Molecular Geomicrobiology 0939950715

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REVIEWS i n MINERALOGY a n d Geochemistry Volume 59

2005

Molec u l a r Geom ic robiolo g y EDITORS: Jillian F. Banfield

University of California, Berkeley Berkeley, California

Javiera Cervini-Silva

University of California, Berkeley Berkeley, California

Kenneth H. Nealson

University of Southern California Los Angeles, California

FRONT COVER: Dinitrogen (N2) bound at the molybdenum-iron-sulfur active site of the nitrogenase enzyme complex, where it is ultimately reduced to biologically-useful ammonia. Nitrogenase is the only enzyme known to catalyze such a transformation, and is found only in prokaryotes, representing a crucial shunt between the inorganic and organic worlds. Created by Jason Raymond and Jill Banfield.

Series Editor: Jodi J. Rosso MINERALOGICAL SOCIETY o f AMERICA GEOCHEMICAL SOCIETY

Copyright 2005

Mineralogical Society of America The appearance of the code at the bottom of the first page of each chapter in this volume indicates the copyright owner’s consent that copies of the article can be made for personal use or internal use or for the personal use or internal use of specific clients, provided the original publication is cited. The consent is given on the condition, however, that the copier pay the stated per-copy fee through the Copyright Clearance Center, Inc. for copying beyond that permitted by Sections 107 or 108 of the U.S. Copyright Law. This consent does not extend to other types of copying for general distribution, for advertising or promotional purposes, for creating new collective works, or for resale. For permission to reprint entire articles in these cases and the like, consult the Administrator of the Mineralogical Society of America as to the royalty due to the Society.

Reviews in Mineralogy and Geochemistry ( Formerly: Reviews in Mineralogy )

ISSN 1529-6466

Volume 59

Molecular Geomicrobiology ISBN 093995071-5





Additional copies of this volume as well as others in this series may be obtained at moderate cost from:

The MINERALOGICAL SOCIETY of AMERICA 3635 Concorde Parkway, Suite 500 Chantilly, Virginia, 20151-1125, U.S.A. www.minsocam.org

Dedication Dr. William C. Luth has had a long and distinguished career in research, education and in the government. He was a leader in experimental petrology and in training graduate students at Stanford University. His efforts at Sandia National Laboratory and at the Department of Energy’s headquarters resulted in the initiation and long-term support of many of the cutting edge research projects whose results form the foundations of these short courses. Bill’s broad interest in understanding fundamental geochemical processes and their applications to national problems is a continuous thread through both his university and government career. He retired in 1996, but his efforts to foster excellent basic research, and to promote the development of advanced analytical capabilities gave a unique focus to the basic research portfolio in Geosciences at the Department of Energy. He has been, and continues to be, a friend and mentor to many of us. It is appropriate to celebrate his career in education and government service with this series of courses in cutting-edge geochemistry that have particular focus on Department of Energy-related science, at a time when he can still enjoy the recognition of his contributions.

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M OLECULAR Geomicrobiology 59

Reviews in Mineralogy and Geochemistry

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From the Series Editor This volume was prepared in advance of a short course entitled “Molecular Geomicrobiology.” The short course, sponsored by the Mineralogical Society of America, the Geochemical Society, the US Department of Energy, and NASA Astrobiology Institute, was held at the University of California, Berkeley, December 3-4, 2005 prior to the fall AGU meeting in San Francisco, California. Errata (if any) can be found at the MSA website www.minsocam.org. Jodi J. Rosso, Series Editor West Richland, Washington October 2005

Preface As geomicrobiologists, we seek to understand how some of nature’s most complex systems work, yet the very complexity we seek to understand has placed many of the insights out of reach. Recent advances in cultivation methodologies, the development of ultrahigh throughput DNA sequencing capabilities, and new methods to assay gene expression and protein function open the way for rapid progress. In the eight years since the first Geomicrobiology volume (Geomicrobiology: Interactions between microbes and minerals; volume 35 in this series) we have transformed into scientists working hand in hand with biochemists, molecular biologists, genome scientists, analytical chemists, and even physicists to reveal the most fundamental molecular-scale underpinnings of biogeochemical systems. Through synthesis achieved by integration of diverse perspectives, skills, and interests, we have begun to learn how organisms mediate chemical transformations, the ways in which the environment determines the architecture of microbial communities, and the interplay between evolution and selection that shapes the biodiversity of the planet. This volume presents chapters written by leaders in the rapidly maturing field we refer to as molecular geomicrobiology. Most of them are relatively young researchers who share their approaches and insights and provide pointers to exciting areas ripe for new advances.   This volume ties together themes common to environmental microbiology, earth science, and astrobiology. The resesarch presented here, the associated short course, and the volume production were supported by funding from many sources, notably the Mineralogical Society of America, the Geochemical Society, the US Department of Energy Chemical Sciences Program and the NASA Astrobiology Institute. We thank Jodi Rosso for her editorial contributions. October 2005

1529-6466/05/0059-0000$05.00

Jillian F. Banfield Javiera Cervini-Silva Kenneth H. Nealson DOI: 10.2138/rmg.2005.59.0

TABLE of CONTENTS 1

The Search for a Molecular-Level Understanding of the Processes that Underpin the Earth’s Biogeochemical Cycles





Jillian F. Banfield, Gene W. Tyson, Eric E. Allen, Rachel J. Whitaker

Characterizing biogeochemical systems......................................................1 Molecular geomicrobiology: opportunities and challenges..........3 Concluding comments..............................................................................................5 Acknowledgments......................................................................................................5 REFERENCES.......................................................................................................................6

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What Genetics Offers Geobiology Dianne K. Newman, Jeffrey A. Gralnick

INTRODUCTION..................................................................................................................9 DEFINITIONS......................................................................................................................10 What is genetics?..........................................................................................................10 How is genetics different from molecular biology and genomics?..............................10 What is a mutant? ........................................................................................................ 11 What is mutagenesis?................................................................................................... 11 TYPES OF GEOBIOLOGICAL PROBLEMS THAT GENETICS CAN SOLVE.............. 11 PRACTICAL CONSIDERATIONS FOR CREATING GENETIC SYSTEMS...................15 Step 1: Isolation and growth.........................................................................................15 Step 2: Methods of mutagenesis...................................................................................17 Genetic polarity in bacteria..........................................................................................20 Step 3: Identifying mutants..........................................................................................20 Step 4: Mutant verification...........................................................................................22 A brief note on phage...................................................................................................22 Step 5: Mutant analysis................................................................................................23 CONCLUSIONS..................................................................................................................23 Acknowledgments....................................................................................................24 References.....................................................................................................................24

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Enzymology of Electron Transport: Energy Generation With Geochemical Consequences Thomas J. DiChristina, Jim K. Fredrickson, John M. Zachara

INTRODUCTION................................................................................................................27 ENZYMATIC BASIS OF IRON AND MANGANESE REDUCTION..............................27 Direct enzymatic reduction at the outer membrane......................................................29 Electron shuttling pathways.........................................................................................32 Fe(III) solubilization by exogenous or bacterially-produced organic ligands followed by reduction of soluble organic-Fe(III).....................................34 ENZYMATIC BASIS OF URANIUM REDUCTION.........................................................35 Involvement of c-type cytochromes in enzymatic U(VI) reduction.............................35 Effect of U(VI) chemical speciation on enzymatic U(VI) reduction activity..............36 Electron donors and competing electron acceptors......................................................37 Subcellular location of enzymatic U(VI) reduction activity........................................38 ENZYMATIC MECHANISM OF TECHNETIUM REDUCTION.....................................39 Involvement of hydrogenases in Tc(VII) reduction.....................................................39 Subcellular location of enzymatic Tc(VII) reduction activity......................................39 MICROBIAL REDUCTION-INDUCED CHANGES IN METAL BIOGEOCHEMISTRY.....................................................................................41 Direct enzymatic effects of dissimilatory metal-reducing bacteria (DMRB) on metal solubility..................................................................................41 Indirect effects of DMRB on metal solubility..............................................................42 REDUCTIVE TRANSFORMATION OF Fe- AND Mn-CONTAINING MINERALS......43 Laboratory studies........................................................................................................43 Field studies..................................................................................................................44 ROLE OF MICROBIAL METAL REDUCTION IN REDOX CYCLING.........................45 Redox cycling in chemically stratified environments..................................................45 Microscale redox cycling.............................................................................................46 SUMMARY..........................................................................................................................46 ACKNOWLEDGMENTS....................................................................................................47 REFERENCES.....................................................................................................................47

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Siderophores and the Dissolution of Iron-Bearing Minerals in Marine Systems Stephan M. Kraemer, Alison Butler, Paul Borer, Javiera Cervini-Silva

Introduction................................................................................................................53 Scope of this review ....................................................................................................53 The iron limitation hypothesis . ...................................................................................54 Biological iron acquisition strategies........................................................54 Iron acquisition by bacteria..........................................................................................54 Iron acquisition by eukaryotic phytoplankton..............................................................55 Role of protozoan grazers in the cycling of iron .........................................................56 sources of iron in HNLC ocean regions ........................................................56 Atmospheric dust as a source of iron...........................................................................57 Iron mineralogy of atmospheric dust............................................................................57 Transformation of iron-bearing minerals during atmospheric transport......................57 Concentrations, speciation and solubility of iron in seawater....58 Iron concentrations as a function of depth in HNLC regions.......................................58 Inorganic iron species...................................................................................................58 Solubility of iron in the presence of iron oxides..........................................................59 Colloidal iron in marine systems..................................................................................62 Photochemistry and redox speciation of iron...............................................................62 organic ligands and iron solubility and speciation............................63 Speciation of soluble iron in the presence of organic ligands......................................63 Marine siderophores.....................................................................................................64 Photo reduction of iron and redox cycling in the presence of siderophores................65 Effect of organic ligands on the solubility of iron oxides............................................67 Dissolution of aerosols and defined iron oxides in seawater........70 Dissolution mechanisms...............................................................................................70 Experimentally observed dissolution rates of aerosol and defined minerals................72 Photo-reductive dissolution in seawater.......................................................................72 Organic ligands and iron oxide dissolution in seawater..................73 Siderophore-promoted dissolution mechanisms..........................................................73 Photo-reductive dissolution mechanisms in the presence of siderophores..................75 Amphiphilic siderophores............................................................................................75 Conclusions .................................................................................................................76 References.....................................................................................................................76

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Geomicrobiological Cycling of Iron Andreas Kappler, Kristina L. Straub

Introduction ...............................................................................................................85 General aspects of the iron cycle..................................................................................85 Solubility and chemical transformation of Fe(II) and Fe(III) minerals........................85 Surface area and reactivity of ferric iron oxides..........................................................87 Ferrihydrite...................................................................................................................87 Forms of iron present in the environment ...................................................................88 Role of iron for microbial energy metabolism.............................................................88 Microbial oxidation of Fe(II).................................................................................89 Competition between chemical and microbial oxidation of Fe(II)..............................89 Aerobic acidophilic Fe(II)-oxidizing microorganisms.................................................89 Aerobic neutrophilic Fe(II)-oxidizing microorganisms...............................................90 Anaerobic Fe(II)-oxidizing phototrophic bacteria.......................................................91 Anaerobic Fe(II)-oxidizing nitrate-reducing bacteria..................................................92 Mechanisms of microbial Fe(II) oxidation . ................................................................93 Formation of Fe(III) minerals by microbial Fe(II) oxidation.......................................95 Microbial dissimilatory reduction of Fe(III)...............................................95 Acidophilic Fe(III)-reducing microorganisms.............................................................96 Microbial reduction of Fe(III) at neutral pH................................................................96 Methods to study mechanisms of microbial Fe(III) reduction.....................................97 Microbial mechanisms of Fe(III) reduction at neutral pH...........................................98 Microbial iron cycling...........................................................................................99 Microbial iron cycling under acidic conditions............................................................99 Microbial iron cycling at neutral pH............................................................................99 Prerequisites for microbial iron cycling at neutral pH...............................................100 Oxygen-dependent microbial cycling of iron.............................................................100 Oxygen-independent microbial cycling of iron..........................................................101 Environmental implications..............................................................................101 Degradation of organic compounds coupled to dissimilatory Fe(III) reduction........101 Iron minerals as adsorbents........................................................................................102 Immobilization of toxic metal ions by microbial Fe(II) oxidation and Fe(III) reduction..................................................................................................102 Formation of reactive iron minerals ..........................................................................102 Some tasks for future investigations.........................................................103 Acknowledgments..................................................................................................103 References...................................................................................................................104

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Molecular-Scale Processes Involving Nanoparticulate Minerals in Biogeochemical Systems Benjamin Gilbert, Jillian F. Banfield

INTRODUCTION..............................................................................................................109 Sources of nanoparticles in the environment............................................................. 111 Impacts of nanoparticles on their surroundings......................................................... 112 Nanoparticles—special properties and implications.................................................. 112 Overview of small size effects in minerals................................................................. 113 PHYSICAL STRUCTURE AND COMPOSITION OF NANOSCALE MINERALS...... 113 Thermodynamic constraints on the structure of nanoparticles................................... 113 The nature of the initial precipitates and subsequent aging....................................... 114 Size dependence of mineral solubility........................................................................ 114 Characterization studies of biogenic nanoparticles.................................................... 115 The effects of water and other surface-bound molecules on nanoparticle structure.. 118 Incorporation of impurity atoms................................................................................. 119 The surfaces of nanoscale minerals............................................................................120 ELECTRONIC STRUCTURE OF NANOSCALE MINERALS......................................121 Introduction to electronic structure of solids..............................................................121 Energy levels in semiconductor minerals...................................................................123 Electronic structure of nanoparticles..........................................................................125 REDOX BEHAVIOR OF NANOPARTICLES..................................................................132 Size effects on nanoparticle redox behavior...............................................................132 Examples of nanoparticle redox behavior..................................................................134 PHOTOCHEMISTRY........................................................................................................137 Size effects on nanoparticle photochemistry . ...........................................................137 Nanoparticle interactions with biomolecules.............................................................138 Examples of nanoparticle photochemistry.................................................................140 The stability of nanoparticles during redox chemistry and photochemistry..............142 Nanoparticle interactions with microorganisms . ......................................................143 Nanoparticle aggregation and its consequences.........................................................144 CONClUSIONS................................................................................................................146 Acknowledgments..................................................................................................146 References...................................................................................................................146

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The Organic-Mineral Interface in Biominerals P. U. P. A. Gilbert, Mike Abrecht Bradley H. Frazer



biominerals: tough structures of life.......................................................157 Introduction to biominerals........................................................................................157 Why biominerals........................................................................................................158 The organic-mineral interface....................................................................................158 Zooming in on the organic-mineral interface.............................................................161 SPECTROMICROSCOPY OF BIOMINERALS...............................................................161 XANES spectroscopy of biominerals.........................................................................161 XANES microscopy of biominerals...........................................................................165 Overcoming charging effects......................................................................................166 THE ORGANIC-MINERAL INTERFACE IN MICROBIAL BIOMINERALS...............168 Prokaryotic biominerals.............................................................................................168 Bacterial cell walls.....................................................................................................170 Capsules......................................................................................................................170 S-layers.......................................................................................................................172 Sheaths........................................................................................................................172 Filaments....................................................................................................................172 The organic-mineral interface in eukaryotIC BIOMINERALS...........175 Eukaryotic biominerals...............................................................................................175 The nano-structure of nacre........................................................................................176 Start and stop signals in nacre growth........................................................................178 Synergy of mechanisms for nacre growth..................................................................179 Biomineral glue: the carboxyl group. .......................................................180 Conclusion...................................................................................................................180 Acknowledgments..................................................................................................181 References...................................................................................................................181

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Catalysis and Prebiotic Synthesis James P. Ferris

Introduction..............................................................................................................187 Formation of the Solar System.......................................................................188 The Early Earth.........................................................................................................189 Atmosphere................................................................................................................189 Primary sources of simple organics............................................................................190 Prebiotic Routes to Biopolymer Precursors............................................192 RNA world.................................................................................................................192 The structure of and prebiotic synthesis of RNA monomers.....................................193 Vesicles.......................................................................................................................196 Chirality.......................................................................................................................196 Prebiotic Polymerization of RNA Monomers.............................................197 Non-Enzymatic Template-Directed Synthesis of RNA...........................198 Alternative Genetic Systems............................................................................199 Examples of Mineral and Metal Ion Catalysis in Prebiotic Chemistry ....................................................................................200 Non-catalytic formation of biopolymers; polypeptides..............................................200 Montmorillonite catalysis of RNA synthesis..............................................................201 Metal ion catalysis of template-directed synthesis.....................................................203 Possible Catalytic Reaction Pathways.........................................................204 Metal ions...................................................................................................................204 Metal ion catalysis of template-directed synthesis of RNA oligomers......................205 A postulate for montmorillonite catalysis..................................................................205 Potential Steps to the Origin of Life from Oligomers........................205 Proposed Experiments...........................................................................................206 Selection of oligomers that bind to other biomolecules.............................................206 Catalysis of template-directed synthesis....................................................................207 Catalysis of RNA ligation...........................................................................................207 Acknowledgments..................................................................................................207 References...................................................................................................................207

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The Evolution of Biological Carbon and Nitrogen Cycling—a Genomic Perspective Jason Raymond

Introduction.............................................................................................................. 211 Using genomics to understand the present (and infer the past)............................................................................................212 Biological nitrogen cycling and diazotrophy.....................................213 Geological clues to the early nitrogen cycle.....................................217 Biological carbon cycling and autotrophy...........................................218 Wood-Ljungdahl (reductive acetyl-COA) pathway...............................219 The rTCA cycle.............................................................................................................220 The Calvin-Benson-Bassham cycle.................................................................223 3-Hydroxypropionate cycle...............................................................................225 Autotrophy, heterotrophy, and the origin of metabolism.............226 References...................................................................................................................229

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Building the Biomarker Tree of Life Jochen J. Brocks, Ann Pearson

INTRODUCTION..............................................................................................................233 The biomarker principle.............................................................................................234 The carbon isotopic composition of biomarkers........................................................237 BIOMARKERS IN GEOMICROBIOLOGY.....................................................................240 Biomarkers as indicators of metabolism....................................................................240 Biomarkers as indicators of the evolution of life and the environment.....................245 Orphan biomarkers and unknown pathways..............................................................248 Building the biomarker tree of life.............................................................251 A phylogenetic approach to the origin and distribution of biomarkers......................251 Acknowledgments..................................................................................................252 REFERENCES...................................................................................................................252

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Population Dynamics Through the Lens of Extreme Environments Rachel J. Whitaker, Jillian F. Banfield

INTRODUCTION..............................................................................................................259 DEFINING A POPULATION – THE LENS OF EXTREME ENVIRONMENTS...........260 SOURCES OF GENOMIC VARIATION...........................................................................261 METHODS FOR IDENTIFYING GENOMIC VARIATION............................................264 BASIC POPULATION PARAMETERS: SELECTION, RECOMBINATION AND GENETIC DRIFT................................................................................................265 SHAPING POPULATION STRUCTURE THROUGH SELECTION AND RECOMBINATION............................................................................................267 IDENTIFYING ADAPTIVE TRAITS...............................................................................271 Recognizing genes under selection in recombining populations...............................271 Recognizing genes under selection in clonal populations..........................................272 CONCLUSION: INTEGRATING GENETIC AND GEOCHEMICAL MOLECULAR TOOLS................................................................................................................................273 ACKNOWLEDGMENTS..................................................................................................274 REFERENCES...................................................................................................................274

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Metabolism and Genomics: Adventures Derived From Complete Genome Sequencing Karen E. Nelson, Barbara Methé

Introduction..............................................................................................................279 Genome Sequencing and Assembly..................................................................279 Simultaneous comparison of multiple genomes....................................281 Functional Genomics – a systems-level approach................................282 Examples of Whole Genome Reconstructions and Derived Information...............................................................................282 Geobacter sulfurreducens.................................................................................283 Thermotoga maritima Metabolism based on Genomics and Comparative Genome Hybridization...............................................284 Comparative genomics and proteomics, Colwellia psychrerythraea 34H .........................................................................................285 Comparisons of multiple genomes, the Vibrios as an example.......286 Metagenomics and Microbial Diversity in Natural Environments...............................................................................289 Other tools to understand gene function...............................................291 SUMMARY........................................................................................................................292 REFERENCES...................................................................................................................292 xiv

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Reviews in Mineralogy & Geochemistry Vol. 59, pp. 1-7, 2005 Copyright © Mineralogical Society of America

The Search for a Molecular-Level Understanding of the Processes that Underpin the Earth’s Biogeochemical Cycles Jillian F. Banfield, Gene W. Tyson, Eric E. Allen, Rachel J. Whitaker Department of Earth and Planetary Sciences and Department of Environmental Science, Policy, and Management, University of California Berkeley Berkeley California, 94720-4767, U.S.A. [email protected]

Characterizing biogeochemical systems Evidence of connections between microbial activity and the Earth’s biogeochemical cycles is all around us, and motivates our interest in the mechanisms of microbial transformations, their rates, and the distribution of microbial activities across environment types and over Earth history. In general, the approach to investigating a geomicrobiological process begins with biological and geochemical characterization of the environment of interest. Geochemical characteristics constrain available metabolisms (e.g., McCollom and Shock 1997) and patterns can reveal processes not recognized initially to be microbially mediated. The membership of microbial communities can be assayed through cultivation and cultivation-independent methods. However, this task is not without its challenges. There is little consensus about the ways in which organisms should be grouped into relevant ecological units such as species (Gevers et al. 2005). Even using standard classification techniques, the extent of microbial diversity appears vast, and recent analyses suggest that current estimates may tremendously under predict the amount of genetic diversity in the biosphere. In addition, a single organism type may contain far more genes than expected based on genomic sequencing of an isolate of that species because species populations can exhibit internal heterogeneity. Thus, it seems that comprehensive characterization of the microbial membership of an environment over space and time is a problem of almost incomprehensible magnitude. Furthermore, microbial census taking is only the first step. Beyond documenting the assemblage of organisms present, we need to know how they are distributed, what are they doing, how they are doing it, and the ways that their activities impact the physical and chemical characteristics of their surroundings. In the near future, the only systems in which it will be plausible to tackle the level of characterization required for relatively comprehensive analyses are the simplest ones. For example, in samples of relatively low geochemical and biological complexity, it is now possible to reconstruct the genomes of the dominant organism types using environmental genomic approaches. These data can be used to classify the individual members of a community into groups based on their genome structure and gene content and to infer some aspects of their metabolism (Tyson et al. 2004). In more complex environments, analyses are likely to be restricted to studies of a subset of biogeochemical processes or organism types. In very complex environments, genomic characterization of a community may be restricted to functional profiling (Tringe et al. 2005). Other profiling using methods such as multi-locus sequence analysis (e.g., Whitaker et al. 2003) may be useful to sample specific genes from 1529-6466/05/0059-0001$05.00

DOI: 10.2138/rmg.2005.59.1

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a much larger number of individuals in a population and to resolve microbial distribution patterns as a function of geochemical conditions and over time. Information about community structure and metabolic potential must be interpreted in the context of fine-scale measurements of temperature, pH, metal and oxygen concentrations, redox potential, etc. A current challenge in uniting geochemical parameters with community structure observations is to document community variation over scales comparable to those over which geochemical gradients occur. The discrepancies in sampling volumes for biological and geochemical characterization may be resolved when new methods such as single cell genomic analysis from environmental samples are combined with micron-scale methods such as microelectrode chemical analyses. Through such approaches it may be possible to decipher the extent to which geochemical gradients structure microbial populations and possibly identify important selective pressures. One of the most severe limitations in our understanding of biogeochemical systems arises from lack of information about the genetic basis for metabolic pathways involved in tasks such as iron oxidation and biomineral formation. Such gaps in our knowledge also restrict derivation of functional inferences from newly acquired genome sequences. A key strategy for deciphering how metabolic tasks are conducted and how these pathways are regulated begins with the development of genetically tractable model organisms (see Newman and Gralnick 2005). Once the sequences of genes involved in processes of interest are known, comparative analyses may provide clues to the evolutionary processes that gave rise to the function (see Raymond 2005). Purified gene products can be assayed to determine substrate specificity, intermediate products, and reaction kinetics, and this information can be incorporated into models for natural systems. Through reconstructions of complex pathways such as those involved in electron transport it is possible to learn how organisms derive energy and shape their surroundings (see DiChristina et al. 2005). For example, the electron transport chain is central to the catalysis of redox reactions involving geochemically abundant chemical species such as nitrate and iron, and can profoundly change the forms of nitrogen and iron in the environment(see Kappler and Straub 2005). Another example involves the microbial production and excretion of specifically tailored iron-binding molecules referred to as siderophores in order address the challenge of iron limitation (see Kraemer et al. 2005). In cases such as this, genetic, genomic, and biochemical approaches can be complemented by high-resolution data for minerals that can now be acquired using spectroscopic methods to reveal the ways in which organic ligands bind to, and interact with, minerals at the molecular level. Spectroscopic methods that provide molecular-scale insights include nano-scale secondary ion mass spectrometry (Guerquin-Kern et al. 2005), micro-scale infrared spectroscopy, nuclear magnetic resonance spectroscopy, and other X-ray-based analytical and imaging methods (see Gilbert et al. 2005). These approaches enable identification of tiny quantities of organic compounds contained in biomineral structures (e.g., Chan et al. 2004) and can reveal surface coordination geometries and reaction mechanisms. Spectroscopic approaches may also provide insights in a wide variety of other systems, including those involving organic molecules and mineral surfaces that may have had special relevance to prebiotic synthesis early in Earth history (see Ferris 2005). Spectroscopic methods also yield isotopic information and can be used to constrain reaction dynamics. The integration of high-resolution, spatially and chemically resolved data from natural inorganic and biological materials is only in a relatively early stage. Such syntheses will certainly generate fundamental new insights into how microorganisms shape their surroundings. Molecularly resolved spectroscopic approaches are also essential tools in efforts to define the structure and constrain mechanisms of reactions involving the mineral products of microbial metabolism. Mineral byproducts of anaerobic respiration (e.g., uranium oxide,

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zinc sulfide) are frequently no more than a few nanometers in diameter (e.g., Moreau et al. 2004). As a consequence of their ultra-small size, these particles may exhibit structures and properties (possibly including redox potential) that depend on the particle diameter (see Gilbert and Banfield 2005). Furthermore, the particle size and size and organization of colloidal aggregates of these particles may depend on the microbial metabolic rate, which itself depends upon solution chemistry (Jin and Bethke 2005). Recent advances in methods for study of nanoparticle structure and properties and for analysis of metabolism make it possible to analyze such connections and predict outcomes in biogeochemical systems.

Molecular geomicrobiology: opportunities and challenges One of the goals of molecular geomicrobiology is to understand how microorganisms function in, respond to, and shape their environments. Complete microbial genomes reveal an organism’s metabolic potential and provide information about genome structure and gene content (see Nelson and Methé 2005). Thus, they provide an important starting point for biogeochemical analyses. However, it should be noted that the complete genomes derive from cultivated strains, so each analysis represents only a snapshot of the genome of a single representative of a population. More importantly, only a small subset of organisms can be cultivated. Despite many successful predictions of new functional capabilities from genomic data (Ramesh et al. 2005), there are innumerable cases where the function of predicted genes cannot be inferred because the gene bears no significant similarity to one for which a function can be assigned. This is not a small problem (Roberts et al. 2005). Typically 30-50% of genes in sequenced genomes are annotated as either hypothetical (a predicted gene with no similarity to any other gene in the databases) or conserved hypothetical (a predicted gene that bears significant sequence similarity to genes in the database, but for which no function has been determined). Given that no more than a few tens of novel genes are ascribed functions per year as the result of typical biochemical and genetic experiments (M. Thelen, pers. comm.), complete functional analysis based on genomic information is a long way off, even for the best studied microbes (e.g., E. coli). This may be one of the largest roadblocks facing geomicrobiologists. Genomic data from isolates have been used extensively in comparative studies for functional and evolutionary analyses (see Nelson and Methé 2005; Raymond 2005). These studies have provided a wealth of evidence that indicates that lateral gene transfer is an important force in genome evolution. It is apparent that genes are moved between organisms that are distantly related, at least occasionally, and that the frequency of transfer increases with decreasing evolutionary distance (e.g., between different species of the same genera; Gogarten and Townsend 2005). Remaining questions relate to the mode and frequency of genetic exchange within populations of very closely related individuals. Are genes taken up from naked DNA or acquired from phage or other organisms with high frequency, only to be discarded in subsequent generations because they rarely, if ever, confer an adaptive advantage? Or are such events still quite rare but their consequences often profound? Genomic data from coexisting members of natural populations may throw light on this question. Genomic analyses can reveal how metabolic traits distribute across lineages and environment types, the rates and mechanisms by which this movement occurs, and the roles that lateral gene transfer can play in biogeochemical cycling. For example, a recent important discovery is that photosystem II genes are found in phage genomes (Lindell et al. 2005). These genes impact levels of a protein involved in photosynthesis, implying that phage can influence

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or even control one of the key steps in the ocean carbon cycle. Other laterally transferred genes with biogeochemical significance include those that are spread on plasmids to confer the ability to degrade specific hydrocarbon contaminants (Wilson et al. 2003; Springail and Top 2004) or resistance to antibiotics or heavy metals (Coombs and Barkay 2004; Nielsen and Townsend 2004). The mobility of genes between lineages has presented a serious challenge to the species concept because the extent to which members of the same “species” share a common evolutionary history is unclear. It is our view that both the genetic characteristics that unite groups and the processes that subdivide them to yield divergent phenotypes are best evaluated through analysis of population genetic heterogeneity. With such information in hand, we may be better positioned to meaningfully delineate organism groups (“species”) with likely ecological relevance. In addition to having the potential to tell us about evolutionary processes that occur over small times scales, environmental genomic data can reveal metabolic traits of organisms that have previously defied cultivation. This information may itself lead to growth of the organism in the laboratory, opening the way for more comprehensive physiological characterization. For example, recently Tyson et al. (2005) designed a cultivation approach based on the recognition that only a single organism type in a community was capable of nitrogen fixation. Population genomic data also reveal differences in metabolic potential that may be responsible for adaptation of otherwise closely related organisms to subtly different environment types. Whitaker and Banfield (2005) describe how methods developed for comparative analysis of (clonal) isolate genomes can be used to identify differences in gene content and sequence amongst closely related individuals and the ways in which population genetic methods may be adapted from macroscopic biology to reveal patterns of selection genome wide. With genomic information for the dominant organisms in a community in hand (whether obtained from isolates or natural populations) it is possible to monitor in situ microbial activity levels in multi-species consortia. For example, gene sequence information can be used to design microarrays to detect mRNA transcripts (see Nelson and Methé 2005) or to identify peptides via mass-based measurements (e.g., mass spectrometry). Functional analyses may reveal the relative contributions of specific organisms or strains to geochemical transformations such as carbon fixation, nitrogen fixation, and metal reduction at a given time, and to deduce how this changes over time. An important limitation for use of genomic data in functional analyses arises if these data do not accurately or fully capture the genetic potential of the community. For example, Ram et al. (2005) used genomic data from one sample to identify proteins in a closely related (but not identical) biofilm sample. Although over 2,000 proteins derived from all of the dominant organism types were detected, it is certain that an important subset were under-detected or missed because the predicted peptides differed from the observed peptides in their amino acid sequence. Thus, full analyses of function in microbial consortia will benefit from comprehensive genomic datasets that capture the full range of sequence types present in a community. Even if complete genomic data are available for one environment type, it is unclear whether they will enable functional analyses in a similar environment due to site-to-site genetic variability. With the exception of studies of organisms that cause plant and animal disease (Bhattacharyya et al. 2002; Holden et al. 2004), population-level differences in the same environment type in geographically separated locations have been little investigated (Whitaker et al. 2003; Escobar-Paramo et al. 2005). Consequently, it is difficult to predict whether the challenges associated with functional analyses of natural communities will be best addressed via better genomic databases, mass spectrometry method development, or both.

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Environmental genomic studies have the potential to constrain the rates of inter-site microbial dispersal and to reveal the relative importance of dispersal vs. in situ diversification in adaptation as environmental conditions change. Clearly, the relative importance of dispersal vs. in situ evolution will vary with environment type, organism type, and the rate and magnitude of the perturbation. However, this balance will impact the level of global-scale biodiversity for each species. Understanding the timescales of microbial evolutionary processes is a tremendous challenge that is perhaps be addressed via studies of geologic systems. In the past, this has been undertaken through direct examination of the fossil record, including analysis of organic biomolecules preserved in ancient rocks. The opportunities and obstacles associated with attempts to tie together biological and geochemical evolution over Earth history are discussed by Brocks and Pearson (2005). Future studies may focus on modern systems for which both a recent geological and biological history can be reconstructed.

Concluding comments The knowledge sought through molecular geomicrobiological studies will find application in a variety of basic and applied fields such as astrobiology and environmental bioremediation. For example, understanding of the range of conditions in which life can persist, and the biochemical bases for the limitations, will constrain the variety of habitat types that may be targeted in the search for life signs on extraterrestrial planets. Molecular-level understanding of biogeochemical processes may be central to an assessment of the biogenicity of mineral biosignatures found there. Furthermore, information about the metabolic pathways for synthesis of organic compounds will assist in evaluation of the sources of organic biosignatures that persist in the geologic record. There has been great deal of interest in the possibility that the enormous problem of contamination of the environment by organic compounds, metals, and radionuclides may be tackled by harnessing the ability of microorganisms to change the chemical speciation of pollutants. Much research has been devoted to exploring this possibility (Madsen 2001; Lovley 2003; Nevin et al. 2003). In addition, understanding of the biogeochemical reactions that occur during stimulated bioremediation can be used to design methods to follow the progress of the treatment and identify key reaction products. For example, recently developed geophysical approaches can sense changes in subsurface transport properties, allowing progress of bioremediation to be evaluated and post treatment changes monitored (Williams et al. 2005). The merging of fields as disparate as molecular microbiology and geophysics is becoming routine, but the results are as yet mostly unknown. We can look forward with optimism to the product of the next decade of research in molecular geomicrobiology.

Acknowledgments Funding for research and development of ideas described here derived from the National Science Foundation Biocomplexity Program, the Department of Energy Genomics: Genomes to Life Program and Basic Energy Sciences Chemical Sciences Program, and the NASA Astrobiology Institute. The contributions of our colleagues and collaborators to this work are gratefully acknowledged.

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Bhattacharyya A, Stilwagen S, Ivanova N, D’Souza M, Bernal A, Lykidis A, Kapatral V, Anderson I, Larsen N, Los T, et al. (2002) Whole-genome comparative analysis of three phytopathogenic Xylella fastidiosa strains. Proc Natl Acad Sci USA 99:12403-12408 Brocks JJ, Pearson A (2005) Building the biomarker tree of life. Rev Mineral Geochem 59:233-258 Chan CS, De Stasio G, Nesterova M, Welch SA, Girasole M, Frazer B, Banfield JF (2004) The role of microbial polymers in templated mineral growth. Science 303:1656-2658 Coombs JM, Barkay T (2004) Molecular evidence for the evolution of metal homeostasis genes by the lateral gene transfer in bacteria from the deep terrestrial subsurface. Appl Environ Microbiol 70:1698-1707 DiChristina TJ, Fredrickson JK, Zachara JM (2005) Enzymology of electron transport: energy generation with geochemical consequences. Rev Mineral Geochem 59:27-52 Escobar-Paramo P, Ghosh S, DiRuggiero J (2005) Evidence for genetic drift in the diversification of a geographically isolated population of the hyperthermophilic archaeon Pyrococcus. Molec Bio Evol 22: 2297-2303 Ferris JP (2005) Catalysis and prebiotic synthesis. Rev Mineral Geochem 59:187-210 Gevers D, Cohan FM, Lawrence JG, Spratt BG, Coenye T, Feil J, Stackenbrandt E, Van de Peer Y, Vandamme P, Thompson FL, Swings J (2005) Opinion: Re-evaluating prokaryotic species. Nat Rev Microbiol 3: 733-739 Gilbert B, Banfield JF (2005) Molecular-scale processes involving nanoparticulate minerals in biogeochemical systems. Rev Mineral Geochem 59:109-155 Gilbert PUPA, Abrecht M, Frazer BH (2005) The organic-mineral interface in biominerals. Rev Mineral Geochem 59:157-185 Gogarten JP, Townsend JP (2005) Horizontal gene transfer, genome innovation and evolution. Nat Rev Microbiol 3:679-687 Guerquin-Kern JL, Wu TD, Quintana C, Croisy A (2005) Progress in analytical imaging of the cell by dynamic secondary ion mass spectroscopy (SIMS microscopy). Biochim Biophys Acta 1724: 228-238 Holden MT, Titball RW, Peacock SJ, Cerdeno-Tarraga AM, Atkins T, Crossman LC, Pitt T, Churcher C, Mungall K, Bentley SD, et al. (2004) Genomic plasticity of the causative agent of melioidosis, Burkholderia pseudomallei. Proc Natl Acad Sci USA 101:14240-14245 Jin Q, Bethke CM (2005) Predicting the rate of microbial respiration in geochemical environments. Geochim Cosmochim Acta 69:1133-1143 Kappler A, Straub KL (2005) Geomicrobiological cycling of iron. Rev Mineral Geochem 59:85-108 Kraemer SM, Butler A, Borer P, Cervini-Silva J (2005) Siderophores and the dissolution of iron-bearing minerals in marine systems. Rev Mineral Geochem 59:53-84 Lindell D, Jaffe JD, Johnson ZI, Church GM, Chisholm SW (2005) Photosynthesis genes in marine viruses yield proteins during host infection. Nature, doi:10.1038/nature04111 Lovley DR (2003) Cleaning up with genomics: applying molecular biology to bioremediation. Nat Rev Microbiol 1:35-44 Madsen EL (2001) Intrinsic bioremediation of organic subsurface contaminants. In: Subsurface Microbiology and Biogeochemistry. Fredrickson JK, Fletcher M (eds) Wiley-Liss Inc., New York, p. 249-278 McCollom TM, Shock EL (1997) Geochemical constrains on chemolithautotrophic metabolism by microorganisms in seafloor hydrothermal systems. Geochim Cosmochim Acta 61:4375-4391 Moreau JW, Webb RI, Banfield JF (2004) Ultrastructure, aggregation-state, and crystal growth of biogenic nanocrystalline sphalerite and wurtzite. Am Mineral 89:950-960 Nelson KE, Methé B (2005) Metabolism and genomics: adventures derived from complete genome sequencing. Rev Mineral Geochem 59:279-294 Nevin KP, Finneran KT, Lovley DR (2003) Microorganisms associated with uranium bioremediation in a highsalinity subsurface sediment. Appl Environ Microbiol 69:3672-3675 Newman DK, Gralnick JA (2005) What genetics offers geobiology. Rev Mineral Geochem 59:9-26 Nielsen KM, Townsend JP (2004) Monitoring and modeling horizontal gene transfer. Nat Rev Biotechnol 22: 1110-1114 Ram RJ, Verberkmoes NC, Thelen MP, Tyson GW, Baker BJ, Blake RC II, Shah M, Hettich RL, Banfield JF (2005) Community proteomics of a natural microbial biofilm. Science 308:1915-1920 Ramesh MA et al. (2005) A phylogenomic inventory of meiotic genes: evidence for sex in Giardia and an early eukaryotic origin of meiosis. Current Biology 15:185–191 Raymond J (2005) The evolution of biological carbon and nitrogen cycling—a genomic perspective. Rev Mineral Geochem 59:211-231 Roberts RJ, Karp P, Kasif S, Linn S, Buckley MR (2005) An Experimental Approach to Genome Annotation. Critical Issues Colloquia Report, Washington D.C. USA: American Academy of Microbiology Springail D, Top EM (2004) Horizontal gene transfer and microbial adaptation to xenobiotics: new types of mobile genetic elements and lessons from ecological studies. Trends Microbiol 12:53-58

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Tringe SG, von Mering C, Kobayashi A, Salamov AA, Chen K, Chang HW, Podar M, Short JM, MathurEJ, Detter JC, Bork P, Hugenholtz P, Rubin EM (2005) Comparative metagenomics of microbial communities. Science 308:554-557 Tyson GW, Chapman J, Hugenholtz P, Allen EE, Ram RJ, Richardson PM, Solovyev VV, Rokhsar DS, Banfield JF (2004) Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428: 37-43 Tyson GW, Lo I, Baker BJ, Allen EE, Hugenholtz P, Banfield JF (2005) Genome-directed isolation of the key nitrogen fixer Leptospirillum ferrodiazotrophum sp. nov. from an acidophilic microbial community. Appl Environ Microbiol 71:6319-6324 Whitaker RJ, Grogan DW, Taylor JW (2003) Geographic barriers isolate endemic populations of hyperthermophilic archaea. Science 301:976-978 Whitaker RJ, Banfield JF (2005) Population dynamics through the lens of extreme environments. Rev Mineral Geochem 59:259-277 Williams KH, Ntarlagiannis D, Slater LD, Dohnalkova A, Hubbard SS, Banfield JF (2005) Geophysical imaging of stimulated microbial biomineralization. Environ Sci Technol 39:7592-7600 Wilson MS, Herrick JB, Jeon CO, Hinman DE, Madsen EL (2003) Horizontal transfer of phn-Ac dioxygenase genes within one of two phenotypically and genotypically distinctive naphthalene-degrading guilds from adjacent soil environments. Appl Environ Microbiol 69:2172-2181

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Reviews in Mineralogy & Geochemistry Vol. 59, pp. 9-26, 2005 Copyright © Mineralogical Society of America

What Genetics Offers Geobiology Dianne K. Newman Division of Geological and Planetary Sciences California Institute of Technology and the Howard Hughes Medical Institute Pasadena, California, 91125, U.S.A. [email protected]

Jeffrey A. Gralnick Department of Microbiology and The BioTechnology Institute University of Minnesota St. Paul, Minnesota, 55108, U.S.A. [email protected]

INTRODUCTION For over 50 years, the Parker Brothers’ board game “Clue” has maintained its position as the classic family detective game. A murder has been committed in the mansion, but we don’t know where, by whom, or how. Was it Professor Plum in the study with a knife, or Miss Scarlett in the ballroom with a candlestick? Through rolls of the dice, fragments of information patiently accumulated piece-by-piece, and the application of logic, players construct a case to figure out “whodunit”. Because there are several potential solutions to the problem, the key challenge is to figure out what happened by understanding how it happened. As for the players of “Clue,” scientists seeking to understand the co-evolution of life and Earth are often confronted with the dilemma of having to parse multiple solutions to an ancient biogeochemical event. For example, in trying to explain the genesis of Archean Banded Iron Formations, we must ask whether it was cyanobacteria in the near shore-environment producing O2, or anoxygenic phototrophs in the oceans directly oxidizing iron (Kappler et al. 2005)? Again, in parallel to “Clue,” typically all we have to work with are isolated scraps of evidence—metamorphosed pieces of rock collected from remote locales on Earth, that contain morphological and/or chemical fossils whose origin and/or meaning is enigmatic. Nevertheless, the legacies of billions of years of evolution—genetic rolls of the dice, subject to natural selection—provide us with a means to interpret these putative biosignatures. By applying the principle of uniformitarianism, we assume that the study of modern organisms can provide us with insights into the composition and behavior of their ancient relatives, thereby allowing us to reconstruct ancient events. This, of course, is a necessary assumption that may not be true, so in the end, all we can really claim is to construct satisfying stories that fit the available data. So how does one go about solving the mysteries of geobiology? Multiple approaches are covered in this book, but our focus in this chapter will be on how the logic of bacterial genetics can be applied to geobiological problems. Because genetics is not often a discipline that geologists are familiar with, we begin our discussion with some definitions. From there, we go on to discuss how genetics can help us understand the past, both generally and through specific examples; we do not discuss how genetics can help us understand modern biogeochemical processes, because we have recently reviewed this elsewhere (Croal et al. 2004a). Finally, 1529-6466/05/0059-0002$05.00

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we close with practical information about how to develop genetic systems in newly-isolated strains of geobiological interest, to guide those seeking to incorporate genetic analysis into their own research.

DEFINITIONS What is genetics? Classical bacterial and phage genetics was pioneered in the 1940s and 50s by Max Delbrück, Salvador Luria, Oswald Avery, Maclyn McCarty, Alfred Hershey, Martha Chase, Joshua Lederberg, Sydney Brenner, Seymour Benzer, Arthur Pardee, François Jacob, and Jacques Monod to name only a few of the key players. The extraordinary history of the development of this discipline (and molecular biology more generally) has been told well by Horace Judson in the book The Eighth Day of Creation (Judson 1996). Thanks to these scientists, genetics became a powerful tool for understanding how basic biological phenomenon worked (e.g., the nature of the gene (Avery et al. 1944), recombination (Lederberg 1946), the regulation of gene expression (Pardee et al. 1959), the nature of the genetic code (Crick et al. 1961), and mutations (Benzer and Champe 1962)). In each of these cases, genetic analysis lead to insights into how things happened, and was predicated upon the construction and analysis of mutants (Beckwith and Silhavy 1992). Accordingly, when we talk about applying genetics to geobiology, we mean performing experiments to understand geomicrobiological processes in mechanistic detail, either by mutagenizing model organisms (e.g., strains that can catalyze a particular geochemical transformation of interest, such as manganese oxidation (van Waasbergen et al. 1996), iron reduction (Coppi et al. 2001; DiChristina et al. 2002; Myers and Myers 2002), arsenate reduction (Saltikov and Newman 2003), methanogenesis (Pritchett and Metcalf 2005)) or by cloning DNA from the environment and expressing it in a foreign host (this is sometimes called “metagenomics” (Beja et al. 2000; Riesenfeld et al. 2004)). For the remainder of this chapter, we will focus our discussion on bacterial genetics to illustrate the more general theory and practice of genetics, whose logic is the same, regardless of the organism in which it is applied. In the context of geobiology, however, it is important to also recognize the recent contributions several labs have made in advancing archaeal genetics (Metcalf et al. 1997; Peck et al. 2000); because archaea catalyze a variety of geochemically significant reactions, that representatives from this group now can be manipulated genetically bodes well for future studies aimed at understanding their impact on the environment.

How is genetics different from molecular biology and genomics? Although modern bacterial genetics is molecular (e.g., gene composition can be readily determined by automated sequence analysis), originally it was not. The key to classical bacterial genetics was the use of deductive reasoning to understand the order and behavior of genetic elements in a genome, accomplished often through elegant assays that required little more than “toothpicks and logic” (Shuman 2003). While sequence information greatly facilitates genetic analysis today, the cornerstone of modern bacterial genetics is essentially the same as it was a half century ago: genetics deconstructs how a system works by making mutants that either eliminate/attenuate the ability of a strain to perform a certain function, or that confer a new property upon it. The challenge and satisfaction of this approach lies in being able to design simple experiments whose results will provide an explanation for a process. With a collection of different mutants, for example, complex biosynthetic processes can be broken down into components, each of which can be reconstructed and understood in detail. Genetic analysis goes hand in hand with physiological, cell biological and/or biochemical approaches that enable the phenotypes (i.e., physical characteristics or behavior) of mutants to be explored in depth.

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In contrast, molecular biology is the science of understanding the chemical composition of important biomolecules such as DNA and protein, and being able to manipulate them. Molecular biology commonly finds application in geobiology through microbial ecology surveys where the 16S gene for ribosomal RNA is cloned and sequenced to determine what types of organisms are present in a given environment (Pace 1997); another application is the use of molecular probes to identify organisms in natural samples through fluorescent in situ hybridization (FISH) (Schrenk et al. 1998; Orphan et al. 2001; Michaelis et al. 2002). In effect, molecular biology permits geobiologists to apply genetics to the environment—to search for the presence and/or expression of particular genes once their function is known (KarkhoffSchweizer et al. 1995; Malasarn et al. 2004). Finally, genomics is the study of genomes with respect to their gene content and organization (also see in this volume Nelson and Methé 2005 and Whitaker and Banfield 2005). It relies heavily upon computational analyses to compare different sequences (from one or more organisms) to each other and to identify motifs in the genes or their translated protein products that are predicted to have a specific function. For example, hypotheses can be generated about what types of reactions a given protein might catalyze, or the conditions under which the gene that encodes it might be expressed; sometimes, genomic analysis can even be used to make predictions about the behavior of entire microbial communities (Tyson et al. 2004). The special advantage of environmental genomic data is that it allows gene expression in communities to be monitored in situ (Ram et al. 2005). It should be emphasized, however, that although much can be learned from genomics, ultimately, predictions about an organism’s (or a community’s) potential to perform a certain function must be tested through classical genetic and/or biochemical analyses to prove that the connection between the presence of a particular gene and a given geochemical state is actually causal as opposed to correlative.

What is a mutant? A mutant is a bacterial strain that differs genetically in some way from the parent strain of the species. While the genotype (e.g., DNA) of the mutant must, by definition, be different from the parent, this is not necessarily the case phenotypically. A single base pair change in the genome could have no effect on the phenotype of the strain, however, genotypically, this strain is now different from the parent and is therefore a mutant.

What is mutagenesis? The capacity to alter the activities of single, or many proteins, from an organism by eliminating the gene(s) that encode them is critical for identifying proteins involved in a process of interest. Mutagenesis is the process of altering the genotype of a strain to make it different from the parent strain (i.e., a mutant). Traditional biochemical methods of identifying an activity in a cell extract can be a complementary method to genetics, but cannot unambiguously identify proteins required for an activity in vivo. If a protein is required for the activity of interest catalyzed by an organism, then removing the capacity of the strain to produce the protein will eliminate the activity. Several methods are used today to mutagenize bacteria, each with different strengths and weaknesses. These will be discussed in detail below.

TYPES OF GEOBIOLOGICAL PROBLEMS THAT GENETICS CAN SOLVE How can genetics help us learn about the geobiology of the past? To answer this question, we must first define what “geobiology of the past” means. Although a wide array of subjects— ranging from dinosaurs to ediacara—could fit this description, we will limit our discussion to microorganisms and how their evolution affected Earth’s near surface environment (i.e.,

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subsurface down to a few kilometers). This choice is justified if one seeks to understand what life was like on this planet for the majority of its history because microorganisms have been in existence much longer than macroscopic organisms. Microorganisms (especially bacteria and archaea) are distinguished by their metabolic diversity rather than their morphological diversity, thus studying the geobiology of the past essentially is an exercise in understanding the evolution of metabolism as recorded in ancient rocks. Because our knowledge of the metabolic diversity of microbial eukaryotes is very limited, we will not consider them here, although we note that this is an area of opportunity for future students of geobiology. Modern microorganisms appear to be capable of generating metabolic energy from any redox reaction that is thermodynamically favorable so long as the constituents involved in the reaction are available in a habitable environment. Their metabolic diversity is based upon their ability to harvest energy from oxidation and reduction reactions, where the oxidant and/or the reductant may be organic or inorganic compounds. In some cases, the substrates and/or products of microbial metabolism are minerals, whereas in others, they are gases. Regardless of what form they come in, microbial substrate consumption or product formation can have a dramatic affect on the geochemistry of the environment. A classic example of this is the evolution of photosystem II, which enabled cells to produce molecular oxygen from water and thereby oxidize the Earth. Prior to this event, however, microbial life had to subsist anaerobically for millions and perhaps billions of years. How did cells cope? What electron acceptors and electron donors did microorganisms use for energy generation? And can we decipher a record of these primitive metabolisms in ancient rocks? These are hard questions, and at first blush, it is not obvious whether genetics can provide the answers. Genetics is an experimental discipline, requiring geobiologists to work with modern microorganisms that we assume behave in much the same way as their ancient relatives. How reasonable is this assumption? One argument in its favor is that the forces of natural selection are conservative: once a particular metabolism is “invented” and is successful, only a limited set of mutations in the genes that confer this metabolism are possible in order for it to be preserved. While evolutionary history records myriad instances in which genetic changes led to the development of novel proteins and hence novel metabolisms, if we focus on a particular metabolism, and the biochemistry of its catalytic core, it is reasonable to infer that biology has only a finite number of solutions to make it work (Kauffman 1993). Moreover, as the complexity or difficulty of a metabolic process increases, we might expect the repertoire of solutions to become even more limited. This conclusion appears to be robust, albeit facilitated through horizontal gene transfer, given the conservation of metabolic genes in the genomes of phylogenetically distant organisms (Doolittle 1999; Friedrich 2002; Nixon et al. 2002; Malasarn et al. 2004; Simonson et al. 2005). Interestingly, microbiologists of the Delft school anticipated these findings nearly a century ago, noting the “manifest unity” in the biochemistry that forms the basis for the ecological relationships of microorganisms in nature (Kluyver 1924). If biochemistry is essentially conservative with respect to metabolism, then using genetics to understand how modern metabolisms work can help us develop a basis for deciphering their origins and how organisms that utilized them may have altered the chemical and physical features of our planet. So what does this mean in practice? If understanding the evolution of metabolism is the goal, there are only two ancient records to work with: one that is recorded in rocks, and one that is recorded in genomes. Let us first consider the former. Rocks preserve two different types of fossils: morphological and chemical. Morphological fossils are the more familiar, as features that stand out from the parent rock are relatively straightforward to identify, and are becoming ever more so given recent innovations in imaging technologies (Watters and Grotzinger 2001; Corsetti and Storrie-Lombardi 2003; Kemner et al. 2004). Once identified, however, whether these features are truly biogenic can be the subject of intense debate, be it at the scale of

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nanoparticles such as magnetite (McKay et al. 1996), micron-sized putative cellular structures (Schopf 1993; Brasier et al. 2002), or centimeter-scale stromatolites (Grotzinger and Knoll 1999). To develop criteria whereby to evaluate the biogenicity of particular structures in ancient rocks, it is helpful to understand how these structures form. This is where genetics can help. For example, if certain conditions prove to be required for the biological formation of a particular structure in modern organisms, and traces of these conditions are absent in an ancient sample, this would argue against its biogenicity. Such an argument was recently made with respect to the magnetite in the Martian meteorite ALH84001, which did not contain a magnetic signature that supported a biogenic origin (i.e., alignment of magnetite in chains) (Weiss et al. 2004). The key assumption in Weiss et al.’s paper was that bacteria organize magnetite into chains by direct molecular control—an assumption that was based on phenomenological observations (Gorby et al. 1988). Recently, genetic analysis has begun to reveal the specific molecular components responsible for this organization (Komeili et al. 2005). The power of bacterial genetics lies is its ability to provide clear and definitive proof that a particular protein is involved in a given function. The case of magnetite is only one example of where genetic analysis can guide our interpretation of the biogenicity of ancient samples. As stated above, in addition to morphological fossils, rocks preserve chemical fossils. These, in turn, come in two varieties: organic and inorganic. It is fair to assume that all organic fossils are of biological origin (the likelihood that prebiotic organic synthesis left preservable traces is extremely small), but it is much harder to know what they mean when we find them, as discussed in the chapter by Brocks and Pearson (2005). Here too, genetics can help. For example, hydrocarbon molecules known as 2-methylhopanes in the sedimentary record can unambiguously be recognized as the molecular fossils of 2-methyl bacteriohopanepolyols (2MeBHPs) that are found in selected modern bacteria. Because cyanobacteria—the only bacteria that engage in oxygenic photosynthesis—are the only known, quantitatively important, source of 2-MeBHPs in the modern environment, it has been inferred that 2-methylhopanes can be used as biomarkers for oxygenic photosynthesis itself (Summons et al. 1999). Thus, Brocks et al. (1999, 2003) interpreted the presence of 2-methylhopanes in sediments of the Archaean Fortescue Group as evidence that photosynthetically-derived O2 first appeared on Earth at least 2.7 billion years ago. However, there is presently no evidence that 2-MeBHPs and oxygenic photosynthesis are functionally related. Our confidence in this critical assumption, as well as in the use of 2-methylhopanes as biomarkers for cyanobacteria (or any other organism in which they exist), would be significantly improved by an understanding of the biochemical function of 2-MeBHPs. Keeping to the theme of O2 evolution, the second class of chemical fossils—inorganic biosignatures—also can be used to shed light on when this critical event occurred. Recently, through the work of Farquhar et al. (2000), mass independent sulfur isotopic signatures from sulfide and sulfate in Precambrian rocks were used to date a major change in the change in the sulfur cycle between 2090 and 2450 million years ago, likely attributable to the rise of O2. Canfield and colleagues have provided additional support for this conclusion, through their work on sulfur isotopic fractionation by archaea and bacteria (Canfield et al. 2000; Shen et al. 2001; Habicht et al. 2002). Central to these studies is the knowledge that sulfur isotope fractionation responds to metabolism—for example, uptake and reduction of sulfate involves kinetic isotope effects that result in the lighter isotope of sulfur being enriched in the sulfide product. The extent of enrichment depends on the growth rate of the organism, which can be controlled by temperature, the nature of the electron donor, and the concentration of sulfate among other factors (Jones and Starkey 1962; Kemp and Thode 1968; Shen et al. 2001)). While great strides have been made in this area without the involvement of genetics, knowledge of the biochemical pathway responsible for sulfate reduction has greatly facilitated interpretations of microbial sulfur isotopic fractionation by bacteria. It is thought that the majority of isotopic

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fractionation occurs when S-O bonds are broken, such as during reduction of adenosine 5′phosphatosulfate (APS) to sulfite by the enzyme APS reductase, with subsequent enzymatic reduction of sulfite to sulfide (Canfield et al. 2005). Genetics affords a means to identify such pathways for more recently discovered geomicrobial organisms that might leave an imprint in the rock record, where the mechanism(s) of isotope effects are not yet fully understood. For example, iron-oxidizing anoxygenic phototrophs have been implicated in the direct deposition of Banded Iron Formations, but at present is it difficult to distinguish their activities from those of cyanobacteria on the basis of iron isotopic fractionation alone (Croal et al. 2004b). Knowledge of what enzymes or molecular components catalyze iron oxidation, where they are localized, and other details of how anoxygenic phototrophs traffic in iron, will position us to better interpret the mechanism of iron isotope fractionation by these bacteria, and thereby develop criteria with which to identify the products of their metabolism in ancient rocks. Even if iron isotopes prove not to provide a unique signature for a particular microbial metabolism, genetic analysis can still be very useful in pointing to potentially novel biosignatures. For example, recent genetic and biochemical results from our laboratory indicate that the enzymes that catalyze iron oxidation are soluble proteins that are localized inside the cell (Croal et al. unpub. data). If true, this implies that the cell has a mechanism for preventing the intracellular precipitation of iron oxide, possibly by chelating ferric iron with an organic molecule that helps release it to the outside where it then precipitates. In the event such a molecule were to exist, and it were shown to be preservable over geologic time scales, this would be an example of a metabolically-specific biomarker discovered through genetics. Whether or not genetic analysis will ultimately lead to the discovery of physiologicallyspecific biomarkers, identification of the genes involved in geobiological processes will provide insight into their evolutionary origin. In this respect, DNA itself is a fossil, as phylogenetic relationships between sequences can provide information about their evolution. A good example of this is the interpretation of the antiquity of anoxygenic photosynthesis based on phylogenies of proteins involved in bacteriochlorophyll biosynthesis (Xiong et al. 2000). Although it is very difficult to date the divergence of groups of proteins, it is reasonable to use phylogeny in tandem with the rock record to infer the relative temporal evolution of different metabolisms (House et al. 2003; Kirschvink et al. 2000). Again, the role genetics plays in this process is to provide the proof that specific genes encode proteins that perform specific functions. Only after this is understood can phylogenetic comparisons be meaningful. As stated above, we caution against the danger of inferring function on the basis of phylogeny alone. Evolution is rife with instances where small sequence changes in an active site of a protein change its substrate specificity (such as in the case of the directed evolution of a fucosidase from a galactosidase; Zhang et al. 1997), thus we cannot be certain that a putative protein actually performs the function we think it does until we do an experiment to prove it. Moreover, there are cases where different proteins have independently evolved that catalyze a similar reaction, yet on the basis of their sequence, they appear to have little in common. A good example of this are the serine proteases, subtilisin and trypsin (Kelly et al. 2005). Thus the absence of a particular gene in the genome of an organism should not be taken as evidence that it cannot perform a certain function. Even for organisms such as Escherichia coli and Salmonella, upon whose DNA the science of bacterial genetics was built, there remain a large number of genes of unknown function. Finally, genetic analysis can provide insights into the conditions that regulate a particular process. As described below, it is straightforward to use molecular reporters to assay for the expression of a gene of interest by exposing the bacteria that harbor the gene to different chemicals, temperatures, or pressures. This has the potential to be useful in making inferences about the paleoenvironment. For example, if evidence were found in the rock record that a particular biomolecule was present that was known only to be produced under conditions

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when oxidized molybdenum [Mo(VI)] was available, this would suggest that the pH of that environment must have been greater than five and the redox potential greater than zero because these are the conditions where Mo(VI) exists in significant quantities (Anbar 2004).

PRACTICAL CONSIDERATIONS FOR CREATING GENETIC SYSTEMS As explained in the previous section, genetics has the potential to be a powerful tool for geobiology, offering insights into: i.) what structures to look for in the rock record, ii.) what they mean when we find them, iii.) what enzymes catalyze their production, and iv.) what conditions regulate their expression. To be able to convert this theory into practice, it is helpful to know where to begin in the laboratory. In this section, we outline the key steps that would need to taken to make an organism genetically tractable. To briefly summarize, the first step is to isolate an organism that will be amenable to genetic analysis; this strain will serve as the standard (or “wild-type”) to which all subsequent mutants will be compared. This of course imposes a limitation on what genetics can offer geobiology, as not all strains can easily be coaxed into growing in the laboratory, much less be straightforward to mutagenize once isolated. Nevertheless, with perseverance and creativity, these difficulties can usually be overcome, leading to the second step: mutagenesis of the strain. Various methods for mutagenesis exist, offering the potential to eliminate/delete genes entirely, introduce pointmutations into specific genes, or introduce genes into an organism. The effects of these different types of mutations can be far ranging, from altering the amino acid composition of a protein and thereby affecting its substrate specificity, to eliminating the ability to make a set of proteins, to changing the regulation of an entire network of genes. After mutagenesis is performed, the third step is to identify the mutants either through a selection or a screen. A selection permits only those mutants that have the desired properties to grow, whereas a screen requires characterizing the behavior of thousands of mutants to identify only rare ones that have the properties/behavior of interest. Depending on the manner in which one has identified candidate mutants, secondary screens may be required to narrow the pool of candidates down to only those that are interesting. For example, if one performs a screen to find genes that control various steps in a biochemical reaction, if the assay for mutant identification involves looking at the rate at which a reaction proceeds, “false” mutants could be identified by the screen that are simply slow to grow but which do not have a specific defect in the reaction of interest. These mutants could be sorted out by measuring the growth rate of all candidates and only continuing to study those whose growth is normal with respect to the parent strain. Once interesting mutants are identified, the fourth step is to determine the nature of the mutation through sequencing and genetic verification. Sequence analysis can help generate hypotheses to explain why the mutant behaves the way it does, and thus infer what affects the process of interest. To test these hypotheses, however, the final step requires physiological, biochemical, or cell biological experiments to be performed in order to study the phenotype of the mutant in detail.

Step 1: Isolation and growth Developing a genetic system in an organism can be a tedious, albeit rewarding process. Development can be greatly enhanced when the organism of interest is a close relative to a microbe with an established genetic system. This was the case for the arsenic-respiring Gramnegative bacterium Shewanella sp. strain ANA-3, which resulted from a targeted-isolation of a strain that could grow strictly anaerobically on arsenate in minimal medium and also make single colonies overnight on Luria-Broth (LB) plates on the bench top. LB is a widely used rich medium in bacterial genetics, as it supports rapid growth and is easy to make. Because this enrichment strategy imposed a strong selection for bacteria that had respiratory versatility, it was not surprising that it resulted in the isolation of a new strain of Shewanella, a genus

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renowned for this property (Nealson and Scott 2004). Since this organism is closely related to other strains of Shewanella that have established genetic systems, Saltikov et al. adapted strategies that had been successfully used in S. oneidensis strain MR-1 to their new isolate (Saltikov et al. 2003; Saltikov and Newman 2003). Several years prior to this, one of the authors had isolated a bacterial strain (Desulfotomaculum sp. strain OREX-4) that could also respire arsenate (Newman et al. 1998; Newman et al. 1997). However, because attempts to grow this strain on plates failed, a genetic system could never be established. Two main lessons regarding the development of a genetic system are illustrated by this example: 1.) Enrich for an organism in a targeted fashion so that a strain can be isolated that exhibits the desired properties and 2.) The ability to readily form colonies on plates is a highly desirable trait, for reasons that will be discussed below. One major benefit of growth on agar plates is facilitating strain isolation. Bacterial colonies on an agar plate typically form from a single cell, meaning that every cell that comprises the colony is identical at the genetic level, or of the same genotype. If a single colony is picked from a solid-surface medium, streaked across a new plate with the purpose of dispersing cells so that individual cells are isolated from their neighbors, and allowed to incubate, all subsequent colonies arising should be both morphologically and genetically identical to the original colony (Fig. 1). This process is typically called ‘colony purification’ and can yield pure cultures of the bacterial strain of interest. Using solid surfaces to culture bacteria is, for all practical purposes, essential for the isolation of mutant strains after they are generated. Solid or liquid medium then can be used to perform basic physiological studies, such as determining optimal growth temperature, nutritional requirements and sensitivity to antibiotics or some other selectable marker. Characterizing the susceptibility of a strain to a selectable marker, such as a heavy metal (e.g., tellurium) or an antibiotic (e.g., kanamycin) is important for many genetic techniques. These techniques require the ability to isolate individuals within a population that carry a genetic difference from the overall population. Often, transposons (or “jumping genes”) that carry resistance determinants for a particular toxic compound are used to make mutants by disrupting

Figure 1. Streak plate. Example of a strain of S. oneidensis streaked for single colonies on solid medium.

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the chromosome at random, but mutants in specific loci can also be generated by replacing the wild-type gene with a resistance determinant. Regardless of the method of mutagenesis, when these resistance determinants are inserted into the chromosome, they confer upon the resulting mutant strain resistance to the toxic compound. With the appropriate solid medium containing this compound, these mutants can be spatially separated and selected from a population that also contains the wild-type (the wild-type, lacking the resistance determinant, will die, so only the mutants will grow). When possible, it is helpful to make low endogenous resistance to several antibiotics a requirement in the isolation of an organism for genetic analysis. If the isolate is naturally resistant to many of the typical antibiotics (e.g., ampicilin, gentamycin, kanamycin, chloramphenicol and tetracycline), however, other strategies can be devised to make mutants— such as employing resistance to heavy metals (Gupta et al. 2002). Two additional properties are also beneficial to establishing genetics in an organism. First is the ability to introduce foreign DNA into the strain, which can be accomplished through transformation, transduction, or conjugation (Madigan et al. 2003). Transformation is when the cell takes up DNA directly—this can be facilitated by electroporation (using electric current to transform DNA into bacteria) or by generating chemically-competent cells (using high concentrations of salt, typically calcium chloride, and inducing the transformation via heat-shock). Transduction is the process whereby phage (i.e., viruses) infect bacterial cells and inject DNA into them which is then incorporated into the chromosome. Finally, conjugation if the process of transferring DNA from one bacterium to another by means of matings that involve the transfer of mobilizable plasmids. Introduction of foreign DNA is important for many targeted and random methods of mutagenesis, and critical for verifying the causality of the phenotype. The second beneficial property is speed of growth. When choosing a new strain for genetic work, the faster the organism grows, the faster its secrets will be unraveled (assuming the creativity of the scientist is not the limiting factor)!

Step 2: Methods of mutagenesis There are three types of mutagenesis that are common in bacterial genetics: chemical, transposon, and targeted. Chemical and transposon mutagenesis typically generate random mutations that are useful when one has no preconceptions about how a system works and seeks to cast a wide net to identify all possible genes involved in a process. In contrast, targeted gene “knockouts” are made when one has an idea of what gene(s) might be involved in a process and wants to test them specifically. We briefly review these methods here, discussing their strengths and weaknesses. I. Chemical and UV mutagenesis. This method of generating mutants is rapid, inexpensive and fairly easy. Cells are treated with a mutagenic agent (e.g., ethyl or methyl methanesulfonate, nitrosoguanidine or ultraviolet light (Madigan et al. 2003)), grown briefly, and then plated for isolated colonies. A balance must be struck in how much to mutagenize the cells: too little treatment results in few mutants in the population and makes it difficult to find them among the remaining wild-type cells, while too much treatment frequently results in multiple mutations in a single cell which complications determinating causality later. Critical parameters to consider are mutagen concentration, mutagen type (some mutagens are stronger than others) and exposure time to the mutagen. In the case of UV mutagenesis, significant killing typically occurs, but this is a necessary side effect of achieving a sufficiently high frequency of mutation in the remaining viable population. The major downside to this type of mutagenesis is the difficulty in identifying the gene, or genes, disrupted by the mutation. This means that more cells must be screened for the defect of interest because a large proportion will be both phenotypically and genotypically wild-type. On the positive side, however, this method of mutagenesis enables subtle phenotypes (such as residues on proteins that affect their substrate specificity, or interactions with other proteins), as well as conditional

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phenotypes (e.g., temperature sensitive mutations) or partial defects to be identified. This is particularly useful in the identification of essential genes, as they mutants can be generated under a condition that permits them to grow, and then shifted to a different condition that renders the mutation lethal. Another benefit of chemical/UV mutagenesis is that it does not depend on introducing foreign DNA into the strain, as the other techniques do (although later, this will be necessary to verify the nature of the mutation—see below). II. Transposon mutagenesis. Transposons are genetic elements that can move in either random, or non-random fashion into and out of chromosomes (Madigan et al. 2003). Facilitating this movement is an enzyme called transposase. These elements are believed to play a role in influencing evolution and can be found in all forms of life. Researchers have modified these elements to be used as tools to generate random mutations. Often times these modifications streamline the element to one or two genes, with one typically encoding resistance to an antibiotic. Plasmids used for transposon mutagenesis will contain both the transposon sequence, and a separate gene encoding the specific transposase. When the plasmid is transformed into a strain, the transposase can be made, which will then facilitate integration of the transposon on the plasmid into the chromosome in a random fashion. The optimal plasmids used for such a procedure are unable to replicate without specific genes, therefore subsequent selection of the population for strains resistant to the antibiotic will eliminate the parent strain. This leaves only mutant strains that have successfully integrated the transposon into their genome. A downside of transposon mutagensis is that genes that are essential for a process under the conditions where the transposon insertion is selected will be missed. This either requires the growth conditions for the selection of the transposon insertion to be different from those where the mutants will be identified, or the use of chemical or UV mutagenesis that permits the study of conditional/partial phenotypes. When a transposon mutant with the desired phenotype is identified, several methods exist to determine the disrupted gene. One way is by cloning the transposon from purified, digested (or sheared) genomic DNA using the antibiotic resistance property. This method will yield genomic DNA flanking the transposon. Primers designed to the transposon can then be used to generate sequence into the flanking region. Alternatively, a process called arbitrary PCR can identify a small portion of sequence adjacent to the transposon directly from genomic DNA without cloning (Caetano-Anolles 1993). If working with a fully sequenced strain, as little as 20 base pairs of sequence is sufficient to identify the location of the transposon. The process of identifying the transposon-mutated gene in an unsequenced strain is more cumbersome, but still straightforward. Two approaches are possible. One can make a genomic library from the mutant strain (a library comprises either plasmids or cosmids or fosmids, the latter holding significantly more DNA than plasmids), introduce this library into an appropriate host, and select for growth of cells that carry the transposon. Alternatively, the sequence identified through arbitrary PCR can be used to probe a genomic library of the wild-type. Using hybridization techniques, a probe consisting of DNA flanking the transposon can identify plasmids in the genomic library containing homologous sequence. Once these have been identified, the plasmids themselves can be sequenced, open reading frames (genes) identified, and the genomic region surrounding the transposon reconstructed. III. Targeted gene knockout. In situations where a particular gene is suspected of being involved in a process (for example, when the genome of an organism of interest has been sequenced, and one can perform genomic analysis on it), it is often helpful to mutagenize that gene to test its involvement. This is called making a targeted gene “knockout.” Several methods exist to specifically eliminate a gene of interest. However, to take advantage of this technique, the sequence of the both the gene, and its surrounding region must be known (Fig. 2A). Simple inactivation of a gene can occur by inserting an antibiotic resistance gene into the gene targeted for knockout. This can be accomplished by cloning the gene, and some flanking sequence, if

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Figure 2. Diagram of targeted gene knockout using a suicide vector. Refer to text for description of figure.

required, into a plasmid that will only replicate in a specific genetic background (Fig. 2B). Good examples of this class of plasmids are those that require the π protein (encoded by the pir gene, derived from the R6K plasmid (Kolter 1981) to replicate. By engineering the plasmid in an E. coli strain that contains the pir gene, one can construct such a vector. The idea is to clone the gene of interest, then modify the gene by inserting an antibiotic resistance cassette into the gene (which can be accomplished either by cloning or by fusion PCR, Fig. 2C). Ideally, this antibiotic resistance cassette will have at least 1,000 base pairs of sequence from the host strain on either side. This is important to facilitate homologous recombination into the genome of the strain of interest. Once the plasmid is constructed that contains the disrupted gene, the next step is to transform the strain with the newly constructed plasmid using a method described above. Strains that become resistant to both of the antibiotics encoded by the antibiotic resistance genes in the plasmid (antiA and antiB) have undergone a single crossover event (Fig. 2C), resulting in the integration of the plasmid into the genome (Fig. 2D). The strain cannot maintain the plasmid itself because it does not produce the π protein. By growing the strain without selecting for the endogenous plasmid resistance (antiA, Fig. 2), a second recombination

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event can occur in some individuals within the population, resulting in the elimination of the plasmid DNA from the genome, along with the wild-type gene of interest (Fig. 2D, result 1). If selection for resistance to antibiotic B is maintained, the second recombination event (Fig. 2D, result 2) that reverts the strain back to wild-type, cannot occur. Variations on this technique can yield mutations such as total gene replacements, or even in-frame (non-polar) deletions of the gene of interest.

Genetic polarity in bacteria Bacteria typically contain a single, circular chromosome. Genes can be arranged in either direction in the genome and are typically clustered into operons. Genes organized in operons tend to be involved in the same process, although this is not always the case (Salgado et al. 2000). An operon is defined as multiple genes sharing the same genetic regulatory elements. A mutation that alters the capacity of the regulatory element to express downstream genes is called a “polar mutation.” This mutation can be any of the types discussed above, but is most often associated with transposon mutagenesis. Because of polarity, a gene disrupted by a transposon may not cause the identified phenotype itself, but a gene (or genes) downstream may be responsible. To attribute a specific process to a specific gene, the problem of polarity must be taken into account, and complementation experiments must be done to demonstrate that a defect can be restored by provision of a particular gene.

Step 3: Identifying mutants Identification of mutants defective in the process of interest is usually limited only by the robustness of the selection/screen, meaning that careful planning and thought should go into its design. We briefly illustrate this with four examples from our laboratory. I. Screen for mutants defective in reducing anthraquinone-2,6-disulfonate (AQDS) in S. oneidensis strain MR-1 (Newman and Kolter 2000). The first screen we performed involved the identification of S. oneidensis mutants that were defective in reducing the soluble humic acid analog AQDS. Because reduced AQDS is orange in color, wells containing mutants defective in this process remained clear whereas other wells turned orange. Mutants were grown overnight, then inoculated into minimal medium containing AQDS in 96-well microtiter plates (96 independent mutant strains per plate) and covered with mineral oil to limit oxygen diffusion into the wells. Two classes of mutants were isolated from this screen: mutants that were completely unable to reduce AQDS, and mutants that reduced AQDS slowly. A screen that can be visually monitored over time can facilitate identification of several classes of mutants with varying degrees of defectiveness. An example of this screen is shown in Figure 3. Note the lighter wells around the outside of the plate are likely due to re-oxidation of AQDS by oxygen diffusing into the plate. These effects could have been avoided by incubating the plates in an anaerobic chamber, which illustrates the importance of screen design for maximal efficiency in identifying potential mutants. II. Screen for iron hydr(oxide) reduction mutants in Shewanella oneidensis strain MR-1 (Gralnick and Newman, unpublished). To identify mutant strains of S. oneidensis defective in the ability to reduce insoluble iron hydr(oxide), we grew mutant strains overnight aerobically in 96-well microtiter plates in LB. These cultures were then transferred to minimal medium that contained iron hydr(oxide) as the sole electron acceptor for growth. Plates were incubated without shaking overnight, then a compound (ferrozine) was used to detect the presence of ferrous iron [Fe(II)], the product of iron hydr(oxide) reduction. Because this is a colormetric assay (when ferrozine binds to Fe(II), a purple color is formed), putative mutants were easily identified by eye as wells that did not significantly change color. These mutants were retested by restreaking from the initial overnight culture, then checked again for their capacity to reduce iron hydr(oxide) to confirm the phenotype. Retesting is very important in this process, as it

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Figure 3. Mutant screen for AQDS reduction-deficient mutants in S. oneidensis. Screen was performed as indicated in the text. Dark wells represent reduced AQDS, circled well represents a mutant defective in AQDS reduction. See (Newman and Kolter 2000) for further details.

allows one to be liberal in the initial round of mutant identification, which permits not only false positives to come through, but also mutants with subtle phenotypes. Using this method, we screened over 8,000 mutants for defects in iron hydr(oxide) reduction, yielding about 60 mutants. It was only after identifying several mutants that we realized a flaw in our screen design—any strain that was unable to grow in minimal medium would be identified as defective in iron hydr(oxide) reduction. Therefore, many of the mutants we isolated were simply unable to grow in minimal medium because they lacked the capacity to produce certain essential amino acids or vitamins absent from the medium (this class of mutants are called auxotrophs). In the next version of this screen, we performed both our initial mutant selection and our screen in a medium containing amino acids and vitamins. This allowed auxotrophic strains to appear phenotypically wild-type rather than appear as mutants defective in iron hydr(oxide) reduction. This example illustrates the importance of screen construction to maximize the probability of identifying interesting mutants. III. Screen for mutants defective in photosynthetic Fe(II) oxidation in Rhodopseudomonas palustris strain TIE-1 (Jiao et al. 2005). In this screen, we used a similar approach to that described to identify mutants defective in iron hydr(oxide) reduction; however, some additional steps were required to maximize the efficiency of the screening process. Transposon-insertion mutants of R. palustris strain TIE-1 were pre-grown aerobically in 96-well microtiter plates, then transferred to photosynthetic medium and grown with hydrogen as the electron donor. Once strains had reached sufficient density (3 days), they were centrifuged and resuspended in a buffer containing Fe(II). After several hours, ferrozine was added to visually observe the presence or absence of Fe(II) in each well. In this screen, wells containing mutants defective in phototrophic Fe(II) oxidation appeared purple (indicating the presence of Fe(II)), whereas clear wells revealed strains with wild-type activity. A key difference from the S. oneidensis screen is that this was a cell suspension assay, because growth of the strain was not required (i.e., the length of the assay was shorter than the doubling time of the cells). In this screen, apparent loss of Fe(II) oxidation activity could have resulted

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from a blockage in a step required for Fe(II) uptake or iron oxidation. To differentiate between these two possibilities, we performed a secondary screen measuring total iron and Fe(II) for filtered and unfiltered samples. In this manner, we were able to narrow the pool of candidate mutants down to only those with defects in Fe(II) oxidation. IV. Selection/screen for mutants defective in magnetite production in Magnetospirillum sp. AMB-1 (Komeili et al. 2004). Finally, perhaps our favorite mutant hunt due to its simplicity was one designed to identify genes required for magnetite formation in Magnetospirillum sp. AMB-1 (Komeili et al. 2004). Cells were first mutagenized and grown under a condition where they did not produce magnetite. They then were pooled and transferred en mass to a condition where they could produce magnetite. Magnets were placed next to the tubes containing the entire mutagenized population to remove magnetic individuals, thus allowing for “mnm” mutants (magnetosome mutants) to be enriched. Individuals that passed through this selection, were screened individually under conditions that promoted formation of magnetite in microtiter plates. Once strains achieved the proper cell density, the entire plate was placed on top of a set of 24 magnets. The magnets were arranged so that they were positioned at the intersection of four individual wells of the microtiter plate. Magnetic strains were pulled toward the edge of their individual well, whereas non-magnetic (or poorly magnetic) strains remained at the center of the well.

Step 4: Mutant verification Regardless of the method of mutagenesis, after identifying a mutant, it is important to verify that a particular gene is responsible for the mutant phenotype (as opposed to the phenotype being due to a random mutation elsewhere in the chromosome). The process of “complementation” is used for this purpose. In complementation, the particular gene, or set of genes thought to be required for a process (usually determined by sequencing around the site of a transposon insertion), can be cloned from the wild-type into a plasmid that has the capacity to replicate in the mutant strain. If the plasmid contains sufficient information to promote expression of the cloned gene, the phenotype of the mutant should be reversed, or complemented, and the mutant’s phenotype should be restored to that of the wild-type. This experiment demonstrates causality, verifying the role of the disrupted gene in the process of interest. Complementing point mutants is a more difficult task. A genomic library must be constructed from a wild-type background and then plasmids (or cosmids) containing the library transferred into the mutant strain of interest. Transformants are then screened for complementation of the mutant phenotype. Once a plasmid is identified that will complement the mutant defect, it can be sequenced to identify the genes it contains. Individual genes can then be cloned to test for complementation, or the original plasmid can be fragmented and sub-cloned to determine the minimal amount of DNA required for complementation. Once the affected gene is identified, the mutant version of the gene can be amplified and sequenced to determine the nature of the original mutation.

A brief note on phage Phage (bacterial viruses) have played a critical role in not only our understanding of genetics and molecular biology, but also facilitating genetic work in a number of organisms. Modified versions of transducing phage that package host genomic DNA at a high frequency can be used to perform many genetic tasks, from generating isogenic strains (two strains that differ genotypically in a single locus) to mapping point mutations and even generating mutant libraries. Because our goal in this chapter is merely to provide an introduction to developing a genetic system, we will not cover genetic techniques associated with phage beyond noting that developing a robust and efficient transducing phage system can add an additional level of sophistication to a genetic system.

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Step 5: Mutant analysis Once mutants have been identified and the involvement of particular genes in the process of interest verified, many new avenues become open for exploration. By identifying a variety of mutants with similar phenotypes, one can begin to construct a model for how a process works at the molecular level. Specific studies can be initiated to study regulation of genes to determine the precise environmental conditions that trigger the organism to catalyze the process of interest. Putative marker genes for this activity may be identified, potentially leading to the design of molecular tools to monitor when this process is active in a given environment (Malasarn et al. 2004). Once regulatory elements (e.g., promoters) have been identified for a gene, a strain can be engineered to “report” when it is expressing that gene. For example, a promoter from a gene of interest can be cloned into a plasmid and used to drive expression of a protein that can be detected by fluorescence or colormetric assay (e.g., green fluorescent protein, GFP or betagalactosidase; reviewed by Kohler et al. 2000). Manipulating various environmental conditions in the laboratory can provide precise information regarding when the engineered strain is expressing the gene of interest by quantifying fluorescence. Finally, the biochemical properties and subcellular localization of the gene product can be studied, either within the host strain, or by cloning and over-expressing the gene that encodes it in another organism. Help in understanding the specific function of genes identified through mutagenesis can come from analyzing the amino acid sequence encoded by the gene. A tool that can provide significant clues is the BLAST (Basic Local Alignment Search Tool) search engine available at NCBI (National Center for Biotechnology Information – www.ncbi.nlm.nih.gov). BLAST can be used to infer functional and evolutionary relationships between amino acid or nucleotide sequences. This program compares sequences entered by the user to a selected database, which can include all known sequences. The program assigns a statistical significance to matches within the database. If the gene of interest encodes a protein with a significant match to a protein of known function in the database, this may suggest it has a similar activity. If the protein has no significant match or is only similar to other proteins of unknown function, there are several additional analyses that can be performed on the sequence to gain insight into its function. Other useful types of searches are motif, post-translational modification and topology. Several programs in each category can be found on the ExPASy (Expert Protein Analysis System) web server (www.expasy.org/tools/), which is maintained by the Swiss Institute of Bioinformatics. Programs found here will allow further analysis of the protein sequence of interest to predict characteristics such as subcellular localization, co-factor binding and transmembrane domains. For example, if the protein is predicted to bind a redoxactive cofactor such as heme, we may hypothesize that it plays a role in electron transfer. As we noted previously, however, it is imperative to remember that database predictions are only suggestive, and must be demonstrated experimentally. However, programs such as these greatly help formulate testable models for the function of a protein.

CONCLUSIONS In this chapter, we have focused our discussion on how bacterial genetics can help unravel the geobiology of the past, and have provided an introduction to basic genetic principles that we hope will encourage those less familiar with genetics to use it as a tool in their research. Not only is making a connection between genetics and geobiology a stimulating intellectual endeavor, but it is also great fun in practice. To close by returning to the analogy with which we started, when it comes to understanding the biogeochemical evolution of the Earth, we must accept that we will never know “whodunit” with absolute certainty, short of the invention of a time machine. But even if after making millions of mutants, we still don’t have a clue about the past, without question, applying genetics to geobiology affords us an excellent

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opportunity to make fundamental discoveries about how modern microorganisms shape the geochemistry of their environment.

Acknowledgments We wish to thank the students of the USC International Course in Geobiology (sponsored by the Agouron Institute), whose enthusiasm for genetics fed our own, and compelled us to think more critically about how genetics can help solve problems in geobiology. In addition, we would like to express our gratitude to the GPS division at Caltech, for nurturing our vision and our work, and the students and postdocs of the Newman lab (past and present) for putting it all together. Special thanks to Laura Croal, Arash Komeili and Doug Lies for constructive comments on the manuscript. We acknowledge the Luce Foundation, Packard Foundation, Agouron Institute, ONR, DARPA, NSF, Beckman Institute, and HHMI for financial support.

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Farquhar J, Bao H, Thiemens M (2000) Atmospheric influence of Earth’s earliest sulfur cycle. Science 289: 756-758 Friedrich MW (2002) Phylogenetic analysis reveals multiple lateral transfers of adenosine-5-phosphosulfate reductase genes among sulfate-reducing microorganisms. J Bacteriol 184:278-289 Gorby YA, Beveridge TJ, Blakemore RP (1988) Characterization of the bacterial magnetosome membrane. J Bacteriol 170:834-841 Grotzinger JP, Knoll AH (1999) Stromatoites in Precambrian carbonates: Evolutionary mileposts or environmental dipsticks? Annu Rev Earth Planet Sci 27:313-358 Gupta A, Meyer JM, Goel R (2002) Development of heavy metal-resistant mutants of phosphate solubilizing Pseudomonas sp. NBRI 4014 and their characterization. Curr Microbiol 45:323-7 Habicht KS, Gade M, Thamdrup B, Berg P, Canfield DE (2002) Calibration of sulfate levels in the Archaen ocean. Science 298:2372-2374 House CH, Runnegar B, Fitz-Gibbon ST (2003) Geobiological analysis using whole genome-based tree building applied to the Bacteria, Archaea, Eukarya. Geobiology 1:15-26 Jiao Y, Kappler A, Croal LR, Newman DK (2005) Isolation and characterization of a genetically-tractable photoautotrophic Fe(II)-oxidizing bacterium, Rhodopseudomonas palustris strain TIE-1. Appl Environ Microbiol 71:4487-4496 Jones GE, Starkey RL (1962) Some necessary conditions for fractionation of stable isotopes of sulfur by Desulfovibrio desulfuricans. In: Biogeochemistry of Sulfur Isotopes. NSF Symposium. Jensen ML (ed.) Yale University Press, New Haven, CT, p 61-79 Judson HF (1996) The Eighth Day of Creation. Cold Spring Harbor Laboratory Press, New York Kappler A, Pasquero C, Konhauser KO, Newman DK (2005) Deposition of banded iron formations by phototrophic Fe(II)-oxidizing bacteria. Geology 33(11):in press Karkhoff-Schweizer RR, Huber DPW, Voordouw G (1995) Conservation of the genes for dissimilatory sulfite reductase from Desulfovibrio vulgaris and Archaeoglobus fulgidus allows their detection by PCR. Appl Environ Microbiol 61:290-296 Kauffman S (1993) The Origins of Order. Oxford University Press, Oxford Kelly SD, Laskowski M, Qasim MA (2005) Tje rp;e pf scaffp;domg om standard mechanism serine proteinase inhibitors. Protein Peptie Lett 12:465-471 Kemner KM, Kelly SD, Lai B, Maser J, O’Loughlin EJ, Sholto-Douglas D, Cai ZH, Schneegurt MA, Kulpa CF, Nealson KH (2004) Elemental and redox analysis of single bacterial cells by X-ray microbeam analysis. Science 306:686-687 Kemp ALW, Thode HG (1968) The mechanism of the bacterial reduction of sulfate and of sulfite from isotope fractionationstudies. Geochim Cosmochim Acta 32:71-91 Kirschvink JL, Gaidos EJ, Bertani LE, Beukes NJ, Gutzmer J, Maepa LN, Steinberger RE (2000) Paleoproterozoic snowball Earth: Extreme climatic and geochemical global change and its biological consequences. Proc Natl Acad Sci USA 97:1400-1405 Kluyver AJ (1924) Unity and diversity in the metabolism of microorganisms. Chemisch Weekblad 21:226 Kohler S, Belkin S, Schmid RD (2000) Reporter gene bioassays in environmental analysis. Fresenius J Anal Chem 366:769-79 Kolter R (1981) Replication properties of plasmic R6K. Plasmid 5:2-9 Komeili A, Vali H, Beveridge TJ, Newman DK (2004) Magnetosome vesicles are present before magnetite formation, and MamA is required for their activation. Proc Natl Acad Sci USA 101:3839-3844 Lederberg J (1946) Gene recombination and linked segregations in E. coli. Genetics 32:505-525 Madigan MT, Martinko JM, Parker J (2003) Bacterial Genetics, Biology of Microorganisms. Prentice Hall, Upper Saddle River, NJ, p. 264-320 Malasarn, D, Saltikov W, Campbell KM, Santini JM, Hering JG, Newman DK (2004) arrA is a reliable marker for As(V) respiration. Science 306:455-455 McKay DS, Gibson Jr. EK, Thomas-Keprta KL, Vali H, Romanek CS, Clemett SJ, Chiller XDF, Maechling CR, Zare RN (1996) Search for past life on Mars: possible relic biogeneic activity in martian meteorite ALH84001. Science 273:924-930 Metcalf WM, Zhang JK, Apolinario E, Sowers KR, Wolfe RS (1997) A genetic system for Archea of the genus Methanosarcina: liposome-mediated transformation and construction of shuttle vectors: Proc Natl Acad Sci USA 94:2626-2631 Michaelis W, Seifert R, Nauhaus K, Treude T, Thiel V, Blumenberg M, Knittel K, Gieseke A, Peterknecht K, Pape T, Boetius A, Amann R, Jorgensen BB, Widdel F, Peckmann J, Pimenov NV, Gulin MB (2002) Microbial reefs in the Black Sea fueled by anaerobic oxidation of methane. Science 297:1013-1015 Myers JM, Myers CR (2002) Genetic complementation of an outer membrane cytochrome omcB mutant of Shewanella putrefaciens MR-1 requires omcB plus downstream DNA. Appl Environ Microbiol 68:2781-93 Nealson KH, Scott J (2004) Ecophysiology of the Genus Shewanella. In: The Prokaryotes. Dworkin M (ed) Springer-Verlag, New York. http://141.150.157.117:8080/prokPUB/chaphtm/394/COMPLETE.htm

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Reviews in Mineralogy & Geochemistry Vol. 59, pp. 27-52, 2005 Copyright © Mineralogical Society of America

Enzymology of Electron Transport: Energy Generation With Geochemical Consequences Thomas J. DiChristina School of Biology Georgia Institute of Technology Environ Sci Technol Building Atlanta, Georgia, 30332, U.S.A. [email protected]

Jim K. Fredrickson and John M. Zachara Fundamental Sciences Division Pacific Northwest National Laboratory P.O. Box 999, MSIN P7-50 Richland, Washington, 99352, U.S.A. [email protected]

[email protected]

INTRODUCTION Dissimilatory metal-reducing bacteria (DMRB) are important components of the microbial community residing in redox-stratified freshwater and marine environments. DMRB occupy a central position in the biogeochemical cycles of metals, metalloids and radionuclides, and serve as catalysts for a variety of other environmentally important processes including biomineralization, biocorrosion, bioremediation and mediators of ground water quality. DMRB are presented, however, with a unique physiological challenge: they are required to respire anaerobically on terminal electron acceptors which are either highly insoluble (e.g., Fe(III)- and Mn(IV)-oxides) and reduced to soluble end-products or highly soluble (e.g., U(VI) and Tc(VII)) and reduced to insoluble end-products. To overcome physiological problems associated with metal and radionuclide solubility, DMRB are postulated to employ a variety of novel respiratory strategies not found in other gram-negative bacteria which respire on soluble electron acceptors such as O2, NO3−, SO42−, and CO2. The novel respiratory strategies include 1) direct enzymatic reduction at the outer membrane, 2) electron shuttling pathways and 3) metal solubilization by exogenous or bacterially-produced organic ligands followed by reduction of soluble organic-metal compounds. The first section of this chapter highlights the latest findings on the enzymatic mechanisms of metal and radionuclide reduction by two of the most extensively studied DMRB (Geobacter and Shewanella), with particular emphasis on electron transport chain enzymology. These advances have drawn significantly upon genomic data for isolated microorganisms from the genera Geobacter and Shewanella (see chapter by Nelson and Methé 2005). The second section emphasizes the geochemical consequences of DMRB activity, including the direct and indirect effects on metal solubility, the reductive transformation of Fe- and Mn-containing minerals, and the biogeochemical cycling of metals at redox interfaces in chemically stratified environments.

ENZYMATIC BASIS OF IRON AND MANGANESE REDUCTION The electron transport systems of gram-negative bacteria are generally described as inner membrane (IM)-associated electron and proton carriers that 1) mediate electron transfer from 1529-6466/05/0059-0003$05.00

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primary donor to terminal electron acceptor and 2) conserve energy released during electron transfer to the generation of ATP (Madigan and Martinko 2006). Figure 1 displays the electron transport chain enzymology of Escherichia coli respiring high concentrations of dissolved O2 as electron acceptor. The E. coli electron transport system is modular in design with a membrane-soluble quinone pool (Q) linking dehydrogenase complexes at the head end with terminal reductase complexes at the terminus. Dehydrogenase complexes include electron donor-specific oxido-reductases (e.g., NADH dehydrogenase) that couple oxidation of specific electron donors to reduction of a series of membrane-associated electron carriers arranged in order of increasingly more positive electric potential (E0′). These electron carriers include flavoproteins (Fp) and FeS proteins that translocate protons across the IM to the periplasm and direct electrons to the Q pool, respectively. Reduced Q is subsequently protonated to QH2 at the inner aspect of the IM. QH2 carries protons across the IM to the periplasm and transfers electrons to cytochrome b556 and b562, two components of the terminal reductase complex that transfer electrons to cytochrome o (topologically located at the inner aspect of the IM) and ultimately to O2. Cytochrome o catalyzes both the translocation of a proton across the IM to the periplasm and the terminal reduction of O2 to H2O. A proton motive force (PMF) is generated by 1) proton translocation across the IM by dehydrogenase complexes, QH2 and cytochrome o, and 2) proton consumption during the terminal reduction of O2 to H2O by cytochrome o. PMF generated in this manner drives ATP synthesis as protons are translocated back into the cytoplasm through an IM-localized ATPase, catalyzing the phosporylation of ADP to ATP (Madigan and Martinko 2006). Fe(III)- and Mn(IV)-respiring DMRB, on the other hand, are presented with a unique physiological problem: they are required to respire anaerobically on terminal electron acceptors found largely in crystalline form or as amorphous (oxy)hydroxide particles presumably unable to contact IM-localized electron transport systems. A DMRB culture actively respiring solid Mn(IV) oxides as anaerobic electron acceptor is displayed in Figure 2. To overcome the problem of respiring solid electron acceptors, Fe(III)- and Mn(IV)-respiring DMRB are postulated to employ a variety of novel respiratory strategies not found in other gram-negative bacteria that respire on soluble electron acceptors such as O2, NO3−, SO42− and CO2 including 1) direct enzymatic reduction of solid Fe(III) and Mn(IV) oxides via outer membrane (OM)-localized metal reductases (Myers and Myers 1992, 2003a; Beliaev and Saffarini 1998; DiChristina et al. 2002) 2) a two-step, electron shuttling pathway in which exogenous electron shuttling compounds (e.g., humic acids, melanin, phenazines, antibiotics, AQDS) are first enzymatically reduced and subsequently chemically oxidized by the solid Fe(III) and Mn(IV) oxides in a second (abiotic) electron transfer reaction (Lovley et al. 1996;

2H+




NADH + H+

2H+ 2e-

FeS

NAD+

2e-

Q QH2 2H+

H+

H+ 2e- b556 2eb562 1/

2O2

o + 3H+

H2O ADP H+ ATP

ELECTRON TRANSPORT SYSTEM

ATPase

Figure 1. Electron transport and proton translocation processes of the E. coli aerobic respiratory chain at high O2 concentrations with NADH2 as electron donor. Fp, flavoprotein; FeS, iron-sulfur protein; Q, quinone pool; b556 and b562, b-type cytochromes; o, cytochrome o.

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Figure 2. DMRB Shewanella putrefaciens strain 200 actively respiring solid Mn(IV) oxides as anaerobic electron acceptor. (A) Anaerobic cell suspensions at the beginning (tube on left side) and end (tube on right side) of a 24-hour anaerobic incubation period (note color change indicative of reductive dissolution of black Mn(IV) particles to clear, soluble reduced Mn), (B) phase contrast micrograph of cells coating the surface of a Mn(IV) oxide particle at the beginning of the anaerobic growth period, (C) epifluorescence micrograph of same field of view as in (B) with acridine orange-stained cells, (D) epifluorescence micrograph of acridine orange-stained cells at the end of the 24-hour anaerobic incubation period (note the absence of the solid Mn(IV) particles).

Coates et al. 1998, 2002; Newman and Kolter 2000; Turick et al. 2002; Hernandez et al. 2004; DiChristina et al. 2005) 3) an analogous two-step reduction pathway involving endogenous, electron shuttling compounds (Newman and Kolter 2000; Saffarini et al. 2002) and 4) a twostep, Fe(III) solubilization-reduction pathway in which solid Fe(III) oxides are first dissolved by exogenous or bacterially-produced organic complexing ligands, followed by uptake and reduction of the soluble organic Fe(III) forms by periplasmic Fe(III) reductases (Arnold et al. 1988; Lovley and Woodward 1996; Pitts et al. 2003). Although the number of DMRB species continues to increase rapidly and has now reached nearly 100 (Lovley et al. 2004), the enzymatic basis of electron transfer to metals has been most extensively studied in metalrespiring members of the genera Geobacter and Shewanella. The following section highlights the latest findings on the enzymatic basis of Fe(III) and Mn(IV) reduction by Geobacter and Shewanella, with particular emphasis on electron transport chain enzymology.

Direct enzymatic reduction at the outer membrane Shewanella and Geobacter catalyze the direct enzymatic reduction of solid Fe(III) and Mn(III,IV) oxides via an electron transport chain arranged in a canonical, highly branched fashion. Hydrogenase and flavin-containing dehydrogenase complexes of both Shewanella and Geobacter (Myers and Myers 1993a; Lloyd et al. 2000) oxidize a variety of electron donors (e.g., H2, NAD(P)H) and transfer electrons to a menaquinone pool (Myers and Nealson 1990; Myers and Myers 1993b; Nevin and Lovley 2002; Saffarini et al. 2002). In S. oneidensis,

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menaquinol diffuses within the IM to the quinol oxidation site of CymA, a 21 kDa tetraheme cytochrome c that oxidizes menaquinol and is thought to transfer electrons to MtrA, a 32 kDa decaheme cytochrome c located in the periplasm (Myers and Myers 2000; Schwalb et al. 2003). In G. sulfurreducens, PpcB (a 36 kDa diheme cytochrome c) essentially carries out the same function as CymA (although CymA and PpcB display little or no amino acid sequence homology), oxidizing the menaquinol pool and transferring electrons to PpcA, a 10 kDa triheme cytochrome c located in the G. sulfurreducens periplasm (Lloyd 2003). As with PpcB, G. sulfurreducens PpcA does not display significant sequence similarity to any S. oneidensis c-type cytochromes, suggesting that they have a different evolutionary origin (Lloyd 2003). Electrons from the S. oneidensis menaquinol pool are transferred to one of four electronaccepting, c-type hemes within CymA, followed by inter-heme electron transfer (according to decreasing heme redox potential) until a final transfer is made to subsequent electron carriers in the periplasmic space (Harada et al. 2002). cymA-deficient mutants of S. oneidensis are unable to reduce NO3−, Fe(III), Mn(IV) or fumarate as electron acceptor (Myers and Myers 1997), an indication that CymA is a central branchpoint of the S. oneidensis electron transport system. ppcB-deficient mutants of G. sulfurreducens, on the other hand, are unable to reduce Fe(III), but retain the ability to reduce fumarate (Butler 2003). The principles of, and rationale for genetic manipulation (including generation of metal respiration-deficient mutants) is discussed in the chapter by Newman and Gralnick (2005). Electron transport from MtrA in S. oneidensis and PpcA in G. sulfurreducens to solid Fe(III) oxides is postulated to proceed via an electron transport chain that spans the periplasmic space and terminates on the outside face of the OM (Myers and Myers 1993a; Leang et al. 2003). Electron transfer to solid Fe(III) and Mn(IV) in G. sulfurreducens proceeds via OmcB, an 87 kDa, 12-heme cytochrome c tentatively assigned to the inner aspect of the OM (Leang et al. 2003). Correspondingly, Fe(III)-grown G. sulfurreducens cells display higher levels of omcB transcripts (Chin et al. 2004; Methé et al. 2005). All G. sulfurreducens OM cytochromes, however, are not necessarily involved in electron transport to Fe(III) since OmcC, an OM cytochrome displaying 73% identity to OmcB, is not required for Fe(III) reduction (Leang et al. 2003; Leang and Lovley 2005) and correspondingly, Fe(III)-grown G. sulfurreducens cells do not display higher levels of omcC transcripts (Chin et al. 2004). The terminal electron transfer step to solid Fe(III) and Mn(IV) oxides in G. sulfurreducens is postulated to be catalyzed by either OmcD or OmcE, two c-type cytochromes that may be exposed on the cell surface (Methé et al. 2005). Results of DNA microarray analysis (as described in the chapter by Nelson and Methé 2005) will identify electron transport chain components with elevated transcript levels during growth on specific electron acceptors. Similar to Geobacter, the Shewanella OM proteins involved in terminal steps of electron transfer to solid Fe(III) and Mn(IV) oxides have not been definitively identified, yet most likely include several c-type cytochromes (Myers and Myers 1992, 2003a). Fe(III) reduction activity is detected in wild-type Shewanella OM fractions (Myers and Myers 1993a), an activity that is severely impaired in Shewanella mutants lacking OM proteins, including several multi-heme c-type cytochromes. The S. oneidensis genome encodes 42 predicted c-type cytochromes (Heidelberg et al. 2002), including those in the mtrDEF-omcA-mtrCAB gene cluster. MtrA and MtrD are decaheme c-type cytochromes that display 99% similarity (Pitts et al. 2003), suggesting they may provide complementary function. MtrD is OM-associated, but may be oriented toward the periplasm (Pitts et al. 2003) and therefore not in position to contact solid Fe(III) directly. MtrB is a putative beta-barrel protein postulated to be involved in OM localization of the c-type cytochromes OmcA and MtrC that are involved in electron transfer to Fe(III) and Mn(IV) (Beliaev and Saffarini 1998; Myers and Myers 2002). mtrB mutants display a complete inability to reduce Mn(IV) and are severely, but not completely, impaired in Fe(III) reduction activity, yet retain the ability to reduce all other electron acceptors (Beliaev

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and Saffarini 1998). MtrC is an OM-localized decaheme, c-type cytochrome required for both Fe(III) and Mn(IV) reduction activity. OmcA, on the other hand, is an OM decaheme c-type cytochrome involved in electron transport to Mn(IV) and not Fe(III) (Myers and Myers 2001). omcA-deficient mutants reduce Mn(IV) at 45% wild-type rates. Interestingly, mtrC overexpression in an omcA-deficient mutant restores Mn(IV) reduction activity to greater than wild-type rates, an indication that the functional roles of MtrC and OmcA at least partially overlap in the electron transport pathway to Mn(IV) (Myers and Myers 2003b). The functions of MtrC and OmcA in Fe(III) reduction remain unclear, yet they are postulated to be major components of the Fe(III) terminal reductase. Some of the most convincing genetic evidence supporting the hypothesis that Shewanella localizes Fe(III) and Mn(IV) reductases to the OM has been derived from genetic studies with S. putrefaciens (DiChristina and DeLong 1994; DiChristina et al. 2002). Genetic mutant complementation analyses (as outlined in chapter by Newman and Gralnick 2005) indicated that a 23.3 kb S. putrefaciens wild-type DNA fragment conferred Fe(III) reduction activity to a set of 10 Fe(III) reduction-deficient mutants of S. putrefaciens. The smallest complementing DNA fragment contained one open reading frame (ORF) whose translated product displayed 87% sequence similarity to Aeromonas hydrophila ExeE, a member of the GspE family of proteins found in Type II protein secretion systems. GspE insertional mutants (constructed by targeted replacement of wild-type gspE with an insertionally inactivated gspE construct) are unable to respire anaerobically on solid Fe(III) or Mn(IV) oxides, yet retain the ability to respire all other electron acceptors including soluble complexes of Fe(III) and Mn(III) (Kostka et al. 1995; Pitts et al. 2003). Nucleotide sequence analysis of regions flanking gspE revealed one partial and two complete ORFs whose translated products displayed 55-70% sequence similarity to the GspD-G homologs of other Type II protein secretion systems. A heme-containing protein complex displaying Fe(III) reductase activity is present in the peripheral proteins loosely attached to the outside face of the wild-type OM, yet is missing from this location in the gspE mutants. Membrane fractionation studies with the wild-type strain support this finding: the heme-containing Fe(III) reductase complex is detected in the OM but not the IM or cytoplasmic fractions. These findings provide the first genetic evidence linking anaerobic Fe(III) and Mn(IV) respiration to Type II protein secretion and provide additional biochemical evidence supporting OM localization of Shewanella Fe(III) and Mn(IV) reductases (DiChristina and DeLong 1994; DiChristina et al. 2002). Gram-negative bacteria secrete soluble exoproteins to the cell periphery or exterior via five known protein secretion systems (Desvaux et al. 2004). Type II protein secretion is part of the main terminal branch of the general secretory (GSP) pathway (Pugsley 1993; Pugsley et al. 1997; Filloux 2004) and is generally comprised of 12-to-16 proteins encoded by a contiguous cluster of moderately-to-highly conserved pul (or gsp) genes, usually in the same order. Pullulanase secretion by the plant cell wall-degrading microorganism Klebsiella oxytoca is one of the best characterized Type II protein secretion systems and a working model for pullulanase secretion has been proposed (Pugsley et al. 1997). Nascent pullulanase is first directed into and across the cytoplasmic membrane where it folds and is transiently anchored to the periplasmic aspect of the cytoplasmic membrane. After processing (signal peptide cleavage, disulfide bond formation, fatty acylation), the mature pullulanase is guided across the periplasmic space by the Type II secretion pseudopilus (GspG, H, I, J complex) and interacts with the OMassociated, multimeric GspD channel. Pullulanase is subsequently attached to the outside face of the outer membrane via a fatty acid tail. The ferE homolog, gspE, is postulated to encode a secretion ATPase that drives the secretion process, including the rapid polymerization and depolymerization reactions associated with pseudopilus extension and retraction (Filloux 2004). Peripherally attached pullulanase cleaves alpha-1,6 linkages in branched maltodextrin polymers such as glycogen or amylopectin of plant cell wall material, thereby releasing linear

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dextrins for bacterial cell uptake and metabolism. Based on the K. oxytoca Type II pullulanase secretion model and the previously reported involvement of S. putrefaciens outer membrane proteins in dissimilatory Fe(III) and Mn(IV) reduction, it has been postulated that the Fe(III) and Mn(IV) respiratory deficiencies of Type II protein secretion mutants are due to their inability to secrete Fe(III) and Mn(IV) terminal reductases to the outside face of the S. putrefaciens outer membrane (DiChristina et al. 2002). A working model of the direct enzymatic pathway for reduction of solid Fe(III) oxides in Shewanella is displayed in Figure 3.

Electron shuttling pathways A variety of Fe(III)-respiring DMRB, including Shewanella and Geobacter, can employ redox-active compounds (e.g., humic acids, melanin, phenazines, antibiotics, AQDS) as exogenous electron shuttles to reduce extracellular Fe(III) oxides (Lovley et al. 1996). The Fe(III) and Mn(IV) reduction-deficiencies of Shewanella Type II protein secretion mutants are rescued by addition of AQDS (DiChristina et al. 2005). S. oneidensis gspD insertional mutants are unable to respire anaerobically on solid Fe(III) or Mn(IV), yet retain the ability to respire all other electron acceptors, including AQDS. The ability to respire 50 mM solid Fe(III) or Mn(IV) is rescued in the S. oneidensis gspD insertional mutants by addition of 50 µM AQDS, an indication that the AQDS electron shuttling pathway is able to overcome the defect in the Type II protein secretion-linked pathway for respiration on solid Fe(III) and Mn(IV). AQDS is toxic to Shewanella cells above a critical threshold concentration and the efflux pump protein TolC protects Shewanella cells from AQDS toxicity by mediating AQDS efflux (Shyu et al. 2002). Electron transfer to AQDS also requires the OM protein MtrB, although its role in AQDS reduction remains unknown (Shyu et al. 2002). Solid Fe(III) reduction by Shewanella is also stimulated by redox-active antibiotics and phenazines (Hernandez et al. 2004). Phenazines are similar in structure to AQDS and function as electron shuttles between Shewanella cells and solid Fe(III) oxides. Redox-active antibiotics (e.g., bleomycin) also function as shuttles for extracellular electron transfer to solid electron acceptors. Bacterially-produced phenazines (e.g., synthesized by Pseudomonas chlororaphis PCL1391) stimulate Fe(III) reduction by bacteria unable to produce them (e.g., S. oneidensis MR-1) (Hernandez et al. 2004). In addition, melanin (a humic acid-like compound synthesized by S. algae BrY in the presence of high concentrations of tyrosine) will enhance rates of Fe(III) oxide reduction (Turick et al. 2002). Melanin may have a dual function by acting as both an electron shuttle and an Fe(II)-complexing agent that prevents Fe(II) from adsorbing to and blocking Fe(III) oxide surface sites. A working model of the exogenous electron shuttling pathway for AQDS-mediated reduction of solid Fe(III) oxides by Shewanella is displayed in Figure 4. Shewanella (and Geothrix fermentans) may also synthesize and release endogenous compounds that shuttle electrons to solid Fe(III) oxides (Newman and Kolter 2000; Nevin and Lovley 2002). Fe(III)-reducing G. metallireducens, on the other hand, does not appear to produce endogenous electron shuttles (Nevin and Lovley 2000). S. algae BrY produces melanin as a soluble electron shuttle for reduction of solid Fe(III) oxides (Turick et al. 2002). S. algae-produced melanin oxidizes c-type cytochromes at the cell surface and reduces solid Fe(III) oxides extracellularly (Turick et al. 2002). S. oneidensis MR-1 mutants defective in menC (encoding o-succinylbenzoic acid synthase) are deficient in menaquinone production and are unable to reduce AQDS, fumarate, thiosulfate, sulfite, DMSO or solid Fe(III) and Mn(IV) (Newman and Kolter 2000). Menaquinone is detected in the spent media of the wildtype strain, but not the menC mutants. Spent medium from the wild-type strain restores Fe(III) reduction activity to the menC mutant, while spent media from menC mutant does not. S. oneidensis MR-1 mutants defective in either menD or menB (encoding components of the menaquinone biosynthetic pathway) are also unable to reduce solid Fe(III) oxides (Saffarini et al. 2002). Vitamin K2 (a menaquinone analog) restores the ability of the menD or menB

Enzymology of Electron Transport

33 Secretion of Fe(III) terminal reductase complex

Fe(II)

Solid Fe(III)

OM D S

Fe(III) terminal reductase complex

H

+

e-

ec-type cytochromes e-

H+ Menaquinone Pool

D S

G H I J

H+

CymA

L

e- donor

F

M C K

IM

N O

E

Dehydrogenase

ADP H+ ATP

ELECTRON TRANSPORT SYSTEM

ATPase

TYPE II SECRETION APPARATUS

Figure 3. Working model for type II protein secretion-linked, direct enzymatic reduction of solid Fe(III)-oxides at the outer membrane.

Solid Fe(III) Fe(II) AQDS AH2DS OM 2e-

c-type cytochromes

H+

e-

H+ Menaquinone Pool

AQDS Terminal reductase e-

H+

e-

IM

CymA

e- donor Dehydrogenase ELECTRON TRANSPORT SYSTEM

ADP

H+

ATP

ATPase

Figure 4. Working model for electron shuttling pathway with AQDS as electron shuttle.

34

DiChristina, Fredrickson, Zachara

mutants (and corresponding membrane fractions) to reduce either Fe(III) or Mn(IV) (Saffarini et al. 2002). It should be noted that the endogenous electron shuttle pathway may be the consequence of cell lysis and inadvertent spillage of menaquinol into the culture medium. Since shuttles can undergo redox cycling they can be effective at low concentrations and therefore even a small fraction of cell lysis could have a significant effect. Lipid-soluble menaquinol or vitamin K2 then diffuses into bacterial membranes and functionally complements the menB, C or D mutants. Definitive evidence on the identity of the endogenous electron shuttle requires further research and will be challenging as exceedingly low concentrations may be involved.

Fe(III) solubilization by exogenous or bacterially-produced organic ligands followed by reduction of soluble organic-Fe(III) A strong electrochemical signal indicative of soluble organic-Fe(III) is detected in a variety of marine and freshwater environments with Au/Hg voltammetric microelectrodes (Taillefert et al. 2002). Soluble organic-Fe(III) may therefore represent a dominant, yet under appreciated electron acceptor in anaerobic aquatic systems. Microbial Fe(III) reduction rates are higher with soluble organic-Fe(III) in pure cultures of S. putrefaciens (Arnold et al. 1988) and in freshwater sediments amended with Fe(III)-chelating compounds such as nitrilotriacetic acid (Lovley and Woodward 1996). S. putrefaciens reduces soluble organic-Fe(III) complexes at rates three orders of magnitude faster than amorphous or crystalline Fe(III) forms (Arnold et al. 1988). The mechanism of formation of soluble organic-Fe(III) generally involves non-reductive dissolution of amorphous Fe(III) oxides by multidentate organic ligands (forming mononuclear complexes with the Fe(III) oxides) at circumneutral pH. The strength of binding between Fe(III) and the complexing organic ligands influences soluble organic-Fe(III) reduction activity: organic ligands with strong Fe(III)-binding capability decrease (and in some cases totally inhibit) Fe(III) reduction activity by S. putrefaciens (Haas and DiChristina 2002). Some Fe(III)-reducing bacteria such as S. algae BrY and G. fermentans generate relatively high concentrations of soluble organic-Fe(III) in the absence of exogenous chelating compounds, an indication that such bacteria synthesize and release organic ligands to solubilize Fe(III) prior to reduction (Nevin and Lovley 2002). Soluble organic-Fe(III) is detected electrochemically in S. oneidensis and S. putrefaciens cultures incubated anaerobically with either ferrihydrite or goethite (Taillefert and DiChristina 2005). Detection of soluble organicFe(III) prior to detection of Fe(II), suggests that soluble organic-Fe(III) is an intermediate in the reduction of solid Fe(III) oxides. Since lactate is the only organic ligand added to the Shewanella batch cultures and lactate-Fe(III) complexes do not react with Au/Hg electrodes, electrochemical detection of soluble organic-Fe(III) suggests that Shewanella synthesizes and releases organic ligands that complex and dissolve Fe(III) prior to reduction. The identity of the bacterially-produced, Fe(III)-solubilizing organic ligands remains unknown. A respiration-linked, soluble organic-Fe(III) terminal reductase has yet to be definitively identified. As described above, Shewanella Type II protein secretion mutants are unable to reduce solid Fe(III) oxides, yet retain the ability to respire all other electron acceptors, including soluble organic-Fe(III). This finding suggests that soluble organic-Fe(III) may be reduced by terminal reductases located in subcellular compartments other than the OM. The S. oneidensis decaheme c-type cytochrome MtrA is a candidate terminal reductase for soluble organic-Fe(III): MtrA is located in the S. oneidensis periplasm and displays soluble organicFe(III) reductase activity when expressed in E. coli (Pitts et al. 2003). The requirement for MtrA in anaerobic respiration of soluble organic-Fe(III), however, has yet to be demonstrated in vivo. In S. frigidimarina, the transcriptional activator IfcR is translated in the presence of soluble organic-Fe(III) and is essential for expression of ifcO and ifcA. IfcO is a putative OM beta-barrel protein postulated to function as a soluble organic-Fe(III) transporter. IfcA is a flavin-containing c-type cytochrome with a small (10 kDa) tetraheme cytochrome domain that

Enzymology of Electron Transport

35

displays soluble organic-Fe(III) reductase activity (Pitts et al. 2003). A working model of the two-step, Fe(III) solubilization-reduction pathway in Shewanella is displayed in Figure 5.

ENZYMATIC BASIS OF URANIUM REDUCTION Members of the genera Shewanella (Lovley et al. 1991), Desulfovibrio (Lovley et al. 1993), Clostridium (Francis et al. 1994), Geobacter (Caccavo et al. 1992), Thermus (Kieft et al. 1999), Pyrobaculum (Kashefi and Lovley 2000), and Desulfosporosinus (Suzuki et al. 2002) display enzymatic U(VI) reduction activity. Shewanella and Geobacter enzymatically reduce U(VI) to U(IV) via a respiratory process that supports anaerobic growth. Although several purified c-type cytochromes display U(VI) reductase activity in vitro, a respirationlinked, U(VI) terminal reductase has yet to be definitively identified in vivo. Enzymatic U(VI) reduction activity is affected by U(VI) chemical speciation, electron donors, and competing electron acceptors. In the following section, the most recent findings on the enzymatic basis of U(VI) reduction by Shewanella and Geobacter are presented along with a discussion of the environmental factors affecting enzymatic U(VI) reduction activity.

Involvement of c-type cytochromes in enzymatic U(VI) reduction Cytochrome c3 of several Desulfovibrio species is involved in electron transfer to U(VI). Cytochrome c3 of U(VI)-reducing (but non-respiring) Desulfovibrio vulgaris Hildenborough displays U(VI) reductase activity in vitro with H2 as electron donor (Lovley et al. 1993). Cytochrome c3 mutants of D. desulfuricans strain G20 are unable to reduce U(VI) with H2 as

Solid Fe(III) Soluble Organic-Fe(III)

Fe(III)

Organic Ligand

Fe(III)

Fe(II) OM

Soluble Fe(III) Organic-Fe(III)

H+

e-

H+ Menaquinone Pool

c-type cytochromes e-

Fe(II)

Organic Ligand

eSoluble Fe(III) reductase

H+

IM

CymA

e- donor Dehydrogenase ELECTRON TRANSPORT SYSTEM

ADP

H+

ATP

ATPase

Organic Ligand

Figure 5. Working model for Fe(III) solubilization-reduction pathway with endogenous organic ligand as Fe(III)-chelating compound.

36

DiChristina, Fredrickson, Zachara

electron donor and are partially impaired in U(VI) reduction activity with lactate or pyruvate as electron donor (Payne et al. 2002). After growth of wild-type D. desulfuricans strain G20 in medium containing uranyl acetate, cytochrome c3 is tightly associated with insoluble U(IV) particles (uraninite) found in the periplasm (Payne et al. 2004). Cytochrome c7 of G. sulfurreducens also displays U(VI) reductase activity in vitro, however, mutants deficient in either cytochrome c3 or c7 retain U(VI) reduction activity in vivo (Lloyd et al. 2003). These findings suggest that either cytochrome c3 and c7 are not the physiological U(VI) reductases in G. sulfurreducens or that the electron transport pathway to U(VI) is highly branched and consists of multiple U(VI) terminal reductases. The highly branched nature of the U(VI) reduction pathway in G. sulfurreducens is reflected by the finding that Fe(III) reduction-deficient ppcA mutants (see above) are also deficient in U(VI) reduction activity (Lloyd et al. 2003). A genetic complementation system has recently been developed to examine the enzymatic mechanism of U(VI) reduction by S. putrefaciens (Wade and DiChristina 2000). S. putrefaciens respiratory mutants unable to reduce U(VI) have been isolated and tested for the ability to respire on a suite of alternate compounds as electron acceptor, including oxygen O2, NO3−, fumarate, trimethylamine-N-oxide (TMAO), dimethyl sulfoxide (DMSO), Mn(IV), Fe(III), chromate (Cr(VI)), arsenate (As(V)), selenite (Se(IV)), pertechnetate (Tc(VII)), thiosulfate (S(II)), and sulfite (S(IV)) (Wade and DiChristina 2000). All U(VI) reduction-deficient mutant strains also lacked the ability to respire NO2−. In particular, U(VI) reduction-deficient mutant strain U14 retained the ability to respire all electron acceptors except U(VI) and NO2−. These results suggest that the electron transport chains terminating with the reduction of NO2− and U(VI) share common respiratory components.

Effect of U(VI) chemical speciation on enzymatic U(VI) reduction activity U(VI) chemical speciation is an important variable controlling enzymatic U(VI) reduction activity. In oxidizing aqueous environments at circumneutral pH (and in the absence of phosphate), U(VI) is found as soluble uranyl ion (UO22+), often in carbonate complexed form (e.g., UO2(CO3)22−, UO2(CO3)34−, CaUO2(CO3)20) or as crystalline solids such as metaschoepite (UO3·2H2O), uranyl phosphates and uranyl silicates. U(IV) precipitates in reducing environments as uraninite (UO2). The relative insolubility of U(IV) (10−8 M at pH > 5; Rai et al. 1990) compared with U(VI) is the basis of alternate bioremediation strategies (Lovley et al. 1991). The uranyl ion readily complexes with either inorganic (e.g., hydroxyl, carbonate, phosphate, sulfate and calcium) or organic (e.g., acetate, malonate, citrate and oxalate) ligands in aqueous solution (Grenthe 1992), and complexation markedly enhances its solubility. The type of complexing ligand changes the reduction potential of U(VI) thus affecting enzymatic reduction activity. In terms of reduction potential, hydroxo complexes are the most easily reduced forms of complexed U(VI), while complexation by carbonate decreases the reduction potential of U(VI). Complexation of U(VI)-carbonate by calcium (forming CaUO2-CO3 complexes) decreases the reduction potential to such an extent that enzymatic U(VI) reduction by S. putrefaciens CN32 nearly ceases (Brooks et al. 2003). Enzymatic U(VI) reduction activity by D. desulfuricans and G. sulfurreducens is also inhibited by formation of Ca-UO2-CO3 complexes. The effect of Ca2+ complexation on enzymatic U(VI) reduction activity are specific to U(VI) reduction since the enzymatic reduction of fumarate and Tc(VII) activities are not inhibited by Ca2+. In the absence of carbonate or at pH < 6 in the presence of carbonate, organic ligands bound to U(VI) also dramatically impact enzymatic U(VI) reduction activity. Citrate, for example, binds U(VI) with varying strength as a function of pH (Pasilis and Pemberton 2003). At pH > 6 and at low citrate concentrations, the highly soluble (UO2)3Cit2 species predominates over the (UO2)2Cit2 species. S. alga BrY reduces U(VI) bound to citrate and other multidentate aliphatic complexes such as malonate and oxalate more rapidly than U(VI)

Enzymology of Electron Transport

37

bound to monodentate aliphatic complexes such as acetate, while the opposite trend is found with D. desulfuricans (Ganesh et al. 1997). U(VI) also adsorbs to carboxyl, phosphoryl and amine functional groups on the S. putrefaciens 200 cell surface, and a ligand exchange reaction may take place between the cell surface or U(VI) terminal reductases and the U(VI) complexes prior to reduction (Haas and DiChristina 2002).

Electron donors and competing electron acceptors U(VI) reduction by Shewanella is coupled to oxidation of hydrogen, lactate, formate or pyruvate (Lovley et al. 1991). U(VI) reduction rates are highest with H2 as electron donor (Liu et al. 2002b). Two explanations have been proposed to account for the increased rate of U(VI) reduction coupled to H2 oxidation (Aubert et al. 2000; Liu et al. 2002b). First, electron flow through the electron transport chain may be more rapid when coupled to H2 rather than lactate oxidation. Periplasmic H2 hydrogenases may pass electrons through the electron transport chain more rapidly than those generated from cytoplasmic membrane-localized lactate dehydrogenase. Secondly, mass flux of neutrally charged H2 to the enzymatic site of oxidation may be faster than negatively charged lactate. The negative charge of the lactate ion inhibits diffusion across the cell surface to the cytoplasmic membrane, thereby requiring an active transport system. The presence of competing terminal electron acceptors also interferes with microbial U(VI) reduction. Thermodynamic calculations predict that electron acceptors should be utilized in order of highest free energy yield, a possible explanation for the inhibition of U(VI) reduction in the presence of nitrate (Finneran et al. 2002). Although the reduction of U(VI) coupled to the oxidation of organic compounds should yield greater free energy than Fe(III) (Cochran et al. 1986), the half-cell potentials of both U(VI) and Fe(III) can vary markedly with their coordination environment and whether they exist in the form of aqueous complexes or solid phases. For example, the half-cell potentials at pH 7 for many common, environmental Fe(III) forms vary from +0.35 V to −0.30 V (Stumm 1992), while those for U(VI) vary from +0.284 V to −0.042 V (Brooks et al. 2003). These variations in half-cell potential are compounded by the effects of reactant concentrations and other aqueous complexants, the similarity in half-cell potential of many Fe(III) and U(VI) forms and their uncertainty, the interfacial chemistry of solid phase electron acceptors, and the poorly understood redox chemistry of surface complexed Fe(II). All of these considerations complicate a rigorous thermodynamic analysis. The effects of pH are also strong because of the proton stoichiometry of reaction, and the redox stability of U(VI) over Fe(III), or vise-versa, may change if pH is not controlled, if reactant concentrations are varied appreciably, or if mineral biotransformation products exhibit different redox chemistry. In spite of these complexities and chemical interrelationships, there are some consistent thermodynamic observations. Ferrihydrite, with its higher redox potential (~−0.070 V at pH = 7 and Fe(II) = 10−5 mol/L) was observed to inhibit bacterial U(VI) reduction, while goethite did not (~-0.250 V at pH = 7 and Fe(II) = 10−5 mol/L) (Wielenga et al. 2000). Electron transport to Mn(IV) provides a greater free energy yield than electron transport to U(VI), and is therefore predicted to be a preferred electron acceptor (Cochran et al. 1986; Langmuir 1997). Bioavailable Mn(IV)-oxides such as birnessite and bixbyite follow this prediction, however, U(VI) is reduced concurrently with less soluble forms of Mn(IV) (Fredrickson et al. 2002). To determine if this finding is due to electron acceptor competition or abiotic oxidation of U(IV) by Mn(IV), S. putrefaciens CN32 was incubated with U(VI) and pyrolusite (β-MnO2) (Liu et al. 2002b). Extracellular, cell surface-associated, and periplasmic UO2(s) aggregates were detected by Transmission Electron Microscopy (TEM) when cells were incubated only with U(VI). Upon addition of pyrolusite, extracellular UO2(s) was depleted but periplasmic and cell surface-associated UO2(s) remained. These results suggest

38

DiChristina, Fredrickson, Zachara

that U(IV) functions as an electron shuttle and is oxidized by the extracellular pyrolusite. U(VI) is completely reduced provided the OM of intact cells physically separates (sequesters in the periplasmic space) UO2(s) from extracellular pyrolusite. Humic acids have recently gained attention for their potential role as shuttles for electron transfer between anaerobically respiring Shewanella and solid Fe(III)-oxides (see above). Addition of AQDS to S. putrefaciens CN32, however, does not enhance the reduction rate of either soluble or insoluble forms of U(VI) (Fredrickson et al. 2000). AQDS actually inhibits U(VI) reduction activity, possibly by diverting electrons away from the U(VI) reduction pathway.

Subcellular location of enzymatic U(VI) reduction activity The subcellular location of enzymatic U(VI) reduction in Shewanella has also been recently examined: Insoluble U(IV) particles are detected extracellularly, on the cell surface and within the periplasmic space of S. putrefaciens CN32 after reduction of soluble U(VI) (Liu et al. 2002a). U(IV) is not detected in the cytoplasm (Fig. 6). U(VI) reductases may therefore be localized within the OM, diffuse (or be transported) across the OM to contact U(VI) reductases located in the periplasm or IM, or both. U(VI) reduction products of Desulfosporosinus have been detected as nanometer-sized UO2(s)-particles (Suzuki et al. 2002). Nanoparticles produced in the periplasm either diffuse or are exported to the cell exterior where they organize extracellularly to form larger aggregates. Aggregation of U(IV) particles prior to export from the cell may result in the periplasmic deposits detected on TEM images of U(VI)-respiring cells (Liu et al. 2002a). U(IV) particles detected in the culture supernatant also leads to the intriguing possibility that anaerobically-respiring Shewanella are able to actively secrete U(IV) particles as a means of avoiding build-up of toxic insoluble U(IV) end-products during U(VI) reduction. S. putrefaciens CN32 is also capable of reducing solid forms of U(VI) such as metaschoepite (Fredrickson et al. 2000), although solid forms of U(VI) have been found to be resistant to microbial reduction in situ (Ortiz-Bernad et al. 2004). The mechanism by which Shewanella species reduce metaschoepite is unknown, but U(VI) terminal reductase localization to the OM to contact solid U(VI) is possible.

Figure 6. Transmission electron microscopy image of an unstained thin section from Shewanella putrefaciens strain CN32 cells incubated with H2 and U(VI) in pH 7 bicarbonate buffer, illustrating the accumulation of nano-size U(IV)O2 particles extracellularly and in association with the periplasmic space and cell surface.

Enzymology of Electron Transport

39

ENZYMATIC MECHANISM OF TECHNETIUM REDUCTION Enzymatic studies on Tc(VII) reduction have largely been focused in E. coli, however the ability to reduce Tc(VII) has been recently found in S. putrefaciens CN32, S. oneidensis MR-1 and S. putrefaciens 200 (Lyalikova and Khizhnyak 1996; Lloyd et al. 1997; Wildung et al. 2000; Payne and DiChristina 2005). Tc(VII) is also reduced under acidic conditions by Thiobacillus thiooxidans (Lyalikova and Khizhnyak 1996), under alkaline conditions by Halomonas strain Mono (Khijniak et al. 2003) and at high temperature by Pyrobaculum islandicum (Kashefi and Lovley 2000). Reduction of soluble Tc(VII) results in formation of Tc(IV) which precipitates as insoluble TcO2·nH2O (hereafter termed TcO2) and may be immobilized in situ. In the absence of aqueous complexing agents, Tc(IV) may also be immobilized via formation of strong surface complexes with hydroxylated surface sites on Al and Fe oxides and clays (Rard 1983; Haines et al. 1987; Meyer et al. 1991; Eriksen et al. 1992; Wildung et al. 2000).

Involvement of hydrogenases in Tc(VII) reduction E. coli possesses four hydrogenases, designated as hydrogenases 1-4. Hydrogenases-1 and 2 share little homology to hydrogenases-3 and 4. Hydrogenases 3 and 4 share high homology to each other and are both expressed as part of the formate-hydrogen lyase complex in E. coli (Bagramyan and Trchounian 2003). The Tc(VII) reductase in E. coli has been identified as the Ni-Fe hydrogenase-3 component of the formate-hydrogen lyase complex (Lloyd et al. 1997). Hydrogenase expression is determined by pH: hydrogenase-4 (encoded by the hyf operon) is expressed under alkaline conditions while hydrogenase-3 (encoded by the hyc operon) is expressed under acidic conditions (Bagramyan and Trchounian 2003). The formate-hydrogen lyase complex in E. coli is composed of formate dehydrogenase plus multiple components of the respective hydrogenase encoding operons (i.e., hyf and hyp operons). The bi-directional nature of hydrogenase-3 (HycE) enables both the production of H2 during formate oxidation and the direct oxidation of H2 under other conditions. S. oneidensis MR-1 does not possess a formate-hydrogen lyase complex and possesses only two hydrogenases, neither of which share significant homology to hydrogenases-3 or 4 of E. coli. The first S. oneidensis MR-1 hydrogenase (Locus SO2098; HyaB) displays high homology to the IM-bound Ni-Fe hydrogenase HydB of Wolinella succinogenes, while the second S. oneidensis MR-1 hydrogenase (Locus SO3920; HydA) displays high homology to the putative D subunit of the NADP-reducing hydrogenase of Thermotoga maritima (Payne and DiChristina 2005). In terms of hydrogenase function, S. oneidensis MR-1 hydrogenases appear most similar to those of Alcaligenes eutrophus in which the cytoplasmic, soluble hydrogenase (HydA) regenerates NADH, while the membrane bound Ni-Fe hydrogenase (HyaB) generates reducing power (Lengeler et al. 1999). In organisms containing only membrane bound hydrogenases, reducing power is generated by reverse electron transport, generally carried out by membrane bound bi-directional hydrogenases (e.g., hydrogenases3 and 4 in E. coli). S. oneidensis MR-1 therefore appears to share close similarity to the hydrogen uptake and utilization systems of A. eutrophus and little sequence or physiological similarity to the H2 uptake and utilization systems of E. coli. Further work needs to be carried out to determine if the S. oneidensis MR-1 hydrogenases display Tc(VII) reductase activity.

Subcellular location of enzymatic Tc(VII) reduction activity The enzymatic reduction of Tc(VII) is electron donor-specific. H2 serves as electron donor in all known Tc(VII)-reducing organisms (Lloyd et al. 2000; Wildung et al. 2000; De Luca et al. 2001), while the ability to couple the oxidation of other carbon sources to the reduction of Tc(VII) occurs in only a small subset of organisms. G. sulfurreducens and D.

DiChristina, Fredrickson, Zachara

40

fructosovorans have an exclusive requirement for H2 as electron donor for Tc(VII) reduction while E. coli is limited to formate and H2 as electron donor. S. oneidensis MR-1 and S. putrefaciens CN32 couple the oxidation of formate, lactate, and H2 to Tc(VII) reduction (Wildung et al. 2000; Payne and DiChristina 2005), but Tc(VII) reduction rates are markedly higher with H2 as electron donor. The reduced Tc(IV) product is generally nanometer-sized TcO2(s) in buffers or media without high carbonate. The identity of the electron donor does not seem to influence the mineralogic nature of the reduction product. The black-colored precipitate is observed in the periplasm, and as 20-50 nm dome-like structures consisting of aggregates of many individual crystallites on the cell surface (Fig. 7). The TcO2(s) is nanocrystalline and exhibits insufficient long-range order to yield a discernable diffraction pattern. The precipitate maintains a Tc solubility (≈10−8 mol/L) that approximates measured values for TcO2·xH2O (as reported by Rard 1999 and associated citations). The physiologic relationship between subcellular and surface associated TcO2(s) is unclear. Limited evidence implies that bioreduced Tc [e.g., Tc(IV)] may exist in the form of carbonate aqueous complexes, or perhaps carbonate precipitates, in high-bicarbonate media (Wildung et al. 2000). Further research on the biogeochemistry of Tc(VII)/Tc(IV) in bicarbonate-containing media and other ligand solutions of geochemical relevance is needed.

A

B

C Figure 7. Transmission electron microscopy image of an unstained thin section from Shewanella oneidensis MR-1 cells incubated with H2 and Tc(VII) in pH 7 PIPES buffer. Tc accumulates predominantly in association with the cell envelope (A, B) as discreet aggregates (C) consisting of Tc(IV)O2 particles approximately 2.2 nm in diameter (arrow).

Enzymology of Electron Transport

41

MICROBIAL REDUCTION-INDUCED CHANGES IN METAL BIOGEOCHEMISTRY Direct enzymatic effects of dissimilatory metal-reducing bacteria (DMRB) on metal solubility The majority of electron acceptors commonly used by prokaryotes (oxygen, nitrate, sulfate, carbon dioxide) exhibit relatively high levels of solubility before and after reduction. In contrast, many of the metals used as microbial electron acceptors exhibit substantially different solubility properties in the oxidized (e.g., pε = 10) versus the reduced (pε = 4 or 2) states (Table 1). Because Fe(III) and Mn(III,IV) exist predominantly as oxyhydroxide minerals in oxic environments (e.g., ferrihydrite and goethite, or birnessite and manganite), DMRB must overcome the fundamental problem of engagement of the cell electron transport system (ETS) with the mineral surface across a solid-liquid interface. DMRB have developed several novel mechanisms for overcoming this problem, as described in preceding sections, including the “shuttling” of electrons by humic acids (Lovley et al. 1996; Lovley and Woodward 1996) or cell metabolites (Newman and Kolter 2000) from terminal points of the ETS to the mineral surfaces, possibly the direct transfer of electrons to metal in the centers of mineral surfaces by multiheme cytochromes associated with the OM (Richardson 2000; Leang et al. 2003), or the solubilization of solid phase-associated Fe(III) as subsequent engagement of Fe(III) reductase(s) as a soluble Fe(III)-organic complex. Regardless of the mechanism, the microbial reduction of Fe and Mn has a profound impact on the geochemical behavior of these metals as Table 1. Solubilities (aqueous concentrations) of select phases of Fe, Mn, U, and Tc at pH 7 in water as a function of pe (pCO2 = 10−3.46 atm, I = 0.01). Solid phase

Oxidizing pε = 10 mol/L

Reducing (pε = 4) mol/L

(pε = 2) mol/L

Fe(OH)3 (ferrihydrite)

2.06×10−8 (Fe3+)

2.06×10−8 (Fe3+) 1.29×10−7 (Fe2+)

2.06×10−8 (Fe3+) 1.29×10−5 (Fe2+)

α-FeOOH (goethite)

8.40×10−13 (Fe3+)

8.36×10−13 (Fe3+) 5.24×10−12 (Fe2+)

8.36×10−13 (Fe3+) 5.24×10−10 (Fe2+)

MnO1.8 (birnessite)

6.03×10−5 (Mn2+)

Soluble as Mn2+ a

Soluble a

γ-MnOOH (manganite)

2.87×10−6 (Mn2+)

Soluble as Mn2+ a (2.83 mol/L maximum)

Soluble a

2.67×10−7 [U(VI)O22+] b

2.67×10−7 [U(VI)O22+] b

1.46×10−7 [U(VI)O22+] 2.0×10−17 [U(IV)(OH)4(aq)] c

Soluble as Tc(VII)O4−

Soluble as Tc(VII)O4−

10−8 [Tc(IV)O(OH)2(aq)]

UO2 (uraninite)

TcO2·nH2O

a. Will precipitate as rhodochrosite [MnCO3(c)] b. 1×10−4 mol/L precipitated as schoepite [β-UO3·2H2O(c)], the primary aqueous complex under the given conditions is UO2(CO3)22−, solubility increases with CO2(g) partial pressure and total aqueous carbonate concentration c. 9.98×10−5 mol/L precipitated as uraninite [UO2(c)], pε is 1.8, U(VI) concentrations decrease with decreasing pε.

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well as broader impacts on the overall geochemical and mineralogic properties of solids and sediments where these processes occur. In contrast to Fe and Mn, U, Tc, and Cr are relatively soluble in oxic environments and typically exist as anionic uranyl carbonate UO2(CO3)34−, UO2(CO3)22− complexes, pertechnetate (TcO4−), or chromate (CrO42−), respectively. The solubility of U(VI) at circumneutral pH is strongly dependent on dissolved carbonate concentration and other associated ligands such as silica or phosphate. Upon reduction to the +4 oxidation state and in the absence of strong complexants, U and Tc can precipitate as the hydrous oxides, UO2 (uraninite) and TcO2, phases that have been identified in anaerobic suspensions of DMRB cells incubated with U(VI) (Gorby and Lovley 1992) or Tc(VII) (Wildung et al. 2000) and appropriate electron donors. The direct enzymatic reduction of U(VI) results in the formation of relatively uniformly-sized nanoparticles (Fredrickson et al. 2002; Suzuki et al. 2002), a factor that can impact their subsequent reactivity and transport. For example, if U(IV) nanoparticles are less than approximately 2-to-5 nm in diameter, they may behave as large molecular clusters and be mobile in solution, while U(IV) particles with larger diameters may not be transported as readily (as described in chapter on nanoparticles by Gilbert and Banfield 2005). It is these marked changes in solubility that have prompted consideration of manipulating the activities of DMRB for the bioremediation of soils and sediments contaminated with metals and radionuclides (Lovley 1995).

Indirect effects of DMRB on metal solubility DMRB can also indirectly influence the biogeochemical behavior of redox active and non-redox active metals. Because the mass content of Fe and Mn is typically higher than trace metals and contaminants in most soils and sediments they tend to have a dominant effect on redox reactions and function as a major sink for electrons from DMRB respiration. Due to their relatively higher mid-point potential, Mn oxides can provide a “buffer” against the net reduction of other metal ions including Fe(III) (Lovley and Phillips 1988; Myers and Nealson 1988) and U(VI) (Liu et al. 2002b). Because Mn(III,IV) oxides are relatively strong oxidants they can also oxidize reduced forms of metals such as Cr(OH)3 (Fendorf and Zasoski 1992) and UO2 (Fredrickson et al. 2002), hence impeding their net microbial reduction unless there are mechanisms that prevent their physical interaction such as the accumulation and isolation of UO2 nanoparticles in the cell periplasm (Fredrickson et al. 2002). Although Fe(III) oxides are not as effective oxidants as Mn oxides, they can potentially impede the reduction of other metals such as U(VI) via competition mechanisms (Wielinga et al. 2000). Hence, the microbial reduction of Mn and Fe oxides can result in redox conditions that are more favorable for the reduction for trace metal contaminants. In addition, Fe(II) and Mn(II) can potentially provide buffer against re-oxidation. Biogenic Fe(II) can also function as a facile reductant of trace metal and radionuclides including U(VI) (Liger et al. 1999), Tc(VII) (Lloyd et al. 2000; Wildung et al. 2004), and Cr(VI) (Wielinga et al. 2001). The rate of Tc(VII)O4− reduction by sediment-associated biogenic Fe(II) was shown to be related directly to the extent of sediment Fe(III) reduction but there was extensive variation among different sediments indicating that the effectiveness of Fe(II) as a reductant was highly dependent upon molecular speciation as opposed to Fe(II) concentration alone (Fredrickson et al. 2004; Hansel et al. 2004). In general, aqueous Fe(II) is poorly reactive with Tc(VII) (Cui and Eriksen 1996) and U(VI) (Fredrickson et al. 2000), probably due to kinetic limitations. In contrast, the rates of chromate reduction by Fe(II)aq are relatively rapid (Wielinga et al. 2001). One area that warrants further investigation is whether biosorbed Fe(II), which can form complexes and precipitates on cell surfaces (Liu et al. 2001a), can also function as a reductant in a manner similar to Fe(II) sorbed on mineral surfaces. The fact that Fe oxides can impede i.e., via competition (Wielinga et al. 2000)

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or promote, i.e., via surface complexation of Fe(II) (Fredrickson et al. 2004), reflects the similarity of the mid-point potentials of these metals and the need to pay careful attention to factors including speciation, concentration, and solubility that can greatly impact the direction and extent of such redox reactions. Trace metals can associate with Fe or Mn oxides as adsorbed or co-precipitated species and are therefore subject to biogeochemical reactions resulting from utilization of the oxide as a terminal electron acceptor by DMRB. Ni2+ and Co2+ co-precipitated with goethite were released when oxide suspensions were subject to reduction by S. putrefaciens CN32, resulting in a net increase in aqueous concentrations of the metal ions (Zachara et al. 2001). Similarly, Ni2+ was also released from a Ni-substituted hydrous ferric oxide upon reduction by strain CN32 although under select conditions a Ni-substituted magnetite (FeIII2FeII1−xNixO4) formed (Fredrickson et al. 2001). Ni2+ was found to inhibit the overall reduction reaction by an undefined chemical mechanism that could be circumvented by addition of AQDS as an electron shuttle. Aluminum release during the bioreduction of an Al-substituted goethite associated with an Atlantic coastal plain sediment was congruent with the production of Fe(II) but the released Al was associated with a sorbed phase (Kukkadapu et al. 2001). DMRB can also promote the mobilization of arsenic as arsenate via the reductive dissolution of the ferric arsenate mineral scorodite (FeAsO4·2H2O) and from iron oxide sorption sites within sediments (Cummings et al. 1999). It is interesting to note that some DMRB are also capable of dissimilatory reduction of arsenate (AsV) to arsenite (AsIII) (Saltikov et al. 2003) but such a reduction reaction was not observed. Although there is considerable potential for mobilization of trace elements associated with metal oxides during bioreduction, the extent to which trace metals remain associated with the solid phase or are released to solution will be a function of the aqueous and solid-phase geochemical composition that ultimately controls the adsorption and precipitation reactions.

REDUCTIVE TRANSFORMATION OF Fe- AND Mn-CONTAINING MINERALS The rate and extent of microbial reduction of Fe(III) and Mn (III,IV) oxides in soils and sediments is a function of complex and highly coupled biological, chemical, and physical factors. Mineralogy plays a critical role with factors such as surface area (Roden and Zachara 1996), extent of structural disorder (Zachara et al. 1998), surface speciation (Roden and Urrutia 2002), and thermodynamics (Liu et al. 2001b) all influencing, to some extent, the reduction process. The physiological state of the organisms, including effects resulting from growth medium composition (Glasauer et al. 2003) and electron donor-acceptor ratios (Zachara et al. 2002), are other key variables that can affect bioreduction of Fe and Mn minerals but are currently poorly understood. The role of cell physiology on metal oxide reduction is currently under-appreciated and its importance warrants further research using physiologically and compositionally defined cultures that better represent and span the range of environmental conditions.

Laboratory studies A general observation made from laboratory studies is that poorly crystalline Fe(III) oxides such as ferrihydrite (Lovley and Phillips 1986) exhibit a greater degree of bioavailability than more crystalline phases such as lepidocrocite (γ-FeOOH), goethite (α-FeOOH), or hematite (Fe2O3), although as a crystalline phase, lepidocrocite is far more bioavailable than the others. The same general trend appears to hold true for Mn oxides in that more highly crystalline phases such as pyrolusite (β-MnO2) are reduced more slowly than amorphous MnO2 or birnessite (Burdige et al. 1992). This effect has been attributed to differences in solubility of these phases and is supported by experiments demonstrating that the maximum rate of Fe(III) reduction was found to correlate positively with the solubility of the oxide (Bonneville et al. 2004). Caution must be exercised when using rates of abiotic reduction of Fe oxides as an

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indicator of their susceptibility to enzymatic reduction as the surface area-normalized rates of bacterial reduction of ferrihydrite, lepidocrocite, goethite, and hematite were found to be quite similar, in contrast to reduction of the same phases by ascorbic acid (Roden 2003). Naturallyoccurring (geologic) Fe oxides have been found to be equally or more reducible than their synthetic counterparts with crystalline disorder and microheterogeneities potentially being dominant factors controlling microbial reduction (Zachara et al. 1998). The presence of solid phase sorbents and organic complexants can also facilitate microbial reduction of Fe oxides, presumably via the removal of reduction product (Fe2+) from oxide and bacterial surfaces (Urrutia et al. 1999). A similar enhancement in the extent of Fe oxide reduction can be achieved by continual replacement of the aqueous phase in semi-continuous cultures (Roden and Urrutia 1999) or in continuous flow columns where soluble Fe(II) is constantly removed (Roden et al. 2000). The products of oxide bioreduction can hence impede further reduction by passivating oxide and cell surfaces (Liu et al. 2001a,b) or can promote reduction by removing products from solution via secondary precipitation reactions. Transformation of Fe- and Mn-bearing minerals to secondary phases is similarly a function of a complex set of biogeochemical variables. A number of laboratory studies have probed the bioreductive transformation of poorly crystalline ferrihydrites to a wide range of phases including more highly ordered Fe(III) oxides such as 6-line ferrihydrite, goethite, lepidocrocite (Zachara et al. 2002, Fredrickson et al. 2003; Hansel et al. 2003; Kukkadapu et al. 2003, 2005), mixed valence oxides such as magnetite and green rust [FeII(6−x)FeIII(x)(OH)12]x+[(A2−)x/2·yH2O]x−] (Lovley et al. 1987; Fredrickson et al. 1998), or Fe(II) phases including siderite (FeCO3), vivianite (Fe3(PO4)2·8H2O), and ferrous hydroxy carbonate (Fe2(OH)2CO3) (Fredrickson et al. 1998; Hansel et al. 2003; Kukkadapu et al. 2003). The extent to which each of these phases may form is influenced by a range of factors including pH and aqueous solution composition (Fredrickson et al. 1998), the relative concentrations of the oxide (electron acceptor) and electron donor (Zachara et al. 2002; Fredrickson et al. 2003), the presence of co-precipitated ions (Fredrickson et al. 2001; Kukkadapu et al. 2004), and hydrodynamic-induced distributions of reduction products (Hansel et al. 2003). The formation of secondary mineral phases is less common or extensive when more highly ordered phases such as hematite or goethite are reduced by DMRB but nonetheless Fe(II) biominerals such as siderite and vivianite have been observed to form under conditions consistent with their solubility (Zachara et al. 1998). The lower solubility of more highly ordered phases such as goethite and hematite support lower concentrations of Fe(III)aq and DMRB-generated Fe(II)aq, relative to ferrihydrite, and therefore hinder precipitation of secondary minerals such as magnetite beyond reorganization on the mineral surface into nanometer-size spinel-like domains (Hansel et al. 2004). In addition to Fe oxides and oxyhydroxides, DMRB can also reduce structural Fe in clay minerals facilitating their dissolution (Kostka et al. 1996, 1999) or changes in clay morphology, structure, and composition (Dong et al. 2003a,b). The extent of microbial reduction of structural Fe(III) in smectite can be substantial, ranging to >90% (Kostka et al. 1999). The microbial reduction of structural Fe in clay can significantly alter clay chemical and physical properties such as was reported for studies with smectite where reduction resulted in reduced swelling pressure, total surface area, and surface charge density (Kostka et al. 1999). Structural Fe(II) in bioreduced ferruginous clays can also promote the reductive dehydrochlorination of organic contaminants such as pentachloroethane and trichloroethane (Cervini-Silva et al. 2003).

Field studies Controlled single-phase, single organism laboratory experiments can provide important mechanistic insights but it is often difficult to predict field behavior from such results due to environmental complexities and heterogeneities. One of the more well studied field sites is

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a crude oil-impacted shallow groundwater aquifer located near Bemidji, Minnesota, USA. Research at this site has documented aspects of microbial community structure (Rooney-Varga et al. 1999) and associated geochemical changes (Baedecker et al. 1993) over the length of the plume as the predominant respiratory process shifted from Fe(III) and Mn(III,IV) reduction to methanogenesis (Anderson and Lovley 1999). A detailed analysis of sediments from within the plume and from the pristine aquifer revealed significant differences in the mass content and identify of Fe(III) oxides consistent with microbial-driven reduction processes (Zachara et al. 2004). Comparisons between the texturally-similar source where bioavailable Fe(III) had been exhausted and Fe(III)-reducing zone sediments where bioavailable Fe(III) remained indicated that dispersed crystalline Fe(III) oxides and a portion of the poorly crystalline Fe(III) oxide fraction had been depleted from the source zone sediment. The presence of residual ferrihydrite in the anoxic plume sediment indicated that some fraction of the Fe(III) oxides were biologically inaccessible, possibly due to their residence in microfractures in the interior of lithic fragments. Interestingly, little evidence was found for biogenic ferrous mineral phases with the exception of thin siderite or ferroan calcite surface precipitates. It is clear that additional field-based research including characterization of samples in concert with modeling and laboratory-based experiments is needed to improve our ability to predict biogeochemical behavior of redox active metals in natural and engineered systems, particularly with regard to mineral biotransformation products and biominerals.

ROLE OF MICROBIAL METAL REDUCTION IN REDOX CYCLING As is the case for most microorganisms, DMRB rarely function alone but are members of complex communities whose collective, intertwined activities are responsible for catalyzing the cycling of elements in the biosphere. Many DMRB, particularly members of the genus Shewanella, are well-adapted to geochemically stratified environments where there is a gradient in electron acceptors available for oxidizing organic matter and H2. These adaptations include a relatively robust and diverse electron transport system that can engage a wide range of electron acceptors and an extensive network of regulatory genes, both two-component and transcriptional regulators (Heidelberg et al. 2002), that allow the organisms to sense and respond to their environment. These organisms play a critical role in such environments by oxidizing fermentation products, such as low molecular weight organic acids, coupled to respiration of Fe and Mn.

Redox cycling in chemically stratified environments The Black Sea is a prime example of a chemically (redox) stratified environment that exists over distances of tens of meters in the water column and where Fe and Mn undergo biogeochemical redox cycling (Nealson and Myers 1992). Fe and Mn are chemically unique ions in redox gradient environments because of their relatively low solubility in the oxidized state. As these metal ions are oxidized, either microbially or abiotically, they can precipitate and be subjected to gravitational settling into anoxic zones (Nealson and Saffarini 1994). As they enter the anoxic zones these precipitates can be reduced by DMRB coupled to organic matter oxidation at which point soluble species of Fe(II) and Mn(II) can diffuse upward into oxic zones. A number of excellent reviews have been published on this subject and the reader is directed to those for more information and examples (Burdige 1993; Nealson and Saffarini 1994; Nealson and Little 1997; Nealson et al. 2002). Previous investigations of redox stratified environments have justifiably focused on detailed geochemical characterization and baseline investigations into associated microbial properties using a combination of cultivation and cultivation-independent methods. The availability of whole genome sequences and the ability to sequence entire microbial communities (metagenomics) provide powerful tools to probe

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microbial processes in these environments in the future (for additional discussion see chapters by Whitaker and Banfield 2005 and Nelson and Methé 2005).

Microscale redox cycling In addition to participating in redox cycling in chemocline environments that can span many meters, more recent research indicates that DMRB also participate in redox cycling over much shorter length scales. Because Fe(II) is rapidly oxidized by O2, neutrophilic microbial Fe oxidation is constrained to microaerobic environments where the abiotic oxidation of Fe(II) is limited by O2 availability and microbiologic rates of Fe(II) oxidation are competitive with abiotic rates. Hence, there is a potential for tight coupling between metal reduction and oxidation steps over short distance scales in environments with sharp microaerobic and anaerobic boundaries. In fact, it has been proposed that Fe(II)-oxidizing organisms localize themselves into a narrow band of cells and associated Fe(III) oxides to facilitate interfacing with DMRB (Roden et al. 2004). Such a coupling was experimentally investigated in microcosms consisting of ferrihydrite coated sand and a co-culture consisting of a lithotrophic Fe(II)-oxidizing bacterium (strain TW2) and the DMRB Shewanella alga strain BrY (Sobolev and Roden 2002). The co-culture exhibited minimal Fe oxide accumulation at the sand-water interface despite measurable dissolved O2 to a depth of 2 mm below the interface whereas a distinct layer of Fe oxide formed at this same interface in microcosms containing BrY alone. Direct microscopic observations revealed close juxtapositioning of both organisms in the upper few mm of sand. Subsequent investigations using the identical experimental system noted relatively low concentrations of Fe(II) in the co-culture relative to the microcosm containing BrY alone and suggested that Fe(III)-binding ligands impeded the formation of Fe(III) oxides and were responsible for a soluble/colloidal Fe(III) phase that facilitated the redox cycling of Fe (Roden et al. 2004). These results established the potential for a tight coupling between microbial metal reduction and oxidation processes to promote rapid microscale cycling of Fe. More research, however, is needed to better define the nature and role of Fe(III)-complexing ligands in microscale Fe cycling and whether interactions between metal-reducing and metaloxidizing extend beyond simply Fe(II)-Fe(III) cycling.

SUMMARY Most of the electron acceptors respired by prokaryotes (O2, NO3−, SO42−, and CO2) are soluble both before and after reduction, while many of the metals respired by DMRB exhibit substantially different solubility properties in the oxidized versus the reduced states. Because Fe(III) and Mn(III,IV) exist predominantly as oxyhydroxide minerals in oxic environments, DMRB must overcome the fundamental problem of engagement of the electron transport system with poorly soluble minerals. Other metals, such as U(VI) and Tc(VII), are relatively soluble in oxic environments, typically as anionic uranyl carbonate complexes and as pertechnetate, respectively. Aqueous Tc(VII) and U(VI) and other soluble electron acceptors are therefore free to enter the cell periplasm through porins or channels in the OM. Upon reduction to the +4 oxidation state, however, U and Tc precipitate as uraninite and hydrous Tc(IV) oxides, phases that have been identified in anaerobic suspensions of DMRB cells incubated with U(VI) or Tc(VII) and appropriate electron donors. The dilemma of reducing soluble electron acceptors to insoluble end-products is no less serious than the one dealing with reduction of solid electron acceptors. The first section of this chapter has highlighted the latest findings on the novel respiratory strategies employed by DMRB to overcome this dilemma, including direct enzymatic reduction, electron shuttling pathways and metal solubilization by exogenous or bacterially-produced organic ligands followed by reduction of soluble organicmetal compounds. The second section has emphasized the geochemical consequences of DMRB activity, including the direct and indirect effects on metal solubility, the reductive

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transformation of Fe- and Mn-containing minerals, and the biogeochemical cycling of metals at redox interfaces in chemically stratified environments.

ACKNOWLEDGMENTS The authors wish to thank David Bates, Justin Burns, Jason Dale, Amanda Payne and Tara Hoyem for help in manuscript preparation. The authors also with to thank Alice Donalkova, Dwayne Elias, David Kennedy, Matthew Marshall, and Andrew Plymale for providing the transmission electron microscopy images. Financial support for this work was provided by the National Science Foundation and the U.S. Department of Energy, Office of Biological and Environmental Research.

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Wielinga B, Mizuba MM, Hansel CM, Fendorf S (2001) Iron promoted reduction of chromate by dissimilatory iron-reducing bacteria. Environ Sci Technol 35:522-527 Wildung RE, Gorby YA, Krupka KM, Hess NJ, Li SW, Plymale AE, McKinley JP, Fredrickson JK (2000) Effect of electron donor and solution chemistry on products of dissimilatory reduction of technetium by Shewanella putrefaciens. Appl Environ Microbiol 66:2451-2460 Wildung RE, Li SW, Murray CJ, Krupka KM, Xie Y, Hess NJ, Roden EE (2004) Technetium reduction in sediments of a shallow aquifer exhibiting dissimilatory iron reduction potential. FEMS Microbiol Ecol 49:151-162 Zachara JM, Fredrickson JK, Li SM, Kennedy DW, Smith SC, Gassman PL (1998) Bacterial reduction of crystalline Fe3+ oxides in single phase suspensions and subsurface materials. Am Mineral 83:1426-1443 Zachara JM, Fredrickson JK, Smith SC, Gassman PL (2001) Solubilization of Fe(III) oxide-bound trace metals by a dissimilatory Fe(III) reducing bacterium. Geochim Cosmochim Acta 65:75-93 Zachara JM, Kukkadapu RK, Gassman PL, Dohnalkova A, Fredrickson JK, Anderson T (2004) Biogeochemical transformation of Fe minerals in a petroleum-contaminated aquifer. Geochim Cosmochim Acta 68:17911805 Zachara JM, Kukkadapu RK, Fredrickson JK, Gorby YA, Smith SC (2002) Biomineralization of poorly crystalline Fe(III) oxides by dissimilatory metal reducing bacteria. Geomicrobiol J 19:179-207

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Reviews in Mineralogy & Geochemistry Vol. 59, pp. 53-84, 2005 Copyright © Mineralogical Society of America

Siderophores and the Dissolution of Iron-Bearing Minerals in Marine Systems Stephan M. Kraemer Department of Environmental Sciences ETH Zürich 8092 Zürich, Switzerland [email protected]

Alison Butler Department of Chemistry and Biochemistry University of California, Santa Barbara Santa Barbara, California, 93106-9510, U.S.A.

Paul Borer Swiss Federal Institute of Aquatic Science and Technology 8600 Dübendorf, Switzerland

Javiera Cervini-Silva Department of Earth and Planetary Science University of California, Berkeley Berkeley, California, 94720-3110, U.S.A. Introduction Scope of this review Metal ions have critical functions in biological processes and provided important biological feedbacks with the environment throughout earth history. For example, Fe, Ni, Mg, Mn, Mo, Cu, W, V, and Zn play an essential role as catalysts in key compounds involved in respiration, photosynthesis, nitrogen fixation, and many other enzymatic processes (da Silva and Williams 2001). It is likely that some of these metal bearing enzymes evolved early in the history of life. However, the availability of metal ions has changed dramatically in the last 3.8 billion years due to changes in atmospheric and marine chemistry (Canfield 1998; Anbar and Knoll 2002; Saito et al. 2003). Homeostasis of metal ions (i.e., the maintenance of an approximately constant intracellular concentration) became a major problem over geologic time scales and in contemporary environments. Iron is an essential nutrient for almost all known organisms due to its important role in important enzymatic processes. While iron is the fourth most abundant element in the Earth crust, its low bioavailability limits primary production in various terrestrial and marine environments. The limitation of primary production in important ecosystems has significant implications for the global carbon cycle and the world climate. This review focuses on geochemical aspects of biological iron acquisition in iron limited “high nutrient low chlorophyll” (HNLC) ocean regions. The purpose of this review is to discuss the effect of biogenic iron specific ligands, the so called siderophores, on the iron speciation, and the dissolution of iron-bearing minerals in the presence of siderophores in these marine systems. 1529-6466/05/0059-0004$05.00

DOI: 10.2138/rmg.2005.59.4

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Important iron sources for algal growth in HNLC ocean regions are the upward mixing of iron rich subsurface waters to the euphotic zone and the atmospheric deposition of dust particles on the sea surface followed by the dissolution of iron from these particles into the surface water. Iron sinks include the scavenging of iron onto inorganic and organic particles in the water and subsequent settling and sedimentation of these particles out of the water column. A number of thermodynamically or kinetically controlled processes influence the redistribution of iron among various chemical species in the water and on the water-solid interfaces along the water column. Among those processes are dissolution and formation of iron-bearing minerals, iron adsorption on and desorption from inorganic and organic particles, (photo-) reduction of Fe(III) and oxidation of Fe(II), and iron complexation by organic and inorganic ligands. Marine micro-organisms and phytoplankton may influence or even regulate these processes through 1) the biological exudation of iron binding ligands; 2) reduction of Fe(III) at the cell surface or Fe(III) photo reduction in the presence of biogenic chromophores which may change the redox speciation of iron; 3) transformation of colloidal iron to more bioavailable forms in the digestive system of grazers; 4) biological recycling of iron from sinking biomass. In iron limiting environments, such biogeochemical processes are critical to enhance iron uptake and/or to decrease iron bioavailability for competing species. Unfortunately, very little is known about the kinetics and fluxes associated with many of these processes. An important impediment to field research in this area is the astoundingly low iron concentration in the HNLC surface waters. Dissolved iron in these ocean areas have a nutrient-like vertical distribution with average sea surface concentrations in the subnanomolar range and increasing concentrations with depth (Johnson et al. 1997). Considering the tremendous problems involved in contamination free sampling and chemical analysis in this concentration range, a remarkable range of information has been gathered on dissolved organic and inorganic iron speciation. A key process of iron cycling in HNLC ocean regions and the focus of this review is the dissolution of iron from dust as part of biological iron acquisition strategies. We will summarize pertinent information from field and laboratory studies and discuss important dissolution mechanisms. For in-depth discussion of related issues the reader is referred to a number of excellent review articles (Jickells 1999; Boyd 2002; Morel and Price 2003).

The iron limitation hypothesis Over glacial/interglacial time scales, CO2 levels in the earth atmosphere are strongly influenced by phytoplanktonic photosynthesis in the oceans. In HNLC ocean areas phytoplankton productivity and consequently the efficiency of the biological CO2 pump is not limited by macronutrients such as phosphate or nitrate, since their concentrations at the HNLC sea surface waters are high. Martin and Fitzwater (1988) proposed that primary productivity in these ocean areas is limited by the low availability of the micronutrient iron. Iron fertilization experiments in the Equatorial Pacific (IRONEX I and II), the Southern Ocean (SOIREE, EisenEx, SOFeX-N and SOFeX-S) and the Sub-Arctic North Pacific (SEEDS, SERIES) have decisively supported this hypothesis (Martin et al. 1994; Coale et al. 1996, 2004; Boyd and Law 2001; Gervais et al. 2002; Tsuda et al. 2003; Boyd et al. 2004). Vast ocean areas are affected by iron limitation and it may be as important as nitrogen and phosphorous limitation of global marine phytoplankton productivity (Moore et al. 2002).

Biological iron acquisition strategies Iron acquisition by bacteria Considering the importance of iron as a limiting nutrient, biological iron acquisition is a key factor determining the ecology of HNLC ocean regions. An important iron acquisition

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strategy among both freshwater and marine cyanobacteria and heterotrophic bacteria involves the production of siderophores under iron limiting conditions (Gonye and Carpenter 1974; Trick 1989; Wilhelm and Trick 1994; Wilhelm 1995; Granger and Price 1999). Siderophores are low molecular weight organic ligands (0.5–1.5 kDa) with high affinity and specificity for iron. The siderophore mediated uptake of iron involves the recognition of the siderophore complex and the transport of the ferric siderophore complex across the cell membrane (Reid and Butler 1991; Butler 1998; Murakami et al. 2000; Guan et al. 2001; Armstrong et al. 2004). Although siderophore exudation is an important bacterial response to iron limitation, not all cyanobacteria and heterotrophic bacteria produce siderophores. While some cyanobacteria have been reported to produce multiple siderophores (Wilhelm and Trick 1994), other strains produce none (Wilhelm 1995). Similar results are reported for heterotrophic bacteria. In a recent study, from a total of 421 strains of heterotrophic marine bacteria which were isolated from marine sponges and seawater, 223 strains were observed not to produce siderophores under iron limiting conditions (Guan et al. 2001). However, the growth of 134 stains out of the total non-siderophore producing strains was stimulated by both cross-streaking of siderophore producing strains and by the addition of siderophores. Similarly, Trick (1989) showed that some marine bacteria produced siderophores which promoted growth of unrelated isolates, while other siderophores only satisfied the iron requirements of the strains that produced them. Granger and Price (1999) isolated strains of heterotrophic bacteria and found that not all of them produced siderophores under the assay conditions, but all took up Fe bound to siderophores. The utilization of ferric siderophore complexes by non-siderophore producing bacterial strains may be a typical pattern in iron limited aquatic systems, since siderophore excretion is considered to be metabolically expensive (Völker and Wolf-Gladrow 1999). However, siderophore production may simply also be a “survive at all cost” response to low iron availability and the high metabolic costs are offset by the potential for survival (Wilhelm 1995).

Iron acquisition by eukaryotic phytoplankton Fe(III)-siderophore complexes may not only be an essential iron source for heterotrophic and phototrophic bacteria (prokaryotes), but also for eukaryotic phytoplankton. It has been reported that that some eukaryotic species are able to acquire iron from various iron complexes and siderophores (Allnutt and Bonner 1987; Soria-Dengg and Horstmann 1995; Kuma et al. 2000; Maldonado and Price 2001), even if they generally do not produce siderophores themselves (for exceptions see Trick et al. 1983; Benderliev and Ivanova 1994; Benderliev 1999). An important process in this context is the reduction of organically bound Fe(III) by a plasma membrane ferrireductase, which promotes the dissociation of Fe(II) from the siderophore complex (Jones et al. 1987; Weger 1999). The inorganic iron is then taken up by membrane transporters. However, the acquisition of iron from iron-siderophore complexes by eukaryotic phytoplankton (e.g., diatoms) is controversially discussed in literature. It has been shown that ferric organic complexes, including model Fe-siderophore complexes like Fe-DFOB are utilized as iron sources by some eukaryotic phytoplankton species (Soria-Dengg and Horstmann 1995; Maldonado and Price 1999, 2001). Several studies found that uptake of iron is largely inhibited by DFO-B additions or at least insufficient to satisfy cellular requirements of eukaryotic phytoplankton (Hutchins et al. 1999; Wells 1999; Timmermans et al. 2001; Eldridge et al. 2004). It is important to note in this context that adaptation of former iron-replete eukaryotic phytoplankton to artificially induced iron limitation may require the activation of membrane bound ferric chelate reductases or high-affinity transport systems over timescales that are longer than typical shipboard incubation experiments (Maldonado and Price 2001; Wells and Trick 2004). Hutchins et al. (1999) observed iron uptake from weaker complexes (e.g., Fe-porphyrin). In contrast to most siderophores, the Fe(III)porphyrin complex exhibits a tetradentate structure, so that iron bound to porphyrin may be

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more accessible to surface reductase of eukaryotic plankton and may be reduced more easily. Consequently, the complexation of iron by siderophores may increases the bioavailability of iron for bacterial species, but also may reduce iron availability for eukaryotic phytoplankton (depending on the nutritional status), unless the iron-siderophore complexes are transformed by secondary processes (e.g., by photochemical processes). Marine algae can change iron speciation by excretion of low and high molecular weight organic substances (Lancelot 1984; Fuse et al. 1993; Myklestad 1995). In iron-limited cultures of the coccolithophore Emiliania huxylei, the release of strong iron-chelating ligands (with conditional stability constants comparable to those of siderophores) was observed under ironlimiting conditions and increased after inorganic iron was added to the cultures (Boye and van den Berg 2000). These observations suggest that the observed ligands were not siderophores (where the release is triggered by iron limitation). An increase of the concentrations of iron binding ligands was also observed as a response to iron fertilization in mesoscale experiments (Rue and Bruland 1997). Potential sources of strong Fe-chelators from phytoplankton in seawater are exudation or the rupture of intact cells. Cell rupture could lead, for example, to the release of compounds with porphyrin-type moieties with a high affinity for binding iron (Witter et al. 2000).

Role of protozoan grazers in the cycling of iron Radiotracer studies indicate that iron is rapidly cycled within the planktonic community by linked biological processes such as grazing, excretion, viral lysis, and bacterial respiration of organic matter (Hutchins et al. 1993). Regeneration of iron among the phytoplankton community does not necessarily require grazing of phytoplankton by heterotrophic protozoan grazers. Regeneration of iron is also accomplished by the grazing of bacteria through photosynthetic protozoan grazers, so called mixotrophs. Mixotrophy—defined as the ability to assimilate organic compounds as carbon sources while using inorganic compounds as electron donors for energy metabolism (Madigan et al. 2000)—is a widespread phenomenon in aquatic habitats and is observed in many ciliates and flagellates (Stoecker 1998). It has been suggested that phagotrophic ingestion of bacteria may be an adaptive strategy for photosynthetic algae to obtain iron for growth in iron limited regions of the sea. In a recent study, it has been shown that the photosynthetic flagellate Ochromonas sp. can obtain iron directly by ingesting bacteria (Maranger et al. 1998). As Ochromonas also excretes some of the Fe it ingests, phagotrophic phytoflagellates may in general play an important role in the Fe cycle by regenerating Fe for themselves and for other microorganisms. Heterotrophic protozoan grazers may also generate bioavailable iron by digestion of refractory iron phases in the acidic food vacuoles. Barbeau et al. (1996) have demonstrated several grazer mediated effects on colloidal ferrihydrite, including a decrease in colloid size, an increase in colloid lability as determined by competitive ligand exchange techniques, and an increase in the bioavailability of colloids to iron limited diatoms. These results indicate that protozoan grazers may significantly enhance the supply of iron to marine phytoplankton from terrestrial sources. It has been estimated that protozoan grazing of colloidal particles (e.g., ferrihydrite) may equal or exceed photoreductive dissolution in increasing iron availability to phytoplankton (Barbeau et al. 1996; Barbeau and Moffett 2000). The effect of grazing on more crystalline iron oxides is not known.

sources of iron in HNLC ocean regions The most important iron sources in HNLC ocean areas are upwelling and atmospheric deposition of iron derived from continental dust (Archer and Johnson 2000; Moore et al. 2002, 2004). The atmospheric flux of iron to the remote HNLC ocean areas is relatively low.

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However, iron deficiency is not only triggered by low total iron concentrations but also by the low bioavailability of iron from atmospheric inputs. As a consequence of its low bioavailability, only a small fraction of the iron input by atmospheric aerosol is solubilized before sedimentation (Zhuang et al. 1990; Fung et al. 2000). The bioavailability of iron is strongly influenced by the mineralogy of aerosol particles which in turn is influenced by the characteristics of soils in the source area of the particles. Iron speciation can further be modified by the chemical environment during atmospheric transport (Jickells 1999). Iron-bearing minerals can be dissolved by protonpromoted and photoreductive dissolution and re-precipitated during drying cycles (Spokes et al. 1994; Siefert et al. 1999; Johansen et al. 2000).

Atmospheric dust as a source of iron A strong link between dust deposition and primary production has been established by observations of increased chlorophyll and particulate organic carbon concentrations at the sea surface during episodic dust deposition events (Ditullio and Laws 1991; Lenes et al. 2001; Bishop et al. 2002). The most important source for atmospheric dust are the arid or semiarid regions of the continents (Duce et al. 1980; Duce and Tindale 1991; Tegen and Fung 1995; Mahowald et al. 1999; Perry et al. 1999; Ginoux et al. 2001). The iron content of atmospheric dust is roughly related to the average crustal abundance of iron (3.5%; Taylor 1964) and depends on the continental source area (Hand et al. 2004) and on size fractionation during transport (Claquin et al. 1999; Jickells 1999). A number of researchers have estimated aeolian iron fluxes to the oceans based on measurements or models of atmospheric dust distributions over the oceans (Duce and Tindale 1991; Tegen and Fung 1995; Mahowald et al. 1999) and on global assumptions or databases of local measurements of the iron content in dust (Archer and Johnson 2000; Gao et al. 2001; Moore et al. 2002).

Iron mineralogy of atmospheric dust The distribution of iron among iron-bearing mineral phases has an important effect on its solubility and lability (Cornell and Schwertmann 2003). The fate of iron during atmospheric transport and after deposition at the sea surface will therefore strongly depend on the mineralogy of aerosol particles. Nanoparticulate hematite (11–170 nm) was observed in Mössbauer studies of atmospheric aerosols collected in a rural area of Poland (Kopcewicz and Kopcewicz 1994, 1998). Reid et al. (2003) report single particle analysis of aerosol particles of Saharan origin collected at Puerto Rico. They found most iron associated with particles with elemental abundances corresponding to illite. The elemental composition of a smaller fraction of high Fe particles suggested kaolinite aggregated with iron oxides. However, the authors noted the difficulty of assigning mineralogical structures to aggregates based on elemental abundances. Falkovich et al. (2001) used SEM-EDS and XRD to analyze individual dust particles of North African origin collected over Israel. They found most iron on particle surfaces and suggested that hematite aggregated with clay minerals were coating particle surfaces. Direct evidence of the presence of hematite and goethite was provided by diffuse reflectance spectrometry of aerosol particles collected from Bermuda, Barbados, and Izaña (Arimoto et al. 2002). Crystalline iron oxides of primarily aeolian origin were also found in deep sea sediments (Bloemendal et al. 1992; Balsam et al. 1995).

Transformation of iron-bearing minerals during atmospheric transport The bioavailability of iron is strongly influenced by it’s speciation in the aerosol particles. Iron-bearing minerals can be transformed by proton-promoted and photoreductive dissolution as well as precipitation during drying cycles (Spokes et al. 1994; Siefert et al. 1998). The observation of significant concentrations of dicarboxylic acids in marine aerosols may promote ligand-controlled and photoreductive dissolution mechanisms (Stephanou and Stratigakis 1993; Sempere and Kawamura 2003).

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Reduction of Fe(III) to Fe(II) dramatically increases its solubility. Reduced iron species are thermodynamically unstable under atmospheric conditions, but photoredox reactions can lead to the significant transient Fe(II) concentrations, particularly in acidic aerosols (Behra and Sigg 1990). Even though Fe(II) is rapidly reoxidized in seawater (Millero et al. 1987; Millero and Sotolongo 1989; King et al. 1995; King 1998), the precipitating Fe(III)-polymers are expected to be thermodynamically and kinetically less stable than iron-bearing minerals of the atmospheric dust. For these reasons, the redox speciation of iron in aerosols is a subject of ongoing research. Most studies of iron redox speciation show that the fraction of Fe(II) in acid extracts of aerosols represents only a small fraction of the total iron (Zhuang et al. 1990; Zhu et al. 1993, 1997; Siefert et al. 1999; Johansen et al. 2000; Chen and Siefert 2004). Large variations of Fe(II) fractions in aerosols collected over the Atlantic and Pacific Oceans were observed by Hand et al. (2004), with significantly higher Fe(II) fractions in fine aerosols ( ferrihydrite > lepidocrocite > goethite (Kuma and Matsunaga 1995). Iron dissolution from aerosol samples in seawater shows more complex dissolution behaviour than pure mineral phases for several reasons. Aerosols can consist of more than one iron-bearing mineral phase with corresponding variations in solubilities and dissolution rates. Moreover, minerals are undergoing extensive transformations due to the harsh conditions during atmospheric transport, leading to the labilization of some fraction of iron from primary minerals including dissolved Fe(II) and Fe(III) as discussed above. Zhuang et al. (1990) have observed fast dissolution of up to 50% of the total iron from aerosol samples immersed into seawater at ambient pH (see corrected value in Yhu et al. 1993). A labile Fe(II) pool of between 0.3–2.2% of the total aerosol iron was quantified by extraction in acidic solutions (Zhu et al. 1993; Zhu et al. 1997; Siefert et al. 1999; Johansen et al. 2000).

Photo-reductive dissolution in seawater Typically, iron oxide photolysis in the laboratory has been investigated by measuring the photo-production of Fe(II). Quantification of iron oxide photolysis in seawater by this methodological approach is constrained by fast reoxidation of photo-produced surface Fe(II) as well as fast reoxidation of Fe(II) that has eventually been released to the solution. Thus, the formation of Fe(II) may not be used as a reliable quantitative measure of photo-reductive iron oxide dissolution in seawater. In recent years other approaches have been taken to quantify iron oxide photolysis in seawater samples. Barbeau and Moffett (2000) have used a novel inert tracer technique to investigate the photo-dissolution of a model iron oxide. Colloidal ferrihydrite uniformly impregnated with an inert tracer (133Ba) was spiked to seawater and the release and accumulation of this tracer in solution was measured under irradiated conditions (natural sunlight). According to these authors, iron oxide photo-dissolution was directly related to the release and accumulation of 133Ba, regardless of the fate of iron. During irradiation of 133 Ba and 59Fe impregnated ferrihydrite, only release and accumulation of 133Ba was observed (Barbeau and Moffett 2000) while iron was most likely re-oxidized. Wells and Mayer (1991) investigated the photo-dissolution of colloidal ferrihydrite and goethite in spiked seawater of pH 8 by measuring the labile portion of total iron as determined by extraction with the complexing agent 8-hydroxyquinoline. The lability of these colloidal iron oxides was found to increase upon irradiation with artificial and natural sunlight, and this was assigned to the rapid cycling of photo-reductive dissolution, rapid reoxidation in solution and precipitation in the presence of unknown chromophores. Pre-irradiation of the

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seawater prior to addition of colloidal Fe(III) eliminated the photoreaction, confirming the role of natural organic chromophores in photo-dissolution of iron oxides. The labile portion of iron in seawater was further shown to correlate positively with its availability to marine algae (Wells and Goldberg 1991). Thus it seems that photo-reductive dissolution of colloidal iron may generate an iron pool that is bioavailable to marine algae, either by generating dissolved Fe(II) of highly labile colloidal Fe(III).

Organic ligands and iron oxide dissolution in seawater Siderophore-promoted dissolution mechanisms Siderophores can influence iron oxide dissolution by acceleration of the dissolution reaction via ligand-controlled and light induced dissolution mechanisms (Kraemer et al. 1999; Borer et al. 2005) and by modifying the solution saturation state of the seawater with respect to the iron oxide (Cheah et al. 2003; Kraemer 2004). A model calculation was performed to illustrate the effect of the concentration of a strong marine siderophore (alterobactin A) on the solution saturation state in the presence of various model iron oxides and a total concentration of 0.1 nM dissolved iron in seawater (Fig. 4). Under these conditions, a small concentration of the siderophore is required to maintain solubility equilibrium (ΔG = 0 kJ/mole). Obviously the equilibrium siderophore concentration increases with increasing thermodynamic stability of the iron oxide. A further increase of the siderophore concentration leads to under-saturation (ΔG < 0 kJ/mole). A quantitative treatment of the effect of the solution saturation state on dissolution rates as derived from the activated complex theory (Lasaga 1981; Aagaard and Helgeson 1982) has been applied to ligand-controlled dissolution (Kraemer and Hering 1997) resulting in an empirical rate law:   ∆G   Rnet = kL [ L ]ads f ( ∆G ) = kL [ L ]ads 1 − exp    2 RT   

(41)

where kL is the rate constant of ligand-controlled dissolution, [L]ads is the adsorbed ligand concentration; ΔG is the Gibbs free energy of reaction (kJ mole−1); R is the gas constant, and T is the absolute temperature (K). Figure 5 illustrates the effect of the solution saturation state expressed as Gibbs free energy change on the net dissolution rate represented as f (ΔG) = [1 − exp(ΔG/(2RT))]. At a ΔG ≤ −3 kJ/mole, f (ΔG) ≥ 0.5, i.e., net dissolution rates are more than half of the maximum dissolution rates. In the model calculation presented in Figure 4, ΔG ≈ −3 kJ/mole at a total siderophore concentration between 0.25 nM (ferrihydrite) and 1 nM (hematite) which is in the range of observed strong ligand concentrations in marine surface water. Based on these considerations, it seems likely that the maintenance of small free siderophore concentrations by marine bacterial exudation may provide the driving force for dissolution mechanisms including ligand-controlled dissolution. Adsorbed siderophores can also accelerate iron oxide dissolution by a ligand-controlled dissolution mechanism (Holmen and Casey 1998; Kraemer et al. 1999; Kalinowski et al. 2000; Maurice et al. 2000, 2001; Cervini-Silva and Sposito 2002; Cocozza et al. 2002; Cheah et al. 2003; Kraemer 2004). As indicated in the rate law for ligand-controlled dissolution (Eqn. 41) the effect of adsorbed siderophores on dissolution rates is linearly related to their adsorbed concentrations. Adsorbed concentrations are non-linearly related to soluble siderophore concentrations via adsorption isotherms (Kraemer et al. 1999, 2002; Cocozza et al. 2002; Neubauer et al. 2002; Cheah et al. 2003). At extremely low dissolved siderophore

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74 8

Ferrihydrite Hematite

6

Goethite Lepidocrocite

∆G [kJ/mole]

4 2 0 -2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

-4 -6 -8 [alterobactin-A]tot [nM]

f (∆G)

Figure 4. Calculated solution saturation state of various iron oxides as a function of the alterobactin-A concentration, assuming a total soluble iron concentrations [Fe(III)tot] = 0.1 nM in seawater. The solution saturation state is expressed as Gibbs free energy change ΔG as calculated by Equation (34) using conditional hydrolysis constants as listed in Table 1. Positive ΔG indicates super-saturation, negative ΔG under-saturation. At equilibrium ΔG = 0. pH = 8.1; logK*FeL = 23.9.

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 -15

-10

-5

0

∆G [kJ/mole]

Figure 5. The effect of the solution saturation state expressed as Gibbs free energy change ΔG on net dissolution rates where f (ΔG) =[1−exp(ΔG/(2RT))]. At equilibrium, f (ΔG) = 0, i.e., the net dissolution rate Rnet = 0. With decreasing Gibbs free energy changes, f (ΔG) approaches unity, i.e., the net dissolution rates approach a constant maximum value.

concentrations reported in literature, adsorbed siderophore concentrations are also expected to be low. Based on this consideration Kraemer (2004) has suggested that direct siderophorecontrolled dissolution mechanisms are insignificant at low siderophore concentrations typically found in natural environments compared to other dissolution mechanisms including proton-promoted dissolution, alkaline dissolution or ligand-promoted dissolution mechanisms driven by other adsorbed ligands. In this context, the more important function of siderophores in oligotrophic natural environments may be to increase the solubility of iron oxides and to drive other dissolution mechanisms by lowering the solution saturation state. This hypothesis is supported by observations of siderophore-promoted dissolution rates of iron oxides in artificial seawater by Yoshida et al. (2002). They have demonstrated that

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micromolar concentrations of a siderophore produced by a marine bacterium Alteromonas haloplanktis accelerate the dissolution rates of goethite and a poorly crystalline iron hydroxide. The dissolution rates increased with increasing siderophore concentrations in a roughly linear relationship at pH 4. Interpolating these results toward nanomolar concentrations suggests that the effect of the siderophores on dissolution rates is negligible at natural concentration levels.

Photo-reductive dissolution mechanisms in the presence of siderophores Photo-reductive dissolution of iron oxides or other particulate iron forms is expected to be slow at seawater pH due to low adsorption of possible photo-reductive ligands (e.g., carboxylic and α-hydroxycarboxylic acids) to iron oxide surfaces, slow release of photoproduced surface Fe(II) to the solution and fast reoxidation of surface Fe(II). Waite et al. (1995) studied diel variations in iron speciation in northern Australian shelf waters and found no correlation between measured particulate iron concentrations and ferrozine active iron (Fe(II)). Therefore, they proposed that particulate iron does not appear to be the dominant source of Fe(II) in seawater. However, for seawater that is characterized by the presence of strong iron complexes (e.g., HNLC waters), dissolution of iron oxides by photo-reductive mechanisms may be enhanced considerably. Recently, Borer et al. (2005) have studied the photo-reduction of goethite and lepidocrocite in the presence of a typical organic photoreductant (oxalate) and two model siderophores, desferrioxamine B (DFO-B) and aerobactin. They have observed that under irradiated and aerated conditions at pH 6, surface Fe(III) is reduced by oxalate, but only a minor part of surface Fe(II) is detached from the surface before reoxidation takes place. Due to the slow detachment of Fe(II) from iron oxide surfaces, in particular for higher crystalline and less soluble iron oxide phases, reoxidation of surface Fe(II) has been shown to limit the overall dissolution rate at circumneutral pH (Sulzberger and Laubscher 1995; Voelker et al. 1997). However, in the presence of siderophores, Fe(II) is efficiently detached from the surface and significant photo-reductive dissolution rates are observed (Borer et al. 2005). Due to the fact that Fe-siderophore complexes have very negative redox potentials at neutral pH, oxidation of dissolved Fe(II)-siderophore complexes is assumed to be very fast (Boukhalfa and Crumbliss 2002), and the trivalent iron state is stabilized against reduction by many ligands. For the reported case of DFOB, oxidation of Fe(II)-DFOB complexes is instantaneous (Welch et al. 2002). These combined observations indicate that siderophores potentially enhance photo-reductive dissolution without contributing to the formation of measurable Fe(II).

Amphiphilic siderophores In addition to the preponderance of α-hydroxycarboxylic-acid-containing siderophores characterized to date from open ocean bacterial isolates, amphiphilic siderophores are also prevalent and many also contain both an α-hydroxycarboxylic-acid, in the form of βhydroxyaspartic acid, as well as a fatty acid that confers the amphiphilicity (e.g., marinobactins and aquachelins, see Fig. 1). The wide diversity of marine bacteria from which amphiphilic siderophores have been isolated suggests this property evolved as a common iron acquisition strategy for marine bacteria (Martinez et al. 2003). Not only could the amphiphilic character of the siderophores function to keep siderophores in close contact with the bacteria (Xu et al. 2002), but importantly this amphiphilicity will increase surface reactivity. The enhanced surface reactivity of photo-reactive siderophores on iron-containing particles may well further promote dissolution of iron minerals; these investigations are in progress. The aquachelins, marinobactin and amphibactins are all produced as suites of siderophores. The amphiphilic siderophores with shorter fatty acids (e.g., C12) partition into vesicle membranes far less than the longer chained fatty acids (C18). The decreased partitioning however increases the availability of particle interactions.

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Kraemer, Butler, Borer, Cervini-Silva Conclusions

Understanding the cycling of iron in marine systems and how it relates to biological nutrient acquisition processes remains a challenge for biogeochemical research. This challenge has been met with impressive vigor and success, considering the difficulty to measure iron concentrations, solubilities, and speciation at sub-nanomolar levels. However, some important information is missing. For example, while it is well known that iron in marine surface waters is bound to strongly complexing ligands, their characterization and identification is difficult. However, indirect evidence suggests that biogenic ligands including microbial siderophores play an important role in marine iron speciation. A further challenge will be the understanding of trace nutrient cycling and the indications of trace nutrient limitation in the geological record, considering the potential importance of iron and other trace nutrients for the global climate and for biological evolution in the past. In this chapter we reviewed the coordination chemistry and redox-/photoredox chemistry of soluble siderophore iron complexes as well as the effect of siderophores on the solubility of iron-bearing minerals, and their dissolution mechanisms and rates. We hope that the discussion of these processes may help to appreciate the complexity of biological influences on marine iron cycling.

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Reviews in Mineralogy & Geochemistry Vol. 59, pp. 85-108, 2005 Copyright © Mineralogical Society of America

Geomicrobiological Cycling of Iron Andreas Kappler and Kristina L. Straub Geomicrobiology Group Center for Applied Geosciences University of Tübingen D-72074 Tübingen, Germany [email protected] [email protected]

Introduction Iron is the most abundant element on Earth and the most frequently utilized transition metal in the biosphere. It is a component of many cellular compounds and is involved in numerous physiological functions. Hence, iron is an essential micronutrient for all eukaryotes and the majority of prokaryotes. Prokaryotes that need iron for biosynthesis require micromolar concentrations, levels that are often not available in neutral pH oxic environments. Therefore, prokaryotes have evolved specific acquisition molecules, called siderophores, to increase iron bioavailability. Acquisition of iron by siderophores is a complex process and is discussed in detail by Kraemer et al. (2005). Here we focus on prokaryotes that generate energy for growth by oxidation or reduction of iron. In both processes single electron transfers are involved. Hence, for a significant extent of energy generation, turnover of iron in the millimolar rather than the micromolar range is necessary. Iron metabolizing organisms have therefore a strong influence on iron cycling in the environment. Microbial iron oxidation and reduction will be discussed, with emphasis on circumneutral pH environments that prevail on Earth. The active metabolic processes outlined above have to be distinguished from indirect biologically induced iron mineral formation in which prokaryotic cell surfaces simply act as passive templates (“passive iron biomineralization”) (e.g., Konhauser 1997).

General aspects of the iron cycle On our planet, iron is ubiquitous in the hydrosphere, lithosphere, biosphere and atmosphere, either as particulate ferric [Fe(III)] or ferrous [Fe(II)] iron-bearing minerals or as dissolved ions. Redox transformations of iron, as well as dissolution and precipitation and thus mobilization and redistribution, are caused by chemical and to a significant extent by microbial processes (Fig. 1). Microorganisms catalyze the oxidation of Fe(II) under oxic or anoxic conditions as well as the reduction of Fe(III) in anoxic habitats. Microbially influenced transformations of iron are often much faster than the respective chemical reactions. They take place in most soils and sediments, both in freshwater and marine environments, and play an important role in other (bio)geochemical cycles, in particular in the carbon cycle. Microbial iron cycling impacts the fate of both organic and inorganic pollutants, including those released from industrial and mining areas (Thamdrup 2000; Straub et al. 2001; Cornell and Schwertmann 2003).

Solubility and chemical transformation of Fe(II) and Fe(III) minerals Different Fe(II), Fe(III) and mixed Fe(II)-Fe(III) minerals are found in the environment and many are used, produced or transformed by microbial activities (Table 1). Fe(III) minerals are characterized by low solubility at circumneutral pH and usually only very low, hardly 1529-6466/05/0059-005$05.00

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Chemical or microbial Fe(II) oxidation with O2 and microbial Fe(II) oxidation with CO2 in the light or with NO3- at neutral pH

Dissolution

Fe(II) minerals

Precipitation (pH increase)

Microbial acidophilic Fe(II) oxidation

Precipitation

Fe(III) minerals

Fe3+

Fe2+ Chemical or microbial Fe(III) reduction at acidic pH

Dissolution (pH decrease)

Chemical or microbial Fe(III) reduction at neutral pH

Figure 1. Microbial and chemical iron cycle.

detectable concentrations in the range of 10−9 M of Fe(III) are present in solution (Fig. 2). However, colloid formation or complexation by organic compounds can lead to elevated concentrations of dissolved Fe(III), even at neutral pH (Cornell and Schwertmann 2003; Kraemer 2004). At strongly alkaline or strongly acidic pH, ferric iron oxides can be dissolved because of their amphoteric character. Ferric iron oxides can be reduced chemically by a range of organic and inorganic reductants. However, the environmentally most important reducing agent for Fe(III) is hydrogen sulfide, which is a common end product of microbial sulfur and sulfate reduction (Thamdrup 2000; Cornell and Schwertmann 2003). In contrast to Fe(III) minerals, some ferrous iron minerals, e.g., siderite or ferrous monosulfides, are considerably more soluble at neutral pH. This leads to concentrations of Table 1. Names and formulas of some important iron minerals. Fe(III) oxides

Fe(III) oxyhydroxides and hydroxides1

Hematite α-Fe2O3

Goethite α-FeOOH

Maghemite γ-Fe2O3

Lepidocrocite γ-FeOOH Ferrihydrite2 Fe5HO8·4H2O

Fe(II) minerals

Mixed Fe(II)-Fe(III) minerals

Ferrous monosulfides ‘FeS’3

Magnetite Fe3O4

Pyrite FeS2

Green rusts FexIIIFeyII(OH)3x+2y−z(A−)z; A− = Cl−; ½ SO42−

Siderite FeCO3

Greigite Fe3S4

Vivianite Fe3(PO4)2 1

For simplicity also commonly referred to as iron oxides. Ferrihydrite frequently is inadequately assigned as Fe(OH)3. However, if the identity of a poorly crystalline iron hydroxide is unknown, this formula can be used as approximation. 3 This term embraces a variety of minerals with slightly varying stoichiometries, i.e., FexSx±1. Only troilite contains iron and sulfur in an exact 1:1 stoichiometry. Troilite rarely occurs on Earth, but is found in iron meteorites and lunar rocks (Lennie and Vaughan 1996). 2

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0 Ferrihydrite Fe5HO8 . 4H2O

log Fe(III) [M]

-5

Fe(OH)2+

-10

Fe(OH)4-

Figure 2. Dominance diagram showing the concentrations of different dissolved Fe(III) species in the presence of ferrihydrite at pH 6-8.

Fe(OH)2+

-15

Fe3+ -20

-25 6

6.4

6.8

7.2

7.6

8

pH

dissolved Fe(II) that can reach the μM range, even in the presence of bicarbonate or sulfide. However, Fe(II) is stable at neutral or alkaline pH only in anoxic environments and is oxidized to Fe(III) minerals by molecular oxygen. At acidic pH, Fe(II) can persist, even in oxic habitats (Stumm and Morgan 1996; Cornell and Schwertmann 2003). Under anoxic conditions, Mn(IV), nitrate, nitrite and nitrous oxide were shown in laboratory studies to oxidize Fe(II) chemically. In anoxic natural habitats, however, Mn(IV) is the only relevant oxidant of Fe(II) (Buresh and Moraghan 1976; Moraghan and Buresh 1977; Myers and Nealson 1988).

Surface area and reactivity of ferric iron oxides The rates of chemical and microbial transformations of iron minerals depend on the number of available reactive surface sites, e.g., on the number of reactive surface-OH functional groups in case of ferric hydroxides (Roden 2003). The mineral surface area in turn inversely depends on the crystal size of the ferric iron oxides. Different iron minerals and samples of the same iron mineral with different crystal sizes vary significantly in surface area and therefore in stability and reactivity. This influences dissolution kinetics, transformation reactions and adsorption of organic and inorganic compounds. Values for surface areas can be determined experimentally by different methods, although these may produce slightly varying results. Surface areas determined by the Brunauer-Emmett-Teller method (BET) as extent of N2-adsorption to an outgassed sample of the respective mineral span from a few m2/g (e.g., 8–16 m2/g for highly crystalline goethite) to a few hundreds of m2/g (e.g., 100–400 m2/g for poorly crystalline ferrihydrite) (Cornell and Schwertmann 2003).

Ferrihydrite Ferrihydrite is widespread in many natural environments. It is frequently used in laboratory studies with Fe(III)-reducing microorganisms and was observed as a product in cultures of Fe(II) oxidizers (Fig. 3). Ferrihydrite is a high-surface area iron oxide that consists of nanometer-sized crystals. Although it has been reported to be hexagonal, its structure remains a matter of debate (Mancaeu and Drits 1993; Jambor and Dutriziac 1998; Janney et al. 2000, 2001). It is a material that exhibits considerable disorder, but it is not amorphous (for more details see Gilbert and Banfield 2005). The crystallinity of the different ferrihydrite species depends on the conditions

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B

A

100 nm

500 nm

Figure 3. Scanning (A) and transmission (B) electron micrographs of ferrihydrite produced by the anoxygenic phototrophic Fe(II)-oxidizing bacterium ‘Rhodobacter ferrooxidans’ strain SW2. Note that particles of the biologically produced ferrihydrite are of nm-size and thus much smaller than microbial cells of typical size.

during synthesis, e.g., formation rate and the presence of organic and inorganic compounds (Cornell and Schwertmann 2003). The small, nanometer-sized crystals of ferrihydrite often aggregate to form colloids with sizes in the μm-range (Fig. 3).

Forms of iron present in the environment In the environment, iron is rarely present as pure, well crystalline mineral phase but rather is found:

• in association with or covered by natural organic matter (e.g., humic substances, biofilm exopolysaccharides)

• in particles to which anions such as phosphate (PO43−) and arsenate (AsO43−) or positively charged metal ions (e.g., Fe2+, Cu2+, Mn2+) have adsorbed

• • • •

as minerals that are mixed or co-precipitated with other minerals (e.g., clays) in minerals in which other cations, e.g., Al, Cr, Mn, partially substitute for iron as nano-sized mineral particles or as aggregates of nano-sized particles (colloids) complexed (e.g., by organic acids) and thus dissolved.

Such complex natural systems provide a huge variety of microenvironments, and thus microniches, for microorganisms with different physico-chemical requirements. In fact, it is hard, if not impossible, to simulate this complexity in the laboratory. This difficulty might be one explanation for the poor growth of many iron-metabolizing bacteria in the laboratory.

Role of iron for microbial energy metabolism Different physiological groups of prokaryotes can use iron as a substrate for energy generation (Fig. 1, Table 2). In the following two sections we will discuss such Fe(II)-oxidizing and Fe(III)-reducing microorganisms in more detail, focusing on electron transfer between cells and iron minerals. Intracellular electron transfer in Fe(III)-reducing bacteria via redox active proteins such as cytochromes was recently reviewed by Lovley et al. (2004). The rapid growth in availability of genomic information will significantly improve our understanding of the electron transport chains of iron cycling microorganisms (e.g., Nelson and Methé 2005). The third section focuses on microbial iron cycling catalyzed by the cooperation of these two

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Table 2. Physiological groups of prokaryotes that catalyze iron redox transformations. Habitat

Electron donor

Electron acceptor

pH

Microbial metabolism

Representative strains

Oxic

Fe(II)

O2

acidic

Fe(II) oxidation

Thiobacillus ferrooxidans Sulfobacillus acidophilus

Fe(II)

O2

neutral

Fe(II) oxidation

Gallionella ferruginea Leptothrix ochracea

Fe(II)

NO3−

neutral

NO3−-dependent Fe(II) oxidation

Acidovorax sp. strain BrG1 Azospira oryzae strain PS

Fe(II)

CO2

neutral

Phototrophic Fe(II) oxidation

‘Rhodobacter ferrooxidans’ strain SW2 Rhodovulum iodosum

Organic or inorganic compounds

Fe(III)

acidic

Fe(III) reduction

Acidiphilium cryptum sp. JF-5 Thiobacillus thiooxidans

Organic or inorganic compounds

Fe(III)

neutral

Fe(III) reduction

Geobacter metallireducens Shewanella oneidensis

Anoxic

physiological groups. Finally, some environmental implications are described and tasks for future investigations defined.

Microbial oxidation of Fe(II) Competition between chemical and microbial oxidation of Fe(II) The chemical oxidation of Fe(II) with oxygen depends mainly on the pH and the concentration of oxygen (Fig. 4). At pH values above 5, the Fe(II) oxidation rate has a firstorder dependence on Fe(II) and O2 concentrations and a second-order dependence on the OH− concentration. Thus, an increase of one pH unit increases the rate of Fe(II) oxidation100-fold. Therefore in O2-saturated water at neutral pH, Fe(II) is readily oxidized to Fe(III) with a halflife in the order of several minutes (Stumm and Morgan 1996). Aerobic, neutrophilic Fe(II)oxidizing microorganisms compete successfully with this fast chemical process. However, some of them thrive only in microoxic niches with low oxygen concentrations and hence a slower chemical oxidation of Fe(II) by oxygen (Emerson 2000). In contrast, under acidic conditions Fe(II) persists for long periods of time, even in the presence of atmospheric O2 levels. Under anoxic conditions, only manganese oxides and nitrite have been shown to oxidize freely dissolved Fe(II) chemically (Myers and Nealson 1988; Moraghan and Buresh 1977). However, neither nitrate nor sulfate react chemically with Fe(II) at appreciable rates at low temperature. Therefore, anaerobic Fe(II)-oxidizing bacteria are the most important catalysts/ oxidants for the generation of Fe(III) in anoxic habitats.

Aerobic acidophilic Fe(II)-oxidizing microorganisms Due to the stability of ferrous iron at acidic pH even in the presence of O2, aerobic acidophilic Fe(II)-oxidizing microorganisms can readily compete with chemical oxidation. However, at acidic pH the redox couple Fe3+/Fe2+ relevant for the redox reaction catalyzed by these bacteria has a redox potential of +770 mV. Therefore, at pH 2 only ~33 kJ/mol iron is produced during the oxidation with O2, since the relevant redox potential of the redox couple O2/H2O is +1106 mV. This difference is just big enough for the synthesis of 1 mol ATP. Under

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F e 3+

0.5

Eh

F eOH 2+

90

O2

F e 2+

Fe 2

+

=

10

0

-0.5

F e(OH) 3,solid µM

H2

F e(OH) 2, solid

-1 0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

pH Figure 4. Eh-pH diagram for Fe(II), Fe(III), O2 and H2 calculated with a Fe2+ concentration of 10 μM. For simplicity Fe(OH)3 is used as approximation for the Fe(III) precipitates that are formed.

such conditions, ~90 mol Fe(II) has to be oxidized to fix 1 mol of CO2 as biomass (Ehrlich 2002). This relationship explains the huge amount of iron that is oxidized by aerobic acidophilic microorganisms, for instance in acid mine drainage (Baker and Banfield 2003; Druschel et al. 2004). Note that at pH values above 2, Fe(III) starts to precipitate and the oxidized product is removed, leading to a lowering of the redox potential of the Fe(III)/Fe(II) couple to less positive values. Since the redox potential of the O2/H2O couple is less pH-dependent (59 mV change per pH unit) than the Fe(III)/Fe(II) couple (177 mV change per pH unit), growth at less acidic pH values is more favorable for aerobic acidophilic Fe(II) oxidizers. A number of lineages of acidophilic iron-oxidizing organisms have been described to date. These were reviewed comprehensively by Nordstrom and Southam (1997) and more recently by Blake and Johnson (2000) and Baker and Banfield (2003). Furthermore, aspects of the population biology of acidophilic microbial communities sustained by iron oxidation are reviewed by Whitaker and Banfield (2005).

Aerobic neutrophilic Fe(II)-oxidizing microorganisms This physiological group of microorganisms uses O2 as electron acceptor for enzymatic oxidation of Fe(II) at neutral pH. To gain energy for growth they have to compete with the chemical oxidation of Fe(II) by O2. Initially, research on oxygen-dependent, neutrophilic Fe(II) oxidizers focused on species of the genera Gallionella and Leptothrix. Organisms of these two groups were already recognized in the 19th century to grow in oxic iron-rich environments. Gallionella ferruginea, a bean-shaped autotrophic bacterium, typically produces twisted stalks that are encrusted with ferric iron minerals (Hanert 1981). Gallionella spp. are very good examples of gradient organisms: growth is observed only under conditions that are neither strongly reducing nor highly oxidizing. The heterotrophic bacterium Leptothrix ochracea forms tubular sheaths which are also covered with ferric iron minerals (Emerson and Revsbech 1994). It has been suggested that the deposition of iron oxide minerals on the stalks or sheaths avoids encrustation of Fe(II)-metabolizing cells. Encrustation of living cells might impair both substrate uptake and metabolite release, and may even cause cell death (Hanert 1981; Hallberg and Ferris 2004).

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A range of novel microaerophilic Fe(II)-oxidizing bacteria were isolated with gradient culture techniques using gradients of Fe(II) and O2 to mimic natural environments. Representatives of the α-, β- and γ-subgroup of Proteobacteria were isolated from groundwater, deep sea sediments and freshwater wetland samples (Emerson and Moyer 1997; Edwards et al. 2003; Sobolev and Roden 2004). More details on aerobic bacterial Fe(II) oxidation at neutral pH are given by Emerson (2000).

Anaerobic Fe(II)-oxidizing phototrophic bacteria About a decade ago anoxygenic phototrophic bacteria were discovered which grow in the light with ferrous iron as sole electron donor (Widdel et al. 1993). Experimental results were in good agreement with the following equation, assuming as the approximate formula of cell mass: 4FeCO3 + 7H2O → + 4Fe(OH)3 + 3CO2 In the meantime, seven cultures of anoxygenic Fe(II)-oxidizing phototrophic bacteria have been established (Table 3). They include representatives of the three major phylogenetic lineages of anoxygenic phototrophs, and furthermore include freshwater and marine species. All known anoxygenic phototrophs oxidized Fe(II) optimally only within the narrow pH-range of 6.5 to 7. This allows them to use Fe(II) as electron donor since the standard redox potential for Fe2+/Fe3+ (+770 mV at pH 1) is shifted at neutral pH to less positive values (around 0 mV) due to the low solubility of Fe(III) (Fig. 4; Widdel et al. 1993; Stumm and Morgan 1996). Therefore, Fe(II) can donate electrons to the photosystems of purple or green bacteria, with midpoint potentials around +450 mV or +300 mV, respectively (Clayton and Sistrom 1978). Fe(II)-oxidizing phototrophic bacteria can oxidize dissolved Fe(II). In addition, they grow with relatively soluble Fe(II) minerals such as siderite or ferrous monosulfide (Kappler and Newman 2004). In contrast, they were unable to utilize less soluble Fe(II) minerals, e.g., pyrite (FeS2) or magnetite (Fe3O4). These results indicate that the phototrophs studied so far may depend on the supply of dissolved Fe(II). Geological records indicate that oceans contained considerable amounts of dissolved ferrous iron and hardly any molecular oxygen in the beginning of the Precambrian. It is therefore intriguing how massive iron mineral deposits, known as banded iron formations (BIFs), were generated at that time. This is even more puzzling, given doubt that the Table 3. Ferrous iron-oxidizing phototrophic bacteria from different phylogenetic groups. Phylogenetic group

Species

Strain

Source

Ref.

Purple sulfur bacteria

Thiodictyon sp.a

F4

Freshwater marsh

(1)

Purple non-sulfur bacteria

‘Rhodobacter ferrooxidans’

SW2

Freshwater ditch

(2)

Green bacteria

Rhodomicrobium vannielii

BS-1

Freshwater

(3)

Rhodopseudomonas palustris

TIE-1

Iron-rich freshwater mat

(4)

Rhodovulum iodosum

N1

Marine sediment

(5)

Rhodovulum robiginosum

N2

Marine sediment

(5)

KoFox

Freshwater ditch

(6)

b

Chlorobium ferrooxidans

a

Mixed culture, highly enriched in Thiodictyon sp.

b

Defined co-culture with chemoheterotrophic ‘Geospirillum’ sp.

References: (1) Croal et al. 2004; (2) Ehrenreich and Widdel 1994; (3) Widdel et al. 1993; (4) Jiao et al. 2005; (5) Straub et al. 1999; (6) Heising et al. 1999

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photochemical oxidation of Fe(II) by UV light (Cairns-Smith 1978; Francois 1986; Anbar and Holland 1992) plays a major role in complex environments such as seawater. Until recently, BIFs were mainly considered the product of chemical or microbial oxidation of dissolved Fe(II) with O2 that was released by cyanobacteria during early oxygenic photosynthesis (Fig. 5)(e.g., Konhauser et al. 2002). Today, the anaerobic oxidation of Fe(II) by anoxygenic phototrophs is regarded as an alternative or additional explanation for the generation of BIFs (Fig. 5) (Widdel et al. 1993; Konhauser et al. 2002). Interestingly enough, in the literature it was speculated that anoxygenic Fe(II)-oxidizing phototrophs participated in the generation of BIFs even before such organisms had been isolated (Hartman 1984). A recent study considering rates of anoxygenic phototrophic Fe(II) oxidation under light regimes representative of ocean water at depths of a few hundred meters suggest that, even in the presence of cyanobacteria, anoxygenic phototrophs living beneath a wind-mixed surface layer provide the most likely explanation for BIF deposition in a stratified ancient ocean (Kappler et al. 2005).

Presence of O2

Absence of O2

Chemical or microbial Fe(II)-oxidation with cyanobacterial O2 O2+Fe2+ --> Fe(OH)3

Microbial Fe(II)-oxidation by anoxygenic phototrophs CO2+Fe2+ + hυ --> Fe(OH)3 + CH2O

Banded Iron Formation

Figure 5. Proposed mechanisms for the deposition of Precambrian banded iron formations in the presence or absence of molecular oxygen: oxidation of Fe(II) either indirectly by cyanobacterially produced O2 or directly by anoxygenic photosynthetic Fe(II)-oxidizing microorganisms.

Anaerobic Fe(II)-oxidizing nitrate-reducing bacteria Furthermore, it was discovered that microorganisms are capable of coupling oxidation of ferrous iron to dissimilatory reduction of nitrate (Hafenbradl et al. 1996; Straub et al. 1996). At pH 7, all redox pairs of the nitrate reduction pathway can accept electrons from ferrous iron because their redox potentials are more positive than that of the redox couple Fe(III)/ Fe(II) (Tables 4 and 5). The first observations of this metabolism were made with a lithotrophic enrichment culture that was transferred successively several times in medium that contained ferrous iron as sole electron donor (Straub et al. 1996). In this culture, ferrous iron oxidation coupled to nitrate reduction definitely supported cell growth; no oxidation of Fe(II) occurred in the presence of heat-inactivated cells or when nitrate was omitted. This type of metabolism is likely to be more abundant than ferrous iron oxidation by anoxygenic phototrophs since it is not restricted to habitats that are exposed to light. Furthermore, most-probable-number studies combined with molecular techniques indicated that the ability to oxidize ferrous iron with nitrate as electron acceptor is widespread among bacteria: members of the α-, β-, γ- and δ- subgroup of the Proteobacteria as well as gram-positive bacteria are probably able to oxidize ferrous iron (Straub and Buchholz-Cleven 1998; Straub et al. 2004). For these reasons, it was not surprising that enrichments of ferrous iron-oxidizing nitrate reducers were successfully established with a variety of marine, brackish or freshwater sediment samples. However, continuous cultivation

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with ferrous iron as sole electron donor Table 4. Redox potentials of redox pairs relevant turned out to be impossible for most of for microbial nitrate reduction at pH 7.0 and these enrichments. After a few transfers, 25 °C (Thauer et al. 1977). ferrous iron was oxidized only in the presence (of low concentrations) of an Redox pair E0′ [mV] organic substrate, e.g., 0.5 mM acetate. − − NO3 / NO2 +430 Accordingly, most Fe(II)-oxidizing nitrate reducers isolated so far need an NO2−/ NO +350 organic co-substrate for growth, i.e. NO/ N2O +1180 grow only mixotrophically with ferrous N2O/ N2 +1350 iron (Straub et al. 1996; Benz et al. 1998; Straub and Buchholz-Cleven 1998; Lack et al. 2002; Straub et al. 2004). For many Table 5. Redox potentials1 of some redox pairs of these strains that need an additional relevant for microbial reduction of iron oxides at organic substrate, it is questioned whether pH 7.0 and 25 °C (Thamdrup 2000). ferrous iron oxidation is beneficial and supports cell growth or whether iron is Redox pair E0′ [mV] just oxidized in a rather unspecific side 2+ reaction. Experiments with Azospira Fe5HO8·4H2O (ferrihydrite)/Fe +2 oryzae strain PS (formerly known as −88 γ-FeOOH (lepidocrocite)/Fe2+ Dechlorosoma suillum) undoubtedly −274 α-FeOOH (goethite)/Fe2+ showed that oxidation of ferrous iron 2+ initiated only after the organic (co−287 α-Fe2O3 (hematite)/Fe ) substrate was completely oxidized Fe3O4 (magnetite)/Fe2+ −314 (Chaudhuri et al. 2001). However, at 1 Slightly varying data can be found in the literature because least for some mixotrophically ferrous redox potentials strongly depend on pH, temperature, iron-oxidizing strains, i.e. Acidovorax concentrations of reactants, crystal size of the iron oxide and thermodynamic data chosen for calculations. sp. strain BrG1, Aquabacterium sp. strain BrG2 and Thermomonas sp. strain BrG3, the situation was more complex because the oxidation of ferrous iron seemed to be regulated. Only if electrons from the organic substrate exceeded those from ferrous iron by a factor of ten or if the concentration of nitrate was limited, ferrous iron oxidation ceased completely (Straub et al. 2004). Recently, some strains were isolated from the deep sea that oxidized Fe(II) with nitrate in the absence of an additional organic substrate. Unfortunately, it is not clear whether these strains can actually grow with ferrous iron as the sole electron donor for several successive generations (Edwards et al. 2003).

Mechanisms of microbial Fe(II) oxidation The mechanism of microbial Fe(II) oxidation has been studied best with the acidophilic Fe(II) oxidizer Thiobacillus ferrooxidans. According to a present model, Fe(II) is oxidized to Fe(III) at the outer membrane of the cell (Blake and Johnson 2000). The electron is then transferred to a copper-containing protein (rusticyanin) which in turn transfers it to a periplasmic c-type cytochrome. From such cytochromes, electrons are finally passed on to O2 via cytochrome oxidase to form water. The exact pathway of the electron transfer from ferrous iron to oxygen is still not completely understood, and slightly varying models are described in the literature. However, there is general agreement that the initial step, i.e. the oxidation of ferrous iron, occurs outside the cell (Blake and Johnson 2000). In addition, it was shown for neutrophilic aerobic Fe(II)-oxidizing Leptothrix spp. that the oxidation of Fe(II) is catalyzed by Fe(II)-oxidizing compounds that are actively secreted by

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the cell (De Vrind-de Jong et al. 1990); an Fe(II)-oxidizing protein with a molecular weight of 150 kDa was identified from spent culture medium of strain Leptothrix discophora (Corstjens et al. 1992). For anaerobic Fe(II) oxidation, it is unknown where in the cell or at the cell surface Fe(II) is oxidized, and it is not understood how the bacteria deal with the poor solubility of the product. In particular, it is unclear how Fe(II)-oxidizing microorganisms either avoid encrustation with ferric iron minerals (such as the phototrophic Fe(II)-oxidizer ‘Rhodobacter ferrooxidans’ strain SW2) or overcome encrustation such as the nitrate-reducing Fe(II)oxidizing strain BoFeN1 (Fig. 6). A microenvironment of lowered pH values in vicinity of the cells was observed around colonies of phototrophic Fe(II) oxidizers (‘Rhodobacter ferrooxidans’ strain SW2) fixed in semi-solid agarose (Kappler and Newman 2004). Such an acidification could explain why these microorganisms do not become encrusted with ferric iron minerals during oxidation of Fe(II) (Fig. 6). With the aerobic Fe(II)-oxidizing strain TW2, deposition of Fe(III) minerals was observed not at the cell surface but at a certain distance from the cells. It was suggested that Fe(III) was released in a ligand-bound dissolved form. The dissolved Fe(III)-ligand complex is thought to

A

B

500 nm

500 nm

Figure 6. Scanning electron micrographs showing (A) cells of the nitrate-reducing Fe(II)-oxidizing strain BoFeN1 highly encrusted with Fe(III) minerals and (B) anoxygenic photosynthetic Fe(II)-oxidizing microorganisms that are associated but not encrusted with Fe(III) minerals.

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diffuse away from the cells. Destabilization of the Fe(III)-ligand complex would finally lead to hydrolysis and precipitation of Fe(III) minerals distant from the metabolically active cells (Roden et al. 2004). The nature of the Fe(III)-ligand and the trigger necessary for destabilizing the dissolved Fe(III)-ligand complex are unknown so far. However, this hypothesis is supported by energetic calculations. The estimated biomass yield for growth was 0.15 mol cell-C per mol oxidized Fe(II), and hence approximately 7.5× more than experimentally observed in gradient cultures. This suggests that a substantial amount of energy is available for synthesis of other cellular components, including Fe(III)-binding ligands.

Formation of Fe(III) minerals by microbial Fe(II) oxidation Microbial oxidation of Fe(II) and precipitation of Fe(III) minerals might be better understood by comparing observations from microbial cultures to results from chemical Fe(II) oxidation experiments (e.g., Cornell et al. 1989). Mono- and dinuclear dissolved species of ferrous iron such as [FeOH]2+ and [Fe2(OH2)]4+ are formed initially during abiotic oxidation of Fe(II). Subsequently, these dissolved species transform into polymeric Fe(III) colloids before they precipitate as poorly crystalline ferrihydrite particles with a size of ~2–5 nm in diameter. Depending on the reaction conditions, the initial precipitation might be followed by further transformations of ferrihydrite. Either “solid-state conversion” to hematite (Fe2O3) by internal rearrangement of iron and oxygen atoms is induced or dissolution to low-molecular weight polynuclear iron species occurs which then transform to better crystalline iron oxides such as goethite (“dissolution-reprecipitation mechanism”) (Hansel et al. 2003; Schwertmann and Cornell 2003). Transformation of ferrihydrite to goethite via dissolution-reprecipitation could be facilitated in particular by enhanced proton activities close to cell surfaces. Lowered pH values and transformation of ferrihydrite to goethite were indeed observed in the vicinity of anoxygenic phototrophic Fe(II)-oxidizing bacteria (Kappler and Newman 2004). The formation of crystalline iron oxides during microbial Fe(II) oxidation might accelerate the speed of Fe(II) oxidation by an autocatalytic mechanism. Excess dissolved Fe(II) has a high affinity for surface-OH groups of iron oxides. These surface OH-groups are electron-donor ligands that increase the electron density of the adsorbed ferrous iron. An increased electron density stabilizes +3 charged iron better than +2 charged iron. Therefore, adsorption of Fe(II) on iron oxide surfaces increases the rate of Fe(II) oxidation (Wehrli et al. 1989; Elsner et al. 2003). An electron transfer from surface-adsorbed Fe(II) through the underlying iron oxide to the cell (where electrons could be accepted by outer membrane compounds) would abolish the need for the Fe(II)-oxidizing microbe to be in direct contact with the dissolved Fe(II). The first evidence for such an electron transfer between adsorbed Fe(II) and Fe(III) from the underlying ferric iron oxide was recently reported by Williams and Scherer (2004). Formation of a variety of different iron minerals by different Fe(II)-oxidizing microorganisms indicates that, apart from medium composition, concentration of possible co-substrates and incubation conditions, the mechanism of Fe(II) oxidation, metabolic rates and the presence of nucleation sites influence (and maybe even control) the mineralogy of the Fe(III) minerals produced. As an example, in a recent report polysaccharide strands were suggested to be extruded to act as a template for formation of akaganeite pseudo–single crystals (Chan et al. 2003)

Microbial dissimilatory reduction of Fe(III) Microbial reduction of ferric iron was known as a phenomenon for many decades before its (bio)geochemical relevance was recognized. It was presumed that microorganisms cause reduction of Fe(III) only indirectly, e.g., by lowering the redox potential or the pH. In addition,

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only few bacteria were known that transferred just few electrons to Fe(III) during fermentative growth (for details see Lovley 1991). This perspective changed notedly with the discovery of bacteria that respire ferric iron and thereby reduce substantial amounts of it (Balashova and Zavarzin 1979; Lovley and Phillips 1988; Myers and Nealson 1988). Today, it is generally accepted that dissimilatory ferric iron-reducing prokaryotes, i.e. organisms that gain energy by coupling the oxidation of organic or inorganic electron donors to the reduction of ferric iron, have a strong influence on the geochemistry of many environments (e.g., Lovley 1997; Thamdrup 2000).

Acidophilic Fe(III)-reducing microorganisms The ability to reduce Fe(III) to Fe(II) under acidophilic conditions seems to be widespread among acidophilic microorganisms, but the degree of Fe(III) reduction varies significantly (Johnson and McGinness 1991). Chemolithotrophic and heterotrophic prokaryotes (bacteria and archaea) are able to couple the reduction of Fe(III) to the conservation of energy. Interestingly enough, acidophilic iron reduction does not require strict anoxia in some strains and proceeds most rapidly even under microoxic conditions (Johnson et al. 1993). Studies with Acidiphilium sp. strain SJH showed that this bacterium is able to reduce a variety of different Fe(III) forms, with the highest reduction rates observed for dissolved Fe(III) (Bridge and Johnson 2000). Barely soluble poorly crystalline iron oxides (e.g., ferrihydrite) were reduced faster than better crystalline iron oxides (e.g., goethite). Apparently, Acidiphilium sp. strain SJH causes dissolution of ferric iron indirectly since direct contact between bacterial cells and solid ferric iron was not necessary for ferric iron reduction to occur. The strain appears to produce an extracellular compound that accelerates Fe(III) dissolution but not reduction. The nature of this extracellular compound and further details of the dissolution process are still unknown (Bridge and Johnson 2000).

Microbial reduction of Fe(III) at neutral pH In the past decade, numerous strains of dissimilatory ferric iron-reducing bacteria and archaea have been isolated from a vast range of habitats. A comprehensive list of Fe(III)reducing microorganisms was recently published by Lovley et al. (2004). The widespread occurrence of Fe(III)-reducing prokaryotes correlates with the ubiquitous presence of ferric iron. Many sediments and soils may contain ferric iron minerals in the range of 50-200 mmol per kg dry matter. Ferric iron is therefore often the dominant electron acceptor although it is barely soluble at neutral pH. According to experimental observations, Fe(III)-reducing microorganisms developed three different strategies to cope with the difficulty of transferring electrons from the cell to the surface of a barely soluble electron acceptor (Fig. 7) (reviewed by Hernandez and Newman 2001; Lovley et al. 2004): A. Physical contact between cell surface/cell surface compounds and ferric iron allows direct delivery of electrons. B. Iron chelators increase the solubility of Fe(III) and hence alleviate Fe(III)reduction. C. Electron-shuttling compounds transfer electrons from the cell to Fe(III) without the necessity of physical contact between cells and ferric iron. Considering the complexity of natural environments and the wealth of microbial capabilities, it is not surprising that different organisms as well as single organisms developed different strategies in order to reduce diverse Fe(III) compounds under varying conditions. For example, some evidence indicates that Shewanella algae and Geothrix fermentans produce and release both Fe(III)-chelators and electron shuttles (Nevin and Lovley 2002a; Lovley et al. 2004). Furthermore, evidence in Geobacter spp. indicates that different cellular compounds are involved in reduction of dissolved Fe(III)-citrate and barely soluble ferrihydrite (Straub

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A Fe(III)-mineral crust on soil particle

Fe(II)

B

Chelator

Chelator

Fe(III)

Shuttlered

Shuttleox

Fe(II)

C

Figure 7. Schematic illustration of different microbial strategies to transfer electrons to ferric iron: (A) Physical contact between cell surface and ferric iron allows direct delivery of electrons; (B) Iron chelators increase solubility of ferric iron; (C) Electron-shuttling molecules transfer electrons to ferric iron. Note: single crystals of ferric iron oxides might be smaller than bacteria (see Fig. 3). However, in nature iron oxides may form crusts on soil particles as depicted here.

and Schink 2004a, Leang et al. 2005). As the methods to study microbial mechanisms of Fe(III) reduction are pivotal, they will be discussed in the next section.

Methods to study mechanisms of microbial Fe(III) reduction Physiological studies of microbial ferric iron reduction at neutral pH are rather difficult. Low solubility of ferric iron is the most prominent obstacle. It impedes the monitoring of cell growth by means of optical density and the separation of cells from iron minerals by simple centrifugation. To circumvent this difficulty, iron chelators (e.g., citrate, EDTA) were applied in many studies to keep iron in solution. However, chelators change the redox potential of ferric iron, may enter the periplasm and can react unspecifically with electron-releasing cellular compounds (reviewed by Straub et al. 2001). In addition, there is growing awareness that culturing microorganisms in rich medium (in particular with other electron acceptors than ferric iron) may cause production of cell compounds which will not be produced under iron-reducing conditions in natural habitats (Glasauer et al. 2003). Caution in the interpretation of results is also necessary when supernatants were prepared either by filtration or centrifugation. Cells of Geobacter spp. were shown to artificially release compounds (e.g., cytochromes) by filtration with 0.2 µm filters as well as by centrifugation (Straub and Schink 2003). In other studies, semi-permeable membranes were used to separate cells and iron oxides physically in order to determine whether prokaryotic cells produce Fe(III)-chelators or electron-shuttling molecules. However, it was recently shown that Fe(III)-chelators and electron-shuttling molecules were unable to diffuse freely through dialysis membranes with the largest pore size available (Nevin and Lovley 2000). Therefore, results from studies with semi-permeable membranes need critical assessment, in particular when positive controls with known electron-shuttling molecules are lacking. To minimize artifacts that might be induced by centrifugation or filtration, further methods were developed to study production of Fe(III)-chelators or electron-shuttling

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molecules in vivo. In a simple one, ferric iron is entrapped in medium solidified with 1% agar (Straub and Schink 2003). Technically more elaborate is the use of iron containing microporous alginate (Nevin and Lovley 2000) or iron-containing glass beads (Lies et al. 2005).

Microbial mechanisms of Fe(III) reduction at neutral pH For Fe(III) reduction, species of the genus Geobacter appear to require physical contact to ferric iron oxides (Nevin and Lovley 2000; Lovley et al. 2004). The latest study with Geobacter sulfurreducens showed that pili (a special type of cell appendages) were produced during growth with poorly soluble Fe(III), but not with dissolved Fe(III)-citrate as electron acceptor. In addition, experiments with a pilus-deficient mutant implied that those pili were not just required for attachment of cells to ferric iron, and conducting-probe atomic force microscopy indicated that the pili were highly conductive. Together these results suggest that Geobacter sulfurreducens attaches and delivers electrons to the surface of ferric iron oxides via pili (Reguera et al. 2005). Initially it was thought that such a physical contact between Fe(III)-reducing prokaryotes and ferric iron minerals is mandatory for the delivery of electrons from the cells to the minerals. Today, it is generally accepted that Fe(III)-reducing microorganisms also use Fe(III)-chelators or electron-shuttling molecules to reduce barely soluble ferric iron oxides (e.g., Hernandez and Newman 2001; Rosso et al. 2003; Lovley et al. 2004). Diffusible chelators and shuttling molecules help to bridge spatial distance between cells and ferric iron oxides (Fig. 7). This is of particular importance since microorganisms and ferric iron oxides are not evenly distributed in natural environments. Plant root exudates and plant debris can release organic acids which are known to chelate Fe(III), e.g., oxalate or citrate. Accordingly, highly elevated levels of dissolved Fe(III) in the range of 20 to 50 µM were reported for soils in laboratory incubations with rice (Ratering and Schnell 2000). In comparison to the nM range of dissolved Fe(III) at neutral pH (Fig. 2), significantly elevated levels of dissolved, presumably chelated Fe(III) in the range of 4 to 16 µM were reported furthermore for freshwater sediment and groundwater samples (Nevin and Lovley 2002b). Plant debris is also the source for phenolic compounds and humic substances which can act as electron-shuttling molecules (e.g., Lovley et al. 1996, 1998). The oxidized form of an electron-shuttling molecule is used as the electron acceptor by the metabolically active cell. The electrons are then transferred from the reduced shuttling molecule in a chemical reaction to ferric iron. It is important that this chemical reaction regenerates the oxidized form of the shuttling molecule (Fig. 7). Prokaryotes that reduce ferric iron oxides only via electronshuttling molecules are not ferric iron-reducing bacteria in a strict sense as their electron acceptor is the shuttling molecule rather than ferric iron. In that respect it is worth mentioning that sulfur-reducing bacteria also can benefit from indirect reduction of ferric iron oxides via sulfur cycling, with sulfide as reductant of ferric iron (Straub and Schink 2004b). The impact of prokaryotes that reduce ferric iron oxides only indirectly with the help of naturally occurring electron shuttles on the total Fe(III) reduction in anoxic environments has not yet been evaluated. Finally, it is useful to discuss what advantages, if any are available to iron-reducing microorganisms that specifically produce and excrete Fe(III)-chelating or electron-shuttling molecules. For a single bacterium, production and release of such specialized molecules might be too expensive, in particular if such molecules are lost or degraded before the costs of biosynthesis have been compensated. However, in bacterial communities (e.g., biofilms, cell aggregates) such expenses might be balanced: each cell contributes just few chelator or shuttle molecules and the whole community benefits from the accessibility of ferric iron as electron

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acceptor. To date, no Fe(III)-chelating or electron-shuttling O compound that was specifically produced and excreted in C NH2 ferric iron-reducing cultures in vivo has been identified. N However, some evidence indicates that Shewanella algae and Geothrix fermentans produce and release both Fe(III)-chelators and electron shuttles (Nevin and Lovley 2002a; Lovley et al. 2004). Furthermore, it was recently N demonstrated that some antibiotics, e.g., phenazine-1Figure 8. Chemical structure of carboxamide (PCN), bleo-mycin and pyocyanine, function phenazine-1-carboxamide, PCN, as electron shuttles between bacteria and Fe(III) minerals a redox-active antibiotic produced (Hernandez et al. 2004). These redox-active antibiotics, by Pseudomonas chlororaphis exemplified in Figure 8 by PCN, structurally resemble that functions as electron shuttle between different microorganhumic substances with regard to aromaticity and redoxisms and ferrihydrite. active functional groups. Pseudomonas chlororaphis can transfer electrons to ferric iron oxides only due to the production and reduction of PCN. In addition to the PCN-producing strain, Shewanella oneidensis, Escherichia coli, Pseudomonas fluorescens, Pseudomonas aeruginosa and Vibrio cholerae were able to reduce PCN and thus indirectly reduce Fe(III) minerals (Hernandez et al. 2004). So far it is unknown whether the antibiotic-producing and/or antibiotic-reducing strains actually gain energy through this indirect Fe(III) reduction. It might just as well be a new microbial mechanism to acquire iron for assimilatory processes (compare to Kraemer et al. 2005). Interestingly enough, appreciable concentrations of phenazines in the range of 27 to 43 ng per g root (with soil) were found in the rhizosphere of wheat plants (Thomashow et al. 1990).

Microbial iron cycling Many reactions relevant to geochemistry are driven and/or accelerated by the activity of prokaryotes. Examples are manifold and include carbon mineralization, nitrogen fixation and sulfate reduction as well as iron transformations. In particular, prokaryotes that gain energy through oxidation of Fe(II) or reduction of Fe(III) have a strong influence on the global iron cycle (for details see Kraemer et al. 2005). For example, in most acidic aerobic environments, Fe(II) would persist if not oxidized by acidophilic Fe(II) oxidizers.

Microbial iron cycling under acidic conditions Understanding microbial cycling of iron at acidic pH has implications for the leaching of ores and the development of (bio)remediation techniques for acid mine drainage. Evidence for in vivo iron cycling was obtained from mining sites and was investigated in more detail in the laboratory (Johnson et al. 1993). Mixed cultures of acidophilic Fe(II)-oxidizing and Fe(III)reducing microorganisms cycled iron between the oxidation states +II and +III when the concentration of dissolved oxygen fluctuated and sufficient electron donor for Fe(III) reducers was supplied. Similarly, iron cycling could also be demonstrated in pure cultures since some acidophiles, e.g., Thiobacillus ferrooxidans and Sulfobacillus acidophilus catalyze both Fe(II) oxidation and Fe(III) reduction under appropriate conditions (reviewed by Johnson et al. 1993; Blake and Johnson 2000).

Microbial iron cycling at neutral pH Ferric iron is the dominant electron acceptor for the mineralization of carbon particularly in anoxic freshwater habitats (Thamdrup 2000). Therefore, processes that regenerate Fe(III) minerals that are again available for Fe(III)-reducing prokaryotes have become of significant interest. Since microbial Fe(III) reduction and Fe(II) oxidation were recognized, microbial

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cycling of iron seemed plausible and is hypothesized for many environments. For example, it was estimated that in marine coastal sediments each iron atom cycled approximately 100 to 300 times before being buried in the sediment (Canfield et al. 1993). However, the natural complexity of habitats aggravates direct measurements of microbial iron transformation reactions and thus microbial iron cycling in vivo has not yet been clearly demonstrated.

Prerequisites for microbial iron cycling at neutral pH Microbial iron cycling needs iron plus appropriate supplementary substrates, i.e., electron donors for Fe(III) reduction and electron acceptors for Fe(II) oxidation (Fig. 9). Furthermore, the nature of the iron minerals formed is crucial for an efficient cycling since not all iron minerals are equally good substrates. For instance, the redox potential of an iron redox couple determines whether it is available as electron donor or acceptor in terms of energetics (Table 5). At pH 7, molecular oxygen and all redox pairs of the nitrate reduction pathway (Fig. 4) can accept electrons from ferrous iron, independently from the Fe(III) mineral produced. The situation is more complex with ferric iron oxides as electron acceptor. The oxidation of acetate (CO2/acetate, E0′ = −290 mV) is energetically favorable just with iron oxides such as lepidocrocite or ferrihydrite. On the other hand, for the reduction of goethite, hematite or magnetite, electron donors with a lower redox potential are necessary, e.g., molecular hydrogen (2H+/H2, E0′ = −414 mV) or formate (CO2/formate, E0′ = −432 mV). Hence, theoretically acetate can fuel microbial cycling of iron only if ferrihydrite or lepidocrocite is the product of microbial Fe(II) oxidation. Furthermore, it is essential that supplementary electron donors and acceptors can diffuse since ferric iron is barely soluble and thus rather immobile in natural environments. The solubility of Fe(III) in equilibrium with ferrihydrite is in the range of 10−9 M (Fig. 2). The solubilities of goethite and hematite are even lower and the Fe(III) concentrations in the presence of these minerals is in the range from 10−10 M to 10−13 M (Kraemer 2004). In natural environments, the concentration of dissolved Fe(II) is controlled by adsorption or precipitation and is therefore insignificant in comparison to solid Fe(II). Dissolved Fe(II) adsorbs to soil particles, cell surfaces and also to the surface of ferric iron oxides (e.g., Liu et al. 2001; Cornell and Schwertmann 2003); in model calculations for a coastal sediment, adsorbed Fe(II) exceeded the concentration of freely dissolved Fe(II) 30fold (Van Cappellen and Wang 1996; Thamdrup 2000).

Oxygen-dependent microbial cycling of iron The product of microbial aerobic Fe(II) oxidation is often identified as poorly crystalline ferrihydrite, a ferric iron oxide that is a favorable electron acceptor for ferric iron-reducing prokaryotes. However, traces of oxygen may repress iron respiration in facultatively anaerobic Fe(III) reducers and can even inhibit the activity of strictly anaerobic ferric iron-reducing

Electron donor,

Fe(III)

e.g. N2

e.g. benzoate

e.g. CO2, H2O

Fe(II)

Electron acceptor, e.g. nitrate

Figure 9. Schematic illustration of microbial iron cycling.

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microorganisms, as shown e.g., for Geobacter spp. (Straub and Schink 2004a). Hence, oxygen-dependent microbial cycling of iron (most likely) always depends on a transition between oxic and anoxic conditions. In natural environments, such transitions are supported by temporary oxygen release by roots, bioturbation by burrowing and boring animals and mixing of sediments by waves or storm events. Of particular interest for oxygen-dependent iron cycling are microaerophilic Fe(II) oxidizers since they thrive in oxic-anoxic transition zones, allowing for microscale microbial redox cycling. Such oxic-anoxic transition zones are characterized by the simultaneous presence of ferrous iron which was produced during anaerobic Fe(III) reduction and of low concentrations of oxygen which reached this zone via diffusion from overlying oxic zones (Sobolev and Roden 2002).

Oxygen-independent microbial cycling of iron The identification of ferrihydrite as the primary product of anaerobic Fe(II) oxidation by phototrophs (Straub et al. 1999; Kappler and Newman 2004) or nitrate-reducing bacteria (Straub et al. 1996, 1998) indicated the possibility of anaerobic iron cycling. Biologically produced ferrihydrite has been shown to be an excellent electron acceptor for Fe(III)-reducing bacteria, which reduced it completely to the ferrous state (Straub et al. 1998, 2004). Similar to ferric iron, nitrate is used as electron acceptor only in anoxic zones after oxygen is depleted. In contrast to iron, nitrate is soluble at pH 7. Finally, it was feasible to show an anaerobic iron cycling in laboratory co-culture experiments (Straub et al. 2004). For these experiments, Fe(II)-oxidizing nitrate reducers were chosen that were unable to oxidize benzoate. As a counterpart, an Fe(III) reducer was selected that utilized benzoate with Fe(III) but not with nitrate as the electron acceptor. Only in experiments that were inoculated with Fe(II) oxidizers plus Fe(III) reducers was benzoate completely oxidized with nitrate in the presence of iron (Fig. 9). Although the transient iron phases in such co-cultures were not analyzed, stoichiometric considerations suggest that iron cycled 6 times between the oxidation states +II and +III in these experiments (Straub et al. 2004). Clearly, the relevance of anaerobic nitrate-dependent iron cycling for the complex flow of electrons in anoxic environments still needs to be determined. Microbial anaerobic iron cycling is possible with the participation of anoxygenic Fe(II)oxidizing phototrophs. Light-dependent, anaerobic cycling of iron may occur in top layers of shallow sediments that are reached by light or in (iron rich) microbial mats.

Environmental implications Microbial reduction of Fe(III) and oxidation of Fe(II) may have left geological imprints during Earth‘s history, and continues to significantly affect modern environments. Due to the considerable amount of iron in soils and sediments, Fe(III) usually represents the most abundant electron acceptor in anoxic soils and freshwater sediments; only in marine sediments is this dominance counterbalanced by the high sulfate concentration of seawater (Thamdrup 2000). Carbon cycling, mobility of micronutrients and in particular the degradation, transformation and (im)mobilization of organic and inorganic pollutants are closely linked in many environments to the microbial iron cycle.

Degradation of organic compounds coupled to dissimilatory Fe(III) reduction In pristine environments, Fe(III)-reducing microorganisms typically couple the reduction of Fe(III) to the oxidation of H2 or other fermentation products such as simple fatty acids or ethanol. Some ferric iron-reducing strains have in addition the ability to oxidize aromatic, organic pollutants such as benzene, toluene, ethylbenzene, phenol, p-cresol and o-xylene (e.g., Lovley et al. 1989; Lovley and Lonergan 1990; Lovley and Anderson 2000; Jahn et al.

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2005). If at contaminated sites ferric iron oxides are available for dissimilatory iron-reducing bacteria and other essential nutrients for microbial growth (e.g., nitrogen, phosphorous, sulfur) are present, microbial Fe(III) reduction has the potential to significantly contribute to the degradation of aromatic pollutants in a process termed ‘natural attenuation’.

Iron minerals as adsorbents Many ferric iron mineral surfaces are positively charged at neutral pH due to their high points of net zero charge (ZPC). The pH ZPCs are ~7.9 for ferrihydrite, ~8.5 for hematite and ~9.0–9.4 for goethite (Cornell und Schwertmann 2003). Such iron oxides therefore constitute good adsorbents for negatively charged compounds like phosphate (PO43−), bicarbonate (HCO3−) and oxyanions of toxic metal ions such as arsenate (AsO43−), arsenite (AsO33−) or chromate (CrO42−). Furthermore, negatively charged natural organic matter (humic substances) also binds strongly to ferric iron mineral surfaces (Stumm and Morgan 1996; Cornell and Schwertmann 2003). Anions were shown to adsorb to ferrihydrite surfaces via replacement of surface hydroxyl groups, leading to tight bonds of almost covalent character. For weak organic acids also an outer-sphere adsorption via weak electrostatic interactions was observed. Cations usually adsorb to iron oxides via hydroxyl-bridged inner-sphere complexes at the oxide surface. A comprehensive overview on adsorption processes on iron oxides is given by Cornell and Schwertmann (2003). Transformation of iron minerals and pH changes in the environment both influence the adsorption of cations and anions to ferric iron oxides. In some cases this has dramatic consequences, as in the well-documented example of arsenic: Arsenite and arsenate both strongly bind to ferric iron oxides (Dixit and Hering 2003). There is evidence from extended X-ray absorption fine structure (EXAFS) studies for inner sphere complexation but the nature of the surface complexes is still controversial (e.g., Waychunas et al. 1993; Shermann and Randall 2003) The microbially induced reductive dissolution of arsenic-loaded iron oxides is thought to play a key role in As-release into the groundwater, which leads to enormous drinking water contaminations observed in countries such as Bangladesh and India (Cummings et al. 1999; Smedley and Kinniburgh 2002; Islam et al. 2004; Harvey et al. 2005). In addition, arsenate can be released into the groundwater when high-surface area ferrihydrite transforms into hematite or goethite with significantly lower surface areas (Ford 2002).

Immobilization of toxic metal ions by microbial Fe(II) oxidation and Fe(III) reduction Reductive dissolution of metal-loaded iron oxides releases adsorbed metal ions into the environment. In contrast, Fe(II) oxidation can lead to the immobilization of toxic metal ions. Either co-precipitation during Fe(II) oxidation (Gunkel 1986; Richmond et al. 2004) or adsorption to synthetic or natural iron oxides potentially provides an applicable biotechnological method to remove toxic metal ions such as arsenic efficiently from drinking water. Natural removal of arsenic by iron oxides was observed when ferrihydrite was precipitated together with arsenic from arsenic- and iron-rich hydrothermal fluids (Pichler and Veizer 1999). In addition to mobilization of adsorbed compounds by dissolution of Fe(III) minerals or immobilization of pollutants by adsorption to or co-precipitation with biogenic iron minerals, iron-metabolizing microorganisms can also have a more direct effect on the fate of pollutants. For example, Fe(III)-reducing microorganisms were shown to convert toxic metal ions from more soluble forms (e.g., Cr(VI) and U(VI)) to less soluble forms that are likely to be immobilized in the subsurface (e.g., Cr(III) and U(IV)) (e.g., Lovley 1993; Lovley and Phillips 1992).

Formation of reactive iron minerals During microbial Fe(III) reduction, different minerals are formed depending on the chemical composition of the medium, on the substrate concentrations and on the incubation

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conditions (e.g., Roden and Zachara 1996; Lovley 1997; Fredrickson et al. 1998; Urrutia et al. 1999; Benner et al. 2002; Zachara et al. 2002; Hansel et al. 2003; Kukkadapu et al. 2004). In particular, the presence of different counter ions leads to the precipitation of different Fe(II) minerals, e.g., iron mono- or disulfides (‘FeS’ or FeS2), ferrous iron phosphate (vivianite), carbonate (siderite) or magnetite (Cornell and Schwertmann 2003). Also transformation of ferrihydrite to the more crystalline iron oxides hematite and goethite was observed during Fe(III) reduction (Hansel et al. 2003). Knowing the products of microbial Fe(III) reduction is quite important since the Fe(II)-species formed as the result of microbial Fe(III) reduction (either Fe(II) minerals or mineral-adsorbed and thus activated Fe(II)-species) can be efficient reductants in contrast to free aqueous Fe(II). They were shown to reduce organic contaminants such as nitroaromatic and chlorinated organic compounds (Hofstetter 1999) but also to reduce inorganic compounds such as U(VI) and Cr(VI) (Buerge and Hug 1999; Liger et al. 1999; Lovley and Anderson 2000; Jeon et al. 2005). Because different Fe(II)-species show different reactivities with respect to reductive pollutant transformation, understanding the mechanisms and conditions leading to different Fe(II)-species is necessary (Haderlein and Pecher 1999; Pecher et al. 2002; Elsner et al. 2003).

Some tasks for future investigations Prokaryotes that gain energy from iron redox transformations have a strong influence on the geochemistry of pristine or polluted environments. This was recognized only in the recent past and still needs to be studied in more detail. In particular the influence of the microbial cycle of iron on the global cycles of other elements, such as carbon, nitrogen or sulfur is not completely understood. The majority of ferrous iron-oxidizing and ferric ironreducing prokaryotes were isolated during the last decade. Therefore, it is not surprising that our knowledge of these prokaryotes and their iron metabolism is still in its infancy. One major task in microbial physiology is to explain how prokaryotes transfer electrons from or to iron minerals. In this context, the combination of physiology with molecular genetics to track the activity of certain proteins is very promising as recently summarized by Croal et al. (2004) and reviewed by Newman and Gralnick (2005). Anticipated genomic data from isolates (as described by Nelson and Methé 2005) and natural communities (as discussed by Whitaker and Banfield 2005) will assist in the identification of targets. Furthermore, the identification of Fe(III)-chelating and electron-shuttling molecules intentionally produced and released by prokaryotes is key to understand the biological and ecological importance of these postulated mechanisms. The advancement of different microscopic methods, e.g., cryo transmission electron or environmental scanning electron microscopy, will help to describe intimate interactions between microorganisms and iron minerals. Finally, consequences of microbial iron transformations for the fate of organic and inorganic pollutants have to be explored in more detail to better understand the process of natural attenuation and to foster remediation of polluted sites. An interdisciplinary approach as pursued in the emerging field of geomicrobiology which comprises such diverse fields as microbial physiology, molecular genetics, geochemistry and mineralogy will certainly help to answer many open questions.

Acknowledgments Parts of the work for this chapter were done by AK in Dianne Newman’s lab at the California Institute of Technology (Caltech) and by KLS in Bernhard Schink’s lab at the University of Konstanz (Germany). The electron micrographs were taken by AK at Caltech/JPL with the help of M. Chi and R.E. Mielke. We would like to thank B. Schink for reviewing the manuscript. AK is supported by an Emmy-Noether fellowship from the German Research Foundation (DFG).

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Reviews in Mineralogy & Geochemistry Vol. 59, pp. 109-155, 2005 Copyright © Mineralogical Society of America

Molecular-Scale Processes Involving Nanoparticulate Minerals in Biogeochemical Systems Benjamin Gilbert Earth Sciences Division Lawrence Berkeley National Laboratory 1 Cyclotron Road MS 90R1116 Berkeley, California, 94720, U.S.A. [email protected]

Jillian F. Banfield Earth and Planetary Sciences University of California Berkeley Berkeley, California, 94720-4767, U.S.A. [email protected]

INTRODUCTION Mineral particles with diameters on the scale of nanometers (nanoparticles) are important constituents of natural environments. The small size of such particles has a host of consequences for biogeochemical systems, which we will review in this chapter. We begin by briefly reviewing what is known about how and when nanoparticles form and the ways in which nanoparticles impact natural processes. Nanoparticles form via a variety of inorganic and biological pathways and may be introduced into the environment as a consequence of human activity. They are widespread in the environment (Banfield and Navrotsky 2001; Penn et al. 2001; Kennedy et al. 2003a,b; van der Zee et al. 2003), although few quantitative studies of their abundance are available. While all crystals begin as very small particles, an important subset retain small size at the Earth’s surface over relatively long time scales, because the combination of low temperature and low solubility inhibits growth. As a consequence, nanoparticles have the potential for a long lifetime in the environment, and widespread transport under certain circumstances. Processes that result in the removal of nanoparticles from an environment include dissolution, settling from air, transport in solution, and crystal growth. Particle aggregation may be an important component of these processes because it will promote settling, limit dispersal via solution transport, and can lead to aggregation-based crystal growth. The presence of nanoparticles can profoundly influence biological systems. Because they are frequently formed in environments that are populated by microorganisms, nanoparticles often adhere to cell surfaces or cell-associated polymers (see Fig. 1 for examples). These coatings can have important consequences for metabolic activity, for example, by restricting communication between the cell and its surroundings. They may also provide protection from predators, inhibit desiccation, screen cells from ultraviolet radiation, and alter the cell buoyancy (e.g., Tebo et al. 1997; Phoenix et al. 2001). The controlled precipitation of nanoscale minerals can lead to formation of integrated organic-inorganic structures with diverse morphologies. In some cases, the shapes provide a record of past microbial activity (i.e., serve as biosignatures, possibly by preserving cell 1529-6466/05/0059-0006$05.00

DOI: 10.2138/rmg.2005.59.6

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Figure 1. Microorganisms can produce copious quantities of nanoparticles. a). Field-emission scanning electron microscopy (SEM) micrograph of spherical aggregates of ZnS nanoparticles (with trace amounts of Fe, As, and Se) in a sulfate-reducing bacteria dominated biofilm growing on wood in neutralized acidmine drainage (Moreau et al. 2003). Draped over the aggregates is a dehydrated filamentous microbial cell of a morphology commonly observed in ZnS-dense regions within the biofilm. Image courtesy of John Moreau. b). High-resolution SEM image of a fractured sulfate reducing bacterium encrusted in aggregates of compositionally mixed ZnS and FeS nanoparticles (Williams et al. 2005). Inset: Higher magnification view of biomineral coating around a fracture cell. Images courtesy of Ken Williams. c). SEM micrograph of iron oxyhydroxide nanoparticle coatings on microbial structures from biofilms from the Piquette Mine, Tennyson, WI. The twisted stalks and cylindrical sheaths are characteristic products of Gallionella ferruginea (an iron oxidizer) and Leptothrix spp. (a putative iron oxidizer), respectively. Image courtesy of Susan Welch and Clara Chan. d). Transmission electron micrograph of aggregated UO2 nanoparticles adhering to the surfaces of sulfate reducing bacteria taken from the Midnite Mine, Washington, USA (Suzuki et al. 2002). Image courtesy of Yohey Suzuki.

morphology, as seen in the iron oxyhydroxide nanoparticle coatings on extracellular sheaths of Leptothrix, Fig. 1). Biominerals constructed from nanoparticles can be generated through deliberate action of the organism in order to create cell architecture (Mann 2000), and magnetic nanoparticles can serve a role in navigation (Bazylinski and Frankel 2004). However, in many cases the function is unclear or the particles may serve no function, and their existence is purely a byproduct of microbial metabolism (e.g., ZnS and UO2 nanoparticles, Fig. 1, and Mn(IV) oxides). Binding of nanoparticles to cell surfaces can also affect the fate and distribution of nanoparticles in the environment, by restricting or facilitating their transport, aggregationbased growth, and mineral transformation pathways. The interactions between nanoparticles, individual cells, and extracellular biomolecules can also act to bind biofilms together (Mayers and Beveridge 1989).

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Sources of nanoparticles in the environment A major pathway for nanoparticle formation in aqueous environments is the precipitation of sparingly soluble dissolved ions derived from both inorganic and biological processes. Precipitation is possible when the concentrations of ions in solution exceed the solubility of a mineral (e.g., see De Yoreo and Vekilov 2003). However, formation of a crystal nucleus is inhibited by an energy penalty associated with creation of a solid-liquid interface. Consequently, ion concentrations must generally exceed the saturation state of the solution for precipitation to occur. The relative energy cost associated with this interface is reduced as the particles grow, providing a driving force for coarsening. Materials that form ultra-small particles are frequently very insoluble with low energy barriers for nucleation. Examples include UO2, with a solubility product for bulk material logKsp ≈ −60, which forms 1–3 nm diameter particles (Suzuki et al. 2002; O’Loughlin et al. 2003); and ZnS, with a solubility product of logKsp ≈ −24, which typically forms 2–3 nm diameter particles (Labrenz et al. 2001; Moreau et al. 2004). In fact, a wide range of both natural and synthetic nanoparticles initially forms in this size range. Inorganic sources of environmental nanoparticles. A variety of inorganic pathways can lead to nanoparticle formation. Chemical weathering reactions that occur when minerals in rocks are exposed to water and air at the Earth’s surface liberate ions that can reprecipitate as nanometer-scale silicate clay minerals, oxides, oxyhydroxides, and phosphates. Common examples include smectite (e.g., montmorillonite (Na,Ca)(Al,Mg)6(Si4O10)3(OH)6⋅nH2O), anatase (TiO2), hematite (α-Fe2O3), and rhabdophane (CePO4⋅H2O). Aqueous clusters of metal sulfides and aluminum oxide are found in some lake and marine environments (Rozan et al. 2000; Casey and Swaddle 2003). Nanoparticulate minerals may nucleate at sites on another mineral surface (Stack et al. 2004). Additional inorganic sources of nanoparticles include impacts, combustion, vaporization (e.g., breakdown of meteorites as they enter the atmosphere), evaporation of sea spray, erosion, faulting, and other mechanical processes. Biological sources of environmental nanoparticles. Microbial activity is a major source of nanoparticles in the environment, and a summary of biogenic minerals is given by Frankel and Bazylinksi (2003). Central to microbial metabolism is the process of energy generation, which harnesses the free energy liberated as the result of coupled oxidation and reduction reactions. Reactions used for energy generation must be thermodynamically favorable, but are often kinetically inhibited. Organisms utilize enzymes to overcome these barriers and may speed up geochemical reactions by several orders of magnitude. In many cases, minerals can serve as either reductant or oxidant in microbial metabolism. In the presence of reduced organic carbon, oxygen or nitrate often serve as the electron acceptor for microbial carbon respiration. However, electrons can be passed to ferric iron, Mn(III)/IV, or sulfate when oxygen and nitrate are not available (Banfield and Nealson 1997; Konhauser 1998; Edwards et al. 2000; Ehrlich 2002). Uranium ions can also be biologically reduced. In either metabolic or co-metabolic processes (Lovely et al. 1991; Abdelouas et al. 2000; Fredrickson et al. 2000), electrons are passed from organic carbon to aqueous uranyl (UO22+) ions, resulting in the formation of insoluble uraninite (UO2) nanoparticles. Ferric iron and manganese minerals that act as electron acceptors are typically fine grained oxides that can dissolve upon reduction (Roden and Zachara 1996; Lovely 1997; Bratina et al. 1998; Quantin et al. 2001). In contrast, for sulfate reduction, aqueous sulfate ions are converted to HS−, which, in the presence of a suitable counter-ion, will precipitate as sulfide nanoparticles. Because reduction of sulfate is quantitatively linked to the oxidation of carbon, these metabolic pathways can generate copious quantities of nanoparticulate sulfides over short time periods (see Fig. 1a,b).

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In the absence of organic carbon for respiration and light for photosynthesis, both carbon fixation and energy generation can be driven by energy harvested from inorganic chemical reactions alone (Lovely 1993). In such chemoautotrophically based ecosystems, bacteria such as Thiobacillus ferrooxidans oxidize sulfide and/or ferrous iron that is present in pyrite (FeS2) (Fowler et al. 1999). The resulting ferric iron and sulfate ultimately precipitate, often as nanoparticulate iron oxyhydroxide (ferrihydrite or goethite, α-FeOOH) or iron sulfate compounds (schwertmannite, Fe16O16(OH)y(SO4)z⋅nH2O, and others). Anthropogenic nanoparticles. Industrial manufacturing and combustion processes can lead to nanoparticle emission into the air or into wastewater effluent. For example, numerous groups are seeking to manufacture stable water-soluble magnetic iron oxide nanoparticles as an injectable diagnostic agent for enhancing contrast in magnetic resonance imaging (Tartaj et al. 2003). Cerium dioxide nanoparticles are of interest as catalysts during combustion processes (e.g., in automotive engines), because of the capacity of the CeO2 lattice to buffer changes in oxygen partial pressures and oxidize carbon monoxide (Bekyarova et al. 1998). These nanoparticles may be released during manufacturing, use, or as the result of product disposal, and could ultimately accumulate in the environment in high enough concentrations to have important ecosystem impacts, such as accumulation in and toxicity for aquatic organisms (Oberdörster et al. 2005).

Impacts of nanoparticles on their surroundings The formation of nanoparticles can influence the local chemical environment in which organisms live. Nanoparticles sequester ions when they precipitate, and can decrease or increase the porosity and permeability of sediments. For example, formation of sulfide nanoparticles removes both metal ions (e.g., Cu, Zn, As, Cd, Fe) and toxic sulfide ions from solution, improving habitability of the environment. Nanoparticle precipitation and dissolution reactions can be sources or sinks for protons, and thus can influence environmental mineralogy through the impact of pH on mineral solubility (Langmuir 1996). For example, the precipitation of iron oxyhydroxide nanoparticles and dissolution of sulfide nanoparticles both lead to environmental acidification, promoting dissolution of surrounding minerals. The solubilization of manganese oxides may liberate adsorbed trace metals that provide nutrients in oligotrophic environments (Bratina et al. 1998). Nanoparticles, with their high surface areas, can play especially important roles in adsorption. For example, nanoparticles of iron oxyhydroxide formed during the neutralization of acidic, ferrous iron-rich solutions can sorb phosphate ions, possibly limiting biological productivity in some environments. Nanoparticle surfaces can also sequester protons or toxic ions such as arsenate (Waychunas et al. 2005).

Nanoparticles—special properties and implications Recently, a great deal of research has shown that nanoscale inorganic solids may exhibit substantially modified properties relative to their bulk counterparts (Alivisatos 1996; Murray et al. 2000; Trindade et al. 2001), with consequences for the inorganic and biological interactions of nanoparticles in the environment. As a foundation for studies of coupled geochemical processes involving nanoparticles, we examine some principles describing how size influences nanoparticle properties and reactivity. The present chapter introduces the physical and chemical consequences of small particle size in minerals, and discusses the effect small particle size has on redox and photochemical reactions. The concepts introduced here can be used for understanding the environmental impact and fate of both natural and engineered nanoscale materials.

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Overview of small size effects in minerals While molecular geochemistry has always been a “nano-science,” the science of nanoscale clusters and nanoparticles is distinct in an important way. The motivating tenet of contemporary nanoscience is that the chemical and physical properties of a solid inorganic material may vary as a function of particle dimensions below a critical size. The definition of the critical size depends both upon the material under consideration and the property of interest. It may refer to the start of a size dependent trend, such the onset of electronic confinement, or to an abrupt change, such as a switch in the thermodynamically stable structure of a mineral. We stress that, in almost all cases, the description of the unmodified (i.e., bulk) material provides an excellent guide to the properties of a nanosized material (even below a critical size). Thus, knowledge of the relevant solid state physics and chemistry for a given mineral is an indispensable foundation for understanding small-size effects. We emphasize, however, that important small size effects are not limited to modifications of nanoparticle structure and properties alone, which we denote as static effects. Static small-size effects in mineral particles include the stabilization of structural phases that are metastable for bulk materials, and the presence elevated strain and disorder, particularly at nanoparticle surfaces. Electrons within solids respond to shifts in the equilibrium positions of atoms, surface-solvent interactions, and the presence of a confining surface itself. The latter effect—quantum confinement—is striking in some materials, but (when present) is neither the only, nor always the dominant, size effect in mineral nanoparticles. A large number of surface effects (e.g., confinement, surface charge, solvent interactions, and surface reconstruction) modulate electronic structure. The kinetics of charge, energy, and material transfer also change at the nanoscale. Geochemical processes are dynamic, and significant size-dependent changes in reaction kinetics may affect processes in natural systems. The impact of nanoparticles on biogeochemical processes can depend on the kinetics of competing pathways. For example, the ability of photoexcited electrons in nanoparticles to reduce biomolecules is governed by the rates of several size-dependent processes that may enhance reactivity or dissipate energy without reaction. Furthermore, the products of various (photo)reduction experiments can be different for nano- versus micron-sized particles, indicating a size effect on reaction pathways (Müller et al. 1997). We start by discussing small-size effects in individual particles. However, nanoparticles are frequently observed to be intimately aggregated, with significant consequences for their behavior in biogeochemical systems.

PHYSICAL STRUCTURE AND COMPOSITION OF NANOSCALE MINERALS Thermodynamic constraints on the structure of nanoparticles One of the better-understood ways that size can influence the structure of nanomaterials is through interfacial energy effects on phase stability. This topic was reviewed in detail by Banfield and Zhang (2002) and Navrotsky (2002). In brief, when a compound can exist in more than one structural form (polymorph), a change in the relative stabilities of the structural variants may occur as the consequence of differences in their surface energies. This effect is only likely at small particle sizes, for which surface areas are large. For example, sizedependent phase stability may explain the precipitation of the hexagonal polymorph of ZnS (wurtzite) in place of the cubic form (sphalerite) in sediments or aqueous solutions, despite the

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higher stability of sphalerite compared to wurtzite in bulk materials up to temperatures over 1000 °C (Scott and Barnes 1972; Qadri et al. 1999; Zhang 2003). However, it should be noted that kinetic effects may also lead to the production of metastable phases (amorphous or highly disordered materials), especially in low-temperature systems where precipitation rates are fast (Schwertmann et al. 1999; Wolthers et al. 2003). It can be difficult to assess whether an observed nanophase is thermodynamically stable or metastable. It can also be difficult to evaluate the structure and natural abundance of amorphous or highly disordered materials.

The nature of the initial precipitates and subsequent aging For many minerals formed at low temperature or by biological processes, the initial precipitates are reported to be amorphous or highly disordered, possibly hydrated nanoparticles. Examples include amorphous iron sulfides, hydrous ferric oxide (HFO), and layered Mn(IV) oxides. These initial materials themselves do not grow beyond the nanoscale, and instead undergo crystallization, dehydration, or other transformations to mineral phases that may subsequently grow. It is widely thought that extracellular bacterial surfaces provide sites for heterogeneous nucleation that lower the free energy barrier for precipitation (Fortin et al. 1997; Warren and Ferris 1998). This model is widely used to describe the precipitation of ferric iron as a result of the activity of iron oxidizing bacteria (Douglas and Beveridge 1998; Ferris 2005). The model is challenged, however, by a recent study of Rancourt et al. (2005), on the precipitation of HFO in the presence of nonmetabolizing bacteria. They showed that while Fe(III) ions adsorb onto functional groups on bacterial surfaces, they remain external to the structure of HFO particles that subsequently form. Rancourt et al. argue convincingly that both inorganic and biological HFO formation always occurs via fast homogeneous precipitation. There is also uncertainty as to the role of biological factors in affecting transformations that occur in nanoparticles after precipitation. While aqueous conditions (solution chemistry, pH, temperature), rather than their inorganic or biological origins, should govern the evolution of mineral precipitates (Konhauser 1998), comparative studies of the structure and reactivity of biominerals versus synthetic analogs frequently reveal differences. The presence of organic matter is thought to be a crucial factor (Ferris 2005). Furthermore, microbial metabolism frequently generates aqueous ions (e.g., Fe(II), or small organic molecules) that can interact with and stabilize or destabilize mineral surfaces during growth, phase transformation, and dissolution (Cornell and Schwertmann 1979; Urrutia et al. 1999; Davis et al. 2000; Thomas et al. 2004). One interesting biological effect is the observation that the association of 2-line ferrihydrite nanoparticles with bacterial cell walls confers significant stability to the mineral against hydrothermal coarsening and transformation into hematite (Kennedy et al. 2004). The stabilization effect is observed with both biogenic and abiotic ferrihydrite in the presence of bacteria. It is inferred that the nanoparticles principally grow by an aggregation-based pathway that is hindered when the particles are immobilized on cell walls. Orientated aggregation (OA) is a significant pathway for nanoparticle growth in which individual particles achieve a common crystallographic orientation (Penn and Banfield 1998). Subsequent elimination of the interfaces between oriented particles can generate larger single crystals, some of which may have unusual morphologies and properties (Banfield et al. 2000). An example of joined UO2 nanoparticles is given in Figure 4.

Size dependence of mineral solubility From the point of view of biogeochemical systems, one of the most important sizedependent materials properties is solubility. As can be seen from the following equation

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(Stumm and Morgan 1996), solubility increases as particle size decreases because the interfacial energy, γ, in J m−2, is a positive contribution to the total energy. ’ log K sp = log K spBULK +

2 3γ S 2.3RT

(1)

In this equation, S is the surface area per mole, R = 8.314 J mol−1 K−1 and T is the temperature in K. For ZnS, this equation predicts that the solubility of 3 nm particles of wurtzite is an order of magnitude higher than for bulk material at room temperature, assuming an effective surface area of ~100 m2/g, log KspBULK = −22.85 (Stumm and Morgan 1996) and γ ≈ 0.5 J m−2 (Zhang et al. 2003). Sphalerite nanoparticles of the same dimensions are predicted to be two orders of magnitude more soluble, assuming log KspBULK = −24.83and γ ≈ 0.8 J m−2. The accuracy of Equation (1) for quantitative predictions is limited. First, it assumes that interfacial energy is not itself size dependent, an assumption that has been questioned previously (Zhang et al. 1999). Second, accurate measurements of the interfacial energy are frequently unavailable, especially for hydrated surfaces that may additionally be coated by organic molecules. More realistic descriptions of the solubility of natural particles as a function of size, phase, and the surface chemical environment are needed.

Characterization studies of biogenic nanoparticles We briefly review recent studies of the structure of important biogenic minerals. Precise structural characterization of biogenic nanoscale materials is challenging due to the small particle size, presence of disorder, and difficulties in isolating mineral precipitates from biomass. Powder X-ray diffraction (XRD) can be a convenient approach for determining nanoparticle structure, but has limitations. First, the width of peaks in diffraction patterns depends inversely upon the number of unit cells of material that make up the diffracting atomic planes. When the number of unit cells is very small (as in nanoparticles), peaks become extremely broad, obscuring structural details and the resulting diffraction pattern resembles those from materials with only short-range order. Given these considerations, structure analysis based upon Bragg’s law can be inaccurate at the smallest particle sizes (Palosz et al. 2002). Direct simulation of diffraction patterns from small structural models can be a more accurate method (Drits and Tchoubar 1990). Furthermore, when nanoscale particles possess significant disorder, XRD-based methods of particle size determination (e.g., via Scherer’s equation; Patterson 1939) are extremely inaccurate (Wolthers et al. 2000). Pair distribution function (PDF) analysis of short-range order in nanoparticles is a promising extension of the powder XRD method (Gilbert et al. 2004; Billinge and Thorpe 1998). Nanoparticle imaging and structure analysis may be performed in the electron microscope by high-resolution transmission electron microscopy (HRTEM) and selected area electron diffraction (SAED). Bright, energy-tunable X-ray sources (synchrotrons) offer additional methods of structural and chemical analysis, including extended X-ray absorption fine structure (EXAFS), and X-ray absorption near-edge structure (XANES) spectroscopies. XANES spectra can be acquired with lateral resolution in scanning transmission X-ray microscopy (STXM) and X-ray photoelectron emission microscopy (X-PEEM). In addition, laboratory-based Mössbauer spectroscopy provides information on the chemical environment of the 57Fe nucleus. Iron oxides and oxyhydroxides. Biological and inorganic processes can lead to the precipitation of hydrous ferric oxides, as discussed above. Subsequent mineral transformations can produce nanocrystalline iron oxides including goethite or hematite (Benner et al. 2002; Hansel et al. 2003). Many environmental factors affect the mineral evolution. For example, low-temperature aging in the presence of water favors goethite formation; hematite is preferentially formed in warmer, dry environments (Cornell and Schwertmann 2003).

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Magnetite (Fe3O4) is a common and mineralogically significant nanocrystalline iron biomineral product of the biological reduction of ferric iron. Dissimulatory iron reducing bacteria (DFeRB) utilize Fe(III) as an electron acceptor for the reduction of organic carbon (Lovely 1987; Bazylinski and Moskowitz 1997). HFO and crystalline ferric iron phases can provide a bioavailable source of ferric iron (Roden and Zachara 1996). Fe(II) released from the mineral surface can re-adsorb onto unreacted HFO, forming mixed-valence green rusts that age into nanoparticulate and poorly crystalline magnetite (Lovley et al. 1987; Sparks et al. 1990; Hansel et al. 2003). Alternative Figure 2. Thin hydrated surface layers can be ferrous iron-bearing minerals may be nanosized products of microbial metabolism. A formed, depending upon the aqueous flow thin (< 1 nm) layer of hydrated magnetite (Fe3O4) rate, the relative concentration of HFO forms on the surface of colloidal goethite particles and the organic electron donor, and the as a result of dissimilatory iron reducing bacterial presence of additional solution species metabolism. [Used by permission of Elsevier from Hansel et al. (2004) Geochimica et Cosmochimica such as phosphate and carbonate, and Acta, Vol. 68, Fig. 7e, p 3217-3229.] ferrous iron complexants (Urrutia et al. 1999; Roden et al. 2000; Fredrickson et al. 2003; Hansel et al. 2003). The rate of ferric iron reduction by Shewanella putrefaciens is correlated more with the surface area of the ferric iron substrate than the solubility of the particular ferric iron phase (Roden and Zachara 1996). However, differences in the solubility of crystalline (goethite and hematite) versus disordered (HFO) materials does affect the quantity and form of magnetite produced. Magnetite formation during the bioreduction of goethite and hematite is apparently limited to approximately 1 nm thick rinds on the initial ferric iron particles (Hansel et al. 2004), as shown in Figure 2. Similar mineral transformations are produced by Fe(II) adsorption onto synthetic iron oxides (Tronc et al. 1984, 1992). Manganese oxides. Numerous microorganisms can enzymatically or indirectly facilitate the oxidation of aqueous Mn(II) by O2 at far higher rates than inorganic pathways, although the biological function(s) of this activity is still unclear (Tebo et al. 1997, 2004). Two recent studies of fresh biogenic material (from the bacterium Pseudomonas putida and from spores of the marine Bacillus sp. strain SG-1) reached similar conclusions on the structure of the first-formed product (Villalobos et al. 2003; Bargar et al. 2005). The material is a hexagonal phyllomanganate assembled from stacked layers of Mn(IV) octahedra with considerable rotational stacking disorder (turbostratic disorder), plus a high negative structural charge due to Mn(IV) vacancies. (For a complete introduction to the complex crystal chemistry of manganese oxide materials, see Burns and Burns 1979, Villalobos et al. 2003, and Tebo et al. 2004). These freshly formed biogenic minerals contained very little Mn(III). By contrast, aged biogenic oxides (Bargar et al. 2005) and Mn(IV) oxides found in soils were observed to have vacancies or Mn(IV) substitution by Mn(III) or heterovalent cations (Fig. 3b) (Isaure et al. 2005; Manceau et al. 2005). Mn(III) incorporation follows the autocatalytic oxidation of surface-sorbed Mn(II), which is an important factor in abiotic transformation of the biogenic mineral. The precipitation and aging of biogenic Mn oxides is depicted in Figure 3a. Sulfides. Transition metal sulfide minerals are common products of the metabolism of sulfate-reducing bacteria. In the case of iron sulfides, the first formed material is thought to be

Molecular-Scale Processes Involving Nanoparticulate Minerals

Figure 3. a). Scheme of the biogenic oxidation of Mn(II), the formation of disordered MnO2 and subsequent aging to layered Mn(IV) and Mn(III) mineral products in the presence of aqueous Mn2+. [Reprinted from Bargar et al. 2005.] b). Layered Mn(IV) oxides found in soils are commonly found to host Mn(III) in octahedral coordination and can incorporate Zn in either tetrahedral or octahedral coordination. The presence of lower valence Mn or other elements will have the potential to introduce electrons into the semiconducting MnO2 sheet. [Used by permission of Elsevier from Isaure et al. (2005) Geochimica et Cosmochimica Acta, Vol. 69, Fig. 7f, p. 1173-1198.]

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amorphous, but further growth and mineral transformation leads to the production of numerous crystalline phases (Benning et al. 2000; Wolthers et al. 2003). Observed biogenic iron sulfide minerals include mackinawite (tetragonal FeS), pyrite (cubic FeS2), marcasite (orthorhombic FeS2) greigite (Fe3S4) and pyrrhotite (Fe7S8). The exact mineral product is highly dependent on solution chemistry (Benning et al. 2000). It has been proposed that the sorption of ferrous iron to functional groups on cell surface membranes provides sites for heterogeneous nucleation, but as discussed above, sorption and precipitation processes may be independent. Iron sulfide minerals including FeS and FeS2 are generally found to be sulfur deficient (Luck et al. 1989), but it is not known whether this is enhanced or suppressed in nanoscale particles. Disordered biogenic phases concentrated by magnetic separation were reported to have a high capacity for cation sequestration (Watson et al. 2000). Disordered nanocrystalline mackinawite exhibits an expanded lattice that incorporates water molecules, and possibly hydroxyl groups, cation impurity atoms, and sulfur vacancies (Wolthers et al. 2003).

Even in the presence of ferrous iron, alternative minerals may precipitate if thermodynamically favored. Labrentz et al. (2000) observed that highly pure ZnS nanoparticles are formed in close proximity to sources of dissolved ferrous iron. A common feature of biogenic ZnS nanoparticles is the presence of stacking faults and wurtzite nanoparticles, probably reflecting the low free-energy difference between the sphalerite and wurtzite phases (Figure 4, and see Moreau et al. 2004). Interestingly, recent studies on synthetic mixed-phase ZnS nanoparticles have indicated a significant effect on the profile and size of the semiconducting band gap (H. Zhang, personal communication).

Other minerals. The reduction of U(VI) in the aqueous uranyl ion (UO22−) to form insoluble uraninite (UO2) nanoparticles can take place as a by-product of microbial metabolism involving sulfate (Fig. 4; Suzuki et al. 2002) or iron reduction (Lovely et al. 1991; Fredrickson et al. 2000). Uraninite nanoparticles can also form abiotically following the reduction of uranyl by ferrous iron (O’Loughlin et al. 2003). Suzuki et al. (2002) used HRTEM to document the presence of biogenic UO2 nanoparticles as small as 1 nm in diameter, and EXAFS spectroscopy to infer that the average particle size is ~1.5 nm. Given the presence of 2–4 nm particles, this result suggests that ultra-small particles or molecular clusters are abundant. The U-O bond length exhibited a 0.007 nm contraction

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Figure 4. Left. High resolution transmission electron microscope (HRTEM) image of biogenic ZnS nanoparticles formed by sulfate reducing bacteria (SRB). The upper nanoparticle has the wurtzite ZnS structure. The lower ZnS nanoparticle contains a mixed sphalerite-wurtzite stacking sequence. [Reprinted from Moreau et al. (2003).] Right. HRTEM of UO2 nanoparticles formed by SRB. Two joined UO2 nanoparticles show a characteristic necking profile indicating that oriented aggregation is an active growth pathway (top left). Image courtesy of Yohey Suzuki.

relative to bulk UO2, indicating the presence of significant surface strain, and an associated increase in solubility. By estimating a relationship between surface stress and interfacial energy, and assuming negligible size-dependent changes in compressibility, this observation was used to predict a billion-fold increase in the solubility of biogenic uraninite relative to the bulk mineral. The uncertainties in the assumptions required for this calculation emphasize the need for additional studies on nanoparticle structure, elastic properties, and solubility.

The effects of water and other surface-bound molecules on nanoparticle structure As the medium in which minerals form, water itself is a key player at every stage in the precipitation and evolution of nanoparticles at and near the Earth’s surface. Rapid precipitation pathways frequently lead to structural incorporation of water molecules. While the interactions between water and bulk mineral surfaces have been an area of intense study (Hochella and White 1990; Henderson 2002), there are relatively few structural or calorimetric studies on solvent interactions with nanoparticle surfaces (but see Navrotsky 2004). Nevertheless, it has been shown that nanoparticle surface interactions with water can be strong and decisive in stabilizing particular mineral structures. Diffraction studies in controlled environments have shown that surface hydration is an important factor in the surface and interior structure of ZnS and γ-Fe2O3 nanoparticles (Zhang et al. 2003; Belin et al. 2004). In these examples, changes in surface hydration caused dynamic structural responses in the nanoparticles. Zhang et al. (2003) demonstrated that adsorption of water drove a structural transformation in ZnS nanoparticles at room temperature without any change in particle size. The waterdriven reaction was not reversible. However, the desorption and re-adsorption of a more volatile solvent (methanol) does cause reversible structural changes, and it is inferred that the activation barriers for structural rearrangements in these nanoparticles are small enough that they can be overcome by surface interactions at room temperature. The findings from the study of ZnS nanoparticles do not imply that natural nanoparticles will always find their energy minimum state, given their size and surface chemical environment.

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But they do emphasize that the dynamic response of the atoms in a nanoscale particle will permit this under certain circumstances. Other groups have sought a description of the phase stability of mineral nanoparticles, based upon surface interactions in the framework of equilibrium thermodynamics. It was recently argued that, because of the high surface area of nanoparticles, the adsorption of hydroxyl groups or oxyanions onto HFO nanoparticles represents a significant change in the stoichiometry, one that can affect the thermodynamic stability of the nanoparticle-sorbate system (Fukushi and Sato 2005). Vayssiéres et al. (1998) interpreted the pH dependence of the phases of iron oxide nanoparticles synthesized with aqueous methods to indicate that the enthalpy of surface adsorption is a governing thermodynamic contribution. Lodziana et al. (2004) have used molecular simulations to describe the very high stability of the hydroxylated θ-Al2O3 (110) surface as a thermodynamically stabilized (i.e., negative interfacial energy) material. However, such characterizations remain controversial, because in the absence of clearly reversible transitions it is difficult to assess whether the observed material is the true thermodynamically stable state. The observations that surface ligands can direct the structure of a nanomaterial may be directly relevant to low-temperature biogeochemical systems. In these environments, small mineral particles are closely associated with solute molecules including small organic compounds, and high molecular weight polymers including proteins and polysaccharides. These compounds may represent a biological influence on the structure of nanoparticles in aqueous systems. In general, molecular simulations that have incorporated realistic surfacesolvent or surface-ligand interactions have revealed strong interactions that stabilize both the surface and interior structure of nanoparticles (Pokrant and Whaley 1999; Rabani 2001; Zhang et al. 2002; Kerisit et al. 2005).

Incorporation of impurity atoms Contaminants (e.g., Zn, As) and nutrients (e.g., phosphate) are readily adsorbed on, coprecipitated with, and incorporated into mineral nanoparticles (Watson et al. 2000; Gunnars et al. 2002; Hochella et al. 2005a,b) and (photo)redox cycles of nanoscale iron and manganese oxides and oxyhydroxides can cause the incorporation and release of these species (Isaure et al. 2005). Since it is widely observed that the availability and transport of aqueous ions is highly correlated to their interactions with colloidal particles (Kimball et al. 1995; Brown et al. 1999), important aspects of nutrient and contaminant biogeochemistry depend on their association with mineral nanoparticles. Furthermore, the reactivity of the nanoparticles themselves can be strongly modified by the incorporation of even low concentrations of impurity atoms. For example, the incorporation of 0.2 mol% Zn into pyrite drastically affects surface photochemical behavior, with no detectable structural modifications (Büker et al. 1999). As explained below, impurity atoms can introduce additional electronic energy levels that can affect mineral reactivity. An understanding of the factors controlling impurity atom incorporation in nanoparticles remains incomplete, both in the materials and earth sciences. As with bulk materials, the ability of nanoparticles to host impurities depends on the structural characteristics of the solid, the size and charge discrepancy between the impurity and intrinsic ion, and the response of the structure to the structural perturbation associated with ion incorporation (Cornell and Schwertmann 2003; Fistul 2004). Careful combined X-ray absorption, X-ray diffraction and structure modeling studies are required to elucidate the crystal chemistry of incorporated atoms in disordered minerals (Isaure et al. 2005; Manceau et al. 2005) Defects such as ion substitutions or vacancies have an associated enthalpy from which their mean concentration can be calculated, assuming thermodynamic equilibrium. However,

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distributing the same number of defects in nanoparticles rather than bulk material would result in the vast majority of nanoparticles being defect-free. Furthermore, there may be an impurity exclusion effect, as solid-state diffusion times to the surface are likely to be quite short (Tang et al 2003). Consequently, the presence of detectable impurity concentrations in nanoparticles is likely to indicate favorable kinetic pathways, rather than thermodynamic equilibrium. An important advance in modeling and predicting impurity incorporation is the recent proposal that it is the affinity of dissolved impurity ions for the surface of a growing nanoparticle that is the key factor determining eventual incorporation (Erwin et al. 2005; commentary by Galli 2005). The excess energy of the impurity once it is in the host lattice is not the dominant factor. Rather, it is the rate of impurity adsorption and desorption at specific surface sites relative to nanocrystal growth kinetics. The implication is that the fast precipitation reactions that follow microbially mediated changes in the oxidation state of environmental ions such as iron, manganese and sulfur might be effective at scavenging reactive aqueous species. There is considerable empirical evidence for significant impurity concentrations in natural nanoparticles. For example, disordered Mn(IV) and Fe(III) oxides and oxyhydroxides are commonly observed to account for the majority of transported contaminant ions (Hochella et al. 2005a,b). Certain impurity atoms can affect the phase stability or enhance the crystallinity of biominerals (Davis et al. 2000; Webb et al. 2005). It is of interest to determine whether the miscibility of substances that form solid solutions is affected by small particle size. For example, bulk ferric iron oxides can accommodate homovalent cation substitution (e.g., Cr3+ and V3+) to 5–10 mol% (Schwertmann et al. 1989; Schwertmann and Pfab 1994), and bulk ZnxFe1−xS forms a solid solution for all values of x (Vaughan and Craig 1978). There are presently no investigations into the stability of these materials as nanoparticles. Most effort has been directed to the production of technological materials. In particular, the introduction of Fe3+ and Cr2+ into TiO2 nanoparticles creates photocatalysts with higher yields (Zhang et al. 1998; Bryan et al. 2004), and the doping of Mn2+ into ZnS nanoparticles enhances their luminescence efficiency (Yu et al. 1996).

The surfaces of nanoscale minerals The surfaces of materials have many distinct structural and electronic properties relative to the bulk or the interior. Surfaces host, mediate, or participate in all relevant biogeochemical processes involving minerals. Decades of surface science provided considerable knowledge about mineral surface structure and surface chemical interactions. However, all such work has been performed on the surfaces of bulk minerals, and it can be argued that the surface science of nanoscale minerals is just beginning. It might be reasonable to expect, a priori, that the constituents of nanoparticle surfaces will include facets and defects (such as edges or vertices) very similar to those found on bulk mineral surfaces, perhaps with a few additional types. However, the results of certain experimental and theoretical studies indicate that this model may be seriously misleading. Zinc sulfide nanoparticles in the 1–5 nm diameter size range are observed to possess significant interior distortion, which is inferred to be provoked by extreme reconstruction at the surface (Gilbert et al. 2004). This model is supported by molecular dynamics calculations showing highly distorted surfaces that, because of high surface curvature, bear little resemblance to facets of bulk minerals (see Fig. 5). Large-scale molecular simulation of goethite nanoparticles within a dissociating model of water, and with dimensions below 10 nm, accumulate protons at highly charged edge sites to a far greater extent than is anticipated from models with simple slab geometries (Fig. 5) (Rustad and Felmy 2005). Nanoparticle surfaces may additionally be stoichiometrically distinct from the interior, as has been determined recently for the surfaces of bulk hematite (Glenn Waychunas, personal communication).

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Figure 5. Molecular modeling studies predict that the surfaces of mineral nanoparticles will exhibit local structural and charge that are distinct from the surfaces found on bulk materials. Left. A cross-section through a molecular dynamics (MD) simulation of the structure of a fully hydrated ~ 3 nm diameter ZnS nanoparticle. No recognizable facets are present on the high curvature surface. Zinc (sulfur) atoms are black (gray); water molecules are white. MD simulation (unpublished) performed by H. Zhang. Right. MD simulations of hydrated goethite nanocrystals predict high protonation along edges at the high angle intersections of (110) faces. The inhomogeneous surface charge distribution could not have been predicted from experimental or theoretical studies of bulk surfaces, and can dominate the effective charge of nanoparticles with certain morphologies. The high edge protonation is favored by ready solvation at high angle edges (high dielectric sites) and by the local high bacisity of edge Fe-O sites (sites labeled 1–3). [Used by permission of Elsevier from Rustad and Felmy (2005) Geochimica et Cosmochimica Acta, Vol. 69, Fig. 6, p. 1405-1411.]

Also, partial transformation caused by surface oxidation or reduction can lead to the formation of maghemite (γ-Fe2O3) layers on magnetite surfaces, and magnetite films on HFO or hematite (cf. Fig. 2) (Hansel et al. 2004). Studies of the mechanism and geometry of adsorbate binding to nanoparticle surfaces are one approach to compare the surfaces of bulk and nanocrystalline minerals. Mercury, arsenic, and copper are known to attach to the surface of goethite (α-FeOOH) via inner sphere coordination and thus are suitable test sorbates for goethite nanoparticles. The coordination environment of Hg adsorbed to 5 nm diameter goethite nanoparticles is modified relative to sorption sites on larger particles, with an expansion of the 2nd and 3rd shell He-O and Hg-Fe distances (Waychunas et al. 2005). However, no significant size-dependent changes in the sorption geometry of As(V) or Cu(II) were detected. At present, there is no general framework for describing nanoparticle surfaces, and both practical and theoretical approaches require considerable development. Modern X-ray methods that probe the structure of water and the chemistry at mineral surfaces must be adapted to the surfaces of nanoparticles (Eng et al. 2000; Cheng et al. 2001).

ELECTRONIC STRUCTURE OF NANOSCALE MINERALS Introduction to electronic structure of solids Electronic structure is key to the reactivity of materials. The electrochemical potential of electrons in solids drives chemical reactions that underpin many of the Earth’s biogeochemical

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cycles. Thus, we begin this section with an introduction to the electronic structure of solids in a form that will permit size-dependent effects and molecular scale reactivity to be addressed. The quantum theory of solid-state materials gives the best quantitative description of electrons in solids and is presented in numerous texts (Ashcroft and Mermin 1976). It is briefly recapped here with an emphasis on the analogies between bonding in molecules and in periodic crystals. Approximate descriptions of electrons in solids. The enormous number of atoms and electrons in solids renders exact solutions of electronic and nuclear motion utterly intractable, and requires drastic simplifying assumptions. The following are two of the most important. First, the nuclear motion is considerably slower than electronic motion, and hence the nuclei are assumed to be static during calculations of electronic structure. Second, the behavior of only a single electron is considered, with an averaged description of the influences of all other electrons and nuclei. Within the framework of this single-electron approximation, Schrödinger’s Equation can be written:

[ Ek + V (r)] ψi (r) = Eiψi (r)

(2)

Equation (2) relates the total energy, Ei, and wavefunction, ψi(r), of an electron in a state i to its kinetic energy, Ek, and to the potential, V(r), within which it moves. The potential energy term principally contains the electrostatic potential of each atomic site, and defines the energy landscape in which the electron moves. An important additional contribution to V(r) describes (approximately) the correlated interactions with all other electrons. Collectively, the energy terms are called the Hamiltonian, H, which completely defines the possible electronic states for a given system. The wavefunction is the most complete description attainable for a quantum particle and represents the probability of finding the particle within an infinitesimal volume of space. The simplest model of size effects on electronic structure relies on the description of an electron traveling freely in space. For a given electron momentum, described using the wave vector k, the energy of a free electron is given by E=

2 k 2 2 me

(3)

where me is the mass of the electron and  = h/2π, where h is Planck’s constant. For numerous materials, the electrons that participate in chemical reactions can be considered as free electrons, but with a modified effective mass, me*. The effective mass captures the effect of the periodic structural environment on the propagation of wave-like electrons within the material. Assuming that the mobility of an electron in a material does not vary with particle size, me* is obtained from a first-principles calculation of the electronic structure of a bulk material (i.e., by obtaining the solutions of Eqn. 2). Solutions of Schrödinger’s equation. There are two important concepts that permit effective solution of the single electron Schrödinger’s equation for bulk materials. First is the realization that the solution wavefunctions (or orbitals) possess the same symmetry as the spatial distribution of atoms with which they are associated. Atoms (and hence the wavefunctions of atomic electrons) possess spherical symmetry. Orbitals that are centered on atoms in a molecule possess the point symmetry of that site; and, as bulk crystals fill space with identical unit cells, the electronic wavefunctions of periodic crystals possess translational symmetry. The last statement is known as Bloch’s theorem, and provided the foundation for an explosive growth in solid-state physics during the 20th century. The second principle is that a wavefunction can be expressed as a linear combination of any set of basis functions that are mathematically complete. This is

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exactly analogous to the use of Fourier series to represent an arbitrary function. An example of this approach is molecular orbital theory, in which a linear combination of atomic orbitals is used to describe new electronic states formed when atoms bond. It permits the efficient numerical simulation of electronic structures, because realistic electronic wavefunctions can be built up as a combination of computationally convenient basis functions. As wavefunctions are (in principle) independent of the choice of basis functions, there is an equivalence between choices. This can be seen by comparing the results of real-space cluster calculations and momentum-space band-structure calculations for the same mineral. Band structure calculations give similar results for the energy positions of electronic levels (bands) in solids that can be derived from cluster calculations, but additionally show how the energy-momentum relation varies with direction of travel in a crystal. An example for sphalerite, the cubic modification of ZnS, is given in Figure 6. The bulk electronic structure of a material is the starting point for understanding the properties and reactivity of nanoparticles of that material.

Energy levels in semiconductor minerals An important focus of this chapter is the role that nanoparticles play in redox reactions in biogeochemical systems. It is necessary, therefore, to introduce the concepts used to describe the behavior of electrons in bulk and nanosized systems. With a few exceptions, biogenic minerals are semiconductors or insulators.

Figure 6. Small cluster (ZnS46−) molecular orbital and full band structure calculations provide approximately equivalent descriptions of electronic bonding in ZnS. The band structure shows how the momentum-energy relation for a delocalized electron traveling within the crystal is modulated by the periodic lattice for various directions of travel. The wave vector, k, is related to the momentum and the symbols on the abscissa axis represent specific directions with respect to the crystal lattice. Integrating over all directions at each electron energy gives the density of states (DOS), i.e., the number of electronic states within a small energy width. Cluster calculations are less accurate than band structure calculations for delocalized electron semiconductors, but correctly show the atomic contributions to the electronic bands. The band gap, which separates the occupied and unoccupied electronic levels is shaded gray. ZnS is a direct semiconductor: no change in momentum is required for electronic transitions from the top of the valence band (VB) to the bottom of the conduction band (CB). The Fermi level, EF lies between the VB and CB. Effective Mass Approximation (EMA) considers only the electronic states at the top of the VB and the bottom of the CB, as indicated by the dashed oval. The band structure is approximately parabolic in this region (E ∝ k2), indicating that the electrons and holes can be modeled as free particles with an effective mass, m*. Cluster energy levels after Vaughan and Sherman (1980). Band structure and DOS calculations used by permission of the American Physical Society, from Wang and Klein (1981) Physical Review B, Vol. 24, Fig. 5, p. 3393-3416.

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The valence and conduction bands and the band gap. The valence band (VB) and conduction band (CB) in solids are the exact analogs of the highest-occupied molecular orbitals (HOMO) and lowest-unoccupied molecular orbitals (LUMO) in molecules. The Fermi energy, EF, can be considered the electronic electrochemical potential within the material. For insulators and semiconductors, EF lies between the (completely occupied) VB and (empty) CB. When a valence electron is excited (by thermal energy or by light absorption) and acquires sufficient energy to jump to the conduction band, a charge deficit, or hole, is created in the VB that acts just like a mobile positive charge. The electronic band gap is the energy required to excite an electron into the conduction band and move it out of the electrostatic field associated with the hole that is created. The spatial scale that defines this distance is the Bohr radius, the radius of the lowest-energy bound state of an electron-hole pair that are mutually attracted because of their opposite electrostatic charges. Such a bound pair is called an exciton. The conductivity of minerals. Electronic conductivity requires mobile charge carriers in bands that are not completely occupied. Energy bands in solids are occupied with electrons up to the Fermi level. Hence, at zero temperature, metallic behavior is expected in a pure solid if the Fermi level lies within a band, while insulating behavior is expected if it lies between bands. At higher temperatures, and for sufficiently small band gaps, thermal energy can excite electrons in the vicinity of the Fermi level. The promotion of a small number of electrons to the conduction band can enable conduction via both CB electrons and the associated VB holes. Pure materials that conduct by this mechanism are called intrinsic semiconductors. For some crystal structures, electronic transitions between the top of the VB and the bottom of the CB require exchange of momentum between the electron and vibrations of the lattice (phonons). Materials in which excitation across the band gap is phonon-assisted are called indirect semiconductors. Solids in which bonding arises from the interactions of s or p atomic levels follow the above description closely. However, other materials are anticipated to be metallic according to the above criteria, but are measured to be very poor conductors (Cox 1995). This is because when bonding involves partially occupied d or f levels, two additional factors can limit electron and hole mobility. First, low overlap between atomic orbitals of neighboring atoms can lead to the localization of outer shell electrons on a single atomic site. Second, energetic barriers may exist that limit charge transfer between neighbors. The origins of such barriers may be associated with electron repulsion or correlation energies, or may be structural. There is a continuum from highly localized to highly itinerant electronic behavior within which many environmentally relevant transition metal oxides and chalcogenides fall. In numerous iron-bearing materials, magnetic ordering of electrons below a threshold temperature can abruptly change the electronic properties from those of an itinerant semiconductor to localized insulator. Even for some bulk minerals such as hematite (Fujimori et al. 1986) and magnetite (Park et al. 1997; Todo et al. 2001), controversy remains with regard to the nature of the VB and CB electronic states. These distinctions, and controversies, persist in nanoscale particles. An additional important class of materials are the extrinsic semiconductors. Impurity atoms or point defects can provide electronic states within the band gap of an insulator or intrinsic semiconductor. The energy required to excite electrons or holes to or from these states can be significantly lower than transitions across the full band gap. Consequently, even very low concentrations of impurities can dominate the conductivity of bulk minerals. The charge carriers introduced into a solid by impurities or defects can be itinerant or localized just as the carriers introduced from atoms intrinsic to the material. Consequently, the incorporation of impurities during nanoparticle nucleation and growth can have a significant effect on reactivity.

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Many of the partly occupied d-shell configurations found in iron- and manganese-bearing compounds have associated magnetic properties. A review of nanoscale magnetism is given by Rancourt (2002). Electron hopping conduction mechanisms. Localized electrons or holes experience an energy barrier against transfer to neighboring atomic sites, but at finite temperatures the thermal energy of the system may be sufficient to overcome the barrier. Thus, localized charge carriers can be mobile through a “hopping” mechanism from site to site. An important example is that of the small polaron, an electron or hole that is transiently trapped at each site because of the distortion of the immediate lattice that it provokes, and which follows it. As with any process with an activation energy barrier, mobility increases with increasing temperature. A theoretical treatment of hopping conductivity (Cox 1995; Rosso et al. 2003) originated from a model electron-transfer reactions between ions in solution (Marcus 1993; Barbara et al. 1996), and is also applicable to charge and exciton transfer between nanoparticles (Adams et al. 2003; see below). Quantum mechanical tunneling is an alternative mechanism that may dominate for acceptor-donor distances less than 14 Å, and is utilized by many proteins and electron shuttles (Moser et al. 1992; Page et al. 1999). Tunneling processes do not pass through an intermediate higher energy state and are therefore distinct from the hopping mechanism. Consider the transfer of an electron from a donor (D) to an acceptor (A). The donor could be a reductant in solution or a site in a crystal that has trapped an electron.

D + A ↔ D+ + A− (4)

The rate of electron transfer, ket, has an Arrhenius-type dependence:  ∆G *  ket = κν exp  −   kBT 

(5)

where ΔG* is the Gibbs free energy of an intermediate state (the activation energy), kB is Boltzmann’s constant, and κ is a prefactor for a given system. Thermal motions of the atomic nuclei bring the system into the most favorable configuration for charge transfer with a certain frequency, given by ν. In the intermediate state, the electron overlaps with both the donor and acceptor. The major contribution to the activation energy is the reorganization energy associated with the geometry of the intermediate state. The activation energy also includes electrostatic interactions between the electron and the atoms or molecules that coordinate the donor and acceptor, such as near-neighbor atoms in a crystal or hydrating solvent molecules. Hopping-based conductivity in minerals is generally highly anisotropic, as determined by the crystal structure and electron spin ordering. For example, conduction in hematite occurs predominantly along the (001) basal planes (Rosso et al. 2003). Conduction in magnetite involves the octahedral sites on which Fe(II) and Fe(III) ions are distributed to the exclusion of purely Fe(III) tetrahedral sites (Cox 1995). As motion of the atoms is required for the system to reach the intermediate state, modifications in the vibrational properties of nanoparticles at small size (e.g., Gilbert et al. 2004) may significantly affect charge (or impurity) transport.

Electronic structure of nanoparticles Pure size-dependent modifications of electronic structure substantially depend on the extent to which the valence electrons are delocalized. When the dimensions of a semiconductor are similar to the Bohr radius, an electron and hole can never be so far apart that the interaction between them can be neglected. Consequently, the lowest energy excitation is not equivalent to the bulk band gap, as defined above, but contains additional terms, principally the (positive) confinement energy and the (negative) electron-hole Coulomb interaction. The confinement energy dominates and the nanocrystal exhibits an increase (or blue-shift) in its band gap. Band

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gap opening is readily observed with ultra violet–visible (UV-vis) absorption spectroscopy, but the individual shifts of the VB and CB are not obtained with this approach. Combined Xray absorption and X-ray emission spectroscopies can resolve the size dependent trends in the energy positions of the occupied and unoccupied bands (Fig. 7). The electronic structure of bulk materials with delocalized electrons is well modeled by band structure methods (using translation symmetry). It is obvious, however, that even the most crystalline nanoparticle lacks long-range periodicity. To date, two approaches have been adopted to deal with this. Effective mass approximation (EMA). Virtually all geochemically relevant optical and redox behavior of materials involves the charge carriers in the vicinity of the top of the VB or the bottom of the CB (region of interest circled in Fig. 6). The effective mass approximation (EMA) is an approach that describes how a perturbation in a material (such as finite size) affects these electrons and holes, disregarding all others. It assumes that the mobility of

Figure 7. Quantum confinement causes shifts in the absolute energy positions of both the valence band maximum (VBM) and the conduction band minimum (CBM). Left. Soft X-ray emission (SXE) and soft X-ray absorption (SXA) spectroscopy provide information on, respectively, the density of occupied and unoccupied electronic states of a material. Spectroscopy at the sulfur L-edge provides the density of states (DOS) with d-character. The d-weighted DOS fine structure and position varies with size for CdS nanoclusters. Right. The energy shifts in the VBM and CBM are plotted versus particle size. The experimental results are compared to theoretical calculations using the infinite potential (IP) vs. finite potential (FP) effective mass approximation (EMA) and tight-binding real-space cluster calculations (TB). Ultraviolet absorption spectroscopy provides only the energy spacing between VBM and CBM. [Used by permission of Elsevier, from Lüning et al. (1999) Solid State Communications, Vol. 112, Figs. 1 & 2, p. 5-9.]

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electrons and holes (represented by the effect mass, and determined by the crystal lattice) is unchanged in nanoparticles. The size-dependent shifts in absolute energy positions of the VB and CB are given by the confinement energy of a hole and electron, respectively. The simplest EMA theory considers an infinite confining potential at the nanoparticlesolvent interface. Simple analytical expressions are obtained for spherical nanoparticles of radius d: 0 ∞ EVB ( d ) = EVB −

2  2 π2 ; d 2 mh*,in

0 ∞ ECB ( d ) = ECB +

2  2 π2 d 2 me*,in

(6)

In this expression, ECB0 and EVB0 are the CB and VB energy positions for the bulk material, and me,in* and mh,in* are the effective masses of the electron and hole inside the nanoparticle. Brus (1984) was the first to consider quantum confinement effects on the redox potential of nanoscale solids, and his predictions for CdSe with an infinite potential are shown in Figure 8. The energy step at the interface must result from either the electron affinity of a solid (~5 eV) or the band gap of the surrounding medium (3.8 eV for aqueous systems, Memming 2001). Several authors have shown that a significant quantitative improvement to the EMA is obtained when a finite confining potential is considered (e.g., see Fig. 7) (Tran Thoai et al. 1990; Schoos et al. 1994; Lüning et al. 1999). Exact analytical expressions are no longer obtained, and electron and hole energies are generally obtained numerically. However, Ferreyra and Proetto

Figure 8. a). Effective mass approximation (EMA) calculations of the size-dependent redox potentials of CdS nanoparticles participating in (photo)redox half-reactions. [CdS]− = negatively charged CdSe nanoparticle. [CdS]= = doubly charged particle. [CdS]* = photoexcited nanoparticle. [Used by permission of the American Institute of Physics, from Brus (1984), Journal of Chemical Physics, Vol. 80, Fig. 2, p. 4403-4409.] b). The size dependence of the energy positions of the valence band (solid lines) and conduction band (dotted lines) of ZnS, FeS2 and hematite at pH 2. Calculations based on the EMA (Eqn 6, main text) with a 4 eV confining potential and effective masses obtained from Wang and Klein (1981), Edelbro et al. (2003), and Guo (2005). Also shown are the stability limits of water. As calculated by Sherman (2005), the redox potential for the reduction of ferric iron in hematite (Fe2+/α-Fe2O3) lies below the hematite CB at pH 2. Hence photoreductive dissolution following photoexcitation of the mineral is possible under these conditions. AVS = absolute vacuum scale; NHE = normal hydrogen electrode.

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(1999) provide an approximate expression for the confinement energies in a finite confining potential. For a spherical particle of diameter d and a confining potential Vout outside: 0 ∞ EVB ( d ) = EVB − EVB (1 − δh );

0 ∞ ECB ( d ) = ECB + ECB (1 − δe )

( 7)

where δe = with an equivalent expression for δh. For particles in water or air, it is generally assumed that me,out* = mh,out* = me, the mass of the free electron. Holes are perfectly confined, so that Vh,out = ∞ and δh = 0. [/(dme,in*)](8me,out*/Ve,out)½,

In the so-called strong confinement regime (for nanoparticle radius similar to or less than the Bohr radius), the electron and hole can be considered as individual particles. Then the band gap is obtained from difference in the size dependent VB and CB energy positions, plus the additional term resulting from the electrostatic interaction of the electron and hole pair. The band gap, Eg, for the same particle considered above is:  2 2 π2  1  1  e2  ( δ + δh )  Eg ( d ) = Eg0 +  2  * + *  − Ee∞ δe − Eh∞ δh  − 3.6 1 − e  εd  4   d  me,in mh,in  

(8)

where e is the charge on an electron and ε is the high frequency dielectric constant of the bulk semiconductor. In this expression, Eg0 is the band gap of the bulk, the second term considers the kinetic energy of the electron and the hole (i.e., the confinement energy—this is an alternative version of Eqn. 7) and the smaller third term is the Coulomb attraction between electron and hole (see discussion in Nanda et al. 2004). Effective mass values may be found directly in the literature (e.g., Landolt-Bernstein 1983) or estimated from band structure calculations (e.g., Edelbro et al. 2003). Limits and extensions of the EMA. The EMA tends to overestimate the band gap in quantum confined materials, even when a finite confining potential is used, an effect that is exacerbated at the smallest sizes. Consistent values for the effective mass can be hard to obtain from the literature; however, the EMA is only weakly dependent on the precise values for the effective mass (Gaponenko 1998; Pelligrini et al. 2005). The EMA is believed to be valid for both direct and indirect semiconductors. Absorption measurements on indirect semiconductors have shown that they retain this characteristic property down to very small sizes (~100 atoms; Delerue et al. 2001) and that confinement effects on band edge positions and band gaps are similar to those in direct semiconductors (Tolbert et al. 1994). However, the UV-vis absorption edge is generally not sharp for the indirect semiconductors, and even for well studied materials such as TiO2, this transition (which reveals the true band gap) has been confused with stronger, direct transitions to higherenergy CB states (Serpone et al. 1995; Monticone et al. 2000). The EMA assumes that there are no size-dependent changes in particle structure, but changes in structure may overwhelm confinement effects in wide band-gap, low-mobility materials. Furthermore, in narrow-band-gap semiconductors, such as PbS (Eg = 0.41 eV), the band edge states are not well approximated as free electrons, and hence quantitative agreement is poor. In such cases, the EMA underestimates the band gap (Pellegrini et al. 2005). Additional refinements of the EMA approach have been performed. Examples include the treatment of multiple bands in the VB and CB (Efros and Rosen, 2000), and inclusion of surface polarization terms (Brus, 1986). Nanda et al. (2004) discuss variations of the finite potential EMA for nanoparticles with slab (2D) and rod (1D) geometries. Many of the above methods assume that important material constants for nanoparticles, such the dielectric constant or the effective mass of the relevant charge carrier(s), are unchanged relative to bulk materials. These assumptions are presently untested. However,

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recent calculations indicate that the dielectric response of a material change very little with decreasing size, with the exception of small near-surface effects (Cartoixa and Wang 2005). Real-space cluster models. As discussed above, band structure and real-space depictions of electronic structure are highly complementary approaches. Real-space methods discard all assumptions of symmetry and are thus suited to nanoparticles. However, they carry a significant price, because whole-nanoparticle simulations must treat hundreds of non-equivalent atoms, while band-structure calculations consider only the number of atoms in a unit cell. The most important input to a cluster calculation is the atomistic real-space structural model itself. Nanoparticle structures are generally assembled by hand, or with classical molecular dynamics structure optimization, and the uncertainty in the structures derived in these ways are a major limitation for subsequent electronic structure calculations, no matter how sophisticated. The nature and structure of nanoparticle surfaces remain particularly obscure, because no experimental method is presently able to directly visualize it. Obviously, uncertainties surrounding the true structures of nanoscale materials cause equally great uncertainties in anticipating their electronic properties. Nevertheless, atomistic simulations provide the clearest visual depictions of nanoparticle structure (Rabani 2001; Pokrant and Whaley 1999) and have played an invaluable role in evaluating theoretical approximations such as the EMA (Lippens and Lannoo 1987; Wang and Zunger 1996; Franceschetti and Zunger 1997). Simulations that are performed with care are experiments in silicio, useful for evaluating and predicting mechanisms or trends in behavior that can be tested experimentally (Zhang et al. 2003; Rustad and Felmy 2005; Kerisit et al. 2005; Erwin 2005). Real-space cluster calculations will prove essential for understanding the electronic structure in complex environmental nanoparticles for which simple models such as the EMA are not applicable (O’Connor and Sposito 2004). Ideally, electronic and physical structure would be simultaneously optimized, as implemented by the Car-Parinello method (Car and Parrinello 1985; Galli and Parinello 1992). The “Quantum Monte Carlo” simulations by the Galli group have demonstrated the value of this approach for showing the effects of surface reconstructions on the electronic structure of nanoclusters (Puzder et al. 2003). However, nanoparticles of diameter greater than ~2 nm remain large even for efficient first-principle calculations. Solvent effects on nanoparticle electronic structure. An interesting consequence of a finite confining potential is that tails of the electron or hole wavefunctions extend into the medium surrounding the particle, as depicted in Figure 9. This generally has a slight effect on the energies of the electron and hole states. However, solvents with a high dielectric constant can stabilize nonuniform charge distributions at the surface (Rustad and Felmy 2005), enhancing the strength of dipolar interactions between nanoparticles (Rabani 1999) and can permit solvent-mediated conductivity (Brus 1996), as discussed below. The simplest approach for estimating solvent effects on electronic energy levels in nanoparticles is to treat the solvent as a continuum characterized by a dielectric constant (Qu and Morais 1999; Rabani et al. 1999; Franschetti et al. 2000). However, it is clear from decades of research on mineral-water interfaces that a continuum description of water is severely limited (Hochella and White 1990). In fact, we require a much more complete description of the nanoparticle–electrolyte interface. Interfacial electrochemistry of nanoparticles. When a solid material is immersed in an electrolyte such as water, redistribution of charge carrying species occurs on both the solid and liquid sides of the interface. This process can produce significant shifts in the absolute energy positions of electronic states at the interface, and hence affect the redox properties of the bulk mineral or nanoparticle.

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Figure 9. The effect of the height of confining potential on the energy levels and wavefunctions of an electron in a nanoparticle can be illustrated by the simple “particle in a box” model. Shown are the three lowest energies, Ei, and wavefunctions, Ψi, for a particle confined by an infinite and a finite potential. Compared to the infinite potential case, the energy levels for a finite potential are more closely spaced, and tails of the wavefunction penetrate into the surrounding medium.

When the two phases touch, the system as a whole reaches thermodynamic equilibrium by equating the electrochemical potentials for all species in the material and the electrolyte. As introduced above, the electrochemical potential for an electron in a semiconductor is equal to the Fermi energy, EF, which lies between the valence and conduction bands. The electrochemical potential associated with the electrolyte is related to the redox potential of the system. The half reaction for a redox reaction in the electrolyte can be written: Red ↔ Ox + e− (9) The reduced and oxidized species in Equation (9) are alternate states of a single system in which an electronic orbital is occupied or unoccupied, respectively. The electronic energies of these states are not equivalent because the solvent cannot reorganize to a ground state configuration on the time-scale of electron transfer reactions, and EOx > ERed. Thus, the reduced and oxidized states of the redox system are analogous to the occupied (valence band) and unoccupied (conduction band) states in a solid, and we may define an effective Fermi energy for the solution, EF,redox, that lies midway between EOx and ERed. The electrochemical potential,  µi , of an ion, electron, or other species, i, is equal to its    chemical potential, μi, plus an additional term for each charged species if it resides within an electric field. µi = µi + zi F φ

(10)

The additional energy term is the product of the charge on the species, zi, the local electrostatic potential, φ, and Faraday’s constant, F. The relation between the chemical potential, μi, and the activity of ion i is given in many textbooks (Lyklema 2001). The electrochemical potential for the electron in Equation (9) is defined as the difference in the values for the oxidized and reduced species,  µ e,redox = µ Red − µOx. (Memming 2001). The electrolyte Fermi energy, EF,redox                                (in eV), is then related to µ e,redox (in J mol−1), by            e EF ,redox = µ e,redox (11) F For the solid phase and aqueous phase Fermi level concepts to be directly comparable, they must be expressed in the same units on the same energy scale, typically in eV on the absolute vacuum scale (AVS). The redox potential associated with a half reaction such as

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Equation (9) is usually expressed in V relative to the normal hydrogen electrode (NHE) or other electrode. As discussed by Xu and Schoonen (2000), the energy of an electron in an energy band can be converted to the associated redox potential by the relation

E (NHE, V) = −E (AVS, eV) − 4.5

(12)

At equilibrium, EF = EF,redox, which requires charge redistribution. Electrons flow into the solution from the material and change the concentration of redox species if EF > EF,redox, and vice versa. This changes the potential at the interface, lowering the free-energy difference due to the term ziFφ in Equation (10) until equilibrium is attained. The VB and CB states at the interface are affected by the local potential, a phenomenon called band bending, as depicted in Figure 10. Energy levels further from the interface than the Debye length, LD, are shielded from the effect of the interfacial potential because of the dielectric properties of the solid. LD is typically greater than 100 Å, depending on the density of charge carriers. Thus, surface charges are not well screened in particles of dimensions smaller than this, and the electronic bands are flat (but shifted) throughout a nanoparticle, as illustrated in Figure 10. This depiction assumes that the kinetics of the redox couple are fast, but it is well known that many environmental systems are far from thermodynamic equilibrium (Schüring et al. 2000). Electric double layer. The distribution of aqueous ions near the surface of a mineral in water is affected by the presence of an electrostatic potential at this interface. Such surface

Figure 10. Semiconductor particles immersed in water reach equilibrium by donating or accepting electrons from solution until the electronic electrochemical potential (the Fermi level) is constant throughout. The Fermi level in the solution is determined by the redox couple(s) in solution. In micrometerscale particles, the change in local charge carrier density causes shifts in VB and CB energy levels (band bending) over a space charge region, dSC. The direction of band bending depends on whether holes (p-type) or electrons (n-type) are the dominant charge carriers (the results for an n-type semiconductor are shown). For nanometer-scale particles of diameter d < dSC the electronic bands inside the entire particle are shifted. The positions of the nanoparticle CB and VB are shown shifted both due to surface charging (which lifts the CB and VB energy positions) and by quantum confinement (which increases the CB-VB separation). After Memming 2001.

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potentials can be created by equalization of the Fermi levels in the solid and the electrolyte (as described above) and by the chemisorption of charged solution species to the mineral surface. For a given mineral surface, strongly interacting charged species are called potential determining ions (PDI) (Lyklema 2001). For example, H+ and OH− are PDI for metal oxides, while HS− can be a PDI for sulfide minerals (Bebie et al. 1998). The pH driven shifts in oxide band energies are quantitatively described by the Nernstian relation that predicts a shift of 0.059 V/pH at 25 °C and 1 atmosphere pressure. However, there are apparently no experimental tests of this relation for nanoscale particles. Similarly, although nanoparticles (and colloids in general) act as ready sorbents for inorganic and organic ions and molecules, the effects of different sorbates on band positions are generally not known. The adsorption of redox active solution species seldom affects the energy band positions.

REDOX BEHAVIOR OF NANOPARTICLES Having completed a brief review of the factors that affect the energies of electronic bands in solids, we are now able to apply this knowledge to the (photo)chemical reactions of nanoparticles. The fundamental reactions in which nanoparticles can participate are depicted in Figure 11.

Size effects on nanoparticle redox behavior The modification of the absolute valence and conduction-band energy levels is a predominant effect on redox behavior when it occurs. As discussed above, doping of a semiconductor, sorption of potential determining ions, and finite particle size may all contribute to such effects. Examples of size effects on redox potential. As shown by Brus (1984), and reproduced in Figure 8a, finite size effects can have a large impact on the redox potentials of semiconductors (see also Franschetti et al. 2000). However, the extent to which valence electrons are delocalized and hence susceptible to finite size effects is unclear for several environmentally

Figure 11. Scheme of the possible redox, photochemical and charge or energy transfer reactions that can take place at the surfaces of semiconductor mineral nanoparticles. D = electron donor (or reductant); A = acceptor (or oxidant); S = surface adsorbed sensitizing ligand; VB = valence band; CB = conduction band; Eg = band gap. − (+) represents a negatively (positively) charged nanoparticle; * represents a nanoparticle containing a photoexcited electron-hole pair.

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relevant materials. For example, sphalerite (ZnS) and pyrite (FeS2) are, respectively, direct and indirect delocalized electron semiconductors in which quantum confinement effects will occur. Figure 8b plots the size-dependent shifts in the VB and CB levels for these materials predicted by the EMA. It is presently difficult to anticipate quantum confinement effects in environmental iron and manganese compounds for which electron and hole effective masses have not been tabulated. Nevertheless, a recent X-ray spectroscopic study identified an increase in the band gap of hematite nanorods ~ 4 nm in diameter by approximately 0.3 eV (Guo 2005). UV-vis spectroscopy of encapsulated iron oxide nanoparticles also indicated size-dependent bandgap opening (Iwamato et al. 2000). In common with most iron (III) and manganese (IV) (oxyhydr)oxide minerals, the conduction and upper valence bands have the character of cation d-states, while the lower valence band is principally composed of oxygen p-like orbitals, although covalency in the metal-oxygen bond causes mixing in the valence band states (Cox 1992; Sherman 1984, 1985, 2005). In Figure 8b, we predict the size dependence of the hematite band gap using an estimate for the effective masses of charge carriers (assuming me* = mh*) consistent with the observations of Guo (2005). Band-gap opening, as depicted in Figure 8, will significantly affect mineral reactivity (Rodriguez et al. 1998). The predictions of Figure 8 require further experimental testing, particularly the use of combined X-ray absorption and emission spectroscopic measurements of nanoparticle band gaps (Lüning et al. 1999; Sherman 2005), and more sophisticated theoretical treatments (O’Connor and Sposito 2005). A complementary approach for understanding the electronic properties of mineral nanoparticles will be studies of the kinetics of surface reactions. Kinetics studies have been used to determine the relative reactivities of different iron minerals to surface redox or complexation reactions (Hering and Stumm 1990; Elsner et al. 2004; Poulton et al. 2004; Peak 2005). Such studies provide a method for determining size-dependent changes in surface reactivity (see below; Madden and Hochella 2005). The roles of surface states. Atoms at the surface of a material are not generally able to attain the same coordination environment that is present within the interior. Thus, atomic sites at a surface may exhibit modified local electronic structure that in some cases can introduce energy levels within the band gap of a semiconductor or insulator (Morrison 1980). Surface states can therefore have the same effect as interior impurity atoms, either facilitating the creation of mobile electron or hole charge carriers or acting as traps for them. For example, underbonded surface anions can donate electrons into the CB of the mineral (cf. Fig. 16). Surface states can also mediate the transfer of charge between an adsorbate and states within a mineral or between two aqueous or adsorbed reagents. In addition, the trapping of electrons at surface states can affect the lifetime of photoexcited electrons and holes, and can determine the rate of electron transfer between neighboring particles (see below). The above phenomena occur at the surfaces of both bulk minerals and nanoparticles. The surfaces of nanoparticles are structurally more diverse, and molecular simulations indicate that inhomogeneous charge distributions can occur at nanoparticle surface and edge sites, modifying the surface Lewis acid or base characteristics relative to large particles (Lucas et al. 2001; Noguera et al. 2002; Rustad and Felmy 2005). Scanning tunneling spectroscopy (Preisinger et al. 2005) and optical luminescence spectroscopy (Chen et al. 1997) are approaches for detecting surface states that lie in the electronic band gap. However, environmental nanoparticles and their synthetic analogs are poorly studied. Undercoordinated surface sites tend to be more reactive and hence are frequently the sites at which molecules bind to nanoparticle surfaces. In place of the initial surface states, new

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surface-ligand molecular orbitals are formed, with discrete energy levels that may no longer reside within the band gap. While ligand binding has been extensively studied for the removal of mid-gap states in engineered nanoparticles (e.g., Green and O’Brien, 1999), ligand binding can also create (photo)redox active mid-gap energy states (e.g., Rajh et al. 2002)

Examples of nanoparticle redox behavior Nanoparticles as molecular-like redox active solution species. Nanoparticles can accept or donate electronic charge, and in this sense can be considered redox active species. Figure 12 shows that CdS nanoparticles can diffuse to, and react with an electrode in an electrochemical cell in a manner similar to an aqueous ion (Kukur et al. 2003). The potential at which the nanoparticles can be reduced (i.e., charged by a single electron) varies with particle size in agreement with confinement effects. However, even for nonaggregated nanoparticles, diffusion rates are considerably slower than dissolved ions or molecules (Scholz and Meyer 1998). The Brownian diffusion rate for nanoparticle transport is given by the Stokes-Einstein equation: D=

kBT 6 πηr

(13)

where η is the solution viscosity, and r is the particle radius. The implication of the results of Figure 12 is that nanoparticles are available to participate in molecular redox reactions with aqueous ions and biomolecules. Below, we discuss the important issue of the stability of individual nanoparticles during (photo)redox reactions. The example above is one of a number of experimental investigations of the charging of nanoparticles that was conducted at electrochemical electrode (Haram et al. 2001; Kukur et al. 2003; McKenzie and Marken 2001). In nature, redox active solution species can inject electrons into the conduction band of a mineral, provided that the redox potential of the aqueous redox reaction is more negative than the position of the CB minimum. For example, Cd(II) and Co(II) can be oxidized on the surface of ZnO and manganese (IV) oxides, respectively (Murray and Dillard 1979; Manceau et al. 1997). In the opposite direction, electrons may transfer from the mineral VB to a strongly oxidizing organic species such as ascorbic acid, which can cause the direct oxidation of manganese oxide minerals (Stone and Morgan 1984; Stumm and Morgan 1996). A negatively charged nanoparticle is free to act as a reductant with a suitable acceptor species. If further charge transfer to the nanoparticle carries an energy penalty (see below),

Figure 12. Semiconductor nanoparticles exhibit molecular-like redox behavior with size-dependent redox potentials. Electrochemical oxidation of a solution of CdSe nanoparticles in acetonitrile at a gold electrode shows a clear trend with increasing particle size (a to d). The position of the oxidation peak (Ep) indicates the valence band maximum and the trend is in quantitative agreement with an effective mass approximation calculation of electronic confinement energies. a = 3.23 nm diameter; b = 3.48 nm; c = 3.73 nm; d = 3.8 nm. [Used by permission of the American Institute of Physics, from Kucur et al. (2003) Journal of Chemical Physics, Vol. 119, Fig. 3, p. 2333-2337.]

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additional reactions with donor species are not favored the excess charge has been lost. An important question is whether oxidant and reductant must be simultaneously surface bound, or whether charged nanoparticles can be formed that are sufficiently stable in solution to act as reactive intermediates. The direct reaction scheme can be written

D < λ > A → D+ < λ > A− (14)

where λ > D represents donor species D adsorbed onto a nanoparticle. Alternatively:

D < λ → D+(aq) + [λ]−

(15a)

[λ] + A(aq) → λ > A (15b) −

−

For electron transfer reactions, the donor (or acceptor) species and the nanoparticle must (1) have a redox potential that coincides with electron energy bands in the minerals, and (2) attain sufficient wavefunction overlap with these bands (Huber et al. 2000). Many singleelectron transfer reactions can occur via electron tunneling to or from species bound to the surface by outer-shell adsorption. However, ligand-particle electron transfer rates are greatly enhanced by surface complexation (Moser et al. 1991); and certain charge transfer reactions, particularly involving Fe d-electron states, require inner-sphere surface coordination to proceed at all (e.g., Ennaoui and Tributsch 1986). The possibility of multiple oxidation states of nanoparticles. The energy required to singly charge a solvated nanoparticle (Fig. 11a) is sensitive to both the nanoparticle size and the dielectric constant of the solvent (Franceschetti et al. 2000). The addition of subsequent electrons to an already charged nanoparticle is possible, but additional energy may be required to compensate for Coulomb repulsion if there is electronic overlap between CB electrons (Brus 1984). In this case, nanoparticles can behave more like atoms with variable redox states (Banin et al. 1999), than bulk minerals within which excess charges may diffuse apart. Atomic-like redox behavior would lead to a quenching of bioreductive processes involving nanoparticles, because the successive charging energies would eventually exceed the reducing power of extracellular electron shuttle molecules. However, this is not observed for the biological reduction of ferric iron minerals, since phases with higher surface areas (i.e., smaller particle size) are more completely reduced (Roden and Zachara 1996; Hansel et al. 2004). For minerals composed of atoms susceptible to valence changes, mineral dissolution is an effective pathway for shedding excess charge. Furthermore, in the small polaron model of charge transport in localized carrier materials, an additional electron at an iron site is spatially localized within a radius less than the near-neighbor bond length (Cox 1992). This implies that interactions between multiple ferrous iron sites will be weak, and that the energy required to add an electron to a ferric iron nanoparticle will not depend on the number of excess electrons already hosted, in contrast to the behavior of delocalized band semiconductors. Reductive transformation of halogenated organic contaminants. Many relevant investigations have been motivated by the desire to identify natural pathways for immobilizing or transforming organic or inorganic contaminants. For example, Fe(II) adsorbed to the surface of ferric iron and non-iron-containing minerals can be a powerful reductant, one that can transform hazardous chlorinated hydrocarbons such as carbon tetrachloride (CT) (Pecher et al. 2002; Elsner et al. 2004) and chromate (Wielinga et al. 2001). Mineral surfaces that stabilize the Fe(III) product of the oxidation of surface-bound Fe(II) can lower the Fe(II)Fe(III) redox potential (Stumm and Morgan 1996; Amonette et al. 2000; Pecher et al. 2002). Structural Fe(II) in mixed-valence iron oxide compounds such as magnetite, particularly biogenic magnetite nanoparticles, can also transform CT. As shown by Figure 13, numerous electron shuttle molecules used by anaerobic DFeRB also possess reduction potentials that can degrade CT, and co-metabolic pathways

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Figure 13. Structural Fe(II) in biogenic magnetite nanoparticles (shown in TEM image, left) is a potent reductant of halogenated organic solvents. Although the redox potentials of common biological electron shuttle molecules are also sufficient to drive the reduction of carbon tetrachloride (right), the mineral nanoparticle driven process is almost 100 times more effective. [Used by permission of the American Chemical Society, from McCormick et al. (2002), Environmental Science and Technology, Vol. 36, Figs. 1 & 4, p. 403-410.]

do contribute to the natural attenuation of this compound. However, in laboratory studies, biogenic magnetite is approximately two orders of magnitude more effective than biomolecular pathways (McCormick et al. 2002; McCormick and Adriaens 2004). Nanoscale magnetite also reduces U(VI) to U(IV) under anaerobic conditions (following surface adsorption) much more rapidly than occurs in solution under reducing conditions (Missana et al. 2003). Surface-promoted redox reactions. Bulk mineral surfaces can mediate charge transfer between solution species that otherwise interact too weakly for effective redox pathways. For example, sulfide minerals—including pyrite, galena, and several doped sphalerite minerals— catalyze the oxidation of thiosulfate to tetrathionate by dissolved molecular oxygen (Xu and Schoonen 1995). A significant nanosize effect was recently observed for hematite-promoted Mn oxidation (Madden and Hochella 2005). While the oxidation of aqueous Mn(II) by oxygen is very slow at pH < ~8.5, it may be promoted following adsorption to mineral surfaces. Surface hydroxyl groups (denoted >OH) mediate electron transfer from molecular oxygen to adsorbed manganese, facilitating the reaction: Mn 2+ +

1 3 OH O2 + H 2O > → Mn(III)OOH + 2H + 4 2

(16)

As shown in Figure 14, the kinetics of this reaction, normalized to surface area, exhibit an increase of more than one order of magnitude for 7 nm diameter hematite nanoparticles compared with 37 nm particles. Madden and Hochella (2005) discuss the possible origins of this striking enhancement of reactivity. Hematite energy bands do not play a direct role in this process; hence, electronic confinement effects are unlikely to be responsible. Possible explanations of the rate enhancement may be understood from Marcus’s original description of the rate of electron transfer (Marcus 1993). By setting ΔG* = (ΔG° + λ)2/4λ in Equation (5),

(

 ∆G o + λ ket = κν exp  − 4λkBT  

)

2

    

(17)

where ΔG° is the standard free energy of the reaction (positive or negative) and λ is the reorganization energy (always positive). Size-dependent changes in either ΔG° or λ are plausible and would modify the reaction kinetics, provided that electron transfer is the rate-limiting step.

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Figure 14. The rate of heterogeneous oxidation of Mn(II) promoted by 7 nm and 37 nm diameter hematite nanoparticles. The smaller hematite nanoparticles promote oxidation at a rate that is almost two orders of magnitude larger than the larger particles. [Reprinted with permission of Elsevier, from Madden and Hochella (2005) Geochimica et Cosmochimica Acta, Vol. 69, Fig. 5, p. 389-398.]

For example, it is likely that the redox potential of Mn(II) adsorbed to the smaller particles is shifted because of a size-dependent modification in the Lewis base character of oxygen atoms on the surface of the hematite to which Mn adsorbs (Noguera et al. 2002, Lewis et al. 2001). If this drives the ΔG° of Reaction (16) more negative, Mn oxidation would not only be more favorable, but ket would increase. Alternatively, small particles may exhibit a higher density of surface sites at which the coordination geometry of adsorbed ions is distorted from the perfectly octahedral configuration preferred by Mn(II). However, Mn(III) complexes tend to prefer a distorted octahedral coordination. Therefore, if the smaller nanoparticles possess greater surface disorder, less structural reorganization may be necessary for the reaction to proceed. This effect would reduce λ, thereby increasing ket The results of Madden and Hochella (2005) demonstrate that the redistribution of both charge and atoms at the surfaces of nanoparticles may strongly influence their reactivity. EXAFS investigations into changes in the coordination environment of Mn(II) bound to hematite nanoparticles in the absence of oxygen may help evaluate these two models.

PHOTOCHEMISTRY A photon of energy greater than the band gap can excite a valence electron to the CB, which leaves a vacant orbital (hole) in the VB. The excited electron (hole) has the ability to reduce (oxidize) chemical species at the surface of the nanoparticle. As with redox chemistry, the ability to do this depends on the absolute electron or hole energy, and hence can be affected by particle size (and pH), as described above.

Size effects on nanoparticle photochemistry Kinetics of recombination and reaction. Following photoexcitation of a nanoparticle, several processes can occur that facilitate or prevent reaction, and these processes may exhibit distinct small-size effects (Gratzel and Frank 1982; Gerischer 1993). Diffusion of an excited electron or hole to the surface and transfer to surface species competes with the recombination of the electron-hole pair and trapping at surface states (Zhang 2000). The transit time to the surface varies as the square of the particle radius, and is generally less than 1 ps for few-nm

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diameter particles (Gratzel and Frank 1982; Huber et al. 2000). The recombination time is principally material dependent, with a weak size dependence caused by enhanced electronhole overlap. Since the recombination time is generally in the range 0.1–1 ns, exited electrons have a far greater probability of reaching the surface of nanoparticles than larger colloidal particles, such as >100 nm diameter iron oxide colloids, in which most electron-hole pairs recombine before reaching the surface (Leland and Bard 1987). Nanoparticle surface states can act as traps for excited electrons with lifetimes that may vary by many orders of magnitude for different nanoparticles. If a photoexcited electron or hole is scavenged by a solution or surface species, recombination within the nanoparticle is no longer possible, and the nanoparticle can remain excited for a considerable time (e.g., minutes) (Leland and Bard 1987). Because both a hole and electron are created following light absorption, both cathodic (i.e., reduction) and anodic (i.e., oxidation) reactions are possible at a nanoparticle surface and may proceed in very close proximity. The kinetics of these reactions are seldom equivalent, providing an opportunity for reaction intermediates to interact. Several studies have concluded that competition between the above processes, plus variation in surface area:volume ratio, leads to an optimum particle size for the maximum efficiency of a given photoreaction that is frequently in the 5–20 nm diameter range (Wang et al. 1997; Almquist and Biswas 2002). Reactions requiring multiphoton absorption. The probability of a photoexcited nanoparticle absorbing a second photon declines with particle size for statistical reasons (Wang et al. 2003), and hence reactions requiring rapid multielectron transfer are highly unlikely for nanoscale particles under environmentally relevant illumination conditions. For example, the products of the photooxidation of ethanol are different for micron-sized versus nanometer-sized ZnS colloids (Müller et al. 1997). Under constant illumination, transfer of two photoexcited holes to adsorbed ethanol can occur readily within 200 ns on the surface of the larger particles. By contrast, the mean time to create two holes in a nanoparticle reaches several seconds, permitting partially oxidized ethanol radicals to diffuse into solution and form more complex organic species. Shifts in electronic band energy positions. The effect of particle size on the photoredox activity of nanoparticles can be illustrated through analogy, with the effect of pH on the photoreduction of methylviologen ions (MV2+) at the surface of colloidal TiO2 particles (Duongdong et al. 1982). The half-reaction, MV+ ↔ MV2+ + e−, has the redox potential E0 = −440 mV versus NHE that is independent of pH. By contrast, the electrochemical potential of the TiO2 CB varies with pH as ECB (TiO2) = ECB (TiO2, pH 0) – 0.059(pH) V (vs. NHE)

(18)

As shown in Figure 15, photoexcited electrons in the CB are sufficiently reducing only above pH around 4–5. Photoreduction of MV2+ is thermodynamically forbidden below this pH threshold. As shown below, for materials that exhibit size-dependent shifts in CB position, thresholds in particle size can exist below which photoredox reactions are enabled. For completeness, it should be recalled that changes in pH can strongly affect the affinity of ionic sorbates to surface binding sites, particularly around the point of zero surface charge (pHzpc) for a material (Kormann et al. 1991). Such effects can complement or compete with shifts of the nominal redox potentials of CB electrons and VB holes.

Nanoparticle interactions with biomolecules It is clear from the study of environmental colloids that mineral surfaces can exhibit high affinities for organic molecules (Yariv and Cross 1979; Amal et al. 1992; Tiller and O’Melia 1993), and many groups have demonstrated effective surface binding of molecules to sulfide

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Figure 15. The photoreduction of methylviologen (MV) at the surface of TiO2 colloids shows a strong pH dependence. a). The energy position of the TiO2 conduction band (CB) displays a linear Nernstian response to increasing pH, while the redox potential of the MV+/MV2+ couple is pH independent. b). The yield of the reduced aqueous ion MV+ vs. pH shows a strong increase around the pH at which electrons in the TiO2 CB are more reducing than MV+. Used by permission of the American Chemical Society, from Duongdong et al. (1982), Journal of the American Chemical Society, Vol. 104, Pages 2977-2985, Figure 4.

and oxide nanoparticles via assorted terminal functional groups. In particular, biomolecules such as amino acids, phospholipids, siderophores, and even DNA have been shown to stabilize sites on nanoparticle surfaces (Konovalova et al. 1999; Jones et al. 2000; Torres-Martínez et al. 2001; Dwarakanath et al. 2004). Recent studies have addressed the detailed electronic structure associated with functional groups on bacterial surfaces, including the position of the Fermi level (Vyalikh et al. 2004; Ireta et al. 1998), which will permit quantitative treatments of nanoparticle-microorganism interactions. As discussed above, strong chemical interactions can create new electronic states in the mineral, modifying (photo)redox reactivity. The coupled redox reactions of iron and manganese oxides and biomolecules play a vital role in the transformation of organic matter and the production of humic materials. Interestingly, while hydrated biofilms may considerably coat the surfaces of minerals, they appear not to seriously hinder the surface adsorption and reaction of either small molecules or metal cations (Templeton et al. 2003; Toner and Sposito 2005), although certain aqueous ions may partition between mineral surface sites and organic functional groups depending upon pH (Warren and Haack 2001). Roles of adsorbed surface species in redox and photochemistry. The complexation of surface atoms of a nanoparticle by adsorbed molecules can introduce additional electronic states within the semiconducting band gap (see Figs. 11f and 16), a phenomenon termed sensitization. Sorbates can significantly increase the rate of photochemical reactions, because the photon energy threshold for creating an excited electron or hole in electronic bands of the nanoparticle can be greatly reduced (Konovalova et al. 1999; Rajh et al. 2002). Charge injection from the adsorbate to the nanoparticle is generally extremely fast (< 0.1 ps), while electron transfer back to the adsorbate, which would permit electron-hole recombination, may take milli- or even microseconds (Moser et al. 1991; Huber et al. 2000).

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A series of important experiments by the group of Rajh have shown that organic molecules possessing enediol groups (–CHOH=COH–) bind to and hybridize with several metal oxide nanoparticles (Fig. 16, Rajh et al. 2002). This process is particularly efficient because undercoordinated cations on the surfaces of Fe2O3 and TiO2 nanoparticles sit within considerably distorted sites. Ligand binding restores local octahedral symmetry and is energetically very favorable. Furthermore, Rajh et al. (2004) showed that single-stranded DNA binds to the surface of TiO2 nanoparticles and retains the ability to hybridize with complementary DNA. Following direct photoexcitation of the nanoparticle, charge transfer occurs readily onto double stranded DNA, but not single-stranded DNA.

Figure 16. Organic molecules containing the endiol group sorb readily to the surfaces of hematite nanoparticles and introduce electronic states into the mineral band gap. These surface ligands “sensitize” the nanoparticles: much less energy is required to excite electrons in these mid-gap states to the mineral CB, enhancing the probability of generating excited electrons with high reducing power following light illumination. After Rajh et al. 2002.

Examples of nanoparticle photochemistry Photofixation of CO2. There is a considerable technological effort behind using colloidal particles for harvesting solar energy, not only for electrical energy production, but also for performing benign photochemistry, such as the splitting of water (Bard and Fox 1995) or the photocatalytic degradation of organic environmental pollutants (Hoffmann 1995). Following Inoue et al. (1995), the photoreductive “fixation” of atmospheric CO2 has been studied by many groups. In most engineered systems, this pathway has always been rather inefficient, although several groups have claimed improvements using chalcogenide nanoparticles including ZnS and CdS (Fujiwara et al. 1997; Fujiwara et al. 1998). The reaction begins with the step: >CO2 hν → >CO2• − • −

(19) • −

where >CO2 indicates a surface-bound CO2 radical. CO2 is extremely reactive and, following desorption into water at pH 7, produces formic acid (HCOOH), CO, and H2 (Fujita and DuBois 2003). First-principles calculations have shown that two effects (one structural, one purely electronic) can together explain the experimental observation that nanoparticles of CdSe are capable of photofixation of CO2 (Wang et al. 2002), a process not observed for bulk CdSe (Nedeljkovic et al. 1986). As shown in Figure 17, CO2 molecules may chemisorb at Se

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Figure 17. Nanoparticles of CdSe can catalyze a step in the photostimulated fixation of molecular CO2, a role that bulk CdSe cannot play. Left. CO2 adsorbed at a site of a selenium vacancy on the nanocrystal surface achieves electronic overlap with the nanoparticle band structure. Following light absorption by the nanoparticle, a conduction band (CB) electron can be transferred to the CO2. The reactive charged molecule has a low barrier for desorption. Right. Energetic diagram for the photoexcitation of CO2. Nonbonding electrons on surface Se atoms produce localized occupied states above the valence band (VB) maximum in the band gap. Light absorption can excite these electrons to the CB. Due to confinement effects, CB electrons in nanoparticles less than ~5 nm in diameter are sufficiently high in energy to transfer to the lowest unoccupied molecular orbital of CO2. The CB minimum in bulk CdSe lies at too low an energy for this step to proceed. After Wang et al. 2002.

vacancies on a nanocrystal surface. Because the CB lies at higher energy in the nanocrystals than in the bulk, photon absorption creates an excited electron with a redox potential capable of reducing the sorbed molecule to produce a reactive charged radical, with a low energy barrier to desorption. Subsequent reactions can create small organic molecules such as formic acid. There is no evidence that nanoparticle production fulfills a biological role of encouraging CO2 fixation. Nevertheless, the above mechanism is one way that nanoscale minerals affect organic carbon transformations. Moreover, it illustrates the capacity for nanoscale minerals to generate reactive radicals. Photogeneration of reactive oxygen species. Photogenerated radicals derived from water or oxygen are commonly found to be reactive intermediates during heterogeneous photochemical reactions (Gerischer 1993; Hoffmann et al. 1995; Riegel and Bolton 1995; Hall 2000; Torres-Martínez et al. 2001; Garrett et al. 2005). The principal reactive species − + (eCB ) hole          by are formed by the capture of a photoexcited electron          or surface-adsorbed (hVB ) molecular oxygen or water, respectively: ν + >O2 h → >O2• − + hVB

(20a )

ν − >H 2O h → >OH• + H + (aq) + eCB

(20 b)

Hydroxyl radicals typically remain surface bound and can be considered as holes trapped at surface states (Lawless et al. 1991), while the superoxide anion, O2• −, may diffuse into solution. Further reduction of O2• − can generate hydrogen peroxide, H2O2, but H2O2 is not evolved under anaerobic conditions. All of the reactive oxygen species can cause cellular damage (Hall 2000), and interactions between nanoparticles and biomolecules can increase the type and number of reactive oxygen species produced photochemically. For example, the yield of O2• − during photoexcitation of TiO2 is greatly enhanced by the presence of low-concentration carotenoid sensitizers (Konovalova et al. 2004). Furthermore, extracellular electron shuttles such as quinones

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(Nevin and Lovely 2000; Newman and Kolter 2000) can act as diffusive secondary reactive intermediates that propagate cell toxicity (Bolton et al. 2000). However, certain strongly bound surface ligands such as halogenated acetic acids may be oxidized directly without a role for reactive intermediates (Pehkonen et al. 1995). The photoactivity of specific minerals cannot always be predicted from thermodynamic considerations and many environmental minerals are not well studied. The combination of sunlight and dissolved oxygen is required for significant photogeneration of cytotoxic radicals, limiting the impact of these processes to near-surface environments.

The stability of nanoparticles during redox chemistry and photochemistry Stability during redox reactions. An excess or deficiency of charge in a nanoparticle may be delocalized within electronic bands or localized at atomic sites, depending upon the mobility of charge carriers within the mineral. Redox active atoms within a nanoparticle may capture the electron or hole through a valence state change, which can lead to nanoparticle dissolution. Dissolution of insoluble ferric iron and manganese oxide minerals may occur following charge injection from a solution donor (Fig. 11a) (Hering and Stumm 1990), which can include biological electron-transfer molecules (Newman and Kolter 2000; Cheah et al. 2003) or molecular “nanowires” (Reguera et al. 2005), sufficiently reducing inorganic (Missana et al. 2003) or organic (McCormick et al. 2002) species, or photoexcited ligands (Voelker et al. 1997). Reductive dissolution of hematite. Direct electrochemical measurements of Fe2O3 nanoparticles drive dissolution, once the electrode potential is sufficiently negative to reduce the iron oxide particles (McKenzie and Marken 2001). The chemical reductive dissolution of hematite can be driven by numerous reductants (Stumm and Morgan 1996), including dissolved sulfide (Dos Santos Afonso et al. 1992; Poulton et al. 2004). When the reductive dissolution of ferric iron oxides was quantified (using a radiologically generated reductant), the amount of dissolved Fe(II) plus adsorbed Fe(II) could not account for all the reductant consumed (Mulvaney et al. 1988). The reductive dissolution of hematite can be written: Fe 2O3 + 6H + + 2e −  → aFe 2+ (aq) + bFe 2+ (sorb)+ cFe 2+ (inc) + 3H 2O

(21)

where the prefactor a is the fraction of reduced iron liberated into solution, b is the fraction adsorbed at the surface, and c is the fraction of electrons incorporated in the interior of the colloidal particles (a + b + c = 2). The prefactors exhibit a strong pH dependence as expected, and vary with colloidal particle size. Around neutral pH, c ≈ 0.25 for 5 nm diameter particles, but c ≈ 0.7 for micron-sized colloids. Thus, some ferrous iron sites are indefinitely stabilized within ferric iron minerals. The partially reduced minerals are likely an intermediate step in the solid-state transformation to magnetite, perhaps with a disordered structure analogous to the that found as a thin layer upon hematite colloids (Fig. 2; Hansel et al. 2004). The partially transformed nanoparticles are reductants themselves, with an activity that depends both on the effective redox potential of structurally incorporated Fe(II) and its mobility, and partitioning between the surface and interior (Williams and Scherer 2004). As expected, higher-surfacearea particles are more effectively dissolved, but even the smallest nanoparticles retain a number of ferric sites. Stability during photoredox reactions. Many semiconductors that are stable against reductive surface processes can be subject to photocorrosion (Gerischer 1980; Memming 2001). If a nanoparticle participates in the light-stimulated reduction of an acceptor molecule, a hole remains in the VB (Fig. 11e or f). From a molecular perspective, a hole is a partially broken bond (albeit a potentially mobile one). Similarly, a photo-oxidation event (Fig. 11d) entails the production of excited electrons that may drive a local valence state change at Fe(III) and Mn(IV) sites and detachment of the atom (i.e., dissolution).

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Photoreductive dissolution of hematite. Sherman (2005) combines oxygen X-ray absorption and emission spectroscopy to determine the conduction and valence-band energy positions of bulk iron and manganese oxides and oxyhydroxides. The position of the hematite CB relative to the redox potential for hematite dissolution determines whether photoexcitation of the mineral (Fig. 11c) can lead to dissolution. Since at pH 2, E0 = 0.655 V for Fe2+/α-Fe2O3, assuming an aqueous concentration of ferrous iron to be [Fe2+] = 10 nM, and ECBhematite = 0.3 eV, direct photodissolution will occur at pH 2, but not at circumneutral pH. At pH 8.3, the bulk hematite CB is predicted to lie ~0.4 eV below the Fe2+/α-Fe2O3 couple on the AVS scale (Sherman 2005). However, as indicated in Figure 8b, hematite nanoparticles with a diameter of ~2 nm are predicted to possess sufficiently reducing CB electrons for photodissolution to proceed at pH 8. Because of the increase in the band gap, higher energy photons would be required to drive photodissolution of the smaller particles. The presence of additional solution species can also favor the photoreduction of hematite, either by sensitizing the mineral surface (Pehkonen et al. 1995) or by complexing soluble Fe(II) so that the electrochemical potential for iron reduction is made more positive (Kraemer 2004) and lies beneath the CB. Ligand promoted photoreductive dissolution of manganese oxide. Manganese oxides in the presence of dissolved organic matter are sensitive to photoreduction (Sunda et al. 1983; Scott et al. 2002; Haack and Warren 2003). Following light absorption by an adsorbed ligand (e.g., humic and fulvic acids; ascorbic acid), fast injection of an electron into the mineral VB (Fig. 11f) can drive reduction of Mn(IV) to Mn(II), which is subsequently soluble. Since this is a two-electron process, the dissolution rate will depend upon the mobility of electrons in the mineral and is likely to exhibit a significant dependence on particle size. Manganese reduction performs an important role in the redox cycling of Mn, and can impact the bioavailability of nutrient and contaminant adsorbates by releasing them into solution (Crerar et al. 1976). Daily cycles in the quantity of oxidized Mn in a biofilm of Mn oxidizers are consistent with sunlightdriven photodissolution (Bourg and Bertin 1996; Haack and Warren 2003). Photodecomposition of sulfides. Metal sulfide particles are susceptible to photodecomposition in the presence of oxygen (which acts as an electron acceptor), producing soluble metal and sulfate ions. Although stability is greater in anaerobic environments, other solution species, such as sulfite ions, can act as electron donors. For example: CdS(s) + SO32 − + H 2O hv → Cd 0 + SO24 − + SH − + H +

(22)

While the thermodynamics of photodecomposition reactions are known in many cases (Gerischer 1980; Memming 2001), it can be hard to predict particle stability for arbitrary solution chemistry (Kormann et al. 1989).

Nanoparticle interactions with microorganisms The possibility of nanoparticle uptake. When biomineralization serves no known structural, navigational, or nutrient storage function, microorganisms almost universally attempt to restrict the precipitation of solids to the extracellular regions. The cell walls of both Gram positive and Gram negative bacteria (both negatively charged at circumneutral pH) present a considerable barrier to nanoparticle transport (Beveridge 1981; Fortin et al. 1997). However, as illustrated by Figure 18, nanoparticle precipitation frequently occurs to high densities in the periplasm. Presently, most studies on nanoparticle uptake are limited to mammalian cell lines, which in contrast to prokaryotic cells are able to acquire nanoparticles by endocytosis (Parak et al. 2002; Alivisatos et al. 2005). Mechanisms of prokaryotic nanoparticle uptake are (1) nonspecific diffusion through membranes, (2) nonspecific membrane damage-mediated uptake and (3) specific uptake following membrane binding. Metal chalcogenide nanoparticles

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Figure 18. Transmission electron microscopy (TEM) micrograph of a 70 nm section of a sulfate reducing bacterium showing periplasmic accumulation of metal sulfide nanoparticles (dark contrast, labeled “ZnS”). Outer and inner cell membranes are marked by arrows and a mineral-free section of the periplasm is labeled “p.” Aqueous metal cations are likely brought into the periplasm as a result of weak chelation by organic acids (e.g. citrate and lactate) during nutrient uptake, where they precipitate with sulfide ions produced by sulfate reduction. Image courtesy of Ken Williams; see Williams et al. 2005.

(such as CdSe) can be internalized within both Gram positive and Gram negative bacterial species via a pyrine-dependent mechanism provided that the surface is labeled with adenine or (AMP) and that the diameter of the coated nanoparticle is less than 5 nm (Kloepfer et al. 2005). As particle uptake exhibits both a dependence on surface label and light exposure, it is likely that a combination of mechanisms 2 and 3 above is responsible for the nanoparticle uptake. Nanoparticle uptake may facilitate gene transfer. It has been shown that the association of DNA with nanoscale precipitates of calcium phosphate minerals greatly enhanced the efficiency of gene transfer into human cell cultures over what was observed for aqueous DNA in the absence of minerals (Shen et al. 2004). It was unclear whether nanoparticles acted as a vector that carried DNA into the cell, or whether the DNA was released from the nanocomposite before uptake. Nevertheless, such studies indicate that extra-cellular biomineral-associated DNA has the potential for colloid-facilitated transport within the environment, while retaining the capacity for subsequent gene transfer.

Nanoparticle aggregation and its consequences The electron micrographs of Figure 1 suggest that biogenic nanoparticles are seldom present as individual particles, but exhibit a marked tendency for aggregation and deposition onto cell bodies. These processes are governed by particle-particle and particle-cell interaction forces that include electrostatic Coulombic forces (resulting from net charge on the nanoparticle surfaces) and short-range dispersion or van der Waals forces. The classical description of colloid stability—DVLO theory—has been intensely investigated for microscopic colloids (Verwey and Overbeek 1948; Lyklema 2001), although quantitative discrepancies remain, particularly at high ionic strength (Israelachvilli 1992; Boström et al. 2001). In particular, suspensions of colloids separated by net repulsive forces are generally found to be less stable against aggregation than predicted by DVLO theory. Furthermore, the validity of this description has not been established for nanoscale particles, for which a continuum description of the electrical double layer may not be appropriate. Nanoparticle transport and settling behavior is highly affected by the colloid properties of the individual particles, but remains a difficult process to quantify (Tiller and O’Melia 1993; Elimelich et al. 1995). Nanoparticle aggregation processes are beyond the scope of this chapter, so we simply state that numerous experimental studies have shown that mineral particles can be unstable against aggregation

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at circumneutral pH, and that the resulting aggregates commonly possess a complex interior porosity with the characteristics of geometric fractals (Meakin 1988; Dickinson 1989; Amal et al. 1990; Mylon et al 2004). Charge transport in aggregated nanoparticles. Aggregates of semiconductor nanoparticles have been shown to be surprisingly efficient at transporting electrons that have been excited to the conduction band (Fig. 11g) (Wang et al. 2003). This process is initiated particularly effectively by photoexcitation of surface sensitizer molecules (Fig. 11f, where A = second nanoparticle). When the rate of back transfer is slow, the lifetime of the excited electron is sufficiently long to permit its transport to a CB of a neighboring nanoparticle. Particle-particle charge transfer separates the original electron and hole, which then persist until consumed in a chemical step, such as redox reaction or radical formation. Charge transfer between two nanoparticles occurs via a mechanism analogous to electron hopping between localized sites in a crystal. The analogy is quite good, because electronic states on the nanoparticle surfaces trap the electron between particle-particle transfer steps. Typical shallow electron traps on TiO2 surfaces are 25–50 meV below the CB (Hoffmann et al. 1995), so thermally activated transport is possible at room temperature (kBT ≈ 25 meV at 25 °C). However, while a single activation energy barrier typically limits hopping between atomic sites in a crystal, there is generally a broad range of surface states on nanoparticles and hence a distribution of activation energies (Nelson 1999). There are now numerous measurements of the conductivity of nanoparticle aggregates. Theoretical descriptions of conductivity are based on percolation (for fractal aggregates; Avnir 1989) or diffusion models (for nonfractal compact aggregates; Nelson 1999). These approaches are not too sensitive to the details of the particle-particle transfer mechanism and can provide good agreement with experiment. The factors that determine whether electron transport occurs predominantly via nanoparticle surface or interior states remain unclear. This issue has been addressed with temperature-dependent conductivity measurements across arrays of magnetite nanoparticles. In one study on 5.5 nm diameter nanoparticles, a conductivity drop was seen below metalinsulator transition temperature, a clear sign of bulk-mineral-like conductivity (Poddar et al. 2002). In a second independent study of 14 nm particles, no temperature-dependent effect was observed, suggesting that surface-mediated conduction dominated (Redl et al. 2004). Significantly, many investigations of the conductivity through aggregated nanoparticles have been performed on dried layers of deposited particles. However, surface hydration can increase electron transport by orders of magnitude, even when direct conductivity through the liquid is not possible. As shown by Brus (1996), in a series of calculations for porous Si, electrons can couple to the dynamics of solvent molecules, which provides an activated intermediate state for transfer between particles. Charge transfer is considerably more favorable in a polar solvent than in air or vacuum because as a consequence of the solvent, the donor and acceptor energy levels do not have to be closely spaced. Exciton transport in aggregated nanoparticles. Long-range energy transfer (also known as Förster energy transfer) is an electrostatically mediated transfer of energy between two molecules or nanoparticles that have resonant excitation energies (Fig. 11h). Significant energy transfer is only possible if the acceptor has a fast-energy-loss pathway to trap the excitation that otherwise can transfer back to the donor. No activation energy is required, but diffusion of excitations is the dominant process for separations less than ~5 Å. Long-range energy has been demonstrated among aggregated nanoparticles (Kagan et al. 1996) and between nanoparticles and a surface (Achermann 2004), but it is presently not known to be significant in biogenic nanoparticle aggregates.

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Gilbert & Banfield CONClUSIONS

Nanoparticles are integral constituents of water bodies, sediments, and soils, and many microorganisms that rely on an inorganic component to their metabolism inevitably interact with nanoscale minerals. Individual nanoparticles have the capacity to act as mobile redox active species that participate in molecular-style redox reactions. The redox potentials of valence and conduction band electrons, and the kinetics of charge transport and particle diffusion can exhibit a strong dependence on particle size. However, the susceptibility of minerals to dissolution upon valence change means that, in contrast to true molecular reagents, charged or photoexcited nanoparticles generally have mechanisms for transformation and energy loss that are not observed in molecular species. Biogenic nanoparticles form extended aggregates with complex structural, surface chemical and charge transport properties. The surfaces of nanoparticles and their aggregates generally exhibit strong affinity for aqueous nutrients, contaminants and organic biomolecules, including DNA, and can promote (bio)geochemical redox reactions, in some cases at significantly greater rates than observed at the surfaces of bulk minerals.

Acknowledgments We thank Pupa de Stasio, Glenn Waychunas, Ken Nealson and Dan Hawkes for reviewing the manuscript, and Clara Chan, John Moreau, Yohey Suzuki, and Ken Williams for generously permitting reproduction of electron micrographs.

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Reviews in Mineralogy & Geochemistry Vol. 59, pp. 157-185, 2005 Copyright © Mineralogical Society of America

The Organic-Mineral Interface in Biominerals P. U. P. A. Gilbert*, Mike Abrecht, Bradley H. Frazer University of Wisconsin-Madison Department of Physics, and Synchrotron Radiation Center 3731 Schneider Drive Stoughton, Wisconsin, 53589, U.S.A. [email protected] * previously publishing as Gelsomina De Stasio

biominerals: tough structures of life Introduction to biominerals Numerous living organisms form minerals. These biogenic minerals, or biominerals, are composite materials containing an organic matrix and nano- or micro-scale amorphous or crystalline minerals. In this chapter we will review the molecular aspects of biomineralization and describe as completely as is currently possible the organic-mineral interface, the location in which organic-mineral interactions occur. Biomineral composite materials include bone, dentine, enamel, statoliths, otoliths, mollusk and crustacean shells, coccolith scales, eggshells, sponge silica skeletons, algal, radiolarian and diatom silica micro-shells, and a variety of transition metal minerals produced by different bacteria (Lowenstam and Weiner 1989; Addadi and Weiner 1997; Banfield and Nealson 1997; Fortin et al. 1997; Fitts et al. 1999; Templeton et al. 1999; Lower et al. 2001a; Mann 2001; Glasauer et al. 2002; De Yoreo and Vekilov 2003; Weiner and Dove 2003; De Yoreo and Dove 2004). From a materials science perspective, organic molecules are soft, compliant and fracture resistant while inorganic crystals are hard and brittle. Biomineral composites combine the best of these properties and minimize the weaknesses: they are both hard and fracture resistant (tough) (Currey 1977; Jackson et al. 1988; Schäffer et al. 1997; Kamat et al. 2000; Gao et al 2003). This is due to several factors: structure, nano-size and chemical composition. Only recently materials scientists have begun to learn how to build a synthetic composite material that outperforms each component taken separately, and have done so inspired by shell nacre (Tang et al. 2003). The mechanisms of biomineral formation are not fully understood (Mount et al. 2004) and while they are of interest in their own right, they may also provide models for new materials concepts, inspire design solutions and give new insights into the genetic control of biological structure (e.g., Schäffer et al. 1997). Lowenstam (1981) introduced the distinction between the biologically induced mineralization, which is enacted extracellularly or on the cell surface by many algae and bacterial species, and the organic-matrix mediated mineralization performed by many animals (later termed biologically controlled mineralization) (Bazylinski and Frankel 2003; Frankel and Bazylinski 2003; Veis 2003). Eukaryotic biominerals often show complex hierarchical structure from the nanometer to the macroscopic scale. This structure confers mechanical strength and toughness: despite being highly mineralized, with the organic component constituting not more than a few percent of the composite material, the fracture toughness exceeds that of single crystals of the 1529-6466/05/0059-0007$05.00

DOI: 10.2138/rmg.2005.59.7

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pure mineral by two to three orders of magnitude (Kamat et al. 2000). The recent discovery of the silica skeleton in Euplectella sp., and its seven levels of structural organization illustrates one brilliant such hierarchy (Aizenberg at al. 2005), as reported in Figures 1 and 2. In eukaryotes biominerals provide a variety of functions including mechanical protection, movement, grinding, gravity or magnetic field sensing. Conversely in prokaryotes biominerals are often formed as a byproduct of a biochemical pathway in which the bacteria oxidize or reduce transition metals or other species in solution, often for metabolic energy generation (Nealson and Stahl 1997; Frankel and Bazylinski 2003).

Why biominerals For prokaryotes and eukaryotes the complex bioinorganic chemistry involved in biomineralization constitutes a distinct evolutionary advantage for the organism performing it. That advantage is the reason biomineralization became as widely spread as is observed in the three kingdoms of life: Archaea, Bacteria, and Eukarya (protists, fungi, plants, and animals), although very few Archaea are known to be biomineral producers. In the cases of eukaryotic biominerals, the biomineral products are clearly of direct use and benefit. In the case of microbial biomineralization, metal oxidation or reduction can be induced - or exploited - by the bacterium, but mineralization itself may have only indirect advantages or even disadvantages. Biomineral formation often occurs extracellularly and is subsequent to oxidation or reduction. In some cases it is detrimental: entombment of the bacterium in its own biomineral products is possible, and the cell either dies or develops an evasion strategy, such as the formation of mineral sheaths (e.g., Leptothrix spp) or stalks (Gallionella ferruginea).

The organic-mineral interface Many biomineralization mechanisms are poorly understood at the molecular level. These include all cases in which highly oriented crystals are formed with the growth of a particular crystal phase, or polymorph (Falini et al. 1996; De Yoreo and Dove 2004). Mollusk shell, bone and some bacterial filaments are examples of such biomineralization: a highly specialized organic matrix directs the formation of a specific crystal phase, habit, size and orientation. In these composites the organic-mineral interaction is so specialized that a mechanism of epitaxial overgrowth, or templation, can be invoked. The particular matrix of organic molecules, produced by the living organism, acts as a template upon which crystals grow epitaxially, or simply, growth is nucleated, and crystal structure, phase, orientation and often habit of the mineral are determined by the organic matrix. Figure 3 shows a biomineralization paradigm. The paradigm of Figure 3 is not general: it applies to some prokaryotic and many eukaryotic biominerals. It is simply intended to guide our reasoning and give a visual model to refer to in this discussion; it is by no means intended to describe and include every biomineral formation mechanism. Many prokaryotic biominerals, in fact, do not follow such a model. Consistently, however, whether the paradigm applies or not, the organic macromolecules are formed first and they direct or induce the growth of specific minerals and their polymorphs. To this day, the organic macromolecular components have been identified in only a few biominerals. This paradigm, therefore, is to be interpreted as a conceptual mechanism, not as a detailed model of interaction between known molecules. The present chapter discusses the possibility of investigating the organic mineral interface, and the chemical bonds formed at that interface, in essence, zooming in on the interface as shown in Figure 3D. In both biologically controlled and mediated biomineralization (Lowenstam 1981), the organic components are formed first, then these bind a few ions, which serve as nucleation sites for crystal growth (Lowenstam and Weiner 1989; Falini et al. 1996; Gotliv et al. 2005). Self-assembly and epitaxial crystal growth subsequently complete the composite structure discussed in Figure 3. We propose that the exploitation of such templation mechanisms can be

Figure 1

Figure 1. Structural analysis of the mineralized silica skeleton of Euplectella sp., a deep sea sponge from the Pacific Ocean. (A) Photograph of the entire skeleton, showing the cylindrical glass cage. (B) Fragment of the cage structure showing the square-grid lattice of vertical and horizontal struts with diagonal elements arranged in a chessboard manner. Orthogonal ridges on the cylinder surface are indicated by arrows. (C) Scanning electron micrograph (SEM) showing that each strut (enclosed by a bracket) is composed of bundled multiple spicules (the arrow indicates the long axis of the skeletal lattice). (D) SEM of a fractured and partially HF-etched single beam revealing its ceramic fiber-composite structure. (E) SEM of the HF-etched junction area showing that the lattice is cemented with laminated silica layers. (F) Contrast-enhanced SEM image of a cross section through one of the spicular struts, revealing that they are composed of a wide range of different-sized spicules surrounded by a laminated silica matrix. (G) SEM of a cross section through a typical spicule in a strut, showing its characteristic laminated architecture. (H) SEM of a fractured spicule, revealing an organic interlayer. (I) Bleaching of biosilica surface revealing its consolidated nanoparticulate nature. Reprinted with permission of AAAS, from Aizenberg et al. (2005), Science, Vol. 309, Fig. 1, p. 275-278.

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Figure 2. The Euplectella sp. Skeletal system structure (left) resembles that of the Swiss Re Tower in London (top right), the Hotel Arts in Barcelona (center right), and the Eiffel Tower in Paris (fragment shown bottom right). The characteristic structure with vertical and horizontal struts to form a checkerboard pattern, and diagonal struts at every other square is optimized for mechanical strength. Image courtesy of Joanna Aizenberg.

Figure 3. Paradigm for the epitaxial overgrowth, or templation, mechanism in biomineralization. The organic matrix (A) is composed of macromolecules, which depending on the particular biomineral may include a single organic molecule, e.g., a polysaccharide or a complex arrangement of proteins and glycoproteins. In all cases the organic components have charged functional groups that attract ions from solution (B). The steric arrangement of organic macromolecules, their sequence, and folding determines the precise position in three dimensions of the ions. Such positions are only compatible with a specific mineral, even more: they are only compatible with a well-determined polymorph of a specific mineral (C). The crystal structure shown (C) is aragonite, the large white ions in (B) are Ca2+, while the small-white and large-dark atoms are C and O, respectively in (C). (D) Zooming in on the organic-mineral interface: the inter-atomic bonds are indicated by dashed lines. A similar mechanism of epitaxial overgrowth takes place in many matrix-mediated eukaryotic and some biologically induced prokaryotic biomineralization mechanisms.

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considered a “genome shortcut”, naturally selected for minimizing the amount of information the organism must transfer down the lineage, while maximizing the performance of the final composite material. Specifically, self-assembly and epitaxial crystal growth are harnessed by the organism, therefore the only information stored in the genome is that involving the synthesis of the organic macromolecules of Figure 3A.

Zooming in on the organic-mineral interface Several authors suggested that the negatively charged amino acids, aspartate and glutamate, along their protein sequences attract positive ions from solution and initiate crystal nucleation and growth (Mann 2001; Weiner and Dove 2003; Gotliv et al 2005). Certainly the concentration of these amino acids in the known and sequenced organic matrix proteins is very high. They usually constitute between 30 and 40 mol% of the matrix protein, and in some cases even more. The recently discovered “Asprich” family of proteins from the bivalve mollusk Atrina rigida contain more than 50 mol% of aspartate and 10 mol% glutamate, hence their name (Gotliv et al. 2005). Therefore, the paradigm by which negatively charged amino acids collect ions from solution, provide the nucleation sites, and direct the epitaxial growth of biominerals deserves further investigation. A novel set of tools is necessary to discover exactly which molecules interact at the organic-mineral interface, and at which specific molecular sites the first chemical bonds are formed, that is, how biogenic mineral formation begins. X-ray spectromicroscopy, used in combination with other microscopic and biological methods, is one such novel tool to explore the chemistry of templation mechanisms at this interface.

SPECTROMICROSCOPY OF BIOMINERALS XANES spectroscopy of biominerals Only recently has the study begun of templation mechanisms in biominerals using X-ray absorption near edge structure (XANES) spectroscopy. We believe that the understanding of organic-mineral templation can be significantly improved with XANES spectroscopy, because this powerful chemical analysis is sensitive to elemental composition, oxidation state, coordination number, and crystal or molecular structure and orientation of minerals and organic molecules (Stöhr 1992). XANES spectroscopy is a technique first introduced in the early 1980s—under X-ray illumination the sample emits electrons and photons, which constitute a spectrum as the X-ray energy varies. In this section a more detailed description will introduce the biomineralogist to this spectroscopy, to the microscopy version of it, and to the combination of spectroscopy and microscopy, termed spectromicroscopy. The best tool for understanding the chemical and physical properties of any material is one that reports on the x,y,z coordinates of atoms in the material. This can be done with great accuracy (1 Å resolution) using X-ray diffraction on periodic structures, such as bulk mineral crystals or crystallized organic macromolecules, when these can be crystallized. For all other systems, which are not periodic—for example, organic macromolecules that cannot be crystallized—one is left with information that can be retrieved with spectroscopies. Various kinds of spectroscopies detect different characteristics of the specimen: the resonances of nuclei, the vibrational states, the electronic structure and many more. All these approaches are limited, compared to diffraction, but they are all that is available for the majority of organic molecules. XANES is one such spectroscopy, and a very powerful one compared to others, although not quite as informative as diffraction. Its main advantage is its wide range of applicability: it can in fact detect and report on the electronic structure of ordered and disordered materials, minerals surfaces, organic macromolecules, their molecular structure, composition, and the chemical bonds that these molecules form with minerals and nano-crystalline mineral precursors.

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For a complete review of the XANES approach see Stöhr 1992. Briefly, XANES spectroscopy probes a specific element according to its absorption of X-ray photons, at energies that are characteristic of the element and the absorption edge. Absorption edges correspond to transitions between occupied atomic-like electronic shells (e.g., 1s, 2p, 3d, etc.) to unoccupied electronic states (molecular antibonding orbitals such as π*, σ*) that are strongly affected by both the absorbing atom itself and by its neighboring atoms. Transitions from the 1st(e.g., 1s), 2nd(e.g., 2s or 2p), 3rd, etc. atomic shells to molecular orbitals are called K- or L- or M-edges. These probe bonds of the absorbing atom to intra-molecular and, to a lesser degree, extramolecular neighbors. For example the transition from 1s to π* of the C=O in the carboxyl group (COOH) has a distinct resonance (peak in the XANES spectrum) at 288.6 eV. XANES spectroscopy can also detect the presence of specific bonds in molecules, and determine the orientation of molecules or functional groups on the surface of solids. Since the absorption edges of low-Z elements (up to atomic number Z = 30, including all non-gaseous elements from Li through Zn), which are the most relevant for biominerals, are in the 10-1000 eV range of binding energies, a source of photons with such energies must be used to observe such edges with XANES. The only tunable sources of 10–1000 eV photons, called the softX-ray range, are synchrotrons. Synchrotron radiation is also emitted by accelerated ions in the universe such as galaxies and nebulas, but those sources are too distant, their photon flux is therefore too low, screened by the atmosphere, and not easily tunable; therefore galaxies and nebulas are not useful as illuminating sources for XANES of materials on Earth. Thus, XANES spectra are acquired at synchrotron facilities while scanning the photon energy across the absorption edges of the relevant elements. When the photon energy reaches or exceeds the binding energy of electrons in a specific atomic shell, the sample photoemits, that is, it emits electrons by the photoelectric effect. The photoelectric effect was first observed by H. Hertz in 1887 (Hertz 1887); but only explained in 1905 by A. Einstein (Einstein 1905; the centennial celebration is undergoing this year). With his explanation Einstein introduced the concept of a photon, which is a quantum of electromagnetic energy. For this fundamental leap in the understanding of light and matter, and not for the more famous special and general relativity theories, Einstein earned the Nobel prize in 1921. In an effort to avoid a quantum-mechanical presentation, we provide in Figures 4-6 an overview of all the information afforded by XANES, and provide via examples an introduction to this extremely powerful approach, tailored to entice the biomineralogist and the geomicrobiologist. Figure 4 illustrates the sensitivity of XANES spectromicroscopy to the elemental composition of samples, and the possibility of spatially mapping the elemental distributions. Figure 5 shows the sensitivity of XANES spectra to the oxidation states of two transition metals at the L-edges. The dramatic differences introduced in the XANES spectral lineshape by changes in oxidation states make it possible to identify with this spectroscopy the chemical species present in the sample. By combining this information with the imaging capability demonstrated in Figure 4, the spatial distribution of elements and their oxidation states can be determined. As mentioned above, XANES spectroscopy is also sensitive to the crystal structure. Figure 6 shows the difference in spectra acquired from CaCO3 and ZnS mineral polymorphs, that is, minerals in which the chemical formula is identical but the crystal unit cell has a different arrangement. Slight differences in the local structural and electronic environment of elements in alternative crystal polymorphs can give clear fingerprints in XANES spectra. XANES spectroscopy is also sensitive to coordination, that is, the number of atoms to which the element under analysis is bonded. Calcium in calcite is 6-coordinated, that is, each Ca atom is bonded to six oxygen atoms all at the same distance (2.35 Å). In aragonite Ca

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Figure 4. X-ray photoelectron emission spectromicroscopy (X-PEEM) image (top left) of Calothrix cyanobacteria embedded in epoxy and microtomed to a 60 nm thick section. In the top left portion of the field of view a Calothrix filament, containing seven bacteria, is visible, while at the center is the cross section of a single-cell, or possibly a filament extending along the direction perpendicular to the image. The distribution maps of sodium, uranium (from uranyl acetate stain), sulfur, phosphorus and calcium are also reported. The distribution maps were obtained by digital ratio of two images, on- and off-peak at the Na L-edge, U O-edge, S L-edge, P L-edge and Ca L-edge respectively, and represent the local concentration of the relevant element: darker gray levels indicate greater elemental density. Notice the high concentration of Na, S, U and Ca in the outer sheaths, and the high density of P corresponding to bacterial DNA. Sample courtesy of Susanne Douglas.

has 9-fold coordination, with five distinct bond lengths; these are: one oxygen at 2.32 Å, two oxygens at 2.43 Å, two oxygens at 2.51 Å, two oxygens at 2.57 Å and two oxygens at 2.66 Å. Consequently, the differences in the crystal field peaks (arrows in Fig. 6A) between aragonite and calcite may be due to both coordination and crystal structure. In both ZnS polymorphs, sphalerite and wurtzite, sulfur atoms are 4–coordinated (as are the Zn atoms), and the nearneighbor environments are almost identical. The crystal structures are different (cubic and hexagonal), therefore the distribution of atoms in the third and higher shell of atoms around the sulfur sites are distinct, and this is the origin of the spectral differences in Figure 6B. XANES spectroscopy has been successfully used to reveal the presence and oxidation state of specific elements in geologic minerals (Sturchio et al. 1998), the structure of synthetic

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Figure 5. (A) Manganese L-edge X-ray absorption near edge structure (XANES) spectra of manganese oxides. The formal Mn oxidation states are given on the right. (Data from Gilbert et al. 2003a). (B) Iron L-edge XANES spectra from ferric (III), ferrous (II) and metallic iron (0), in the minerals and metal indicated. (Data from Frazer et al. 2005)

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Figure 6. (A) Ca L-edge XANES spectra from calcite and aragonite, trigonal-rhombohedral and orthorhombic polymorphs of CaCO3, respectively. The two main peaks, common to both spectra are the L3 and L2 edges, respectively. Two additional peaks at ~346 and ~350 eV (arrows), due to the crystal field, are prominent in calcite but have lower intensity and different line shape in aragonite. (B) XANES sulfur L-edge spectra from sphalerite and wurtzite, cubic and hexagonal polymorphs of ZnS, respectively. Notice the difference in line shape between 165 and 170 eV. Used with permission of the American Chemical Society, from Gilbert et al. (2003), J. Phys. Chem. A, Vol. 107, Fig. 1, p. 2839-2847.

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materials (Bozek et al. 1990), elemental speciation in soils and sediments (Myneni et al. 1997; Beauchemin et al. 2003; Zawislanski et al. 2003) and other environmentally relevant samples (Myneni 2002a,b; Myneni et al. 1999). Many other experiments on the microlocalization of trace elements in eukaryotic cells (De Stasio et al. 1993, 1996, 2001; Gilbert et al. 2000) and the identification of prokaryotic biomineral products (Labrenz et al. 2000; Lawrence et al. 2003; Lopez-Garcia 2003; Chan et al. 2004), also attest to the power and breadth of XANES spectroscopy and spectromicroscopy. As mentioned, the lineshape of XANES spectra gives information on the molecular and/ or crystal structures surrounding the element under analysis. The interpretation of spectral lineshape and peak assignment, however, can be complicated. When the molecular or crystal structure is known, and relatively simple, ab initio calculations can be used to simulate the XANES spectrum. A comparison of experimental and calculated spectra enables peak assignment to specific molecular structures. Specific peaks can be considered “spectral signatures” of specific molecular features. XANES is extremely sensitive to carbon chemistry: examples of molecular features that generate well-established spectral signatures are C≡C, C=C, C–O, C=O, C–O, as well as C–C–C bond angles, conjugation of adjacent bonds, etc. A material that contains several of these molecular features exhibits a XANES spectrum resulting from the combination of their corresponding spectral signatures: the “building blocks” (Stöhr 1992). For other edges, e.g., Si or S at the L-edge, simulations of XANES spectra are not currently adequate because the electronic structure is too complex to be calculated. In these cases, the spectral signatures do exist and are measurable, but they are not univocally assigned to specific bonds or molecular structures. Unknown minerals, however, such as sub-micron silicate inclusions, can still be identified by empirical comparison with spectra from known, macroscopic, reference silicate minerals (De Stasio et al. 2003; Gilbert et al. 2003b).

XANES microscopy of biominerals XANES spectroscopy has been used to study the same kind of molecular interactions discussed hereafter, but without spatial resolution. Examples include organic-mineral interaction at the binding sites in metalloproteins (Benfatto et al. 2003) or between metal ion and humic macromolecules (Myneni et al. 1999; 2002a). There are practical reasons that, until recently, completely precluded the spectromicroscopy of biomineralized structures, as described below first from a spectroscopy, then from a microscopy point of view. XANES spectroscopy can be performed in two ways: by detecting either fluorescence photons or photoemission electrons (photoelectrons) from a solid sample surface. Fluorescence XANES signal is most intense for high Z elements (Z > 30). These elements have their core shell electrons at binding energies much greater than 1000 eV, therefore the corresponding absorption edges detectable by XANES spectroscopy can only be detected in the “hard-X-ray” regime. On the contrary, low Z elements, which include all the organic elements C, N and O, have their absorption edges below 1000 eV: the C K-edge is at 285 eV, the N K-edge at 400 eV, and the O K-edge at 531 eV. Since none of these edges is easily accessible to the hard-Xray fluorescence range, the organic components of biominerals have never before been studied with fluorescence XANES. Photoelectron XANES, also known as total electron yield or TEY-XANES, is much more intense than fluorescence below 1 keV, where the Si, P, S, and Ca L-edges, and the C, N and O K-edges are located. In this spectral region, a strongly space-averaged TEY spectroscopy

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has always been possible on an insulating biomineral. This is, however, not particularly informative, given the highly organized microscopic structure of biominerals. Spectromicroscopy with X-ray PhotoElectron Emission Spectromicroscope (X-PEEM) adds spatial resolution to the TEY-XANES experiment, down to the 10 nm level (Frazer et al. 2004). Until recently, however, X-PEEM could only image and analyze the chemistry of conductive sample surfaces. Insulating samples such as minerals and biominerals could not be analyzed without major charging problems. Transmission X-ray microscopy experiments (e.g., scanning transmission X-ray microscopy, STXM (Kilcoyne 2003; Tyliszczak 2004), which do not suffer from charging, are limited to very thin solid samples (few atomic monolayers) or dilute liquid samples. Most biominerals, therefore, are excluded from this powerful analysis.

Overcoming charging effects We recently optimized a differential-thickness coating method (De Stasio et al. 2003) that enabled us to extensively study mineral and biominerals surface with X-PEEM and do highresolution imaging and XANES analysis on them (35 nm or better) (Gilbert et al. 2003-2). The coating approach is shown in Figure 7. We have used this coating approach on a variety of insulators, including wood, quartz, zircons, glass slides, tribological polyphosphate and nano-diamond films, cells in culture, mollusk shells and bone. In all these cases the coating completely removed charging and enabled micro- and nano-XANES spectroscopy of insulators. Figure 8 shows a representative example of the results enabled by differential-thickness coating. As aforementioned, the combination of

Figure 7. Schematic diagram showing the preparation steps (top to bottom) for the differential thickness coating. First the biomineral (e.g., a mollusk shell) is embedded in epoxy, then the surface is polished with grit, down to 50 nm if high-resolution imaging is desired, then a thick coating (500 Å) of platinum is deposited by magnetron sputtering on the sample, while masking and not coating the central area, typically 3 mm in diameter, which will then be analyzed by X-PEEM. Finally, a thin coating (10 Å) is deposited on the whole sample surface. The photoelectron escape depth is on the order of 30 Å at the C K-edge, therefore photoelectrons from the shell can be collected through the 10 Å coating at the center. The thicker coating layer around the central region ensures perfect conductivity and a good electrical contact with the sample holder, therefore the sample can be kept at a reliable and stable voltage, it does not charge when electrons are extracted by X-ray illumination, and XANES analysis can be performed (De Stasio et al. 2003).

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Figure 8. Distribution map of Ca in the nacreous layer of pinctada margaritifera, the Tahitian black pearl oyster. Dark indicates higher Ca concentration. The Ca-poor regions between nacre tablets are thicker organic matrix strata that could be due to seasonal changes, as suggested by other researchers of abalone nacre (Lin and Meyers 2005), although the thickness and spacing are different in pinctada. This map was acquired using the spectromicroscope for photoelectron imaging of nanostructures with X-ray s (SPHINX) instrument, which is an XPEEM (Frazer et al. 2004), on a fragment of nacreous layer from pinctada embedded in epoxy and polished.

XANES spectroscopy and X-PEEM microscopy is called spectromicroscopy. From an image such as the one in Figure 8, specific regions of interest can be selected (with the computer mouse), and spectra (e.g., C K-edge XANES spectra) can be extracted, showing different spectroscopic signatures characteristic of the crystals and the organic matrix. This powerful technique can investigate both the organic and the inorganic components of biominerals. Real-time, full-field imaging can be done with a maximum field of view of 180 µm in diameter. At this low magnification the area of interest in a biomineral can easily be identified, then zooming in to higher magnification down to a field of view of 1.7 µm allows highresolution imaging and spectroscopic analysis of biomineral nanostructures. The usual mode of data acquisition consists of acquiring stacks of images while scanning the photon energy, therefore obtaining “movies” that can then be played independent of the synchrotron source. In these movies the third coordinate is energy rather than time, and each pixel (typically 512 × 512 pixels in each image) contains the full XANES spectrum. The number of spectra simultaneously acquired is therefore 2 × 105. The resulting complexity in data analysis and interpretation initiated a considerable effort in software design, which is in constant evolution. From each one of these movies, all the elemental composition, oxidation state, coordination number, molecular or crystal structure information is available, and can be retrieved after data acquisition. Once carbon XANES spectra from the bound mineral-templating and unbound organic matrices of biominerals are obtained, the difference between those spectra reveals the organic-mineral interaction. Interpretation of the data is then done by comparison with the extensive literature on carbon XANES spectroscopy in individual amino acids and organic compounds (Stöhr 1992; Kaznacheyev et al. 2002; Carravetta et al. 1998; Myneni 2002b; Lawrence et al. 2003), or by comparison with reference molecules prepared and analyzed separately for a specific interaction. Two main limitations remain for XANES spectromicroscopy with the X-PEEM approach: the samples must be compatible with ultra-high vacuum, and must be flat. The vacuum compatibility requirement arises from the necessity to collect photoelectrons, which would recombine with gas molecules if these were present in the experimental chamber. The flatness requirement arises from the necessity to keep the sample at high voltage (typically –20 keV) to accelerate electrons away from the sample surface and towards the electron optics column. If the samples have high surface corrugation, greater than ~1 µm, severe distortions of the electric field provoke imaging artifacts and distortions, and in extreme cases even arching and

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sparking which preclude analysis. Surface corrugations lower than 0.5 µm in height, however, are very easy to obtain on solid samples such as minerals and biominerals by conventional surface polishing. Another complication in the XANES X-PEEM approach is the difficulty in separating co-localized mixed phases. In the presence of multiple proteins in a biomineral (for example bone), carbon K-edge spectra may be too complicated to interpret. In that case it is necessary to acquire spectra from separate single-components and deconvolve individual contributions to XANES spectra of the mixture. Separation and/or purification of single components may not be possible. Furthermore, the components may not be spectroscopically distinguishable. If the individual organic components contributing to XANES spectra are known and spectroscopically distinct, singular value decomposition or cluster analysis methods can be used to deconvolve and quantify their contributions (Pickering et al. 2000; Lerotic et al. 2004).

THE ORGANIC-MINERAL INTERFACE IN MICROBIAL BIOMINERALS Prokaryotic biominerals Microbes or prokaryotic cells, which include Bacteria and Archea, are single-celled organisms. They are small, ranging in size from 200 nm to 7 µm, and lack the tissue differentiation and sophisticated external structures immediately apparent in single- and multicelled eukaryotes. They also lack nuclei and membrane-bound internal organelles, with the notable exception of magnetosomes, surrounded by a phospholipid bilayer, in magnetotactic bacteria. Another exception are Gemmatata obscuriglobus, recently discovered bacteria with a double membrane surrounding their nucleoid, making them appear very similar to eukaryotes (Fuerst and Webb 1991; Lindsay et al. 2001). Prokaryotes, however, are among the most abundant organisms on Earth and can be found in virtually every known environment. In the driest location on Earth, the Atacama desert in Chile, 103 bacteria per gram of soil, can be found in the immediate underground (Maier et al. 2004). That number increases dramatically in more hospitable locations, up to 109 bacteria/g of soil in the rolling hills of Tuscany or the rain forests. Bacteria have also been found as deep under the Earth’s crust as man has drilled: over 6000 m underground in a South African mine (Takai et al. 2001; Newman and Banfield 2002). Prokaryotes not only inhabit all natural waters, soils and sediments, they are also capable of surviving in extremes of temperature, pH, or salinity. Additionally, unlike eukaryotes, which depend on glycolysis and require glucose as an energy source and oxygen as an oxidant, prokaryotes adapt to extract energy from diverse and often even multiple chemical reactions (Nealson and Stahl 1997). Sources of metabolic energy include redox reactions of minerals and ions in solution, as well as other inorganic molecules. One of the most eclectic of bacteria, Shewanella putrefaciens, can extract energy from reducing iron and manganese oxides, or sulfur, or fumarate or nitrate or many other compounds, in anaerobic conditions, depending on their availability. If oxygen is available instead, Shewanella becomes an aerobic organism using molecular oxygen to oxidize its energy source (organic carbon or hydrogen) (Myers and Nealson 1990). Returning to the definitions of the different biomineralizations, microbes mostly perform biologically induced mineralization (Lowenstam 1981; Frankel and Bazylinski 2003). Magnetotactic bacteria are an exception, and are, together with coccoliths, the most studied microbes to exhibit biologically controlled mineralization (Bazylinski and Frankel 2003). Biologically induced mineralization is especially significant for bacteria in anaerobic habitats, because in these conditions bacteria respire with sulfate and/or various metals as terminal electron acceptors in electron transport (Frankel and Bazylinski 2003).

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Bacterial communities and biofilms thrive in environments rich in metal ions in solution and play an important role in mineral and/or rock dissolution, formation and deposition. From a materials science point of view, prokaryotes can be considered rock-catalysts: they enact or induce chemical transformations that lead to geochemical cycling and biomineral formation. Minerals formed by biologically induced mineralization are generally nucleated and grown extracellularly as a result of metabolic activity of the organism and subsequent chemical reactions involving metabolic byproducts. Microbes secrete one or more organic macromolecules that react with ions or compounds in the environment, resulting in the subsequent deposition of mineral particles. This biomineralization may be unintended (Frankel and Bazylinski 2003) or advantageous for the organism (Chan et al. 2004). The minerals formed are often nanoparticles with considerable particle-size distributions (Frankel and Bazylinski 2003). A more thorough review of the general characteristics of prokaryotic nanoparticulate biominerals is given elsewhere in this volume (Gilbert and Banfield 2005). As catalysts of biomineralization, however, prokaryotes are ideally configured. The larger the volume of an organism, the smaller the surface/volume ratio. Therefore the smallest organisms, microbes, are the most efficient in rapidly exchanging nutrients and waste byproducts with the surrounding environment. This metabolic advantage also implies that every bacterium can produce many times its body weight in biominerals. This efficiency has a price: bacteria, more likely than other larger organisms, are prone to become encrusted in their biomineral products. Furthermore, the microbial cell walls have a strong negative charge, with multiple sites available for metal binding. Metal ions in solution interact with the charged surface of the cell wall and initiate the formation of minerals. In other microbes, additional structures such as sheaths, capsules, S-layers and filaments provide binding and nucleation sites for mineralization. In addition, bacteria can induce mineralization by secreting extracellular polysaccharides and enzymes that, when released into the surrounding environment, transform minerals already present or induce the precipitation of new minerals and metastable mineral precursors. In the first case, the organic-mineral interface of Figure 3D is located on the surface of bacteria or on extruded but still connected structures, whereas in the second case the biomineralization occurs entirely extracellularly and away from the cell bodies. In both cases, the organic macromolecules induce nucleation and growth of the minerals, and are formed first. In prokaryotic biomineralization, however, a combination of the cell physiology and the chemistry of the surrounding environment determine the mineralization process and the final mineral product. No general statements, therefore, can confidently be made, and the paradigm of Figure 3A is certainly not widely applicable to prokaryotic biomineralization. The only general conclusion, perhaps, is that as a result of prokaryotic biomineralization the mineral changes redox state and the microbe gains energy, while in eukaryotic mineralization there is seldom a redox change, and the organism expends energy to form the biomineral. The structure and dynamics of the microbe-mineral interface can be studied with atomic force microscopy (Lower et al 2001a). The interactions at that interface were also reviewed by Juniper and Tebo (1995). Several groups did spectroscopic analysis of the minerals formed by microbes. Among these, several studies used extended X-ray absorption fine structure (EXAFS) spectroscopy, which explores the structure of the 2-3 nearest neighboring shells of atoms in minerals of biogenic origin. These studies include Suzuki et al. (2002), Tebo et al. (2004), and Villalobos et al. (2005). Another article analyzed the changes in elemental concentrations between adhering and suspended bacteria using hard X-ray fluorescence spectromicroscopy (Kemner et al. 2004). Other studies used XANES or STXM-XANES spectromicroscopy, to analyze the oxidation states of biomineral products (Grush et al. 1996; Tonner et al. 1999; Toner et al. 2005). The latter studies, being all in the soft-X-ray region have the potential of analyzing both the mineral and the organic components of biominerals. This is, again, due to the location in energy of the absorption thresholds of organic elements, C, N, O, etc. However, to

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the best of our knowledge all those studies have focused on the mineral components of bacterial biomineralization. We will later describe the first two cases in which the organic and mineral components, and the interface between them was analyzed using XANES spectromicroscopy (Chan et al 2004; Lawrence et al. 2003). Biomineralization on various prokaryotic structures is reviewed hereafter, including cell walls, capsules, S-layers, sheaths, and filaments.

Bacterial cell walls Bacterial cell walls can be classified into one of two groups based on their reaction to Gram’s stain, a stain used for visible light microscopy. The cell walls of both Gram-positive and Gram-negative bacteria are negatively charged and may induce biomineral formation. However, Gram-negative cells have been shown to precipitate only a fraction of the quantity of minerals produced by Gram-positive cells (Beveridge and Fyfe 1985). The cell wall for Gram-positive bacteria is made up of a layer of peptidoglycans and is separated from the interior of the cell by the plasma membrane. Peptidoglycans are composed of repeating dimers of N-acetylglucosamine and N-acetylmuramic acid. Each N-acetylmuramic acid molecule exhibits a side stem, which is a peptide with four or five amino acids. These stems covalently bond with other stems on neighboring peptidoglycan strands to form a strong and enduring 3-dimensional macromolecular structure that surrounds the bacterium. This cell wall is 15-25 nm thick (Fortin and Beveridge 2000). Both the glycan strands and peptide stems of peptidoglycans are rich in carboxyl groups and give the cell wall a net negative charge. Secondary polymers like teichoic or teichuronic acids, which contain negatively charged phosphoryl groups, are also bound in the peptidoglycan structure and increase the negativity of the cell surface. The large number of anionic reactive sites provided by the peptidoglycan layer is the main source of surface catalysis or mineralization in Gram-positive bacteria. The cell walls of Gram-negative bacteria are more complex, both structurally and chemically. The peptidoglycan layer is much thinner than in Gram-positive cells (3 nm), contains no secondary polymers and is bound on both sides by membranes composed of lipid-protein bilayers. The outer membrane of Gram-negative bacteria is unique and asymmetric: the inner layer is composed of phospholipids but the outer layer contains an unusual lipopolysaccharide (LPS) layer, which is found uniquely in prokaryotes. LPS is a large complex molecule with three components: a lipid core, a core polysaccharide and a short polysaccharide chain that contains unique and species-specific sugar sidechains. The core polysaccharide is rich in anionic phosphate and carboxyl groups and gives the cell wall a net negative charge. The sugar sidechains can extend up to 40 nm from core polysaccharides and may also contain negatively charged carboxyl groups (Langley and Beveridge 1999). In contrast to Gram-positive bacteria, the peptidoglycan layer is not only considerably thinner, but also shielded by the outer membrane in Gram-negative bacteria. Metal ions in the environment, therefore, cannot reach the peptidoglycans, presumably by the same mechanism excluding the Gram stain, and the biomineralization site is constituted of the numerous phosphate and carboxyl groups in the LPS layer (Fig. 9). Active cell metabolism can slow down biomineral formation on the cell wall. A clear example of this behavior is given by Bacillus subtilis cells. During metabolism, a membraneinduced proton motive force continuously pumps protons into the cell wall. Therefore metal ions must compete with protons for anionic cell wall sites, and the result is that these bacteria bind more minerals dead than alive (Urrutia et al. 1992).

Capsules Both Gram-positive and Gram-negative cells can possess additional outer layers that also

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Figure 9. Transmission electron micrograph of Shewanella putrefaciens, a Gram-negative bacterium, exposed to nanocrystalline hematite. The crystals adhere to the cell wall due to its negative charge. This example illustrates that the bacterial cell wall not only binds ions from solution, but also alreadyformed mineral crystals. Reproduced with permission of the American Society of Microbiology, from Glasauer et al. (2001), Applied and Environmental Microbiology, Vol. 67, Fig. 5, p. 5544-5550.

induce biomineralization. Among these, capsules are highly hydrated amorphous matrices of exopolysaccharides or polypeptides, and strongly attached to the cell wall (see Fig. 10A). Capsules extend up to 1 µm away from the cell, and serve as protective shields for bacteria and, as cell walls, contain numerous carboxyl groups (see Fig. 10B). They contain 99 % water and allow for efficient transport of nutrients and waste products (Schultze-Lam et al. 1993). The negatively charged polysaccharides filter and capture the positive cations from solution and induce precipitation away from the cell, thereby protecting the organism from becoming encrusted with minerals.

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Figure 10. (A) TEM micrograph of bacteria, surrounded by exopolysaccharide (EPS) capsules, to which clay nanoparticles adhere. Reproduced with permission from www.nwri.ca/envirozine/images/bacteria_ e.gif. (B) SEM image of another bacterium exhibiting the remains of a capsule. Bacteria in this ground water sample were not fixed, nor treated in any way, therefore the morphology of the 99% water-containing capsule is altered by dehydration. Sample courtesy of Clara S. Chan.

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Capsules also stabilize the metal ion concentration around the cell wall. This is particularly advantageous when the metal ion concentration in the surrounding environment, which naturally fluctuates, reaches toxic levels. Experiments have shown that mutated forms of Klebsiella aerogenes, which do not produce capsules, were unable to survive in concentrations of metals in which the capsule-forming wild-type strains thrived (Bitton and Freihofer 1978).

S-layers S-layers are paracrystalline surface layers 5 to 25 nm thick, containing many ordered repeats of a single protein or glycoprotein. Both Archea and Bacteria may form S-layers. These self-assemble into a well-ordered two-dimensional shell around the bacterium (Sleytr 1997). S-layer proteins or glycoproteins assemble into regular patterns in which the unit cell has 2-, 3- 4- or 6-fold rotational symmetry. The well ordered lattice contains pores that are identical in size and morphology. Since the S-layer becomes the outermost layer for the bacterium, it can serve several functions. In addition to determining the external morphology and shape of the cell, S-layers can be extremely resistant to external chemical challenges such as salts, detergents and even enzymes, and thus provide a protective armor for the cell (Schultze-Lam and Beveridge 1994). An S-layer with well-defined pore size is a barrier for compounds with a large molecular weight and therefore acts as a molecular sieve. S-layers may promote cell adhesion to crystalline surfaces and can also provide a method of surface recognition. Before S-layer formation, the proteins and glycoproteins forming this layer have negative charges, while after formation, as the proteins self-assemble into the ordered structure, the charged amino acids are embedded within the layer, and in most cases the final S-layer presents a net neutral charge at the cell surface. However, some S-layer proteins retain exposed anionic residues and are capable of inducing biomineralization (Schultze-Lam et al. 1993). The cyanobacteria Synechococcus spp, have a 6-fold symmetry S-layer as their outermost surface. This strain showed that strontium and calcium carbonates and other minerals can form on the S-layer (Fortin and Beveridge 2000).

Sheaths Sheaths are well-defined biomineralized structures, such as hollow cylinders, that often surround chains of filamentous cells, and can be sites of biomineralization. Once biomineralized, the sheaths can remain long after the bacteria have died and decomposed. Leptothrix spp oxidizes ferrous iron in solution by secreting a complex matrix of heteropolysaccharides that catalyzes Fe oxidation and precipitation as iron oxyhydroxide (FeOOH) nanoparticles (Banfield et al. 2000). This bacterium thrives in high concentration of Fe and Mn, and leaves behind long sheaths as shown in Figure 11.

Filaments Other bacteria induce the formation of biomineral filaments. The microbial mineral filaments of Figure 12 are formed by iron-oxidizing bacteria that have not yet been isolated nor phylogenetically identified. All these filaments show an unprecedented ~ 2 nm wide, up to 10 μm-long, curved pseudo-single crystals of akaganeite (β-FeOOH) in their cores (Chan et al. 2004), as presented in Figure 12B. The filaments are 20-200 nm wide, tangled, and composed of 2-line ferrihydrite (FeOOH·nH2O), surrounding the akaganeite cores (Fig. 12B). Formation of akaganeite in solution requires the presence of chloride, and is unexpected in fresh water. Chan et al. (2004) therefore suggested that akaganeite formation is catalyzed by organic polymers extruded by the bacteria. In this model, chemical bonds are formed between an organic molecule and ions in solution or amorphous nanoparticles, which are precursors of the crystal cores. As in other biominerals, the organic molecule acts as a template for a particular mineral polymorph, in this case, akaganeite. The paradigm of Figure 3A therefore

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applies to this biomineral formation, although akaganeite crystal cores are formed upon aging

Figure 11. SEM micrograph of the FeOOH sheaths formed by Leptothrix spp in the Piquette mine, Tennyson, WI. Sample courtesy of Clara S. Chan.

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Figure 12. (A) SEM image of mineralized filaments produced by Fe-oxidizing bacteria in the Piquette abandoned and flooded mine in Tennyson WI. The filaments, approximately 100 nm in diameter are mineralized by FeOOH adhesion to the polysaccharide chains immediately after being extruded by the bacterium. On the right hand-side of the image a thinner, faint, strand is visible, possibly a non-mineralized polysaccharide fibril. Image used with permission of the American Journal of Science from De Stasio et al. (2005), American Journal of Science, Vol. 305, Fig. 4. Sample courtesy of Clara S. Chan. (B) TEM micrograph of a mineralized filament similar to the one in (A). The outer structure is formed by 1-2 nm wide 2-line ferrihydrite nanoparticles, while the central core of each filament exhibits a ~2 nmwide crystalline core of akaganeite (β-FeOOH). This crystal core is only 2-3 unit cells wide, and can be identified as akaganeite by its distinctive crystal spacing (0.75 nm). Image courtesy of Jillian F. Banfield.

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of the mineral filaments, not as FeOOH nanoparticles are nucleated and grown. Bacterially extruded polymer fibrils were analyzed using the SPHINX spectromicroscope, and identified as polysaccharides by comparison of their carbon K-edge XANES spectra with those from representative reference compounds (Fig. 13). Mineralized filaments also revealed a polysaccharide spectrum at the carbon K-edge. FeOOH nanoparticles form a ~50 nm thick coating around the polysaccharide fibrils, hence the carbon signal is much lower, relative to the uncoated filaments. Most interestingly, carbon spectroscopy from mineralized filaments revealed a new peak, which was absent from spectra of non-mineralized polysaccharide fibrils. This spectral signature was interpreted as a σ* resonance of a C-O single bond involved in FeOOH binding. It is likely that the C-O groups that interact with FeOOH originate from the carboxyl groups (O=C-O−) of acidic polysaccharides (e.g., alginate). Acidic polysaccharides have an excess of COO− groups that have high affinity for binding positive ions. Chan et al. (2004) concluded that carboxyl groups in the unidentified biofilm polysaccharide chains must be the sites at which FeOOH amorphous nano-precipitates form chemical bonds, templating for the formation of akaganeite crystal cores upon aging (Fig. 13). This is a relatively simple biomineral, in which the biomineral composite has only three components: an unidentified COO−-rich polysaccharide, akaganeite crystal fibers and ferrihydrite nanoparticles. Because of its simplicity, its analysis (still incomplete) suggested a possible templation mechanism, which could be inferred at the molecular level (Chan et al. 2004). Biomineralization of these bacterial filaments has common features with many other biominerals. As a careful reader may have already noticed in all the biominerals reviewed thus far, it is always negatively charged groups along the organic macromolecules that direct the interaction with positively charged mineral ions, such as Fe3+ or Ca2+. In the case of the

Figure 13. SPHINX image and spectra of the filaments produced by iron-oxidizing bacteria. (A) mineralized filaments from the biofilm contain the akaganeite crystal core described in the text. (B) Carbon K-edge XANES spectra from non-mineralized (NM) fibrils and the mineralized (M) filament in (B), and reference organic molecules: alginate, albumin, lipid and DNA. Notice the similarity of the spectra from the NM fibrils and M filaments with the polysaccharide spectrum, and the additional structure in the one from the M filament: the peak at 292.4 eV was assigned to the C-O bond in carboxyl groups. Data from Chan et al. 2004.

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microbial acidic polysaccharides, negatively charged COO− groups are responsible. In cell walls, capsules, S-layers and sheaths, either acidic polysaccharides (rich in COO− groups) or peptide sequences rich in negatively charged amino acids (also exhibiting carboxyl groups) enact the nucleation and biomineral growth. The paradigm of Figure 3, therefore, has one more identified component: at the interface of the inorganic and mineral components is most frequently, perhaps always, a carboxyl group. We will now discuss the biomineralization paradigm in eukaryotes, and highlight the similarities of the core mechanisms.

The organic-mineral interface in eukaryotIC BIOMINERALS Eukaryotic biominerals The majority of animals mineralize at least part of their bodies, usually as internal skeletons or external armors, using a variety of proteins and minerals with calcium carbonates, calcium phosphates, and silica being the most common (Currey 2005). Other eukaryotic biominerals contain a variety of elements, including barium, strontium, iron, manganese, magnesium, copper, zinc, and sulphur. These complex composites, often hierarchically organized, include bone, teeth, eggshell, mollusk shells, crustacean shells, corals, sponge skeletons, the statoliths through which trees sense gravity and grow vertically even on the steepest mountain slopes, the otoliths in the inner ear of most animals, from humans to zebra fish (Söllner et al. 2003), warm jaws (Lichtenegger et al. 2002), and many more composites, in excess of 70 biominerals known nowadays. See Weiner and Dove (2003) and Mann (2001) for the most recent complete lists of biominerals. Eukaryotic biominerals can be distinguished from their abiotic counterparts because of their uniform crystal size and habit and the regular nanostructures that result from biologically controlled mineralization (Weiner and Dove 2003). The control, again, is enacted by the organic matrix and its macromolecules: proteins, glycoproteins, and carbohydrates. Mollusk shells, and in particular that of red abalone (Haliotis rufescens), have been widely studied for their very regular repeating crystalline domains and astounding properties. The nacre layer, or mother of pearl, at the inner surface of the abalone shell has a fracture resistance 3000 times greater than that of aragonite, the pure mineral of which it is composed. The toughening effect is due to well-defined nanolayers of organics at the interfaces between micro-tiles of aragonite (Kamat et al. 2000; Currey 2005). In nacre and many other eukaryotic biomineral structures, the stiff mineral tiles absorb the bulk of the externally applied loads. The alternating organic layers, in turn, provide toughness, prevent the spread of the cracks into the interior of the structure, and even confer a remarkable capacity for recovery after deformation (Smith et al. 1999). Two other structural characteristics of eukaryotic biominerals contribute to the superior mechanical properties of skeletons made from them. First, at the lowest level, they are often made of tiny crystals that are smaller than the “Griffith length” necessary for cracks to spread (Gao et al. 2003). Second, the precision with which they can be laid down (changing their main orientation over a few micrometers, for instance) allows exquisite adaptations to the loads to which the skeletons are subjected (Currey 2005). Nacre is composed of approximately 95 mass percent aragonite and 5 mass percent organic macromolecules. We note that various groups have studied other systems of marine biominerals. For instance, in studies on marine sponges such as Tethya aurantia that form silica needles, research has focused on the role of the proteins and their possible use in organosilicon chemistry. The ultimate goal there is to manufacture silicon based polymeric materials in milder conditions than those used in today’s industry. The proteins responsible for biological silica

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synthesis have received a lot of attention recently, including their very own name, “silicateins” (Shimizu et al. 1998; Shimizu and Morse 2000; Weaver and Morse 2003; Pozzolini et al. 2004). In this section, we will focus on nacre and promote our opinion that the key to nacre formation lies at the organic-mineral interface. Understanding the role of that interface is thus pivotal to the development of biomimetics, that is, the field that imports biologically inspired concepts and mechanisms into the design and fabrication of new materials.

The nano-structure of nacre Mollusk shell and pearl nacre presents a highly regular brick and mortar arrangement in which aragonite tiles, 500 nm thick along the c-axis, 10-20 µm wide along the a and b axes (Mann 2001), and polygonal in shape, form extremely flat layers (Fig. 14). Subsequent layers of aragonite tiles and organic matrix, composed of silk-like proteins and glycoproteins, keep alternating across the entire thickness of the nacreous layer (Currey 1977; Jackson et al. 1988; Schäffer et al. 1997). The regularly repeating layering of nacre, the semi-transparency of aragonite and the pitch of this periodic structure (500 nm), which falls in the middle of the visible light wavelength range (400-700 nm), all combine to generate the iridescence typical of mother of pearl. As the observation angle varies, the color perceived changes due to the variation in apparent spacing between the semi-transparent layers of crystals. Furthermore, there is considerable crystallographic alignment, with the c-axes of most tiles lying in the direction perpendicular to the tiled planes. Aragonite is an orthorhombic polymorph of CaCO3, whereas the outer prismatic layer of all mollusk shells is formed by columns of the trigonal-rhombohedral calcite polymorph. In the prismatic layer the c-axes are along the long axis of each prismatic column, perpendicular to the shell surface and parallel to the nacreous layer c-axes. Epithelial cells form a layer along the inner surface of the shell, called mantle, and secrete all the macromolecules of the organic matrix (see Figs. 15 and 16). Mechanically nacre is stiff and resistant to fracture; it therefore combines the behavior of flexible materials that can absorb energy by rearranging their molecular conformation (distortion and deformation), and that of hard and stiff materials. On the other hand, it does not suffer from the limitations of its components, as it is neither compliant (as most soft materials) nor brittle (as most hard materials). Jackson et al. (1988) reported the Young’s modulus of

Figure 14. SEM micrograph of red abalone nacre tiles seen at a fractured edge.

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Figure 15. Schematic, not to scale, of a vertical cross-section of the outer edge of the shell and mantle of red abalone (Haliotis rufescens) with an enlargement indicating the thickness of each shell structure. The size of the extrapallial space is exaggerated for clarity. Used by permission of the American Chemical Society, from Zaremba et al. (1996) Chemistry of Materials, Vol. 8, Fig. 1a, p. 680.

Figure 16. A proposed model for the organic matrix structure in nacre of the bivavlve shell Atrina serrata, observed in the hydrated state by cryo-TEM. Note that silk was found to be present in both phases, the water-soluble and water-insoluble matrices. Reproduced with permission of Elsevier, from Levi-Kalisman et al. (2001), J. Structural Biology, Vol. 135, Fig. 1, p. 8-17.

nacre in the bivalve Pinctada umbricata to be approximately 70 GPa and 60 GPa for dry and wet samples, respectively, whereas the tensile strength is a corresponding 170 MPa and 140 MPa. The work of fracture varies between 350 and 1240 J/m2 (up to 3000× higher than that of CaCO3) (Jackson et al. 1988). Interestingly, the organic layers are thick along the c-axis, very thin and hard to detect,

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or non-existent on the lateral surfaces of tiles (see Fig. 16). The thick organic layers between tile layers are consistent with the model in which sliding of the tiles give nacre its resistance to fracture (Lin and Meyers 2005). It is conceivable that upon sliding, the chains of looped organic macromolecules stretch and break loops without breaking the main molecular chain, thus conferring nacre with its elastic behavior (Smith et al. 1999).

Start and stop signals in nacre growth Several groups studied the growth of abalone nacre and other mollusks (Addadi and Weiner 1985; Lowenstam and Weiner 1989; Belcher et al. 1996; Zaremba et al. 1996; Lin and Meyers 2005). In nacre the organic matrix is a true matrix: its continuous sheets are formed first (Fig. 3A) and they provide the many nucleation sites, which initiate crystal growth to fill the voids in the three-dimensional organic matrix. This the “start” signal. The position and nature of the nucleation points determines the crystal species and polymorph (Falini et al. 1996), while the structure of the voids in the matrix, presumably, determines crystal habit and size. Laterally, along the a and b axes, crystal growth is stopped by crystal-crystal contacts. At the surface of nacre, which is the growth front, tiles are piled up as stacks of coins, or cones, or Christmas trees, as many authors have called them (see Fig. 15). Lateral growth along the a and b (in plane) directions occurs in these cones until adjoining terraces come in contact. This is one of the “stop” signals, and explains the polygonal appearance of nacre tiles. Vertically, however, the reproducibly perfect thickness of 500 nm must be controlled by another extremely accurate “stop” signal, transduced by the preformed matrix macromolecules. Such signals, and the matrix molecules involved in the growth cessation, are still unknown (Lowenstam and Weiner 1989). Since the aragonite tiles have a relatively small thickness in the c direction (the pure mineral aragonite crystals are much more elongated along that direction, and are much longer than 500 nm), there must be a signal stopping this growth. This signal may be linked to stereochemical adsorption of proteins in the growth of calcite crystals demonstrated by Addadi and Weiner (1985) and Addadi et al. (1987). It can be speculated that the host animal produces the proteins that stop growth in a periodic manner (Lin and Meyers 2005). Another relevant observation is that the size of the aragonite tiles does not depend on the size of the animal. The growth of nacre in space and time has been analyzed in vivo and in vitro using the flat pearl system (Fritz et al. 1994). They found that nacre growth begins with the secretion of proteins that mediate the precipitation of calcite. Other proteins then induce a phase transition from calcite to aragonite (Zaremba 1996; Belcher and Gooch 2000; Lin and Meyers 2005). Some of the matrix macromolecules involved in nacre formation have been identified. Among the known molecules are the insoluble β-chitin central sheet in each organic matrix layer (Addadi and Weiner 1985), insoluble silk fibroin protein layers above and below this sheet, and unidentified soluble acidic macromolecules. Even without identification, however, these macromolecules can be extracted from nacre, exposed to non-shell β-chitin and silk fibroin in a saturated solution of CaCO3 and induce nucleation and growth of aragonite, not calcite (Falini 1996). Aragonite formation is induced by the macromolecules even when seeding calcite crystals! (Thompson et al. 2000). This is particularly clear proof of the role of these unknown acidic macromolecules in polymorph selection, since aragonite is much less stable than calcite. The Thompson et al. (2000) experiment proves that nucleation and polymorph selection are independent in nacre formation. The recent discovery and sequence of Asprich proteins contributes to the clarification of the nature of these acidic macromolecules (Gotliv et al. 2005). Again, as already noted in microbial biominerals, it is the negatively charged amino acids aspartate and glutamate in acidic glycoproteins in mollusk shell that are

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believed to initiate biomineral formation (Mann 1988, 2001; Mann et al. 2000; Weiss et al. 2000; Weiner and Dove 2003; Gotliv et al. 2005). Stereochemical recognition determines the specific interactions between aspartic acidrich proteins and certain faces of various calcium dicarboxylate crystals, which are used as model systems. The specific faces have carboxylate groups oriented perpendicular to the face and can therefore optimally complete the coordination polyhedron around the protein bound calcium ions (Addadi and Weiner 1985). A more recent study, which explored the subtle links between atomic scale dynamics and macroscopic crystal faces, clarifies further this issue and reconciles the stereochemical recognition model with the simple mechanistic model of crystal growth by step propagation across crystallographic faces, the terrace-ledge-kink model (De Yoreo and Dove 2004). A possible stereochemical recognition model for abalone nacre is reported in Figure 17.

Synergy of mechanisms for nacre growth Several mechanisms likely conjoin in the formation of nacre. These are:

• Heteroepitaxial nucleation: in this case nucleation and growth of each aragonite tile are detrmined by the organic matrix sheet beneath it (Schäffer et al. 1997).

• Epitaxial crystal growth of the ith crystal layer, connected to the (i − 1)th crystal layer

Figure 17. Unit cell of aragonite: (a) perspective view (b) normal view showing schematic position of (Asp-Y)n and β-pleated organic matrix sheet. Notice protruding Ca ions on (001) face: black atoms are Ca, small black are C and gray are oxygen. This model is in perfect agreement with the paradigm of Figure 3. Reproduced with permission of Elsevier, from Lin and Meyers (2005), Materials Science & Engineering, Vol. 390, Fig. 5, p. 27-41.

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• After nucleation on the organic matrix sheet, the growth of aragonite may be mediated or catalyzed by proteins in solution (Falini et al. 1996).

All of these mechanisms, and possibly others not yet discovered, are likely to control in synergy the growth and architecture of nacre.

Biomineral glue: the carboxyl group. We have already highlighted the similarity of all organic-mineral interface in prokaryotic biominerals. In the few biominerals thus far analyzed at the molecular level, including prokaryotic and eukaryotic, it is often negatively charged carboxyl groups (COO-) that attract positive ions from solution, and these nucleate for biomineral crystal growth. The carboxyl groups are located either along polysaccharide chains or in acidic amino acids, along the sequences of protein and glycoprotein rich in aspartate or glutamate (Mann 1988, 2001; Mann et al. 2000; Weiss et al. 2000; Weiner and Dove 2003; Gotliv et al. 2005). The very reason the latter are commonly called aspartate and glutamate, and not aspartic and glutamic acid, highlights the fact that they are nearly always deprotonated, and therefore their carboxyl group terminations are negatively charged at physiological pH (the typical pK for both is 4.4; Stryer 1995). Carboxyl-group-rich proteins and/or polysaccharides are the most common and most effective cation-binding macromolecules that any organism can assemble to bind mineral precursors and either control or induce biomineralization. We hypothesize that this is why this molecular functional group was selected in many biominerals as the organic-mineral interface of Figure 3D. In this hypothesis, the carboxyl group is a molecular “glue” of choice for biominerals. Interestingly, even when this hypothesis is not confirmed in specific biominerals, by analyzing with XANES an intact and pristine biomineral from both the mineral and the macromolecule perspective, it is always possible to identify spectral signatures, even if the bond sites and functional groups involved are not those expected.

Conclusion Frequently in biology a gene or a protein is identified but its function is unknown for decades. Even now, in the most intensely studied genomes, 25% or more of the genes are yet to be associated with a function. In biomineralization it is quite the contrary: most often the function, namely, the formation of a specific biomineral structure is identified, but the molecule or molecules responsible for it are unknown. We know, however, that composite biominerals form as a result of complex chemical interactions between organic and inorganic matrices, and that the former acts as a template for the latter, according to a paradigm presented in Figure 3. Few approaches enable the simultaneous analysis of both the organic and mineral components in biominerals and their interface. XANES spectromicroscopy studies of that interface might reveal some of the molecular details of templation mechanisms (e.g., Figs. 13 and 17). We note that at this interface, in diverse eukaryotic and prokaryotic biomineralization, there is frequently a carboxyl group. Acidic amino acids or polysaccharides with excess carboxyl groups are the most common and most effective cation-binding chemical species that any organism can assemble to bind mineral precursors and initiate templation. In this sense COO- is a biomineral preferred “glue”.

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Ultimately, once the molecular-scale chemistry of the interface is elucidated in more biominerals, it may be possible to harness it and synthesize novel biomimetic composite materials that self-assemble and, as natural biominerals, outperform the sum of their components. Two conceivable avenues towards bio-inspired synthetic materials are: (i) templation by structured organic surfaces, such as self-assembled monolayers or Langmuir-Blodgett films and functionalized polymers; and (ii) precipitation from solution with growth modifiers, such as ions, proteins, and synthetic polymers (Han and Aizenberg 2003). The first biomineral-inspired man-made material synthetically reproduced the nacre assembly with alternating organic and inorganic matrices, using synthetic organic molecules and clay crystals. Remarkably, the tensile strength of the prepared multilayers was similar to that of nacre, and the Young’s modulus approached that of lamellar bone (Tang et al. 2004). We envision a future with many more of these synthetic materials, assembling and structuring themselves at different scales as biominerals have done for well over 500 million years. Impact resistant cars, trains and spacecrafts, in which cracks do not propagate might one day have an attractive mother of pearl luster. In the meantime, the best we can do is to analyze and understand at the molecular level the formation mechanisms of biominerals. The paradigm introduced here includes some prokaryotic and many eukaryotic biomineralization mechanisms. In prokaryotic biominerals, however, the organic components are fewer and simpler to analyze, while the mineral diversity is enormous. This is a distinct advantage offered by prokaryotes for understanding biomineral formation. Following that paradigm as an incomplete but useful starting point, and analyzing as many prokaryotic biominerals as possible, we anticipate that the mechanisms of biomineral formation will be further elucidated. The general rules, the exceptions and the anomalies that are characteristic of the living world will eventually be clear for the biomineral world.

Acknowledgments We thank Jill Banfield and Clara Chan for their expert, friendly and continued collaboration, and most importantly for bringing GDS into the exciting adventure of discovering templation of akaganeite crystal fibers. That experiment sparked her interest in biomineralization mechanisms, a field now impossible to abandon! We thank Ben Gilbert and Ronke Olabisi for critically reviewing this manuscript. GDS acknowledges the support of the UW-Graduate School, the Department of Physics, the Synchrotron Radiation Center, Air Force grant FA9550-05-1-0204 and NSF grant PHY-0523905. X-PEEM experiments were performed at the UW-Synchrotron Radiation Center, supported by NSF-DMR 0084402.

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Reviews in Mineralogy & Geochemistry Vol. 59, pp. 187-210, 2005 Copyright © Mineralogical Society of America

Catalysis and Prebiotic Synthesis James P. Ferris NY Center for Studies on the Origins of Life and Department of Chemistry Rensselaer Polytechnic Institute Troy, New York, 12180-3590, U.S.A. [email protected]

Introduction Little is know about the origins of life on Earth. Most scientists believe this event occurred some time within a billion years after the Earth formed 4.6 billion years ago (Ga). It is also possible that the Earth was “seeded” by life transported here by another body like a meteorite or by extra-terrestrials. Most scientists in this field assume that life originated on Earth or in our solar system because there would be little data on which to base a proposal for the origin of life at a location outside our solar system. As it is we have a very rudimentary knowledge of the environments on the primitive Earth in the first billion years on our planet. Some relevant books and reviews include Brack (1998), Fry (2000), Zubay (2000), Orgel (2004), and Ferris (2005). Ten years ago it appeared that we had made good progress on understanding about when life arose and what the environmental conditions on the Earth were at that time. Carbon isotope studies on rocks present on the Earth 3.8 Ga suggested life arose in or slightly after that time period (Mojizsis et al. 1996). In addition, microfossils found in rocks dated to be 3.5 Ga suggested were consistent with the presence of life 3.5 Ga (Schopf 1993). These data have been challenged recently (Brasier et al. 2002) so it is not certain the proposed microfossils were originally living organisms. Also the carbon isotope studies have been challenged (Moorbath 2005). But new and entirely different findings suggest that that the Earth had liquid water and an environment suitable for life 4.3 Ga. (Watson and Harrison 2005). In addition it has been proposed that the early Earth had an atmosphere with a mixing ratio of hydrogen of 0.3 (Tian et al. 2005). This suggests the possibility of an atmosphere compatible with reduced organic compounds. So the good news is that this is an active area of research and there is a chance that enough data will accumulated so that a more accurate picture of the conditions on the early Earth will arise from the current confusion. One of the first problems to consider in studies on origin of life is what is life? How would the first, very primitive life form be recognized? This life form would have just barely transited from the non-living to the living so may be lost in the large excess of inanimate material from which it arose. The first life was probably expending all its energy just staying alive so would be hard to recognize. Another problem is the only models of life we have are the highly evolved forms that surround us on the Earth today. Was the biochemistry of the first life just a simpler form of our protein – DNA world or was it entirely different? We don’t know. Many scientists have proposed a definition of life. The definitions proposed often describe a model of the first life that the individual is investigating. Those postulating an RNA world propose the need for RNA or structures that were precursors to RNA. Those investigating the need for cell membranes propose the need for the origin of life in a contained like a vesicle to 1529-6466/05/0059-0008$05.00

DOI: 10.2138/rmg.2005.59.8

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protect it from losing the essential molecules to maintain this first life. My favored definition is a system of biomolecules that it capable of replication and mutation. This definition does not require that the assemblage of molecules be confined within a membrane nor does it specify the nature of the replicating system present. It has the advantage of being applicable to a variety of different replicating systems that may live in a variety of different environments.

Formation of the Solar System The Big Bang, the starting point for the formation of the universe occurred 13.7 Ga. This event has been described as the simultaneous appearance of plasmas of primary particles everywhere at the same time in the Universe. The next stage in the evolution in the Universe was the spontaneous formation of the lighter elements (hydrogen, helium, lithium and beryllium) from the primary particles by the spontaneous combination of neutrons and protons. Star formation occurred next where there was a greater density of particles and elements in the Universe. This process was the result of a greater density of elements and particles in a region of the Universe that condensed to form stars. Once star formation took place there was a sufficiently high concentration of particles and elements to form carbon in a three-body reaction. The next stage in the evolution of the universe was star formation where the primary particles were concentrated in a star with the initial elements where the close proximity resulted in the formation of carbon via a three-body reaction. Once carbon formed the elements up to and including iron-58 formed spontaneously. Additional energy was required to form the elements with atomic masses greater that iron. This additional energy was provided when the star’s fusion reactor shut down because the nuclear fuel, hydrogen and helium, were consumed. The star then collapsed and then exploded violently and the energy released powered the formation of the elements with masses greater than that of iron-58. The energy released during the supernova blew the elements that formed by the nuclear fusion reactions into the interstellar medium. Some stars containing high levels of carbon distributed large amounts of carbon into the interstellar medium. These violent explosions distributed clouds of dust in the interstellar medium that contained carbon, silicates and an array of other elements. The action of cosmic rays and other energy sources on the carbon initiated reactions with the abundant interstellar hydrogen to generate simple hydrocarbons. These dust clouds have a lifetime of about 108 years. They then collapse, possibly as a result of an external force like a supernova, and star formation together with the formation of the planets and other bodies in the solar system was repeated. When a dust cloud collapses to form a solar system the bulk if the material in the cloud forms the protostar and the remainder of the dust forms small bodies called planetismals that continue to accrete dust and smaller bodies to eventually form plants as they orbit the protostar. Comets form in the outer solar system where they accumulate gases, water, ice, silicates, and organic compounds. The material in comets has undergone the least change of all the orbiting material because it is far away from the radiation released by the protostar when it accumulated sufficient mass to initiate fusion reactions. Comets may have delivered organics and water to the surface of the primitive Earth. After the comets formed they orbited the Sun in the vicinity of the giant planets, Jupiter, Saturn and Uranus. The strong gravitational forces of these planets accelerated many of these comets out of the Solar System to the Oort cloud. This process probably resulted in the passage of comets near the inner solar system and some of the comets may have impacted with the Earth and brought organics and water to its surface. Asteroids orbit the Sun between Mars and Jupiter in our solar system. This collection of small bodies never coalesced to planets because the strong gravitational field emanating from

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Jupiter perturbs them. There are collisions between asteroids as they orbit the Sun. These collisions resulted in the formation of dust and smaller bodies (meteorites) that are ejected from the asteroid belt. Some of this material impacts on the Earth. It is postulated from spectral studies that that some of the asteroids contain organic compounds and the dust and meteorites may released from these asteroids may have brought the organic compounds and water to the primitive Earth that initiated the origin of life. This process of accumulating dust and meteorites on the Earth continues today but at a much slower rate than it occurred on the primitive Earth. It is estimated that about 30,000 tons of dust per year is accreted on the surface of the Earth today. This general model for the formation of our solar system suggests that there will be an abundance of solar systems around other stars in the universe. This appears to be the case since over 150 extrasolar planets have been discovered. Most of the extrasolar planets are equal to or greater than the size of Uranus while few even approach the size of Earth. Planets the size of Uranus or greater may be gas giants and would not be likely to have life, as we know it, on them. The failure to detect Earth-size planets is probably not due to their absence but rather to the detection method used. It is based on the gravitational perturbation of the motion of their star, which is very small for low mass, Earth-size planets. If Earth-size planets are discovered does this mean that the conditions on them are conducive to life? Not necessarily, but it does seem likely that some of these rocky bodies will at least have microbial life. It should be noted that some scientists believe that the Earth was formed under a very restricted set of favorable conditions that may not be present on comparable Earth-size bodies. They also feel that civilizations of the type present on Earth are unlikely to be present on these Earth-size planets (Ward and Brownlee 2000).

The Early Earth It is difficult to obtain data greater than 4 Ga about the primitive Earth’s from the rock record. This is because plate tectonics has resulted in the subduction of most of the Earth’s crust where it was subjected to high temperatures and pressures. This resulted in the destruction of much of the evidence in the rock record of earliest life on Earth. One thing that appears to be known from the rock record is that the oxidation level of the Earth’s crust and mantle 3.8 Ga is the same as it is today (Delano 2001). The discovery of zircons that are 4.0–4.3 Ga old opened up a new window on the primitive Earth in the 4–4.5 Ga time period (Watson and Harrison 2005). This suggests that the impacts of larger bodies, such as comets or meteorites that would have heated the Earth to over 100 °C had decreased to a low level by 4.3 Ga. The high temperatures and pressures resulting from plate tectonics do not alter these refractory zircons. They also contain inclusions of other elements that may provide additional insight into the chemical processes on the primitive Earth prior to 4 Ga. There is evidence from lunar material of a sharp increase in the impacts with the Moon at 3.9 Ga. If it is assumed that the same heavy bombardment occurred on the Earth at 3.9 Ga it could have extinguished much of the life on Earth at that time. If this happened it might have required that the origin of life occurred again after these impact decreased in intensity. It is possible that some life survived the impacts in a niche, like the deep ocean, and this life was the ancestor of life on Earth today. Alternatively, the first life on Earth may have arisen after 3.9 Ga.

Atmosphere If the oxidation level of the Earth’s crust and mantle was the same 3.9 Ga as today then it is unlikely that a reducing atmosphere was present on the primitive Earth (Delano 2001). If the recent claim that the Earth’s atmosphere contained 30% hydrogen is correct (Tian et al. 2005)

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this hydrogen may have altered the overall oxidation state of the atmosphere but may not have changed the oxidation states of the other gases present in the atmosphere. The atmosphere of the Earth is believed to have formed as a result of outgasing the Earth’s crust and mantle. It is unlikely that the gases emitted 3.8 Ga would differ from the carbon dioxide, water, sulfur dioxide and other oxidized gases emitted from volcanoes today. It is not clear whether the presence of 30% hydrogen in an atmosphere containing oxidized gases atmosphere could have been a precursor to the reduced organics that resulted in the protein and nucleic acids, central to life on the Earth today. The large deposits of limestone and other carbonates on Earth suggest that carbon dioxide was an important constituent of the primitive atmosphere. The oceans may have been slightly acidic as a consequence the dissolved carbon dioxide and the acidic oxides of sulfur emanating from volcanoes. The rate of precipitation carbonate minerals would have determined the length of time the oceans and other bodies of water were acidic and were probably not favorable for the origin of life. It is encouraging that there are an increasing number of new findings about the ancient Earth. The field of the origins of life suffers from the lack of definitive data about the environment on the primitive Earth. Consequently, there are a plethora of hypotheses but few facts the support or refute them.

Primary sources of simple organics Earth’s atmosphere. The Miller-Urey experiment was the first laboratory experiment designed to investigate routes to organic compounds on the primitive Earth. In this experiment Stanley Miller passed an electric discharge through a mixture of methane, ammonia, hydrogen and water vapor at 100 °C. After allowing the experiment to proceed for one week the water was analyzed and Miller detected the presence of amino acids. Later he also found hydroxy acids, carboxylic acids and other products. This experiment was carried out with reduced carbon and nitrogen compounds because Harold Urey believed that the atmosphere of the early Earth contained these gases. Up to the present time scientists did not think the primitive Earth had a reducing atmosphere and it was generally agreed that he Miller–Urey reaction conditions are not a valid model for the source of simple organics on the primitive Earth. The proposal that the atmosphere of the primitive Earth contained 30% hydrogen reopens the debate on whether reduced carbon and nitrogen compounds were formed by the Miller–Urey reaction. Even if reduced organics are formed, the rapid photolysis of carbon, nitrogen and sulfur compounds such as methane, ammonia and hydrogen sulfide by solar ultraviolet light suggests that they were present in very small amounts. Meteorites. Meteorites emanating from the asteroid belt may have been an important source of organics on the primitive Earth. It is estimated that greater than 1021 kg of the original asteroid belt reached the Earth’s surface in the form of dust and meteorites (Vokrouhlicky and Farinella 2000). This would correspond to a layer of material weighing 5 × 106 kg/m2 if spread evenly over the surface of the Earth. If the carbon content of that material is about ~1% this would have been equivalent to a layer of carbon 25 m thick over the surface of the Earth (Private communication from Michael Gaffey). Small meteorites are not destroyed when they hit the Earth’s atmosphere or surface since they break into small chunks. Large meteorites also survive their impact with the atmosphere but they generate so much energy when they hit the Earth’s surface that the organics present in them are destroyed. Dust particles from asteroids and comets float down through the atmosphere and make a soft landing on the Earth’s surface. Meteorites are particularly important since they are a direct source of the extraterrestrial material delivered to the primitive Earth. The Murchison meteorite, which fell in the town of Murchison, Australia, was collected shortly after it fell and was therefore less likely to have been contaminated organic compounds indigenous to the Earth. It contains about 2% organic material

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with the bulk of it present as a polymeric organic matrix called kerogen and the remainder of the material present as soluble small organic molecules. The latter is a complex mixture of organic compounds including amino acids, purines, pyrimidines, and carboxylic acids (Table 1) (Pizzarello et al. 2001). Other carbonaceous meteorites contain some of the same compounds so it appears likely that these compounds were present on the primitive Earth. Comets. As noted previously comets formed in the outer regions of the solar system where the solar radiation is lower so the structures of the organics more closely reflect those in the dust cloud from which the solar was formed. It has been possible to determine the structures of some of the compounds by spectroscopic analysis of the coma and tails of comets (Table 2). The energetic particles and UV radiation of the Sun not only melts the ice in a comet to release the organics it also degrades high molecular weight organics and generates the lower molecular weight degradation products. Missions are in progress to directly collect cometary dust and return it to the Earth for structure analysis. For example, the Stardust mission sent a probe that arrived at comet Wild2 in January 2004. It collected dust emanating from the comet and is now returning to Earth where the captured dust will be parachuted to Earth on January 15, 2006. The European Space Agencies Rosetta Mission launched a probe to comet in March 2004. Upon arrival in 2014

Table 1. Soluble organics in the Murchison meteorite.a Class Aliphatic hydrocarbons Aromatic hydrocarbons Dicarboxylic acids Carboxylic acids Pyridine carboxylic acids Dicarboximides Sulfonic acids Amino acids Amines Amides Hydroxy acids

Concentration (ppm)

Compounds Identified

> 35 15-28 >30 >300 >7 >50 67 60 8 n.d. 15

140 87 17 20 7 3 4 74 10 4 7

a

Adapted from (Pizzarello 2001)

Table 2. Some organic compounds observed in comets. Name

Formula

Name

Formula

Methanol

CH3OH

Formic acid

HCOOH

Formamide

HCONH2

Methyl formate

HCOOCH3

Methane

CH4

Acetylene

C2H2

Ethylene

C2H4

Ethane

C2H6

Methylacetylene

CH3C2H

Hydrogen cyanide

HCN

Acetonitrile

CH3CN

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it will launch a lander to the comet to analyze it’s surface and subsurface. The probe will undertake extensive analysis of the comet and volatiles emanating from it. Hydrothermal systems. Hydrothermal systems provide an ecosystem where heat from the Earth’s interior, rather than sun light, provides the energy to support life at the bottom of the sea. These “black” and “white smokers” occur at crustal spreading centers where heat driven circulation of water through crustal material brings dissolved compounds in proximity to iron-containing magma. Here the iron reduces oxidized substances in the water. For example, dissolved sulfate is converted to sulfides and the resultant smoker is a precipitate of metal sulfides formed in the neighborhood of the vents when they exit the 300 °C vent into the 4 °C ocean water. Laboratory studies have demonstrated the reduction of molecular nitrogen at temperatures of 500-700 °C and pressures of 0.1 GPa. Nitrogen oxides have also been reduced under high temperature and pressures of hydrothermal systems using iron sulfides and magnetite as reductants (Brandes et al. 1998). More complex organics that may have been destroyed by the high temperatures in a hydrothermal vent would have been stable if formed a short distance away from the vent. The heat flow from the vent also drives the circulation of ocean water through vent regions that is “off axis” or away from the area close to the magma. The combination of the reducing agents, e.g. metal sulfides that precipitated in the vicinity of the vent, the possible mineral catalysts in the crust and the lower temperatures could have served to generate complex structures that were stable under these reaction conditions. Some laboratory studies have been successful modeling the chemistry in hydrothermal systems. Laboratory findings have demonstrated the possibility of generating methane thiol (CH3SH) from carbon dioxide (Heinen and Lauwers 1996). Carbon dioxide is reduced to acetic acid in the presence of methane thiol (Huber and Wachtershauser 1997). Dipeptides are formed in the reaction of amino acids with carbon monoxide, an iron and nickel sulfide catalyst or methane thiol at 100 °C (Huber and Wachtershauser 1998). Reaction of carbon monoxide with iron sulfate at 250 °C generates the Krebs cycle compound pyruvate (Cody et al. 2000). So far it has not been possible to demonstrate the formation of more complex biomolecules in simulations of the reactions in hydrothermal systems but studies of this type are in progress. At the present time it appears that hydrothermal systems may have served as a source of simple organics that were converted to more complex structures in other environments on the primitive Earth.

Prebiotic Routes to Biopolymer Precursors RNA world Biopolymers are essential structures in life today. Protein enzymes catalyze the synthesis and transformation of chemical processes that drive metabolic and other processes in the cell. DNA stores the genetic information in its sequences. DNA transfers this information to RNA, which in turn brings it to the ribosome, the site of protein synthesis. RNA brings genetic information to the ribosome and it also catalyzes a key step in protein synthesis in the ribosome. The observation that RNA could both store genetic information in its sequences and catalyze reactions, process that are carried our by the DNA and proteins in contemporary life, led to the proposal that the first life could have had one essential biopolymer, RNA. This was the genesis of the RNA world. A big advantage of this postulate was only one biopolymer would have to be formed by prebiotic processes rather than two. Perhaps some simple peptides may have served as catalysts in the RNA world. Eventually the RNA evolved the ability to catalyze the synthesis of proteins that in turn evolved to catalyze the synthesis of DNA.

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Since the emphasis in this review is a world where one type of biopolymer that can both store information and catalyze reactions the prebiotic synthesis of proteins or protein-like structures will not be emphasized.

The structure of and prebiotic synthesis of RNA monomers RNA is a biopolymer composed of monomers linked together. The monomer is composed of three units, bases, ribose and phosphate (Fig. 1). Bases. Two purines, adenine and guanine and two pyrimidines, uracil and cytosine are the principal bases in RNA. Purines are present in small amounts in the Murchison and other carbonaceous meteorites. They are also formed from hydrogen cyanide (HCN) via two routes. The polymerization of HCN generates a black substance called the “HCN Polymer” from which adenine may be extracted in low yields after water hydrolysis. The second route is via the HCN tetramer, an intermediate in the polymerization process, that when photolyzed yields a substituted imidazole (Fig. 2). This imidazole may also be prepared by the reaction of the HCN tetramer with formamidine. Reaction of the substituted imidazole with HCN generates adenine. A variety of other purines can be prepared by the reaction of the imidazole formed by photolysis of the HCN tetramer with other simple molecules.

B (a)

RNA Monomers

HO 5' 3'

O

B

O

P O 5'

-O

2'

O-

3'

OH OH nucleoside

NH2 (b)

Bases

B=

N N N H adenine

N

O

N

N

2'

HN

HO

NH2 N N

O 3' O OH O P O 5' O OO O P O-

O N

NH

N

N

OH O

O O

NH2 O

HN N

O OH O O P O O O3'-end

Figure 1. RNA structural elements.

N H uracil

Pyrimidines

N 5'-end

O

N H cytosine

O

NH2

N

2'

O

N

NH

RNA Oligomer

O

NH2

Purines

(c)

3'

OH OH activated nucleotide

O

N N H guanine

P O

5'

O-

OH OH nucleotide

N

B

O

NH2 N N

OH OH

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Figure 2. The reaction pathway from HCN to some purine bases.

Pyrimidines may be synthesized by the reaction of cyanoacetylene or cyanoacetaldehyde, (the hydrolysis product of cyanoacetylene) with cyanate, guanidine or urea. Pyrimidines have also been isolated from the product mixture obtained by the hydrolysis of the HCN polymer (Ferris and Hagan 1984). Purines and pyrimidines have also been isolated by the hydrolysis of the products formed the polymerization of a mixture of HCN and ammonia that was kept at −78 °C for 27 years (Miyakawa et al. 2002). Most of the products obtained in this study were comparable to those obtained in the reactions carried out for shorter times at room temperature (Ferris et al. 1978). The various sources observed for purines and pyrimidines described above suggest that it is likely that they were present on the primitive Earth. Ribose. The five-carbon sugar ribose is the backbone of nucleoside monomers. Initially it was proposed that the prebiotic synthesis of ribose proceeded by the Formose reaction, the self-condensation of formaldehyde catalyzed by divalent metal ions like calcium. This route dropped from favor because the yield was very low and about 35 other sugars that would have reactivity comparable to that of ribose were also formed so that it was not obvious why these didn’t compete with ribose in subsequent reactions to form nucleosides. Recent investigations suggest that ribose could have been present on the primitive Earth. It may have been possible to selectively isolate the cyanamide (NH2C≡N) adduct of ribose from other reaction products (Springsteen and Joyce 2004). The adduct forms 7-fold faster than the comparable adduct with arabinose and 30 times faster than the adduct with glucose. It crystallizes from water under conditions where none of the other adducts crystallize and this adduct is much more stable than free ribose. The only problem is that cyanamide reacts with formaldehyde, glycolaldehyde and glyceraldehydes, compounds present in the Formose reaction mixture to give adducts that do not react further to give the ribose adduct. So it is necessary to postulate that cyanamide appeared in these mixtures after these intermediates were consumed. Three other routes to ribose have been reported that suggest that ribose was present on the primitive Earth. In one approach the use of a magnesium ion - lead ion mixture as a catalyst for the Formose reaction. This catalytic cocktail reduced the number of products from 35 to the 4 possible pentoses (Zubay 1998). Also of interest was the observation that the catalyst also directs the conversion of any one of the 4 pentoses to a mixture of the all four of them. This lead-magnesium ion mixture also catalyzes the conversion of hexoses to the 4 pentoses. A second prebiotic approach to ribose is the reaction of formaldehyde with calcium hydroxide in the presence of borate. The borate forms a complex with the hydroxyl groups

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of ribose and ribose precursors so that the subsequent reaction with formaldehyde is to form hexoses and polymeric products are inhibited (Ricardo et al. 2004). A third route to ribose is a stepwise synthesis by first reacting glycolaldehyde phosphate with formaldehyde to generate glyceraldehydes phosphate. This in turn reacts with glycolaldehyde to give ribose 2,4- diphosphate. Unfortunately neither phosphate is in 5′-position where it may have functioned as a point for linking nucleotides together via the 5′-phosphate group (Müller et al. 1990). Nucleosides. There is an early report of a prebiotic synthesis of purine nucleosides. It is a dry phase heating reaction of purine bases with ribose in the presence of sea salts or MgCl2 to get 1–8% yields of purine nucleosides (Fuller et al. 1972). No nucleosides were observed when the same reaction procedure was performed using pyrimidine bases in place of the purine. The low yields of purine nucleosides and the absence of formation of pyrimidine nucleosides indicates that this is not a likely prebiotic pathway to nucleosides. Limited progress has been made in other approaches to the synthesis of nucleosides but so far none of the proposed prebiotic syntheses has generated nucleosides in sufficiently high yields to believe that these monomers would be present in amounts large enough to have been starting materials for RNA synthesis. This is an area that requires new ideas and experiments. Nucleotides. Nucleotides can be synthesized from nucleosides by heating them in the solid phase with acid phosphates like ammonium dihydrogen phosphate (NH4H2P04) (Osterberg and Orgel 1972; Osterberg et al. 1973). The reactions are catalyzed by amides like urea. The reaction probably proceeds by driving off the ammonia of the ammonium dihydrogen phosphate to give phosphoric acid that catalyzes the phosphorylation. When this dry heating reaction is carried out with uridine about a 70% yield of a mixtures of the phosphorylated adducts of uridine is obtained. Linear polyphosphates are formed by heating acid phosphates like sodium dihydrogen phosphate (NaH2PO4) (Osterberg and Orgel 1972). The linear polyphosphates are converted to cyclic trimetaphosphate (Fig. 3), which reacts with nucleosides in basic solution to yield triphosphates or with 5′-nucleotides under less basic conditions to give triphosphates in a series of reaction steps (Fig. 3). The yields of phosphorylated products formed by dry heating suggest that these may have been abundant on the primitive Earth if there was a plausible prebiotic synthesis of nucleosides. Another concern is that the conditions under which ribose synthesis and phosphorylation

Figure 3. A reaction pathway from a 5′-AMP and trimetaphosphate to ATP.

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reactions occur are very different. It is difficult to imagine how this series of steps required for nucleoside synthesis occurred on the primitive Earth. More experimental studies are required here.

Vesicles Vesicles, also called lipsomes, are small enclosed, compartments separated from their aqueous environments by a lipid bilayer. It is proposed that enclosed compartments similar to vesicles may have contained a suite of molecules that constituted the first life. It has been observed that an organic fraction obtained from the Murchison meteorite formed vesicles and other structures when mixed with water (Deamer and Pashley 1989). It is not likely that these vesicles are enclosed within a lipid bilayer. It is known that under the proper conditions vesicles form from linear carboxylic acids containing 9 or more carbon atoms when the pH of the solution is close to the pKa of the carboxylic acids. Since carboxylic acids are present in the Murchison meteorite this may be a possible explanation for the formation of vesicles. As discussed in detail below, montmorillonite clay, which enhances the rate of formation of vesicles from carboxylic acids, also catalyzes the formation of RNA oligomers (Hanczyc et al. 2003; see discussion in “Prebiotic Polymerization of RNA Oligomers.” The prebiotic synthesis of linear carboxylic acids may have proceeded in hydrothermal systems where the high temperatures, pressures and the presence of iron may have produced them from carbon monoxide and carbon dioxide (McCollum et al. 1999; Rushdi and Simoneit 2001). The above discussion assumes that the first life required a container to maintain the integrity of the living system. Since this first replicater was extremely simple and probably was unable to catalyze the formation of the fatty acids needed to form vesicles it may have replicated while attached to a surface. In the simplest case the community of RNAs may have been attached to a mineral surface where it captured the necessary nutrients of life as they flowed past. There they could have also bound the metal ions that may have been required for the functioning of this extremely primitive system.

Chirality Life on Earth is composed of a specific handedness of the molecules that it utilizes. For example the proteins are composed of L-amino acids and the RNA and DNA of D-nucleotides (Fig. 4). An L-amino acid is the mirror image of the corresponding D-amino acid and each mirror image molecule is called an enantiomer. It is not known why contemporary life has amino acids with the L-configuration and nucleotides with the D-conformation. One proposal is that it was a chance event that occurred during evolution when life based on D-amino acids and L-nucleotides was not able to compete with L-amino acids and D-nucleotides because of a favorable mutation in the latter. The latter life forms gradually took over the Earth and these configurations have been frozen in place ever since. Another theory is based on the observation of circularly polarized infrared light in the vicinity of interstellar dust clouds (Bailey et al. 1998). Circularly polarized light can be either left- or right-handed. It is proposed that if there is circularly polarized infrared radiation then there will also be circularly polarized UV radiation as well. It is known from laboratory studies that irradiation of a mixtures of enantiomers with one handed circularly polarized UV light results in the faster rate of loss of one of the enantiomers so that an excess of the other enantiomer is left behind (Flores et al. 1977). This postulate has some support by the observation of an excess of the L-enantiomer in some of the amino acids found in the Murchison meteorite (Pizzarello and Cronin 2000). If it is assumed that there is a correlation between the handedness of the circularly polarized light and the L-configurations of the interstellar amino acids reaching the Earth via meteorites then one could conclude that the

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Figure 4. Enantiomers. (a) the amino acid alanine, (b) the nucleoside adenosine.

observed conformations resulted from the interstellar circularly polarized light. So far there has been no proof of this postulate.

Prebiotic Polymerization of RNA Monomers The condensation of RNA monomers to oligomers in aqueous solution at pHs near neutrality is not favored energetically. The reactivity of the 5′-phosphate group must be activated for the reaction to proceed. In contemporary biochemistry the 5′-phosphate group is activated by the attachment of a diphosphate group to the 5′-nucleotide. The nucleotide triphosphate group is quite stable in aqueous solution and only generates RNA polymers in the presence of a polymerase enzyme. It was observed that some amine derivatives of the 5′phosphates (phosphoramidates) are effective activating groups for the monomeric nucleotides. Adducts of imidazole (Fig. 5a,b) and 1-methyladenine (Fig. 5c) have been observed to be effective activating agents (Weimann et al. 1968; Prabahar and Ferris 1997). Some montmorillonite clays have been shown to be effective catalysts of the oligomerization of both phosphorimidazolides and phosphoro-1-methyladenylides. It was possible to generate oligomers (short polymers) that contained 10 monomer units (10 mers) (Ferris and Ertem 1992). It was then observed that a synthetic 10 mer could be elongated to a 40-50 mer using “feeding reactions” where the activated monomer is added daily to the reaction mixture. The 40-50 mers were observed after feeding for 14 days (Ferris et al. 1996;

Figure 5. Activated nucleotide monomers where B is a purine or pyrimidine base. (a) nucleoside 5′phosphorimidazolide (ImpB), (b) nucleoside 5′-phosphoro-2-methylimidazolide (2-MeImpB), (c) nucleoside 5′-phosphoro-1-methyladenium (1-MeadpB), (d) α- nucleoside 5′-phosphorimidazolide (α-ImpB).

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Ferris 2002). The previous studies were performed using imidazole as the activating group. It was recently observed that 40 mers were formed in 1 day when 1-methyladenine is used as the activating group (Huang and Ferris 2003). It is possible to form longer oligomers of RNA because the catalyst selectively directs the reaction pathway to the formation of a limited number of reaction products. If the catalyst were not selective then the formation of longer oligomers would not occur because all possible isomers would be formed and these side reactions would consume all the activated monomers before 40-50 mers are formed. It has been calculated that it would be required to form 1048 isomers weighting 1028 grams, an amount equal to the mass of the Earth, to prepare a mixture that contained two identical 40 mers (Joyce and Orgel 1999). Sequence selectivity was demonstrated in the montmorillonite-catalyzed reaction of a mixture of the four activated nucleotides. Here 80 % of the dimers formed were 8 of the 16 possible dimers (Ertem and Ferris 2000; Miyakawa and Ferris 2003). In the second study of the reaction of active ImpA with ImpC the pentamer fraction contained 4 main products in yields of 4.3–13 % while random synthesis would have generated 512 products with a 0.2% yield of each (Miyakawa and Ferris 2003). Selectivity was also observed in the formation of phosphodiester bonds. The montmorillonite-catalyzed reaction of ImpA generates oligomers in which about 67% of bonds formed were 3′,5′-linked. In the absence of catalysis the percentage of 3′,5′-phosphodiester bonds are about 20%. The outcome of the reaction of D,L- ImpA has been investigated to determine if oligomers form from a D,L-mixture. It was not expected that there would a selective reaction of only one of the enantiomers during montmorillonite catalysis because montmorillonite does not have D- and L-conformations. A preponderance of homochiral oligomers over what was expected was observed. That is there was an excess of both all D and all L-enantiomers over the expected yields of D,L- and L,D- dimers. Similar results were observed with the trimers formed. Heterochiral products predominate in the reaction of D,L-ImpU.

Non-Enzymatic Template-Directed Synthesis of RNA RNA is essential to early life because information is stored in its sequences of purines and pyrimidines. The process of template-directed synthesis preserves this information. Information is preserved in contemporary biological systems because the sequences in the RNA can be replicated in a two-step process from the complementary chain (Fig. 6a). The key to the replication process is the selective hydrogen bonded interactions with G and C (Fig. 6b) and those with A and U. In contemporary biology the monomers of the nucleotides that are activated

Figure 6. Template-directed synthesis. (a) schematic replication of a template of the 8 mer of C and the formation of an 8 mer of G, (b) Watson-Crick hydrogen bonding between G and G.

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with the triphosphate group interact with their complementary bases in the RNA and then the monomers are coupled by a polymerase enzyme to form the complementary chain. The original chain and the complementary chains dissociate and the template directed synthesis takes place on the complementary chain to regenerate the original chain. The errors in this process provide a mechanism by which evolution occurs. Leslie Orgel and coworkers discovered that template-directed synthesis does occur to a limited extent in the absence of catalysis. The best example of this is the formation of oligomers of G by the reaction of 2-MeImpG (Fig. 5b) on a poly(C) template (Fig. 6a) (Inoue and Orgel 1981). Unfortunately the other possible template-directed syntheses are not as successful. The synthesis of oligo(A)s on a poly(U) template gives lower yields of shorter of oligo(A)s. The reactions of activated pyrimidine nucleotides on polypurine templates does not yield oligopyrimidines. Replication of an RNA chain is not possible in the G–C system because the formation of the complementary oligo(C)s on a poly(G) template was not successful. These observations suggest that the RNA world will need a catalyst (RNA?) or some changes in the reaction conditions or reagents to initiate the replication of RNA. It has been estimated that a library consisting of about 1020 sequences of 40 mers is likely to contain at least one self-replicating RNA molecule (Joyce and Orgel 1999). Mineral and/or metal ion catalysis may have generated the catalytic RNAs that catalyzed RNA replication but to date none been discovered.

Alternative Genetic Systems It was noted in “Nucleosides” that there are deficiencies with the currently proposed prebiotic syntheses of nucleosides and nucleotides. In addition, a plausible prebiotic formation of the activated monomers has not been accomplished. These deficiencies prompted the search for simpler monomers from which to form a replicating system with the expectation that this simpler system would in turn invent the RNA world. Carboxylic acid ester groups, polypeptides with interacting charged side chains, link some of the proposed polymers. In one instance it was proposed that a replicating clay mineral initiated the first life that catalyzed the formation of organic molecules that were more efficient than the clay as catalysts and they took over clay life to form life based on organic molecules (Cairns-Smith 1982). There are no experimental data to support the later hypothesis. A more complicated group of alternative biopolymers have incorporated some of the structural features of RNA, such as being linked by phosphoester bonds and the utilization of the same bases and base pairings as RNA. Eschenmoser and coworkers initiated a research program to undertake a systematic investigation of structures that are similar to RNA and not the prebiotic synthesis of biopolymers (Eschenmoser 1999). Their first synthetic target was homo-DNA in which the five-membered ring of RNA was replaced with a sixmembered ring. Next they prepared pyranose-RNA (p-RNA), which Figure 7. Nucleotide also had a six-member ring in which the phosphodiester bond differed of threose. in location from that of homo-DNA. p-RNA also differed from homoDNA in that would form a double helix with a complementary strand. A recent addition to the RNA analogs is TNA, which is based on the four-carbon sugar threose (Fig. 7). This is especially interesting since it not only forms double helices with complementary TNA strands it also forms a double helix with a complementary RNA strand even though threose only contains one less carbon atom than ribose (Schoning et al. 2000). This structure has the advantage of a simple evolution of RNA from TNA by template-directed synthesis of RNA monomers on a TNA template. The TNA would have to have catalyzed the biosynthesis of the RNA monomers.

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A similar approach to preRNAs is peptide RNA (PNA), which was prepared as an analog of RNA, since it has about the same distances between the monomer units as RNA (Fig. 8). The other advantage of PNA is that it has no chiral centers. It forms double helices with itself and with RNA. Once it forms a double helix with RNA it assumes the chirality of the RNA to which it is bound. It can be used as a template for the formation of the complementary RNA (Böhler et al. 1995; Schmidt et al. 1997). One problem is that it has no charges on its backbone so longer oligomers are not likely to be soluble in water. There are no complete prebiotic synthesis of any of these pre-RNAs so it has not been demonstrated how they could have been formed on the primitive Earth (Miller 1997).

Figure 8. PNA. (a) monomer, (b) oligomer.

Examples of Mineral and Metal Ion Catalysis in Prebiotic Chemistry Minerals and metal ions have been investigated as potential catalysts for prebiotic reactions. There are few general principles to guide the experimentalist in choosing a particular metal ion to catalyze a proposed prebiotic process. Their selection has been mainly based on (1) their ability to bind reactants (2) their utilization in contemporary biological systems to catalyze reactions similar to proposed prebiotic reactions (3) just try available metal ions. Examples of minerals and metal ions used to bring about prebiotic reactions and what is known about the source of their catalytic activity will be outlined here. It is my view that these catalysts were central to the formation of the biopolymers that were essential to the origin of life since it is unlikely that polymers could have formed without them.

Non-catalytic formation of biopolymers; polypeptides The simplest role that a mineral could play in the formation of biopolymer is to serve as a matrix on which polymers would bind and growth. These minerals are not catalysts so the key to the polymer growth is the increase in the strength of binding of the oligomer to the mineral as it increases in length. In addition, the rate of oligomer formation must be greater than its rate of hydrolysis. This procedure requires that the substrate binds to the mineral but does not require that the mineral catalyze the reaction (Ferris et al. 1996; Orgel 1998). Feeding reactions in which a condensing agent that is used to drive polypeptide formation from amino acids on mineral surfaces may generate polypeptides containing up to 50 monomer units. Hydroxyapatite, Ca5(PO4)3(OH), binds the acidic amino acid glutamic acid via the Ca2+ of the mineral. After 50 feedings of the glutamic acid together with the condensing agent, 1,1′-carbonyldiimidazole (CDI) (Fig. 9), in the presence of hydroxyapatite yields polypeptides containing up to 45 monomer units. Similar results were ob-

Figure 9. The condensing agent 1,1′-carbonyldiimidazole (CDI).

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tained using the clay mineral illite (Hill et al. 1998). Aspartic acid oligomers were formed on hydroxyapatite using 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDAC) as the condensing agent but oligomer formation decreased when CDI was used as the condensing agent (Liu and Orgel 1998). The positively charged amino acid arginine was elongated on both illite and FeS2 using CDI as the condensing agent. Variation of the structure of the amino acids used and the surface of the mineral indicates that the extent of oligomer formation depends on a number of factors (Hill and Orgel 1999). This procedure has not been successful with amino acids with not net charge, such as alanine, since they have weak binding interactions with the mineral surfaces.

Montmorillonite catalysis of RNA synthesis Structure and properties of montmorillonite. Montmorillonite is a 2D sheet of corner linked SiO4 tetrahedra bound to layers of edge-linked AlO6 octahedra (Fig. 10). The montmorillonite platelets associate with each other via interlayer cations and van der Waals forces. Montmorillonite is a clay mineral Key: formed by the weathering of volcanic Oxygen ash. Its composition varies depending on the other elements present in the Hydroxyl exchangeable cations environment where it is formed. Mag2+ 2+ water layers Aluminum nesium ion (Mg ) ferrous ion (Fe ) and ferric ion (Fe3+) are often incorporated Silicon into the octahedral layer positions. In addition, Al3+ may be substituted for Magnesium, Iron tetravalent silicon (Si4+) in the tetrahedral silicate layer. While the theoretical formula is Al4Si8O20OH)4, the actual formula for a Wyoming montmorillonite, with Fe3+ and Mg2+ in the octahedral layer and Al in the tetrahedral layer and 0.67 monovalent exchangeable cations is (Al2.33Fe0.68Mg0.47)(Si7.71 Figure 10. The layer structure of montmorillonite. Al0.29O20(OH)4X0.67. Since the number of oxygen atoms in the montmorillonite sheet is constant the lattice has a net negative charge that is balanced by the charge of 0.67 cations. The associated cations that neutralize this negative charge usually reside in the interlayers between the montmorillonite sheets. Dry montmorillonite expands when water is added due to the solvation of the interlayer cations. When organics bind in the interlayer the sheets come further apart if the binding energy between the sheets is less than the binding energy of the organic compound. Van der Waals interaction between organic molecules and the silicate layer is often the force that attracts organic compounds to bind in the clay interlayer. Montmorillonite found in deposits on Earth usually contains a mixture of cations in its interlayer that reflect those present in the environment where the montmorillonite is formed. Na+, Ca2+ usually predominate. It is possible to exchange this mixture of cations with a single cation in the laboratory to obtain a homoionic montmorillonite. This is usually done before investigating possible catalytic activity so as to avoid chemical processes due to the interlayer cations. Substances with multiple negative charges may bind at the acidic edges of the montmorillonite sheets. These include polyphosphates, dicarboxylic acids and polyanionic polymers. The edges have Al+3 with three oxygens bound to it. The fourth oxygen atom is not there because that is the bond where the sheet was broken. A water molecule will bind at Al+3 via the lone pair of electrons on the water molecule. This coordination enhances the acidity

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of the water molecules and generates an acidic site that can transfer a proton to a basic or a negatively charged molecule. RNA oligomer formation. In most of our studies on the montmorillonite-catalyzed formation of RNA oligomers homoionic Na+-montmorillonite was used. It was observed that other alkali and alkaline earth cations also gave catalytically active montmorillonites with the exception of Mg2+. When other divalent cations like Ni2+ and Cu2+ serve as the exchangeable cations the montmorillonite does not catalyze RNA oligomer formation. It was also observed that the acid titration exchange procedure of (Banin 1973; Banin et al. 1985) was essential for the preparation of most of these catalytically active montmorillonite. In most instances an alternative procedure where the clay is treated with an excess of NaCl to give Na+montmorillonite does not generate a catalytic montmorillonite. Not all montmorillonites catalyze oligomer formation. There may be a correlation between the increase in the iron content of the clay lattice and the increase in catalytic activity (Ferris et al. 1990). This potential correlation breaks down for the nontronites where Fe3+ replaces almost all the Al3+. Binding studies established that purine nucleotides bind more strongly to montmorillonite than do the pyrimidine nucleotides. This is consistent with the greater van der Waals interactions between the purine ring and the silicate surfaces of the montmorillonite than the smaller pyrimidine ring (Kawamura and Ferris 1999; Ertem and Ferris 2000). That the reaction of activated mononucleotides occurs in the clay interlayer was determined by first treating the montmorillonite with tetraalkyl ammonium salts (Ertem and Ferris 1998). Dodecyltrimethylammonium cations (Fig. 11a) inhibit oligomer formation while tetramethyl ammonium ions (Fig. 11b) did not. The inhibition resulting from the substituted quarternary ammonium salts is due to the positively charged quarternary ammonium group binding to the negatively charged clay lattice and hydrophobic interactions between the long alkyl groups of the quarternary ammonium salts in the interlayer. These alkyl groups fill up the interlayer and bind so strongly that they are not replaced by the activated RNA monomers.

Figure 11. Quarternary ammonium salts. (a) dodecyltrimethylammonium, (b) tetramethylammonium.

It has also been observed that the deoxypyrophosphate derivative, dA5′ppdA, (Fig. 12) inhibits oligomer formation (Wang and Ferris 2001). It is proposed that the strong inhibition by dA5′ppdA is the result of the van der Waals interaction of both adenine rings to the silicate layers so that it is not possible for an RNA monomer, with only one adenine ring, to displace it from the interlayer. The chemical reactivity of the 3′-OH of deoxynucleotides is much less than that of the 2′,3′-hydroxyl groups of ribonucleotides so that reaction at the 3′-hydroxyls of dA5′ppdA does not occur (Ferris and Kamaluddin 1989). The possibility that the catalysis occurred at the edge sites of montmorillonite was also

Figure 12. Deoxyadenosine pyrophosphate (dA5′pp5′dA).

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investigated (Ertem and Ferris 1998). Trimethylsilyl groups bound to the edge silanol groups or fluoride ion bound to the trisubstituted edge aluminum groups (Fig. 10) resulted in some levels of inhibition as determined from the slightly shortened chain lengths of the oligomers formed. This finding suggests the absence of edge catalysis because the formation of oligomers was not strongly inhibited. As noted in “Prebiotic Polymerization of RNA Monomers,” the preparation of oligomers containing 40–50 mers has been achieved by two experimental approaches. These are feeding reactions where activated monomers are added daily for 14 days to a decamer primer yielded 50 mers and reactions where 1-methyladenine is used instead of imidazole as an activating group generate 35–40 mers in a one day (Huang and Ferris 2003). It is not clear why it has not been possible to generate oligomers longer than 40–50 mers. One possibility is the binding of the oligomers to montmorillonite increases, as the oligomers grow longer. Since oligomers must be mobile on the clay surface for them to react with activated monomers to increase in length, very strong binding may decreases their mobility on the surface so they block the catalytic sites in the interlayer. Another possibility is that the oligomers formed fill up the interlayer so no more activated monomers can be accommodated there. Uranyl ion catalysis of RNA oligomer formation. Uranyl ion (UO22−) catalyzes of the formation of oligo(C)s, oligo(A)s and oligo(U)s with chain lengths up to 10, 16 and 10 mers, respectively, from the corresponding phosphorimidazolides of nucleotides starting compounds (Sawai et al. 1989) (Sawai et al. 1992). There are some similarities between the reactions catalyzed by UO22− and montmorillonite. The optimal pH for both catalysts is 8; cyclic dimers are formed in high yields from the activated pyrimidine nucleotides. Mainly 2′,5′- phosphodiester bonds form in reactions of activated pyrimidine nucleotides catalyzed by either UO22− or montmorillonite. There are some differences in the reaction of ImpA catalyzed by UO22− or montmorillonite. Neither cyclic dimers nor high yields of 3′,5′-linked oligomers are formed in the UO22− catalyzed reactions of ImpA. Higher yields of oligomers are observed in the UO22− catalyzed reactions because the extent of the hydrolysis reaction of the activated monomers is much lower than that observed with montmorillonite. Catalysis of RNA oligomer formation by lead and other metal ions. Lead (Pb2+), zinc (Zn2+) and lanthanide metal ions have been observed to catalyze the formation of 5–10 mers from ImpA. Pb2+ is second only to UO22− as a catalyst. The ratio of the yields of oligomers formed from the lanthanide metal ions to the yield of hydrolyzed activated monomer increases with the atomic weight of the metal ion where the highest ratio is 49 with Lutitium (Lu3+) (Sawai 1988; Sawai and Yamamto 1996). Zn2+ gave results similar to those of Pb2+ except the longest oligomers observed were only tetramers and the oligomers formed had mainly 2′,5′links (Sawai and Orgel 1975). The Pb2+ catalysis is enhanced when the reaction is performed in the eutectic liquid phase of water at −18 °C for 20–40 days when most of the water is present as ice crystals. Ice predominates reactants and the Pb2+ catalysts are concentrated in the small liquid phase. Here oligomers as long as 17 mers are formed with overall yields of 80–90% (Kanavarioti et al. 2001; Monnard et al. 2002, 2003). The higher yields and longer oligomers are due in part to the high concentrations of reactants and the slower rate of hydrolysis of the activated monomers. It is postulated that base stacking and ordered monomer assemblies on the ice surfaces enhances the chain lengths in the eutectic phase but there is no specific data that supports this claim.

Metal ion catalysis of template-directed synthesis Zn2+ and Pb2+ catalyze the template-directed synthesis of RNA oligomers from ImpG and ImpA (Fig. 6a) (Sleeper et al. 1979; Lohrmann et al. 1980). Pb2+ catalyzes the poly(C) template-directed synthesis of mainly 2′,5′-linked oligo(G)s that containing up to 40 mers.

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Pb2+ also catalyzes the formation oligo(A)s on a poly(U) template but the maximum chain length is only 7 mers. Here 75% of the phosphodiester bonds are 3′,5′-linked. Zn2+ catalysis of the template-directed synthesis of oligo(G)s in the reaction of ImpG on a poly(C) template gives oligomers as long as 30 mers that are 75% 3′,5′-linked. When the reaction is performed in the absence of Zn2+ the oligomers formed are mainly 2′,5′-linked. Zn2+ does not catalyze the template-directed formation of oligo(A)s. In the investigation of the reaction pathway it was observed that 2 Zn2+ per ImpG gave optimal oligomer yields. One of the zinc ions can be replaced with Mg2+. X-ray structure analysis of the Zn2+-nucleotides complexes show Zn2+ binding at both N7 and the phosphate of the nucleotide. It is tentatively proposed that in this complex one Zn2+ binds to N(7) and either Zn2+ or possibly Mg2+ binds to the imidazole (Birdson and Orgel 1980). No postulates on the reaction pathway of the template-directed reactions catalyzed by Pb2+ have been proposed other than the coordination of the lead ion with the 2′-hydroxyl of the nucleotide. The main basis for this suggestion is the preferential formation of 2′,5′-phosphodiester bonds (Sleeper et al. 1979). Other metal ions have been tested as catalysts for the template-directed synthesis of oligo(G)s from ImpG besides Pb2+ and Zn2+ (van Rode and Orgel 1980). Of the 17 tested, 4 (Bi3+, Sn2+, Sb3+, and Mn2+) exhibited catalytic activity. All of these generated lower yields of oligomers than were formed by Pb2+catalysis and all the oligomers were linked by 2′,5′phosphodiester bonds. The divalent transition metal ions that would be expected to coordinate with imidazole (Co2+, Ni2+, Cu2+and Cd2+) protect ImpG from hydrolysis and also inhibited catalysis. It is proposed that these metal ions bind to the imidazole of the activating group where they protect ImpG from hydrolysis and at the same time they inhibit oligomer formation. The research on the metal ion catalysis of template-directed synthesis stopped when it was observed that when the imidazole activating group was replaced with 2-methylimidazole metal ions were not required to enhance the formation 30 mers of oligo (G) s on a poly(C) template (Inoue and Orgel 1981).

Possible Catalytic Reaction Pathways Since metal ions, RNA templates and montmorillonite clay all catalyze the reactions of nucleotides activated with imidazole at the 5′-position it may be possible to gain insight into the reaction pathway on montmorillonite by reviewing the mechanisms proposed for the catalysis by metal ions and RNA templates.

Metal ions UO22− and Pb2+ are effective while Zn2+ and lanthanides metal ions are much less effective catalysts of the reactions of ImpN where N is A, U, G, C and I. In most cases the products formed are mainly 2′,5′-linked. It is proposed that UO22− forms a complex with the activated monomers that binds them in the correct orientations for the 2′-hydroxyl group of one monomer to react with the activated phosphate of the other monomer (Shimazu et al. 1993). In studies where the nucleotide base is inverted in the nucleotide, a α-nucleotide, (Fig. 5d) the longest oligomers formed are half as long as those formed from the natural β-nucleotide and the bonds formed were linked mainly by 3′,5′-phosphodiester bonds (Sawai et al. 1997). It was not noted whether the UO22− complex, formed from UO22− monomers, binds α-ImpA in the proper orientations for reaction and whether this complex can also bind oligomers in the correct orientation for elongation. Reactions catalyzed by soluble metal ions are probably initiated by complex formation between the metal ion and the activated nucleotide. If the metal ion binds two or more activated

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nucleotides then they may be held in the proper orientation for reaction in the complex. An alternative proposal is the metal ion binds the activated nucleotide in a way that activates the 2′-hydroxyl group of the activated nucleotide. This postulate is supported by the observation that the best catalyst among the lanthanide series of metal ions is Lu3+, in which the bound water has the greater acidity, and as such should be the most effective metal ion for activating the 2′-hydroxyl group of the nucleotide (Sawai and Yamamto 1996).

Metal ion catalysis of template-directed synthesis of RNA oligomers Metal ion catalyzed template-directed synthesis enhances the length of the oligomers formed over that of the catalysis by either the metal ions or the templates alone. The most successful metal ion catalysts are Pb2+ and Zn2+. Pb2+ catalyzes the poly(C) template-directed synthesis of mainly 2′,5′- linked G oligomers and the synthesis of mainly 3′,5′-linked an oligomers on a poly (U) template. Zn2+ catalyzes the template-directed synthesis of a mainly 3′,5′-linked G oligomers on a poly(C) template. The observation that metal ions were not responsible for catalysis when the mononucleotide is activated with 2-methylimidazole suggests that they may not have a direct role in the activation of the mononucleotide for reaction. Rather the metal ion may just change the geometry of the double helix of the poly(C) template with the growing G oligomers so that the activated nucleotides are lined up for reaction (Birdson and Orgel 1980). This favorable orientation does not require the activation of either the 2′- or 3′-hydroxyl groups by a metal ion.

A postulate for montmorillonite catalysis If it is assumed that similar pathways are followed in oligomer formation for metal ion, template-directed and montmorillonite-catalyzed synthesis of RNA oligomers it may be possible propose a possible mechanism for the montmorillonite-catalyzed synthesis of RNA oligomers. The observation that a 2-methylimidazole activating group is all that is needed to generate long oligomers from activated nucleotides suggests that the optimal orientation of the activated monomers for reaction is the key factor in the montmorillonite-catalyzed reaction. The failure of certain montmorillonites to be catalysts may be due to differences in the geometry of their interlayer from those of the catalytic clays. The proper orientation of the activated bound nucleotides has also been proposed to be an important factor in UO22− and Pb2+ catalysis as well. While activation of the 2′-hydroxyl group may be a factor with some of the metal ions it appears not to be a factor in the montmorillonite catalysis. The selectivity observed for reaction at either the 2′- or 3′-hydroxyl groups may be due to factors specific for each catalyst. The difference in selectivity for montmorillonite may be due to a difference in the orientation of the activated nucleotide when bound to the clay interlayer. This difference in orientation may be responsible for the selectivity for the formation of the 3′, 5′-link with purine nucleotides and 2′, 5′- links with pyrimidine nucleotides.

Potential Steps to the Origin of Life from Oligomers Research on the formation of RNA oligomers is based on the assumption that the requisite activated RNA monomers formed spontaneously on the primitive Earth. Progress has been made in their formation but no plausible prebiotic synthesis of the activated RNA monomers or the monomers that would the basis of any other genetic polymer has been reported at the time of this writing (see discussion in “Alternative Genetic Systems”). In this Section we will assume that a catalyst was found that generated a mixture of informational biopolymers on the primitive Earth that were sufficiently long to store genetic information and to catalyze reactions.

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The next question is how far away are these biopolymers from the formation of the first life? Since these polymers were formed catalytically it is reasonable to assume that there was selectivity in the synthesis of these oligomers so a limited number of isomers were produced (see discussion in “Prebiotic Polymerization of RNA Monomers”). Because it is a catalytic process a continuous supply of these structurally related oligomers were formed continuously. If the first life was based on a self-replicating system capable of mutations then the key step was the origin of the replication of these oligomers. In one scenario it is assumed that the first life is an array of oligomers bound on the mineral that catalyzed their formation. This would require that a subgroup of these oligomers, or the mineral-oligomer-complex, had the ability to catalyze their own synthesis while bound to the mineral. This group of catalytic oligomers would rapidly take over the mineral surface if their rates of replication were greater than the catalytic formation of oligomers by the mineral. In this scenario the subgroup of oligomers would serve as the catalysts and the templates for the synthesis. It is assumed that the rate of catalysis by the biopolymer will increase as a consequence of the selection of the more rapidly forming oligomers. Another important catalyst was one that catalyzed the ligation of the oligomers formed by mineral catalysis. This generates longer biopolymers with greater capability of storing more genetic information. Greater information storage will be essential for the evolution of the more complex biochemical machinery needed for the formation of more sophisticated forms of life. At some point in the above process the biomolecules will have to become independent from the mineral catalyst to which they are bound. This is because the higher molecular weight oligomers will bind more strongly to the minerals surface and block the catalytic sites. This dissociation could be a stepwise process where the oligomer-catalyst complex is encapsulated within a vesicle and then the oligomers are released from the catalyst. The direct incorporation of the catalyst-oligomer complex into a vesicle is an alternative to life originating on the surface of a mineral catalyst. A possible scenario for such an event has been described (Hanczyc et al. 2003; also see discussion in “Vesicles”). Here montmorillonite catalyzes both the formation of RNA oligomers and the vesicle that encapsulated the oligomercatalyst complex. The one flaw with this particular scenario is that the conditions necessary for the formation of the biopolymer destroys the vesicle (Monnard et al. 2002). It seems likely that there may be an alternative approach that will be successful in the incorporation of the mineral-catalyst inside a vesicle without destroying it. One can imagine the ingestion of molecules in the vesicle that elute the oligomers from the catalyst. Of course these molecules should do this without inhibiting the catalyst. The stages leading to a self-replicating system after the initial trapping of the biopolymer inside a vesicle will be similar to the ones described above for life originating in a family of biopolymers bound to a catalytic mineral. The main difference will be the need for the fission of the vesicle into two or more vesicles once it has reached a critical size (Walde et al. 1994).

Proposed Experiments Selection of oligomers that bind to other biomolecules The oligomers formed by montmorillonite catalysis are long enough to fold into threedimensional structure that can bind to other RNA oligomers or other biomolecules (Joyce and Orgel 1999). Studies should be performed to determine if this binding does indeed occur. The selectivity observed in the oligomers formed by montmorillonite catalysis suggests that it is likely, if binding is observed, that an appreciable fraction of these oligomers will bind. If

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this occurs it possible that a small fraction of these oligomers will catalyze reaction(s) of the biomolecules to which they are bound.

Catalysis of template-directed synthesis One of the essential catalytic processes required for the first life if the replication of RNA by template-directed synthesis. This is essential for the reservation of genetic material as well as the preservation of catalytic RNA. As noted in “Non-enzymatic Template – Directed Synthesis,” the non-enzymatic template-directed synthesis is only successful in the formation of G oligomers on a C-template or on a template that contains more cytidine nucleotides than guanosine nucleotides. Catalysis is needed to enhance the rates of formation of RNAs that contain A, U and C nucleotides. The potential catalysts include RNA oligomers and minerals. There are examples of the template-directed synthesis of G-oligomers on a polyC template bound to minerals (Schwartz and Orgel 1985; Holm et al. 1993). So far no minerals have been detected that enhance the rates of template-directed synthesis of activated nucleotides over that from RNA templates alone.

Catalysis of RNA ligation Some of the oligomers formed by montmorillonite catalysis are long enough to be catalysts but the bulk of the oligomers are not. A ligation catalyst that would link these smaller oligomers would enhance the pool of longer oligomers. An RNA catalyst of this type would generate longer oligomers that have similar structures because it would have a template that would simultaneously bind two oligomers close enough so that they could form a phosphodiester bond between them. While it is likely that the ligation catalyst would be another RNA molecule it is also possible that a mineral would catalyze phosphodiester bond formation between two oligomers.

Acknowledgments Research support was provided by NSF grant CHE-0413739 and NASA grant NAGS12750 that supports the NY Center for Studies on the Origins of Life.

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Reviews in Mineralogy & Geochemistry Vol. 59, pp. 211-231, 2005 Copyright © Mineralogical Society of America

The Evolution of Biological Carbon and Nitrogen Cycling—a Genomic Perspective Jason Raymond Microbial Systems Division Biosciences Directorate Lawrence Livermore National Laboratory Livermore, California, 94550, U.S.A. [email protected]

Introduction Carbon and nitrogen are essential to all living organisms, owing to their abundance and remarkable characteristics when participating in chemical bonds. Their essentiality dates back to the very origin of life, where current theories hypothesize either a prebiotic abundance of organic compounds rich in carbon and nitrogen, or an ability to assimilate them inorganically through abiotic reactions that might have been catalyzed on ancient mineral surfaces. This chapter details the core reactions essential to the assimilation of these elements in biologically useful forms—the so-called fixation of carbon and nitrogen—focusing on recent literature and insights from comparative genomics and phylogenetics. Though considerable debate continues on the antiquity of these pathways, especially whether or not they might have been present in the last common ancestor (LCA) of modern organisms, it is clear that carbon and nitrogen fixation pathways were of crucial importance to the primitive ancestors of extant life. Furthermore, the biological assimilation of inorganic carbon (autotrophy) and atmospheric nitrogen (diazotrophy) represent pivotal juxtapositions of biological and geological cycles. It is thought that atmospheric CO2 concentrations have decreased substantially since the proposed origin of life some 3.8 billion years ago, due in large part to either primary (fixation) or secondary (e.g., weathering) influence by biota (Hayes 1994; Rye et al. 1995; Des Marais 1997; Lowe and Tice 2004). Though the biosphere accounts for a relatively small fraction of the total carbon on Earth, the rate of carbon flux through the biosphere far exceeds that through any geological reservoirs (Des Marais 1997). Biological carbon fixation is closely balanced to carbon recycling through biological oxidation, and the future stability of this and other CO2 reservoirs (and our ability to influence or understand them) depends critically on these biological underpinnings (e.g., Falkowski et al. 2000). Conversely, nitrogen, especially the atmospheric N2 reservoir, is remarkably stable, owing largely to the stability of the N-N triple bond and the relative inertness of the molecule. In fact, many environments are considered nitrogen limited, meaning that biologically available nitrogen is essentially locked up in biomass. Thus many ecosystems are dependent upon diazotrophs, prokaryotes that can convert atmospheric N2 into ammonia by way of the enzyme nitrogenase. As is detailed below, this enzyme is thought to be ancient and is extremely sensitive to oxygen; nitrogen-fixing bacteria and archaea are either anaerobic or have evolved elaborate mechanisms for shielding nitrogenase from molecular oxygen. It is estimated that as nitrogenase fixes of the same order per year as all anthropogenic and abiotic processes combined, including the industrial Haber-Bosch process and the lightning-catalyzed production of nitrate that (prior to Haber’s revolutionary invention) formed the basis for profitable mining industries, especially in arid areas such as Chile’s Atacama. 1529-6466/05/0059-0009$05.00

DOI: 10.2138/rmg.2005.59.9

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Raymond Using genomics to understand the present (and infer the past)

At present, 254 prokaryotic and 20 eukaryotic genomes have been completed, with over 400 and 200 more, respectively, progressing through various pipelines around the world. The term comparative genomics—once an applied discipline unto itself—is arguably redundant, as the wealth of sequence data now available obliges genomics to be inherently comparative. More specifically, given a single gene or chromosome sequence, ab initio prediction of a protein’s structure, function, and interactions are still far beyond current capabilities, and simply predicting the position and arrangement of coding sites given a “universal” genetic code is imprecise even in the simplest prokaryotic genomes. The bootstraps upon which our vast repositories of genetic information are propped stem ultimately from the comparison of new sequences with previously characterized evolutionary “relatives”—homologous proteins that have been painstakingly expressed and characterized. Thus our understanding hinges on our knowledge of molecular evolution, how processes such as natural selection and horizontal gene transfer alter gene sequences and the function of an organism’s proteins. Widely used function prediction front-ends, such as NCBI’s BLAST sequence vs. database comparison tool, are essentially distilled algorithms for determining a protein’s phylogeny, in particular for finding homologs with known functions. Fast database search tools are typically sufficient for assessing homology between sequences and annotating a newly sequenced genome, but a wealth of additional information can be obtained by looking not just at whether a group of sequences is related, but how they have changed through time. The textbook example is how natural selection has promulgated variability in the immunoglobulin antigen-recognition domain, thereby increasing the robustness of vertebrate immune systems (Tanaka and Nei 1989). Phylogenetic analysis of enzyme families, such as those presented in this chapter for proteins involved directly in carbon and nitrogen cycling, give insight into how metabolic capabilities and pathways have evolved. For example (as is discussed below), since diverging from its homologs in chlorophyll biosynthesis pathways, it is possible to elucidate how the enzyme nitrogenase has become progressively more specific for atmospheric dinitrogen as a substrate, even though the mechanism, structure, and cofactor complement of the enzyme has been largely retained in these functionally diverse pathways. Likewise, one can envisage how the reductive tricarboxylic acid cycle, used by diverse and deeply-branching organisms for “fixing” CO2 into biomass, likely evolved from a much simpler pathway by duplication of a few ancient genes followed by improved substrate specificity. Taken as a whole, the function of the expressed protein complement encoded within an organism’s genome comprises its metabolome and, in essence, comprises its phenotype: how and under what conditions an organism can thrive. Connecting the dots between the genome-encoded genotype and the functional phenotype represents the next major frontier in biology, with two major hurdles that so-called functional genomics must overcome. The first is that, at a given time and under varying conditions, not all of the protein complement of a genome is translated into protein. High-throughput determination of this “business end” is presently being attacked both at the transcriptional level, using microarrays and gene chips to quantify mRNA levels, and also at the proteome level, in particular using high resolution mass spectrometers to identify fragments of proteins being expressed by an organism under varying conditions. Theoretical advances have also been made using metabolic network modeling methods such as flux balance analysis, which can correctly segregate important versus redundant metabolic pathways by optimizing flux through a metabolic network under a given set of (environmentally-imposed) boundary conditions. The second major hurdle in connecting genotype to phenotype is the remarkable paradox presented by so-called hypothetical proteins of unknown function, paradoxical because their

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presence and relative abundance has continued almost unabated since the first genomes became available a decade ago. Specifically, this means that every new genome sequenced will have on average of 1/3 of its putative proteins with no clear homologs in any protein database or in any other genome (Bork 2000)—a veritable Red Queen principle that appears only to be solvable by bench-top biochemical characterization. However, it is not difficult to envision how high-throughput proteomics and microarray analysis will give insight into the function of hypothetical proteins, giving insight into whether and when a particular protein is expressed (Ram et al. 2005). As we learn how to decode and interpret their genomes and proteomes, microorganisms present an unrivalled diagnostic tool for understanding the physical chemistry and dynamics of natural environments, highly-tuned to their surroundings and possessing a remarkable capacity to adjust their metabolic repertoire and/or community organization in response to changing conditions. This raises the attractive possibility that an understanding of microbial evolution can provide a glimpse into the past. While this appears to hold true for many cases, for example the near-concurrence in the geological and biological records of the evolution of oxygenic photosynthesis and mitochondrial-based respiration, the plasticity with which organisms, pathways, and individual genes have evolved and, in the latter case, been horizontally transferred stand as important caveats. In particular, relying on so-called “universal” phylogenies to infer characteristics of ancient organisms is fraught by the assumption that modern lineages have somehow remained metabolically “frozen,” a difficult tenet to hold in light of the remarkable geological changes the Earth has undergone and given the proclivity and non-linearity through which evolution sometimes acts.

Biological nitrogen cycling and diazotrophy In modern organisms, assimilation of inorganic nitrogen is universally shunted through ammonia (as the ammonium cation), representing the central inorganic nitrogen source and further suggestive of a pivotal role in the early evolution of life. Though nitrate and nitrite are important inorganic nitrogen sources for extant organisms, these compounds were of fleeting abundance on the early Earth and probably played a minor role until their concentration increased following the oxidation of Earth’s atmosphere (see discussion below). Numerous transmembrane permeases transport ammonium into cells, whereafter it is enzymatically incorporated into carbon skeletons of central metabolites. This juxtaposition of the carbon and nitrogen cycles is carried out by a highly conserved set of enzymes comprising the GS/GOGAT cycle, illustrated in Figure 1. The enzyme glutamine synthetase (GS) adds ammonium to glutamate to form glutamine, which then is used as a substrate in the amination of 2-oxoglutarate by glutamine:2-oxoglutarate aminotransferase (GOGAT or glutamate synthase). This results in the cyclic formation of two molecules of glutamate—regenerating the original glutamate substrate and freeing a second glutamate to be used in other metabolic reactions. The products of this central cycle, glutamate and glutamine, serve as the ultimate nitrogen donors for all additional nitrogen-containing metabolites through a cascade of aminotransferase reactions. Regulatory response to combined nitrogen availability is tightly queued to the intracellular ratio of glutamine to 2-oxoglutarate, further underscoring the centrality of this intersection between carbon and nitrogen assimilatory pathways. This overlap between pathways has also garnered substantial interest in the evolutionary history of the key enzymes, glutamine synthetase and glutamate synthase. Glutamine synthetase is a multisubunit enzyme (homododecameric in bacteria, homooctamer in many eukaryotes, including Homo sapiens) that catalyzes the ATP-dependent condensation of ammonium with glutamate. The wide distribution of this enzyme across all three domains of life argues that the enzyme is ancient, notably comprising two major classes

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Figure 1. GS/GOGAT cycle, as discussed in the text. Boxes over arrows indicate enzymes responsible for the biochemical reactions shown. Pathways are abbreviated for simplification.

(GSI and GSII) of enzymes whose duplication origin has been argued to predate the LCA. Early attempts to date major divergences on the tree of life used this early duplication as time zero, along with an argued “clock-like” evolution among GS proteins that remains controversial (Pesole et al. 1991; Brown et al. 1994). So far, the GSI family is exclusive to bacteria, whereas eukaryotes possess only GSII. Some bacteria, most notably the rhizobia, express both forms of GS (Turner and Young 2000). Archaea also contain GSI, with euryarchaeotes sharing a highly similar GSI (GSI-α) with Gram-positive bacteria and Thermotoga, with horizontal gene transfer as a very likely explanation for this distribution (see below). Sulfolobus and other crenarchaeotes, however, possess GSI’s that are phylogenetically distinct from the GSI-α or GSI-β (bacterial) clades, with functional and phylogenetic features intermediate between the bacterial and euryarchaeal enzymes (Cabello et al. 2004). Within both classes of GS are examples of HGT, potentially confounding their evolutionary history. Some of the strongest early evidence for HGT came from analysis of the GSI family, where interdomain transfer between Gram-positive bacteria and euryarchaeotes is clear) which, intriguingly, is similar to the pattern of interdomain (bacteria ↔ archaea) transfer evident in analysis of nitrogenase genes (discussed below) (Pesole et al. 1995; Nesbo et al. 2001). It is not yet clear whether these similar patterns of gene transfer are in fact linked—for example, as an adaptation specific to biological nitrogen cycling—or the result of a more extensive, nonspecific exchange of genetic information (as is suggested by recent whole genome analysis of methanogens; e.g., Deppenmeier et al. 2002). HGT also obfuscates the phylogeny of the GSII family, noted originally among the rhizobia (Turner and Young 2000). The evolution of GOGAT/glutamate synthase, shown in Figure 2, is even more perplexing. The enzyme itself comes in several different flavors that vary by their obligatory (two) electron donor, which can be ferredoxin, NADH, or NADPH. Most bacteria possess NADH- or NADPH-dependent GOGAT’s, with some specificity within taxa such as NADH dependence in many proteobacteria versus NADPH dependence in Gram-positive Bacillus subtilis (Suzuki and Knaff 2005). The GOGAT from cyanobacteria is ferredoxin-dependent, as is that found in the plastids of photosynthetic eukaryotes. Alternatively, non-photosynthetic eukaryotes and

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the non-plastid (e.g., nuclear) component of plants and algae contains NADH-dependent GOGAT’s, suggesting a linear recruitment of cyanobacterial GOGAT by algae following plastid endosymbiosis. Archaea contain NADPH-dependent GOGAT enzymes, though highly truncated in structure and evidently not possessing the two-subunit heterodimeric structure endemic to bacterial GOGAT’s (Vanoni and Curti 1999). Temple et al. (1998) have suggested that this abridged gene may represent an ancestral, minimal form of the enzyme. Subsequently, Dincturk and Knaff (2000; Dincturk 2001) and Nesbo et al. (2001) noted patterns of homology suggesting one or multiple interdomain transfers between archaea and bacteria. The centrality of these enzymes in ammonia assimilation strongly suggests that the GS/GOGAT cycle—or an analogous pathway—was operational in the LCA, which leads to the conclusion that observed HGT’s were in fact orthologous replacements, whereby the acquisition of a more efficient pathway assumes the role of a preexisting one, which is eventually lost due to purifying selection. Biologically-utilizable nitrogen compounds are often in limited supply in natural systems, and a complex array of mechanisms for assimilating and recycling nitrogenous compounds are known. For instance, while ammonia represents the nucleus of the biological nitrogen cycle, most organisms have mechanisms for transporting nitrogenous compounds (particularly nitrogen-rich amino acids such as lysine and glutamine) across the membrane for assimilation, effectively bypassing potential dependence upon ammonia. Found exclusively among prokaryotes (more specifically, bacteria and methanogenic archaea) are a diverse group of so-called diazotrophs that are able to assimilate nitrogen from atmospheric N2 using a heterotetrameric enzyme known as nitrogenase. Using a high potential electron donor such as ferredoxin, the nitrogenase complex successively reduces atmospheric dinitrogen into two molecules of ammonia, with an optimal net reaction of: N2 + 8H+ + 8e− + 16 ATP → 2NH3 + H2 + 16 ADP + 16Pi The metabolic “cost” of dinitrogen reduction—16 ATP’s hydrolyzed—is unparalleled among biochemical reactions and may be two or more times as high under natural conditions. Active nitrogen fixation is thought to account for as much as 40% of the total ATP synthesized in diazotrophs (Daesch and Mortenson 1968). Therefore it is not surprising that nitrogenase is expressed in diazotrophs as a last-resort enzyme, when other more readily assimilated nitrogenous compounds are exhausted. It is worth noting that the net reduction of dinitrogen as shown is an exergonic reaction (dG° = −15.2 kJ/mol) and at least in theory could be used to generate energy (the high cost of running nitrogenase is thought to be involved with breaking the highly stable N–N triple bond) (Howard and Rees 1996). The evolutionary history of nitrogenase has been of considerable interest since sequences for its component proteins first became available. Though prebiotic sources of nitrogen might have been prevalent on the early earth as a result of planetary accretion and impact events, in particular NH3 would have been subject to ultraviolet photolysis and thereby progressively depleted (Kuhn and Atreya 1979; Raven and Yin 1998). The abiotic synthesis of nitrates and nitrites, an important source of fixed nitrogen generated by lightning strikes in the modern oxidized atmosphere, is thought to have been limited under an early neutral-to-mildly-reducing atmosphere, especially as Archean CO2 levels decreased (Navarro-Gonzalez et al. 2001). Navarro-Gonzales et al. argue that these conditions culminated in a nitrogen crisis during the Neoproterozoic or Archean, and that such conditions would have favored the development of the nitrogenase enzyme complex. It is feasible that alternative sources of nitrogen might have been abiotically synthesized under conditions hypothesized for the early Earth, such as cyanide if methane were present in quantities assumed by some models (Pavlov et al. 2000), and potentially offset or delayed the onset of this fixed nitrogen crisis. While such hypotheses are still in need of validation, some interesting correlations can be drawn from examination of the biological record. The evolutionary history of nitrogenase

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suggests that it likely evolved from a complex not directly involved in reduction of dinitrogen, and which may have had a broad specificity that may (or may not) have been associated with nitrogen assimilation (Silver and Postgate 1973; Burke et al. 1993; Fani et al. 2000; Raymond et al. 2004). For example, site-directed nitrogenase mutants as well as nitrogenase itself have been shown to have some competency for reducing cyanide, generating methane as well as ammonia, which could then serve as a nitrogen source (Kao et al. 2003; Pickett et al. 2004). This idea is intriguing given the hypothesis that Archean nitrogen reservoirs might have progressed from ammonia to cyanide and ultimately to dinitrogen, transitions quite possibly captured in the evolutionary history of nitrogenase and other nitrogen assimilating enzymes. The importance of ammonia to early organisms, arguments for an early Earth nitrogen crisis, and the solitary role of nitrogenase as a shunt into the atmospheric dinitrogen reservoir suggest that this enzyme might have been of critical importance to early life, especially as increasing numbers of organisms depleted the limited supply of bio-available nitrogen. The presence of the nitrogenase enzyme among diverse phyla of bacteria and in methanogenic archaea have led some authors to argue that nitrogenase was present in the last common ancestor (LCA) of extant organisms (Fani et al. 2000; Normand et al. 1992). While compelling based on the arguments above, the evolution of nitrogenase has been very complex and includes examples of: gene duplication resulting in numerous families of nitrogenase homologs; horizontal gene transfer within and between the bacterial and archaeal domains; and apparent loss of nitrogenase from many lineages. Raymond et al. (2004) recently proposed a scenario whereby nitrogenase was not present in the LCA, but was “invented” in methanogenic archaea and subsequently horizontally transferred into bacteria. While difficult to argue whether this late-origin proposal is more parsimonious than the LCA hypothesis, the former has an advantage in not invoking the perplexing disappearance of nitrogenase from many early branching lineages, such as eukaryotes, crenarchaeotes, and most phyla of early branching bacteria.

Geological clues to the early nitrogen cycle Phylogenetic and genomic analyses, the essential role of nitrogenous compounds in biochemistry, and the ubiquity of nitrogen-assimilating enzymes suggests that biological interactions with N2, NH3, and possibly other nitrogenous compounds such as cyanide began early in the evolution of life. Determining which of these pathways might be the most primitive or could have been dominant among early organisms remains considerably more ambiguous. The GS/GOGAT pathway, universally required for the synthesis of nitrogenous compounds by amine and amide transfers, appears to have been an early invention, possibly predating the LCA as it is found across all three domains of life. This system might have been sufficient to support the synthesis of nitrogenous compounds if ammonia was present in ample concentrations on the Archean Earth. However, arguments for an early nitrogen crisis (or, at the very least, as a limited supply of nitrogenous compounds became locked up in biomass) suggest a rapidly increasing necessity for alternative shunts for assimilating nitrogen from the environment, such as nitrogenase for reducing atmospheric dinitrogen to ammonia (Kasting and Siefert 2001). Though there remains debate as to whether nitrogenase was present in the LCA, it seems likely that the enzyme evolved during the Archean, prior to the evolution of cyanobacteria and the ensuing oxidation of the Earth’s atmosphere (Navarro-Gonzalez et al. 2001). Unfortunately the arguably diagnostic isotopic signatures in carbon fractionation that result from different modes of autotrophy have thus far been substantially less forthcoming for nitrogen isotopes. Nitrogenous compounds are typically very soluble and characterized by a short residence time in sediments and crustal rocks. Beaumont and Robert (Beaumont and Robert 1998, 1999) determined isotopic compositions of cherts ranging from 3.5 billion to 500 million years in age, noting a transition from mostly negative to all positive values

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occurring during the transition from the Archean to the Proterozoic. This concurs with the invention of oxygenic photosynthesis and the ensuing oxidation of Earth’s atmosphere, likely representing the increasing availability (due to abiotic fixation of nitrogen as well as O2dependent biological nitrification) of soluble oxidized nitrogen such as NO3−2. Importantly, though negative excursions during the Archean are consistent with assimilation of NH3 as well as the biological fixation of dinitrogen by nitrogenase, these signatures typically not regarded as diagnostic for either process.

Biological carbon cycling and autotrophy Autotrophy is typically defined as the autocatalytic, reductive conversion of CO2 into the low-molecular-weight building blocks of biosynthesis (e.g., Hugler et al. 2003), and by root derivation (self-nourishing) applies to organisms that derive energy for this reaction from light (photosynthesis) or inorganic compounds (chemotrophy). The following discussion gives a broad glimpse of the four known pathways used by autotrophs to assimilate CO2, focusing in brief on the unique or key enzymes of each pathway and discussing the current evolutionary understanding of these enzymes. The four pathways are summarized in Figure 3, and it must be emphasized that key steps in the 3-hydroxypropionate pathway are still being elucidated. Finally, the distribution of these pathways in the three domains is discussed, as is the possible relevance to the origin and early evolution of life. Though not covered in detail here, it is worthwhile to briefly detail and clarify some of the other carbon assimilatory pathways that have been elucidated. A remarkable diversity of organisms exist which can use various C1 compounds—organic or inorganic compounds containing, among other elements, only a single carbon atom—as sources of both energy and cell carbon. Methylotrophs are an evolutionary distinct class of organisms that can utilize C1 compounds in oxidation states ranging from formaldehyde to methanol to methane (including aminated, sulfurated, and halogenated methane). Methanotrophs are a subset of methylotrophs that specifically metabolize methane using either O2-dependent methane monooxygenases, as is found in recently sequenced Methylococcos capsulatus and other proteobacteria, or a recently elucidated anaerobic pathway found in domain Archaea that roughly appears to operate as methanogenesis in reverse (Hallam et al. 2004). C1 assimilation pathways are also known for numerous other compounds, such as formate, cyanide, and carbon monoxide, and organisms able to use CO as both sole carbon and energy source raise intriguing exceptions to the long-held “CO2-only” definition of autotrophy. Carbon fixation in methanotrophs, on the other hand, takes place through one of two non-autotrophic pathways, the serine or ribulose monophosphate/allulose pathways, by which formaldehyde (the common product of C1-utilization) is incorporated. Intriguingly, these two pathways also incorporate CO2, but are effectively mixotrophic because of their dependence on formaldehyde. However, some methanotrophs, such as M. capsulatus, also fix carbon via the Calvin cycle and thereby are true autotrophs. Yet another class of so-called anaplerotic pathways are important in both autotrophs and heterotrophs, and anaplerotic enzymes catalyze the direct incorporation of CO2 into organic compounds for the regeneration of essential intermediates of central metabolism. The familiar Krebs/TCA cycle, which carries out the energy-generating breakdown of pyruvate into three CO2 molecules, occupies a central position in metabolism as its products serve as biosynthetic precursors for an enormous repertoire of compounds, including amino acids, nucleotides, cofactors, and lipids. Anaplerotic enzymes allow the direct replenishment of Krebs/TCA cycle intermediates, effectively “recapturing” CO2 that is lost by running the cycle. The very presence of these anaplerotic shunts gives some insight into how CO2-fixing pathways might have

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Figure 3. Four known pathways for autotrophic carbon fixation. Enzymes are in boxes, denoting in particular those that are discussed in the text. The 3-hydroxypropionate cycle schema adapted from (Herter et al. 2002).

evolved. Indeed, there are over distinct 400 reactions—almost 5% of the total—annotated in the KEGG database that directly consume or produce CO2, arguing that biological interactions with CO2 have been of foremost importance in the evolution of life.

Wood-Ljungdahl (reductive acetyl-COA) pathway In recent years, considerable progress has been made in enzymatic and mechanistic understanding of the Wood-Ljungdahl or reductive acetyl-CoA pathway. This progress has come particularly in the way of crystal structures for two key enzymes in the pathway. The Wood-Ljungdahl pathway and enzymes involved are of particular interest to evolutionary biologists, bearing many hallmarks that suggest an ancient origin and an important role among primitive autotrophs. For instance, the stepwise condensation of C1 units to form C2 and C3 compounds such as acetate and pyruvate, respectively, represents schematically the simplest form of autotrophy. Furthermore, the enzymes catalyzing each step bear arguably “primitive” characteristics—unique metal clusters and a corresponding sensitivity to molecular oxygen, as well as a distribution among many early-branching lineages on the tree of life. Note that the

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pathway is not cyclic/autocatalytic, though the Wood-Ljungdahl pathway has the distinction among all known autotrophic pathways in being net exergonic, according to: 2CO2 + 4H2 + n ADP + nPi → CH3COOH + 2H2O + n ATP with an overall change in free energy of dG° = −95 kJ/mol (Muller 2003). This pathway is unique among known autotrophic assimilation pathways in allowing fixation concomitant with generation of ATP. Importantly, this dual role of energy generation and carbon fixation is not universal among microorganisms, many of which have components of the Wood-Ljungdahl pathway but are not autotrophs. Whereas methanogens, acetogens, and aerobic carboxydotrophs depend upon the pathway for chemoautotrophy and subsequent biosynthesis (and thereby actively take up CO2 from the environment), many non-autotrophic anaerobic fermenters utilize this pathway to further reduce CO2 generated from pyruvate decarboxylation to CO (Ragsdale 2004). The key enzyme for the reduction of CO2 to CO is carbon monoxide dehydrogenase (CODH), which catalyzes this reversible reaction using a variety of electron donors. The enzyme is one of a handful of enzymes that requires elemental nickel, though an oxygen-tolerant CODH that apparently utilizes molybdenum rather than nickel has been characterized from carboxydotrophs (Meyer et al. 1990). The other key enzyme in this pathway, acetyl-CoA synthase (ACS), has also been shown to contain two nickel atoms complexed to an ironsulfur cluster (despite their common nickel-containing metal clusters, ACS and CODH are not thought to be homologous) (Hegg 2004). In autotrophs, the ACS enzyme forms a bifunctional complex with CODH, where CO generated by CO2 reduction is passed through a protein “channel” from CODH to ACS, then combined with methane and coenzyme A (CoA) to form acetyl-CoA (Hegg 2004; Ragsdale 2004). Figure 4 shows the CODH phylogeny, inferred from sequences of completely sequenced genomes. As can be observed, gene duplication has been extensive, and there is a lack of overall taxonomic cohesion, as in the paraphyly of methanogens. Bootstrap support and conserved sequence signatures suggest that the phylogeny is robust overall, and that horizontal gene transfer is a good explanation for some of the topology. A number of more distant CODH homologs are excluded from this tree. These include the hydroxylamine reductase/hybrid cluster protein—to which CODH is still closely related enough to be “converted” into by sitedirected mutagenesis (Heo et al. 2002)—along with very distantly related CODH homologs from other bacteria and archaea that may carry out unrelated functions.

The rTCA cycle With the exception of aerobic carboxydotrophs briefly mentioned above, both the WoodLjungdahl pathway and reverse or reductive tricarboxylic acid (rTCA) cycle are typically the realm of anaerobic or microaerobic autotrophs, thanks in part to O2-sensitivity of many of their key enzymes. The rTCA cycle, as its name implies, is the conceptual “reverse” of the familiar TCA (also Krebs or citric acid) cycle which, rather than oxidizing acetate (to two molecules of CO2) concomitant with the production of reducing equivalents, uses reducing equivalents to fix CO2 ultimately to acetyl-CoA. The net production of acetyl-CoA is also found in the Wood-Ljungdahl pathway, though the rTCA cycle differs in being endergonic and cyclic/network autocatalytic (cycle intermediates can serve as templates for their own synthesis and—as “carriers” of assimilated carbon—are not consumed as the cycle propagates). Three key enzymes of the rTCA cycle distinguish it from the archetype Krebs cycle: fumarate reductase, ATP citrate lyase, and 2-oxoglutarate:ferredoxin oxidoreductase. 2-oxoglutarate:ferredoxin oxidoreductase catalyzes the carboxylation of succinyl-CoA,

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Figure 4. CODH protein phylogeny, constructed as described in the legend for Figure 2. Numbers indicate duplicate CODH copies in a single genome. Many organisms in fact have two or more CODH paralogs, and the interspersed bacterial-archaeal phyla indicate a highly nonvertical evolution likely beset by horizontal gene transfer. Methods as given in Figure 2.

forming 2-oxoglutarate. ATP citrate lyase catalyzes the citrate “cleavage” that closes the cycle, forming oxaloacetate and acetyl-CoA. 2-oxoglutarate:ferredoxin oxidoreductase is a member of the diverse thimine diphosphate-dependent 2-oxoacid oxidoreductases and, along with its homolog pyruvate:ferredoxin oxidoreductase (PFOR), is able to “reverse” the oxidative decarboxylations carried out by unrelated dehydrogenases of the Krebs cycle, incorporating CO2 into biomolecules. The evolutionary history of PFOR, key in synthesizing pyruvate from acetyl-CoA in Wood-Ljungdahl as well as rTCA autotrophs, is shown in Figure 5. The phylogeny is congruent in many respects with the 16S rRNA-based tree of life, but the enzymes are clearly overrepresented in organisms that have an obligately or facultatively anaerobic lifestyle (with notable exceptions to both of the previous points). As with the Wood-Ljungdahl pathway, the rTCA cycle is also found among diverse prokaryotes, many of which are early-branching lineages on the tree of life. The pathway was first described from the obligately anaerobic, non-oxygen producing phototroph Chlorobium tepidum, where the electrons generated during photosynthesis eventually enter into the rTCA cycle for carbon fixation (Evans et al. 1966). So-called Knallgas bacteria (metabolizing via 2H2 + O2 → 2H2O), such as members of phylum Aquificales, are microaerophiles that use the rTCA cycle. The pathway is also found in autotrophic Crenarchaeota, sulfate-reducing bacteria, and in some chemolithotrophic epsilon Proteobacteria (Shiba et al. 1985; Schafer et al. 1986; Schauder et al. 1987; Hugler et al. 2005). Remarkably, several of the key enzymes from the rTCA cycle are found among a broad range of aerobic heterotrophs. Citrate lyase, for example, has homologs in humans and other vertebrates that function in fatty acid metabolism

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A.variabilis|1964 Nostoc sp|2284 C.watsonii|1367 100 Synechocystis 6803|2805 99 75 A.variabilis|4948 Nostoc sp|3176 100 87 S.elongatus|858 C.aggregatum|1493 36 C.tepidum tls|1594 100 100 D.acetoxidans|1527 D.acetoxidans|5881 74 G.metallireducens|1832 100 G.sulfurreducens pca|97 100 D.aromatica|1842 97 R.rubrum|2304 60 C.aurantiacus|1199 68 M.capsulatus|13637 91 S.typhi|1244 100 E.coli O157H7|2086 100 100 S.flexneri 2a|1720 L.lactis|422 100 D.desulfuricans|1201 90 D.vulgaris hildenborough|2993 90 M.thermoacetica|732 C.jejuni|1392 95 H.mobilis|1691 76 100 C.perfringens|2124 88 C.acetobutylicum|2199 D.hafniense|204 38 C.acetobutylicum|2466 97 C.thermocellum|1873 100 D.hafniense|2349 54 100 S.solfataricus|1088 46 S.tokodaii|1662 S.tokodaii|1937 74 S.solfataricus|2501 100 A.fulgidus dsm4304|2021 M.thermoacetica|326 65 C.thermocellum|2458 * M.thermoacetica|2093 49 100 T.acidophilum|618 T.volcanium|849 24 100 M.barkeri|2496 100 M.mazei|1340 100 M.acetivorans|32 24 60 M.burtonii|1125 A.fulgidus dsm4304|1678 M.thermoautotrophicum|1700 35 M.kandleri|82 45 99 M.jannaschii|271 48 M.maripaludis|1505 P.furiosus|966 61 P.abyssi|1344 100 P.horikoshii|710 99 100

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Figure 5. Pyruvate:ferredoxin oxidoreductase (PFOR) protein phylogeny. As discussed in the text, PFOR is a decarboxylating enzyme found in both heterotrophs and autotrophs and functions reversibly as a key carboxylating enzyme in the rTCA and Wood-Ljungdahl pathways. There is a clear stratification of bacteria (top clade) and archaea (bottom clade), with distributions loosely consistent with phylum-level taxonomy (shown in boxes; asterisks indicate notable deviations). Methods as given in Figure 2.

and biosynthesis (Wahlund and Tabita 1997). Thus detection of the rTCA cycle as a suspected

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autotrophic pathway in new organisms must be based on multiple factors—phenotype and the presence of all key genes—rather than the presence of any single diagnostic gene. The rTCA cycle has been argued by some authors to have been a pivotal pathway not only in the evolution of life but in the origin of metabolism. The pathway occupies a unique position at the hub of most metabolic networks, with metabolites used as precursors in biosynthesis of all major classes of biochemical compounds. Several of the enzymatic steps have plausible prebiotic analogs, most notably discussed by Wächtershäuser in his hypothesis for primitive sulfur-linked “thioanalogs” of modern rTCA cycle intermediates (Wächtershäuser 1990). This had the argued advantage of overcoming the unfavorable kinetics of several reactions, e.g., beta-carboxylations, of the rTCA cycle as well as inevitably linking this early metabolism with a sulfur/pyrite-rich interface. Thermodynamically unfavorable reactions could thereby be coupled to pyrite oxidation, much as coupling to ATP hydrolysis provides the basis for difficult modern biochemical reactions (Bebie and Cody 2000). These arguments have also been carried forth by Smith and Morowitz (2004), who argue that such a network-autocatalytic (self-replicating), redox-intermediate cycle is exactly what natural selection might be expected to produce. However, in the absence of catalysis, detractors point out that several critical steps in the pathway are simply not expected as favored outcomes (Orgel 2000). Because many of these arguments focus on pre-LCA metabolism, they are simply beyond the resolution of comparative genomics and phylogenetics. While the centrality of the rTCA cycle in metabolic charts is striking, it could just as likely represent a favorable but relatively recent reorganization of pre-existing metabolic pathways.

The Calvin-Benson-Bassham cycle The Calvin-Benson-Bassham (CBB) cycle represents the best known of the autotrophic pathways, most notably because it is the only one present in eukaryotes, making it amenable to now-infamous large scale preparations from e.g., spinach for biochemical and biophysical studies. The key enzyme of the pathway, ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO), is the key enzyme of the cycle. The enzyme itself falls somewhere between evolutionary enigma and embarrassment, having a turnover rate of only a few carboxylation reactions per second and also carrying an intrinsic, energetically wasteful proclivity for using molecular oxygen, rather than carbon dioxide, as a substrate. Photoautotrophs, plants in particular, attempt to compensate for RuBisCO’s inefficiencies by overexpressing the enzyme, and as a result RuBisCO is by far the most abundant protein in perhaps the largest biomass group on the planet, making it arguably the most abundant enzyme on Earth. The CBB cycle is in essence split into three separate phases. The first stage, carboxylation or carbon fixation, takes place by the RuBisCO-catalyzed addition of CO2 to ribulose bisphosphate, a C5 sugar that is synthesized from ribose-1,5-bisphosphate by the other key/ unique enzyme of the Calvin cycle, phosphoribulokinase (PRK). The carboxylation step is exergonic and essentially irreversible, though the overall cycle is net endergonic. The resultant C6 sugar is subsequently cleaved to two C3 3-phosphoglycerates, which are converted to glyceraldehydes 3-phosphates (G3P)—a highly utilized carbon “currency” in many cells— during the so-called reductive phase of the CBB cycle. The reductive phase requires a net input of ATP and NADPH. Because a 3C compound is output, the Cycle must turn three times per G3P generated, so that cycle intermediates are not depleted. The regeneration phase of the cycle then incorporates a number of ATP-dependent aldolase and isomerase steps to regenerate the 5C precursor to RuBisCO carboxylation. The competing oxygenase activity of RuBisCO results in a process termed photorespiration, whereby O2 rather than CO2 combines with ribulose-1,5-bisphosphate, yielding one molecule of 3-phosphoglycerate and another of 2-phosphoglycolate. This latter compound must be

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salvaged—reconverted into usable 3-phosphoglycerate in a series of reactions that require both ATP and NADH. As this salvage pathway does not result in fixed carbon and requires energy and reducing equivalents to amend, photorespiration may reduce the efficiency of the Calvin cycle by as much as 50%. To counter the effects of photorespiration, a suite of novel carbon concentrating mechanisms (CCM’s) have evolved to ramp up the ratio of CO2 to O2 inside cells and chloroplasts (e.g., Badger et al. 2002), responsible for, among other adaptations, the carboxysome that is among the largest known enzyme aggregates. Notably, some recent evidence suggests that photorespiration may in fact be important in higher plants as an energy sink or source of metabolites (Wingler et al. 2000). The evolutionary history and diversity of the Calvin cycle has been studied in great detail, as evidenced by the enormous repository of RuBisCO gene sequences (~25,000) available in GenBank. A disproportionately large number of these sequences are from eukaryotes, so the focus here is constrained to the more diverse RuBisCO (large subunit) sequences available from completed prokaryotic genomes. As illustrated in Figure 6, several distinct clades are evident in the phylogenetic tree, and importantly some of these represent proteins not known to function within the Calvin cycle, as evident based on active site substitutions, absence of PRK, and experimental verification. True RuBisCO’s fall into two groups, so-called form I and form II (with subgroups within each) that highlighted on the tree. Form III and IV homologs of RuBisCO, the so-called RuBisCO-like-proteins (RLP’s), have become a recent focus of several investigations. A suite of recent experiments by Tabita and colleages has illustrated quite compellingly that the form III RuBisCO homologs are able to fix atmospheric CO2, using a functional analog of PRK to generate a substrate for the enzyme (Finn and Tabita 2004). While it is not evident that these methanogens use this alternative pathway (most are Wood-Ljungdahl autotrophs) as a primary mode of carbon fixation (Sprott et al. 1993), this may serve an important anaplerotic role and provides an exciting window into RuBisCO evolution and possible engineering. Conversely, the distantly-related form IV RuBisCO homologs, found in a diverse range of organisms including several anoxygenic phototrophs (Ashida et al. 2003), is argued to be involved with methionine salvage pathways and is not able to fix CO2. Importantly, with the exception of RuBisCO and PRK, all of the enzymes used in the Calvin cycle are found in functions known from other pathways. At least schematically, the Calvin cycle bears tantalizing similarities with the aforementioned ribulose monophosphate (RuMP) pathway found in many methanotrophs, functioning to incorporate C1 units in the form of formate rather than CO2 (analogously driving C1 + C5 → C6 fixation in the process). Thus it can be argued that most of the enzymes of the Calvin cycle were already present in the ancestors of the first Calvin cycle autotrophs, and that “invention” of RuBisCO and PRK, combined with recruitment of enzymes from the RuMP pathway, could have resulted in a new, O2-tolerant form of autotrophy. The timing of the appearance of the Calvin cycle appears to have been closely linked to the development of bacterial aerobic respiration and photosynthesis, events that were both tied to the progressive and irreversible oxidation of Earth’s atmosphere. As the rTCA cycle and Wood-Ljungdahl pathways are both inhibited at high oxygen concentrations, it seems reasonable that RuBisCO came about in response to the challenge of an atmosphere that was increasingly oxic and with decreasing CO2 availability. The taxonomically-diverse RLP’s, recently discovered to be involved with methionine salvage, present a plausible ancestral pathway from which RuBisCO was recruited and came to function as a carboxylase, and structure-function analyses are presently providing a testbed for this hypothesis (Ashida et al. 2005). The unique dual oxygenase/carboxylase role of the enzyme would have been much less of a disadvantage as the Precambrian CO2:O2 ratio was still high, and feasibly could have served a useful additional role in ameliorating high levels of oxygen.

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C.watsonii Synechocystis 6803 T.erythraeum 46 N.punctiforme Form IA 35 100 A.variabilis (cyanobacteria) N.ostoc sp 100 83 G.violaceus 100 T.elongatus S.elongatus 100 R.metallidurans 92 100 N.europaea Form IB M.capsulatus (β,γ-proteobacteria, Synechococcus WH8102 99 marine cyanos) 100 P.marinus MED4 100 100 P.marinus CCMP1375 58 P.marinus MIT9313 B.fungorum Form II R.sphaeroides 100 100 (α,β-proteobacteria) S.meliloti B.japonicum 36 R.palustris CGA009 100 97 P.abyssi 100 P.horikoshii P.furiosus A.fulgidus dsm4304 Form III M.jannaschii (Archaea: M.acetivorans euryarchaeotes) M.barkeri 100 M.mazei 95 M.burtonii Form IV (b) M.magnetotacticum (Bacteria, D.aromatica 100 Archaea) R.rubrum 92 C.tepidum tls 100 C.aggregatum B.bronchiseptica Form IV (a) H.mobilis (Bacteria) Exiguobacterium B.subtilis 100 B.anthracis Ames 0581 99 100 B.cereus ATCC14579 65

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Figure 6. RuBisCO large subunit phylogeny, showing the four different clades or Forms of RuBisCO that have been established in previous evolutionary studies. As discussed in the text, Forms I and II are key enzymes in Calvin-Benson-Bassham cycles, whereas Forms III and IV are RuBisCO-like proteins, recently shown to function in alternative carbon fixation or methionine salvage pathways, respectively. Methods as given in Figure 2.

3-Hydroxypropionate cycle This pathway was first discovered only fairly recently in the filamentous anoxygenic phototroph Chloroflexus aurantiacus, of evolutionary interest because of its early-branching position on the tree of life. In fact many of the steps in this unique pathway are only now being fully resolved, particularly by the investigations of Fuchs and colleagues (Herter et al. 2002). However, it has also recently become clear that the cycle is more widespread than previously thought, apparently functioning in many aerobic autotrophic crenarchaeota (Hugler

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et al. 2003). Schematically, the pathway as presently elucidated incorporates a combination of novel reactions with enzymatic steps recruited from other metabolic pathways, including the rTCA cycle and propionyl-CoA pathway which carries out odd-chain fatty acid oxidation. Carbon fixation ultimately leading to pyruvate actually comprises a bicyclic pathway; in the first cycle, two carboxylations take place to give rise to a glyoxylate intermediate, which then condenses with an intermediate product from the first half of the first cycle (which accounts for the third carbon) (Herter et al. 2002). Several rearrangements, schematically similar to a reversed glyoxylate cycle but technically like a reversed citramalate pathway, eventually give rise to the cleaved pyruvate end-product. Though this remarkable pathway has now been solved in considerable detail, its evolution still remains enigmatic. Somewhat perplexing is that, despite its discovery in type strain C. aurantiacus, some close relatives of this organism (within phylum Chloroflexi) apparently do not have a 3-hydroxypropionate cycle, instead apparently having a functioning CBB cycle (Ivanovsky et al. 1999). Furthermore, many of these organisms are content to grow (photo)heterotrophically and are often found in microbial mats with abundant biomass to facilitate such a lifestyle. As shown in Figure 7, this pathway is found only in a few different groups of aerobic prokaryotes, suggesting that it, as with the Calvin cycle, might have arisen as an ad hoc autotrophic solution to the oxidation of Earth’s atmosphere, bypassing the oxygen-sensitive enzymes that catalyze critical roles in the rTCA cycle and Wood-Ljungdahl pathway. This speculation has some support from genomic data as it appears that, while homologs to several of the key enzymes that C. aurantiacus uses are present in crenarchaeotes, some enzymatic steps may be different in different crenarchaeota, suggesting evolutionary convergence or some plasticity in pathway operation (Menendez et al. 1999). The relatively recent discovery of this cycle and the apparently diverse means by which organisms carry it out suggest that there are pathways for autotrophic carbon fixation that have yet to be discovered, and resolving these as well as known pathways will no doubt shed additional light on the origin and evolution of microbial-environmental interactions.

Autotrophy, heterotrophy, and the origin of metabolism For several decades, origin of life research focused on establishing a diverse synthetic library of prebiotic compounds upon which early life might have been built. A so-called heterotrophic origin assumes that a relatively complex milieu of chemical compounds would have been present on the early earth, supplied exogenously (e.g., during cometary accretion) or through abiotic reactions involving prevalent inorganic precursors such as N2, CO2, and H2 (and typically involving a mineral catalyst and/or an energy source such as lightning to overcome energetic barriers). Plausible synthetic sources for many essential ingredients of prokaryotic cells have been illustrated, and it is thought that as early cells became more complex, enzymatic routes of synthesis eventually replaced their prebiotic counterparts as their prebiotic precursors became increasingly scarce. While impressively successful in the breadth of biologically-relevant metabolites that have been obtained, heterotrophic theories have not been without criticism. Opponents cite that reaction conditions often invoke conditions that are quite different than geochemical evidence suggests might have been available on the early Earth, as well as the fact that it is difficult to imagine a single environment where all of the necessary catalysts and conditions would allow precursors to accumulate. Thus about two decades ago, autotrophic origin of life theories proposed that prebiotic reactions might have involved only inorganic precursors, catalyzed for example on mineral surfaces or within FeS “membranes” (Russell et al. 1988; Wächtershäuser 1988). Such schemes arguably allow for locally high concentrations of metabolites to accumulate and invoke a comparably simple set

Figure 7. Distribution of autotrophs on the tree of life, based on the established carbon fixation pathways discussed in the text, signature genes present in completed genomes, and experimental detection of pathways as inferred from current literature. squares=reductive TCA cycle, circles=Calvin-Benson-Bassham cycle, triangles=Wood-Ljungdahl pathway, diamonds=3-hydroxypropionate cycle. Gradient-filled shapes indicate pathways that are found in some, but not all, members of a particular clade. For example, members of the gamma proteobacteria, which includes A. vinelandii and P. aeruginosa, are known to use the Calvin cycle, though neither of these two organisms specifically is an autotroph.

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of necessary catalysts and precursors, for example envisaging environments not unlike modern hydrothermal systems. Experimental corroboration of the metabolic world attainable by autotrophic prebiotic pathways is providing support, but a number of important gaps remain to be filled (see e.g., Cody 2004). Both schools of thought at least broadly agree that the individual abiotic steps within an increasingly complex metabolic network were progressively assumed by enzyme (or ribozyme) catalysts. Of relevance to this discussion, if the autotrophic theory were true, the simplest—though by no means the most likely—assumption would be that primitive carbon and nitrogen cycles were functioning in the earliest organisms so as to directly assimilate N2 and CO2. Certainly enzymes such as nitrogenase and carbon monoxide dehydrogenase have been noted for their unique metal cluster complement (Rees and Howard 2003), which poses the intriguing idea (though without much support) that these metal clusters represent the stillfunctional remnants of prebiotic mineral catalysts that carried out analogous reactions. While these putative glimpses of prebiotic chemistry through modern enzymology are tantalizing, it seems just as likely that many generations of overprinting have occurred; distinct and unrelated enzymes have evolved to carry out these ancient reactions more efficiently than their predecessors. The rTCA cycle and the Wood-Ljungdahl pathway both bear hallmarks of a primitive autotrophy, as compellingly argued by a number of authors (Wächtershäuser 1990; Morowitz 1999; Cody 2004; Smith and Morowitz 2004). As Figure 7 illustrates, both pathways operate in a number of deeply-branching anaerobes, whereas the CBB and 3hydroxypropionate cycles are found in clades closer to the “tips” of the tree of life, suggesting more recent origins. Furthermore, the rTCA and W-L pathways contain enzymes with diverse metal clusters that show sensitivity to molecular oxygen, and concomitantly are found predominantly in anaerobes and microaerobes. Each pathway also boasts specific attractive features such as being network autocatalytic (rTCA), net exergonic (W-L), centrally connected to diverse biosynthetic pathways (rTCA), and catalyzing reactions between arguably “primitive” metabolites (W-L). Importantly however, their sporadic though widespread distribution on the tree of life prevents either of these pathways from being definitively argued to have been present in the last common ancestor, and the possibility remains open that they originated as specific, convergent CO2-fixing adaptations in early Earth niches—still ancient, but far removed from the de facto origin of life. Contrarily, several lines of reasoning suggest that the Calvin-Bensen-Bassham cycle represents the youngest autotrophic pathway. This is especially remarkable as the pathway is the dominant mechanism for autotrophic carbon fixation on the modern Earth, primarily as a result of the cyanobacterial endosymbiosis that gave rise to plastids and chloroplasts in modern algae and plants. Based on phylogenetic analysis of RuBisCO and its diverse homologs not involved in the Calvin-Benson-Bassham cycle, the CBB cycle appears likely to have originated during the anoxic-oxic transition, in close conjunction with the invention of oxygenic photosynthesis and O2-based respiration. This new autotrophy permitted a new legion of aerobic autotrophs the ability to fix carbon while using O2 as a high potential terminal electron acceptor and was tightly linked to both photosynthesis and respiration, allowing CO2 fixation to occur within a much more favorable energetic milieu than anaerobic habitats previously afforded. In the 2-2.5 billion years that have ensued since the appearance of the Calvin cycle, it is perhaps surprising that autotrophy has maintained any predominance, especially as heterotrophy—the ability to degrade and recycle organic biomass—has assumed hegemony in many families of microorganisms and most higher eukaryotes. However, as our understanding of global carbon cycling improves, it seems clear that the role of autotrophs has anything but diminished.

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Reviews in Mineralogy & Geochemistry Vol. 59, pp. 233-258, 2005 Copyright © Mineralogical Society of America

Building the Biomarker Tree of Life Jochen J. Brocks Research School of Earth Sciences The Australian National University Canberra, ACT 0200, Australia [email protected]

Ann Pearson Department of Earth & Planetary Sciences Harvard University Cambridge, Massachusetts, 02138, U.S.A. [email protected]

INTRODUCTION It is the great challenge of geomicrobiology to study microorganisms in the context of their environments, both in Earth’s distant past and in the present. Planet Earth and its biosphere have evolved together, and a chronicle of Earth’s ecosystems and their geochemical cycles is recorded in sedimentary rocks spanning billions of years. A relatively new and very powerful approach to read these subtle microbial and environmental signatures in ancient rocks is the study of molecular fossils, or biomarkers, within the context of the biochemistry and phylogeny of their origins. Biomarkers are organic compounds (primarily lipids) that have particular biosynthetic origins and can be preserved in sediments and sedimentary rocks. The most valuable biomarkers are taxonomically specific, i.e., they can be assigned to a defined group of organisms, and are resistant to degradation. Reading the biomarker signatures in rocks can give information about the ancient record of anoxic conditions in the water column (e.g., Summons and Powell 1986), the intensity of UV radiation penetrating lakes (Leavitt et al. 1997), hypersalinity in evaporitic environments (Grice et al. 1998), and the function of microbial communities at methane seeps (Hinrichs et al. 1999). Biomarkers have helped to reconstruct the first appearance of major groups of organisms (e.g., McCaffrey et al. 1994; Moldowan et al. 1994; Moldowan and Talyzina 1998; Brocks et al. 2005), elucidate events of global climate change (e.g., Brassell et al. 1986), record major perturbations and reorganization of geochemical cycles (e.g., Logan et al. 1995; Kuypers et al. 1999) and document catastrophic losses in biodiversity (e.g., Grice et al. 2005). They are even used as tools to help in the discovery of major new petroleum reservoirs (for a review see Peters et al. 2004). The field of biomarker research is young and many new applications wait to be discovered. This review will explain how biomarkers form, how they are extracted from sedimentary rocks, and how they are used to reconstruct ancient and modern microbial ecosystems. It ends with a look to the future of biomarker research and the on-going efforts to reconcile the biomarker record with the tree of life. The review includes examples of the detection of important metabolic pathways, of the appearance of new biomarkers (and by inference new taxonomic groups) in the rock record, and of the reconstruction of geochemical processes. We will concentrate on biomarkers from bacteria, archaea and unicellular eukaryotes, excluding 1529-6466/05/0059-0010$05.00

DOI: 10.2138/rmg.2005.59.10

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the molecular fossils of plants and other non-microscopic eukaryotes. We highlight biomarker research using outstanding recent examples with a pedagogic emphasis on concepts but with no claim for completeness. More encyclopedic reviews were given by Peters et al. (2004) and Brocks and Summons (2004). For readers who desire more background in organic geochemistry, excellent introductions to the nomenclature, chemistry, and biology of lipids can be found in textbooks by Killops and Killops (2005) and Madigan and Martinko (2005).

The biomarker principle The origin of biomarkers. In lakes and oceans, the organic matter from dead organisms usually is almost quantitatively (> 99.9%) recycled back into carbon dioxide and water (Hedges and Keil 1995). The biological degradation of most proteins, nucleic acids and carbohydrates proceeds rapidly as dead biomass sinks through the water column, and it continues in the surface layers of the sediments. However, a small fraction of organic matter escapes the remineralization process and accumulates. Molecules that are especially recalcitrant, such as pigments, lipids and many structural macromolecules, will become concentrated (Tegelaar et al. 1989). With the onset of reducing conditions, the remaining sedimentary organic matter is degraded further by anaerobic heterotrophic organisms such as sulfate reducers, fermenters and methanogens (Megonigal et al. 2004); the chemical structure of the remains is altered by biological and chemical processes (Hedges and Keil 1995; Hedges et al. 1997; Rullkötter 1999). These alterations are referred to collectively as diagenesis. Smaller molecular units and degradation-resistant macromolecules are cross-linked and form kerogen, an amorphous and exceedingly complex structural network of biochemical subunits (e.g., Derenne et al. 1991; de Leeuw and Largeau 1993). During the formation of kerogen, vulcanization reactions mediated by sulfur and polysulphides often play an important role in connecting smaller molecular units, such as lipids, to the macromolecular aggregate, thus protecting them against further structural alterations (Sinninghe Damsté and de Leeuw 1990). Over millions of years, and with increasing burial depth and geothermal heat, most lipids will undergo structural rearrangement via cracking and isomerization reactions. These processes create a vast range of homologues and stereo- and structural isomers. Through reduction, elimination and aromatization the biomarkers typically lose all of their functional groups. The resultant products are geologically-stable hydrocarbon skeletons. Structures 1 and 2; 3 and 4; and 5 and 6 show examples of biolipids and their diagenetic hydrocarbon products. Bitumen is defined as the fraction of organic matter that can be extracted from sediments and sedimentary rocks using organic solvents, and it includes diagenetic components that have been thermally cracked, or released, from the kerogen. With increasing burial temperature and pressure, the thermal degradation of kerogen in organic-rich sedimentary rocks will generate enough liquid bitumen and natural gas for the expulsion of hydrocarbons in the form of petroleum. Petroleum reservoirs are, in fact, gigantic accumulations of biomarkers and other cracking products of sedimentary organic matter. However, at burial temperatures in the sedimentary unit exceeding 150–250 °C, most residual gas and liquid hydrocarbons have been expelled and the kerogen dehydrogenated to a highly aromatic, black carbon phase. This is the upper survival temperature for biological molecules over geologic time (Brocks and Summons 2004). The thermal destruction of biomarkers with deep burial is the primary complication in the search for biogenic molecular remains in very ancient, billion-year-old sedimentary rocks (Brocks et al. 2003a). During the experimental analysis of biomarkers, organic-rich, black sedimentary rocks are crushed to powder, and the powder is extracted with solvents such as methanol and dichloromethane using conventional reflux extraction or automated solved extractors (ASE). The bitumen extracts are usually yellow to dark brown, highly complex mixtures, containing hundreds of thousands of compounds. To simplify further analyses, the bitumen is fractionated into saturated hydrocarbons, aromatics, and polar compounds (usually those containing the

Building the Biomarker Tree of Life 1

isorenieratene

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3

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lanosterol HO

4

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lanostane 4

OH OH

X

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bacteriohopanepolyols (BHP)

Y 2 3

OH

Z

X, Y = H, OH Z = OH, OR, NHR

22 30

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pentakishomohopane

*

R1 R2

28 2 3

35

a: R1 = H; R2 = H b: R1 = H; R2 = Me c: R1 = Me; R2 = H R 20

24 heteroatoms O, N, and S) using normal-phase (SiO chromatography. The fractions are * 2-gel) then analyzed by gas chromatography-mass spectrometry (GC-MS). 7 steranes

The ratios of different homologues and stereo- and structural isomers contained in a sample of bitumen may contain a plethora of information about conditions during burial R and diagenesis, as well as the source organisms. For example, the relative abundance of stereoisomers such as 22S vs. 22R hopanes (see 6), 20S vs. 20R steranes (see 7), or triaromatic steroids 8 with steroids intact and cleaved side chain, often helps to estimate relative burial triaromatic 8 temperatures (e.g., Brocks et al. 2003a). A high relative abundance of biomarkers such as = H, Me, Et6 lacking the C-28 and C-30 aromatic carotenoids (e.g., 2), 28,30-bisnorhopanesR (hopanes methyl groups) (Schoell et al. 1992), or the pentacyclic triterpane gammacerane 9, produced by ciliates grazing on bacteria in the anoxic zone (Sinninghe Damsté et al. 1995), may indicate anoxic and/or sulfidic conditions. 4

Molecular 9fossils as markers for biosynthetic pathways. For many geobiologists, gammacerane the most interesting application of biomarkers is the reconstruction of ancient microbial ecosystems and the concurrent environmental conditions, at time-scales of millions to billions

22 30

6

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R1

2 3

a: R1 = H; R2 = H b: R1 = H; R2 = Me c: R1 = Me; R2 = H

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R 20

*

7

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R = H, Me, Et

gammacerane

of years. Although lipids usually lose their functional groups during diagenesis, and often also stereochemical specificity, the remaining hydrocarbon skeletons retain useful biological and ecological information. For instance, the aromatic carotenoid okenane 10, a molecule detected in 1,640 million-year-old sedimentary rocks of the McArthur Basin in northern Australia, is regarded as a biomarker for purple sulfur bacteria of the family Chromatiaceae (Brocks et al. 2005). The only known biological precursor of okenane is the red-colored phototrophic pigment okenone 11, and okenone is found exclusively in Chromatiaceae. As the Chromatiaceae have a very specific ecology, the presence of okenane has been used to predict that the waters of the McArthur Basin 1,640 million years ago were sulfidic up into the photic zone.

10

okenane

11

okenone

O

Me O

The distribution of carotenoids in the biosphere is comparatively well known due to their intense color. Therefore, the interpretation of okenane as a biomarker for purple sulfur bacteria appears be robust. However, the interpretation of many other biomarkers is more complex. 12 tooxidosqualene Strictly speaking, biomarkers are not markers for taxonomic groups or for environmental conditions. Biomarkers are the products of biosynthetic pathways that may occur in unrelated O organisms. An example of potential misinterpretation is the carotenoid isorenieratane 2. Isorenieratane 2 is commonly interpreted as a specific biomarker for green sulfur bacteria tail-to-tail (Chlorobiaceae). However, the precursor of isorenieratane 2, isorenieratene 1, also occurs in the gram positive bacterial group Actinomycetales (Krügel et al. 1999; Phadwal 2005), 13 crocetane and a genome library search for genes involved in isorenieratene biosynthesis suggests that cyanobacteria may have this capacity as well (Woodward Fischer, personal communication).

O

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In general, the distribution of genes for carotenoid biosynthesis in Bacteria is characterized by horizontal gene transfer and gene duplication events (Phadwal 2005). Thus the capacity for biosynthesis of isorenieratene 1 might appear in other lineages as well. Additionally, the aromatic end-group of isorenieratene also may form by abiotic, diagenetic aromatization of cyclohexyl (ionene) end-groups (Koopmans et al. 1996). Thus, isorenieratane may form in sedimentary environments by alteration of a wide range of carotenoids, including β-carotene. Therefore, isorenieratane 2 is not a biomarker solely for Chlorobiaceae. Isorenieratane is a product of all biosynthetic and abiotic pathways that can produce and alter suitable precursors. A second example of the ambiguities in the interpretation of biomarkers is the phylogenetic distribution of the biosynthetic pathway leading to sterols. The C30 steroid hydrocarbon lanostane 4 has two common diagenetic precursors, lanosterol (produced by animals and fungi) and cycloartenol (biosynthesized by plants). The enzyme oxidosqualene cyclase (OSC) catalyzes the formation of lanosterol 3 and cycloartenol from the precursor compound oxidosqualene 12. These compounds are the initial steroidal products that feed the long, complex biosynthetic pathways that produce all known eukaryotic steroids. However, although most biology textbooks state that steroid biosynthesis is one of the defining characteristics of eukaryotes, lanostane 4 is not always a biomarker for Eucarya. OSC also is expressed in at least three groups of bacteria: the Methylococcales (Bird et al. 1971), okenane Myxococcales (Kohl et al. 1983), Planctomycetales (Pearson et al. 2003). Several of these 10 and species produce detectable amounts of lanosterol, as well as down-stream (modified) sterols. Therefore, the compound lanostane is a biomarker for eukaryotes and bacteria. However, fossil steroids that have an additional alkyl substituent at position C-24 (see 7) in the side chain must still be regarded as diagnostic for eukaryotes, as no group of bacteria is known O okenone to alkylate the steroid side chain (Brocks et al. (yet) to possess the biosynthetic 11 capacity 2003b; Volkman 2003).

12

oxidosqualene O

The carbon isotopic composition of biomarkers

tail-to-tail

13 crocetane Fractionation of the stable isotopes of carbon, 12C and 13C, occurs in association with biological reactions and remains imprinted in the isotopic signatures of biomarker molecules. In addition to the intrinsic taxonomic utility of certain lipids, the isotopic ratios of these compounds can provide insight about the environmental conditions and metabolic capabilities of the source organisms. As such, compound-specific isotopic analysis is a valuable additional O tool for understanding the modern and ancient geologic record. archaeol 14 12 13 Carbon occurs naturally as three isotopes, C, C, and 14C; with fractional abundances of O

0.989, 0.011, and 10−12, respectively. The last, 14C, is radioactive, and its half-life of 5730 years OH yields useful radiocarbon chronologies only over the most recent few tens of thousands of years. Therefore, most isotopic analyses of individual biomarkers focus primarily on the two stable isotopes of carbon. Isotopic composition is expressed most commonly as the ratio of 13C to 12C head-to-head in the substance, relative to the ratio in a standard material. The units of isotopic fractionation HO O reference material is a carbonate rock are parts per thousand, or “permil” (‰), and the standard (VPDB; Vienna Pee Dee Belemnite), which by definition has a δ13CVPDB value of 0‰. Crenarchaeol O 15 O

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 13 C 12 C Sample δ13C =  13 12  C C Standard 

  1000  

There are numerous factors that determine the final δ13C values of total biomass and of individual biomarker compounds. Some of the principles most fundamental to geobiological interpretations are summarized below; however, the reader is encouraged to examine the more comprehensive reviews by Hayes (1993, 2001), from which most of this material was adapted. The isotopic composition of the carbon source. The δ13C values of biomarkers depend in complex ways on the pathway(s) and condition(s) of carbon fixation, as described below; but the baseline for these values necessarily is that of the inorganic carbon source from which the biomass is produced. The speciation of inorganic carbon is governed by acid-base equilibria between CO2, HCO3−, and CO32− species. CO2 + H2O ↔ H2CO3 ↔ HCO3− + H+ ↔ CO32− + 2H+ The relative abundance of each component is dependent on the pH of the local environment and varies among fresh-water, marine, fluvial, hydrothermal, and other systems of geologic interest. The isotopic distribution, however, is a function primarily of temperature, and in the case of CO32−, also of the partitioning between dissolved and mineral [M[II]CO3(s)] phases (Bottinga 1969; Emrich and Vogel 1970). These equilibrium isotope exchange reactions favor the incorporation of 13C into the more stable (lower energy) bonding environment; the primary result is a relative isotopic depletion of 13C in CO2(aq) and CO2(g) relative to HCO3− or CO32−. The equilibrium fractionation factor, αA-B, is defined as the 13C/12C ratio of A (RA) divided by the 13C/12C ratio of B (RB): A↔B α A− B ≡

RA (1000 + δ A ) = RB (1000 + δ B )

Equilibrium fractionation factors at common biosynthetic or environmental temperatures were summarized by Falkowski and Raven (1997). However, in many systems of interest to geobiology, such as thermal springs, temperatures may exceed the values presented here. Bottinga (1969) and Richet et al. (1977) have presented equations to extrapolate values of α for the reaction CO2(aq) ↔ HCO3− to higher temperatures. Values of αA-B decrease at higher temperatures, resulting in smaller isotopic differences between the carbon species. Typical values of δ13C for inorganic carbon substrates in geological settings are −6 to −8‰ for atmospheric CO2; −2 to +2‰ for dissolved HCO3− and CO32−; and a range of values for CO2(aq) dependent largely on the relative amount of i) respiration-derived CO2 (having lower δ13C values), or ii) residual, isotopically heavy CO2 such as is found in strongly methanogenic systems. Fractionation associated with fixation of inorganic carbon to biomass. Carbon isotope fractionation in biological reactions most commonly results from unidirectional kinetic isotope effects (KIE). These effects result from the effectively slower rate of reaction for a 13 C-containing bond relative to the same bond in which the carbon atom is 12C. Thus, isotopic fraction ultimately is controlled at the atomic level by the reaction rates at individual sites within molecules. A → 13B, rate k1

13 12

A → 12B, rate k2; k2 > k1

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When inorganic carbon is fixed into biomass, these reactions necessarily are controlled by the activity of specific enzymes, each of which has an associated KIE. The biological transformation of substrate (A) to product (B) is accompanied by a fractionation factor that most commonly is written using the ε notation, for the subsequent convenience of relating εA-B to δA and δB when ε is a small number. Because 13C preferentially remains in A, εA-B always is a positive number and products are “lighter” than reactants. εA-B ≡ (αA-B − 1)1000 εA-B ≈ δA − δB The enzymes utilized by organisms to fix carbon are categorized most easily by the metabolic pathways in which they are used and by the species of inorganic carbon substrate for which they are specific (see Bott and Thauer 1989; and Hayes 2001, for compilations of substrates and isotopic fractionations, respectively). The latter is important, as it is clear that organisms utilizing pathways specific for HCO3− necessarily begin with a substrate that is heavier isotopically than organisms that fix CO2 directly. The recent discovery of novel metabolic pathways in some species of microbes (e.g., the newly discovered 3-HP pathway; Strauss and Fuchs 1993; Raymond, this volume)—and the possibility that mixotrophic or hybrid metabolisms are expressed by species in the environment—also complicates the picture considerably. However, the currently available information can be divided into pathways that typically yield isotopically “heavy” biomass (rTCA and 3-HP); pathways yielding isotopically intermediate biomass (Calvin-Benson cycle and some methanogens); and pathways yielding isotopically light biomass (homoacetogens, some methanogens, other Acetyl-CoA pathway organisms, and all methanotrophs). The above discussion does not include strict heterotrophs, because the carbon isotopic composition of their lipids and biomass broadly reflects their carbon sources. The δ13C values of biomass of heterotrophic microbes and macroscopic heterotrophs appear to be united by the principle “you are what you eat, plus 1‰” (DeNiro and Epstein 1978). This rule of thumb is based on the weak carbon isotopic fractionation associated with the glycolytic respiratory metabolism of most heterotrophs. Methanotrophy and methylotrophy, on the other hand, may be considered here as special cases of autotrophy rather than heterotrophy, as they involve fixation of C1 metabolites and thus are accompanied by large ε values. For further discussion of fractionations associated with aerobic methanotrophy see Summons (1994); and for separate treatment of methylotrophic metabolism see Summons (1998). Anaerobic methanotrophy has not yet been explained mechanistically, although the first in situ isotopic measurements (Hinrichs et al. 1999) immediately indicated large values of εCH4-biomass. Application of δ13C analyses at the molecular level. Isotopic analysis of lipid biomarker molecules can provide valuable information about the geobiology and biogeochemistry of contemporary and ancient systems. Compound-specific measurements usually employ the “continuous-flow,” isotope-ratio mass spectrometric methods developed by Hayes (Matthews and Hayes 1978; Hayes et al. 1990). Initial studies focused on lipids extracted from samples of ancient geologic age (e.g., Freeman et al. 1990) and showed the utility of this approach to describe a diversity of biomarkers and their origins. Such molecular-level analyses rely on the intrinsic advantages provided by lipids: their volatility permits separation by gas chromatography, and lipids are thermally and diagenetically stable. An important, but complicating, factor in the interpretation of δ13C values of individual lipids is the extent to which the lipids themselves are fractionated isotopically relative to the total biomass of the source. Intracellular fractionations are the consequence of the diversion of metabolic intermediates such as pyruvate and acetate, which are necessary for the biosynthesis of lipids, into other pathways such as the citric acid cycle and/or for the biosynthesis of amino

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acids. A detailed mathematical treatment of the isotopic consequences of such branched pathways is given in Hayes (2001), but in general, the fractionation between biomass and lipids (εbiomass-lipid) results in a more negative value of δ13C for the lipid due to fractionation during the decarboxylation of pyruvate to acetate. This is true for organisms expressing the Calvin cycle, but it is not necessarily true for species that synthesize acetate directly or for those that have alternative biosynthetic routes to acetate (e.g., van der Meer et al. 2001) or isoprenoids (Schouten et al. 1998a). Organisms that have unusual metabolisms have not yet been studied thoroughly, and the range of variability in expression of εbiomass-lipid needs further exploration.

BIOMARKERS IN GEOMICROBIOLOGY There are far too many examples of the informative use of biomarkers in geobiology to review all of them in this chapter. Therefore, in the section that follows we present a series of case studies and outstanding examples of biomarker geochemistry. These examples are grouped by metabolic pathway, biogeochemical process, and/or by the phylogeny of the source organism. Each is an attempt to illustrate the unique contributions that the analysis of lipid biomarkers can make to the field of geomicrobiology.

Biomarkers as indicators of metabolism Methanotrophic methanogens: the anaerobic oxidation of methane. The need for an anaerobic sink for the methane produced in marine sediments was recognized by geochemists as early as the 1970s-1980s (e.g., Reeburgh 1976; Alperin and Reeburgh 1985). Profiles of dissolved CH4 in sedimentary pore waters indicated that in most environments it did not reach the sediment-water interface and therefore was not oxidized by O2 in the deep ocean. Zehnder and Brock (1979) first proposed—and later Hoehler et al. (1994) expanded upon—the hypothesis that the anaerobic oxidation of methane (AOM) could be achieved by methanogenic archaea utilizing a specialized metabolism designed to run “in reverse.” They suggested that the energetic expense of this reaction could be overcome by consumption of the H2 by-product or other reducing equivalents by syntrophic, sulfate-reducing bacteria (SRB). Such a process would require a close physical association in the form of an aggregate or consortium. However, it was only recently that the microorganisms were identified that putatively are involved in this process. Currently they are classified broadly among three new categories of Euryarchaeota, ANME-1, ANME-2, and ANME-3 (Boetius et al. 2000; Orphan et al. 2001, 2002; Knittel et al. 2005). At least one of these groups (ANME-2) indeed appears to live in consortia with SRB. Much still remains to be learned about the biology of AOM, including the possibility that there are ANME-group archaea or SRB that are capable of oxidizing CH4 independently, without a syntrophic partner. Prior to the discovery of the consortia that mediate AOM, however, there was evidence from δ13C values of biomarker lipids that archaea were involved in AOM. The history behind the discovery of this process represents an outstanding case in which isotopic analysis of biomarkers led directly to conclusions about geomicrobiology and environmental metabolisms. Bian (1994) first observed (but could not yet explain) depleted values of δ13C for crocetane 13, a C20 isoprenoid isomer of phytane, in sediments of the Kattegat Strait between Denmark and Sweden. Subsequent work by Hinrichs et al. (1999) on sediments from coastal California, USA, showed similarly extreme isotopic depletions for both archaeal and bacterial biomarkers from a CH4-rich, anaerobic sediment. Hinrichs et al. (1999) showed that the lipids archaeol 14 and sn-2-hydroxyarchaeol, which are glycerol diethers typical of methanogenic euryarchaeota, had δ13C values ≤ −100‰. Such light values indicated that consumption of isotopically-depleted CH4 must serve as the primary carbon source for the archaea from which the lipids were derived. The earlier δ13C values for crocetane 13 and the related isoprenoid

10

okenane

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12 oxidosqualene O Me hydrocarbon 2,6,10,15,19-pentamethyl­icosane (PMI), could finally be explained as Oproducts okenone 11 O of similar CH4-consuming archaea (Bian et al. 2001). tail-to-tail

13 12

crocetane

oxidosqualene O

O

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archaeol

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crocetane OH

derived from archaeal Recent work has suggested that crocetane and C20 isoprenoids head-to-head diethers may be biomarkers specific for HOthe archaea associated with ANME-2-type consortia. O O The ANME-1 archaea (frequently observed as individual cells and filaments; Orphan et al. archaeol 14 associated with tetraethers 15 (Blumenberg et al. 2002) may be typified by the C40 isoprenoids Crenarchaeol O 15 O 2004). There may be further taxonomic potential to be discovered within archaeal biomarkers,O OH and more may be learned if ANME archaea are brought into pure or enrichment culture. O

HO

16 15

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[5]-ladderane

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head-to-head OMe O

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C18:1ω9 fatty acid

ω9

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OH

O HO

OMe

16 the [5]-ladderane relatives of the normally methanogenic After early of the assimilation of CH4 by chlorobactene 18 reports O archaea, numerous other cases of AOM were documented using lipid biomarkers. Isotopicallydepleted isoprenoid ethers have been found in association with Mediterranean mud volcanoes (Pancost et al. 2000); cold-temperature CH4 seeps (Pancost et al. O 2001; Zhang et al. 2003); ω seeps (Teske et al. 2002; Schouten et al. 2003b); in the Black Sea (Thiel hot-temperature CH C fatty acid 4 1 OH 17 18:1ω9 18 2 ω et al. 2001; Michaelis et al. 2002; Wakeham et al. 2003); and in the spectacular carbonate chimneys of the serpentinite-hosted hydrothermal system of the Lost City (Kelley et al. 2005). There is also considerable evidence for the association of bacterial groups with the AOM chlorobactene 18These process. microbes are probably involved in the metabolism of the organic end-products of AOM and/or in the incorporation of 13C-depleted DIC. However, it remains possible that some unidentified groups of bacteria assimilate CH4 directly. Hopanoids (Elvert et al. 2000; Pancost et al. 2000; Thiel et al. 2001, 2003) and fatty acids derived from phospholipids (e.g., Hinrichs et al. 2000) with highly depleted δ13C signatures are frequently found in the same samples as the archaeal biomarkers for AOM. 2

9

Anammox: the discovery of autotrophic denitrification. In a now-classic paper entitled “2 kinds of lithotrophs missing in nature”, Broda (1977) postulated the anaerobic oxidation of ammonia by nitrate or nitrite-reducing bacteria. Oxidation of NH4+ with subsequent reduction

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of NO2− would represent a means to provide energy for lithoautotrophic fixation of CO2 oxidosqualene 12 both to biomass and a form of denitrification with associated effects on the global nitrogen cycle. O

NH4+ + NO2− → N2 + 2H2O

ΔG° = −360 kJ/mol

The elegance and simplicity of Broda (1977) make the paper virtually required reading for the student of geobiology; however, the predicted “anammox” tail-to-tail reaction and more specifically the associated organisms remained undiscovered for a further 20 years. Anammox was finally 13 crocetane revealed in association with nitrogen-rich wastewater reactors (Mulder et al. 1995; Strous et al. 1997; van de Graaf et al. 1997), and the organisms responsible for the reaction were identified as members of an unusual bacterial group, the Planctomycetales (Strous et al. 1999). Recently it has been suggested that anammox may be responsible for up to 30–50% of the denitrification occurring in the global ocean (Dalsgaard et al. 2003, 2005; Kuypers et al. 2005), all of which had been previously assigned to the activity ofO denitrifying, anaerobic heterotrophs. archaeol Not all members of14the Planctomycetales are capable of anammox metabolism; most O members of this group appear to be heterotrophs or chemoorganotrophs. However, it is OH within the anammox genera (Brocadia, Kuenenia, Scalindua) that the unique lipids known as ladderanes 16 are found (Sinninghe Damsté et al. 2002). Ladderanes are believed to be critical components of the anammox organelle, the “anammoxosome” (van Niftrik et al. head-to-head 2004). Specifically, ladderanes may provide a diffusional barrier to the toxic intermediate of O purpose, ladderanes possess a unique ‘ladder’ ammonium oxidation, hydrazine (N2H4). ForHthis O of concatenated cyclobutane rings (see 16) that can be stacked into a dense membrane; and due Crenarchaeol O to a lack of branched groups, they are apparently biosynthesized from acetate rather 15methyl than from isoprene. The C20 moieties may be connected via ether or ester linkages to a glycerol backbone (Sinninghe Damsté et al. 2002).

O O HO

16

OMe

[5]-ladderane

O

It is unknown at present to what extent intact ladderanes, or more likely, their O ω degradation products, are preserved in the geologic record. Because of the highly strained C fatty acid 1 OH 17 18:1ω9 18 2 ω nature of cyclobutane rings, these high-energy structures should be unstable and prone to rapid degradation. However, the ring-opening products of the cyclobutane groups may yield diagnostic and geologically stable biomarker products. Searching for these products, it may chlorobactene 18to determine eventually be feasible how far back into the geologic record the anammox process persists. Simple consideration of biogeochemical cycles suggests that anammox would not have developed as a significant process until after the advent of oxygenic photosynthesis, since the reaction is dependent on sufficient quantities of NO2−. Nitrite is a product of the aerobic oxidation of NH4+ by O2 (nitrosification, by genera such as Nitrosomonas or Nitrosobacter). 2

9

The limited δ13C data that are currently available for ladderane lipids are consistent with an autotrophic metabolism for the anammox bacteria. Schouten et al. (2004) observed δ13C values between −55‰ and −58‰ for samples of ladderanes from the water column of the Black Sea. The isotopic fractionation of lipid relative to substrate (CO2) was 32‰ to 49‰ (lipids depleted relative to substrate) for the samples from the Black Sea and for samples taken from laboratory enrichment cultures. These data could be consistent with a metabolic pathway dependent on autotrophic synthesis of Acetyl-CoA. Such metabolism is also broadly consistent with an ancient origin of these species and of this chemoautotrophic pathway. Ladderanes are not the only unusual lipids to be found within the Planctomycetales. Species of Pirellula and Planctomyces (Kerger et al. 1988) contained abundant C18:1ω9 fatty

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O

acid 17, more commonly component of eukaryotes (in the above nomenclature for fatty 14 aarchaeol O atoms in the molecule, ‘:1’ to the number of acids, C18 refers to the total number of carbon C=C double bonds in the chain, and ‘ω9’OHdesignates the position of the double bond counted from the last (ω = omega) carbon atom in the chain). They also contained unique distributions of 3-hydroxy-fatty acids, which were suggested to be sufficiently diagnostic biomarkers for planctomycetes in some environmental settings. Unlike most bacteria, the Planctomycetales head-to-head lack peptidoglycan in their cell walls and HO as such contain no muramic acid (e.g., König et al. 1984; Stackebrandt et al. 1986). Although Omany planctomycetes, including the anammox genera, contain “nucleoids” which confine the cellular DNA, Gemmata obscuriglobus is the Crenarchaeol O 15 only species known to contain a “nucleoid” which is double membrane-bound as found in O eukaryotes (Fuerst and Webb 1991; Lindsay et al. 2001). G. obscuriglobus also is among O the few bacterial species that biosynthesize sterols, and it is the only species (prokaryotic or eukaryotic) in which those sterols are not subsequently demethylated at position C14 (see HO lanostane 4) (Pearson et al. 2003). It remains unknown what intracellular roles all of the OMe unusual planctomycete lipids serve, or how far back in the geologic record the molecular [5]-ladderane 16 O fossils of this group may extend.

17

C18:1ω9 fatty acid

O

ω2 18

ω9

2

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The Planctomycetales represent an unusual and divergent microbial lineage which chlorobactene 18 traditionally has been difficult to place within the tree of life. Using approaches based on alternative phylogenetic treeing methods for 16S rRNA genes (Brochier and Philippe 2002) and on the relationships among C1 metabolic pathway genes (Chistoserdova et al. 2004), it has been suggested that the Planctomycetales may be the most deeply branching of the bacterial taxa. By these analyses, the planctomycetes are basal to the rest of the bacteria and are the closest bacterial relatives to the archaea and eukaryotes. Aromatic carotenoids, biomarkers for phototrophic oxidation of sulfide. Anoxic conditions in aquatic systems, and particularly the enigmatic Oceanic Anoxic Events such as OAE1b in the mid-Cretaceous, have become critical research areas for the geosciences. Periods of prevailing anoxia in large basins might be responsible for the widespread deposition of black shales, increased accumulation of petroleum source rocks, changes in global biogeochemical cycles, extreme shifts in climate, major mass extinctions, and concomitant biological radiations. Currently the only biomarker proxies available to study the most extreme form of anoxia, photic zone euxinia, are biomarkers of the phototrophic green and purple sulfur bacteria (Summons and Powell 1986; Requejo et al. 1992; Brocks et al. 2005; Grice et al. 2005). Green sulfur bacteria (family Chlorobiaceae) are only distantly related to other phototrophic bacterial groups (Fig. 1). Chlorobiaceae only use photosystem I (PS I) and are strictly anaerobic, exploiting reduced sulfur species, such as hydrogen sulphide, as electron sources. In microbial mats, the requirement for sulfide and light restricts their habitat to the anoxic zone millimeters below the mat surface. In planktonic environments they live in a layer below the anoxic-oxic boundary, but within the photic zone. To adjust to the wavelength distribution and attenuated intensity of light at depth, Chlorobiaceae commonly possess an abundance of accessory carotenoid pigments. Green-pigmented species of planktonic Chlorobiaceae grow in a thin layer at water depths up to ~13 m, and their major carotenoid pigments are chlorobactene 18 and hydroxychlorobactene (Imhoff 1995), both of which yield the sedimentary biomarker, chlorobactane 19. Brown-pigmented Chlorobiaceae inhabit a zone deeper than the green-pigmented species; they are usually found at water depths up to 18 m,

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but were also observed in the Black Sea at 80 m (Repeta et al. 1989). Their pigment system is dominated by the carotenoids isorenieratene 1 and β-isorenieratene, the precursors for the OO ω ω hydrocarbon biomarkers isorenieratane 2 and β-isorenieratane (Liaaen-Jensen 1965). C fatty acid C fatty acid 1 1 OH 18:1ω9 1717 18:1ω9 OH 18 18 2 2 ω ω 2

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Purple sulfur bacteria of the family Chromatiaceae are a sub-group of the γ-proteobacteria and utilize the PS II photosynthetic reaction center. Although unrelated to Chlorobiaceae, R the Chromatiaceae have similar environmental requirements. Their preferred physiology cheilanthane 20 is phototrophic oxidation of reduced sulfur under anoxic conditions. However, they are R = isoprenyl generally more tolerant to oxygen and can exploit a more versatile range of electron donors, including hydrogen. In microbial mats, as well as in planktonic environments, they grow in a thin layer directly above the zone of green sulfur bacteria but below the oxycline. Several head-to-tail species of planktonic Chromatiaceae have an accessory pigment system based on the redhexaprenol 21 OH

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Figure 1. SSU rRNA phylogenetic tree annotated with structure numbers of biomarkers discussed in the text (adapted after Brocks and Summons 2004).

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colored monoaromatic carotenoid okenone 10, which has a 2,3,4-trimethylaryl end-group. Planktonic species are commonly observed at water depths around 10 meters or less, and very rarely deeper than 20 meters (Van Gemerden and Mas 1995). Okenone 11 is the only known precursor for the hydrocarbon biomarker okenane 10, which represents the sole known proxy for purple sulfur bacteria in the fossil record. Biomarkers of green and purple sulfur bacteria are particularly valuable tracers for the study of marine anoxic conditions in the Precambrian (> 542 million years (Ma) ago), as body fossils of animals that give evidence about anoxic conditions in younger rocks do not exist in this period.

Biomarkers as indicators of the evolution of life and the environment Phototrophic sulfur bacteria and a sulfidic ocean in the Proterozoic. Today, Earth’s oceans are teeming with complex life, and even deep marine trenches contain enough oxygen to support macroscopic organisms. However, oceans in the distant past were fundamentally different. For the first two billion years of its existence, the ocean-atmosphere system was almost entirely anoxic (Fig. 2) (Holland 1994), but around 2450 to 2320 Ma ago, the disappearance of mass-independent fractionation of sulfur isotopes indicates that the concentration of atmospheric oxygen rose from previously trace levels to at least 10−5× the present level (Farquhar et al. 2000; Bekker et al. 2004; Holland 2004). Soon after this initial rise of oxygen, fossil soils (paleosols) begin to show typical oxic weathering patterns that suggest atmospheric O2 quickly may have reached 15% of its present value (Rye and Holland 1998). However, the deep oceans remained mostly or entirely anoxic until at least ~1,800 Ma ago, the point in geological history when the last Paleoproterozoic banded iron formations (BIFs; iron silicates and iron carbonates) disappeared (Fig. 2). Surprisingly, the state of the ocean in the following “mid-Proterozoic” interval (~1,800 to ~800 Ma) remains particularly mysterious. One model suggests that deposition of BIFs ceased ~1,800 Ma ago because FeII emitted from mid-oceanic ridges was precipitated immediately on the oxygenated sea-floor as FeIII-hydroxides (Holland 1994). However, according to a competing model (Canfield 1998), FeII was not removed as oxidized rust but precipitated as FeII-sulfides in sulfidic ocean waters. Evidence is accumulating from the isotopic composition and distribution of sulfides (Canfield 1998; Shen et al. 2003; Poulton et al. 2004), sulfates (Kah et al. 2001) and molybdenum (Arnold et al. 2004), that appears to support Canfield’s model. It is also possible that a hybrid of both models existed, resembling a ‘marble cake’ ocean (A. H. Knoll, personal communication). If large areas of the world ocean were euxinic (anoxic and sulfidic) in the midProterozoic, then our understanding of more than one fifth of Earth history would change

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Figure 2. Geological time chart beginning with (a) the formation of Earth ~4.6 billion years ago (Ga); (b) anoxic, non-sulfidic oceans; (c) onset of oxygenation of the atmosphere (Bekker et al. 2004; Holland 2004); (d) disappearance of banded iron formations as indicator of changing ocean chemistry (Holland 2004); (e) the informally defined “mid-Proterozoic” interval with possible widespread, anoxic and sulfidic marine conditions (Canfield 1998); (f) major radiation of eukaryotic algae (Knoll 1992); (g) first appearance and major radiation of multicellular organisms and animals. 0.54 Ga marks the PrecambrianCambrian boundary. P = Paleozoic, M = Mesozoic, C = Cenozoic.

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radically. Geochemical cycles would have been altered, and many bioessential elements, such as nitrogen, molybdenum, and copper would have been rare. Trace-metal limitation may explain why familiar forms of life, such as modern algae and animals, arose so late in Earth history (Anbar and Knoll 2002). In the euxinic ocean, organisms requiring oxygen would have been marginalized, restricted to surface waters and shorelines. The ocean would have promoted extensive growth of green and purple sulfur bacteria wherever sulfidic conditions rose into the photic zone. The earliest evidence for the existence of phototrophic sulfur bacteria comes from a biomarker study on the 1,640 Ma Barney Creek Formation in the McArthur Basin, northern Australia (Brocks et al. 2005). Well preserved, organic-rich dolostones of the Barney Creek Formation were deposited in deep waters of the rift basin. The lipids extracted from these sedimentary rocks contain some of the oldest, clearly indigenous biomarkers known to date (Summons et al. 1988b). Significantly, the samples contain relatively high concentrations of isorenieratane 2, chlorobactane 19, and okenane 10. This indicates that the basin was stratified, and euxinic conditions extended—at least episodically—into the photic zone of the water column. This ancient assemblage of biomarkers had two further characteristics which were radically different from any younger bitumen. Steroids alkylated at C-24 in the side chain and diagnostic for eukaryotic organisms were present at levels close to or below detection limits. In contrast, aromatic steroids without side chain alkylation but which were methylated at C-4 (see 8) were very abundant. These biomarkers, together with high relative concentrations of 3β-methyl-hopanes, suggest aerobic type-I methanotrophic bacteria were abundant members of the population. Aerobic methanotrophs are typically found in sulfate-starved environments (95% genomic coverage (Lander and Waterman 1988). Figure 3 relates species abundance, sequencing effort, and genome coverage for a community of microorganisms. As shown, a sequencing budget in the range of 100 Mbp should allow analysis of population dynamics for the more abundant species of any community or for the majority of species in relatively simple communities (i.e., those with few members). In highly diverse populations, population variability in target species may be assessed though directed approaches that screen for clones from certain organism types before sequencing (Stein et al. 1996; Handelsman et al. 1998; Henne et al. 1999; Beja et al. 2000; Rondon et al. 2000; DeLong 2002; Hallam et al. 2003; Brady et al. 2004; Handelsman 2004; Riesenfeld et al. 2004; Daniel 2005; DeLong 2005; Schleper et al. 2005). In addition, variation in specific genome regions of interest may be identified by combining environmental genomics with targeted PCR amplification and gene sequencing.

BASIC POPULATION PARAMETERS: SELECTION, RECOMBINATION AND GENETIC DRIFT We have described how to define microbial populations and recognize fine-scale patterns of variation within them. The patterns of individual-level variation observed in a population (through the methods described above) result from the dynamic interaction of evolutionary

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Figure 2. Methods for environmental genomics. Microbial cells are sampled from the environment. DNA extraction results in a mixture of genomes in proportion to their representation in the sample if there is no bias in DNA extraction between species. DNA is fragmented using restriction digests or by sheering in to fragments that are approximately the same size. Each fragment is inserted into a vector to make a clone library. Each species representation in the clone library should be proportional to their representation in the extracted DNA if there is no cloning bias. A random sample from the clone library is then sequenced in two directions. Resulting paired-end sequences are then assembled using assembly software.

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forces, including selection, recombination and neutral genetic drift. We describe each of these forces, and then discuss how they interact to structure diversity in natural populations. One of the primary forces that shape populations is natural selection. There are many different types of natural selection which act to fix or preserve variation within a population. For example, when a mutation confers a selective advantage, that mutation will be selected for across generations and in time may become fixed (i.e., variation is removed) in the population through positive selection. Alternatively, when mutations are deleterious, purifying selection (i.e., selection against individuals with a harmful mutation) will generally remove it from the population. Natural selection may also preserve diversity within a population by favoring multiple variants in a process referred to as diversifying selection (Kreitman and Hudson 1991). The relative intensity of selection for different mutations or genome changes will vary. The relative importance of selection as an evolutionary force has been the subject of great debate in evolutionary literature (Nei 2005). Another important evolutionary force in natural populations is the redistribution of genetic variation among individuals. This occurs through transfer of DNA between cells and its incorporation into the recipient chromosome via recombination. In most microorganisms, these processes are not linked to reproduction as they are in sexually reproducing eukaryotes. Instead, genes are transferred between individuals though transduction (i.e., by viruses), transformation (i.e., direct uptake of DNA from the environment), or conjugation (i.e., unidirectional transfer of chromosomes between individuals). The processes of gene transfer and recombination lead to greater variation in individual fitness within a population, because

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both beneficial and deleterious mutations are continually re-sorted into new combinations (Burt 2000; Goddard et al. 2005). Genetic drift is an evolutionary process in which gene frequencies change at random from one generation to the next (Fisher 1930; Kimura 1983). These random deviations generally have a greater effect on small populations than on large ones. Genetic drift may result in the fixation or removal of mutations from a population without any selection, and is therefore considered a neutral force. Variation introduced into a population may be fixed or removed by selection or random genetic drift and it can be redistributed between individuals through recombination. However, these forces do not act in isolation. Instead, the interaction among evolutionary forces influences the overall structure of diversity within a population. There is a substantial body of work in theoretical population biology and experimental evolutionary biology that predicts the characteristic footprints that each of these evolutionary processes leave in the patterns of sequence variation (Li 1997; Souza et al. 2002). Based on this work, it is possible to interpret historic evolutionary processes from the distribution of variation among individuals in a population.

SHAPING POPULATION STRUCTURE THROUGH SELECTION AND RECOMBINATION The structure of diversity within a population (e.g., the extent of genome-level heterogeneity, its distribution among individuals within the population, and the abundance distribution of the genotypes) represents the outcome of interactions among several evolutionary forces. Conversely, after identifying the structure of a microbial population, we can infer the combination of forces that created it. One of the primary interactions that defines microbial population structure is the balance between selection and recombination. We begin a discussion of the interaction between recombination and selection by considering extremes of population structure (also see Fig. 4): (i) a clonal structure, resulting from natural selection in populations where recombination events are rare, and (ii) a recombinant structure, in which recombination occurs fast enough to widely distribute genetic variation prior to selection events. (i) When all genes in the genome are physically linked to one another, natural selection that effects one position must affect the entire genome. As clonal populations evolve in natural environments, the linked fate (linkage) of genes across the genome results in a dynamic process known as periodic selection. This process begins when a single individual randomly acquires an adaptive mutation that increases its fitness (i.e., its survival and rate of asexual reproduction relative to other individuals in the population). The faster reproduction of this individual clone relative to other individuals within a population results in an increase in the overall frequency of its genotype (clonal expansion). Eventually, if the adaptive mutation confers a great enough advantage, this single adaptive genotype may outcompete all other types in the population and become fixed. Replacement of all individuals in a population by a single genotype periodically purges all variation from the population and is known a selective sweep. After a selective sweep, individuals in the population will diversify as they acquire random neutral genetic mutations until the next adaptive mutation is introduced, resulting in another genome-wide selection event. Population structures of some bacteria show evidence of periodic selection events that purge genetic diversity (Majewski and Cohan 1999; Palys et al. 2000; Feil et al. 2003). This process has also been documented in experimental populations of E. coli, at least during the initial period of adaptation (Levin 1981; Lenski and Travisano 1994). Cohan predicts

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C. Diversity over time in clonal and recombining populations Number of genotypes

Figure 4. A. Periodic selection purges diversity in a clonal population

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Figure 4. Comparing clonal and recombining populations. A simplified model showing the difference in diversity (number of genome types) for two populations that differ only in recombination rate. A. Shows a clonal population structure which begins with a single clone represented here by a single black circle. Over time, this clone acquires random neutral mutations (solid arrows) at different positions shown by small white spots. When a selection event occurs (grey rectangle) only the individual with the mutation at approximately seven o’clock position in the genome can survive and the population is purged of diversity. This clone will begin the process of neutral divergence again at the beginning. B. Shows a recombining population structure. Between mutation events recombination distributes mutations among individuals to create new genotypes (dashed arrows). This processes results in an increases the total number of genotypes in the population. Here again there is a selection event (for the same adaptive mutation) however, because this mutation has been distributed between individuals through recombination three different genotypes survive. This shows that in recombining populations some of the diversity that has accumulated is preserved. In this model three clones are left after the selection event to diversify through mutation and recombination. C. Illustrates the difference in diversity estimated as number of different genotypes in a clonal (top) and recombining (bottom) population over time. As above all other population parameters; mutation rate, population size and the type and frequency of selection are the same between populations.

diversification between clonal populations will lead to the development of ecological species (ecotypes) in which each clonal population is specifically adapted to a unique environmental niche (Cohan 1994; Cohan 2002). In this model of ecological speciation, ecotypes are defined as populations that are genetically cohesive (cohesion is the result of periodic selection events that purge diversity) and ecologically distinct (Gevers et al. 2005). (ii) Recombination disrupts the physical linkage between regions of the genome, and thus reduces the ability of selection events to purge diversity from the population (see Fig. 4). This occurs because gene variants are distributed among individuals such that selection for a gene that confers an adaptive advantage increases the frequency of that gene in the population without affecting the frequency of unlinked regions of the genome. Recombination is a cohesive force because a beneficial adaptation can be spread throughout the population, restricting the divergence of the lineage in which the gene arose. Conversely, barriers to recombination allow species divergence. The rise of independent lineages as the result of barriers to recombination is analogous to the biological speciation in sexual eukaryotes.

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Evidence for recombination among members of archaeal populations was first observed through community genomic analysis of a single biofilm growing in extremely metal rich acid mine drainage (Tyson et al. 2004). Tyson et al. reconstructed partial and near complete composite genome sequences for the five dominant organism types in the biofilm community. Each genome sequence was a composite because it was derived from closely related but not identical sequence reads. Population level analyses were possible because sequencing reads can be arrayed against the composite sequence. The authors recognized evidence for recombination in transitions between two types of sequences observed within a single sequence read (see Fig. 5). One recombination event was detected on average every 3–5 Kbp across the entire genome of the archaeal species Ferroplasma type II. Ferroplasma type II is one of two discrete coexisting Ferroplasma lineages that would have been grouped as a single species based on 16S rRNA gene sequence analysis. Despite extensive evidence of recombination within Ferroplasma type I and Ferroplasma type II populations, there was evidence for only very limited recombination between them. The authors suggested that the decrease in recombination rates with increasing genetic divergence might represent a species boundary. It may be inferred that the level of sequence divergence between these populations (on average 77% nucleotide identity) prevents homologous recombination from initiating (Majewski and Cohan 1998). Genome rearrangements and differences in gene content may also create barriers to homologous recombination and lead to sympatric speciation (occurring within a contiguous population) (Vetsigian and Goldenfeld 2005). A second study of archaea cultured from extremely high salinity salterns environments used multilocus sequence analysis of 40 Haloarculum strains to identify evidence for recombination (Papke et al. 2004). This study suggests that the recombination occurs at 5× the rate of mutation in this species. Further analysis revealed that all but one of the recent recombination events observed in this population occurred between individual clones that were closely related (Whitaker et al. 2005). The decrease in extent of genetic exchange with increasing evolutionary distance is a phenomenon that can ultimately result in the formation of biological species, as described above. It should be possible to use genetic information to infer the relative rates of recombination and selection within populations. Figure 4 shows, for example, two cases where selection events occur with the same frequency for two organisms but the organisms differ in their recombination rates. In one population, recombination events are infrequent so that after a selection event genome diversity is purged (a single genome type is observed), where in the second the recombination events are frequent and their effects can accumulate, thus maintaining greater levels of genetic diversity after selection. Multilocus sequence studies of bacteria associated with human and plant disease have revealed a range of population structures from purely clonal to completely recombinant (Suerbaum et al. 1998; Feil et al. 2000; Falush et al. 2001; Feil and Spratt 2001; Suerbaum et al. 2001; Enright et al. 2002; Feil et al. 2003; Sarkar and Guttman 2004). Population structures between the two extremes have evidence for clonal expansion of one clonal type (possibly resulting from natural selection) and recombinant genomes. This is called an epidemic population structure, where a single clone is overrepresented but does not completely overtake the entire population (Smith et al. 1993; Fraser et al. 2005). Multilocus analysis of the thermoacidophilic crenarchaea Sulfolobus islandicus from a geothermal hot spring in Kamchatka, Russia represents the first in-depth analysis of the balance between recombination and selection within a single geographically isolated endemic population (Whitaker et al. 2005). The boundaries of this population were defined prior to this study, facilitating assessment of the interaction between selection and recombination and their relative effects on population structure. Whitaker et al. (2005) showed that recombination is

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A. Environmental Genomics

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Figure 5. Recognizing population structure using environmental genomics or MLST molecular methods. A. Illustrates alignments of sequencing reads to the composite sequence (above) and alignments of paired end sequences (below) in clonal and recombining population structures. Dots indicate nucleotides that are identical to the composite sequence. Letters identify SNPs. Because each sequence comes from a different individual in the population, genotypes are inferred through assembly. Sampling of variation at each position is limited by the amount of coverage in the clone library. B. Illustrates how multilocus sequence analysis can be used to detect recombination in genomes of cultured isolates. Genotypes 1 and 2 show variants at each of the seven genetic markers (represented as black or grey segments). Mosaic combinations of loci resulting from recombination between Genotypes 1 and 2 are shown below (Genotypes 3 and 4).

three to six times as frequent as mutation within this single hot spring community. This study also identified evidence for natural selection in one of the six protein-encoding genes analyzed, indicating that recombination was rapid enough to allow genetic loci to evolve independently. In addition, this population was shown to have an epidemic population structure in which clonal expansions increased the frequency of a single clone but did not completely purge diversity. The Tyson et al. (2004) environmental genomics study demonstrated that environmental genomics analysis will allow simultaneous sampling of multiple populations within the same sample and comparison of population dynamics among them. Notably, significant differences in population dynamics were inferred for bacterial and archaeal groups in the biofilm through methods described in Figure 5A. A very low incidence of nucleotide polymorphisms was seen in the Leptospirillum group II population, suggesting a near clonal structure that may have resulted from a recent selective sweep or from a decrease in diversity following a founding event by a single clone (Tyson et al. 2004). By comparison, the genomic heterogeneity of the Ferroplasma type II population indicated that it had not recently experienced a genome-wide selective sweep. The higher diversity in this population may have resulted from frequent recombination limiting the purging effects of negative selection or from a rarity of adaptive mutations or selection events leading to dominance by a single genotype. Above we introduced two extremes of population structures that are clonal and recombinant. We discussed three extreme island-like environments in which population dynamics have been described (high salt, high temperature, high acidity) and in which the archaeal recombination rates are rapid compared to selection rates. However, in the case of the acid mine drainage biofilm, the bacteria exhibit an apparently clonal population structure,

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suggesting lower recombination rates relative to selection. It remains to be seen whether high relative recombination rates are a more common characteristic of archaeal than bacterial populations in extreme environments. Identifying the structure of a population is essential to understanding how diversity is shaped by environmental change. Below we will describe how defining a population also determines the methods that may be used to identify specific traits that are under natural selection in natural microbial systems.

IDENTIFYING ADAPTIVE TRAITS There are innumerable examples that illustrate how microbial communities are shaped by, and shape their environments (for example see Kappler and Straub 2005). However, much remains to be learned about the interplay between the environment and natural selection on microbial populations at the genome level. This is because until the advent of environmental genomics, we lacked the ability to correlate subtly different genotypes and phenotypes to the microenvironments to which they are adapted. Genomic characterization of microbial communities allows for the comprehensive identification of genetic traits that lead to differential adaptations within and among lineages. With enough genome coverage and population genomics tools, it may be possible to discern the environmental parameters that exert key selective pressures. For example, consider one clonal type found in a geothermally heated stream at 45 °C and a second found only above 65 °C (Miller and Castenholz 2000). Population genomics will lead to identification of the genetic differences that result in their niche specificity. The availability of genome sequences also enables the development of methods to evaluate gene expression, an approach that may help identify traits important for adaptation to specific conditions. For example, relative levels of expression can be monitored using microarrays that specifically bind mRNAs predicted from gene sequence information (Nelson and Methé 2005). In addition, proteomics on environmental samples can be used to identify the more abundant proteins in a culture or natural sample. For example, Ram et al. (2005) assessed the protein composition from an acid mine drainage biofilm sample. Similar proteomics experiments could also provide some information about relative protein abundance between samples. Prior microarry and proteomic studies have used genome sequences from isolated organisms. The availability of new data that capture gene sequence heterogeneity within populations will allow expression analyses with resolution to the strain level. The combination of genomic and gene expression analysis methods may provide an unprecedented opportunity to link the effects of natural selection to environmental dynamics within microbial communities. Below, we discuss how the environmental genomics data itself can be used to identify traits that are under selective pressure by exploring individual-level variation across the genomes of a population.

Recognizing genes under selection in recombining populations Many methods have been developed for identifying genes under selection in sexual eukaryotes (Bamshad and Wooding 2003). The application of these tools to recombinant microbial populations is appropriate because recombinant microbial populations lack the extensive hitchhiking (physical linkage of genes, thus shared fate) that is a characteristic of clonal populations. One pattern within environmental genomic data that may be used to identify genes under positive selection relies on the assumption that positive selection in recombining populations may result in relatively low levels of neutral diversity relative to the rest of the genome. Purging of neutral diversity from regions of the genome subject to positive selection is analgous to the purging of diversity in clonal populations through selective sweeps. However, because the population is recombinant, selective forces act upon the gene rather than the genome. As

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shown in Figure 4, adaptive genes at a single genome position become fixed in a population while the other regions are left variable. Large regions of low variation were found in the genomes of two Ferroplasma species identified within the acid mine drainage community (Tyson et al. 2004). Identification of the function of genes within invariant regions will lead to determining genes that are under positive selection in this environment. Comparisons of invariant regions among samples collected across space and time may provide insight into ecologically relevant adaptations to specific environmental conditions. Where genes vary between individuals within a recombinant population, comparison of the relative proportion of nonsynonymous and synonymous substitutions among gene variants may identify the action of natural selection. As described above, nonsynonymous substitutions that change the amino acid sequence of a protein may result in a phenotypic change. If there were no natural selection, the relative frequency of nonsynonymous substitutions per nonsynonymous site (Ka) to synonymous substitutions per synonymous site (Ks) will be equal. A skew toward a higher relative frequency of synonymous substitutions suggests that nonsynonymous mutations are quickly removed because they are deleterious (negative selection). On the other hand, a higher relative frequency of nonsynonymous substitutions observed among gene variants in a population suggests that amino acid changes are beneficial (positive selection). Estimating the Ka/Ks ratio for different variable genes across the genome can be used to identify loci that are under positive and negative selection (McDonald and Kreitman 1991). Using codon-based methods for determining relative rates of nonsynonymous and synonymous substitutions (Yang 1997) Beilawski et al. identified specific residues responsible for differential light adaptation in proteorhodopsin genes recovered across light gradients in the ocean environment (Bielawski et al. 2004). Testing for genes under positive or diversifying selection across the genome without a priori assumptions of where they occur can uncover unexpected adaptive responses to different environmental conditions.

Recognizing genes under selection in clonal populations Identifying specific genes under selection in populations with a clonal structure is difficult, because the action of selection on different genome regions cannot be isolated in a linked genome. However, if the presence of a novel gene acquired through HGT or the position of a mobile element confers a selective advantage, it may be recognized through careful assembly and comparative genomic analyses between clonal types. In community genomic data, the conserved location of mobile elements or novel genes within a single clonal type indicates an adaptive function. Differential position between individuals within a clonal population indicates that they move within a genome faster than they can be selected for or that they are essentially neutral. Recognizing of differences in gene content or position is also possible using similar methods in recombinant populations. For example, comparisons between syntenous genomic regions of the Ferroplasma type I populations from the acid mine drainage community and the near complete genome sequence of a Ferroplasma type I strain isolated from the same environment revealed differences in the position of mobile elements and differential insertion of novel genes (Allen and Banfield 2005 Figure 2). If the differential position of a mobile element is identified within an assembled genome sequence, its adaptive function may be inferred by examining the function of genes in its vicinity. For example, insertion of a novel gene into a recognizable regulatory element may suggest that modified expression confers adaptive function (Schneider and Lenski 2004). Similarly, if mobile elements are inserted within a gene, this suggests that loss of a particular function confers some advantage (Zinser et al. 2003). If the function of the genes around mobile elements is known, this allows a connection between the position of element and the adaptive trait (Cooper et al. 2001).

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Likewise, the adaptive significance of novel genes acquired through horizontal gene transfer may be inferred if their function is known. One challenge with such analyses to date is that the functions of many of the inserted genes are unknown. Without known function, it is difficult to determine the adaptive significance or even whether novel genes are expressed. Ram et al. (2005) presented an interesting approach to resolving this challenge using the detection of protein products (via mass spectrometry) in a natural biofilm community. The authors noted that protein products of novel, hypothetical genes were detected less commonly than those of genes for which a probable function could be ascribed. In addition, detection rates were especially low for genes encoded in blocks of putative phage or plasmid origin that were apparently inserted into the genome of a bacterial species of the Leptospirillum genera. Because proteins must be expressed to confer a function, this approach may be used to determine the adaptive importance of novel genes. This approach may further be used to determine the levels of expression of genes in different populations through comparative proteomics.

CONCLUSION: INTEGRATING GENETIC AND GEOCHEMICAL MOLECULAR TOOLS The focus of this chapter has been the relatively unexplored subject of microbial population dynamics. Because the topic is new, there is only a small literature to review. Consequently, we have emphasized tools and approaches that could be applied as data become available. We have described forms of variation, methods to assess them, and what their patterns can tell us about microbial evolution. At the beginning of this chapter, we asked a series of questions concerning how best to describe diversity within microbial populations. While we have described the tools needed to resolve these fundamental challenges, we have left these questions unanswered. For example, we asked whether biodiversity is partitioned into definable species and whether species are even the most ecologically relevant units of diversity in microbial systems. It is our view that both the genetic characteristics that unite groups and the processes that subdivide them into divergent phenotypes are best evaluated through population genomic analyses. With such information in hand, we may be better positioned to establish meaningful delineations between species and to understand mechanisms of diversification and lineage cohesion in microorganisms. As the field develops, two additional steps will be necessary. The first is the use of these methods to sample populations over time and across space. Each analysis described here represents a single snapshot in the natural history of a population. These snapshots allow develop-ment of hypotheses about mechanisms generating diversity (e.g., through rapid recombination, through selection for a certain genetic trait) that can be tested through comparisons between samples collected across time and space. For example, if an inserted mobile element is hypothesized to up-regulate genes important in metal resistance, sampling strategies designed to target multiple populations with different levels of metal contaminants might reveal the loss of this adaptation. Especially exciting are comparisons among geographically isolated endemic populations adapted to distinct local environments. Comparative population dynamics among isolated populations of similar microbes will shed light on their unique natural history and provide a means by which to correlate evolutionary and geological dynamics. The second step is to place microbial communities into a well-defined geochemical context. Characterizations of the geochemical gradients in time and space along which populations evolve will provide an essential basis for linking specific adaptations to environmental change. It is our hope that the methods described here, in combination with molecular tools to simultaneously characterize the organic and inorganic structure

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of microbial communities (Gilbert et al. 2005), set the stage for this development. We urge geomicrobiologists to embrace all of these new molecular-level methods to explore population dynamics. With these tools, we can begin to unravel the intimate links between microorganisms and their environment and how geochemical changes drive microbial evolution.

ACKNOWLEDGMENTS We thank Javiera Cervini-Silva, Chris Belnap, K. Blake Suttle and Stephen M. Wald for helpful reviews and the National Science Foundation Biocomplexity Program, the NASA Astrobiology Institute, and the Department of Energy Microbial Genome Program and Genomics: Genomes to Life programs for support of research and development of ideas presented in this chapter.

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Reviews in Mineralogy & Geochemistry Vol. 59, pp. 279-294, 2005 Copyright © Mineralogical Society of America

Metabolism and Genomics: Adventures Derived From Complete Genome Sequencing Karen E. Nelson and Barbara Methé The Institute for Genomic Research 9712 Medical Center Drive Rockville, Maryland, 20850, U.S.A. [email protected]

[email protected]

Introduction The genomic era has provided us with hundreds of complete microbial genome sequences (current estimates as of July 12, 2005, are 266 microbial genomes completed and an additional 730 in progress; see http://www.GenomesOnline.org) (Mongodin et al. 2005a). Collectively, the sequencing of individual genomes and whole communities has enabled the realization of a level of genetic diversity and complexity that was previously unappreciated (Venter et al. 2004; Mongodin et al. 2005b). This is particularly evident when the results of these endeavors are related to the study of physiological processes and metabolic capabilities of both the individual species and community members from a range of environments. Often, species are found to harbor the genetic material for metabolic pathways that had not been identified or tested in the laboratory setting, and it has become increasingly evident that we are some distance away from understanding the tremendous biological, physiological and metabolic diversity and potential that clearly exists in the microbial world. The chemical process of sequencing allows for the determination of the primary structure of a region of DNA (the main information carrier in a cell). The result of this process is a determination of the exact order of the four-nucleotide building blocks (adenine, cytosine, guanine and thymidine abbreviated A, C, G, T, respectively) that make up the DNA region in question. Completing the entire genome sequence of an organism thus provides a comprehensive representation of the entire sequence of the organism under study and its genome structure including the presence of chromosomes and in the case of prokaryotes, the presence of plasmids.

Genome Sequencing and Assembly Upon completing the sequence of a microbial genome, a thorough analysis of the genetic data should follow (detailed in Fig. 1). This process typically begins with the identification of all open reading frames (ORFs). A variety of ORF finding software is available to enable gene identification. Once gene predictions are completed, assignment of biological functions is made possible by searching all the ORFs against a database of non-redundant sequences. Among the most popular tools for searching sequence databases is BLAST (Basic Local Alignment Search Tool) (Altschul et al. 1990), which performs pair-wise sequence comparisons and seeks to define regions of local similarity, as opposed to optimal global alignments between entire sequences. Hidden Markov models (HMMs), which are statistical representations of consensus sequences describing a family of protein sequences, are also frequently used to accurately search large data sets of genome sequence. The goal of HMM searches is to 1529-6466/05/0059-0012$05.00

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Overview of Genome Sequencing and Analysis Sequencing and Assembly Library construction

Annotation and Analysis

Functional Analysis

Gene finding

Microarrays

Template preparation

Homology searches

Metabolomics

Sequencing reactions

Initial role assignments

Proteomics

Initial random assembly

Metabolic pathways Gene families

Gap closure sequence editing

Comparative genomics

Re-assembly

Structural Proteomics

Metabolomics Operon prediction regulatory elements repeats

Closed Genome Sequence

Figure 1. A schematic of some of the major components of a microbial genome sequencing project, as well as some of the post genomic applications that can be applied.

determine if the query sequence is a member of a protein family for which an HMM has been described. The collective results of investigations such as BLAST and HMM searches are used to characterize the gene prediction and the results are stored in relational databases designed to allow for further data mining. Typical information that is obtained about an ORF prediction includes: assignment of a biological role, common name, percent identity and similarity of other sequence matches, the pair-wise sequence alignment, and taxonomy associated with the match assigned to the predicted coding region. In addition to ORF analysis and gene identification, a number of other features of the genome can be identified using a variety of computer algorithms. TopPred for example allows for the identification of membrane-spanning domains (Claros and von Heijne 1994). Signal peptides and the probable position of a cleavage site in secreted proteins can be detected with SignalP (Nielsen et al. 1997). Genes coding for untranslated RNAs can be identified by database searches at the nucleotide level, and searches for tRNAs can be performed using tRNAScan-SE (Lowe and Eddy 1997). Repetitive sequences can be identified by various repeat finding programs, as well as by using an algorithm based on suffix trees which are versatile data structures that are particularly useful in genomic analyses for solving many string (sequences of characters) matching problems (Delcher et al. 1999). The determination of metabolic pathways can be aided by comparison of genome annotation to known pathways. Resources which can enable this analysis include the Kyoto Encyclopedia of Genes and Genomes or KEGG database (http://www.genome.ad.jp/kegg/). Operons represent a basic organizational unit of genes on prokaryotic chromosomes. A variety of computational methods have been suggested which can predict operon structure (Chen et al. 2004) although no one method of choice currently exists.

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Simultaneous comparison of multiple genomes Bioinformatics tools have had to be (and continue to be) developed and refined so that they can handle large quantities of biological sequence information, as well as provide the capacity to compare genome information derived from closely as well as distantly related strains and species. MUMmer (Delcher et al. 1999) allows the rapid alignment of whole genome sequences. This is made possible by an algorithm that is based on a suffix tree data structure. The NUCmer utility that is also included with the system can align sequences from genomes that have not been closed, being capable of aligning thousands of smaller assemblies to another sequence data set. The PROmer utility permits the alignment of genomes for which the proteins are similar but the DNA sequence is too divergent to detect similarity. The Comprehensive Microbial Resource (CMR) (http://www.tigr.org/tigr-scripts/CMR2/ CMRHomePage.spl) is one of the few publicly available tools that allows for access to all the prokaryotic genomes or any subset of prokaryotic genomes that have been completed to date (Peterson et al. 2001). The CMR was introduced primarily to reduce annotation inconsistency across completed genome, and displays the primary annotation taken from the original sequencing center where the data was generated, and an annotation generated by an automated annotation process at The Institute for Genomic Research (TIGR) (Peterson et al. 2001). Complex queries based on role assignments, database matches, protein families, membrane topology and other features are feasible. The CMR also provides access to web-based tools that allow for data mining using pre-run homology searches, whole genome dot-plots, batch downloading and traversal across genomes using a variety of datatypes. Querying data can be based on a variety of gene properties including molecular weight, hydrophobicity, G+C-content, functional role assignments, and taxonomy. When viewing an individual genome, graphical displays highlight genes placed linearly on regions of the chromosome, or as a complete circle for an entire chromosome. At an even broader level, the CMR presents comparative information between microbial genomes (Peterson et al. 2001). The Genome Properties (Haft et al. 2005) is a relational database system that includes tools and web interfaces for the investigation of the metabolism, phenotypes, and other biological properties of microbial species. The results of searches from the Genome Properties system reflects gene content, phenotype, and phylogeny for example, with the results of HMM searches allowing for a deduction of basic characteristics that include families of proteins that are conserved in function. In addition, some properties can be derived from curation, publications on the organism of interest, and other forms of evidence (Haft et al. 2005). Finally, reconstruction of biochemical pathways and transporter profiles associated with an organism of interest provides an overview of the metabolic capacity of the cell, and often reveals new aspects of the basic biochemistry of the species (see for example Nelson et al. 1999, 2002; Nierman et al. 2001). Some environmental species such as Pseudomonas putida for example (Nelson et al. 2002) have revealed a higher number of metabolic pathways for the conversion of atypical compounds than have been previously identified. Other organisms such as Caulobacter crescentus that have been sequenced for insights into biological processes such as cell cycle control have revealed the presence of unsuspected pathways such as the beta ketoadipate pathway for the metabolism of atypical compounds (Nierman et al. 2001). Considering that on average 40% of each microbial genome is considered to be hypothetical or conserved hypothetical proteins, it is obvious that a significant amount remains to be elucidated about the biology of microbial species. It should be highlighted that although tremendous insight is gained into the metabolic diversity of the species that is being analyzed, many other pathways are likely missed due to the limited characterization of many of these species that is reflected in the high number of conserved hypothetical and hypothetical proteins that remain at the end of the average genome sequencing project.

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The burgeoning information produced from genome sequencing, annotation and comparative analyses has led to advances in the field of functional genomics. Functional genomic approaches seek to capitalize on the knowledge of genomes through the application of technologies in a comprehensive manner (from a whole organism or “systems-level”) to elucidate genes, their functions and products, and how these products interact with the ultimate goal of understanding how an organism functions in the manner in which it does. Examples of functional genomic approaches include but are certainly not limited to the following discussion. Microarray technology (Dharmadi and Gonzalez 2004) can be used to examine gene expression patterns by measuring relative gene transcript abundance of the cellular mRNA pool (the transcriptome) or can be used to determine the presence or absence of genes (using DNA) in a query genome relative to a reference genome in a process known as comparative genomic hybridization (CGH). Proteomic approaches include 2-dimensional gel electrophoresis and mass spectrometry which seek to measure proteins synthesized in a cell (the proteome) and can provide information on how those proteins function and interact with each other. Metabolomic approaches can measure changes in low molecular weight chemical complement of a cell (metabolome) using among other techniques liquid and gas chromatography, mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy (Nielsen and Oliver 2005). Structural proteomics is another field of study benefiting from the increase in genome information. This area of research aims to provide information on protein identification at the level of the genome (proteome identification), characterizing three-dimensional structures of proteins and determining structure-function relationships. Experimental methods for determining protein structures typically include techniques such as X-ray crystallography or NMR spectroscopy (Forster 2002). However, elucidation of three-dimensional structures by these techniques is still limited. Therefore, computational methods are an active area of interest for providing new tools that can augment traditional experimental techniques in determining three-dimensional structure. Approaches include comparative or homology modeling which seeks to create a three-dimensional protein model of an unknown structure using sequence similarity to proteins of known structure (proteins whose structures have been solved; Centeno et al. 2005). Other approaches rely on de novo or ab initio structure prediction of proteins to elucidate three-dimensional protein structure based on using only the primary amino acid sequence (Klepeis et al. 2005).

Examples of Whole Genome Reconstructions and Derived Information Metabolic reconstructions for the genomes of a number of microbial species of both environmental and pathogenic significance have been successfully completed. The reconstruction of biochemical pathways and transporter profiles associated with an organism of interest provides an overview of the metabolic capacity of the cell, and often reveals new aspects of the basic biochemistry of the species. Although at present no computational tool can accurately predict all of the potential metabolic pathways and regulatory networks of the cell, the development of these reconstructions has been aided through the use of a variety of genome tools and information including genome annotations, in some cases predictions based on the Genome Properties tool (http://www.tigr.org/Genome_Properties/) and sequence searches against transporter databases (http://www.membranetransport.org/) as well as other comparative genomic and functional genomic approaches. Presented in detail below are several examples of genome analyses in which various computational and functional genomic approaches have been applied to understand the organism in question at a systems-level of biology.

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One example of a successful approach using computational predictions to elucidate metabolic functions is shown by the results of the data mining of P. putida KT2440, a metabolically versatile saprophytic soil bacterium that has been certified as a biosafety host for the cloning of foreign genes. The genome of P. putida strain KT2440 is a single circular chromosome, 6,181,863 bp in length with an average G+C content of 61.6% (Nelson et al. 2002). A total of 5420 ORFs with an average length of 998 bp were identified. Genome analysis reveals metabolic pathways for the transformation of a variety of aromatic compounds including ferulate, coniferyl- and coumaryl alcohols, aldehydes and acids, vanillate, phydroxybenzoate, protocatechuate, many of which may arise during the decomposition of plant materials (Nelson et al. 2002). P. putida KT2440 appears to modify the diverse structures of these aromatics to common intermediates that can be fed into central pathways. Initial steps in the metabolism of ferulic acid, 4-hydroxybenzoate and benzoate for example, could be mediated by different enzymes with all routes ultimately converging via protocatechuate or catechol to the 3-oxoadipate pathway. This convergent strategy is also seen with substrates that can be metabolized by the phenylacetyl-CoA pathway (Nelson et al. 2002). Consistent with the extensive metabolic versatility for the degradation of aromatics, the genome sequence of P. putida KT2440 includes many putative transporters for aromatic substrates, including multiple homologs of the Acinetobacter calcoaceticus BenE benzoate transporter, and of the P. putida PcaK 4-hydroxybenzoate transporter. In addition, KT2440 has 23 members of the BenF/PhaK/OprD family of porins that includes outer membrane channels implicated in the uptake of aromatic substrates. Strain KT2440 also possesses approximately 350 cytoplasmic membrane transport systems, 15% more than P. aeruginosa, including twice as many predicted ATP-Binding Cassette (ABC) amino acid uptake transporters. This is consistent with its ability to colonize plant roots, since root exudates are rich in amino acids, and reflects a physiological emphasis on the metabolism of amino acids and their derivatives for successful competition in the rhizosphere. The details of this study, metabolic reconstruction and all references associated with the publication of the genome can be found in (Nelson et al. 2002).

Geobacter sulfurreducens Analysis of the Geobacter sulfurreducens genome (Methé et al. 2003), a bacterium known primarily for its ability to carry out extensive metal reduction in subsurface environments revealed many significant and unsuspected capabilities, including evidence of aerobic metabolism, one-carbon and complex carbon metabolism, motility, and chemotactic behavior, which had not been previously revealed even though this bacterium has been extensively studied. These characteristics coupled with the possession of many c-type cytochromes (111 putative c-type cytochromes were identified) and many other genes predicted to be important in electron transport revealed an ability of this organism to create alternative, redundant electron transport networks offering new insights into the process of metal reduction in subsurface environments. As a member of a family of dissimilatory metal-ion reducers, G. sulfurreducens prefers to couple acetate or hydrogen oxidation to the dissimilatory reduction of iron (III) to iron (II), thereby linking the global iron and carbon cycles. In this respiratory process, G. sulfurreducens and other members of its family have solved the riddle of using insoluble metal-oxides as terminal electron acceptors. They employ a strategy of direct contact on the metal-oxide facilitating deposition of transported electrons external to the cell. This contrasts with its use of other soluble electron acceptors such as fumarate and with most other forms of respiration including aerobic respiration, in which the terminal electron acceptor is reduced inside the cytoplasm of the cell. Among the metals reduced by Geobacter spp. is uranium

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(VI) to uranium (IV), which has the added benefit of decreasing uranium solubility and leading to its precipitation. This ability to precipitate uranium from solution is currently under investigation as an in situ (in place) strategy for bioremediating uranium contaminated subsurface environments (Anderson et al. 2003). The capacity to move electrons external to the cytoplasm as part of its respiratory process also creates an electrical current, which can be captured via the growth of Geobacter spp. as a biofilm on energy harvesting electrodes (Anderson et al. 2003; Bond and Lovley 2003). The genome analysis and metabolic prediction analysis of G. sulfurreducens revealed conclusively that this bacterium, possesses an ability to play critical roles in the global cycling of metals and carbon, and provided new insights into its potential as an agent of bioremediation of metals including uranium and in the generation of electricity (Anderson et al. 2003; Bond and Lovley 2003; Methé et al. 2003). Functional genomic approaches such as the use of microarray technology to examine global gene expression patterns have provided confirmations of genome predictions and discerned new information regarding G. sulfurreducens physiology. For instance a G. sulfurreducens microarray fabricated from PCR amplicons representing the complete genome and printed on glass slides was used to examine growth when using soluble iron as a sole electron acceptor. cDNA targets synthesized from mRNA extracted from cells grown when using soluble iron as a sole electron acceptor were compared to those derived from cells grown under identical conditions except for using fumarate as a sole electron acceptor. In this experiment differential expression of a number of transcription factors was determined consistent with the prediction based on genome analyses of tight gene regulation in this organism. Elucidation of increased expression of a number of metal efflux transporters during growth with iron suggested they may play an important role in metal homeostasis under this condition which was previously unknown (Methé et al. 2005b).

Thermotoga maritima Metabolism based on Genomics and Comparative Genome Hybridization The Thermotoga maritima strain MSB8 genome revealed a number of pathways for the metabolism of plant compounds including hemicellulose and xylan, as well for the metabolism of sugars (Nelson et al. 1999). The bacterium also has a significantly high number of transporter systems that are devoted to the import of polysaccharides and oligopeptides and that appear to be a reflection of the environmental niche that this bacterium occupies. The results of a recent study (Mongodin et al. 2005b) highlight the dynamic nature of the genome of members of this genus and support the idea that there has been extensive lateral gene transfer (LGT) in the Thermotoga lineage. This genome variability is independent of the closeness of strains based on 16S rRNA phylogenetic analysis, and it highlights the limitations of using 16S rDNA sequencing and analysis as a tool to describe microbial species diversity (also see Whitaker and Banfield 2005). Although T. maritima has so far not been shown to be competent, most certainly due to the lack of efficient molecular biology tools, various type II secretion pathway proteins and type IV pilin-related proteins that function in natural competence in other bacterial species could be identified in the T. maritima MSB8 genome (Nelson et al. 1999). Homologs of various competence genes could also be identified, suggesting that there may be an inherent system for the uptake of exogenous DNA, thereby facilitating the exchange of DNA with other organisms. From the whole-genome CGH study (in which genomic DNA from query strains of T. maritima were compared to a reference strain, T. maritima MSB8, using microarray technology), it is evident that T. maritima strains vary in the total number of genes and metabolic capabilities when compared to the reference T. maritima MSB8 (Mongodin et al. 2005b).

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For example, strain PB1platt is most divergent in terms of metabolic capabilities, and either does not use the plant polymers pectin or xylan, or glycerol, maltose, tagatose, or cellobiose for energy, or it uses systems that are divergent from those employed by strain MSB8 and were therefore not detectable using microarrays. Compared to the other Thermotoga strains used in this study, strain PB1platt was isolated from a unique environment, the upcoming produced fluids (oil-water-gas mixtures) from the Prudhoe Bay oil fields (for details of the organic chemistry of such materials see Brock and Pearson 2005). These geothermally heated reservoirs may represent isolated pockets of microbial communities situated deep down below the permafrost soil hostile to hyperthermophilic life. Therefore, microorganisms such as PB1platt could represent survivors from the times where this crude oil had been formed. It is also possible that they have invaded their hot biotope very recently during the procedure used for secondary oil recovery, i.e., when seawater (which may possibly harbor some dormant hyperthermophiles which had originated from submarine vents) is pumped down into the oil reservoirs. In both hypotheses, strain PB1platt had to adapt to an environment in which sugars and plant polymers are not (or are no longer) available. Therefore, LGT and genome plasticity are important features for genetic and metabolic evolution of the Thermotogales, and most likely in other microbial species. It is also likely that shared regulatory elements/promoters among microbial species have enabled the efficient activity of acquired genes. Alternatively, regulatory elements from other locations in the chromosome can be tapped to regulate these acquired genes and/or pathways, allowing for the success of these transfer events.

Comparative genomics and proteomics, Colwellia psychrerythraea 34H By volume, most of Earth’s biosphere is cold and marine, with 90% of the ocean’s waters at 5 °C or colder and fully 20% of Earth’s surface environment is frozen, including permanently frozen soil (permafrost), terrestrial ice sheets (glacial ice), polar sea ice, and snow cover (Bowman et al. 1997). In terms of metabolically active biomass, these permanently cold environments are colonized principally by cold-adapted microorganisms. Psychrophilic bacteria which make up much of this active biomass are generally defined as having growth temperature optima of