Advances in Microbial Physiology, Volume 53

Contents CONTRIBUTORS TO VOLUME 53 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix The Bacterial Resp...

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Contents

CONTRIBUTORS

TO

VOLUME 53 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ix

The Bacterial Response to the Chalcogen Metalloids Se and Te Davide Zannoni, Francesca Borsetti, Joe J. Harrison and Raymond J. Turner

1. 2. 3. 4. 5. 6. 7. 8.

Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . Biological Uses of Se and Te . . . . . . . . . . . . . Resistance Towards Se and Te Oxyanions . . . . Microbial Processing of Metalloid Chalcogens . Chalcogens and Bacterial Physiology. . . . . . . . Other Chalcogens and Metalloids . . . . . . . . . . Concluding Remarks . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

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Gaining Insight into Microbial Physiology in the Large Intestine: A Special Role for Stable Isotopes Albert A. de Graaf and Koen Venema

1. 2. 3. 4. 5. 6.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . The Gut Microbial Ecosystem . . . . . . . . . . . . Stable Isotopes . . . . . . . . . . . . . . . . . . . . . . . Genomic Inventories of Intestinal Bacteria . . . Proteomic Aspects of Intestinal Microbial Life. Metabolomics . . . . . . . . . . . . . . . . . . . . . . . .

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CONTENTS

7. Metabolic Flux Analysis Applied to the Gut . . . . . . . . . . . . 8. Emerging Picture of the Role of Microorganisms Integrated in Man . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9. New Aspects in the Study of Intestinal Bacterial Physiology . 10. Conclusions and Future Prospects . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Iron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Copper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Bacterial Physiology, Regulation and Mutational Adaptation in a Chemostat Environment Thomas Ferenci

1. General Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. The Chemostat Environment and Its Applications to Studies of Bacteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. The Physiological Changes in an Organism Inoculated into a Chemostat: The Example of Glucose-Limited Escherichia coli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Variations in Responses Within and Between Species . . . . . . . 5. Steady State or Constant Change in a Chemostat Population? 6. Mutation Rates and Mutators in Chemostat Populations . . . . 7. Mutational Takeovers and Population Changes . . . . . . . . . . . 8. A Mutational Sweep in Detail: The Physiological Advantage and Spread of mgl Mutations in Glucose-Limited E.coli . . . . . 9. Other Mutations in Chemostat Populations and Their Physiological Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10. Emerging Diversity in Chemostat Populations . . . . . . . . . . . . 11. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Metallosensors, The Ups and Downs of Gene Regulation Amanda J. Bird

CONTENTS

4. Zinc . . . . . . . . . . . 5. Cadmium . . . . . . . 6. Conclusions . . . . . Acknowledgements References . . . . . .

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AUTHOR INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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SUBJECT INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Colour Plate Section to be found in the back of this book

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Contributors to Volume 53

AMANDA J. BIRD, Division of Hematology, Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, UT 84132, USA FRANCESCA BORSETTI, Department of Biology, Unit of General Microbiology, Faculty of Sciences, University of Bologna, Via Irnerio 42, 40126 Bologna, Italy ALBERT A. de GRAAF, Wageningen Center for Food Sciences, P.O. Box 557, 6700 AN Wageningen, The Netherlands; Department of Surgery, University of Maastricht, Maastricht, The Netherlands THOMAS FERENCI, School of Molecular and Microbial Biosciences G08, The University of Sydney, NSW 2006, Australia JOE J. HARRISON, Department of Biological Sciences, University of Calgary, Calgary, Alta., Canada RAYMOND J. TURNER, Department of Biological Sciences, University of Calgary, Calgary, Alta., Canada KOEN VENEMA, Wageningen Center for Food Sciences, P.O. Box 557, 6700 AN Wageningen, The Netherlands; TNO Quality of Life, P.O. Box 360, 3700 AJ Zeist, The Netherlands DAVIDE ZANNONI, Department of Biology, Unit of General Microbiology, Faculty of Sciences, University of Bologna, Via Irnerio 42, 40126 Bologna, Italy

The Bacterial Response to the Chalcogen Metalloids Se and Te Davide Zannoni1, Francesca Borsetti1, Joe J. Harrison2 and Raymond J. Turner2 1

Department of Biology, Unit of General Microbiology, Faculty of Sciences, University of Bologna, Via Irnerio 42, 40126 Bologna, Italy 2 Department of Biological Sciences, University of Calgary, Calgary, Alta., Canada

ABSTRACT Microbial metabolism of inorganics has been the subject of interest since the 1970s when it was recognized that bacteria are involved in the transformation of metal compounds in the environment. This area of research is generally referred to as bioinorganic chemistry or microbial biogeochemistry. Here, we overview the way the chalcogen metalloids Se and Te interact with bacteria. As a topic of considerable interest for basic and applied research, bacterial processing of tellurium and selenium oxyanions has been reviewed a few times over the past 15 years. Oddly, this is the ﬁrst time these compounds have been considered together and their similarities and differences highlighted. Another aspect touched on for the ﬁrst time by this review is the bacterial response in cell–cell or cell–surface aggregates (bioﬁlms) against the metalloid oxyanions. Finally, in this review we have attempted to rationalize the considerable amount of literature available on bacterial resistance to the toxic metalloids tellurite and selenite.

ADVANCES IN MICROBIAL PHYSIOLOGY, VOL. 53 ISBN 978-0-12-373713-7 DOI: 10.1016/S0065-2911(07)53001-8

Copyright r 2008 by Elsevier Ltd. All rights reserved

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Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Tellurium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Selenium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Biological uses of Se and Te . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Use in Medicine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Use in Structural Biochemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Use in Selective Bacterial Growth Media . . . . . . . . . . . . . . . . . . . . 3.4. Isolates from the Environment with Te and Se Oxyanions . . . . . . . . 3.5. Applications of Te and Se in Biotechnology/Industry/Bioremediation. 4. Resistance toward Se and Te oxyanions . . . . . . . . . . . . . . . . . . . . . . . 4.1. Tellurium and TeR Determinants . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Tellurate Resistance Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Selenite/Selenate Resistance Genes . . . . . . . . . . . . . . . . . . . . . . . 5. Microbial processing of metalloid chalcogens . . . . . . . . . . . . . . . . . . . . 5.1. Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3. Biofilms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Chalcogens and bacterial physiology . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. Selenium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Tellurium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3. Mechanism(s) of Chalcogen Toxicity . . . . . . . . . . . . . . . . . . . . . . . 7. Other chalcogens and metalloids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1. Polonium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2. Other Metalloids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ABBREVIATIONS Ch COX DPA GSH NMR MBC MBEC MIC PDTC QS R ROS

chalcogen cytochrome c oxidase dipicolinic acid reduced glutathione nuclear magnetic resonance minimum bactericidal concentration bioﬁlms eradication concentration minimum inhibitory concentration pyridine-2,6-bis-thiocarboxylic acid quorum sensing organic constituent reactive oxygen species

. .2 . .3 . .4 . .5 . .6 . .7 . .7 . .8 . .9 . 10 . 11 . 13 . 14 . 21 . 21 . 22 . 22 . 29 . 30 . 38 . 38 . 42 . 45 . 49 . 49 . 50 . 50 . 51 . 52

THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS

SAM SCV Se SEM SEM-EDS SRB TA Te

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S-adenosylmethionine small colony variant Selenium scanning electron microscopy scanning electron microscopy energy dispersive spectroscopy sulfate reducing bacteria toxin-antitoxin tellurium

1. INTRODUCTION Considering that heavy metals have been reasonably abundant throughout the majority of the Earth’s history, one needs to acknowledge that bacteria have had to deal with their toxic forms since the beginning. This view, pointed out by Silver and Phung (2005a), implies that metal resistance in bacteria is not a recent evolutionary event. Although levels of metals in localized environments become higher from time to time due to geological events, human activities have provided unique metal combinations and levels from industrial and pollution events. Regardless of the explanation of tolerance and biogeochemical interaction between heavy metals and bacteria, there is an amazingly wide occurrence of bacterial genetic elements with deﬁned metal resistances. Thus, bacteria have found ways to eke out a life with such metals and the chalcogens Se and Te. Metal metabolism and resistance in bacteria has been of interest since the 1970s when it was recognized that microorganisms are involved in the transformation of metal compounds in the environment (Jernelov and Martin, 1975; Saxena and Howard, 1977; Summers and Silver, 1978). This area of research is beginning to be referred to as environmental bioinorganic chemistry or microbial biogeochemistry. Bacterial processing of selenium and tellurium oxyanions has been explored since these early years and remains a topic of interest. Here we overview the ways in which the chalcogen metalloids Se and Te interact with bacteria. Tellurite toxicity and resistance in bacteria has been reviewed a few times (Walter and Taylor, 1992; Taylor, 1999; Turner, 2001). While the focus of the literature on selenium in bacteria has been primarily on its incorporation into the amino acid selenocysteine (the 21st amino acid) (Bo¨ck et al., 1991), an overview of selenium processing in bacteria has been published (Turner et al., 1998). However, this is the ﬁrst time these compounds have been considered together and their similarities and differences highlighted.

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2. CHEMISTRY Wilhelm Blitz of the Institute of Inorganic Chemistry at the University of Hannover, Germany, coined the term ‘‘chalcogen’’ sometime around 1930. Whereas other groups of elements had names, the Group 16 elements (formally Group VI A in USA labeling and VI B in European labeling) O, S, Se, and Te lacked a good collective term. The result was the term ‘‘chalcogens’’ (‘‘ore formers’’ from chalcos old Greek for ‘‘ore’’) for these elements and ‘‘chalcogenides’’ for their compounds (Fischer, 2001). The chalcogen elements (pronounced with a hard ‘‘C’’ as in chemistry) other than oxygen can be generally referred to in chemical structures by ‘‘Ch’’ and this abbreviation will also be used here. The most common compounds of the non-oxygen chalcogens are chalcogenide glasses. The most abundant materials in the earth’s crust are silicates (various compounds of silicon dioxide). ‘‘Chalcogenide glasses’’ are distinguished from these as non-silicate glasses. Se and Te generate compounds that are structurally related to their sulfur analogues, but that exhibit different properties and reactivities and are thus considerably more toxic. As one descends the column, the chalcogens become larger and more polarizable than sulfur. Selenium has a lower electronegativity and forms weaker bonds than sulfur (Whitham, 1995). The chemists ﬁnd that selenium can be easily introduced into molecules as a radical, a nucleophile, or an electrophile. Tellurium has even greater metallike properties and is a true metalloid. In part, due to its polarizability, the C–Se bond is weaker than C–S bonds; C–Te bonds are weaker yet and tend to decompose in aqueous environments. This difference in bond energies may explain why telluromethionine and tellurocysteine amino acids have not been naturally found while selenocysteine (the 21st amino acid) has been found in all but a few organisms. Although a considerable amount of selenium chemistry has been studied, tellurium chemistry is still somewhat in the dark ages. Se and Te can exist in a number of redox states, namely: Ch2 or ChðIIÞ ! Ch0 or Chð0Þ 2 ! ChO2 3 or ChðIVÞ ! ChO4 or ChðVIÞ

Selenide ðSe2 Þ ! elemental ðSe0 Þ 2 ! selenite ðSeO2 3 Þ ! selenate ðSeO4 Þ

THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS

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Telluride ðTe2 Þ ! elemental ðTe0 Þ 2 ! tellurite ðTeO2 3 Þ ! tellurate ðTeO4 Þ

Bacteria are exposed to these elements mainly as their oxidized ions in the form of the oxyanions, as well as in organometalloid forms (RCh). However, the exact ionic form of the chalcogen to which microorganisms are exposed is unknown. For example, in solution at physiological pH, As(III) is primarily in the form of the undissociated acid arsenic trioxide [As(OH)3] and not the oxyanion arsenite (Ramirez-Solis et al., 2004). At physiological 1 2 pH, Se(IV) is predominantly HSeO 3 (pKa ¼ 2.6 and pKa ¼ 7.3) and Se(VI) 2 is SeO4 . Although selenide is a key metabolic intermediate, its ionic form is probably not Se2 but HSe. Te(IV) at pH 7.0 exists at a ratio of HTeO 3/ TeO2 of 104/1. Te(VI) would likely be TeO2 3 4 . Thus, the standard reduction potential of the Te/TeO2 3 couple (0.42 V) at basic pH would be 2 4+ raised to 0.12 V for the couple HTeO 3 /TeO3 at pH 7.0, with no Te present due to its instability in water (Di Tomaso et al., 2002).

2.1. Tellurium Tellurium was named from the Latin ‘‘tellus’’, meaning ‘‘earth’’, and was discovered by F.J. Mueller von Reichenstein in 1782 from ores mined in the gold districts of Transylvania (Bragnall, 1966; Cooper, 1971). Tellurium is occasionally found native, but is more often found as the telluride of gold (calaverite) or combined with other metals. It is recovered commercially from anode muds produced during the electrolytic reﬁning of blister copper. The U.S., Canada, Peru, and Japan are its main producers. The concentration of total Te in the earth’s crust is estimated to be 0.002 ppm ranking Te as approximately 75th in abundance of earth’s elements (Bragnall, 1966; Cooper, 1971). Crystalline tellurium has a silvery-white appearance and when pure it exhibits a metallic luster. Amorphous tellurium is found by precipitating tellurium from a solution of tellurous acid. Tellurium is a p-type semiconductor, and shows greater conductivity in certain directions, depending on the alignment of the atoms. Tellurium has been used in blasting caps, and is added to cast iron for chill control and to steel for toughness. It is increasingly being used in ceramics and photovoltaic cells (Lide, 2005) and is presently very popular as a coloring and property-modifying agent in various types of glasses. It is also used as a reagent (tellurium chloride and tellurium dioxide) in producing the black ﬁnish on silverware. The addition of Te0 and Te diethyldithiocarbamate as primary vulcanizing agents to rubber allows it to withstand temperature ﬂuctuations and

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enhance the overall lifetime of natural and synthetic rubber. Usually in combination with Pt, Te is also used as an accelerant/catalyst in a variety of reactions. In metallurgy, Te is used to modify and improve the properties and machinability of cast iron, lead, and copper alloys; its addition to lead decreases the corrosive action of acids and improves its strength and hardness. In the environment, Te exists in its elemental (Te0), inorganic – (telluride 2 (Te2), tellurite (TeO2 3 ), and tellurate (TeO4 )), and organic (dimethyl telluride (CH3TeCH3)) forms (Cooper, 1971). Of these, its oxyanion forms are more common than its non-toxic, elemental state (Summers and Jacoby, 1977). Presently, sparse research into anthropogenic emissions of Te-based compounds has been conducted and the implications of Te in the air have yet to be investigated.

2.2. Selenium Berzelius discovered selenium in 1818. Its name is derived from the Greek word selene, meaning ‘‘moon’’. Selenium is found in a few rare minerals such as crooksite and clausthalite. Previously, it has been obtained from ﬂue dusts remaining from processing copper sulﬁde ores, but the anode metal from electrolytic copper reﬁneries now provides the main source, as for tellurium. Elemental selenium has been said to be practically non-toxic and is considered to be an essential trace element; however, hydrogen selenide and other selenium compounds are extremely toxic, and resemble arsenic in their physiological reactions. Selenium exists in several allotropic forms, although three are generally recognized. Selenium can be prepared with either an amorphous or a crystalline structure. Amorphous selenium is either red (in powder form) or black (in vitreous form). Crystalline monoclinic selenium is deep red; crystalline hexagonal selenium, which is the most stable variety, is a metallic gray. Selenium exhibits both photovoltaic action and photoconductive action; therefore, it ﬁnds use in photocells. Selenium is also able to convert a.c. to d.c. electricity and is used in rectiﬁers. As a p-type semiconductor, selenium has many uses in electronic and solid-state applications. Selenium is used in Xerography for copying documents. Like tellurium, it is used by the glass industry as an additive to stainless steel (Lide, 2005). As opposed to tellurium, selenium is a very important, essential element for most organisms, including humans. This requirement stems from its incorporation into proteins as part of the 21st amino acid as selenocysteine

THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS

7

(Bo¨ck et al., 1991) and many selenoproteins have now been identiﬁed (Gromer et al., 2005). Although considered a key trace element, Se can be highly toxic depending on its concentration and speciation. In the environment, Se occurs in a variety of oxidation states from the water-soluble oxyanions selenite 2 (SeO2 3 ) and selenate (SeO4 ). Under anoxic conditions, it is found in its insoluble elemental form of Se(0) and mineralized selenides. Redox transformations can occur in natural systems to increase or decrease the mobility and bioavailability of the element. Although such transformations can occur abiotically (Myneni et al., 1997; Zhang et al., 2004), the reduction of selenate and selenite to elemental Se clearly involves microorganisms.

3. BIOLOGICAL USES OF SE AND TE 3.1. Use in Medicine Tellurium found applications in the treatment of microbial infections prior to the discovery of antibiotics. Early documentation in 1926 reports its use in the treatment of syphilis. Its oxyanion tellurite, TeO2 3 , has been used in microbiology since the 1930s when Alexander Fleming reported its antibacterial properties (Fleming, 1932; Fleming and Young, 1940). In 1984, it was suggested that TeO2 could be a potential antisickling agent of red 3 blood cells in the treatment of sickle cell anemia (Asakura et al., 1984). In 1988, tellurium-containing immuno-modulating drugs were proposed as treatment agents for AIDs; however, little has been done on it since (Jacobs, 1989). This compound, AS-101, inhibits the production of IL-10, IFNgamma, IL-2R, and IL-5 (Shohat et al., 2005). A new use of tellurium compounds is in bone marrow stem cell protection during chemotherapy. Trichloro[dioxoethylene-O,O0 ]tellurite shows promise compared with other compounds (Guest and Uetrecht, 2001). In another recent example, organoselenium and organotellurium compounds are being explored as pharmaceuticals for defense against oxidative and nitrosative stress (Klotz et al., 2003). Selenium is intrinsically useful through its role in selenocysteine. Selenium has undergone a revolution since the days when it was only considered to be a toxin. Now, Se is not only recognized as an essential trace element, but has started to be considered the champion of antioxidants (Tapiero et al., 2003) and in cancer prevention (Fleming et al., 2001). It would be impossible in the context of this review to highlight all the eukaryotic biology of selenium.

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The reader is directed toward some other reviews for such purposes (Neve, 1991; Whanger et al., 1996; Burk, 2002; Hatﬁeld, 2002; Klein, 2004). However, it is worth pointing out here that selenium can modify the toxicity of other heavy metals including mercury (Watanabe, 2002) and arsenate (Gailer et al., 2002; Manley et al., 2006) that occur through glutathione–Se–As/Hg complexes (Gailer et al., 2000, 2002).

3.2. Use in Structural Biochemistry The functional and structural properties of chalcogen analogues of sulfurand oxygen-containing amino acids in peptides and proteins is now possible with new synthetic and recombinant technologies. Applications are being increasingly explored with both natural and synthetic proteins (reviewed by Moroder, 2005). Selenocysteine has been recognized as a tool for the production of selenoenzymes with new catalytic activities. By exploiting the highly negative redox potential of selenols, disulﬁde replacement with diselenide is well suited to increase the robustness of cysteine frameworks in cystine-rich peptides and proteins and can even be used in the de novo design of non-native cysteine frameworks. The isomorphous character of seleniumand tellurium-containing amino acids can be easily exploited for the production of metalloid mutants of proteins. Such modiﬁed proteins have been shown to be useful in protein spectroscopy. Both selenomethionine and telluromethionine have been incorporated into proteins as heavy metal derivatives of proteins in protein crystallography and nuclear magnetic resonance (Boles et al., 1995; Budisa et al., 1995). Tellurium acts as a phasing vehicle for solving X-ray diffraction patterns and in NMR as an internal probe to examine structure/function biochemistry following the 125 Te signal. The ﬁrst use of telluromethionine as a tool for phasing X-ray diffraction data was its incorporation in dihydrofolate reductase (Boles et al., 1994) and was also used in solving the structures of the phage P22 tailspike protein (Steinbacher et al., 1997) and pyrrolidone carboxypeptidase (Boles et al., 1997). To carry out these experiments, the protein is expressed in a bacterial methionine auxotroph host, typically Escherichia coli, in minimal media supplemented with selenomethionine or telluromethionine. Bioincorporation of telluromethionine is difﬁcult due to its toxicity, presumably because of isotope effects on enzyme activities. Additionally, telluromethionine in aqueous solution is unstable and degrades to produce the toxic TeO2 3 .

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3.3. Use in Selective Bacterial Growth Media Tellurite has been extensively explored as an additive to growth media for the selection and identiﬁcation of various microorganisms, particularly those resistant to tellurite, for almost 90 years. It is often employed in selective media to isolate a wide range of pathogens including: Corynebacterium diphtheriae, Vibrio cholerae (Shimada et al., 1990), Shigella spp. (Rahaman et al., 1986), and verocytotoxigenic E. coli O157:H7 (the ‘‘hamburger disease’’ bacterium) (Zadic et al., 1993; Kormutakova et al., 2000). Considerable work has been focused on the pathogenic E. coli O157:H7. This E. coli strain contains the terABCDEF TeR determinant on its chromosome as part of the O pathogenicity island (Taylor, 1999; Taylor et al., 2002). Because of the high level of resistance, several groups have explored the use of tellurite-enriched media for its identiﬁcation and isolation. Tellurite is reduced in these strains resulting in a dark black colony that led to the adage ‘‘Beware the Black E. coli’’ (see Fig. 1). Although the resistance from this toxin-producing E. coli originates from the ter resistance determinant (see below), there is diversity in the number of gene copies present and there are even examples without the ter genes (Taylor et al., 2002). Tellurite is highlighted as a key selection ingredient (De Boer and Heuvelink, 2000) and is also used in media to select Shiga toxin-producing E. coli (STEC) O26 (Hiramatsu et al., 2002). However, a study on E. coli O46 and O15:H7 suggests that there is no correlation between the TeR and the ability to produce Shiga toxin (Taylor et al., 2002). In addition to E. coli strains, tellurite has been used in selection media for other organisms, including Mycobacterium avium complex (Afghani and Fujiyama, 2001), which also give black colonies, and in the selective media for methicillin-resistant Staphylococcus aureus (MSRA) (Zadic et al., 2001). Furthermore, tellurite is also used as an additive to culture media for the isolation of pathogeneic Vibrio spp. (Donovan and van Netten, 1995). Ceﬁxime-tellurite media has been used for isolating organisms from minced beef (Dogan et al., 2003), rectal swabs of cattle (Yilmaz et al., 2002), raw vegetables (Fujisawa et al., 2002), and sprouts (Fujisawa et al., 2000). Tellurite and tellurate have also been proposed for use in selective media for fecal Streptococci (Saleh, 1980). It is clear that tellurite has proven to be a useful amendment for selection media in clinical laboratory settings and will continue to do so. However, this approach should be used with caution since non-pathogenic strains can acquire tellurite resistance determinants, for example the ter genes present in the pathogenic E. coli O157:H7, thereby appearing in many clinical assays as false positives. Conversely, as the biochemistry of Te is far from understood, it needs to be recognized that as

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Figure 1 Biogeochemical transformation of tellurite and selenite by bacteria. The coloration of black cells (tellurite) and red-orange (selenite) is due to the reduction to Ch(0) product within the cells. (A) Pseudomonas aeruginosa grown in microtitre plate planktonically with tellurite. (B) P. aeruginosa grown on Calgary Bioﬁlm Device pegs with tellurite. (C) P. aeruginosa grown in microtitre plate planktonically with selenite. (D) P. aeruginosa grown on Calgary Bioﬁlm Device pegs with selenite. (E) E. coli grown on solid Luria Bertani broth showing the black colonies. (F) Thin section electron micrograph of E. coli grown in the presence of tellurite. The ﬁgure shows the precipitation of black crystals along the membrane. (G) E. coli harboring various tellurite resistance determinants. The non-colored culture of the ars is reﬂective of the resistance being an efﬂux system. (See plate 1 in the color plate section.)

yet unidentiﬁed physiological responses to this chalcogen may give rise to false negatives.

3.4. Isolates from the Environment with Te and Se Oxyanions Apart from the isolation and selection of infectious organisms described above by the augmentation of growth media with potassium tellurite, other bacteria have also been selected through the use of chalcogen oxyanions. Tellurite was found to be an excellent selective agent for Agrobacterium spp.

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(Mougel et al., 2001). It was also utilized to characterize a gene segment in an unculturable rove beetle symbiont that was found to have a functional terZABCDEF tellurite resistance operon (Piel et al., 2004). This organism is thought to be closely related to Pseudomonas aeruginosa. Recently, a proposal to use tellurite in a bioassay for quantiﬁcation of cell viability in environmental samples has been put forward (Lloyd-Jones et al., 2006). The assay is based on the assumption that the tellurite reduction to the black precipitate only occurs in metabolically competent bacteria. Yurkov’s group has been involved in isolating bacteria from various unique and extreme environments. His group has concentrated investigations on the microorganisms located in close proximity to the hydrothermal vent of the Juan de Fuca Ridge in the Paciﬁc Ocean (Rathgeber et al., 2002). Ocean hydrothermal vents emit an array of heavy metal/metalloid compounds into the aquatic environment, including TeO2 3 . Tellurite- and selenite-reducing strains were isolated in large numbers from the bacterial bioﬁlms and sulﬁde-rich rocks near the hydrothermal vents. The isolates were found to be from the genus Pseudoalteromonas, were salt-, pH-, and heat-tolerant, and gave rise to very high MICs (1500–2500 mg K2TeO3) (Rathgeber et al., 2002). Some of these organisms were found to utilize 2 SeO2 3 or TeO3 as terminal electron acceptors. Recently, a strain performing anaerobic respiration on tellurate (TeO2 4 ) was isolated from the hydrothermal vent sulﬁde worm Paralvinella sulfincola (Csotonyi et al., 2006).

3.5. Applications of Te and Se in Biotechnology/Industry/ Bioremediation There are unique challenges in following fates of genetically modiﬁed bacteria released into the environment. The exploitation of microorganisms for the bioremediation of contaminated areas is of particular interest. The use of antibiotic resistance markers for following released organisms has deleterious ramiﬁcations in the spread of multi-drug resistance. Sanchez-Romero et al. (1998) have shown that the kilAtelAB tellurite resistance determinant can be used to trace Pseudomonas putida following environmental release for organic degradation. Tellurite has also been used to detect and quantify the release of Pseudomonas pseudoalcaligenes KF707 in soils for polychlorinated biphenyl (PCB) degradation (Zanaroli et al., 2002). Bacteria can mediate bioremediation of Se and Te either through direct sequestration, bioreduction, or biomethylation. In sequestration, bacteria do not biotransform the chalcogen oxyanions into a less toxic compound; the accumulation may occur either through uptake or interaction with

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surface biomolecules acting in the form of an ion-exchange matrix. Bioreduction instead reduces the more toxic oxyanion forms to the ‘‘non-toxic’’ Ch(0) form. This process usually occurs intracellularly, leading to the precipitation of the metallic form within the cell. Finally, biomethylation leads to volatile methyl derivatives that disperse into the atmosphere. The dimethyl chalcogens can undergo reactions with OHd , NO3 radicals, and ozone. The methylated products and the reactive products can interact with atmospheric particles leading to atmospheric residence times from hours to days (Atkinson et al., 1990). Thus, the chalcogen can travel considerable distances providing detoxiﬁcation of local contamination sites through dilution by dispersal. Below, we explore some examples of Se and Te bioremediation studies. Bioremediation of selenium-contaminated environments has been reviewed by Frankenberger and Arshad (2001). The tellurite resistance determinants kilAtelAB, ter, tehAB, and arsABC were investigated for use in tellurite remediation. The use of the plasmidborne tellurite resistance determinant tehAB was found to facilitate the largest amount of uptake of tellurite from the external media (Turner et al., 1994a). Highly resistant microbes could also potentially be used for Te bioremediation. Strains of marine purple non-sulfur bacteria with resistance to 5 mM tellurite were found to decrease the concentration of tellurite in the external media 100-fold and led to accumulation of Te(0) deposits in the cells (Yamada et al., 1997). Similar levels of activity have been reported for strains of obligate anaerobes (Yurkov et al., 1996). This bioreduction, leading to sequestration and chemical transformation of chalcogen oxyanions, could have promise for aquifer contamination sites. Tellurium oxyanions could also be remediated through biotransformation via volatilization through production of methylated derivatives. Methylation and reduction processes are discussed further below. Both aerobic and anaerobic reduction processes of selenium oxyanions are considered to be useful for removing toxic forms of Se from Se-contaminated water. In certain aquatic systems, the effective bioremediation must include the physical removal of the precipitated Se(0) to prevent its re-oxidation to Se(IV) and Se(VI) and subsequent remobilization (Zhang et al., 2004). An interesting approach to the bioremediation of metals is to combine phytoremediation with microbial remediation. This would involve metal processing through plant rhizoﬁltration and is based on plant rhizobacteria interactions. An example is the isolation of Bacillus mycoides and Stenotrophomonas maltophilia from the rhizosphere of Astragalus bisulcatus, a plant that is able to hyper-accumulate selenium. These organisms were highly efﬁcient in reducing selenite to Se(0) (Vallini et al., 2005).

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Microbial bioﬁlms show potential for various industrial processes. The ability of metalloids to adsorb to and/or react with microbial biomass has been exploited as a means for detecting industrial pollutants in rivers (Mages et al., 2004). Moreover, bioﬁlms grown on membrane bioﬁlm reactors are now being explored as a means to extract SeO2 4 from industrial waste-water and mine tailings, either chalcogens alone or in the presence of other metals such as chromate or arsenate (Chung et al., 2006b,c). Bioremediation of selenium-contaminated water sources is promising. Early experiments have explored bioremediation of chalcogens using algal–bacterial mixtures (Gerhardt et al., 1991). Drainage water treatment using the selenate-respiring bacterium Thauera selenatis has been explored (Macy et al., 1993). In a system at the Panoche Water District in California, USA, a medium-packed biological reactor amended with acetate as the carbon source demonstrated a 98% reduction in selenium oxyanions levels. The Se was bioprocessed to Se(0) and then removed using Nalmet 8072, a Se precipitant coagulant (Cantaﬁo et al., 1996). Another example using this organism utilized wheyamended fermentor to removal of up to 98% selenium oxyanions in the contaminated drainage water (Bledsoe et al., 1999).

4. RESISTANCE TOWARD SE AND TE OXYANIONS The ﬁeld of toxic metal resistance microbiology has been frequently reviewed in the past 25 years. Notably, a number of extensive reviews have been written by Simon Silver and others (Trevors et al., 1985; Silver, 1996, 1998; Silver and Phung, 1996; Summers, 2005). However, with the exception of As, the metal oxyanions have not received much attention. In fact, in the recent review of Silver and Phung (2005a), the authors dedicate barely a paragraph to tellurium and do not discuss selenium. In general, the so-called heavy metals (although it would be more correct to refer to them as ‘‘toxic metals’’ as some heavy metals are not very toxic; Mo, for example) are toxic as they form stable long-lived complexes with sulfur, thus disrupting the thiol chemistry within the cell. The chalcogens Se and Te are no different and demonstrate interesting thiol chemistry within bacteria as described in the sections below. A lack of understanding of the toxicity of the Ch oxyanions has hampered investigations of the mechanism of several of the cloned resistance determinants. Although oxidative damage has been suggested as a mode of toxicity, generally speaking, tellurite resistance determinants generally do not provide global protection to other oxidants and tend to be very speciﬁc to

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tellurite. Recruitment of genes from the metabolic operons responsible for managing the toxicity of normal metabolites or from the genes targeted by the toxic agent are common themes in resistance determinants (Summers, 2005). This can be seen in the resistance mechanisms displayed for other metals. However, this is less evident in tellurite resistance to date. Furthermore, it is interesting to note that there is no cross-resistance observed for any of the deﬁned resistance determinants, suggesting a speciﬁc evolution of Ter genes.

4.1. Tellurium and TeR Determinants Genes responsible for tellurite resistance in various organisms have been isolated and characterized by a number of groups. The Ter genes ﬁrst appeared associated with plasmids; however, several determinants and plasmid homologues have now been found associated with the chromosome. Tellurium resistance mediated by plasmids was ﬁrst described by Anne Summer and Diane Taylor in the 1970s (Summers and Jacoby, 1977; Taylor and Summers, 1979; Taylor et al., 1988). Taylor reviewed bacterial tellurite resistance in 1999, and the mechanisms of toxicity in E. coli were explored in 2001 by Turner. It has been recognized for some time that metal resistance determinants are found on conjugative plasmids (Summers and Jacoby, 1977; Izaki, 1978). Reviews focusing on plasmid-mediated tellurite resistance (Ter) include: Walter and Taylor (1992), Taylor (1999), and Turner (2001). Plasmid-encoded tellurite resistance determinants are generally associated with plasmids of the H and P incompatibility groups (Hou and Taylor, 1994). Additionally, a number of chromosomal genes have been found to be associated with tellurite resistance or to directly mediate tellurite resistance. To date, ﬁve genetically distinct chromosomal and plasmid-borne bacterial tellurite resistance systems have been described (Taylor, 1999; Turner et al., 1999; Turner, 2001; Taylor et al., 2002). However, there are also several unrelated Ter determinants emerging from various bacterial families, suggesting that these determinants provide some selective advantage in natural environments. The nature of this advantage may be unrelated to the Ter phenotype, as the levels of resistance demonstrated in the laboratory do not correlate with the levels of tellurium ion species present in the ecological or pathogenic environment. An interesting characteristic of the genes encoding Ter is that many confer other phenotypes as well. Thus, it is highly likely that the genes identiﬁed to be associated with tellurite resistance may act by

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encoding ‘‘moonlighting’’ enzymes. Our present understanding of the Ter determinants is summarized below. 4.1.1. ter The majority of plasmids within the incompatibility Groups HI-2 and HII confer protection against colicins and resistance to potassium tellurite (Taylor and Summers, 1979; Taylor, 1999). The isolated tellurite resistance determinants mediated by these plasmids convey a very high MIC (1024 mg/ml) and has been primarily studied in the plasmids pMER610 and R478 (Jobling and Ritchie, 1987, 1988; Whelan et al., 1995). The phenotypes of resistance to tellurite, bacteriophage (Phi), and pore-forming colicins (PacB) are associated with a large cluster of genes (terZABCDEF) referred to as the ter Ter determinant (Walter and Taylor, 1992; Whelan et al., 1995, 1997). This determinant was later identiﬁed to be on the chromosome of E. coli H157:O7, associated with the pathogenicity island (Tarr et al., 2000; Taylor et al., 2002). A few studies have been performed on the regulation of the ter operon. The terABCDE operon from the plasmid pMER610 was initially considered to be inducible (Jobling and Ritchie, 1987) but later was shown to be constitutively expressed (Hill et al., 1993). A study examining the ter determinant in pathogenicity islands of pathogens using reverse transcriptase-PCR analysis demonstrated that the majority of ter genes showed constitutive expression. However, a few isolates were recently found to be telluriteregulated and involved induction of the terB and terC genes (Taylor et al., 2002). Transposon mutagenesis suggests that only the terB, -C, -D, and -E genes are required for Ter (Kormutakova et al., 2000) and the data from Taylor et al. (2002) suggest a common transcriptional region for strains with high MIC. However, those with intermediate levels of resistance probably have separate tellurite-regulated promoters before terCDE and terZ as well as before terB. The regulatory sensor protein has not yet been identiﬁed. A terZABCDE operon was identiﬁed in Proteus mirabilis and was also found to be inducible as a single transcript (Toptchieva et al., 2003). This ter operon was inducible by tellurite and to a lesser extent by oxidative stress inducers, such as hydrogen peroxide and methyl viologen. The promoter resembled the OxyR-consensus sequence. From this work, it appeared that this determinant was common in the Proteus genus. Overall, the ter operon may be differentially regulated in different organisms. Other examples of the ter determinant include the E. coli KL53 conjugative plasmid pTE53, which contains homologous terBCDEF genes responsible for the TeR (Kormutakova et al., 2000) as well as the so-called

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protective region of genes terXYW (Vavrova et al., 2006). The terF gene was not required in this case to mediate full resistance. Burian et al. (1998) noted that this plasmid gives twice the tellurite uptake, compared with the cured strain. There is a considerable degree of homology between the ter genes on IncHI2 plasmid R478, which originated in Serratia marcescens, and pTE53 from the E. coli clinical isolate. The biochemical mechanism of resistance toward tellurite by the ter determinant remains unknown. However, it is clear that reduced uptake or efﬂux is not involved (Lloyd-Jones et al., 1991, 1994; Turner et al., 1995a). Additionally, there was no increased accumulation of tellurite from the media (Turner et al., 1994a). From the accumulation of Te(0) crystals in E. coli expressing this Ter, it has been inferred that this determinant facilitates the reduction (Lloyd-Jones et al., 1994). No in vitro reduction using cell extracts has been demonstrated (Lloyd-Jones et al., 1991). However, as the Te(0) deposits are closely associated with the membrane and a required protein, TerC, an integral membrane protein, it remains a viable hypothesis that ter components tap into the electron pool in the membrane for functionality (Lloyd-Jones et al., 1994). Additionally, the ter operon is able to protect against tellurite-mediated thiol oxidation (Turner et al., 1999). The resistance to channel-forming colicins has been reviewed by Alonso et al. (2000b). No clues arise from our present understanding of colicins and other cholicin resistance mechanisms on how the ter determinants might mediate Pac and Phi resistance. Bioinformatic analysis suggests a weak homology between TerC and some transporters. TerD is homologous to the cAMP binding protein. TerA, TerD, and TerE show some homology and are related to a stress response protein in a variety of organisms. Overall, bioinformatic analysis does not give any clues to biochemical activity other than the fact that ter operon homologues are found on the chromosomes of a wide range of bacteria. The observation of co-resistance to phage, colicins, and tellurite remains an unresolved biochemical and physiological link. 4.1.2. tehAB The tehAB genes were ﬁrst described as a Ter determinant that was believed to have originated from the IncHII plasmid pHH1508a (Walter and Taylor, 1989; Walter et al., 1991). Later, these genes were localized on the E. coli genome (Taylor et al., 1994). The genes were thought to be speciﬁc to E. coli at that time based on hybridization and PCR approaches. However, through genome sequencing projects, homologues have been clearly shown to be present on many other bacterial genomes. In E. coli, cloning the tehAB genes into a multicopy plasmid or over-expressing them behind an inducible

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promoter leads to a tellurite MIC of 128 mg/ml. These genes do not appear to mediate resistance when encoded on the chromosome and no difference in the basal resistance is observed in a deletion mutant. No work to date has explored the expression and regulation of these genes on the chromosome. TehA is a polytopic integral membrane protein of 36 kDa with a putative topology of 10 transmembrane helices. This protein shows homology to C4-dicarboxylate transporter/malic acid transport proteins. Intriguingly, it was observed that four of its transmembrane helices are homologous to the small multidrug resistance (SMR) protein family (Turner et al., 1997). It was shown that both full length TehA and a truncated construct in which helices were removed except for the SMR homologous region could transport quaternary ammonium compounds, which are substrates of the SMR proteins. The SMR proteins have a conserved Glu-14 that is crucial to their activity as a proton drug antiporter and is thought to play a role in binding both ligands (Gutman et al., 2003). In fact, TehA contains a glutamic acid in the transmembrane region that could play a similar role. TehB is a 23-kDa cytoplasmic protein that associates weakly with the membrane. TehB contains three conserved motifs found in S-adenosyl-methionine (SAM)-dependent non-nucleic acid methyltransferase (Liu et al., 2000). Mutagenesis of key residues of these motifs eliminated the resistance mediated by the tehAB determinant. It was also shown that TehB undergoes a conformational change upon SAM and tellurite binding (Liu et al., 2000) and SAM can be photochemically reacted with TehB (R.J. Turner, unpublished results). Although a SAM-dependent depletion of tellurite in cultures is observed, tellurium methylation has not been directly observed with this determinant. In fact, expression of this determinant on a plasmid decreases the presence of methylated tellurides in the head gas of cultures and actually decreased the methylsulﬁde levels (van Fleet-Stalder and T.G. Chasteen, personal communication). A study of Liu and Taylor (1999) suggests that TehB has the ability to mediate resistance on its own and that it is partially responsible for the natural resistance of Streptococcus (Liu and Taylor, 1999). Additionally, overexpression of tehB from Streptococcus pneumoniae in E. coli causes a ﬁlamentous morphology in E. coli (Liu and Taylor, 1999). Morphological changes upon the over-expression of Ter determinants are a common theme. The tehAB determinant is very relevant to the physiological state of the cell. In order to mediate full resistance, the cell must have a functioning cysteine biosynthetic pathway, ubiquinone biosynthesis, nicotinamide metabolism, and a thioredoxin/glutathione/glutaredoxin system (Turner et al., 1995a). This suggests that the oxidoreductases and thiol-redox balance are important (Turner et al., 1995a, 1999). Additionally, cysteine residues

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were found to be functionally important for both TehA and TehB (DyllickBrenzinger et al., 2000). Each protein contains three cysteines and can withstand the loss of single cysteine residues; however, TehA and TehB mutants lacking more than one of these cysteines had a decreased tellurite resistance level. The work also demonstrated that TehB is a dimer and at least one Cys is involved in tellurite binding. These results, taken together, make clear that thiol biochemistry is fundamental to the mechanism of these genes. Although the biochemical mechanism of TehAB-mediated tellurite resistance is unknown, one can hypothesize a mechanism based on the observations to date. TehB is clearly a methylase but does not lead to (CH3)nTe products. As TehAB requires glutathione to mediate resistance, it is possible that glutathione participates in the TehB reaction leading to a GSTeCH3. This may follow a similar mechanism that is displayed in eukaryotic multiresistance proteins that utilize glutathione conjugation of drugs and efﬂux of the product (Deeley and Cole, 2006). TehA could then transport the GSTeCH3 compound via a proton antiport mechanism. Preliminary metabolomic experiments examining small molecular weight compounds in the media support such an idea (R.J. Turner, unpublished results). A direct efﬂux mechanism, in which there is no change in molecular form has been ruled out, giving this hypothesis further support (Turner et al., 1995a). 4.1.3. kilAtelAB/klaABtelB IncP plasmids do not normally express the Ter phenotype. However, a cryptic determinant was identiﬁed on the IncP plasmid RP4 or RK2 (Taylor and Bradley, 1987). The RK2 plasmids have a complex network of coregulated genes known as the kil-kor operon. A normally cryptic Ter was identiﬁed on some isolates as RK2TeR was mapped to the kilA locus, giving MICs of 256 mg/ml (Walter and Taylor, 1989). The operon comprises three genes, klaA, -B, -C, which in the Ter versions are referred to as kilAtelAB (Walter et al., 1991; Turner et al., 1994b,c). All three of the genes are required for resistance (Turner et al., 1994b). Furthermore, a single mutation – Ser125 to Cys125 in TelB – was identiﬁed as being responsible for the appearance of the resistance (Turner et al., 1994c). Due to nomenclature changes of the kil-kor region, and in order to clearly identify the Ter version, this determinant is also referred to as klaABtelB. The kil-kor region of the RK2 plasmid is responsible for plasmid maintenance and was named for cell killing or killing override (Goncharoff et al., 1991). The KilA (KlaA) was observed to have a strong lethality phenotype and was also found to inhibit assembly of lambda phage tails (Saltman et al.,

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1992). The growth inhibition phenotype of the operon was examined and it was demonstrated that all three genes provide some level of retarded growth (Turner et al., 1994b). In this study, cells expressing KilA were found to form non-septated ﬁlaments with distinctive evaginations or blebs on the membranes. A working hypothesis for this phenotype is that KilA inhibits the cell chaperone GroEL, as suggested by in vitro experiments (Rochet and Turner, unpublished results). Though the mutation is in TelB, all three genes are required for resistance (Turner et al., 1994c). KlaA (28 kDa) and KlaB (42 kDa) are cytoplasmic proteins while TelB is an integral membrane protein of 32 kDa. There is a cysteine pair (Cys125/Cys132) in a putative loop in TelB and both cysteines are required for resistance (Turner et al., 1994c). This suggests that thiol chemistry is also involved for this Ter. However, as opposed to the tehAB determinant, kilAtelAB is much less dependent on the physiological state of the cell to mediate full resistance (Turner et al., 1995a). Additionally, this determinant is able to protect against the tellurite-dependent glutathione oxidation in a cell (Turner et al., 2001). Reduced uptake or efﬂux of tellurite has been ruled out for this determinant as the resistance mechanism (Turner et al., 1995a). The operon appears to be unique to the IncP plasmid. KlaA is found on the chromosome of very few organisms such as Burkholderia spp., Proteus vulgaris, Paracoccus denitrificans, Roseobacter spp., and Acinetobacter spp. However, there is some annotation confusion in that KlaA in these organisms is referred to as TelA and designated as a putative toxic anion resistance protein. KlaB (TelA) is found on the chromosomes of many organisms and annotated as a hypothetical oxyanion resistance. TelB is found in only a few organisms again showing a strong conserved domain to the TrbC conjugal transfer protein. A telAB version is beginning to be identiﬁed on other plasmids such as pADPTel in P. putida CR30RNS (Hirkala and Germida, 2004). Overall, sequence analysis does not lead to any further clues as to the biochemical mechanism of resistance. Due to the lethality phenotype associated with this determinant, fewer microbial and biochemical studies have been performed. At this time, the biochemical mechanism of this determinant remains elusive. 4.1.4. tpmT The tpm gene was cloned from the tellurite resistant Pseudomonas syringae pathovar pisi (Cournoyer et al., 1998). This gene encodes a SAM-dependent thiopurine methyltransferase enzyme, which led the authors to propose that the resistance probably occurs through a volatilization of tellurite/selenite

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into dimethyl telluride/selenide, a biochemical mechanism that is also involved in the detoxiﬁcation of thiopurine drugs and their analogues. Analysis of the genome of P. putida KT2440, a strain that has a high metal tolerance (Canovas et al., 2003), was also found to have tpmT and an arsRBCH, both of which likely contribute to the tellurite resistance in this organism.

4.1.5. cysM/cysK Chromosomally encoded genes, homologous to those involved in cysteine biosynthesis, have been isolated and are inferred to be involved in tellurite resistance. The cysM gene from S. aureus SH1000 was found to be functionally homologous to the O-acetyl serine (thiol)-lyase B family of cysteine synthase proteins. A deletion in this gene gives increased sensitivity to tellurite and could mediate TeR when transformed into E. coli (Lithgow et al., 2004). A clone of a single reading frame from pMip233, an IncHI3 plasmid, conﬁrmed resistance against both tellurite and pore-forming colicin B. The sequence of this clone is also homologous with O-acetyl serine sulfhydrylase (Alonso et al., 2000a). This group designated this gene cysK and mediated resistance to 41000 mg/ml. This enzyme is a pyridoxal 50 -phosphatedependent enzyme and catalyzes the transformation of O-acetyl-L-serine and S2 to L-cysteine and acetate. The cysK gene was cloned and characterized in Azospirillum brasilense, where its deletion led to an eightfold decrease in tellurite resistance (Ramirez et al., 2006). Somehow this reductase-like enzyme mediates resistance to pore-forming colicins and towards tellurite. This dual phenotype is similar to that of the ter determinant above, yet no homology exists between them. Experiments using E. coli ton and tol mutants harboring pB22 (the cysK clone from plasmid Mip233 Inc HI3) indicate that the product of tolC, but not that of tonB, is required for both the PacB and Ter phenotypes (Vilchez et al., 1997). A homologue of cysK was also identiﬁed in Geobacillus stearothermophilus V (formerly Bacillus sterothermophilus) that is naturally resistant to tellurite (Vasquez et al., 2001). Vasquez’s group has explored this organism’s tellurite resistance (Vasquez et al., 1999) and has isolated different fractions from cell lysates that demonstrate a NADH-dependent reduction of tellurite (Moscoso et al., 1998). Additionally, the gene iscS (cysteine disulfurase) was cloned and shown to be responsible for some of the resistance in this organism and it could confer resistance in E. coli (Tantalean et al., 2003). This work suggests that there may be several genes that are involved in tellurite metabolism in this organism.

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It is tempting to suggest from the above ﬁndings that tellurite resistance in many organisms is due to genes involved in cysteine biosynthesis. However, if this were the case, then one must question why all organisms do not display high levels of resistance. Furthermore, a cysK mutant of E. coli shows no change in its basic level of sensitivity to tellurite (Turner et al., 1995b).

4.2. Tellurate Resistance Genes Tellurate (Te(VI), TeO2 4 ) toxicity is of the same order of that of tellurite. However, this oxyanion has been far less studied probably due to its markedly lower solubility in aqueous buffers, further indicating that our understanding of the electronic forms of tellurium metalloids remains poor. For the most part, the tellurite resistance determinants ter, klaABtelB, teh, and ars do not mediate resistance to tellurate (R.J. Turner, unpublished results). Few studies have been performed exploring the effects of tellurate on microbes. To our knowledge, no electron microscopy or other tools have been used to investigate tellurate exposure to microbes. E. coli cultures, both planktonic and bioﬁlm, exposed to tellurate just below their MIC turn a gray color, compared with the black seen with tellurite. It is not clear if the difference in color and darkness is due to different levels of Te(0) accumulation or to a different metalloid product. E. coli nitrate reductase, which contributes to basal levels of tellurite resistance, may reduce selenate, Se(VI), to selenite, Se(IV), and tellurite, Te(IV), to Te(0), but does not show any tellurate, Te(VI), reduction activity (Avazeri et al., 1997). The ubiE gene of G. stearothermophilus V encodes a methyltransferase, which upon cloning into E. coli produced dimethyl telluride in the head gas of cultures amended with tellurate, but not tellurite (Araya et al., 2004). Although the biochemistry of UbiE is not completely worked out, the process is likely to be SAMdependent.

4.3. Selenite/Selenate Resistance Genes Selenite is 100- to 1000-fold less toxic than tellurite and thus speciﬁc resistance determinants have not evolved. Likewise, selenate is much less toxic than selenite (Frankenberger and Engberg, 1998). Nonetheless, a few studies have now been done that describe genes involved in resistance and/or bioconversion of Se oxyanions. The tpmT gene is proposed to mediate selenite methylation in addition to tellurite methylation (Cournoyer et al., 1998).

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The ubiE gene of G. stearothermophilus described above, which mediates tellurate resistance through methylation, was found to also volatilize selenite and selenate (Swearingen et al., 2006). The methylated compounds were dimethyl selenide and dimethyl diselenide.

5. MICROBIAL PROCESSING OF METALLOID CHALCOGENS 5.1. Reduction The biochemical role of reduction and its tenuous correlation to susceptibility is an important unresolved factor in the study of microbial tolerance to Se and Te oxyanions. When cultures of microorganisms are exposed to Se or Te oxyanions, a reaction occurs that leads to the formation of crystals or nanoparticles of the metalloid in a reduced form (see also Fig. 2). There are several examples that suggest that the chemistry leading to reduction may provide a base level of resistance to an organism (Avazeri et al., 1997); however, many of the genetic resistance determinants described above act independently of this. For instance, while working to clone the ter operon from pTE53, Burian et al. (1998) discovered ‘‘white-colony’’ variants with a level of tellurite resistance comparable to ‘‘black-colony’’ variants harboring the same plasmid. In this case, a 3.5-fold relative decrease in TeO2 3 uptake was noted and the authors concluded that an insertion mutation had

Figure 2 There are a number of outcomes for a chalcogen oxyanion within the cells. The primary process dictates how toxic the oxyanion will be to the microorganism and the related damage as well as any transformation of the oxyanion.

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occurred in an unknown chromosomal gene likely to be responsible for membrane transport of this anion. In another example, cultures of bacteria harboring kilAtelAB were observed to reduce TeO2 3 at a slower rate that growth controls lacking this determinant (Turner et al., 1994a). An attractive explanation for this was the hypothesis that kilAtelAB may encode a 2 TeO2 3 efﬂux system. However, the difference in the overall TeO3 reduction rate of the kilAtelAB transformed culture was the result of slower metabolism in the transformant (Turner et al., 1994a) and not due to reduced uptake (Turner et al., 1995a). This section is focused on the bioreduction of Se and Te oxyanions by bacteria and fungi. This biological process has a history of application to clinical microbiology as well as to electron microscopy and may also be important in the biogeochemical cycling of minerals. A frustrating limitation of the data presented here is that it cannot resolve whether there is a true correlation between metalloid reduction and the mechanism(s) of resistance. 5.1.1. Selenium Selenium is found in four inorganic oxidation states. Comparative biological toxicity of several selenium compounds representing the different oxidation states of this element were originally evaluated in rats by Franke and Painter (1938) and in humans by Vinceti et al. (2001). The soluble oxyanions selenate and selenite were poisonous in concentrations of ppm. In contrast, elemental selenium Se0 (0) is highly insoluble and relatively non-toxic and occurs as a prevalent chemical species under anoxic conditions (Barceloux, 1999). Selenide, S2 (-II), is both highly reactive and highly toxic, but is readily oxidized to Se0 through several possible, energetically favorable inorganic and/or biochemical reactions (Turner et al., 1998). A variety of bacteria from soil and aquatic environments have the ability to reduce Se(VI) and Se(IV) oxyanions to insoluble Se(0). Representative genera include Wolinella, Pseudomonas, Sulfurospirillum, Enterobacter, Thaurea, Bacillus, and Citrobacter (Zhang and Frankenberger, 2005; Sidique et al., 2006). Reduction of selenium oxyanions leading to bioaccumulation may also be mediated by plants (Hurd-Karrer, 1937). There is a great deal of interest in using resistant rhizosphere bacteria in conjunction with plant life as low cost treatments to manage contamination in selenium-laden efﬂuents (Di Gregorio et al., 2005; Vallini et al., 2005). Deposition of selenium particles may occur in the extracellular milieu for some microorganisms (Klonowska et al., 2005). For others, bioaccumulation of reduced selenium is intracellular, frequently in association with the cell wall or membrane (Gerrard et al., 1974). Four different types of

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biochemical mechanisms have been proposed that can account for the formation of nanoparticles of elemental selenium in cultures amended with Se(VI) or Se(IV). These are: (1) the Painter-type reactions of SeO2 or 4 SeO2 with reduced thiols, in particular with reduced glutathione, (2) the 3 enzymatic reduction of selenium oxyanions by periplasmic as well as cytosolic oxidoreductases, (3) inorganic reactions with bacterial metabolites, and (4) the reduction–oxidation reactions of Se oxyanions involving the siderophore pyridine-2,6-bisthiocarboxylic acid (PDTC). Some of these pathways have been described previously in E. coli (Turner et al., 1998). Below, we describe these four putative mechanisms focusing on the reduction to Se(0) (see also Fig. 3 for a general scheme). Painter (1941) was the ﬁrst to observe the high reactivity of selenium oxyanions with thiol groups, particularly in proteins in toxic cereal grains during chemical analysis of poisonous plants growing in seleniferous soils. He discovered that selenium forms selenotrisulﬁdes (RS-Se-SR), which may be produced according to the following reaction: 4RSH þ H2 SeO3 ! RS-Se-SR þ RSSR þ 3H2 O

(1)

Figure 3 Biochemical pathways for the biological reduction of selenium and tellurium. The chalcogen (Ch, denoting Se or Te) oxyanions may be reduced by bacteria to form elemental precipitates through four generalized routes via: (1) enzymatic reduction, (2) methylation, (3) dissimilatory reduction concomitant with sulfate reduction, (4) Painter-type reactions with the thiols of proteins as well as glutathione, and (5) a chemical reaction with the siderophore PDTC and the products of PDTC hydrolysis. The italicized numbers refer to reactions listed and detailed in the text.

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It is important to note that the ﬁrst probable step in metabolic processing of SeO2 4 is an enzymatic (discussed below) or abiotic reduction–oxidation reaction to form SeO2 3 . The latter is a slow but energetically favorable reaction with glutathione (Shamberger, 1985). In this manner, some cells 2 may process SeO2 4 by the same pathways as SeO3 . As an interesting aside, the formation of selenotrisulﬁdes by E. coli has since been conﬁrmed in vivo using 77Se NMR to examine bacterial cultures amended with SeO2 3 (Rabenstein and Tan, 1988). Ganther (1968) identiﬁed that an analogous, Painter-type reaction occurs between SeO2 and the tripeptide glutathione. Recent evidence suggests 3 that when glutathione functions as an electron donor, the reduction of SeO2 also leads to the formation of superoxide anions (O 3 2 ) (Kessi and Hanselmann, 2004). The proposed equation for this reaction is: 6GSH þ 3H2 SeO3 ! 3GS-Se-SG þ O 2

(2)

O 2

may be removed by the combined enzymatic In biological systems, activity of superoxide dismutase and catalase. Generation of reactive oxygen species (ROS) such as O 2 and H2O2 in this process may account for the observed oxidative stress response of bacterial cells exposed to selenium oxyanions (Bebien et al., 2002). Regardless of these later ﬁndings, Ganther (1971) also demonstrated that the biological reduction of selenodiglutathione (GS-Se-SG) was mediated by the cellular enzyme glutathione reductase (GR): GS-Se-SG þ NADPH ! GSH þ GS-Se þ NADPþ

(3)

As a terminal step in this biochemical pathway, elemental selenium may be produced by an inorganic reaction between the unstable glutathione selenopersulﬁde (GS-Se) and a proton (H+), regenerating a single molecule of glutathione in the process: GS-Se þ Hþ ! GSH þ Se0

(4)

Thioredoxin is a ubiquitous protein with a redox-active dithiol/disulﬁde in the active site. Work with thioredoxin reductase (TR) extracts from E. coli suggest that thioredoxin (Trx) may reduce selenodiglutathione (Bjornstedt et al., 1992). Oxidized Trx can in turn be reduced by TR in a NADPHdependent manner to regenerate reduced thioredoxin (Bjornstedt et al., 1992; Kumar et al., 1992). This may be represented by the following two reactions: Trx-ðSHÞ2 þ GS-Se-SG ! Trx-S2 þ GSH þ GS-Se

(5)

Trx-S2 þ NADPH þ Hþ ! Trx-ðSHÞ2 þ NADPþ

(6)

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The selenopersulﬁde product from (5) may then undergo the spontaneous dismutation reaction (4) that generates Se0. It is important to note that the mechanism of biological reduction of SeO2 differs from the inorganic reduction–oxidation reaction that 3 produces Se0, particularly with respect to the generation of ROS (Kessi and Hanselmann, 2004). In summary, the likely ﬁrst step in the biochemical mechanism for generating elemental selenium in bacterial cultures involves the reaction between Se oxyanions and reduced thiols, followed by the subsequent action of glutathione reductase and/or thioredoxin reductase. Other biomolecules that contribute to the biological process of metalloid reduction are redox active enzymes, many of which are components of bacterial electron transport chains. Various enzymatic systems, such as nitrate 2 (NO 3 ) and nitrite (NO2 ) reductases as well as sulfate (SO4 ) and sulﬁte (SO3 ) reductases, are suspected to be involved in the overall reduction of 0 2 2 2 SeO2 4 and SeO3 to Se . For example, the reduction of SeO4 to SeO3 may be carried out by the E. coli periplasmic nitrate reductase NapA, or through the action of the cytoplasmic nitrate reductases NarGHIJ or NarZUWV (Avazeri et al., 1997). Selenite generated in this fashion can undergo further reduction via reactions (1) through (6). In another example, De Moll-Decker and Macy (1993) have suggested that reduction of SeO2 to Se0 in T. selenatis may be catalyzed by a 3 periplasmic dissimilatory nitrite reductase. Similarly, a dissimilatory sulﬁte reductase from Clostridium pasteurianum has shown a high selenite reductase activity (Harrison et al., 1984). More recently, the work of Kessi (2006) has demonstrated that there is metabolic interference between selenite and sulﬁte as well as selenite and nitrite metabolism in logarithmic-growing Rhodobacter capsulatus. However, R. capsulatus stationary phase cells that can no longer reduce nitrite or sulﬁte still may metabolize selenite, suggesting that nitrite, sulﬁte, and selenite reduction may be catalyzed by independent pathways in this microorganism (Kessi, 2006). Overall, these studies suggest that the catalytic speciﬁcity of oxidoreductases for SeO2 4 and SeO2 may be different or even absent from certain classes and/or 3 families of these enzymes. It is also interesting to note that in sulfate-reducing bacteria (SRB), SO2 4 reduction is linked to the concomitant precipitation of sulfur and selenium in SeO2 3 amended cultures. It is likely that this is not due to the direct action of redox active enzymes; rather Hockin and Gadd (2003) postulated that this was due to the following inorganic reaction: þ SeO2 ! Se0 þ 2S0 þ 3H2 O 3 þ 2HS þ 4H

(7)

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A ﬁnal mechanism for metalloid reduction involves siderophores – ironspeciﬁc (Fe3+) chelators produced by microorganisms under nutrientlimited conditions as part of an iron acquisition system. The chelator PDTC, which is produced by Pseudomonas stutzeri and P. putida, has an ability to bind a broad range of metals, including many transition metals, lanthanides and actinides (Cortese et al., 2002). Many toxic metals form insoluble precipitates with PDTC, including toxic selenium and tellurium oxyanions (Zawadzka et al., 2006). These workers proposed that SeO2 may be 3 reduced and bound by PDTC or its hydrolysis product, dipicolinic acid [pyridine-2,6-bis(carboxylic acid)] (DPA). The authors represented this reaction qualitatively, but not stoichiometrically, as the following two linked reactions: PDTC þ H2 O ! DPA þ Hþ þ H2 S þ e

(8)

þ SeO2 ! Se0 þ S0 þ H2 O þ DPA 3 þ PDTC þ H2 S þ H þ e

(9)

SeO2 4

SeO2 3

and may be reduced through several mechTo summarize, anisms within bacteria, encompassing energetically favorable reactions with thiols, reduction–oxidation reactions mediated by enzymes, inorganic precipitation with bioenergetically produced sulﬁde, and precipitation via reactions with siderophores and their hydrolysis products. 5.1.2. Tellurium Similar to selenium, tellurium has four inorganic oxidation states: the II, 0, IV, and VI valence states. Te(II) is chemically reactive and is naturally incorporated into organic tellurides. In fact, reduction to dimethyl telluride is responsible for the hallmark garlic breath of acute tellurium toxicity in animals (Hollins, 1969; Taylor, 1996) as well as in humans (Blackadder and Manderson, 1975; Yarema and Curry, 2005). Biomethylation of Te is further discussed below. The tellurium oxyanions, tellurite and tellurate, have been considered in the literature as strong oxidizers and this chemical attribute is considered to be the explanation for their toxicity in vivo (Taylor, 1999). Gram-negative bacteria are especially sensitive to tellurium oxyanions; hence, there is a history of using potassium tellurite (K2TeO3) as a selective agent in microbiological growth medium for the isolation of pathogenic bacterial species from food, clinical, and environmental samples (Zadic et al., 1993; Donovan and van Netten, 1995). Klett (1900) was the ﬁrst to note the biochemical transformation of TeO2 3 into a black, insoluble precipitate that, at the time, was presumed to be metallic tellurium. This chalcogen is considered to be

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relatively non-toxic in its elemental state (Te0), although there is no published data that explicitly addresses this assumption. In support of this notion, it was observed that E. coli growth on agar plates amended with black Te0 precipitates from spent cultures is similar to growth on agar plates without the reduced metalloid (R.J. Turner, unpublished data). The capacity to reduce tellurium is not restricted to resistant microorganisms, nor is it unique to pathogens (Harrison et al., 2005c). A variety of bacterial aerobic and anaerobic phototrophs (Moore and Kaplan, 1992), hydrothermal vent heterotrophs (Rathgeber et al., 2002), eukaryotes, such as fungi (Kuhn and Jerchel, 1941) and plants (Schreiner and Sullivan, 1911), and the mitochondria in animal tissues (Barrnett and Palade, 1957) may carry out various reactions leading to black precipitates. In contrast to selenium, bacterial deposition of tellurium crystallites is almost exclusively intracellular (van Iterson and Leene, 1964a,b; LloydJones et al., 1994; Klonowska et al., 2005). Transmission electron microscopy (TEM) indicates that metalloid precipitation usually occurs in close physical proximity to the cell wall and/or lipid membranes. Reduction of tellurium oxyanions may also occur through four documented processes: (1) a Painter-type reaction with glutathione (Turner et al., 2001); (2) catalytic reduction by periplasmic and cytoplasmic oxidoreductases (Avazeri et al., 1997); (3) a reduction–oxidation reaction involving the iron siderophore PDTC (Zawadzka et al., 2006); and (4) direct or indirect reduction by electrons siphoned from the membrane-bound respiratory chain (Trutko et al., 2000). Tellurium and selenium chemistry are similar in many regards; this is exempliﬁed again as the ﬁrst three mechanisms of tellurite reduction are similar to those presented previously for selenium. However, tellurium oxyanions may differ in their site-speciﬁc interaction(s) with components of the bacterial respiratory chain; it has recently been shown that the redox state of several electron transport redox components can be affected by tellurite (Borsetti et al., 2007). In membrane fragments isolated from cells of the facultative phototroph R. capsulatus, addition of tellurite induces an acceleration of the QH2:cyt c oxidoreductase activity, an effect which is both speciﬁcally inhibited by antimycin A and dependent on the presence of the membrane-associated thiol:disulﬁde oxidoreductase DsbB. These results not only blur the proposal by Trutko et al. (2000) that membrane-bound oxidases are involved in tellurite reduction but also exclude the possibility that the oxyanion has a general oxidizing effect on the membrane redox components. Microbiologists generally accept that the crystalline precipitates produced by microorganisms growing in the presence of K2TeO3 are metallic tellurium. This is founded on two sets of observations: (1) chemical data,

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including the observation that these precipitates rapidly dissolve in the presence of a strong oxidizing agent, such as bromine (Morton and Anderson, 1941); and (2) X-ray diffraction analysis of tellurium precipitates from C. diphtheriae and Group D Streptococci (Tucker et al., 1962, 1966). It is interesting to note that pure, metallic tellurium ore is a lustrous silverwhite, which starkly contrasts the black, reduced tellurium precipitates recovered from bacterial cultures. To date, this discrepancy has neither been sufﬁciently addressed nor adequately resolved in the literature. It is unclear whether any other organic material may be associated with the tellurium precipitates found in vivo. For instance, scanning electron microscopy energy dispersive spectroscopy (SEM-EDS) suggests that tellurium precipitates may be in proximal association with organosulfur compounds (Zawadzka et al., 2006).

5.2. Methylation A common biological response to Se and Te exposure is methylation. Over a hundred years ago, it was observed that upon Se or Te exposure, a distinct and unpleasant garlic-like odor emanated from their biological acquisition. We now recognize that this odor originates from methylated derivatives of the chalcogens. Such methylated forms in microbes include: dimethyl selenide, CH3SeCH3; dimethyl selenenyl sulﬁde, CH3SeSCH3; dimethyl diselenide, CH3SeSeCH3; dimethyl telluride, CH3TeCH3; and dimethyl ditelluride, CH3TeTeCH3 (Chasteen and Bentley, 2003). Of these compounds, the methylated tellurides are considered to have the more ‘‘disagreeable character’’. An interesting anecdote is that garlic as a nutriceutical appears to have the ability to reduce cholesterol levels, and garlic tends to accumulate Te; incidentally, tellurite has been shown to have hypocholesterolaemic effects (Larner, 1995). Methylation of selenium and tellurium in microorganisms has been extensively reviewed by Chasteen and Bentley (2003) and the reader is directed there for an extensive overview. Chalcogen methylation in bacteria appears to be reasonably common, and examples of methylated products have been reported from the IV and VI, as well as 0, redox states of Te and Se. Similarly, some organisms will convert organochalcogen compounds such as selenomethionine or telluromethionine to methylated derivatives (Chasteen and Bentley, 2003). The biochemical mechanism of the methylation has been explored in only a few organisms; nonetheless, such work has led to the assumption that the methyl group originates from S-adenosylmethione (SAM). Reports also suggest the possibility that methyl

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cobalamin contributes to Se methylation as well (Thompson-Eagle et al., 1989). Overall, the methylation of Se is thought to occur via a form of the Challenger mechanism. This includes a series of reduction methylation steps alternating the redox state of the Se from VI to IV and ﬁnally a dual reduction of dimethylselenone through a Se(III) to Se(II) to dimethylselenide (Challenger, 1945). This mechanism was later modiﬁed to account for dimethyl diselenide via a methyl selenide intermediate (Reamer and Zoller, 1980). The majority of studies have explored mixed microbial populations in soils, waters, sediments, and efﬂuents from metal-contaminated areas as well as in sewage sludge that has not changed much from such early reports (Chau et al., 1976; Cooke and Bruland, 1987). Methylated metals and metalloids are commonly observed in gases released from anaerobic wastewater treatment facilities, presumably due to the microbial activity (Michalke et al., 2000). Chasteen and Bentley (2003) provide a list of organisms that have been identiﬁed with the biomethylation of selenium. The surprise in the list is that Rhodobacter sphaeroides, Rhodocyclus tenuis, and Rhodospirillum rubrum have the ability to use Se(0) and Te(0) as a substrate (Van FleetStalder and Chasteen, 1998). This provides the possibility of biomining of minerals of these chalcogens. A point to consider is that methylation and reduction are likely to be mutually exclusive activities. A study that alludes to this examines P. fluorescens K27 where although, considerable dimethyl telluride is produced from methylation, reduction is still the fate of one third of the amended tellurite (Basnayake et al., 2001).

5.3. Biofilms Microbial bioﬁlms are cell–cell or solid–surface attached assemblies of bacteria that are surrounded by an extracellular matrix of polymers. Growth in a bioﬁlm is part of the natural ecological cycle for the vast majority of microbes (Kolter and Greenberg, 2006) and is regarded as a developmental process likened to differentiation in multicellular organisms (Hall-Stoodley et al., 2004; Harrison et al., 2005f). A typical bioﬁlm forms when bacteria stick to a surface and become permanently attached, triggering a change in physiology. The bacteria then grow and divide to form layers, clumps or stalk, and mushroom-shaped microcolonies, all under the control of speciﬁc bioﬁlm genes (Stoodley et al., 2002). At every stage of growth, bioﬁlm bacteria are generally more resilient to antimicrobials than their planktonic counterparts. For example, E. coli bioﬁlms are up to 100 times more tolerant

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to antibiotics and disinfectants than the corresponding logarithmic-growing planktonic cells (Spoering and Lewis, 2001; Harrison et al., 2005b). The exploration of bioﬁlm susceptibility to metal toxicity has only just begun (Teitzel and Parsek, 2003; Harrison et al., 2004a). Recently, Teitzel and Parsek (2003) demonstrated that P. aeruginosa bioﬁlms are up to 600 times more tolerant to heavy metal cations than logarithmic-growing planktonic cells. Harrison and coworkers have shown a comparable trend in E. coli bioﬁlms exposed to the selenium and tellurium 2 oxyanions, selenite (SeO2 3 ) and tellurite (TeO3 ) (Harrison et al., 2005b,c,d). For bacterial bioﬁlms, this tolerance is time dependent. In fact, bioﬁlms can be eradicated under certain test conditions by a number of 2 metal ions including SeO2 and TeO2 (Harrison et al., 2004a,b, 3 3 /TeO4 2005a,b).A 24-h challenge period using potassium tellurite gives an MIC1 of 0.006 mM, an MBC2 of 0.016 mM, and an MBEC3 of 0.014 mM for E. coli; MIC of 0.073 mM, MBC of 3.1 mM, and an MBEC of 4.4 mM for P. aeruginosa; and MIC of 0.18 mM, MBC of 40.73 mM, and an MBEC of 0.73 mM for S. aureus. For sodium selenite, there was no difference between the MIC, MBC and MBEC, which were: 8.1 mM for E. coli, 28 mM for P. aeruginosa, and 16 mM for S. aureus (Harrison et al., 2004a). Although the physiology of bioﬁlms is considerably different from planktonic growth, the metabolism and thus the degree of protection conferred to microbes by bioﬁlm formation varies from organism to organism. In terms of application, understanding the molecular mechanisms that contribute to metal tolerance is a logical ﬁrst step in utilizing bioﬁlms for bioremediation of metal(loid) contaminated soils and wastewaters. This section explicitly focuses on a multifactorial model of metal tolerance that may be used to explain the reduced susceptibility of bioﬁlms to watersoluble oxyanions of selenium and tellurium. It is important to emphasize that the altered susceptibility of bioﬁlms to these compounds occurs in the absence of speciﬁc selenium and tellurium genetic resistance determinants; therefore, microbial bioﬁlm formation is an innate strategy for microorganisms to survive exposure to metal toxicity. Bacterial bioﬁlms derive their astonishing tolerance to metal toxicity from at least six contributing factors (Harrison et al., 2005e,f), many of which also contribute to antibiotic tolerance (Lewis, 2001, 2005). This includes: (1) structure-dependent metabolic gradients arising from restricted penetration of nutrients and oxygen into the bioﬁlm; (2) a distinct bioﬁlm physiology controlled by a set of 1

MIC: minimum inhibitory concentration – inhibition of planktonic growth. MBC: minimum bactericidal concentration – killing of planktonic bacteria. 3 MBEC: minimum bioﬁlm eradication concentration – killing of bioﬁlm bacteria. 2

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bioﬁlm-speciﬁc genes; (3) sequestration of ions in the bioﬁlm matrix; (4) an adaptive physiological response to metal toxicity; (5) self-generated genetic diversity within the community that gives rise to variant cell phenotypes; and (6) a small population of specialized survivor cells termed ‘‘persisters’’. These factors and their potential contribution to the reduced susceptibility of bacterial bioﬁlms to chalcogen toxicity are outlined in the sections below. 5.3.1. Structure and Susceptibility The architecture of mature bioﬁlms is irregular but complex, and communities are intermingled with networks of ﬂuid-ﬁlled channels (Lawrence et al., 1991). Bioﬁlm structure is mechanically elastic and the constituent cells have metabolic plasticity, which together allow the bacteria to be malleable in the face of environmental factors, such as shear, chemical, and nutritive stresses (O’Toole and Kolter, 1998; Stoodley et al., 1999). There are many elegant studies showing metabolic gradients within solid surfaceattached bioﬁlms, a stratiﬁcation that is correlated to the restricted penetration of oxygen and nutrients from the liquid phase to the microbial community (Huang et al., 1998; Xu et al., 1998; Werner et al., 2004). As a result, bacteria growing in a bioﬁlm can possess very different and distinct physiological states, even when separated by as little as 10 mm (Xu et al., 2000). In general, the bacteria nearest the substratum are in an anoxic zone and are thus slow-growing, which leads to an intrinsic resistance to killing by antibiotics relative to the fast-growers in the outer layers of the bioﬁlm (Walters et al., 2003; Borriello et al., 2004). This structure-dependent metabolic heterogeneity may also explain, in part, the tolerance of the bacterial bioﬁlms to metal(loid) ions. For example, since the reduction of TeO2 3 to Te0 is suggested to be correlated with speciﬁc electron transport activity of the respiratory chain (Trutko et al., 2000), differential expression of electron transport chain components in aerobic, anaerobic, and microaerophillic regions of a bioﬁlm may play a role in bioﬁlm tolerance to TeO2 3 . This structure–function relationship has recently been examined by comparing the bioﬁlm susceptibility of a parental E. coli strain to its isogenic twin-arginine translocase (tat) mutant (Harrison et al., 2005c). E. coli strains lacking this membrane-associated, protein transport apparatus have a variety of cell envelope-related defects, including abnormal cell division as well as hypersensitivity to detergents and hydrophobic drugs (Stanley et al., 2001). E. coli DtatABC mutants also have an impaired ability to form bioﬁlms, in particular under nutrient-restricted conditions (Ize et al., 2004; Harrison et al., 2005c). Bioﬁlms of E. coli DSS640 (DtatABC) that

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lack the highly organized microcolony structure of the isogenic, parental strain (E. coli TG1) still possess elevated tolerance to antimicrobials, including a 10-fold increased tolerance to TeO2 3 in comparison to the corresponding planktonic cells. However, this tolerance is still diminished relative to the wild type E. coli bioﬁlms (Harrison et al., 2005c). This indicates that bioﬁlm structure (and the interdependent metabolic stratiﬁcation in the community) is only one amongst several contributing factors to chalcogen tolerance. 5.3.2. Biofilm Physiology A comparative study has shown that, with respect to the different forms of microbial growth, bioﬁlms and planktonic cultures of the same bacterial 2 2 strain may process SeO2 3 , TeO4 , and TeO3 in different ways (Harrison et al., 2004b). P. aeruginosa ATCC 27853 bioﬁlms and planktonic cultures reduce selenium and tellurium oxyanions to orange and black end-products, respectively. In all cases, P. aeruginosa is highly tolerant to killing by these metalloid oxyanions. Similarly, planktonic cultures of S. aureus ATCC 29213 are able to process these compounds to produce elemental precipitates and are resilient to their toxicity. However, the corresponding S. aureus bioﬁlm cultures do not produce colored end-products typical of metalloid reduction and are two- and ﬁvefold more susceptible to killing by 2 TeO2 4 and TeO3 , respectively. Although the change in bioﬁlm cell physiology decreases tolerance to tellurium oxyanions, in this case it also demonstrates that chalcogen biochemistry and chemistry may be altered in a bioﬁlm. An additional factor in bioﬁlm physiology is the process of cell–cell signaling by quorum-sensing (QS). Many groups have examined QS control of bioﬁlm formation. Although, in some cases, QS does not appear to be involved, there are many bacterial species in which QS does inﬂuence bioﬁlm development (Parsek and Greenberg, 2005). In relationship to defense against chalcogen toxicity, some bacterial species, such as P. aeruginosa, upregulate expression of cellular defense machinery, for instance genes against ROS including superoxide dismutase (sodAB) and catalase (katA) (Hassett et al., 1999). This is important as oxidative stress is involved in tellurite toxicity (Rojas and Vasquez, 2005). Sublethal amounts of both selenite and tellurite increase superoxide dismutase activity and this effect mimics the early cellular response to oxidative stress (Bebien et al., 2002; Borsetti et al., 2005). In this fashion, the change in physiology innate to the bioﬁlm lifestyle may afford an additional level of protection for bacteria against the oxidative toxicity of metalloid oxyanions.

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5.3.3. Sequestration in the Biofilm Matrix The precise composition of a bioﬁlm extracellular matrix varies with the environment as well as the genotype(s) of the constituent microorganism(s) (J.J. Harrison, H. Ceri, and R.J. Turner, submitted work). In general, the bioﬁlm matrix is a highly charged, viscoelastic hydrogel that comprises oligonucleotides (Whitchurch et al., 2002), species-speciﬁc proteins (Branda et al., 2006), amino acids (Sutherland, 2001b), and assorted polysaccharides (Sutherland, 2001a; Wozniak et al., 2003; Branda et al., 2005). Selenium and tellurium oxyanions may equilibrate across the bioﬁlm matrix at a slowed rate due to steric and/or ionic hindrance, similar to other charged molecules (Stewart, 2003). The bioﬁlm itself has pH and reduction–oxidation gradients (Pringault et al., 1999) that can affect anion speciation; in these microenvironments, certain constituents of the extracellular matrix may bind, as well as react with, these oxyanions. Sequestration of chalcogens by the bioﬁlm matrix is thus considered here as a potential contributor to metal tolerance. The extracellular polymeric substances produced by E. coli have been well studied. Colanic acid is the major extracellular polysaccharide for many E. coli strains (Potrykus and Wegrzyn, 2004) and is important for the ability of this microorganism to form bioﬁlms (Whitﬁeld and Roberts, 1999). Colanic acid is anionic and would thus have a low afﬁnity for binding both 2 SeO2 3 and TeO3 . This is evidenced by the ability of these ions to eradicate bioﬁlms, which necessitates that the oxyanions completely penetrate the matrix (Harrison et al., 2005b). Furthermore, the organic chelator sodium diethyldithiocarbamate can be used to coordinate and precipitate transition metals and metalloid oxyanions in vitro. Its use produces visible metal–chelator complexes in Cu2+-treated bioﬁlms; however, when used against Se or Te oxyanion-treated E. coli bioﬁlms, no precipitate is seen (Harrison et al., 2005b). Rather, E. coli in bioﬁlms have the propensity and 2 2 2 capacity to reduce SeO2 to their elemental 4 , SeO3 , TeO4 , and TeO3 forms, which is predominantly an intracellular phenomenon in this microorganism (Harrison et al., 2005c). Overall, this indicates that the extracellular matrix of E. coli bioﬁlms may sequester only small quantities of selenium and tellurium. In contrast, some microorganisms may produce chemically reactive metabolites that cause the precipitation of metalloid oxyanions in the extracellular matrix. For instance, sulfate-reducing bacteria (SRB) produce sulﬁde (S2) through dissimilatory sulﬁde biogenesis. Under low redox conditions and in the dark, precipitation of elemental selenium in SRB bioﬁlms may occur via an abiotic reaction with bacterially generated S2 (Hockin and Gadd, 2003). Boils of Shewanella oneidensis growing in anaerobic conditions

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may also reduce SeO2 3 and accumulate elemental selenium in the extracellular milieu (Klonowska et al., 2005). However, the amount of SeO2 3 reduced is highly dependent on the nature of the electron donor and terminal electron acceptor. It is important to note that S. oneidensis retains the capacity to reduce TeO2 3 and accumulate elemental tellurium within the cell (Klonowska et al., 2005). The coordination and/or reaction of chalcogens with components of the bioﬁlm matrix could sequester toxic anions away from the bacterial cells. This may provide a level of protection commensurate with the kinetics of the reaction equilibrium, which may restrict diffusion as well as alter biological availability of the chalcogen oxyanions. 5.3.4. Adaptive Stress Responses Szomolay et al. (2005) have proposed that reaction-diffusion limited penetration of bioﬁlms may result in low levels of antimicrobial exposure to bacterial cells in deep regions of the community. Cells sheltered in this fashion may be able to enter an adapted physiological state that is resistant to the antimicrobial. To date, there have been no studies examining the global changes to the bacterial transcriptome that accompany chalcogen exposure. However, proteomic ﬁngerprinting of bioﬁlm and planktonic E. coli exposed to TeO2 indicate that the proteome undergoes global 3 changes after only a few hours of exposure to this toxic compound. By contrast, similarities in protein proﬁles obtained for bioﬁlms and planktonic cultures suggest that certain features of this adaptive response may be shared by both modes of bacterial growth (N.J. Roper, personal communication). Although the understanding of this physiological adaptation is still superﬁcial, proteomic ﬁngerprinting suggests that TeO2 3 elicits a complex biological response from bacterial bioﬁlm populations. Accordingly, an adaptive stress response of bioﬁlms (vs. the innate physiological difference of bioﬁlms compared with planktonic cells) may further contribute to chalcogen tolerance. 5.3.5. Genetic Diversity and Colony Morphology Variants In many instances, bioﬁlm growth leads to the formation of colony morphology variants that may display altered phenotypic traits relative to the parental, colonizing strain. Small colony variant (SCV) cells, which are frequently recovered from bioﬁlms of clinical and/or rhizosphere Pseudomonas spp., are an example of this (Ha¨uXler, 2004; Kirisits et al., 2005; van den Broek et al., 2005). Typically less motile, these SCV isolates are superior at

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forming bioﬁlms compared with their progenitors and occur at a frequency in the population that is increased by exposure to certain antibiotics, metal ions, and H2O2 (Drenkard and Ausubel, 2002; Davies et al., 2007). It was recently discovered that TeO2 3 may trigger the formation of SCV cells in bioﬁlms of P. fluorescens (J.J. Harrison, M.L. Workentine, H. Ceri, and R.J. Turner, unpublished data). Variant formation may be an important contributor to chalcogen tolerance, as the switch to the SCV phenotype is correlated with the emergence of multidrug and metal resistance (Drenkard and Ausubel, 2002; Davies et al., 2007). SCV cells of S. aureus are frequently auxotrophic for menadione or hemin, two compounds that are required for the biosynthesis of menaquinone and cytochromes, respectively (McNamara and Proctor, 2000). S. aureus strains bearing inactivating mutations in menD or hemB, which are required for the synthesis of menadione and hemin, produce stable SCV cells (von Eiff et al., 2006). S. aureus menD and hemB mutants with a stable SCV 2 phenotype are hypersensitive to SeO2 3 and/or TeO3 (von Eiff et al., 2006). Similar to P. aeruginosa SCV cells, S. aureus SCV cells are hyperadherent (Vandaux et al., 2002), providing an additional link between bioﬁlm formation and altered susceptibility to chalcogen toxicity. Although the molecular mechanism that triggers the formation of SCV cells is unknown, it was shown that the reversion of P. aeruginosa PA14 and P. chlororaphis O6 SCV cells to a normal colony morphotype requires the sensor kinase GacS (Davies et al., 2007). In many rhizosphere and laboratory strains of Pseudomonas spp., gacS is naturally prone to inactivating mutations (Duffy and Defago, 2000; Sa´nchez-Contreras et al., 2002; van den Broek et al., 2005). Moreover, phenotypic variation in P. fluorescens is mediated by two site-speciﬁc recombinases, XerD and Sss, which appear to introduce mutations into gacA and/or gacS (Martinez-Granero et al., 2005). The stability of many types of biological systems is increased by genetic diversity, and Boles et al. (2004) have recently reported that P. aeruginosa may introduce genetic diversity into the bioﬁlm population in a recAdependent manner. In this manner, genetic diversity may act as ‘‘insurance’’ for microbial survival in a diverse range of environmental stresses. In the analogous case of SCV cells, genetic diversity introduced to the bioﬁlm community by XerD and Sss may lead to the variation in cell phenotype, that appears to be linked to metal(loid) resistance. 5.3.6. Persister Cells The tolerance of bacterial bioﬁlms to antimicrobials may be explained, in part, by the presence of a large number of specialized survivor cells termed

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‘‘persisters’’ within the adherent community. This subpopulation of cells is estimated to account for 0.0001 to 0.001% of the logarithmic-growing bacteria in planktonic culture (Moyed and Bertrand, 1983), but may represent as much as 1–10% of the cells in a bioﬁlm (Spoering and Lewis, 2001). These slow-growing phenotypic variants are able to withstand exposure to chemically and structurally unrelated bactericidal agents (Lewis, 2005). A hallmark of the persistent phenotype is biphasic population killing kinetics by antibiotics and disinfectants that are either time-dependent (Sufya et al., 2003; Balaban et al., 2004; Keren et al., 2004a) or concentration-dependent (Brooun et al., 2000; Spoering and Lewis, 2001). Both of these characteristics are common to cell death kinetics in bioﬁlms exposed to bactericidal 2 2 concentrations of metal ions, including SeO2 3 , TeO4 , and TeO3 (Harrison et al., 2005a,b). Bacterial persistence is best understood in E. coli and has a wellestablished genetic basis linked to the expression of chromosomal toxin– antitoxin (TA) modules (Moyed and Bertrand, 1983; Moyed and Broderick, 1986; Scherrer and Moyed, 1988; Black et al., 1991, 1994; Korch et al., 2003). The fraction of persisters in the E. coli population is controlled, in part by the TA module high persistence (hipBA) operon (Keren et al., 2004b), which encodes a toxin (HipA) and an antitoxin (HipB). The mechanism of persister formation is only partially understood, but involves the interaction of HipA with a downstream target to arrest macromolecular synthesis (Keren et al., 2004b; Korch and Hill, 2006). Mutants bearing inactivating mutations in hipBA produce a smaller proportion of persisters in stationary-phase cultures and in bioﬁlms than wild type E. coli (Keren et al., 2004b). In fact, certain alleles of hipA increase the frequency of persisters in the bacterial population. The hipA7 allele is a gain-of-function mutation known to mediate a 20-fold increase in relative size of the persister cell population produced by E. coli approaching stationary phase (Korch et al., 2003). Stationary phase cultures of the E. coli hipA7 mutant produce up to an 80-fold increase in the relative size of the bacterial population 2 surviving exposure to TeO2 4 and TeO3 (Harrison et al., 2005b). These data suggest that persister cells, which occur at increased frequency in bioﬁlm bacterial populations, may mediate time-dependent tolerance to metalloid oxyanions. 5.3.7. Fungal Biofilms Up to this point, this review has focused on bacterial bioﬁlms. Similarly, fungi may also form surface-adherent bioﬁlms that are innately multidrug and metal resistant. Bioﬁlm formation by fungi is best characterized for

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Candida spp. This genus of polymorphic yeast produces bioﬁlms through a stepwise developmental process involving cellular differentiation (Parahitiyawa et al., 2006). For instance, C. albicans produces exopolymer entrenched, mature bioﬁlms that are composed of a basal layer of yeast cells from which hyphae extend into the liquid medium (Chandra et al., 2001). To date, the inﬂuence of chalcogens on fungal bioﬁlms has been examined only in C. tropicalis. Bioﬁlms of this microorganism continue growing in twice the concentration of SeO2 3 required to sterilize a planktonic culture of equivalent cell density (Harrison et al., 2006). C. tropicalis bioﬁlms are also highly resistant to TeO2 (Harrison et al., 2006). Surprisingly, low 3 concentrations of SeO2 affect the pattern of cellular differentiation in the 3 bioﬁlm and thus change the structure and organization of the surfaceadherent community. In particular, SeO2 3 inhibits the transition from yeast to hyphal cell morphotypes; thus exposed bioﬁlms consist of only a ﬂat layer of yeast cells that lack both microcolony structure as well as hyphae (J.J. Harrison, R.J. Turner, and H. Ceri, unpublished data). Similarly, microscopic investigation of Aspergillus parasiticus Var. globosus revealed morphological changes to the fungi which were more marked with increased concentration of either selenite or tellurite (Zohri et al., 1997). Since the emergence of drug resistance coincides with multiple cell morphotypes in bioﬁlm maturation (Chandra et al., 2001), metal(loid) ions may alter bioﬁlm susceptibility to natural or synthetic antimicrobial agents, including the chalcogens. These studies suggest that bioﬁlm formation may also be a strategy for fungi to survive exposure to metalloid toxicity.

6. CHALCOGENS AND BACTERIAL PHYSIOLOGY 6.1. Selenium The biochemistry of selenium in microbes has been discussed extensively (Heider and Bo¨ck, 1993; Turner et al., 1998; Stolz and Oremland, 1999; Birringer et al., 2002; Stolz et al., 2002, 2006) (see Fig. 2 for a general scheme). As mentioned above, Se is a trace element incorporated into several proteins in bacteria, archaea, and eukaryotes as selenocysteine (Sec) and selenomethionine. To date, hundreds of microbial selenoproteins have been identiﬁed. They belong to 10 principal selenoprotein families, although this number is destined to increase thanks to the development of innovative bioinformatic tools that allow the identiﬁcation of new classes of selenoproteins (Zhang et al., 2005). Analysis of the composition of

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selenoproteomes revealed that most are redox proteins, which use selenocysteine to coordinate redox-active metals (Mo, Ni, or W) or are involved in Sec-thiol redox catalysis (Kryukov and Gladyshev, 2004; Zhang et al., 2005). At present, very little information exists concerning the metabolic processes responsible for assimilation of inorganic selenium (such as selenate and selenite) into these selenoproteins. It was noted in the selenate-resistant E. coli strains, that sulfate uptake was inhibited by selenate. This occurs to a lesser extent in wild type strains, suggesting a connection between sulfate and selenate transport (Springer and Huber, 1973). Due to the similarities in the chemical properties of selenium and sulfur, it was proposed that the two elements were assimilated through the same pathway (Shrift, 1969; Stadtman, 1974). Selenate uptake in the model system of E. coli is considered to follow the sulfate uptake system for the most part, for incorporation into selenocysteine. However, early studies suggest that there may be an alternative uptake pathway as inhibitors of the pathway of sulfur incorporation do not completely stop selenite assimilation (Brown and Shrift, 1982). Studies with Salmonella typhimurium have demonstrated that, whereas selenate (SeO2 4 ) is transported by the sulfate permease (CysTWA system), selenite (SeO2 3 ) is taken up by a different process, involving neither the sulfate nor the sulﬁte assimilatory pathways (Brown and Shrift, 1980). Analogous studies in E. coli have demonstrated that transport of selenate is repressed by the presence of cysteine in the medium as well as sulfate uptake. Selenite transport, in contrast, is not repressed in the presence of this amino acid, suggesting that a distinct carrier for this oxyanion could exist (Brown and Shrift, 1982). Selenite seems to be accumulated by the sulfate carrier only when it is present at high concentrations, reﬂecting the low afﬁnity of the sulfate permease for the oxyanion compared with sulfate anion (Lindblow-Kull et al., 1985). Selenite uptake in C. pasteurianum was reported to occur via both a unidirectional ATPase (probably the sulﬁte uptake system) as well as via the DpH component of the proton motive force (Bryant and Laishley, 1989). The existence of an alternative carrier for selenite was also suggested for Selenomonas ruminatum, a species that cannot metabolize sulfate and selenate (Hudman and Glenn, 1984) as well as the phototrophic bacterium R. sphaeroides. In R. sphaeroides, a polyol ABC transporter has been indicated as the possible carrier of selenite into the cytoplasm (Bebien et al., 2001). It is worth noting that the uptake of the oxyanions arsenite (As(III)) and antimonite (Sb(III)) in E. coli AW3110 is facilitated by the glycerol facilitator GlpF, an aquaglyceroporin that helps the movement of neutral substrates but not ions (Sanders et al., 1997; Meng et al., 2004).

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In prokaryotes, selenium also participates in dissimilatory processes in which transformations result in the transduction of energy and/or detoxiﬁcation. Microbes that can use selenium oxyanions as terminal electron acceptors are widespread amongst prokaryotes and this ability is often used to distinguish between closely related species (Stolz and Oremland, 1999). The majority of these microorganisms are able to reduce selenate to selenite and to elemental selenium that is ﬁnally accumulated inside the cells as dense orange-red precipitates. The ability to use selenate as an alternative acceptor is often associated with the ability also to use arsenate (Stolz et al., 2002). By contrast, only a few species have been isolated for their ability to use selenite as an electron acceptor, namely the haloalkaliphilic Bacillus selenitireducens and three strains of an Aquificales sp. (Switzer-Blum et al., 1998; Takai et al., 2002). Reduction of selenate and selenite to elemental selenium, which is insoluble and less toxic, may inﬂuence the mobility and hence the bioavailability of the element in the environment (Oremland et al., 1990, 1991; Steinberg and Oremland, 1990). Although the remobilization of selenium by oxidation is possible, this biotic process is very slow compared with dissimilatory reduction (Dowdle and Oremland, 1998). Overall, it appears that microbial metabolism is predominately responsible for the biogeotransformation of selenium oxyanions in the environment. Once inside the cell, selenium derived from selenate or selenite may be incorporated into polypeptides as selenocysteine and selenomethionine. In order for this to occur, selenium oxyanions must be reduced to selenide. Selenite is reduced to selenide by the Painter reaction with GSH, the most abundant reduced thiol in the cytoplasm of the cells (Painter, 1941; Fahey et al., 1978). Although several Se-glutathione intermediates may be produced (such as GSSeO 2 and GSOSeSR), the principal adduct formed and shown by 77SeNMR is selenodiglutathione GS-Se-SG (Milne et al., 1994). Selenate may also react with GSH, albeit slowly (Shamberger, 1985), although selenate reduction to selenite catalyzed by periplasmic or membrane-associated nitrate reductases may be the ﬁrst step for the further incorporation of selenium. In the form of selenide, selenium can be incorporated into the free-amino acid selenocysteine by the enzyme O-acetylserine (thiol)-lyase (coded by cysK gene) or modiﬁed by the Sel system to give the speciﬁc aminoacyltRNASec. The free amino acid is not directly ligated to tRNA but can be esteriﬁed to tRNACys and subsequently inserted randomly into proteins in place of cysteine (Kaiser and Young, 1975). The effect of this replacement is deleterious for the cells as it can alter enzyme activities. The Sel system comprises four gene products (SelA, SelB, SelC, and SelD) and is directly involved in the biogenesis of Sec-proteins. SelC is the Sec-speciﬁc-tRNA

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(tRNASec), SelA and SelD are enzymes essential for the formation of the tRNASec from seryl-tRNA and SelB is an elongation factor that speciﬁcally recognizes the tRNASec (Stadtman, 1996). Importantly, tRNASec is not directly derived from ligation of selenocysteine to tRNA, but is derived from modiﬁcation of an L-serine residue previously ligated to tRNASec by L-serine ligase. SelD is a selenophosphate synthase, which produces an activated form of selenium, selenophosphate. SelA is the selenocysteine synthase that converts seryl-tRNASec to aminoacrylyl-tRNASec. Selenocysteine is incorporated into the polypeptide chains in response to the UGA codon. Though UGA is universally recognized as a stop codon, it can cue selenocysteine incorporation into polypeptide chains. This occurs through an RNA stemloop structure designated Sec insertion sequence (SECIS). SECIS elements are present immediately downstream of UGA codons in bacteria (Zinoni et al., 1990; Liu et al., 1998) and in 30 untranslated regions in archea (Rother et al., 2001). SelB is the elongation factor that recognizes the mRNA context of selenocysteine codons and, additionally, it can discriminate tRNASec from other tRNA species (Heider and Bo¨ck, 1993). This recognition of selenocysteine has come a long way from early experiments exploring the physiology of selenium on bacterial growth, which include the work of Huber et al. (1967) in which the effect of selenate was evaluated by its ability to replace sulfur. It was noted that the majority of added selenate was incorporated into proteins. Phototrophic bacteria have been shown to grow in the presence of 0.1–10 mM selenate or selenite. When phototrophic bacteria are grown in the presence of selenium oxyanions, small amounts of the oxyanions are reduced and/or methylated. A study in R. sphaeroides indicated that volatilization of selenite or selenate occurs only at low levels and is an insigniﬁcant fate for the selenium oxyanions taken up by this organism (Van Fleet-Stalder et al., 2000). Selenite was processed more efﬁciently than selenate in this organism. Although volatilization is not signiﬁcant, R. sphaeroides grown in the light produced more reduced volatile selenium than cultures kept in the dark (Van Fleet-Stalder et al., 1997). The selenium uptake system in R. sphaeroides operates at very low concentrations of selenium oxyanions. Its poor initial afﬁnity for selenite and even lower afﬁnity for selenate seems to be compensated for by a very effective subsequent reduction to trap any selenium that enters the cell (Van Fleet-Stalder et al., 2000). The physiology of the organism appears to initially put any excess Se into a form very similar to selenomethionine, or even selenomethionine itself. Any further Se excess appears to be completely reduced to the detoxiﬁed red elemental form Se(0), which has a very low bioavailability (Combs et al., 1996).

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It has been suggested that members of the Rhodospirillaceae family utilize oxidized compounds, including Te and Se oxyanions, to get rid of the excess electrons produced in anaerobic photosynthesis (Moore and Kaplan, 1992, 1994). This working hypothesis has recently garnered support by data indicating that tellurite reduction is likely to be mediated by the thiol:disulﬁde oxidoreductase DsbB by extracting reducing equivalents from the ubiquinone-pool (Borsetti et al., 2007). Selenite reduction is observed in R. sphaeroides f. sp. denitrificans under photosynthetic conditions after approximately 100 h lag time, the result of the induction of a molybdenumdependent enzyme (Pierru et al., 2006). This group concluded that there are several pathways of selenite reduction in this organism, at least one involving such an enzyme. Another example of Se oxyanion utilization in respiration is a novel, strictly anaerobic, hyperthermophilic, facultative organotrophic archaeon that utilizes carbon dioxide as the carbon source and can use hydrogen as an electron donor and arsenate, thiosulfate, or selenate as electron acceptors (Huber et al., 2000). This organism is related to Pyrobaculum aerophilum which can utilize arsenate, selenate, and selenite for electron acceptors. An organism of key importance in selenium microbial physiology is T. selenatis, which contains a respiratory selenate reductase that allows growth on selenate by utilizing it as a terminal electron acceptor (Schro¨der et al., 1997).

6.2. Tellurium Unlike selenite and selenate, no microorganism has been isolated for its ability to use tellurite as a terminal electron acceptor for growth. Tellurate, which is less toxic than tellurite, has recently been shown to sustain the anaerobic growth of a strain, EC-Te-48, isolated from hyperthermophilic vents (Csotonyi et al., 2006). Furthermore, it has been observed that nitrate reductases (NRs A and Z) from E. coli present tellurite and selenate reduction activities, leading to the deposition of Te0 and Se0. A soluble nitrate reductase is also able to reduce tellurite in anaerobically grown cells, though this activity does not allow the growth of the microorganism under anaerobic conditions without nitrate as a terminal electron acceptor. Interestingly, E. coli is able to utilize selenate and tellurite for anaerobic respiration when the NRA is induced in large amounts (Avazeri et al., 1997). Additionally, tellurite was found to negate the induction effect of nitrate on NRA, while Se oxyanions decrease the nitrate reduction by 50% (R.J. Turner and G. Giordano, unpublished results). This suggests that

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NR activity on Se and Te oxyanions is an unfavorable reaction for both the enzyme and the cell. Periplasmic and membrane-bound nitrate reductases from Ralstonia eutropha, P. denitrificans, Paracoccus pantotrophus, and the phototrophic bacterium R. sphaeroides have shown the ability to reduce tellurite and selenite in vitro as well, suggesting that tellurite- and selenate-reducing activities are a general feature of different denitrifying species. However, the catalytic activity of the isolated Nap enzyme from R. sphaeroides is too low to justify the high level of resistance of the microorganism, suggesting that other mechanisms may contribute to the resistance phenotype (Sabaty et al., 2001). Indeed, tellurite resistance without metal accumulation has been observed in some obligately aerobic photosynthetic bacteria, showing that tellurite resistance does not strictly depend on reduction to Te(0) (Yurkov et al., 1996). Trutko et al. (2000) have proposed that components of the respiratory chain of Gram-negative bacteria are involved in the reductive process. They postulate that the location of tellurium deposits is dependent on the plasma membrane position of the active site of terminal membrane-bound oxidases. However, they have also shown that rate of tellurite reduction does not always correlate with the intensity of respiration. Indeed, in cells of P. aeruginosa PAO ML 4262, the stimulation of cytochrome c oxidase (COX) activity by addition of ascorbate-dichlorophenolindophenol drastically lowers the Te0 deposition in cells (Trutko et al., 2000). This ﬁnding compromises the hypothesis that COX plays a direct role in reducing tellurite but is in line with other reports indicating that COX activities in membranes from P. pseudoalcaligenes KF707 and R. capsulatus cells grown in the presence of tellurite, drop concurrently with a drastic decrease of the soluble c-type heme content (Di Tomaso et al., 2002; Borsetti et al., 2003a). Additionally, despite the polarity of the respiratory COX in the membrane, R. capsulatus and P. pseudoalcaligenes KF707 accumulate elementary tellurium in the cytosol only (Di Tomaso et al., 2002; Borghese et al., 2004), suggesting that reduction of tellurite to Te0 is unlikely to be performed by respiratory cytochrome c oxidases. By contrast, whether the modiﬁcations observed in the plasma membrane redox chains of P. pseudoalcaligenes KF707 and R. capsulatus are speciﬁcally required to survive in the presence of tellurite or simply reﬂect toxic effects of the anion on the electron transport system, remains a matter for debate. This problem has recently been challenged in isolated plasma membrane vesicles of R. capsulatus. Borsetti et al. (2007) showed that tellurite (0.25–2.5 mM) alters the redox equilibrium of the Q/QH2-bc1-c2/cy segment of the redox chain. This effect is blocked by the bc1 complex-speciﬁc inhibitor antimycin A and it is absent from membranes

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of R. capsulatus MD22, a mutant lacking the thiol:disulﬁde oxidoreductase DsbB. The latter ﬁnding is particularly important because it suggests for the ﬁrst time a possible molecular mechanism by which tellurite can perturb the plasma membrane redox components facing the periplasmic space. Little is known about the entry of tellurium oxyanions into bacterial cells. The observation of a ‘‘white’’ tellurite resistance variant (Burian et al., 1998) is an important factor to consider when analyzing the location of reduction of Te(IV) to Te(0) in the cell and the existence of speciﬁc transporters. It also leads to the question of how uptake is related to toxicity and resistance. A ﬁrst report suggests that tellurite may be transported into E. coli cells by the phosphate transporter (Tomas and Kay, 1986). This conclusion was derived from two observations: ﬁrst, TeO2 3 is a strong competitive inhibitor of the transport of phosphate in wild type strain and second, some mutants defective in phosphate transport are collaterally resistant to high levels of tellurite. Indeed, sensitivity to the anion is restored by a plasmid carrying the phoB region, which is involved in phosphate transport. Likewise, tellurite uptake in R. capsulatus cells is a DpH-dependent process strongly repressed by the K+/H+ exchanger nigericin and by the sulfhydryl reagent NEM (Borsetti et al., 2003b). These observations support the idea that R. capsulatus imports tellurite by a phosphate transporter belonging to the Pit 2 family, which catalyze the transport of H2PO across the inner 4 /HPO4 + membrane in an electro-neutral way, working as a H /solute symport system (Van Veen, 1997; Harris et al., 2001). However, these data do not exclude the existence of additional mechanisms for the uptake of the oxyanion; indeed, recent results in aerobically grown cells of R. capsulatus suggest that tellurite may enter the cells by exploiting other carriers, such as an as yet uncharacterized monocarboxylate transporter (R. Borghese, personal communication) in a DpH-dependent manner as previously shown by Borsetti et al. (2003b). These tellurite transport experiments are challenging as ‘‘friendly’’ radio-isotopes of tellurite do not exist. Even so, a few studies have utilized this approach to demonstrate levels of uptake (Lloyd-Jones et al., 1991, 1994). With the advent of a spectroscopic method to examine free tellurite concentration via the chelator diethyldithiocarbamate, tellurite transport can be more easily explored (Newman et al., 1989; Turner et al., 1992b). Using this assay, the Ter determinants, ter, kilAtelAB, and tehAB were shown not to mediate any change in the uptake rate (Turner et al., 1995a). However, it was observed that the arsenite/arsenate/antimonite resistance determinant arsABC, an ATP-dependent efﬂux pump, does in fact give rise to reduced tellurite accumulation suggesting that the ars is a general chalcogen efﬂux transporter (Turner et al., 1992a). Finally, it cannot be

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ruled out that, along with other oxyanions, GlpF (discussed above regarding Se oxyanion transport) may also facilitate movement of telluro compounds.

6.3. Mechanism(s) of Chalcogen Toxicity While different elements have different toxicity levels toward different bacterial groups, in general the toxicity levels of the different chalcogen oxyanions, from most toxic to least toxic, is: 2 2 2 2 TeO2 Tellurite is 3 4TeO4 AsO2 4AsO4 ; SbO2 4SeO3 SeO4 . toxic at MIC’s on the order of 0.006–0.8 mM, whereas selenite ranges from 8.1 to 28 mM for organisms such as E. coli, S. aureus, and P. aeruginosa (Harrison et al., 2004a). The study of the toxicity of these oxyanions in eukaryotes is far more developed. Information within these eukaryotic studies could provide clues to toxicity in prokaryotic systems. We brieﬂy overview this research below. Selenium exempliﬁes Paracelsus’ statement: ‘‘It is the dose that makes the poison’’. Indeed, selenium is essential for animals and humans to guarantee growth and reproductive functions (Fan, 1990). Deﬁciency in humans results in a condition known as Keshan’s disease, a cardiomyopathy found in some areas of China, where the selenium concentration in soils is low (Chen et al., 1980). Another Se-responsive disease, also reported in regions of China, Siberia, and Korea, is an osteoarthropathy called Kaschin-Beck disease (Ge and Yang, 1993). Conversely, excess selenium in a diet leads to a pathological status deﬁned as ‘‘selenosis’’ and acute selenium overexposure can cause several characteristic symptoms. For more detailed information on nutritional and toxicological aspects, see Fan (1990) and Goldhaber (2003). There are many proposed mechanisms by which selenium and its derivatives cause toxicity in eukaryotic cells. Metalloid Se can undergo redox reactions with thiols (Klassen et al., 1985), which can compromise the function of structural, enzymatic, and regulatory proteins (Park et al., 2000a,b; Gupta and Porter, 2002; Hartwig et al., 2002; Chung et al., 2006a). By reacting with thiols, and glutathione in particular, selenium initiates a ROS production, involving the formation of selenide (RSe). This intermediate may enter a redox cycle and generate the superoxide anion and oxidative stress, or may form free radicals that could inhibit further enzymes or cause damage to cell membranes and DNA (Chaudier et al., 1992; El-Demerdash, 2001; Abul-Hassan et al., 2004; Garcia et al., 2005). Another mechanism by which selenium and its derivatives may exert their toxicity in eukaryotic cells is through selenium substitution for sulfur in methionine, forming

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selenomethionine, which may be mis-incorporated into proteins. This interaction could explain the teratogenic action of these compounds (Combs and Combs, 1986) and the damage to keratin-containing proteins in adults exposed to high levels of selenium in their diet (Fan, 1990). It is important to note that the toxicity of Se depends on the chemical form of the element, as this determines its bioavailability and ability to enter the organism and/or cells. In addition, the negative action of the metalloid can be altered by its interactions with other substances, such as sulfate, methionine, cysteine, heavy metals (As, Cd, Cu, Pb, Hg, Ag, Zn), and vitamins C and E (Fan, 1990). Hence, a selenium effect is a result of a balance between antioxidant and pro-oxidant abilities in the cells. Superoxide production may be the major mechanism of selenium toxicity under aerobic conditions in prokaryotic cells as well, as suggested by several studies (Turner et al., 1998; Bebien et al., 2002; Kessi and Hanselmann, 2004). Selenite is the only known compound that induces both iron and manganese superoxide dismutases (SodB and SodA, respectively) in E. coli. The effect of the oxyanion on the proteomic response of the microorganism strengthens the hypothesis that selenium toxicity involves several molecular circuits and it is not directed to a speciﬁc and single target. 6.3.1. Tellurite Tellurium biochemistry in the context of animal and human toxicology was last reviewed by Taylor (1996). Despite many chemical homologies between selenium and tellurium, a nutritional role has never been identiﬁed for tellurium; moreover, tellurium at low concentrations induces both acute and chronic toxicity in a variety of organisms. Nevertheless, several studies have shown that trace amounts of tellurium are present in body ﬂuids, such as blood and urine (Goulle´ et al., 2005). Tellurocysteine and telluromethionine can be found in bacteria (Boles et al., 1995; Budisa et al., 1995, 1997), yeast (Yu et al., 1993), and fungi (Ramadan et al., 1989) as a result of misincorporation of tellurium in place of sulfur or selenium, thus allowing the expression of protein analogs useful for protein structural studies. One possibility is that tellurium may act as a metabolic antagonist of selenium, inhibiting the catalytic activity of certain enzymes. This could be the case for the cytosolic glutathione peroxidase (GSHpx) in hepatocytes in which (121Te)-tellurite was shown to form adducts on the protein, with resulting inhibition of the catalytic reduction of hydrogen peroxide by the enzyme (Garberg et al., 1999). A contrasting situation is the one in which tellurium and/or its oxyanion forms act on the enzyme squalene monooxygenase, the second enzyme in the committed pathway for cholesterol biosynthesis.

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Tellurium blocks cholesterol synthesis, causing a transient demyelination of peripheral nerves (Wagner-Recio et al., 1991; Wagner et al., 1995). The same effect has been observed with selenite and other methylselenium compounds (Gupta and Porter, 2002). The sensitivity of squalene monooxygenase to tellurium and selenium compounds is due to the binding of these compounds to vicinal cysteines; the methylation of tellurium in vivo may enhance the toxicity of tellurium for this enzyme (Laden and Porter, 2001). It has also been observed that tellurite (TeO2 3 ) ions induce the alteration of the erythrocyte membrane and this activity is thiol-dependent as well (Deuticke et al., 1992). Finally, most of the Te(IV) derivatives are also able to inactivate cysteine proteases, but not other families of proteases. This seems to be related to the ability of Te(IV) compounds to react with the thiolcatalytic site of cysteine proteases. Te (VI) compounds do not exhibit any such inhibitory activity as they are inert towards thiol moieties (Albeck et al., 1998). Overall, the above work suggests that tellurium compounds interact with biological systems by speciﬁc chemical interaction with endogenous thiols. The precise biochemical explanation for the toxicity of oxyanions of the different chalcogens remains largely unknown in bacteria. In general, it has been assumed that the toxicity of tellurite is a consequence of the strong oxidizing properties. From various experiments examining tellurite resistance mechanisms, one can infer possible toxic pathways, which involve normal bacterial physiology. For example, one recognizes that the redox chemistry of the reaction of tellurite with nitrate reductase is unbalanced, as nitrate reductase would undergo two electron reductions and the reduction of TeO2 to Te0 would require four electrons. The two electron reduced 3 2 TeO3 would likely result in the formation of a radical species or radical oxygen ions being formed. Furthermore, the reaction of tellurite with glutathione would sequester glutathione and change the glutathione/ glutaredoxin/ thioredoxin redox balance as well as produce H2O2 and O2d , as discussed above. This latter observation corresponds with the evidence that the induction of the cambialistic superoxide dismutase (i.e. functional with Mn or Fe) of R. capsulatus leads to a signiﬁcant increase in tellurite resistance. Interestingly, it has also been reported that SOD activity is increased by the addition of a sublethal amount of K2TeO3 (Borsetti et al., 2005). If tellurite is allowed to enter the metabolism of the cell, it can be incorporated into enzymes as telluromethionine and tellurocysteine as well replacing both sulfate/sulﬁte and/or phosphate in various biochemical events. Tellurite toxicity in cells of the obligate aerobe and PCB degrader P. pseudoalcaligenes KF707, has been linked to the production of ROS (Tremaroli et al., 2006). This study also indicates that although Od 2

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generation is clearly linked to tellurite reduction by reduced thiol (RSH) oxidation, the time courses of the two processes are different. Experiments with the iscS gene from G. stearothermophilus V complementing E. coli iscS and sodA sodB mutants, support this evolving hypothesis of tellurite toxicity. The reduction of tellurite generates superoxide and other ROS and the primary targets of the superoxide damage in E. coli may be [Fe-S] clusters (Tantalean et al., 2003). The interaction of tellurite with the electron transport chain via the DsbB link to the quinone pool leads to a short circuit in the electron transfer pathways of R. capsulatus (Borsetti et al., 2007) and also avoids overreduction of the quinone pool with a consequent stimulation of lightdependent electron transport under highly reducing conditions. Thus, under unfavorable growth conditions, i.e. phototrophic growth under anaerobiosis, sub-inhibitory amounts of tellurite might exert a positive effect on the redox state of the electron transport components of the facultative phototroph R. capsulatus. E. coli exposed to tellurite displays a rapid loss in free thiol content (Turner et al., 1999). In addition to this key observation, the transmembrane DpH gradient is dissipated and intracellular ATP levels are rapidly depleted (Lohmeier-Vogel et al., 2004). Tellurite exposure also causes a large change in the proteome ﬁngerprint whether grown planktonically or as a bioﬁlm (N.J. Roper, J.J. Harrison, J.M. Howell, H. Ceri and R.J. Turner, unpublished data).

6.3.2. Tellurate Tellurate (TeO2 4 ) is about 2- to 10-fold less toxic than tellurite in most organisms studied (Harrison et al., 2004a). However, due to its poor solubility in aqueous conditions, very little has been done with this form of tellurium. Basnayake et al. (2001) observed that adding tellurite and tellurate to cultures of P. fluorescens was more toxic than added individually. This work suggests a synergistic toxic effect of tellurate and tellurite on bacterium. At this time, our understanding of Te physiology does not provide any clues for this observation. As the observations of dark cells are also seen in cultures exposed to tellurite, it is likely at least some similar biochemistry is occurring or that tellurate is being reduced to tellurite. However, at this point it is not clear what enzyme or process would carry this reaction out, as it is clear that nitrate reductase does not have this capacity (Avazeri et al., 1997).

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6.3.3. Selenite/Selenate Very few studies have looked at the biochemical mechanism behind the selenite toxicity in bacteria because, at most concentrations, there is little effect on the growth rate or accumulated biomass (Hudman and Glenn, 1984). Lohmeier-Vogel et al. (2004), using 31P-NMR on E. coli, did not see any effect on ATP levels or on the transmembrane DpH gradient, in contrast to the case of tellurite exposure. Examining the free thiol oxidation in E. coli upon selenite exposure, one observes an initial loss of RSH content but this oxidation recovers over a short time (Turner, unpublished results). This is probably because the glutathione reductase can accept the GSSeSG as a substrate. Similarly, selenate is essentially non-toxic to most bacteria (Huber et al., 1967, 2000). It is unknown if this is due to simply a lack of uptake or the inability to reduce selenate to selenite. However, it is clear that nitrate reductases can perform this task (Avazeri et al., 1997; Sabaty et al., 2001). Overall, the toxicity of tellurite can be considered to be a combination of speciﬁc targeted thiol chemistry and the resulting production of oxygen radical species that contributes to poisoning of the cell’s electrochemistry. The difference in toxicity between the oxyanions of the different chalcogens may lie in the rate or ability to repair thiol oxidation and/or process RS Ch(II) species (such as GSSe vs. GSTe) and the rate of ROS production, all of which leads to subsequent damage to respiratory and biosynthetic pathways. On a ﬁnal note, it has been observed that addition of selenite with tellurite will increase the tellurite MIC; i.e. selenite protects against the toxic affects of tellurite (R. Turner, unpublished results). This suggests that the preferential, and less toxic, biochemical paths and kinetics of selenite can dominate over that of tellurite. Thus, tellurite has a less noxious relationship with the cell.

7. OTHER CHALCOGENS AND METALLOIDS 7.1. Polonium Nothing has been done exploring polonium (Po) speciﬁcally due to its extremely low natural abundance and its high radioactivity. The chemistry of soluble forms of Po are expected to be similar to that of the other chalcogens; however, the toxicity level would be greater due to the radiation.

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Although no speciﬁc investigations exist, studies by Momoshima et al. (2001, 2002) on aquatic samples suggest that the microorganisms are able to generate volatile Po species most likely through methylation. Additionally, they may have the ability to reduce oxyanion forms of Po, leading to precipitates, as the authors indicate there was some accumulation of Po in some form as well. This observation is supported by a previous study that demonstrated that phytoplankton and bacteria accumulate 210Po (Wildgust et al., 1998). Other early work from the 1960s by various Russian groups simply used Po as a source of radiation to explore radiation tolerance of microbes.

7.2. Other Metalloids By deﬁnition, the metalloids include the elements Bo, Al, Si, Ge, As, Sb, Te, and Po. Of these, considerable research has been focused on the resistance of arsenite, arsenate, and antimonite. The microbiology of these will not be discussed here and the reader is referred to the following reviews: Rosen et al. (1999), Rosen (2002c), Bhattacharjee et al. (2000), Rosen (2002a,b), Mukhopadhyay et al. (2002), Silver and Phung (2005a,b). However, the arsenate resistance determinant arsABC is worth bringing up here. ArsAB is an ATP-dependent efﬂux pump and ArsC is an arsenate reductase, reducing arsenate to arsenite. Turner et al. (1992a) observed that this operon also provides tellurite resistance and effectively efﬂuxes tellurite, keeping tellurite accumulation low and thus preventing the ‘‘blackening’’ of the cells through tellurite reduction. Surprisingly, ArsC, the thiol-dependent arsenate to arsenite reductase, was required for full resistance. Along the same lines, arsenate reductase activity was found in the plasmid pI258 from S. aureus. This reductase demonstrated selenate reduction and was inhibited by tellurite (Ji et al., 1994). The overlap of the biochemistry of arsenate with the chalcogen oxyanions remains mostly under-appreciated and under-investigated.

8. CONCLUDING REMARKS An interesting biological puzzle of tellurite resistance arises from the observation that levels of resistance frequently observed are higher than the concentrations typically experienced in the environment. Levels of selenite

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resistance are closer to environmental levels and the biochemistry of the cells may have naturally evolved in order to coexist with this metal. Perhaps, the striking difference between the cellular response against tellurite and the cellular response against selenite results from the evolutionary pressure of speciﬁc intracellular, periplasmic, and electron transfer redox components. The recent observation that the thiol:disulﬁde oxidoreductase DsbB allows the transfer of oxidizing equivalents from tellurite to membrane-embedded quinols opens new perspectives for both microbial physiologists and biochemists. The possibility that membrane-bound disulﬁde proteins might act as ‘‘electron conduits’’ between periplasmically localized metalloids and redox complexes raises the question of whether metalloids can be considered toxic per se or whether they might also act as ‘‘electron sinks’’ under unfavorable reducing conditions. Another biological puzzle concerns how metalloids get into cells and their subsequent cytosolic fate. No speciﬁc carriers have been identiﬁed although several non-speciﬁc mechanisms are known. Further, the reduction mechanisms are still uncertain for both tellurite and selenite to their less toxic elemental forms, while it is clear they are linked to generation of toxic ROS. Thus, in the case of tellurite and probably also that of selenite, the cellular response is more likely to be a suicide mechanism than a rescue mechanism. In conclusion, tellurite resistance has proved to be the greatest challenge in the metal and metalloid resistance ﬁeld. The level of frustration was clearly illustrated in a recent review by Silver and Phung (2005a) where the area of tellurite resistance was given as a single paragraph indicating that the research had not progressed much in 10 years in comparison to the research on other metals. However, as seen here, advances have indeed been made in both understanding the mechanism of toxicity of this metalloid and the various biochemical processes it interacts or interferes with. We are hopeful that this review helps to bring ‘‘tellurium microbiology’’ out of the dark age.

ACKNOWLEDGMENTS This work was supported by MIUR (PRIN 2005) to D.Z. and NSERC to R.J.T. R.J.T. is grateful for discussions with Andrew J. Percy and discussions on the bioﬁlm research with Howard Ceri. J.J.H. was supported by an AHFMR studentship and a NSERC CGSD award. We also thank Bronwyn Hasslam for the careful reading of the manuscript.

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Watanabe, C. (2002) Modiﬁcation of mercury toxicity by selenium: practical importance? Tohoku J. Exp. Med. 196, 71–77. Werner, E., Roe, F., Bugnicourt, A., Franklin, M.J., Heydorn, A., Molin, S., Pitts, B. and Stewart, P.S. (2004) Stratiﬁed growth in Pseudomonas aeruginosa bioﬁlms. Appl. Environ. Microbiol. 70, 6188–6196. Whanger, P., Vendeland, S., Park, Y.C. and Xia, Y. (1996) Metabolism of subtoxic levels of selenium in animals and humans. Ann. Clin. Lab. Sci. 26, 99–113. Whelan, K.F., Colleran, E. and Taylor, D.E. (1995) Phage inhibition, colicin resistance, and tellurite resistance are encoded by a single cluster of genes on the IncHI2 plasmid R478. J. Bacteriol. 177, 5016–5027. Whelan, K.F., Sherburne, R.K. and Taylor, D.E. (1997) Characterization of a region of the IncHI2 plasmid R478 which protects Escherichia coli from toxic effects speciﬁed by components of the tellurite, phage, and colicin resistance cluster. J. Bacteriol. 179, 63–71. Whitchurch, C.B., Tolker-Neilsen, T., Ragas, P.C. and Mattick, J.S. (2002) Extracellular DNA required for bacterial bioﬁlm formation. Science 295, 1487. Whitﬁeld, C. and Roberts, I.S. (1999) Structure, assembly and regulation of expression of capsules in Escherichia coli. Mol. Microbiol. 31, 1307–1319. Whitham, G.H. (1995) Organosulfur Chemistry. Oxford University Press, Oxford. Wildgust, M.A., McDonald, P. and White, K.N. (1998) Temporal changes of 210Po in temperate coastal waters. Sci. Total Environ. 214, 1–10. Wozniak, D.J., Wycoff, T.O., Starkey, M., Keyser, R., Azadi, P., O’Toole, G.A. and Parsek, M.R. (2003) Alginate is not a signiﬁcant component of the extracellular polysaccharide matrix of PA14 and PAO1 Pseudomonas aeruginosa bioﬁlms. Proc. Natl. Acad. Sci. USA 100, 7907–7912. Xu, K.D., Stewart, P.S., Xia, F., Huang, C. and McFeters, G.A. (1998) Spatial physiological heterogeneity in Pseudomonas aeruginosa bioﬁlm is determined by oxygen availability. Appl. Environ. Microbiol. 64, 4035–4039. Xu, K.D., McFeters, G.A. and Stewart, P.S. (2000) Bioﬁlm resistance to antimicrobial agents. Microbiology 146, 547–549. Yamada, A., Miyagishima, N. and Matsunaga, T. (1997) Tellurite removal by marine photosynthetic bacteria. J. Mar. Biotechnol. 5, 46–49. Yarema, M.C. and Curry, S.C. (2005) Acute tellurium toxicity from ingestion of metal oxidizing solutions. Pediatrics 116, 319–321. Yilmaz, A., Gun, H. and Yilmaz, H. (2002) Frequency of Escherichia coli O157:H7 in Turkish cattle. J. Food Protect. 65, 1637–1640. Yu, L., He, K., Chai, D., Yang, C. and Zheng, O. (1993) Evidence for telluroamino acid in biological materials and some rules for assimilation of inorganic tellurium by yeast. Anal. Biochem. 209, 318–322. Yurkov, V., Jappe`, J. and Vermeglio, A. (1996) Tellurite resistance and reduction by obligately aerobic photosynthetic bacteria. Appl. Env. Microbiol. 62, 4195–4198. Zadic, P.M., Chapman, P.A. and Siddons, C.A. (1993) Use of tellurite for the selection of verocytotoxigenic Escherichia coli O157. J. Med. Microbiol. 39, 155–158. Zadic, P.M., Davies, S., Whittaker, S. and Mason, C. (2001) Evaluation of a new selective medium for methicillin-resistant Staphylococcus aureus. J. Med. Microbiol. 50, 476–479.

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Zanaroli, G., Fedi, S., Carnevali, M., Fava, F. and Zannoni, D. (2002) Use of potassium tellurite for testing the survival and viability of Pseudomonas pseudoalcaligenes KF707 in soil microcosms contaminated with polychlorinated biphenyls. Res. Microbiol. 153, 353–360. Zawadzka, A.M., Crawford, R.L. and Paszczynski, A.J. (2006) Pyridine-2,6bis(thiocarboxylic acid) produced by Pseudomonas stutzeri KC reduces and precipitates selenium and tellurium oxyanions. Appl. Environ. Microbiol. 72, 3119–3129. Zhang, Y. and Frankenberger, W.T., Jr. (2005) Removal of selenium from river water by a microbial community enhanced with Enterobacter taylorae in organic carbon coated sand columns. Sci. Total Environ. 346, 280–285. Zhang, Y., Zahir, Z.A. and Frankenberger, W.T., Jr. (2004) Fate of colloidal particulate elemental selenium in aquatic systems. J. Environ. Qual. 33, 559–564. Zhang, Y., Fomenko, D.E. and Gladyshev, V.N. (2005) The microbial selenoproteome of the Sargasso Sea. Genome Biol 6, R37. Zinoni, F., Heider, J. and Bo¨ck, A. (1990) Features of the formate dehydrogenase mRNA necessary for decoding of the UGA codon as selenocysteine. Proc. Natl. Acad. Sci. USA 87, 4660–4664. Zohri, A.A., Saber, S.M. and Mostafa, M.E. (1997) Effect of selenite and tellurite on the morphological growth and toxin production of Aspergillus parasiticus var. globosus IMI 120920. Mycopathologia 139, 51–57.

Plate 1 Biogeochemical transformation of tellurite and selenite by bacteria. The coloration of black cells (tellurite) and redorange (selenite) is due to the reduction to Ch(0) product within the cells. (A) Pseudomonas aeruginosa grown in microtitre plate planktonically with tellurite. (B) P. aeruginosa grown on Calgary Bioﬁlm Device pegs with tellurite. (C) P. aeruginosa grown in microtitre plate planktonically with selenite. (D) P. aeruginosa grown on Calgary Bioﬁlm Device pegs with selenite. (E) E. coli grown on solid Luria Bertani broth showing the black colonies. (F) Thin section electron micrograph of E. coli grown in the presence of tellurite. The ﬁgure shows the precipitation of black crystals along the membrane. (G) E. coli harboring various tellurite resistance determinants. The non-colored culture of the ars is reﬂective of the resistance being an efﬂux system. (For b/w version, see page 10 in the volume)

Gaining Insight into Microbial Physiology in the Large Intestine: A Special Role for Stable Isotopes Albert A. de Graaf1,2,3 and Koen Venema1,3 1

Wageningen Center for Food Sciences, P.O. Box 557, 6700 AN Wageningen, The Netherlands 2 Department of Surgery, University of Maastricht, Maastricht, The Netherlands 3 TNO Quality of Life, P.O. Box 360, 3700 AJ Zeist, The Netherlands

ABSTRACT The importance of the human large intestine for nutrition, health, and disease, is becoming increasingly realized. There are numerous indications of a distinct role for the gut in such important issues as immune disorders and obesity-linked diseases. Research on this longneglected organ, which is colonized by a myriad of bacteria, is a rapidly growing ﬁeld that is currently providing fascinating new insights into the processes going on in the colon, and their relevance for the human host. This review aims to give an overview of studies dealing with the physiology of the intestinal microbiota as it functions within and in interaction with the host, with a special focus on approaches involving stable isotopes. We have included general aspects of gut microbial life as well as aspects specifically relating to genomic, proteomic, and metabolomic studies. A special emphasis is further laid on reviewing relevant methods and applications of stable isotope-aided metabolic ﬂux analysis (MFA). We argue that linking MFA with the ‘-omics’ technologies using innovative modeling approaches is the way to go to 3

Present address: TNO Quality of Life, P.O. Box 360, 3700 AJ Zeist, The Netherlands.

ADVANCES IN MICROBIAL PHYSIOLOGY, VOL. 53 ISBN 978-0-12-373713-7 DOI: 10.1016/S0065-2911(07)53002-X

Copyright r 2008 by Elsevier Ltd. All rights reserved

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establish a truly integrative and interdisciplinary approach. Systems biology thus actualized will provide key insights into the metabolic regulations involved in microbe–host mutualism and their relevance for health and disease.

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1. Why Study Intestinal Microbial Physiology? . . . . . . . . . . . . . . 1.2. Purpose of This Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. The gut microbial ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Butyrate is Important . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Gut pH Matters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Microbes Extend Our Genome . . . . . . . . . . . . . . . . . . . . . . . 2.4. Microbes Keep Our Immune System on Standby . . . . . . . . . . 2.5. Methods to Study Bacterial Physiology: Many Fields of Science Come Together . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6. Stable Isotopes Offer Unique Insights . . . . . . . . . . . . . . . . . . 3. Stable isotopes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. What are Stable Isotopes? . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. How are Stable Isotopes Detected? . . . . . . . . . . . . . . . . . . . 3.3. What Information can be Retrieved from Stable Isotope Experiments? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Important Fields of Applications of Stable Isotopes in Biomedicine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Basics of Metabolic Flux Analysis . . . . . . . . . . . . . . . . . . . . . 3.6. MFA in Detecting Microbial Metabolic Stress . . . . . . . . . . . . . 4. Genomic inventories of intestinal bacteria . . . . . . . . . . . . . . . . . . . . 4.1. General Aspects: Cataloguing Intestinal Microbial Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. The Microbiome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Stable Isotope Probing: Clues to Metabolic Function from Genomics Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Proteomic aspects of intestinal microbial life . . . . . . . . . . . . . . . . . . 5.1. Functions of Intestinal Bacterial Enzymes . . . . . . . . . . . . . . . 5.2. Proteomic Studies of the Gut Microbiota: A Largely Unprobed Area? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3. Can Stable Isotopes Help in Proteomics? . . . . . . . . . . . . . . . 6. Metabolomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. Microbial Products and What They Can Mean to Us. . . . . . . . 6.2. Tracing the Fate of Prebiotics: In Vitro Models and Stable Isotopes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3. Evidence of Cross-Feeding. . . . . . . . . . . . . . . . . . . . . . . . . . 7. Metabolic flux analysis applied to the gut . . . . . . . . . . . . . . . . . . . . 7.1. Insights into Bacterial Metabolic Routes. . . . . . . . . . . . . . . . . 7.2. Get Quantitative: Mass Balances Reveal a Lot. . . . . . . . . . . . 7.3. Stable Isotope-Aided Quantification of Pathways: Functional Genomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. Emerging picture of the role of microorganisms integrated in man . .

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8.1. Energy Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2. Innate Immune System . . . . . . . . . . . . . . . . . . . . . 8.3. Intestinal Microbiota: Is There a Link With Obesity? . 8.4. Role of Stable Isotopes . . . . . . . . . . . . . . . . . . . . . 9. New aspects in the study of intestinal bacterial physiology . 9.1. Microbes at War: Population Competition Models . . . 10. Conclusions and future prospects . . . . . . . . . . . . . . . . . . 10.1. Toward a Systems Biology of the Gut . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. INTRODUCTION 1.1. Why Study Intestinal Microbial Physiology? What happens in our intestine? The human gastro-intestinal (GI) tract is the primary site of food intake, food perception, and food conversion. In the ﬁrst part, the small intestine, highly active enzymatic hydrolysis of carbohydrates, fats, and proteins takes place and the resultant digestive products are absorbed. Thus, the bulk of our food intake is processed by the small intestine. What then is the function of the second part of the intestine, the large intestine (or colon)? Until recently, the large intestine was considered just a storage place for undigested food components. However, the past 5–10 years have changed this view drastically. Nowadays, the large intestine is called the ‘forgotten organ’. The cells of microorganisms (totaling approximately 1014 cells) present in the colon outnumber the cells of the host by a factor of 10 and all these bacteria contribute to nutrient processing. The biochemical (metabolic) potential of this complex assemblage of different microorganisms is considered equal to that of the liver. This community of mostly anaerobic bacteria inﬂuences human gut physiology and health by performing a number of activities including fermentation of dietary compounds which escape digestion in the small intestine, processing of mucosal cells shed in the small intestine, and of intestinally secreted mucus. Thus, polymers of sugars are degraded by the colonic microbes into gases such as hydrogen, carbon dioxide, and methane as well as short-chain fatty acids (SCFAs) (Bergman, 1990; Cummings, 1991), notably butyrate, propionate, and acetate (Fig. 1). These SCFA are taken up by the host and contribute to its energy and health status. In addition, the microbial community produces a variety of other health-related compounds including vitamins (Hill, 1995) and other growth-promoting compounds. However, toxic, mutagenic, and carcinogenic substances (Cummings and Macfarlane, 1997) may be formed that negatively affect the host. It is also known that the colon plays a role

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Propionate

Carbohydrates

PropionylCoA Diacetyl Glycerol CO2

Succinate

Malate

Pyr

Acetolactate Lactate Acetate

Acetic Aldehyde

Formate CO2

Ethanol

AcetoAcetylCoA CO2

AcAcetate CrotonylCoA

3-Hydroxybutyrate

n-Butanol

Butyrate

Figure 1 Schematic overview of anaerobic bacterial metabolic pathways involved in carbohydrate metabolism. The dashed reaction represents the Wood–Ljungdahl pathway of acetate formation from CO2. Boxes show the short-chain fatty acids (SCFAs) from C1 (formate) up to C4 (butyrate), and lactate.

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2,3-Butanediol

CO2

AcetylCoA

Acetoin

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in the modulation of the immune system (Chadwick and Anderson, 1995), the transformation of bile acids (Aries and Hill, 1970), and the provision of a barrier against pathogenic bacteria (Hill, 1995). The above facts already indicate that the processes going on inside the colon are important for us. There is indeed evidence that the bacterial metabolic processes in the colon are becoming an increasingly important issue for man. Numerous indications of a correlation between intestinal health status and the occurrence of various intestinal diseases, such as colon cancer, inﬂammatory bowel disease (IBD), and irritable bowel syndrome (IBS) (Chadwick and Anderson, 1995) have been reported. The general hypothesis is that there is a strong correlation between proteolytic fermentation in the (distal) colon and the occurrence of colon cancer and IBD (Macfarlane et al., 1992a; Levine et al., 1998). Protein fermentation leads to the production of microbial metabolites that can be toxic to the host (Macfarlane et al., 1992b; Macfarlane and Macfarlane, 1995). In particular, these may include sulfur-containing metabolites (Hill et al., 1995; Rowland, 1995). Carbohydrate fermentation, on the other hand, leads amongst others to the production of SCFA, which are considered to be health promoting (Cummings, 1991, 1995). During carbohydrate fermentation, protein is incorporated into microbial biomass (Birkett et al., 1996), preventing fermentation of protein. However, most carbohydrates are completely fermented in the proximal colon, which leads to the depletion of these substrates in the distal colon. Here, the microorganisms switch to fermenting protein. It is hypothesized that it would therefore be of importance to prolong the fermentation of carbohydrates. This can be accomplished for instance by including more slowly fermentable carbohydrates in the diet (e.g. certain types of resistant starch). More generally, the idea developed that changing the diet in such a way that harmful bacteria (or their harmful activities) are suppressed and beneﬁcial bacteria (or their beneﬁcial activities) are stimulated, may contribute to improving gut health. However, the microbial processes occurring in the colon are hitherto largely unknown, because the accessibility of the lumen of the human colon is severely limited in practice.

1.2. Purpose of This Review The studies discussed above all served to demonstrate that there is a strong yet intricate link between intestinal bacterial metabolism and gut health. There is also little doubt that nutrition has an impact on the composition and activity of the intestinal microbiota and thereby inﬂuences human health and well-being. However, the mechanisms behind these processes are

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still largely unknown. If we want to be able to improve human health by optimizing nutrition, we must know what are the regulatory mechanisms that govern how our intestinal bacteria react to various food ingredients, and also what will be the determinant processes for the reaction of the host (i.e. our own body) in return. While this may seem a distant goal, a key step in this research, i.e. the characterization and analysis of the bacterial physiological behavior, has become possible using the powerful analytical methods available today. While genomics, proteomics, metabolomics, and bioinformatics immediately come to mind, it is becoming increasingly clear that the ﬁnal endpoint of the genome, proteome, and metabolome, i.e. the ﬂuxome, is perhaps the most closely linked with physiology. Indeed, it is argued that fully assembled metabolic pathways in living systems, rather than genes or proteins, are the true units of function in biology and biochemistry. A corollary is that measurement of metabolic ﬂuxes (biochemical kinetics) is thereby required to understand biochemical control and gene function (Hellerstein, 2004). Studying the dynamics of an organism’s metabolic ﬂuxes in response to various stimuli (such as different nutritional components) provides the unique and key insights to understand physiological regulation. As pointed out above, it is precisely this regulation which needs to be discovered so as to ﬁnally enable a targeted modulation of intestinal bacterial metabolism to achieve beneﬁcial effects on human health. Therefore, the present review will put a special focus on the application and perspectives of metabolic ﬂux analysis (MFA) and its main enabling technology, stable isotope labeling, to study intestinal microbial physiology.

2. THE GUT MICROBIAL ECOSYSTEM 2.1. Butyrate is Important As already eluded to above, SCFAs (primarily acetate, propionate, and butyrate) are the major end products of bacterial fermentation in the intestine and affect key functions of the colonic epithelium in vivo. These compounds are probably key participants in gut maintenance (Bergman, 1990; Kruh et al., 1991; Mariadason et al., 2000) and may also be beneﬁcial contributors to the peripheral metabolism in humans (Macfarlane and Cummings, 1991). Therefore, SCFA have been the subject of numerous investigations. Butyrate is considered the most important of the SCFA. In the large intestine, butyrate is present in millimolar concentrations. Butyrate is metabolized by epithelial cells and is responsible for 70% of their

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energy needs (Roediger, 1982; Scheppach, 1994). In addition, it acts as a signaling metabolite, affecting epithelial cell proliferation and differentiation (Gamet et al., 1992; Gibson et al., 1992). In addition to its established role in regulating viability, differentiation, and proliferation (Velazquez et al., 1997), butyrate was reported to be effective in cancer suppression (McIntyre et al., 1993; Singh et al., 1997). Furthermore, butyrate might be beneﬁcial in preventing mucosal inﬂammation, because decreased availability of butyrate has been associated with distinct forms of colitis (Harig et al., 1989; Chapman et al., 1994; Ahmad et al., 2000). Moreover, butyrate enemas have been shown to be an effective treatment for mucosal inﬂammation in both humans and animal models of colitis in some studies (Scheppach et al., 1992; Gibson and Rosella, 1995; Butzner et al., 1996; D’Argenio et al., 1996; Fernandez-Banares et al., 1999; Kanauchi et al., 1999; Okamoto et al., 2000; Andoh et al., 2003; Cherbut et al., 2003). Experimental work to explain the molecular mechanisms of this anti-inﬂammatory property of butyrate largely focused on cultured intestinal epithelial cells, where butyrate was shown to modulate IL-8, and macrophage inhibitory protein 2. In addition, butyrate has been shown to inhibit activation of the transcription factor NF-kB in cultured epithelial cells (Wu et al., 1999; Inan et al., 2000). Furthermore, butyrate has been shown to have an anti-inﬂammatory effect on human monocytes by the potent inhibition of IL-12 and upregulation of IL-10 production (Saemann et al., 2000). Moreover, butyrate resulted in an increase of IgA-producing cells and mucosal IgA concentrations (Morita et al., 2004; Roller et al., 2004), the secretion of anti-inﬂammatory cytokines (Fusunyan et al., 1998, 1999; Saemann et al., 2002; Bocker et al., 2003), and decreased activity of myeloperoxidase (Butzner et al., 1996; Cherbut et al., 2003), an enzyme which aids in the defensive properties of phagocytic cells of the human immune response. The (regulation of the) ﬂux of butyrate (and the other SCFA for that matter) by (speciﬁc) intestinal microorganisms in the colon and the use of the SCFA in the rest of the body, however, is still largely unknown. It is important therefore to know which bacterial metabolic routes exist in vivo for the synthesis of SCFA. Knowledge of the regulation of these metabolic routes will allow the development of dietary strategies to inﬂuence SCFA metabolism, possibly even for therapeutic purposes.

2.2. Gut pH Matters Carbohydrate fermentation results in lowered cecal pH concomitant with the production of SCFA (Cummings and Bingham, 1987). In sudden death individuals, a significant trend from high to low concentrations of SCFA

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has been found on passing distally from cecum to descending colon, while pH changed from 5.670.2 in the cecum to 6.670.1 in the descending colon (Cummings et al., 1987). Interestingly, there seems to be an inverse effect of pH on SCFA metabolism as well; the response of human fecal microbial communities in anaerobic continuous culture showed markedly higher ﬁnal butyrate concentrations at pH 5.5 compared with pH 6.5, whereas acetate and propionate were higher at pH 6.5 (Walker et al., 2005). Changes in colon pH are also reported to alter the metabolism of protein, bile acids, nitrate, sulfate, and other substances (Cummings and Bingham, 1987). In contrast to SCFA, products of protein fermentation, such as ammonia, branched chain fatty acids, and phenolic compounds, progressively increase from the proximal (right) to the distal (left) colon, as does the pH of gut contents (Macfarlane et al., 1992a). Epidemiological studies found a marked correlation between pH and the incidence of colon cancer (e.g. Levy et al., 1994), which in a subsequent study appeared to be associated with higher animal protein and fat consumption (O’Keefe et al., 1999). However, a direct linkage between colon cancer and alkaline colonic pH was questioned in other experimental studies (Hove et al., 1993). This demonstrates that it is difﬁcult to establish unequivocally the cause–effect relationships between intestinal processes and human health due to the many interactions present. Clearly, extensive additional systematic research is necessary to elucidate the regulation of intestinal bacterial metabolism, the reaction of the host, and the interaction between both.

2.3. Microbes Extend Our Genome The colonic microbiota represents an enormous and largely unexplored potential of metabolic pathways for synthesis and degradation of compounds (Sekirov and Finlay, 2006). This has, for instance, been described for fatty acids (Juste, 2005). Microbial activity may be relevant here in various ways; bacteria may detoxify food components (Humblot et al., 2005), but also produce toxins themselves. Due to the multitude of chemical reactions they can carry out, intestinal bacteria may contribute significantly to drug metabolism (Shu et al., 1991; van de Kerkhof et al., 2005). This may be exploited by having a drug activated by intestinal bacteria that is otherwise absorbed before it can exert its action, or by applying bacteria themselves to act directly on gut epithelial cells (O’Hara et al., 2006). Clearly, while metabolic activation of drugs by intestinal host cells has already been demonstrated (Gharat et al., 2001), bacteria must also be considered able to carry out speciﬁc desired biotransformations of food (glucosinolates, ﬂavonoids, etc.) and drugs. However, the establishment of

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the highly speciﬁc biotransformations needed for drug efﬁciency may prove difﬁcult in such a complex microbiota as that found in the colon.

2.4. Microbes Keep Our Immune System on Standby Gut bacteria also play an important role in the development and modulation of our immune system. A healthy mucosa shows a certain chronic, basal inﬂammatory activity in the lamina propria. This is essential in relation to its barrier function and is closely related to the intestinal microbiota. It is assumed that a number of important health issues, such as IBS, are associated with an aberration of this inﬂammatory activity (Collins, 2001). In other words, it seems that we need our intestinal bacteria to keep up a certain basal level of activity of our intestinal immune system. The interaction between bacteria, their metabolic products, and the colonic epithelium is of pivotal importance for the inﬂammatory reaction. Recently, the importance of mast cell activity for intestinal function in relation to intestinal comfort has been described (O’Sullivan et al., 2000; Barbara et al., 2004; Siddiqui and Miner, 2004), and may well be modiﬁed by nutritional intervention (Rydzynski and Dalen, 1994; Ju et al., 1996; Ganessunker et al., 1999; Larauche et al., 2003). Unfortunately, a single biomarker for epithelial health is lacking. One has to rely on markers of permeability, e.g. production of certain tight junction proteins (Nusrat et al., 2000; Ma et al., 2004), Paneth cell defensins (Bevins, 2006), transport of paracellular and transcellular inert permeability markers (Baumgart and Dignass, 2002), inﬂammation (Saemann et al., 2000), mucus composition (Szentkuti et al., 1990), cell turnover and apoptosis (Andoh et al., 2003), mast cell activation (O’Sullivan et al., 2000), and others. Healthy individuals can be subjected to investigating these biomarkers as many of these can be determined by non- or minimally invasive, but sophisticated, techniques. Furthermore, in patients with established increased inﬂammatory activity, such as ulcerative colitis (UC) and pouchitis, these techniques can also be implemented. The intestinal immune system and the mucus layer are both important for human host defence and can be affected by microbial metabolites (amongst others butyrate).

2.5. Methods to Study Bacterial Physiology: Many Fields of Science Come Together Not surprisingly, recent years have seen the development of specially adapted experimental techniques to study the colon and its inhabitants. These include in vitro model systems, cell culture models, animal models, microdialysis, and breath tests (Table 1).

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Table 1 Some characteristics

References

In vitro model systems

-From simple anaerobic cultivation tubes to fully ﬂedged sophisticated computer-controlled in vitro model systems -Enable quantitative studies under deﬁned conditions using an inoculum isolated either from feces or, in an invasive manner, from speciﬁc sites along the GI tract -The effect of e.g. prebiotics on the composition and activity of the microbiota has been studied in such a model -From CaCO-2 or HT29 cell lines to intact intestinal mucosal strips -Enable the selective study of the properties of different types of (transformed) colon cells in isolation -The advantage of mucosal strips over cell cultures is that conditions probably better approach the in vivo situation because the strips include an intact basolateral lining of cells, as e.g. reﬂected in Km values for butyrate uptake that differ markedly from isolated colonocytes -Especially dogs and pigs, with several methods of sampling: either (i) dissection after sacriﬁcing the animals, (ii) use of stomas implanted in the living animal to probe speciﬁc luminal sites in the GI tract, or (iii) multicatheterization of blood vessels so as to gain access to arterial, portal, and hepatic venous blood all at the same time in the living, conscious animal

Minekus et al. (1999), Jensen and Jorgensen (1994), van Nuenen et al. (2003), Venema et al. (2003)

Cell culture models

(Monogastric) animal models

Boren et al. (2003), Jorgensen and Mortensen (2000), Jorgensen and Mortensen (2001) Deutz et al. (1998), Rerat (1985), Wunsche et al. (1979)

ALBERT A. DE GRAAF AND KOEN VENEMA

Experimental technique

Breath tests and gas analyses

Rooyackers et al. (2004), Jansson et al. (2004)

Christian et al. (2002), Jensen and Jorgensen (1994), Slater et al. (2006)

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Microdialysis

-Upon chemical analysis of blood samples, the techniques described in the line above allow for detailed quantitative studies of net splanchnic absorption and/ or intestinal production of metabolites, but such studies generally bear a (strong) invasive character -Can be used safely with low-grade invasiveness in humans with small catheters placed in speciﬁc tissue beds of interest -Allowing continuous sampling of the interstitial space over prolonged periods of time without taking any biopsies -Quantitative measures for metabolic activity are not easily obtained because the exact amount of tissue involved in the dialysis is not known. Results of intraperitoneal microdialyses were shown to strongly depend on catheter position -Enable non-invasive assessments of especially intestinal (carbohydrate) metabolism -Information is limited and quantitative aspects are not trivial since measured values represent overall metabolism of the complete organism

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The challenge for the coming years is to establish a truly integrative research approach that will enable the analysis and correlation in a meaningful manner of the many diverse aspects involved in intestinal microbial metabolism and its interaction with the human host. This will require the cooperation between scientists from various different scientiﬁc disciplines such as microbial physiology, human physiology, and gastroenterology, as well as highly technical disciplines including genomics, proteomics, metabolomics, analytical sciences, and bioinformatics and possibly nano-technology.

2.6. Stable Isotopes Offer Unique Insights Perhaps one of the greatest difﬁculties in the research of intestinal metabolism and function is the requirement for adequate, minimally invasive experimental techniques that allow for research in humans in vivo. Such techniques will have to cope with severely limited possibilities for manipulation of experimental conditions as well as for material sampling. The use of stable isotopes may prove to be a key factor to success here. Stable isotope-labeled molecules follow the same metabolic routes, and function identically in physiological processes as their natural unlabeled counterparts. The isotopic label, however, allows for their speciﬁc detection at any desired stage after their administration, allowing indirect monitoring of the processes in which they are involved. Over the past 20 years, stable isotopelabeling techniques have proven to be powerful tools to get quantitative as well as qualitative information about the metabolic processes in living organisms in general, including microorganisms, plants, animals, and humans, and also in the colon in vivo (Moran and Jackson, 1990; Wolfe, 1992; de Graaf, 2000; Shulman and Rothman, 2001; Pouteau et al., 2003; Kelleher, 2004; McCabe and Previs, 2004; Dolnikowski et al., 2005; Ratcliffe and Shachar-Hill, 2006). The use of isotopically labeled compounds enables the selective study of that part of the metabolism in which the isotopic tracer is involved, offering ample possibilities to probe microbial as well as host metabolism, or both. Isotopic labeling in compounds can be conveniently and specifically detected by mass spectrometry and/or nuclear magnetic resonance (NMR)-based analytical techniques. One indirect yet important feature of stable isotopes is that their application involves and bridges many disciplines at the same time, providing scientists with ample opportunities to come across different ﬁelds than their own, and stimulating cross-fertilization of ideas. Scientists from the life sciences working with stable isotopes will acquire a sense for analytical issues, and they will necessarily have to interact with physicists and

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mathematicians to undertake their modeling work successfully. The complexity of human metabolism may make researchers in the medical ﬁeld refrain from in-depth mechanistic analyses of metabolic regulation, yet the impressive results obtained from stable isotope studies in microbial applications in recent years may stimulate the design of studies to tackle more complex issues. In return, analytical scientists will broaden their views because they need to ﬁnd solutions that suit both the experimental constraints associated with biological material, as well as the requirements of the data modelers to reach accurate and precise parameter estimates and statistically significant results. Mathematicians and physicists are forced to make sense out of data that are often imprecise and show limited reproducibility, yet the inherent ability of such investigations to detect structure and logic even in complex multivariate data adds to the level and quality of conclusions that can ultimately be derived from experimental work. Put differently, working with stable isotopes conveys a natural inclination toward systems biology thinking (Kelleher, 2004).

3. STABLE ISOTOPES 3.1. What are Stable Isotopes? Isotopes were discovered in the 1910s after experiments conducted by F. Soddy gave the ﬁrst demonstration that most of the elements in nature are composed of atoms identical from the chemical point of view but slightly different in weight. Very soon after the discovery of deuterium, for which H.C. Urey was awarded the Nobel Prize in 1934, researchers launched the idea of using stable isotopes in kinetic/dynamic investigations. Thus, early studies on fat metabolism in mice with deuterium were done by R. Schoenheimer and D. Rittenberg (Ratner et al., 1987), and studies in nutrition by using 15N, 13C, and 18O soon followed. Tracer methods ﬁnd applications in nearly every ﬁeld of science, be it typical life science ﬁelds (medicine, biology, physiology, nutrition, toxicology, biotechnology), or more technical areas (physics, chemistry, agriculture, geoscience, engineering), which have now become an integral part of everyday life. The common issues for all these isotope labeling applications concern the possibility of tracing the entity of interest, called tracee, which may be a substance, or a component of a substance, like a radical, a molecule, or an atom. An ideal tracer has the same physical, chemical, or biological properties as the tracee, but it presents some unique characteristic that enables

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its detection in the system where the tracee is also present. The production of an isotopic tracer involves the substitution of one or more naturally occurring atoms in speciﬁc positions in the tracee molecule with an isotope of that atom with a less common abundance. Either stable or radioactive isotopes can be used as tracers. Natural abundancies of a number of stable isotopes relevant for life science research are displayed in Table 2. Mass differences of isotopes are due to different numbers of nuclear neutrons, so that the chemical properties are not affected. Both stable and radioactive isotopes of an element take part in the same chemical reactions of the element. The use of a labeled tracer requires the assumption that the labeled molecule, or atom, will not be discriminated from the unlabeled in, for example, chemical or enzymatic reactions, and will trace the position or movement of the unlabeled molecules. Some isotopic effects (like evaporation processes or root uptake into plants) can be observed, especially for light elements or molecules, and should be taken into account. Radioactive tracers have been used very intensively in the earlier years of life sciences research (Table 2). However, their use has diminished much in favor of stable isotopes after the health risks of radioactivity became apparent (and radioactive waste processing costs rose significantly).

Table 2 Stable and radioactive isotopes used in life science research Element

Isotope

Stable or radioactive

% Natural abundance

H

1

Stable Stable Radioactive Stable Stable Radioactive Stable Stable Stable Stable Stable Stable Stable Stable Radioactive Stable Stable Radioactive

99.985 0.015 –a 98.89 1.108 – 99.63 0.37 99.76 0.037 0.204 95.00 0.76 4.22 – 0.014 100 –

C N O S

P a

H H 3 H 12 C 13 C 14 C 14 N 15 N 16 O 17 O 18 O 32 S 33 S 34 S 35 S 36 S 31 P 32 P 2

Abundance listed for the stable isotopes only.

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3.2. How are Stable Isotopes Detected? Detection of stable isotopes is based either on their speciﬁc masses, or on nuclear properties such as spin. Consequently, mass spectrometry (MS) and NMR are the main detection methods for stable isotopes used today. Mass spectroscopy is by far the more sensitive of both techniques. A great variety of mass spectrometer instruments and applications dedicated to the detection of compounds with different characteristics exists. Newest time-of-ﬂight (TOF) and Fourier transform (FT) mass spectrometers reach such high mass accuracies that, from the measured mass of a molecule, its unique composition in terms of number of carbons, nitrogens, oxygens, and protons can be derived unequivocally, allowing for easy identiﬁcation. Especially for MS instruments that do not have such high mass resolution, the spectrometer is often run ‘hyphenated’ with a chromatographic separation technique (either liquid chromatography (LC) or gas chromatography (GC)) to allow for better separation of signals, namely in both the (retention) time and the mass dimension. Illustrative examples of such two-dimensional approaches used for isotopic studies of metabolism include LC-MS of glycerol and glucose (McIntosh et al., 2002), LC-MS of amino acids (van Eijk and Deutz, 2004), GC-MS of amino acids (Christensen and Nielsen, 1999) in protein hydrolysates, volatile fatty acid detection by GC-MS, and GC-combustion-isotope ratio mass spectrometry (GC-C-IRMS) (Morrison et al., 2004), as well as IRMS approaches for breath test analysis (Stellaard and Elzinga, 2005). The latter authors describe also the use of infrared (IR) spectroscopy for gas isotopic analysis. One important difﬁculty in determination of isotopic enrichments by mass spectrometry is the presence, especially with larger molecules, of background isotope signals that stem from the natural abundance of stable isotopes, 13C in most cases making the largest contribution. Powerful matrix calculation protocols that allow for easy correction of those mass isotopomer peaks have recently been developed (Wahl et al., 2004) (isotopomers are molecules having an identical chemical composition but different masses due to the presence of one or more isotopes). NMR spectroscopy is far less sensitive than MS and can detect only nuclei that possess a nuclear spin. Fortunately, these include such important isotopes as 2H, 13C, 15N, and 17O. Moreover, NMR in contrast to MS offers the advantage that it also allows one to determine the position of an isotopic label within a molecule. This has proved such an enormous asset in studies of biosynthetic pathways that the number of isotope-aided NMR studies in the life science ﬁeld is seemingly endless, and growing every day. The interested reader may ﬁnd useful information and references in de Graaf (2000).

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3.3. What Information can be Retrieved from Stable Isotope Experiments? Basically, stable isotopes can convey two sorts of information: how fast a speciﬁc metabolic process is running, and what the products of the processes are. That is, the speed of incorporation of an isotopic label in a molecule gives information on the synthesis rate of that metabolite, and the position of the label in the molecule gives information on the biosynthetic pathway of the molecule. In special cases, namely experimental protocols where a steady state in time of the degree of isotopic labeling of the concerned molecules is established, it is the steady-state degree of isotopic enrichment at different positions in the concerned molecules that gives information on the synthesis rate (or, equivalently, the biosynthetic pathway ﬂux) of the molecule (Fig. 2). The principles of kinetic analysis from isotopic labeling experiments have been described in detail (Wolfe, 1992). Recent interesting examples of ‘classical’ kinetic analysis and biosynthetic pathway elucidation can be found in papers by Teusink et al. (2003) and Bacher et al. (1999), respectively.

endogenous tracee synthesis

Ra tracer infusion Inf

whole body compartment

TTR

Rd

disposal

Figure 2 Principle of whole body synthesis rate determination using stable isotopes. Ra, rate of appearance of unlabeled, newly synthesized metabolite; Rd, rate of disappearance (due to metabolism, excretion, etc.) of metabolite; and Inf, rate of infusion of stable isotope. The tracer–tracee ratio (TTR) is determined experimentally by NMR or MS from a sample of the traced compartment. Because Inf is a parameter that is set by the researchers and TTR can be measured, Ra and Rd can be determined.

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A great variety of dedicated experimental protocols for isotope-aided studies have been developed to suit special needs, some of which are relevant here. Incubation of microorganisms with [U-13C]glucose for multiple doubling times followed by biomass hydrolysis and isotope labeling NMR analysis of the proteinaceous amino acids allows for overall analysis of the bacterial intermediary metabolism (Szyperski, 1995). Similarly, incubation of mammalian cells with [1,2-13C2]glucose and subsequent mass isotopomer analysis of cellular metabolites allows the characterization of cellular intermediary metabolism (Boren et al., 2003). Primed, continuous infusion of 13C-labeled essential amino acids combined with LC-MS detection of amino acid labeling allowed the study of protein turnover in man (Engelen et al., 2000). An alternative technique needs only a single tracer dose to be injected but is mathematically more involved and requires also biopsies to be taken (Zhang et al., 2002). Deuterium labeling of non-essential amino acids combined with mass isotopomer distribution analysis (MIDA) (Hellerstein and Neese, 1999) of body proteins allow the determination of their synthesis rates (Busch et al., 2006). The indicator amino acid oxidation method, which consists of infusion of [1-13C]phenylalanine and monitoring its oxidation product 13CO2 in exhaled air at different supplementation rates of another essential amino acid, may be used to determine dietary amino acid requirements such as for L-lysine (Zello et al., 1993). The use of multiple tracers in a single experiment may offer speciﬁc advantages. The aim of tracer studies is to gather quantitative information about a speciﬁc metabolic function. In case the measured isotope enrichment may be affected by other metabolic events, the necessary correction can be performed when a second tracer, which is known to be metabolized by all interfering metabolic events but not by the function of interest, is added simultaneously (Stellaard, 2005). A special case of this principle is the simultaneous administration of two tracers through both the intravenous and oral routes of administration, which permits the understanding of dynamic pictures of relevant processes such as ﬁrst-pass splanchnic bed retention of nutrients in humans (Matthews et al., 1993). Smartly designed multiple tracer techniques may also be used to resolve multiple biosynthetic pathways leading to the same metabolite, as in the case of, for example, arginine metabolism (Lau et al., 2000), homocysteine remethylation metabolism (Davis et al., 2004), and gluconeogenesis in humans (Ekberg et al., 1999). Application of multiple substrates has also led to impressive results in quantiﬁcation of complex microbial metabolic pathway networks (Petersen et al., 2000) and theoretical frameworks have been established that allow for optimal experimental design of stable isotope labeling experiments (Wiechert et al., 2001).

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3.4. Important Fields of Applications of Stable Isotopes in Biomedicine As mentioned previously, stable isotopes have been applied to almost every imaginable ﬁeld of the life sciences. In the following, a number of references are given to literature on biomedical applications of stable isotopes that are relevant to the study of microbial physiology, mammalian metabolism, and their interaction. Stable isotope studies have been highly instrumental in the elucidation of (microbial) biosynthetic pathways (Bacher et al., 1999). Relatively recent examples that may bear relevance to investigating gut microbial pathways are, for example, a study on pathways of methanol conversion in anaerobic bacteria (Paulo et al., 2003), a study of propionate metabolism by sludges from bioreactors treating sulfate- and sulﬁde-rich wastewaters (Lens et al., 1998) and a study demonstrating how the presence of the Bifidobacterium pathway of acetate formation (Wolin et al., 1998) can be inferred from isotope labeling data. Stable isotopes have found widespread application in MFA of microbes (see following paragraphs); a recent review is Wiechert (2001). The technique can now be applied on a large scale for screening of metabolic ﬂux distributions in microorganisms. Illustrative applications on Bacillus subtilis are described (Fischer and Sauer, 2005). Many examples of stable isotope work in mammalian systems are available. The reader looking for an overview of recent work may ﬁnd reviews on the application of isotope tracers to the study of metabolism in mouse models (McCabe and Previs, 2004), on the estimation of ﬂuxes in mammalian metabolic physiology (Kelleher, 2004), and on the application of stable isotopes in obesity research (Dolnikowski et al., 2005) useful. Organs that have been very intensively studied with stable isotopes include the heart (Des Rosiers et al., 2004), the liver (especially hepatic gluconeogenesis) (Previs and Brunengraber, 1998), muscle, and brain (Shulman and Rothman, 2001). Interesting areas where significant progress in recent years has been reported include lipogenesis (Bederman et al., 2004), nitric oxide metabolism in disease (Luiking and Deutz, 2003), intestinal and renal metabolism of L-citrulline and L-arginine (Boelens et al., 2005), and interorgan protein metabolism (Engelen et al., 2005). Stable isotopes have also been applied to the study of colonic metabolism (Pouteau et al., 2003); these studies will be reviewed in the section on MFA below. Stable isotopes are also beginning to be applied to the study of the interaction of human and gut microbial metabolism. For instance, knowing that SCFA are important products of gut microbial metabolism, and that they are taken up by the host, a further question is what exactly they are

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used for in our bodies. SCFA are known to be oxidized to CO2 in colonic tissue, contributing to colonocyte energy metabolism (Jorgensen and Mortensen, 2000). The fate of butyrate carbons in metabolism of HT29 colon cells was probed with [1,2-13C2]butyrate (Boren et al., 2003) and demonstrated their utilization in the citric acid cycle. The study of amino acid metabolism in man is another example where stable isotopes (in this case 15N) have been instrumental in demonstrating man–microbe interactions. Amino acids from circulating blood may exchange via enterocytes even with the colonic lumen, causing intermingling of endogenous and microbial amino acid metabolism. A number of studies employing 15N-labeled urea have been performed to assess this issue. Urea diffuses into the colon where it is hydrolyzed by bacterial urease to ammonia before being assimilated. Thus, 15N labeling of amino acids upon 15 N urea administration is a clear indication of the activity of microbial nitrogen metabolism (Fig. 3). The microbial origin of a significant fraction in body protein of several amino acids that cannot be transaminated in mammalian tissue (e.g. lysine and threonine) has thus been shown (Lien et al., 1997; Metges, 2000). This implies that our intestinal bacteria have the potential to supply us with essential amino acids! While the examples mentioned mostly pertained to studies focusing on metabolites of intermediary metabolism, recent applications with high relevance to the study of intestinal microbial physiology also target the synthesis of macromolecules, notably proteins and nucleic acids. Only a few stable isotope-aided studies of proteins targeting the intestine have appeared in the literature. These studies are focused on the synthesis of mucins and mucoproteins (Faure et al., 2002), mainly looking at the effect of food components (Coefﬁer et al., 2003; Faure et al., 2005). Studies on nucleic acids are especially relevant for the study of intestinal microbiota (see next chapter). Stable isotope probing (SIP) approaches, as they are called, involve incubation of microorganisms with a stable isotopelabeled substrate under conditions resembling the environmental situation. After a sufﬁciently long incubation, microbial nucleic acids are isolated and the heavier fractions (i.e. those that show incorporation of the stable isotope) are separated and analyzed by, for example, PCR and ﬁngerprinting techniques. Hereby, in situ isotope tracking techniques allow the unraveling of the substrate utilization of microbes in their natural habitat by linking the isotopic signature of biomarker molecules to their inherent phylogenetic information (Maneﬁeld et al., 2004). These techniques are useful for a broad and unprejudiced activity-screen in complex communities, and also to verify whether selected groups of microbes utilize a certain substrate or not. Recent reviews have been written (Dumont and Murrell, 2005; Egert et al.,

enterocytes

serosa (blood)

from arterial

92

intestinal lumen

diet urea

microbial compartment

NH3

urea

His, Lys, Thr

AA

AA

microbial

Protein

endogenous

Protein NH3

AAdisp

Protein NH3 to portal vein

Figure 3 Schematic representation of gut-associated nitrogen metabolism, compiled from information in Metges (2000) and references therein. Colored circles symbolize 15N isotope label originating from urea (red) or ammonia (blue), respectively, with fading color intensity indicating isotope dilution. Urea diffuses from the blood through the enterocytes into the intestinal lumen, where it is hydrolyzed by bacterial urease into ammonia and carbon dioxide. Ammonium is the preferred non-speciﬁc microbial nitrogen substrate for synthesis of e.g. amino acids. Microbially synthesized amino acids may partially be released into the gut lumen and taken up by ileal enterocytes (in the colon, bacterial cell densities are so high that microbially synthesized amino acids probably never reach colonocytes). Therefore, bacteria may supply a significant portion of the body’s requirement for indispensable amino acids. 15N label appearance in histidine, lysine, and threonine upon 15N-urea or 15N-ammonia administration is proof of microbial activity since these amino acids cannot be endogenously transaminated. However, after administration of e.g. the 15N-labeled indispensable amino acid leucine, 15N label will appear in other branched-chain indispensable amino acids as well as in dispensable amino acids (AAdisp) since the body is able to transaminate leucine, valine, and isoleucine. Due to extensive amino acid exchange between blood, enterocytes, and intestinal microbiota, interpretation of 15N labeling experiments is often ambiguous. Combining nitrogen-15 labeling with carbon-13 or carbon-14 labeling as done e.g. in Torrallardona et al. (2003), therefore, may constitute a useful approach to arrive at unequivocal conclusions. (See plate 2 in the color plate section.)

ALBERT A. DE GRAAF AND KOEN VENEMA

Val, Ile, Leu

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2006). Since little is known about the actual in situ functions of human colonic microorganisms, SIP techniques appear particularly promising in investigating these. Ideally, to get good insight into the role of the intestinal microbiota, one would like to be able to probe bacterial and host metabolism simultaneously using stable isotopes, in a single approach. However, an integrative study of gut microbial metabolism of a relevant substrate (e.g. carbohydrate) that covers all the microbial products, and also monitors their metabolism in the host, is still lacking today. The setup of such a study would need to include the basic features of MFA. First, a careful mass balancing helps to ensure that all metabolites are accounted for. Second, tracing the routes where the microbially produced metabolites go using stable isotopes gives an insight into the host processes that receive input from gut microbial activities. Finally, determining the velocities of the various pathways involved allows us to address the (relative) importance of the pathways for the host. Likewise, the reverse routes (man to microbe) can be probed using stable isotopes. It is interesting to note that, whereas historically the ﬁelds of stable isotope studies in microorganisms and in mammalian cells and organisms seem to have developed much on their own, MFA is now starting to integrate both ﬁelds (Ramakrishna et al., 2001; Lee et al., 2003; Antoniewicz et al., 2006). Because of the key role MFA can play in integrating different ﬁelds and disciplines of science, the following sections will give a brief introduction to MFA with references to important literature.

3.5. Basics of Metabolic Flux Analysis At the heart of MFA as it is being used today to characterize microbial metabolism under steady-state conditions, stands the concept of metabolite balancing (FBA, ﬂux balance analysis). Introduced to its full functionality by Stephanopoulos and Vallino (1991), this simple principle has proved very powerful in analyzing metabolic networks, and even in predicting their behavior under various environmental and genetic conditions (Schuster et al., 1999; Edwards and Palsson, 2000; Burgard et al., 2004). Special powerful computational methods like minimization of metabolic adjustment (MOMA) (Segre et al., 2002; Holzhutter, 2004), regulatory on/off minimization (ROOM) of metabolic ﬂux changes (Shlomi et al., 2005), and ﬂux coupling analysis (FCA; Burgard et al., 2004) have been developed especially for the latter purpose. Metabolite balancing applies the principle of material conservation for each and every metabolite pool in the metabolic

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network. At steady state, metabolite pools are constant, hence the total of all metabolite ﬂuxes entering a speciﬁc pool must equal the total of ﬂuxes leaving that same pool. This yields, for every metabolite pool, one linear equation relating all ﬂuxes connecting with that pool. Available measurement data on ﬂuxes (such as substrate uptake rates and product excretion rates) provide additional equations. Setting up the equations for every pool in the network then yields a high-dimensional system of linear equations, with the ﬂuxes as unknowns, that can be solved mathematically using matrix procedures, provided the system is (over)determined (i.e. there are more independent equations than there are unknowns). Here comes the problem with FBA: it turns out that in practice, there are insufﬁcient data to fully determine the equation system. Therefore, a solution can be obtained only when additional assumptions (such as on the stoichiometry of the electron transport chain, or a closed balance of cofactors such as NAD(P), etc.) are made. This has the drawback that the calculated ﬂuxes depend more or less strongly on the assumptions made, with the associated risk of introducing important systematic errors. At this point, stable isotopes came in to help, providing additional measurement data to solve the ﬂux analysis problem without having to rely on assumptions (Wiechert and de Graaf, 1996; de Graaf, 2000) (Fig. 4). The reason why isotopic labeling data allow this is that now a material conservation balance for each and every single carbon (in case of 13C labeling) for every metabolite in the network can be drawn up, resulting in a greatly increased number of equations. Although the number of unknown ﬂuxes also increases in the procedure, for the reason that isotopic labeling patterns also depend on backward ﬂuxes (Wiechert and De Graaf, 1997; Wiechert et al., 1997), the vastly increased amount of experimental data coming with the additional labeling information generally allows the solving of the new equations without having to rely on assumptions. Soon after that, it became obvious that a further increase in information could be obtained by including isotopomer measurement data. Isotopomer information holds knowledge as to whether a molecule is labeled in a single position, or two or more positions at the same time, and also which position(s) is/are labeled. Consequently, considering only natural carbon-12 and its stable isotope 13C, a molecule with a backbone of N carbons can exist as 2N different isotopomers. Extending the mass balancing principle up to the level of isotopomers proved to be difﬁcult at ﬁrst because the equations were no longer linear. However, an analytical solution was developed in due time (Mollney et al., 1999; Wiechert et al., 1999), making the full potential of MFA available. Theoretical properties of isotopomer labeling equation systems along with their associated consequences for experimental

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isotope-labeled substrate

pathway A

pathway B metabolism

FA 13 C 12 C

FB

FB

product metabolite = F A/ F B

Figure 4 Principle of pathway ﬂux determination at (pseudo)steady state using stable isotopes. Via pathway A, carbons 1 and 2 of the substrate end up in carbons 1 and 2 of the product P, respectively, leading to an [1-13C]P isotopomer. In contrast, via pathway B, these carbons end up in carbons 2 and 1, respectively, leading to the [2-13C]P isotopomer (the fate of carbons 3 and 4 of the substrate is ignored here). When the labeling of pool P has reached steady state, the ratio of the [1-13C]P and [2-13C]P isotopomers present in the total pool equals the ratio FA/FB of the ﬂuxes in the two pathways A and B. Once the total rate of synthesis of P is measured independently, the absolute ﬂuxes FA and FB are known.

design have been analyzed (Wiechert and Wurzel, 2001; Isermann and Wiechert, 2003). The interested reader may ﬁnd a number of tutorial reviews on MFA useful (de Graaf, 2000; Wiechert, 2001, 2002). Recent advances concentrate on providing mathematical frameworks that also allow metabolic ﬂuxes from full isotopomer data sets under nonsteady-state conditions to be derived (Wiechert and Noh, 2005). This may provide a much hoped-for basis for the integration of the full potential of isotopomer labeling with ‘classical’ kinetic tracer approaches, both with regard to theoretical and experimental considerations.

3.6. MFA in Detecting Microbial Metabolic Stress One of the main advantages of MFA is that it visualizes the ﬁnal effect of genetic, proteomic, and metabolomic responses to the changing environment

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of an organism. In other words, it reveals how the organism adapts itself at the metabolic level to changing requirements. This offers unique insights into physiological regulation that cannot usually be obtained from genetic, proteomic, or metabolomic analyses alone. To illustrate this point, the reader is referred to work by Petersen and colleagues (Petersen et al., 2000, 2001, 2003), on the amino acid-producing bacterium Corynebacterium glutamicum. This microorganism shows astonishingly little phenotypic change upon deletion/inactivation of important genes for anaplerosis (pyruvate carboxylase, phosphoenolpyruvate (PEP) carboxylase) or gluconeogenesis (PEPcarboxykinase) (de Graaf et al., 2001). This has severely hampered a targeted metabolic engineering for increased L-lysine production by genetic modiﬁcation (Sahm et al., 2000). Whereas the organism does not respond at the genetic and proteomic level to inactivation of the aforementioned genes, metabolite levels changed up and down only to relatively small extents, with no clear picture discernible. Carbon-13 isotopomer-aided MFA experiments in contrast showed very clear-cut responses of the metabolic ﬂuxes concerned (Petersen et al., 2001). Taking into account key kinetic data of the enzymes involved, a mathematical model of the relevant pathways was constructed of which the unknown parameters were subsequently tuned using the available experimental data on enzyme activities (‘genome’/‘proteome’), metabolite concentrations (‘metabolome’), and ﬂuxes (‘ﬂuxome’). Amazingly, this model could quite accurately predict the effects of genetic manipulations on L-lysine production (Petersen et al., 2003), making it the ﬁrst working small-scale systems biology model to our knowledge. As of today, research in the medical ﬁeld aiming at elucidation of disease mechanisms largely proceeds by way of making statistical correlations of observable physiological parameters (‘phenotype’) with measured metabolite concentrations (‘metabolome’), enzyme activities (‘proteome’), or gene transcription rates (‘genomics’/‘transcriptomics’). In our view, the advent of ‘-omics’ technologies in recent years has changed the scale of this approach, but not the paradigm. The example with C. glutamicum demonstrates that much can be gained from including MFA results, and from developing appropriate modeling frameworks as opposed to purely statistical analysis procedures. There is nowadays a growing consensus that, before a certain disease actually appears, there is a long preceding time period during which the metabolism is continuously stressed, until the point where normal regulatory mechanisms can no longer compensate (van der Greef et al., 2004), and the disease becomes manifest. Although changes in gene expression, enzyme activities, and/or metabolite levels may be apparent during the pre-disease period, pathway regulation often is so complex that a clear picture is very

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hard to obtain from statistical analysis of these data alone. From the example of C. glutamicum, we can expect that metabolic ﬂuxes may offer a significantly improved view. A ﬁrst example pertains to protein turnover measured with stable isotopes in chronic obstructive pulmonary disease patients (Engelen et al., 2000), where no apparent net unbalance in protein metabolism was found, but whole body protein synthesis and breakdown were significantly increased in patients (i.e. the turnover, or ﬂux, was increased). Summarizing, it is predicted that stable isotope-aided methods, especially MFA, will significantly improve our understanding in the near future of metabolic regulation in response to the dynamic environment. In the special case of the intestinal microbiota interacting with our own metabolism, expectations are also high that stable isotopes will permit key insights into the metabolic regulations both on the microbial, and the host side in due course. The following sections will give an overview of important knowledge gained in recent years in the ﬁeld of intestinal microbial metabolism on the genomic, proteomic, and metabolomic level as well as on the level of metabolic ﬂuxes, with a special focus on studies that employed stable isotopes.

4. GENOMIC INVENTORIES OF INTESTINAL BACTERIA The human GI tract is colonized by a microbial community that develops in complexity during life, resulting in a climax community of microbial cells in adults which outnumber the host cells by an order of magnitude (Blaut, 2003; Zoetendal et al., 2006). In addition to this temporal development, the GI tract community is characterized by a distinct spatial variation of microbial communities that progressively develops in size and diversity distally from the stomach (Blaut, 2003; Zoetendal et al., 2006), culminating in a staggering 1012 microorganisms per gram of colonic content. The microorganisms in our large intestine contribute significantly to nutrient processing and are important for health and disease. While the enumeration of bacteria by conventional culture techniques has been imprecise and time consuming, analysis of the ecology of the intestinal microbiota has been greatly improved by designing 16S-rRNA-targeted oligonucleotide probes. Nowadays, many tools and techniques are available to characterize comprehensively the microbial diversity in the human gut (Wilson and Blitchington, 1996; Zoetendal et al., 1998; Suau et al., 1999; Rigottier-Gois et al., 2003). Use of these in molecular studies (Hugenholtz et al., 1998; Zoetendal et al., 2004a, 2004b) have shown that the majority of the

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microorganisms in our gut have not yet been cultivated as pure cultures in the laboratory, either because we do not know the nutritional requirements or growth conditions of these microorganisms (Finegold et al., 1983; Rigottier-Gois et al., 2003), or because they are damaged or dead (Ben-Amor et al., 2005). Despite the fact that, over the past few years, the use of these molecular techniques has given important insight into structure and spatial organization of the human intestinal microbiota (Hugenholtz et al., 1998; Zoetendal et al., 2004a, 2004b), only a limited number of tools are to hand to investigate the activity of the microbiota at the level of individual species. Several recent developments, discussed below, that aimed to characterize the activity of (particular species within) a microbial community have allowed a more detailed picture of the link between its structure and function. Frequently, these methods and tools have been developed for other ecosystems, but they have recently found their way to the anaerobic system of the GI tract. Food passes relatively quickly through the stomach and the small intestine. That, in combination with a hostile environment in the upper GI-tract (gastric acid, bile, pancreatic enzymes, immune system) precludes a dense colonization of this region of the gut, although up to 107 cells per gram of content may be present. Transit of undigested and indigestible food components through the colon is much slower. This allows for the development of the diverse microbiota present in the large intestine. Here, the microbes thrive on a variety of substrates, including some originating from the host, such as mucin. It appears that we have established an intimate relationship with the microbial world, a relationship of a largely symbiotic nature. Speciﬁc host–microbial interactions develop that are now starting to be understood and are essential for maintaining intestinal health (Hooper et al., 2002; Freitas et al., 2003). In an elegant paper (Backhed et al., 2005), the term ‘mutualism’ has recently been introduced as a more proper way to account for the fact that both host and microbes proﬁt from their coexistence. In the following sections, we will discuss the principal ﬁndings of studies on diversity of the intestinal microbiota, look in some detail into the results of stable isotope-aided studies, and review the conclusions drawn from such studies on gut microbial functionality.

4.1. General Aspects: Cataloguing Intestinal Microbial Communities Microbial functionality represents perhaps the greatest unexplored realm of gastrointestinal biology with respect to our understanding of the effects

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of microbial activity on health and disease. The introduction of molecular biological techniques into intestinal microbial ecology in recent years has uncovered the vastness of microbial diversity in the GI tract. Considerable attention has been given to determine the spatial and temporal microbial diversity by high-throughput genetic approaches that are mainly based on analysis of the microbial signatures in 16S ribosomal RNA (rRNA) sequences (Amann and Ludwig, 2000; Backhed et al., 2005). All three domains of life have been detected in the GI tract, but the Bacteria are highly dominant. A total number of 1014 cells and 41000 species have been reported (Egert et al., 2006). Of the more than 200,000 rRNA gene sequences currently present in databases, only approximately 1% are annotated as being derived from the human intestinal bacteria, of which approximately 80–90% represent uncultured bacteria (Backhed et al., 2005). The bacterial divisions that dominate are the Cytophaga, Flavobacterium, Bacteroides, and the Firmicutes, each estimated to make up about 30% of the bacteria. Only 6 additional divisions (of a total of 55 discovered to date) have been reported to occur in the human large intestine, which make the diversity in the GI tract at the division level among the lowest (Hugenholtz et al., 1998; Backhed et al., 2005). Diversity present in the GI tract is hypothesized to be the result of strong host selection and coevolution and reﬂects natural selection at the microbial level and at the host level. At the microbial level, lifestyle strategies affect the competitiveness of individual species in a complex mixture. These strategies include, for instance, growth rate, substrate use (part of which is host derived, such as mucus), and ability to cope with the hostile environment (such as the intestinal immune system). At the host level, deleterious effects of bacteria can reduce host ﬁtness, resulting in fewer hosts and therefore, less habitat for the microorganisms to grow in. On the other hand, an activity that promotes host ﬁtness will create more habitats. One such positive interaction alluded to already earlier is for instance the production of butyrate, which is used as a source of fuel by the colonocytes (Roediger, 1982). Although this mutualistic coexistence between microbiota and host is generally accepted to be correct, it is also believed that the intestinal microbiota is responsible for numerous intestinal diseases, such as colon cancer and IBD. And even though there generally is a symbiotic relation between microbiota and host, the individual microorganisms live in constant battle with each other. For instance, they compete for substrates for growth (from dietary origin, but also mucus and exfoliated epithelial cells) and adherence sites (receptors), and they produce metabolites that may kill or slow down growth of other microorganisms when present in high concentrations (e.g. SCFA, lactate, bacteriocins). By contrast, however, these microorganisms also live in symbiosis, where one

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species may produce a nutrient/metabolite that is required for growth of another species, e.g. in the case of acetogenesis or methanogenesis, where H2 produced by some members of the microbiota is used to produce acetate or methane, respectively, by other members of the community. Normal colonization by the human intestinal commensal microbes stimulates a range of important functions, such as postnatal intestinal maturation, maintenance of the mucosal barrier, protection against pathogens, and the development and maturation of the immune system (Cummings and Macfarlane, 1997; Falk et al., 1998; Elson et al., 2005). Over adulthood, the composition of the microbiota is rather stable, but speciﬁc for each individual. This is in part determined by genetic factors (Zoetendal et al., 1998). Diet has the potential to inﬂuence the activity and composition of the microbiota, although that is generally believed to be only a temporary effect, unless the dietary components responsible for the change in composition and/or activity are taken on a frequent basis. Although increasing insight has been obtained into the microbial diversity, there is very limited knowledge of the metabolic function of the human intestinal microbes, the way the diet affects metabolic ﬂuxes, and how the produced metabolites affect the health of the host. Even though it has been possible to determine production of microbial metabolites (even in vivo using stable isotope-labeled substrates, see below) by the collective microbiota, it has until recently not been possible to determine which microorganisms are primarily responsible for the production of these metabolites in situ.

4.2. The Microbiome Our gut microbiota can be pictured as a microbial organ placed within a host organ. It is believed that the collective microbiota can carry out more biochemical conversions than the liver, our most metabolically active organ with respect to the multitude of different biochemical reactions it can effect. The collective microbial genome, termed metagenome or microbiome, which contains more than 100 times the number of genes in the human genome, encodes biochemical pathways that we have not had to evolve ourselves. In a recent review in Nature Medicine, Sekirov and Finlay (2006) concluded that ‘Together with our microbes we are a human–bacterial superorganism with immense metabolic diversity and capacity’ . In September 2006, the full genome sequence of 279 bacterial and 23 archeal genomes was sequenced (resource: Comprehensive Microbial Resource Home Page: http://cmr.tigr.org/tigr-scripts/CMR/CmrHomePage.cgi), and another 17 genomes were in progress. Also, a large number of genomes of bacteria that

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can be found in the human gastrointestinal tract or have been associated with human disease have been sequenced (Table 3). Most of these microorganisms have been chosen due to either their pathogenic potential or their potential probiotic (i.e. health beneﬁcial) activity. These sequencing activities already provide an enormous wealth of data with respect to the (potential) metabolic activity of these individual microorganisms. However, metagenomics provides insight into the genetic potential of complex microbial communities. Assuming that each bacterial species within the GI tract has an average genome size of 3 Mb, the human intestinal microbiome probably comprises several thousand Mb, and thus in size equals that of the human genome. However, due to a much higher gene density, it vastly exceeds the human genome’s coding capacity (Relman and Falkow, 2001; Backhed et al., 2005). In addition, an estimation of the total microbial genomic content in an individual should consider the genetic redundancy within these communities. Also, the total microbiome in a human population should, in addition to this redundancy, consider the individual composition of the microbiota (Egert et al., 2006). Compelling evidence suggests that disruption of the intestinal microbial ecosystem contributes to a number of diseases. However, without understanding the interactions between the human and microbial genomes, it is impossible to obtain a complete picture of the effects of the intestinal microbiota on health and disease. Elegant studies (Hooper et al., 1999; Freitas et al., 2001) have indicated a cross-talk between the members of the microbiota and the host. They have shown that soluble factors of Bacteroides thetaiotaomicron, a prominent member of the microbiota, results amongst others, in changes in the expression of glycosyl residues on host membrane-associated glycosylated proteins. In particular, the upregulation of fucosylated glycans (Hooper et al., 1999) by B. thetaiotaomicron revealed a novel signaling collaboration between host and microbe to produce nutrients for growth for the microbe. In a follow-up study, the genome-wide response of the host was carried out (Hooper et al., 2001; Stappenbeck et al., 2002). These studies used single cultivable microbial species and focused mostly on host-related genes and functionalities. The microbiome of the complex gut microbiota has only recently been identiﬁed as a target for exploration. Novel hydrolase genes were discovered in uncultured rumen bacteria (Ferrer et al., 2005) and beta-glucanase genes were identiﬁed from uncultured bacteria that colonize the large bowel of mice (Walter et al., 2005). Two metagenomic libraries constructed from DNA from fecal samples of healthy individuals and patients with Crohn’s disease (CD) (Manichanh et al., 2006) and screened for 16S rRNA genes revealed a greatly reduced diversity in the Firmicutes in CD patients, which

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Table 3 Fully sequenced genomes and those in progress for human gastrointestinal bacteria Completely sequenced genomes Actinomyces naeslundii Anaplasma phagocytophilum Bacillus anthracis Bacillus subtilis Bacteroides fragilisb B. fragilisb Bacteroides thetaiotaomicron Bartonella henselae Bartonella quintana Bifidobacterium longum Bordetella bronchiseptica Bordetella parapertussis Bordetella pertussis Borrelia burgdorferi Borrelia garinii Brucella abortus Brucella melitensis Brucella suis Burkholderia mallei Burkholderia pseudomallei Burkholderia thailandensis Campylobacter jejunib C. jejunib Chlamydia abortus Chlamydia pneumoniae Clostridium acetobutylicum Clostridium perfringens Clostridium tetani Corynebacterium diphtheriae Corynebacterium jeikeium Coxiella burnetii Desulfovibrio desulfuricans Ehrlichia chaffeensis Enterococcus faecalis Escherichia coli Francisella tularensis Fusobacterium nucleatum Haemophilus ducreyi Haemophilus influenzae Helicobacter hepaticus Helicobacter pylori Lactobacillus acidophilus Lactobacillus helveticus Lactobacillus johnsonii

Genome sizea (Mb) 3.0 1.5 5.2 4.2 5.2 5.3 6.7 1.9 1.6 2.3 5.3 4.8 4.1 1.5 1.0 3.3 3.3 3.3 5.8 7.3 6.7 1.6 1.8 1.1 1.2 4.1 3.1 2.8 2.5 2.5 2.0 3.7 1.2 3.4 4.6 1.9 2.2 1.7 1.9 1.8 1.7 2.0 2.0 2.0 (Continued )

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Table 3 (continued ) Completely sequenced genomes Lactobacillus plantarum Lactobacillus sakei Lactobacillus salivarius Lactococcus lactis subsp. lactis Legionella pneumophila Leptospira interrogans Listeria innocua Listeria monocytogenes Mycobacterium avium paratuberculosis Mycobacterium bovis subsp. bovis Mycobacterium leprae Mycobacterium tuberculosis Mycoplasma pneumoniae Mycoplasma pulmonis UAB Neisseria gonorrhoeae Neisseria meningitidis Nocardia farcinica Pasteurella multocida Porphyromonas gingivalis Prevotella intermedia Propionibacterium acnes Pseudomonas aeruginosa Rickettsia conorii Rickettsia felis Rickettsia prowazekii Rickettsia typhi Salmonella enterica S. enterica serovar Typhi Salmonella typhimurium Shigella boydii Shigella dysenteriae Shigella flexneri Shigella sonnei Staphylococcus aureus Staphylococcus epidermidis Staphylococcus haemolyticus Staphylococcus saprophyticus Streptococcus agalactiae Streptococcus mutans Streptococcus pneumoniae Streptococcus pyogenes Streptococcus thermophilus Treponema denticola ATCC Treponema pallidum Tropheryma whipplei

Genome sizea (Mb) 3.3 1.9 2.1 2.4 3.4 4.6 3.1 3.0 4.8 4.3 3.3 4.4 0.8 1.0 2.2 2.3 6.3 2.3 2.3 2.7 2.6 6.3 1.3 1.6 1.1 1.1 4.6 4.8 5.0 4.6 4.6 4.6 5.0 2.8 2.5 2.7 2.6 2.2 2.0 2.1 1.8 1.8 2.8 1.1 0.9 (Continued )

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Table 3 (continued ) Completely sequenced genomes Ureaplasma urealyticum parvum Vibrio cholerae Vibrio parahaemolyticus Vibrio vulnificus Wolinella succinogenes Yersinia pestis Yersinia pseudotuberculosis Genomes in progressc Lactobacillus gasseri Lactobacillus casei Lactobacillus rhamnosus Lactobacillus helveticus Lactobacillus delbrueckii Lactobacillus reuteri Lactobacillus brevis Leuconostoc mesenteroides Pediococcus pentosaceus Propionibacterium freundereichii Streptococcus mitis

Genome sizea (Mb) 0.8 4.0 5.2 5.1 2.1 4.8 4.8 Approximate genome size 1.8 2.6 2.4 2.4 2.3 2.5 2.0 2.0 2.0 2.6 2.0

a

Rounded to the nearest decimal; average of genome size of all sequenced strains where applicable. b Two strains of this species have been sequenced, with different genome sizes. c GI tract species listed are those for which we have information that they are being sequenced.

is in agreement with the hypothesis that the intestinal microbiota has an important role in CD development. The human gut microbiome initiative (HGMI) has been proposed as an extension of the human genome project. New and cost-effective approaches now allow fast and reliable highthroughput sequencing of millions of basepairs. The ﬁrst published results from such a sequencing effort analyzed 78 million bases (Gill et al., 2006) in 140,000 sequence reads from DNA libraries from two healthy human adults. The study revealed that metabolic function analyses of identiﬁed genes of the sequenced microbiome has identiﬁed enrichment of genes encoding metabolism of carbohydrates, amino acids, and xenobiotics and methanogenesis compared with other sequenced microbial genomes. At least 81 different glycosyl hydrolases have been found in the microbiome, indicating its capacity to cleave a vast array of different (mostly food derived) carbohydrate linkages. Also, an overrepresentation of butyrate kinase (statistically increased with a factor of 9.30 (odds ratio) compared with other microbial genomes) was found. It was speculated that this corroborated the important commitment of the gut microbiota to generating this biologically

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important compound, which serves as the principal energy source for colonocytes. About 50% of the total of 50,164 open reading frames (ORFs) predicted matched to the database, of which 5700 were present in both subjects. Of the total number of ORFs, only 25% could be assigned unambiguously to members of the Archeae or Bacteria. The remainder could not be assigned unambiguously to either of the two, or did not match any known ORFs. The 78 million bases sequenced would represent approximately 1–3% of the total microbiome. Approximately 40% of the sequence reads could not be assembled into contigs, most likely because of low abundance of the microorganisms, from which the sequence originated, within the specimens studied. It should be mentioned that, even though the colon is colonized by a myriad of different microorganisms, only a limited number of species/genera make up the majority of the microbiota. Only approximately 15 16S rRNA-targeted probes for bacterial genera/phyla are required to measure approximately 90% of the bacterial cells in fecal samples from human adults (Harmsen et al., 2002). Therefore, any clone library obtained from an actual sample from the gut will be dominated by genomic DNA from these dominant species, even without the bias generated by the cloning procedure itself. Given the extensive sequencing efforts it would take to sequence the full complement of the intestinal microbiome, the true metabolic potential of the microbiota will not be unraveled in the near future. Since the major species probably make up the major activities, this may not have to be our major goal for the moment. Also, similarities and differences between the microbiota of different individuals (Gill et al., 2006; two subjects were studied) need to be studied in more detail to be able to decipher the major activities carried out by the human intestinal microbiota. Current screening approaches of intestinal metagenomic libraries are not fully established. Screens can be based either on nucleotide sequences or on enzyme activities but both strategies have limitations. PCR and hybridization techniques require primers or probes based on previously cloned (i.e. known) genes. Functional analysis enables the discovery of new classes of genes, but this requires the expression from the cloned inserts of active enzymes in heterologous hosts (usually Escherichia coli). In addition, appropriate assays must be available, and most phenotypes of interest, e.g. butyrate production, might not be suitable for high-throughput selection. Recently, an elegant screen has been developed that enables the rapid identiﬁcation of clones with a desired inducible metabolic activity within large clone libraries (Uchiyama et al., 2005). The method is based on the commonly observed substrate induction of genes encoding biodegradative pathways, and relies on a promoter-trap system to trap genes that encode catabolic pathways in front of a gene that encodes green ﬂuorescent protein

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(GFP). GFP-positive, induced clones are selected by ﬂuorescence-activated cell sorting. However, one of the constraints of this method, as with activity screening in a heterologous host, is that it requires the regulatory machinery of the expression host to recognize both the promoter and the substrate (Handelsman, 2005), and therefore the method may have limited applicability. Thus, although these studies begin to deﬁne the functional activities of the human gut microbiome, future in-depth metagenomics studies are needed to provide deeper coverage of the microbiome, which has been termed the second human genome, and to study the relationship between microbiota and health and disease.

4.3. Stable Isotope Probing: Clues to Metabolic Function from Genomics Data Even though the picture of the complete microbiome may be incomplete, the ﬁrst studies toward the metabolic function of individual members within the collective microbiota are being undertaken, and the ﬁrst clues are known. These concern studies where stable isotope-labeled substrates are used to investigate the role and activity of certain members within the microbiota on speciﬁc substrates. In Section 5, we will describe the metabolomics approaches to this. Here, we focus on the contribution of speciﬁc microorganisms to the fermentation of the substrates. We have discussed that molecular DNA technologies allow for a comprehensive and integrated approach to assessing the structure of microbial communities, providing a perspective in GI tract microbiology. Although the application of these tools has significantly advanced our understanding of the gut microbial diversity, it does not provide functional insight on which microbes are relevant for speciﬁc dietary conversions (de Vos, 2001; Egert et al., 2006). The real challenge here is to develop and apply methodologies for analyzing the functionality of the microbiome. For this, it is important to know which microorganisms are responsible for the observed activities, elucidating dominant microbial functionalities in the human GI tract, the impact of speciﬁc dietary components on these functionalities, and ultimately the effect on gut health. Stable isotopes can play an important role in answering these questions. To couple the microbial diversity to metabolic function, in situ SIP approaches appear very promising (Egert et al., 2006). Typically, in nucleic acid-based SIP studies, 13C-labeled compounds that act as substrates in the food chain are delivered to cultures of (intestinal) bacteria (Fig. 5). Subsequently, the ribosomal DNA or RNA of the microbial community is

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isolated and subjected to density gradient centrifugation to isolate the heavy, labeled fraction of nucleic acids. These heavier fractions stem from bacteria that have consumed the substrate and incorporated the isotopic label in their nucleic acids. Either general or group-speciﬁc PCR ampliﬁcation allows 16S rDNA fragments to be ampliﬁed (Satokari et al., 2001; Heilig et al., 2002). These can then be characterized by high-throughput rDNA sequence analysis providing insight into the microbial diversity of these fractions. By following the development of the rDNA sequence diversity in time, the speciﬁc groups of microbes involved in the food chain from, for example, carbohydrates to SCFA can be reconstructed. In addition, the spatial diversity can be probed. This approach has been shown to be useful to determine the substrate utilization in a variety of microbial communities (Radajewski et al., 2000; MacGregor et al., 2002). The approach has also been used in the gut, although until recently restricted to earthworms and larvae of the cockchafer and the cetoniid beetle (Egert et al., 2003, 2004, 2005; Lemke et al., 2003). Recently, we have taken this SIP strategy and applied it to a human gut microbial community (Egert et al., 2007). In this study, 16S rRNA-based SIP and NMR spectroscopy-based metabolic proﬁling were used to identify bacteria fermenting glucose (as a model substrate) under conditions simulating the human intestine. An in vitro model of the human intestine was inoculated with a GI tract microbiota resembling that of the small intestine and subsequently 40 mM of uniformly labeled 13 C-glucose was added. RNA was extracted from lumen samples after 0 (control), 1, 2, and 4 h of incubation and fractionated by density gradient ultracentrifugation. Phylogenetic analysis of the 16S rRNA revealed a microbial community dominated by microorganisms closely resembling lactic acid bacteria and Clostridium perfringens, not unlike the microbiota in the terminal ileum. Fingerprints of the most-labeled rRNA fraction identiﬁed Streptococcus bovis and C. perfringens as the most active glucose fermenters in the model. Accordingly, NMR analysis identiﬁed lactate, acetate, butyrate, and formate as the principal fermentation products, constituting up to 96% of the 13C-carbon balance. Thus, RNA-SIP combined with metabolic proﬁling allowed the detection of differential utilization of the general model carbohydrate glucose, indicating that this approach holds great potential to identify bacteria involved in the fermentation of relevant dietary oligo- and polymeric carbohydrates in the human large intestine as well. RNA is the most responsive (sensitive) biomarker for SIP analyses because it occurs in greater cellular copy numbers, has a higher turnover rate than does DNA and is produced more or less independent of cellular replication (Maneﬁeld et al., 2002). Owing to fewer variations in its GC content compared with DNA, ribosomal 16S-based RNA-SIP might also be less

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susceptible to GC effects that interfere with the separation of labeled 16S rRNA. It should be realized though that it is exactly this difference in GC content between different members of the microbiota which results in the fact that a density gradient of unlabeled RNA already contains RNA in the heavier fractions. For instance, biﬁdobacteria can be found in these fractions (our own unpublished results). Therefore, to study properly which microorganisms contribute to fermentation of certain substrates, molecular ﬁngerprinting techniques, such as T-RFLP or DGGE, are required to assess the enrichment of the heavy fractions for those microorganisms actually using the substrate (Fig. 5; Egert et al., 2007). Human intestinal samples seem particularly suited to an RNA-SIP approach because these samples contain large numbers of highly active cells, resulting in quick and sufﬁcient labeling of RNA. However, in view of these same large numbers of cells in the human GI tract, together with the nutrient-rich environment and the broad range of potential substrates (e.g. carbohydrates, proteins, hostderived substrates) that can be fermented, human intestinal samples necessitate a sensitive RNA-analysis approach to cope with label dilution. The use of in vitro gut models that closely mimic the environmental conditions in the GI tract and that are easy to sample enables detailed analyses of successive label incorporation into the RNA of different community members over time. Such cross-feeding effects (i.e. the use by one member of the microbiota of labeled metabolites derived from the initially added substrate produced by a different member) will help to identify food chains in intestinal systems. This may lead to the generation of hypotheses that need to be tested in vivo. Yet, application of SIP in human trials is challenging. It remains to be shown (i) whether a labeled substrate can be effectively delivered through the intestinal tract into the target region and homogenously distributed there, and (ii) whether the (singly or pulsed) applied substrate concentrations can be adjusted in a way that prevents dilution within the colon, while still enabling sufﬁcient labeling of microbial (16S r)RNA. Figure 5 Principle of RNA-based stable isotope probing (SIP) for detection and characterization of microbes that actively metabolize the labeled substrate. 13 C-labeled substrates are incubated in (a) simple in vitro models (test tube or ﬂask), (b) sophisticated in vitro systems, or (c) in vivo. Samples obtained from these experiments [in the ﬁgure only shown for samples from (a)] are subjected to RNA isolation and density gradient centrifugation. After separation of the gradient in fractions, molecular ﬁngerprinting techniques, such as DGGE (Zoetendal et al., 2004a) or T-RFLP (Egert et al., 2003) can be used to determine the presence (usually enrichment) in the heavier fractions of those microorganisms that specifically fermented the substrate and this can be compared with the diversity present in an unlabeled, control sample. (See plate 3 in the color plate section.)

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5. PROTEOMIC ASPECTS OF INTESTINAL MICROBIAL LIFE 5.1. Functions of Intestinal Bacterial Enzymes Intestinal bacteria produce large amounts of extracellular enzymes, especially for degradation of mucins and dietary carbohydrates. These enzymes may be freely soluble or remain membrane bound; the latter are generally found to be more active. Mucin oligosaccharide chain-degrading bacteria have been isolated from feces of healthy subjects (e.g. Salyers et al., 1977; Hoskins et al., 1985; Derrien et al., 2004) and their enzymes studied. It was concluded that certain Bacteroides, Bifidobacterium, and Ruminococcus strains are numerically dominant populations degrading mucin oligosaccharides in the human colon due to their constitutive production of the requisite extracellular glycosidases including blood group antigen-speciﬁc alpha-glycosidases, sialidase, beta-glycosidases, alpha-galactosidase, and beta-N-acetyl-hexosaminidases. Enterococcus faecalis produced predominantly cell bound glycosidases (Salyers et al., 1977; Hoskins et al., 1985). Oligosaccharide side chains of human colonic mucins contain O-acetylated sialic acids and glycosulfate esters. Although these substituents are considered to protect the chains against degradation by bacterial glycosidases, sialate O-acetylesterase, N-acetylneuraminate lyase, arylesterase, and glycosulfatase activities have been found in fecal extracts (Corﬁeld et al., 1992). Thus, mucin oligosaccharide chains terminating in O-acetylated sialic acids are unlikely to be protected from degradation by enteric bacteria. High levels (2–565 U/g) of amylase activity have been observed in human feces, with over 92% of amylase activity being of extracellular origin, whereas only about 9% of activity was associated with particulate material and washed cells (Macfarlane and Englyst, 1986). Bacterial cell-bound amylases were considerably more efﬁcient in breaking down starch, however, than were the soluble enzymes which occurred in cell-free fecal supernatant ﬂuids. Other hydrolytic and reductive bacterial enzymes measured in human colonic contents include beta-glucuronidase (GN), beta-glucosidase (GS), arylsulfatase (AS), azoreductase (AR), and nitroreductase (NR). These enzymes can be involved in production of mutagenic or genotoxic metabolites (McBain and Macfarlane, 1998). Cell-associated AS and extracellular GS were found to be approximately twice as high in the distal colon compared with the proximal bowel, while AR changed little throughout the

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gut. Upon studying 20 pure cultures of intestinal bacteria these authors found that various bacterial strains were active producers of GS, GN, NR, and AR. However, only few of the isolated bacteria produced AS in small amounts. In recent years, research on bacterial intestinal proteins seems to have focused more on the dark sides of our commensals, with a particular interest in the roles of enzymes and toxins in gut diseases. Infectious enteric Helicobacter pylori have been shown to produce matrix metalloproteinases (MMPs) that participate in degradation of the extracellular matrix also on HT29 colon epithelial cells, allowing bacteria to invade (Yanagisawa et al., 2005). Bacterial ﬂagellin, a speciﬁc microbial ligand of Toll-like receptor-5 (TLR-5), is released by commensal and enteroinvasive microbes. Flagellin exposure to an in vivo mouse model of injured colon, but not to intact colon, was found to significantly aggravate colonic inﬂammation, increase mouse mortality, enhance histopathological damage in the colonic mucosa, and to cause severe apoptosis in colonic epithelium (Rhee et al., 2005). These results demonstrated that bacterial ﬂagellin plays an important role in the development and progress of colitis, via TLR-5 engagement. Chitinase 3-like-1 (CHI3L1) is a putative key molecule involved in the dysregulation of host/microbial interactions that appears to play a central role in the development of IBD. A very recent study (Mizoguchi, 2006) demonstrated that CHI3L1 is required for the enhancement of adhesion and internalization of infectious bacteria in colonic epithelial cells. The expression of CHI3L1 protein was found to be clearly detectable in lamina propria and colonic epithelial cells in several murine colitis models and UC and Crohn’s disease patients but absent in normal controls (Mizoguchi, 2006). It was concluded that CHI3L1 contributes to the facilitation of bacterial invasion into the intestinal mucosa and the development of acute colitis, presumably by enhancing the adhesion onto and invasion of bacteria into colonic epithelial cells.

5.2. Proteomic Studies of the Gut Microbiota: A Largely Unprobed Area? Proteomic studies of the intestinal microbiota, in principle, could be useful to study expression patterns of proteins and enzymes in response to dietary components and thereby provide a rationale for the development of new active ingredients (e.g. pre- and probiotics). Yet, the full power of proteomic analysis remains to be demonstrated in this area also.

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The number of studies reported in this ﬁeld is strikingly small and most studies concentrate on cultivated bacteria. This may have to do with the difﬁculty of accessing the colon; but more likely, the complex fecal nature of the samples causes severe problems especially with the widely used two-dimensional gel techniques the performance of which is susceptible to sample impurities and difﬁculties in reproducibility. It can be anticipated that less susceptible techniques such as surface-enhanced laser desorption/ ionization (SELDI)-TOF analysis (Barzaghi et al., 2004), a protocol that has been successfully applied to a wide range of Gram-positive and -negative bacteria, will accelerate progress of the study of proteomics of the gut microbiota in the near future. Nevertheless, technically impressive methodologies have been developed that allow the characterization of hundreds of microbial proteins from bacteria relevant to the colon in a single experiment. For instance, a nano-high-performance liquid chromatography/mass spectrometry (nano-HPLC/MS) system was established to separate proteins of E. coli in a two-dimensional manner by combining strong cation exchange (SCX) and reversed phased (RP) chromatography (Vollmer et al., 2003). Peptides were eluted online to an iontrap MS instrument and further analyzed by tandem MS fragmentation for identiﬁcation using the Swiss Prot Database. Differentially expressed proteins on glucose and lactose were identiﬁed. Similarly, lactic acid bacteria that are widely used in the agro-food industry have been characterized by proteomic techniques as reviewed in Champomier-Verges et al. (2002). More recently, the proteome of biﬁdobacteria has received considerable attention. Adaptation to and tolerance of bile stress are among the main limiting factors to ensure survival of biﬁdobacteria in the intestinal environment of humans. Comparing protein patterns of strains grown with or without bile showed 34 different proteins whose expression was regulated (Sanchez et al., 2005). These proteins included general stress response chaperones, proteins involved in transcription and translation and in the metabolism of amino acids and nucleotides, and several enzymes of glycolysis and pyruvate catabolism, indicating that bile salts induce a complex physiological response rather than a single event in biﬁdobacteria. In a second study, a strong cation exchange-reversed phase-tandem mass spectrometry strategy was used to catalogue the most abundantly expressed proteins of a probiotic Bifidobacterium infantis strain (Vitali et al., 2005). These authors were able to obtain a global view of the B. infantis proteome with 136 proteins identiﬁed by multidimensional protein identiﬁcation technology (MudPIT) analysis that were subsequently compared to available genomic information. Yuan et al. (2006)

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very recently published an even more comprehensive proteomic study on Bifidobacterium longum NCC2705 in which they succeeded in identifying 369 protein entries by MALDI-TOF-MS and/or ESI-MS/MS. The identiﬁed proteins represent 21.4% of the predicted 1727 ORFs in the genome and correspond to 30% of the predicted proteome. Interestingly, this study also aimed to characterize cellular pathways related to important physiological processes. Comparing proteome maps during growth on glucose and fructose suggested the presence of a speciﬁc transport system for fructose in B. longum. Interestingly, the proteome of biﬁdobacteria in the GI tract of the human infant is being studied (Te Biesebeke et al., 2004). Over a period of 9 weeks, fecal samples were collected from infants and studied with two-dimensional gel electrophoresis. A change in protein expression over time was observed. Detailed analyses of these changes using MS-analyses are in progress (Te Biesebeke et al., 2004). As was already apparent from enzymatic analysis discussed above, intestinal bacteria express many proteins that deploy their activities outside the cell, be it freely soluble or, in many cases preferably, attached to the cell wall. This makes sense because such essential factors as both the substrate and the opportunities to attach to the gut wall are located on the outside of the cell. A well-known intestinal bacterium, Clostridium difficile, has been analyzed for its cell wall-associated proteome recently (Wright et al., 2005). This bacterium causes disease of the large intestine, particularly after treatment with antibiotics, due to production of two toxins (A and B). In addition to these toxins, C. difficile expresses cell wall-associated virulence factors including cell wall protein Cwp66, highmolecular weight surface layer protein (HMW-SLP), and the ﬂagella. However, the genome sequence predicted many more cell wall-associated proteins that could play a role as virulence factors, and indeed the study found, among 49 different identiﬁed cell wall proteins, a number of paralogs of HMW-SLP that present interesting targets for further research (Wright et al., 2005). The application of proteomics to complex microbial assemblages (metaproteomics) still presents considerable challenges (Wilmes and Bond, 2006). The most extensive metaproteomic study to date combined proteomics with metagenomics to study a low-complexity natural bioﬁlm (Ram et al., 2005). 2033 individual proteins of the 12,148 predicted proteins (from the metagenome sequence) were identiﬁed. Summarizing, proteomic studies of gut microbiota are still very few which is a pity because they provide fascinating views on how the intestinal bacteria forage for food, attach to the host, and send out toxins to defend themselves.

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5.3. Can Stable Isotopes Help in Proteomics? The reason for the limited number of proteomic studies on the gut microbiota primarily lies in experimental problems especially related to sample acquisition and preparation. Also, proteomic techniques are less amenable to large-scale rapid screening protocols than transcriptome and metabolome technologies, due to required slow separation technology as well as a complex mass spectrometry-based identiﬁcation with associated need for tryptic digestion of the sample. Faster, chip-based technologies (such as SELDI (Issaq et al., 2002) and antibody-based protein chips (Binder et al., 2006)) are in continuous development (see Ramachandran et al., 2005 for a recent review) but their application to map the complete proteome of the intestinal microbiota, containing many unknown proteins, and many important proteins attached to the bacterial cell walls, is still to be awaited. Stable isotopes do not seem to have properties that could change that situation. Nevertheless, stable isotopes play a significant role in proteomics as a means to provide standards for quantiﬁcation. Isotope-coded afﬁnity tags (ICAT) (Gygi et al., 1999) is probably the best example; this technique employs isotopic reagents for labeling two different populations of proteins that can subsequently be compared against each other quantitatively using mass spectrometry, allowing e.g. the determination of organelle location of proteins (Dunkley et al., 2004). SILAC (Stable Isotope-Labeling with Amino acids in cell Culture) (Ong et al., 2002) provides another, inexpensive and accurate procedure that can be used as a quantitative proteomic approach in any cell culture system simply by comparing the protein proﬁles measured by mass spectrometry from cell cultures grown in unlabeled culture medium vs. those grown in deuterium-labeled medium. Stable isotopes can be used to monitor protein synthesis and determine protein fractional synthesis rates (FSRs), i.e. protein metabolic ﬂuxes, using the MIDA approach introduced by Hellerstein and Neese (1999). This technique, because it uses mass spectrometry analysis of peptide protein fragments, is possibly relatively easy to combine with existing mass spectrometry-based protein proﬁling approaches. The technique as originally published is technically involved and therefore requires close attention to potentially confounding factors and analytic performance for optimal application. However, a new development of MIDA was recently described (Busch et al., 2006) that employs 2H2O labeling to permit sensitive, quantitative, and operationally simple measurements of protein turnover in vivo, especially for proteins with slow constitutive turnover. While this technique appears best suited to slowly turning-over proteins, it does bring the prospect of dynamic protein proﬁling closer. It is to be

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awaited whether this prospect will turn into reality in the near future, and whether future developments will be suited also to probe the proteome of the colonic microbiota.

6. METABOLOMICS Of the functional genomics toolbox, metabolomics is the most recent addition. The technique involves the non-targeted, holistic analysis of changes in the total set of metabolites in a sample (the metabolome) in response to environmental or cellular changes. Only now is the metabolomics approach technically feasible, due to the enormous improvements made in the past few years in analytical chemistry and bioinformatics. There have been enormous improvements in the separation and detection of metabolites. Moreover, progress in bioinformatics makes it now possible to process and interpret large sets of biochemical data generated through this non-biased holistic approach. Metabolites are low molecular weight organic compounds (o1000 Da) that participate in general metabolic reactions or are required for the maintenance, growth, and normal functioning of a cell (Beecher, 2003). Metabolites mostly play a role in cellular metabolism and as carriers of energy and reducing equivalents. The total number of different metabolites that are present in any given cell is as yet unknown. In total, almost 20,000 microbial metabolites have been described so far (Vicente et al., 2003). However, many of these metabolites are only present in relatively few microorganisms. From the recent annotation of microbial genome sequences, between 241 and 794 metabolites were deduced to be present in microorganisms (Vaidyanathan and Goodacre, 2003). Since around 40% of the genes present in the microbial genomes have an unknown function, the actual number of metabolites may be approximately three times more (van der Werf et al., 2005). There are several reasons why metabolomics is the functional genomics technology of choice. First of all, the information that can be derived from the metabolome corresponds to a very different perspective on cellular functioning than those of the genome, transcriptome, and proteome. While genomic studies are highly instrumental in uncovering the genetic potential of the gut microbiome, and the transcriptome reﬂecting the functional response, the proteome and the metabolome together determine the actual functionality of a cell. The biochemical level of the metabolome is closest to that of the function of a cell (the phenotype), and thus the study of the metabolome (together with the ﬂuxome) is the most relevant in order to

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comprehend biological functioning. This is especially so since changes in the levels of individual enzymes have, in general, little effect on metabolic ﬂuxes, but do have an effect on the concentrations of the metabolites (Sanford et al., 2002; Goodacre and Kell, 2003) produced in the microbial pathway containing that enzyme. However, although metabolomics is thus ‘the preferred choice’ of the currently established ‘-omics’ technologies, from a more technical point of view, there are still several challenges. Many metabolites, especially signal molecules, are present only transiently and in very low concentrations. The sensitivity and dynamic range of analytical instrumentation, when applied in non-target mode, is still not as high as it should be, and thus these metabolites may not be measured. In addition, since the intestinal microbiota is composed of 41000 different species, it may be impossible to relate production of certain metabolites to speciﬁc members of the microbiota. By contrast, interaction of microbial metabolites, from the intestinal microbiota, with the host is basically restricted to the extracellular metabolites, simplifying matters again. In addition, whereas the microbial composition may be very different between subjects and even vary considerably with time (Barcenilla et al., 2000), we must assume that the microbiota as a whole performs a stable set of activities within a population, given the enormous functional overlap (redundancy) between microorganisms. Also, the use of stable isotopes may help to shed light on what microbes are doing, both on the level of identiﬁcation of microbes that are actively fermenting a given substrate (using SIP, Section 4), and on the level of metabolite production, as is discussed below.

6.1. Microbial Products and What They Can Mean to Us The proximal colon receives food residues and other substrates from the small intestine and is therefore rich in carbohydrate and protein. It is generally accepted that carbohydrates are the preferred substrate for most members of the colonic microbiota. The carbohydrates are used to obtain energy, while any available protein is incorporated into biomass. However, fermentable carbohydrates may become depleted more distally along the colon, leading to decreased activity of saccharolytic bacteria. Conversely, the proteins and peptides that are present throughout the colon can be utilized by protein or amino acid fermenting bacteria when the carbohydrate is depleted. Numerically important proteolytic species identiﬁed in the large bowel include species belonging to the genera Bacteroides, Propionibacterium, Clostridium, Fusobacterium, Streptococcus,

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and Lactobacillus (Macfarlane and Cummings, 1991). Studies in sudden death individuals have shown that concentrations of metabolites from proteolytic fermentation are higher in the distal colon compared with the proximal colon (Cummings et al., 1987; Macfarlane et al., 1992a; Smith and MacFarlane, 1996, 1997): e.g. distal concentrations of phenolic compounds were four times that detected in proximal regions. Therefore, the presence of fermentable carbohydrates inﬂuences proteolytic fermentation in the colon, as also recently shown using stable isotopes (De Preter et al., 2004). Although carbohydrate fermentation predominates in the large intestine as a whole, the fermentation of proteins becomes quantitatively more important distally. It is also interesting to note that the majority of colorectal cancers occur in the distal side of the colon (Hughes et al., 2000; Hope et al., 2005) where the SCFA concentration is at its lowest, the concentrations of proteolytic metabolites is at its highest, and contact of the intestinal epithelium with luminal contents is increased due to the more solid nature of luminal contents and also due to the slower transit through this segment of the bowel. Therefore, it is speculated that protein degradation in the colon is relevant for colon cancer (Hughes et al., 2000). Similarly, it is speculated that there is a correlation between the occurrence of protein fermentation in the distal colon and the onset of UC (Roediger et al., 1997; Levine et al., 1998). This is, however, all circumstantial evidence, and hard proof is lacking. And therefore, little is known about the biological role in vivo of these potentially toxic metabolites derived from proteolytic fermentation. It is believed that carbohydrate fermentation results in the production of beneﬁcial microbial metabolites such as the SCFAs (primarily acetate, propionate, and butyrate), while protein metabolism may lead to what are generally considered toxic metabolites, such as hydrogen sulﬁde, ammonia, and phenolic and indolic compounds. Hydrogen sulﬁde is also produced by the sulfate-reducing bacteria (SRB), which characteristically couple oxidative phosphorylation with the reduction of sulfate to sulﬁde. Butyrate is the principal energy source of colonic epithelial cells. Up to 70% of the energy used by these cells comes from microbially produced butyrate (Roediger, 1980). Butyrate has been implicated in colorectal tumorigenesis since it exerts a multitude of anti-tumor effects in transformed cells in vitro, such as modulation of cell proliferation, differentiation, and apoptosis (Young and Gibson, 1994). In vivo, luminal butyrate concentrations are inversely correlated with tumor size in experimental colorectal tumorigenesis, and direct rectal or cecal installation of butyrate reduced the size and number of tumors in experimental carcinogenesis. It is, therefore, no surprise that considerable experimental effort is being expended in order

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to deﬁne the effects of butyrate and the mechanisms by which it acts. However, the cellular effects of butyrate are complex, especially since those in one cell system may be the complete opposite of those in a different but related cell system. This so-called ‘butyrate paradox’ has been observed in relation to cell proliferation, differentiation, and apoptosis (Gibson et al., 1999b). The biological basis for these contrasting effects has not been deciphered. However, it is hypothesized that the responses of cells to butyrate may depend on the cells’ state of activation, independent of butyrate oxidation (Gibson et al., 1999b). The formation of toxic hydrogen sulﬁde (H2S) by human commensal bacteria, either from protein fermentation or sulfate reduction by SRB, is assumed to promote the development of inﬂammatory intestinal diseases (Levine et al., 1998; Ohge et al., 2005), particularly in the distal part of the human colon. Here, as discussed above, carbohydrate availability is limited because the quickly fermentable carbohydrates have already been fermented in the proximal colon. Therefore, the microbiota switches to protein fermentation, with concurrent production of putrefactive metabolites. It is unknown where SRB display the highest activity, but the presence of electron acceptors throughout the colon (e.g. acetate, H2) suggests that SRB may be active throughout the whole large intestine, although they have to compete for H2 with acetogens and methanogens. As touched upon above, there is speculation that there is a correlation between putrefaction and the occurrence or start of onset of UC and colon cancer. Although this is circumstantial evidence, the current belief is that H2S, in particular, may be responsible for this (Levine et al., 1998; Ohge et al., 2005) as it blocks oxidation of butyrate in colonic epithelial cells. Roediger et al. (1993) showed inhibition of butyrate oxidation by H2S in vitro in both rat and human colonocytes at a concentration of 2 mmol/L. Using human colon tissue, Christl et al. (1994) showed that 1 mmol sulﬁde/L significantly increased cell proliferation rates and other changes normally seen in UC. Studies have shown that dietary protein does contribute to sulﬁde production in the large intestine (Magee et al., 2000). In general, the higher the intake of protein, the higher is the production of sulﬁde in the colon, with a production of 3 mmol after a dietary protein intake of 200 g/day. Other sources of sulfur present in the colon are from inorganic sulfate and mucin. Daily intake of inorganic sulfate is estimated to range from 1.5 to 16 mmol/kg. Estimated amounts of mucin (an unknown part of which is sulfated) excreted in the lumen of the GI tract are 4100 g/day (Lichtenberger, 1995). Sulfate from mucin can be liberated by numerous members of the microbiota that contain sulfatases (e.g. Bacteroides), after which the liberated sulfate may become available for SRB. It is estimated that SRB derive 1.5 to 2.6 mmol

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sulfate/day from sulfated mucin (McGarr et al., 2005). Therefore, it is possible that a combination of dietary protein, sulfated mucins, and inorganic sulfur additives in food could result in fecal sulﬁde concentrations that may lead to pathological processes. Hydrogen sulﬁde also functions as a neuromodulator, but whether it modulates visceral perception and pain in humans is currently unknown. A recent study in rats (Distrutti et al., 2006) investigated the role of H2S in modulating the nociception (ability to feel pain) to colorectal distension (CRD), a model that mimics some features of IBS. Treating rats with NaHS resulted in a dose-dependent attenuation of CRD-induced nociception. It was concluded that H2S release in the colon might actually be beneficial in treating painful intestinal disorders. This contrasts with the current belief that H2S is deleterious to health. Equivalent with the ‘butyrate paradox’, there seems to be a ‘H2S paradox’ as well. How much of this paradox is based on differences in dose–response is presently unknown. The other proteolytic toxic metabolites are also deleterious for health. The branched chain fatty acids (BCFAs), which are produced by fermentation of the branched chain amino acids valine, leucine, and isoleucine, can cause liver problems (Mortensen and Clausen, 1996). Ammonia is toxic to the colonic epithelium and promotes colon cancer in rats. In addition, it is a potential liver toxin and has been implicated in the onset of neoplastic growth (Clinton, 1992; Macfarlane and Macfarlane, 1995). The production of phenolic and indolic compounds by intestinal bacteria has been associated with a variety of disease states in humans, including schizophrenia (Macfarlane and Macfarlane, 1995). In addition, they appear to act as co-promoter in the development of colorectal cancer (Rowland, 1995). Other metabolites that are produced by the intestinal microbiota but have not been discussed so far included gases, primarily H2, CO2, CH4, but 4250 other vapors can be detected in expired breath and are assumed to be produced partly in the colon (Brydon et al., 1986; Levitt et al., 1995; Suarez et al., 1998). Depending on the speed of production and accumulation (possibly up to 25 L/day), these gases may cause intestinal symptoms such as abdominal cramps and urge of defecation, while impaired transit and tolerance to gas has been implicated in IBS (Suarez, 2000; Serra et al., 2001). Also, certain minor food components may be fermented into bioactives, such as the conversion of sinigrin (a glucosinolate) into allyl-isothiocyanate (Krul et al., 2002), which has been shown to inhibit metastasis of human hepatoma cells (Hwang and Lee, 2006), or the breakdown of ﬂavonoids into several different phenolic compounds, such as 3-methoxy-4-hydroxyphenyl acetic acid, 4-hydroxyphenyl acetic acid, 3,4-dihydroxyphenyl acetic acid, 3-(3-hydroxyphenyl) propionic acid, 2,4,6-trihydroxybenzoic acid, 3-(4-hydroxy-3-methoxyphenyl)

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propionic acid, and 3-hydroxyphenyl acetic acid (Gao et al., 2006). The biological activity of most of these is unknown, but 3,4-dihydroxyphenyl acetic acid has been shown to have an anti-proliferative activity (Gao et al., 2006). In addition, compounds such as nitrosamines may be formed by the reaction of secondary amines with nitrite at low pH. It goes beyond the scope of this review to discuss these in any detail. Microbial metabolites may inﬂuence the metabolic integrity of intestinal epithelial cells and induce mucosal immune responses. In recent experiments, the effects of the microbial metabolites butyrate, iso-valerate, and ammonium on CaCO-2 cells was investigated (van Nuenen et al., 2005). Barrier function was determined by measuring transepithelial electrical resistance. The barrier function of CaCO-2 cells remained intact in this study after exposure with the type and concentrations of metabolites used. However, addition of phenolic compounds, above a certain threshold value, had a dramatic effect on the transepithelial electrical resistance (Venema et al., unpublished data). These experiments need to be conﬁrmed and extended. This can be done in in vitro experiments, but also in other models more closely simulating the real situation. In the same set of experiments (van Nuenen et al., 2005), the effect of microbial metabolites on cytokine production by macrophages was tested as well. In these experiments, the macrophage cell line U937 was cultured alone, or in combination with CaCO-2 cells. Production of TNF-a and IL-10 was measured. These experiments showed that CaCO-2 monoculture cells did not secrete detectable levels of TNF-a or IL-10 after metabolite exposure (in the presence or absence of stimulation with LPS) (van Nuenen et al., 2005). In the U937 monoculture experiments, addition of 50 and 100 mM butyrate or iso-valerate, or of 20 and 40 mM ammonia resulted in a dose-dependent inhibition of TNF-a secretion compared with LPS (positive control), while lower, more physiological concentrations (4–20 mM for butyrate and iso-valerate; 2 and 4 mM for ammonia) stimulated TNF-a secretion (dose independently). IL-10 secretion by these macrophages in monoculture was suppressed by all metabolites in all concentrations compared to LPS (dose dependently), except for the lowest concentration of ammonia, for which IL-10 secretion by macrophages was almost twice as high as with LPS. This shows that the immune system underlying the colonic epithelium may be differentially inﬂuenced by the different metabolites, which in turn may also be present in ﬂuctuating concentrations, and therefore metabolites may have stimulating or suppressing effects on production of cytokines depending on concentration. An experiment that still needs to be performed would include a mixture of these metabolites, to determine whether the effects of one of the metabolites dominates over that of the others.

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The absorption of SCFA and butyrate from the colonic lumen is a very efﬁcient process as only 5–10% is excreted in the feces. At least 60% of the SCFA uptake is effected by simple diffusion of protonated SCFA involving hydration of luminal CO2 (Topping and Clifton, 2001). The remainder occurs by active transport through two transporters, the monocarboxylate transporter isoform 1 (MCT1), which is coupled to a transmembrane proton gradient, and the sodium-coupled monocarboxylate transporter (SMCT1) (Gupta et al., 2006). How does butyrate exert such a wide array of effects? The ability of butyrate to regulate gene expression is often attributed to its induction of histone hyperacetylation through the inhibition of histone deacetylase (HDAC). Hyperacetylation of histones disrupts their association with DNA, resulting in more ‘open’ chromatin structure that facilitates access of transcription factors to speciﬁc genes. This has been demonstrated by the fact that trichostatin A, which specifically inhibits HDAC, mimics many of the effects of butyrate (Gibson, 2000). However, it is likely that butyrate has other intracellular targets. These include the hyperacetylation of non-histone proteins, alteration of DNA methylation, selective inhibition of histone phosphorylation, and the modulation of intracellular kinase signaling (Daly and Shirazi-Beechey, 2006). This multiplicity of effects may underlie the ability of butyrate to modulate gene expression at several levels including transcription, mRNA stability, and elongation. The response to butyrate is complex, involving multiple distinct mechanisms/pathways (Daly and Shirazi-Beechey, 2006), with different pathways operative in different cells. As said before, butyrate is transported across the membrane by MCT1. The expression of MCT1 is significantly downregulated in the human colon during the transition from normality to malignancy. This leads to a reduction of butyrate uptake and may contribute to the development of colonic neoplasia (Daly and Shirazi-Beechey, 2006). Currently, the mechanism of the effects observed is unknown. In addition to the observed effect on the inhibition of histone deacetylase, the promoters of several genes contain a highly conserved sequence, the butyrate response element. It is likely that effects of butyrate are realized through one of these mechanisms. However, it is currently unknown what the mechanism of action is for other microbial metabolites.

6.2. Tracing the Fate of Prebiotics: In Vitro Models and Stable Isotopes Prebiotics are deﬁned as non-digestible food ingredients that beneﬁcially affect the host by selectively stimulating the growth or activity of one or a

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limited number of bacterial species already resident in the colon and presumed to be health promoting (Gibson and Roberfroid, 1995), for example, by increasing numbers of indigenous biﬁdobacteria (Finegold et al., 1983; Gibson and Roberfroid, 1995; Gibson et al., 1999a). Many carbohydrates are reported to be prebiotic, including fructooligosaccharides, galactooligosaccharides, isomaltooligosaccharides, and lactulose (Gibson and Roberfroid, 1995; Gibson et al., 1999a). Apart from their activity on the composition of the large intestinal microbiota, prebiotics also affect the microbial metabolite pool in the colon. Most measurements on microbiota and metabolites in humans are performed in feces, which is basically the only non-invasive material that can be obtained from healthy volunteers. A drawback of analysing fecal samples is the fact that they do not represent quantitatively what happens in the proximal part of the colon where fermentation of most prebiotics takes place. SCFA from prebiotic fermentation are predominantly produced in the proximal part of the colon and will be absorbed by the body to a considerable extent during subsequent transit of the chyme to the distal colon and rectum, which may take anywhere from 24 to 472 h depending on the individual. Consequently, the amount and ratio of SCFA recovered in the feces will not reﬂect those resulting from fermentation of the prebiotics in the proximal colon, but rather that of local production in the distal colon. To be able to properly study the microbial processes occurring in the proximal colon, various in vitro models simulating the fermentation processes occurring in the lumen of the colon have been developed (McBain and Macfarlane, 1997; Minekus et al., 1999; De Boever et al., 2000). Although we acknowledge the existence of other in vitro models, it goes beyond the scope of this review to discuss these here. Here, we would like to exemplify the advantages of these models in studying the activity of the intestinal microbiota on the basis of some of our own results in a dynamic, computer-controlled in vitro model of the large intestine. This model features, amongst others, peristaltic movements and removal of microbial metabolites (Minekus et al., 1999). The model allows frequent sampling in time, such that time series can be prepared for MFA (see below) and SIP (Egert et al., 2007). In this manner, better insight is obtained in the chronological order of the processes that underlie fermentation of undigested dietary components, and the microbial pathways involved in the fermentation of these substrates. The model closely simulates the in vivo conditions of the GI tract of humans during the passage of food under average conditions of a population. Validation of the colon model was done with regard to the composition of the microbiota, the enzymatic activity of the microbiota, and the production and concentration of

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SCFA using data from sudden death individuals (Cummings et al., 1987; Macfarlane et al., 1992a). Since the in vitro system offers the possibility of separating the test substrate from other compounds, the effects of substrates and especially the mechanisms behind this effect can be properly studied. A mass balance can be made in this system, because in principle all metabolites that are produced are collected. This is a major advantage over in vivo studies. Even if in vivo samples from the lumen of the colon and the blood would be taken, not all produced SCFA would be measured (Cummings and Macfarlane, 1991). This is because butyrate especially is used as a substrate by colonocytes (Roediger, 1982) and therefore only low amounts of this microbial metabolite are found in the bloodstream (Cummings and Macfarlane, 1991). In the in vitro model all SCFA are detected, either in the lumen of the model, or in the collected dialysis ﬂuid. The potential of this system as a tool to study fermentation of dietary components was demonstrated in experiments with a variety of substrates, including pectin (Minekus et al., 1999), fructooligosaccharides (Minekus et al., 1999), inulin (van Nuenen et al., 2003), lactulose (Venema et al., 2003), tagatose (Venema et al., 2005), resistant starch (Venema et al., 2004), and lactitol (Minekus et al., 1999). Parameters such as total SCFA production and the SCFA ratio were determined in time to characterize the fermentation. This allowed the mechanistic study of the effects of food components on microbial metabolite production at its most active site, the proximal colon. For instance, when lactulose was added, a biﬁdogenic effect, and thus prebiotic effect, was shown in vitro (Venema et al., 2003), as reported previously in the literature (Terada et al., 1991; Mizota, 1996). This study showed that, after in vitro addition of lactulose, the microbiota hardly produced any butyrate any more. Apparently, lactulose changed the activity and/or composition of the microbiota such that butyrate is no longer produced. The effects of lactulose on butyrate production would not have been evident in vivo, because in vivo a multitude of other substrates are available to the microbiota, such as resistant starch, mucin, desquamated epithelial cells, etc. Here, the value of being able to separate, in vitro, the mixture of in vivo substrates (amongst others the test compound, other dietary components, desquamated cells, and mucin) from the test substrate (in this case lactulose), was great and made it possible to investigate the underlying mechanism of speciﬁc stimulation of microorganisms by lactulose. It should be mentioned though that these models have limitations and cannot mimic all conditions prevailing in vivo, and also may not be able to allow growth of all microorganisms present in vivo. There are indications that the administration of prebiotics suppresses the generation and accumulation of toxic metabolites from protein

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fermentation, and through a suppression of toxic metabolites, the incidence of colon cancer may decrease. However, substantial evidence supporting these beneﬁcial effects of prebiotics is currently lacking, mainly due to the inaccessibility of the colon and the unavailability of reliable tracers. In vitro, the addition of 10 g/day of inulin, the best studied prebiotic to date, resulted in a twofold reduction in the production of ammonia and an undetectable production of BCFA (Van Nuenen et al., 2003). Interestingly, the addition of a protein fermentative microorganism, C. difficile, increased the production of these protein-fermentative metabolites. Addition of inulin to this situation reduced ammonia, BCFA, and production of phenolics (van Nuenen et al., 2003). A recent study investigated in vivo whether the administration of a selected prebiotic (lactulose) would result in a reduced concentration of one or more protein-fermentative metabolites in the colon (De Preter et al., 2004). Ten grams of lactulose were given at breakfast and at supper for 2 weeks. Before and after these 2 weeks, a test meal containing [2H4]tyrosine and lactose-[15N]ureide was consumed. Urinary p-[2H4]cresol and total 15N were measured. This study showed a significant reduction in both urinary biomarkers, and provides direct evidence that in vivo also, colonic protein degradation is reduced by the administration of lactulose as a fermentable carbohydrate, resulting in a lower concentration of potentially toxic metabolites. The full metabolome upon addition of prebiotics can be studied, either from fecal material or from in vitro derived samples, of which the latter are more clean, but do not necessarily contain all metabolites observed in real samples. As far as we are aware, initiatives to measure a full metabolome of fecal matter have not been undertaken. In studies in in vitro models of the large intestine, a ﬁrst attempt to measure as many metabolites as possible has been made (our own unpublished results). Here, the holistic approach of metabolomics was taken. Apart from metabolites that are usually studied and measured in intestinal microbiology, such as the SCFA, lactate, ammonia, etc., several other extracellular microbial metabolites have been identiﬁed using this metabolomics approach. These include ethanol, acetaldehyde, methanethiol, and dimethylsulﬁde. None of these should raise surprise, as all are known to be microbial metabolites, but they are not generally measured or detected in samples related to the (lumen of the) GI tract. In these types of experiments, however, it is unclear which metabolite is produced from which substrate, emphasizing once more that our knowledge about the activities of the intestinal microbiota is woefully inadequate. Use of stable isotopes may ﬁll some of the gaps in this knowledge, as exempliﬁed in the studies described above (De Preter et al., 2004).

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6.3. Evidence of Cross-Feeding An interesting aspect of the biﬁdogenic nature of certain prebiotics, is that upon feeding these carbohydrates, the butyrate production is also increased. Yet, biﬁdobacteria are uncapable of producing butyrate. This indicates that there is more occurring than just biﬁdobacteria fermenting the prebiotics, and this is either an indication of other species fermenting the prebiotics as well, or an indication of cross-feeding. Probably both occur. The gut microbiota, as we have seen, is a very complex community that comprises hundreds of different species of microbes from different genera. One of the great puzzling questions is, how these microbes can work together so well to perform the functions that the microbiota generally does, and how it is possible that the microbiota composition can adapt itself to such rapidly and strongly changing conditions as those in the colon, without significant disturbance of its overall function. Advancements of science in this area of gut research are among the most interesting today. Some striking results are discussed here. Oxalobacter formigenes, a strictly anaerobic bacterium found in the human colon, presents a beautiful example of how the beneﬁts of cometabolism extend beyond the gut wall, and intestinal microbial metabolism really is integrated with host metabolism (Stewart et al., 2004). Oxalate is ingested in a wide range of foods and beverages and is formed endogenously as a waste product of metabolism. Bacterial, rather than host, enzymes are required for the intestinal degradation of oxalate in man and mammals. The bacterium primarily responsible is O. formigenes (Stewart et al., 2004). Oxalate is excreted in urine and the loss of O. formigenes may be accompanied by elevated concentrations of urinary oxalate, increasing the risk of recurrent calcium oxalate kidney stone formation. The interesting points here are that O. formigenes has an obligate requirement for oxalate (produced by the host) as a source of energy and cell carbon. In return, the host is saved from kidney stone formation. But colleague microbes would also beneﬁt. In O. formigenes, the proton motive force needed for energy conservation is generated by the electrogenic antiport of oxalate and formate by the oxalate–formate exchanger. Thus, O. formigenes produces formic acid which in turn, as a cross-feeding substrate, may serve as a one-carbon donor in the metabolism of many other intestinal microbes. Even when considering a single aspect of the colonic microbiota’s function, butyrate production, the situation is complex and confusing. Many different species of butyrate-producing bacteria are present. In a study of seven of those, namely strains of Roseburia sp., Faecalibacterium prausnitzii, and Coprococcus sp. from the human gut that produce high levels of butyric

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acid in vitro, distinct patterns of available butyrate pathway enzymes and fermentation patterns were discovered (Duncan et al., 2002). Strains of Roseburia sp. and F. prausnitzii possessed butyryl coenzyme A (CoA): acetate-CoA transferase and acetate kinase activities, but no butyrate kinase activity. Although unable to use acetate as a sole source of energy, these strains showed net utilization of acetate during growth on glucose, indicating that in the gut also they need a co-substrate for growth. In contrast, Coprococcus sp. strain L2-50 possessed a complete set of detectable butyrate synthetic enzyme activities: butyrate kinase, acetate kinase, as well as butyryl-CoA:acetate-CoA transferase. Yet, this strain was found to be a net producer of acetate instead of butyrate! The use of lactate by intestinal bacteria also presents some puzzling questions. The microbial community of the human colon contains many bacteria that produce lactic acid, but lactate is normally detected only at low concentrations (o5 mM) in feces from healthy individuals. To study which microorganisms are mainly responsible for lactate utilization in the human colon, bacteria able to utilize lactate and produce butyrate were isolated from fecal samples (Duncan et al., 2004). Out of nine such strains identiﬁed, four were found to be related to Eubacterium hallii and two to Anaerostipes caccae, while the remaining three represented a new species within a clostridial cluster. Interesting in view of the results discussed above, no significant ability to utilize lactate was detected in the butyrate-producing species Roseburia intestinalis, Eubacterium rectale, or F. prausnitzii (raising the question of which co-substrate of acetate these bacteria employ in the gut). Whereas E. hallii and A. caccae strains used both D- and L-lactate, the remaining strains used only the D isomer. Lactate utilization was prevented by the presence of glucose. However, when grown on starch in separate co-cultures with a starch-utilizing Bifidobacterium adolescentis isolate, two E. hallii strains and one A. caccae strain formed butyrate and the lactate produced by B. adolescentis became undetectable (Duncan et al., 2004). The effects of changes in the gut environment upon the human colonic microbiota are poorly understood. Studies of the response of human fecal microbial communities to alterations in pH (5.5 or 6.5) and peptides (0.6 or 0.1%) yielded surprising results (Walker et al., 2005). SCFA proﬁles differed markedly between conditions. Moreover, very substantial changes in the levels of the bacterial groups Bacteroides and Roseburia were monitored by using ﬂuorescence in situ hybridization with a panel of speciﬁc 16S rRNA probes. These ﬁndings suggested that a lowering of pH resulting from substrate fermentation in the colon may boost populations of butyrateproducing bacteria, while at the same time curtailing the growth of Bacteroides sp. (Walker et al., 2005).

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In a recent study, cross-feeding of microorganisms on acetate and lactate to form butyrate was investigated using stable isotopes (Morrison et al., 2006). [U-13C6]Glucose was used to show that M+2 and M+4 isotopomers were the principal butyrate species produced from glucose fermentation, via [13C2]acetyl CoA as intermediate (Fig. 6). Lactate conversion to acetate, propionate, and butyrate were also observed. Conversion of propionate or butyrate into other SCFA was negligible. The degree of interconversion was dependent on which individual provided the fecal sample, indicating some degree of host speciﬁcity in microbial activity between different individuals. For instance, in only two of the ﬁve fecal samples, lactate to propionate conversion was found (Morrison et al., 2006). In addition, the authors studied butyrate production from fructooligosaccharides, a prebiotic stimulating biﬁdobacteria. Biﬁdobacteria produce primarily acetate and lactate, but not butyrate. Yet, addition of fructooligosaccharides to fecal batch cultures significantly increased butyrate production, and the stable isotope data allowed the conclusion that as much as 80% of this butyrate was derived from interconversion of extracellular acetate and lactate, with acetate being quantitatively more significant (Morrison et al., 2006).

7. METABOLIC FLUX ANALYSIS APPLIED TO THE GUT What are the best parameters to characterize physiology? The end function of gene expression, protein synthesis, and establishment of metabolite pools is to maintain organism life. From life in its simplest form, prokaryotes, we learn that cell physiology foremost serves to maintain maximal growth rate under the prevailing environmental conditions in the organism’s habitat. This primarily implicates the directing of appropriate material in the various biosynthetic pathways, and the supply of sufﬁcient metabolic energy to drive these processes. Furthermore, transport processes of metabolites and ions are the basic means by which the cell ensures a closed material balance, and homeostasis of its inner environment. Therefore, metabolic ﬂuxes of the primary metabolism, together with membrane transport ﬂuxes, can be considered parameters that are very closely linked with cellular physiology. Thus, it seems worthwhile to measure and monitor these ﬂuxes in addition to the genomic, proteomic, and metabolomic characterizations addressed above. MFA, as this activity is called, has seen a tremendous development in the past few decades and stable isotopes have played a key role in this progress, as indicated previously.

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Figure 6 Probing colonic SCFA metabolism with stable isotopes. Using the principle of isotope dilution, the colonic bacterial synthesis rate of butyrate can be determined after continuous infusion of [1-13C]butyrate in the cecum by measuring the [1-13C]butyrate tracer–tracee ratio (TTR) in the portal blood (relevant carbon labeling patterns are indicated by squares). In this case, the bacterial metabolism will be producing unlabeled butyrate. Cross-feeding of butyrate-producing gut bacteria on acetate may be evidenced by infusing [U-13C2]acetate in the cecum, and measuring the abundance of [1,2-13C2]-, [3,4-13C2]-, and [U-13C4]isotopomers in portal butyrate (relevant 12C/13C carbon isotopomer patterns are indicated by circles). In this case, if butyrateproducing bacteria take up acetate, part of the acetyl-CoA pool will get labeled with [1,2-13C2]acetyl units which leads to the mentioned butyrate isotopomers.

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The aim of MFA applied to the gut is to identify and quantify significant metabolic ﬂuxes in vitro and in vivo mediated by the microbial conversion of substrates in the colonic intraluminal environment. The fermentation of complex carbohydrates as well as of proteins deserves special attention here, as SCFA primarily result from carbohydrate fermentation while protein fermentation yields amongst others sulfur-containing compounds that compromise health (see previous section). The human in vivo approach necessitates non- or minimally invasive investigation techniques. Hence, the primary investigation tool is that of the use of stable isotopes, which is increasingly used in dietary interventions (Labayen et al., 2004). One may anticipate testing 13C-labeled carbohydrate substrates with a varying degree of polymerization, as well as isotopically labeled proteins. The metabolic fate of the isotopically labeled atoms can be followed by high-throughput mass spectroscopic analysis of the various metabolites in body samples including blood, urine, feces, and epithelial biopsies. These analyses can be coupled to dedicated gas chromatography-TOF-mass spectrometry (GC-TOF-MS) for the analysis of volatile organic compounds in the exhaled air, and their isotopic labeling. In addition, metabolite concentrations can be determined in these samples, and genomic and proteomic expression proﬁles may be recorded from biopsy samples. The resulting data set provides a basis for the correlation of gut microbial metabolic activity with host responses, and ultimately human health. Newest developments in experimentation technology that can be applied in this area include the use of targeted administration of isotopically labeled substrates to the colon using e.g. pH-sensitive coated capsules (Tuleu et al., 2002; Oo et al., 2003). In the following sections, illustrative results pertinent to MFA of the gut microbiota as well as the host metabolic response will be discussed.

7.1. Insights into Bacterial Metabolic Routes The ﬁrst important step in developing MFA of the colonic microbiota is the definition of the metabolic network that is operative. This would seem a task of unprecedented difﬁculty given that we are dealing with a highly complex and diverse symbiotic community of microbes that altogether form a microbiome with a genome coding capacity vastly exceeding that of the human genome as referred to previously. Moreover, it is well documented that considerable variations in both intensity and type of microbial metabolic activity occur along the GI tract (Jensen and Jorgensen, 1994; Metges, 2000). The cecum and proximal colon are the metabolically most active parts. As has been elegantly demonstrated to be the case for termites

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(see Brune and Friedrich, 2000 and references therein), there very likely will be distinct structure–localization–function relationships of bacterial metabolism also in the human gut. One genus of bacteria may supply the substrate for another, thus leading to a diverse and dynamic yet functionally stable microbial ecosystem. For instance, lactate produced by lactic acid bacteria and biﬁdobacteria is rapidly converted to butyric acid by clostridia and Eubacterium sp. (Bourriaud et al., 2005). Bacteria and Archeae performing saturation of unsaturated fatty acids, reduction of nitrite to ammonia, reduction of sulfate to sulﬁde, reduction of CO2 to methane, and reduction of CO2 to acetate provide possible hydrogen sinks (Jensen and Jorgensen, 1994) and by their action lower the partial pressure of intestinal hydrogen gas, thus creating thermodynamic conditions that allow for increased overall fermentation rates (Backhed et al., 2005). The different bacterial genera present in the colon play distinct roles in the metabolic chain of polysaccharide processing (Backhed et al., 2005; Bourriaud et al., 2005; McGarr et al., 2005), from depolymerization, sugar utilization, and production of intermediate metabolites such as hydrogen, lactate or ethanol, and conversion of these intermediates into end products (SCFA, methane). How then, given all this complexity, can one think of setting up a metabolic network to perform ﬂux analysis? The approach should be to consider the microbiota as a whole rather than concentrating on all individual members. One can then proceed in a meaningful manner knowing that the microbiota performs only a limited number of major metabolic functions (Backhed et al., 2005; McGarr et al., 2005), including:

breakdown of polysaccharides producing lactate, volatile SCFAs (formic acid, acetate, propionate, butyrate, valerate) and related metabolites, as well as gases (carbon dioxide, hydrogen, and methane); breakdown of dietary peptides to amino acids for incorporation into biomass or for subsequent fermentation, with concomitant production of putrefactive metabolites; breakdown of endogenous (i.e. produced by the host) proteins especially mucins and other mucosal proteins.

Thus, one may start by ﬁrst accounting for the quantitatively major processes, after which the network can be reﬁned more and more, so as to include also the quantitatively minor (but possibly at least as interesting) pathways such as bile acid metabolism and production of vitamins. Following this approach, the strategy is now to build the pathway network by putting together all available pieces of information on gut microbial

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pathways and their approximate metabolic ﬂuxes from the literature. In the following, relevant available literature data is reviewed. To quantify carbohydrate digestion in the small intestine or fermentation in the large intestine in vivo, 13C-labeled carbohydrate substrates such as starch are administered orally and the appearance of products such as glucose in blood (Korach-Andre et al., 2004) or carbon dioxide in exhaled breath (Christian et al., 2002) has been monitored. Using mathematical modeling the relative amounts of the substrate digested in the small and large intestine can be determined (Christian et al., 2002). Small intestinal and oro-cecal transit time (OCTT) can be measured with the lactose-[13C]ureide breath test (Priebe et al., 2004). Considerable efforts have been made to quantify accurately the microbial SCFA production. This is not an easy task due to the very active metabolism of the colonocytes and the liver which interferes as soon as the SCFA are released in the gut lumen (Fig. 7). In an elegant protocol, Kien et al. (1996) used [2-2H3]acetate and [1-13C] sugars infused into the colonic lumen of pigs to determine the rate of microbial acetate synthesis as well as the fraction of the sugars metabolized to acetate in a single experiment. Using [1-13C]butyrate infusion in the colon and sampling of portal blood, these authors later determined microbial butyric acid production in pigs (Kien et al., 2002) and showed that butyrate is also produced endogenously by these animals (Kien et al., 2000). Pouteau et al. (2003) have developed and applied protocols to determine SCFA production in humans, including intragastric infusion to evaluate ﬁrst-pass splanchnic retention of SCFA. Isotopic tracers have been highly instrumental for the clariﬁcation of metabolic pathways involved in biosynthesis of compounds, as described in an exquisite review by Bacher et al. (1999). By detecting doubly-13C-labeled acetate produced from [3-13C]glucose, Wolin et al. (1998) could establish that in a fecal suspension isolated from an infant, the Bifidobacterium pathway was the major glucose fermentation pathway used. These authors also demonstrated the operation of the Embden–Meyerhof–Parnas as the major glycolytic pathway leading to SCFA in fecal suspensions of adults (Wolin et al., 1999). In these experiments, they found that a considerable portion of microbially produced acetate was formed via the Wood–Ljungdahl pathway of CO2 reduction. The analysis of amino acid metabolism by isotope labeling is complicated by the fact that these compounds are very actively turned over in each and every organ of the body, and most of them are rapidly de- and reaminated in transaminase reactions. Thus, all indispensable branched-chain amino acids become 15N labeled after intravenous application of only 15N-labeled leucine (Lien et al., 1997), because they are in rapid transaminase

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Figure 7 Assessing colonic SCFA production in vivo is not a straightforward task. SCFA are mainly produced by bacterial fermentation in the colonic lumen, but endogenous production may also take place in liver and peripheral organs (as in the case of acetate). Part of the microbially produced SCFA may ‘disappear’ in the lumen itself due to uptake and metabolization by other microorganisms (cross-feeding). A large part of SCFA produced in the colonic lumen may be disposed of in colonocytes and never even reach the bloodstream (as in the case of butyrate). The liver also has an active metabolism of SCFA, and almost completely prevents butyrate from reaching the peripheral bloodstream. Using the principle of isotope dilution in the experimental conﬁguration depicted in the ﬁgure, the measured TTR of arterial SCFA reﬂects the sum of SCFA coming from the liver and SCFA produced in peripheral organs, rather than the true colonic production. To probe the latter correctly, intraluminal infusion is required. Ra and Rd signify rates of appearance and disappearance, respectively, due to active metabolism in the various organs.

equilibrium with their respective precursor keto acids. Amino acids from circulating blood may exchange via enterocytes with the colonic lumen, causing mixing of endogenous and microbial amino acids. As much of 20–30% of liver-produced ureum may be used by intestinal bacteria for amino acid and protein synthesis (Moran and Jackson, 1990) (cf. Fig. 3). A number of studies employing 15N-labeled urea have been performed to assess this issue. Urea diffuses into the colon where it is hydrolyzed by

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bacterial urease to ammonia before being assimilated. The microbial origin of a significant fraction of lysine and threonine in body protein has been established from 15N urea labeling experiments (Lien et al., 1997; Metges, 2000). Studies with combined 15NH4Cl and 14C-polyglucose in pigs (Torrallardona et al., 2003) conﬁrmed this fact and, following sampling at different locations along the GI tract, gave strong indications that absorption of amino acids from microbial origin mainly occurs in the ileum and not in the colon. An important implication of these ﬁndings is that, as with vitamins, metabolic requirement cannot be equated with dietary requirement. Closely related to this issue is the determination of the daily requirement for essential amino acids (for a recent overview on this subject see Kurpad and Young (2003), which is currently done via tracer-based protocols such as the indicator amino acid oxidation technique (for example, threonine (Wilson et al., 2000) and lysine (Kriengsinyos et al., 2002)). Dietary fat apparently is a minor substrate for the colon due to the high efﬁciency of the fat uptake. Also, the bile acid cycle is highly efﬁcient. Nevertheless, a minor (1–5%) fraction of bile salts reach the colon where anaerobic bacteria from, for example, the genus Clostridium metabolize them to secondary bile acids, especially lithocholic acid and deoxycholic acid (DCA) (McGarr et al., 2005). The latter is partly absorbed but cannot be reconverted to cholic acid by the liver. Serum levels of DCA in patients with colon cancer have been shown to be consistently higher than in healthy subjects (McGarr et al., 2005). The sulfated amino acid taurine is an important substrate for bile acid conjugation in the liver and a more highly preferred sulfur source for fecal microbial metabolism (McGarr et al., 2005). Since taurine conjugation is increased in individuals on a high animal protein diet, investigation of the colonic metabolism of this amino acid in relation to colon cancer may be relevant. A ﬁrst taurine turnover study, on whole body level, employing [1,2-13C2]taurine has recently appeared (Rakotoambinina et al., 2004), showing very low turnover in healthy adults. Toxic nitrogen containing and/or aromatic end products of bacterial fermentation may present a health risk especially on animal protein-rich diets. Pre- and/or probiotic intakes have various claimed beneﬁcial effects which generally are difﬁcult to prove. De Preter et al. (2004) and Geboes et al. (2005) introduced the use of lactose-15N2-ureide and [2H4]tyrosine as useful quantitative indicators for pre-/probiotics efﬁciency, namely from the capacity of a product to suppress the generation and accumulation in urine of (i) 15N label derived from toxic bacterial ammonium, and (ii) toxic p-[2H4]cresol, a bacterial degradation product from tyrosine fermentation.

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With the help of the information presented above, an overall metabolic network of gut microbial metabolism can be constructed. To complete the model, transport routes that account for uptake of substrate and removal of products (e.g. via epithelial absorption and feces) have to be included.

7.2. Get Quantitative: Mass Balances Reveal a Lot In a consistent metabolic network, considered at (quasi)steady state, the possible ﬂuxes are constrained by stoichiometry relations that reﬂect the mass balances (Schilling et al., 2000). As a consequence, the ranges within which intracellular metabolic ﬂuxes can vary can be predicted and conclusions can be drawn on how the metabolic network responds under different conditions of, for example, substrate availability. While this approach has been extensively and successfully applied with microorganisms (Reed and Palsson, 2003), it may equally well be used to determine and even predict metabolic ﬂuxes in mitochondria (Ramakrishna et al., 2001). The next step in setting up MFA of the colonic microbiota therefore is to gather as much quantitative experimental data as possible on ﬂuxes that represent the inputs and outputs of that metabolic network in order to reduce the available ﬂux solution space (Wiback et al., 2004). In other words, measured rates of, for example, carbohydrate intake by the gut, SCFA use by the colon, etc., contribute to determine the bacterial intracellular metabolite ﬂuxes. Careful balancing of SCFA production in anaerobic cultures of fecal bacteria for instance has already provided important insights in their regulation by carbohydrate availability and growth rate (Macfarlane and Macfarlane, 2003). The potential of this procedure to derive conclusions on important ﬂuxes of intermediary metabolism such as the activity of the citric acid cycle (i.e. mitochondrial function) even in a complex organ, has recently been demonstrated with isolated perfused livers (Arai et al., 2001; Lee et al., 2003; Yokoyama et al., 2005). For the colon, experimental procedures as discussed in the previous section can be employed to obtain relevant data. Of course, to be really successful, MFA of the colonic microbiota has to be performed in vivo with the colon functionally operative in the entirety of the functioning body. Indeed there is a true perspective that this can actually be accomplished. Namely, the ﬂux balancing approach is also possible in vivo using multi-catheterized blood sampling approaches (Ten Have et al., 1996). Basically, this method employs measurement of blood ﬂow and sampling of blood upstream and downstream of organs, allowing the set up of a net material balance across each organ. This approach has been used to study interorgan amino acid metabolism during acute liver failure in pigs

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and humans, where it led to a much improved insight in the role played by different organs (muscle, kidney, spleen, liver, portally drained viscera) during pathogenesis of the disease (Olde Damink et al., 2002; Ytrebo et al., 2006). When the method is combined with isotopic tracers, the total disposal rate of a traced compound (e.g. an amino acid) in an organ can be derived from the loss of tracer across that organ. This allows the calculation of the true uptake or production of this amino acid in that organ as net balance plus disposal (Bruins et al., 2002, 2003). The use of suitably chosen isotopically labeled colon substrate tracers will further allow an increase in the analyzing power of ﬂux analysis, much the same as discussed previously for single microorganisms. Therefore, once stable isotopes are included, there is genuine reason to be optimistic about the prospect of MFA of the gut microbiota also in vivo in the near future.

7.3. Stable Isotope-Aided Quantification of Pathways: Functional Genomics What is the practical route for MFA of the colonic microbiota? Once the network is deﬁned, the ﬁrst step as we have seen is to construct material and metabolite balances over the colon. On the experimental side, this will involve the measurement of the input of non-digested material from the small intestine into the colon, the measurement of differential metabolite appearance rates in portal vs. arterial blood, and correcting the results for material lost in feces as well as for products of digestion in the small intestine (cf. Fig. 7). This analysis yields the basic input–output analysis of the colon, which however still has to be completed by taking into account the material metabolized by the gut wall. Stable isotopes may be employed at this stage in addition to the net balancing to determine the total metabolite disposal and true production rates as explained previously. This is an enterprise that may involve elaborate and strongly invasive experiments. Next comes the probing of the actual bacterial intracellular metabolic network. While this needs ﬁnally to be done for the in vivo situation in the intact functioning colon, useful a priori information on the regulation of gut bacterial metabolism may be derived from carefully planned in vitro experiments such as those on SCFA production discussed above (Macfarlane and Macfarlane, 2003). These experiments should include product proﬁles, cross-feeding effects, inﬂuence of thermodynamic constraints and pH, among others. This work could be performed in validated in vitro colon model systems such as described in a previous section (Minekus et al., 1999).

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Of course, in vitro conditions will most certainly differ considerably from those in vivo, which will in turn affect the microbiota composition and hence the overall metabolic activity pattern. Nevertheless, such in vitro models may be highly instrumental in model development, i.e. setting up the basic metabolic network of involved pathways and transport routes that one expects. Then, this overall network might be decomposed into a limited set of sub-networks each characteristic for a certain genus of bacteria, much in the same way as elementary mode balancing (Schuster et al., 2002; Cakir et al., 2004). Changes in microbiota composition then will only affect the relative contributions of those modes, and not the pathway network model as a whole. FBA will subsequently reveal to which extent the resulting equation system is under-determined from a mathematical point of view, given the measured material balance data. Subsequently, intelligent strategies may be employed (Mollney et al., 1999; Isermann and Wiechert, 2003) to design stable isotope labeling experiments that will produce the additional data necessary to completely solve the metabolic ﬂux network thus constructed, in vitro and also in vivo. Subsequently, after the experiments using the required isotopically labeled substrates are actually conducted, the data resulting from MS and/or NMR analyses will be used in a non-linear least squares ﬁtting procedure to yield the full set of ﬂuxes in the metabolic network model. The ﬁnal perspective of MFA developed along these lines is a map of metabolic pathway activities in the colonic microbiota, that can be decomposed into sub-maps of methanogenic, mixed acid, and other typical microbial fermentation modes, each eventually attributable to a different genus of microorganisms. One may then proceed to investigate how these ﬂux maps differ between subjects with differences in microbiota composition, or how these ﬂux maps change upon feeding different prebiotic substrates, or how these ﬂux maps relate to host disease parameters, etc. Ideally, this information is correlated with data from the various ‘-omics’ platforms, leading to a true functional genomics application (Hellerstein, 2004).

8. EMERGING PICTURE OF THE ROLE OF MICROORGANISMS INTEGRATED IN MAN In recent years, the picture of the role played by the gut microorganisms within us has become increasingly clear. All evidence points at a truly mutualistic relationship. Our guts provide a habitat for incredible numbers

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of mostly anaerobic bacteria in an otherwise deadly, oxygen-rich environment. What we are getting in return has been largely derived from studies that compared Germ-Free (GF) rodents with those that have acquired a microbiota since birth (conventionally raised CONV-R). A recent overview can be found (Backhed et al., 2005). Important ﬁndings from these studies show that GF rodents show disturbed bile acid balances which affect cholesterol homeostasis, they show reduced cardiac weight and output, they are more vulnerable to vitamin deﬁciencies (including vitamin K, B6, B12, biotin, folic acid, and pantothenate), they extract less energy from their diet, their immune system development is different (e.g. strongly reduced serum IgM and IgG levels), and they are unable to metabolize dietary oxalates, leading to kidney stone formation. Interestingly, GF mice are resistant to IBD and are less susceptible to arthritis and colitis, indicating that there is also a risk involved with carrying around our microbiota.

8.1. Energy Balance Large intestinal fermentation can account for 10% of our daily energy supply (Bergman, 1990). Thus, the colonic microbiota plays a very significant role in whole body energy supply. Studies with GF and CONV-R rodents have shown that CONV-R animals were able to extract significantly more energy from their diets than GF counterparts, as judged from the fact that they had 40% more total body fat while consuming less food per day (Backhed et al., 2004). This corroborates ﬁndings from other studies (Scheppach et al., 1991; Pouteau et al., 2005) that evidenced increased serum acetate concentrations and turnover, correlating with colonic carbohydrate fermentation. Microbially produced butyrate is the preferred and most important energy source for colonocytes (Csordas, 1996). These points all more or less reﬂect a direct effect, i.e. additional energy produced by microbial fermentation of substrates entering the colon that would otherwise be useless to the host. There is however reason to believe that indirect effects may be at least as important. The colon functions within the whole of the intestine and associated visceral organs in controlling body energy balance (Badman and Flier, 2005). Gut and organs together play a key sensing and signaling role in the physiology of energy homeostasis. The gut, the pancreatic islets of Langerhans, elements in the portal vasculature, and even visceral adipose tissue communicate via neural and endocrine pathways with the controllers of energy balance in the brain. Signals reﬂecting energy stores, recent nutritional state, and other parameters are integrated in the central nervous system, particularly in the hypothalamus, to coordinate

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energy intake and expenditure (Badman and Flier, 2005). We are only beginning to uncover all the different features of this complex regulatory network, and expectations are high that understanding of the mechanisms that control energy balance will provide clues for therapies to ﬁght the metabolic syndrome.

8.2. Innate Immune System Only a thin layer of epithelial cells known as enterocytes separates the host from the intestinal lumen. These cells must form an effective barrier against incursions and introgressions by the intestinal microbiota. Interestingly, part of this barrier function appears to be carried out by intestinal bacteria themselves: the puriﬁed adhesin of a B. adolescentis strain was found to inhibit the adhesion of enteropathogenic E. coli and C. difficile to an intestinal epithelial cell line (Zhong et al., 2004). However, to offer protection in case that the barrier function becomes impaired, the bulk of cells aligned below the layer of enterocytes are immune cells (Chin, 2004). The intestinal immune response and the mucosal layer therefore are both very important for human host defence and can be affected by the gut microbial fermentation products of carbohydrates and proteins, of which notably SCFA and sulfur-containing compounds have been studied in most detail. Of the SCFA, butyrate has best been studied. Butyrate was found to decrease colitis in animal models. Moreover, butyrate resulted in an increase of IgA-producing cells and mucosal IgA concentrations, the secretion of anti-inﬂammatory cytokines and decreased myeloperoxidase (MPO) activity. Most of these parameters have been studied using cell lines or animal models. However, in patients with UC, sodium butyrate enemas are found to improve inﬂammatory scores, clinical symptoms, and intestinal permeability. Apart from possible anti-inﬂammatory effects, butyrate also inﬂuences the intestinal mucus production (Cassidy et al., 1982; Finnie et al., 1995; Barcelo et al., 2000). In vivo, the number of goblet cells was found to increase and dose-dependent increases in mucus secretion were observed (Barcelo et al., 2000) upon addition of butyrate. However, intestinal mucus also serves as substrate for bacterial fermentation (degradation of proteins and saccharide-side chains). In addition, intestinal mucus is a source of sulfur for SRB, which are able to use the sulfate liberated from mucins for the production of hydrogen sulﬁde. This sulﬁde can inhibit butyrate oxidation by the epithelial cell (Roediger et al., 1993) and is associated with apoptosis, loss of goblet cells, and distortion of the crypt cell architecture. Limited information is available about the effects of sulfur-rich diets on

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SRB and their effect on intestinal inﬂammatory parameters and mucus production in humans. Furthermore, the interactive effects of sulfur-rich diets (i.e. increased sulﬁde production) and carbohydrate consumption in humans are still largely unknown. In view of the rising number of clinical cases with intestinal complaints, there is a need for increased research efforts in this area. For example, IBS, a functional bowel disorder, affects a large number of subjects. Prevalence data show a frequency ranging from 3 to 20% in the general population of which a large part does not seek medical attention but has complaints of pain, diarrhea, and/or constipation (Verne, 2004). Moreover, the prevalence of IBS is found to increase. A disturbed inﬂammatory response, role of mast cells, and an abnormal colonic fermentation are all observed in IBS and beneﬁcial effects have been reported using pre- and probiotics. Because of the magnitude of the population affected by this disorder, it is worthwhile to study the mechanisms behind this. Needless to point out, further insight into the health effects of SCFAs and sulﬁde with regard to major intestinal mucosal functions is important also for healthy subjects.

8.3. Intestinal Microbiota: Is There a Link With Obesity? As pointed out above, the colon functions within the whole of the intestine and associated visceral organs in maintaining the host energy balance. An important aspect is the role that intestinal microorganisms play in cholesterol and bile acid metabolism, performing deconjugation and metabolism of bile salts. Disturbed bile acid metabolism was observed in GF rodents, but it has also been found that the SCFA acetate can interfere directly with lipid metabolism. By contrast, acetate production (presumably microbial) after lactulose ingestion in overweight subjects was recently shown to result in short-term decrease in free fatty acid level and glycerol turnover related to a decrease of lipolysis (Ferchaud-Roucher et al., 2005), both factors believed to help in preventing insulin resistance. By contrast, however, acetate may also stimulate lipid synthesis (Wolever et al., 1995), and it remains to be settled whether acetate has a long-term beneﬁcial effect. Interestingly, the latter study also showed that another SCFA, propionate, inhibited lipid synthesis from acetate. Backhed et al. (2004) found that conventionalization of GF mice with a microbiota harvested from cecum of CONV animals produced a 60% increase in body fat content and insulin resistance despite reduced food intake. An increased absorption of monosaccharides from the intestine was detected in conventionalized mice, resulting in de novo lipogenesis in the liver. In addition, they showed that Fiaf, a lipase inhibitor,

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was suppressed in the intestinal epithelium of conventionalized mice and that this suppression was essential for the microbiota-induced deposition of fat in adipose tissue. These ﬁndings suggest that, at least in the mouse, the gut microbiota affects energy harvest from the diet and energy storage in adipose tissue. Whether this is also the case in humans remains to be determined. Thus, the effects of colonic SCFA production or microbial activity in general on lipid metabolism is difﬁcult to predict. The true role of the colon in regulation of lipid metabolism is very likely to be an even more complex one, involving multiple neural and endocrine pathways. The recent ﬁnding that ingestion of dietary fat stimulates cholecystokinin (CCK) receptors, but at the same time leads to attenuation of the inﬂammatory response by way of the efferent vagus nerve and nicotinic receptors, may be an interesting foretaste of the type of regulatory hardwiring that can be expected. Here, we have a novel neuro-immunologic pathway, controlled by nutrition, that may help to explain the intestinal hyporesponsiveness to dietary antigens. Our intestine developed during evolution for optimal survival on natural diets. The recent rise to epidemic dimensions of obesity-linked diseases correlates, in a timely manner, with a shift in dietary habits toward a reduced intake of dietary ﬁber, an increased intake of simple sugars, a high intake of reﬁned grain products, an altered fat composition of the diet, and a dietary pattern characterized by a high glycemic load (Suter, 2005). Recent epidemiological research (Maskarinec et al., 2006) of a large ethnically diverse population showed that on an individual level, fat and protein consumption predicted a higher BMI, and dietary ﬁber intake predicted a lower BMI. Similarly, a higher consumption of meat, poultry, and ﬁsh was related to excess weight, whereas fruit and vegetable intake were inversely associated with excess weight. There is growing evidence of the high impact of dietary ﬁber and foods with a low glycemic index on single risk factors (e.g. lipid pattern, diabetes, inﬂammation, endothelial function, etc.) as well as the development of the endpoints of atherosclerosis (especially coronary heart disease; Suter, 2005). A recent review (Hyman, 2006) pointed out that it is the glycemic load, rather than the glycemic index, that affects the neuroendocrine–immune signaling. Dietary ﬁber is one of the main factors lowering the glycemic load, whence its beneﬁcial effects in reducing weight. This author also points out that bacterially produced fatty acids lower cholesterol production in the liver. In line with this observation, it is tempting to hypothesize that obesity may, at least in part, be associated with deprivation of proper substrate for colonic microbial fermentation. Obviously, in obesity, the normal regulatory response to high leptin levels is blunted by another, as yet unknown, regulatory component. If this blunting is associated

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with substrate limitation of the microbiota, this would hint at a colon-linked regulator. There is an interesting parallel with polyphosphate metabolism in bacteria that could support this view: many bacteria in carbon-rich media, when confronted with certain dietary component limitations, shift to accumulation of large amounts of polyphosphate, probably for energy storage (Ault-Riche et al., 1998). Physiological studies could be instrumental in ﬁnding which aberrations in metabolic, proteomic, and/or genomic proﬁles linked to the colon can be found in obesity, after which subsequent research may uncover the relevant regulatory consequences of these aberrations. Such studies may proﬁt considerably from the integration of enteric neurobiological approaches (Grundy, 2004; Grundy and Schemann, 2005), a fascinating and rapidly developing ﬁeld.

8.4. Role of Stable Isotopes Can stable isotopes contribute to knowledge in the ﬁeld of host–microbe interaction? Yes, they can. The key advantage of stable isotope methods is that they are very potent in tracing the fate of substrates entering the colon on the metabolic level, and therefore allow for a speciﬁc correlation of host responses to colon-derived metabolic events. This helps in discriminating colonic microbiota-related effects against those having an endogenous origin, and therefore allow a clearer picture of host–microbe interaction to emerge. For instance, supplementation with resistant starch (RS) has been shown to improve colonic lesions in a dextran sulfate sodium (DSS)-induced colitis model in rats. To ﬁnd out whether it is the increased colonic butyrate production that accelerates the healing process, Moreau et al. (2004) measured the ceco-colonic uptake of butyrate and its oxidation into CO2 and ketone bodies in control and DSS-treated rats fed a ﬁber-free basal diet or a RS-supplemented diet. After cecal infusion of [1-13C]butyrate, concentrations and 13C-enrichment of butyrate, ketone bodies, and CO2 were quantiﬁed in the abdominal aorta and portal vein, and portal blood ﬂow was measured. These measurements allowed the authors to determine the utilization of butyrate specifically by the colonic mucosa, and to conclude that increased utilization of butyrate by the mucosa is subsequent to evidence of healing, and appears to be a consequence rather than a cause of the RS healing effect (Moreau et al., 2004). In another study (Pouteau et al., 2005), it was tested whether acetate from colonic fermentation of inulin would stimulate peripheral acetate turnover in dogs. Dogs were administered with simultaneous infusions of

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[1-13C]acetate i.v. and [1,2-13C2]acetate intrarectally. After switching from a control diet to a 3% inulin-enriched diet, initially no changes in whole body acetate concentration and turnover were seen after 4 days, but after 21 days the whole body acetate turnover had increased significantly by 31%. While it was determined that a significant acetate production occurred in the colon, no [1,2-13C2]acetate tracer was recovered in the peripheral circulation. This led to the conclusion that the occurring colonic fermentation of inulin indirectly stimulated whole body acetate turnover (Pouteau et al., 2005). While these examples concern the effect of microbial processes on the host, the effects of host metabolism on microbial processes may also be probed by stable isotopes. One example obviously is the measurement of SCFA synthesis on various diets (a direct effect of host behavior), a nice example of a more indirect interaction concerns mucin. Mucus and mucosal proteins represent an important substrate for intestinal bacteria. Faure et al. (2002) have developed a method to measure intestinal mucoprotein FSR (%/day) in vivo by using the ﬂooding dose method with the stable isotope L-[1-13C]valine. Free L-[1-13C]valine enrichments in the intracellular pool were determined by GC-MS, whereas L-[1-13C]valine enrichments in puriﬁed mucoproteins or intestinal mucosal proteins were measured by gas chromatography-combustion-isotope ratio mass spectrometry. Using this method, Faure et al. (2005) compared rats fed isonitrogenous diets (12.5% protein) containing 30% (group 30) and 100% (control group) of the theoretical threonine requirement for growth. The mucin FSR was significantly lower in the duodenum, ileum, and colon of group 30 compared with controls. Because mucin mRNA levels did not differ between these two groups, mucin production in group 30 probably was impaired at the translational level. These results clearly indicated that restriction of dietary threonine significantly and specifically impairs intestinal mucin synthesis. In clinical situations associated with increased systemic threonine utilization, threonine availability may limit intestinal mucin synthesis and consequently reduce gut barrier function in the absence of adequate dietary threonine intake. In another study, glutamine was found to stimulate gut mucosal protein synthesis (Coefﬁer et al., 2003). While the above examples pertain to in vivo studies, stable isotope-based methods are equally potent in sorting out regulatory effects at the cellular level, as e.g. evidenced in HT29 cells where it was found that, upon butyrate supplementation, these colon cells replace glucose for butyrate as an energy substrate (Boren et al., 2003). Therefore, stable isotope methods offer prospects for rapid screening protocols such as stable isotope-based dynamic metabolic proﬁling (SIDMAP) (Boros et al., 2003).

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9. NEW ASPECTS IN THE STUDY OF INTESTINAL BACTERIAL PHYSIOLOGY 9.1. Microbes at War: Population Competition Models Some of the species of adherent intestinal microorganisms in the intestine have exploited and adapted to particular microniches in different compartments of the colon with its extremely large surface area created by the complex involution of crypts and villi. These bacteria are continuously competing for survival. The ability to persist and propagate or be ultimately eliminated, is dependent to a large extent upon the armory of each combatant (Chin, 2004). Susceptibility or immunity of each strain to the arsenal of bacteriocins or quorum sensing factors produced by another constitutes a community at war. Yet, seemingly in stark contradiction, this scenario may be vital for their very existence, as we will see shortly. The weaponry of intestinal microbes is diverse and sometimes ingenious. In addition to ‘fair’ weapons such as bacteriocins, they can use less direct but potentially even more powerful tricks. For instance, very active excretion of acetate may induce growth limitation of a competitor who is susceptible to acetate uncoupling, as, for example, described for a Clostridium sp. (Baronofsky et al., 1984). As another example, some Bifidobacterium strains produce adhesins that competitively inhibit adherence of E. coli and C. difficile to intestinal epithelial cells, providing themselves with an increased resistance against being washed out of the colon (Zhong et al., 2004), while stimulating washout of their competitors. Similarly, Lactobacillus plantarum is able to produce a protein that may prevent adhesion of E. coli carrying type-1 ﬁmbriae to bind to mannose-containing glycans (Pretzer et al., 2005). Competition experiments of gut microbial strains (Kato et al., 2005) have shed light on the mechanisms that allow stable coexistence of enemies in bacterial cultures. A cellulose-degrading deﬁned mixed culture consisting of ﬁve intestinal bacterial strains was established that showed no change in cellulose-degrading efﬁciency, while all members stably coexisted through 20 sub-cultures. The mechanisms responsible for the observed stability were investigated by constructing ‘knockout communities’ in which one of the members was eliminated. Thereafter, the roles played by each eliminated member and its impact on the other members of the community were evaluated from measured dynamics of the community structure and the cellulose degradation proﬁles of these mixed cultures. Integration of the results showed different synergistic and detrimental relationships between different

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sets of the ﬁve bacterial strains. An obvious synergistic effect was that the aerobic bacteria introduced anaerobic conditions, which permitted anaerobic Clostridium to supply metabolites (acetate and glucose) for their growth in return. A detrimental effect was the inhibition of cellulose degradation due to excessive acetate production by another Clostridium sp. As an important conclusion, the balance of the various types of relationships (both positive and detrimental) is apparently essential for the stable coexistence of the members of this mixed culture (Kato et al., 2005). In this type of investigation, stable isotope analysis may be helpful. They may offer powerful tools to interpret the results of cross-feeding experiments in mixed cultures by their ability to trace back speciﬁc metabolic transitions (cf. Fig. 6). This has, for instance, allowed the unequivocal detection of the presence of biﬁdobacteria in a human colonic microbiota (Wolin et al., 1998) by establishing 13C-labeling patterns that are speciﬁc for the unique Bifidobacterium pathway of hexose catabolism. To better understand the mechanisms governing the stable coexistence of different competing bacterial strains, the approach of building a predictive theoretical model is worth serious consideration. This requires us to be philosophical to a certain extent. The outcome of the struggle for life of bacteria in the human colon to some level will reﬂect the results of coevolution. At ﬁrst, one might be inclined to expect that one or another single species (let’s take us, humans, for a moment) may have such enormous competitional advantages that it will outgrow all others. However, this is obviously never the case in reality. If there is one thing that research on the colon shows, it is that even we as humans cannot live a healthy life without the help of those seemingly insignificant microbes inside us. Apparently, there is much evolutionary advantage in sharing resources and surviving as a consortium, rather than alone (Pfeiffer et al., 2001). The fact that the colonic microbiota is able to vary considerably in composition and time, while remaining able to perform a rather stable overall metabolic function, must reﬂect intrinsic principles or ‘laws’ governing their dynamic yet persistent coexistence. It is the task of biological modelers to sort out those principles. Indeed, they are already on the job and interesting parallels with such seemingly far-off ﬁelds as game theory have already been discovered. The rationale here is that by evolving toward optimal properties, organisms change their environment, which in turn alters the optimum. Evolutionary game theory provides an appropriate framework for analyzing evolution in such ‘dynamic ﬁtness landscapes’ (Pfeiffer and Schuster, 2005). Indeed, theoretical simulations correctly predicted that an ensemble of toxinproducing, toxin-sensitive, and toxin-resistant strains of E. coli is able to coexist when living in spatially structured, non-transitive interaction

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(Kerr et al., 2002; Kirkup and Riley, 2004). A corollary of this observation is that bacteriocins promote, rather than eliminate, microbial diversity in the gut too. This in fact corroborates the experimental ﬁndings (Kato et al., 2005) discussed above. Further interesting progress in this area is certain to be awaited in coming years.

10. CONCLUSIONS AND FUTURE PROSPECTS 10.1. Toward a Systems Biology of the Gut The combination of high-throughput methods of molecular biology with advanced mathematical and computational techniques has propelled the emergent ﬁeld of systems biology into a position of prominence. Unthinkable a decade ago, it has now become possible to screen and analyze the expression of entire genomes, simultaneously assess large numbers of proteins and their prevalence, and characterize in detail the metabolic state of a cell (population). Because of these general advances in life sciences, research on the physiology of the intestinal microbiota as it functions within and in interaction with the host is rapidly growing in intensity also. The literature covered in this review bears testimony to the fact that our knowledge on a wide range of issues related to gut microbes and the role they play in the mutualistic relationship with their host has expanded enormously in recent years. Having said this, it must however be admitted that all this knowledge in fact is still fragmentary and that a fully integrated picture of host–microbe interactions has yet to be established. Notably, the mechanisms by which the metabolic activity of our intestinal microbiota inﬂuence processes leading to disease, are still very far from being understood. Their elucidation requires an understanding of metabolic regulation that so far has been limited by a failure to consider regulation within the context of the whole network (Sweetlove and Fernie, 2005), in this case of microbial and host metabolic and signaling pathways. Several approaches that provide tools for the required integration of data, models, and thinking (Davis and Hord, 2005) are now appearing in the literature. Lee et al. (2005) discuss how, for the design of cells that have improved metabolic properties for industrial applications, informative high-throughput analysis and predictive computational modeling or simulation must be combined to generate new knowledge through iterative modiﬁcation of an in silico model and experimental design. Such new modeling approaches should aim to take full advantage of genome sequence data, transcription proﬁling, proteomics and

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metabolite proﬁling, and integrate global metabolic models with genetic and regulatory models for strain improvement (Smid et al., 2005). The applicability of this approach has already been demonstrated in a study that describes the in-depth analysis of the intracellular metabolite concentrations, metabolic ﬂuxes, and gene expression (metabolome, ﬂuxome, and transcriptome, respectively) of lysine-producing C. glutamicum at different stages of batch culture revealing distinct phases of growth and lysine production (Kromer et al., 2004). The integrated approach was valuable for the identiﬁcation of correlations between gene expression and in vivo activity for numerous enzymes, and allowed, for the ﬁrst time, an integrated overview of the regulation of C. glutamicum intermediary metabolism when this organism switches to L-lysine production. The way to go that is emerging from the literature (Hellerstein, 2004; Boros, 2005) is obviously to link up stable isotope-aided MFA with the existing ‘-omics’ technologies. Innovative modeling frameworks that are able to integrate data from all these four platforms are required for this purpose. Such models should allow the determination of regulatory properties of the studied organism from the experimental data by incorporating such diverse information as pathway structures, ﬂux balance constraints, isotopic labeling routes, thermodynamic constraints, enzyme kinetic properties, statistical correlations, and employing suitable minimization criteria. This challenge is formidable but methods are in very rapid development, and are rapidly gaining predictive power for metabolic regulation (Wiback et al., 2004). Recent developments in this ﬁeld include the introduction of ‘scale-free’ networks (Barabasi and Albert, 1999), the implementation of constraints imposed by kinetic and equilibrium constants in the isotopomer distribution analysis (Selivanov et al., 2005), and hybrid cooperations between kinetics-based dynamic models and FBA-based static models (Yugi et al., 2005; Kitayama et al., 2006), while the importance of including new levels of the metabolic regulatory hierarchy (such as protein–protein interaction) has also been pointed out (Sweetlove and Fernie, 2005). With these recent advances in theoretical aspects of network thinking and a postgenomic landscape in which our ability to quantify molecular changes at a systems level is unsurpassed, the time is ripe for the development of this new level of understanding of metabolic network regulation in the world of our intestinal microbiota, with its immensely complex network of intricate microbe–microbe and microbe–host interactions. One of the more simple but invaluable lessons to be learned pertaining to this ﬁeld is that, together with a ﬁber-rich diet, a good breakfast is the best guarantee to make that network do what is was designed for – keep us lean and healthy (Hyman, 2006).

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intestinal lumen

enterocytes

serosa (blood)

from arterial

diet urea

microbial compartment

NH3

urea

His, Lys, Thr

AA

AA

Val, Ile, Leu

microbial

Protein

endogenous

Protein NH3

AAdisp

Protein NH3 to portal vein

Plate 2 Schematic representation of gut-associated nitrogen metabolism, compiled from information in Metges (2000) and references therein. Colored circles symbolize 15 N isotope label originating from urea (red) or ammonia (blue), respectively, with fading color intensity indicating isotope dilution. Urea diffuses from the blood through the enterocytes into the intestinal lumen, where it is hydrolyzed by bacterial urease into ammonia and carbon dioxide. Ammonium is the preferred non-speciﬁc microbial nitrogen substrate for synthesis of e.g. amino acids. Microbially synthesized amino acids may partially be released into the gut lumen and taken up by ileal enterocytes (in the colon, bacterial cell densities are so high that microbially synthesized amino acids probably never reach colonocytes). Therefore, bacteria may supply a signiﬁcant portion of the body’s requirement for indispensable amino acids. 15N label appearance in histidine, lysine, and threonine upon 15N-urea or 15N-ammonia administration is proof of microbial activity since these amino acids cannot be endogenously transaminated. However, after administration of e.g. the 15N-labeled indispensable amino acid leucine, 15 N label will appear in other branched-chain indispensable amino acids as well as in dispensable amino acids (AAdisp) since the body is able to transaminate leucine, valine, and isoleucine. Due to extensive amino acid exchange between blood, enterocytes, and intestinal microbiota, interpretation of 15N labeling experiments is often ambiguous. Combining nitrogen-15 labeling with carbon-13 or carbon-14 labeling as done e.g. in Torrallardona et al. (2003), therefore, may constitute a useful approach to arrive at unequivocal conclusions (For b/w version, see page 92 in this volume).

Plate 3 Principle of RNA-based stable isotope probing (SIP) for detection and characterization of microbes that actively metabolize the labeled substrate. 13C-labeled substrates are incubated in (a) simple in vitro models (test tube or ﬂask), (b) sophisticated in vitro systems, or (c) in vivo. Samples obtained from these experiments [in the ﬁgure only shown for samples from (a)] are subjected to RNA isolation and density gradient centrifugation. After separation of the gradient in fractions, molecular ﬁngerprinting techniques, such as DGGE (Zoetendal et al., 2004a) or T-RFLP (Egert et al., 2003) can be used to determine the presence (usually enrichment) in the heavier fractions of those microorganisms that speciﬁcally fermented the substrate and this can be compared with the diversity present in an unlabeled, control sample (For b/w version, see page 108 in this volume).

13

C-labeled substrate

a

b

c

incubation

density gradient centrifugation

Density

RNA extraction

gradient fractionation H2O

labeled RNA molecular comparison of fractions

unlabeled RNA

Bacterial Physiology, Regulation and Mutational Adaptation in a Chemostat Environment Thomas Ferenci School of Molecular and Microbial Biosciences G08, The University of Sydney, NSW 2006, Australia

ABSTRACT The chemostat was devised over 50 years ago and rapidly adopted for studies of bacterial physiology and mutation. Despite the long history and earlier analyses, the complexity of events in continuous cultures is only now beginning to be resolved. The application of techniques for following regulatory and mutational changes and the identiﬁcation of mutated genes in chemostat populations has provided new insights into bacterial behaviour. Inoculation of bacteria into a chemostat culture results in a population competing for a limiting amount of a particular resource. Any utilizable carbon source or ion can be a limiting nutrient and bacteria respond to limitation through a regulated nutrient-speciﬁc hunger response. In addition to transcriptional responses to nutrient limitation, a second regulatory inﬂuence in a chemostat culture is the reduced growth rate ﬁxed by the dilution rate in individual experiments. Sub-maximal growth rates and hunger result in regulation involving sigma factors and alarmones like cAMP and ppGpp. Reduced growth rate also results in increased mutation frequencies. The combination of a strongly selective environment (where mutants able to compete for limiting nutrient have a major ﬁtness advantage) and elevated mutation

ADVANCES IN MICROBIAL PHYSIOLOGY, VOL.53 ISBN 978-0-12-373713-7 DOI: 10.1016/S0065-2911(07)53003-1

Copyright r 2008 by Elsevier Ltd. All rights reserved

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rates (both endogenous and through the secondary enrichment of mutators) results in a population that changes rapidly and persistently over many generations. Contrary to common belief, the chemostat environment is never in ‘‘steady state’’ with ﬁxed bacterial characteristics usable for clean comparisons of physiological or regulatory states. Adding to the complexity, chemostat populations do not simply exhibit a succession of mutational sweeps leading to a dominant winner clone. Instead, within 100 generations large populations become heterogeneous and evolving bacteria adopt alternative, parallel ﬁtness strategies. Transport physiology, metabolism and respiration, as well as growth yields, are highly diverse in chemostat-evolved bacteria. The rich assortment of changes in an evolving chemostat provides an excellent experimental system for understanding bacterial evolution. The adaptive radiation or divergence of populations into a collection of individuals with alternative solutions to the challenge of chemostat existence provides an ideal model system for testing evolutionary and ecological theories on adaptive radiations and the generation of bacterial diversity.

1. General introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. The chemostat environment and its applications to studies of bacteria . 3. The physiological changes in an organism inoculated into a chemostat: The example of glucose-limited Escherichia coli . . . . . . . . . . . . . . . . . 3.1. Transport and Membrane Permeability. . . . . . . . . . . . . . . . . . . . . 3.2. Metabolism and Energetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Stress Regulation and Gene Expression . . . . . . . . . . . . . . . . . . . 3.4. Antibiotic Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Quorum Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Variations in responses within and between species . . . . . . . . . . . . . . 5. Steady state or constant change in a chemostat population? . . . . . . . . 6. Mutation rates and mutators in chemostat populations . . . . . . . . . . . . . 7. Mutational takeovers and population changes . . . . . . . . . . . . . . . . . . . 8. A mutational sweep in detail: The physiological advantage and spread of mgl mutations in glucose-limited E. coli . . . . . . . . . . . . . . . . 9. Other mutations in chemostat populations and their physiological effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1. Changes in the lac System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2. Outer Membrane Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3. rpoS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4. mlc and malT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5. ptsG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.6. Metabolic Changes and Cross-Feeding . . . . . . . . . . . . . . . . . . . . 9.7. Amplification and Other Genomic Rearrangements . . . . . . . . . . . . 10. Emerging diversity in chemostat populations . . . . . . . . . . . . . . . . . . . . 10.1. Diversity in Regulatory Strategies . . . . . . . . . . . . . . . . . . . . . . .

. 171 . 173 . . . . . . . . . .

177 177 181 183 187 188 189 191 192 195

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201 201 201 203 204 205 205 206 207 211

CHEMOSTAT ENVIRONMENT

10.2. Diversity in Transport Strategies . . . . . . . . . . . . . 10.3. Diversity in Metabolic and Bioenergetic Strategies. 11. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. GENERAL INTRODUCTION Unusually for microbiology, a science in which empirical observations predominate, continuous culture methods originated from conceptual and formal analyses of the growth properties of bacteria. In 1950, two milestone papers proposed and demonstrated that bacterial cultures can be grown indeﬁnitely by pumping fresh medium at a ﬁxed rate into a culture vessel with a constant volume (Monod, 1950; Novick and Szilard, 1950a). The medium controls growth in that one component becomes limiting, preventing growth beyond a particular density. In synthetic media containing a single component such as glucose, amino acid or ion at a concentration lower than generally used in batch culture, growth is capped by a single nutrient and is characteristically called a nutrient-limited chemostat. The behaviour of bacteria in a chemostat more or less follows the kinetic formulations derived and reﬁned in several studies (Monod, 1950; Novick and Szilard, 1950a; Herbert et al., 1956; Pirt, 1975; Dykhuizen and Hartl, 1983; Panikov, 1995). Nevertheless, derived growth equations rely on empirical observations describing bacterial growth with sub-saturating concentrations of individual nutrients (Monod, 1949). Inherent assumptions in the description of chemostat cultures are saturable growth kinetics and a stable halfsaturation constant for the given substrate/organism pairing. Another assumption is the constancy of biomass yield from substrate for the studied organism. This review will not deal with growth kinetics in detail, but the concept of absolute constants in bacterial growth relevant to chemostat culture has been questioned and discussed elsewhere (Ferenci, 1999a). At several points in this review, entrenched views on the properties of chemostats will be reassessed in view of the plasticity in bacterial characteristics due to physiological and mutational adaptations in chemostat-grown populations. My approach will be more empirical than formal and I will mainly focus on describing the complexities of events in chemostats rather than forcing bacterial behaviour into equations or models. There are more than 5000 papers dealing with chemostat cultures so some added focus was needed to keep this review to manageable proportions.

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Several reviews and books deal with some aspects of chemostat culture (Kubitschek, 1970; Pirt, 1975; Dykhuizen, 1993; Kovarova-Kovar and Egli, 1998). This discussion will concentrate on basic principles in bacterial physiology and mutational adaptation arising from studies of chemostats limited by a single nutrient. Many lessons can be learned from single organismsingle limitation experiments so I will not consider multi-stage chemostats (Novick, 1959; Lovitt and Wimpenny, 1981) or other ingenious-stat variations. Neither chemostats inoculated with more than one type of organism (e.g. phage and bacteria, Horne, 1970; or inter-species competitions, Veldkamp and Jannasch, 1972) nor continuous cultures with more than one limiting nutrient (e.g. multiple sugars; Egli et al., 1993; Dykhuizen and Dean, 2004) will be discussed. Also not considered are numerous studies involving chemostats for the improvement of strains, directed gains in function, processes or plasmid stability. The motivation for this review is actually no different to that in a 50-year-old paper on a similar topic (Moser, 1957). It is sobering to re-quote some of the opening words of Moser: ‘‘population dynamics has been studied in bacteria for many years, but today this ﬁeld has become an attractive and promising subject of experimental and theoretical investigation because of two factors. First, the astounding advances made in our knowledge of the genetic mechanisms of bacteria. Second, the development of devices for the continuous growth of large bacterial populations y’’. These ideas can be re-stated after 50 years but there is certainly a resurgence of interest in the area of chemostat research for three reasons. These are, ﬁrstly, the revival in the use of chemostats for gene expression comparisons under stable physiological conditions (Hoskisson and Hobbs, 2005 and references therein); secondly, the mushrooming interest in experimental evolution using a variety of selection environments including chemostats (Dykhuizen, 1990; Watt and Dean, 2000; Elena and Lenski, 2003; Adams, 2004); and thirdly the better understanding of the chemostat environment in eliciting physiological responses (Ferenci, 1999b, 2001). The last point impacts on the other two, because a better understanding of the physiological state of nutrient-limited bacteria is half the battle in interpreting gene expression studies and predicting the kind of beneﬁcial mutations that occur in evolving populations. Regulatory and mutational changes giving rise to physiological and metabolic adaptations go hand-in-hand. Hence, the ﬁrst aim of the review is to describe the rapid regulatory transitions in one well-studied system (glucose limitation in E. coli chemostat cultures) and then to proceed to discussion of ‘‘steady states’’, mutational processes and what mysterious assemblages constitute a long-term chemostat population.

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2. THE CHEMOSTAT ENVIRONMENT AND ITS APPLICATIONS TO STUDIES OF BACTERIA Not all chemostats are equal. There are numerous chemostat designs adopted in research besides those produced commercially. Two major hurdles in adopting chemostats for the usual microbiology laboratory are cost and space considerations. These costs are particularly problematic when multiple parallel cultures are needed as in most physiological and evolutionary studies; comparisons of gene expression under replicate or manipulated conditions, replicate parallel evolution experiments or testing competitive ﬁtness of several strains all need more than one culture. In the author’s laboratory, simpliﬁed chemostats with less rigorous control shown in Fig. 1 are used, made of modiﬁed Schott bottles. Four such cheap chemostats with reduced working volumes can be run simultaneously in a small area. The level of control of environmental parameters such as pH, dissolved O2, biomass is less than in the most sophisticated commercial equipment, but a well-buffered medium and relatively low biomass levels prevent secondary limitations. The dangers of secondary limitations have been documented (Ihssen and Egli, 2004) so an awareness of the carrying capacity of home-made chemostats is needed. Further miniaturization of chemostat cultures to 10 mL working volumes has been reported recently for metabolic comparisons (Nanchen et al., 2006). Even more exciting developments are on the horizon with the description of continuous ﬂow microﬂuidic devices in which the properties of individual cells can be observed (Balagadde et al., 2005; Groisman et al., 2005; Zhang et al., 2006). Once generally available, these will provide a new impetus for chemostat studies with the added advantage of on-line monitoring of expression and visualization of individual microbes under controlled conditions. Other recent technical advances for particular applications are in the tricky control of oxygen availability in studying transitions between aerobiosis and anaerobiosis (Alexeeva et al., 2002) and in the prevention of wall growth (change of cells from planktonic to bioﬁlm forms adhering to the chemostat vessel) (de Crecy-Lagard et al., 2001; Kashiwagi et al., 2001). Whatever the design of the chemostat used, the experimental choices available to the researcher are: (a)

the choice of organism. The inoculum is of course determined by the interests of the experimenter, but when a choice of strain within a species or between laboratory strains is available, several factors can inﬂuence the course of experiments. The strain variations and polymorphisms that inﬂuence the behaviour in chemostats are considered in Section 4.

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medium inflow controlled by a Watson-Marlow 302S pump air inlet from a Hy-Flo air pump air inlet through medium break

overflow outlet

sampling port Screw-cap 100 ml Schott bottle sealed with silicone

Water bath

80 ml working volume

sparger stirrer

Figure 1 A simple positive-pressure chemostat. Four such chemostats can be concurrently operated on a multi-place stirrer base at the same dilution rate using the multi-channel peristaltic pump and individual air pumps attached to each inlet. The working volume is set by the positioning of the overﬂow outlet. The positive-pressure medium break prevents back-contamination of medium.

(b)

the choice of limiting nutrient. Limitation with carbon source, nitrogen source, phosphorus source, etc. can all give the same growth rate in a chemostat, but the gene expression patterns are very different (Hua et al., 2004). The hunger responses to speciﬁc limitations are considered in Section 3. The cell volumes of E. coli also differ under different forms of limitation (Shehata and Marr, 1971), reinforcing differences

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in growth and physiology. Of course, different limitations also result in very different selection pressures on cultures (Section 9.4) and were reported to give different mutation rates (Novick and Szilard, 1951). the choice of medium and concentration of limiting nutrient. The concentration of nutrient pumped into a chemostat determines the cell density achieved. The density of a culture not only inﬂuences quorum sensing aspects (Liu et al., 2000), but high densities also can bring on secondary limitations such as low oxygen availability or trace metal requirement. Unless high cell yields are absolutely needed, cultures at around 108 cells/mL will avoid most of these secondary effects of high density. the dilution rate for running the chemostat. The dilution rate, D, theoretically sets the growth rate in continuous culture (Monod, 1950). Any dilution rate that does not result in washout of bacteria results in nutrient limitation, but the growth rate and concentration of regulatory molecules (s factors, cAMP, ppGpp) are highly dilution rate dependent (Notley and Ferenci, 1996; Notley-McRobb et al., 1997; Teich et al., 1999). The production of particular enzymes and pattern of gene expression is thus very sensitive to dilution rate (Matin, 1979; Ferenci, 1999b). The regulatory differences brought on by differences in D are discussed in Section 3. Mutation frequency and ﬁtness effects of mutations are also dependent on the dilution rate (Notley-McRobb et al., 2003) and are discussed in Sections 6 and 9. It also follows that no arbitrary dilution rate represents a state ‘‘characteristic’’ of a nutrient-limited condition. sampling times for particular tests of the culture, be they for gene expression or sampling for mutations. The time-course of events in chemostats after inoculation with a batch-cultured inoculum is more complex than simply a transition between two states. The incorrect but widespread belief that chemostats reach a steady state with constant properties is discussed in Section 5. The implications for reproducible sampling are also described in Section 5 and the time-scale of mutational take-overs and evolution experiments is discussed in Section 7.

So what can and cannot be studied with chemostats? Broadly speaking, two research applications were foreseen by the inventors of chemostat culture and these are still the most widely studied. Firstly, chemostats permit bacteria to be grown in one growth phase, with a ﬁxed doubling time and with better control of the environment and cell density. In chemostats, studies of physiological and regulatory phenomena should be less affected by growth transitions, metabolic shifts or quorum sensing. The medium and

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atmosphere can also be controlled to look at the effect of individual environmental changes without modifying doubling times. An excellent recent example of this is the study of the effects of temperature on cellular composition without changes in growth rate (Cotner et al., 2006). Many other such studies are in the literature and Section 3 will include examples in various areas of bacterial physiology. Nevertheless, as noted above, the properties of cells in a nutrient-limited state are a function of D and several other factors noted above, so it is important not to lose sight of the effect of limitation and growth rate on gene expression, metabolism and physiology in the interpretation of chemostat experiments. The second fundamental area of application of chemostats is in the study of mutational adaptation in populations grown continuously under a more or less constant selection pressure. The strong competition between members of chemostat populations under nutrient limitation is itself a strong selection condition where mutants with better ability to utilize nutrients have a ﬁtness advantage (Harder et al., 1977; Dykhuizen and Hartl, 1983). As shown in Sections 6–9, recent data suggest that mutational changes are evident very shortly after the start of chemostat cultivation. The ability to propagate chemostat populations for extended periods (weeks to months) further allows the study of successions of mutations and the gains in new characteristics. Results of the past 10 years also suggest that chemostat populations become highly heterogeneous in remarkably short time periods. This also makes chemostats good systems for the study of the generation of bacterial diversity, as discussed in Section 10. The nature of alternative beneﬁcial mutations permitting ﬁtness under nutrient limitation also reveals much about the redundancy of metabolic and physiological responses to the same environment. The emergence of diversity in chemostats further sees the appearance of intra-population interactions aside from competition, such as cross-feeding (Treves et al., 1998). Section 10 describes how the chemostat environment itself evolves over time, as a consequence of the changes in populations. In reality, there are still very limited examples of studies with chemostats in which the total complexity of changes in gene expression, physiology, metabolism, as well as mutational adaptations are well analysed. As far as I am aware, lactose- and glucose-limited cultures of E. coli are the closest to this breadth of coverage. The lactose studies have been reviewed recently (Watt and Dean, 2000) so much of the subsequent discussion will focus on glucose-limited chemostats. With E. coli, there is also the possibility of comparisons between different types of limitation, permitting identiﬁcation of responses that are nutrient speciﬁc. Most importantly, E. coli is the only bacterium in which the regulatory and mutational adaptations can be

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considered in enough detail as two parts of the same story, namely the acquisition of ﬁtness in a new environment, as considered in the later sections.

3. THE PHYSIOLOGICAL CHANGES IN AN ORGANISM INOCULATED INTO A CHEMOSTAT: THE EXAMPLE OF GLUCOSE-LIMITED ESCHERICHIA COLI So what happens when batch-cultured bacteria are inoculated into a chemostat? The initial transitions are similar in many ways to the set of changes in bacteria approaching stationary phase. The regulatory signals emanating from reduced growth rates (effects on ppGpp and RpoS levels) and reduced availability of carbon source in the case of glucose limitation (effects on cAMP levels) determine the global shifts in gene expression and physiological responses (Ferenci, 1999b). A major difference is that stationary phase bacteria rapidly deplete remaining carbon source on the way to starvation whereas the chemostat environment maintains a low but biologically signiﬁcant level of nutrition and low growth rates. Batchcultured bacteria go through transient peaks of alarmones like cAMP in approaching starvation, but as shown in Fig. 2, chemostat cultures continue to maintain higher levels of cAMP when the bacteria reduce glucose levels to the micromolar range and begin to grow at the rate determined by the dilution rate. It is worth noting that the residual glucose level is different with particular D values; the higher the D and growth rate, the higher the residual glucose (Senn et al., 1994). In turn, this means that cAMP, RpoS and ppGpp levels are also distinct at different dilution rates (see Section 3.3), which is why the choice of D is signiﬁcant in studies of gene expression. Metabolic regulation is also a function of D and the balance of glucose converted to CO2 or acetate is different at low and high dilution rates. The detailed effects of D on transport, regulation, metabolism and physiology are discussed below.

3.1. Transport and Membrane Permeability The residual glucose concentration in a chemostat is a function of D, but is of the order of micromolar or below (Senn et al., 1994). The most obvious physiological response in bacteria to such low nutrient levels is to improve scavenging ability for limiting nutrient (Harder and Dijkhuizen, 1983). Bacterial afﬁnity for nutrients is far from constant. Afﬁnity differs between

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Growth (A=580 nm)

res

idu

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al expre

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al dens

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Figure 2 Transitions in E. coli upon acclimatization to a glucose-limited environment. In a chemostat inoculated with E. coli and established at a dilution rate of 0.3 h1 and 1 mM glucose in the feed medium, most of the major transitions occur within the ﬁrst 8 h. Bacterial density stabilizes at the carrying capacity determined by the limiting medium, glucose levels drop markedly to the mM range, global regulatory circuits involving cAMP are activated and gene expression responsive to nutrient limitation (mgl, mal genes) are highly expressed. The curves are based on data in Notley-McRobb et al. (1997).

oligotrophic bacteria and bacteria like E. coli which needs to adapt to more than one habitat (Button, 1985), but neither is the afﬁnity for limiting nutrient constant in the same bacterium under different environmental conditions (Ferenci, 1999a). The importance of transport to chemostat behaviour was recognized long ago (Hansen and Hubbell, 1980; Harder and Dijkhuizen, 1983) and several regulatory adaptations ensure the expression of multiple sets of genes responsible for high-afﬁnity transport systems under nutrient limitation. Depending on the substrate, the expression of particular cytoplasmic membrane transporters as well as outer membrane porins in Gram-negatives is modiﬁed by nutrient limitation. This is true not just for glucose limitation; for example, phosphate limitation induces PhoE (involved in outer membrane permeability of anions; Overbeeke and Lugtenberg, 1980) and Pst proteins (involved in high-afﬁnity, binding protein-dependent transport of phosphate; Medveczky and Rosenberg, 1971). Typical of many such limitational adaptations, PhoE complements the general porins and the Pst system complements a lower afﬁnity system (Pit; Rosenberg et al., 1977) that bacteria use when phosphate is not at low concentration.

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A common theme in all kinds of nutrient limitation is that outer membrane composition is modiﬁed, including in organisms other than E. coli (Hancock et al., 1982; Sterkenburg et al., 1984). The compositional changes mainly affect the porin proteins responsible for outer membrane permeability (Nikaido and Vaara, 1985). In E. coli, the relative proportion of the less selective porins OmpF and OmpC is controlled by nutrient availability; the larger-channel, more permeable OmpF protein is preferentially expressed at low nutrient levels (sub-micromolar glucose at D ¼ 0.3–0.4 h1), whereas OmpC is more prevalent with excess nutrient (Liu and Ferenci, 1998). Interestingly, OmpC, which imparts reduced permeability and greater levels of antibiotic and detergent resistance on E. coli, is present in preference to OmpF at even lower dilution rates (D ¼ 0.1 h1) involving further reduced residual glucose levels. In other words, closer to starvation, the response of E. coli is towards self-protection and reduced permeability (Ferenci, 1999b). The complex relationship between dilution rate and porin expression involves control by several global transcriptional regulators (Liu and Ferenci, 2001). Another outer membrane adaptation peaks at intermediate dilution rates in glucose-limited chemostats. The level of LamB protein is 20-fold elevated at D ¼ 0.5–0.6 h1. As for OmpF, LamB is also lowered in amounts with nutrient excess or starvation conditions (Death et al., 1993). LamB is a sugar-selective porin (Death et al., 1993) produced from the lamB gene regulated as part of the mal regulon (Boos and Shuman, 1998). LamB and the mal regulon are rapidly induced on transition to glucose limitation (Fig. 2). The porin encoded by lamB improves glucose scavenging at micromolar glucose levels and is induced by the combined elevation of intracellular cAMP and endogenous inducers under glucose limitation (Notley and Ferenci, 1995). The maltotriose produced under glucose limitation is the result of multiple intracellular metabolite pool changes in chemostat-grown E. coli (Tweeddale et al., 1998). The combined effect of OmpF and LamB changes under glucose limitation at intermediate dilution rates is to increase permeability to limiting nutrients. The importance of these regulatory adaptations is apparent from the results that mutants lacking one or both of these porins are less ﬁt in chemostat culture (Death et al., 1993; Liu and Ferenci, 1998). As well as the outer membrane changes described above, periplasmic and cytoplasmic membrane components also change under glucose limitation. In most forms of carbon source limitation, the concentration of cAMP is a major factor in the control of high-afﬁnity cytoplasmic membrane transporters. cAMP levels in carbon-limited chemostats are much higher than in exponential batch culture, as discussed in Section 3.3. The cAMP effect is

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very broad and contributes to the elevated expression of many binding protein-dependent transport systems in E. coli, as shown in proteomic analyses (Wick et al., 2001; Ihssen and Egli, 2005). Transport systems involving periplasmic binding proteins generally have higher substrate afﬁnities than facilitators, symporters or phosphotransferase-type transporters (Furlong, 1986; Ferenci, 1996). The high-afﬁnity systems provide scavenging ability for amino acids, inorganic ions and sugars and allow bacteria to utilize micromolar concentrations of substrates. Speciﬁcally for glucose, limitation results in the induction of the high-afﬁnity MglBAC system, involving the periplasmic glucose–galactose binding protein, which is half-saturated at sub-micromolar glucose levels (Death and Ferenci, 1993). The rapid increase in mgl expression on entry into glucose limitation is shown in Fig. 2. The induction of the Mgl system is in stark contrast to nutrient-excess situations, where glucose down-regulates transporters for other substrates and represses systems like the mal regulon and mglBAC by cAMP-mediated catabolite repression (Death and Ferenci, 1994; see Section 3.3). The usage of binding protein-dependent transport systems entails an afﬁnity advantage but also a cost in terms of energetics. Binding protein-dependent systems are more expensive in ATP input than alternative transporters with lower afﬁnity such as proton-coupled symporters or the phosphotransferase system (Muir et al., 1985; Driessen et al., 1987). As noted in Fig. 3, glucose can be recognized by several alternative cytoplasmic membrane transport systems besides Mgl (Lengeler, 1993), but these are of lesser importance under glucose-limiting conditions (Ferenci, 1996). In particular, the afﬁnity advantage of transport at low substrate concentrations makes binding protein-dependent transport a ﬁtness beneﬁt in chemostats; with limiting glucose, mutants lacking the glucose–galactose binding protein-dependent Mgl system are much less competitive than wild-type bacteria or those lacking the glucose phosphotransferase system (Death and Ferenci, 1993). Despite the cost, active transport also contributes to the ﬁtness of yeast in sugar-limited chemostats compared with transport dependent on facilitated diffusion (Weusthuis et al., 1994). A broad generalization in microbial physiology based on the examples above is that there is an in-built redundancy and energetic cost–beneﬁt trade-off inherent in adapting nutrient transport to different nutrient levels. The importance of transport to growth in chemostats is also supported by theoretical studies (Hansen and Hubbell, 1980; Shoemaker et al., 2003). Additional strong evidence that elevated scavenging ability for limiting nutrients in chemostats is important in ﬁtness comes from analysis of longer term, mutational adaptations that occur in continuous cultures. As noted from the ﬁrst studies onwards, chemostat-evolved mutant bacteria are better

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11 12

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Figure 3 Redundancies in glucose transport and metabolism in E. coli. Through the outer membrane, glucose can pass through porins OmpC (1), OmpF (2) and LamB (3). Glucose is a substrate of the PEP:glucose phosphotransferase system (4), PEP:mannose phosphotransferase system (5), the glucose–galactose binding proteindependent Mgl system (6) and the GalP symporter (7) in the cytoplasmic membrane. When phosphorylated either with the PTS or glucokinase, glucose-6-phosphate can be converted to pyruvate using three alternative reactions sequences (glycolysis (8), the Entner–Doudoroff pathway (9) or the pentose phosphate pathway (10)). Reduced cofactors in glucose oxidation are oxidized by alternative enzymes (NDH-1 and NDH-2 (11 and 12)) which in turn can feed into alternative respiratory chains (cytO (13) or cytD (14)). Pyruvate can be oxidized to CO2 via the tricarboxylic acid cycle (15) or a variation using glyoxylate cycle enzymes (16). Carbon for biosynthesis is taken from both carbohydrate metabolism and pyruvate metabolism (17). Acetate production from pyruvate (18) can also take place depending on environmental conditions. Nutritional and growth rate-dependent factors in chemostats also inﬂuence at least three global regulatory circuits involving cAMP (19), RpoS (20) and ppGpp (21) under glucose-limited conditions. See text for discussion and references.

at uptake of limiting nutrient (Novick and Szilard, 1951), as described in more detail in Sections 8–10.

3.2. Metabolism and Energetics The range of metabolic ﬂexibility of E. coli is even more extensive than seen with transport adaptations. Growth on glucose can involve alternative metabolic pathways and result in non-identical outputs (yields, metabolic products) in different situations. As summarized in Fig. 3, three known pathways can convert hexose phosphate to pyruvate, alternative pathways take pyruvate to CO2; there are alternative respiratory chain components, as well as non-constant fermentation balances or products in E. coli (see references

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in Neidhardt, 1996). The choice between alternatives is highly sensitive to the environment, including the chemostat environment. Recent studies have conﬁrmed that the dilution rate setting under glucose limitation can strongly inﬂuence metabolic processes (Kayser et al., 2005; Nanchen et al., 2006). The proportion of glucose metabolized via the pentose phosphate pathway, the glyoxylate pathway and indeed almost all ﬂuxes were non-linearly dependent on the dilution rate setting (Nanchen et al., 2006). Transcription of metabolic genes is also differentially regulated at different growth rates and also with different forms of limitation (Hua et al., 2004). Consistent with likely shifts in metabolism, metabolite pools are also strongly affected by dilution rate, as shown by metabolome analysis and comparisons between bacteria grown at different dilution rates. Slow dilution rates (at 0.1 h1) induced changes characteristic of stressed bacteria, such as trehalose accumulation (Tweeddale et al., 1998). An unexpected recent ﬁnding was that the glyoxylate cycle, normally associated with acetate incorporation and not with glucose utilization, was expressed in glucose-limited bacteria (Fischer and Sauer, 2003; Maharjan et al., 2005). The switch to the glyoxylate cycle can also be partly observed at the transcriptional level (Franchini and Egli, 2006). There is also increased production of isocitrate lyase (AceA) after prolonged glucose limitation (Wick et al., 2001). There are alternative views on why the glyoxylate cycle may be beneﬁcial under glucose limitation (Wick et al., 2001; Fischer and Sauer, 2003). Also, the presence of the pathway is not universal in glucoselimited cultures; some E. coli laboratory strains such as the W3110 lineage does not use the glyoxylate cycle under glucose limitation (Fischer and Sauer, 2003). This is one of many strain-variable properties discussed in Section 4 that fog a uniﬁed view of even a single species. Respiration rates are also dilution rate dependent (Kayser et al., 2005). Although it is outside the scope of this review to consider the control of the respiration/fermentation balance and the related ﬁeld of aerobiosis/ anerobiosis regulation, these are active ﬁelds of research using chemostats. Amongst the topics addressed, the regulation of acetate production due to NADH/NAD ratio (Vemuri et al., 2006), the role of global transcriptional regulators (Alexeeva et al., 2003) and the effect of alternative electron acceptors (Wang and Gunsalus, 2003) have all been investigated using chemostats. On the energetics side, it was also shown that transcription of ATPase genes decreases moderately with increasing growth rate (Kasimoglu et al., 1996) and the metabolic balance changes due to mutations in ATPase (Noda et al., 2006). Chemostats also allow analysis of a fundamental question in bioenergetics, namely whether there is evolutionary selection for optimal efﬁciency or

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rate in bacterial metabolism. There is an assumption that in general, metabolic efﬁciency is dependent on the trade-off between the rate and yield of energy metabolism (Pfeiffer et al., 2001). Relevant to the above question is that, perhaps paradoxically, a characteristic of bacteria is that microbial growth yields are often 50% less than optimal (Westerhoff et al., 1983). Energetic efﬁciency has been tested in chemostat culture and it was concluded that rate and not efﬁciency is optimized under nutrient limitation (Demattos and Neijssel, 1997). Bacteria in populations may not have the luxury of evolving in an energy-efﬁcient way and in situations where there is kinetic growth competition amongst members of the same culture, perhaps there is more of a selection for rate than yield (Fong et al., 2003). A recent chemostat study has also looked at competition between energetically efﬁcient and inefﬁcient yeasts with temporal and spatial variation (MacLean and Gudelj, 2006). How prolonged nutrient limitation in an unstructured environment with a single resource or niche inﬂuences metabolic strategies shifts the yield-rate trade-off is discussed in Section 11. A long-observed feature of chemostat growth that still cries out for a metabolic explanation is the concept of maintenance energy (Pirt, 1965; Tempest and Neijssel, 1984). Numerous studies have documented the decreasing efﬁciency of biomass production at slow dilution rates and extrapolating energy demands to zero growth rates suggested that bacteria need some energy for maintaining viability at slow or zero growth rates. A comparison of measured maintenance coefﬁcients was assembled recently and also shows inconsistencies between different strains (Nanchen et al., 2006). A molecular explanation of maintenance energy is still lacking however, and it needs testing whether elevated stress metabolism such as the synthesis of trehalose and many stress resistance proteins by starvation is responsible for the maintenance energy effect in starving or close-to-starving cells.

3.3. Stress Regulation and Gene Expression Several features of the chemostat environment contribute to altered gene expression relative to nutrient-excess bacteria and the extent of the transcriptional changes is evident from microarray comparisons (Hua et al., 2004; Franchini and Egli, 2006). Undoubtedly many regulators are involved in the adaptation to the chemostat environment and the sections below focus on the role of obviously important global controllers. This is not to say that the ones considered are totally responsible for chemostat adaptation. Many other transcriptional regulators change signiﬁcantly in concentration in the chemostat environment, but have not yet been fully

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investigated in this context. For example, hns transcription and H-NS protein level, with major inﬂuences on DNA structure and transcription (McLeod and Johnson, 2001), both increase signiﬁcantly with increasing D in glucose- and ammonia-limited chemostats (X. Liu and T. Ferenci, unpublished results) (Liu et al., 2000). In contrast, himA gene transcription and IHF, also involved in the regulation of many genes (Goosen and Van De Putte, 1995), increase greatly with decreasing D (Liu, 2001). The full implications of these changes have not been studied yet and it needs to be kept in mind that the overall patterns of regulation will be even more complicated than is apparent from the examples described below. 3.3.1. Starvation and Stress Signals and RpoS E. coli senses reduced growth rate as an indicator of stress and elevates RpoS protein levels (Hengge-Aronis, 2000) even when nutrient limitation is the sole problem in the environment. Consequently, the general stress response controlled by RpoS, with its hundreds of coregulated genes (Patten et al., 2004; Weber et al., 2005), is highly expressed in chemostats (Notley and Ferenci, 1996). The level of RpoS rises with decreasing dilution rate and especially sharply at slow growth rates near or below D ¼ 0.1 h1. This trend is seen both with glucose- as well as ammonia-limitation, and RpoS levels are if anything even higher under N-limitation (Liu and Ferenci, 2001). Paradoxically, almost none of the genes controlled by RpoS are of physiological beneﬁt in directly overcoming nutrient limitation. Is the seemingly unnecessary induction of RpoS in chemostats at low dilution rates a failure of regulation or an artefact of the chemostat system? Several lines of argument could be used to support the RpoS response in chemostats as a sensible reaction to environmental signals. Chemostats are sometimes criticized as unnatural environments and it may be argued that low levels of nutrient without other stresses is not commonly met in the habitats of bacteria like E. coli. In more natural situations, a mix of simultaneous stresses such as osmotic, pH or oxidative stress is present. An alternative explanation is that even when temporarily present on its own, nutrient limitation is ecologically sensed as a useful signal for hard times to come. Despite some merit in these arguments, there is accumulating evidence that the induction of the general stress response in chemostats may be an indication of a fault in the sensing and processing of stress responses. The observation that rpoS polymorphisms and rpoS null mutants are common in natural populations does suggest that E. coli and Salmonella in nature can beneﬁt from losing RpoS function (Ferenci, 2003). This indicates that expression of the general stress response is sometimes a burden even in

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natural habitats, as it is in chemostat culture. Consistent with this view, rpoS mutants are selected in continuous culture under glucose limitation to avoid the RpoS cost (Notley-McRobb et al., 2002a). Recent studies suggest that mutational loss of RpoS is a consequence of the trade-off between stress responses and nutritional responses to hunger in general (Ferenci, 2005). This balancing of stress/nutrition capabilities occurs not just in chemostats, but also inﬂuences growth with poorer carbon sources like acetate that occur in natural habitats (King et al., 2004). The sigma factor-dependent competition in transcription documented in other studies (Farewell et al., 1998; Nystrom, 2004) imposes two alternative and mutually exclusive choices on E. coli, namely either to optimize stress responses based on RpoS, or to utilize nutrients dependent on RpoD (Ferenci, 2005). Because of s factor competition, differences in RpoS levels affect both stress responses (controlled by RpoS) as well as vegetative gene expression due to RpoD (or s70) (Jishage et al., 1996; Jishage and Ishihama, 1997; Nystrom, 2004). The latter controls genes involved in nutrient utilization so ﬁtness in chemostats is inversely proportional to RpoS protein level within a strain (King et al., 2004). Hence many polymorphisms affecting stress response and nutritional properties are seen within the species E. coli. As noted in Section 6, polymorphisms and strain variation in RpoS protein levels found in natural isolates can strongly inﬂuence regulation and ﬁtness properties in a chemostat culture. 3.3.2. Nutritional Status and cAMP The extracellular levels of all essential nutrients are signals sensed by bacteria like E. coli. Depleting levels of phosphorus, nitrogen or sulphur sources have their own sensing and response mechanisms (Wanner, 1993; Ikeda et al., 1996; Gyaneshwar et al., 2005). In considering glucose limitation, here too, major shifts in gene expression occur with reduced levels of extracellular carbon source (Hua et al., 2004; Franchini and Egli, 2006). Indeed, the scale of the changes in switching from glucose-excess to glucose-limited environments is extensive because many of the changes are controlled by cAMP and Crp protein, which represent truly ‘‘global’’ regulators of E. coli (Saier, 1996). cAMP and Crp together control expression of about 200 operons and many more genes (Gosset et al., 2004; Zheng et al., 2004). As demonstrated in chemostats, cAMP levels (both intracellular and excreted) are much elevated under glucose but not nitrogen limitation (Harman and Botsford, 1979; Matin and Matin, 1982; Notley-McRobb et al., 1997). The control of cAMP levels occurs through regulation of adenylate cyclase, which is in turn is regulated by glucose availability

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through components of the PEP:glucose phosphotransferase system and Crp (Takahashi et al., 1998; Park et al., 2006). Reduced extracellular glucose levels trigger an increase in cAMP synthesis (Fig. 2). After a major jump in cAMP concentration when glucose drops below about 0.3 mM (NotleyMcRobb et al., 1997), cAMP levels continue to rise more moderately with decreasing dilution rates and further reduction of extracellular glucose levels. The precise shape of the dilution rate–cAMP response curve is strainspeciﬁc however, particularly at low dilution rates, due to unidentiﬁed variations even between E. coli K-12 strains (Notley-McRobb et al., 1997). Relevant to the chemostat environment, the physiological response to high cAMP levels is the much-elevated expression of a large number of nutrient transport proteins (Section 3.2; Wick et al., 2001; Hua et al., 2004; Franchini and Egli, 2006). These include many binding protein-dependent transporters besides the mglBAC and the mal-lamB gene products speciﬁcally useful in scavenging glucose. Extrapolating to natural environments, the expression of systems speciﬁc for other carbon sources has been interpreted as a response that can broaden the possibilities and prepare E. coli to utilize any available substrate in a nutrient-limiting environment (Ihssen and Egli, 2005).

3.3.3. Growth Rate and ppGpp The growth rate of E. coli has a major effect on the rate of macromolecular synthesis (Maaloe and Kjeldgaard, 1966). The ribosomal content of E. coli in glucose-limited chemostats rises linearly with dilution rate in the measured interval between D ¼ 0.2 and 0.7 h1 (Yun et al., 1996). The concentration of the alarmone ppGpp, produced by RelA and SpoT, controls many of the growth rate-related changes in bacteria (Cashel et al., 1996) although changes to nucleotide concentrations (Schneider and Gourse, 2003) and DNA supercoiling may also contribute (Travers and Muskhelishvili, 2005). The intracellular concentration of ppGpp increases with decreasing dilution rate in glucose-limited chemostats, particularly sharply at Do0.1 h1 (Teich et al., 1999). The multitude of ppGpp-dependent phenotypes in bacteria has been reviewed recently (Braeken et al., 2006). Signiﬁcant to regulation in chemostats are the interaction of ppGpp with RNA polymerase (Artsimovitch et al., 2004) and the role of ppGpp in diverting transcription to promoters differentially regulated by sigma factors (Jishage et al., 2002). Both ppGpp and RpoS are elevated and function synergistically to provide a general repositioning of transcription in stressed cells (Nystrom, 2004).

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Table 1 Examples of physiological responses to environmental stresses studied with chemostat cultures Organism E. coli E. coli E. coli E. coli E. coli Bacillus cereus Streptococcus mutans Streptococcus mutans Xanthomonas campestris Lactococcus lactis

Stress Oxidative stress: paraquat effects on metabolomes Nitric oxide stress: transcriptome Water activity UV, sunlight killing effects Heavy metal effects (Zn): transcriptome Acid tolerance: effects of growth rate Acid effects on bioﬁlms Acid effect on membrane lipids Acid stress on enzyme activities Acid stress and energetics

Reference Tweeddale et al. (1999) Flatley et al. (2005) Roller and Anagnostopoulos (1982) Berney et al. (2006) Lee et al. (2005) Thomassin et al. (2006) Li et al. (2001) Quivey et al. (2000) Esgalhado and Roseiro (1998) O’Sullivan and Condon (1999)

3.3.4. Other Studies of Regulation in Chemostats Chemostats can also be used to impose controlled changes to the environment and to study transcriptional and physiological responses aside from the general stress response. As summarized in Table 1, virtually any stress can be imposed on bacterial chemostat cultures by manipulating culture conditions or modiﬁcations such as illumination of the culture. As also included in this non-exhaustive list, Table 1 shows environmental conditions such as the effect of low pH that can be applied to studies of many different types of bacteria.

3.4. Antibiotic Sensitivity A number of chemostat studies have reached the conclusion that growth rate is an important factor in antibiotic susceptibility and bacteria growing at slow dilution rates are not as sensitive as fast-growing organisms (Brown et al., 1990 and references therein). This generalization does not hold for all forms of limitation, and interestingly different limitations affected

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chlorhexidine susceptibility in opposite ways; susceptibility increased with growth rate for carbon-limited chemostats but decreased with phosphorus limitation (Brown et al., 1990). In some cases, as with polymyxin sensitivity, the difference in susceptibility was ascribed to changes in lipopolysaccharide content at different dilution rates (Wright and Gilbert, 1987). Undoubtedly, changes in porin content from OmpF to OmpC described in Section 3.1 and responses associated with the general stress response in Section 3.3 also contribute to altered access and effects of antibiotics at slow growth rates. There is also some evidence that antibiotic resistance through efﬂux is also a function of growth rate. The expression of the E. coli acrAB efﬂux system was increased at low D and was higher under glucose limitation (Rand et al., 2002), possibly contributing to resistance at slow growth rates. Furthermore, decreased susceptibility to ciproﬂoxacin and tetracycline at slow dilution rates was associated with a higher proportion of persister cells at low D (Sufya et al., 2003). Persister cells, that are not killed by antibiotics as readily as the rest of a population, have been recently implicated in the emergence of treatment-resistant organisms in clinical settings (Balaban et al., 2004; Kussell et al., 2005). Chemostats potentially offer an excellent system to study the factors governing the generation and population distribution of persisters in much more detail.

3.5. Quorum Sensing Population density and the circulation of extracellular signal molecules (autoinducers) are important factors in bacterial behaviour (Reading and Sperandio, 2006). Several classes of autoinducer molecules are produced by bacteria and regulate multiple physiological responses (Camilli and Bassler, 2006). Chemostats offer an ideal way of studying the production and effects of autoinducers because population density in chemostats is directly controlled by the medium composition (Section 2). Early studies of autoinduction of bioluminescence indeed used chemostats to demonstrate density effects (Rosson and Nealson, 1981). The effect of growth rates at constant densities can also be studied and the production of one autoinducer, AI-2, has been studied in this way (DeLisa et al., 2001). AI-2 production is strongly elevated at high growth rates and was subject to stress-induced perturbations (DeLisa et al., 2001). Other physiological effects of density on gene expression and metabolism can also be measured in chemostats by changing the population density (Liu et al., 2000). For example, the expression of porin genes is controlled by E. coli population characteristics and ompF transcription is strongly repressed

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at high density (Liu et al., 2000). The regulation of the stress regulator RpoS by density is more controversial, and different results were obtained in different laboratories (Liu et al., 2000; Ihssen and Egli, 2004). It remains to be established whether a secondary limitation or strain differences (see Section 4) explain the different population density results obtained. Quorum sensing is an important factor in bioﬁlm formation by different types of bacteria (Parsek and Greenberg, 2005). To remove variables from bioﬁlm formation, chemostats have been modiﬁed to study the colonization of surfaces like glass rods inserted into continuous cultures (Keevil, 2001; Li et al., 2001). Of course, the bacteria in bioﬁlms are not subject to the same thoroughly mixed local environment that planktonic bacteria enjoy, but at least some environmental aspects can be controlled in this way. Indeed recent evidence suggests the bioﬁlm bacteria in a continuous culture grow faster than planktonic bacteria and can survive washout at fast dilution rates (Bester et al., 2005). The effect of dilution rate on bioﬁlm buildup and the effect of antibiotics can be effectively studied in such systems (Molin et al., 1982; Anwar et al., 1992).

4. VARIATIONS IN RESPONSES WITHIN AND BETWEEN SPECIES The choice of organism in chemostat studies is of course determined by the aims of the experiment, but several strain characteristics have been shown to affect behaviour in continuous culture. A comparison of more than 70 isolates of E. coli taken straight from natural habitats resulted in an approximately threefold range of maximal growth rates in glucose minimal medium (Mikkola and Kurland, 1991). Chemostat cultivation resulted in the slower isolates rapidly adapting to the laboratory environment with altered growth kinetics. Ribosomal function was one property that changes after chemostat culture; translational kinetics is highly variable in natural isolates but converges on that of laboratory K-12 strains after growth in glucose-limited chemostats (Mikkola and Kurland, 1992; Rang et al., 1997). The maximal growth rate of such isolates changed within 200 generations and other more subtle changes possibly occurred even earlier. Hence care is needed in studying the growth and ecological behaviour of natural isolates in chemostats to avoid pitfalls due to acclimatization in laboratory culture. Even when strains well adapted to the laboratory, such as E. coli K-12, are available, at least three strain characteristics can change the course of

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chemostat experiments. Firstly, the propensity for wall growth (bioﬁlm formation) and the rapidity of appearance of adherent mutants is species- and strain-variable (Larsen and Dimmick, 1964). The formation of a bioﬁlm in a culture vessel alters the environment of a chemostat sub-population and can locally affect gene expression and conditions for selecting further mutations. Fortuitously rather than by design, the strain used in my laboratory for long-term evolution experiments does not appear to readily mutate to wall growth. Other strains of E. coli readily produce curli adhesins (Vidal et al., 1998; Prigent-Combaret et al., 2000) and rapidly change the characteristics of the chemostat culture away from a homogeneous environment. The genetic basis of the differences in adhesiveness between strains is not always known, so some empirical testing is advisable. There could well be a range of genetic pathogenicity elements that affect adhesion and it may be relevant that pathogenic O157:H7 bacteria show high adhesion in chemostat culture (James and Keevil, 1999). A second source of variation in chemostat behaviour between strains is subtle but with signiﬁcant consequences. As introduced in Section 3.3, strain differences affecting the RpoS (or sS) s factor can inﬂuence gene expression, stress responses, competitive behaviour and the selection of beneﬁcial mutations in long-term cultures (Ferenci, 2005). Such global differences are common and many surveyed strains within the species E. coli contain characteristic and distinct levels of RpoS protein even when grown under identical conditions (King et al., 2004). As discussed in Section 7, the level of RpoS inﬂuences which mutations are enriched and the evolutionary pathway with prolonged continuous culture. The third area of strain variation is in metabolism, as introduced in Section 3.2. Even in the well-studied utilization of glucose by E. coli, many discrepancies are present in the literature. To give one example of differences due to metabolic strain variation, acetate is produced from glucose in most E. coli strains under aerobic conditions when glucose is in excess (or in chemostats at high dilution rates; el-Mansi and Holms, 1989). Still, for unknown reasons, the amount of glucose converted to acetate by different E. coli lab strains is highly uneven (Luli and Strohl, 1990). A more comprehensive view of metabolic variation is available from metabolome analysis and comparative proﬁling of strains across the species E. coli. Most strikingly, the metabolome proﬁles of separate E. coli isolates are highly distinct and less than 30% of metabolite pools are conserved in all strains (Maharjan and Ferenci, 2005). There were even metabolome differences between laboratory strains of E. coli K-12. Metabolomic divergence into A, B1, B2 and D subgroups resembles the divergence seen in taxonomic trees within the species derived from gene sequence comparisons (Pupo et al.,

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1997; Maharjan and Ferenci, 2005). Hence, chemostat studies of metabolism with a particular strain need to be interpreted in this wider context. Finally and more broadly problematic in microbiological studies, the possibility of non-constancy of properties does exist even with the same labelled strain of the same species (O’Keefe et al., 2006). Differences in storage conditions, errors in handling and subculturing can readily create divergence in stocks (Johnson et al., 2001) and hence differences in strain characteristics. A good example came from comparison of the variations in stocks of the E. coli W3110 strain used in different laboratories in Japan (Jishage and Ishihama, 1997).

5. STEADY STATE OR CONSTANT CHANGE IN A CHEMOSTAT POPULATION? For applications needing stable and reproducible levels of gene expression, an important consideration is the timing of samples after chemostats are inoculated. When is the most reproducible time for stable comparisons? As shown in Fig. 2, within 24 h of inoculation, bacterial cell densities approach a constant value and the initial hunger responses also plateau. Of course, this is true only if the inoculum does not contain high levels of limiting nutrient which need to be depleted or washed out to attain limitation. With high volumes of inocula, an overshoot of bacterial density is observed before limitation sets in. An attainment of a constant bacterial density or yield is not however a clear-cut indicator of a steady state. As shown in independent studies with different organisms, the concentration of limiting nutrient continues to drop for many generations in chemostats even after a steady biomass level is reached, indicating a lack of a genuine steady state (Rutgers et al., 1987; Kovarova-Kovar and Egli, 1998). Nevertheless, classic chemostat texts recommend, and recent studies frequently use, 5–8 vol. of medium passing through a chemostat vessel before a ‘‘steady state’’ is reached (Hua et al., 2004; Nanchen et al., 2006). In one study with Saccharomyces cerevisiae, even 10–14 vol. changes were used to ‘‘avoid strain adaptation’’ for expression studies (Boer et al., 2003). It is highly unlikely that a steady residual nutrient level is reached in most of these studies. Moreover, at D ¼ 0.1 h1, or 0.1 vol./h, 5 vol. is equivalent to 50 h. With E. coli at least, this length of time is sufﬁcient to see almost a complete replacement of a population by mutants and is even sufﬁcient to see the initiation of a second round of mutational sweeps (Notley-McRobb et al., 2003; Section 7).

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So what is more problematic, the lack of attainment of a steady state or the danger of mutational changes? My personal view is that the latter is more likely to lead to misleading results in measuring expression or other physiological properties of chemostat-inoculated bacteria. Sampling for expression studies is therefore advisable soon after the initial rapid depletion of limiting nutrient and the attainment of a constant cell density. As shown in Fig. 2, the time-course of expression changes in genes susceptible to glucose limitation reach a fairly stable, elevated level within 24 h. The threshold concentration for glucose limitation in eliciting the hunger response is below approximately 0.3 mM, which occurs (with E. coli and glucose limitation) within the ﬁrst 6–10 h (Notley-McRobb et al., 1997). The entrenched idea of steady states and chemostats as constant environments is probably due to two factors. The ﬁrst is the easy but misleading description of chemostats in terms of simple equations. Starting with Monod but continuing to this day in many text-books, a basic assumption is that bacteria have ﬁxed properties (Pirt, 1975; Panikov, 1995) with deﬁnable enzyme-like characteristics. This assumption is untenable except when a gross simpliﬁcation is sufﬁcient. The maximal growth rate, or mmax, of bacteria in chemostats changes with prolonged culture (Mikkola and Kurland, 1992) and the afﬁnity, or Ks is also non-constant. Indeed, this has been demonstrated directly as a change of Ks at different dilution rates (Wick et al., 2002). The molecular explanation is probably distinct for different limiting substrates, but for E. coli/glucose, changes in apparent Ks are due to shifting expression of transporter genes at different dilution rates (Ferenci, 1996, 2001). These molecular differences probably explain the deviations from simple Monod saturation kinetics noted in numerous studies (e.g. Shehata and Marr, 1971). The second contributing factor in historically viewing chemostat populations as being in steady state was the unrecognized rapidity and extent of mutational changes. In the absence of gross morphological differences and lack of techniques for the analysis of genetic changes in populations, it was easy to overlook the impact of mutations on chemostat behaviour. Given the mutational adaptations considered below, there is no truly safe extended phase in which cultures exhibit constant properties.

6. MUTATION RATES AND MUTATORS IN CHEMOSTAT POPULATIONS In all life-forms, evolution is dependent on the availability of mutations in a population. Mutation supply and the exploration of a wide range of

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mutational variations is dependent on both the population size and the mutation rates within populations (de Visser et al., 1999). Most chemostat populations involve high numbers (usually1010–1012) of bacteria, so the generation of a large number of spontaneous mutations is inevitable. Given that loss-of-function mutations occur in 1 in 105 or 106 newly divided cells, and even rarer gain-of-function mutations occur in 1 in 109–1010 bacteria, the possibility of beneﬁcial mutations leading to organisms ﬁtter than the inoculated strain is ever-present. The ﬁrst chemostat studies already demonstrated that in tryptophan-limited chemostats, E. coli mutants with better tryptophan transport take over the population although the mutations were not characterized (Novick and Szilard, 1950b). Similar trends in transport improvement occur in glucose-limited populations of E. coli or yeast (Dykhuizen and Hartl, 1981; Helling et al., 1987; Dunham et al., 2002) and probably all other chemostat populations. Detailed studies with E. coli populations of different sizes indicated that mutation availability is not a problem with commonly used (41010) populations, but does affect the rate and shape of mutational take-overs in small populations (Wick et al., 2002). The effect of limiting population size was demonstrated in the progression of glucose-limited E. coli cultures towards better glucose scavenging ability. By measuring residual glucose levels in chemostats over 500 h (Wick et al., 2002), a small population (107 bacteria) was found to exhibit a step-wise improvement in afﬁnity whereas a continuum of increased afﬁnities was observed when 1011 cells were evolving. Evolution towards low residual glucose levels was also more reproducible between parallel cultures with large populations, consistent with the expectation that the availability of beneﬁcial mutations was more subject to chance in small populations. The constancy of mutation rates was not experimentally measured in Wick et al. (2002), but several studies measured mutation rates in chemostats (Novick and Szilard, 1951; Kubitschek and Bendigkeit, 1964). Mutation rates vary with the type of limiting nutrient (Novick and Szilard, 1950b). Different dilution rates also change mutation rates, with low dilution rates giving a 30-fold increase in the rate of accumulation of mutations with no selective advantage (e.g. resistance to phage T5 causes no beneﬁt or loss of ﬁtness under glucose limitation; Notley-McRobb et al., 2003). The mutational differences can be rationalized as a manifestation of the broader phenomenon of stress-induced mutagenesis, which is more often studied in stationary phase cultures and in colonies on plates (Bjedov et al., 2003). It should be emphasized that there has been no detailed study on the mechanism of mutation rate regulation in chemostats, although continuous cultures would be ideal for controlling the level of stress and analysis of the cellular systems that regulate mutation rates (Tenaillon et al., 2004; Foster,

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2005). For example, the study of the expression of the SOS response and error-prone DNA polymerases would add greatly to our understanding of mutation supply under nutrient limitation and the regulation of stressinduced mutagenesis (Tippin et al., 2004). Mutation rates in chemostats could also increase if DNA repair processes such as mismatch repair were less highly expressed under nutrient stress. This kind of regulation has been proposed for stationary phase changes in mutation rates (Feng et al., 1996). There has been one study on the effect of chemostat culture on the regulation of DNA repair involving mutY (Notley-McRobb et al., 2002b), but chemostat culture could also add much to understanding the contribution of other pathways of DNA repair in controlling mutation supply. For example, mutS expression is affected in stationary phase in many E. coli strains (Li et al., 2003), so is also likely to be altered in chemostat culture. The availability of mutations is also strongly inﬂuenced by the occurrence of mutator mutations in natural and chemostat populations of E. coli. Amongst environmental and especially clinical isolates of E. coli, individual strains are present with orders of magnitude higher mutation rates than seen in ‘‘normal’’ E. coli (Leclerc et al., 1996). Actually, surveys of mutation frequencies in large strain collections show that even ‘‘normal’’ mutation rates are subject to a wide range of variation (Bjedov et al., 2003) as well as are subject to different levels of regulation (Li et al., 2003). The isolates with greatly elevated mutation rates often contain mutator mutations, such as in mutS with over 100-fold increase in mutation rates (Li et al., 2003) or other defects in DNA repair (Cox, 1976; Denamur and Matic, 2006). Mutators were also present in some long-term experimental lineages (Sniegowski et al., 1997) as well as in signiﬁcant numbers in 6 of 11 chemostat populations under prolonged glucose limitation (Notley-McRobb et al., 2002c). As shown in pioneering studies by Cox and others, mutator strains are associated with a ﬁtness advantage in chemostat populations and outcompete bacteria with normal mutation rates (Gibson et al., 1970; Cox and Gibson, 1974; Trobner and Piechocki, 1984). The ﬁtness beneﬁt comes not from the mutator mutation itself but the ability to hitchhike with a greater number of beneﬁcial mutations that arise in the mutator sub-population (Miller et al., 2000; Tenaillon et al., 2001; Denamur and Matic, 2006). This process of secondary selection has been demonstrated in evolving chemostat populations and both the mutator mutation and the linked beneﬁcial mutation identiﬁed (Notley-McRobb and Ferenci, 2000a; Notley-McRobb et al., 2002b, c). In Fig. 4, the mutS mutation hitchhiked with beneﬁcial mgl mutations. For reasons not yet explained, mutY and mutS mutations were the predominant mutators and occurred in over 30% abundance in the chemostat populations

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>99% mgl mutS+ <1% mgl mutS

66% mgl mutS+ 33% mgl mutS

% of mutants in population

100

2

90 80 70

mgl mutS mlc

1

60 50 40 30 20 10 0 0

100

200

300

400

Time (h) Figure 4 Changes in chemostat populations resulting from mutations in mgl, mutS and mlc. Glucose-limited conditions in chemostats at dilution rate 0.3 h1 were sampled to test individual isolates for three characteristics at different time points. For details of the mgl, mutS and mlc assays, see Notley-McRobb and Ferenci (2000a).

studied (Notley-McRobb et al., 2002b). The appearance and even predominance of mutators was followed by eventual elimination of mutators through the appearance and spread of beneﬁcial mutations in the non-mutator subpopulation (Notley-McRobb et al., 2002c). The elimination of mutators was not predicted in earlier models of mutator spread in populations (Tenaillon et al., 1999), probably because the inherent heterogeneity of evolving chemostat populations was not recognized at this time (see Section 10). Nevertheless, the transient enrichment of mutators has been demonstrated in environments where mutational adaptation is paramount, and which explains the natural occurrence of mutators in hostile surroundings and amongst antibiotic-resistant bacteria (Oliver et al., 2000; Denamur and Matic, 2006).

7. MUTATIONAL TAKEOVERS AND POPULATION CHANGES Fitness increases resulting from natural selection require mutations that confer an advantage in a particular environment. As noted already by

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Novick and Szilard (1950b), chemostats provide a controlled environment in which it should be possible to follow how mutations spread in populations. Are the same mutations always present in an evolving chemostat population? Do they sweep in a particular sequence? What are beneﬁcial mutations in a molecular and physiological context? How rapidly do mutations arise and spread? How does the course of evolution change with small variations of growth rate or environment? Do chemostats result in a single winner clone? Or diversity? All these questions and others can be usefully tackled with chemostats, and the sections below report on progress on these topics. Before discussing these however, a broader perspective needs introducing, because many of the neo-Darwinian ideas on mutation-selection came from outside the bacterial world and because a series of classic studies on the spread of mutations in bacterial populations form the background to subsequent discussions. An excellent review on the early work in this area is by Dykhuizen (1990). Although experimental evolution studies have a long history (references in Kassen and Rainey, 2004), the ﬁrst inﬂuential studies of bacterial population changes in experimental cultures was by Ryan (Atwood et al., 1951) using serial transfer. The evidence that mutations swept through populations was indirect and relied on following the proportion of easily followed mutations (with neutral ﬁtness effects) in populations. The ﬂuctuation in the proportion of bacteria with spontaneously arising phage T5 resistance mutations was observed in several independent chemostat lines (Novick and Szilard, 1950b; Kubitschek, 1970; Helling et al., 1987). The occasional transitions in the proportions of such neutral mutations, called periodic selection events, were classically attributed to the displacement of the original population by a mutant with a growth advantage; the reduction in the proportion of T5-resistant mutants during such sweeps was explained by the likelihood that the beneﬁcial mutation arises in the predominant T5-sensitive members of the population. The kinetics of such mutational sweeps was formalized by Moser (1957), who assumed the sweeping mutant is of a single ‘‘predominant’’ form and other mutants are a minor proportion of the population. In this model, mutations swept to ﬁxation and new rounds of mutations arose successively from the predominant type. A discussion of the consequences of this model are presented in Kubitschek (1974) and Dykhuizen (1990). But in the absence of identiﬁed beneﬁcial mutations, the real behaviour of populations could not be easily analysed. Identiﬁed changes in glucose-limited populations have now permitted testing of how mutations really sweep populations and Section 8 will deal with one example in detail. A second historically important aspect of mutational sweeps is the question of time-scale and how mutations and ﬁtness effects of mutations

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distribute themselves over time. There has been a general assumption that experimental evolution studies require hundreds or thousands of generations. A well-known example of long-term studies is the Lenski experiments on serially subcultured E. coli (Lenski et al., 1991), now being analysed to 20,000 generations (Woods et al., 2006). In chemostats, glucose-limited E. coli has been grown for over 1800 generations at D ¼ 0.2 h1 (Helling et al., 1987). In none of these long-term cultures is there evidence of a bacterium reaching a constant, optimal state and population changes were still occurring. In replicate serial-dilution cultures, the ﬁtness achieved by different populations after 10,000 generations was not identical. The greatest step-wise increases in ﬁtness accrued in the ﬁrst 2000 generations, and ﬁtness increases slowed over subsequent generations (Lenski and Travisano, 1994; Elena and Lenski, 2003). In chemostats also, the rapid sweeps with large ﬁtness effects occur early in the culture period (within the ﬁrst two weeks at D ¼ 0.1 h1; Maharjan et al., 2006). Beyond that, incomplete, slower sweeps with weaker beneﬁts are apparent after three weeks and beyond (see Fig. 1 in Maharjan et al., 2006). A crucial question that can be answered with chemostat cultures is whether a bacterial population stays uniform after many generations in the same environment. Does a population acquiring a succession of beneﬁcial mutations become a super-ﬁt clone or does it diverge in the selection environment? This question is important in distinguishing between two historically inﬂuential models of how mutations contribute to ﬁtness. As discussed by Dykhuizen in a bacterial context, the end result distinguishes between the Fischer and Wright views of evolution, towards either single or multiple ﬁtness solutions in evolving populations (Dykhuizen, 1990). The experimental evidence is beginning to favour the divergence model. Coexistence of distinct clones has been found in long-term experimental evolution studies (Rosenzweig et al., 1994; Elena and Lenski, 1997; Rozen and Lenski, 2000), although some of these coexistences were explained by the formation of cross-feeding consortia partitioning metabolism in the population (Treves et al., 1998; Porcher et al., 2001). Nevertheless, population heterogeneity was evident in various other studies of experimental evolution in E. coli (Finkel and Kolter, 1999; Papadopoulos et al., 1999; Notley-McRobb and Ferenci, 1999a; Schneider et al., 2000; Friesen et al., 2004). In chemostats, the level of divergence is much greater than previously expected (Maharjan et al., 2006). We need to therefore consider how mutational events occur and contribute to this level of divergence in chemostat populations, as discussed in Section 10.

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8. A MUTATIONAL SWEEP IN DETAIL: THE PHYSIOLOGICAL ADVANTAGE AND SPREAD OF MGL MUTATIONS IN GLUCOSE-LIMITED E. COLI In every aerobic glucose-limited culture investigated, regulatory mutations affecting mgl expression spread through the population (Notley-McRobb and Ferenci, 1999a; Notley-McRobb et al., 2003). Much-elevated levels of MglB protein were found in proteomics studies of long-term glucose-limited chemostat cultures (Wick et al., 2001). The mgl mutations are also genetically well characterized. The strong beneﬁt of mglD (also called galS; Weickert and Adhya, 1993) inactivation or changes in the mgl operator results in constitutive mglBAC expression and consequent improvement of glucose uptake through the cytoplasmic membrane (Death and Ferenci, 1993). The Mgl system also catalyses galactose uptake, so it is possible to follow Mgl expression changes speciﬁcally using galactose transport assays. As shown in Fig. 4, the proportion of bacteria with Mgl changes rapidly increases over a very short period in a glucose-limited chemostat, due to the strong ﬁtness advantage of mgl-constitutive bacteria. This mutational sweep reproducibly appeared within four days or 15 generations at slow dilution rates (0.1 h1) but after 13–23 days or over 260 generations at fast dilution rates (0.6 h1) in the same aerobic medium (Notley-McRobb et al., 2003). Several aspects of mgl mutation spread in chemostat populations are instructive of how the interaction between cellular physiology, ﬁtness effects and the environment shape a mutational sweep. The unexpected complexity of sweeps is illustrated by the events in the population shown in Fig. 4. The ancestral population was not simply replaced by a new population with a mutated mgl allele. Three concurrent events were taking place at the ﬁrst point arrowed in Fig. 4. Firstly, multiple alleles at the mgl locus were responsible for the sweep, with up to 13 different sequence changes detected in the same culture (Notley-McRobb and Ferenci, 2000a). Secondly, some mgl mutations occurred in cells that also acquired a mutS mutation (or vice versa), but others were mutS+. Thirdly, between the ﬁrst and second arrows, the mgl mutations associated with mutS were largely eliminated and the mgl mutants that won out were essentially all mutS+. To explain this, it is necessary to postulate yet another beneﬁcial mutation, which provides additional ﬁtness to the mgl mutS+ bacteria. This third mutation did not arise in mgl mutS types, leading to their elimination. The mutS mutation, as noted in Section 6, does not have a negative ﬁtness effect that could explain the rapid purging. Hence over two days or so, what appears to look like a smooth takeover by mgl bacteria in Fig. 4 is actually a complex process, with mutations in at least three genes changing frequencies in the population.

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Yet another lesson from Fig. 4 is on the constancy of growth rates in chemostat populations. As is evident from the speed of the sweeps, subpopulations with beneﬁcial mutations are growing considerably faster and growing through many more generations than the ancestral bacteria over the same period. The dilution rate clearly does not equate to speciﬁc growth rate for sweeping mutant populations. This makes it misleading to plot generations as a time-axis as is often done for long-term chemostat studies. Isolates with multiple beneﬁcial mutations will have undertaken many more generations than can be calculated from chemostat theory. The multiplicity of mutant alleles involved in mgl sweeps in chemostats is not conﬁned to mutations in this gene. The spread of rpoS mutations (Notley-McRobb et al., 2002a), malT mutations (Notley-McRobb and Ferenci, 1999b) as well as the mlc mutations shown in Fig. 4 are all due to concurrent increases in the frequencies of different alleles. In the mlc sweep, 15 different mlc sequence changes appeared during the sweep (Notley-McRobb and Ferenci, 2000a). Although the physiological effect on phenotype is similar and all mlc mutations in chemostats result in a loss of function of the regulator Mlc, the genetic effect is to partition the population into bacteria with distinct chromosomal histories. The hitchhiking of other mutations in separate chromosomes (such as mutS in Fig. 4) results in the seeds of genetic heterogeneity that arises with prolonged chemostat culture. From the gradients of the lines in Fig. 4, it is evident that mgl mutations exhibit a faster rate of population take-over than mlc mutations. This is of course because the ﬁtness increase imparted by the mutations is not equivalent. The ﬁtness effects can themselves be compared in chemostats by competing strains carrying a mutation against either ancestor or a strain in which the mutated gene has been replaced by a wild-type allele (see examples in Dean, 1989; Notley-McRobb et al., 2003; King et al., 2004). The greatest ﬁtness increases are reproducibly associated with the earliest recognized sweeps and involve mgl and rpoS mutations in glucose-limited chemostats of E. coli (Notley-McRobb et al., 2003). The observed gradients of individual sweeps decreases after about two weeks of culture and population transitions do not result in a complete take-over (Maharjan et al., 2006). In general, this pattern of mutational change in chemostats ﬁts with the view that the biggest ﬁtness increases occur early in evolving populations (Lenski and Travisano, 1994). The mgl mutations evident in chemostat populations are a mixture of mglD mutations inactivating the repressor of the mglBAC genes (Weickert et al., 1991) and operator mutations upstream of mglBAC (Notley-McRobb and Ferenci, 1999a). Initially, mglD mutations predominate, but after several weeks of chemostat culture, the ratio of mglO to mglD mutations

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increases (Notley-McRobb and Ferenci, 1999a; Notley-McRobb et al., 2003). The trend towards mglO-containing bacteria is explained by a slight difference in Mgl expression. mglO mutants have a measurably higher transport activity at low sugar concentrations than mglD bacteria. The likely explanation for the increased ﬁtness of mglO mutations is that operator mutations escape repression not only by MglD, but also other repressors in the LacI/GalR family, that recognize operators resembling the mglO sequence (Notley-McRobb and Ferenci, 1999a). Another curious aspect of the mgl mutations in chemostats was the strong bias in the type and position of DNA changes found by sequencing a large number of mutations. Particularly in the operator, speciﬁc hotspots for transition mutations were noted at symmetrical positions on opposite strands of the operator sequence (Notley-McRobb and Ferenci, 1999a). A possible explanation of the targets is that the mutations arise at positions made sensitive to chemical change under stress (Wright, 2004). Wright proposed that stretches of DNA with the potential to form stemloop structures of high stability, such as at mglO, lead to instability of the exposed bases in this region. The unpaired bases are particularly susceptible to change under stress, such as at slow growth rates in chemostats (Wright, 2004). This kind of mechanism may explain the bias in mgl mutations, as one of a number of adaptive mutational mechanisms in stressed cells (Foster, 2005). Although mgl mutations universally appear in aerobic chemostat cultures of E. coli, no such mutations appeared in anaerobic glucose-limited populations (Manche et al., 1999). Under fermentative conditions, ptsG mutations were the main source of transport-improving mutations. The ptsG mutations do not increase afﬁnity anywhere as much as mgl mutations so why the absence of mgl mutations? This unexpected result does have a physiological basis; anaerobic conditions do not permit functioning of the Mgl system. The lack of anaerobic Mgl activity is not due to transcriptional regulation, but occurs at the protein function level. How this occurs is still unknown, but may be linked to the inability of E. coli to utilize several sugars under anaerobic conditions (Mat-Jan et al., 1991). A rationalization can be offered for the absence of the Mgl system under fermentative conditions; the expensive ATP usage (two ATPs in transport and glucose phosphorylation) of binding protein-dependent transporters results in an energetic impasse when only two ATPs are produced in fermentation pathways (Muir et al., 1985). Whatever the explanation, this example of differences between anaerobic/aerobic mutational adaptations demonstrates how sensitive evolutionary pathways are to physiological effects on ﬁtness.

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9. OTHER MUTATIONS IN CHEMOSTAT POPULATIONS AND THEIR PHYSIOLOGICAL EFFECTS Attempts to identify beneﬁcial mutations occurring in chemostat cultures have been documented with several E. coli/substrate limitation combinations, although surprisingly few changes have been characterized in both genetic and physiological detail. The sections below deal with such better understood examples.

9.1. Changes in the lac System The historically important studies with the lactose–E. coli combination provided the ﬁrst deﬁned examples of chemostat mutations (Novick and Horiuchi, 1961). Many historical aspects of mutational adaptations and polymorphisms in the lac system were discussed in a review (Watt and Dean, 2000) so only a brief synthesis is offered here. When lactose-limited chemostats are initiated, the most obvious observed change is the signiﬁcant increase in b-galactosidase production and the accumulation of mutants with constitutive expression of the lac operon (Novick and Horiuchi, 1961). Later in the life of the chemostat, further increases in expression result from ampliﬁcation of the lac region of the chromosome (Horiuchi et al., 1962), and the extent of chemostat-selected lac ampliﬁcations were recently characterized using a lactose analog (lactulose) as limiting substrate (Zhong et al., 2004). The accumulation of b-galactosidase is not however the major gain in ﬁtness in utilizing lactose in chemostats (Dykhuizen et al., 1987; Dean, 1989); the ‘‘cell wall’’ components (Dean, 1989), namely LacY in the cytoplasmic membrane and porins in the outer membrane, exert a greater inﬂuence on adaptation in the chemostat environment. This idea is supported by LacY being a site of positive selection in natural isolates (Wagner and Riley, 1996) and, more conclusively, that chemostats select for lacY mutants with increased afﬁnity for lactose (Tsen et al., 1996). Lactose limitation in continuous culture also selects for both regulatory and structural gene changes in porins (Zhang and Ferenci, 1999), as discussed below.

9.2. Outer Membrane Changes Except for some bulky or anionic nutrients, outer membrane permeability is dependent on passive diffusion through porin pores in E. coli and other

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Gram-negative bacteria (Nikaido, 2003). For a disaccharide like lactose, the channel accessibility is more restricted than for monosaccharides; there is a greater than 10-fold difference in permeation rates through OmpF between glucose and lactose (Nikaido and Vaara, 1985). Lactose uptake at low nutrient levels is indeed limited by outer membrane permeability in E. coli and lactose-limited chemostats most commonly enrich mutants with structural gene changes that remove the channel constriction (L3 loop) in the OmpF porin (Zhang and Ferenci, 1999). Increased expression of OmpF was also noted, but the mutations causing this increase were not identiﬁed. But what about smaller substrates like monosaccharides? There has been relatively little effort in identifying mutational outer membrane changes until recently, but the evidence suggests that, even for glucose, extensive outer membrane differences contribute to ﬁtness (Maharjan et al., 2006). Glucose can pass through more than one porin channel across the outer membrane, as shown in Fig. 3. In glucose-limited chemostats, mutations are present that affect one or more of these three paths so all three can be improved by selection. The levels of OmpC, OmpF and LamB are elevated in one or more classes of isolate after 26 days of glucose limitation. Some isolates have more than one protein in increased amounts; Class 4 isolates (Maharjan et al., 2006) increased both LamB and OmpF, whereas Class 2 isolates have all three at a higher level. Altogether, ﬁve different combinations of outer membrane changes were noted with altered expression of one or more porins. Genetic analysis suggested that, even in individual protein pattern classes, several mutational causes could give rise to the same observed changes. The overall conclusion is that outer membrane permeability is subject to strong selection in chemostats even for monosaccharide substrates and multiple mutations are accumulated to improve permeability at low concentrations. The causes of LamB protein elevation are due to mutations in mlc and malT (see Section 9.2) but OmpC regulation changes cannot yet be explained. The alteration in OmpF levels in some classes is also largely unsolved, but could result from mutations in any of numerous inputs into the control of porin levels (Pratt et al., 1996; Liu and Ferenci, 2001). However, OmpF increases in some isolates can be ascribed to rpoS mutations (Section 9.3), because RpoS negatively regulates ompF transcription (Pratt et al., 1996; Liu and Ferenci, 2001). The selection condition in chemostats, driving bacterial outer membranes to ever-increasing ﬁtness under glucose limitation, involves a high level of evolutionary specialization and a loss of some properties that make E. coli ﬁt in its normal habitat. The outer membrane changes in chemostats provide a good example of this type of antagonistic pleiotropy. All the outer

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membrane changes noted above increased permeation not just to nutrients, but also to antibiotics and detergents (Zhang and Ferenci, 1999; Maharjan et al., 2006). Isolates from chemostats with porin changes are much more sensitive to several antibiotics including large b-lactams like cloxacillin and detergents like SDS or bile salts, which make chemostat isolates sensitive to their normal intestinal habitat. Indeed, it is possible to use chemostats to explore the level of antagonistic pleiotropy between selection for better nutrition and resistance to antibiotics by incorporating low levels of antibiotic into a glucose-limited chemostat. The competitive ﬁtness of bacteria in such an environment is very much dependent on the level of outer membrane permeability and the concentration of antibiotic (Maharjan and Ferenci, unpublished results).

9.3. rpoS As discussed in Section 3.3, mutational sweeps affecting rpoS can be readily studied in chemostat cultures. In some strains of E. coli with high endogenous RpoS protein levels (including our usual MC4100 derivative used in experimental evolution studies; Notley-McRobb and Ferenci, 1999a), mutational loss of RpoS is a major ﬁtness gain and indeed the earliest identiﬁed major sweep. In glucose-limited cultures at D ¼ 0.1 h1, rpoS null mutants constitute 420% of the population by the second day (4–5 vol. of medium) and in chemostats limited by diluted Luria broth, mutants are already evident within three culture volumes (King et al., 2006). Populations of other E. coli strains such as the commonly used MG1655 strain, on the other hand, show few detectable rpoS mutants in the ﬁrst four days of culture but exhibit attenuated rpoS phenotypes after hundreds of generations of cultivation (unpublished observations cited in Ihssen and Egli, 2004). The differences in rpoS selection between strains has been extensively discussed recently (Ferenci, 2003, 2005). An exploitable advantage of studying the rpoS sweep in chemostats is that it can be adopted as a model system for the analysis of what makes mutations sweep populations. The interplay between the environment and the beneﬁt derived from mutations in rpoS can be easily followed using simple population-level screens. The spread of rpoS mutations is detectable by either the staining of colonies with iodine (measuring the RpoS-dependent level of glycogen; Hengge-Aronis and Fischer, 1992) or by growth on minimal acetate plates (measuring the RpoS-dependent negative effect on use of poor substrates; King et al., 2004). Changes in the proportions of wild-type and mutant bacteria can hence be readily followed in chemostat samples.

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The trade-off between the need for RpoS-dependent stress resistance and the selection pressure to lose RpoS for improved ﬁtness can be studied by varying the chemostat environment and looking at the speed of mutational sweeps under different conditions (King et al., 2006). An unexpected ﬁnding from such studies was that there is a signiﬁcant difference in the ﬁtness properties of rpoS mutants in aerobic and anaerobic conditions (King et al., 2006). In anaerobic chemostats under glucose limitation, RpoS has a role not needed under aerobic conditions (King and Ferenci, 2005). Hence studying the beneﬁt of mutations in different environments in chemostats has the additional advantage of revealing subtleties in cellular physiology.

9.4. mlc and malT Mutations in malT and mlc arise in glucose-limited chemostat cultures grown with slow dilution rates (D ¼ 0.1–0.3 h1). As noted in Section 8, Mlc is a repressor and MalT an activator of the mal regulon (Boos and Shuman, 1998). One physiological target of mlc and malT mutations in chemostats is the outer membrane LamB protein, whose expression is controlled as part of the mal regulon (Boos and Shuman, 1998). As discussed in Section 3.1, LamB facilitates glucose entry across the outer membrane at low concentrations. The timing of appearance of mlc and malT mutations in replicate glucose-limited populations is always after rpoS and mgl mutations become common (Notley-McRobb et al., 2003). A possible physiological rationale for this sequence is that outer membrane permeability becomes a signiﬁcant bottleneck after the mgl mutations have greatly increased ﬂux through the cytoplasmic membrane. At low dilution rates, different populations often have either mlc or malT mutations, but sometimes both (Notley-McRobb et al., 2003). The increase in mal expression resulting from mlc mutations is less than from malT changes, so there may be some additional advantage in acquiring malT mutations after mlc has spread (Notley-McRobb and Ferenci, 1999b). By contrast, mlc mutations do have a property not shared by malT in that mlc mutations also increase expression of ptsG (Tanaka et al., 1999; Plumbridge, 2002; Seeto et al., 2004). The ptsG-encoded glucose phosphotransferase transporter is not as important as Mgl under glucose limitation, but could potentially increase ﬁtness by a smaller amount. Indeed, in a recently analysed population under glucose limitation, mutants with increased ptsG expressions were present (Maharjan et al., 2006). The mlc mutations in chemostats include a wide range of base changes and insertions resulting in loss of repressor activity (Notley-McRobb and

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Ferenci, 1999b). The malT mutations are different in that they are all point mutations affecting a few regions in MalT protein resulting in active activator even in the absence of inducer (Schlegel et al., 2002). Interestingly, the beneﬁt conferred by high expression of the mal regulon under glucose limitation is evident only at slow dilution rates (D ¼ 0.1–0.3 h1). At D ¼ 0.6 h1, malT mutations do not appear in chemostat populations (Notley-McRobb et al., 2003). As noted in Section 3.1, mal and lamB expression is already optimal at fast dilution rates so a mutation to mal constitutivity is not needed. Perhaps surprisingly, a malTconstitutivity mutation confers a competitive disadvantage at faster growth rates, for unknown reasons (Notley-McRobb et al., 2003). Hence as seen also for rpoS mutations, the mutational sweeps in populations are highly sensitive to growth rate and environmental factors.

9.5. ptsG Improvements in glucose transport across the cytoplasmic membrane are not solely due to Mgl changes under glucose limitation. As noted in Section 8, the PEP:glucose phosphotransferase system is a major player in glucose uptake under all conditions, but has an especially important role in glucoselimited chemostats with low O2 availability. In the absence of Mgl-dependent glucose transport, PtsG overexpression and gains in afﬁnity were found in isolates under these conditions. Interestingly, the same base change within ptsG was responsible for both the regulation and afﬁnity change, and occurred at just two positions in ptsG in parallel populations (Manche et al., 1999; Notley-McRobb and Ferenci, 2000b). In aerobic chemostats, isolates with mutations affecting ptsG were present, but occurred after mutations affecting mgl and mal regulation (see Section 9.4) and are distinct from the mutations in anaerobic cultures. The mutations affecting PtsG levels in aerobic chemostats (Maharjan et al., 2006) are outside ptsG or mlc and have not yet been fully characterized.

9.6. Metabolic Changes and Cross-Feeding The landmark ﬁnding of the Adams group, that bacterial isolates from glucose-limited chemostats have evolved alternative metabolic strategies (Helling et al., 1987; Rosenzweig and Adams, 1994; Treves et al., 1998; Adams, 2004), has greatly extended our understanding of the scope of what may be happening in chemostat populations. Six of the 12 glucose-limited

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populations after 4400 generations contained acetate-scavenging bacteria with mutations up-regulating acetyl-CoA synthetase (Treves et al., 1998). The mutations were of different types but were all upstream of the acs gene. The important characteristic of these acetate scavengers is that they crossfeed off other chemostat strains that produce elevated levels of acetate when in the glucose-limited environment (Helling et al., 1987). The acetate producers and users have different growth efﬁciencies when using glucose and also compete against ancestor and each other in a way indicative of intercellular interactions. The signiﬁcance of this ﬁnding is that an environment which should, in the simplest view, result in a ﬁt strain efﬁcient for glucose utilization actually supports more than one adaptational strategy. The conclusion that evolved long-term chemostat bacteria do not converge on a unique metabolic strategy is strongly supported by recent data. In analysis of the highly diverged population discussed in Section 10, it was apparent that the growth yields on glucose were very heterogeneous within a population. The same culture contained competing strains with either reduced or elevated growth yields, or those unchanged from the ancestor. The mutation(s) responsible for these changes still need to be identiﬁed.

9.7. Amplification and Other Genomic Rearrangements At the genetic level, the examples from glucose-limited chemostats above involved mutations affecting single genes through base changes or inactivation through insertion sequences. Another genetic solution to increased ﬁtness, as seen within the lactose chemostats, is for bacteria to increase gene copy number through ampliﬁcation of the lac region (Horiuchi et al., 1962). Ampliﬁcation of genomic material (Romero and Palacios, 1997) and rearrangements due to mobile genetic elements are of considerable importance in bacterial evolution (Arber, 2000). Moreover, there is recent discussion as to whether transient ampliﬁcation is of signiﬁcance in adaptive or stressinduced mutations (Pettersson et al., 2005). The latter could also be signiﬁcant in evolution in the nutrient-stressed chemostat environment. So how important is ampliﬁcation in chemostat culture? Strong evidence for the frequent occurrence of adaptive duplications of ﬁtness-conferring genes came from studies of Salmonella typhimurium grown in chemostats limited by arabinose, sorbitol or melibiose (Sonti and Roth, 1989). All cultures showed a duplication of a similar large region of the chromosome, covering the genes responsible for transport and metabolism of these sugars. Bacteria with chromosomal duplications rose to over 90% of the population within 50–100 h of limitation. Interestingly, the proportion

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of bacteria with duplications dropped sharply after about 100 h (Sonti and Roth, 1989). Presumably, other types of mutations had bigger ﬁtness advantages and bacteria with these can displace bacteria with duplications. Whether the further mutations are to some extent dependent on the duplications is not known. Nevertheless, duplications are common in populations of E. coli (Haack and Roth, 1995) and the lactose and arabinose examples do illustrate that ampliﬁcations can contribute to overcoming nutrient limitation by increasing gene copy number. A similar conclusion was found with Saccharomyces cultures; ampliﬁcations of transporter genes and genomic rearrangements were common in glucose-limited chemostats (Dunham et al., 2002). In glucose-limited cultures of E. coli, evidence is less extensive but some mgl mutations do show instability in phenotypes, possibly due to unstable ampliﬁcation events (Maharjan et al., 2006). The stability of the genome with respect to mobile elements has not been well analysed with chemostat cultures, but the serial dilution regime of E. coli in glucose medium does result in major reassortment of insertion sequences after 10,000 generations (Papadopoulos et al., 1999). It would be surprising if similar rearrangements did not also occur in chemostats and there is data from the analysis of mutations in rpoS and mgl genes that IS elements were responsible for some of the loss of function mutations (Notley-McRobb and Ferenci, 1999a; Notley-McRobb et al., 2003). The contribution of mobile elements to chemostat diversity is worthy of further study.

10. EMERGING DIVERSITY IN CHEMOSTAT POPULATIONS This section should be prefaced by the comment that the results discussed below are strain dependent and selection condition dependent. There are replicate populations that exhibit the properties outlined below; there is also accumulating published and unpublished evidence that changes of strain, limiting nutrient or dilution rate results in a different mix of evolved strains. This should not come as a surprise, considering the data above showing that rpoS, mgl and malT mutations have different ﬁtness advantages in different environments and different backgrounds. So this discussion is speciﬁc to one E. coli laboratory strain in a glucose-limited chemostat at D ¼ 0.1 h1 but is instructive of the huge diversity that one clonal population can achieve. Also, in this section, only heterogeneity based on mutational change is considered. Recent studies in a number of systems indicate that stable genetic change is not the only source of variation in populations; epigenetic

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variation, leading to differences in gene expression within the same genetically identical population, is also evident in bacterial cultures (Laurent et al., 2005; Smits et al., 2006). These phenomena are undoubtedly evident in chemostat populations and add to the complexity within the chemostat community, but is not further discussed here. The genetic and phenotypic diversity apparent in chemostats does not have a strong theoretical foundation. It has often been argued that evolutionary diversiﬁcation follows from selection for specialization on alternative resources (Kassen and Rainey, 2004; MacLean, 2005). In a chemostat limited by a single resource, divergence should not happen, if the niche exclusion principle is strictly followed (Hardin, 1960). Also, from the perspective of chemostat operation, the open ﬂow system in chemostats was considered not to be useful in the development or study of inter-cellular interactions (Jannasch and Egli, 1993). Nevertheless, beginning with the work of the Adams group (reviewed in Adams, 2004), analysis of individuals within a chemostat population has demonstrated that intra-population interactions do take place and involves the establishment of a new community environment in the chemostat (Kurlandzka et al., 1991; Treves et al., 1998). In other words, even with a single, constantly applied limitation, bacterial populations establish a new ecological framework through mutational adaptation. This section focuses on how easily bacteria by-pass the competitive exclusion principle and establish complex interacting societies within a single chemostat culture. The extent of divergence in chemostats has been largely underestimated because limited numbers of isolates from any population were analysed in earlier studies and few differentiating phenotypes were recognized. The identiﬁed mutational changes described above do offer means of following shifts in populations. For example, including a transcriptional lacZ fusion into the ancestor allows detection of several levels of mal expression resulting from different mutations (Notley-McRobb and Ferenci, 1999b). The heterogeneity in populations also becomes visibly obvious on agar plates when indicator plates are used to show differences in lac fusion expression. The differences on lactose–EMB (eosin–methylene blue) indicator media shown in Fig. 5 are the result of mlc and malT mutations as well as smaller changes due to rpoS and other unidentiﬁed mutations. The colony size differences on the plates are also heritable and small colony types are often associated with inefﬁcient glucose throughput (see Section 10.3). Figure 5 depicts the extensive heterogeneity after 30 days of culture at D ¼ 0.1 h1 in a glucose-limited chemostat. Other simple plate assays can detect mutational changes in chemostats; diversity can also be seen using staining methods for rpoS and by testing sensitivity to inhibitors such as a-methyl glucoside, 3-amino-1,2,4-triazole (AT) or antibiotics (Maharjan et al., 2006).

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Figure 5 Heterogeneity in evolving chemostat populations, as detected with indicator plates. A glucose-limited E. coli culture in chemostats with a dilution rate 0.1 h1 was diluted after 30 days and spread on eosin–methylene blue (EMB) indicator plates containing lactose as fermentable substrate. The ancestor strain used (BW2952; Notley-McRobb and Ferenci, 1999a, b) has a deletion of chromosomal lac genes but has a lacZY transcriptional fusion to malG, which is regulated as part of the mal regulon. As discussed in the text (Section 9), several different beneﬁcial mutations change mal regulation in evolving chemostat populations. The colony colour and colony size are both heritable characteristics. (See plate 4 in the color plate section.)

When 40 or more isolates were recently compared for multiple characteristics, several different phenotypic groupings within one glucose-limited population were noted (Maharjan et al., 2006). A similar level of diversity was found in eight chemostat populations also analysed with 40 isolates each after 20–26 days, although the observed combination of mutations was not identical in each culture (Seeto and Ferenci, unpublished results). As shown in Table 2,

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Table 2 Differences between co-existing isolates in a long-term (26-day) chemostat culture of E. coli described in Maharjan et al. (2006) and Maharjan et al. (2007) Examples from one population Class 1

BW4003 BW4005 BW4021 BW4023 BW4036 BW4039

Class 2

BW4029

Class 3

BW4001 BW4006–BW4012 BW4020

Class 4a

BW4002 BW4035

Class 4b

BW4030

Class 4c

BW3767 BW4004 BW4022 BW4024–BW4028

Properties a. Unchanged Glc transport b. Unchanged regulation c. Use of alternative resources a. Unchanged Glc transport b. Unchanged regulation c. Increased metabolic efﬁciency d. Reduced respiration a. Increased transport (through PtsG) b. Global regulation: reduced ppGpp c. Reduced metabolic efﬁciency d. Increased respiration a. Very high Glc transport (through Mgl) b. Global regulation: rpoS mutation (L247stop) c. Unchanged or increased metabolic efﬁciency d. Increased respiration a. Very high Glc transport (through Mgl) b. Global regulation: rpoS mutation (H191P) c. Unchanged or increased metabolic efﬁciency d. Increased respiration a. Very high Glc transport (through Mgl) b. Global regulation: rpoS mutation (E315 stop) c. Unchanged or increased metabolic efﬁciency d. Increased respiration

Fitness strategy Different ecotype K strategist

r strategist

Mixed

Mixed

Mixed

regulatory, physiological and metabolic divergence was observed amongst coexisting isolates. The different combination of properties gives rise to the conclusion that members of the same population gain ﬁtness through alternative survival strategies. Class 1 isolates in Table 2 have persisted in the

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chemostat environment but do not take up or metabolize glucose better than the ancestor, and indeed one of these Class 1 isolates is less ﬁt under glucose limitation than the ancestor (Maharjan et al., 2006). The simplest explanation for the behaviour of BW4005 and Class 1 is that it is competing for an alternative niche present in the chemostat, just as the acetate scavengers did in the Adams studies (Adams, 2004). The BW4005 does not specialize in utilizing acetate however, and its niche was not identiﬁed. Still, these ﬁndings extend the Adams studies that a chemostat develops into a new environment when a population evolves. The Class 2 isolate is interesting in that it conserves glucose rather than transports it effectively or quickly (Maharjan et al., 2007). Indeed, its characteristic is to increase growth yield on glucose and decrease wasteful respiratory metabolism. This class is a K strategist from an ecological point of view (MacArthur and Wilson, 1967) and utilizes a limiting resource with increased efﬁciency. The Class 2 is minor numerically, but shows that kinetic adaptation is not the only answer to resource limitation. Class 3 isolates have the opposite approach to ﬁtness and use glucose with elevated transport rates and also elevated respiration (Maharjan et al., 2007). The combined effect of increased transport and metabolism is a signiﬁcant 20% decrease in the growth yield on glucose. The numerically signiﬁcant Class 3 isolates used a high rate/low efﬁciency approach to ﬁtness so are r strategists from an ecological viewpoint (MacArthur and Wilson, 1967). The Class 4 isolates have similar properties and are numerically the most common. They are all the best-evolved from the glucose transport point of view and strongly outcompete ancestor when placed in a reconstituted glucose-limited environment. Members of this group have heterogeneous growth yields, though most isolates are more efﬁcient than ancestor in turning glucose into biomass (Maharjan et al., 2007). Class 4 isolates have a mixed approach, increasing scavenging rates, but not at the cost of metabolic efﬁciency. The difference between the Class 4 sub-classes is in the allele of rpoS present; the different alleles indicate separate parallel evolutionary routes and acquisition of similar characteristics in three separate lines. The contribution of regulatory, transport and metabolic differences within one population is discussed below.

10.1. Diversity in Regulatory Strategies The majority of the identiﬁed mutations arising in chemostat populations, discussed in Section 9, alter regulation. This predominance of regulatory mutations is no different to results with other experimental evolution

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populations, in which changes in regulation are the most obvious differences (Elena and Lenski, 2003). What is interesting though is that the regulatory mutations are not universally distributed in different isolates from the same population. The coexistence of bacteria with different combinations of regulatory changes is good evidence that there are redundant solutions to ﬁtness under glucose limitation. One clear example of regulatory redundancy is the change of mal regulation through two alternative mutations (see Section 9.4 on mlc and malT). Both mutations overcome the bottleneck of lamB expression and hence increase transport across the outer membrane. Isolates with mlc or malT mutations can coexist and contribute to overall divergence in populations; further beneﬁcial mutations in malT or mlc backgrounds increase the possibility of different phenotypes through epistatic effects. The role of mlc in ptsG regulation also distinguishes mlc mutants from malT organisms and possibly the path to further ﬁtness through yet additional mutations. Another regulatory change, and an early one in glucose-limited bacteria, is the functional loss of a major sigma factor (see Section 9.3 on RpoS). The rapid sweep by rpoS mutants is not however to complete ﬁxation. In all populations studied, close to 1% of bacteria remain rpoS+ for many generations. It is possible that this persisting sub-population differs from the ancestor and acquired an alternative ﬁtness property, but the nature of this is unknown. Nevertheless, continued presence of the rpoS+ sub-population allows further changes to occur in different regulatory backgrounds and can result in entirely different ﬁtness solutions. In all eight studied populations studied, rpoS+ bacteria eventually recover in frequency and constitute a signiﬁcant proportion of the population after about four weeks (Maharjan et al., 2006; S. Seeto, unpublished results). Contributing to diversity, three types of adaptation were found amongst rpoS+ bacteria (Classes 1, 2 and 3 in Table 2). Class 3 bacteria have another characteristic phenotype indicative of regulatory change. When tested for sensitivity to AT, Class 3 isolates were all highly sensitive. The sensitivity to AT is dependent on the level of his gene expression, as AT inhibits histidine biosynthesis (Rudd et al., 1985). In turn, his expression is dependent on ppGpp so AT sensitivity was used as a possible screen for changes in ppGpp levels. The increased sensitivity in Class 3 isolates may therefore indicate yet another change in global gene regulation, if indeed the Class 3 isolates can be conﬁrmed to contain lowered ppGpp levels. The examples above are probably a small fraction of the regulatory changes in chemostat populations, because only a minority of the phenotypic changes were explained up to now. The metabolic and transport changes below cannot be explained by the effects of the so far identiﬁed

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regulatory mutations, so must be due to yet unrecognized changes. Nevertheless, it is clear that no single regulatory solution provides a winning combination for success in a glucose-limited chemostat.

10.2. Diversity in Transport Strategies Changes in outer membrane and cytoplasmic transporters are common in chemostat-adapted mutants. Diversity in populations arises because different combinations of outer and inner membrane changes can provide equivalent ﬁtness contributions (Maharjan et al., 2006). As discussed in Section 9.2, a single population can accommodate at least ﬁve different combinations of outer membrane changes, due to up-regulation of one or more porins in individual isolates. The redundancy shown in Fig. 3, with three parallel pathways for glucose permeation across the outer membrane, allows any or all of these pathways to be increased in amount or structurally altered to overcome glucose limitation. So it is perhaps not so surprising that coexisting long-term chemostat isolates exhibited changes affecting one or more of these components. The relative ﬁtness contribution of each outer membrane change still needs investigation, but it would be surprising if the ﬁtness changes were not cumulative in small increments. The same level of redundancy in cytoplasmic membrane transport, with the alternatives shown in Fig. 3, also permitted the selection of isolates with increases in PtsG or Mgl transporters or both (Classes 3, 4 in Table 2; Maharjan et al., 2007). We do not know the full physiological consequences of improving one transport path as against another, but one impact is predictable. The PtsG system is energized by phosphoenol pyruvate, so an increased PtsG function demands higher usage of this glycolytic product. By contrast, the operation of the Mgl system needs more ATP, so evolution of high Mgl activity requires an increased ATP supply. It would not be surprising that the evolution of increased glycolytic ﬂux helped the former whereas increased ATP production (possibly through respiration) the latter. Although this is pure speculation, it is these sorts of secondary effects that could drive the selection of further divergent metabolic adaptations described in the next section.

10.3. Diversity in Metabolic and Bioenergetic Strategies A remarkable feature of long-term chemostat populations is that bacteria adopt ecological survival strategies covering the gamut of metabolic rate or

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yield strategies. As summarized in Table 2, glucose conversion into biomass is faster but less efﬁcient in some ﬁt strains whereas others rely on metabolic efﬁciency for surviving in the competitive chemostat environment. Recent bioenergetic and metabolomic results (Maharjan et al., 2007) are indicative of more extensive and rapid metabolic variegation than previously recognized in experimental populations (Rosenzweig et al., 1994; Fong et al., 2005). Yeast chemostat cultures were also changed in metabolic properties within 250 generations of glucose-limited growth (Dunham et al., 2002) but the scope of in-population divergence and the rapidity of accumulation of such changes was unrecognized. In an ecological context, these results obviate the postulated need for multiple niches or resources in bacterial diversiﬁcation (Kassen and Rainey, 2004). Also, contrary to formal predictions (Pfeiffer, et al., 2001), derivatives of a single parental strain in a constant environment diverge to take both sides of what was proposed to be a fundamental rate/yield trade-off affecting growth energetics. The high plasticity of the yield/rate balance suggests that metabolic properties are under frequent re-adjustment in bacterial evolution and not a fundamental property of organisms (Ferenci, 2005). The rapid divergence in bacteria under sub-optimal nutritional conditions needs to be recognized as a potential source of biodiversity and intra-species heterogeneity (Feil, 2004). The details of the mutations causing shifts in yields and rates are not yet known. In line with the discussion of transport and regulation above, it would not be surprising if the redundancy in metabolic capabilities shown in Fig. 3 allowed beneﬁcial mutations to change pathways or respiratory chains individually, resulting in diversity. It is known that mutational changes to the parallel but energetically different respiratory chains of E. coli can increase growth yields (Calhoun et al., 1993), and this is a possible explanation for events in glucose-limited chemostats. Metabolic differentiation could also explain the evolution of cross-feeding when high-ﬂux, low-yield bacteria produce a new resource (acetate or other fermentation product). Even low levels of these products could support a low number of other organisms beginning to specialize on the alternate resource.

11. CONCLUSIONS Chemostats are excellent experimental systems for controlling the environmental state of bacteria in a reproducible manner. The set environment is at the same time a stimulus for changes in gene expression as well as a selection condition for ﬁtter mutants. The culture conditions allow many variables to

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be studied, but it is important to understand the effects imposed by settings such as dilution rate, culture density, limiting substrate and timing of samples. Despite the experimental constancy, it is impossible to keep biological variation out of consideration when dealing with chemostat cultures as with any other living system. Perhaps the most exciting application of chemostats is the ability to follow evolution almost in real time. Another beauty of following bacterial populations in chemostats is that it reveals so much about the physiological basis of ﬁtness. Each identiﬁed mutation discussed above deﬁnes a property in the ancestor bacteria that acts as a bottleneck under a particular, set environmental condition. For this reason alone, there is no better experimental system for studying the interaction of bacteria with an environmental situation. Many of the changes observed were unexpected, which is one of the excitements in experimental evolution studies. It is particularly the unforeseen results that lead to a better understanding of natural phenomena, such as the limitation of global regulatory circuits in reacting to the environment. The identiﬁcation of the yet unknown mutations will no doubt lead to further surprises. The most recent conclusion from studies of glucose-limited bacteria is that there are many alternative ways of becoming ﬁt in even a relatively constant selective environment. In evolutionary terms, these alternatives are indications of the multiple ﬁtness peaks on an evolutionary landscape (Wright, 1932, 1980). Indeed, there is good indication that mutations in chemostats can reveal for the ﬁrst time what these multiple ﬁtness peaks look like in physiological rather than conceptual terms. With hindsight, the multiplicity of parallel cellular capabilities in bacteria (as in Fig. 3) makes the alternative paths to ﬁtness inevitable. As discussed by others, redundancy in capabilities leads to robustness in biological systems (Kitano, 2004; Wagner, 2005a). Robustness in avoiding total damage from deleterious mutations has been the main grounds for suggesting robustness as of signiﬁcance in biology (Wagner, 2005b). Our results with chemostat populations suggest that redundancy in cellular functions also opens up a greater repertoire for beneﬁcial change in bacterial populations (Maharjan et al., 2006). It will be interesting in the future to see whether the results with the E. coli/ glucose system holds true for other organisms and other forms of limitation.

ACKNOWLEDGEMENTS This review is dedicated to the two highly committed assistants, Lucinda Notley-McRobb and Shona Seeto, who rapidly learned that chemostats do

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not stop over weekends and holidays and whose dedication contributed to virtually all our chemostat results in the past 10 years. I also thank Beny Spira and Katherine Phan for reading the manuscript.

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Plate 4 Heterogeneity in evolving chemostat populations, as detected with indicator plates. A glucose-limited E. coli culture in chemostats with a dilution rate 0.1 h 1 was diluted after 30 days and spread on eosin–methylene blue (EMB) indicator plates containing lactose as fermentable substrate. The ancestor strain used (BW2952; Notley-McRobb and Ferenci, 1999a, b) has a deletion of chromosomal lac genes but has a lacZY transcriptional fusion to malG, which is regulated as part of the mal regulon. As discussed in the text (Section 9), several different beneﬁcial mutations change mal regulation in evolving chemostat populations. The colony colour and colony size are both heritable characteristics (For b/w version, see page 209 in this volume).

Metallosensors, The Ups and Downs of Gene Regulation Amanda J. Bird Division of Hematology, Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, UT 84132, USA

ABSTRACT In fungal cells, transcriptional regulatory mechanisms play a central role in both the homeostatic regulation of the essential metals iron, copper and zinc and in the detoxiﬁcation of heavy metal ions such as cadmium. Fungi detect changes in metal ion levels using unique metallo-regulatory factors whose activity is responsive to the cellular metal ion status. New studies have revealed that these factors not only regulate the expression of genes required for metal ion acquisition, storage or detoxiﬁcation but also globally remodel metabolism to conserve metal ions or protect against metal toxicity. This review focuses on the mechanisms metallo-regulators use to up- and down-regulate gene expression.

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Iron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Iron-Responsive Transcriptional Regulation: An Overview . 2.2. Iron-Responsive Gene Activation in S. cerevisiae . . . . . . . 2.3. Iron-Responsive Gene Repression in S. cerevisiae. . . . . .

ADVANCES IN MICROBIAL PHYSIOLOGY, VOL. 53 ISBN 978-0-12-373713-7 DOI: 10.1016/S0065-2911(07)53004-3

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2.4. GATA Factors and Iron-Responsive Transcriptional Regulation . 2.5. Iron-Responsive Transcription in the Absence of Oxygen . . . . . 2.6. The Iron Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Copper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Copper-Responsive Gene Activation . . . . . . . . . . . . . . . . . . . . 3.2. The Copper Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zinc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Zinc-Responsive Transcriptional Regulation: An Overview. . . . . 4.2. Zap1-Dependent Activation of Gene Expression . . . . . . . . . . . . 4.3. Zap1-Dependent Repression of Gene Expression. . . . . . . . . . . 4.4. Zap1 and Zinc Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cadmium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Cadmium: An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Cadmium-Responsive Activation of Gene Expression . . . . . . . . 5.3. The Cadmium Sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. INTRODUCTION A number of transition metal ions are essential for growth but are potently toxic when in excess. For example, copper is an essential cofactor of the cytochrome oxidase complex that allows cells to utilize a non-fermentable carbon source. However, excessive copper levels can catalyse the generation of hydroxy radicals that cause damage to DNA, protein and lipids. Consequently, it is important that essential metals ions such as copper are maintained at a level that is sufﬁcient but not toxic to cell growth. Other transition metals such as cadmium are toxic and have no known cellular function. Cells therefore need systems to rapidly detoxify these heavy metal ions. In fungi, transcriptional regulation of gene expression plays a central role in metal ion homeostasis and in the detoxiﬁcation of non-essential metals. Metallo-regulatory factors are a class of transcription factors whose activity is regulated by cellular metal ion status. By regulating the expression of genes involved in metal ion uptake, compartmentalization, reutilization, sequestration and/or conservation, these factors ensure metal levels are maintained at an optimal level for cell growth. This article reviews the most recent advances that have been made in understanding how fungi sense and globally change metabolism in response to the essential metals iron, copper and zinc and the non-essential metal cadmium at the transcriptional level.

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2. IRON 2.1. Iron-Responsive Transcriptional Regulation: An Overview Iron is an essential redox active metal that serves as a catalytic cofactor for a wide variety of enzymes. Although iron is an abundant element in the Earth’s crust, it is generally found as insoluble ferric oxides. As a consequence, most microorganisms strive to obtain iron from the environment. Conversely, the redox active nature of iron allows it to react with hydrogen peroxide to form hydroxy-radicals, a free radical species that can damage many cellular components. In fungi, transcriptional regulatory mechanisms play a central role in maintaining the balance between iron deﬁciency and iron toxicity (Rutherford and Bird, 2004). In the yeast Saccharomyces cerevisiae, two paralogous iron-responsive transcriptional activators, Aft1 and Aft2, increase target gene expression during iron deﬁciency. In other fungal systems, an iron-responsive subset of the GATA family of transcription factors mediates gene repression during iron sufﬁciency (Rutherford and Bird, 2004). Recent studies have shown that these factors not only regulate the expression of genes involved in iron acquisition but also have roles in the global remodelling of metabolic pathways to reduce the cellular requirement for iron during periods of iron starvation. Here, I have focused on some of the newer roles these factors play in both the up-regulation and down-regulation of various cellular processes.

2.2. Iron-Responsive Gene Activation in S. cerevisiae In S. cerevisiae, Aft1 regulates the expression of genes that are involved in iron acquisition, release of iron from intracellular stores and iron conservation. The most widely studied genes are those that are involved in iron acquisition or mobilization (Fig. 1). These include genes that are involved in iron uptake (FTR1, FET3, FET4) reduction of ferric iron (FRE1-2), delivery of copper to the Fet3 ferrioxidase (ATX1, CCC2), iron-siderophore uptake (ARN1-4 and FIT1-3), release of iron from stores in the vacuole (FET5, FTH1, SMF3) and degradation of haem (HMX1) (Yamaguchi-Iwai et al., 1996; Lin et al., 1997; Yun et al., 2000; Protchenko et al., 2001; Rutherford et al., 2001; Jensen and Culotta, 2002; Portnoy et al., 2002; Protchenko and Philpott, 2003). A number of recent studies have shown that an alternative way of surviving iron starvation is to decrease the cells, reliance on iron. Transcriptional regulation of gene expression plays a major role in this

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Figure 1 Schematic representation of proteins that are involved in the iron acquisition or iron sparing response during iron starvation. Shown are the protein products of genes that are transcriptionally up-regulated during iron deﬁciency in S. cerevisiae. The topology and total number of transmembrane domains of membrane proteins was based on the following studies, Arn1 (Kim et al., 2005), Ftr1 (Severance et al., 2004), Fet3 (De Silva et al., 1995), Smf3 (Courville et al., 2004), Fth1/Fet5 (Urbanowski and Piper, 1999), Vht1 (Stolz et al., 1999), Mrs4 (Pebay-Peyroula et al., 2003) and Ccc2 (Payne and Gitlin, 1998). A dotted line has been used to separate proteins that are involved in iron acquisition or iron sparing/conservation. Circling arrows indicate proteins whose cellular localization is regulated by iron (Aft1) (Yamaguchi-Iwai et al., 2002) or iron-bound siderophores (Arn1) (Kim et al., 2002) while grey arrows indicate the direction of transport of copper (Cu), iron (Fe) or biotin. Iron in black circles represents siderophore-bound iron. Cth2-dependent posttranscriptional degradation of mRNA (wavy lines) has been shown for a limited number of genes and cellular processes (respiration/haem synthesis). See Puig et al. (2005) and text for further mRNA targets of Cth2.

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iron-sparing response (Fig. 1). Aft1 mediates iron conservation through the activation of CTH2 expression and the subsequent Cth2-dependent mRNA decay (See Section 2.2). Aft1 also ensures that alternative iron-independent biosynthetic pathways are induced during iron starvation. For example, biotin synthesis requires three enzymes, Bio2, Bio3 and Bio4 to synthesize biotin from the precursor 7-keto, 8-amino-pelargonic acid. Bio2 (biotin synthase) contains a 4Fe–4S cluster and catalyses the rate limiting step in biotin synthesis. During iron deprivation, cells rely on Aft1-dependent induction of VHT1 biotin transporter, since BIO2, BIO3 and BIO4 transcripts are less abundant under these conditions (Shakoury-Elizeh et al., 2004). The induction of the Vht1 iron-independent pathway by Aft1 therefore ensures cells obtain sufﬁcient amounts of the essential nutrient biotin while sparing iron that would be incorporated into Bio2. While the functional role of Aft1 in iron acquisition has been well documented, the function of its paralogue Aft2 it still unclear. The high conservation of the Aft1 and Aft2 DNA-binding domains allows both factors to bind to a core CACCC promoter element (FeRE). However, differences in the ﬂanking nucleotides allow the preferential binding of Aft1 or Aft2 at some promoters. Aft1 preferentially binds to FeREs that are primarily found in the promoters of genes involved in iron acquisition (e.g. FET3, ARN1) (Rutherford et al., 2003; Courel et al., 2005). FeREs that Aft2 speciﬁcally or preferentially binds to are generally located in the promoter of genes involved in organelle iron transport or use (e.g. SMF3, ISU1, MRS4) (Rutherford et al., 2003; Courel et al., 2005). Thus, Aft1 and Aft2 respond to the same cellular signal (low iron), have a signiﬁcant overlap in target gene expression but have diverged far enough that they each regulate a distinct set of genes involved in iron homeostasis. So why have cells maintained Aft2? Despite Aft2 speciﬁcally regulating a number of genes involved in iron homeostasis, a strain that lacks Aft2 displays no mutant phenotype under iron-limiting conditions (Blaiseau et al., 2001; Rutherford et al., 2001). A clue to Aft2 function might be obtained by understanding why speciﬁc genes are primarily Aft2 targets. For example, Aft2 increases the expression of genes that are required for Fe–S cluster formation/mitochondrial Fe transport during iron deﬁciency. Fe–S cluster synthesis is a vital cellular process (Kispal et al., 2005) and yet, during iron starvation, a number of mRNA transcripts (NFS1 ISA1 and YAH1) that encode proteins involved in Fe–S synthesis are down-regulated in a Cth2-dependent manner (see Section 2.3; Puig et al., 2005). Similar examples of differential regulation of Fe–S synthesis/transport genes are observed in other yeast. For example, the levels of ISU1 and ATM1 transcripts increase while NFS1, ISA1 and YAH1 transcripts decrease during

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iron deﬁciency in Candida albicans (Lan et al., 2004b). Isu1 could be a key determinant in the maintenance of Fe–S cluster synthesis when iron is limiting. One potential hypothesis is that Aft2-dependent regulation channels iron into essential Fe–S cluster synthesis allowing cell survival during conditions of severe iron deﬁciency. Consistent with this model, an aft1/aft2 mutant strain is more sensitive to the lack of iron than an aft1 mutant (Blaiseau et al., 2001; Rutherford et al., 2001). A second phenotype of the aft1/aft2 mutant strain is hypersensitivity to hydroxyurea (HU) (Dubacq et al., 2006). HU is a speciﬁc inhibitor of the iron-binding protein ribonucleotide reductase that catalyses the rate limiting step in dNTP synthesis. Aft2-dependent mobilization of iron stores may therefore be of importance under conditions of high iron demand, such as when iron-binding proteins like ribonucleotide reductase are being rapidly synthesized. Finally, it remains possible that Aft2 is induced or functions under a yet to be identiﬁed condition. A genome wide screen has identiﬁed aft2 to be synthetically lethal with hsp82 (Zhao et al., 2005). Hsp82 is a heat shock protein (one isoform of the S. cerevisiae Hsp90) that is required for the folding of a subset of ‘difﬁcult-to-fold’ proteins, such as the transcription factors Hap1 and Gcn2 (Burnie et al., 2006). Although it remains to be tested whether this result was a false positive, this result would suggest that Aft2 functions in a pathway that is distinct from Aft1. Further studies will have to decide whether Aft2 is a major player in iron homeostasis or whether it is simply the weak counterpart of Aft1.

2.3. Iron-Responsive Gene Repression in S. cerevisiae An alternative way of conserving iron is to alter metabolism such that nonessential iron binding proteins are no longer synthesized. One way in which this is globally achieved in S. cerevisiae is through the Aft1- and Aft2dependent induction of the CTH2 gene (Puig et al., 2005). CTH2 encodes an mRNA-binding protein that shares homology with the mammalian tandem zinc ﬁnger protein tristetraprolin (TTP). In mammalian cells, TTP binds to AU-rich elements (AREs) within the 30 untranslated regions (30 -UTRs) of mRNAs, promoting deadenylation and increased rates of mRNA turnover (Lai et al., 2003). Similarly, in S. cerevisiae Cth2 decreases mRNA transcript stability by binding to AREs that are located in the 30 -UTRs of a large number of transcripts. Many of the transcripts encode proteins that bind iron or are found in iron-dependent processes (Puig et al., 2005). For example, mRNAs involved in haem and Fe–S cluster synthesis, mitochondrial respiration, sterol metabolism and fatty acid metabolism are targeted for

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degradation in a Cth2-dependent manner. Thus, Cth2-mediated degradation plays a central role in the iron-sparing response. However, the transcriptional regulation of CTH2 expression by Aft1 is the key determinant that ensures that this mechanism only occurs in low iron. Apart from the extensive Cth2-mediated post-transcriptional control, other complementary mechanisms exist at the transcriptional level. For example, the GLT1 gene is transcriptionally down-regulated in response to iron starvation (Shakoury-Elizeh et al., 2004). GLT1 encodes the Fe–S containing enzyme glutamate synthase that catalyses the formation of glutamate from glutamine and a-ketoglutarate. Repression of GLT1 in response to iron starvation maps to a CGG palindrome promoter sequence indicative of a binding site for a bi-nuclear Zn-cluster family member. Currently, it is unknown which member of the bi-nuclear Zn-cluster family binds to this site. The identiﬁcation of these regulatory pathways reveals how multiple mechanisms function in concert. For example, the remodelling of biotin metabolism in response to iron deﬁciency requires both the Aft1-dependent induction of VHT1 and CTH2 gene (Shakoury-Elizeh et al., 2004; Puig et al., 2005). The increased expression of CTH2 results in the increased Cth2-mediated degradation of BIO2 and BIO3 mRNA. This concurrent regulation by Aft1 therefore precisely coordinates the activation of biotin uptake pathway while down-regulating biotin synthesis in response to the same low iron environmental signal.

2.4. GATA Factors and Iron-Responsive Transcriptional Regulation The majority of fungi lack Aft1 and Aft2 homologues and instead regulate transcription in response to iron using a GATA-type transcription factor. GATA factors are a large group of regulatory proteins that use zinc ﬁnger domains to bind to a promoter element containing the core sequence 50 -GATA-30 . The iron-responsive GATA factors include Fep1 from Schizosaccharomyces pombe, Sfu1 from C. albicans, SRE from Neurospora crassa, SREA from Aspergillus nidulans, SreP from Penicillium chrysogenum and Urbs1 from Ustilago maydis (Voisard et al., 1993; Haas et al., 1997, 1999; Zhou et al., 1998; Pelletier et al., 2002; Lan et al., 2004b). All of these factors function by repressing gene expression under iron-replete conditions. Analogous to Aft1 from S. cerevisiae, these factors regulate genes required for iron homeostasis including high-afﬁnity iron uptake and siderophore

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uptake (Haas et al., 1999; Oberegger et al., 2002; Pelletier et al., 2002, 2003; Lan et al., 2004b). Iron-responsive GATA factors regulate the expression of additional genes required for specialized functions. It is well known that genes involved in iron acquisition are essential for virulence of pathogenic fungi (Ramanan and Wang, 2000; Knight et al., 2005). Genomic proﬁling in C. albicans has revealed that a broad range of genes required for pathogenicity are regulated either directly or indirectly in response to changing iron levels. These include genes encoding secreted hydrolytic enzymes, cell wall/surface proteins and genes required for blastospore to hyphal transitions (Lan et al., 2004b). With a few exceptions (e.g. S. cerevisiae), the majority of fungi possess genes required for siderophore biosynthesis. Genes required for siderophore synthesis are frequently up-regulated in response to iron depletion allowing fungi to efﬁciently compete with other microorganisms for limited iron (reviewed by Philpott, 2006). In S. cerevisiae, four proteins Hap2, Hap3, Hap4 and Hap5 form the CCAAT-binding factor that activates the transcription of a range of genes required for respiration in response to glucose depletion. Hap2, Hap3 and Hap5 form a heterotrimeric DNA-binding domain while the Hap4 subunit contains the transactivation domain function. The levels of Hap4 limit the activity of the CCAAT-binding factor (Kwast et al., 1998). In the presence of glucose, HAP4 is repressed. As a consequence, the CCAAT-binding factor is only active in the absence of glucose. In S. pombe, the Php2, Php3 and Php5 homologues of Hap2, Hap3 and Hap5 play an important role in mediating gene repression in response to iron deprivation (Mercier et al., 2006). In iron-replete media, the Php2, Php3 and Php5 complex activates the expression of the genes pcl1+, sdh4+ and isa1+ (Mercier et al., 2006). Pcl1, Sdh4 and Isa1 are all iron-binding proteins. During iron starvation recruitment of a fourth protein, Php4 to the Php2/3/5 complex leads to the down-regulation of pcl1+, sdh4+ and isa1+ gene expression. Php4 shares little homology with Hap4, with the exception of a small motif that is required for Hap4 to associated with the Hap2/3/5 complex. The key to the iron-responsiveness of these genes lies in the regulation of php4+ expression. php4+ is a target of iron-responsive GATA factor Fep1 (Mercier et al., 2006). Consequently, php4+ is only expressed during iron starvation. The differences between the regulatory mechanism used by S. cerevisiae and S. pombe to conserve iron have been summarized in Fig. 2. It is likely that other fungi use a similar mechanism to mediate ironresponsive gene repression. Microarray analysis of C. albicans identiﬁed over 1100 mRNA transcripts whose abundance changed in response to iron status (Lan et al., 2004b). Comparable to S. cerevisiae, mRNA transcripts

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Figure 2 Regulatory mechanisms used by S. pombe and S. cerevisiae to regulate gene expression/transcript abundance in response to iron. Mechanisms involved in the up-regulation of gene expression during iron deﬁciency (1) or the down-regulation of gene expression/transcript abundance (2). See text for further details.

encoding proteins involved in many iron-dependent processes such as Fe–S cluster synthesis, haem synthesis and mitochondrial respiration were less abundant during iron starvation. Notably, mRNA transcripts of the C. albicans PHP4 homologue (orf 19.8298/19.861) but not the CTH2 homologue were more abundant during iron starvation in an Sfu1-dependent manner. Genomic proﬁling also suggests Sfu1 acts as a transcriptional activator at some promoters, which could be consistent with Sfu1 regulating a repressor. It therefore seems likely that both C. albicans and S. pombe use a transcriptional regulatory network to repress gene expression in response to iron depletion. These iron-responsive regulatory networks emphasize mechanisms that up- and down-regulate gene expression are important in metal homeostasis.

2.5. Iron-Responsive Transcription in the Absence of Oxygen Many iron-dependent pathways require oxygen and will not function in its absence. For example, haem synthesis requires oxygen. As a consequence, high-afﬁnity iron uptake that requires a haem-containing reductase cannot

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occur under anaerobic conditions. In the absence of oxygen, cells therefore retain the need for essential iron-dependent processes such as Fe–S cluster synthesis but do not need oxygen-requiring iron metabolic pathways such as high-afﬁnity iron uptake and haem synthesis. Here, I have focused primarily on the transcriptional regulatory mechanisms used in S. cerevisiae to alter metabolism under these conditions. A more detailed picture of the cellular remodelling of iron metabolism and other metabolic pathways in response to cellular oxygen tension can be found in a number of excellent reviews (Kwast et al., 1998; Kaplan et al., 2006). In the absence of haem/oxygen, cells up-regulate the expression of genes required for iron-siderophore and low-afﬁnity iron uptake and downregulate the expression of genes required for high-afﬁnity iron uptake. These changes in gene expression are mediated by a number of factors. Cells sense oxygen tension through the haem-binding regulator Hap1 (reviewed by Kwast et al., 1998; Mense and Zhang, 2006). In the presence of haem, Hap1 induces the expression of a wide range of genes involved in mitochondrial respiration and the oxidative stress response (Ter Linde and Steensma, 2002). One of the most important targets of Hap1 is the repressor of hypoxic genes Rox1 (Lowry and Zitomer, 1984). Rox1 represses genes required for growth under anaerobic conditions and genes required for oxygendependent functions. When oxygen levels are low, the derepression of these latter genes becomes important for using the limited oxygen more efﬁciently. Four Rox1 target genes that are important for iron homeostasis are FET4, SMF3 and potentially FIT2 and FIT3 (Jensen and Culotta, 2002; Ter Linde and Steensma, 2002). Fet4 is a low-afﬁnity iron, zinc and copper transporter, Smf3 helps mobilize iron from the vacuolar store, while Fit2 and Fit3 are glycosylphosphatidylinositol anchored cell wall proteins that facilitate iron-siderophore uptake (Portnoy et al., 2000; Protchenko et al., 2001; Jensen and Culotta, 2002; Waters and Eide, 2002). The derepression of these genes during low oxygen or anaerobic conditions provides an entry route for iron in the absence of the high-afﬁnity iron uptake system. ISU2, which encodes a scaffold protein for Fe–S cluster formation (Lill and Muhlenhoff, 2006), is also a putative target gene of Rox1 (Ter Linde and Steensma, 2002). Analogous to the up-regulation of ISU1 in response to low iron, the increased expression of ISU2 in the absence of oxygen could be a mechanism to drive Fe–S cluster synthesis during iron-limitation (see Section 2.2). In the absence of oxygen or haem, cells need only to induce the expression of a subset of the iron regulon (Crisp et al., 2003; Kaplan et al., 2006). For example, the Aft1 target genes involved in high-afﬁnity iron uptake (e.g. FTR1, FET3) are not required while genes involved in siderophore mediated iron uptake (e.g. ARN1-4) are retained. A recent study in S. cerevisiae has

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shown how several regulatory factors allow the differential expression of Aft1 target genes under conditions of haem deﬁciency (Crisp et al., 2006; Fig. 3). Analysis of two Aft1 target promoters, FET3 and ARN1 revealed Aft1, Tup1 and Cti6 are central to this process. Tup1 is a general repressor of transcription that acts in a complex with Cyc8 (previously Ssn6) (Keleher et al., 1992). Cti6 is a PHD ﬁnger-containing protein that binds to the Tup1/ Cyc8 complex and relieves repression by recruiting the SAGA complex (Papamichos-Chronakis et al., 2002). When cells are iron starved, Aft1 binds to both the FET3 and ARN1 promoters where it recruits Tup1 and Cti6 (Crisp et al., 2006). Both FET3 and ARN1 are induced under these conditions. Aft1-dependent activation of FRE2 also requires the recruitment of the Tup1/Cyc8 complex suggesting that Tup1 and Cyc8 might have a global role in Aft1-target gene activation (Fragiadakis et al., 2004). During haem deﬁciency, Cti6 is lost from the FET3 promoter but is retained at ARN1 (Crisp et al., 2006). Under these conditions, the Tup1/Cyc8 complex acts as a repressor at the FET3 promoter. It is not known how the Tup1/Cyc8 complex functions as a co-activator in iron-limiting/haem-rich media but as a repressor in the absence of iron and haem. Importantly, it is the retention of Cti6 at the ARN1 promoter that is both necessary and essential for the activation of ARN1 during haem deﬁciency. Retention of Cti6 at the ARN1 promoter requires a 14-bp promoter sequence that is absent from the FET3 promoter. In addition to a role in the absence of haem and iron, Cti6 is required for growth under iron-limiting conditions and a number of Aft1

Figure 3 Differential regulation of gene expression in response to haem deﬁciency. Shown are regulatory factors recruited to speciﬁc promoters under the listed conditions. For further details and precise stoichiometries of factors that interact with Hap1, see Lan et al. (2004a) and Mense and Zhang (2006). It is currently unknown if Cti6 and Tup1 play a role in the regulation of FET4 expression.

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target genes are up-regulated in cti6 null strains (Puig et al., 2004). Thus, there is still much to learn about the relationship between Aft1 and Cti6 at various promoters. So far, most studies have focused on the role of a single metallo-regulator in the activation or repression of a target gene in response to one metal. These studies elegantly demonstrate how combinations of regulatory factors can allow differential gene expression in response to multiple environmental signals.

2.6. The Iron Sensors An important part of metal homeostasis is the ability of metallo-regulators to be able to ‘sense’ the intracellular levels of a speciﬁc metal ion. Studies of iron responsive factors have revealed very different mechanisms in the way Aft1- and GATA-like proteins sense iron. The regulation of Aft1 by iron is based on its cellular localization. When iron is limiting Aft1 accumulates in the nucleus, while in iron-rich media Aft1 is preferentially found in the cytoplasm. Mutation of the nuclear export signal (NES) results in constitutive Aft1 activity conﬁrming the importance of nuclear accumulation of Aft1 for maximal target gene expression (Yamaguchi-Iwai et al., 2002). The signal controlling nucleocytoplasmic shuttling is not iron directly but a signal that stems from the mitochondrial Fe–S machinery. In cells that are defective in mitochondrial Fe–S cluster biogenesis, Aft1 activates target gene expression even in the presence of high cytosolic iron levels (Chen et al., 2004). These data suggest that the binding of a Fe–S cluster or a Fe–S cluster containing protein could inactivate Aft1. Notably, a conserved Cys–X–Cys motif that potentially could form a metal-binding domain is located next to the DNA-binding domain and is essential for Aft1 iron sensing (YamaguchiIwai et al., 1995). Nuclear and cytosolic Fe–S cluster synthesis requires the export of an unknown Fe–S precursor from the mitochondria through the Atm1 transporter. Three additional cytosolic proteins Nar1, Cfd1 and Nbp35 are then required for the assembly of mature cytosolic Fe–S cluster proteins (Rouault and Tong, 2005; Lill and Muhlenhoff, 2006). Although Aft1 activity is constitutive in the absence of Atm1, it is regulated in cells lacking Nar1, Cfd1 and Nbp35 (Rutherford et al., 2005). Therefore, Aft1 is not directly sensing the maturation of a cytoplasmic/nuclear Fe–S cluster protein but instead senses the unknown compound that is exported from the mitochondria via Atm1. It is currently unknown if Aft1 directly senses this intermediate or whether an intermediary protein senses the mitochondrial Fe–S signal and in turn regulates Aft1 activity.

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A number of proteins have been identiﬁed that directly interact with Aft1. Grx3 and Grx4 are nuclear monothiol glutaredoxins. The only other monothiol glutaredoxin in S. cerevisiae is Grx5 (Wheeler and Grant, 2004). Grx5 is found in mitochondria and is required for the activity of enzymes containing Fe–S clusters (Rodriguez-Manzaneque et al., 2002). It is thought that Grx5 acts as a thiol reductase by potentially reducing mixed disulphides formed between proteins and glutathione (Tamarit et al., 2003). The cellular role of Grx3 and Grx4 is unknown. Cells lacking both Grx3 and Grx4 have constitutive Aft1 activity while over-expression of GRX4 leads to the reduced expression of Aft1 target genes (Ojeda et al., 2006). The conserved Cys–X–Cys motif required for Aft1 iron sensing is necessary for the interaction with both Grx3 and Grx4. While the activity of Aft1 is clearly affected by Grx3 and Grx4, it remains unclear what precise role they play in iron sensing. The interaction of Grx3 and Grx4 with Aft1 was shown by two-hybrid analysis to be independent of iron levels. Under normal growth conditions, Grx3 and Grx4 are nuclear localized while Aft1 cycles between the nucleus and cytosol. Grx3 and Grx4 are therefore only in the same compartment as Aft1 during iron deﬁciency. A simple model where glutathione is the iron-signalling molecule and the Grx3/Grx4-dependent removal of a glutathione adduct from Aft1 leads to its inactivation seems unlikely. A glutathione-deﬁcient gsh1 strain has constitutive Aft1 activity and so far there is no evidence that Aft1 forms a mixed disulphide with glutathione (Rutherford et al., 2005; Ojeda et al., 2006). Further studies will therefore be needed to understand why Aft1-dependent activation of gene expression requires two monothiol glutaredoxins. Finally, in addition to iron-dependent regulation Aft1 regulates the expression of its own gene and a number of signalling pathways alter Aft1 activity in response to various environmental signals (Lee et al., 2002; Haurie et al., 2003). It is unknown if Aft2 is regulated in a similar manner to Aft1. The majority of studies to date have examined the role of Aft1 during iron starvation where it is maximally active. Although Aft1 is preferentially found in the cytosol under iron-replete conditions a small amount of Aft1 still resides in the nucleus. Could Aft1 have a functional role in the nucleus under iron-replete conditions? A recent study has demonstrated that an aft1 mutant strain has increased rates of chromosomal loss and non-disjunction events suggesting it may be required for the proper segregation of chromosomes (Measday et al., 2005). Chromosome spread analysis localized Aft1 to discrete foci, a number of which overlap with the centromeric protein Ndc10. Aft1 also interacts with the centromere binding factor Cbf1 in

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two-hybrid analysis and an aft1 mutant strain displays a synthetic growth defect with a cbf1 mutant (Measday et al., 2005). It is currently unknown whether any of these growth or chromosomal defects can be rescued by high iron or exacerbated by growth under severely iron-limiting conditions. Whether these affects are a result of the induction of an unknown Aft1 target gene that is required for maintaining chromosomal stability or whether Aft1 has a more direct role at the centromere, they provide an exciting new cellular role for Aft1. Iron-responsive GATA family members bind to GATA elements located in the promoter regions of target genes and repress gene expression in response to iron. In S. pombe, repression is mediated by the Fep1-dependent recruitment of the Tup11/Tup12 repression complex (Znaidi et al., 2004). Tup11 is a homologue of Tup1 from S. cerevisiae. Similarly, in C. albicans, Tup1 is required for iron-dependent repression of Sfu1 target genes (Knight et al., 2002). A number of studies have begun to address how these factors sense iron. Studies with Fep1 have shown that it is stable and localized to the nucleus in the presence or absence of iron suggesting that iron regulates DNA-binding activity and/or the interaction with Tup11/12 (Pelletier et al., 2005). The iron-responsive GATA family members contain an atypical DNA-binding domain that contains two zinc ﬁnger domains that surround a region containing four conserved cysteine residues. The four conserved cysteine residues are necessary for DNA-binding activity in all family members while the requirement of one or both of the zinc ﬁnger domains in this process is dependent on the family member (Rutherford and Bird, 2004). In Fep1, mutagenesis of the conserved cysteine residues leads to the loss of iron-responsive gene repression in vivo (Pelletier et al., 2005). However, mutagenesis of the equivalent cysteines in the GATA factor Sre leads the constitutive repression of siderophore synthesis (a process regulated by Sre) (Harrison and Marzluf, 2002). It is currently unknown if this difference is a result of additional Sre-independent iron-responsive regulation in N. crassa or if it results from an intrinsic disparity in the regulation of Sre and Fep1 by iron. An obvious model is that iron directly binds to the DNA-binding domain allowing these factors to bind to DNA and/or interact with the Tup1 repressor complex. In vitro studies suggest Sre binds iron while recombinant Fep1 can only be puriﬁed from cells in the presence of iron (Harrison and Marzluf, 2002; Pelletier et al., 2002). Thus, the iron-responsive GATA family members may directly sense iron while Aft1 senses iron deﬁciency indirectly through Fe–S cluster biosynthesis, a process that is affected by cellular iron content.

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3. COPPER 3.1. Copper-Responsive Gene Activation Copper is an essential nutrient that is found in a variety of enzymes including Cu,Zn superoxide dismutase, cytochrome c oxidase and multicopper oxidases. When in excess, copper can catalyse the generation of reactive hydroxy radicals that damage DNA, proteins and lipids. As a consequence, copper homeostasis is stringently maintained by multiple complementary mechanisms. Copper-responsive transcriptional regulation is of particular importance in fungi where a variety of factors control genes required for copper acquisition and detoxiﬁcation. In S. cerevisiae, copper levels are controlled through the actions of Mac1 and Ace1. Mac1 is active during copper starvation and induces the expression of genes required for high-afﬁnity copper uptake (CTR1, CTR3) and reduction of Cu2+ to Cu+ (FRE1, FRE7) (Gross et al., 2000). Ace1 protects cells from copper toxicity by inducing the expression of copper-chelating metallothioneins (CUP1, CRS5) and the antioxidant superoxide dismutase (SOD1) (Gralla et al., 1991; Gross et al., 2000). In addition to these classical target genes, extensive genomic proﬁling has identiﬁed new Mac1 copperregulated target genes (e.g. YFR055w, CRR1) (De Freitas et al., 2004; van Bakel et al., 2005). However, the roles of these newly identiﬁed Mac1 target genes in copper homeostasis are currently unknown. Both microarray analysis and individual studies have reiterated the tight relationship between copper and iron homeostasis. For example, both high-afﬁnity iron and copper uptake rely on a haem-binding oxidoreductase of the FRE family. Akin to the regulation of high-afﬁnity iron uptake by Aft1, Mac1-dependent activation of CTR1 expression is repressed in a Tup1-dependent manner in the absence of haem (Crisp et al., 2006). Thus, copper-responsive regulators play a primary role in copper acquisition and copper detoxiﬁcation. In other fungi, copper-responsive regulators regulate a number of additional genes. In Podospora anserina, the Mac1 orthologue GRISEA remodels metabolic pathways to spare copper. In response to copper limitation, P. anserina uses an iron-dependent respiration pathway with an alternative terminal oxidase (Borghouts et al., 2001). Coincident with this change in respiratory pathways, the Mac1 ortholog GRISEA induces the expression of a manganese-requiring superoxide dismutase gene (SOD2) (Borghouts et al., 2001). Thus, copper is not needed for cytochrome c oxidase and Cu,Zn superoxide dismutase production and cells are protected from reactive

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oxygen species by SOD2. In S. pombe, Cuf1 is functionally comparable to Mac1 but contains a DNA-binding domain that is homologous to Ace1 (Beaudoin and Labbe, 2001; Beaudoin et al., 2003). Cuf1 activates genes required for copper uptake and release from the vacuolar stores while represses the expression of genes required for high-afﬁnity iron uptake (Labbe et al., 1999; Bellemare et al., 2002). This dual regulation by Cuf1 ensures genes that are ineffective in the absence of copper are not expressed. The ability of fungi to undergo dimorphic transitions to ﬁlamentous forms is essential for virulence (Lengeler et al., 2000). A number of recent studies have implicated copper-responsive transcription factors to be important in this transition. For example, ectopic expression of the C. albicans Mac1 in S. cerevisiae promotes ﬁlamentous growth in diploids and invasive growth in haploids (Huang et al., 2006). In the opportunistic pathogen Cryptococcus neoformans, the blue copper oxidase laccase is essential for virulence. Copper-responsive transcriptional regulation is important for laccase production at a number of levels. In an oxy2 mutant, laccase production is decreased (Nyhus and Jacobson, 2004). oxy2 is thought to be allelic to the C. neoformans MAC1. Regulation of genes required for copper uptake therefore plays an important role in copper delivery to laccase. In the presence of copper, laccase gene expression (CNLAC1) is induced (Zhu et al., 2003). Although not directly tested, this response is likely mediated by the C. neoformans Ace1 homologue. Thus, copper regulators are important under both copper-limiting and copper-replete conditions. Phenotypic switching is a mechanism by which spontaneous variants are generated within infecting population of microorganisms. Differences in phenotypes of the variants provide a mechanism for the rapid adaptation to environment changes. In Candida glabrata, phenotypic switching is associated with increased expressions of Mac1, Amt1 (C. glabrata Ace1 homologue) and a number of their target genes (Srikantha et al., 2005). This switch is independent of copper levels and suggests that these factors and their target genes may have important unknown roles in colonization and virulence.

3.2. The Copper Sensors The copper sensing domains of Mac1 and Ace1 have been well characterized (reviewed by Rutherford and Bird, 2004). Mac1 contains two Cys–X–Cys–X4–Cys–X–Cys–X2–Cys–X2–His domains designated C1 and C2. Each of these domains can bind four Cu+ ions in a polycopper cluster and is found within a transactivation domain. Inactivation of Mac1 is mediated by a copper-dependent intramolecular interaction between the Mac1

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DNA-binding domain and the C2 transactivation domain. The copper-detoxifying factor Ace1 is constitutively expressed and resides in the nucleus in an inactive form. When exposed to copper, Ace1 binds Cu+ cooperatively to form a polycopper cluster. This in turn leads to a conformational switch that converts Ace1 into a DNA-binding active form. The metallo-regulation of Mac1 and Ace1 occurs independently suggesting that no common pathway signals the changes in copper status to both factors (Keller et al., 2005). These two copper-sensing domains are highly conserved in other fungal copper sensors. However, different fungal species have evolved various ways in which they use these same domains to control activity. For example, inactivation of Cuf1 by copper is mediated by a copper-dependent interaction between the DNA-binding domain and N-terminal Cys-rich region (C1) (Beaudoin and Labbe, 2006). This interaction in Cuf1 masks the nuclear localization signal (NLS), sequestering Cuf1 in the cytosol in the presence of copper (Beaudoin and Labbe, 2006) in contrast to the constitutive nuclear localization of Mac1 (Jensen and Winge, 1998). In Yarrowia lipolytica, Crf1 (an Ace1 ortholog) is localized to the nucleus in the presence of copper while having a cytosolic residence during copper limitation (Garcia et al., 2002). It is therefore possible that the formation of the N-terminal polycopper cluster in Crf1 may expose a NLS or mask an NES, which leads to copper-dependent nuclear retention. It is currently unknown if there is any advantage or reasons for the differences in the regulation of copper sensing domains in different fungi. Studies in S. cerevisiae have demonstrated that a speciﬁc subset of copper-binding proteins, copper chaperones, bind and deliver copper to intracellular compartments or copper-requiring proteins (Field et al., 2002). No copper chaperone has been identiﬁed for Mac1. Cellular signalling pathways may also be important for regulation since Mac1 needs to be phosphorylated before it can bind to DNA (Heredia et al., 2001). The differences in cellular localization may therefore arise from differences in copper sensing or regulation by signalling pathways in different species.

4. ZINC 4.1. Zinc-Responsive Transcriptional Regulation: An Overview Zinc is an essential nutrient that is found in a wide range of enzymes and in many regulatory proteins. Unlike copper and iron, zinc is not redox active.

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However, when in excess, zinc is toxic to cell growth. Although the precise reason for this toxicity is unknown, it may arise from zinc binding to improper sites in some proteins. Cells therefore need to employ a variety of homeostatic mechanisms to maintain an optimal level of zinc. The only characterized fungal factor that regulates gene expression in response to zinc is Zap1 from S. cerevisiae (Zhao and Eide, 1997). This review has therefore focused on the importance of zinc-responsive transcriptional control for cell survival from studies with Zap1. Zap1 homologues are not present in every fungal genome. For example, S. pombe contains no known zinc-responsive transcription factor and yet the transcript abundance of the ZYM1 metallothionein increases under zinc-replete conditions (Borrelly et al., 2002). Although it is unknown whether this response is transcriptional or posttranscriptional, it suggests that very distinct factors, such as the ironresponsive regulators Aft1 and Fep1, may control gene expression in response to zinc in different fungi.

4.2. Zap1-Dependent Activation of Gene Expression Zap1 activates gene expression in response to zinc starvation by binding in a site-speciﬁc manner to 11 bp zinc responsive elements (ZREs) that are located in the promoter regions of all known target genes (Zhao et al., 1998). The current predicted number of target genes is 49 of which 14 have been conﬁrmed by direct experimental analyses. The characterized targets of Zap1 are illustrated in Fig. 4. Not surprisingly, a number of these genes mediate zinc uptake (ZRT1, ZRT2 and FET4) or zinc release from the vacuole stores (ZRT3) (Zhao and Eide, 1996a, 1996b; Lyons et al., 2000; Waters and Eide, 2002). Zap1 also activates the expression of two additional zinc permease genes, ZRC1 and ZRG17, that encode proteins involved in zinc inﬂux into the vacuole and endoplasmic reticulum (ER), respectively (Yuan, 2000; MacDiarmid et al., 2003; Ellis et al., 2005). Although counterintuitive, the increased expression of ZRC1 during zinc deﬁciency is a proactive mechanism to protect zinc-starved cells from sudden exposure to high levels of zinc (MacDiarmid et al., 2003). The regulation of ZRG17 expression is potentially a mechanism to control zinc inﬂux into the ER. Zrg17 transports zinc into the ER as a heterodimeric complex with Msc2 (Ellis et al., 2004, 2005). If neither protein functions as a homodimer then zinc transport would depend upon increased ZRG17 expression and the corresponding increase in Zrg17 protein levels. Thus, Zap1 plays a critical role in maintaining cytoplasmic and possibly ER zinc levels, during zinc deﬁciency.

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Figure 4 The protein products of genes that are transcriptionally regulated by Zap1. Proteins have been separated according to their cellular roles in either zinc acquisition or zinc sparing response. Proteins that fall into neither of these categories or whose role zinc homeostasis has yet to be determined, are in their own class (other). The topology and total number of transmembrane domains of membrane proteins was based on the following studies, Zrt1, Zrt2, Zrt3 (Zhao and Eide, 1996a), Zrc1, Zrg17 (Palmiter and Findley, 1995), Dpp1 (Han et al., 2004) and Izh1/2 (Yamauchi et al., 2003). Grey arrows indicate the direction of zinc transport and black arrows indicate whether a gene is up-regulated (m upright arrow) or downregulated (k inverted arrow) by Zap1.

Analysis of a subset of the remaining target genes has provided further incite into areas of metabolism that alter during zinc deﬁciency. One unpredicted correlation was the tight coordination of phospholipid metabolism with zinc metabolism. During zinc deﬁciency, Zap1 induces the expression of the diacylglycerol pyrophosphate phosphatase gene DPP1, the phosphatidylinositol synthase gene PIS1 and the ethanolamine kinase gene EKI1 (Han et al., 2001, 2005; Kersting and Carman, 2006). The regulation of these genes by Zap1 has substantial effects on the levels of both major and minor phospholipids during zinc deﬁciency (Iwanyshyn et al., 2004). For example,

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the increased levels of vacuolar membrane-associated Dpp1 results in the reduction of the vacuole membrane phospholipids diacylglycerol pyrophosphate and phosphatidate while the induction of PIS1 leads to increased levels of phosphatidylinositol. Zinc depletion additionally leads to a decrease in the levels of phosphatidylethanolamine, which is partly attributed to decreased expression of the CHO1 phosphatidylserine synthase (Iwanyshyn et al., 2004). Notably, this decrease does not require Zap1, but is instead mediated through an UASINO promoter element by an unknown mechanism. Phospholipids have a wide range of functions including altering the efﬁciency of membrane permeases and acting as secondary messengers. Therefore, one of the most interesting questions remaining to be answered is why is there the remodelling of phospholipid metabolism in response to zinc starvation? Other Zap1 target genes include IZH1 and IZH2 (also known as PHO36) that encode proteins with homology to vertebrate membrane steroid receptors (Lyons et al., 2004). Recent studies have revealed that Izh2 is localized to the plasma membrane where it acts as the receptor for the plant defensin, osmotin (Narasimhan et al., 2005). On binding osmotin, Izh2 induces apoptosis via the Ras2 signalling pathway. In humans, the two Izh homologues function as receptors for the hormone adiponectin. Binding of adiponectin to the receptors stimulates increased glucose uptake and fatty acid catabolism (Kadowaki and Yamauchi, 2005). In yeast, deletion of IZH2 leads to multiple defects in lipid and phosphate metabolism and zinc sensitivity while over-expression of IZH1 or IZH2 led to decreased activity of a Zap1 reporter construct (Karpichev et al., 2002; Lyons et al., 2004). It therefore appears that the Izh receptors may control signalling cascades that can affect lipid, phosphate and zinc homeostasis. It remains unknown if any other ligand, apart from osmotin mediates Izh signal transduction. Finally, the cellular roles of a number of conﬁrmed Zap1 target genes (e.g. ZPS1) have yet to be identiﬁed.

4.3. Zap1-Dependent Repression of Gene Expression An emerging theme amongst fungal transcription activators is their role in mediating gene repression. Studies of three Zap1 target genes (ZRT2, ADH1 and ADH3) have demonstrated how Zap1, a transcriptional activator, can lower gene expression (Fig. 5). The role of Zap1 in mediating gene repression was ﬁrst revealed when the expression of ZRT2 was examined over a range of zinc levels (Bird et al., 2004). ZRT2 expression increased, as cells become zinc starved but decreased upon severe zinc limitation. This atypical proﬁle results from the arrangement and afﬁnity of ZREs located within the

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Figure 5 Down-regulation of gene expression by Zap1. Schematic representation of the mechanisms Zap1 uses to repress gene expression. TATA elements (stars), ZREs (black boxes) major RNA transcripts (thick wavy line) or minor RNA transcripts (thin wavy line) are shown.

ZRT2 promoter. When cells become starved of zinc, Zap1 binds to two high-afﬁnity ZREs within the ZRT2 promoter and activates gene expression. As cells become severely zinc starved, transcription is blocked as a result of Zap1 binding to a third low-afﬁnity ZRE element that maps to the ZRT2 transcriptional initiation site. This orderly expression pattern ensures that the Zrt2 low-afﬁnity zinc uptake system is only produced when it has the capacity to transport zinc. ADH1 encodes the major zinc-dependent alcohol dehydrogenase utilized by yeast during fermentation while ADH3 encodes the major mitochondrial zinc-dependent alcohol dehydrogenase. Zap1 mediates the repression of ADH1 by binding to the ADH1 promoter and inducing the expression of the ZRR1 RNA transcript (Bird et al., 2006a). ZRR1 acts in cis by transcriptional interfering with the binding of the two major activators of ADH1 expression, Rap1 and Gcr1. A similar mechanism is thought to occur at the ADH3 promoter through induction of the intergenic transcript ZRR2. The decrease in ADH1 and ADH3 gene expression is paralleled by the Zap1-dependent activation of ADH4 (Lyons et al., 2000). ADH4 encodes a mitochondrial alcohol dehydrogenase that resembles the ironbinding ADHII from Zymomonas mobilis. Despite the homology to the iron-binding alcohol dehydrogenases, Adh4 is thought to be a zincmetalloprotein (Drewke and Ciriacy, 1988). So why would cells spare zinc by decreasing ADH1 and ADH3 expression only to then incorporate zinc into Adh4? One possibility is that there is still a saving in zinc. Adh1 and Adh3 form tetramers where each subunit binds two zinc atoms while Adh4 is predicted to be a dimer with one zinc atom per subunit. A second possibility is that some property of Adh4 is advantageous during zinc starvation. Whatever the reason turns out to be, the use of intergenic transcripts to repress gene expression reveals yet another mechanism by which cells can

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coordinate both the increased and decreased expression of genes in parallel pathways with one factor.

4.4. Zap1 and Zinc Sensing As zinc levels increase due to increased expression zinc transporters, such as ZRT1, or decreased expression of genes like ADH1, cells no longer require Zap1. Thus, to avoid zinc overload, one of the most important properties of Zap1 is its ability to be inactivated by zinc. Like many transcription factors, Zap1 autoregulates the expression of its own gene to maximize the levels of Zap1 protein under conditions where it is active. However, the most novel regulatory attribute of Zap1 is its ability to be inactivated by zinc at a post-translational level (Bird et al., 2000). Zap1 contains two transactivation domains that are independently regulated by zinc. Activation domain 1 (AD1) is located at the N-terminus of the protein and is surrounded by two regions that are essential for zinc responsiveness (Herbig et al., 2005). The Zap1 DNA-binding domain is also necessary for inactivation of AD1 by zinc. AD1 contains no known zinc-binding motif but binds multiple zinc ions in vitro, while mutations to cysteine and/or histidine residues that surround AD1 lead to constitutive AD1 function. These results have led to a model in which zinc binding to AD1 stimulates an intramolecular interaction between AD1 and the DNAbinding domain thereby masking AD1 function (Herbig et al., 2005). The second activation domain (AD2) of Zap1 is autonomously regulated by zinc and uses a novel regulatory zinc ﬁnger pair motif to sense zinc (Bird et al., 2003, 2006b). The distinguishing features of zinc ﬁnger pairs are hydrophobic residues located within each ﬁnger that mediate an interﬁnger protein–protein interaction (Wang et al., 2006). Two features of the Zap1 AD2 pair are unusual. First, an activation domain maps precisely to the one of the zinc ﬁngers and second the zinc bound to the pair is highly labile in nature (Bird et al., 2003; Wang et al., 2006). It is thought that under zinc-replete conditions, the increased occupancy of this pair with zinc results in the formation of the zinc ﬁnger pair masking residues critical for activation domain function. It remains unknown why Zap1 would need two independent zinc sensors. Possible reasons include either differential regulation of these activation domains in response to various environmental signals, the ability of them to sense different cellular zinc levels giving a graded response to zinc or allowing differential target gene expression.

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5. CADMIUM 5.1. Cadmium: An Overview Cadmium is a non-essential, toxic, heavy metal ion. Its toxicity arises from its ability to block sulphdryl groups in enzymes, compete with zinc in proteins and generate oxidative stress (McMurray and Tainer, 2003; Waisberg et al., 2003). Thiol-containing compounds, such as glutathione and phytochelatins (PC), play an important role in protecting fungi from cadmium toxicity (reviewed by Mendoza-Cozatl et al., 2005). For example, in S. pombe cadmium–glutathione and cadmium–PC complexes are transported to the vacuole where they can be stored in stable, high molecular weight complexes. Given the high sulphur content of glutathione and PC, it is not surprising that cadmium detoxiﬁcation is intricately linked to sulphur metabolism. Here, I have highlighted what is known about transcriptional regulation of gene expression in response to cadmium in S. cerevisiae and S. pombe.

5.2. Cadmium-Responsive Activation of Gene Expression In S. cerevisiae, cadmium leads to the activation of the transcriptional activators Yap1, Yap2 and Met4. Yap1 and Yap2 are two basic leucine zipper (bZIP) transcription factors that, like Aft1 and Aft2, have overlapping as well as unique functions (Fernandes et al., 1997; Cohen et al., 2002). Yap1 preferentially regulates genes required for the detoxiﬁcation of reactive oxygen species. Notably, Yap1 activates the expression of YCF1 (required for vacuolar import of cadmium–glutathione complexes) and GSH1 (required for glutathione biosynthesis) (Wemmie et al., 1994; Wu and Moye-Rowley, 1994; Cohen et al., 2002). Yap2 preferentially controls genes required for protein turnover and protein folding (Cohen et al., 2002). The induction of these latter genes may be important in the removal and replacement of damaged proteins. Met4 plays a pivotal role in cadmium detoxiﬁcation. Met4 remodels metabolism such that sulphur-depleted isoforms of several carbohydrate metabolism enzymes are used instead of sulphur-rich forms (Fauchon et al., 2002). For example, under normal conditions, cells use the Pdc1 pyruvate decarboxylase. Pdc1 contains 12 Met and 4 Cys residues. In the presence of cadmium, cells repress PDC1 expression and induce PDC6. The Pdc6 isoform is a relatively sulphur-poor enzyme containing 4 Met and 1 Cys residues (Fauchon et al., 2002). This decrease in sulphur-containing

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protein synthesis allows the sulphur that is saved to be rerouted into antioxidant glutathione (Lafaye et al., 2005). This remodelling therefore serves two purposes: it limits damage to speciﬁc pathways while sparing sulphur for glutathione production. Finally, a Met4-dependent pathway may be one mechanism by which cells delay the G1–S transition of the cell cycle in response to cadmium. This delay/arrest ensures that cell division does not occur in the presence of damaged proteins (Yen et al., 2005). The bZIP regulator, ZIP1, in S. pombe mediates many similar responses. Zip1 activates the expression of a subset of genes required for sulphur uptake and metabolism in response to cadmium and mediates growth arrest of cells when exposed to high levels of cadmium (Harrison et al., 2005). It is noteworthy that a strain lacking Pap1 (the S. pombe homologue of Yap1) is sensitive to cadmium suggesting that the protective role against cadmium of these bZIP proteins is conserved in other yeast (Toone et al., 1998). Thus, transcriptional control of genes involved in the control of the sulphur metabolism, cadmium chelation, protein turnover and cell cycle progression all contribute to cadmium detoxiﬁcation.

5.3. The Cadmium Sensors How do transcriptional regulators sense a toxic metal such as cadmium? Studies with Yap1 and Met4 have revealed two very different regulatory mechanisms (Fig. 6). Yap1 has two different redox centres that control the nuclear accumulation of Yap1 in response to speciﬁc oxidative stresses (Azevedo et al., 2003). When exposed to peroxides, the thiol peroxidase Hyr1 (previously Gpx3) mediates the formation of an intramolecular disulphide bond between Cys303 and Cys598 in Yap1. This disulphide bond is thought to induce a conformational change that masks the NES leading to the nuclear accumulation of Yap1 and target gene activation (Delaunay et al., 2000, 2002). Nuclear retention of Yap1 can also be induced through chemical modiﬁcation of reactive cysteine residues (Cys598, Cys620 and Cys629) that lie close to the NES (Azevedo et al., 2003). Furthermore, a minimal domain which included the residues Cys598, Cys620 and Cys629 from Yap1 accumulated in the nucleus only after exposure to cadmium (Azevedo et al., 2003). Thus, cadmium most likely controls Yap1 activity by directly binding to cysteine residues in the C-terminal redox centre instigating Yap1 nuclear retention. Ubiquitylation plays an important role in controlling the activity and stability of Met4. Polyubiquitylation is a general mechanism by which a protein is marked for degradation by the 26S proteasome. The covalent

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Figure 6 Cadmium-responsive transcriptional control. Factors involved in the regulation of gene expression in the presence or absence of cadmium. A ‘U’ in a circle represents ubiquitin, the proteasome has been indicated by the letter ‘P’ and UBP represents the unknown cadmium inducible/responsive deubiquitinylating enzyme. Shown are the different mechanisms of Met4 regulation in minimal media+methionine (MM+Met) and in rich media.

attachment of a polyubiquitin chain to a protein requires a cascade of E1, E2 and E3 enzymes to activate and transfer ubiquitin. A common class of E3 enzymes (or ubiquitin ligases) is the SCF (Skp1-Cullin/Cdc52-F box protein) complex. The variable member of the ubiquitin ligase complex is the F-box protein that determines substrate speciﬁcity. When methionine is added in excess to minimal media, Met4 is polyubiquitylated and degraded by the proteasome (Rouillon et al., 2000). The F-box protein Met30 is speciﬁc for Met4 and mediates this process. An alternative regulatory mechanism exists in rich media. Here, Met4 is oligo-ubiquitylated but is not degraded (Kaiser et al., 2000; Flick et al., 2004, 2006). Under these conditions, Met4 remains in the nucleus and activates a small subset of its target genes (Kuras et al., 2002). Cadmium has the ability to override both regulatory mechanisms

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leading to the constitutive activation of Met4. Exposure of cells to cadmium in minimal media in the presence of high methionine leads to the rapid dissociation of Met30 (Barbey et al., 2005). This, in turn, prevents Met4 ubiquitylation and subsequent degradation. In vitro, cadmium does not inhibit the ability of Met4 to be ubiquitylated suggesting that the dissociation of Met30 is not simply mediated by direct cadmium binding (Barbey et al., 2005). In rich media, cadmium leads to the deubiquitylation of Met4 returning it to its active form (Barbey et al., 2005; Yen et al., 2005). The cadmium inducible/responsive deubiquitinylating enzyme is currently unknown. In S. pombe, cadmium responsive regulation of Zip1 also occurs through ubiquitin-mediated degradation (Harrison et al., 2005). In the absence of cadmium the F-box protein Pof1, which is speciﬁc for Zip1, interacts with a phosphorylated form of Zip1 leading to its polyubiquitylation and degradation. In the presence of cadmium, Zip1 is stabilized suggesting that the interaction between the transcription factor and F-box protein is key to cadmium responsiveness.

6. CONCLUSIONS The study of fungal metallo-regulatory factors has greatly advanced our understanding of the importance of metals in biology. Many mechanisms are used to sense different metal ions and to globally remodel metabolism according to metal ion status. It has now become clear that during metal ion deﬁciencies, metallo-regulatory proteins play central roles in both metal acquisition and in metal ion conservation. Transcription factors that respond to metals are found in all eukaryotes. For example, in humans MTF-1 is activated by zinc, the hypoxia-inducible factor 1 (HIF-1) is activated by copper while Nrf2, a bZIP factor that is subject to ubiquitin-mediated degradation, is stabilized in the presence of cadmium (Andrews, 2001; Giedroc et al., 2001; Stewart et al., 2003; Martin et al., 2005). In Chlamydomonas, the copper-responsive factor, Crr1, is required for both the activation and repression of genes involved in copper homeostasis (Quinn et al., 2000; Moseley et al., 2002; Kropat et al., 2005). In Arabidopsis, FIT1 regulates gene expression in response to iron deﬁciency (Colangelo and Guerinot, 2004) while the transcript abundance of many genes is regulated by iron, copper and zinc levels (Petit et al., 2001; Connolly et al., 2002; Tarantino et al., 2003; Mukherjee et al., 2006; Talke et al., 2006). Metallo-regulation at the transcriptional level is observed in other multicellular model systems. For example, in Drosophila, MTF-1 activates

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metallothionein gene expression in response to copper and activates the expression of the CTR1B copper importer during conditions of copper deﬁciency (Balamurugan and Schaffner, 2006) while in Caenorhabditis elegans, genes encoding ferritin and aconitase are transcriptionally regulated by iron (Gourley et al., 2003). Lessons learned from studies of metalloregulators in fungi therefore provide a foundation for understanding how factors sense metals and how metal ion availability affects basic cellular metabolism. With the increasing number of metallo-regulatory factors being identiﬁed, the future with transcriptional regulators looks up and not down.

ACKNOWLEDGEMENTS I would like to thank Dennis Winge, Paul Cobine, Aaron Atkinson and Oleh Khalimonchuk for helpful comments and discussion on the manuscript. A.B. is a member of the Winge lab, whose work is supported by grant CA 61286 from the National Cancer Institute, National Institutes of Health.

REFERENCES Andrews, G.K. (2001) Cellular zinc sensors: MTF-1 regulation of gene expression. Biometals 14, 223–237. Azevedo, D., Tacnet, F., Delaunay, A., Rodrigues-Pousada, C. and Toledano, M.B. (2003) Two redox centers within Yap1 for H2O2 and thiol-reactive chemicals signaling. Free Radic. Biol. Med. 35, 889–900. Balamurugan, K. and Schaffner, W. (2006) Copper homeostasis in eukaryotes: teetering on a tightrope. Biochim. Biophys. Acta 1763, 737–746. Barbey, R., Baudouin-Cornu, P., Lee, T.A., Rouillon, A., Zarzov, P., Tyers, M. and Thomas, D. (2005) Inducible dissociation of SCF(Met30) ubiquitin ligase mediates a rapid transcriptional response to cadmium. EMBO J. 24, 521–532. Beaudoin, J. and Labbe, S. (2001) The ﬁssion yeast copper-sensing transcription factor Cuf1 regulates the copper transporter gene expression through an Ace1/ Amt1-like recognition sequence. J. Biol. Chem. 276, 15472–15480. Beaudoin, J. and Labbe, S. (2006) Copper induces cytoplasmic retention of ﬁssion yeast transcription factor cuf1. Eukaryot. Cell 5, 277–292. Beaudoin, J., Mercier, A., Langlois, R. and Labbe, S. (2003) The Schizosaccharomyces pombe Cuf1 is composed of functional modules from two distinct classes of copper metalloregulatory transcription factors. J. Biol. Chem. 278, 14565–14577. Bellemare, D.R., Shaner, L., Morano, K.A., Beaudoin, J., Langlois, R. and Labbe, S. (2002) Ctr6, a vacuolar membrane copper transporter in Schizosaccharomyces pombe. J. Biol. Chem. 277, 46676–46686.

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Author Index Abe, T., 105 Abee, T., 98 Abt, B., 238 Abul-Hassan, K.S., 45 Adams, J., 172, 175, 192, 195–196, 205–208, 213 Adams, W.B., 40 Adhya, S., 197, 199 Adkins, E.M., 241, 245 Adriano, J.M., 39 Aebersold, R., 114 Afghani, B., 9 Agabian, N., 236–238 Aguilar Netz, D.J., 235 Agustines, M., 110 Aharonowitz, Y., 19–20, 28 Ahmad, M.S., 79 Ahmed, S., 82, 89, 91, 142 Ahn, K., 45 Aiba, H., 185, 204 Aikawa, K., 9 Ailabouni, A., 131 Ajami, A., 89 Akimenko, V.K., 28, 32, 43 Akkermans, A.D., 97, 100, 107 Akotia, V., 81 Akpolat, O.M., 30, 48 Alam, J., 256 Alam, M.T., 173 Alam, S., 256 Albeck, A., 47 Albeck, M., 47 Albendea, C., 45 Albert, R., 146 Alder, N.P., 256 Alexandraki, D., 241 Alexeeva, S., 173, 181 Allison, D.G., 37, 186 Allue, J.L., 45

Alonso, G., 16, 20 Alric, M., 82, 122–123, 135 Al-Soud, W.A., 111 Altman, E., 181 Amann, R., 99, 107 Amarri, S., 83, 131 Anagnostopoulos, G.D., 187 Anderson, R.P., 76–77 Anderson, T.F., 28 Andersson, D.I., 206 Andersson, L., 174, 185 Andoh, A., 79, 81 Andrews, B., 243–244 Andrews, G.K., 256 Angermayr, K., 237–238 Anglade, P., 112 Anisimova, L.A., 28, 32, 43 Antoine, J.M., 101 Antonelli, E., 119 Antoniewicz, M.R., 93 Anwar, H., 188 Aposhian, H.V., 8 Appleman, M.D., 28 Apuy, J.L., 256 Arai, K., 134 Araya, M.A., 20–22, 48 Arber, W., 197, 206 Aries, V., 76 Arigoni, D., 88, 90, 131 Armes, L.G., 50 Arnold, F.H., 173 Arshad, M., 12 Artsimovitch, I., 186 Asakura, T., 7, 182 Aschmann, S.M., 12 Ash, J.S., 256 Askeland, E., 234–237 Askwith, C.C., 234 Asmuss, M., 45

270

Athenstaedt, K., 249 Atkinson, R., 12 Atwood, K.C., 195 Aude, J.C., 253 Augenlicht, L.H., 78 Ault-Riche, D., 140 Ausubel, F.M., 36 Avazeri, C., 21–22, 26, 28, 42–43, 48–49 Awada, M., 89, 114 Azadi, P., 34 Azevedo, D., 254 Azpiroz, F., 119 Babidge, W., 117 Baca, B.E., 20 Bacher, A., 88, 90, 131 Bachofen, R., 41 Backhed, F., 98–99, 101, 130, 136–137, 139 Badman, M.K., 137 Badran, A.M., 133 Badrane, H., 192, 206, 213 Badry, E.A., 28, 30–34, 37–38 Baetz, K., 243–244 Bahler, J., 254, 256 Bailey, L.B., 89 Bailey, M.J., 91, 107 Baker, B.J., 113 Balaban, N.Q., 37, 187 Balagadde, F.K., 173 Balamurugan, K., 257 Balk, J., 235, 242–243 Ball, R.O., 89, 133 Ballevre, O., 84, 90–91, 131, 142 Bamba, T., 79 Bammens, B., 133 Banﬁeld, J.F., 113 Bank, S., 90, 131, 144 Banta, S., 134 Baquero, F., 194 Barabasi, A.L., 146 Baraige, F., 112 Barbara, G., 81 Barbey, R., 255–256

AUTHOR INDEX

Barcelo, A., 138 Barceloux, D.G., 23 Barcenilla, A., 116, 126 Bard, M., 242 Bar-Joseph, Z., 243 Barkla, D.H., 118 Barnoud, D., 131 Baronofsky, J.J., 143 Barraco, P., 256 Barrault, M.B., 254 Barrett, J.A., 129, 187 Barrnett, R.J., 28 Barth, S., 256 Bartram, P., 79 Barzaghi, D., 112 Basilio, A., 115 Basit, A.W., 129 Baskunov, B.P., 28, 32, 43 Basnayake, R.S.T., 30, 48 Bassilian, S., 82, 89, 91, 142 Bassin, C., 187 Bassler, B.L., 187 Bassler, J., 235 Basu, P., 38, 40 Bates, D., 36 Baudouin-Cornu, P., 256 Baumgart, D.C., 81 Baxa, U., 8 Baylor, M.R., 15 Bazett-Jones, D.P., 14 Beatty, J.T., 11, 28 Beaudoin, J., 237–238, 244, 246–247 Bebien, M., 25, 33, 39, 46 Becker, K., 36 Bedekovics, T., 235 Bederman, I.R., 90 Beecher, C.W., 115 Begum, S., 113 Bell, C.J., 79 Bellalou, J., 173 Bellemare, D.R., 246 Belli, G., 243 Belliveau, B.H., 13 Ben-Amitai, D., 7

AUTHOR INDEX

Ben-Amor, K., 98 Bendigkeit, H.E., 192 Bengert, G.A., 30 Benoit, G., 190 Bensoussan, L., 120 Bentley, R., 29–30 Bentley, W.E., 188 Benz, R., 177 Berg, O.G., 206 Bergman, E.N., 75, 78, 137 Bergomi, M., 23 Berkman, O., 93 Berks, B.C., 32 Bernalier, A., 82, 122–123, 135 Berney, M., 187 Berthiaume, F., 93, 134 Bertrand, K.P., 37 Besser, T.E., 15 Bester, E., 188 Bevins, C.L., 81 Beyersmann, D., 253 Beysen, C., 89, 114 Bhattacharjee, H., 50 Biddanda, B.A., 175 Bienenstock, J., 80 Bilge, S.S., 15 Binder, S.R., 114 Bindi, A.B., 40 Bingham, S.A., 79–80, 117 Bird, A.J., 231, 233, 244, 246, 250–252 Birkenbihl, R.P., 256 Birkett, A., 77 Birringer, M., 38 Bisognano, C., 36 Bius, J.H., 30, 48 Bjedov, I., 193 Bjornstedt, M., 25 Black, D.S., 37 Blackadder, E.S., 27 Blackshear, P.J., 236 Blagoev, B., 114 Blaiseau, P.L., 235–236 Blake, R.C., 113 Blanchard, J.L., 192

271

Blankman, E., 250, 252 Blattner, F.R., 9, 14–15 Blaut, M., 97 Blazquez, J., 194 Bledsoe, T.L., 13 Blessing, H., 45 Blitchington, R.B., 97 Bloemberg, G.V., 35–36 Blot, M., 197, 206 Boccazzi, P., 173 Bochner, B.R., 36, 212 Bo¨ck, A., 3, 7, 38, 41 Bock, H.D., 82 Bocker, U., 79 Boelens, P.G., 90 Boer, V.M., 191 Boesten, R., 113 Bohannan, B.J., 145 Bohmig, G.A., 79, 81 Boivin, M.A., 81 Boles, B.B., 36 Boles, J.O., 8, 46 Bond, P.L., 113 Bonhoeffer, S., 144, 182, 213 Bonilla, I., 36 Bonnaud, E., 101 Bonneau, L., 46 Bonnet, R., 97 Booijink, C.C., 113 Boone, C., 236, 243–244 Boos, W., 178, 203–204 Borek, V., 27 Boren, J., 82, 89, 91, 142 Borghese, R., 33, 43, 47 Borghouts, C., 245 Borgmann, E., 82 Born, T.L., 112 Boronin, A.M., 28, 32, 43 Boros, L.G., 82, 89, 91, 142, 146 Borrelly, G.P., 248 Borriello, G., 32 Borsetti, F., 1, 28, 33, 42–44, 47–48 Bost, M.C., 134 Botsford, J.L., 185

272

Botstein, D., 192, 206, 213, 233, 235, 237, 240, 248, 251 Boucherie, H., 243 Bouige, D., 46 Boulding, E.T., 110 Bountra, C., 81 Bourriaud, C., 130 Bowman, R.H., 13 Boyle, J.D., 32 Boyle, M.A., 79 Boza, J., 91, 142 Bracken, W.M., 45 Brackett, D.J., 142 Bradley, D.E., 18 Braeken, K., 186 Bragnall, K.W., 5 Branch, W.J., 80, 117, 123 Branda, S.S., 34 Brandolin, G., 234 Breslin, N.P., 81 Bressan, R.A., 250 Breuille, D., 91, 142 Briat, J.F., 256 Brigidi, P., 112 Brink, E.J., 123 Brobech Mortensen, P., 80 Broderick, S., 37 Bron, P.A., 143 Brooun, A., 37 Brown, G.E., 7 Brown, K., 113 Brown, M.R.W., 186 Brown, N.L., 16, 28, 44 Brown, P.O., 192, 206, 213, 233, 235–237, 240, 245, 248, 251 Brown, T.A., 39 Brown, W.C., 40 Bruchert, V., 107 Bruins, M.J., 82, 135 Bruland, K.W., 30 Brune, A., 107, 109, 129 Brunengraber, H., 89–90, 131 Bruun, L.D., 107 Bryant, R.D., 39

AUTHOR INDEX

Bryden, L.J., 15 Brydon, W.G., 119 Buchanan, B.B., 41 Buchczyk, D.P., 7 Budisa, N., 8, 46 Bueno, L., 81 Bugnicourt, A., 32 Bunnett, N.W., 81 Burgard, A.P., 93 Burian, J., 15, 22, 44 Burk, R.F., 8 Burke, P.V., 238, 240 Burkle, A., 45 Burland, N.T., 9, 14–15 Burnett, S., 33 Burnie, J.P., 236 Burtscher, H., 79, 81 Busch, R., 89, 114 Butler, E., 248 Button, D.K., 177 Butzner, J.D., 79 Buzzelli, J., 40 Byron, K., 118 Cabello, A., 115 Cabiscol, E., 243 Cagney, G., 236 Cakir, I., 9 Cakir, T., 136 Calderon, I.L., 20, 48 Caldwell, D.E., 32 Calhoun, M.W., 214 Camadro, J.M., 235–236 Camenisch, G., 256 Camilli, A., 187 Campbell, J.K., 173 Campbell, N.H., 256 Campbell, R.G., 89 Campo, P., 194 Canovas, D., 20 Cantaﬁo, A.W., 13 Canton, R., 194 Capati, C., 255 Capdevila, S., 36

AUTHOR INDEX

Carlin, L., 140 Carlino, U.B., 190 Carlson, D.E., 41 Carlson, M., 241 Carman, G.M., 249–250 Carnevali, M., 5, 11, 43 Carter, T.L., 236 Cascante, M., 82, 89, 91, 142, 146 Cases, I., 20 Cashel, M., 185, 212 Cassidy, M.M., 138 Castaneda, M., 20 Castermant, J., 46 Castillo, L., 89 Cave, D.R., 125 Cayuela, C., 98, 101 Cebula, T.A., 193 Cellier, M.F., 234 Centelles, J.J., 82, 89, 91, 142 Ceri, H., 28, 30–34, 36–38, 45, 48 Chadwick, V.S., 76–77 Chai, D., 46 Chait, R., 37, 187 Chakraborty, S., 234 Challenger, F., 30 Chaloupka, R., 234 Champ, M.M., 141 Champomier-Verges, M.C., 112 Chandra, J., 38 Chandramouli, V., 89 Chang, C.-H., 18 Chang, J.C., 131 Chapman, M.A., 79 Chapman, P.A., 9, 27 Chardon, P., 101 Charlebois, R., 40 Charvin, G., 207 Chasteen, T.G., 21–22, 29–30, 41, 48 Chatham, J.C., 90 Chau, Y.K., 30 Chaudier, J., 45 Chauvin, J.P., 39 Chayvialle, J.A., 138 Chegwidden, K., 177

273

Chen, C.M., 50 Chen, D., 254, 256 Chen, J.Y., 45, 246 Chen, O.S., 242 Chen, X., 45, 249, 256 Cheng, B., 97–98, 109, 243–244 Cherbut, C., 79, 130 Chernikova, T.N., 101 Cheung, B.P.K., 38 Chevalier, A., 236 Chich, J.F., 112 Chikhale, P.J., 80 Child, M.W., 80, 126 Chin, J., 138, 143 Chinkes, D.L., 89 Cho, H., 173 Choi, E.J., 45 Choi, H., 173 Choi, Y., 45 Chotani, G., 116 Christensen, B., 87 Christian, M.T., 83, 131 Christl, S.U., 79, 118–119 Chu, F., 34 Chung, J.,, 13 Chung, Y.M., 45 Church, G.M., 93, 253 Ciriacy, M., 251 Cirino, G., 119 Claeyssens, S., 91, 142 Clamp, J.R., 110 Clark, D.P., 200 Clark, S., 248 Clausen, M.R., 119 Claustre, J., 138 Clayton, N., 81 Clendenin, W.M., 193 Clifton, P.M., 121 Clinton, S.K., 119 Coats, B.S., 89 Coca, M.A., 250 Coefﬁer, M., 91, 142 Cohen, B.A., 253 Cohen, D.P., 113

274

Cohen, G., 20 Colangelo, E.P., 256 Cole, S.P., 18 Colleran, E., 15 Collier, C.T., 97–98 Collier, P.J., 186 Collins, M.D., 89, 97, 114 Collins, S.M., 81 Combourieu, B., 80 Combs, G.F., 41, 46 Combs, S.B., 46 Comte, B., 90 Condon, S., 187 Cong, Y., 100 Connolly, E.L., 256 Conrads, T.P., 114 Constantinidou, C., 185 Conway, P.L., 189 Cooke, T.D., 30 Cooper, J.R., 131 Cooper, K., 87 Cooper, W.C., 5–6 Corﬁeld, A.P., 110 Corinaldesi, R., 81 Corner, G.A., 78 Cornivelli, L., 250 Cortese, M.S., 27 Cosenza, V., 79 Costerton, J.W., 30, 32, 188 Cotgreave, I.A., 46 Cotner, J.B., 175 Cottrell, G.S., 81 Courbeyrette, R., 236 Courel, M., 235 Cournoyer, B., 10, 19, 21 Coursange, E., 197 Courtin, O., 45 Courville, P., 234 Cox, E.C., 193 Cox, G.B., 44 Cox, S.G., 248 Crawford, R.L., 27–29 Cremon, C., 81 Crenn, P., 133

AUTHOR INDEX

Criddle, R.S., 41, 49 Crisp, R.J., 240–242, 245 Crooks, M., 247 Crossett, B., 113 Csordas, A., 137 Csotonyi, J.T., 11, 42 Cuber, J.C., 138 Cuevas, W.A., 185 Cui, Z.J., 143–145 Culotta, V.C., 233, 240, 247 Cummings, J.H., 75, 77–80, 100, 117–118, 123, 137 Cunnane, S.C., 139 Curle, C., 26 Curry, S.C., 27 Cvitkovitch, D.G., 187–188 Dahlmans, V.E., 88 Dahout-Gonzalez, C., 234 Dalal, V., 79 Daldal, F., 33, 47 Dalen, H., 81 Daly, K., 121 Damsz, B., 250 Danby, S.G., 186 Dancis, A., 233, 238, 242, 244 Dandekar, T., 93 Daniels, K., 246 Daniels, R., 186 Danot, O., 204 D’Argenio, G., 79 Darmaun, D., 133 Das, S.K., 84, 90 Daum, G., 249 Dauner, M., 87 Davey, M., 236 Daviaud, D., 79 David, F., 90 David, M., 7 Davidson, R.C., 246 Davies, D.G., 30 Davies, J.A., 36 Davies, N.A., 134 Davies, S., 9

AUTHOR INDEX

Davis, C.D., 145 Davis, H.M., 87 Davis, R.W., 236–238 Davis, S.R., 89 De Boer, E., 9 De Boever, P., 122 De Castro, C.L., 90 De Crecy-Lagard, V., 173 De Freitas, J.M., 245 De Giorgio, R., 81 de Graaf, A.A., 73, 84, 87, 89, 93–96, 99, 101, 106–107, 109, 122, 136 de Graaf, I.A., 80 de Jager, M.H., 80 de Ligt, R.A., 120 De Lorenzo, V., 11, 20 de Los Reyes-Gavilan, C.G., 112 De Mattos, M.J.T., 173, 181, 214 De Moll-Decker, H., 26 De Preter, V., 117, 124, 133 De Silva, D.M., 234 De Visser, J.A.G.M., 192 De Vos, W.M., 73, 93, 97–101, 106–107, 109–110, 113, 122, 143, 146 de Vries, M.C., 113 De Vuyst, L., 117, 124, 133 de Waard, P., 107, 109, 122 De Winde, J.H., 191 Dean, A.M., 169, 172, 176, 198, 200 Death, A., 174, 178–179, 185, 191, 197 Deboy, R.T., 104–105 Dechelotte, P., 91, 142 Deeley, R.G., 18 Defago, G., 36 Defer, N., 78 Degano, R., 247 Degener, J.E., 105 Dejong, C.H., 90 Delaunay, A., 254 Delavari, P., 190 Delisa, M.P., 188 Delle Cave, M., 79 Delle Valle, N., 79 Delort, A.M., 80

275

Demange, P., 8, 46 Demattos, M.J.T., 182 DeMeio, R.H., 7 Demeter, J., 235, 237 DeMoll-Decker, H., 13 Denamur, E., 193–194 Denis-Pouxviel, C., 79 Denton, M.B., 8 Depege, N., 256 Deplancke, B., 122 Derrien, M., 110, 113 Des Rosiers, C., 90 Deuticke, B., 47 Deutz, N.E.P., 82, 87, 89–90, 97, 107, 109, 122, 134–135 Dey, M., 193 Dhut, S., 254, 256 Di Gregorio, S., 12, 23 Di Tomaso, G., 5, 43 Diakos, C., 79, 81 Diaz-Orejas, R., 11 Diez, N., 129 Dignass, A.U., 81 Dijkema, C., 90, 107, 109, 122 Dijkhuizen, L., 177 Dimmick, R.L., 189 Dimmitt, R.A., 100 Ding, H., 137, 139, 243–244 Diop, L., 98 Distrutti, E., 119 Dockery, J., 35 Dodson, B., 83, 127, 131 Doebeli, M., 197 Dogan, H.B., 9 Dolnikowski, G.G., 84, 90 Dominguez, A., 247 Dominguez-Abascal, F., 79 Dong, H., 112 Donohue, J., 28 Donovan, S.M., 81 Donovan, T.J., 9, 27 Doornbos, R.P., 120 Dore´, J., 97–98, 101 Dorel, C., 189

276

Dorr, A., 129 Dowdle, P.R., 40 Drahovska, H., 15 Drenkard, E., 36 Drewke, C., 251 Driessen, M., 179 D’Souza, C., 246 Dubacq, C., 236 Ducrotte, P., 91, 142 Duda, V.I., 28, 32, 43 Dudley, R.E., 45 Duesterhoeft, S., 248 Duffy, B.K., 36 Dufour, Y.S., 173 Dugan, M.E., 91, 131–132 Dumon, H.J., 137, 141–142 Dumont, M.G., 91 Duncan, A.,, 118, 138 Duncan, S.H., 80, 116, 125–126 Dungan, J., 236–238 Dunham, M.J., 192, 206, 213 Dunkley, T.P., 114 Dunlap, B., 8 Dunlap, R.B., 8, 46 Duperchy, E., 197 Dupree, P., 114 Dusel, G., 79 Dwarakanath, A.D., 138 Dykhuizen, D.E., 169, 171–172, 175, 192, 194, 196, 200 Dyllick-Brenzinger, M., 17 Eastburn, K.K., 81 Eastwood, M.A., 119 Eckburg, P.B., 104–105 Eckerskorn, C., 8, 46 Eckhardt, K., 256 Edwards, C.A., 83, 127, 131 Edwards, J.S., 93, 134 Efendic, S., 89 Egert, M., 93, 99, 101, 106–107, 109, 122 Eggeling, L., 89, 96

AUTHOR INDEX

Egli, T., 172–173, 176–177, 179–180, 183–185, 187–188, 190–192, 197, 203, 207 Ehrlich, G.D., 32 Eichinger, D., 88, 90, 131 Eide, D.J., 234, 240, 248–249, 250–252 Eikmanns, B.J., 96 Eisenreich, W., 88, 90, 131 Eisner, H.D., 118 Eiteman, M.A., 181 Ekberg, K., 89 Elborough, K., 101 El-Demerdash, F.M., 45 Elena, S.F., 172, 196, 211 Elia, M., 137 Elkins, J.G., 33 Ell, P.J., 129 Ellen, R.P., 187–188 Ellis, C.D., 248 Ellis, C.J., 77, 117–118 Ellwood, D.C., 77 El-Mansi, E.M., 189 El-Meleigy, M., 46 El-Omar, E.M., 117 Elson, C.O., 100 Elsworth, R., 171 Elthon, T., 245 Elzinga, H., 87, 131 Emili, A., 236 Enfors, S.O., 174, 185 Engelberg-Kulka, H., 41 Engelen, M.P., 89–90, 97 England, R.R., 186 Englyst, H.N., 110 Engman, L., 46 Enss, M.L., 81 Epping, E., 34 Eriksson, M., 256 Ernstberger, H., 190 Escanero, J.F., 45 Esgalhado, M.E., 187 Espitalier-Noel, G., 80 Eubel, J.K., 7 Evans-Galea, M., 252

AUTHOR INDEX

Evenepoel, P., 133 Ezan, E., 254 Fahey, R.C., 40 Fairweather, N., 113 Falk, P.G., 100–101 Falkow, S., 101 Fan, A.M., 45–46 Fanning, A., 80 Farewell, A., 184 Fauchon, M., 253 Faure, M., 91, 142 Faustoferri, R., 187 Fava, F., 11 Fay, L.B., 91, 142 Fedi, S., 5, 11, 43, 47 Feil, E.J., 213 Fejdi, P., 15, 22, 44 Fekete, Z., 235 Feldman, M.W., 145 Fell, D.A., 93, 136 Felschow, D., 114 Feng, E., 112 Feng, G., 193 Feng, L.S., 252 Ferchaud-Roucher, V., 139 Ferea, T., 192, 206, 213, 233, 235, 237, 240 Ferenci, T., 169, 171–172, 174, 176–180, 183–185, 187–206, 208–213, 215 Fernandes, A.R., 246 Fernandes, L., 253 Fernandez-Banares, F., 79 Fernie, A.R., 145–146 Ferrer, M., 101 Fett, J.P., 256 Field, J.A., 90 Field, L.S., 247 Figurski, D.H., 18 Findlay, K., 32 Findley, S.D., 249 Finegold, S.M., 98, 122 Finkel, S.E., 197 Finlay, B.B., 80, 100

277

Finnie, I.A., 138 Finot, P.A., 91, 142 Fioramonti, J., 81 Fiorucci, S., 119 Fischer, D., 203 Fischer, E., 90, 180–181 Fischer, W., 4 Flamme, I., 256 Flatley, J., 187 Fleischer, S., 107 Fleming, A., 7 Fleming, J., 7 Flick, K., 255 Flier, J.S., 137 Flint, H.J., 80, 116, 126 Flohe, L., 38 Foglia, G.R., 36 Foladori, P., 43 Foley, C.S., 81 Folino, M., 79 Fomenko, D.E., 38–39 Fong, S.S., 182, 213 Fonty, G., 82, 122–123, 135 Forchhammer, K., 3, 7 Foster, P.L., 193, 199 Foster, S.J., 20 Fox, T., 245 Fraenkel, E., 243 Fragiadakis, G.S., 241 Fraley, C.D., 140 Franchini, A.G., 180, 183–185 Franchini, F., 83, 131 Francia, F.,, 28, 42–43, 48 Francois, P., 36 Frangeul, L., 101 Franke, K.W., 23 Frankenberger, W.T., 7, 12, 23, 29 Franklin, M.J., 32 Fraser-Liggett, C.M., 104–105 Fredericks, J., 33 Freitas, M., 98, 101 Frenais, R., 137, 141–142 Friedman, L., 34 Friedrich, M., 129

278

Friedrich, M.W., 107, 109 Friesen, M.L., 197 Froguel, P., 249 Fuchs, G., 88, 90, 131 Fuchs, J.A., 50 Fuentes, D.E., 20, 22, 48 Fuentes, L., 45 Fujimoto, M., 79 Fujimura, S., 9 Fujisawa, T., 9 Fujiyama, D., 9 Fujiyama, Y., 79, 81 Fukunaka, A., 234, 242 Fuller, M.F., 92, 132 Funchain, P., 193 Furet, J.P., 80 Furlong, C.E., 179 Furne, J.K., 77, 117–119 Fusunyan, R.D., 79 Gadd, G.M., 26, 34 Gaertner, K., 81 Gailer, J., 8 Gaither, L.A., 248, 251 Galea, C., 240 Gamet, L., 79 Ganapathy, V., 121 Ganessunker, D., 81 Ganther, H.E., 25 Gao, K., 120 Garber, E.A., 50 Garberg, P., 46 Garbisu, C., 41 Garcia, E., 235, 237 Garcia, J.J., 45 Garcia, S., 247 Garcia-Puges, A., 79 Gardner, J.L., 89, 114 Gardner, W.L., 41 Garin, J., 25, 33, 46 Garny, K., 188 Gasch, A.P., 248, 251 Gaskins, H.R., 81, 97–98 Gassull, M.A., 79

AUTHOR INDEX

Gatti, D.L., 50 Ge, K., 45 Geboes, K.P., 117, 124, 133 Geironson, L., 111 Gelb, M.H., 114 Gennis, R.B., 214 Gentry, D.R., 185 George, G.N., 8, 41 Gerard, B., 193 Gerber, G.K., 243 Gerber, S.A., 114 Gerdes, R.G., 46, 177 Gerhardt, M.B., 13 Germida, J.J., 19 Gerrard, T.L., 23 Gerrish, P.J., 192–193 Ghannoum, M.J., 38 Gharat, L., 80 Ghoos, Y., 133 Ghose, A., 7 Giardina, C., 79 Gibbons, J., 246 Gibson, G.R., 77, 80, 97, 117, 119, 122–123 Gibson, K.E., 202 Gibson, P.R., 79, 117–118, 121 Gibson, T.C., 193 Gidrol, X., 236 Giedroc, D.P., 256 Gifford, D.K., 243 Gilbert, P., 37, 186 Gill, S.R., 104–105 Gine, J.J., 79 Giordano, G., 21–22, 26, 28, 42, 48–49 Gitlin, J.D., 234 Gladyshev, V.N., 38–39 Glass, R.S., 8 Glenn, A.R., 39, 49 Glossenger, J., 114 Gloux, K., 101 Godelle, B., 194, 196 Godon, J.J., 97 Goebel, B.M., 97–99 Goering, P.L., 45

AUTHOR INDEX

Goldhaber, S.B., 45 Golyshin, P.N., 101 Golyshina, O.V., 101 Gomes, C., 20 Gomollon, F., 79 Goncharoff, P., 18 Gonzalez, A., 129 Gonzalez, C., 20 Gonzalez-Huix, F., 79 Gonzalez-Lara, V., 79 Goodacre, R., 115–116 Goodman, M.F., 193 Goodman, M.T., 140 Goodman, S.H., 28 Goosen, N., 183 Gordon, D.B., 243 Gordon, J.I., 98–101, 104–105, 130, 136–137, 139 Gordon, M., 251 Gosset, G., 185 Goulle´, J.P., 46 Goupry, S.M., 141 Gourley, B.L., 257 Gourse, R.L., 185 Grady, E.F., 81 Grahn, M.F., 79 Gralla, E.B., 245 Gramet, G., 97–98 Grant, C.M., 243 Green, F.B., 13 Green, J., 187 Greenberg, E.P., 30, 33, 188 Greenblatt, J., 236 Greenway, D.L.A., 186 Gregory, J.F., 89 Grifﬁths, R.I., 107 Groisman, A., 173 Groisman, I., 41 Gromer, S., 7 Groothuis, G.M., 80 Gross, C., 245 Grosse, S., 39, 42 Grundy, D., 141 Guant, L., 45

279

Gudelj, I., 182 Guerinot, M.L., 256 Guespin-Michel, J., 207 Guest, I., 7 Guilhaus, M., 112 Guller, L., 15, 22, 44 Gun, H., 9 Gunsalus, R.P., 181–182 Gupta, N., 45, 47, 121 Gupta, S., 180 Gu¨rleyu¨k, H., 41 Gutman, N., 17 Guyoneaud, R., 34 Guzzo, J., 243–244 Gyaneshwar, P., 184 Gygi, S.P., 114 Haack, K.R., 206 Haas, H., 237–238 Habeeb, R.L., 15 Hagen, K.D., 13 Hainsworth, E., 114 Hale, B., 253 Halestrap, A.P., 79 Halkman, A.K., 9 Hall-Stoodley, L., 30 Hamer, G., 176–177 Han, G.S., 249–250 Han, S.H., 249 Hancock, R.E.W., 177 Handelsman, J., 106 Hanikenne, M., 256 Hankemeier, T., 115 Hanna, M.N., 187–188 Hannett, N.M., 243 Hanselmann, K.W., 25–26, 46 Hansen, C.L., 173 Hansen, S.R., 177, 180 Hansson, L., 101 Hara, A., 123 Hara, K., 249 Harashima, T., 246 Harbison, C.T., 243 Harder, W., 175, 177

280

Hardin, G., 207 Harig, J.M., 79 Harman, I., 81 Harman, J.G., 185 Harmsen, H.J., 105 Harrigan, G.G., 142 Harris, C.I., 92, 132 Harris, R.M., 44 Harrison, C., 254, 256 Harrison, G., 26 Harrison, J.J., 1, 28, 30–34, 36–38, 45, 48 Harrison, K.A., 244 Harrison, M.D., 248 Harrison, P.R., 7 Hartl, D., 192 Hartl, D.E., 171, 175, 200 Hartwig, A., 45 Haruta, S., 143–145 Hasegawa, D., 12 Hasegawa, P.M., 250 Hassan, H., 80 Hassett, D.J., 33 Hatada, M., 8 Hatﬁeld, D.L., 8 Ha¨uXler, S., 35 Haurie, V., 243 Havekes, L.M., 88 Havenaar, R., 82, 119, 122–123, 135 Hayes, P.C., 134 Hayhurst, E.J., 20 Hazelton, G.A., 45 He, K., 46 He, T., 105 Heber, D., 120 Hecht, V., 180–181 Hecketsweiler, B., 91, 142 Heider, J., 3, 7, 38, 41 Heilig, H., 98 Heilig, H.G., 107 Heiner, A.M., 131 Heinzle, E., 146 Heitman, J., 246 Hellerstein, M.K., 78, 89, 114, 136, 146

AUTHOR INDEX

Helling, R.B., 192, 195–196, 205 Hellingwerf, K.J., 173, 181–182 Henderson, C., 116 Henderson, T.A., 37 Hengge, R., 183 Hengge-Aronis, R., 183, 203 Hennig, U., 82 Henning, S.M., 120 Hensel, R., 30 Herbert, D., 171 Herbig, A., 252 Heredia, J., 247 Hernandez, V.J., 185 Hernandez-Navarro, A., 253 Herrero, E., 243 Herweck, F., 79 Hettich, R.L., 113 Heulin, T., 23, 28, 35 Heuveling, J., 183 Heuvelink, A.E., 9 Heydorn, A., 32 Hieter, P., 243–244 Hilgetag, C., 136 Hill, M.J, 75–77 Hill, S.M., 15 Hill, T.M., 37 Hinojosa, J., 79 Hinton, J.C., 32 Hippler, M., 256 Hiramatsu, R., 9 Hirayama, H., 40 Hirkala, D.L., 19 Hirner, A.V., 30 Hixson, C., 114 Hjelle, J.D., 45 Hoa, N.T., 81 Hoac, T., 243–244 Hobbs, G., 172 Hobman, J.L., 16, 28, 44, 185 Hockin, S.L., 26, 34 Hodges, S., 134 Hodgetts, S.J., 236 Hofer, I., 10 Hoffman, P.S., 15

AUTHOR INDEX

Hoffmann, S., 45 Hogg, R.W., 199 Hoja, U., 234 Hold, G.L., 117 Hollibaugh, J.T., 40 Hollins, J.G., 27 Holman, P., 245 Holmgren, A., 25 Holms, W.H., 189 Holstege, F.C., 245 Holt, L., 79 Holzhutter, H.G., 93 Hong, J., 185 Hooper, L.V., 98, 100–101, 137, 139 Hooreman, M., 189 Hope, M.E., 117 Hord, N.G., 145 Horikoshi, K., 40 Horiuchi, T., 200, 205 Horl, W.H., 79, 81 Horne, M.T., 172 Horth, P., 112 Hosaka, T., 186 Hoskins, L.C., 110 Hoskisson, P.A., 172 Hou, Y., 14, 16, 44, 50 Houry, W.A., 236 Hove, H., 80 Howard, P.H., 3 Howitt, S.M., 44 Hoyer, L.L., 38 Hoyle, B.D., 32 Hsing, W.H., 202 Hsu, Y.C., 236 Hua, Q., 174, 180, 183–185, 190 Huang, C., 32–33, 112 Huang, G.H., 246 Huang, L., 112 Huang, N., 79 Huang, P., 112 Hubbard, A.K., 79 Hubbell, S.P., 177, 180 Huber, H., 42, 49 Huber, R., 8, 42, 46, 49

281

Huber, R.E., 39, 41, 49 Hudman, J.F., 39, 49 Hugenholtz, J., 146 Hugenholtz, P., 97–99 Hughes, M.N., 187 Hughes, R., 117–118 Huh, S.H., 45 Hui, D., 10 Huijser, P., 256 Huis in’t Veld, J.H., 82, 122–123, 135 Humblot, C., 80, 119 Humm, A., 46 Hurd-Karrer, A.M., 23 Hutton, M., 79 Huys, G., 117, 124, 133 Hwang, E.S., 119 Hylemon, P.B., 119, 130, 133 Hyman, M.A., 140, 146 Igarashi, Y., 143–145 Iglewski, B.H., 33 Igliki, F., 133 Ihssen, J., 173, 179, 185, 187–188, 203 Ikeda, I., 81 Ikeda, T.P., 184 Ikemura, T., 105 Im, E., 111 Imaizumi, K., 81 Inada, T., 185, 204 Inagaki, F., 40 Inan, M.S., 79 Ineson, P., 107 Innis-Whitehouse, W., 81 Iovino, P., 79 Irwin, B., 37 Isbister, J.D., 112 Isermann, N., 94–95, 136 Ishihama, A., 184, 189–190, 198, 203 Ishii, M., 143–145 Isnard, A.D., 254 Issaq, H.J., 114 Ito, Y., 249–250 Iwamoto, G.K., 81 Iwanaga, T., 79

282

Iwanyshyn, W.M., 249–250 Iwata, A., 184 Iyer, V.R., 245 Izaki, K., 14 Ize, B., 32 Jackson, A.A., 84, 132 Jacob, J., 7 Jacobs, J.L., 7 Jacobson, E.S., 246 Jacoby, G.A., 6, 14 Jalan, R., 134 James, B.W., 189 Janaky, T., 235 Jannasch, H.W., 172, 207 Jansson, K., 83 Jappe`, J., 12, 43 Jaron, S., 233, 235–236, 240 Jarrin, C., 101 Jarvis, G., 101 Jelacic, S., 15 Jellema, R.H., 115 Jennings, E.G., 243 Jensen, B.B., 82–83, 129–130 Jensen, K.F., 173 Jensen, L.T., 233, 240, 247, 252 Jeong, D.W., 45 Jerchel, D., 28 Jernelov, A., 3 Jhanke, G., 45 Ji, G., 50 Jickling, G., 17 Jin, J., 250 Jishage, M., 184, 186, 190 Jobin, C., 79 Jobin, M.P., 187 Jobling, M.G., 15 Johnson, A.D., 241 Johnson, J.R., 190 Johnson, R.C., 183 Johnston, C.N., 249 Jones, B.J., 257 Jones, G., 77 Jones, N., 254, 256

AUTHOR INDEX

Jones, T., 236–238 Jordan, M.I., 184 Jorgensen, H., 82–83, 129–130 Jorgensen, J., 82, 91 Joseph, P., 253 Joubert, L., 188 Joyce, A.R., 213 Ju, H.R., 81 Jung, Y.K., 45 Junot, C., 254 Juste, C., 80 Kadowaki, T., 249–250 Kagelari, O., 79 Kahn, R., 234 Kain, R., 117 Kaiser, I.I., 40 Kaiser, P., 254–256 Kaldalu, N., 37 Kamon, J., 249 Kanauchi, O., 79 Kapaniris, O., 118, 138 Kaplan, J., 234, 240–242, 245 Kaplan, S., 28, 42 Kaplanek, P., 236 Karaolis, D.K.R., 190 Karlson, U., 29 Karnbrock, W., 46 Karpichev, I.V., 250 Kashiwagi, A., 173 Kashket, E.R., 143 Kasimoglu, E., 182 Kasper, H., 79, 118 Kassen, R., 194, 207, 213 Kasumov, T., 90 Kataoka, M., 123 Katayama, S., 254, 256 Kato, S., 143–145 Katschinski, D.M., 256 Katsuno, M., 173 Kay, W.W., 44 Kayser, A., 180–181 Kearns, D.B., 34 Keelan, M., 9, 14–15

AUTHOR INDEX

Keevil, C.W., 188–189 Keilbaugh, S.A., 79 Keleher, C.A., 241 Keles, S., 245 Kell, D.B., 116 Kelleher, J.K., 84–85, 90, 93 Kelleher, M., 245 Keller, G., 243, 247 Kellermann, J., 8, 46 Kelley, W.L., 36 Kelly, A.J., 37 Kennington, E.A., 236 Kepner, J., 131 Keren, I., 37 Kerr, B., 145 Kersting, M.C., 249 Kessi, J., 25–26, 46 Keston, A.S., 85 Keyser, R., 34 Khachane, A.N., 101 Khalili, A., 34 Khandelwal, S., 45 Khodursky, A., 169, 200 Khodursky, A.B., 181 Kick, L.S., 89 Kidd, M., 80 Kien, C.L., 131 Killeen, E., 256 Kim, A.M., 32 Kim, I.Y., 45 Kim, J.H., 245 Kim, K.K., 250 Kim, K.-S., 18 Kim, M.S., 45 Kim, N., 45 Kim, T.S., 45 Kim, T.Y., 145 Kim, Y.K., 45, 89, 114, 234 Kimata, K., 204 Kimmel, E., 241, 245 King, T., 184, 189, 198, 202–203 Kingston, D.G., 80 Kinoshita, A., 146 Kirchhof, M., 183

283

Kirchner, T., 79 Kirdar, B., 136 Kirisits, M.J., 35 Kirkup, B.C., 145 Kishony, R., 187 Kispal, G., 235 Kita, S., 249 Kitamura, T., 249 Kitano, H., 215 Kitayama, T., 146 Kjeldgaard, N.O., 185 Klaassens, E.S., 113 Klapper, I., 35 Klassen, C.D., 45 Klausner, R.D., 233, 242, 244 Kleerebezem, M., 113, 143 Klein, E.A., 8 Klett, A., 27 Klonowska, A., 23, 28, 35 Klopprogge, K., 146 Klotz, L.O., 7 Klucar, L., 9, 15, 22, 44 Knight, S.A., 238, 244 Kochetov, G.A., 146 Koh, G.Y., 137, 139 Koike, S., 97–98, 109 Kokavec, J., 15 Kokkotou, E., 111 Kolonel, L.N., 140 Kolter, R., 30, 32, 34, 197 Komorowski, R.A., 79 Korach-Andre, M., 131 Korber, D.R., 32 Korch, S.B., 37 Kori, A., 184, 189, 198, 203 Kormutakova, R., 9, 15 Kornberg, A., 140 Kosman, D.J., 234 Kovarova-Kovar, K., 172, 190 Kowalik, L., 37, 187 Kownatzki, D., 94, 136 Koyasu, S., 249 Kozlowski, F., 130 Krafft, T., 42

284

Kramer, M., 252 Kramer, U., 256 Kratchmarova, I., 114 Kreienbring, F., 82 Krempf, M., 84, 90, 131, 139, 141 Kriaris, M.S., 110 Kriengsinyos, W., 133 Krishnan, S., 79 Kristensen, D.B., 114 Krogan, N., 236 Kromer, J.O., 146 Kroncke, K.D., 7 Kropat, J., 256 Kropﬂ, K., 12 Kruh, J., 78 Krul, C., 119–120 Kryukov, G.V., 39 Ktistaki, E., 241 Kubitschek, H.E., 172, 192, 195–196 Kuchel, P.W., 146 Kuenen, J.G., 175 Kuge, S., 254 Kuhl, M., 34 Kuhn, D.M., 38 Kuhn, R., 28 Kuipers, E.J., 120 Kuipers, O.P., 207 Kuleasan, H., 9 Kull, F.J., 39 Kumar, S., 25 Kunkle, M., 8 Kupchak, B.R., 250 Kuras, L., 255 Kurland, C.G., 188–189, 191 Kurlandzka, A., 207 Kuroda, M., 39 Kurpad, A.V., 133 Kussell, E., 187 Kustu, S., 184 Kusunoki, M., 79 Kvint, K., 184, 186 Kwast, K.E., 238, 240 Kwok, T., 243–244

AUTHOR INDEX

LaBaer, J., 114 Labarre, J., 25, 33, 46, 253–254 Labayen, I., 129 Labbe, S., 231, 237–238, 244, 246–247 Lacroix, C., 46 Laden, B.P., 47 Lafaye, A., 254 Lagniel, G., 25, 33, 46, 253–254 Lai, S.C., 201 Lai, W.S., 236 Lai, Z.S., 138, 143 Laine´, G., 46 Laishley, E.J., 26, 39 Laity, J.H., 252 Lallet, S., 235 Lambert, J., 143 Lamers, W.H., 135 Lampert, S.M., 234 Lampis, S., 12, 23 Lan, C.Y., 236–238, 241 Lan, R.T., 190 Landau, B.R., 89 Lange, H., 235 Langlois, R., 246 Lappin-Scott, H.M., 32 Larauche, M., 81 Larner, A.J., 29 Larsen, D.H., 189 Larson, D.N., 114 Lau, M., 242 Lau, T., 89 Lauer, K.P., 112 Lauquin, G.J., 234 Laurent, M., 207 Lavoinne, A., 91, 142 Lawrence, J.R., 32 Lawson, S., 13 Le Bizec, B.J., 141 Le Bourhis, A.-G., 97–98 Le Marchand, L., 140 Le, N.H., 194 Le, T.T.T., 189 Lear, W., 40 Lebioda, L., 8, 46

AUTHOR INDEX

Lecannu, G., 79 Leclaire, J., 45 Leclerc, J.E., 193 Lederer, H.M., 79 Lee, B.C., 41 Lee, B.L., 7 Lee, B.R., 185 Lee, D.Y., 145 Lee, H.C., 241 Lee, H.J., 119 Lee, J.I., 201 Lee, K., 93, 134 Lee, L.J., 187 Lee, P.W., 146 Lee, S.H., 45 Lee, S.Y., 45, 145 Lee, T.A., 255–256 Lee, T.I., 243 Lee, W.N., 82, 89, 91, 142 Leene, W., 28 Lehnert, B.E., 45 Lei, X.-H., 36 Leibler, S., 37, 187 Leibold, E.A., 257 Leighton, T., 41 Leinfelder, W., 3, 7 Lejeune, P., 189 Lemke, T., 107, 109 Lendenmann, U., 172, 176–177 Lengeler, J.W., 179 Lengeler, K.B., 246 Lens, P.N., 90 Lenski, R.E., 172, 192–193, 196–197, 199, 206, 211 Leong, S.A., 237 Lesuisse, E., 235–236, 238, 244 Letscher, D., 134 Lettinga, G., 90 Levchenko, A., 173 Leverve, X., 131 Levine, J., 77, 117–118 Levitt, M.D., 77, 117–119 Levy, R.D., 80 Lew, D.P., 36

285

Lewandowski, Z., 32 Lewinski, K., 8 Lewis, G.E., 13 Lewis, K., 30–31, 37 Lewis, T.A., 27 Ley, R.E., 98–99, 101, 130, 136 Li, B.G., 193 Li, J., 50 Li, Q., 112 Li, T., 112 Li, Y.H., 187–188 Liang, T.W., 81 Lichtenberger, L.M., 118 Lide, D.R., 5–6 Lien, K.A., 91, 131–132 Lightfoot, F.G., 138 Lill, R., 235, 240, 242–243 Lilley, K.S., 114 Lim, H.C., 185 Lim, S., 82, 89, 91, 142 Lin, H.Y., 174, 185 Lin, S.J., 233 Lindblow-Kull, C., 39 Linden, T., 256 Lithgow, J.K., 20 Liu, B., 129 Liu, M., 17 Liu, S., 37 Liu, X., 112 Liu, X.F., 240 Liu, X.Q., 174, 177–178, 183, 188, 202 Liu, Y., 120 Liu, Z., 39, 41 Ljungh, S., 111 Ljungqvist, O., 83 Lloyd, B.H., 15 Lloyd, S., 90 Lloyd-Jones, G., 11, 15–16, 22, 28, 44 Lobo, C., 173 Lobreaux, S., 256 Lodola, C., 20 Loguinov, A., 245 Lohmeier-Vogel, E.M., 48–49 Lombardi, G., 79

286

Lombardia, L., 253 Longin, R., 189 Lorenz, R.G., 100 Lorusso, L., 40 Losick, R., 34 Louis, P., 126 Lovitt, R.W., 172 Lowry, C.V., 240 Loyola, C.A., 20 Loza-Tavera, H., 253 Lu¨tkemeier, P., 47 Lu, J., 120 Lucchini, S., 32 Ludwig, W., 99 Lugtenberg, B., 177 Lugtenberg, B.J., 35–36 Luiking, Y.C., 90 Luis Moroder, L., 46 Luk, E., 247 Luli, G.W., 189 Lundquist, T., 13 Lundqvist, H., 46 Luo, H., 252 Luxon, P.L., 30 Luypaerts, A., 133 Lyons, A., 80 Lyons, J., 89 Lyons, T.J., 248, 250–251 Ma, J.F., 33 Ma, T.Y., 81 Maaloe, O., 185 Maathuis, A., 107, 109, 122 Macarthur, R.H., 208, 210 MacDermott, R.P., 79 MacDiarmid, C.W., 248 Macfarlane, G.T., 75, 77–78, 80, 100, 110, 117, 119, 122–123, 134–135 Macfarlane, S., 77, 119, 134–135 MacGregor, B.J., 107 Mack, C., 96 Mackay, W.G.,, 127 Mackie, R.I., 97–98, 109 Maclean, R.C., 182, 207

AUTHOR INDEX

Macsharry, J., 80 Macy, J.M., 13, 26, 42 Madara, J.L., 81 Madden, S., 8 Madoff, R.D., 118 Maeda, Y., 50 Maest, A.S., 40 Magee, E.A., 117–118 Mages, M., 12 Magnuson, A., 83 Maguin, E., 112 Magyarosy, A.C., 41 Mahadevan, R., 134, 146 Maharjan, R., 196–197, 199, 201–202, 204–206, 208, 210–212, 215 Maharjan, R.P., 180, 190, 208–209, 211–213 Mahieu, L., 46 Mai, D., 236 Makino, W., 175 Malagelada, J.-R., 119 Malagoli, C., 23 Malek, J., 182 Manche, K., 199, 204 Manchester, J.K., 101 Manderson, WG., 27 Maneﬁeld, M.,, 91, 107 Manegatti, M., 5, 43 Mangold, M., 101 Manichanh, C., 101 Manley, S.A., 8 Mann, C., 236 Mann, M., 114 Manning, S., 175, 196, 205, 207 Mao, E., 193 Maranas, C.D., 93 Marano, M.A., 89 Marciniak, J.Y., 182 Mardis, M.J., 37 Margolles, A., 112 Marguerie, G., 253 Marhan, S., 107 Mariadason, J.M., 78, 118 Marino, M.E., 89, 114

AUTHOR INDEX

Marks, L., 133 Marliere, P., 173 Marol-Bonnin, S., 82, 122–123, 135 Marques, L.L.R., 30–31, 36 Marquis, R., 187 Marr, A.G., 174, 191 Marsh, J.B., 84, 90 Marteau, P., 82, 101, 107, 122–123, 135 Martin, A.L., 3 Martin, F., 256 Martin, I., 9, 14–15 Martin, J.C., 116 Martin, L., Martin, L.J., 130, 137, 141–142 Martin, M., 36 Martin, P.M., 121 Martinez, J.A., 129 Martinez-Ballarin, E., 45 Martinez-Granero, F., 36 Martinez-Salmeron, J.F., 79 Marx, A., 94 Marzluf, G.A., 237, 244 Maskarinec, G., 140 Mason, C., 9 Mathan, M., 79 Mathisen, G.E., 98, 122 Matic, I., 193–194 Matin, A., 174–175, 185 Matin, M.K., 185 Mat-Jan, F., 200 Matskevich, V., 252 Matsumoto, M., 9 Matsunaga, T., 12 Matsushita, K., 182 Matthews, D.E., 87, 89 Matthews, R.C., 236 Mattick, J.S., 34 Mazzacca, G., 79 Mcauliffe, J., 184 McBain, A.J., 110, 122 McCabe, B.J., 84, 90 McCall, K., 252 McCormick, T., 38 McCracken, V.J., 100

287

McCulloch, A., 93, 134 McDermott, T.R., 33 McDonald, P., 50 McDonald, W.H., 255 McEvoy, J.L., 237 McFeters, G.A., 32–33 McGarr, S.E., 119, 130, 133 McIntosh, T.S., 87 McIntyre, A., 79 McKay, L.F., 119 McKee, W.B., 110 McLaren, A., 81 Mcleod, S.M., 183 McMurray, C.T., 253 McNamara, P.J., 36 McVey Ward, D., 240 McWilliam Leitch, E.C., 80, 126 Measday, V., 243–244 Medveczky, N., 177 Mehring, M., 30 Meier, S., 234 Meier-Eiss, J., 197, 206 Meijer, D.K., 80 Mencarelli, A., 119 Mendoza-Cozatl, D., 253 Meng, Y.L., 39 Mense, S.M., 240–241 Merchant, S., 256 Mercier, A., 238, 246 Merrin, J., 37, 187 Meshalkina, L.E., 146 Messing, B., 133 Metges, C.C., 91–92, 129, 132 Mettraux, C., 91, 142 Meyer, P.D., 82, 123–124 Meyer, S., 174, 185 Michalke, K., 30 Michel, C., 79, 130 Michelacci, F., 33, 47 Michiels, J., 186 Midtvedt, T., 98, 100–101 Mikkola, R., 188–189, 191 Milanick, M.A., 248 Millan-Plano, S., 45

288

Millard, S., 118, 138 Miller, J.H., 193 Miller, L.G., 40 Miller, S., 8 Miller, T.L., 90, 131, 144 Milne, J.B., 40 Mimouni, D., 7 Min, B.M., 45 Minchin, S.D., 185 Minekus, M., 82, 122–123, 135 Miner, P.B., 81 Misell, L.M., 89, 114 Mishina, M., 9 Mistou, M.Y., 112 Mitra, B., 39 Mitra, R.D., 253 Mitsuoka, T., 123 Mitsuyama, K., 79 Miwa, Y., 9 Miyagishi, M., 249 Miyagishima, N., 12 Miyazaki, Y., 9 Mizoguchi, E., 111 Mizota, T., 123 Mizutani, T., 182 Moeller, I., 79 Moennoz, D., 91, 142 Molenaar, D., 143, 146 Moles, J., 79 Molin, G., 188 Molin, S., 32, 189 Mollney, M., 89, 94, 96, 136 Momoshima, N., 50 Monahan, K., 187 Monod, J., 171, 174 Montigon, F., 91, 142 Mooney, M., 252 Moore, J., 117 Moore, M.D., 28, 42 Moore, R.E., 233 Morales, N.M., 190 Moran, B.J., 84, 132 Morano, K.A., 244, 246 Moreau, N.M., 141

AUTHOR INDEX

Morell, P., 47 Morelli, A., 119 Moreno-Sanchez, R., 253 Morgan, B.A., 254 Mori, H., 174, 180, 183–185, 190 Moris, M., 186 Morita, T., 79 Moro, F., 138 Moroder, L., 8 Morrison, D.J., 83, 87, 127, 131 Morselli-Labate, A.M., 81 Morshed, M.G., 9 Mortensen, P.B., 82, 91, 119 Morton, H.E., 28 Morton, R., 183 Moscoso, H., 20 Moseley, J.L., 256 Moser, H., 172, 195 Mostafa, M.E., 38 Mougel, C., 10 Moyed, H.S., 37 Moye-Rowley, W.S., 253 Mravec, J., 15 Muhlenhoff, U., 240, 242–243 Muir, J., 77 Muir, M., 179, 200 Mukai, Y., 231, 237–238, 244 Mukherjee, I., 256 Mukherjee, P.K., 38 Mukhopadhyay, C.K., 256 Mukhopadhyay, R., 50 Mukopadhyay, R., 5 Munshi, M.M., 9 Murakami, K., 249 Murat, J.C., 79 Murgia, I., 256 Murillo, L.A., 236–238 Murray, H.L., 243 Murray, R.D., 131 Murrell, J.C., 91, 107 Murthy, S.N., 79 Muskhelishvili, G., 185 Mutzel, R., 173 Myneni, S.C.B., 7

AUTHOR INDEX

Nagai, R., 249 Nagele, E., 112 Nagrath, D., 134 Nagy, A., 137, 139 Nagy, J., 113 Nakayama, Y., 146 Nalin, R., 101 Nanchen, A., 173, 180, 182, 191 Naquin, R., 256 Narang, A., 180 Narasimhan, M.L., 250 Navarro, E., 79 Naylor, C.P., 80, 117, 123 Nayyar, S.N., 185 Nealson, K.H., 187 Nebe, T., 79 Nedredal, G.I., 134 Neef, A., 101 Neese, R.A., 89, 114 Neidhardt, F.C., 180 Neijssel, O.M., 182, 214 Nelson, K.E., 104–105 Nerenberg, R., 13 Nesme, X., 10 Neubauer, P., 174, 185 Neuefeind, T., 46 Neve, J., 8 Neveu, N., 46 Newman, R.A., 44 Newman, R.D., 13 Newport, G., 236–238 Newton, J.M., 129 Ng, C., 174, 183, 188 Ng, L.K., 9, 14–15 Nguyen, P.G., 84, 90, 131, 137, 141–142 Nie, X.Y., 246 Niedermeyer, G., 110 Nielsen, J., 87 Nikaido, H., 177, 201 Nikolaev, E.V., 93 Nilsson, I., 188 Nishiumi, E., 182 Nishizono, S., 81 Niu, Y., 120

289

Niwano, K., 9 Noah, L., 137, 141–142 Noda, S., 182 Noh, K., 95 Nomura, A.M., 140 Norgren, L., 83 Notley, L., 174, 178, 183 Notley-Mcrobb, L., 174, 178, 180, 184–185, 187, 191–199, 201–206, 208–212, 215 Noumachi, W., 173 Novick, A., 171–172, 174, 180, 192, 194–195, 200, 205 Noviski, N., 89 Nunan, K.M., 13 Nusrat, A., 81 Nygren, J., 83 Nyhus, K., 246 Nystrom, T., 184, 186 Oberegger, H., 238 O’Brien, M., 111 Ochi, K., 186 Ochsner, U.A., 33 Oddie, K.M., 13 O’Dea, K., 77 Oden, K.L., 214 Odom, D.T., 243 Odom, J.D., 8, 46 Oehme, F., 256 O’Gara, F., 36 O’Halloran, T.V., 233 O’Hara, A.M., 80 Ohge, H., 118 Ohno, Y., 79 Ohteki, T., 249 Ojeda, L., 242–243 Okamoto, T., 79 O’Keefe, K.J., 190 O’Keefe, S.J., 80 Olde Damink, S.W., 134 Oleke, B.C., 23 Oliver, A., 194 Olson, M.E., 36

290

O’Mahony, C., 80 O’Mahony, L., 80 O’Morain, C.A., 81 Ong, S.E., 114 Onoe, K., 182 Oo, C., 129 O’Regan, P., 80 Oremland, R.S., 38, 40 Orlandi, S., 119 Osaki, S., 50 Osborn, A.M., 16, 28, 44 Osborn, S., 44 Oshima, T., 174, 180, 183–185, 190 Osiewacz, H.D., 245 Osterreicher, C.H., 79, 81 O’Sullivan, E., 187 O’Sullivan, M., 81 Oswald, W.J., 13 O’Toole, G.A., 32, 34 Ouni, I., 255 Ourania, R., 118 Ovari, M., 12 Overbeeke, N., 177 Owen, W., 89 Owira, P., 80 Pace, N.R., 97–99 Paganelli, G.M., 79 Pagano, I., 140 Page, M.D., 256 Painter, E.P., 23–24, 40 Palacios, R., 206 Palade, G.E., 28 Paliy, O., 184 Palmer, T., 32 Palmiter, R.D., 249 Palsson, B.O., 93, 134, 146, 182, 213 Pan, L.J., 138, 143 Pan, X., 246 Pandey, A., 114 Pang, C.P., 201 Panikov, N.S., 171, 191 Panja, A., 79 Papadopoulos, D., 197, 206

AUTHOR INDEX

Papamichos-Chronakis, M., 241 Parahitiyawa, N.B., 38 Paraskeva, C., 79 Parasuram, P., 252 Pardo, J.M., 250 Parekh, N.R., 107 Park, E., 45 Park, H.S., 45 Park, I.S., 45 Park, S.J., 182 Park, Y.C., 8 Park, Y.H., 185 Parker, S.B., 257 Parkos, C.A., 81 Parmar, R., 79 Parolini, O., 79, 81 Parra, M.D., 129 Parsek, M., 35 Parsek, M.R., 30–31, 33–34, 188 Parson, W., 238 Parsons, A.B., 236 Pasquinelli, G., 81 Passador, L., 33 Paszczynski, A.J., 27–29 Patlan, V., 186 Patten, C., 183 Patti, J.M., 8 Patton, E.E., 255 Paulo, P.L., 90 Payne, A.S., 234 Payne, W.L., 193 Pean, M., 131 Pebay-Peyroula, E., 234 Pedram, A., 81 Pelaez, F., 115 Pelletier, B., 231, 237–238, 244 Pelletier, E., 101 Pelzer, A., 45 Pena, M.M., 246 Pencharz, P.B., 89, 133, 139 Pereira, Y., 254 Perez, J.M., 20, 48 Perna, N.T., 9, 14–15 Peronnet, F., 131

AUTHOR INDEX

Perozziello, G., 173 Petat, C., 236, 253 Peterkofsky, A., 185 Peters, G., 36 Petersen, S., 89, 96 Peterson, D.A., 98–99, 101, 130, 136 Petit, J.M., 256 Petrakis, T., 241 Pettersson, M.E., 206 Pfeiffer, T., 144, 182, 213 Pﬂieger, D., 254 Pham, P., 193 Philippe, C., 119 Phillips, J., 77 Philpott, C.C., 233–235, 237–238, 240 Phung, L.T., 3, 13, 50–51 Pichuantes, S., 20 Pickering, I.J., 8, 41 Piechocki, R., 193 Piel, J., 10 Pierru, B., 42 Pignol, D., 42–43, 49 Pijl, H., 88 Pilawa, S., 38 Piller, F., 101 Piloquet, H., 139 Pilpel, Y., 253 Pilyugin, S.S., 180 Pinto, R., 193–194 Piper, M.D.W., 191 Piper, R.C., 234 Pirt, S.J., 171–172, 182, 191 Pitts, B., 32 Plaisancie, P., 138 Plishker, M.F., 21–22 Plugge, C.M., 107, 109–110, 122 Plumbridge, J., 204 Polen, T., 183 Polli, J., 80 Pollington, A., 240 Pomare, E.W., 80, 117, 123, 137 Pommerenke, B., 107 Pommier, J., 21–22, 26, 28, 42, 48–49 Pool, W., 82, 123

291

Poole, R.K., 177, 187 Pop, M., 104–105 Popham, D.L., 184 Porcelli, I., 32 Porcher, E., 196 Porter, T.D., 45, 47 Portnoy, M.E., 233, 240 Poser, B., 47 Postma, P.W., 179, 185, 190 Pot, B., 117, 124, 133 Pot, I., 243–244 Pothoulakis, C., 111 Potrykus, J., 34 Pouteau, E., 84, 90, 131, 137, 139, 141–142 Powers, L., 131 Powers, P., 131 Poynton, H., 245 Poyton, R.O., 238, 240 Prade, L., 46 Prado, M., 247 Prasad, P.D., 121 Prats, G., 190 Pratt, L.A., 202 Prenner, E.J., 8 Prensier, G., 189 Presser, T.S., 40 Preston, T., 83, 87, 127, 131 Pretzer, G., 143 Previs, S.F., 84, 90 Priebe, M.G., 131 Prigentcombaret, C., 189 Prince, R.C., 8, 41 Pringault, O., 34 Proctor, R.A., 36 Pronk, J.T., 179, 191 Prost, L., 35 Protchenko, O., 233, 240 Pryde, S.E., 116, 126 Pufahl, R.A., 233 Puig, S., 234–237, 242 Pulimood, A.B., 79 Pullan, S.T., 187 Pupo, G.M., 190

292

Qiao, W., 252 Quadroni, M., 179–180, 185, 197 Quake, S.R., 173 Quinlivan, E.P., 89 Quinn, J.J., 79, 256 Quinn, J.M., 256 Quivey, R.G., 187 Raangs, G.C., 105 Raasi, S., 255 Rabenstein, D.L., 25 Rabiei, M., 38 Rabot, S., 80, 119 Radajewski, S., 107 Radke, J., 246 Radman, M., 193 Raﬁi, M., 133 Ragab, A.M., 46 Ragas, P.C., 34 Rahaman, M.M., 9 Rainey, P.B., 194, 207, 213 Rakotoambinina, B., 133 Ram, R.J., 113 Ramachandran, N., 114 Ramadan, S.E., 46 Ramakrishna, B.S., 79 Ramakrishna, R., 93, 134 Ramanan, N., 238 Ramirez, A., 20 Ramirez-Solis, A., 5 Ramos-Montoya, A., 146 Rand, J.D., 186 Randi, M.R., 43 Rang, C.U., 189 Rashford, J., 233, 235, 237, 240 Rasoulpour, R.J., 79 Rastall, R.A., 122 Ratcliffe, R.G., 84 Rathgeber, C., 11, 28 Ratner, S.,, 85 Ray, E., 233, 235–236 Razak, A.A., 46 Reading, N.C., 187 Reamer, D.C., 30

AUTHOR INDEX

Rech, S., 42 Reches, M., 41 Rechkemmer, G., 79 Redd, M.J., 241 Redhead, D.N., 134 Redler, B., 83 Reed, J.L., 134 Reed, S.I., 255 Reeves, G.T., 180 Reeves, P.R., 190 Regalla, L.M., 250 Rege, B., 80 Reilly, M.P., 7 Relman, D.A., 101, 104–105 Remesy, C., 79 Ren, B., 243 Ren, Y.X., 138, 143 Renga, B., 119 Rensen, P.C., 88 Rensing, C., 39 Rerat, A.A., 82 Resch, A., 41 Reszko, A.E., 90 Revhaug, A., 134 Reyes-Duarte, D., 101 Rhee, S.H., 111 Rhodes, J.M., 138 Richardson, C.J., 118 Richet, E., 204 Richter, F., 79 Rickard, K., 118 Ridlon, J.M., 119, 130, 133 Riedel, C., 96 Rieder, C., 88, 90, 131 Riedesel, H., 81 Riegler, M., 111 Riera, J., 79 Rigottier-Gois, L., 97–98, 101 Riley, M.A., 145, 196, 201 Rinaldi, N.J., 243 Rinas, U., 180–181 Rist, B., 114 Ritchie, D.A., 15–16, 28, 44 Rittenberg, D.,, 85

AUTHOR INDEX

Rittmann, B.E.,, 13 Rivilla, R., 36 Roberfroid, M.B., 122 Robert, F., 243 Roberts, I.S., 34 Robins, R.J., 130 Robinson, A.K., 248 Robinson, N.J., 248 Roca, J., 101 Rochet, V., 97–98 Rodarte, G., 236–238 Rodgers, J., 248 Rodrigues-Pousada, C., 253–254 Rodriguez Lemoine, V., 16, 20 Rodriguez-Manzaneque, M.T., 243 Roe, F., 32 Roediger, W.E., 78, 99, 117–118, 123, 138 Rogers, J., 79 Rojas, D.M., 33 Rolfes, R., 235, 237 Roller, M., 79 Roller, S.D., 187 Rombeau, J.L., 79 Romero, D., 206 Romero-Gomez, M., 134 Romijn, J.A., 88 Rooker, M., 9, 14–15 Rooyackers, O., 83 Roper, N.J., 28, 30–34, 37 Ros, J., 243 Rose, C., 134 Rose, D., 42, 49 Rose, M.R., 196 Roseiro, J.C., 187 Rosella, O., 79 Rosen, B.P., 5, 39, 50 Rosenberg, D.W., 79 Rosenberg, H., 177 Rosenzweig, F., 192, 206, 213 Rosenzweig, R.F., 196, 205, 207, 213 Rossol, S., 79 Rosson, R.A., 187 Roth, H., 131

293

Roth, J.R., 206, 212 Rothenberger, D.A., 118 Rother, M., 41 Rothman, D.L., 84, 90 Rotte, C., 235 Rouault, T.A., 242 Rouch, D.A., 16, 28, 44 Rouillon, A., 255–256 Roviezzo, F., 119 Rowland, I.R., 77, 119 Rozen, D.E., 196 Rudd, K.E., 212 Ruppin, E., 93 Russo, T.A., 190 Rutgeerts, P., 133 Rutgers, M., 190 Rutherford, J.C., 233, 235–236, 242–244, 246 Rutten, E.P., 90 Ryan, F.J., 195 Rydzynski, K., 81 Rye Clausen, M., 80 Saadi, S., 18 Saadia, R., 80 Saarela, M., 107 Saavedra, C.P., 20–22, 48 Sabaty, M., 42–43, 49 Saber, S.M., 38 Sacher, M., 42, 49 Saemann, M.D., 79, 81 Saetre, A., 17 Saftic, S., 188 Sagliocco, F., 243 Sahm, H., 89, 96 Said, H.M., 81 Saier, M.H., 185 Saito, M., 9 Sakazaki, R., 9 Sakihama, Y., 40 Sakono, M., 81 Saleh, F.A., 9 Saltman, L.H., 18 Salyers, A.A., 110

294

Samaranayake, L.P., 38 Samaranayake, Y.H., 38 Samuel, B.S., 104–105 Samuels, M., 254 Sanchez, B., 112 Sanchez-Lombrana, J.L., 79 Sanchez-Romero, J.M., 11 Sanders, O.I., 39 Sanderson, I.R., 79 Sanford, K., 116 Sangurdekar, D.P., 181 Santini, D., 81 Santini, J.M., 38 Santos, V.A., 101 Sapin, C., 98, 101 Sartor, R.B., 79 Sasaki, M., 79 Sasaki, R., 234, 242 Sata, S., 9 Satokari, R.M., 107 Sauer, K., 30 Sauer, N., 234 Sauer, U., 90, 173, 180–182, 191 Sauer, W.C., 91, 131–132 Sawers, G., 3, 7 Saxena, J., 3 Saxer, G., 197 Schade-Serin, V., 89, 114 Schaffner, W., 257 Schellhorn, H., 183 Schemann, M., 141 Scheppach, W., 78–79, 118, 137 Scheppe, M.L., 193 Scherrer, R., 37 Schertzberg, M., 183 Scheu, S., 107 Schicker, A., 173, 180, 182, 191 Schilling, C.H., 93, 134 Schlegel, A., 204 Schmitt, P., 187 Schneider, D.A., 185, 196–197, 206 Schneider, L.K., 195 Schoenheimer, R., 85 Schoeser, M., 238

AUTHOR INDEX

Schols, A.M., 89–90, 97 Schro¨der, I., 42 Schreiner, O., 28 Schrenzel, J., 36 Schreurs, W.J., 143 Schuldiner, S., 17 Schultz, J., 241 Schuren, F., 113 Schuster, S., 93, 136, 144, 182, 213 Schweizer, E., 234 Sebat, J.L., 27 Seckler, R., 8 Sediari, L., 119 Seeram, N.P., 120 Seeto, S., 174, 180, 191–194, 196–199, 201–206, 208–213, 215 Segel, I.H., 41, 49, 80 Segre, D., 93 Sekine, S.I., 186 Sekirov, I., 80, 100 Selivanov, V.A., 146 Semenkovich, C.F., 137, 139 Sen, S., 134 Senn, H., 176–177 Sentenac, A., 253 Seok, Y.J., 185 Serra, J., 119 Severance, S., 234 Shachar-Hill, Y., 84 Shah, D., 37 Shah, M., 113 Shakoury-Elizeh, M., 235, 237 Shamberger, R.J., 24, 40 Shanahan, F., 80 Shane, B., 89 Shaner, L., 246 Sharp, R.R., 196, 213 Shauger, A.E., 184 Shehata, T.E., 174, 191 Sheikh, B., 243–244 Shen, W.C., 246 Sherburne, R., 14 Sherburne, R.K., 15 Shibata, Y., 249

AUTHOR INDEX

Shibutani, Y., 7 Shim, J., 45 Shimada, T., 9 Shimizu, K., 174, 180, 183–185, 190 Shimizu, T., 249 Shingler, V., 186 Shirazi-Beechey, S.P., 121 Shlomi, T., 93 Shlyapnikov, M.G., 28, 32, 43 Shoemaker, J., 180 Shohat, B., 7 Shohat, M., 7 Shrift, A., 39 Shu, Y.Z., 80 Shulman, R.G., 84, 90 Shuman, H., 178, 203 Siddik, Z.H., 44 Siddiqui, A.A., 81 Siddons, C.A., 9, 27 Sidique, T., 23 Siefke, C., 94 Siekel, P., 15, 22, 44 Sies, H., 7 Silar, P., 245 Silhavy, T.J., 202 Silver, S., 3, 13, 50–51 Silverberg, B.A., 30 Simon, I., 243 Simpson, S.C., 196 Singer, M.V., 79 Singh, B., 79 Singh, P.K., 36 Sinskey, A.J., 173 Sipos, K., 235 Sisson, G., 15 Slater, C., 83, 87 Slootweg, T., 11 Slupska, M.M., 193 Small, G.M., 250 Smeets-Peeters, M., 82, 122–123, 135 Smid, E.J., 146 Smidt, H., 93, 98–99, 101, 106–107, 109, 122 Smith, E.A., 117

295

Smith, N.R., 41 Smits, M.A., 143 Smits, W.K., 207 Sa´nchez-Contreras, M., 36 Snel, J., 143 Snell, P., 129 Sniegowski, P.D., 193 Snozzi, M., 172, 176–177 Soave, C., 256 Soergel, K.H., 79 Soeters, P.B., 82, 134–135 Soll, D.R., 246 Solovjeva, O.N., 146 Sommer, H., 79 Song, L.X., 50 Sonnenburg, J.L., 98–99, 101, 130, 136 Sonti, R.V., 206 Sorgenfrei, O., 146 Sosa, A., 20 Soto, F., 256 Soucaille, P., 116 Soularue, P., 253 Souza, V., 193 Spadafora, P.J., 139 Sperandio, V., 187 Spizzo, T., 248 Spoering, A., 30, 37 Springer, S.E., 39 Springﬁeld, J.R., 77, 117–119 Sproule, K.M., 30–31, 34, 37–38 Sredni, B., 7, 47 Sredni, D., 7 Srikantha, T., 246 Srinivasan, C., 252 Stackebrandt, E., 11, 28, 42 Stacpoole, P.W., 89 Stadtman, T.C., 39, 41 Stams, A.J., 90 Stanghellini, V., 81 Stanley, N.R., 32 Stappenbeck, T.S., 101 Stark, P.R., 114 Starkey, M., 34–35 Stearman, R., 233, 244

296

Steen, H., 114 Steensma, H.Y., 240 Steinbacher, S., 8, 46 Steinberg, N.A., 40 Steiner-Mordoch, S., 17 Steipe, B., 8, 46 Stell, A.L., 190 Stellaard, F., 87, 89, 131 Stemmler, T.L., 5 Stensonholst, L., 188 Stephanopoulos, G.N., 93, 134 Sterkenburg, A., 177 Stevens, A.M., 173 Stewart, C.S., 116, 125–126 Stewart, D., 256 Stewart, J., 193 Stewart, P.S., 32–35 Stillman, D.J., 250 Stingl, U., 107 Stockl, J., 79, 81 Stofﬂer, G., 237–238 Stolz, J.F., 38, 40, 234 Stoodley, P., 30, 32 Storey, D.G., 36 Strand, I., 83 Strap, J.L., 188 Stremick, C.A., 30–31, 33, 36, 45, 48 Strengman, E., 245 Strike, P., 15–16, 28, 44 Strohl, W.R., 189 Strompl, C., 101 Stroobant, P., 96 Struhl, K., 253 Stuchlik, S., 15, 22, 44 Su, N.Y., 254, 256 Su, T., 245 Suarez, F.L., 119 Suau, A., 97 Sufya, N., 37, 186 Sugano, M., 81 Sugimoto, M., 146 Sugiyama, K., 79 Sugiyama, T., 249 Sullivan, M.X., 28

AUTHOR INDEX

Sultanul Aziz, K.M., 9 Summers, A.O., 3, 6, 13–15 Suter, P.M., 140 Sutherland, I.W., 34 Sutren, M., 97 Sutter, V.L., 98, 122 Suyk-Wierts, J.C., 134 Suzina, N.E., 28, 32, 43 Suzuki, Y., 9 Svensater, G., 187–188 Swearingen, J.W., 21–22 Sweetlove, L.J., 145–146 Swierczek, S., 252 Swings, J., 117, 124, 133 Switzer-Blum, J., 40 Szczypka, M.S., 253 Szentkuti, L., 81 Szilard, L., 171, 174, 180, 192, 194–195 Szomolay, B., 35 Szyperski, T., 89 Tabet, J.C., 254 Tacnet, F., 254 Taddei, C., 5, 43 Taddei, F., 193–194 Tadler, S.C., 196 Tagami, H., 204 Tagne, J.B., 243 Tainer, J.A., 253 Taira, K., 249 Takahashi, H., 185 Takahashi, K., 79 Takahashi, T., 9 Takai, K., 40 Takata, Y., 140 Takekawa, S., 249 Takezawa, Y., 182 Takizawa, K., 9 Talke, I.N., 256 Tamarit, J., 243 Tan, K.-S., 25 Tanabe, H., 79 Tanaka, Y., 204

AUTHOR INDEX

Taneja, R., 80 Tang, S., 241 Tannock, G.W., 101 Tantalean, J.C., 20, 48 Tapiero, H., 7 Tarantino, D., 256 Tari, L.W., 17 Tarr, P.I., 15 Tavan, E., 98 Taylor, A., 27, 46 Taylor, B.A., 138 Taylor, D.E., 3, 9, 12, 14–19, 21, 23–24, 27–28, 38, 44, 46, 48, 50 Te Biesebeke, R., 113 Teich, A., 174, 185 Teitzel, G.M., 30–31 Teixeira De Mattos, M.J., 190 Telford, J.N., 23 Telling, R.C., 171 Tempest, D.W., 182 Ten Have, G.A., 134 Tenailleau, E., 130 Tenaillon, O., 193–194, 196 Ter Linde, J.J., 240 Terada, A., 123 Terauchi, Y., 249 Teusink, B., 88, 146 Tew, K.D., 7 Theg, S.M., 256 Thelen, M.P., 113 Thelin, A., 101 Theondel, M., 36 Thiele, D.J., 234–237, 242, 245–246, 253 Thomas, D., 255–256 Thomas, J.W., 28 Thomassin, S., 187 Thompson, C.M., 243 Thompson-Eagle, E.T., 12, 29 Thorell, A., 83 Thuillier, F., 133 Tichonicky, L., 78 Tiedeman, J.S., 233, 235, 237, 240 Timmis, K.N., 101 Tippin, B., 193

297

Tobe, K., 249 Toda, T., 254, 256 Toews, A.D., 47 Tokunaga, T.K., 7 Toledano, M.B., 254 Tolker-Neilsen, T., 34 Tolmachev, V., 46 Tomas, J.M., 44 Tomita, F., 182 Tomita, M., 146 Tomizawa, J., 200, 205 Tomlin, K.L., 28, 31–34 Tompkins, R.G., 134 Tong, A., 236, 243–244 Tong, W.H., 242 Toninello, A., 44 Toone, W.M., 254 Topalidou, I., 241 Topping, D.L., 121 Toptchieva, A., 15 Torrallardona, D., 92, 132 Tottey, S., 256 Touati, D., 25, 33, 46 Toupance, B., 194 Townsend, D.M., 7 Toyonaga, A., 79 Travers, A., 185 Travisano, M., 196–197, 199 Tremaroli, V., 33, 47 Tresan, R.B., 13 Treves, D.S., 175, 196, 205, 207, 213 Trevors, J.T., 13 Trezeguet, V., 234 Trobner, W., 193 Troger, J., 256 Trott, A., 244 Trugnan, G., 98, 101 Trutko, S.M., 28, 32, 43 Tsang, P.W.K., 38 Tsay, R., 89 Tsen, S.D., 201 Tseng, C.P., 182 Tsuchida, A., 249 Tsui, H.C.T., 193

298

Tsujikawa, T., 79, 81 Tsuno, N.H., 249 Tsunoda, M., 249 Tu, N., 15, 22, 44 Tucker, F.L., 28 Tuleu, C., 129 Tumpling, W., 12 Turecek, F., 114 Turna, J., 9, 15, 22, 44 Turnbaugh, P.J., 104–105 Turner, P.E., 190 Turner, R.J., 1, 3, 12, 14, 16–19, 21–24, 26, 28, 30–34, 36–38, 42–46, 48–50 Tweeddale, H., 178, 180, 187 Tyers, M., 255–256 Tyson, G.W., 113 Tzamarias, D., 241 Tzeng, C.M., 140 Uchida, S., 249 Uchiyama, T., 105 Ueda, S., 184 Ueta, R., 234, 242–244 Uetrecht, J., 7 Ulgen, K.O., 136 Ung, S., 48–49 Ungerstedt, U., 83 Urabe, I., 173 Urbanowski, J.L., 234 Vaara, M., 177, 201 Vagstad, A., 250 Vahouny, G.V., 138 Vaidyanathan, S., 115 Vaisanen, M.L., 80 Valachovic, M., 242 Valdes, J.J., 188 Valentine, J.S., 245 Valkova, D., 15 Vallini, G., 12, 23 Vallino, J.J., 93 van Bakel, H., 245 Van Dam, K., 179, 182, 190 van de Kerkhof, E.G., 80

AUTHOR INDEX

Van De Putte, P., 183 van den Bogaard, A.E., 134 Van den Broek, D., 35–36 van den Heuvel, E.G., 82, 123 van der Greef, J., 96 van der Heijden, R., 96 van der Laan, M., 245 van der Meer, R., 143 van der Vossen, J.M.B.M., 82, 123 van der Werf, M.J., 115 van der Woude, J.C., 120 van Eijk, H.M., 87 Van Fleet-Stalder, V., 30, 41 Van Iterson, W., 28 van Leeuwen, P.A., 90 van Lier, J.B., 90 van Netten, P., 9, 27 van Nuenen, M.H., 119–120 van Nuenen, M.H.M.C., 82, 123–124 Van Tassell, R.L., 80 Van Veen, H.W., 44 Vandaux, P., 36 Vandenbroek, P.J.A., 179 Vanderleyden, J., 186 Vandijken, J.P., 179 Vanhoutte, T., 117, 124, 133 Vargas, C.N., 192, 195–196, 205 Vary, J.C., 15 Vasquez, C., 20 Vasquez, C.C., 20–22, 33, 48 Vassylyev, D.G., 186 Vassylyeva, M.N., 186 Vaughan, E.E., 97–98, 107, 110, 113 Vavrova, S., 15 Veening, J.W., 207 Veenstra, T.D., 114 Velazquez, O.C., 79 Veldkamp, H., 172 Vemuri, G.N., 181 Vendeland, S., 8 Venema, K., 73, 82, 93, 99, 101, 106–107, 109, 120, 122–124 Venkataraman, K., 252 Veprek, B., 3, 7

AUTHOR INDEX

Verbeke, K., 117, 124, 133 Verberkmoes, N.C., 113 Verbrugghe, K., 117, 124, 133 Vercellotti, J.R., 110 Verkade, P., 81 Vermeglio, A., 12, 21–23, 25–26, 28, 33, 35, 39, 42–43, 46, 48–49 Vermeulen, M., 119 Vermunt, S.H., 123 Verne, G.N., 139 Verstraete, W., 122 Veyrier, F., 234 Vicente, M.F., 115 Vidal, O., 189 Vik, S., 34 Vilaire, G., 238 Vilchez, G., 16, 20 Villa, N.Y., 250 Villacieros, M., 36 Vinceti, M., 23 Vinella, D., 185 Vinh, J., 254 Vitali, B., 112 Vitkup, D., 93 Vivian, A., 19, 21 Vivoli, G., 23 Vlegels, E., 177 Voisard, C., 237 Volkert, T.L., 243 Vollmann, A., 42, 49 Vollmer, M., 112 Von Eiff, C., 36 Von Engelhardt, W., 81 von Lieres, E., 96 Vonk, R.J., 131 Voshol, P.J., 88 Vulpe, C., 245 Wachters-Hagedoorn, R.E., 131 Wada, M., 182 Waddington, W.A., 129 Wagner, A., 215 Wagner, B., 107, 109 Wagner, M., 47

299

Wagner, R.R., 201 Wagner, S.A., 110 Wagner-Recio, M., 47 Wahl, S.A., 87 Wahren, J., 89 Waisberg, M., 253 Wait, R., 113 Wajngot, A., 89 Waki, H., 249 Waldron, S., 87 Walker, A.W., 80, 126 Wallace, J.L., 119 Walmsley, A.R., 50 Walmsley, R., 45 Walper, J.F., 28 Walter, E.G., 3, 14–16, 18 Walter, J., 101 Walters, M.C., 32 Wang, B., 112 Wang, F., 248 Wang, H., 112 Wang, H.N., 181 Wang, J., 112, 237 Wang, J.D., 138, 143 Wang, P., 246 Wang, Q.Y., 138, 143 Wang, T., 137, 139 Wang, Y., 238 Wang, Z., 252 Wanner, B.L., 184 Ward, T.R., 15 Wasinger, V., 112 Wasserman, D.H., 90 Watanabe, C., 8 Watanabe, K., 105 Watanabe, S., 19, 21 Waters, B.M., 240, 248 Watson, J.H.P., 77 Watson, R.B., 114 Watt, W.B., 172, 176, 200 Watzl, B., 79 Waugh, M., 246 Weaver, C.T., 100 Weaver, G.A., 131

300

Weaver, L.T., 83, 87, 127, 131 Webb, D.C., 44 Weber, H., 183 Weber, J., 180–181 Weerapreeyakul, N., 80 Wegrzyn, G., 34 Wei, E.T., 23 Wei, K., 112 Weickert, M.J., 197, 199 Weilenmann, H., 191–192 Weilenmann, H.U., 187 Weiner, J.H., 3, 12, 14, 16–19, 21–24, 26, 28, 38, 42, 44, 46, 48–50 Weintraub, A., 8 Weitman, H., 47 Welling, G.W., 105 Welty, F.K., 84, 90 Wemmie, J.A., 253 Wen, X., 79 Wen, Z., 45 Wendisch, V.F., 183 Wenger, R.H., 256 Werner, A., 245 Werner, E., 32 Werner, M., 253 West, S.E., 33, 110 Westerhoff, H.V., 182 Weusthuis, R.A., 179 Whanger, P., 8 Wheeler, G.L., 243 Whelan, K.F., 15 Whitchurch, C.B., 34 White, K.N., 50 Whited, G., 116 Whitehall, S.K., 248 Whiteley, A.S., 91, 107 Whitﬁeld, C., 34 Whitham, G.H., 4 Whitman, W.B., 41 Whittaker, S., 9 Wiback, S.J., 134, 146 Wick, L.M., 179–180, 185, 191–192, 197 Wickenheiser, E.B., 30 Wiechert, W., 87, 89–90, 94–96, 136

AUTHOR INDEX

Wiersma, A., 143 Wijmenga, C., 245 Wildgust, M.A., 50 Wilding, I., 129 Wilkens, L.R., 140 Wilkins, T.D., 80, 110 Williams, C.R., 200 Williams, H.H., 23 Williams, L., 179, 200 Williams, N.S., 79 Williams, R., 134 Williamson, J., 89 Williamson, P.R., 246 Williamson, W.M., 11 Wilmes, P., 113 Wilson, A.J., 118 Wilson, D.C., 133 Wilson, E.O., 208, 210 Wilson, K.H., 97 Wilson, T.H., 201 Wimpenny, J.W., 172 Winge, D.R., 233, 235–236, 242–243, 245, 247, 250–252 Winkler, M.E., 193 Winkworth, C.L., 196 Winstone, T.L., 17 Winterstein, C., 33, 47 Wintz, H., 245 Winzerling, J.J., 8 Wittenberg, C., 255 Wittmann, C., 146 Wohlschlegel, J.A., 255 Wolever, T.M., 139 Wolfaardt, G., 188 Wolfe, R.R., 84, 88–89 Wolin, M.J., 90, 131, 144 Wong, M.H., 101 Wong, P.T.S., 30 Wood, C.M., 79 Woods, J.H., 136 Woods, R., 196 Worsham, M.B., 40 Wouters, E.F., 89–90, 97 Wouters, J.T.M., 177

AUTHOR INDEX

Wozniak, D.J., 34 Wright, A., 113 Wright, B.E., 199 Wright, N.E., 186 Wright, S., 214 Wright, W.F., 13 Wu, A.L., 253 Wu, C.Y., 252 Wu, G.D., 79 Wu, H.Y., 81 Wu, Q., 8 Wunsche, J., 82 Wurzel, M., 94–95 Wycoff, T.O., 34 Wykes, L.J., 133 Wyrick, J.J., 243 Xia, F., 32 Xia, Y., 8 Xiqui, M.L., 20 Xu, A., 120 Xu, J., 101 Xu, K.D., 32 Xu, P., 237 Yakimov, M.M., 101 Yamada, A., 12 Yamaguchi-Iwai, Y., 233–234, 240, 242–244 Yamai, S., 9 Yamato, Y., 40 Yamauchi, T., 249–250 Yamdagni, R., 8 Yanagisawa, N., 111 Yang, C., 46, 174, 180, 183–185, 190 Yang, G., 45 Yang, H., 79 Yarema, M.C., 27 Yarmush, M.L., 93, 134 Yates, J.R., 255 Yau, J.Y.Y., 38 Ye, J., 38 Yee, A., 41 Yen, J.L., 254–256

301

Yerry, S., 90, 131, 144 Yeung, A., 193 Yeung, S.K.W., 38 Yilmaz, A., 9 Yilmaz, H., 9 Yin, L., 79 Ying, T., 112 Yokomizo, T., 249 Yokota, A., 182 Yokoyama, S., 186 Yokoyama, T., 134 Yomo, T., 173 You, L.C., 173 Youderian, P., 20, 48 Young, G.P., 79, 117 Young, M.Y., 7 Young, P.A., 40 Young, R.A., 243 Young, V.R., 89, 133 Younis, H.S., 8 Ytrebo, L.M., 134 Yu, E.Y., 8 Yu, H.N., 8 Yu, L., 46 Yu, P.L., 180 Yu, Y.M., 89 Yuan, D.S., 248 Yuan, J., 112 Yuen, K., 243–244 Yugi, K., 146 Yun, C.W., 233–234 Yun, D.J., 250 Yun, H.S., 185 Yurkov, V., 11–12, 28, 42–43 Yurkova, N., 11, 28 Zadic, P.M., 9, 27 Zadra, I., 237–238 Zagorec, M., 112 Zahir, Z.A., 7, 12 Zair, Y., 139 Zanaroli, G., 11 Zannoni, D., 1, 5, 11, 28, 33, 42–44, 47–48

302

Zarzov, P., 256 Zawadzka, A.M., 27–29 Zeitlinger, J., 243 Zello, G.A., 89 Zeng, M., 112 Zeyl, C.W., 192 Zhang, E., 201–202 Zhang, H., 255 Zhang, L., 240–241 Zhang, S., 246 Zhang, X.J., 89, 112 Zhang, Y., 7, 12, 23, 38–39, 90, 112, 131, 144 Zhang, Z.G., 173, 185 Zhang, Z.S., 138, 143 Zhao, H., 248–249, 252 Zhao, R., 236, 246 Zheng, D.L., 185 Zheng, O., 46

AUTHOR INDEX

Zhong, S., 200 Zhong, S.S., 138, 143 Zhou, L.W., 237 Zhou, T., 50 Zhu, L., 112 Zhu, X., 246 Zhu, Z., 247 Ziglio, G., 43 Ziman, M., 36 Zinoni, F., 3, 7, 41 Zitomer, R.S., 240 Zlabinger, G.J., 79, 81 Znaidi, S., 244 Zoetendal, E.G., 97–98, 100, 107, 109 Zohri, A.A., 38 Zoller, W.H., 30 Zuckermann, F.A., 81 Zumbrennen, K.B., 257 Zurakowski, D., 89

Subject Index Note: The page numbers taken from ﬁgures and tables are given in italics.

acetate-scavenging bacteria, 206 adenylate cyclase, 185–186 ADH1 gene, 250–252 ADH3 gene, 250–251 ADH4 gene, 251 alarmone ppGpp, 186 amino acids branched chain, 119 generation by gut bacteria, 91–92 metabolism by isotope labeling, 131–134 in humans, 91 3-amino-1,2,4-triazole (AT), 208 ammonia, 119 anaerobic bacteria, 76 Anaerostipes caccae, 126 anaplerosis, 96 antagonistic pleiotropy, 202–203 anti-inﬂammatory cytokines, 79 anti-inﬂammatory effect, 79 Arabidopsis, 256 arginine metabolism in humans, 89 ArsAB, 50 ArsC, 50 arsenate reductase, 50 Aspergillus nidulans, 237 Aspergillus parasiticus Var. globosus, 38 Astragalus bisulcatus, 12 autoinducers, 188 Bacillus, 23 Bacillus mycoides, 12 bacterial ﬂagellin, 111 bacteriocins, 143, 145 Bacteroides, 118

Bacteroides thetaiotaomicron, 101 biﬁdobacteria, 122, 125–127 Bifidobacterium, 131, 143 Bifidobacterium adolescentis, 126 bioﬁlms, see microbial bioﬁlms biomethylation of tellurium, 27–28 branched chain amino acids, 119 branched chain fatty acids (BCFAs), 119 butyrate, 78–79, 117–118, 121, 123, 126, 131 C. albicans, 38, 237–239, 244, 246 iron deﬁciency in, 236 C. difficile, 124 C. diphtheriae, 29 C. glutamicum, 96, 146 C. neoformans, 246 C. pasteurianum, 26, 39 C. tropicalis, 38 cadmium responsive activation of gene expression, 253–254 transcriptional control, 253–254 sensors, 254–256 toxicity, 253 Caenorhabditis elegans, 257 cAMP, 179–180 Candida glabrata, 246 Candida spp, 37–38 carbohydrate fermentation, 77, 79 metabolism, 76, 83, 253 ceﬁxime-tellurite media, 9

304

cell-cell signaling, 33 Cfd1 cytosolic protein, 242 chalcogenides comparison among, 4 glasses, 4 chalcogens, see also polonium; selenium; tellurium bacterial exposure, 5 bacterial physiology and, 38–45 biological reduction, 22–29 biochemical pathways for, 24 compounds, 4 efﬂux transporter, 44–45 mechanism of toxicity, 45–49 metabolic intermediates, 5 methylation in bacteria, 29–30 microbial processing of metalloid, 22–29 reactions, 4–5 sequestration by bioﬁlm matrix, 34–35 use in selective bacterial growth media, 9–10 challenger mechanism, 30 chemostat, physiological changes in organisms inoculated into metabolism and energetics, 181–183 stress regulation and gene expression, 183–187 transport and membrane permeability, 177–181 chemostat-adapted mutants, diversity in transport strategies, 213 chemostat cultures, organisms in, response to environmental stress, 187 chemostat environment applications to bacterial studies, 173–177

SUBJECT INDEX

changes resulting from mutations, 195 mutational takeovers and population changes, 195–197 mutation rates and mutators in, 192–195 nutrient-limited, 171 variation in behavior among various species, 189–191 chemostats, divergence in, 207–211 heterogeneity detected in indicator plates, 209 in metabolic and bioenergetic strategies, 213–214 in regulatory strategies, 211–213 in transport strategies, 213 Chitinase 3-like-1 (CHI3L1), 111 Chlamydomonas, 256 chlorhexidine, 188 cholecystokinin (CCK) receptors, 140 Citrobacter, 23 13 C-labeled carbohydrate substrates, 131 Clostridium, 133, 144 Clostridium perfringens, 107 cloxacillin, 203 colanic acid, 34 colon cancer, 77, 80, 118, 133 colorectal cancer, 117, 119 colorectal distension (CRD), 119 colorectal tumorigenesis, 117 copper homeostasis, 245, 256 copper-responsive gene activation, 245–246 sensors, 246–247 Coprococcus sp., 125 CRD-induced nociception, 119 Crohn’s disease (CD), 101 patients, 111 Crp protein, 185 CTH2 gene, 236–237 Cu, Zn superoxide dismutase, 245 cytochrome c oxidase (COX), 43, 245 cytochrome oxidase complex, 232

SUBJECT INDEX

cytoplasmic nitrate reductases, 26 cytosolic Fe–S cluster synthesis, 242 cytosolic glutathione peroxidase (GSHpx), 46 deuterium labeling, 89 diacylglycerol pyrophosphate phosphatase gene (DPP1), 249–250 dietary fat, 133 dipicolinic acid, 27 Drosophila, 256 E. coli, 26, 39, 44, 105, 176, 193 adaptive stress responses, 35 bacterial persistence, 37 bioﬁlms formation by, 34 divergence amongst coexisting isolates in a chemostat culture, 209, 210, 211 DSS640, bioﬁlms of, 32–33 nitrate reductases (NR) from, 43 selenite toxicity, 45–46 strains, 32, 144–145 under aerobic conditions, 190 thiol oxidation in, 49 thioredoxin reductase (TR) from, 25 transport and membrane permeability, 177–181 E. coli, in glucose-limited chemostats antibiotic sensitivity, 187–188 metabolism and energetics, 181–183 mgl mutations in aerobic glucoselimited, 198–200, 207 quorum sensing, 188–189 stress regulation and gene expression, 183 growth rate and ppGpp, 186 nutritional status and cAMP, 185–186 physiological responses to environmental stresses, 187 starvation and stress signals and RpoS, 184–185

305

Embden–Meyerhof–Parnas, 131 enterobacter, 23 Enterococcus faecalis, 110 environmental bioinorganic chemistry, 3 environmental stress, response of organisms to, 187 eosin–methylene blue (EMB), 209 ethanolamine kinase gene (EKI1), 249 Eubacterium hallii, 126 Eubacterium rectale, 126 Eubacterium sp., 130 F. prausnitzii, 125, 126 FBA, 136 Fe–S cluster synthesis, 235–236, 240, 242, 244 Fiaf, 139–140 ﬁngerprinting techniques, 91 ﬂagellin, 111 ﬂuxome, 78 formic acid, 125 Fourier transform (FT) mass spectrometers, 87 fractional synthesis rates (FSRs), 114 fructooligosaccharides, 122, 123, 127 fungi, metalloregulators in, 232–257 G. stearothermophilus, 22 V, 21, 48 galactooligosaccharides, 122 galS, 198 GATA factors, iron-responsive transcriptional regulation and, 237–239 GC-combustion-isotope ratio mass spectrometry (GC-C-IRMS), 87 gene expression cadmium responsive activation of, 253–254 in response to haem deﬁciency, 241 Zap1-dependent activation of, 248–250 repression of, 250–252

306

gene repression iron-responsive, 236–237 in S. cerevisiae, 236–237 Zap1-dependent, 250–252 germ-free (GF) rodents, 137 GlpF, 39 GLT1 gene, 237 gluconeogenesis, 96 in humans, 89 glucose–galactose binding protein, 180 glutamine, 142 glutathione reductase (GR), 25 green ﬂuorescent protein (GFP), 105–106 Grx3 monothiol glutaredoxin, 243 Grx4 monothiol glutaredoxin, 243 Grx5 monothiol glutaredoxin, 243 gut metabolic ﬂux analysis applied to, 127–136 pH 79–80 gut-associated nitrogen metabolism, 92 gut bacteria amino acid generation by, 91–92 effect on immune system, 81 metabolic activation of drugs by, 80–81 gut microbial ecosystem, importance of butyrate, 78–79 gut microorganisms, role of, 136–137 energy balance, 137–138 innate immune system, 138–139 in obesity, 139–141 haem synthesis, 239 Hap2 protein, 238 Hap3 protein, 238 Hap4 protein, 238 Hap5 protein, 238 Helicobacter pylori, 111 histone deacetylase (HDAC), 121 homocysteine remethylation metabolism in humans, 89 Hsp82 protein, 236

SUBJECT INDEX

humans amino acids metabolism by isotope labeling in, 91 arginine metabolism in, 89 gastro-intestinal (GI) tract, 75–77, see also gut; intestinal bacteria gluconeogenesis in, 89 homocysteine remethylation metabolism in, 89 hydrogen sulﬁde (H2S), 118, 119 hydroxyurea (HU), 236 hypocholesterolaemic effects, 29 IBD, see inﬂammatory bowel disease IBS, see irritable bowel syndrome IL-10, 120 inﬂammatory bowel disease (IBD), 77, 137 in situ SIP approach, 106–109 intestinal bacteria, genomic inventories, 97–98 microbial communities, 98–100 microbiome, 100–106 stable isotope probing, 106–109 intestinal bacterial enzymes, functions of, 110–111 intestinal bacterial metabolism, 75– 78 and gut health, 77– 78 metabolic activation of drugs by, 80–81 MFA in, see metabolic ﬂux analysis role of stable isotopes in, 84–85 intestinal bacterial physiology, 143–145 methods to study, 81–84 intestinal diseases, 77 intestinal microbiota, proteomic studies of, 111–113 intestinal symptoms, from gaseous metabolites, 119 inulin, 123, 124, 141–142 iron homeostasis, 235–236, 245 sensors, 242–244 starvation, 234, 239

SUBJECT INDEX

iron-responsive gene activation in S. cerevisiae, 233–236 iron-responsive gene repression in S. cerevisiae, 236–237 iron-responsive transcriptional regulation, 233 and GATA factors, 237–239 iron-responsive transcription in absence of oxygen, 239–242 irritable bowel syndrome (IBS), 77, 81, 119 symptoms, 119 Isa1+ gene, 238 Isa1 protein, 238 iscS gene, 48 isomaltooligosaccharides, 122 isotope-coded afﬁnity tags (ICAT), 114 isotopomers, 87, 94 IZH1 gene, 250 IZH2 gene, 250 Kaschin-Beck disease, 45 Keshan’s disease, 45 kilAtelAB, 11, 12, 18–19, 23 b-lactams, 203 lactitol, 123 Lactobacillus plantarum, 143 lactose-limited chemostats, 201 lactulose, 122, 123 ingestion, 139 lamB gene, 179 LamB protein, 179, 202, 204 leucine, 131 macrophage cell line, 120 maltotriose, 179 MalT protein, 205 marine purple non-sulfur bacteria, 12 mass isotopomer analysis, 89 mass isotopomer distribution analysis (MIDA), 89 mass spectrometry (MS), 87

307

mast cell activity for intestinal function, 81 matrix metalloproteinases (MMPs), 111 metabolic ﬂux analysis (MFA), 78, 93–95 applied to gut, 127–136 of colonic microbiota, 134–135 in detecting microbial metabolic stress, 95–97 metabolites, 115, 120 gaseous, 119 toxic, 117 metabolomics, 115–116 metal ion homeostasis, 232, 242 metalloids, 50 methicillin-resistant Staphylococcus aureus (MSRA), 9, see also S. aureus methylation of chalcogens, 29–30 methyl cobalamin, 29–30 a-methyl glucoside, 208 mglD, 198 mgl mutations in aerobic glucoselimited E.Coli, 198–200 microbial bioﬁlms, 13, 30–32 adaptive stress responses, 35 formation, 189 fungal bioﬁlms, 37–38 genetic diversity and colony morphology variants, 35–36 matrix, sequestration in, 34–35 persister cells, 36–37 physiology, 33 structure and susceptibility, 32–33 minimization of metabolic adjustment (MOMA), 93 mitochondrial Fe–S cluster biogenesis, 242 mlc and malT mutations in chemostats, 204–205 monocarboxylate transporter isoform 1 (MCT1), 121 mucins, 98, 110, 118, 119, 142 multiple tracer techniques, 89

308

mutational takeovers and population changes, 195–197 mutation rates and mutators in chemostat populations, 192–195 mutations in chemostat populations and their physiological effects ampliﬁcation and other genomic rearrangements, 206–207 changes in lac system, 201 metabolic changes and cross-feeding, 205–206 mlc and malT, 204–205 outer membrane changes, 201–203 ptsG, 205 rpoS, 203–204 mutators, 194 Mycobacterium avium complex, 9 myeloperoxidase, 79 N. crassa, 237, 244 NapA, 26 Nar1 cytosolic protein, 242 NarGHIJ, 26 NarZUWV, 26 Nbp35 cytosolic protein, 242 neuromodulator, 119 nitrate reductases (NR), 42 periplasmic and membrane-bound, 43 nitrosamines, 120 NMR spectroscopy, 87 non-essential metals, detoxiﬁcation of, 232 nutrient-limited chemostat, 171 OmpC, 179 OmpF protein, 179 organochalcogen compounds, 29 oro-cecal transit time (OCTT), 131 oxalate, 125 Oxalobacter formigenes, 125 oxygen, 239–242 P. aeruginosa, 10–11, 31, 33, 43 ATCC 27853 bioﬁlms, 33

SUBJECT INDEX

PA14, SCV cells of, 36 selenite toxicity range, 45 P. anserina, 245 P. chlororaphis O6, SCV cells of, 36 P. denitrificans, 43 P. fluorescens bioﬁlms of, 36 K27, 30 phenotypic variation in, 36 tellurate addition, 48 P. pseudoalcaligenes, 43 KF707, 47 P. putida, 27 Paracoccus pantotrophus, 43 Paralvinella sulfincola, 11 Pcl1+ gene, 238 Pcl1 protein, 238 PCR techniques, 91 pectin, 123 Penicillium chrysogenum, 237 periodic selection events, 196 periplasmic nitrate reductase, 26 persister cells, 31, 188 PhoE, 178 phosphatidylinositol synthase gene (PIS1), 249–250 phosphoenol pyruvate, 213 phosphotransferase system, 180 planktonic cultures, 33 plant rhizoﬁltration, 12 polonium, 49–50, see also chalcogens polychlorinated biphenyl (PCB) degradation, 11 polyubiquitylation, 254, 256 porin(s), 188, 201–202 genes, 188–189 proteins, 179 potassium tellurite, 27 prebiotic carbohydrates, 122 prebiotics, effects on intestinal microbiota, 121–124 proteins, involved in iron acquisition, 234

SUBJECT INDEX

protein fermentation, 77, 80 microorganisms for, 124 proteomic studies of intestinal microbiota, 111–113 proton-coupled symporters, 180 Pseudomonas, 23 Pseudomonas pseudoalcaligenes, 11 Pseudomonas putida, 11 Pseudomonas spp, 35, 36 Pseudomonas stutzeri, 27 Pst proteins, 178 ptsG mutations, 200 PtsG system, 213 pyridine-2,6-bisthiocarboxylic acid (PDTC), 24 quorum-sensing (QS), 33, 143, 188–189 R. capsulatus, 26, 28, 43, 47, 48 R. sphaeroides, 30, 39, 43 radioactive tracers, 86 Ralstonia eutropha, 43 Ras2 signalling pathway, 250 reactive oxygen species (ROS), 25 reduced thiol (RSH), 48 regulatory on/off minimization (ROOM), 93 resistant starch (RS), 123 healing effect, 141–142 rhizosphere bacteria, 23 Rhodocyclus tenuis, 30 Rhodospirillum rubrum, 30 ribonucleotide reductase protein, 236 RNA as biomarker, 107 Roseburia intestinalis, 126 Roseburia sp., 125, 126 RpoS, 189, 202 protein, 184, 190 rpoS, 203–204 S. aureus, 9, 31 ATCC 29213, planktonic cultures of, 33

309

SCV cells of, 36 selenite toxicity range, 45 S. cerevisiae, 191, 233, 236, 238–240, 243–248, 253, 256 iron-responsive gene activation in, 233–236 repression in, 236–237 S. pombe, 237–239, 244, 246, 248, 253–254 Saccharomyces, 207 S-adenosylmethione (SAM), 29 Salmonella typhimurium, 39, 206 SCFA, 75, 77, 78, 79, 91, 107, 117, 123, 131 acetate, 139 concentration in case of death, 79–80 metabolism, effect of pH on, 80 metabolism with stable isotopes, 128 from prebiotic fermentation, 122 production, colonic, 132 production in anaerobic cultures of fecal bacteria, 134 uptake in colonic lumen, 121 schizophrenia, 119 SCV cells of S. aureus, 36 Sdh4+ gene, 238 Sdh4 protein, 238 selenate toxicity, 49 selenite/selenate resistance genes, 21–22 selenite toxicity, 49 selenium allotropic forms, 6 applications in biotechnology/ industry/ bioremediation, 11–13 in bioﬁlms, 34 biological reduction, 23–27 biological uses, 7–8 biomethylation, 29–30 chemical properties, 7 compounds, 6 in diet, 45–46 occurrence, 6 oxyanions, nitrate reductase (NR) activity on, 42–43 oxyanions, resistance towards, 13–14

310

precipitation in SRB bioﬁlms, 34–35 toxicity in bacteria, 49 toxicity in eukaryotic cells, 45–46 use in structural biochemistry, 8 selenocysteine, 8 selenodiglutathione, 25 selenomethionine, 29 Selenomonas ruminatum, 39 selenopersulﬁde, 26 selenosis, 45 sensors cadmium, 254–256 copper-responsive, 246–247 iron-responsive, 242–244 zinc-responsive, 252 Shewanella oneidensis, 34–35 Shiga toxin-producing E. coli (STEC) O26, 9 short-chain fatty acids, see SCFA siderophores, 27 biosynthesis, 238 SILAC (stable isotope-labeling with amino acids in cell culture), 114 silicates, 4 sinigrin, 119 SIP techniques, 93 small colony variant (SCV) cells, 35 sodium-coupled monocarboxylate transporter (SMCT1), 121 sodium selenite, 31 stable isotopes, 85–86 applications in biomedicine, 90–93 for cross-feeding of microorganisms on acetate and lactate, 127 detection, 87 metabolic ﬂux analysis using, 93–95 probing of intestinal bacteria, 106–109 role in host–microbe interaction, 141–142 role in proteomic study of gut microbiota, 114–115

SUBJECT INDEX

role in study of biosynthetic pathway, 88–89 SCFA metabolism with, 128 stable isotope-aided quantiﬁcation of metabolic pathways, 135–136 stable isotope-aided studies, 89 on nucleic acids, 91 of proteins, 91 stable isotope-based dynamic metabolic proﬁling (SIDMAP), 142 stable isotope-labeling amino acid metabolism by, 131–134 techniques, 84 stable isotope probing (SIP), 91 Stenotrophomonas maltophilia, 12 Streptococcus bovis, 107 stress-induced mutagenesis, 193–194 sulfatases, 118 sulfate-reducing bacteria (SRB), 26, 34, 117, 118–119 Sulfurospirillum, 23 T. selenatis, 26 tagatose, 123 taurine, 133 tellurate, 9 resistance genes, 21 toxicity, 48 tellurides, methylated, 29 tellurite, 9–10, 10–11, 29, 42, 45 resistance determinants, 11, 12 toxicity, 46–48 use in selective bacterial growth media, 9–10 tellurium appearance, 5 applications in biotechnology/ industry/ bioremediation, 11–13 bioﬁlms, 34 biological reduction, 27–29 biological uses, 7–8

SUBJECT INDEX

biomethylation, 27–28 combination with Pt, 6 environmental forms, 6 methylation, 29–30 occurrence in earth’s crust, 5 oxyanions, resistance towards, 13–14 recovery, 5 structural biochemistry, 8 Ter determinants and, 14–21 toxicity in bacteria, see tellurate, toxicity; tellurite, toxicity telluromethionine, 8, 29 Thauera selenatis, 13 Thaurea, 23 thioredoxin reductase (TR) from E. coli, 25 thioredoxin (Trx), 25 time-of-ﬂight (TOF) mass spectrometers, 87

tracer–tracee ratio (TTR), 88 tristetraprolin (TTP) protein, 236 U937, 120 ubiE gene, 21, 22 ubiquitylation, 254, 256 urea, 132–133 ureum, 132 Ustilago maydis, 237 VHT1 gene, 237

311

Wolinella, 23 Wood–Ljungdahl pathway, 131 Yarrowia lipolytica, 247 Zap1-dependent activation of gene expression, 248–250 Zap1-dependent repression of gene expression, 250–252 Zap1-dependent sensing, 252 zinc homeostasis, 249 responsive transcriptional regulation, 247–248 sensing, 252 starvation, 250 Zap1-dependent activation of gene expression, 248–250 repression of gene expression, 250–252 sensing, 252 zinc responsive elements (ZREs), 248, 251 ZRC1 zinc permease gene, 248 ZRG17 zinc permease gene, 248 ZRT1 gene, 252 ZRT2 gene, 250–251 Zymomonas mobilis, 251