The Pennsylvania State University

The Graduate School

College of Earth and Mineral Sciences

MICROBIAL TRACE METAL REQUIREMENTS:

LIMITING NUTRIENTS AND POTENTIAL BIOSIGNATURES

A Thesis in

Geosciences

by

Aubrey L. Zerkle

© 2006 Aubrey L. Zerkle

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

August 2006

The thesis of Aubrey L. Zerkle was reviewed and approved* by the following:

Christopher H. House Associate Professor of Geosciences Thesis Adviser Chair of Committee

Susan L. Brantley Professor of Geosciences

Lee R. Kump Professor of Geosciences

Hunter J. Carrick Associate Professor of Aquatic Ecology

Donald E. Canfield Professor of Biology University of Southern Denmark

Katherine H. Freeman Associate Department Head of Graduate Programs Professor of Geosciences

*Signatures are on file in the Graduate School

ABSTRACT The present study employs a multi-disciplinary approach in both field and laboratory settings, including phylogenetics and genomics, microbiology, and geochemistry, to investigate the importance of trace metals in microbial metabolic functions and biogeochemical cycling in modern and ancient systems. The general objectives of this research were: 1) to elucidate how early life contributed to global biogeochemical cycles and co-evolved with earth surface chemistry; and 2) to examine potential resulting biomarkers. Progressive oxygenation of the Earth’s atmosphere has forced changes in the redox state of the oceans through geologic time. These fluctuations in ocean redox chemistry have altered the availability of bioactive trace metals necessary in microbial metabolisms. Genomics and phylogenetic investigations suggest that the use of metals in biology has evolved along with changes in metal availability, and different metals have responded in different ways. Furthermore, metal use in extant prokaryotes shows some predictable patterns, especially with metabolism, that could provide metal biosignatures in natural populations. Laboratory experiments recreating ancient ocean metal concentrations indicate that an increase in Mo availability resulting from the initial rise in atmospheric O 2 at the end of the Archean (~3.45 Ga) could have stimulated rates of N2 fixation to modern levels. Furthermore, N2 fixation under enhanced Fe availability associated with widespread anoxia in the Archean and some Phanerozoic oceans could contribute to negative 15 N biosignatures preserved in the rock record. Finally, field investigations of meromictic Green Lake (Fayetteville, New York) provide evidence for the importance of both biotic and abiotic processes in phosphorus cycling in stratified aqueous systems, similar to oceans proposed to have existed during the mid-Proterozoic and many other periods of earth history.

iii TABLE OF CONTENTS

List of Tables ...... vi List of Figures ...... viii Preface...... xi Acknowledgements ...... xv

Chapter 1. GENOMICS INVESTIGATION OF BIOGEOCHEMICAL SIGNATURES .1 Abstract ...... 1 Introduction ...... 2 Using Microbial Genomes to Infer Past Biochemistry ...... 2 Biological Carbon Isotope Fractionation ...... 3 The Microbial Metallome ...... 5 Materials and Methods ...... 8 Estimation of a Consensus Tree of Life ...... 8 Predicted Carbon Isotope Fractionation ...... 9 Computing the Model Metallome ...... 9 Composite Organism Phylogeny and a Consensus Tree of Life ...... 11 Inferred Carbon Isotope Signatures of Ancient Organisms ...... 12 Model Metallomes of Modern and Ancient Organisms ...... 15 Comparing Model Metallomes from Modern Genome Sequences ...... 15 Evolution of the Metallome from Modern Genome Sequences ...... 20 Evolution of the Metallome from Metallo-enzyme Functions ...... 21 Conclusions ...... 25 Acknowledgements ...... 27 References ...... 27 Tables ...... 44 Figures ...... 51

Chapter 2. METAL LIMITATION OF CYANOBACTERIAL N FIXATION ...... 60 Abstract ...... 60 Introduction ...... 60 Materials and Methods ...... 67 Culturing Conditions ...... 67 Media Preparation ...... 67 Growth Analyses ...... 69 Acetylene-Ethylene Reduction Assays ...... 69 Results ...... 70 Discussion ...... 71 Metal Limitation ...... 71 Implications for N fixation Through Time ...... 73 Conclusions ...... 76 Acknowledgements ...... 77 References ...... 77 Tables ...... 89

iv Figures ...... 90

15 Chapter 3. EFFECT OF METAL AVAILABILITY ON Nbiomass OF N 2-FIXING CYANOBACTERIA...... 95 Abstract...... 95 Introduction...... 95 Methods...... 97 Results...... 98 Discussion...... 99 Acknowledgements...... 101 References...... 101 Tables...... 106 Figures...... 108

Chapter 4. BIOGEOCHEMICAL P CYCLING IN A MEROMICTIC LAKE...... 112 Abstract...... 112 Introduction...... 112 Geologic Setting...... 113 Materials and Methods...... 114 Results...... 115 Discussion...... 117 Biological P Cycling...... 117 Abiotic P Cycling...... 119 Conclusions...... 120 Acknowledgements...... 121 References...... 121 Tables...... 126 Figures...... 128

Appendix A: Supplemental Material for Chapter 1...... 135 Tables...... 136

Appendix B: Supplemental Material for Chapter 2...... 148 Additional Materials and Methods...... 148 Tables...... 150 Figures...... 155

Appendix C: Supplemental Material for Chapter 4...... 163 Figures...... 164

v LIST OF TABLES

Table 1.1A. Carbon isotopic composition of cells grown on CO 2...... 44

Table 1.1B. Carbon isotopic composition of cells grown on DIC or methane...... 47

Table 1.2. Metallome calculations for organisms and groups in this study...... 49

Table 1.3. Rules used to assign the timing of evolution for individual metallo-enzymes.50

Table 2.1. Growth rates and N 2 fixation rates for cyanobacteria grown in different media metal concentrations...... 89

Table 3.1. Nitrogen isotope composition of cyanobacteria published in the literature...106

Table 3.2. Nitrogen isotope composition of cyanobacteria from this study...... 107

Table 4.1. Total and dissolved Fe, Mn, and P concentrations measured in Green Lake.126

Table 4.2. Particulate Fe, Mn, and P concentrations measured in Green Lake...... 127

Table A.1. Fe-utilizing enzymes...... 136

Table A.2. Zn-utilizing enzymes...... 139

Table A.3. Cu-utilizing enzymes...... 141

Table A.4. Mo-utilizing enzymes...... 142

Table A.5. Mn-utilizing enzymes...... 143

Table A.6. Co-utilizing enzymes...... 144

Table A.7. Ni-utilizing enzymes...... 145

Table A.8. W-utilizing enzymes, synonyms, moles of W required per mole of enzyme (when unknown assumed 1), and inferred timing of evolution...... 146

Table A.9. V-utilizing enzymes...... 147

Table B.1. Total growth yields and calculated substrate consumption...... 150

Table B.2. Nitrogen fixation kinetics with growth phase...... 151

Table B.3. Growth rates and nitrogen fixation rates for cyanobacteria grown autotrophically ...... 152

vi

Table B.4. Nitrogen fixation rates for cyanobacteria grown autotrophically with progressive culture density...... 153

Table B.5. Results from continuous culture experiments...... 154

vii LIST OF FIGURES

Figure 1.1. Carbon isotope fractionation factors from substrate to biomass in prokaryotes and the predicted resulting 13 C...... 51

Figure 1.2. Phylogenetic trees for composite organisms...... 52

Figure 1.3. Carbon isotopic signatures from the literature superimposed on a consensus tree of life ...... 53

Figure 1.4. Comparisons of average F i values predicted by whole genomes...... 54

Figure 1.5. Phylogenetic comparisons of average F i values...... 55

Figure 1.6. F Cu versus F Ni ...... 56

Figure 1.7. Evolutionary scenarios for the metallome based on extrapolating our phylogenetic results...... 57

Figure 1.8. Evolutionary scenario for the metallome based on metallo-enzyme functions...... 58

Figure 1.9. Model metallomes for the LCA...... 59

Figure 2.1. Growth curves and ethylene production measured under Fe-limited conditions...... 90

Figure 2.2. Growth rates and N 2 fixation rates calculated under Fe-limited conditions...... 91

Figure 2.3. Growth curves and ethylene production measured under Mo-limited conditions...... 92

Figure 2.4. Growth rates and N 2 fixation rates calculated under Mo-limited conditions...... 93

Figure 2.5. Trends in growth and N 2 fixation through geologic time...... 94

Figure 3.1. Nitrogen isotopic composition of biomass measured in N 2-fixing cyanobacteria in culture studies and from the field...... 108

Figure 3.2. Nitrogen isotopic composition of cyanobacterial biomass under Fe- and Mo- limited conditions...... 109

viii Figure 3.3. Nitrogen isotopic composition of cyanobacterial biomass with growth and N 2 fixation rates...... 110

Figure 3.4. N isotopic ratios for cyanobacteria from the literature versus [Fe]...... 111

Figure 4.1. Geographic Location of Green Lake...... 128

Figure 4.2. Physicochemical characteristics of Green Lake in November, 2005...... 129

Figure 4.3. Concentrations of total and dissolved Fe, Mn, and P with depth in the water column of GL...... 130

Figure 4.4. Environmental scanning electron microscopy (ESEM) images of particulates collected on filters from 18 m (top) and 23 m (bottom)...... 131

Figure 4.5. Particulate Fe, Mn, and P concentrations with depth in the water column of GL...... 132

Figure 4.6. Microbial P cycling in Green Lake...... 133

Figure 4.7. Macroscopic P cycling in Green Lake...... 134

Figure B.1. Total growth yields...... 155

Figure B.2. Nitrogen fixation kinetics with growth phase...... 156

Figure B.3. Growth curves for autotrophic experiments...... 157

Figure B.4. Nitrogen fixation curves for autotrophic experiments...... 158

Figure B.5. Growth rates and nitrogen fixation rates for autotrophic experiments...... 159

Figure B.6. Nitrogen fixation rates for autotrophic experiments with progressive culture density...... 160

Figure B.7. Metal-free continuous culture Photostat...... 161

Figure B.8. Nitrogen fixation rates for continuous culture experiments...... 162

Figure C.1. Geochemical profiles and cell counts from GL in September 2002...... 164

Figure C.2. Geochemical from GL in April 2003...... 165

Figure C.3. Geochemical profiles from GL in June 2003...... 166

Figure C.4. Dissolved metal concentrations for GL in June, 2004...... 167

ix Figure C.5. Particulate metal concentrations for GL in June, 2004...... 168

x PREFACE The science of Geobiology is interested in how changes in the geosphere have affected the evolution of life over geologic time, and vice versa. One of the most important ways in which biology interacts with the earth is through chemistry. The bulk biological elements (H, C, N, O, P, and S) make up 98% of life. Due to their common occurrence in biological form, the geochemical cycling of these elements is intimately tied to biological processes. Of special interest are phosphorus and nitrogen, which make up DNA, RNA, and proteins, among other things. These bulk biological elements are thought to be the major nutrients limiting primary productivity over geologic timescales. Other elements are used in trace quantities in some or all species, primarily as structural elements or cofactors catalyzing metabolic processes in enzymes. Of particular interest in this group are the “trace elements”, transition metals that can exist in multiple redox states and are found in trace quantities in natural systems (e.g., Fe, Zn, Mn, Mo, Cu, Co, Ni, W, and V). Due to the redox sensitivity of these elements, their abundance and availability to biological systems in natural environments is highly dependent on environmental factors, such as pH and oxidation-reduction potential. In this way the chemistry of the environment can directly mediate the ability of organisms to perform metabolic reactions. This research explores in detail the importance of trace metals in microbial metabolic functions, and the importance of microorganisms in biogeochemical cycling, in modern and ancient systems. The general scientific strategy used was to examine this interaction in modern systems and extrapolate the results to ancient environments. This approach was taken in theoretical, laboratory, and field studies, using a wide range of interdisciplinary methods, including phylogenetics and genomics, microbiology, and geochemistry. Chapter 1 presents details of a theoretical investigation of metal use in prokaryotes, based on the occurrence of genes encoding for metallo-enzymes in whole genome sequences of modern organisms. The results of this study are published along with investigations of biomass 13 C signatures by C. House in the American Journal of Science (Zerkle, House, and Brantley, 2005, Biogeochemical signatures through time as inferred from whole microbial genomes: American Journal of Science 305: 467-502). The established model “metallomes”

xi indicate that the use of metals in prokaryotes generally follows the hierarchy: Fe >> Zn > Mn >> Mo, Co, Cu >> Ni > W, V. Metal use varies with metabolism, oxygen tolerance, optimum growth temperature, and phylogeny. Some trends are apparent, including an elevated Ni signature in methanogens, that could provide a unique biosignature for this metabolic function in natural populations. Model metallomes were further extracted onto phylogenetic trees to examine the evolution of metal use through time, and compared to metallome evolution determined from the function and occurrence of metallo-enzymes in modern organisms. This analysis indicates that the portion of metals used in biology has evolved with changing metal bioavailability. The biological use of Cu and Mo has developed along with bioavailability; biological use of Fe and Mn has developed counter to bioavailability; and biological use of Zn, Co, and Ni has not changed significantly through time. Lists of metallo- enzymes examined in this study are presented in Appendix A. Chapter 2 introduces an experimental investigation into the importance of the trace metals Fe and Mo on biological nitrogen cycling. Fe and Mo form the cofactor for the primary nitrogenase enzyme, used by cyanobacteria (and other prokaryotes) to fix atmospheric N 2 into bioessential compounds, replacing reduced nitrogen lost by denitrification. This development of ocean redox chemistry due to rising atmospheric O 2 during the Precambrian led to fluctuations in Fe and Mo availability that could have significantly impacted the ability of prokaryotes to fix nitrogen. In experiments replicating ancient marine metal concentrations, low rates of cellular N2 fixation were measured in cyanobacteria cultures with Fe and Mo concentrations reflecting an anoxic Fe-enriched Archean ocean. With decreased [Fe] and higher [Mo] representing a sulfidic

Proterozoic ocean, N 2 fixation, growth, and biomass C:N were similar to those observed with metal concentrations of the fully oxygenated oceans of the Phanerozoic. These results suggest that an initial rise in atmospheric oxygen could actually have enhanced nitrogen fixation rates to near modern marine levels, rather than inhibiting it as previously suggested. Chapter 2 focuses specifically on heterotrophic batch experiments, and is currently in press for publication in Geobiology (Zerkle, House, Cox, and Canfield,

2006, Metal limitation of cyanobacterial N 2 fixation and implications for the Precambrian nitrogen cycle: Geobiology ). Further experiments conducted in continuous culture and under autotrophic growth conditions are detailed in Appendix B.

xii Chapter 3 presents results of nitrogen isotopic analyses of biomass from the same cultures, and a shortened version was recently submitted for consideration for publication in Geology (Zerkle, House, Junium, and Canfield, submitted , Enhanced Fe availability contributes to negative δ15 N excursions in the geologic record: submitted to Geology ).

The results of these analyses indicate that cyanobacteria fixing N2 under conditions of varying metal availability produce biomass with significantly different δ15 N values. Under Fe-enriched or Mo-limited conditions ([Fe]/[Mo] > 0.5), cyanobacteria produce biomass up to 3‰ lower in δ15 N than identical organisms grown in Fe-limited conditions, such as the modern oceans ([Fe]/[Mo] < 0.5). Differential fractionation by organisms fixing nitrogen under enhanced Fe availability could contribute to negative δ15 N values in Archean rocks, and in some Mesozoic and Cenozoic sediments deposited during proposed periods of increased ocean anoxia. Chapter 4 explores microbial involvement in biogeochemical cycling of phosphorus in meromictic Green Lake (GL), NY. GL is a stratified aqueous system, with an upper oxygenated layer separated by a sharp redox interface from anoxic and sulfidic waters below. Phosphorus is a major nutrient limiting primary productivity and organic carbon burial in modern and ancient systems. Furthermore, increasing evidence suggests that for extended periods of earth history (e.g., the mid-Proterozoic and the end Permian) the oceans experienced redox-stratified conditions similar to GL. Metal oxide coatings on 3- cell walls of modern marine phytoplankton are important scavengers of PO 4 . Reductive dissolution of these biogenic oxide-coatings could have contributed to increased P release under anoxic conditions in the geologic past. Profiles of dissolved and total Fe, Mn, P, and soluble reactive phosphate from GL suggest that P is released primarily from reductive dissolution of abiotic Mn-oxides at the redox interface (chemocline). Particulate elemental distributions indicate that some P is sorbed onto cyanobacterial Fe- (hydr)oxide coatings in surface waters, but this pool can only account for about 10% of the total P supply to the chemocline. P is rapidly taken up (possibly via biosorption) and recycled by a dense community of sulfur bacteria inhabiting the chemocline. The intense recycling of P by sulfur bacteria could counteract the release of P from metal-hydroxides (both biotic and abiotic) at the redox interface in such stratified systems. Chapter 4 focuses on data collected in November, 2005. This chapter is currently under revision for

xiii submission to Limnology and Oceanography (Zerkle and Kump, in prep , Influence of biosorption on phosphorus cycling across the redox interface of a meromictic lake: for submission to Limnology and Oceanography ). Appendix C documents further data (including geochemical profiles, bacterial cell counts, and trace metal analyses) collected from September, 2002, to June, 2004.

xiv ACKNOWLEDGEMENTS

I would like to thank my advisor, Christopher House, for his guidance and support throughout this research, and for always encouraging creativity and independence. I would also like to thank the Biogeochemical Research Initiative in Education program, and my BRIE co-advisor Susan Brantley for additional support and for contributing to my scientific breadth. In addition, I thank my committee members – Lee Kump, Hunter Carrick, and Donald Canfield – for their time and valuable suggestions. I with to gratefully acknowledge my colleagues at Pennsylvania State University: Julie Maresca, Jon Kittleson, and Chris Junium. Julie and I have spent many, many hours pumping purple water and discussing science in the field and out. Jon spent many hours teaching me trace metal sample preparation and running the ICP-MS. Chris helped me to run C and N analyses on the XP and greatly contributed to my understanding of the nitrogen cycle. I would also like to gratefully acknowledge my friends and colleagues of the former Danish Center for Earth Systems Science (now Nordic Center for Earth Evolution), for hosting and welcoming me during multiple extended visits. I am indebted to Raymond Cox, Peter Søholt, and Mette Andersen. Raymond spent many hours helping me set up cyanobacteria experiments, running the GC, and fixing the membrane-inlet mass spectrometer. Peter made everything run smoothly in the lab, and Mette made everything run smoothly out of the lab. Most of all, I wish to thank my wonderful friends, and my mother and father, who refuse to ever stop believing that I am anything less than brilliant. Their love and support over the years has made this possible. Funding for this research was provided by the Penn State Biogeochemical Research Initiative for Education (BRIE), sponsored by NSF (IGERT) grant DGE- 9972759, the NASA Astrobiology Institute, the Dansk Grundforskningsfond, and graduate research scholarships from the Penn State Department of Geosciences, the Geological Society of America, and SOAS International School of Aquatic Sciences.

xv Chapter 1

Genomics Investigation of Biogeochemical Signatures

ABSTRACT Throughout geologic time, a strong feedback has existed between the geosphere and the biosphere; therefore biological evolution and innovation can be linked to the evolution of ancient environments on Earth. Here we deduce geochemical signatures and phylogenetic relationships of prokaryotes from whole genome sequences and use this link to infer geochemical aspects of the biosphere through time. In particular, we have investigated two potential biosignatures for modern and ancient biochemistry: the magnitude of microbial carbon isotopic fractionation, and the use of metals in microbial cells. The distribution of carbon fixation pathways on microbial phylogenies suggests that: (1) both low and moderate carbon isotope fractionation were established quickly in the early evolution of life; and (2) methanotrophic and ethanotrophic metabolisms capable of producing biomass with extreme 13C depletions are not primitive, but rather evolved after the major groups of Prokaryotes had already diverged. The universal importance of the TCA cycle, which results in low carbon isotopic fractionation, indicates it may have evolved especially early making it perhaps the most likely carbon fixation pathway for the last common ancestor (LCA). Low isotopic fractionation by the reductive TCA cycle can be considered consistent with carbon isotope ratios found in 3,800 Ma Isua sediments. Additionally, moderately fractionated biomass from up to 3,500 Ma can now likely be attributed to carbon fixation by anoxygenic photoautotrophs using the reductive pentose phosphate cycle (Calvin cycle). In addition to carbon, cells require a number of other elements that could potentially provide biosignatures, including bioactive trace metals. We calculated “model metallomes” for 52 prokaryotes based on the number of atoms of trace metals required to express one molecule of each metallo-enzyme coded for in the corresponding genomes. Our results suggest that the use of metals in prokaryotes as a group generally follows the hierarchy: Fe >> Zn > Mn >> Mo, Co, Cu >> Ni > W, V. However, model metallomes vary with metabolism, oxygen tolerance, optimum growth temperature, and phylogeny. The model metallome of methanogens shows a unique metal signature, suggesting that elevated

1 requirements of nickel and tungsten might be translated to the expressed metallome providing a biosignature for methanogenesis. The model metallomes of diazotrophs and cyanobacteria do not show unique signatures; however changes in enzyme expression under some conditions could still translate into a metal biosignature in the expressed cellular metal content. In a separate analysis, we made inferences about the timing of the evolution of individual metallo-enzymes based on their function and occurrence in modern organisms. The results suggest that fluctuations in the redox state of the Earth's oceans and atmosphere have forced changes in the proportions of metals used in biology. In this model, biological use of copper and molybdenum has developed along with bioavailability; biological use of iron and manganese has developed counter to bioavailability; and biological use of zinc, cobalt, and nickel has not changed significantly through time. Using this technique, we estimated a model metallome for the LCA based on the metallo-enzymes we infer to have been present at that time. This metallome for the LCA differs greatly from one extrapolated from the distribution of model metallomes on microbial phylogenies, supporting the idea that gene loss, metal substitution, and lateral gene transfer have been important in shaping the enzymatic composition of extant organisms. INTRODUCTION Using Microbial Genomes to Infer Past Biochemistry Microbial genomes provide information about metabolic capabilities and gene regulation in extant organisms, as well as molecular clues to the events leading to the evolution of these genes in the geologic past (Macalady and Banfield, 2003). In this context, modern prokaryotic genomes can provide a link between the geosphere and the biosphere through the evolutionary signatures they contain. Here we demonstrate this by using genomes to examine the modern phylogenetic distribution of two potentially important biochemical signatures, and interpreting their evolution and possible distribution in ancient life. We used the gene content of prokaryotic genomes to predict cellular metal requirements in modern organisms, and to determine how metal use and biological carbon isotope fractionation have evolved through geologic time. We first refined the topology of the tree of life using whole genome sequences of composite phylogenetic groups of prokaryotes. We then mapped biochemical characteristics onto

2 the topology of this composite tree, and used the result as a foundation for discussion about microbial biochemistry on early earth. As part of this study, we have reviewed and updated the literature on biological carbon isotope fractionation by prokaryotes from carbon dioxide and methane, establishing a range of fractionation factors produced by specific carbon fixation pathways. We have also examined the cellular metallome inferred from gene content, and evaluated the use of a model metallome as a biosignature for specific microbial metabolisms. Ultimately we suggest that such analyses will be useful in interpreting biosignatures in both modern prokaryotic ecosystems and in the rock record. Biological Carbon Isotope Fractionation In recent decades, the biological fractionation of carbon isotopes has become an important means of recognizing and studying ancient life (for a review, see Schidlowski, 12 2001). Autotrophic microorganisms preferentially incorporate CO2 into their biomass to a varying degree based partly on their carbon fixation pathway. Therefore, specific carbon fixation pathways recognizably affect the isotopic signature of organic matter preserved in the geologic record. Among the Bacteria and the Archaea, the two prokaryotic domains of life, there are four known CO2 carbon fixation pathways: (1) the reductive tricarboxylic acid (TCA) cycle; (2) the 3-hydroxypropionate cycle (3-HP); (3) the reductive pentose phosphate cycle (or Calvin Cycle), using Rubisco (PP); and (4) the reductive acetyl-CoA pathway

(AP). There may be additional presently unknown CO2 carbon fixation pathways, operating in organisms such as the recently described ‘Candidatus Chlorothrix halophila’, recovered from a hypersaline microbial mat (Klappenbach and Pierson, 2004). Cell carbon can also be fixed from methane or other hydrocarbon gases. In the Bacteria, methanotrophy is accomplished using the enzyme methane monooxygenase in either the RuMp pathway (RuMP) or the Serine Pathway (S) (Hanson and Hanson, 1996). In the Archaea, it has been suggested that anaerobic methanotrophy (AOM) occurs using a modified methyl co-enzyme M reductase (Hallam et al., 2003; Krüger et al., 2003). The consumption of non-methane hydrocarbons, such as ethane, is known in the Actinobacteria and in the γ-Proteobacteria (Bokova, 1954; Davis et al., 1956; de Bont, 1976) but the process, ethanotrophy (E), remains considerably less studied than bacterial

3 methanotrophy despite the potential of ethane as a global microbial substrate (D’Hondt et al., 2003).

Figure 1.1A shows the degree of carbon isotopic fractionation from either CO2 or

CH4 to cellular biomass observed in organisms using different carbon fixation pathways (data found in Tables 1.1A and 1.1B; adopted and updated from House et al., 2003a; also reviewed by Knoll and Canfield, 1998; van der Meer et al., 2001a). Fractionation factors in Figure 1.1A are given in ε (Hayes, 2002):

3 ε = lnα A−B ×10 (1-1) where A is the substrate being used, B is the resulting biomass, and α is the fractionation factor, the ratio of the rate of carbon fixation for 12C to the rate of carbon fixation for 13C, calculated as

13 3 δ C A +10 α A−B = 13 3 (1-2) δ CB +10 Note that ε only defines a relative value of fractionation produced by the specific pathway, and that the actual carbon isotopic compositions produced will vary according to the isotopic composition of the initial substrate used. The results in Figure 1.1A indicate that the reductive pentose phosphate cycle, acetyl-CoA pathway, and some methanotrophic pathways can yield larger isotopic fractionations than the reductive TCA cycle and 3-hydroxypropionate cycle (Preuß et al., 1989; House et al., 2003a; Londry and Des Marais, 2003). House et al. (2003a) also showed that biomass produced by acetyl-CoA pathway utilizing microbes can vary over a remarkably wide range, with ε values of 2.7 to 8.0 ‰ for the Archaeoglobales and ε values of 4.8 to 26.7 ‰ for the methanogens. The magnitude of carbon isotopic fractionation observed in methanogens is dependent on the growth status of the culture, with isotopic fractionation increasing as the cultures proceeded toward stationary growth phase. Furthermore, Londry and Des Marais (2003) have shown that different sulfate reducers using the acetyl-CoA pathway can demonstrate widely different fractionations (ε values of both 10.0 and 30.5‰). The fractionation associated with bacterial methanotrophy by RuMP has been shown to be up to about 30 per mil for the particulate methane monooxygenase, but lower for the soluble methane monooxygenase (Summons et al., 1994; Jahnke et al., 1999). Finally, the fractionation associated with anaerobic

4 methane oxidation has been estimated to be around 32 ‰ based on studies of natural cell carbon from seep environments (Orphan et al., 2001; Orphan et al., 2002). Figure 1.1B shows the predicted carbon isotopic composition produced by natural

microbial cells using each of the pathways shown, assuming fixation of CO2 with a

typical carbon isotopic composition of about –7 ‰, or CH4 with a composition of –55 ‰. The global range of carbon isotopic compositions for methane is between –50 to –110 ‰ (Whiticar, 1999), but is typically heavier (about –55‰) in marine seeps subject to microbial methane oxidation (Whiticar, 1999; Orphan et al., 2004). This figure provides only an approximate guide to the expected carbon isotopic signatures that each metabolic pathway should produce in a natural setting, because the actual isotopic composition of

either CO2 or CH4 can be highly variable in nature. Nevertheless, there is expected to be demonstrably different values of carbon isotopic composition found in natural microbial biomass. The most 13C-depleted carbon is produced by cells that have grown on natural biogenic methane. Methanotrophic cells using either an aerobic pathway (RuMP) or an anaerobic pathway (AOM) typically have cellular biomass that is around –85 per mil (Orphan et al., 2001, 2002) due to both a 13C-depleted methane source and isotopic

fractionation during carbon fixation. Of the CO2 fixation pathways, the acetyl-Co A pathway can produce the most 13C-depleted biomass, but in other cases produces significantly less 13C-depleted biomass (from δ13C of about –45‰ to about –10‰). In methanogens and acetogens, this pathway produces biomass that is highly 13C-depleted (δ13C about –30‰). The same level of 13C-depletion is found for natural microbial cells using the reductive pentose phosphate cycle with type I rubisco (δ13C around –30‰), and somewhat less 13C-depletion is found for cells using the reductive pentose phosphate cycle with type II rubisco (δ13C around –20‰; Roeske and O’Leary, 1984, 1985; Robinson and Cavanaugh, 1995). Finally, the two remaining carbon fixation pathways, the 3-hydroxypropionate cycle and the reductive TCA cycle, produce the least 13C- depleted biomass (with δ13C values expected between 0‰ to –20‰). The Microbial Metallome In addition to carbon and other traditional macro-nutrients such as nitrogen and phosphorous, organisms require more than 20 elements for cellular growth. In fact, a biological cell can be compositionally described by: (1) its genome, which contains a

5 blueprint of all molecules required to carry out cellular processes (DNA); (2) its proteome, the individual proteins encoded for by the genome and expressed; or (3) its metallome, the twenty-some elements (including carbon, nitrogen, phosphorous, as well as bioactive trace metals) that make up both DNA and proteins and are involved in cellular reactions (Frausto da Silva and Williams, 2001). Bioactive metals are essential nutrients that function as structural elements and catalytic centers in metalloproteins and metal-activated enzymes involved in virtually all cell functions, including DNA and RNA synthesis, respiration and photosynthesis, electron transport, and cellular detoxification (Holm et al., 1996; Frausto da Silva and Williams, 2001). Iron, for example, is almost universally required for life, forming iron-sulfur proteins involved in mitochondrial electron transport, and important heme proteins used for electron transfer, oxidase formation, storage, and transport. Other metals have more limited biological uses associated with specific microbial physiologies. Molybdenum, for example, is an important enzyme in the nitrogen cycle, used in nitrate reductase and the nitrogenase enzyme necessary for nitrogen fixation (Burgess and Lowe, 1996; Eady, 1996; Hille, 1996; Kisker, 1997); nickel is found in enzymes catalyzing carbon reduction reactions of acetogenic and methanogenic organisms (Hausinger, 1987; Ragsdale and Kumar, 1996; Eitinger and Friedrich, 1997; Taha et al., 1997); and tungsten is most commonly used by hyperthermophiles and methanogenic archaea (Johnson et al., 1996; L’vov et al., 2002). Conflicting ideas exist as to the extent of variance in the metallome of prokaryotic organisms. Previous work indicates that E. coli exert tight genetic control over cellular metal concentrations, even for relatively low-toxicity metals such as zinc (Outten and O’Halloran, 2001). These researchers conclude that heterotrophic microbial cells cultured in the laboratory contain a background metal content, or “metal quota”, that is conserved regardless of the metal content of the media. Other studies (for example Raven, 1988) suggest that organisms with specialized metabolic functions, such as nitrogen fixation (diazotrophy), may require up to 100 times more of a specific trace metal to sustain growth under nitrogen-limited conditions. Specialized prokaryotic trace metal requirements in excess of background cellular metal content could potentially provide a useful fingerprint of physiological function in poorly characterized microbial communities in the environment.

6 In addition to providing a possible link between cellular chemistry and physiology, the metallome also offers the most direct chemical link between biological systems and the environment. An element that is widely used by biological systems must be ‘biologically available’, or present in an easily extractable form, from the atmosphere or from solutions. Conditions such as temperature and the oxidation state of the atmosphere and oceans have fluctuated through geologic time, considerably affecting the bioavailability of redox sensitive elements. Some researchers suggest that decreased organic matter production and carbon isotope fractionation during the mid-Proterozoic could have been due to metal limitation of nitrogen fixation and primary productivity in a sulfidic ocean (Canfield, 1998; Anbar and Knoll, 2002). The trace metal preferences and sensitivities of cyanobacteria could also reflect evolution in a sulfidic environment (Saito et al., 2003). Modern biological systems have evolved sophisticated methods of acquiring essential metals under metal-limited conditions, and can significantly alter geochemical cycling in natural environments. For example, in terrestrial settings bacteria can enhance weathering rates through the production of chelators to promote solubilization of metals from minerals in soils (Kalinowski et al., 2000; Liermann et al., 2000; Brantley et al., 2001; Liermann et al., in revision). In response to low concentrations of some essential metals in the surface of modern oceans due to phytoplankton depletion (Bruland, 1980; Bruland, 1989; Martin, 1989; Rue and Bruland, 1995), various microorganisms release strong complexing agents and catalyze redox reactions that modify the bioavailability of these metals and promote their rapid cycling in the upper water column (for a review see Morel and Price, 2003). We examine the trace metal content of the modern prokaryotic metallome predicted by the presence or absence of genes coding for metallo-enzymes in whole genome sequences. We utilize these data to determine if prokaryotic metallomes could provide physiological biosignatures, and to clarify the evolution of biological chemistry and its inter-dependence with the Earth surface environment. Due to the nearly universal biological role of iron, it is assumed that all organisms require cellular iron concentrations far in excess of other redox-sensitive metals. For this reason, we focused only on the predicted metallome contribution of non-iron trace metals (Zn, Mn, Cu, Mo, Co, Ni, W, and V) from prokaryotic genomes. Iron-containing enzymes were considered

7 only if they contained an additional trace metal. In a second analysis, we estimated the approximate timing of evolution of metallo-enzymes (including those containing iron) from their functions and occurrences in modern organisms. This view of metallome evolution is then compared to modeled changes in the bioavailability of these metals through time from the literature (Saito et al., 2003). MATERIALS AND METHODS Estimation of a Consensus Tree of Life We estimated the topology of the tree of life from the combined gene content of taxonomic groups of prokaryotes. For this, we downloaded data matrices containing the presence or absence of homologous gene families in individual prokaryotes, as calculated in House et al. (2003b). We then combined the data matrices for individual organisms in each group to produce a representative data matrix scoring the presence or absence of gene families in a specified percentage of individual organisms in that group, including gene families present in greater than 1, 10, 15, 20, 25, 50, and 99% of the organisms in the group (combined matrices available in online at http://www.geosc.psu.edu/~chouse/ajs1.html). We constructed phylogenetic trees based on the presence and absence of informative gene families in composite genomes, as previously described for individual organisms (Fitz- Gibbon and House, 1999; House et al., 2003b). Parsimony and distance analyses were performed on the combined matrices using PAUP version 4.0b (Swofford, 2002); compatibility and threshold parsimony analyses were applied using the Phylip software package (Felsenstein, 1993). We compared these results to published trees based on rRNA (Fox et al., 1980; Woese, 1987), concatenated protein datasets (Hansmann and Martin, 2000; Brown et al., 2001; Brochier et al., 2002; Matte-Tailiez et al., 2002), and other gene content methods (Gerstein, 1998; Gerstein and Hedgyi, 1998; Snel et al., 1999; Tekaia et al., 1999; Lin and Gerstein, 2000; Wolf et al., 2001; Bansal and Meyer, 2002; Clarke et al., 2002; Korbel et al., 2002; Li et al., 2002; Blank, 2004; Yang et al., 2005), and produced a consensus microbial tree of life based on this comparison. This tree does not represent a strict consensus, but simply our best estimate of phylogenetic relationships based on these disparate studies.

8 Predicted Carbon Isotope Fractionation We produced a compilation of experimentally determined carbon isotope fractionation values as a function of carbon fixation pathways from data compiled in House et al., 2003a (data from Calder and Parker, 1973; Wong et al., 1975; Pardue et al., 1976; Quandt et al., 1977; Sirevåg et al., 1977; Fuchs et al., 1979; Mizutani and Wada, 1982; Belyaev et al., 1983; Holo and Sirevåg, 1986; Ruby et al., 1987; Preuß et al., 1989; Mirkin and Ruby, 1991; Popp et al., 1998; Jahnke et al., 2001; House et al., 2003a) and updated with additional published literature (Madigan et al., 1989; Summons et al., 1994; Jahnke et al., 1999; van der Meer et al., 2001a, 2001b; Orphan et al., 2001, 2002; Londry and Des Marais, 2003; Londry et al., 2004; Tables 1.1A and 1.1B; Figure 1.1A). We superimposed the results onto our consensus microbial tree of life based on the phylogenetic distribution of carbon fixation pathways (Andreesen and Gottschalk, 1969; Sirevag, 1974; Fuchs and Stupperich, 1978; Fuchs et al., 1980; Schauder et al., 1987, 1989; Holo, 1989; Altekar and Rajagopalan, 1990; Windhovel and Bowien,1990; Meijer et al., 1991; Beh et al., 1993; Strauss and Fuchs, 1993; Kandler, 1994; Vorholt et al., 1995; Delwiche and Palmer, 1996; Ishii et al., 1996; Wahlund and Tabita, 1997; Ishii, 1998; Shively et al., 1998; Menendez et al., 1999; Watson et al., 1999; Wirsen et al., 2002; Hügler et al., 2003; Macalady and Banfield, 2003). We then used parsimony to extrapolate carbon isotopic fractionation at various nodes on the tree, including the last common ancestor (LCA). Computing the Model Metallome We compiled a list of known metallo-enzymes utilizing the bioactive metals iron, zinc, copper, molybdenum, manganese, cobalt, nickel, tungsten, and vanadium, and the number of atoms of metal required per molecule of enzyme, when known (data tables available in Appendix A; Burgess and Lowe, 1996; Dismukes, 1996; Hille, 1996; Holm et al., 1996; Johnson et al., 1996; Lipscomb and Sträter, 1996; Ragsdale and Kumar, 1996; Berman et al., 2000; Frausto da Silva and Williams, 2001; L’vov et al., 2002; and references therein). We created a mysql database of genome content for the complete genomes of 52 prokaryotes from data downloaded from the NCBI site (www.nbi.nih.gov or ftp.ncbi.nih.gov), which contains reannotated microbial genomes for maximum similarity. For each gene in a genome, the NCBI database contains information including, but not limited to, fields for the gene name, the Clusters of Orthologous Groups (COG) code

9 (Tatusov et al., 1997), and the COG product. We then used multiple arrangements of the common names and synonyms of each of the metal-containing enzymes we identified to search within our mysql genome database for the presence of a gene or genes encoding each metallo-enzyme. The presence of genes coding for metal-containing enzymes in each genome was then used to compute a “model metallome” for each organism. In computing the model metallome, we made a series of assumptions. First, for each gene known to code for a metallo-enzyme, we assumed that one enzyme molecule would be

expressed. We then defined the model metallome, TMe, for this one enzyme per gene system

by summing all the metal atoms in the expressed enzymes, such that if Mei is the total number of atoms of any individual metal, i, in that model metallome, then

TMe = ∑ Mei (1-3) i

We calculated the fractional metal contribution of each metal i to the model metallome (Fi), by the following equation:

Mei Fi = ×100 (1-4) TMe Due to the nearly ubiquitous use of iron in biological systems, iron-containing enzymes were considered only if they contained an additional metal cofactor. In that case, only the number

of non-Fe atoms appeared in the TMe summation. Thus, strictly, Fi is the fractional metal

contribution to a model metallome where Fe atoms have been excluded. Values of Mei, TMe,

and Fi for individual metals are shown for each organism in Table 1.2. In any natural system, the microbial metallome can be altered either by changing the genome through the addition or loss of genes encoding for a metallo-enzyme, or by changing the proportion of genes expressed by the organism. In order to make generalizations about

TMe across a wide range of microbial cellular functions, we assumed that all enzymes encoded in the genome are produced in equal proportions within the cell at all times. Thus, the “model metallome” represents the metals in all metallo-enzymes that can be produced by an organism (excluding Fe-only enzymes). At any given time under any given environmental condition, the actual expressed cellular metallome in the organism may differ substantially from the theoretical “model metallome” we calculate, due to changes in gene expression.

10 We then sought to look at how the model metallome varied across the consensus

phylogenetic tree. We tested sets of individual Fi values of organisms within phylogenetic groups for similarity using the chi-square (Pearson goodness of fit) test for general association of variables. The test was used to determine whether or not the organisms have statistically similar model metallomes within the groups, and statistically different model metallomes between groups. This particular statistical analysis is generally weakened by the occurrence of counts of less than five for the variable in question; therefore tests performed

using each individual Fi count were checked against tests combining Fi counts less than five,

commonly FNi, FV, and FW. The model metallome for phylogenetic groups was statistically significant in all but two cases: the model metallomes for the firmicutes fit into three statistically significant categories that were unrelated to previously recognized phylogenetic groupings, and the model metallome of methanogenic archaea fit into two statistically significant groups, Methanosarcina sp. and chemoautotrophic methanogens (excluding M. thermoautotrophicum). We extrapolated these model metallomes on a consensus tree representing our analysis of combined gene families considered along with the most widely accepted microbial tree topologies (see discussion below). We calculated the extrapolated

metallomes for each node by computing the mean Fi for each metal of all branches

diverging from the node. For example, in Figure 1.7A the Fi values assigned to the

branching point of the firmicutes and actinobacteria is calculated as an average of the Fi values for the three groups of firmicutes and the Fi values for the actinobacteria. In turn,

the Fi values of earlier nodes on the tree were calculated based on the values determined for the lineages that diverge from them. COMPOSITE ORGANISM PHYLOGENY AND A CONSENSUS TREE OF LIFE We estimated the topology of the tree of life from the combined gene content of taxonomic groups of prokaryotes. Trees based on gene families present in ≥20% of individual genomes in each taxonomic group are shown in Figure 1.2A (maximum parsimony) and in Figure 1.2B (compatibility). We chose to present the trees based on 20% because using gene families present in ≥25% of genomes in a group resulted in composite genomes with very few gene families unique to each group, and thus produced primarily unresolved trees. Trees produced with gene families present in ≥15% and ≥20% of genomes

11 in a group were nearly identical. Additionally, a cutoff point of 20% produced the most consistent trees between the various tree-building methods we employed. In general, different tree-building methods have different strengths and weaknesses, leading to differing tree topologies. Therefore, based on comparisons of our results with trees in the literature constructed from ribosomal RNA, the concatenation of large protein-data sets, and other gene content methods (see references above), we developed a tree best representing the present consensus on microbial relationships (Figure 1.2C). For the consensus tree, the two groups of gram positive bacteria (the firmicutes and the actinobacteria) were united based on their overall similar cell structure, in spite of the limited molecular support for this grouping (Brown et al., 2001; Wolf et al., 2001; Fu and Fu-Liu, 2002; Blank, 2004). Our resulting tree of life, shown in Figure 1.2C, is broadly similar to that based on rRNA (for example: Fox et al., 1980; Woese, 1987) with the following exceptions: (1) the methanogenic archaea are united together based on whole genome analysis (Slesarev et al., 2002; House et al., 2003b; Yang et al., 2005); and (2) cyanobacteria have been placed as a sister group to the gram positive bacteria (Gerstein, 1998; Hansmann and Martin, 2000; Blank, 2004; this study). Our trees of composite organisms (Figure 1.2A and 1.2B) differ from the consensus tree (Figure 1.2C) in that our genomic trees failed to unite the Proteobacteria, and placed the Thermoplasmales at the base of the tree, presumably due to difficulties in analyzing genomes of differing size (House and Fitz-Gibbon, 2002). INFERRED CARBON ISOTOPE SIGNATURES OF ANCIENT ORGANISMS Based on the observation that the reductive TCA and 3-hydroxypropionate cycles

result in low carbon isotope fractionation between CO2 and biomass, the reductive pentose-

phosphate cycle and acetyl-CoA pathway result in moderate fractionation between CO2 and biomass, and pathways using other lighter substrates result in extremely 13C depleted biomass (as in Figure 1.1), we superimposed the distribution of modern carbon fixation pathways and the resulting carbon isotope signatures onto a modified version of our consensus tree of life (Figure 1.3). From extrapolation by parsimony, we then inferred the carbon fixation and approximate isotopic signature for the common ancestors of various ancient prokaryotic groups, including the last common ancestor (LCA). For ethanotrophy (E), a biological process for which fractionation factors have not been measured, we assumed some degree of biological fractionation from natural ethane, which is itself isotopically

12 depleted. For example, measured δ13C values for ethane in deep sea sediments are around -60 ‰ due to biological fractionation during synthesis (Oremland et al., 1988; Waseda and Didyk, 1995; Paull et al., 2000). The results shown in Figure 1.3 suggest the following: (1) both low and moderate carbon isotope fractionation were established quickly in the early evolution of life; and (2) methanotrophic and ethanotrophic metabolisms capable of producing biomass with extreme 13C depletions are not primitive, but rather evolved after the major groups of Prokaryotes had already diverged. Based on the distribution of carbon fixation pathways in Figure 1.3, it appears more or less equally parsimonious to assign low or moderate carbon isotope fractionation to the base of the Bacteria and the base of the Archaea, and thus to the last common ancestor. This conclusion follows because it is difficult to extrapolate the relative antiquities of the reductive TCA cycle, acetyl CoA pathway, and reductive pentose phosphate cycle. For example, the last common ancestor of the Archaea could have exhibited high fractionation through either a variant of the acetyl CoA pathway as found in methanogens, or through the reductive pentose phosphate cycle, as found in the haloarchaea (Rawal et al., 1988). It has been suggested that the acetyl Co-A pathway is the oldest carbon fixation pathway (Huber and Wachtershauser, 1997; Martin and Russell, 2004). Alternatively, the wide phylogenetic distribution of the reductive pentose phosphate cycle (Rawal et al., 1988; Ivanovsky et al., 1999), and the ubiquitous occurrence of diverged forms of Rubisco in both Bacteria and Archaea support the antiquity of this pathway (Finn and Tabita, 2003; Hügler et al., 2003). The Rubisco enzyme, however, could have had a complex history, originally evolving as an oxygenase (Hanson and Tabita, 2001). The TCA cycle, which seems to have evolved originally as a reductive pathway (Romano and Conway, 1996), is universally important in biosynthesis and therefore must have evolved especially early making it perhaps the mostly likely carbon fixation pathway for the last common ancestor (Wachtershaüser, 1990; Morowitz et al., 2000). For the purposes of extrapolating carbon isotopic signatures to the LCA, if the reductive TCA cycle is the oldest carbon fixation pathway, then the LCA would demonstrate low fractionation. We expect that carbon isotopic signatures of biomass identified experimentally in modern organisms using different carbon fixation pathways have been manifested in

13 isotopic compositions of ancient microorganisms, and subsequently preserved in microfossils (House et al., 2000; Ueno et al., 2001; Kaufman and Xiao, 2003) and other organic matter in the rock record (for example, Hayes et al., 1983). Carbon isotopic studies of Precambrian sediments have traced a distinctive isotopic signature of highly fractionated biomass, resulting from biological carbon fixation, to ~3,500 million years ago (Schidlowski et al., 1983; Hayes et al., 1992). For example, recent studies of Buck Reef Chert, South Africa, have shown that photosynthetic mats were an important microbial ecosystem in shallow water environments at 3,416 million years ago (Tice and Lowe, 2004). These laminated cherts contain carbonaceous matter with a carbon isotopic composition of -20 to -30 ‰, consistent with fractionation by the reductive pentose phosphate cycle (Tice and Lowe, 2004). It is now possible to partially explain the isotopic fractionations found throughout the Archean through anoxygenic photoautotrophy. In the past, such an explanation was hindered (House, 1999) because the known deeply diverging lineages of anoxygenic phototrophs, Chloroflexus and Chlorobium, do not use the reductive pentose phosphate cycle; and therefore do not demonstrate sufficiently large isotopic fractionations during carbon fixation to explain the isotopic record prior to the evolution of Cyanobacteria or photosynthetic Proteobacteria (like Chromatium). However, it is now known that Oscillochloris (Keppen et al., 2000), a Green Non-sulfur Bacteria, uses the reductive pentose phosphate cycle for carbon fixation (Ivanovsky et al., 1999). This lineage, along with the recently described ‘Candidatus Chlorothrix’ which has been shown not to the use the 3-hydroxypropionate cycle (Klappenbach and Pierson, 2004), may be modern examples of the kinds of anoxygenic photoautotrophs that built microbial mats 3,500 million years ago producing organic material with moderately high 13C-depletions. Furthermore, the prospect that anoxygenic photoautotrophs, such as Oscillochloris, were mat builders in the early Archean is consistent with reported “cyanobacteria-like” microfossils of that Eon (Schopf and Packer, 1987; Schopf, 1993; Schopf et al., 2002), as the morphology of Oscillochloris is remarkably similar to that of filamentous cyanobacteria (Gorlenko and Pivovarova, 1977; Keppen et al., 1994). Compared to younger Archean sediments, a relatively low fractionation has been observed in turbidites from the 3,800 million years old Isua terrain in central West

14 Greenland (Rosing, 1999), and a large amount of 13C-depletion has been reported in graphite inclusions in apatite from the 3,850 million years old Akilia Island metasediments in southwest Greenland (Mojzsis et al., 1996). For the Isua terrain, the carbon isotopic composition of the organic matter in turbidite sediments (about –19‰; Rosing, 1999) and inclusions in various mineral phases (down to about -18‰; Ueno et al., 2002) could be consistent with fractionation by a primitive autotroph using the reductive TCA cycle (van der Meer et al., 2000). However, since the carbon isotopic results from the more ancient Akilia Island seem to show greater 13C-depletion, an explanation of this carbon is more complicated (also see Knoll, 2003). The Alkilia Island results would suggest that: 1) the last common ancestor of life dates from earlier than 3,850 million years ago; 2) processes other than biologic carbon fixation have contributed to the apparently large magnitude fractionations reflected in the ancient Akilia samples; and/or 3) the reductive TCA cycle is not the oldest carbon fixation pathway. (Note: during final preparation of this manuscript, Lepland et al., 2005, published concerns about the relevance of Akilia apatites as a recorder of the past record of life on Earth based on their inability to reproduce the earlier results of Mojzsis et al., 1996). For example, it is possible that life originated and diversified extensively before the 3,800 to 3,900 million year old sedimentary units were deposited. Growing evidence suggests that the Earth was habitable before the oldest preserved sediments. Zircons from arc volcanism, dated from 4,200 to 4,400 million years ago, have been recovered from Jack Hills, Western Australia, suggesting that oceans were present at that time (Mojzsis et al., 2001; Wilde et al., 2001). Additionally, studies of the history of lunar impacts suggest that the Earth was comparatively peaceful from ~4,400 to ~3,900 million years ago, between the completion of an accretionary event ~4,450 million years ago and a late intensive bombardment at ~3, 900 million years ago (Ryder, 2003), allowing for a period of prior planetary habitability. MODEL METALLOMES OF MODERN AND ANCIENT ORGANISMS Comparing Model Metallomes from Modern Genome Sequences We hypothesized that the prokaryotic model metallome could provide a further biosignature of microbial physiology in the environment. To test this hypothesis, we examined the model metallomes of modern prokaryotes predicted by the presence or absence

15 of genes encoding for metallo-enzymes in whole genome sequences, and compared these values across specific metabolisms, oxygen tolerance, optimum growth temperature, and phylogeny (Figure 1.4 and Figure 1.5). When making broad comparisons across groups of

organisms, if we suppose equal gene expression and a relatively constant TMe, then a higher

Fi value for a specific metal means that the organism generally requires more atoms of that metal, if all enzymes are being expressed equally. Results of these comparisons suggest that the use of metals in prokaryotes when analyzed as a group follows the general hierarchy Fe >> Zn > Mn >> Co, Cu, Mo >> Ni > W, V (Figure 1.4 and Figure 1.5). As explained above, iron is nearly universally required for life and therefore not even considered in the model metallome as we define

it. Zinc (FZn 30-50%) forms metallopeptidases, metallophosphatases, and many other zinc polymerases involved in DNA and RNA binding and synthesis (Lipscomb and Sträter,

1996). Manganese (FMn 20-30%) catalyzes several redox reactions, hydration- dehydration, isomerizations, phosphorylation-dephosphorylation, phosphoryl transfer, and dioxygen production in photosynthesis (Dismukes, 1996; Yachandra et al., 1996).

Cobalt (FCo 10-20%) forms the cobalamin cofactor of the essential vitamin B12, which is

required for a number of enzymes (Marsh, 1999). Copper (FCu 10-20%) forms the common ‘blue’ copper proteins used in electron transfer, often associated with oxidative enzymes and energy capture (Guss and Freeman, 1983; Solomen et al., 1996). And molybdenum (FMo 5-20%), in addition to its involvement in nitrogen metabolism (in nitrogenase and nitrate reductase), is used in a wide variety of other enzymes that catalyze an oxygen atom transfer or two-electron reaction, including xanthine oxidase, sulfate reductase, and fumarate dehydrogenase (Burgess and Lowe, 1996; Eady, 1996; Hille, 1996; Kisker, 1997; Frausto da Silva and Williams, 2001). Nickel, tungsten, and vanadium have much more limited biological uses, as discussed below. We compared model metallomes for organisms with specialized metabolic functions to determine if their use of specific metallo-enzymes produces a unique metal signature that could be apparent in the expressed cellular metallome (Figure 1.4A). We chose to compare organisms that perform methanogenesis, nitrogen fixation, and oxygenic photosynthesis, as these are metabolic functions that are important in elemental

16 cycling in the environment and are each associated with one or more specific metallo- enzymes. Model metallome comparisons indicate that of the metabolisms we tested, only the model metallome of methanogens contains a unique signature. Methanogenic archaea

have significantly elevated FNi and FW (FNi ~9% and FW ~5% for methanogens, versus FNi

~2% and FW ~2% for non-methanogens; Figure 1.4A and 1.6A). Nickel is extremely important in methanogenesis, forming the cofactor of two primary enzymes involved in methane production, methyl-coenyzme M reductase and acetyl-CoA synthase. It is also

used in carbon monoxide dehydrogenase, which can catalyze the reduction of CO2 to carbon CO and subsequent assembly of acetyl-CoA, key to pathway of carbon fixation for acetogenic methanogens (Ragsdale and Kumar, 1996). In non-methanogens nickel is additionally used in urease, some hydrogenases, and rarely in superoxide dismutase (Frausto da Silva and Williams, 2001). Hyperthermophilic methanogens use tungsten in aldehyde ferredoxin oxidoreductase (AOR) enzymes, and in some thermophilic methanogens tungsten is also used preferentially to molybdenum in formylmethanofuran

dehydrogenase, catalyzing the first step in the conversion of CO2 to CH4 (L’vov et al., 2002). Our examinations indicate the model metallomes of nitrogen fixers and cyanobacteria, on the other hand, do not contain unique metal signatures. We calculate only

slightly higher FMo values in nitrogen-fixing microorganisms (FMo ~12% for diazotrophs,

versus FMo of 10% for non N-fixers), despite their requirement for molybdenum in the Fe-Mo cofactor of the nitrogenase enzyme responsible for nitrogen-fixation. In the absence of molybdenum, some organisms can form alternative nitrogenases containing vanadium and iron or iron only (Eady, 1996; Burgess and Lowe, 1996; Howard and Rees, 1996), so that

diazotrophs additionally have a small but insubstantial FV (1-2%) not found in most non N- fixers, as the only other well-understood use of vanadium in biology is in haloperoxidases (Frausto da Silva and Williams, 2001). Due to the substantial requirement of manganese in photosystem II, which is responsible for oxygenic photosynthesis (using 4 atoms of manganese per molecule of enzyme; Yachandra et al., 1996), we expected that oxygenic

phototrophs might exhibit elevated FMn values over non-phototrophs. However, the two

17 cyanobacteria in our database have a mean of 23% FMn, an increase of only 1% over non- phototrophs.

High FNi and FW values in methanogenic archaea support the idea that these organisms may contain elevated cellular nickel and tungsten concentrations in excess of other organisms, providing a biosignature for methanogens utilizing the known Ni- and W- containing enzymes in the environment. In fact initial measurements of the cellular metal content of Methanosarcina demonstrate high nickel and tungsten concentrations (House, unpublished data). Furthermore, in our calculations of a model metallome we ignored gene expression, assuming that all enzymes were expressed to the same extent in all organisms at all times. However, in different environments under different conditions, specific proteins will be expressed in higher quantity than others in a given organism. For example, Raven (1990) calculated an increase in both manganese and iron requirements for photosynthesis at low photon flux densities and with different ATP sources and CO2 assimilation mechanisms. Additionally, growth in the absence of fixed nitrogen requires a high concentration of the nitrogenase enzyme, and as a result some diazotrophs accumulate 10 to 20% of the total cell protein as nitrogenase under nitrogen-limited conditions (Martinez-Argudo et al., 2004). If the metallo-enzymes responsible for specific metabolic capabilities such as nitrogen fixation and oxygenic photosynthesis are expressed in excess of other enzymes under conditions when these metabolisms are active, then this would result in an amplification of the metal in the expressed cellular metallome beyond that predicted by our model. Thus the expressed metallome could still potentially contain a biosignature for the activation of these metabolisms in nature. Next we compared the model metallomes of strict aerobes and strict anaerobes

(Figure 1.4B). This comparison indicates that anaerobes have elevated FNi and FW and

slightly lower FCu and FMo than aerobes (average for anaerobes: FNi and FW > 3%, FCu ~8%,

FMo ~9%; average for aerobes FNi and FW ~2%, FCu ~10%, FMo ~11%). A closer examination

of FNi for individual organisms (Figure 1.6A) clearly shows that the increased nickel signature for anaerobes is due to the bias in our anaerobic database for methanogenic archaea, which have elevated FNi as explained above. This examination also shows that most anaerobes have FCu less than 9%, while aerobes have FCu values up to twice that (Figure 1.6A). The two exceptions were M. jannaschii and A. fulgidus. This is difficult to explain for

18 A. fulgidus; however, M. jannaschii has a relatively small total number of identified metallo- enzymes, most likely due to poor genome annotation (TMe of 30% for this organism, much

less than the average TMe of 51% for all organisms examined), such that even though it only requires 3 atoms of copper (in amine oxidase and nitrate reductase), this leads to a higher overall FCu. We also compared the model metallomes of organisms with varying optimum growth

temperatures (Figure 1.4C). Increasing trends in FMo and FW are seen with increasing thermophily (Figure 1.4C and Figure 1.6B). FMo increases from around 8% in the one psychrophile we examined to greater than 12% in hyperthermophiles. At high temperatures, molybdenum forms the primary cofactor of enzymes such as formyl methanofuran

dehydrogenase (L’vov et al., 2002). FW is ~ 4% for hyperthermophiles, ~3% for thermophiles, ~2% for mesophiles, and ~1% for the one psychrophile examined. As mentioned above, tungsten is used in AOR enzymes, best known from the hyperthermophile Pyrococcus furiosus (Mukund and Adams, 1991; Chan et al., 1995; Kletzin et al., 1995). Hyperthermophilic archaea also contain two other AOR-type tungstoenzymes that catalyze the oxidation of aldehydes, formaldehyde ferredoxin oxidoreductase and glyceraldehyde ferredoxin-oxidoreductase (Johnson et al., 1996). In some thermophilic organisms, tungsten can also be substituted for molybdenum in molybdo-enzymes such as formylmethanofuran dehydrogenase (L’vov et al., 2002). Despite the small increase in FW, tungsten is such a minor contributor to the total metallome (≤6% for all organisms in this study), that unless the proportion of tungsten-containing proteins expressed in an organism or group of organisms (i.e. hyperthermophiles) is found to be higher than that of other metallo-enzymes, tungsten will not be a useful biomarker for thermophiles. Preliminary studies indicate cellular tungsten concentrations in all hyperthermophiles studied thus far are less than 0.1 parts per billion (Cameron et al., 2004). Similar comparisons can be made between model metallomes across phylogenetic

groups (Figure 1.5). These comparisons suggest relatively constant FZn, FMn, FCu, and FCo,

with fluctuating FMo, FNi, and FW. The average archaeal metallome predicts higher FMo

(~13%), FNi (~4%), and FW (~4%), when compared to the average bacterial metallome (FMo

10%, FNi ~2%, and FW ~2%; Figure 1.5A). Individual groups of Archaea have much more widely variant model metallomes (Figure 1.5B) than individual groups of Bacteria (Figure

19 1.5C). All archaeal and bacterial groups shown in Figure 1.5B and 1.5C have statistically significant model metallomes, except for the firmicutes, which had to be separated into three

groups unrelated to previous phylogenetic groupings. Mean values for FMo, FCo, FNi, and FW for Archaeal groups all vary significantly (Figure 1.5B). Again, the most clear signals are elevated FNi and FW for Methanosarcina sp. and chemoautotrophic methanogens, and elevated FW for the methanogens and thermophilic Thermoplasmales. Compared to the groups of Archaea, groups of Bacteria are fairly uniform, with only the Firmicutes (a mean of

all three statistically significant groups) and cyanobacterial FNi differing significantly. Evolution of the Metallome from Modern Genome Sequences Model metallomes of phylogenetic groups can be mapped onto our consensus tree of life (Figure 1.2C), and used to infer evolutionary changes (Figure 1.7). Extrapolation

to the base of the tree of life by consecutive calculation of each mean Fi from the Fi of

converging branches projects a model metallome for the LCA: FZn ~40%, FMn ~20%,

FMo, FCo, and FCu ~10% each, FNi and FW ~3% (Figure 1.7). The predicted model metallome of the LCA is very similar to the average model metallome we calculated from

genomes of modern anaerobes in this study, as in Figure 1.4 (FZn = 44%, FMn = 21%, FMo

= 9% , FCo = 11%, FCu = 8%, FNi = 3% and FW = 3%; Figure 1.9). This similarity is supported by the isotopic evidence for life more than 3,800 million years ago (see discussion above), prior to the oxygenation of the Earth’s atmosphere (Rye and Holland, 1998). A hypothesis for the possible evolution of trace metal use in the microbial metallome can be formed by assuming that Bacteria and Archaea with mean Fi values evolved from the last common ancestor, and then specific groups of Bacteria and Archaea evolved from these organisms (Figure 1.7). The predicted changes in the metallome with each evolutionary step may have been caused by: 1) changes in trace metal availability due to fluctuating redox conditions in the organism’s environment; 2) competition between evolving organisms for available trace metals; 3) opportunistic evolution of enzymatic uses for widely available but previously unused trace metals; 4) gain of genes encoding for metallo-enzymes by lateral gene transfer; and/or 5) loss of genes encoding for metallo-enzymes along with unnecessary metabolic functions. Early in the evolution of life, it is likely that changes in trace metal availability due to variable redox conditions

20 and competition with other organisms were important in driving biological metal use. As complex life evolved and developed specialized lifestyles, lateral gene transfer and loss of extraneous genetic material likely became important in shaping trace metal utilization (for example, Gogarten et al., 2002). Evolution of the Metallome from Metallo-enzyme Functions Another way to view the evolution of the use of metals in biology is by considering the timing of evolution of modern metallo-enzymes, based on their individual function and occurrence in life today (Figure 1.8). Although some enzymes are believed to have initially developed for one biological use and later modified and adapted to fit a different function (for example nitrogenase; Silver and Postgate, 1973; Normand and Bousquet, 1989), we assume a unique origin for each individual metallo-enzyme, and assign it an inferred timing of evolution based on the rules presented in Table 1.3. The metallo-enzymes can then be mapped onto a geochemical timeline based on the inferred timing of major evolutionary events and we can examine how the use of these metals has developed with changes in global ocean redox chemistry (Figure 1.8). Note: a growing body of evidence indicates that beginning around 1,800 million years ago, after an initial pulse of oxygen into the atmosphere, the Earth’s oceans experienced a prolonged period of deep water euxinia, before

a second pulse of O2 brought atmospheric levels to near current values (Canfield, 1998; Shen et al., 2002; 2003; Arnold et al., 2004; Poulton et al., 2004; shown as a dark hatch in Figure 1.8B). This could have dramatically altered the bioavailability of many redox-sensitive metals during this time (Figure 1.8B; Saito et al., 2003). Some researchers have speculated that this crisis in metal bioavailability could have forced a change in the use of specific metals in metallo-enzymes (Anbar and Knoll, 2002; Saito et al., 2003). However, as we have no conclusive evidence for biological innovation at this time, we can only speculate as to what these changes might have been. Therefore, although the bioavailability of metals during the mid-Proterozoic calculated by Saito et al. (2003) is shown in Figure 1.8B, our interpretation of the evolution of metal use with bioavailability considers only a single oxygenation event. The metals we examined fall into one of three categories: (1) metals with biological uses that developed along with their bioavailability (Cu and Mo); (2) metals with biological

21 uses that developed counter to their bioavailability (Fe and Mn); and (3) metals with biological uses that did not fluctuate significantly through time (Zn, Co, Ni, W, and V). The biological uses of both copper and molybdenum have increased along with their bioavailability since oxygenation of the Earth’s atmosphere. We speculate the maximum number of metallo-enzymes utilizing copper doubled from 10 to 20 enzymes with oxygenation. Today copper is commonly used in ‘blue proteins’ involved in electron transfer, especially in oxidative enzymes and energy capture (Guss and Freeman, 1983). Copper enzymes are also commonly used in oxidase reactions involving small non-metal compounds that only became available to any extent with the rise of dioxygen, including phenols, amines, ferrous ions, and oxidized nitrogen species (Solomen et al., 1996). Similarly, the maximum number of metallo-enzymes utilizing molybdenum more than doubled from 13 to 27 enzymes with oxygenation. Today, molybdenum enzymes catalyze reactions ranging from six-electron reductions (e.g., nitrogenase) to oxygen atom transfer. The increased use of copper and molybdenum in oxygen transformation is consistent with an increase in the biological availability of these metals with complete oxidation of the oceans (Figure 1.8). Under oxidizing conditions such as the modern ocean, copper is moderately available as 2+ 2- Cu , and molybdenum forms the highly mobile molybdate anion, MoO4 , which is unreactive in seawater, exhibiting a conservative profile with an average concentration of 105 nanomolar (Emerson and Huested, 1991). In contrast to Cu and Mo, biological use of iron and manganese has increased counter to their bioavailability since oxidation of Earth’s atmosphere (Figure 1.8). We speculate that the maximum number of metallo-enzymes using iron has nearly tripled from 26 to 74 enzymes with oxygenation, while the maximum number of enzymes using manganese has increased from 13 to 18, despite the predicted decrease in the availability of these metals in Earth systems with progressive oxidation of the atmosphere (Figure 1.8). Despite the low solubility under oxidizing conditions of minerals containing these elements, both Fe and Mn are ubiquitous in natural environments. Reduced ferrous iron (Fe2+) is readily available under anoxic conditions such as on the early Earth, where it could have easily been used to form early metallo-enzymes (Holland, 1973). However, in a transitional sulfidic ocean iron would have been highly complexed with sulfides, and might have existed in concentrations three orders of magnitude lower than Archean seawater (Lewis and Landing, 1991, 1992; Saito et

22 al., 2003). In modern oxygenated oceans iron is estimated to exist at no more than nanomolar concentrations, due to the formation of insoluble Fe-hydroxides (Gordon et al., 1982; Landing and Bruland, 1987; Martin and Gordon, 1988; Bruland et al., 1991; Donat and Bruland, 1995). Despite the decreasing availability of iron under surface conditions with the progressive oxidation of the Earth, the use of iron in transformations of molecular oxygen and in dealing with oxygen radical toxicity seems to have increased with the rise of oxygen in the Earth’s atmosphere. Iron’s use in enzymes dealing with oxygen radicals (for example, superoxide dismutase), and in multiple reductase enzymes (sulfite, nitrite, etcetera) could have evolved in organisms living in anoxic environments where iron was still abundant. In contrast, the wide use of iron in molecular oxygen transformations in most dioxygenases suggests that mechanisms for acquiring iron in oxic environments might have evolved very early in life as an adaptation to rising oxygen in the Earth’s atmosphere (Raven, 1995). For instance, some microorganisms secrete ligands, called siderophores, designed almost exclusively to bind and take up Fe3+ (for example, Neilands, 1984; Hughes and Poole, 1989). These siderophores are low-molecular weight organic molecules, commonly catechols or hydroxamates, with formation constants for ferric iron as high as 1051 (Hider, 1984; Hughes and Poole, 1989; Winkelmann, 1991; Hersman et al., 1995). Manganese has similar redox sensitivity, and can therefore substitute into some iron- containing enzymes (such as superoxide dismutase). Other manganese enzymes are used in a wide variety of functions, most notably dioxygen production, as in photosystem II, catalase, superoxide dismutase, and in the organelles of eukaryotes (Frausto da Silva and Williams, 2001). These functions might have evolved later in life after organisms began to evolve molecular oxygen and learn to deal with free oxygen radicals. Furthermore, over some pH ranges manganese minerals tend to have higher solubility than iron minerals even under oxidizing conditions. Biological uses of other metals (Zn, Co, Ni, W, and V) do not seem to have fluctuated significantly through time (Figure 1.8). This may be because organisms established very specific roles for these metals early in life, or because the metals have been subsequently replaced in metallo-enzymes by more bioavailable cofactors. Zinc, for example, is used in many ubiquitous enzymes such as metallopeptidases, metallophosphatases, and other zinc polymerases involved in DNA and RNA binding and synthesis (Lipscomb and Sträter, 1996).

23 Zinc is readily available in modern freshwater and marine systems, where it can exist at up to 0.45 micromolar concentrations, nearly half of which is the bioavailable Zn2+ ion (Turner et al., 1981; Stumm and Morgan, 1996). However Saito et al. (2003) calculate that zinc would have been scarce in early anoxic oceans, and suggest that during this time prokaryotes (specifically cyanobacteria) either used less zinc due to its scarcity or made use of alternate metal centers. They further suggest that organisms may have obtained zinc enzymes later through lateral gene transfer. Cobalt and nickel-containing enzymes are most commonly used in metabolisms based on methane, carbon monoxide, and hydrogen, such as those likely operating on early earth. Nickel is rarely used in higher organisms, and most nickel enzymes are confined to anaerobic prokaryotes, especially methanogens. The main biological use of cobalt is as the cobalamin cofactor of vitamin B12, which it is speculated to have evolved 2.7-3.5 billion years ago based on its synthesis without an oxidative step (Scott, 1998; 2001; 2003). Therefore the use of cobalt in B12 likely evolved in early life when Co was more highly available than today (Figure 1.8B). Cobalt can also replace zinc in the carbonic anhydrase of some eukaryotic phytoplankton (Roberts et al., 1997), and possibly in Synechococcus (Smith et al., 1999). From Figure 1.8, we calculated a model metallome for the LCA, as before, based on the number of atoms of each metal required per metallo-enzyme inferred to be present in early life. This model metallome for the LCA can be compared to our previous model metallome for the LCA based on phylogenetic extrapolation from modern genome sequences, from Figure 1.7. The enzyme-predicted model metallome (Figure 1.9A) suggests quite a different set of trace metal requirements for the LCA than the model metallome based upon the genome (Figure 1.9B). The enzyme-predicted model metallome has a much higher

FZn, with lower Fi for all other trace metals examined, especially copper and molybdenum. However, the model metallome based upon the genome is much more similar to the average model metallome we calculated from genomes of modern anaerobes in this study (Figure 1.4 and Figure 1.9C). This similarity could be a consequence of the technique used to extrapolate the model genome–based metallome of the LCA. Or, if the second model of metallome evolution based on the known functions of modern metallo-enzymes is more accurate, this could illustrate the point that the modern prokaryotic genome has been greatly affected by gene loss, lateral gene transfer, and/or substitution of alternate metal centers with changing

24 metal availability. Gene loss can occur in organisms with lack of necessity for expression with metabolic function. Analysis of proteins and whole genome sequences in modern prokaryotes support the idea that lateral gene transfer has extensively affected the genetic composition of early life (for example Gogarten et al., 2002). Additionally, numerous metallo-enzymes form functional analogues with metal substitutions, including nitrogenase, superoxide dismutase, and carbonic anhydrase. CONCLUSIONS We utilized the information contained within whole genome sequences to investigate the modern distribution of two potentially important microbial biosignatures (carbon isotopic signatures and cellular metal content), and to interpret their evolution and possible distribution in early life. Specifically, we used the gene content of prokaryotic genomes to refine the topology of the tree of life, to predict cellular metal requirements in modern organisms, and to determine how metal use and biological carbon isotope fractionation have evolved through geologic time. Previous work indicates that the reductive TCA and 3-hydroxypropionate cycles result in low carbon isotope fractionation between CO2 and biomass, the reductive pentose-

phosphate cycle and acetyl-CoA pathway result in moderate fractionation between CO2 and biomass, and pathways using other lighter substrates result in extremely 13C depleted biomass. The distribution of organisms using these carbon fixation pathways on microbial phylogenies suggests that: (1) both low and moderate carbon isotope fractionation between inorganic carbon dioxide and cellular biomass were established quickly in the early evolution of life; and (2) methanotrophic and ethanotrophic metabolisms capable of producing biomass with extreme 13C depletions due to growth on a fractionated carbon substrates are not primitive, but rather evolved after the major groups of Prokaryotes had already diverged. The most likely carbon fixation pathway for the last common ancestor is the universally important TCA cycle, which produces low carbon isotopic fractionation in biomass. Low fractionation by the reductive TCA cycle can be considered consistent with carbon isotope ratios found in 3,800 year old Isua sediments. Additionally, moderately fractionated biomass from up to 3,500 years ago can now likely be attributed to carbon fixation by anoxygenic photoautotrophs using the reductive pentose phosphate cycle.

25 To investigate microbial trace metal use from genome sequences, we calculated “model metallomes” for 52 prokaryotes based on the number of atoms of trace metals required to express one molecule of each metallo-enzyme coded in their corresponding genomes. Our results suggest that the use of metals in prokaryotes follows the general hierarchy: Fe >> Zn > Mn >> Mo, Co, Cu >> Ni > W, V. Model metallomes vary with metabolism, oxygen tolerance, optimum growth temperature, and phylogeny. The model metallome of methanogens contains a unique metal signature, suggesting that elevated requirements of nickel and tungsten may be translated to the expressed metallome, providing a biosignature for methanogenesis. The model metallomes of diazotrophs and cyanobacteria do not contain unique metal signatures; however changes in enzyme expression under some conditions might still translate into a biosignature in the expressed cellular metal content. Metallome comparisons also indicate that the model metallomes of aerobic organisms generally require more copper than those of anaerobic organisms, while tungsten and molybdenum show increasing importance in model metallomes with increasing optimum growth temperature. Most taxonomic groups we tested have statistically significant model metallomes; however, individual groups of Archaea have much more widely variant model metallomes than individual groups of Bacteria. Finally, we estimated a model metallome for the LCA by extrapolating model metallomes of modern phylogenetic groups on a tree of life based on our analysis of composite organisms along with the most widely accepted tree topologies. This extrapolated model metallome for the LCA is very similar to the average model metallome of anaerobic organisms calculated in this study. In a separate analysis, we made inferences about the timing of the evolution of individual metallo-enzymes based on their function and occurrence in modern organisms. The results suggest that fluctuations in the redox state of the Earth's oceans and atmosphere have forced changes in the proportions of metals used in biology. In this model, biological use of copper and molybdenum has developed along with bioavailability; biological use of iron and manganese has developed counter to bioavailability; and biological use of zinc, cobalt, and nickel has not changed significantly through time. The model metallome of the LCA suggested by this method is much different than that extrapolated from modern genomes, supporting the idea that gene loss, metal substitution, and lateral gene transfer have been important in shaping the enzymatic composition of extant organisms.

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43 Table 1.1A. Published results of growth experiments estimating carbon isotopic fractionation from carbon dioxide to cell biomass, ‰. The carbon fixation pathways are those listed in Figure 1.1. Where available, isotopic fractionation to corresponding metabolic products are given in parentheses (MP; methane in most cases, except acetate for A. woodii). Updated from House et al., 2003a.

T ε (‰) from CO2 to TAXON Pathway (°C) cells (MP) Ref

22.2 (43.6), 29.0 Acetobacterium woodii AP-MP 28 (ND), 1 15.8 (43.4) Acidianus brierleyi 3-H 65 3.6 2 Agmenellum quadrupicatum PP 39 15.9 3 39 22.2, 23.9, 19.6 4 Alkaligenes eutrophus PP 28 28.2 1 Ammonifex degensii ? 70 4.1, 4.2 2 Anacystis nidulans PP 39 13.1, 18.0 3 33 16.0, 21.5, 18.0 5 Aquifex aeolicus TCA 85 5.4, 5.4 2 Archaeoglobus fulgidus AP 85 2.7, 5.8 2 Archaeoglobus lithotrophicus AP 85 8.0 2 Candidatus "Brocadia ? 35 18.7, 17.0, 22.3, 6 anammoxidans" strain Delft 21.2, 15.8 Chlorella sorokiniana PP 39 22.6 4 Chloroflexus aurantiacus 3-H 55 7.6 7 Chromatium tepidum PP 50 20.5 8 Coccochloris elebens PP 39 12.3, 16.0 3 Desulfobacter hydrogenophilus TCA 28 10.0, 8.9, 14.2 1 30 13.7 9 30 15.4 10 D. autotrophicum AP 28 39.3, 38.2 1 30 10.0 9 30 9.9 10 Desulfotomaculum acetoxidans AP 30 30.5 9 30 29.7 10 Ferroglobus placidus AP 85 3.5 2 Hydrogenobacter thermophilus TCA 70 5.5 2 Metallosphaera sedula 3-H 65 -3.0 11 65 3.1 2 Methanobacterium sp. AP-MP 37 25.1 (36.4), 12 23.2 (34.9) 46 18.8 (33.7), 12 24.5 (35.7) References: 1 = Preuß et al., 1989; 2 = House et al., 2003; 3 = Calder and Parker, 1973; 4 = Pardue et al., 1976; 5 = Mizutani and Wada, 1982; 6 = Schouten et al., 2004; 7 = van der Meer et al., 2001b; 8 = Madigan et al., 1989; 9 = Londry and Des Maraies, 2003; 10 = Londry et al., 2004; 11 = van der Meer et al., 2001a; 12 = Belyaev et al., 1983.

44 Table 1.1A. Continued.

T ε (‰) from CO2 to TAXON Pathway (°C) cells (MP) Ref

Methanobacterium thermoauto. AP-MP 65 19.1 (34.1) 13 65 15.0 (24.6) 2 Methanococcus igneous AP-MP 85 20.2 (28.4) 2 6.2 (25.0), 10.7 Methanococcus jannaschii AP-MP 85 (19.6), 2 13.7 (17.7), 17.7 (19.6) M. thermolithotrophicus AP-MP 41 7.4 (26.2) 2 20.5 (21.1), 13.4 45 (23.0), 2 4.8 (16.5) 51 13.5 (27.0) 2 60 19.7 (26.4) 2 22.7 (29.0), 25.8 65 (29.6), 2 26.0 (30.8), 26.7 (29.5), 24.8 (29.8), 22.0 (23.2), 19.4 (24.9), 8.7 70 24.9 (27.9) 2 20.3 (30.0), 12.7 Methanopyrus kandleri AP-MP 100 (27.6) 2 17.7 Methanosarcina barkeri AP-MP 37 19.5 (17.2) , 7.9 2 Methanothermus fervidus AP-MP 85 13.1 (30.6) 2 Microcoleus chthonoplastes PP 39 17.1 4 Osillatoria williamsii PP 39 5.0 3 39 17.3 4 Pyrobaculum aerophilum TCA 100 2.9 2 Pyrodictium occultum ? 102 2.3 2 Pyrolobus fumarii ? 105 3.8 2 Rhodopseudomonas capsulata PP 10.8 14 Rhodospirillium rubrum PP 20 21.1 15

12.7 14 Schizothrix calcicola PP 39 13.2 4 Sulfolobus solfataricus 3-H 85 0.2 2 Synechococcus PP 57 22.5 5 15.5, 16.4, 18.6, 18.6, 16 17.2, 17.1, 15.6, 16.9, 18.9, 18.1 References: 2 = House et al., 2003; 3 = Calder and Parker, 1973; 4 = Pardue et al., 1976; 5 = Mizutani and Wada, 1982; 13 = Fuchs et al., 1979; 14 = Quandt et al., 1977; 15 = Sirevåg et al., 1977; 16 = Popp et al., 1998; 17 = Jahnke et al., 2001.

45 Table 1.1A. Continued.

T ε (‰) from CO2 to TAXON Pathway (°C) cells (MP) Ref

Synechococcus lividus PP 47 12.8 4 70 11.1 Thermocrinis ruber TCA 85 3.3 17 Thermoproteus neutrophilus TCA 85 8.7 1 85 2.0 2 Thiobacillus novellus PP 30 5.1 2 References: 1 = Preuß and others, 1989; 2 = House and others, 2003; 4 = Pardue and others, 1976; 17 = Jahnke and others, 2001.

46 Table 1.1B. Published results of growth experiments estimating carbon isotopic fractionation from dissolved inorganic carbon (DIC) or methane to cell biomass, ‰. The carbon fixation pathways are those listed in Figure 1.1. Updated from House et al., 2003a.

T ε (‰) from TAXON Pathway (°C) DIC to Cells Ref

27.1, 25.3, Candidatus "Brocadia ? 35 29.5, 6 anammoxidans" strain Delft 24.2, 30.6 Chlamydomonas reinhardii PP 20 36.8 15 Chlorobium limicola TCA 30 9.5 14 Chlorobium phaeovibrioides TCA 30 10.4, 9.5 14 Chlorobium thiosulfatophilum TCA 20 20.1 15 12.2, 10.8, Chlorobium vibrioforme TCA 30 10.7 14 28 11.4 18 Chloroflexus aurantiacus 3-H 55 13.7 19 55 12.7 18 55 13.6 7 Chromatium strain D PP 20 31.0 15 Chromatium vinosum PP 35 32.7 20 30 27.3 14 Metallosphaera sedula 3-H 65 2.0 11 Nitrosomonas europaea PP 13.8, 13.2 21 Oscillochloris trichoides PP 28 20.1 18 Thiobacillus neapolitanus PP 25.8 22 Thiocapsa roseopersicina PP 28 22.0 18 Thiomicrospira sp. L-12 PP 25.5 22 Thiomicrospira crunogena PP 24.5, 23.3 21 References: as in Table 1.1A; additionally 18 = Ivanovsky and others, 1999; 19 = Holo and Sirevåg, 1986; 20 = Wong and others, 1975; 21 = Mirkin and Ruby, 1991; 22 = Ruby and others, 1987.

47 Table 1.1B. Continued.

T ε (‰) from CH4 TAXON Pathway (°C) to Cells Ref

ANME-1 AOM ~25 to 35 23 23, ANME-2 AOM ~30 to 35 24 CEL 1923 RuMP 10 31.2 25 15 25.7 20 24.1 25 23.0 30 23.1 35 22.1 Methylococcus capsulatus RuMP 37 31.3, 28.9, 24.4, 26 19.5, 19.6, 17.9, 17.6, 19.3, 15.8, 18.6, 14.5, 3.8, 28.4, 27.6, 18.2 37 14.4, 12.4, 25 13.9, 13.6, 12.8, 24.1, 26.0 Methylomonas methanica RuMP 29.3 26 Methylophylus methylotrophus RuMP 5.6 25 Methylosinus trichosporium S 30 9.9 26 5.2, -3.7, 7.6, - 30 0.6, 25 9.2, -7.3, 14.7, - 7.8 References: 23 = Orphan and others, 2002; 24 = Orphan and others, 2001; 25 = Jahnke and others, 1999; 26 = Summons and others, 1994.

48 Table 1.2. The organisms considered in this study, their groupings in discussed analyses (including phylogenetic groups, optimum growth temperature, oxygen tolerance, and any specialized metabolisms considered), the number of atoms of each individual metal (Me) in a given model metallome (Mei), the total model metallome (TMe), and the fractional contribution of each metal to the model metallome (FMe, in %). Abbreviations are as defined below.

O2 Microorganism Group Opt T pref Met

Aeropyrum pernix K1 Crenarchaeota Hyp A Agrobacterium tumefaciens C58 α-Proteobacteria Mes A Nf Aquifex aeolicus VF5 Hyp M Archaeoglobus fulgidus DSM4304 Hyp AN Bacillus halodurans C-1 25 Firmicutes(1) Mes A Bacillus subtilis 168 Firmicutes(1) Mes A Brucella melitensis α-Proteobacteria Psy A Campylobacter jejuni NCTC 11168 δ/ε-Proteobacteria Mes M Caulobacter crescentus CB15 α-Proteobacteria Mes A Chlorobium tepidum TLS Therm AN Nf Chlostridium acetobutylicum ATCC 824 Firmicutes(2) Mes AN Clostridium perfringens Firmicutes(2) Mes AN Corynebacterium glutamicum Actinobacteria Mes A/FA Deinococcus radiodurans R1 Mes A Escherichia coli K-12 Strain MG1655 γ-Proteobacteria Mes A/AN Fusobacterium nucleatum ATCC 25586 Mes AN Haemophilus influenzae Road KW20 γ-Proteobacteria Mes FAN Halobacterium sp. NRC-1 Mes AN Helicobacter pylori 26695 δ/ε-Proteobacteria Mes M Lactococcus lactis IL1403 Firmicutes(3) Mes A Mesorhizobium loti MAFF303099 α-Proteobacteria Mes A Nf Methanococcus jannaschii DSM 2661 Methanoarchaea(1) Hyp AN Meth Methanopyrus kandleri AV1 9 Methanoarchaea(1) Hyp AN Meth Methanosarcina acetivorans C2A Methanoarchaea(2) Mes AN Meth Methanosarcina mazei Goel Methanoarchaea(2) Mes AN Meth Methanobacterium thermoautotrophicum Therm AN Meth Mycobacterium leprae Actinobacteria Mes A Neisseria meningitidis Z2491 β-Proteobacteria Mes A Nf, Nostoc sp. PCC 7120 Cyanobacteria Mes A Ph Pseudomonas aeruginosa PA01 γ-Proteobacteria Mes A Pyrobaculum aerophilum IM2 Crenarchaeota Hyp FA Pyrococcus abyssi Pyrococcales Hyp AN Pyrococcus furiosus DSM 3638 Pyrococcales Hyp AN Pyrococcus horikoshii OT3 Pyrococcales Hyp AN Psy = psychrophile; Mes = mesophile; Therm = thermophile; Hyp = hyperthermophile; A = aerobic; AN = anaerobic; M = microaerophilic; F = facultative; Nf = nitrogen fixer; Meth = methanogen; Ph = oxygenic photoautotroph.

49 Table 1.3. Rules used to assign the timing of evolution for individual metallo-enzymes.

1. Enzymes involved in basic cellular machinery, such as those involved in DNA repair and replication, RNA transcription, protein translation, respiration and electron transport, and carbon assimilation are considered to have been present in very early life.

2. Enzymes involved in specific microbial metabolisms (such as methanogenesis, bacterial sulfate reduction, nitrogen fixation, and oxygenic photosynthesis) are considered to have evolved at the same time as the metabolism.

3. Enzymes involved in other anaerobic microbial metabolisms, such as anaerobic respiration and fermentation, are considered ancient.

4. Enzymes involved in aerobic metabolisms and iron acquisition, enzymes requiring or transforming molecular oxygen or oxygen radicals (such as H2O2), and enzymes used to transform oxidized nitrogen and sulfur species, are considered to have evolved after the rise of oxygen to near atmospheric levels at ~2.2 billion years ago.

5. Enzymes that are only found in plants or animals are considered to have evolved after the diversification of multicellular organisms at 542 million years ago.

6. Enzymes that have only been documented in one species of Bacteria or Archaea, or enzymes that were not found in any of the organisms considered in this study, are considered to have evolved recently, unless enzymatic function suggested otherwise.

50 45 A. From CO2 From CH4 B. AOM 40 acetate S methane 35

30 RuMP CH4 CO2 AP 25 AP-MP 20 /oo (1000 ln α ) o

ε 15 PP

10 3-HP

5 TCA EXTREME MODERATE 0 TCA 3-HP PP AP-MP AP RuMP S AOM -90 -80 -70 -60 -50 -40 -30 -20 -10 0 13 o Pathway of C fixation Resulting δ C /oo

Figure 1.1A. A generalized summary of published carbon-isotopic fractionation factors from carbon dioxide or methane to biomass by prokaryotes grouped by their known or suspected carbon fixation pathway (modified from House et al., 2003a, with data from Tables 1.1A and 1.1B). Note that e only defines a relative value of fractionation produced by the specific pathway, and that the actual carbon isotopic compositions produced will vary according to the isotopic composition of the substrate used. The carbon fixation pathways from carbon dioxide are: the reductive TCA cycle (TCA), the 3-hydroxypropionate cycle (3-HP), the reductive pentose phosphate cycle (PP), and the acetyl-CoA pathway in species that form a metabolic product from carbon dioxide such as methane or acetate (AP - MP), or in species that do not (AP). The changing carbon isotopic fractionation that has been observed for methanogens with growth phase (listed in AP -MP) is shown as a thin solid line extending up to the values expected for a culture at stationary growth. Fractionation factors observed in the metabolic products produced (acetate and methane) are shown as white boxes. Other work has shown that larger fractionations to methane can be obtained during methanogenesis in open systems (Botz et al., 1996; Valentine et al., 2004). The published fractionation factors for AP have been connected by a thin solid line; while these organisms all use the acetyl-CoA pathway, they demonstrate a wide range of isotopic fractionations. The carbon fixation pathways from methane are bacterial methanotrophy using the RuMP pathway (RuMP) or the Serine Pathway (S), and anaerobic methanotrophy (AOM). The shape of the bars indicates the estimated relative importance of that pathway in producing a particular fractionation factor. For the RuMP pathway, the different fractionation factors associated with soluble versus particulate methane monooxygenase is connected by a solid line because both forms of the enzyme is found in some species causing them to show a reduction in fractionation at the initiation of stationary growth. B. The predicted carbon isotopic composition produced by natural microbial cells using each of the pathways shown, assuming fixation of CO2 with a typical carbon isotopic composition of about -7 per mil, or CH4 with a composition of -55 per mil. The labels "extreme", "moderate", and "low" refer to terminology used in fig. 3 and the corresponding discussion.

51 A. Maximum Parsimony B. Compatibility C. Consensus

α-Proteobacteria α-Proteobacteria α-Proteobacteria β-Proteobacteria β-Proteobacteria β-Proteobacteria γ γ-Proteobacteria -Proteobacteria γ-Proteobacteria

Firmicutes Cyanobacteria δ/ε-Proteobacteria

Cyanobacteria Actinobacteria Cyanobacteria Actinobacteria Firmicutes Actinobacteria δ/ε-Proteobacteria δ/ε-Proteobacteria Firmicutes

Crenarchaeota Crenarchaeota Crenarchaeota

Methanoarchaea Methanoarchaea Methanoarchaea

Pyrococcales Pyrococcales Pyrococcales Thermoplasmales Thermoplasmales Thermoplasmales 100 changes 100 changes

Figure 1.2A. Composite organism genomic tree generated by maximum parsimony. B. Composite organism genomic tree generated by compatibility. C. Consensus tree representing our analysis of combined gene families considered along with the most widely accepted microbial tree topologies.

52 α-Proteobacteria (PP, S) β-Proteobacteria (PP) γ-Proteobacteria (PP, AP, RuMP, E) δ/ε-Proteobacteria (TCA, AP) Cyanobacteria (PP) Firmicutes (PP, AP) Actinobacteria (E) Green Sulfur (TCA)

? Green Non-sulfur (PP, 3-HP) Aquificales (TCA)

Crenarchaeota (TCA, 3-HP)

Methanoarchaea (inc. ANME groups) (AP, AOM)

Archaeoglobus (AP) Haloarchaea (PP)

= Low fractionation resulting in biomass δ13C > about -20 permil.

= Moderate fractionation resulting in biomass δ13C between about -20 & -40 permil.

= Biomass showing extreme 13C depletion (< -60 permil).

Figure 1.3. Carbon isotopic signatures from the literature (Figure 1.1; Tables 1.1A and 1.1B) superimposed on our consensus tree of life (Figure 1.2C). For this analysis, we additionally added single organisms with known carbon fixation pathways that did not have representative whole genome sequences or did not fit into previously assigned phylogenetic groupings. Carbon fixation pathways are noted as in Figure 1.1, with the addition of ethanotrophy (E). Note that we positioned the Haloarchaea as a sister group to the Methanoarchaea and Archaeoglobus, within the Euryarchaeota. This placement is primarily based on arguments presented in House and Fitz-Gibbon (2003), however it is currently the subject of debate (for example, Korbel et al., 2002; Yang et al., 2005). Additionally, the anaerobic methane oxidizing archaea (ANME groups) have been included with the Methanoarchaea (Orphan et al., 2001). Ancient isotopic signatures are extrapolated using simple parsimony.

53 0.25 A.

0.20 N fixers Ox photo Methanogens 0.15 Fi

0.10

0.05

Zn/2 Mn/2 Mo Co Cu Nix2 Wx2 Vx2 Metal, i 0.25 B.

0.20 Aerobes Anaerobes 0.15 Fi

0.10

0.05

Zn/2 Mn/2 Mo Co Cu Nix2 Wx2 Vx2 Metal, i 0.25 C. Psychrophile (1) 0.20 Mesophiles Thermophiles 0.15 Fi Hyperthermophiles

0.10

0.05

Zn/2 Mn/2 Mo Co Cu Nix2 Wx2 Vx2 Metal, i

Figure 1.4. Average Fi values predicted by whole genomes compared across: A. specific metabolisms; B. oxygen tolerance; and C. optimum growth temperature. Note the scaling (Zn/2, Mn/2, Nix2, Wx2, and Vx2).

54 0.25 A.

0.20 Bacteria Archaea 0.15 Fi

0.10

0.05

Zn/2 Mn/2 Mo Co Cu Nix2 Wx2 Vx2 Metal, i 0.25 B.

Crenarchaeota 0.20 Methanosarcina sp. Chemoautotrophic 0.15 methanogens Fi Pyrococcales Thermoplasmales 0.10

0.05

Zn/2 Mn/2 Mo Co Cu Nix2 Wx2 Vx2 Metal, i 0.25 C.

0.20 Proteobacteria Cyanobacteria 0.15 Actinobacteria Fi Firmicutes 0.10

0.05

Zn/2 Mn/2 Mo Co Cu Nix2 Wx2 Vx2 Metal, i

Figure 1.5. Average Fi values compared across phylogenies: A. Bacteria and Archaea; B. individual groups of archaea; and C. individual groups of bacteria. Note the scaling as in Figure 1.4 (Zn/2, Mn/2, Nix2, Wx2, and Vx2).

55 0.14 A. 0.12

Aerobes 0.10 Methanogens

0.08 Other anaerobes Ni F 0.06

0.04

0.02

0.05 0.10 0.15 0.20 FCu 0.25 B. Psy Meso Therm Hyp

0.20

0.15 i Mo F W 0.10

0.05

20 40 60 80 100 120 Optimum growth T (°C)

Figure 1.6A. FCu versus FNi values for aerobes, methanogens, and non-methanogenic anaerobes. Average values for each group are shown as open symbols. B. FMo and FW for organisms plotted against optimum growth temperature (for organisms with a range of optimum growth temperatures, the mean temperature was used). Average values for each group (abbreviated as in Table 1.1) are connected with a line and shown as open

56 α-Proteobacteria A. β-Proteobacteria

3 γ-Proteobacteria

δ/ε-Proteobacteria 6 Cyanobacteria

W V Actinobacteria Mo Zn Co 4 11 Ni 1 2 3 3 Cu Firmicutes Mn Crenarchaeota

10 1 2 Methanoarchaea

8

9 Pyrococcales

Thermoplasmales

B. LCA

3% W 12% Mo 39% Zn 11% Co 11 3% Ni 9% Cu 23% Mn

2% 4% 10% 14% 43% 36% Bacteria 11% Archaea 6 12% 10 LCA - 1%Ni - 2%Mo 2% LCA - 3%Zn - 1%Cu - 1%W + 4%Zn 9% 3% + 1%Co + 2%Mo + 1%W 8% 23% 23%

2% 2% 8% 2% 3% 5% 11% 11% 34% 11% 42% 43% 11% 43% 16% 37% 11% 10% 1% 11% 9 2% 3% 13% 8% 4% 10% 8% 3% 8% 27% 9% 22% 23% 22% 24% Proteos Cyanos GC Gram + Crens Eurys Bact - 1%Zn Bact - 1%Cu Bact - 1%Cu Arch - 2%Zn Arch - 1%Co - 1%Mn - 1%Cu - 1%Co + 1%Ni - 1%Ni - 2%Mo - 1%Mn - 1%W - 3%Mo + 1%Zn +1%Mo + 1%Mo + 4%Mn + 1%Cu + 1%Co + 1%Mn + 1%Ni + 2%Mo + 1%W

Figure 1.7A. Model metallomes extrapolated onto a consensus tree of life. B. Possible scenario for metallome evolution based on this extrapolation.

57

2

2

Multi-cellular life Origin of the EarthOriginMethanogenesis of life Bacterial(?) Nitrogen sulfate (?) fixation reduction (?) Oxygenic photosynthesisInitial riseEukaryotes of O Second rise of O Modern

A. B. O 2 H2S -4

Fe 20 21 22 25 26 74 88 Fe -5 Log Approximate Concentration (M) -6

Zn 40 42 51 Mo -7

Co Ni -8 Cu 5 6 7 10 20 30 Mn Ni Fe -9 Mo 8 9 13 27 29 Mn Mn 11 12 13 18 22 -10 Mo Co Enzymes using a given metal Co 1416 2019 -7 -10 W 7 8 9 -13 Zn -16 Ni 1 5 6 -19 V 1 2 3 Cu -22 -25 4 3 2 1 0 4 3 2 1 0 Billion years ago Billion years ago Figure 1.8A. A second way to view the evolution of the use of metals in biology, considering the timing of evolution of known modern metallo-enzymes based on their individual function and occurrence in life today (using rules outlined in Table 1.3). The inferred evolutionary timing of these enzymes is based on the estimated timing of major biological innovations (from Young, 1992; Golubic et al., 1995; Beaumont and Robert, 1999; Brocks et al., 1999; Summons et al., 1999; Shen et al., 2001; Habicht et al., 2002; Battistuzzi et al., 2004) and the estimated timing of oxygenation of the Earth's atmosphere (Holland, 1984; Knoll et al., 1986; Holland et al.,1990; Des Marais et al., 1992; Canfield and Teske, 1996; Rye and Holland, 1998). The numbers on the bars refer to the number of metallo-enzymes containing a given metal inferred to be in use during that time (for example, 88 Fe-containing enzymes were considered to exist on Earth today), and the thickness of the bars is scaled to reflect the proportions of each metal used among total metal-containing enzymes at any given time. B. Approximate concentrations of the considered metals and other species in seawater with progressive oxygenation of the oceans, based on assumptions made by Saito et al. (2003), except molybdenum concentrations, which were derived from other sources (Bertine and Turekian, 1973; Collier, 1985; Lewis and Landing, 1992; Stumm and Morgan, 1996; Morford and Emerson, 1999; Anbar and Knoll, 2002). The hatched area refers to the period of time during which the Earth's oceans likely experienced a prolonged period of deep water euxinia (based on the most recent dates from Poulton et al., 2004).

58 A. LCA - metallo- B. LCA - genome C. Modern anaerobes enzymes sequences

4%W 3%W 3%W 5%Mo 53%Zn 12%Mo 39%Zn 9%Mo 44%Zn 10%Co 11%Co 2%Ni 11%Co 3%Ni 6%Cu 3%Ni 11 8%Cu 9%Cu 20%Mn 23%Mn 21%Mn

Figure 1.9. Model metallomes for: A. the LCA, based on metallo-enzymes inferred to be present at this time, from Figure 1.8; B. the LCA, based on extrapolation of model metallomes from our consensus tree in Figure 1.7; and C. the average of modern anaerobes, from Figure 1.5.

59 Chapter 2

Metal Limitation of Cyanobacterial Nitrogen Fixation

ABSTRACT Nitrogen fixation is a critical part of the global nitrogen cycle, replacing biologically available reduced nitrogen lost by denitrification. The redox-sensitive trace metals Fe and Mo are key components of the primary nitrogenase enzyme used by

cyanobacteria (and other prokaryotes) to fix atmospheric N2 into bioessential compounds. Progressive oxygenation of the Earth’s atmosphere has forced changes in the redox state of the oceans through geologic time, from anoxic Fe-enriched waters in the Archean to partially sulfidic deep waters by the mid-Proterozoic. This development of ocean redox chemistry during the Precambrian led to fluctuations in Fe and Mo availability that could have significantly impacted the ability of prokaryotes to fix nitrogen. It has been suggested that metal limitation of nitrogen fixation and nitrate assimilation, along with increased rates of denitrification, could have resulted in globally reduced rates of primary production and nitrogen-starved oceans through much of the Proterozoic. To test the first

part of this hypothesis, we grew N2-fixing cyanobacteria in cultures with metal concentrations reflecting an anoxic Archean ocean (high Fe, low Mo), a sulfidic Proterozoic ocean (low Fe, moderate Mo), and an oxic Phanerozoic ocean (low Fe, high

Mo). We measured low rates of cellular N2 fixation under [Fe] and [Mo] estimated for the Archean ocean. With decreased [Fe] and higher [Mo] representing sulfidic Proterozoic

conditions, N2 fixation, growth, and biomass C:N were similar to those observed with metal concentrations of the fully oxygenated oceans that likely developed in the Phanerozoic. Our results raise the possibility that an initial rise in atmospheric oxygen could actually have enhanced nitrogen fixation rates to near modern marine levels,

providing that phosphate was available and rising O2 levels did not markedly inhibit nitrogenase activity. INTRODUCTION Nitrogen fixation is an energetically expensive process by which a limited but diverse group of prokaryotes, called diazotrophs (Young, 1992), can produce biologically + useful NH4 from inert dissolved N2 when other forms of inorganic nitrogen are

60 unavailable. Nitrogen is an essential element in all organic tissue, required for the formation of proteins and amino acids. The modern marine nitrogen cycle involves a complex series of primarily biological transformations between oxidized and reduced - - forms of nitrogen, the most important species being nitrate and nitrite (NO3 and NO2 ), + ammonium (NH4 ), and dinitrogen (N2). Although nitrogen must be incorporated into cell + biomass in a reduced form (e.g., NH4 ), N2 is the dominant form of nitrogen in the oceans. This means that the global balance between denitrification, or the loss of - - bioavailable N (conversion of NO3 /NO2 to N2) and N2 fixation, or the gain of + bioavailable N (conversion of N2 to NH4 /organic N) is important in supporting marine primary productivity over geologic time scales (Karl et al., 2002; Mahaffey et al., 2005). Nitrogen fixation is clearly an important source of nitrogen for primary production in the modern oligotrophic oceans (e.g., Capone et al. 1997; Karl et al., 1997).

Estimates of the annual N contribution from N2 fixation in modern oceans range from 0.06 to 25 mol N x 1012 y-1 (for reviews see Arrigo, 2005, and Mahaffey et al., 2005).

Much of the research on N2 fixation in the marine environment has focused on the relatively conspicuous, non-heterocystous, filamentous cyanobacterium Trichodesmium (Carpenter and Romans, 1991; Capone et al., 1997; Capone et al., 2005). This planktonic diazotroph is ubiquitous in low-nutrient tropical and subtropical seas and often forms massive near-surface blooms (Carpenter and Capone, 1992; Capone et al., 1997; Orcutt et al., 2001; Davis and McGillicuddy, 2006). Trichodesmium is unusual in that it makes macroscopic aggregates of filaments, contains gas vacuoles to control buoyancy, and fixes nitrogen only in the light. In addition, it has recently been shown that Trichodesmium can utilize the organic phosphonate compounds in the ocean’s dissolved phosphorus pool (Dyhrman et al., 2006), a seemingly unique adaptation amongst marine cyanobacteria possibly conveying a further competitive advantage to survival in nutrient- starved marine environments. Although Trichodesmium has traditionally been considered the dominant marine

N2 fixer (along with heterocystous cyanobacterial symbionts, e.g., Carpenter et al., 1999), recent studies have shown that small unicellular diazotrophic cyanobacteria can also contribute a substantial amount of fixed nitrogen to the modern oceans (Zehr et al., 2001; Montoya et al., 2004). Examination of natural samples using molecular methods has

61 revealed an extremely high phylogenetic diversity in genes for nitrogen fixation machinery (nifH) in the environment (Zehr et al., 1995, 1998, 2000), suggesting the

abundance of previously undescribed N2-fixing organisms. These and other field-based studies indicate that the relative intensity of N2 fixation for these various groups of diazotrophs is highly variable and heterogeneous (Mahaffey et al., 2005).

Diazotrophs fix N2 using the nitrogenase enzyme complex, a two component metallo-enzyme consisting of a dinitrogenase and a dinitrogen reductase. Both proteins

are oxygen-sensitive, iron-sulfur proteins. All known N2-fixing organisms contain a dinitrogenase reductase with an iron (Fe) cofactor, and a dinitrogenase with an iron- molybdenum (Fe-Mo) cofactor, containing 2 moles Mo and 38-50 moles Fe per mole of enzyme complex (Miller and Orme-Johnson, 1992). In the absence of molybdenum, some organisms can produce two genetically distinct but homologous alternative nitrogenases, containing an iron-vanadium (Fe-V) cofactor, or a cofactor based on Fe alone (Eady, 1996). The alternate enzymes have been found only secondarily to the Fe-Mo nitrogenase in a select subset of diazotrophs, excluding marine organisms. The Fe-Mo nitrogenase is

more efficient in N2 binding and reduction than either of the alternative nitrogenases, approximately 1.5 times more efficient than the Fe-V nitrogenase and up to 100 times as efficient as the Fe-Fe nitrogenase (Joerger and Bishop, 1988; Miller and Eady, 1988). Some diazotrophs can also produce a second Fe-Mo-dependent nitrogenase enzyme under anoxic conditions (Thiel et al., 1995, 1997; Thiel and Pratte, 2001). All nitrogenase systems studied exhibit a very low substrate specificity (Burris, 1991), and can additionally catalyze the reduction of other doubly or triply bonded compounds, including acetylene, cyanide, and N2O. One exception is an unusual Mo-dependent nitrogenase described in Streptomyces thermoautotrophicus UBT1, presumably linked to carbon monoxide reductase activity (Gadkari et al., 1992; Ribbe et al., 1997).

A number of environmental factors can control the extent of N2 fixation in modern environments, including the presence of fixed nitrogen compounds, oxygen concentration, and availability of other nutrients (phosphorus) and metal cofactors (Fe + and Mo). In general, nitrogenase synthesis is repressed in the presence of NH4 , and is induced by the depletion of fixed N substrates. The nitrogenase protein is irreversibly inactivated by molecular oxygen and reactive oxygen species (Gallon, 1992). This

62 oxygen sensitivity is especially troublesome in diazotrophic cyanobacteria, which produce oxygen during photoautotrophic growth via photosystem II. These organisms have developed a number of complex strategies for separating nitrogenase from

photosynthetic O2 production (for a review, see Berman-Frank et al., 2003). In some cyanobacteria, nitrogen fixation is confined to specialized cells, called heterocysts, which

have high rates of respiration and decreased permeability to dissolved O2 (i.e. spatial separation). Other organisms fix nitrogen only at night, while photosynthetically fixing

carbon and producing O2 in the daylight (i.e. temporal separation). In Trichodesmium, N2

fixation is confined to the photoperiod and occurs simultaneously with O2 photosynthesis through both temporal and spatial segregation controlled by a circadian clock (Berman- Frank et al., 2001; Chen et al., 1999; Lin et al., 1998). Availability of phosphorus, another major constituent of biomass and potentially

limiting nutrient, could also be important in controlling N2 fixation in some parts of the modern oceans (Sañudo-Wilhelmy et al., 2001; Mills et al., 2004). Iron is a critical metal cofactor for all nitrogenase enzymes (Howard and Rees, 1996), as well as the majority of redox enzymes involved in respiration and photosynthesis (Frausto da Silva and Williams, 2001). Thus Fe is a potential limiting nutrient for both photoautotrophic growth

and N2 fixation in the modern Fe-depleted oceans (Sunda and Huntsman, 1995;

Falkowski, 1997; Kutska et al., 2002). Fe requirements of N2-fixing phytoplankton have + been estimated to be from 2 to 100 times higher than for organisms assimilating NH4 (Raven, 1988; Sañudo-Wilhelmy et al., 2001; Kustka et al., 2003; Tuit et al., 2004). A number of studies have shown that Fe can regulate growth and nitrogenase activity in Trichodesmium in both the laboratory and the field (Rueter, 1988; Rueter et al., 1990; Paerl et al., 1994; Berman-Frank et al., 2001). A smaller quantity of molybdenum is required for synthesis of the primary nitrogenase enzyme, with 2 moles of Mo per molecule of the enzyme complex. N2 fixation requires a high concentration of intracellular nitrogenase, and this enzyme can account for up to 20% of the total cell protein in diazotrophs under nitrogen-limited conditions (Martinez-Argudo et al., 2004). As a result, some phytoplankton show a

significant increase in cellular Mo concentrations when fixing N2 (Tuit et al., 2004). It

has even been suggested that enhanced cellular [Mo] could be used to recognize N2

63 fixation by uncultured prokaryotes in the environment (Zerkle et al., 2005). Mo has been

proposed to limit modern N2 fixation primarily through the competitive inhibition of molybdate uptake by high concentrations of sulfate in seawater (Howarth and Cole, 1985; Cole et al., 1993; Marino et al., 2003), but it is unclear how important this competition is in nature (Paerl et al., 1987; Paulsen et al., 1991). Molybdenum is directly involved in regulating the expression of the alternate nitrogenase enzymes in Azotobacter vinelandii (Premakumar et al., 1984; Jacobson et al., 1986; Joerger and Bishop, 1988; Jacobitz and Bishop, 1992) and Anabaena variabilis (Thiel, 1993), and has been shown to stimulate

growth and N2 fixation in methanogens (Lobo and Zinder, 1988; Kessler et al., 1997). A. vinelandii can additionally extract Mo from silicates using a high-affinity ligand during diazotrophic growth under Mo-depleted conditions (Liermann et al., 2005).

Due to the strong environmental controls listed above, the development of N2 fixation and the nitrogenase enzyme has been tightly linked to the evolution of the Earth’s surface through geologic time. Nitrogen fixation likely commenced early in Earth’s history, before the initial rise in atmospheric oxygen (Broda and Peschek, 1983). The nitrogenase enzyme is found in diverse microorganisms distributed across both prokaryotic domains (Young, 1992; Zehr et al., 2000; Zehr and Turner, 2001), and shows a high degree of conservation of structure, function, and amino acid sequence (Dean and Jacobson, 1992), indicating an ancient origin. Phylogenetic analyses also support the origin of nitrogenase from a single common ancestor, possibly predating the emergence of the Bacteria and Archaea (Broda and Peschek, 1983; Fani et al., 2000; Raymond et al.,

2004). However these analyses cannot rule out the possibility that N2 fixation might have evolved later and been subsequently distributed via horizontal gene transfer (e.g., Raymond et al., 2004).

The timing of the initial evolution of N2 fixation is debated, and depends on when the biological requirement for fixed nitrogen exceeded its abiotic production on the early Earth. In an anoxic atmosphere, the primary abiotic source of fixed nitrogen would have been the production of nitric oxide (NO) by lightning, requiring oxygen atoms obtained

via the splitting of CO2 and H2O (Kasting and Siefert, 2001). In a mildly reducing

atmosphere dominated by CH4 and CO2 (Pavlov et al., 2000; Catling et al., 2001; Kasting and Siefert, 2001, 2002), NO produced by lighting could have been a significant source

64 of fixed N, and the primitive nitrogenase might have evolved as an Fe-dependent

respiratory enzyme for anaerobic heterotrophs utilizing N2 as an electron sink (Berman-

Frank et al., 2001). High CH4 concentrations could have limited the production of NO by lightning around 2.2 billion years ago (Ga) (Navarro-Gonzalez et al., 2001). However,

photochemical models predict that atmospheres containing N2 and CH4 can also produce significant HCN (hydrogen cyanide) that is then hydrolysed in solution to form + bioavailable NH4 (Zahnle, 1986; Kasting and Siefert, 2001). Under this scenario, nitrogenase could have originally evolved as a “detoxyase”, used as protection against cyanide in the ancient ocean (Postgate and Eady, 1988; Fani et al., 2000). With a further rise in O2, decreasing availability of free ammonia and cyanides could have caused the enzyme to refine its specificity of both substrate and metal cofactor (Fani et al., 2000).

Alternatively, if concentrations of atmospheric CO2 were low from the beginning of

Earth’s history, this would have limited the amount of NO formation from N2 and CO2, causing a crisis in fixed N that could have selected for N2 fixation very early in life, as early as ~3.5 Ga before present (Kasting and Siefert, 2001). The rise in atmospheric oxygen between 2.4 and 2.2 Ga (Rye and Holland, 1998; Farquhar et al., 2000; Bekker et al., 2004) and the resulting change in ocean redox chemistry would have altered the speciation of nitrogen compounds, as well as other nutrients, including phosphate and bioactive metal cofactors. The deposition of extensive banded iron formation in the Archean (3.8 to 2.5 Ga) testifies to prolonged periods of Fe- enriched ocean bottom water (Holland, 1984; Beukes and Klein, 1990). Under such

circumstances Fe would be abundantly available to N2-fixers. Mo, on the other hand, 2- would be limiting, as it is most available as MoO4 , which would be difficult to generate under low Archean levels of atmospheric oxygen (Farquhar et al., 2000; Canfield, 2005). Additionally, phosphate availability may have limited primary production during this time due to adsorption of phosphate by iron-rich minerals (Bjerrum and Canfield, 2002). With rising atmospheric oxygen concentrations, the Fe-enriched Archean ocean gave way to an ocean containing at least partially sulfidic bottom waters by about 1.9 Ga (Canfield, 1998; Shen et al., 2002; Shen et al., 2003; Arnold et al., 2004; Poulton et al., 2004; Kah et al., 2004; Rouxel et al., 2005; Brocks et al., 2005). This ended the deposition of banded iron formations, alleviating the primary phosphate sink. At the same time, bioavailable

65 ammonia would be oxidized to nitrite and nitrate, and nitrogen removal due to denitrification at the oxic/anoxic transition could have depleted fixed nitrogen in the ocean (Fennel et al., 2005). Additionally, the speciation of redox-sensitive metals would have been dramatically altered. In sulfidic waters, bioactive trace metals including Mo and Fe are readily removed to the sediments via reduction to insoluble sulfides and scavenging by organic ligands and other transition metals (Helz et al., 1996; Lewis and Landing, 1991; Malcolm, 1985). Ancient seawater metal concentrations are not well constrained; however, initial models suggest that Fe availability would have been greatly reduced under sulfidic conditions (Berman-Frank et al., 2003; Saito et al., 2003), and that Mo concentrations in the surface ocean would have remained less than 10% of modern marine levels if at least 10% of the seafloor was covered by sulfidic waters (Anbar and Knoll, 2002). The removal of Fe and Mo with the onset of mid-Proterozoic sulfidic deep waters

could have limited global N2 fixation, contributing to a period of exceptional nitrogen stress on the biosphere, and possibly constraining rates of global primary production (Anbar and Knoll, 2002). With complete oxygenation of the oceans in the late Neoproterozoic (Canfield and Teske, 1996), [Mo] would have risen, possibly allowing more efficient fixation of nitrogen by the Fe-Mo nitrogenase in prokaryotes (as well as assimilation of newly abundant nitrate by the Mo-bearing nitrate reductase enzyme in eukaryotes), relieving nitrogen stress and allowing eukaryotic diversification (Anbar and Knoll, 2002). Thus, the history of Fe and Mo availability could have had a profound effect on the activity of the biosphere, possibly influencing the pace of biological evolution. To test this history of nitrogen fixation activity and possible consequences for primary production, we examined the effect of varying metal concentrations on metabolic rates in cyanobacteria cultures. Previous metal-limitation experiments have focused primarily on the effects of Fe-limitation on metabolic rates in Trichodesmium spp. (Rueter, 1988; Rueter et al., 1990; Paerl et al., 1994; Berman-Frank et al., 2001), and on the role of Mo and V in controlling expression of alternate nitrogenase enzymes (e.g., Jacobson et al., 1986; Thiel et al., 1993). Our study extends this previous work to examine the effects of both Fe- and Mo-limitation (and co-limitation) on N2 fixation and

66 growth rates in a representative organism, and interprets the results in light of the development of ancient ocean redox chemistry. As an initial model organism, we chose Anabaena variabilis strain ATCC 29413, a freshwater heterocystous cyanobacterium. A. variabilis has a sequenced genome and a well-characterized set of three nitrogenase enzymes, including the primary Fe-Mo nitrogenase that functions identically to other diazotrophs, a second Fe-Mo nitrogenase that functions in vegetative cells only under anoxic conditions, and an alternate Fe-V nitrogenase that is regulated by the presence or absence of Mo (Thiel, 1993; Thiel et al., 1995, 1997). This organism also has a robust physiology and a laboratory doubling time 3 to 5 times faster than marine cyanobacteria, allowing metabolic rates to be measured and compared between greater numbers of experiments. MATERIALS AND METHODS Culturing conditions We grew A. variabilis str. ATCC 29413 in a modified ATCC 819, nitrogen-free media (media preparation explained below). We included 10% fructose as a carbon source to stimulate growth and N2 fixation (Haury and Spiller, 1981). Cultures were prepared using standard aseptic techniques, in acid-washed polycarbonate vessels under constant light and optimal pH (7.1) and temperature (33°C). All experiments were conducted on a shaker table under aerobic conditions in 100 mL batch cultures. Media preparation Stock ATCC 819 media was prepared without addition of Fe or trace metal solutions. Initial phosphate and sulfate concentrations were constant in all experiments (0.2 mM and 0.3 mM, respectively). Metal impurities were removed from the base media by a chelex ion-exchange column (Morel et al., 1979). Media blanks resulted in [Fe] and [Mo] below detection by inductively-coupled plasma mass spectrometry (ICP-MS; method detection limit of 0.4 nM for Fe, 0.01 nM for Mo) and [V] < 10 nM. We then added a modified version of the trace metal mix A5, without addition of Mo or V.

Separate stock solutions of Fe-citrate (2.4 mM Fe), and Na2MoO4 x 2H2O (1M Mo), were prepared in 0.5% ultrapure nitric acid (total of ≤ 0.001% per liter final media) and analyzed by ICP-MS. Appropriate aliquots of Fe and Mo stock solutions were then added to the media to achieve desired media metal concentrations. One mg/L EDTA was

67 included to keep metals in solution. Cultures examined with standard optical microscopy did not contain any noticeable precipitates. We therefore assumed that filtered media metal concentrations were representative of unfiltered media metal concentrations. Filtered samples from select experiments were screened for contamination by ICP-MS after the experiments were terminated. However, these analyses measured the media metal concentrations at stationary growth phase, and do not necessarily reflect the metal concentration during late linear growth phase when nitrogenase assays were performed (e.g., Figure 2.1; see discussion of growth below). Therefore our results are plotted

against calculated media metal concentrations before inoculation, denoted [Mo]i and

[Fe]i. Cell pellets were then spun down, rinsed, and resuspended in deionized H2O before inoculation to prevent further addition of metals from the inoculant. Metal-limited experiments are defined here as experiments conducted in media with [Mo]i or [Fe]i less than that measured in media prepared using the subscribed ATCC

819 recipe (referred to as “optimal”, [Mo]i = 1700 nM and [Fe]i = 24,000 nM; Table

2.1). Fe-limited experiments were conducted at [Mo]i of 100 nM and [Fe]i of 2 to 2400

nM; Mo-limited experiments were conducted at [Fe]i of 2400 nM and [Mo]i of 0.1 to 100 nM (Table 2.1). Included in these experiments were cultures grown in Fe and Mo concentrations replicating those estimated for an Fe-enriched anoxic Archean ocean, a sulfidic Proterozoic ocean, and a fully oxygenated Phanerozoic ocean. We assumed Fe concentrations in the micromolar range for the Archean and the nanomolar range for the mid-Proterozoic (Holland 1973; Berman-Frank et al., 2003; Saito et al., 2003; Canfield, 2005). “Phanerozoic” experiments were run at similar nanomolar Fe concentrations (2 nM). This is at the high end of bioavailable [Fe] measured in the modern oceans (Gordon et al., 1982; Landing and Bruland, 1987; Martin and Gordon, 1988; Bruland et al., 1991;

Donat and Bruland, 1995); however, experiments conducted at lower [Fe]i resulted in

very little growth and N2 fixation rates below detection by our methods. Ancient [Mo] values were after those modeled by Anbar and Knoll (2002; 1 and 10 nM), and Phanerozoic [Mo] values were after those measured in modern oceans (100 nM; Collier, 1985; Morford and Emerson, 1999; Emerson and Huested, 1991).

68 Growth analyses All experiments were performed on duplicate or triplicate cultures. Growth was tracked by optical density measurements at 600nm using a UV-VIS spectrophotometer and calibrated to cell counts under light microscopy. Reported growth rates represent linear regressions of growth during linear growth phase (e.g., Figure 2.1). Initial experiments indicated that N2 fixation rates can vary with growth phase (Appendix B), so all experiments were continued through stationary growth to ensure that nitrogenase activity was measured during late linear growth phase (e.g., Figure 2.1). Since phosphate was available at non-limiting concentrations (~0.2 mM) (Thiel, 1988) and phosphate uptake is not known to be dependent on [Fe] or [Mo], we examined carbon to nitrogen ratios of the cyanobacterial biomass as a proxy for ecological stoichiometry and thus general nutrient status (e.g., Sterner and Elser, 2002). Biomass C:N was analyzed using a Costech/Thermo-Finnigan Delta Plus XP coupled elemental analyzer, continuous flow, isotope ratio mass spectrometer (EA-CF-IRMS) in the Isotope Biogeochemistry Lab at The Pennsylvania State University. Acetylene-ethylene reduction assays Nitrogenase activity was measured on duplicate analyses of each culture during late linear growth phase (e.g., Figure 2.1) using the standard acetylene-ethylene technique (Dilworth, 1967; Schollhorn and Burris, 1967). For each acetylene reduction assay, a 5 mL subsample of culture was placed in a 15-mL serum vial fitted with rubber stopper and stir bar. The assay was initiated by withdrawing 1 mL of air from the vial and injecting 1 mL (10% headspace) of acetylene, generated by adding calcium carbide to water. Cultures were stirred vigorously, and 1 mL of headspace was withdrawn every 10 minutes for 50 minutes and analyzed for ethylene concentration using a gas chromatograph equipped with flame ionization detector and Haysep T column. Ethylene production was converted to N2 production by assuming a standard 4:1 ratio of moles N2 fixed per mole acetylene reduced, and standardized to cell counts. Nitrogen fixation rates given are the mean of rates calculated from both the first 30 minutes and 40 minutes of ethylene production. Cells grown with fixed nitrogen (~18 mM NaNO3) did not produce ethylene in excess of that measured in blanks (data not shown).

69 RESULTS Our results show that cyanobacterial growth is dramatically affected by Fe concentration in the media. A rapid drop to around 33% of the optimal growth rate is

observed when [Fe]i is decreased from 24,000 nM to 240 nM (Table 2.1; Figure 2.1, 2.2).

Cellular nitrogen fixation rates were little influenced by [Fe]i in this concentration range,

but dropped to about 60% of optimal rates below 240 nM. Both growth and N2 fixation rates drop below a threshold Fe concentration of around 240 nM, corresponding to a shift in C:N ratios from values lower to greater than the Redfield ratio observed for phytoplankton in the modern oligotrophic (nutrient-depleted) ocean (106:16 = 6.625, Redfield, 1963; Table 2.1; Figure 2.2). Experiments with no added Fe yielded growth

rates around 1-2% of optimal and N2 fixation rates below detection by the acetylene reduction method.

Growth rates also dropped with decreasing initial Mo concentration ([Mo]i), but less dramatically than with Fe limitation (Table 2.1; Figure 2.3, 2.4). Even at very low

[Mo]i (0.1 nM), relative growth rates never dropped below ~33% of optimal. Nitrogen

fixation rates, on the other hand, remained constant with decreasing [Mo]i down to 5 nM, and then rapidly decreased to ~20% of optimal between 1 and 5 nM. Both growth and N2 fixation rates drop below a threshold Mo concentration of around 5 nM, although no significant corresponding increase in C:N is observed (Table 2.1; Figure 2.4). In all of our Mo-limited experiments C:N remains below Redfield proportions (5 to 6). Two experiments were run under conditions of both Mo- and Fe-limitation (Table 2.1). Both experiments grew very slowly (growth rates 10% and 20% of optimal) and demonstrated moderate N2 fixation rates (40% and 50% of optimal). The one biomass sample analyzed from a metal co-limited experiment had the highest C:N ratio of all samples measured (~8).

Relatively low N2 fixation rates were measured in “Archean” experiments (high Fe, low Mo), as in other Mo-limited experiments (Table 2.1). Growth rates, although ~33% of optimal, were more than twice those of the other ocean conditions examined. Consistent with all of the Mo-limited experiments (Figure 2.4), the Archean conditions resulted in biomass C:N ratios (C:N = 5.4) significantly lower than modern Redfield

ratios. “Proterozoic” experiments (low Fe, moderate Mo), in contrast, showed cellular N2

70 fixation rates twice those of Archean conditions, accompanied by a dramatic drop in growth rate (Figure 2.5). With a further ten-fold increase in [Mo] (low Fe and high Mo, as in the Phanerozoic oceans), both growth rates and cellular N2 fixation rates remained relatively constant (Figure 2.5). These experiments also produced biomass with similar C:N ratios to those of mid-Proterozoic experiments (Table 2.1; C:N = 7.9 for mid- Proterozoic experiments, C:N = 7.3 for modern experiments), indicating a similar nutrient status. DISCUSSION Metal limitation

Decreasing [Fe] in our experiments led to somewhat lowered rates of cellular N2 fixation; however, the primary importance of Fe seems to be in generally maintaining cyanobacterial growth (and thus nutrient status). Fe-limitation dramatically decreases growth in A. variabilis, similar to earlier studies with Trichodesmium (Rueter, 1988; Rueter et al., 1990; Paerl et al., 1994; Berman-Frank et al., 2001). This strong Fe control on growth is consistent with the use of Fe/S proteins in the majority of redox enzymes involved in respiration and photosynthesis, including cytochrome oxidases, rubredoxin and ferredoxin electron transfer proteins (Frausto da Silva and Williams, 2001; Ferreira and Straus, 1994). The ability of these organisms to fix N2 even at very low [Fe]i (~2 nM)

is also consistent with recent evidence that Fe requirements of N2 fixers are only slightly

higher than phytoplankton growing on NH4 or NO3 (Sañudo-Wilhelmy et al., 2001; Flynn and Hipkin, 1999). Decreasing [Fe] also led to a switch from low to high C:N ratios. Our

results suggest a decoupling of growth rates and cellular N2 fixation rates in A. variabilis,

with biomass C:N more tightly coupled to growth rates than to N2 fixation rates. Low cellular C:N at high growth rates presumably reflects an increase in the production of N- rich biomolecules, such as protein and RNA, during eutrophic (nutrient-rich) growth (Gourse et al., 1996). Diazotrophic cyanobacteria can also accumulate nitrogen in the form of the storage protein cyanophycin (Simon, 1971). The pattern of cyanophycin production in cyanobacteria differs with nitrogen source (Kolodny et al., 2006) and is temporally and spatially regulated by nitrogenase activity over a 12-hour cycle of light and dark (Li et al., 2001). It is unknown how growth rates directly regulate cyanophycin production over longer time scales. Maximum cyanophycin production is induced

71 primarily by phosphate starvation in the γ-Proteobacteria Acinetobacter calcoaceticus (Elbahloul and Steinbuchel, 2006; Elbahloul et al., 2005); however, our experiments were not conducted under phosphate-starved conditions so it is unlikely that this directly contributed to the lowered C:N ratios.

Molybdenum, on the other hand, has a dramatic effect on cellular N2 fixation rates without significantly affecting overall growth and nutrient status. This supports the results obtained by Zahalak et al. (2004), suggesting that growth in A. variabilis was not

significantly hindered at [Mo]i of 10 nM. In addition to its use in nitrogenase, Mo catalyzes other oxygen atom transfers or two-electron reactions important in the nitrogen and sulfur cycles (e.g., nitrate reductase and sulfate reductase; Hille, 1996; Kisker, 1997; Frausto da Silva and Williams, 2001); however, it is not used in any proteins or enzymes directly involved in respiration or photosynthesis. Therefore Mo regulates growth in

cyanobacteria only indirectly, through its importance in N2 fixation. Optimal N2 fixation

rates are maintained down to [Mo]i of 5 nM, below which the ability of the organisms to

fix N2 dramatically plummets. Since only Mo concentrations were changed between Mo- limited experiments (Figure 2.3), the trend in N2 fixation rates with [Mo]i suggests either a significant input from the Fe-V nitrogenase at lower Mo levels, or that the Fe-Mo nitrogenase in this organism is fully functional at [Mo] below 10 nM. Thiel and others (1993) have shown that the expression of the Fe-V nitrogenase in A. variabilis is regulated by Mo concentrations, and can be repressed by Mo concentrations as low as 1 nM. No vanadium was added to our experiments, and blanks showed background [V] of < 10 nM, identical to that measured in supernatant after microbial growth (data not shown), indicating no measurable bacterial V uptake. The Fe-V nitrogenase in A.

vinelandii requires orders of magnitude higher [V] for maximum activity (10 µM V2O5; Bishop et al., 1982). Additionally, the activity of the Fe-Mo nitrogenase remains significantly higher than that of the Fe-V nitrogenase at 30°C (Miller and Eady, 1988). We then suggest that the involvement of [V] in regulating nitrogenase activity in our experiments is negligible, and that we are therefore measuring the response of the primary Fe-Mo nitrogenase to metal availability.

72 Implications for N fixation through time There are admittedly limitations in assuming biological uniformitarianism when applying results from laboratory experiments performed on modern organisms to ancient earth ecosystems. The morphology and physiology of marine nitrogen fixers during the Precambrian was possibly as diverse and heterogeneous as in the modern oceans (Zehr et al., 1995, 1998, 2000; Paerl and Zehr, 2000). However, some assumptions can be made

about ancient marine N2 fixers and their enzymes. Cyanobacteria are the primary N2 fixers in modern oceans (e.g. Capone et al., 1997). It is uncertain as to when N2-fixing cyanobacteria emerged, but molecular biomarkers for cyanobacteria (2-methylhopanes) are found in 2.7 Ga shales from Western Australia (Brocks et al., 1999). Additionally, 2.7 Ga stromatolites from Australia have been interpreted as accreted by oxygenic phototrophs in a sulfate-limited environment (Buick, 1992), providing further evidence for the existence of cyanobacteria at that time. As cyanobacteria would have been the most important primary producers and consumers of nutrients, it is reasonable that these organisms would have been in place as the dominant N2 fixers by the late Archean. Therefore, experimental observations on the effects of metal limitation on diverse groups of cyanobacteria (e.g., Berman-Frank et al., 2001; this study) are essential to resolving the history of the Precambrian nitrogen cycle. Anoxygenic phototrophs could have

significantly contributed to N2 fixation on the early earth as well, especially if sulfidic conditions existed in wide areas of the photic zone (e.g., Brocks et al., 2005). Diazotrophic cyanobacteria differ from each other primarily in morphology and in strategies for dealing with oxygen sensitivity. Anabaena variabilis is a filamentous heterocystous cyanobacterium. Free-living heterocystous cyanobacteria are relatively rare in the modern oceans, although phylogenetic and paleontological studies suggest that heterocysts had evolved by at least 2.1 Ga (Tomitani et al., 2006). ATCC 29413 is also a freshwater cyanobacterial strain. Fe and Mo availability can differ between freshwater and marine systems due to environmental parameters such as pH, ionic strength, and possibly sulfate concentrations (e.g., Marino et al., 1990; Marino et al., 2003), although freshwater and marine cyanobacteria have developed similar strategies for adapting to fluctuations in metal availability (e.g. Ferreira and Straus, 1994; Erdner et al., 1999). A. variabilis also has a well-characterized molybdate transport system that is similar to those

73 of other diazotrophic microorganisms studied (e.g., Rhodobacter capsulatus and Azotobacter vinelandii) (Thiel et al., 2002). An analogous strain (PCC 7937) is known to be capable of producing a high affinity siderophore for Fe scavenging (Kerry et al., 1988), but siderophores are useful only when Fe is present in an insoluble form, i.e. as an Fe-oxide mineral (e.g., Neilands, 1981). Freshwater and marine phytoplankton can also have different N and P requirements, leading to different elemental stoichiometries. Our measurements indicate that A. variabilis has C:N stoichiometry similar to modern marine phytoplankton, primarily governed by growth rates, as discussed above. Furthermore, the nitrogenase enzyme is presumably ancient, and shows a high degree of conservation in structure, function, and amino acid sequence (Dean and Jacobson, 1992). All cyanobacterial nifH genes sequenced (encoding for the dinitrogenase reductase protein in nitrogenase), including that of A. variabilis, cluster together in phylogenetic analyses (Zehr et al., 1997). Therefore, it is reasonable to assume that the Fe-Mo nitrogenase in A. variabilis will respond to metal limitation in a manner similar to that of other cyanobacterial diazotrophs, modern and ancient. If we consider A. variabilis and the corresponding Fe-Mo nitrogenase enzyme as

a conserved model for early N2 fixation, we can then examine our results in light of the development of ocean redox chemistry (Figure 2.5). The trends presented in Figure 2.5 imply that the ten-fold increase in [Mo] estimated to occur with increasing oxidation of the atmosphere during the early Proterozoic could actually have stimulated nitrogen fixation rates to near modern levels. Whether reduced rates of N2 fixation at low [Mo] would have imposed N limitation during the Archean, however, is uncertain. In a largely

anoxic atmosphere and ocean, once N2 was reduced it would remain present as the + ammonium ion (NH4 ), with limited production of nitrate from ammonia oxidation. Thus, the loss of fixed nitrogen from the oceans due to denitrification would also have been limited (Falkowski, 1997). In addition, phosphorus, not nitrogen, may have been the limiting nutrient during the Archean (Bjerrum and Canfield, 2002). By contrast, in the early and middle Proterozoic with a sulfidic ocean, the iron oxide sink for phosphorus would have been substantially decreased, as evidenced by the disappearance of banded iron formations at this time (Isley and Abbott, 1999). Ancient phosphate concentrations are difficult to constrain, but it is reasonable to assume that the

74 marine phosphate pool would have increased due to the removal of this Fe-oxide sink. The availability of reduced nitrogen, on the other hand, could have dramatically decreased during this time. In the presence of molecular oxygen, all ammonia produced by N2 fixation would be rapidly oxidized to nitrate, and nitrogen removal due to denitrification at the oxic/anoxic transition could have depleted fixed nitrogen in the ocean (Fennel et al., 2005). Nitrogenase activity in cyanobacteria is also markedly inhibited by oxygen, with maximum N2 fixation rates in cultures of Trichodesmium occurring at oxygen concentrations of 2 to 5% of present atmospheric levels (PAL) (Berman-Frank et al., 2005). Thus the estimated increase of atmospheric oxygen to 5 to

18% of PAL (Canfield and Teske, 1996) could also have contributed to limiting N2 fixation simply by increased inhibition of the nitrogenase enzyme. The trends we present here suggest that with a ten-fold increase in oceanic [Mo] supplied by oxidative

weathering, enhanced N2 fixation rates in surface waters could have at least partially compensated for the increased loss of available nitrogen, if sufficient strategies for

dealing with O2 inhibition had evolved by this time. Anoxygenic phototrophic sulfur

bacteria could have further contributed to N2 fixation if sulfidic conditions extended into the photic zone (Kump et al., 2005). Our results do not preclude the involvement of the alternate nitrogenase enzymes

in ancient N2 fixation. One scenario for the evolution of N2 fixation proposes that the ancestral nitrogenase enzyme was less specific with respect to its metal cofactor, and thus able to respond to the fluctuations in metal availability (Normand and Bousquet, 1989; Anbar and Knoll, 2002). As explained above, Fe was much more available in the Archean oceans. The vanadium concentrations of ancient seawater are unknown, but vanadium sulfides are more soluble than Mo sulfides, so conceivably V could have been more abundant than Mo in a sulfidic ocean (Frausto da Silva and Williams, 2001). This history of metal availability suggests that the Fe-V nitrogenase could have been an evolutionary intermediate between the Fe-Fe and Fe-Mo nitrogenase enzymes, evolving increasing enzyme efficiency along with metal specificity (Anbar and Knoll, 2002). There is some phylogenetic support for this evolutionary sequence (Raymond et al., 2004). However, the alternate nitrogenase enzymes have not been found in any marine diazotrophs studied, and are thus far known only to exist secondarily to the Fe-Mo

75 nitrogenase in nonmarine heterocystous organisms (Bergman et al., 1997). This phylogenetic distribution suggests that if the nitrogenase was present before the divergence of Bacteria and Archaea, then either the alternate enzymes have been lost and the Fe-Mo enzyme has evolved independently in multiple lineages, or the most primitive nitrogenase was Mo-dependent (Raymond et al., 2004). Alternately, all three nitrogenase enzymes could have evolved in methanogens and subsequently transferred to bacteria via horizontal gene transfer (Raymond et al., 2004). Regardless, the results we present here suggest that the Fe-Mo nitrogenase is fully functional at even moderate [Mo], and thus the selection pressure to evolve the more efficient Fe-Mo nitrogenase would have been in place during the mid-Proterozoic. Little is known about the effects of metal limitation on the alternate nitrogenase enzymes, and this will be an important line of future inquiry.

The results of our experiments do not support metal limitation of N2 fixation as contributing to a period of exceptional nitrogen stress during the mid-Proterozoic. However, this result is highly dependent on the [Mo] assumed for the mid-Proterozoic

oceans. In our experiments where Mo alone is limiting, a dramatic change in N2 fixation rates is observed when [Mo] is within one order of magnitude higher than that estimated for the Precambrian oceans (between 1 nM and 5 nM Mo; Figure 2.3). This tight

coupling between [Mo] and N2 fixation rates at low [Mo] suggests that modest variations in [Mo] could have had major effects on the nitrogen cycle during this time. Bioactive metals are also important in other parts of the nitrogen cycle, including eukaryotic nitrate assimilation (Anbar and Knoll, 2002). The effects of metal limitation on this and other nitrogen metabolisms (including denitrification, which utilizes enzymes requiring Fe, Cu, and Mo; Averill, 1996), must be considered to form a complete picture of how variations in metal availability could have acted on the global nitrogen cycle. CONCLUSIONS

Our culture studies of A. variabilis reveal low rates of N2 fixation in high Fe and low Mo concentrations estimated for an anoxic Fe-enriched Archean ocean. With decreased [Fe] and increased [Mo] representing metal concentrations in a sulfidic

Proterozoic ocean, N2 fixation, growth, and biomass C:N were similar to those observed for near-modern marine metal concentrations. These results suggest that an initial rise in atmospheric oxygen could actually have enhanced nitrogen fixation rates to modern

76 marine levels, provided that phosphate was available and nitrogenase was not markedly

inhibited by rising O2. The tight coupling between [Mo] and cyanobacterial N2 fixation rates at Mo concentrations near that of the estimated Precambrian oceans, however, indicates that modest variations in [Mo] during this time could have dramatically affected the nitrogen cycle. Further work is needed both in the geochemical realm to better constrain the concentrations of trace metals and phosphorus in the Precambrian oceans, and in the biological realm to continue investigations into the effects of metal-limitation

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88 Table 2.1. Growth rates, N2 fixation rates, and C:N measured for experiments conducted at various initial media metal concentrations.

[Fe]i [Mo]i Growth N2 fixation (nM) (nM) rate x 107 RGR rate x 10-2 RNR C:N Optimal 24,000 1700 3.0 100% 10 100% 5.6 Fe-limited 2400 100 2.0 67% 10 100% 5.9 240 100 1.0 33% 9 90% 5.9 120 100 0.8 27% 6 60% 7.5 24 100 0.7 23% 6 60% 7.0 12 100 0.6 20% 6 60% 7.2 2 100 0.5 17% 4 40% 7.3 Phan 0 100 0.04 1% BD BD 6.3 Mo-limited 2400 100 2.0 67% 10 100% 5.9 2400 10 2 67% 10 100% 6.1 2400 5 2 67% 10 100% 5.5 2400 2 1.5 50% 7 70% 5.0 2400 1 1 33% 2 20% 5.9 Arch 2400 0.5 1 33% 2 20% 5.6 2400 0.1 1 33% 1 10% 5.7 Fe/Mo-limited 24 10 0.6 20% 5 50% ND 2 10 0.3 10% 4 40% 7.9 Prot -1 -1 -1 -1 Growth rates given in cells mL day , N2 fixation rates given in nmoles cell minute , and C:N ratios are atomic. Relative growth rates (RGR) and relative N2 fixation rates (RNR) are expressed as % of rates measured in optimal experiments. Experiments conducted in metal concentrations replicating those estimated for the ancient oceans are indicated by “Arch” (Archean), “Prot” (mid-Proterozoic), and “Phan” (Phanerozoic).

89 3.0 (a) Growth - Fe-limitation late linear 2.5

2.0 optimal 2400 nM Fe linear 240 nM Fe growth 1.5 24 nM Fe 2.4 nM Fe OD (600 nm) 1.0

0.5

0 1 2 3 4 5 6 Days

14 (b) N2 fixation - Fe-limitation

12 -4 10 x 10 -1 8

6

4 nmoles cell

2

0 10 20 30 40 50 Time (minutes)

Figure 2.1. (a) Partial growth curves for representative Fe-limitation experiments, run at [Mo]i = 100 nM and [Fe]i as noted in legend (except "optimal", see Table 2.1). Also shown are examples of linear and late linear growth modes. (b) Ethylene production (nmoles ethylene normalized per cell) measured during late linear growth mode in the corresponding experiments.

90 8 oligotrophic growth

7

C:N RR eutrophic 6 growth

5

100

80 RGR (%) 60

40

RNR (%) 20

1 10 100 1000 10000 100000

[Fe]i (nM) Figure 2.2. Relative growth rates (RGR, in % of optimal) and relative nitrogen fixation rates (RNR, in % of optimal) for Fe-limitation experiments, along with C:N ratios of the biomass produced. The mean val- ues are shown as symbols, with bars indicating any range in the values measured. "RR" denotes the Redfield ratio for modern marine phytoplankton (C:N = 106:16 = 6.625, Redfield, 1963). "Oligotrophic" and "eutrophic" refer to low nutrient and high nutrient growth, respectively. Data are given in Table 2.1.

91 3.0 (a) Growth - Mo-limitation

2.5

2.0

optimal 1.5 10 nM Mo

OD (600 nm) 2 nM Mo 1.0 1 nM Mo

0.5

0 1 2 3 4 5 6

14 (b) N2 fixation - Mo-limitation 12 -4 10 x 10 -1 8

6

4 nmoles cell

2

0 10 20 30 40 50 Time (minutes)

Figure 2.3. (a) Partial growth curves for representative Mo-limitation experiments, run at [Fe]i = 2400 nM and [Mo]i as noted in legend (except "optimal", see Table 2.1). (b) Ethylene production (nmoles ethylene normalized per cell) measured during late linear growth mode in the corresponding experiments.

92 8

7 RR C:N 6

5

100

80

RGR (%) 60

40

20 RNR (%)

0.1 1 10 100 1000 10000

[Mo]i (nM) Figure 2.4. Relative growth rates (RGR, in % of optimal) and relative nitrogen fixation rates (RNR, in % of optimal) for Mo-limitation experiments, along with C:N ratios of the biomass produced. Abbreviations and symbols are as in Figure 2.2. Data are given in Table 2.1.

93 low Mo mod Mo high Mo 5 ) -2 high Fe low Fe low Fe

-1 1.2

4 Growth rate x 10 1.0 (cells mL minute -1 3 0.8 -1

0.6 day 2 fixation rate x 10 -1 2 ) (nmoles cell

N 0.4

7 1 0.2 Arch PhanProt

3 2 1 0 Time (Ga)

Figure 2.5. Experimentally-produced trends in cyanobacterial growth rates and N2 fixation rates with varying [Fe]i and [Mo]i replicating one scenario for ocean metal concentrations in the Archean (Arch), Proterozoic (Prot), and Phanerozoic (Phan). Data are given in Table 2.1.

94 Chapter 3

15 Effect of Metal Availability on δ Nbiomass of N2-fixing Cyanobacteria

ABSTRACT Negative δ15N values are found almost exclusively in Archean sediments from before 2.5 Ga, and in some Mesozoic and Cenozoic sediments deposited during proposed periods of increased ocean anoxia. These isotopic excursions have been attributed to the 15 production of marine organic matter with low δ N by enhanced N2 fixation during

periods of fluctuating nutrient dynamics. N2-fixing organisms in the modern oceans

generally produce biomass with little isotopic discrimination from source N2, and it is not understood how negative δ15N compositions are produced in the field. Here, we show

that N2-fixing cyanobacteria grown in Fe-enriched media ([Fe] ≥ 50 nM) produce biomass up to 3‰ lower in δ15N than identical organisms grown in the Fe-limited conditions of modern oceans ([Fe] < 50 nM). We suggest that enhanced Fe availability during periods of widespread ocean anoxia could contribute to the formation organic 15 matter with negative δ N by N2 fixation. INTRODUCTION

The global marine nitrogen cycle is controlled by a balance between N2 fixation (diazotrophy), which produces organic N with little to no isotopic discrimination relative 15 to source N2 (δ N ≤ 0‰) (Delwiche and Steyn, 1970; Minagawa and Wada, 1986,

Macko et al., 1987), and denitrification, which returns light N2 to the atmosphere, leaving - 15 the marine NO3 pool relatively enriched in N (Wellman et al., 1968; Delwiche and Steyn, 1970; Miyake and Wada, 1971). Denitrification is the dominant process in the modern oceans, producing nitrate with an average δ15N of around +4.7‰ (Sigman et al., 1997; Altabet et al., 1999). As a result, modern sedimentary organic matter exhibits average δ15N values of +6 to +7‰ (Peters et al., 1978; Sweeney et al., 1978). Nitrogen isotope signatures of organic matter can be preserved in the rock record directly as kerogen (e.g., Beaumont and Roberts, 1999), or indirectly as ammonium + (NH4 ) structurally incorporated into clay minerals (e.g., Itihara and Suwa, 1985). In the rock record, negative δ15N values (down to -6.2‰) have been found almost exclusively in Archean sediments from before 2.5 Ga, and in Mesozoic and Cenozoic sediments

95 deposited during periods of widespread oceanic anoxia (for a recent compilation, see Papineau et al., 2005). For example, bulk δ15N values from organic matter-rich sediments deposited during the Cretaceous Oceanic Anoxic Event II (Rau et al., 1987; Kuypers et al., 2004) and black shales deposited under anoxic conditions in the mid-late Devonian (Levman and von Bitter, 2002) reach values as low as -3‰. Negative δ15N values in Precambrian organic matter are generally interpreted as reflecting either the dominance

of marine N2 fixation at low atmospheric oxygen prior to the evolution of denitrification (Beaumont and Roberts, 1999; Papineau et al., 2005), or chemolithoautotrophic bacteria + using inorganic NH4 derived from hydrothermal fluids (e.g., Pinti et al., 2001). Similarly, negative δ15N excursions in Phanerozoic sediments are interpreted to represent

a temporary enhancement of isotopic input from N2 fixation due to more complete denitrification and/or enhanced phosphate availability during periods of widespread anoxia (Rigby and Batts, 1986; Rau et al., 1987; Calvert et al., 1992; Jenkyns et al., 2001; Sephton et al., 2002; Levman and von Bitter, 2002; Kuypers et al., 2004; Junium and Arthur, submitted).

Although N2 fixation is commonly invoked to explain the occurrence of negative δ15N in the rock record, the mechanism of isotopic discrimination during diazotrophy is not thoroughly understood. Previous work has demonstrated that in laboratory settings 15 biomass δ N produced by N2-fixing organisms is independent of phylogeny, growth

phase, carbon source, O2 protective mechanism, and nitrogenase activity (Beaumont et al., 2000). Additionally, the average δ15N of diazotrophic cyanobacteria grown in the laboratory is commonly significantly more depleted in 15N than similar species sampled 15 15 in the field (with mean δ Nculture = -2.5‰ versus mean δ Nfield = -0.5‰; Table 3.1; Figure 3.1). Recent studies suggest that availability of the metals Fe and Mo, which make

up the cofactor and other structural elements for the primary enzyme responsible for N2 fixation (nitrogenase), can dramatically affect the growth and N2 fixation ability of cyanobacterial diazotrophs (Rueter, 1988; Rueter et al., 1990; Paerl et al., 1994; Berman- Frank et al., 2001; Chapter 2). Additionally, anoxic conditions associated with negative δ15N excursions in the rock record would have resulted in redox-induced changes in the bioavailability of metals, including Fe and Mo (Holland, 1984; Beukes and Klein, 1990; Anbar and Knoll, 2002). To examine the link between metal availability and the extent of

96 isotopic discrimination between N2 and organic matter produced during N2 fixation, we measured the δ15N of biomass from modern diazotrophic cyanobacteria grown in various media Fe and Mo concentrations. We tested δ15N fractionation under “optimal” or prescribed media metal concentrations (high Fe and high Mo), Fe-limited conditions, and Mo-limited conditions (Table 3.2). Our results suggest that the extent of δ15N

fractionation between cyanobacterial biomass and atmospheric N2 is highly dependent on

Fe availability. Cyanobacteria show less isotopic discrimination during N2 fixation under low [Fe] similar to that measured in modern oceans, and maximum isotopic

discrimination during N2 fixation under high [Fe] similar to that estimated for Archean

oceans. Increased isotopic discrimination during N2 fixation under enhanced Fe availability could contribute to trends in δ15N preserved in the rock record. METHODS We grew Anabaena variabilis strain ATCC 29413, a freshwater nitrogen-fixing

cyanobacterium, in ATCC medium 616 with no NaNO3 and variable added initial Fe and

Mo concentrations ([Fe]i and [Mo]i; Table 3.2). Culturing and media preparation methods are presented in Chapter 2. Growth was tracked by optical density measurements at 600nm using a UV-VIS spectrophotometer and calibrated to cell counts under light microscopy. Cell pellets were harvested during late linear or stationary growth phase, rinsed three times with deionized water, dried, and stored at 4ºC. Elemental and isotopic analyses were performed using a Costech/Thermo-Finnigan Delta Plus XP coupled elemental analyzer, continuous flow, isotope ratio mass spectrometer (EA-CF-IRMS). All analyses were performed in the Isotope Biogeochemistry Lab at The Pennsylvania State University. Dried samples were powdered, weighed, and sealed in silver boats. Boats were combusted in a Costech elemental analyzer at 1020oC with a ‘zero blank’ helium atmosphere autosampler. Isotopic ratios are reported using delta notation relative to

atmospheric N2. Reference gases were calibrated relative to IAEA N3 and N1 isotopic standards. Run-to-run variations in nitrogen isotopes from instrument variability were calibrated using a well-characterized in-house caffeine standard. Standard precision was often better than +/- 0.15 ‰ but is reported as +/- 0.2‰ to reflect known isotopic values of IAEA N standards. Sample sizes were weighed to produce at least 2000 mV nitrogen peak; however, analyses of standards indicate that sample peaks greater than 1200 mV do

97 not deviate statistically from reported standard values. Ammonium analyses were performed on filtered supernatant from sampled cultures by a standard spectrophotometric technique. RESULTS 15 δ N measured in biomass from N2-fixing cyanobacteria grown in different media metal concentrations are listed in Table 3.2. In all Mo-limitation experiments ([Fe]i =

2400 nM, [Mo]i = 0.1 to 100 nM) cyanobacteria produced uniformly depleted biomass with δ15N values as low as -2.0‰ (mean of -0.9‰; Figure 3.2). In contrast, in Fe- 15 limitation experiments ([Mo]i = 100 nM, [Fe]i = 0.01 to 2400 nM) biomass δ N values were near 0‰ to slightly positive, an average of 0.9‰ more enriched than under Mo- limitation (Figure 3.2). In general, biomass δ15N values decrease log linearly with increasing [Fe]i. The greatest isotopic fractionation is produced under “optimal” metal concentrations, with both high initial Mo (~1700 nM) and high initial Fe (24,000 nM). In cultures with optimal metal concentrations, cyanobacterial biomass δ15N is highly depleted, ranging from -2.4‰ to -1.8‰ with an average of -1.9‰ (Figure 3.2). From 15 these optimal conditions, further increasing [Mo]i to ~20 µM results in biomass with δ N values of up to 0.6‰ higher. Additionally, biomass δ15N concentrations exhibit a 2 2 stronger correlation with measured growth rates (R = 0.4) than N2 fixation rates (R = 0.02) for corresponding cultures (rates presented in Chapter 2; Figure 3.3). The strong correlation between biomass δ15N and [Fe] is consistent with previous measurements of N2-fixing cyanobacteria in the laboratory and in the field (Table 3.1; Figures 3.1, 3.4). Previous analyses of cultures of Anabaena spp. grown in prescribed 15 media Fe concentrations (Log [Fe] = 4) show an average δ Nbiomass of -2.3‰ (ranging from -2.9 to -0.6‰; Minagawa and Wada, 1986; Macko et al., 1987; Beaumont et al., 2000). Laboratory cultures of Trichodesmium IMS 101 grown at high Fe concentrations (Log [Fe] = 3) produced biomass similarly low in δ15N (-3.6 to -1.3‰; Carpenter et al., 1997). However, in the modern oceans where metal concentrations are highly varied but generally Fe-limited (e.g., Falkowski et al., 1997), Trichodesmium spp. produce biomass 15 with a wide range of δ N values, from -2.1 to +4.1‰, with an average of -0.5‰ (Wada and Hattori, 1976; Wada, 1980; Macko et al., 1987; Minagawa and Wada, 1984, 1986;

98 Carpenter et al., 1997). These results suggest that in general, diazotrophic cyanobacteria produce biomass with little to no isotopic discrimination (δ15N ≥ -0.5‰) at [Fe] < 50 nM, and biomass depleted in 15N (δ15N = -2.4 to -0.5‰) at [Fe] ≥ 50 nM. DISCUSSION Our results indicate that the extent of δ15N fractionation between cyanobacterial

biomass and atmospheric N2 in A. variabilis is strongly correlated to both [Fe] and growth rates (Figures 3.2, 3.3). The two trends are not mutually exclusive, since growth rates in this and other N2-fixing cyanobacteria are highly dependent on [Fe] (Rueter, 1988; Paerl et al., 1994; Berman-Frank et al., 2001; Chapter 2). The mechanism of

isotopic discrimination during N2 fixation is not thoroughly understood; however,

discrimination could be dominated by either transport processes (diffusion of N2 across the cell membrane) or enzymatic processes (binding and reduction by the nitrogenase enzyme). Here we propose two possible causes for the observed trends in δ15N fractionation, based on each of these scenarios. Other possibilities we considered do not support the observed trends (see discussion below). Isotopic discrimination could result from diffusion of dinitrogen across the cell membrane. Oceanic phytoplankton respond to Fe-limitation by reducing cell size, increasing the cell surface area-to-volume ratio for more efficient nutrient diffusion

(Sunda and Huntsmann, 1995, 1997). More efficient diffusion of N2 into and out of the cell could lead to less overall fractionation. Conversely, at higher [Fe] with smaller

surface area to volume ratios, N2 would diffuse into and out of the cell less efficiently, leading to more isotopic fractionation. We did not observe an appreciable size difference between A. variabilis cells grown in high Fe versus low Fe conditions under standard light microscopy; however, this was not rigorously quantified, and the possibility needs to be explored further. Isotopic discrimination could instead be primarily the result of enzymatic

processes, i.e. N2 binding and reduction by the nitrogenase enzyme. Convincing evidence suggests that N2 binds to a Fe-S cluster at the FeMo cofactor site, where reduction occurs (e.g., Scott, 1990; Dance, 1994). Fe-limitation could hinder the ability of the organisms to produce the Fe-rich nitrogenase enzyme (with 38-50 moles Fe per mole of enzyme complex; Miller and Orme-Johnson, 1992), resulting in minor structural changes to the

99 protein configuration due to insufficient availability of Fe atoms. We speculate that such structural alteration might logically result under low [Fe], due either to mechanical error in protein production or a genetically programmed response to Fe stress. Structural modification of the nitrogenase enzyme could then lead to differential substrate binding specificity, and correspondingly differential isotopic discrimination. Other explanations we considered do not support the observed trends in N isotope 15 fractionation, because they either predict that δ N ratios should directly correlate to N2 fixation rates rather than growth rates, or they predict a trend in isotopic fractionation the + opposite of what we observe. For example, we considered that heavy NH4 or other biomolecules enriched in 15N (e.g., amino acids; Macko et al., 1987) could be diffusing 15 out of N2 fixing organisms when N replete, leading to lighter overall biomass δ N. 15 However, in this instance we would expect δ N to vary with N2 fixation rates rather than growth rates, and ammonia was below detection in all cultures examined. Other fractionation mechanisms that are directly dependent on nitrogenase efficiency (e.g., substitution of the non-Fe electron donor flavodoxin for the Fe-containing ferredoxin;

Erdner et al., 1999) would additionally predict less fractionation with more efficient N2 13 fixation (i.e. higher [Fe]), similar to trends in δ C with CO2 fixation (Laws et al., 1995, 1997; Bidigare et al., 1999) but corollary to what we observe here.

Differential isotopic discrimination at varying [Fe] during N2 fixation presents a mechanism by which fluctuations in ocean redox chemistry could directly contribute to trends in δ15N values preserved in ancient sediments. Although the exact timing of the

evolution of N2 fixation is debated (e.g., Kasting and Siefert, 2001), it is likely that it had arisen by 2.2 Ga (for a review, see Chapter 2). Fossil evidence indicates the occurrence of

N2-fixing cyanobacteria at this time (e.g., Tomitani et al., 2006), and as these organisms would have been the most important primary producers and consumers of nutrients, it is reasonable that they would have been the dominant N2 fixers on the early earth. Modern oxic ocean basins are severely Fe-limited due to the formation of insoluble Fe- hydroxides. As a result, Fe concentrations of < 1 nM have been measured in some open ocean areas (Gordon and et al., 1982; Landing and Bruland, 1987). At low [Fe] such as those of the modern oceans (0.1-1 nM), our model predicts little isotopic discrimination, producing greater δ15N values, as seen in modern oceans as well as most records of

100 Phanerozoic δ15N after the rise of oxygen (Figure 3.4). In contrast to the modern oceans, reduced ferrous iron is abundant in anoxic basins. During the Archean, prolonged periods of Fe-enriched anoxic ocean bottom water are indicated by the deposition of extensive banded iron formations between 3.8 to 2.5 Ga (Holland, 1984; Beukes and Klein, 1990). At high [Fe] ratios such as those estimated for the Archean ocean (1-10 µM), our model predicts negative biomass δ15N values (Figure 3.4). In experiments replicating Archean metal concentrations (1 nM Mo and 2400 nM Fe; Holland, 1973; Anbar and Knoll, 2002; Canfield, 2005), we measured biomass δ15N values down to -2.0‰, consistent with light δ15N ratios preserved in Archean sediments and some Phanerozoic sediments associated with expanded anoxia (Figure 3.4; Beaumont and Roberts, 1999; Papineau et al., 2005). Our results indicate that oceanic [Fe] would only have to be ≥ 50 nM during these anoxic periods to produce organic matter with significantly negative δ15N values, if the isotopic signal is produced primarily by cyanobacteria fixing atmospheric nitrogen. ACKNOWLEDGMENTS We thank P. Søholt and L. Salling for laboratory assistance, P. Polissar and D. Walizer for technical assistance, and K. Freeman for the opportunity to measure organic matter nitrogen isotopic compositions by EA-CF-IRMS. This project was funded by the Penn State Biogeochemical Research Initiative for Education (BRIE), sponsored by NSF (IGERT) grant DGE-9972759, the NASA Astrobiology Institute, the Dansk Grundforskningsfond, and an SOAS International School of Aquatic Sciences Graduate Scholarship to A. Zerkle. We also thank M. Arthur, S. Brantley, H. Carrick, and L. Kump for helpful discussion and comments. REFERENCES Altabet M. A., Pilskaln C., Thunell R., Pride C., Sigman D., Chavez F., and Francois R. (1999) The nitrogen isotope biogeochemistry of sinking particles from the margin of the Eastern North Pacific. Deep-Sea Research I 46, 655-679.

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105 15 Table 3.1. δ N of biomass measured in N2-fixing cyanobacteria from the literature.

Organism location δ15N Ref

CULTURE Anabaena cylindrica -0.6 1 -2.6 2 Anabaena sp. strain IF -2.85 3 -2.93 -2.74 Trichodesmium IMS 101 -1.3 4 -3.4 -3.6 FIELD "blue-green algae" East China Sea -0.6 1 Trichodesmium erythraeum N. Pacific -2.0 East China Sea -2.1 1 -1.2 Trichodesmium spp. Caribbean Sea -0.7 4 -0.5 -0.7 -0.6 -0.4 -0.3 -0.3 -0.3 -0.2 East China Sea 1.0 1 -2.1 5 South Florida -0.2 6 N. Pacific 0.5 7 (Phillipine Sea) -0.5 -1.7 0.0 N. Pacific 4.1 8 0.4 -1.6 -1.5 Trichodesmium thiebautii East China Sea -0.8 1 1 = Minagawa and Wada, 1986; 2 = Beaumont et al., 2000; 3 = Macko et al., 1987; 4 = Carpenter et al., 1997; 5 = Minagawa and Wada, 1984; 6 = Macko et al., 1984; 7 = Wada and Hattori, 1976; 8 = Wada, 1980

106 15 Table 3.2. δ N (and standard deviation, SD) of biomass measured in N2-fixing A. variabilis grown in various initial media metal concentrations from this study.

[Fe]i [Mo]i (nM) (nM) δ15N SD

24,000 22,000 -1.3 0.2 24,000 1700 -1.9 0.3 2400 100 -1.4 0.2 2400 10 -0.6 1.4 2400 5 -0.7 0.2 2400 2 -0.9 0.1 2400 1 -1.0 0.0 2400 0.5 -2.0 0.0 2400 0.1 -0.8 0.0 240 100 -0.7 0.1 120 100 -0.6 0.0 48 100 -1.0 0.0 24 100 1.0 0.0 2.4 100 -0.3 0.4 2.4 10 -0.3 0.0 0.01 100 0.4 0.0

107 9 8 Field 7 Culture 6 5

4

Frequency 3 2

1

0 -4 -3 -2 -1 0 +1 15 o δ N /oo

δ15 Figure 3.1. N composition of biomass measured in N2-fixing cyanobacteria in culture studies and from the field. Data are given in Table 3.1.

108 (a) Mo-limitation (~2400 nM Fe)

2

1

0 N ‰ 15 δ -1 Mean = -0.9‰

-2

-3 10 1001 [Mo]i (nM)

(b) Fe-limitation (~100 nM Mo) 2

1

0 N ‰ 15

δ -1 y = -0.2Ln(x) + 0.02 R2 = 0.7

-2

-3 0.1 10 1000 100000 [Fe] i (nM)

Figure 3.2. δ15N composition of A. variabilis biomass produced under (a) Mo-limited conditions, and (b) Fe-limited conditions. Also shown in (b) are δ15N values produced under "optimal" metal concentrations ([Fe]i = 24,000 nM and [Mo]i = 1700 nM; black circles) and with "added" Mo ([Fe]i = 24,000 nM and

[Mo]i = 22,000 nM; grey circles). Data are given in Table 3.1.

109 δ 15 (a) N - N 2 fixation rates

1

0 N ‰

15 -1 δ R2 = 0.02 -2

-3 0.2 0.4 0.6 0.8 1.0 1.2 -10 N2 fixation rates x 10

(b) δ 15N - Growth rates

1

0 N ‰

15 -1 δ

-2 R2 = 0.4

-3 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Growth rates x 107

δ15 -1 -1 Figure 3.3. Biomass N versus (a) N2-fixation rates (in moles N2 cell minute ), and (b) growth rates (in cells mL-1 day-1) reported in Chapter 2.

110 5

4 Anabaena variabilis (culture, this study) Anabaena spp. (culture) 3 Trichodesmium IMS 101 (culture) 2 Trichodesmium spp. (field) / oo

o 1 BS N

15 0 Modern δ oceans -1

-2 Archean ocean -3

-4 0.01 0.1 1 1000 10000 10000010010 [Fe] (nM)

δ15 Figure 3.4. N composition of biomass from N2-fixing cyanobacteria from this study and from the litera- ture. A. variabilis from this and other studies is a freshwater strain, Trichodesmium is a marine strain impor- tant in the modern oceans (e.g., Capone et al., 1997). Results from culture studies are plotted versus the pre- scribed initial [Fe] added to the media (Minagawa and Wada, 1986; Macko et al., 1987; Carpenter et al., 1997; Beaumont et al., 2000; this study). The symbol for δ15N composition of field populations represents a mean with error bars indicating one standard deviation. These measurements were collected from diverse environments, including the Northern Pacific Ocean (Wada and Hattori, 1976; Wada, 1980), the Caribbean Sea (Carpenter et al., 1997), and South Florida (Macko et al., 1984), but are plotted here versus a moderate estimate for modern marine [Fe]. Also shown are boxes representing a range of [Fe] measured in modern oceans (Gordon et al., 1982; Landing and Bruland, 1987) and in the Black Sea ("BS"; Lewis and Landing, 1991), and a range of [Fe] estimated for the Archean ocean (Holland, 1973; Canfield, 2005). The dashed line represents a best-fit line through the data, with an R2 value of 0.4.

111 Chapter 4

Biogeochemical Phosphorus Cycling in a Meromictic Lake

ABSTRACT 3- Recent evidence points to the importance of surface scavenging of PO4 by metal-oxide coatings on phytoplankton cell walls. Reductive dissolution of these biogenic oxide-coatings could potentially contribute to increased P release under anoxic conditions. Here we present profiles of dissolved and total Fe, Mn, P, and soluble reactive phosphate from a stratified (meromictic) lake, suggesting that P is released primarily from reductive dissolution of abiotic Mn-oxides at the redox interface (chemocline). Particulate elemental distributions indicate that some P is sorbed onto cyanobacterial Fe- (hydr)oxide coatings in surface waters, but this pool can only account for about 10% of the total P supply to the chemocline. P is rapidly taken up (possibly via biosorption) and recycled by a dense community of sulfur bacteria inhabiting the chemocline. The intense recycling of P by sulfur bacteria could counteract the release of P from metal-hydroxides (both biotic and abiotic) at the redox interface in such stratified systems. INTRODUCTION Phosphorus (P) is important in fuelling primary productivity in both marine and lacustrine settings, and is generally thought to be the key nutrient limiting organic carbon burial over geologic timescales (Van Cappellen and Ingall, 1994, 1996; Tyrell, 1999). The role of abiotic Fe- and Mn-(hydr)oxides in controlling P cycling is well documented (e.g., Berner, 1973; Hongve, 1997; Wheat et al., 1996); however, recent evidence suggests the importance of biogenic metal-(hydr)oxides in P cycling as well (Sañudo- Wilhelmy et al., 2004; Fu et al., 2005). These studies report that much of the measured P content included in the Redfield ratio of marine phytoplankton (Redfield, 1963) could largely reflect the contribution of a surface-adsorbed P pool. This external P pool is present on cells from a broad taxonomic range of eukaryotic and prokaryotic phytoplankton (both marine and freshwater) in the laboratory and in the field, and can account for 15 to 46% of total cellular P in natural populations (Fu et al., 2005). P is 3- presumably sorbed to cell surfaces as phosphate (PO4 ), via inorganic ligand-exchange

112 reactions with adsorbed metal hydroxides (specifically of Mn; Sañudo-Wilhelmy et al., 2004). The degree of P retention in sediments has been shown to be redox dependent. Phosphorus retention increases under oxic conditions, presumably due to bioaccumulation by aerobic benthic organisms and sorption of P onto Fe(III) (hydr)oxides. Conversely, P burial is less efficient when bottom waters are low in oxygen (Ingall et al., 1993; Ingall and Jahnke, 1994, 1997). Reductive dissolution of biotic metal- oxide coatings could potentially contribute to the release of P under anoxic conditions (Kump et al., 2005). To date, the importance of biogenic metal-oxide coatings on P cycling has been studied extensively only in oxic cultures and in phytoplankton blooms from well-oxygenated surface waters. The contribution of sorbed P to nutrient cycling under anoxic or suboxic conditions is unknown. Meromictic lakes with permanently stagnant depths provide an ideal setting for studies of physical and biogeochemical phenomena. These lakes often possess an upper, wind-mixed, oxic water column (the mixolimnion) overlying anoxic and/or sulfidic deep waters (the monimolimnion). These two distinct water bodies are often separated by a sharp redox interface (the chemocline), which can support high densities of microbial biomass and can accumulate elevated levels of products from biogeochemical processes. Additionally, these environments are of interest because similarly redox-stratified conditions could have dominated the oceans during numerous periods of earth history (e.g., Canfield, 1998). Here we examine the general controls on phosphorus distribution within a meromictic lake, specifically focusing on the role of microbial adsorption on P cycling across the chemocline. Our results suggest that phosphorus cycling across the redox interface is controlled by both abiotic processes and biological uptake. GEOLOGIC SETTING Fayetteville Green Lake (GL) is a small (0.26 km2), permanently stratified lake located approximately 1.7 km northeast of Fayetteville, New York (Brunskill and Ludlam, 1969; Figure 4.1). The lake was formed by glacial meltwaters during the late Wisconsinian, and occupies a steep-sided basin that cuts directly into the lower Syracuse Formation and Vernon Shale (Thompson et al., 1990). Calcium and sulfate, leached from the gypsum-rich Vernon Shale, enters the lake at depth causing permanent density

113 stratification (Takahashi et al., 1968; Brunskill and Ludlam, 1969; Torgersen et al., 1981). GL has a long history of scientific study dating back to the late 1800’s, due both to its meromixis and to its impressive accumulations of benthic calcium carbonate bioherms and perennial “whiting” events of carbonate precipitation in surface waters (for a review, see Thompson et al., 1990). It is also notable for its thriving microbially dominated ecosystem. The mixolimnion is dominated by oxygenic phototrophs (e.g., Synechococcus), which are responsible for the bioherms and whiting events (Thompson et al., 1990; Thompson and Ferris, 1990). The sulfate-rich groundwater supports high rates of microbial sulfate reduction and buildup of H2S in the deep waters (up to 1.2 mM; Brunskill and Ludlam, 1969; Figure 4.2). Very high rates of productivity are sustained at the chemocline by a dense population of anoxygenic phototrophic purple sulfur bacteria (e.g. Lamprocystis, Chromatium) and green sulfur bacteria (e.g. Chlorobium) (Culver and Brunskill, 1969; Fry, 1986). Anoxygenic phototrophic sulfur bacteria are common in the chemocline of meromictic lakes (e.g., Tonolla et al., 2003). These organisms oxidize H2S or other reduced sulfur compounds using energy from photosynthesis, and can thrive at even very low light levels (e.g., Overmann et al., 1992). MATERIALS AND METHODS Field work for this study was completed in November, 2005. A detailed geochemical profile (depth, temperature, oxidation-reduction potential (ORP), turbidity, and specific conductivity) was taken in situ from 0.5 to 30 m depth, using a YSI 6600 multiparameter logger.

Dissolved oxygen was measured with a hand-held O2 probe at the surface. Water samples were brought to the surface with a peristaltic pump and collected in acid-washed Nalgene bottles. Waters were sampled from the mixolimnion (at 13, 18, and 19 m), the monimolimnion (at 21, 22, 23, 25, and 31 m), and the chemocline (at ~20 m during this time, as evidenced by the sharp changes in ORP, conductivity, and oxygen concentration; Figure 4.2). Samples for determination of dissolved [Fe], [Mn], [P], and soluble reactive phosphate (SRP) were filtered through 0.45 µm Supor polysulfone-ester inline filters, demonstrated to have low blanks in these elements (Cullen and Sherrell, 1999). Total Fe, Mn, and P were unfiltered water samples. All water samples were fixed with 2% ultrapure HNO3 (except for SRP) and immediately frozen. Filtrate was collected on 0.45 µm filters from each depth and kept at 4°C for particulate analysis.

114 Sections of filters from just above (18 m) and below (23 m) the chemocline were examined on an FEI Quanta 200 Environmental Scanning Electron Microscope (ESEM), at the Materials Characterization Laboratory (MCL) at Pennsylvania State University, to qualitatively determine the portion of biotic versus abiotic particulates. All filters were prepared using acid-washed plastic tweezers in a trace metal clean laminar flow hood. Select filters were rinsed with an oxalate reagent to remove surface adsorbed Fe, Mn, and P, following protocols outlined in Tovar-Sanchez et al. (2003). Briefly, dried filters were incubated for 5 minutes with 10 mL of oxalate reagent and rinsed twice with deionized

H2O. The oxalate washing procedure presumably reduces oxide coatings on cell walls to release sorbed metals and nutrients, without damaging cells or leading to cell lysis

(Tovar-Sanchez et al., 2003). All filters were subsequently digested in ultrapure HNO3 at 150ºC for 2-3 hours. Nitric acid is sufficient to solubilize nearly all naturally occurring oxide, carbonate, sulfide, and sulfate minerals (Weast and Astle, 1982). Digestion of silicate phases commonly require the use of HF; however, the very low levels of terrestrial input to Green Lake via surface runoff (Takahashi et al., 1968; Brunskill and Ludlam, 1969; Torgersen et al., 1981) and previous studies indicate that silicate phases do not make up a significant portion of particles in the deep waters (Brunskill, 1969).

Dissolved filters were then dried and resuspended in 2% ultrapure HNO3. Fe, Mn, and P were analyzed on water and filter samples by inductively coupled plasma mass spectrometry (ICP-MS) on a Finnigan MAT ELEMENT High Resolution ICP-MS at the MCL. Instrument detection levels were 0.01 µM for P, 0.0002 µM for Mn, and 0.0005 µM for Fe. SRP was analyzed on a Hach DR/4000V spectrophotometer at 890 nm using the Phosporus, Reactive (Orthophosphate) method 8048. RESULTS In November 2005, the chemocline of FGL was around 20 - 25 meters, as evidenced by an increase in specific conductivity, along with decreases in O2 concentration and ORP (shown in grey in Figure 4.2 and subsequent figures). The small increase in temperature commonly associated with the chemocline (up to 2ºC; Appendix C) was not observed on this sampling trip. The spike in turbidity is caused by the exceedingly high density of sulfur bacteria at the redox interface (up to 107 cells/mL;

115 Thompson et al., 1990; Appendix C), and water samples from 20 m depth were visibly

purple in color. Additionally, samples from 20 m and below smelled strongly of H2S. Here we present elemental profiles only from November 2005; however [Fe], [Mn], particulate Fe and Mn, and SRP profiles show similar trends to those from June 2004 and May 2006 (Appendix C). Concentrations of total and dissolved Fe, Mn, and P are very low in the mixolimnion (Table 4.1; Figure 4.3). Total and dissolved Fe and Mn peak at the chemocline and decrease into the monimolimnion, although the Fe peak occurs slightly deeper (~21 m). Dissolved P and SRP remain low across the chemocline, and then increase at depth in the monimolimnion. Total P shows a peak across the redox- interface before increasing in the monimolimnion as well. ESEM examination of GL filtrate revealed that nearly all of the suspended particulates collected on 0.45 µm filters appeared biogenic, based generally on size (0.5 – 2 µm) and morphology (spherical to rod-shaped) (as observed in Thompson et al., 1990; Figure 4.4). Particulate Fe peaks directly above the chemocline (19 m; Table 4.2; Figure 4.5). Particulate Mn concentrations were very low, less than 0.1 µM throughout the water column. Particulate P concentrations were the highest (consistent with the predominance of biotic particles), peaking at ~0.8 µM at the chemocline. Particulate P:Fe ratios varied from 0.7 (in the monimolimnion) to 17 (at the chemocline). Particulate P:Mn ratios were highly variable (from 1 to ~200) and also lowest in the monimolimnion. We used an oxalate rinse to remove Fe, Mn, and P that was adsorbed to biogenic oxide coatings (e.g., Tovar-Sanchez et al., 2003; Sañudo-Wilhelmy et al., 2004; Fu et al., 2005). Non-rinsed (N) samples can then be compared to rinsed (R) samples to reflect the surface-adsorbed concentrations (N – R; Table 4.2; Figure 4.5). Mn particulate concentrations were so low as to prevent accurate determination of a difference between the two filter analyses. Non-rinsed filter concentrations of Fe and P are significantly greater than rinsed filter concentrations directly above the chemocline (at 19 m) and at depth (31 m), as expected; however, within the chemocline (from 21 to 23 m), non-rinsed filter concentrations of Fe and P are less than rinsed filter concentrations, at amounts significantly greater than measured filter blanks (0.07 µM for P and 0.05 µM).

116 DISCUSSION Biological P Cycling The increase in both dissolved and total P at depth suggests that phosphorus is supplied to the lake primarily by regeneration in the sediments, likely due to microbial degradation of organic matter (Figure 4.3). There could be a minor P contribution to the mixolimnion from surface runoff; however, hydrologic data indicate that groundwater is the primary source of water to GL (Takahashi et al., 1968; Brunskill and Ludlam, 1969; Torgersen et al., 1981). Although dissolved P and soluble reactive phosphate (SRP) concentrations remain very low throughout the rest of the water column (< 0.4 µM), there is a large increase in total P across the chemocline, reaching a maximum at the redox interface (> 3 µM). This P maximum corresponds to the peak in turbidity (Figure 4.2), suggesting that phosphate is immediately taken up by the dense community of sulfur bacteria at the chemocline. ESEM examination of filtrate revealed that nearly all of the suspended particulates are biogenic (Figure 4.4), supporting this P as being primarily microbially associated. Recent evidence suggests that much of the measured P content included in the Redfield ratio of marine phytoplankton (Redfield, 1963) largely reflects the contribution of a surface-adsorbed P pool (Sañudo-Wilhelmy et al., 2004; Fu et al., 2005). To quantify the extent to which biogenic P in the GL water column was internal versus externally sorbed, we used a trace metal-free oxalate rinse to remove Fe, Mn, and P adsorbed to biogenic surfaces (Tovar-Sanchez et al., 2003). Non-rinsed (N) samples reflect the total particulate concentrations, rinsed (R) samples presumably reflect the intracellular concentrations, and the difference between total and intracellular (N – R) should then reflect the surface-adsorbed concentrations. Trends in adsorbed P concentrations (represented by N – R) directly track trends in adsorbed Fe (Figure 4.5). The oxalate rinse removed P and Fe from filters directly above the chemocline (at 19 m; N – R > 1), corresponding to a previously observed increase in chlorophyll content that likely indicates a high density of cyanobacteria (Thompson et al., 1990). We interpret this as a release of P from cellular oxide coatings, as seen with other freshwater and marine cyanobacteria (Sañudo-Wilhelmy et al., 2004; Fu et al., 2005). However, these researchers observed a co-release of P with Mn, suggesting the importance of Mn-oxide

117 coatings (Sañudo-Wilhelmy et al., 2004); our observation of a co-release of P with Fe in rinsed samples indicates adsorption by bacterial Fe-oxide coatings instead. The prevalence of Fe- over Mn-oxide cell coatings could be due to their differential reactivities in this redox-stratified environment: Fe-oxides form more rapidly than Mn- oxides, and Mn-oxides are more quickly reduced (e.g., Balzer, 1983). Previous work indicates growing phytoplankton can access the P adsorbed to cell surfaces (Sañudo- Wilhelmy et al., 2004; Fu et al., 2005). These studies suggest that P sorption could be the first step in biological P uptake, and/or that the sorbed P pool could serve as an extracellular P reserve. The sorption of P onto Fe-oxide coatings in the GL mixolimnion could indicate a similar scavenging mechanism (Figure 4.6). In contrast, within the chemocline (from 21-23 m) the oxalate rinse actually increased measured P and Fe concentrations (N – R < 1), up to 0.3 µM for P and 0.2 µM 0 for Fe. The oxalate rinse has a very negative redox potential (E H of -631 mV for oxalic acid) and works because it should reduce oxide coatings on cell surfaces, releasing metals and sorbed nutrients as ionic species that are subsequently carried away in the wash steps (Sañudo-Wilhelmy et al., 2004). If, on the other hand, P was sorbed to cell walls in a non- oxide phase (such as might occur under reducing conditions of the chemocline), then different reactions could result. The enrichment suggests that, in samples within the chemocline, the wash step results in the production of a new phase containing Fe and P that is either retained on the filter, or is more easily solubilized during acid digestion. Filters were dried prior to oxalate rinse, and the measured enrichment is significantly greater than P and Fe concentrations measured in filter blanks (0.07 µM P and 0.05 µM Fe), indicating the enrichment is not due to the precipitation of a new mineral phase. Therefore, the rinse must result in the reduction of a Fe- and P-containing solid phase that is already present, producing a reduced version that is more soluble during acid digestion.

One likely possibility is the presence of an Fe-phosphate phase: Fe(III)PO4 x 2H2O is not

soluble in HNO3, however reduced forms are, including Fe(II)3(PO4)2 (Weast and Astle, eds., 1982). Colloidal forms of Fe-(hydroxo) phosphates containing both ferrous and ferric Fe are common in the redox boundary of seasonally anoxic lakes (e.g., Buffle et al., 1989). We hypothesize that a Fe(III)-phosphate phase could be the dominant form of Fe and P sorbed to cells in the chemocline. This phase is not soluble during digestion with

118 nitric acid, and therefore stays in the solid phase and is not measured during analysis. The oxalate rinse reduces the Fe(III)-phosphate to Fe(II)-phosphate, which is dissolved during 2+ 3- acid digestion, releasing Fe and PO4 ions that are then measured in the analysis. This scenario would explain the enrichment of both P and Fe in oxalate-rinsed filters (Figure 4.5) Green sulfur bacteria (Chlorobium spp.) have been shown to sorb FeS and MnS to cell membranes in sulfide-limited environments (Garcia-Gil and Borrego, 1997). Previous work has shown that the GL water column is severely P-limited, with dissolved N:P ratios as high as 1000 in the mixolimnion (and lowest at the chemocline, ~100; Johnson and Kump, unpublished data). Therefore, in this P-limited environment, organisms could be scavenging P directly as colloidal Fe-phosphates. The particulate P:Fe ratios we measured (1-16) are very similar to those reported for Fe-P oxyhydroxide colloids (~20-30; Buffle et al., 1989). These colloids would be small enough to be sorbed by bacteria but too small to be retained on our filters (most with a diameter around 0.1 µm; Buffle et al., 1989). The sulfur bacteria community at the chemocline has been observed to be a dynamic system, and we suggest that organisms could be moving up to the redox interface, sorbing P in the form of colloidal Fe(III)(hydroxo) phosphates, and then settling back down into the chemocline. Microscopic examination of organisms from the GL chemocline indicates that some cyanobacteria and purple sulfur bacteria are 3- accumulating PO4 in the form of intercellular polyphosphate granules (Macalady, pers. comm..). The presence of polyphosphate has been described in green sulfur bacteria as well, and seems to be a common feature in phototrophs (Cole and Hughes, 1965; Kulaev and Vagabov, 1983; Pfennig, 1989; Baneras and Garcia-Gil, 1998). Here we suggest a two-step process for P uptake by sulfur bacteria similar to that suggested for cyanobacteria by Sañudo-Wilhelmy et al. (2004): organisms at the redox interface sorb Fe-(hydroxo) phosphate colloids in the P-limited chemocline. This external phosphate pool is subsequently transferred into the cell for storage as polyphosphates (Figure 4.6). Abiotic P Cycling The sharp increase in total P at the redox interface indicates the release of substantial amounts of P via abiotic reductive dissolution and immediate uptake by organisms. The amount of biogenically sorbed P that we measured in the mixolimnion

119 (~0.2 µM) is still less than 10% of the total P inventory of the chemocline (~3 µM). This discrepancy strongly suggests the presence of a significant P-scavenging component missing from our analyses. Our particulate sampling technique (collection on inline filters) provides only a single snapshot in time, and resulted in the collection of fine suspended particulates primarily biological in origin (Figure 4.4). No sediment traps were deployed to collect rapidly settling particle fluxes. Therefore we infer the importance of rapidly settling particulates (composed primarily of Mn-oxides) in shuttling P to the chemocline. Abiotic Fe-hydroxides and Mn-oxides have long been recognized as important scavengers or carrier phases for phosphorus and trace metals in aquatic environments (e.g., Sigg 1985; Buffle et al., 1989; Davison, 1993; Balistrieri et al. 1994; Hamilton- Taylor and Davison, 1995; Hongve, 1997). We observe peaks in total P, Fe, and Mn at the chemocline of GL, suggesting the reductive dissolution of rapidly settling abiotic Fe and Mn (hydr)oxide phases and release of associated P at the redox boundary (Figure 4.7). The peak in total Fe occurs below Mn as a consequence of the differential reactivities of Fe- and Mn-oxides: Mn-oxides have a higher redox potential than Fe- oxides, and so are more readily reduced (e.g., Balzer, 1982). The peak in total P corresponds with the higher peak in Mn, suggesting that Mn-oxides are the primary abiotic P shuttle in GL. Manganese is then removed from solution in the monimolimnion, likely by the formation of rapidly settling Mn-carbonates (e.g., Emerson 1976; Matisoff et al., 1980). Fe is likely removed in the monimolimnion by the formation of fine suspended Fe-sulfides (e.g., Forstner, 1982). CONCLUSIONS Phosphorus cycling across the chemocline of Green Lake is controlled by both abiotic and biotic processes. P is regenerated in the sediments, where it diffuses up through the water column and is rapidly recycled across the redox interface. P is primarily released by the reductive dissolution of rapidly settling abiotic Mn-oxides. Some P is sorbed onto cyanobacterial Fe-(hydr)oxide coatings in the mixolimnion, but this can only account for about 10% of the total P supply to the chemocline. The dense community of phototrophic sulfur bacteria inhabiting the chemocline immediately takes up this released phosphate to fuel high levels of productivity. Evidence for the presence

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125 Table 4.1. Concentrations of total (T) and dissolved (D) Fe, Mn, and P, and soluble reactive phosphate (SRP) with depth in the water column of GL. Measurement error is included as one standard deviation (SD). All concentrations are given in µM.

Depth (m) PT SD PD SD SRP SD MnT SD MnD SD FeT SD FeD SD

13 0.43 0.0 0.27 0.0 0.05 0.0 0.01 0.0 18 0.25 0.0 0.20 0.0 0.28 0.0 0.01 0.0 0.03 0.0 0.02 0.0 19 0.48 0.0 0.11 0.0 0.32 0.0 1.48 0.1 0.11 0.0 0.07 0.0 0.03 0.0 20 3.25 0.1 0.15 0.0 0.39 0.0 33.27 2.1 33.89 1.4 0.24 0.0 0.15 0.0 21 1.40 0.1 0.16 0.0 0.30 0.1 20.86 1.2 19.96 0.3 1.92 0.0 1.61 0.0 22 1.16 0.0 0.24 0.0 0.30 0.0 11.55 0.5 12.00 0.1 1.19 0.1 0.79 0.0 23 1.05 0.1 0.20 0.0 0.43 0.1 10.75 0.6 10.31 0.4 0.94 0.0 0.58 0.0 25 0.88 0.1 0.15 0.0 9.16 0.8 9.43 0.5 0.70 0.1 0.40 0.1 31 4.35 0.3 4.29 0.2 2.68 0.0 6.81 0.1 6.65 0.4 0.54 0.0 0.36 0.0

126

Table 4.2. Concentrations of non-rinsed particulate (N), rinsed particulate (R), and adsorbed (A = N – R) Fe, Mn, and P with depth in the water column of GL. Measurement error is included as one standard deviation (SD). All concentrations are given in µM.

Depth (m) PN SD PR SD PA MnN SD MnR SD MnA

13 0.037 0.00 0.041 0.00 -0.004 0.012 0.00 0.006 0.00 0.006 18 0.060 0.00 0.100 0.01 -0.040 0.015 0.00 0.008 0.00 0.007 19 0.261 0.01 0.141 0.01 0.120 0.007 0.00 0.103 0.01 -0.095 20 0.731 0.02 0.767 0.00 -0.036 0.037 0.00 0.033 0.00 0.004 21 0.240 0.00 0.529 0.01 -0.288 0.015 0.00 0.007 0.00 0.008 22 0.101 0.01 0.403 0.02 -0.302 0.101 0.01 0.003 0.00 0.097 23 0.245 0.01 0.304 0.01 -0.059 0.023 0.00 0.013 0.00 0.010 25 0.179 0.00 0.179 0.01 0.000 0.003 0.00 0.001 0.00 0.002 30 0.242 0.00 0.097 0.00 0.145 0.004 0.00 0.000 0.00 0.004

Table 4.2. (Continued).

Depth (m) FeN SD FeR SD FeA

13 0.026 0.00 0.019 0.00 0.007 18 0.013 0.00 0.016 0.00 -0.003 19 0.262 0.01 0.020 0.00 0.242 20 0.045 0.00 0.045 0.00 0.000 21 0.110 0.00 0.161 0.01 -0.051 22 0.032 0.00 0.248 0.03 -0.216 23 0.292 0.01 0.436 0.01 -0.145 25 0.130 0.00 0.097 0.01 0.033 30 0.248 0.00 0.013 0.00 0.235

127 VT

NH

NY MA

RI CT PA Green Lake New York State Thruway OUTLET 10 Green Lakes 0 100 200 20 meters Syracuse State Park 30 Fayetteville 40 43º N N 50 N I-81

INLET Lafayette Rt. 20

5 km 76º W

Figure 4.1. Geographic Location of Green Lake, along with bathymetry (after Brunskill and Ludlam, 1969).

128 Temp (°C) Eh (mV) Diss O 2 (mg/L) Sulfate (ppm)

4 6 8 10 12-10014 1002003000 1 2 3 4 5 6 1200 1300 1400 1500 0

SO 2- 10 4

20

Depth (m) Chemocline

30

H2S

40

50 1.5 2.0 2.5 0 10 20 20 40 60 Sp cond (mS/cm) Turbidity (NTU) Sulfide (ppm)

Figure 4.2. Physicochemical characteristics of Green Lake in November, 2005. At this time the chemocline was around 20 m, as evidenced by the sharp changes in ORP, conductivity, and oxygen concentration. Sulfate and sulfide profiles from Turano and Rand, 1967; normalized to the current chemocline depth). Eh profile is from September 2002, consistent with profiles taken over the last five years (Appendix C).

129 [Fe] µM [Mn] µM [P] µM 0.5 1.51.0 2.0 10 20 30 1 2 3 4

Total Dissolved 10 (< 0.45 µM)

SRP 15

20 Depth (m) 25

30

35

Figure 4.3. Concentrations of total (T) and dissolved (D) Fe, Mn, and P (shown here as SRP only) with depth in the water column of GL. Error calculated as one standard deviation is less than the size of the symbol except where shown.

130 a

b

Figure 4.4. Environmental scanning electron microscopy (ESEM) images of particulates collected on filters from (a) 18 m and (b) 23 m.

131 Total - rinsed [Fe] µM [Mn] µM [P] µM (“adsorbed”), µM 0.2 0.60.4 0.2 0.4 0.2 0.60.4 0.8 -0.2 0 0.2

Fe Total particulate Oxalate rinsed P 10

15

20 Depth (m)

25

30

35

Figure 4.5. Concentrations of total particulate, rinsed, and adsorbed (total - rinsed) Fe, Mn, and P with depth in the water column of GL. Error calculated as one standard deviation is less than the size of the symbol except where shown.

132 3- PO 4 FeOOH Cyanobacterium

O2

H S reductive sorption 2 Fe-phosphates dissolution S bacterium

2+ 3- Fe PO 4

S bacterium Fe-phosphates

3- PO 4 transport? polyphosphate P storage Fe-phosphates

3- PO4

Figure 4.6. Release of Fe and P in rinsed particulates above the FGL chemocline suggests that P is sorbed onto Fe-oxide coatings on cyanobacterial cells in the mixolimnion, in a process similar to that suggested for marine phytoplankton (Sanudo-Wilhelmy, 2004). In the chemocline however, we see evidence for the sorption of col- loidal Fe-phosphates on cell surfaces.

133 Fe(III)-(hydr)oxides 3- Mn(IV)-oxides sorbed PO 4 O diffusion 2 H S reductive 2 dissolution uptake 2+ 2+ 3- P Fe Mn PO4 organic

Fe(II)-sulfides ?? Mn(II)-carbonates burial

regeneration burial

Figure 4.7. Processes primarily responsible for P cycling in GL. Phosphate diffuses up from the sediments to the P-limited mixolimnion. Both inorganic and organic processes influence P cycling across the chemocline. Reductive dissolution of rapidly settling Fe- and Mn-oxides at the redox boundary releases dissolved Fe, Mn, and sorbed P (Hongve, 1997). Phosphate is immediately taken up by the dense bacterial community inhabit- ing the chemocline. Fe and Mn are either recycled or carried to the , possibly as Fe-sulfides and Mn- carbonates, as suggested in previous studies.

134 Appendix A

Supplemental Material for Chapter 1

135 Table A.1. Fe-utilizing enzymes, synonyms, and inferred timing of evolution.

Inferred point Enzyme Synonyms of evolution aconitase LCA alkene epoxidase recent mulit-cellular arachidonate 15-lipoxygenase life aromatic ring oxygenase rise of O2 arthromyces ramosus peroxidase rise in O2 biphenyl-2,3-diol 1,2-dioxygenase rise in O2 calcineurin recent catalase A rise in O2 catalase Hpii rise in O2 catechol dioxygenase rise in O2 catechol dioxygenase extradiol; pyrocatechase; catechol- rise of O2 1,2-oxygenase; pyrocatechol 1,2- dioxygenase; catechol 1,2- dioxygenase; catechol 2,3- dioxygenase; metapyrocatechase chloroperoxidase rise in O2 coproporphyrinogen III oxidase rise in O2 cyclooxygenase 1 rise in O2 cyclooxygenase 2 rise in O2 cytochrome C peroxidase rise in O2 cytochrome f LCA cytochrome P-450 LCA cytochromes a LCA cytochromes b LCA cytochromes c LCA cytosolic ascorbate peroxidase rise in O2 dihydroorotate dehydrogenase B rise in O2 dihydroxybiphenyl dioxygenase rise in O2 ferredoxin LCA mulit-cellular ferritin life fumarate dehydrogenase LCA glutamate synthase LCA guanylate cyclase recent mulit-cellular haemerythrin life mulit-cellular haemoglobin life mulit-cellular haemoglobin life mulit-cellular homogentisate 1,2-dioxygenase life horseradish peroxidase rise in O2 hydrogenase (Fe only) LCA hydrogenase (Ni-Fe) LCA hydrogenase (Ni-Fe-Se) LCA hydroxyamine reductase rise in O2

136 Table A.1. Continued.

Inferred point Enzyme Synonyms of evolution

hydroxyglutaryl-coA dehydratase LCA hydroxyphenylpyruvate dioxygenase rise in O2 indole amino dioxygenase tryptophan LCA isopenicillin N synthetase acv cyclase; delta-(alpha- aminoadipyl)-cysteinyl-valine cyclase; IPN synthase rise in O2 lignin peroxidase rise in O2 lipoxygenase rise in O2 lipoxygenase-1 rise in O2 lysyl oxidase LCA methane mono-oxygenase methanogenesis

methane monooxygenase hydroxylase rise in O2 myeloperoxidase rise in O2 mulit-cellular myoglobin life naphthalene dioxygenase rise in O2 nitrite reductase rise in O2 nitrite reductase to NH3 rise in O2 nitrogenase (Fe only) N fix nitrogenase (Fe-Mo) N fix nitrogenase (Fe-V) N fix NO synthase rise in O2 peroxidase C rise in O2 phenol hydroxylase phenol 2-monooxygenase rise in O2 phenol hydroxylase rise in O2 phenylalanine hydroxylase rise in O2 phthalate dioxygenase rise in O2 phthalate dioxygenase reductase rise in O2 proline hydroxylase rise in O2 proline oxidase rise in O2 prolyl oxidase LCA mulit-cellular prostaglandin synthetase life protocatechuate dioxygenase rise in O2 protocatechuate dioxygenase rise in O2 mulit-cellular purple acid phosphatase life pyruvate ferredoxin oxido-reductase LCA ribonucleotide reductase LCA rubredoxide LCA rubrerythrin SRB Stearoyl-CoA,hydrogen-donor:oxygen stearoyl delta-9 desaturate oxidoreductase; stearoyl-CoA desaturase rise in O2 succinate reductase plants sulfur reductase rise in O2

137 Table A.1. Continued

Inferred point Enzyme Synonyms of evolution

a-taurine/-ketoglutarate dioxygenase rise in O2 toluene 2-monooxygenase; toluene- rise in O2 benzene-2-monooxygenase; toluene- 3-monooxygenase; toluene-4- toluene mono-oxygenase monooxygenase mulit-cellular transferrin life tyrosine hydroxylase rise in O2 xanthine dehydrogenase rise in O2 xanthine oxidase rise in O2 2-oxoglutarate-dept oxygenases (5) aspartic acid 2-oxoglutarate- rise in O2 dependent dioxygenase; lysyl 2- oxoglutarate dioxygenase; procollagen-lysine, 2-oxoglutarate 5- dioxygenase; proline, 2-oxoglutarate 3-dioxygenase; thymine, 2- oxoglutarate dioxygenase

138 Table A.2. Zn-utilizing enzymes, synonyms, moles of Zn required per mole of enzyme (when unknown assumed 1), and inferred timing of evolution.

mol Zn/ Inferred mol point of Enzyme Synonyms enzyme evolution

3' nucleotidase 1 LCA alcohol dehydrogenase (Zn) alcohol dehydrogenase oxidoreductase; 1 LCA retinol dehydrogenase; aldehyde dehydrogenase alkaline phosphatase phosphodiesterase/alkaline phosphatase 2 LCA aminopeptidases (11) D-aminopeptidase; methionyl 1 LCA aminopeptidase; leucine aminopeptidase; X-pro aminopeptidase; methionine aminopeptidase; deblocking aminopeptidase; cytosol aminopeptidase; proline aminopeptidase; membrane alanine aminopeptidase; aspartyl aminopeptidase; prolyl aminopeptidase ACE angiotensin-converting enzyme 1 recent aryl sulphatases aryl-sulfate sulfohydrolase 2 rise of O2 aspartate transcarbamylase* 1 LCA Beta-lactamase 1 recent calcineurin 1 recent carbonic anhydrase carbonic anhydrase/acetyltransferase 1 LCA carboxypeptidases (3) zinc-binding carboxypeptidase; metal- 1 LCA dependent carboxypeptidase; metal- dependent amidase/aminoacylase/carboxypeptidase; thermostable carboxypeptidase; D-alanyl- D-alanine carboxypeptidase; dipeptidyl carboxypeptidase collagenase (Zn) 1 LCA dehydroquinate synthase 3-dehydroquinate synthase 2 LCA DNA polymerase 1 LCA DNA primase 1 LCA elastase 1 recent excinucleases 1 LCA galactonate dehydratase d-galactonate-d-fuconate dehydratase 1 recent gelatinase (Zn) pepsin 1 recent glutame dehydrogenase glutamic dehydrogenase; NADP-GDH , L- 1 LCA glutamate:NADP+ oxidoreductase[deaminating] hydroxyacylglutathione hydrolase; glyoxalases II* hydroxyacylglutathione hydrolase 2 recent *not found in genomes of present study

139 Table A.2. Continued.

mol Zn/ Inferred mol point of Enzyme Synonyms enzyme evolution

phosphohydrolases (6) pyrophosphohydrolase; NTP pyrophosphohydrolase 2 LCA phospholipase C 3 LCA protease (Zn) Zn-dependent metalloprotease 1 LCA protein kinase C 2 LCA reverse transcriptase 1 recent RNA polymerase 1 LCA sigma-factor IIIA* 1 LCA stromelysin (Zn) matrix metalloproteinase11; MMP11 2 LCA superoxide dismutase (Cu-Zn) 1 rise of O2 thermolysin 1 recent tRNA synthetase 1 LCA *not found in genomes of present study

140 Table A.3. Cu-utilizing enzymes, synonyms, moles of Cu required per mole of enzyme (when unknown assumed 1), and inferred timing of evolution.

mol Cu/ mol Inferred point of Enzyme Synonyms enzyme evolution

amine oxidases (3) flavin monoamine oxidase; 2 LCA pyridoxamine oxidase ammonia monooxygenase* 1 rise in O2 ascorbate oxidase 1 plants auracyanin* 1 recent (1 sp.) azurin 1 LCA caeruloplasmin 5 mulit-cellular life copper ATP-ase 1 LCA 1 recent coproporphyrin decarboxylase cytochrome oxidase 1 rise in O2 diamine oxidase* 1 plants dopamine -hydroxylase dopamine mono-oxygenase 1 mulit-cellular life ethylene receptor 1 plants galactose oxidase 1 recent (1 sp.) glycine oxidases 1 rise in O2 haemocyanin 1 mulit-cellular life laccase urishiol oxidase 1 mulit-cellular life lysine oxidase lysyl oxidase 1 rise in O2 metallothionein 2 LCA methane oxidase methane monooxygenase 1 methanogenesis nitrite reductase 1 rise in O2 nitrous oxide reductase 6 nitrogen fixation peptide glycine - hydroxylase* peptidylglycine 2 rise in O2 phenol oxidase catechol oxidase 1 rise in O2 phenylalanine hydroxylase* L-Phenylalanine,tetrahydrobiopterin: 1 rise in O2 oxygen oxidoreductase phytocyanin* 1 oxygenic photosynthesis plastocyanin* 1 oxygenic photosynthesis rusticyanin* 1 oxygenic photosynthesis superoxide dismutase (Cu-Zn) 1 rise in O2 tyrosinase tyrosine monooxygenase; tyrosinase 2 rise in O2 oxidoreductase ubiquinone oxidase* 1 LCA *not found in genomes of present study

141 Table A.4. Mo-utilizing enzymes, synonyms, moles of Mo required per mole of enzyme (when unknown assumed 1), and inferred timing of evolution.

mol Mo/ mol Inferred point of Enzyme Synonyms enzyme evolution

aldehyde ferredoxin 1 rise of O2 oxidoreductase* aldehyde oxidase glyceraldehyde oxidase 1 rise of O2 arsenite oxidase* 1 rise of O2 biotin-S-oxide reductase 1 LCA cytochromes b (4) cyt B556 (succinate 1 LCA dehydrogenase); cyt b6/f- complex; menaquinol:cyt c oxidoreductase; cyt b529aa

DMSO reductase 2 rise of O2 formate dehydrogenase* 1 LCA formyl methanofuran 1 methanogenesis dehydrogenase hydroxycarboxylate viologen HVOR 1 rise of O2 oxidoreductase molybdopterin oxidase 1 rise of O2 molybdopterin oxidoreductase 1 rise of O2 nitrate reductase (3) periplasmic/assimilatory/res- 1 N fix piratory nitrate reductase nitrate to nitrite terminal respiratory oxidase 1 rise of O2 nitrogenase (Mo) 1 N fix plant nitrate reductase 1 plants polysulfide reductase* 1 rise of O2 pyridoxine 5'-phosphate pyridoxal oxidase oxidase 1 rise of O2 pyrogallol transhydrolase 1 recent sulfite oxidase 1 rise of O2 trimethylamine Noxide reductase* 1 rise of O2 xanthine dehydrogenase 1 rise of O2 xanthine oxidase 1 rise of O2 *not found in genomes of present study

142 Table A.5. Mn-utilizing enzymes, synonyms, moles of Mn required per mole of enzyme (when unknown assumed 1), and inferred timing of evolution.

mol Mn/ mol Inferred point Enzyme Synonyms enzyme of evolution

acid phosphatase phosphotransferase; acid 1 recent phosphomonoesterase; phosphomonoesterase; glycerophosphatase; orthophosphoric-monoester phosphohydrolase arginase arginine amidinase; canavanase; L- 4 LCA arginine amidinohydrolase

catalase (Mn) 1 rise of O2 concanavalin A 1 plants dinitrogen reductase regulatory 2 nitrogen protein fixation enolase 2 LCA galactosyl transferase 1 LCA glutamate synthetase 1 LCA glycosyl aminase 1 recent imidazoleglycerol phosphate D-erythro imidazoleglycerol- 1 LCA dehydratase phosphate dehydratase-histidinol phosphate phosphatase

isocitrate dehydrogenase oxalosuccinate decarboxylase 1 LCA isopropylmalate synthase alpha-isopropylmalate synthase; 1 LCA alpha-IPM synthetase/synthase; 3- carboxy-3-hydroxy-4- methylpentanoate 3-methyl-2- oxobutanoate-lyase NRAMP-2 1 LCA oxalate oxidase* oxalate; oxygen oxidoreductase 1 rise of O2 peroxidase (Mn) ligninase 1 rise of O2 phosphoenolpyruvate phosphopyruvate carboxylase; 1 LCA carboxykinase phosphoenolpyruvate carboxylase; ATP:oxaloacetate carboxy-lyase

phosphotriesterase 2 recent photosystem II 1 oxygenic photosynthesis proline dipeptidase 1 LCA pyruvate carboxylase 4 LCA ribonucleotide reductase (Mn) 1 rise of O2 superoxide dismutase (Mn) 1 rise of O2 *not found in genomes of present study

143 Table A.6. Co-utilizing enzymes, synonyms, moles of Co required per mole of enzyme (when unknown assumed 1), and inferred timing of evolution.

mol Co/ mol Inferred point of Enzyme Synonyms enzyme evolution

acetate synthetase* 1 methanogenesis adenylate kinase myokinase; ATP-AMP 1 LCA transphosphorylase; ATP:AMP phosphotransferase aldehyde decarbonylase 1 plants bromoperoxidase 1 rise of O2 cobalt transporter COT 1 GRR1; HOXN 1 LCA diol dehydratase 1 LCA ethanolamine deaminase ethanolamine ammonia-lyase 1 LCA xylose isomerase; glucose isomerase glucosephosphate isomerase 1 LCA glutamate mutase 1 LCA glycerol dehydratase* 1 LCA lysine mutase lysine 2,3 amino-mutase 1 LCA methane synthetase* 1 methanogenesis methionine aminopeptidase 1 LCA methionine synthetase* S-adenosylmethionine synthetase 1 LCA methylmalonyl CoA-mutase 1 rise of O2 mm-CoA carboxytransferase 1 LCA nitrile hydratase 1 LCA prolidase Xaa-Pro dipeptidase 1 LCA ribonucleotide reductase 1 rise of O2 vitamin B12 cobalamin; cyanocobalamin 1 LCA *not found in genomes of present study

144 Table A.7. Ni-utilizing enzymes, synonyms, moles of Ni required per mole of enzyme (when unknown assumed 1), and inferred timing of evolution.

molNi/mol Inferred point of Enzyme enzyme evolution

acetyl-coenzyme A synthase 1 methanogenesis bacterial hydrogenases 1 methanogenesis carbon monoxide dehydrogenase 1 methanogenesis glyoxalase I* 1 recent methyl-coenyzme M reductase 2 methanogenesis urease 2 LCA *not found in genomes of present study

145 Table A.8. W-utilizing enzymes, synonyms, moles of W required per mole of enzyme (when unknown assumed 1), and inferred timing of evolution.

mol W/ mol Inferred point of Enzyme Synonyms enzyme evolution

acetylene hydratase* 1 LCA aldehyde dehydrogenase (W) glycolaldehyde:NAD+ 1 LCA oxidoreductase; glycolaldehyde dehydrogenase; aspartate- semialdehyde dehydrogenase; succinate-semialdehyde dehydrogenase; N-acetyl-glutamate semialdehyde dehydrogenase; glycine betaine aldehyde dehydrogenase aldehyde ferredoxin- AOR 1 LCA oxireductase formaldehyde ferredoxin- FOR 1 LCA oxireductase glyceraldehyde ferredoxin- GAPOR 1 LCA oxireductase carboxylic-acid reductase* CAR 1 LCA DMSO reductase 2 rise of O2 formate dehydrogenase (W) 1 LCA formyl methanofuran 1 methanogenesis dehydrogenase *not found in genomes of present study

146 Table A.9. V-utilizing enzymes, synonyms, moles of V required per mole of enzyme (when unknown assumed 1), and inferred timing of evolution.

Inferred point of Enzyme Synonyms mol V/ mol enzyme evolution

bromo-peroxidase 1 plants chloroperoxidase 1 recent (1 sp.) Iodide:hydrogen-peroxide iodo-peroxidase* oxidoreductase 1 rise of O2 nitrogenase (V) 1 N fix *not found in genomes of present study

147 Appendix B

Supplemental Material for Chapter 2

ADDITIONAL MATERIALS AND METHODS Autotrophic experiments (Table B.3 to B.4; Figures B.3 to B.6) were conducted in media prepared as for heterotrophic experiments, without added fructose and buffered

with NaHCO3 (10 mM). Experiments were performed in acid-washed glassware fitted with a rubber stopper, since stopper-fitted polycarbonate vessels were not available. Unfortunately, the use of glassware means that metal concentrations in these experiments were not well constrained, and subsequent ICP-MS analyses of supernatant indicates contamination of either the experiments or the supernatant samples (which were processed using new sampling bottles with higher metal blanks), therefore the experiments are only referred to as “optimal”, “Fe-limited”, and “Mo-limited”. “Optimal” experiments were performed with media metal additions to 24,000 nM Fe and ~1740 nM Mo; “Fe-limited” experiments were performed with media metal additions to ~2 nM Fe and ~100 nM Mo; “Mo-limited” experiments were performed with media metal additions to 2400 nM Fe and 1 nM Mo; experiments attempted at 2 nM Fe and 10 nM Mo (x 4) failed. 9.5 mL carbon dioxide (5 % total headspace) was injected through a filter into each bottle daily. Autotrophic cultures never reached stationary growth phase even after 10 days, so nitrogen fixation rates were measured at the latest possible date. Continuous cultures were maintained in a metal- and glass-free photostat specially designed for these experiments (Table B.5; Figure B.7). The photostat consisted of three 1 L polypropylene bottles connected via acid-washed polypropylene connectors and Tygon tubing, ran by two peristaltic pumps. The 500 mL culture was maintained in the central bottle, one bottle served as a media reservoir input, and one bottle served to collect culture output. Each bottle was also fitted with a tube and filter for air input/output. Multiple continuous cultures were maintained over two laboratory seasons at optimal metal concentrations. The main problems with the photostat were: 1) the outflow clogging (tubes were changed); 2) the pumps quitting overnight (new pumps were acquired); and 3) the culture becoming contaminated. Even at optimal metal concentrations, the organisms grow so slowly that the culture would only stabilize when

148 the pumps were kept on the lowest speed setting. Continuous cultures would not stabilize even at the lowest pump speed under metal-limited conditions; instead the culture density depleted to near 0. Therefore the “optimal” nitrogenase experiments were performed on cultures that had stabilized for up to 10 days, however all metal-limited experiments were performed after the reservoir had replaced the original media once.

149 Table B.1. Total growth yield and calculated substrate consumption for Fe and Mo.

Total Substrate consumption [Mo] [Fe] Linear T T growth (nM) (nM) growth rate yield [Fe] [Mo]

optimal 1740 24000 11.90 3.00 1.25E+06 1.72E+07 Fe-limited 100 2400 6.19 2.00 8.33E+06 2.00E+08 100 240 4.93 1.00 4.17E+07 1.00E+08 100 120 3.92 0.83 6.88E+07 8.25E+07 100 24 3.53 0.70 2.92E+08 7.00E+07 100 12 3.47 0.60 5.00E+08 6.00E+07 100 2 3.24 0.45 2.25E+09 4.50E+07 100 0.01 0.61 0.04 Mo-limited 100 2400 6.19 2.00 8.33E+06 2.00E+08 10 2800 6.30 2.00 7.14E+06 2.00E+09 5 2800 6.43 2.00 7.14E+06 4.00E+09 2 2800 5.05 1.50 5.36E+06 7.50E+09 1 2800 5.44 1.00 3.57E+06 1.00E+10 0.5 2800 5.60 1.00 3.57E+06 2.00E+10 0.1 2800 5.71 1.00 3.57E+06 1.00E+11 co-limited 10 2 3.01 0.30 1.50E+09 3.00E+08 10 24 2.92 0.60 2.50E+08 6.00E+08 2 2 2.78 Growth yield in cells mL-1 x 107, growth rates in cells mL-1 day-1 x 107, substrate consumption (calculated by growth rate / [Fe] x 1000) in cells day-1 nmolFe-1.

150 Table B.2. Nitrogen fixation rates with growth phase (defined in Figure 2.1).

-1 -1 -1 nmol N2 cell min x 10

[Mo]i early mid late (nM) linear linear linear

0.5 0.5 0.3 0.2 1 0.6 0.3 0.2 5 4 4 1 10 4 4 1

151 Table B.3. Growth and N2 fixation rates for cultures grown autotrophically (with CO2) compared with those grown heterotrophically (with fructose).

7 -1 Growth rate x 10 N2 fixation rate x 10

Expt. CO2 Fructose CO2 Fructose

Optimal 0.8 3 1 Mo-limited 0.4 0.5 2.5 0.4 Fe-limited 0.7 1 3.5 0.2 -1 -1 -1 -1 Growth rates in cells mL day and N2 fixation rates in nmoles cell minute .

152 Table B.4. N2 fixation rates for cultures grown on CO2 with progressive culture density.

-1 N2 fixation rate x 10 Mo- Fe- Day OD limited OD limited

3 0.222 0.134 0.9 6 0.661 1.0 0.476 0.9 7 0.865 1.0 0.579 0.8 8 1.012 1.0 0.669 0.6 9 1.384 0.6 0.715 0.3 10 1.644 0.4 -1 -1 N2 fixation rates in nmoles cell minute .

153 Table B.5. Nitrogen fixation rates (in nmoles cell-1 minute-1), relative nitrogen fixation rates (RNR, in % of optimal), C:N ratios, and δ15N of biomass (‰) for organisms grown at variable metal concentrations in continuous culture.

N2 [Fe]i [Mo]i fixation (nM) (nM) rate RNR C:N δ15N

24,000 1700 0.80 100% 6.6 -0.34 24,000 1700 0.80 100% 6.5 -0.44 24,000 1700 8.1 -1.42 24,000 1 0.26 33% 2400 100 0.19 24% 2400 100 0.19 24% 240 100 0.50 63% 2 10 0.40 50%

154 (a) Maximum culture density versus [Fe] 14

12 7 10 x 10

-1 8

6

cells mL 4

2

0.01 0.1 1 10 100 1000 10,000 100,000

[Fe]T (nM)

(b) Maximum culture density versus [Mo] 14

12 7 10 x 10

-1 8

6

cells mL 4

2

0.1 1 10 100 1000 10,000

[Mo]T (nM)

Figure B.1. Growth yield, in terms of maximum culture density achieved during stationary growth, for cyanobacteria under optimal, Fe-limited, Mo-limited, and co-limited (both Fe- and Mo-limited) conditions, versus (a) initial [Fe] and (b) initial [Mo] added to the media (shown here as total metal concentrations in the system, [Fe]T and [Mo]T). Growth yields show a strong correlation with [Fe]T rather than [Mo]T, consistent with growth rates and supporting our claim that general growth is controlled by [Fe] in this organism. Data are given in Table B.1.

155 -1 4 Early linear growth x 10 -1 Mid linear growth 3 Late linear growth minute -1 cell

2 2

1 nmoles N

0 5 10

[Mo] i (nM)

Figure B.2. Nitrogen fixation rates for Mo-limitation experiments with growth phase. Data given in Table B.2.

156 (a) Growth - Optimal conditions 3.0

2.5 Fructose 2.0 CO2 1.5

OD (600 nm) 1.0

0.5

0 2 6 84 10 12 Days

(b) Growth - Fe-limitation 1.0

0.5 OD (600 nm)

0 2 6 84 10 12 Days

(c) Growth - Mo-limitation 2.0

1.5

1.0 OD (600 nm) 0.5

0 2 6 84 10 12 Days

Figure B.3. Growth curves for cultures grown autotrophically (on CO2) compared with those grown heterotrophically (on fructose). Shown are (a) optimal experiments, (b) Fe- limitation experiments, and (c) Mo-limitation experiments. See Additional Materials and Methods for explanation of methodology.

157 (a) N2 fixation - Fe-limitation 3 -4 Fructose

CO2 2

1 nmoles/cell x 10

0 10 20 30 40 50 Time (minutes)

(b) N2 fixation - Mo-limitation

3 -4

2

1 nmoles/cell x 10

0 10 20 30 40 50 Time (minutes)

Figure B.4. Nitrogen fixation curves for cultures grown autotrophically compared with those grown heterotrophically. Shown for (a) Fe-limitation and (b) Mo-limitation (“optimal” experiments failed). See Additional Materials and Methods for explanation of methodology.

158 3

7 Fructose

CO2

2 Optimal Mo-limited

Growth rate x 10 1 Fe-limited

-1 10

8 Optimal

6 Fe-limited Mo-limited 4 fixation rate x 10 2

N 2

Figure B.5. Growth rates (in cells mL-1 day-1) and nitrogen fixation rates (in nmoles cell-1 minute-1) for cultures grown autotrophically compared with those grown heterotrophically. Data given in Table B.3.

159 12 (a) N2 fixation - CO2 with growth

10 -1

8 Fe-limited 6 Mo-limited 4 fixation rate x 10 2

N 2

4 62 108 120 Days

12 (b) N2 fixation - CO2 with culture density

-1 10

8

6

fixation rate x 10 4 2 N 2

1.0 1.50.5 2.0 OD (600 nm)

-1 -1 Figure B.6. Nitrogen fixation rates (in nmoles cell minute ) for cultures grown on CO2, plotted against (a) the number of days grown and (b) the progressive culture density. Data given in Table B.4.

160 AIR IN AIR IN SAMPLES OUT AIR OUT MEDIA OUT FILTER CLAMP WASTE IN X FILTER MEDIA IN WASTE OUT FILTER

VITA- LITES PERISTALTIC PUMP

PERISTALTIC PUMP SAMPLER STIR PLATE STIR PLATE WASTE MEDIA CULTURE

Figure B.7. Metal-free continuous culture photostat.

161 N2 fixation - continuous culture

0.8 Optimal 100 nM Mo 10 nM Mo 1 nM Mo 0.6 “Archean” 0.4 fixation rate 2 N 0.2 “Proterozoic”

1 10 100 1,000 10,000 100,000

[Fe]i (nM) Figure B.8. Nitrogen fixation rates (in nmoles cell-1 minute-1) for continuous culture experiments performed at various metal concentrations. Data give in Table B.5. See Additional Materials and Methods for explanation of methodology.

162 Appendix C

Supplemental Material for Chapter 4

163 ORP (mV) Temperature (°C) pH Turbidity (NTU) Cells/mL (x10 6) 0 5 10 15 20 -400-300-200-100 100 6.5 7.0 7.5 8.0 0 20 4060 80 1 2 3 4 50 0

Auto-fluorescent Prokaryotes Total Prokaryotes 10

20 Depth (m) 30

40

50 0 0 20 40 60 3000 3500 4000 4500 5000 100 200 300 400 500 PAR2/PAR1 Specific conductivity Dissolved Oxygen (%) (µS/cm)

Figure C.1. Geochemical profiles (taken by Sonde) and acridine orange cell counts of GLfrom September 2002 (ORP non-corrected). At this time the chemocline resided at approximately 21.5m, as demonstrated by the sharp decrease in oxidation-reduction potential (ORP) and dissolved oxygen, along with the increase in turbidity.

164 Temperature (°C) pH Turbidity (NTU) 3 4 5 6 7 8 9 5.6 6.46.0 6.8 0 10 20 30 40 50

5

10

15

Depth (m) 20

25

30

35

40 -250 -200 -150 -100 -50 0 2600 3000 3400 ORP (mV) Specific conductivity (µS/cm) Figure C.2. Geochemical profiles (taken by Sonde) of GL from April 2003 (ORP non-corrected). At this time the chemocline resided around 19 - 22 m, with the turbidity peak at ~21.3 m.

165 Temperature (°C) pH Turbidity (NTU) 5 10 15 20 25 7.5 8.07.06.5 8.5 0 200 400 600 800

5

10

15

Depth (m) 20

25

30

35

40

45

-200 -100 0 100 1700 2100 2300 ORP (mV) Specific conductivity (µS/cm)

Figure C.3. Geochemical profiles (taken by Sonde) of GLfrom June 2003 (ORP non-corrected). At this time the chemocline resided around 20-22 m, with the turbidity peak at 21.5 m.

166 µM [Fe] µM [Mn] µM 0.02 0.06 0.10 0.14 0.18 0.5 1.5 2.5 2 4 6 8 10 12 14 16 180

Cu 5 Zn

10

15

20

25

30

35

40

45

Figure C.4. Dissolved metal (< 0.1 µm) concentrations for GL in June, 2004. Mo, W, V, Co, and Ni concentrations were below detection in all samples. The chemocline was around 20-22 m, with the turbidity maximum at 21.3 m (sonde data from this trip was lost).

167 µ [Cu] M µ [Zn] µM [Fe] M [Mn] µM 0.00020.00040.00060.00080.00100.00120.00140.00160 0.005 0.010 0.015 0.020 0.025 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.05 0.10 0.15 0.20 0.25 0.300

> 0.45 µm 0.1 - 0.45 µm 5

10

15

20

25

Depth (m) 30

35

40

45

50

Figure C.5. Particulate metal concentrations for GL in June, 2004. During this sampling trip waters were consecutively filtered on 0.1 and 0.45 µm filters. Whole filters were not digested, but rather filtrate was rinsed from filters with a known concentration of HNO3.

168 Curriculum Vitae: Aubrey Lea Zerkle

Department of Geosciences Pennsylvania State University 207 Deike Building University Park, PA 16802 [email protected]

Education Ph.D., Department of Geosciences, The Pennsylvania State University, 2006. Dissertation Research: Microbial Trace Metal Requirements: Limiting Nutrients and Potential Biosignatures Advisor: Christopher H. House

M.S., Department of Geology, University of Illinois at Urbana-Champaign, 2001. Masters Thesis: Microbial and Environmental Influences on Black Band Disease of Scleractinian Corals on Curacao, Netherlands Antilles. Advisor: Bruce W. Fouke

B.S. with Distinction, Department of Geology, University of Illinois at Urbana-Champaign, 1999.

Recent Awards, Grants, and Fellowships Ohmoto Graduate Fellowship (2005) International School of Aquatic Sciences Graduate Scholar (2004) P.D. Krynine Memorial Research Grants (2003, 2004, & 2005) Geological Society of America Research Grants (2001 & 2002) Tait Scholarship in Microbial Biogeochemistry (2001) Research Fellow, Penn State’s Biogeochemical Research Initiative in Education program, NSF-IGERT (2001-2005)

Professional Experience Graduate Research Assistant, Penn State Astrobiology Research Center, NASA Astrobiology Institute, Pennsylvania State University (2005-2006).

Graduate Research Fellow, Biogeochemical Research Initiative in Education (BRIE), and Teaching/Research Assistant, Department of Geosciences, Pennsylvania State University (2001-2005).

International School of Aquatic Sciences (SOAS) Graduate Scholar, Danish Center for Earth System Science, Institute of Biology, University of Southern Denmark, Odense, DK (Fall 2004).

Visiting scientist, Danish Center for Earth System Science (DCESS), Institute of Biology, University of Southern Denmark, Odense, DK (Fall 2003).

Summer Research Assistant, Penn State Astrobiology Research Center (PSARC), NASA Astrobiology Institute (Summer 2001).

Graduate Research Fellow, Texas-Alumni Fellowship, Dept. Geology, University of Illinois (1999-2001).

Undergraduate Research Assistant, Dept. Geology, University of Illinois (1998-1999).