Conserved and Specific Proteome Responses To

CONSERVED AND SPECIFIC PROTEOME RESPONSES TO GROWTH ON FERRIC CITRATE VERSUS FUMARATE IN VARIOUS GRAM- NEGATIVE DISSIMILATORY IRON REDUCING BACTERIA: Anaeromyxobacter dehalogenans 2CP-1, Shewanella oneidensis MR-1, Geobacter sulfurreducens PCA AND Geobacter bemidjiensis Bem

A Thesis

Presented to the Faculty of the Graduate School of

Cornell University

In Partial Fulfillment of the Requirements for the Degree of

Master of Science

Qiaochu Wang

August 2015

ABSTRACT

A growing list of Gram-negative iron reducing bacteria are known to be also capable of reducing soluble U(VI) to insoluble U(IV) biologically, providing potential uranium bioremediation strategies. Though model organisms have been studied for years, our knowledge of them is still limited and iron reductases of many other poorly characterized bacteria remain unknown. In this thesis, bottom-up proteomics using iTRAQ labeling was employed to analyze differential protein expression in Anaeromyxobacter dehalogenans

2CP-1, Shewanella oneidensis MR-1, Geobacter sulfurreducens PCA and Geobacter bemidjiensis Bem grown on ferric citrate versus fumarate. In all organisms, there were modest increases in enzymes associated with overall activity (oxidative phosphorylation,

TCA cycle, RNA polymerase) – consistent with iron being a more energetic electron acceptor than fumarate. For A. dehalogenans, our analyses suggested which cytochromes are iron reductases (A2cp1_0127 and A2cp1_1731). Additional studies such as heterologous expression or knock out mutations will be needed to prove this function.

BIOGRAPHICAL SKETCH

Qiaochu had obtained a Bachelor of Engineering degree from Southwest Jiaotong

University in China before he came to the U.S. for his graduate study at Cornell University in

2013. He knew that he would probably put his whole career in environmental engineering when he gave up the chance to switch his major from environmental engineering to electrical engineering in his sophomore year.

During his undergraduate study, his main concentration is technologies can be applied in wastewater treatment plants and his graduation project for bachelor’s degree is the technological design of a hospital wastewater treatment plant. He also did research in the application of enhanced coagulation technique in the removal of heavy metals from source water.

After the first academic year of his M.S. program at Cornell University, he became interested in environmental proteomics, which made his way finishing this thesis research.

Although his undergraduate study was more focusing on physical and chemical processes, which made him a novice in environmental microbiology, he made efforts to learn knowledge of microbiology, biochemistry, etc. to strengthen his background, enabling him to continue his proteomics research. Though it may not be a field to make money, he decided to stay in Environmental Engineering field and will try to find a chance to become a

PhD student in this field.

iii

To my beloved Mum and Dad &Xiaowei

iv ACKNOWLEDGEMENTS

Firstly, I would like to give my appreciation to Prof. Ruth Richardson and Prof. James

Gossett, for their unstoppable wisdoms and great patience in guiding me through the two- year academic journey and finally finished my thesis successfully. Their ways of thinking about problems are the most valuable things I have learned at Cornell University.

Secondly, I would like to express my love and appreciation to my parents who have always been giving their endless love to me, supporting my education financially and spiritually. All these can never be paid back but I would try my best to guarantee their health and happiness. I would also like to thank my girlfriend for her love and accompanying, as well as her support and understanding when I face difficulties. I will try my best to let her feel the same.

Thirdly, I want to give my special thanks to my colleagues Annie Otwell and Cristina

Fernandez-Baca for their friendliness and patience in helping me to learn many laboratory techniques during my research, which truly benefit me a lot.

Then I would like to thank all other faculty members of Environmental Processes:

Prof. Leonard Lion, Prof. James Bisogni, Prof. Damian Helbling and Dr. Monroe Weber-Shirk for their selflessly spreading knowledge both inside the class and outside the class.

v Last but not least, I want to give my thanks to all the people that have helped me, laughed with me and been my friend. This includes the entire Richardson Lab Group, all advisors and friends I met at Cornell University.

vi TABLE OF CONTENTS

ABSTRACT ...... III BIOGRAPHICAL SKETCH ...... III ACKNOWLEDGEMENTS ...... V TABLE OF CONTENTS ...... VII LIST OF TABLES ...... X LIST OF FIGURES ...... XII LIST OF ABBREVIATIONS ...... XV CHAPTER 1. INTRODUCTION ...... 1

1.1 URANIUM CONTAMINATION ...... 1

1.2 BIOREMEDIATION OF URANIUM ...... 2 CHAPTER 2. BACKGROUND ...... 4

2.1 DISSIMILATORY IRON REDUCTION...... 4 2.1.1 Concept ...... 4 2.1.2 Access to Iron ...... 4 2.1.3 Dissimilatory Iron Reducing Bacteria (DIRB) ...... 5 2.1.4 Uranium Reduction ...... 6

2.2 DIRB IN BIOREMEDIATION AND BIOENERGY ...... 7 2.2.1 Heavy Metal and Uranium Contamination ...... 7 2.2.2 Chlorinated Solvents Dechlorination ...... 10 2.2.3 Microbial Fuel Cells ...... 11

2.3 DIRBS IN THE ENVIRONMENT ...... 12 2.3.1 As Members of Microbial Communities ...... 12 2.3.2 Environmental Significance ...... 13

2.4 SELECTED DIRBS STUDIED IN THIS THESIS ...... 15 2.4.1 Overview ...... 15

vii 2.4.2 Anaeromyxobacter dehalogenans 2CP-1 ...... 16 2.4.3 Shewanella oneidensis MR-1 ...... 20 2.4.4 Geobacter sulfurreducens PCA ...... 23 2.4.5 Geobacter bemidjiensis Bem ...... 26

2.5 PROTEOMICS METHODS FOR STUDYING MICROORGANISMS...... 28 2.5.1 Overview ...... 28 2.5.2 Comparison to Genomics Study ...... 28 2.5.3 LC-MS/MS Based Proteomics ...... 29 2.5.4 Proteomics Methods ...... 30 CHAPTER 3. OBJECTIVES ...... 34

3.1 OVERVIEW ...... 34

3.2 IDENTIFYING SHIFTS IN THE CULTURE PROTEOMES ACROSS THE FOUR SPECIES GROWN ON IRON

VERSUS FUMARATE ...... 34

3.3 PRIORITIZING CONSERVED HYPOTHETICAL PROTEINS FOR FURTHER CHARACTERIZATION ...... 34

3.4 DISCOVERY OF C-TYPE CYTOCHROMES AND OTHER MEMBRANE-BOUND OXIDOREDUCTASES

POSSIBLY INVOLVED DIRECTLY IN IRON-REDUCTION ...... 35 CHAPTER 4. MATERIAL AND METHODS ...... 36

4.1 CULTURE CONDITIONS ...... 36 4.1.1 Anaeromyxobacter dehalogenans 2CP-1 and Geobacter sulfurreducens PCA .... 36 4.1.2 Shewanella oneidensis MR-1 ...... 36 4.1.3 Geobacter bemidjiensis Bem ...... 37

4.2 LC-MS/MS BASED SHOTGUN PROTEOMICS ...... 38 4.2.1 Protein Samples Preparation and Trysin Digestion ...... 38 4.2.2 Multiplex LC-MS/MS Analysis using iTRAQ Tags ...... 38

4.3 DATA PROCESSING ...... 40 4.3.1 Peptide Data Cut-off ...... 40 4.3.2 Normalization ...... 47 4.3.3 Peptide-to-Protein Rollup ...... 52 4.3.4 Protein Abundance Data Processing and Analysis ...... 57

4.4 BIOINFORMATICS TOOLS ...... 63 4.4.1 SPOCS ...... 63 viii 4.4.2 BLAST ...... 63 4.4.3 KEGG Pathway Database ...... 64 4.4.4 Pfam Protein Family Database ...... 64 4.4.5 PSORTb ...... 65 CHAPTER 5. RESULT AND DISCUSSION ...... 66

5.1 PROTEIN STATISTICS ...... 66 5.1.1 Protein Detection Rate ...... 66 5.1.2 Protein Ratios between Iron and Fumarate Conditions ...... 86

5.2 ACROSS-SPECIES ANALYSIS ...... 90 5.2.1 Overview ...... 90 5.2.2 Up-Regulated Orthologs ...... 91 5.2.3 Down-Regulated Orthologs ...... 96

5.3 ORGANISM-SPECIFIC ANALYSIS ...... 100 5.3.1 Proteins in Important Pathways ...... 100 5.3.2 c-Type Cytochromes ...... 114 5.3.3 Highly Up-Regulated Hypothetical Proteins ...... 129 5.3.4 Other Up-Regulated Proteins of Interest ...... 134 CHAPTER 6. CONCLUSIONS & SUGGESTIONS FOR FURTHER STUDY ...... 139

6.1 SUMMARY OF KEY FINDINGS ...... 139

6.2 SUGGESTIONS FOR FUTURE WORK ...... 141 APPENDICES ...... 143

APPENDIX A. NON-NORMALIZED LOG2-TRANSFORMED PEPTIDE INTENSITIES ...... 143

APPENDIX B. NORMALIZED LOG2-TRANSFORMED PEPTIDE INTENSITIES ...... 145

APPENDIX C. SPOCS-IDENTIFIED ORTHOLOGOUS PROTEINS DETECTED IN ALL FOUR ORGANISMS (106*4) ...... 148

APPENDIX D. TOP 20 UP-REGULATED AND DOWN-REGULATED PROTEINS OF FOUR ORGANISMS ...... 158 REFERENCES ...... 166

ix LIST OF TABLES

Table 2.1: Genome information of four organisms...... 15 Table 4.1: Labeling schemes of 4plex iTRAQ reagents……………………………………………...... 39 Table 4.2: Example of raw peptide data………………………………………………………………...... …41 Table 4.3: Peptide intensities of Anaeromyxobacter dehalogenans………………………...... …43 Table 4.4: Detected proteins expressed by duplicate genes in four organisms...…………...... 44 Table 4.5: Peptide intensities of Anaeromyxobacter dehalogenans (after filtering steps)...... 46 Table 4.6: Example of protein abundance data (A. dehalogenans)……………………....…...... 58 Table 4.7: Example of protein abundance ratio data (A. dehalogenans)...... 59 Table 4.8: Example of normal protein ratio with annotations and t-test results (A. dehalogenans).....……………………...... ……...... ………………………...... …………62 Table 5.1: Proportion of genome-predicted and detected proteins of A. dehalogenans (classified by annotations)……………………...... ……………………...... …...... …67 Table 5.2: Proportion of genome-predicted and detected proteins of S. oneidensis (classified by annotations).……………………...... ………………………...... ……………………...... 69 Table 5.3: Proportion of genome-predicted and detected proteins of G. bemidjiensis (classified by annotations)...……………………...... ………………..... …...... ….....…………………....71 Table 5.4: Proportion of genome-predicted and detected proteins of G. sulfurreducens (classified by annotations). ……………………...... ………………………...... ……………...... 73 Table 5.5: Protein detection rate of four species (classified by annotations)...... 75 Table 5.6: Proportion of genome-predicted and detected proteins of A. dehalogenans (classified by subcellular locations as predicted by PSORTb)...... 76 Table 5.7: Proportion of genome-predicted and detected proteins of S. oneidensis (classified by subcellular locations as predicted by PSORTb)...... 79 Table 5.8: Proportion of genome-predicted and detected proteins of G. bemidjiensis (classified by subcellular locations as predicted by PSORTb)...... 81

x Table 5.9: Proportion of genome-predicted and detected proteins of G. sulfurreducens (classified by subcellular locations as predicted by PSORTb)...... 83 Table 5.10: Protein detection rate of four species (classified by subcellular locations as predicted by PSORTb)……………………...... ………………………...... …………....……...... …85 Table 5.11: Orthologs up-regulated over four organisms....……………………...... …...... 91 Table 5.12: Annotations of orthologs up-regulated over four organisms...…………..………….....93 Table 5.13: Orthologs down-regulated over four organisms...... 96 Table 5.14: Annotations of orthologs down-regulated over four organisms...... 97 Table 5.15: c-Type cytochromes detected in A. dehalogenans...... ……...... ……………...... …115 Table 5.16: c-Type cytochromes detected in S. oneidensis...... 120 Table 5.17: c-Type cytochromes detected in G. sufurreducens...... 123 Table 5.18: c-Type cytochromes detected in G. bemidjiensis...... 127 Table 5.19: Highly up-regulated hypothetical proteins in all four organisms...... 130 Table 5.20: Other up-regulated proteins of interest in A. dehalogenans...... 134 Table 5.21: Other up-regulated proteins of interest in S. oneidensis...... 135 Table 5.22: Other up-regulated proteins of Interest in G. sulfurreducens...... 136 Table 5.23: Other up-regulated proteins of interest in G. bemidjiensis...... 137 Table 6.1: Proteome results of all four organisms...... 138

xi LIST OF FIGURES

Figure 2.1: 16S rRNA gene-based phylogenetic tree of selected iron and sulfate reducing bacteria………………………...... ………………………...... ……...…………………...... ………………...16 Figure 2.2: Proposed electron transport in Shewanella oneidensis…...... 23 Figure 2.3: Proposed electron transport in Geobacter sulfurreducens…...... 26 Figure 2.4: Structure of the iTRAQ reagent…...... 31 Figure 2.5: iTRAQ 4plex reagents workflow…...... 33 Figure 4.1: Log2-transformed peptide Intensities box plot of A. dehalogenans (non-normalized)...... …48 Figure 4.2: Normalization parameters in InfernoRDN users interface...... 49 Figure 4.3: Log2-transformed peptide intensities box plot of A. dehalogenans (normalized)...... 51 Figure 4.4: Distribution of log2-transformed normalized peptide intensities of A. dehalogenans...... 53 Figure 4.5: Distribution of log2-transformed normalized peptide intensities of S. oneidensis...... 54 Figure 4.6: Distribution of log2-transformed normalized peptide intensities of G. sulfurreducens...... 55 Figure 4.7: Distribution of log2-transformed normalized peptide intensities of G. bemidjiensis...... 56 Figure 4.8: Log2-transformed average protein abundance ratio (ferric citrate to fumarate) distribution...... 60 Figure 5.1: Proportion of genome-predicted and detected proteins of A. dehalogenans (classified by annotations)...... 68 Figure 5.2: Proportion of genome-predicted and detected proteins of S. oneidensis (classified by annotations)...... 70

xii Figure 5.3: Proportion of genome-predicted and detected proteins of G. bemidjiensis (classified by annotations)...... 72 Figure 5.4: Proportion of genome-predicted and detected proteins of G. sulfurreducens (classified by annotations)...... 74 Figure 5.5: Normalized protein detection rate of four species (classified by annotations).....76 Figure 5.6: Proportion of genome and detected proteins of A. dehalogenans (classified by subcellular locations as predicted by PSORTb)...... 78 Figure 5.7: Proportion of genome and detected proteins of S. oneidensis (classified by subcellular locations as predicted by PSORTb)...... 80 Figure 5.8: Proportion of genome and detected proteins of G. bemidjiensis (classified by subcellular locations as predicted by PSORTb)...... 82 Figure 5.9: Proportion of genome and detected proteins of G. sulfurreducens (classified by subcellular locations as predicted by PSORTb)...... 84 Figure 5.10: Normalized protein detection rate of four species (classified by subcellular locations as predicted by PSORTb)...... 85 Figure 5.11: Distribution of protein ratio of four species (protein numbers)...... 87 Figure 5.12: Distribution of protein ratio of four species (protein frequencies)...... 88 Figure 5.13: Detection and regulation status of proteins involved in each step of TCA Cycle (A. dehalogenans).…………...... …...... …………...... …...... …...... 101 Figure 5.14: Detection and regulation status of proteins involved in each step of TCA Cycle (S. oneidensis)…………...... …...... …………...... …...... …………..…...... 103 Figure 5.15: Detection and regulation status of proteins involved in each step of TCA Cycle (G. sulfurreducens)...…………...... …...... …………...... ………...... …...... 105 Figure 5.16: Detection and regulation status of proteins involved in each step of TCA Cycle (G. bemidjiensis)…………...... …...... …………...... …………...... …...... 107 Figure 5.17: Detection and regulation status of proteins involved in each step of oxidative phosphorylation (A. dehalogenans)……………………...... ………………………...... 109 Figure 5.18: Detection and regulation status of proteins involved in each step of oxidative phosphorylation (S. oneidensis)...... 110 Figure 5.19: Detection and regulation status of proteins involved in each step of oxidative phosphorylation (G. sulfurreducens)...……………………...... ……...... …...... 111 xiii Figure 5.20: Detection and regulation status of proteins involved in each step of oxidative phosphorylation (G. bemidjiensis)...... 112

xiv LIST OF ABBREVIATIONS

AD Anaeromyxobacter dehalogenans GS Geobacter sulfurreducens GB Geobacter bemidjiensis SO Shewanella oneidensis DIRB Dissimilatory Iron Reducing Bacteria LC Liquid Chromatography MS Mass Spectrometry iTRAQ Isobaric Tag for Relative and Absolute Quantitation FeC Ferric Citrate Fum Fumarate SPOCS Species Paralogy and Orthology Clique Solver BLAST Basic Local Alignment Search Tool

MQ Menaquinone MtrA/B/C Methylransferase A/B/C

CymA Cytoplasmic-Membrane Cytochrome A

PpcA/D Periplasmic Cytochrome A/D

OmcA/B/E/S/Z Outer Membrane Cytochrome A/B/E/S/Z

OmpB Outer-Membrane Protein B fccA Flavocytochrome c flavin subunit A

NrfA Formate-dependent Nitrite Reductase subunit A

xv CHAPTER 1. INTRODUCTION

1.1 Uranium Contamination Uranium, a chemical element with atomic number 92 in the actinide series of the periodic table, is well known as the fissile material to produce early nuclear weapons and to fuel nuclear power plants to generate electricity.

Above-ground nuclear tests by countries during the Cold War between the Soviet

Union and the United States spread a significant amount of fallout from uranium daughter isotopes around the world (Warneke et al. 2002).

In civilian fields, the anthropogenic use of uranium for research purposes and fuel production has resulted in widespread environmental contamination. Combustion of coal and application of phosphate fertilizers release trace amounts of uranium, which is also a potential source of contamination (Markich 2002).

Soluble uranium salts are toxic, though their toxicity is not as strong as other heavy metals like lead or mercury. Alpha radiation, the primary form of Uranium 238 decay, has a very short range, and will not penetrate skin, so toxic effects are the main risks posed by uranium exposure, but ingestion of uranium will cause radiological hazard. Normal functioning of the brain, heart, kidney, liver and other systems such as reproductive system

1 can be affected by uranium exposure. Possible ways of exposure include inhaling dust in air

or ingesting contaminated water or food.

1.2 Bioremediation of Uranium After the end of the Cold War, the U.S. Department of Energy (DOE) began efforts to

identify as well as remediate contaminated areas around the country, including 120 sites in

36 states and territories covering 7280 km2, most of which were contaminated with uranium

(McCullough, Hazen, and Benson 1999).

Bioremediation of uranium is different from biodegradation of organic substances.

Unlike organic compounds, toxic metals cannot be degraded, only their biological

accessibility and solubility can be altered to achieve the goal of protecting human and

environmental health. Luckily, the chemistry of uranium provides a possible approach to

effect bioremediation: when uranium is reduced from U(VI) oxidation state to U(IV), its

solubility decreases, causing the immobilization (Langmuir 1978). A growing list of Gram-

negative iron reducing bacteria are known to be also capable of reducing uranium

biologically by utilizing it as the terminal electron acceptor. These iron reducing bacteria

provide us with a potential uranium bioremediation strategy. As iron reducing bacteria and

uranium reducing bacteria often overlap with each other (Stolz and Oremland 2011) and the

same pathways might be involved in transporting electrons to Fe(III) and U(VI), it would be

helpful to study Fe(III) reduction in order to understand U(VI) reduction better. Shewanella

oneidensis and Geobacter sulfurreducens are two members of this list and their abilities for

reducing Fe(III) have been deeply studied for years. Model pathways for transporting

2 electrons to terminal metal electron acceptors like Fe(III) have been proposed for these two organisms, making them model organisms in studying iron as well as uranium reduction processes (Shi et al. 2007). Another Gram-negative bacterium, Anaeromyxobacter dehalogenans, is also capable of uranium and iron reduction but is much less well understood than the model genera, Shewanella and Geobacter, and has shown a vast range of metabolic diversity but functional annotation of the genome is limited and neither iron nor uranium reductases have been discovered.

Shotgun proteomics can give us expression information for as many proteins as possible. By comparing proteins expression in cells grown with different electron acceptors, proteins deeply involved in terminal electron transport to metal could be identified, helping us understand the Fe(III) reduction processes.

3 CHAPTER 2. BACKGROUND

2.1 Dissimilatory Iron Reduction

2.1.1 Concept Dissimilatory metal reduction is a process used by microorganisms to conserve

energy through oxidizing organic or inorganic electron donors and transferring electrons to a

metal or metalloid. In this process, as terminal electron acceptors, the metal or metalloid

accepts electrons and get reduced. During this type of metabolism, electrochemical

gradients are created during the process, providing energy required for growth.

Due to the potential of facilitating bioremediation in areas with heavy metal and/or

nucleotide contamination, the dissimilatory iron reduction process becomes a research

focus because organisms conducting this process are often versatile in available electron

acceptors. Organisms that can conduct these processes, particularly with iron as electron

acceptor, are called dissimilatory iron reducing bacteria (DIRB). These microorganisms are

also essential for development of microbial fuel cells.

2.1.2 Access to Iron Fe(III) reduction often takes place in sediments, soils and subsurface. When pH is

higher than 4 in these environments, the availability of soluble Fe(III) is limited and the

predominant forms of Fe(III) are its oxides or hydroxides with low solubility (Stolz and

Oremland 2011). To access the insoluble forms of Fe(III), iron reducing bacteria have been 4 found to utilize soluble electron shuttles as well as Fe(III)-chelating compounds. Direct

electron transfer is achieved with outer membrane enzymes, nanowires or pili (Coker et al.

2012).

2.1.3 Dissimilatory Iron Reducing Bacteria (DIRB) Much of the Fe(III) reduction in sedimentary environments was considered to result

from abiotic processes until dissimilatory Fe(III) reducers were found to enzymatically

catalyze nearly all of the Fe(III) reduction in anaerobic, nonsulfidogenic environments (Derek

R. Lovley 1993). Dissimilatory iron reduction occurs most often anaerobically in aquatic

sediments, submerged soils, and aquifers.

There are numbers of microorganism that are capable of reducing Fe(III) to Fe(II), as

well as reducing U(VI) to U(IV). Most of them are Gram-negative bacteria and some of them

are highly versatile, for instance, Geobacter sulfurreducens and Anaeromyxobacter

dehalogenans. Some Firmicutes are also capable of conducting the dissimilatory iron

reduction process, like Desulfotomaculum reducens (Tebo and Obraztsova 1998).

Geobacter sulfurreducens can use fumarate as electron acceptor, while it can also

oxidize a fermentation product, acetate. Besides reducing Fe(III) to Fe(II), it is able to utilize

sulfate as terminal electron acceptor, producing H2S. The overall energy-generating reaction,

with acetate and Fe(III) as electron donor and acceptor respectively, is:

- - + CH3COO + 8Fe(III) + 4H2O = 2HCO3 + 9H + 8Fe(II); ∆G°' = -370.6 kJ/mol

When acetate and fumarate are serving as electron donor and acceptor,

respectively, the reaction becomes:

5 - 2- - + 2- CH3COO + 4 C4H2O4 + 4H2O = 2HCO3 + H + 4 C4H4O4 ; ∆G°' = -239.3 kJ/mol

In Geobacter strains, organic substrate is completely oxidized through the TCA cycle and electrons are shuttled to a membrane-bound electron transport chain. During the oxidative phosphorylation process (a membrane-bound enzymatic pathway), electrons from donors are shuttled to a terminal electron acceptor. Hydrogen ions (protons) are pumped out of the cytoplasmic membrane thus a proton motive force is created. With proton motive force, ATP synthase is able to synthesize ATP from ADP and Pi, storing chemical energy in

ATP molecules. Geobacter sulfurreducens possesses outer membrane-bound c-type cytochromes OmcE and OmcS which are suggested to conduct extracellular electron transport with microbial nanowires (type IV pili), transferring electrons to insoluble Fe(III) oxides (Reguera et al. 2005). The result of a knock-out mutation experiment showed that the mutants that lack the conductive pili are not able to utilize Fe(III) oxides as electron acceptors to grow (Reguera et al. 2005).

2.1.4 Uranium Reduction To remove uranium from contaminated sites, the soluble form U(VI) needs to be transformed to the insoluble U(IV). Several species of dissimilatory iron reducing bacteria are also capable of reducing uranium and the list is growing (Wall and Krumholz 2006).

Some of which, for example, Geobacter metallireducens and Shewanella algae can respire uranium to grow, though most others like Desulfovibrio desulfuricans can only reduce uranium without coupling the process to growth. The mechanism of microbes reducing U(VI) to U(IV) for ATP generation is not well-understood, but there is a reasonable hypothesis that

6 a phosphorylation involving electron transport exists in their growth with metals as terminal

electron acceptors (Wall and Krumholz 2006). The reduction process of U(VI) to U(IV) is

similar to the reduction process of Fe(III) to Fe(II), and the same enzymes might be involved

in the reactions. Iron reducing bacteria are also believed to reduce uranium indirectly, since

the product of Fe(III) reduction, soluble Fe(II), can reduce U(VI) to U(IV) abiotically (Du et al.

2011). It is not surprising that microbes reducing Fe(III) and U(IV) overlap with each other

(Stolz and Oremland 2011). This makes it important to study dissimilatory iron reducing

bacteria in order to get better understanding of uranium reduction to apply better uranium

bioremediation.

2.2 DIRB in Bioremediation and Bioenergy

2.2.1 Heavy Metal and Uranium Contamination Heavy metals exist naturally in the earth and become concentrated due to a series of

human activities. Release of these concentrated materials causes heavy metal

contamination. They can enter human tissues via inhalation of dust in the air or by ingesting

contaminated water or food. Heavy metal poisoning of humans is usually treated by

applying chemicals to solubilize heavy metals in the human body by chelation. Thus this

chemically inert form of heavy metals can be excreted without further interaction with the

body. The purpose of bioremediation is also to remove pollutants from the “body” of a

contaminated site, though immobilization (precipitation), rather than mobilization

(solubilization) is usually the strategy in the bioremediation of heavy-metal-contaminated

subsurface systems.

7 Bioremediation is an environmental engineering technique that can be applied at sites contaminated by metals, organic compounds, and inorganic chemicals. This technique uses organisms to remove or neutralize pollutants. The EPA definition of bioremediation is

“a treatment that uses naturally occurring organisms to break down hazardous substances into less toxic or non-toxic substances” (http://www.epa.gov/). In the case of uranium bioremediation, the general idea is to transform uranium compounds in contaminated sites to an insoluble form to remove it from groundwater.

Microbial uranium reduction could be applied to bioremediation of uranium contamination sites. Contamination of ground water with uranium occurs near sites where uranium is mined or processed (Wall and Krumholz 2006). As it was reported that U(VI) is in the form of uranyl-carbonate complexes in most surface and ground waters (Derek R. Lovley and Phillips 1992b), microorganisms that are capable of reducing uranyl-carbonate complexes are valuable in applying bioremediation. Two microorganisms — Geobacter metallireducens (Gorby and Lovley 1992) and Desulfovabrio desulfuricans (Derek R. Lovley and Phillips 1992a) — have demonstrated their capability of reducing U(VI) in uranium- carbonate complexes. The general idea of utilizing these uranium reducers is to let them take U(VI) as electron acceptor while electron donors (organic compounds) are already on site or supplied as part of the engineered bioremediation process. These electron donors include acetate, lactate or ethanol. As the bacteria are growing, U(VI) will accept electrons originally from electron donors during the anaerobic reduction process and thus gets reduced to U(IV) form. The insoluble U(IV) form of uranium, mostly UO2, is precipitated from underground water and immobilized on the soils, thus the remediation of the groundwater

8 is achieved. In this way, microbial uranium reduction process can be used to concentrate soluble uranium that is dispersed in a large volume of liquid to pure and compact solid of insoluble uranium.

A long-term trial of uranium bioremediation is ongoing at the former uranium ore processing facility in Rifle, Colorado (R. T. Anderson et al. 2003; Vrionis et al. 2005). A traditional bioremediation approach was used: acetate was injected as electron donor into a galley of wells placed in two closely placed rows that were perpendicular to groundwater flow. U(VI) concentrations decreased to below the treatment goal after about two months and began to increase. The increase might be the result of transport of uranium from an up- gradient source, desorption of uranium from sediments or/and U(IV) reoxidation abiotically or biotically (Wall and Krumholz 2006), which is a disadvantage of such bioremediation strategies as operators have to keep the site anaerobic far into the future.

The advantages of applying microbial uranium reduction to bioremediation over other proposed uranium contamination treatment techniques like treatment with chelating compounds (Derek R. Lovley and Phillips 1992b) include: 1) the ability to treat U(VI) in the form of uranium-carbonate complexes; 2) the ability to concentrate dispersed soluble uranium into solid, insoluble uranium; 3) the potential to treat organic contaminants along with uranium contaminants by using the organic contaminants as electron donors; 4) high uranium removal efficiency (amount of uranium reduced per unit of biomass); and 5) the potential to be applied to both ground water and surface water bioremediation (Derek R.

Lovley 1993).

9 Different techniques are applied to realize bioremediation of uranium and they can

be generally classified as in situ or ex situ. In situ bioremediation treats the contaminated

material, in this case uranium, at the site, while ex situ method removes and treats uranium

elsewhere or above ground at the site.

2.2.2 Chlorinated Solvents Dechlorination

Geobacter lovleyi is a Gram-negative, rod-shaped delta-Proteobacterium conducting

anaerobic respiration. Geobacter lovleyi strain SZ was first isolated from a sample from non-

contaminated creek sediment by Sung et al. (Sung et al. 2006). It was the first Geobacter

recognized as capable of reducing tetrachloroethene (PCE) to cis-1,2-dichloroethene (cis-

DCE). G. lovleyi shares features with members of Geobacteraceae family, with Geobacter

thiogenes as its closest relative suggested by genetic and phenotypic analyses. These

features include the capability of utilizing both acetate and hydrogen as electron donors and

reducing oxidized metals. Pyruvate can serve as an alternate electron donor for Geobacter

lovleyi Strain SZ (Sung et al. 2006). Feasible electron acceptors’ oxidation include: PCE and

TCE to cis-DCE, Fe(III) to Fe(II), malate to succinate, U(VI) to U(IV), nitrate to ammonia.

Additionally, another strain of G. lovleyi, strain KB1 was also identified to be able to conduct

PCE-to-cis-DCE-dechlorination(Wagner et al. 2012), it is a member of the KB1

bioaugmentation culture(Duhamel and Edwards 2006). A. dehalogenans, which is a focus of

this thesis, is capable of dechlorinating chlorophenols (Sanford, Cole, and Tiedje 2002).

The appearance of alternate electron acceptors such as nitrate and U(VI) does not

inhibit the reductive dechlorination (Sung et al. 2006). This ability of reducing priority

10 pollutant PCE to cis-DCE makes Geobacter lovleyi a potential microbe species to be applied

to PCE bioremediation. But other microorganisms such as Dehalococcoides mccartyi would

be needed to achieve complete dechlorination, in which the desired final products are

ethene or ethane.

2.2.3 Microbial Fuel Cells Electricigens, which refer to microorganisms that can be used to generate electricity

in a solid electrode, share some properties with iron reducing bacteria, as both of the two

types of microbes can transfer electrons to an extracellular insoluble electron acceptor.

While iron reducing bacteria transfer electrons to electron acceptors such as hydrous ferric

oxides (HFO) through nanowires, electricigens can transfer electrons to an electrode to

generate electricity. Actually the presence of nanowires is evidence of the bacteria’s ability

of transporting electrons to distant electron acceptors in thick biofilms (DEREK R. Lovley et

al. 2008). Therefore, it is not surprising that these two types of bacteria would share similar

features.

Studies with Desulfuromonas and Geobacter species demonstrated their ability to

grow by completely oxidizing organic compounds to carbon dioxide while electrodes served

as the sole electron acceptor. More than 95% of the electrons from the oxidation of electron

donors can be recovered as electricity (Derek R. Lovley 2006). Studies in the laboratory of

Byung Hong Kim at the Korea Institute of Science and Technology demonstrated that

Shewanella species could also produce electricity from oxidizing lactate (Gorby et al. 2006).

The efficiency of Shewanella species generating electricity from an organic compound is

11 however low due to the fact that Shewanella species only incompletely oxidize lactate to acetate.

2.3 DIRBs in the Environment

2.3.1 As Members of Microbial Communities Since many different species of microbes can grow under anaerobic conditions, there is never a pure culture for any of these microbes in the natural environment. That is to say, interactions among various species of microbes are always involved in the mixed culture of sediments. These interactions include competition and cooperation.

Geobacter sulfurreducens, Anaeromyxobacter dehalogenans and Shewanella oneidensis can all grow under anaerobic conditions. These versatile microbes share many electron donors and acceptors. For example all three organisms can grow on H2 as electron donor when other carbon sources are provided. G. sulfurreducens and A. dehalogenans both grow on acetate as electron donor and carbon source, completely reducing acetate to carbon dioxide and water. When Fe(III) is the sole electron acceptor, it gets reduced.

Researchers have even employed the same media (Derek R. Lovley and Phillips 1988;

Wrighton et al. 2011) to culture A. dehalogenans and G. sulfurreducens in laboratory research. These two organisms, when at the same site, may present a relationship of competition, in which they compete for donor substrate and iron. The expression of genes may be regulated as a response to these competitive surroundings.

Different DIRB species may also appear in a relationship of cooperation. A Gram- positive bacterium, Desulfotomaculum reducens, can utilize butyrate as electron donor and

12 iron or sulfate as an electron acceptor. During iron respiration, one butyrate molecule is incompletely oxidized to 2 acetate molecules, donating 4 electrons to be transported to electron acceptors, ideally, or released as 2H2. As mentioned above, acetate can be utilized by A. dehalogenans and G. sulfurreducens as electron donor. So at the electron donor side of the co-culture of these two microbes, a cooperative relationship is formed when D. reducens provides its metabolic product to A. dehalogenans as the latter’s electron donor.

In fact D. reducens and A. dehalogenans/G. sulfurreducens form a relationship more complicated and interesting than a simple cooperation. They can all reduce U(VI) or Fe(III), which can make their relationship in a co-culture competitive at the electron acceptor side.

These multi-DIRB cultures can contribute to uranium bioremediation. These organisms can act simultaneously to immobilize uranium at contaminated sites and their uranium reductases can lead to effective biomolecular assays which can monitor the activities of multiple uranium reducers simultaneously at the site.

Interaction between DIRBs and other groups of microbes can also be both cooperative and competitive. As mentioned before, DIRBs can utilize acetate and H2 as electron donors and acetate and H2 are fermentation products of fermenting organisms.

DIRBs also compete with Sulfate Reducing Bacteria (SRB) and denitrifiers for these electron donors.

2.3.2 Environmental Significance Microbial iron reduction has a greater general environmental influence than any other microbial metal reduction process (Derek R. Lovley 1993), since its involvement in

13 many environmental phenomena were directly shown or implicated, such as organic substance decomposition in various waters or sediments; the buildup of magnetite in the

Banded Iron Formations coupled with organic matter oxidation (Derek R. Lovley 1993); and the formation of other Fe(II) minerals and the discharge of phosphate and trace metals into water following Fe(III) reduction.

The environmental significance of microbial uranium reduction comes from the fact that the U(VI) is soluble in most natural water bodies while U(IV) is highly insoluble

(Langmuir 1978). Reduction of U(VI) to U(IV) which makes uranium precipitate is the most universally significant fate for dissolved uranium (R. F. Anderson et al. 1989; Klinkhammer and Palmer 1991; Alexander J. B., n.d.). Some roll-type or sand stone uranium ores were formed with a mechanism where soluble uranium could reductively precipitate from groundwater (Hostetler and Garrels 1962; Jensen 1958; Langmuir 1978). Anaerobic uranium reduction was suggested to be carried by abiotic reduction by sulfide or H2 in earlier years

(Hostetler and Garrels 1962; Jensen 1958; Langmuir 1978). But neither sulfide nor H2 is an effective uranium reductant at the typical temperature and pH of ground waters and aquatic sediments (Derek R. Lovley 1993). Besides, uranium reduction was limited in sterilized anaerobic sediments. These observations suggest that uranium reduction in the environment is achieved with uranium-reducing enzymes (Derek R. Lovley et al. 1991). The accumulated data have not identified dedicated uranium reductases yet. Mutants of G. sulfurreducens were constructed with several genes involved in dissimilatory iron reduction.

These mutants’ abilities to reduce Fe(III) were negatively affected. However there was not a good correlation between effects on reduction rates of Fe(III) and U(VI). The capability of

14 U(VI) reduction was decreased but not eliminated, implying that the reduction of uranium

may happen at multiple locations of the cell.(Wall and Krumholz 2006).

2.4 Selected DIRBs Studied in This Thesis

2.4.1 Overview Proteomic data from experiments with four species of Dissimilatory Iron Reducing

Bacteria are presented in this thesis: Anaeromyxobacter dehalogenans 2CP-1, Shewanella

oneidensis MR-1, Geobacter sulfurreducens PCA and Geobacter bemidjiensis Bem. Genome

information of these four strains are provide by IMG (https://img.jgi.doe.gov/), shown in

Table 2.1. The phylogenetic relatedness of these species is shown in Figure 2.1. They are all

Gram-negative cells belonging to the Proteobacteria phylum which possess cytoplasmic

membranes and an outer membranes containing a lipopolysaccharide (LPS) layer. Many

significant metabolic pathways take place in the cytoplasmic membrane as this membrane

possesses a variety of protein complexes conducting key reactions such as Nicotinamide

Adenine Dinucleotide (NADH) dehydrogenation and Adenosine Triphosphate (ATP)

synthesis. Outer membranes also contain proteins with key functions such as protein

translocation, signal transduction (Koebnik, Locher, and Van Gelder 2000), and extracellular

electron transport. Although all four organisms are Proteobacteria, they have physiological

and genomic differences as discussed below.

15 Table 2.1: Genome information of four organisms.

Genome Size Genes Number Protein-coding Bases # G+C # (%) Total # Strains Genes (%) A. dehalogenans 2CP-1 5029329 3757738 (74.72%) 4540 4485 (98.79%) S. oneidensis MR-1 5131416 2354767 (45.89%) 4657 4502 (96.67%) G. sulfurreducens PCA 3814139 2324233 (60.94%) 3552 3465 (97.55%) G. bemidjiensis Bem 4615150 2781399 (60.27%) 4106 4034 (98.25%)

Figure 2.1: 16S rRNA gene-based phylogenetic tree of selected iron and sulfate reducing bacteria. Strains studied in this thesis are noted with red circles. Tree provided by Stephen Callister at PNNL (unpublished)

2.4.2 Anaeromyxobacter dehalogenans 2CP-1

16 2.4.2.1 Description Anaeromyxobacter dehalogenans is a slender rod-shaped Gram-negative bacterium found in soil. This bacterium, whose movement is achieved by gliding, can form a spore-like structure (Sanford, Cole, and Tiedje 2002). The first strain of this bacterium, 2CP-1 was isolated from a culture enriched from a soil sample taken from a stream near Lansing,

Michigan, based on its ability to grow with acetate as an electron donor to dechlorinate 2-

CPh (2-chlorophenol) (Sanford, Cole, and Tiedje 2002). Once it was isolated and its ability to reduce metals was demonstrated, the evidence of its presence in uranium-contaminated

U.S. DOE (Department of Energy) sites was documented near Oak Ridge, Tennessee. This evidence is based on 16-S ribosomal RNA gene-based analysis, and it suggested the involvement of this species in in situ metal reduction (North et al. 2004).

A. dehalogenans was grouped, by 16S rRNA gene phylogenetic analysis, in the delta-

Proteobacteria close to the Myxobacteria, which have distinguishing characteristics such as strictly aerobic metabolism, fruiting body formation, surface motility and sporulation. A. dehalogenans’ ability to perform anaerobic respiration was the first to be found in

Myxobacterium (Thomas et al. 2008), which are otherwise aerobic and colonial microbes.

2.4.2.2 Genome Information The complete genome sequence information of Anaeromyxobacter dehalogenans strain 2CP-1 can be accessed through the Joint Genome Institute (http://jgi.doe.gov/). The genome contains 5,029,329 bps in total, 4,555,335 of which are DNA coding bases. 74.72% of the total number of base pairs are G/C pairs. This complete genome consists of 4540

17 genes, 4485 (98.79%) of them are protein coding genes, among which 3134 genes have been assigned functional annotations by now. The rest of the genes are RNA genes (55,

1.21%) (Thomas et al. 2008).

A. dehalogenans possesses one of the highest G+C percentage (74.72%) among any described organism (Thomas et al. 2008). Haywood-farmer and Otto (Haywood-Farmer,

Otto, and Huelsenbeck 2003) demonstrates that G+C content suggests that A. dehalogenans is evolutionarily closer to the Myxobacteria than to other delta-Proteobacteria, though it shares genotypic traits with the anaerobic majority of the delta-Proteobacteria That it is a

Myxobacterium was also suggested by the genome analysis. The mosaic nature of its genome suggests that A. dehalogenans may have descended from aerobic ancestors with complex lifestyles (Thomas et al. 2008).

2.4.2.3 Metabolism A. dehalogenans is able to grow both microaerophilially and anaerobically and it prefers the latter. Anaerobic metabolism has been also found in other members of delta-

Proteobacteria group such as Geobacter, Desulfovibrio or Desulfomonile. A. dehalogenans shows great versatility utilizing different electron donors and acceptors. Along with some

Geobacter, it was demonstrated as one of the few species capable of growing by both chlororespiration (dechlorination) and metal reduction, found to couple Fe(III) reduction to the oxidation of acetate (He and Sanford 2003). Fe(III) compounds that can be used by A. dehalogenans include HFO, ferric citrate and ferric pyrophosphate. Acetate was demonstrated as the best electron donor considering growth and dechlorination activity.

18 The acetate threshold concentrations for chlororespiration, amorphous Fe(III) reduction and ferric citrate reduction were reported to be 69±4, 19±8 and <1 nM, respectively (He and

Sanford 2004). H2, formate, succinate, pyruvate and lactate were also used as electron donors. It utilizes ortho-substituted halophenols, nitrate, nitrite, fumarate and oxygen as terminal electron acceptors (Sanford, Cole, and Tiedje 2002).

The versatility of A. dehalogenans makes it a potential organism for bioremediation of uranium or herbicide contamination such as bromoxynil (Cupples, Sanford, and Sims

2005), where it reduces the contaminant anaerobically while oxidizing organic matter. A. dehalogenans has also been proved to use an electrode as the sole electron donor to conduct both metal reduction and chlororespiration (Strycharz et al. 2010). Field tests also showed that , compared to control soils without electrodes, uranium removal could be improved if an electrode is providing electrons (Torres, Kato Marcus, and Rittmann 2008).

2.4.2.4 C-type Cytochromes Typically, bacteria with low versatility in lithotrophic metabolism tend to possess a modest number of c-type cytochromes and those cytochromes typically have fewer than five heme-binding motifs. Bacteria that have respiratory versatility often possess numerous c-type cytochromes, many of which have multiple heme-binding motifs. These bacteria include Anaeromyxobacter spp., Shewanella spp. and Geobacter spp. (Thomas et al. 2008). S. oneidensis and G. sulfurreducens now are regarded as model organisms for studying dissimilatory iron reducing bacteria while no progress has been published functionally characterizing c-type cytochromes of A. dehalogenans. A major obstacle has been the lack of

19 genetic tools that are effective in generating Anaeromyxobacter deletion mutants (Nissen et al. 2012). The GeneBank® database confirmed 20 c-type cytochrome-coding genes in its genome, but Nissen et al. suggested later that its genome encodes 69 c-type cytochrome, 27 and 25 of which were expressed with ferric citrate and solid MnO2 as electron acceptors, respectively. (Nissen et al. 2012)

2.4.3 Shewanella oneidensis MR-1

2.4.3.1 Description Shewanella oneidensis is a Gram-negative bacterium first isolated in 1988 from a sample from Lake Oneida, NY, which is the origin of its species name (Venkateswaran et al.

1999). Shewanella oneidensis, as a facultative gamma-Proteobacterium, is able to survive and proliferate in both aerobic and anaerobic environments. It possesses cytochromes MtrC and OmcA on its outer membrane, known to be involved in the reduction of Fe(III) (Lower et al. 2007). When the aqueous concentration of metal is low in its immediate environment,

Shewanella oneidensis is able to produce pili to transfer electron to insoluble metals. When the electron acceptors are in high concentrations, the bacterium forms biofilms in sediment and soil (Venkateswaran et al. 1999).

2.4.3.2 Genome Information The complete gene sequence information for Shewanella oneidensis strain MR-1 has been determined by Heidelberg et al. (Heidelberg et al. 2002) and recently re-annotated by the Joint Genome Institute (http://jgi.doe.gov/). The genome contains 5,131,424 bps in total, 4,406,969 of which are DNA coding bases. 45.89% of the total number of base pairs

20 are G/C pairs. This complete genome consists of 4701 genes, 4550 (96.79%) of them are protein coding genes, among which 3669 genes have functional annotations. The other 151 genes are RNA genes (3.21%).

Thirty-nine c-type cytochromes were identified in the genome, reflecting the species’ great metabolic versatility (Heidelberg et al. 2002). The most similar proteomes to S. oneidensis from organisms that already have available genome information are those of the gamma-Proteobacteria. Genome analysis identified a lambda-like phage genome containing

51,857 base pairs, both integrated in S. oneidensis genome and in nonintegrated form. This suggests that it is a functional phage (Heidelberg et al. 2002) representing a valuable tool for genome engineering of S. oneidensis and it is partially a reason why S. oneidensis is well studied.

2.4.3.3 Metabolism Because of its remarkably versatile respiratory capabilities, Shewanella oneidensis strain MR-1 becomes a significant model organism for studies with bioremediation of metals

(Heidelberg et al. 2002). As with other facultatively anaerobic bacterium, S. oneidensis conducts an aerobic respiration with oxygen as terminal electron acceptor when oxygen is present in the environment. Under anaerobic conditions, however, S. oneidensis is able to utilize alternative terminal electron acceptors such as Mn(III), Mn(IV), Fe(III), U(VI), Cr(VI), nitrate, fumarate, sulfite and elemental sulfur (Charles R. Myers and Nealson 1988; Nealson and Saffarini 1994). It has been suggested to reduce mercury ion to elementary mercury

(Wiatrowski, Ward, and Barkay 2006). With its capabilities to reduce various metals and the

21 fact that cellular respiration is not restricted by surrounding heavy metals, S. oneidensis is a potential choice to be applied to heavy metal bioremediation.

A membrane-bound electron transport pathway utilizing c-type cytochromes involved in dissimilatory metal reduction was proposed by Shi et al.(Shi et al. 2007), as shown in Figure 2.2. CymA (locus tag SO_4591), anchored in the cytoplasmic membrane, delivers electrons to MtrA (SO_1777), a periplasmic decaheme c-type cytochrome. MtrB

(SO_1776) facilitates MtrA to transfer electrons to the outer membrane-bound OmcA

(SO_1779)/MtrC(OmcB, SO_1778) complex. This complex is responsible for transferring electrons to extracellular Fe(III) to reduce it. In addition, MtrD, MtrE and MtrF (not shown in the Figure), which are paralogs of MtrA, MtrB and MtrC, respectively, were identified and serve a similar function to their homologs (not shown in the figure). The exact factor(s) triggering the expression of MtrD/E/F was unclear but proposed to be non-planktonic growth.(Richardson et al. 2012). Interestingly almost all of these proteins are encoded in one area of the gnome, which can be told from the locus tags.

Figure 2.2: Proposed electron transport in Shewanella oneidensis (Shi et al. 2007). MQ, Menaquinone; MtrA/B/C, Methylransferase A/B/C; OmcA/B, Outer-membrane Cytochrome A/B; CymA, Cytoplasmic-membrane c-type cytochrome A.

2.4.4 Geobacter sulfurreducens PCA

2.4.4.1 Description Geobacter sulfurreducens strain PCA was firstly discovered in a sample from hydrocarbon contaminated soil in Norman, Oklahoma (Caccavo et al. 1994). The bacterium is one of the predominant metal reducing bacteria found below the surface. It is a rod- shaped, Gram-negative Proteobacterium conducting aerobic and anaerobic respiration, firstly known to reduce sulfur. It is also capable of reducing metals with organic compounds

23 as electron donors (Caccavo et al. 1994). G. sulfurreducens is able to attach to an electrode and remain viable for relatively long periods of time, completely oxidizing organic matter and transferring electrons to the electrode thus generating electricity (Methé et al. 2003).

16s rRNA gene sequence phylogenetic analysis placed it under the delta subgroup of proteobacteria (Caccavo et al. 1994).

2.4.4.2 Genome Information The complete gene sequence of Geobacter sulfurreducens strain PCA was revealed by Methé et al. (Methé et al. 2003). It has been re-annotated recently by the Joint Genome

Institute (http://jgi.doe.gov/). The genome contains 3,814,128 bps in total, 3,467,568 of which are DNA coding bases. G. sulfurreducens has a smaller genome than A. dehalogenans and Shewanella oneidensis, according to this data. 60.94% of the total number of base pairs are G/C pairs. This complete genome consists of 3481 genes, 3481 (97.50%) of them are protein coding genes, among which 2845 genes currently have functional predictions.

Eighty-seven RNA genes occupy 2.5% of the whole genome.

G. sulfurreducens is a delta-Proteobacteria, like A. dehalogenans,. The genome also reveals its capability of aerobic metabolism, ability of one-carbon and complex carbon metabolism, and chemotactic behavior. Its potential to be used in bioremediatioin of radioactive metals as well as playing roles in global metals and carbon cycling was revealed.

Analysis of gene distribution patterns across lineages revealed that only two homologous genes were exclusively found in G sulfurreducens and S. oneidensis: both encoding c-type cytochromes. This suggests that the metal reduction capabilities of these species are not

24 simply related to the set of genes they share, but also involve expansion of specific gene

families and novel genes’ presence. (Methé et al. 2003).

2.4.4.3 Metabolism Unprecedented numbers of putative c-type cytochromes (88) were found during the

genome analysis, implying a high versatility of its metabolism. G. sulfurreducens is capable of

growing on diverse electron acceptors such as Fe(III), elemental sulfur, fumarate and malate

(reduced to oxaloacetate) with acetate as electron donor. Hydrogen is also reported to be

an available electron donor for G. sulfurreducens to reduce Fe(III) (Caccavo et al. 1994).

However, microbial reduction of Fe(III) was not feasible when sulfur, lactate, propionate,

butyrate, fumarate or glucose were provided as electron donors. Methé et al. (Methé et al.

2003) reported its ability to use an electrode as electron acceptor, demonstrating its

potential to be used as microbial fuel cells to generate electricity.

An electron-transport pathway of Geobacter spp. during Fe(III) reduction has been

proposed by several researchers (Shi et al. 2009; Weber, Achenbach, and Coates 2006), as

shown in Figure 2.3. The outer membrane tetraheme c-type cytochrome, OmcE, and

hexaheme c-type cytochrome, OmcS, are believed to locate on the cell surface to transfer

electrons to type IV pili, a conductive nanowire which relays electrons directly to Fe(III) (Shi

et al. 2009). They receive electrons from quinol pool in the cytoplasmic membrane,

facilitated by PpcA(Bird, Bonnefoy, and Newman 2011).

Figure 2.3: Proposed electron transport in Geobacter sulfurreducens (Bird, Bonnefoy, and Newman 2011). MQ, menaquinone; PpcA/D, Periplasmic c-type cytochrome A; OmcB/E/S, Outer-membrane Cytochrome B/E/S; OmpB, Outer-membrane Protein B

2.4.5 Geobacter bemidjiensis Bem

2.4.5.1 Description Geobacter bemidjiensis is a Gram-negative bacteria with slightly curved rod-shape. It is an iron reducing bacteria that is motile by means of monotrichous flagella. Strain Bem was firstly isolated and enriched from sediments samples collected in Bemidji, MN.(Nevin et al.

2005) G. bemidjiensis was analyzed as part of a comprehensive phylogenetic study of

Geobacteraceae (Holmes, Nevin, and Lovley 2004). Strain Bem was suggested to fall under the Geobacter genus of Geobacteraceae family under delta-Proteobacteria class.

26 2.4.5.2 Genome Information The complete genome information of Geobacter bemidjiensis strain Bem was firstly revealed by Aklujkar et al. and has been updated recently on Aug. 5th, 2014 by the Joint

Genome Institute (http://jgi.doe.gov/). The result exhibits the genome containing 4,615,150 bps in total, 4,044,510 of which are DNA coding bases. 60.27% of the total number of base pairs are G/C pairs, similar to its relative Geobacter sulfurreducens. This complete genome consists of 4106 genes, 4034 (98.25%) of them are protein coding genes, among which 2925 genes have function predictions. Seventy-two genes are RNA genes, occupying 1.75% of the whole genome.

Characterization of the genome sequence of G. bemidjiensis indicates several differences in metabolism compared to previously sequenced non-subsurface

Geobacteraceae. The genome suggested that besides using benzoate as electron donor, G. bemidjiensis is potentially able to detoxify other aromatic pollutants without degrading them. And it was implicated to have enhanced abilities to respire, detoxify and avoid oxygen

(Muktak Aklujkar et al. 2010).

2.4.5.3 Metabolism G. bemidjiensis is capable of coupling the reduction of soluble and insoluble Fe(III) to the oxidation of variety of organic substrates including acetate, butyrate, lactate, benzoate, butanol, pyruvate and succinate, etc. It can also utilize inorganic hydrogen as electron donor. Its available electron acceptors besides ferric iron include: fumarate, AQDS

(Anthraquinone-2,6-disulfonate), Mn(IV) oxide (Nevin et al. 2005). Like the other three

27 strains mentioned above, c-type cytochromes with multiple heme groups are very abundant

in G. bemidjiensis cells (88 proteins were annotated as c-type cytochrome) and are believed

to be involved in electron transference to metal electron acceptors. It was suggested to be

auxotrophic for 4-aminobenzoate, making it the first Geobacter species identified to have a

vitamin requirement.

2.5 Proteomics Methods for Studying Microorganisms

2.5.1 Overview The proteome of a bacterial cell is the complete set of proteins expressed by the

bacterium’s genome. Proteomics is the study of proteomes, and proteins’ structures and

functions (N. L. Anderson and Anderson 1998; Blackstock and Weir 1999)The term

“proteomic” was firstly coined in 1997 (James 1997). The creation of this term is to make an

analog to the study of the genome (genomics).

2.5.2 Comparison to Genomics Study Proteome characterization is more complicated than genome characterization. An

organism’s genome is relatively constant while the proteome of the organism will be

different among cells grown under different conditions and changes with time.

Environmental stress will dramatically change the proteome, but the genome will still

remain unchanged if mutation or horizontal gene transfer are not occurring. Cell

differentiation in the human body is a result of differential expression of the human

genome. In other words, the proteomes of different type of cells are different. Instead of

providing the information of what proteins are to be expressed potentially, as what

28 genomics study is, proteomics could confirm the expressions of proteins and may also

provide a quantitative measure of the proteins.

Compared to genomics study, proteomics gives us insight into the systems biology of

organisms because of the three main reasons listed below:

1. While the structure and content of a microorganism’s genome changes slowly, the

proteome could vary a lot at different phases of the microorganism’s growth, and/or in

response to the environmental conditions. Thus proteomics gives us a dynamic view of what

the microbe is doing, rather than the knowledge about what the microbe is ‘potentially’

able to.

2. Besides the identification of existing proteins in the cell, proteomics can also

quantitate the abundance of proteins. Giving the fact that protein abundance will change a

lot due to different rates/types of degradation and different efficiencies of translation, the

protein quantitation offered by some proteomics techniques could be a very helpful tool to

study the responses of organisms to their surroundings.

3. Many transcripts express more than one protein, through diverse splicing (in

eukaryotes) or PTM (Post-Translational Modifications). Proteomics offers insight into splice

sites, providing real-time information about what protein the transcript is expressing.

2.5.3 LC-MS/MS Based Proteomics While 2D-GE is still being used for differential proteomics studies, modern proteomic

approaches for complete proteome characterization (shot-gun proteomics) are mainly

29 relying on a LC-tandem MS system, in which LC stands for Liquid Chromatograph and MS

stands for Mass Spectrometer. A peptide mixture from the samples is obtained by trypsin

digestion of proteins. The mixture is then injected in to the LC system, which can fractionate

peptides either online or offline (Cravatt, Simon, and Yates Iii 2007). Peptides are separated

in the LC and then sent in to a first MS system. In the first MS, peptide molecules are ionized

and the mass-to-charge ratio of each peptide ion is measured to report the molecular mass

of the peptide. Specific peptide ions are then selected and fragmented before being

delivered to a second MS system. In the second MS system, the mass-to-charge ratio of each

fragment is measured, producing a MS/MS spectrum. Computational algorithms are then

used to combine the information of molecular mass reported by MS1 and the spectra

reported by MS2 to do a comparison with a database of predicted peptide masses and its

predicted fragmentation spectrum (Keller and Hettich 2009). A peptide is identified when a

match is found between predicted masses and MS Spectra.

2.5.4 Proteomics Methods

2.5.4.1 Bottom-up (Shotgun) Proteomics Bottom-up (shotgun) proteomics refers to the strategy of proteomic research that

identifies as many proteins as possible in a mixture. The strategy combines High

Performance Liquid Chromatography with tandem Mass Spectrometry (Alves et al. 2007;

Washburn, Wolters, and Yates 2001; Wolters, Washburn, and Yates 2001; Hu et al. 2007;

Fournier et al. 2007; Nesvizhskii 2007). Bottom-up proteomics is often used to do non-

targeted protein identification in a complex sample. It is used to profile the whole dynamic

proteome (Wu and MacCoss 2002), but only a portion of the total proteins will be detected 30 through the pipeline due to such factors as poor ionization, interference from other

peptides, and limitations of databases used in the study.

2.5.4.2 Isobaric Tag for Relative and Absolute Quantitation (iTRAQ) The iTRAQ is an isobaric labeling method used in combination with bottom-up

techniques in quantitative proteomics to determine relative abundances of the same

protein from different samples (P. L. Ross et al. 2004; Zieske 2006; Gafken and Lampe 2006).

Figure 2.4: Structure of the iTRAQ reagent (Sadowski et al. 2006)

The iTRAQ reagent molecule consists of one charged reporter group with mass

ranging from 114-117 Da, one balance group with mass ranging from 31-28 Da and one

peptide reactive group, as shown in Figure 2.4. So the mass of the isobaric tag remains

constant among all of the different reagents. The peptide reactive groups will covalently

bind to the N-terminus of peptides. Thus peptides from different samples are labeled with

tags with different reporter mass and peptides from same samples are labeled with the

same tag. Reagents with different reporter masses are used on peptide mixtures from

different samples. Then peptide pools from different samples are mixed. During the 31 fragmentation, the link between balance group and reporter group is broken, generating a reporter ion from each molecule of iTRAQ reagent. The reporter ions’ intensities in MS2 spectra will report the relative abundances of peptides they bind to. From the intensities of different reporter ions measured by MS2, relative abundances of the same peptide from different samples can be calculated (Boehm et al. 2007) (Figure.2.5). Some factors will affect the applicability and attainable dynamic range of protein quantification by iTRAQ, such as isotopic contamination, signal-to-noise ratio and background interference (Ow et al. 2009).

Figure 2.5: iTRAQ 4plex reagents workflow. GINTSELTTIR is an example peptide sequence.

33 CHAPTER 3. OBJECTIVES

3.1 Overview

Dissimilatory iron reducing bacteria play important roles in bioremediation of metal-

contaminated and radionuclide-contaminated sites. This thesis focuses upon applying

comparative shotgun proteomics methods to four selected Gram-negative bacteria capable

of conducting dissimilatory iron and uranium reduction: Anaeromyxobacter dehalogenans

2CP-1, Shewanella oneidensis MR-1, Geobacter sulfurreducens PCA and Geobacter

bemidjiensis Bem. Cultures were grown under two conditions: on ferric citrate and on

fumarate as electron acceptor. In both cases the electron donor was held constant.

Additionally, comparative genomics algorithms were used to identify orthologous proteins

clusters and analyze proteome trends across organisms.

3.2 Identifying Shifts in the Culture Proteomes across the Four Species Grown on Iron versus Fumarate

The first objective was to identify the core proteome, which refers to orthologous

proteins always observed under either growth conditions (iron or fumarate) across the four

species. Orthologous proteins that are consistently regulated on iron versus fumarate are

also to be identified. This may expand our knowledge about the conserved cellular

responses to growth via iron reduction.

3.3 Prioritizing Conserved Hypothetical Proteins for Further Characterization

34 Results of previous analysis of genome-wide expression patterns in DIRB report a

substantial number of differentially expressed hypothetical proteins or proteins with only

vague annotations. This makes it difficult to learn more about the anaerobic respiratory

processes. One of the objectives of this thesis is to prioritize core proteins that are non-

functionally or only generally characterized proteins for further characterization. In

particular, there is interest in hypothetical proteins that are: (1) Conserved across the study

organisms’ genomes; (2) detected in the proteomic dataset; and (3) differentially expressed

on iron versus fumarate.

3.4 Discovery of c-Type Cytochromes and Other Membrane-bound Oxidoreductases Possibly Involved Directly in Iron-reduction

C-type cytochromes and oxidoreductases are highly involved in electron transport

chains – including those involving dissimilatory metal reduction. However, for most genome-

encoded cytochromes or other oxidoreductases, their exact functions in the metal reduction

process remain unknown. Another objective of this thesis is profiling the changing pattern of

c-type cytochromes and oxidoreductases that are strongly regulated in cells grown with

Fe(III) versus fumarate. This may provide a clue for further investigation about the detailed

function of these proteins, especially in the poorly characterized DIRB, A. dehalogenans.

35 CHAPTER 4. MATERIAL AND METHODS

4.1 Culture Conditions

4.1.1 Anaeromyxobacter dehalogenans 2CP-1 and Geobacter sulfurreducens PCA A. dehalogenans 2CP-1 and G. sulfurreducens PCA were grown and harvested by

Annie Otwell, Microbiology PhD student, Cornell University. Cultures were grown in 2-L glass

bottles with butyl rubber stoppers under anaerobic conditions with N2 headspace at 30

degrees Celsius. The media used to grow both A. dehalogenans and G. sulfurreducens

contained (per liter) 0.1 g of KCl, 1.5 g of NH4Cl, 0.6 g of NaH2PO4, 2.5 g of NaHCO3, 5 ml of

vitamin mix, and 10 ml of trace mineral mix as previously described (Derek R. Lovley and

Phillips 1988; Wrighton et al. 2011). Sodium acetate (20 mM) was added as electron donor

for both organisms. Either 50 mM of sodium fumarate (for fumarate cultures) or 60 mM of

ferric citrate (for Fe (III) cultures) from anoxic stock solutions were added as electron

acceptor. Bacterial cells were harvested in mid-late exponential phase of growth under

anoxic atmosphere by centrifugation (10,000xg, 10 min). Pellets were then collected and re-

centrifuged in 50-ml conical tubes at 10000xg. Pellets were then frozen at -80 degree Celsius

in the freezer. All four kinds of cultures (two different electron acceptors for each organism)

were run in triplicates.

4.1.2 Shewanella oneidensis MR-1

36 S. oneidensis MR-1 was grown anaerobically with an N2 headspace at Pacific

Northwest National Laboratory (PNNL) by Dr. Michael. Wilkins. The M1 medium modified

from Zachara et al. (Zachara et al. 1998) was used to grow S. oneidensis. It contained (per

liter) 0.1 g of KCl, 1.5 g of NH4Cl, 0.6g of NaH2PO4, 0.213 g of Na2SO4, 0.1 g of CaCl2, 0.3 g of

NaOH and 0.907 g of PIPES buffer. Vitamin solutions and trace minerals as well as amino

acids (arginine, glutamate and serine) were added as previously described by McLean et

al.(McLean et al. 2008). Lactate (20 mM) was provided as electron donor and 40 mM of

fumarate (for fumarate culture) or ferric citrate were added as electron acceptors. Cells

were harvested in mid exponential phase by centrifugation at 8,000xg for 3 minutes under

anaerobic conditions.

4.1.3 Geobacter bemidjiensis Bem G. bemidjiensis Bem was also grown at PNNL by Dr. Wilkins. In each experimental

bottles, cultures were grown with fresh water media containing (per liter) 0.1 g of KCl, 0.2 g

of NH4Cl, 0.6 g of NaH2PO4, 2.5 g of NaHCO3, 10 ml of vitamin mix and 10 ml of trace mineral

mix per liter (Derek R. Lovley and Phillips 1988; Wrighton et al. 2011). Each bottle was

amended with 20 mM of acetate as electron donor and either 40 mM of fumarate (for

fumarate culture) or 42.6 ± 1.3 mM of ferric citrate. Cultures were grown under anaerobic

conditions with an N2 headspace in triplicates for each growth condition (fumarate or ferric

citrate). Cells were anaerobically harvested in mid exponential phase and by centrifugation

at 8,000xg for 3 minutes. Pellets were then immediately frozen on liquid nitrogen and

stored at -80 degrees Celsius, until protein was extracted.

37 4.2 LC-MS/MS based Shotgun Proteomics

4.2.1 Protein Samples Preparation and Trypsin Digestion Protein samples’ preparation, digestion and LC-MS/MS analysis were done by our

collaborators at PNNL. Cells were washed with 0.392 M oxalate (prepared with 0.167 M

ammonium oxalate and 0.225 M oxalic acid) until the supernatant was nearly colorless.

Pellets were then suspended in 100 mM NH4HCO3 at pH around 8 and lysed by bead beating

with zirconia/silica beads (0.1mm, Biospec Products, Inc.) and proteins were extracted as

previously published(Merkley et al. 2015). Briefly, lysates were centrifuged at 5,000 rpm for

5 min to pellet out cellular debris, mineral phases and beads. Dry urea and DTT

(Dithiothreitol) were added to the supernatant. Samples were then denatured for 30 min at

60 degrees Celsius and diluted with 100 mM NH4HCO3 to 10% of its concentration. CaCl2 (1

M) was added at 1 mM and trypsin was added in a protein-to-trypsin mass ratio of 50:1

(Merkley et al. 2015). Samples were then digested overnight for around 18 hours at 37

degrees Celsius. Potentially remaining insoluble iron (of Fe(III)-grown cultures) and beads

were pelleted by brief centrifugation and the supernatants were desalted with 1 mL/50 mg

C18 columns (Supelco) (Merkley et al. 2015). Eluents were concentrated to a volume less

than 10 μL and diluted to 50 μL with 50 mM NH4HCO3. After clean-up, concentration of

peptides in the solutions was measured by the BCA assay. Samples were diluted to 0.25

μg/μL (Merkley et al. 2015).

4.2.2 Multiplex LC-MS/MS Analysis using iTRAQ Tags The iTRAQ 4plex reagents were used to label peptides from different samples before

the LC-MS/MS analysis. The labeling scheme is shown in Table 2.1. Samples of same scheme

38 number were run simultaneously in one run. For each scheme, peptides from different samples were pooled and separated with reversed-phase liquid chromatography (RPC) system. The RPC column was coupled to a tandem MS (Mass Spectrometry) system

(Orbitrap Velos) via an electrospray ionization interface (Livesay et al. 2008). Higher energy

C-trap dissociation (HCD) was used to fragment parent ions coming out from first MS. The molecular mass of each peptide was determined by the MS1 by measuring the mass-to- charge ratios of the parent ions. Then the mass-to-charge ratios of fragments from each parent ion were measured by MS2 to generate a fragments spectrum. Computational algorithms were used to compare the generated spectrum with spectra predicted from putative peptide sequences suggested by the genome information to determine which peptide was detected. The quantities of the same peptide coming from different samples were estimated by the iTRAQ reporter ions’ peak intensities in the fragments spectrum and then used to calculate protein ratios of cells grown on different electron acceptors.

Table 4.1: Labeling schemes of 4plex iTRAQ reagents. Biological duplicates are denoted with “1” or “2”.

Scheme # Sample Name* iTRAQ Label SO_Fum_1 114 SO_FeC_1 115 1 AD_Fum_1 116 AD_FeC_1 117 GB_Fum_1 114 GB_FeC_1 115 2 GS_Fum_1 116 GS_FeC_1 117 SO_Fum_2 114 SO_FeC_2 115 4 AD_Fum_2 116 AD_FeC_2 117 GB_Fum_2 114 5 GB_FeC_2 115 GS_Fum_2 116

39 GS_FeC_2 117 *SO: S. oneidensis MR-1, AD: A. dehalogenans 2CP-1, GB: G. bemidjiensis Bem, GS: G.sulfurreducens PCA, Fum: fumarate, FeC: ferric citrate

4.3 Data Processing

4.3.1 Peptide Data Cut-off There were in total four multiplex runs of this experiment and after each run, a

datasheet was generated by the computer working online. This datasheet gives information

about all the peptides detected in this run, including peptide sequences, iTRAQ reporter ion

intensities, the proteins that each peptide belongs to and whether each detected peptide

comes from more than 1 protein or not. An example of the datasheet is shown below in

Table 4.2. This example came from the Run 1 in which samples of duplicate 1 of Shewanella

oneidensis grown on fumarate (SO_Fum_1), duplicate 1 of Shewanella oneidensis grown on

ferric citrate (SO_FeC_1), duplicate 1 of Anaeromyxobacter dehalogenans grown on

fumarate (AD_Fum_1), duplicate 1 of Anaeromyxobacter dehalogenans grown on ferric

citrate (AD_FeC_1) were injected in to the LC-MS/MS.

40 Table 4.2: Example of raw peptide data (Run 1, Duplicate 1 of Shewanella oneidensis and Anaeromyxobacter dehalogenans). Peptides are listed alphabetically. Intensities of iTRAQ tag for each peptide are reported.

iTRAQ Channel 114 115 116 117 Peptide SO_Fum_1 SO_FeC_1 AD_Fum_1 AD_FeC_1 Protein Multi Protein AAAAAAAPAR 437134.5 240576.27 503226.66 689213.06 A2cp1_1551 0 AAAAAAASAASK 1654.77 2064.11 A2cp1_2135 0 AAAAAALSEATSHRSQLAQIERQR 11415.61 23798.53 A2cp1_0709 0 AAAAALEPAAAHR 35877.67 14207 38844.55 57989.77 A2cp1_0220 0 AAAAAPAPAAPPAAPPAPAAAAGFR 20745.12 15217.66 90489.46 59743.28 A2cp1_0834 0 AAAAAPAVAPAAAGAGAGK 11109.08 4895.9 33153.34 32318.03 A2cp1_3465 0 AAAAASSTSVPQGETTVR 36003.45 20220.49 SO_3207 0 AAAAEALAR 85967.88 67654.74 160525.86 510463.47 A2cp1_0920 0 AAAAEALGAAGAVEQAPALAALGR 41001.38 38376.53 A2cp1_2916 0 AAAAEALGR 140512.09 119902.43 636187.56 661233.44 A2cp1_2916 0 AAAAEKPQVSK 68557.58 3632.91 820174.26 644006.57 A2cp1_3455 0 AAAAEMEGAFGDGAAAALLADGDHR 32283.4 47443.04 A2cp1_1037 0 AAAAGDNVTLHYHR 25239.39 A2cp1_4284 0 AAAAGPTEVGEVK 7021.08 8072.67 75304.2 60889.56 A2cp1_0887 0 AAAAGVPVIASVSAPSSLAVDLAR 14740.51 12011.21 69709.41 69930.91 A2cp1_0877 0 AAAALATAGEAR 45601.61 69713.55 323786.12 480690.78 A2cp1_2356 0 AAAALGINQGVAK 408330.89 55854.44 32263.06 48816.37 SO_3265 0 AAAAMPAAPGPAAEPAGTR 1691.95 3394.48 8952.12 A2cp1_4152 0 AAAAPAPAK 946392.28 820731.03 2117716.21 2860865.25 A2cp1_2022 0 AAAAPAPAKK 34404.02 13296.31 39815.02 39343.24 A2cp1_2022 0 AAAAPGPEAAIPVQTSLAPLIDK 21628.94 4627.14 275607.16 144527.8 A2cp1_2161 0 AAAAPPPR 171876.05 194379.23 274361.72 438784.81 A2cp1_4198 0 ... … … … … … …

41 In Table 4.2 the first column (Peptide) of the table gives the sequences of the detected peptides. Column 2-5 (SO_Fum_1, SO_FeC_1, AD_Fum_1, AD_FeC_1) give the corresponding iTRAQ reporter ions’ intensities in the MS/MS spectrum. These intensities can be further used to calculate abundance ratio of peptides. Column 6 (Protein) uses proteins’ locus tags to provide information about which protein a certain peptide comes from and column 7 (Multi Protein) tells whether the peptide is found in multiple proteins in the database (>0) or it is unique to one protein in the database (0).

After all four runs of these samples were accomplished, four datasheets of the above-mentioned kind were generated. Data coming from same organism were compiled into one datasheet resulting in four datasheets (one for each organism). Table 4.3 is an example datasheet of compiled peptide intensities for A.dehalogenans.

42 Table 4.3: Peptide intensities of Anaeromyxobacter dehalogenans.

Peptide AD_FeC_1 AD_Fum_1 AD_FeC_2 AD_Fum_2 Protein Multi Protein AAAAAAAPAR 689213.06 503226.66 A2cp1_1551 0 AAAAAAASAASK 2064.11 1338.75 649.11 A2cp1_2135 0 AAAAAAELSR 696.26 485.91 A2cp1_0826 0 AAAAAALSEATSHRSQLAQIERQR 23798.53 11415.61 A2cp1_0709 0 AAAAAAPAAAPAAGAPAPAPAPAAPAPAPTAADPAAQR 246.48 292.72 A2cp1_1988 0 AAAAADVK 262501.75 893281.25 A2cp1_4035 0 AAAAAEAAGAWPDAVR 124853.38 108853.42 A2cp1_4481 0 AAAAAGGYAALAR 72452.43 50622.2 A2cp1_0169 0 AAAAALEPAAAHR 57989.77 38844.55 A2cp1_0220 0 AAAAAPAPAAPPAAPPAPAAAAGFR 59743.28 90489.46 23858.35 46220.24 A2cp1_0834 0 AAAAAPAVAPAAAGAGAGK 32318.03 33153.34 A2cp1_3465 0 AAAAAVTLR 164689 163128.41 A2cp1_3455 0 AAAAEALAR 510463.47 160525.86 314918.16 126351.95 A2cp1_0920 0 AAAAEALGAAGAVEQAPALAALGR 38376.53 41001.38 10499.16 11109.74 A2cp1_2916 0 AAAAEALGR 661233.44 636187.56 A2cp1_2916 0 AAAAEELAR 1507099.62 1282176.38 A2cp1_0270 0 AAAAEKPQVSK 644006.57 820174.26 108113.61 97081.58 A2cp1_3455 0 AAAAEMEGAFGDGAAAALLADGDHR 47443.04 32283.4 22247.6 15394.04 A2cp1_1037 0 AAAAFAERDPTPGSER 69492.78 69766.16 A2cp1_3541 0 AAAAGDNVTLHYHR 25239.39 62347.83 53644.79 A2cp1_4284 0 AAAAGLAR 1657789 589543.19 A2cp1_2329 0 AAAAGPTEVGEVK 60889.56 75304.2 A2cp1_0887 0 AAAAGVPVIASVSAPSSLAVDLAR 69930.91 69709.41 23476.83 23890.21 A2cp1_0877 0 … … … … … … …

43 Similar to Table 4.2, the first column of Table 4.3 shows the peptide sequences, column 2-5 give iTRAQ reporter ions’ intensities of four Anaeromyxobacter dehalogenans samples (two replicates for each condition: ferric citrate or fumarate). Column 6 and 7 are of same meanings as in Table 4.2.

To ensure an unbiased comparison among all four samples when calculating protein ratios across conditions, the following criteria were used to filter out some detected peptides:

1. Peptide must be detected in all four samples for the organism to be processed in the next step. That is to say, for each peptide in the first column of Table 4.3, there must be one effective value in each cell from column 2 to column 5 in this row to let this peptide proceed to next process.

2. To avoid ambiguous peptides, each peptide must map to a unique protein to be carried through into next step. The value in Column 7 (Multi Protein) must be 0 to make this peptide useable. A value above 0 will also happen with duplicate genes. Gene duplication is widely existing in organisms’ genomes. If the contained gene is a protein-coding gene, the expression of both of the duplicate regions will produce the same protein.

According to these criteria, peptides expressed by duplicate genes would be cut off.

Since they may still contain some useful information, they were processed separately here.

Peptides from three additional proteins were processed and their abundance ratios were calculated.

44 Table 4.4: Detected proteins expressed by duplicate genes in four organisms.

Duplicate Genes Ratio 1 Ratio 2 Annotation A2cp1_2017/A2cp1_2367 0.8165 0.9424 elongation factor Tu SO_A0112/SO_A0115 0.9319 0.7568 putative lipoprotein GSU2859/GSU2871 0.2435 0.2865 elongation factor Tu

After applying these two filtering criteria, an example datasheet of remaining peptides is shown in Table 4.5.

45 Table 4.5: Peptide intensities of Anaeromyxobacter dehalogenans (after filtering steps). List is in order of gene number on A. dehalogenans’s genome.

Peptide AD_FeC_1 AD_Fum_1 AD_FeC_2 AD_Fum_2 Protein IDLPDDVTQLLAQHIR 40495.8 19246.44 26391.47 7296.58 A2cp1_0001 AHNNYLEVK 167791.9 110865.7 63969.21 42659.72 A2cp1_0002 ALPEQTVTLK 12900033 8806090 6995602 4541728 A2cp1_0002 EEVPVEYAGEPLK 318238.3 227859 1370736 811352.7 A2cp1_0002 FDKVPFAPVDPALLLEM*IER 30954.42 25412.86 23042.53 20079.76 A2cp1_0002 FDKVPFAPVDPALLLEMIER 40615.6 26181.82 76743.21 104349.3 A2cp1_0002 FDKVPFAPVDPALLLEMIERT 10577.16 25521.65 10458.07 12822.34 A2cp1_0002 GGEEADQGFTAVVMPMR 282287.3 182469.4 465200.1 273324.3 A2cp1_0002 GGEEADQGFTAVVMPMRI 122004.5 80781.27 54495.57 51377.6 A2cp1_0002 ISLLSSDK 8721472 4089273 6226137 2862667 A2cp1_0002 IVGLPAEDFPALPR 520447.3 326278.1 60265.34 49330.35 A2cp1_0002 KLLAEAAESGEEQPEAK 307772 186344 214419.4 189200.8 A2cp1_0002 LGFVENSAIFR 8933130 4949462 4694102 2728192 A2cp1_0002 LIEGLFPDYK 5480216 3465080 4176428 2735315 A2cp1_0002 LLAEAAESGEEQPEAK 156958.2 110479.8 734543.6 559599.1 A2cp1_0002 SNEVVFELADDLSPGVLK 309929.6 314134.6 546954 489243.1 A2cp1_0002 SQGIVEKK 1569667 1224702 57342.02 50377.53 A2cp1_0002 STMPILSHVLLEAK 1056562 569442.9 598427.9 366301.2 A2cp1_0002 TFFAVSNDETR 21847306 13572592 5346908 3330667 A2cp1_0002 VLSQNPDLGEAK 361795.3 181851.3 194270 123149.1 A2cp1_0002 VLSQNPDLGEAKEEVPVEYAGEPLK 44024 26094.34 86536.35 79219.02 A2cp1_0002 YIM*EVLQAVK 68616.24 27186.36 11484.79 10970.73 A2cp1_0002 YIMEVLQAVK 5956141 3934016 2287097 1510193 A2cp1_0002 … … … … … …

46 4.3.2 Normalization Peptide intensity data were compiled into .csv files and imported into InfernoRDN, a developed version of DanteR (Polpitiya et al. 2008; Taverner et al. 2012) to be further processed.

Firstly a log2-transformation was done to the datasheet and box plot of each sample was generated to check the labeling efficiency of isobaric tags. As we injected the same amount of biomass into the pipeline, the box plots should theoretically align well. This is based on an assumption that ratio of protein mass to the total mass of an organism will remain the same in same species of organism (Callister et al. 2006).If the box plots don’t align very well, it indicates different labeling efficiencies or amounts actually injected and a normalization has to be applied. Figure 4.1 shows the box plots of log2-transformed peptide intensities of four samples of A. dehalogenans as an example. Plots for other three species can be found in the Appendix.

Figure 4.1: Log2-transformed peptide Intensities box plot of A. dehalogenans (non-normalized). Left to right: AD_FeC_1, AD_Fum_1, AD_FeC_2, AD_Fum_2.

48 To counteract the negative effect of different labeling efficiencies, a Central

Tendency Normalization (Callister et al. 2006) was applied on the data with InfernoRDN. For

Central Tendency Normalization, in Mean Tendency mode we have algorithm:

Imax: Highest average peptide intensities among the four datasets of each species (j=samples, k=peptides)

Ij (j=1,2,3,4): Average peptide intensities of each dataset

Pj,k (j=1,2,3,4; k=1,2,3,4,…): Peptide intensity values of each dataset

Nj,k (j=1,2,3,4; k=1,2,3,4,…): Normalized peptide intensity values of each dataset

= ( = 1,2,3,4)

𝐼𝐼𝑚𝑚𝑚𝑚𝑚𝑚 𝑀𝑀𝑀𝑀𝑀𝑀�𝐼𝐼𝑗𝑗� 𝑗𝑗 , = , × ( = 1,2,3,4; = 1,2,3,4, … ) 𝐼𝐼𝑚𝑚𝑚𝑚𝑚𝑚 𝑁𝑁𝑗𝑗 𝑘𝑘 𝑃𝑃𝑗𝑗 𝑘𝑘 𝑗𝑗 𝑘𝑘 Setup of the parameters of normalization𝐼𝐼𝑗𝑗 is shown in Figure 4.2.

Figure 4.2: Normalization parameters in InfernoRDN users interface.

49 The normalization was directly applied to original peptide intensities (not log2 transformed values), so the Divide Adjustment was selected and the highest mean value among the four samples was taken as a reference to normalize the datasets. Figure 4.3 shows the box plots of log2-transformed peptide intensities of A. dehalogenans after the

Central Tendency Normalization as an example. Box plots for other three species can be found in the Appendix

Figure 4.3: Log2-transformed peptide intensities box plot of A. dehalogenans (normalized). Left to right: AD_FeC_1, AD_Fum_1, AD_FeC_2, AD_Fum_2.

51 The alignment of box plots were improved and the data were ready for further processing.

4.3.3 Peptide-to-Protein Rollup Before rolling peptide intensities up to protein abundances, the distribution of normalized peptide intensities was checked to see if it fits the rules of statistics. A good fit demonstrates an unbiased peptide cut-off step and a reasonable normalization. Peptide intensities distribution of four organisms are shown in Figure 4.4-4.7.

Figure 4.4: Distribution of log2-transformed normalized peptide intensities of A. dehalogenans. Replicates for the iron conditions are shown on the left and fumarate are shown on the right.

Figure 4.5: Distribution of log2 transformed normalized peptide intensities of S. oneidensis. Replicates for the iron conditions are shown on the left and fumarate are shown on the right.

Figure 4.6: Distribution of log2 transformed normalized peptide intensities of G. sulfurreducens. Replicates for the iron conditions are shown on the left and fumarate are shown on the right.

Figure 4.7: Distribution of log2 transformed normalized peptide intensities of G. bemidjiensis. Replicates for the iron conditions are shown on the left and fumarate are shown on the right.

56 As shown in Figures above, all log2-transformed peptide intensities are in acceptable distribution condition, compared to a normal distribution curve. Then a QRollup method

(Polpitiya et al. 2008) was applied to these datasheets to calculate protein abundance.

In the QRollup method, all peptides detected in both replicates under both conditions were used. Intensities of peptides coming from a same protein were summed up and the average value of these intensities was used as a proxy for the relative abundance of this protein in the proteome.

The QRollup was done on the normalized peptide intensities after reverse- transforming the data (antilog2). With the intention of getting as much protein abundance data as possible, 100% of peptide was set to be count into the calculation and the minimum presence was set to 0. The “Include One-Hit-Wonders” option was not checked. So based on a default parameter in InfernoRDN, for each protein, at least two peptides would be contained in the datasheet to calculate the protein abundance, proteins with only one peptide detected will be cut off. Then a datasheet of protein abundance was generated for each organism.

4.3.4 Protein Abundance Data Processing and Analysis

4.3.4.1 Generating Protein Ratio Data After rolling up to protein abundance with the QRollup method, series of protein abundance data were acquired. Table 4.6 shows an example of the generated data: part of

A. dehalogenans protein abundance data.

57 Table 4.6: Example of protein abundance data (A. dehalogenans)

Protein Pep Count* AD_FeC_1 AD_Fum_1 AD_FeC_2 AD_Fum_2 A2cp1_0002 23 3202429 3010512 2932866 2541626 A2cp1_0004 11 397197 395614.6 356253 349066.4 A2cp1_0005 21 2271358 2449307 1542724 1768699 A2cp1_0008 4 109741.8 99730.74 55899.09 60808.73 A2cp1_0012 23 1034170 1209568 939975.9 1131610 A2cp1_0030 3 55295.63 59075.6 74812.35 83170.89 A2cp1_0039 5 460877.7 474426.1 435408.6 415833.9 A2cp1_0045 2 85685.19 127817.1 157322.1 284824.3 A2cp1_0048 7 326766.4 587902.2 108410.8 207133.1 A2cp1_0052 5 417046.1 261869.9 303520.2 186808.3 A2cp1_0053 2 1907664 808672.9 2202592 901421.7 A2cp1_0054 5 153688.2 96283.79 70047.73 46995.75 … … … … … … * PepCount column gives the number of unique peptides used in calculation for each protein.

First column of this Table gives gene locus tags. The numbers in second column are numbers of unique peptides used to calculate the abundance of the corresponding proteins.

Column 3 to 6 show protein abundance readings (average peptide label intensities) under 2 duplicates for each of the two growth conditions (ferric citrate and fumarate).

Then datasheets of protein abundance ratios were generated simply by calculating the ratio of values in Column 3 (AD_FeC_1) to values in Column 4 (AD_Fum_1), and ratio of values in Column 5 (AD_FeC_2) to values in Column 6 (AD_Fum_2). Thus two protein ratios for each detected protein were acquired. The ratios in Table 4.7 are all plain ratios (not log2 transformed).

58 Table 4.7: Example of protein abundance ratio data (A. dehalogenans)

Protein Pep Count Ratio1 Ratio2 Average A2cp1_0002 23 1.063749 1.153933 1.108841 A2cp1_0004 11 1.004000 1.020588 1.012294 A2cp1_0005 21 0.927347 0.872237 0.899792 A2cp1_0008 4 1.100381 0.919261 1.009821 A2cp1_0012 23 0.854992 0.830653 0.842822 A2cp1_0030 3 0.936015 0.899502 0.917758 A2cp1_0039 5 0.971442 1.047073 1.009258 A2cp1_0045 2 0.670373 0.552348 0.611361 A2cp1_0048 7 0.555818 0.523387 0.539602 A2cp1_0052 5 1.592570 1.624768 1.608669 A2cp1_0053 2 2.359006 2.443465 2.401235 A2cp1_0054 5 1.596200 1.490512 1.543356 … … … … …

Figure 4.8 shows the distribution of log2-transformed average protein abundance ratios (X-axis) for the organisms under ferric citrate to fumarate.

Figure 4.8: Log2-transformed average protein abundance ratio (ferric citrate to fumarate) distribution. Top left: A. dehalogenans, top right: G. bemidjiensis, bottom left: G. sulfurreducens, bottom right: S. oneidensis.

60 In the plots, the blue dash line indicates the normal distribution curve with mean

value of 0 and the red solid line is the probability intensity curve of each distribution. The

discrepancy between the two curves indicates the extent of the distribution being different

from a normal distribution. As shown in the distribution plots, mean values of the four

distributions are around 0, though some discrepancy exists. Proteins with a ratio around

zero are at the same expression level under both ferric citrate and fumarate conditions.

4.3.4.2 Processing Protein Ratio Data Gene annotations describing proteins’ functions were pulled from

GenBank® (http://www.ncbi.nlm.nih.gov/genbank/), a collection of annotations of publicly

available DNA sequences (Benson et al. 1999).

One-sample Student’s t-tests (Haynes 2013) were applied to the protein ratios to test

the confidence level of the data. Note that the one-sample Student’s t-tests should be

applied to a statistical population theoretically in normal distribution, so the t-tests were

applied on log2-transformed ratios. The null hypothesis is: H0: μ = 0, where μ = the average

protein ratio; i.e. the protein ratio is unity. A p-value below 0.05 indicates a great reliability

and most of time we use p ≤ 0.1 as the confidence threshold. The Table 4.8 below shows an

example of the ratio data with t-test results and gene annotations (A. dehalogenans). This

Table is sorted by gene number (locus tag).

61 Table 4.8: Example of normal protein ratio with annotations and t-test results (A. dehalogenans).

Protein Pep # Avg. t-stat p-value Ratio_1 Ratio_2 Annotation* A2cp1_0002 23 1.109 1.781 0.120 1.064 1.154 DNA polymerase III subunit beta A2cp1_0004 11 1.012 1.052 0.188 1.004 1.021 DNA gyrase subunit B A2cp1_0005 21 0.900 2.448 0.090 0.927 0.872 3-oxoacyl-(acyl-carrier-protein) synthase III A2cp1_0008 4 1.010 0.045 0.480 1.100 0.919 hypothetical protein A2cp1_0012 23 0.843 8.379 0.027 0.855 0.831 isoleucyl-tRNA synthetase A2cp1_0030 3 0.918 3.057 0.072 0.936 0.900 hypothetical protein A2cp1_0039 5 1.009 0.161 0.429 0.971 1.047 prolyl-tRNA synthetase A2cp1_0045 2 0.611 3.628 0.061 0.670 0.552 methyl-accepting chemotaxis sensory transducer A2cp1_0048 7 0.540 14.52 0.015 0.556 0.523 hypothetical protein A2cp1_0052 5 1.609 33.59 0.007 1.593 1.625 phenylacetate--CoA ligase A2cp1_0053 2 2.401 35.21 0.006 2.359 2.443 amino acid-binding ACT domain-containing protein A2cp1_0054 5 1.543 8.946 0.025 1.596 1.491 phenylacetate--CoA ligase A2cp1_0055 2 1.270 2.649 0.083 1.190 1.351 pyruvate ferredoxin/flavodoxin oxidoreductase A2cp1_0056 13 1.361 1.999 0.108 1.216 1.506 thiamine pyrophosphate TPP-binding domain-containing protein A2cp1_0063 3 1.480 2.704 0.081 1.330 1.629 hypothetical protein A2cp1_0064 16 1.216 4.496 0.050 1.179 1.253 electron transfer flavoprotein subunit alpha/beta A2cp1_0065 6 0.816 1.659 0.128 0.744 0.888 electron transfer flavoprotein subunit alpha A2cp1_0066 3 1.038 0.277 0.381 0.949 1.128 beta-lactamase domain-containing protein … … … … … … … … * In protein annotations, “hypothetical protein” refers to a protein that is predicted by genome sequencing to be exist but its function remains unclear (Gore and Chakule 2011; Zarembinski et al. 1998; Nan et al. 2009)

62 4.4 Bioinformatics Tools Several bioinformatics tools were used to analyze the generated protein ratio data

and the protein coding genes on organisms’ genomes. Unless stated otherwise, the

annotations and classes of proteins were taken from NCBI’s genome information for each

organism.

4.4.1 SPOCS SPOCS (Species Paralogy and Orthology Clique Solver) software

(http://cbb.pnnl.gov/portal/software/spocs.html) was employed in comparative genomics

analyses. It was a software written in C++ language by Curtis et al. to predict and visualize

paralogous and orthologous relationships among proteins encoded on microbial genomes

(Curtis et al. 2013). Orthologs are genes in different species that evolved from a common

ancestral gene by speciation. They may retain the same function during evolution. While

paralogs are genes related by duplication within a genome that may evolve new functions

(Remm, Storm, and Sonnhammer 2001). This software can provide flexible, readily

extensible ortholog predictions for user-defined species. In this analysis, the genomes of

nine organisms were analyzed with SPOCS: Anaeromyxobacter dehalogenans 2CP-1,

Shewanella oneidensis MR-1, Geobacter sulfurreducens PCA, Geobacter bemidjiensis Bem,

Desulfotomaculum reducens MI-1, Desulfotomaculum_acetoxidans Str. 5575,

Desulfobulbus_propionicus DSM 2032, Desulfococcus_oleovorans Hxd3 and

Desulfovibrio_desulfuricans ATCC 27774.

4.4.2 BLAST

63 BLAST (Basic Local Alignment Search Tool), which can be feely accessed at http://blast.ncbi.nlm.nih.gov/Blast.cgi is an algorithm firstly developed by Altschul et al. of

NCBI (Altschul et al. 1990). Its latest version is 2.2.31+ released on Jun. 15, 2015. BLAST is actually a family of programs to compare primary biological sequence information, such as the amino-acid sequences of different proteins (BLASTP) or the nucleotides sequences of

DNA (BLASTN). BLASTP was used in this thesis research to determine the similarities among proteins.

4.4.3 KEGG Pathway Database KEGG PATHWAY database (http://www.genome.jp/kegg/pathway.html), developed by Kanehisa et al. (Kanehisa and Goto 2000), is a collection of pathway maps representing knowledge on the molecular interactions and reaction networks within organisms for metabolism, genetic information processing, environmental information processing, cellular processes, etc. It provides information about which genes are involved in which steps of particular pathways, so it could be used to conduct systematic analysis of gene functions as well as linking them with higher order functional information. In this thesis, identified proteins were assigned to specific pathways to be analyzed in terms of their regulation in response to the different growth conditions.

4.4.4 Pfam Protein Family Database The Pfam protein family database (http://pfam.xfam.org/) was firstly developed by

Sonnhammer, Eddy, and Durbin (Sonnhammer, Eddy, and Durbin 1997). Its latest version

Pfam 28.0 was released in May 2015 containing information about 16,230 protein families.

64 It is a database of curated protein families (Finn et al. 2014) defined by two alignments of

peptide sequences to SEED (a genomic information database (Overbeek et al. 2005))

database. and a profile hidden Markov model (HMM) (Eddy 1998; Krogh et al. 1994). In this

thesis, Pfam information of each detected protein was retrieved from the database to get

more functional predictions besides their annotations. It provided functional

characterizations of protein domains and can be especially helpful for suggesting functions

of vaguely characterized proteins or even hypothetical proteins.

4.4.5 PSORTb PSORT, a bioinformatics tool predicting subcellular locations of proteins in cells from

the amino acid sequence information (Emanuelsson 2002; K. Nakai 2000), was firstly

available in 1999 (Kenta Nakai et al. 1999) but early versions did not support predictions for

Gram-negative bacteria. Its improved version for Gram-negative bacteria, PSORTb V1.0

(http://www.psort.org/psortb/), was released in 2003 (Gardy et al. 2003). Predicted

subcellular locations were reported as one of the following categories: cytoplasmic,

cytoplasmic membrane, periplasmic, outer membrane, extracellular, unknown. PSORTb

V3.0.2 (Gardy et al. 2005; Nancy et al. 2010), the latest version of this software was

employed in this thesis research to determine subcellular locations of detected proteins,

assisting in characterizing proteins with unclear or unknown functions, and to highlight

which proteins are associated with the cell surface.

65 CHAPTER 5. RESULT AND DISCUSSION

5.1 Protein Statistics

5.1.1 Protein Detection Rate Overall we detected between 7.3% (G. bemidjiensis) and 30.1% (A. dehalogenans) percent of the predicted proteins coded on the genome of the four study organisms. Results are discussed below for each organism.

5.1.1.1 By Protein Annotations Protein annotations on publically available genomes give general information about the function of a protein. To examine the detection trends for interesting classes of proteins, in this part of the thesis, proteins were roughly classified into five categories by their annotations in selected groups: c-type cytochromes; oxidoreductases/dehydrogenases

(other than c-type cytochromes); hypothetical proteins (proteins whose existence has been predicted by computational analysis of genomic DNA sequences, but for which there is no experimental evidence of its expression and/or activity in vivo); ribosomal proteins, and other annotated proteins. Numbers of proteins in these categories predicted by the whole genome were available from GenBank® data.

66 Table 5.1: Proportion of genome-predicted and detected proteins of A. dehalogenans (classified by annotations).

Normalized Predicted Detected % detected Detection Rate c-Type cytochromes 20 9 45.0% 1.49 Oxidoreductases/dehydrogenases 305 132 43.3% 1.44 Hypothetical proteins 1430 246 17.2% 0.57 Ribosomal proteins 57 47 82.5% 2.74 Other annotated proteins 2703 926 34.3% 1.14 Number of proteins 4515 1360 30.1% 1.00 *In Normalized Detection Rate column, overall detection rate is set to 1.00 as a reference value, rates of other categories were converted to a number based on their ratio to the overall rate. These numbers can provide information about detection efficiencies of different categories of proteins.

In total 1360 proteins were detected in AD, presenting 30.1% of all the protein- coding genes. Ribosomal proteins have the highest detection rate partially because they are often very abundant in the cytoplasm of active cells. C-type cytochromes may be of great

interest to us since they play key roles in mediating respiration-associated electron-transfer

processes (Shi et al. 2007). The GenBank® database predicted 20 c-type cytochromes, 9 of

which were detected. However, subsequent researchers predicted 69 c-type cytochromes

based on the presence of conserved heme-binding motifs and sequence similarities (Nissen

et al. 2012; Wagner et al. 2012), and 17 of them were detected.

67 Predicted Proteins Detected Proteins c-Type cytochrome, Oxidoreductases/dehydro Ribosomal Oxidoreductases/dehy 9, 0.7% genase, 305, 6.76% protein, 57, drogenase, 132, 9.7% 1.26%

hypothetical, 1430, 31.67%

hypothetical, 246, 18.1%

Others, 926, 68.1% Others, 2703, 59.87% c-Type Ribosomal cytochrome, protein, 47, 20, 0.44% 3.5%

c-Type cytochrome Oxidoreductases/dehydrogenase c-Type cytochrome Oxidoreductases/dehydrogenase hypothetical Ribosomal protein hypothetical Ribosomal protein Others Others

Figure 5.1: Proportion of genome-predicted and detected proteins of A. dehalogenans (classified by annotations).

68 Table 5.2: Proportion of genome-predicted and detected proteins of S. oneidensis (classified by annotations).

Normalized Predicted Detected % detected Detection Rate c-Type cytochromes 32 9 28.1% 1.54 Oxidoreductases/dehydrogenases 291 105 36.1% 1.98 Hypothetical proteins 455 16 3.5% 0.19 Ribosomal proteins 55 52 94.6% 5.19 Other annotated proteins 3737 650 17.4% 0.96 Number of proteins 4570 832 18.2% 1.00

As shown in Table 5.2, 52 out of 55 ribosomal proteins (94.6%) were detected in S. oneidensis cells, carrying the highest detection percentage among categories, consistent with the A. dehalogenans strain. 105 (36.1%) Oxidoreductases/dehydrogenase were detected, providing us good chance to look into these proteins connected closely to microbial metabolism. The GenBank® database confirmed 32 c-type cytochrome-coding genes in its genome, but Nissen et al. suggested later that its genome encodes 40 c-type cytochrome (Nissen et al. 2012), 9 of which were detected in our study. Though

Hypotheticals are approximately 10% of genome-encoded proteins, their detection percentage was lowest (3.5%). In S. oneidensis cells, we detected MtrA (SO1778), MtrC

(SO1779), and OmcA (SO1780) which are part of a proposed single operon (Shi et al. 2007).

69 Predicted Proteins Detected Proteins c-Type c-Type Oxidoreductases/dehydro cytochrome, Oxidoreductases/dehy cytochrome, genase, 291, 6.4% 9, 1.1% drogenase, 105, 12.6% 32, 0.7%

hypothetical, 16, 1.9% hypothetical, 455, 10.0%

Others, 3737, 81.8% Others, 650, 78.1%

Ribosomal protein, 55, Ribosomal 1.2% protein, 52, 6.3% c-Type cytochrome Oxidoreductases/dehydrogenase c-Type cytochrome Oxidoreductases/dehydrogenase hypothetical Ribosomal protein hypothetical Ribosomal protein Others Others

Figure 5.2: Proportion of genome-predicted and detected proteins of S. oneidensis (classified by annotations).

70 Table 5.3: Proportion of genome-predicted and detected proteins of G. bemidjiensis (classified by annotations).

Normalized Predicted Detected % detected Detection Rate c-Type cytochromes 79 4 5.1% 0.69 Oxidoreductases/dehydrogenases 302 46 15.2% 2.08 Hypothetical proteins 1028 18 1.8% 0.24 Ribosomal proteins 56 28 50.0% 6.82 Other annotated proteins 2656 206 7.8% 1.06 Number of proteins 4121 302 7.3% 1.00

Only 302 proteins out of 4196 genes were detected in G. bemidjiensis, representing

7.2% of the whole genome, which is the lowest detection rate among the four species in this study. But the data still confirmed the presence of 46 oxidoreductases/dehydrogenases and

4 c-type cytochromes.

71 Predicted Proteins Detected Proteins c-Type c-Type cytochrome, cytochrome, 79, 1.9% Oxidoreductases/dehyd 4, 1.3% rogenase, 302, 7.3% Oxidoreductases/dehydr ogenase, 46, 15.2%

hypothetical, 18, 6.0%

hypothetical, 1028, 24.9% Ribosomal protein, 28, 9.3% Others, 2656, 64.5% Others, 206, 68.2%

Ribosomal protein, 56, 1.4%

c-Type cytochrome Oxidoreductases/dehydrogenase c-Type cytochrome Oxidoreductases/dehydrogenase hypothetical Ribosomal protein hypothetical Ribosomal protein Others Others

Figure 5.3: Proportion of genome-predicted and detected proteins of G. bemidjiensis (classified by annotations)

72 Table 5.4: Proportion of genome-predicted and detected proteins of G. sulfurreducens (classified by annotations).

Normalized Predicted Detected % detected Detection Rate c-Type cytochromes 88 17 19.3% 0.67 Oxidoreductases/dehydrogenases 196 100 51.0% 1.76 Hypothetical proteins 1086 167 15.4% 0.53 Ribosomal proteins 56 44 78.6% 2.72 Other annotated proteins 2002 664 33.2% 1.15 Number of proteins 3428 992 28.9% 1.00

In G. sulfurreducens, 17 out of 88 c-type cytochromes and 100 out of 196 oxidoreductases/dehydrogenases were detected in the study, providing rich information to look into the differential expression of redox-linked proteins in this strain. There are 79 and

88 predicted c-Type cytochrome-coding genes in G. bemidjiensis strain Bem and G. sulfurreducens strain PCA, respectively. These two species contains more c-type cytochrome coding genes than above-mentioned A. dehalogenans strain 2cp-1 (20 c-type cytochromes annotated by GenBank® but up to 69 suggested by Nissen et al. (Nissen et al. 2012)) and S. oneidensis train MR-1 (32 c-type cytochromes predicted by GenBank®, 40 suggested by

Nissen et al. (Nissen et al. 2012)). Genetic studies in S. oneidensis and G. sulfurreducens have identified some key c-type cytochromes involved in iron reduction in these organisms (Shi et al. 2007; Bird, Bonnefoy, and Newman 2011). In this study we detected all OmcS, OmcB, and

OmcZ in G. sulfurreducens.

73 Predicted Proteins Detected Proteins c-Type c-Type cytochrome, cytochrome, Oxidoreductases/dehydro 17, 1.7% Oxidoreductases/dehy 88, 2.6% genase, 196, 5.7% drogenase, 100, 10.1%

hypothetical, hypothetical, 167, 16.8% 1086, 31.7%

Others, 2002, 58.4% Others, 664, 66.9%

Ribosomal Ribosomal protein, 44, protein, 56, 4.4% 1.6%

c-Type cytochrome Oxidoreductases/dehydrogenase c-Type cytochrome Oxidoreductases/dehydrogenase hypothetical Ribosomal protein hypothetical Ribosomal protein Others Others

Figure 5.4: Proportion of genome-predicted and detected Proteins of G. sulfurreducens (classified by annotations)

74 Comparing data from the four species, we see some common trends in the detection rates of proteins with different predicted functions among the four species as shown in

Table 5.5.

Table 5.5: Protein detection rates of four species (classified by annotations)

Protein Detection Rates Other c-Type Hypothetical Ribosomal Oxidoreductases/ annotated Species* cytochromes proteins proteins dehydrogenases proteins Overall A2cp1 45.0% 43.3% 17.2% 82.5% 34.3% 30.1% SO 28.1% 36.1% 3.5% 94.6% 17.4% 18.2% Gbem 5.1% 15.2% 1.8% 50.0% 7.8% 7.3% GSU 19.3% 51.0% 15.4% 78.6% 33.2% 28.9% Normalized Protein Detection Rates Other c-Type Hypothetical Ribosomal Oxidoreductases/ annotated Species cytochromes proteins proteins dehydrogenases proteins Overall A2cp1 1.49 1.44 0.57 2.74 1.14 1.00 SO 1.54 1.98 0.19 5.19 0.96 1.00 Gbem 0.69 2.08 0.24 6.82 1.06 1.00 GSU 0.67 1.76 0.53 2.72 1.15 1.00 * A2cp1: A. dehalogenans strain 2cp-1 SO: S. oneidensis strain MR-1 Gbem: G. bemidjiensis strain bem GSU: G. sulfurreducens strain PCA

In the normalized data presented in the lower part of the Table, a value above 1 means that the detection rate of proteins in this category is higher than the overall average detection rate within this species. Similarly, a value below 1 indicates a lower detection rate of this category of protein than the overall average detection rate within this species. With respect to c-type cytochromes, A. dehalogenans and S. oneidensis both have higher rates

(>1) than the overall average value; however G. bemidjiensis and G. sulfurreducens species have lower rates (<1) than the overall average. In all four organisms, the category of ribosomal proteins had the highest normalized ratio – not surprising as ribosomes are

75 required for growth under all conditions and cells may have hundreds to thousands of ribosomes per cell, besides, ribosomes are cytoplasmic so easier to be extracted.

Normalized Protein Detection Rate

Overall Others Ribosomal protein hypothetical Oxidoreductase/dehydrogenase Protein Functions Protein cytochrome c

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 Normalized ratio (Overall=1)

GSU Gbem SO A2cp1

Figure 5.5: Normalized protein detection rates of four species (classified by annotations). Ribosomal proteins have highest detection rates in all four species. Oxidoreductases/dehydrogenases are the second highest.

5.1.1.2 Detection by Predicted Protein Locations PSORTb V3.0.2 (Gardy et al. 2005; Nancy et al. 2010)was employed in this thesis

research to determine subcellular locations of detected proteins, assisting in the

characterization of proteins with unclear or unknown functions.

Table 5.6: Proportion of genome-predicted and detected proteins of A. dehalogenans (classified by subcellular locations as predicted by PSORTb).

Normalized Genome Detected Percentage Detection Rate Cytoplasmic 1967 954 48.50% 1.61 Cytoplasmic membrane 950 75 7.89% 0.26 Periplasmic 103 37 35.92% 1.19 Outer membrane 67 13 19.40% 0.64 Extracellular 44 8 18.18% 0.60 Unknown 1384 273 19.73% 0.65 Number of proteins 4515 1360 30.12% 1.00

76 Some key metabolic pathways take place in the cytoplasmic membrane (Singleton and others 2004), mediated by membrane-bound enzymes. In A. dehalogenans 75 out of

950 (7.9%) cytoplasmic membrane-bound proteins were detected. Outer membrane-bound proteins may be involved in electron transfer to extracellular electron acceptors such as metal oxides. Thirteen outer membrane proteins were detected representing 19.4% of the group.

77 Predicted Proteins Detected Proteins

Extracellular, 8, 0.6% Unknown, 273, 20.1% Unknown, Outer 1384, 30.7% Cytoplasmic, membrane, 1967, 43.6% 13, 1.0%

Extracellular, 44, 1.0% Periplasmic, 37, 2.7% Cytoplasmic, Outer 954, 70.1% membrane, Cytoplasmic 67, 1.5% Cytoplasmic membrane, membrane, Periplasmic, 950, 21.0% 75, 5.5% 103, 2.3%

Cytoplasmic Cytoplasmic membrane Cytoplasmic Cytoplasmic membrane Periplasmic Outer membrane Periplasmic Outer membrane Extracellular Unknown Extracellular Unknown

Figure 5.6: Proportion of genome-predicted and detected proteins of A. dehalogenans (classified by subcellular locations as predicted by PSORTb).

78 Table 5.7: Proportion of genome-predicted and detected proteins of S. oneidensis (classified by subcellular locations as predicted by PSORTb).

Normalized Genome Detected Percentage Detection Rate Cytoplasmic 1768 583 33.0% 1.81 Cytoplasmic membrane 893 43 4.8% 0.26 Periplasmic 123 38 30.9% 1.70 Outer membrane 102 11 10.8% 0.59 Extracellular 41 6 14.6% 0.80 Unknown 1643 151 9.2% 0.50 Number of proteins 4570 832 18.2% 1.00

In S. oneidensis, only 4.8% (43 out of 893) cytoplasmic membrane-bound proteins were detected, presenting the lowest detection efficiency among all categories. One hundred and two outer membrane-bound proteins were predicted from the genome, among which 11 (10.8%) were detected.

79 Predicted Proteins Detected Proteins

Extracellular, 6, 0.7%

Unknown, Unknown, Outer 151, 18.1% 1643, 36.0% membrane, 11, 1.3% Extracellular, 41, 0.9%

Cytoplasmic, Periplasmic, Outer 1768, 38.7% 38, 4.6% membrane, Cytoplasmic, 102, 2.2% 583, 70.1% Cytoplasmic membrane, Cytoplasmic Periplasmic, 893, 19.5% membrane, 123, 2.7% 43, 5.2%

Cytoplasmic Cytoplasmic membrane Cytoplasmic Cytoplasmic membrane Periplasmic Outer membrane Periplasmic Outer membrane Extracellular Unknown Extracellular Unknown

Figure 5.7: Proportion of genome-predicted and detected proteins of S. oneidensis (classified by subcellular locations as predicted by PSORTb).

80 Table 5.8: Proportion of genome-predicted and detected proteins of G. bemidjiensis (classified by subcellular locations as predicted by PSORTb).

Normalized Genome Detected Percentage Detection Rate Cytoplasmic 1913 251 13.1% 1.79 Cytoplasmic membrane 847 8 0.9% 0.13 Periplasmic 97 9 9.3% 1.27 Outer membrane 66 4 6.1% 0.83 Extracellular 59 1 1.7% 0.23 Unknown 1139 29 2.6% 0.35 Number of proteins 4121 302 7.3% 1.00

As only 302 (7.20%) proteins were detected in total, of which only 8 were located in

the cytoplasmic membrane and only 4 in the outer membrane, data from G. bemidjiensis provided very few opportunities to detect changes in pathways taking place in membrane- associated proteins when cells were grown with different electron acceptors (ferric citrate vs. fumarate).

81 Predicted Proteins Detected Proteins

Extracellular, 1, 0.3% Unknown, 1139, 27.6% Outer Unknown, Cytoplasmic, membrane, 4, 29, 9.6% Extracellular, 1913, 46.4% 1.3% 59, 1.4% Cytoplasmic, 251, 83.1%

Cytoplasmic Periplasmic, 9, Outer membrane, 3.0% membrane, 847, 20.6% 66, 1.6% Cytoplasmic Periplasmic, membrane, 8, 97, 2.4% 2.6%

Cytoplasmic Cytoplasmic membrane Cytoplasmic Cytoplasmic membrane Periplasmic Outer membrane Periplasmic Outer membrane Extracellular Unknown Extracellular Unknown

Figure 5.8: Proportion of genome-predicted and detected proteins of G. bemidjiensis (classified by subcellular locations as predicted by PSORTb).

82 Table 5.9: Proportion of genome-predicted and detected proteins of G. sulfurreducens (classified by subcellular locations as predicted by PSORTb).

Normalized Genome Detected Percentage Detection Rate Cytoplasmic 1629 746 45.8% 1.58 Cytoplasmic membrane 728 50 6.9% 0.24 Periplasmic 72 28 38.9% 1.34 Outer membrane 59 13 22.0% 0.76 Extracellular 37 7 18.9% 0.65 Unknown 903 148 16.4% 0.57 Number of proteins 3428 992 28.9% 1.00

In G. sulfurreducens, totally 992 proteins were detected, most of which were cytoplasmic proteins. Cytoplasmic and periplasmic proteins are easier to be extracted. It is no wonder cytoplasmic and periplasmic proteins are most abundant among examined categories for all organisms.

83 Predicted Proteins Detected Proteins

Extracellular, 37, 1.1%

Extracellular, 7, 0.7% Unknown, Unknown, 903, 26.3% Cytoplasmic, Outer 148, 14.9% 1629, 47.5% membrane, 13, 1.3% Outer membrane, Cytoplasmic, 59, 1.7% Periplasmic, 746, 75.2% 28, 2.8% Periplasmic, Cytoplasmic 72, 2.1% membrane, 728, 21.2% Cytoplasmic membrane, 50, 5.0%

Cytoplasmic Cytoplasmic membrane Cytoplasmic Cytoplasmic membrane Periplasmic Outer membrane Periplasmic Outer membrane Extracellular Unknown Extracellular Unknown

Figure 5.9: Proportion of genome-predicted and detected proteins of G. sulfurreducens (classified by subcellular locations as predicted by PSORTb).

84 Detection rate data from the four species were then compared to see if there is any common trend in the detection of proteins at different predicted locations.

Table 5.10: Protein detection rates of four species (classified by subcellular locations as predicted by PSORTb).

Protein Detection Rates

Species C* CM P OM E U Overall A2cp1 48.50% 7.89% 35.92% 19.40% 18.18% 19.73% 30.12% SO 32.98% 4.82% 30.89% 10.78% 14.63% 9.19% 18.21% Gbem 13.12% 0.94% 9.28% 6.06% 1.69% 2.55% 7.33% GSU 45.79% 6.87% 38.89% 22.03% 18.92% 16.39% 28.94% Normalized Protein Detection Rates

Species C CM P OM E U Overall A2cp1 1.61 0.26 1.19 0.64 0.60 0.65 1.00 SO 1.81 0.26 1.70 0.59 0.80 0.50 1.00 Gbem 1.79 0.13 1.27 0.83 0.23 0.35 1.00 GSU 1.58 0.24 1.34 0.76 0.65 0.57 1.00 * C = Cytoplasmic; CM = Cytoplasmic Membrane; P = Periplasmic; OM = Outer Membrane; E = Extracellular; U = Unknown.

Normalized ratio of proteins detected by locations

Overall Unknown Extracellular Outer membrane Periplasmic Cytoplasmic membrane Protein locations Protein Cytoplasmic

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00 Normalized ratio (Overall=1)

GSU Gbem SO A2cp1

Figure 5.10: Normalized protein detection rates of four species (classified by subcellular locations as predicted by PSORTb)

As shown in Table 5.10 and Figure 5.10, cytoplasmic proteins had the highest detection efficiencies, partially because soluble proteins in the cytoplasm are more easily

85 extracted from cells during sample preparation. Periplasmic proteins also have higher

detection efficiencies than overall, the reason being pretty similar to that of the cytoplasmic

proteins. On the other hand, proteins bound to the cytoplasmic membrane were

underrepresented compared to the average. Such proteins can be difficult to extract.

Unsurprisingly they have the lowest detection efficiency in all four species. Many proteins in

this category are important to cellular respiration. For example the oxidative

phosphorylation process is an important pathway for most organisms to produce ATP and

transfer electrons to terminal electron acceptors (Mitchell and Moyle 1967). Most enzymes

involved in oxidative phosphorylation are membrane-bound proteins (Dimroth, Kaim, and

Matthey 2000).

5.1.2 Protein Ratios between Iron and Fumarate Conditions Generated protein ratios were firstly calculated for the replicate samples, then the

ratios were log2-transformed. A log2 ratio above 0 indicates an up-regulation under ferric

citrate, which means this certain protein’s average peptide ion intensity was higher under

ferric citrate than under fumarate. A ratio below 0 suggests a down-regulation under ferric

citrate, implying a higher abundance of proteins under fumarate. An area chart was then

created for each of the four species to present the distribution of protein ratios (see

Appendices).

Two combined Figures presenting the log2 ratio distributions of all four organisms

are shown below. Figure 5.11 shows the absolute number of proteins distributed across the

86 ratio range, and Figure 5.12 shows the frequency of proteins distributed across the ratio range.

87 225

200

175

150

125

100

Protein Numbers 75

0 -6.4 -5.6 -4.8 -4 -3.2 -2.4 -1.6 -0.8 0 0.8 1.6 2.4 3.2 4 4.8 5.6 6.4

Log2-Transformed Protein Ratio

A2cp1 SO GSU Gbem

Figure 5.11: Distribution of protein ratios of four species (protein numbers).

88 0.18

0.16

0.14

0.12

0.1

0.08

Probabilities 0.06

0.04

0.02

0 -6.4 -5.6 -4.8 -4 -3.2 -2.4 -1.6 -0.8 0 0.8 1.6 2.4 3.2 4 4.8 5.6 6.4 Log2-Transformed Protein Ratio

A2cp1 SO GSU Gbem

Figure 5.12: Distribution of protein ratios of four species (protein frequencies).

89 Though some little offset may exist, the mean values of log2 ratio distributions

estimated from these Figures were still within a small range around 0. The distribution for G.

bemidjiensis is in an irregular shape, which seems abnormal. The reason may be that

detected proteins of this species is too few (only 302 out of 4121) to present a smooth

curve. However, protein ratios reported in this study are still useful when we dig into

regulation of single protein. A. dehalogenans possesses higher probabilities around 0. This

may imply that fewer proteins of this species were strongly up- or down-regulated in

expression in switching between electron acceptors than with the other three species.

5.2 Across-Species Analysis Across-species analysis was done with help of SPOCS. SPOCS can help identify

orthologous proteins in these species by recognizing characterized motifs of DNA sequences.

5.2.1 Overview The four species mentioned in this thesis belong to a SPOCS group containing, in

total, 9 microorganism species. Besides A. dehalogenans, S. oneidensis, G. sulfurreducens

and G. bemidjiensis, there are Desulfotomaculum reducens, Desulfotomaculum_acetoxidans,

Desulfobulbus_propionicus, Desulfococcus_oleovorans and Desulfovibrio_desulfuricans, all of

which are capable of reducing metals. There are, in total, 4135 clusters containing genes

from at least two organisms. (729 clusters contain more than one clique, which refers to the

subgroup of genes in the same cluster). There are 247 clusters containing genes from all of

the nine organisms mentioned above. Four thousand four hundred and eighty-seven cliques

contain genes from at least one of the four organisms analyzed in this thesis, among which

90 525 contain genes from all of the four species analyzed in this thesis, 106 of them were detected in all four species (see Appendices).

5.2.2 Up-Regulated Orthologs

91 Table 5.11: Orthologs up-regulated by all four organisms when supplied with ferric citrate, versus fumarate.

Cluster Avg. Ratio Avg. Ratio Avg. Ratio Avg. Ratio ID Nodes Edges A2cp1 Ratio 1&2 Gbem Ratio 1&2 GSU Ratio 1&2 SO Ratio 1&2 1.70 1.17 0.88 1.50 490 6 15 A2cp1_0980 2.03* Gbem_1015 1.92 GSU1183 1.11 SO_1095 1.09 2.36 2.66 1.35 0.67 1.21 2.01 0.70 1.08 599 9 36 A2cp1_1228 1.33 Gbem_1300 1.48 GSU1586 1.07 SO_1203 1.04 1.44 0.95 1.44 1.01 1.28 1.08 1.39 1.85 851 6 15 A2cp1_1888 1.46 Gbem_0889 1.33 GSU0994 1.54* SO_2222 1.76* 1.64 1.58 1.69 1.67 1.35 0.21 1.35 1.36 911 9 36 A2cp1_2022 1.34* Gbem_0937 1.17 GSU2853 1.55* SO_0235 1.66 1.33 2.13 1.74 1.97 1.50 0.68 1.21 0.87 934 9 36 A2cp1_2045 1.39* Gbem_0960 1.39 GSU2831 1.26* SO_0256 1.04 1.27 2.10 1.31 1.22 2.32 6.72 1.44 1.64 1485 9 36 A2cp1_3517 2.18* Gbem_0913 4.70 GSU2875 1.39* SO_3939 1.54* 2.04 2.68 1.33 1.45 2.30 1.00 2.14 2.74 1803 5 10 A2cp1_4408 1.97* Gbem_3077 1.69 GSU1835 2.79* SO_4410 2.34* 1.63 2.38 3.44 1.94 1.26 9.76 8.58 1.79 1841 9 36 A2cp1_4503 1.25* Gbem_3950 6.05 GSU0113 9.86* SO_4747 1.58* 1.24 2.34 11.1 1.37 1.41 0.53 8.18 1.58 1842 9 36 A2cp1_4504 1.15 Gbem_3951 1.40 GSU0112 11.5* SO_4748 1.80* 0.90 2.28 14.8 2.01 1.03 0.89 17.3 2.44 1843 9 36 A2cp1_4505 1.01 Gbem_3952 1.13 GSU0111 16.0* SO_4749 2.23* 0.99 1.37 14.7 2.02 1) Cluster ID is an ID number given to a cluster of orthologous genes 2) Nodes number: the number of orthologous genes in this clique (maximum value is 9 – which suggest a highly conserved protein) 3) Edges number: the number of bi-directional (reciprocal) best hits in this clique 4) Avg. Ratio: Average plain protein abundance ratios of cells growing under ferric citrate to growing under fumarate. Average ratios with “*” have p-values below 0.1. 5) Ratio 1&2: Protein abundance ratios reported by the two replicates

92 Ten clusters were identified in the data as upregulated on iron in all four organisms, as shown by average ratios in Table 5.11. Shading of a single ratio indicates that this ratio is below 1, which means a down-regulation on this replicate, making the regulation status of this gene uncertain. Most ratios are below 2-fold, implying that these shifts in protein abundance were small. The annotations of proteins mentioned in Table 5.11 are correspondingly listed below in Table 5.12. Annotations of unconfidently regulated genes are shaded.

93 Table 5.12: Annotations of orthologs up-regulated by all four organisms when supplied with ferric citrate, versus fumarate.

Cluster ID A2cp1 Gbem GSU SO O-acetylhomoserine/O- O-acetyl-L-homoserine O-acetyl-L-homoserine O-acetylhomoserine (thiol)- 490 acetylserine sulfhydrylase sulfhydrylase sulfhydrylase lyase MetY transcription elongation transcription elongation N utilization substance 599 NusA antitermination factor factor NusA factor NusA protein A NusA Fe-S type, tartrate/fumarate anaerobic fumarate 851 subfamily hydro-lyase subunit class I fumarate hydratase fumarate hydratase, class I hydratase FumB alpha 30S ribosomal protein S19 911 30S ribosomal protein S19 30S ribosomal protein S19 30S ribosomal protein S19 RpsS DNA-directed RNA DNA-directed RNA polymerase DNA-directed RNA DNA-directed RNA 934 polymerase alpha subunit subunit alpha polymerase subunit alpha polymerase subunit alpha RpoA 1485 30S ribosomal protein S9 30S ribosomal protein S9 30S ribosomal protein S9 30S ribosomal protein S9 RpsI 1803 glutamine synthetase, type I type I glutamine synthetase glutamine synthetase, type I glutamine synthetase GlnA F0F1 ATP synthase subunit F0F1 ATP synthase subunit ATP synthase F1 beta subunit 1841 F0F1 ATP synthase subunit beta beta beta AtpD F0F1 ATP synthase subunit F0F1 ATP synthase subunit F0F1 ATP synthase subunit ATP synthase F1 gamma 1842 gamma gamma gamma subunit AtpG F0F1 ATP synthase subunit F0F1 ATP synthase subunit ATP synthase F1 alpha 1843 F0F1 ATP synthase subunit alpha alpha alpha subunit AtpA

94 This Table contains some core proteins in cellular metabolism and cellular processing

— including the F0F1type ATP synthase, proteins of glutamine metabolism, proteins of fumarate metabolism, RNA polymerase (the enzyme responsible for all transcription in the cell), a transcriptional regulator (NusA) and the molecular chaperone DnaK. F0F1-type ATP synthase of bacteria is a protein complex consisting of two parts: F0 and F1, mainly serving two important physiological functions (Deckers-Hebestreit and Altendorf 1996, 1). This enzyme catalyzes the synthesis of ATP from ADP and inorganic phosphate with proton motive force generated by the transmembrane transportation of hydrogen ions. When the ion gradient is low, it can function as ATPase consuming ATP to generate a transmembrane ion ingredient. In Gram-negative bacteria, the F0 complex responsible for ion translocating is cytoplasmic membrane-bound and the F1 complex is located in the cytoplasm, catalyzing

ATP synthesis and hydrolysis. As to the F0 complex, only subunit b of it in A. dehalogenans

(A2cp1_4493) and S. oneidensis (SO_4751) was detected in this study, and it is hard to tell the regulation condition of these two proteins, as A2cp1_4493 possesses two ratios: 1.32 and 0.80 (data not shown), while SO_4751 possesses two ratios: 1.46 and 0.70 (data not shown). This may due to the variable detection efficiency of membrane-bound proteins. F1 possesses five subunits (α, β, γ, δ and ε), β subunit were confidently up-regulated (Table

5.12 cluster ID: 1841). Although up-regulation of α and γ (Table 5.12 cluster ID: 1842, 1843) subunits was not significant in A. dehalogenans and G. bemidjiensis, other data still demonstrate a trend of up-regulation of these two subunits. It is unclear if the up-regulation of F1 complex of all four species is a specific response to iron or a more general response to more robust growth. The fact that RNA polymerase is also universally upregulated in the

95 organisms suggests the latter explanation. The redox tower suggests that Fe(III)/Fe(II) redox couple has high redox potential than fumarate/succinate couple (0.2V versus 0.03V). It is not surprising the ATP synthase is more abundant in cells grown on ferric citrate, as the greater redox difference between NADH and Fe(III) could allow more H+ to be pumped across cell membrane.

Glutamine synthetase (Table 5.12 cluster ID: 1803) can use ammonia produced by amino acid degradation and glutamate to synthesize glutamine (Liaw, Kuo, and Eisenberg

1995). O-Acetyl-L-homoserine sulfhydrylase functions as a homocysteine synthase in the pathway of methionine synthesis (Yamagata 1989). The connection between these amino acid synthesis reactions and iron reduction is unclear and it is likely an indirect effect as with

RNA polymerase and ATPase. The up-regulation of fumarate hydratase (Table 5.12 cluster

ID: 851) is surprising and interesting. This enzyme is involved in one step of the TCA

(Tricarboxylic Acid) cycle. It catalyzes the reversible reaction of fumarate to malate. The up- regulation of this protein caused by the presence of ferric citrate as electron acceptor instead of fumarate warrants a closer investigation. It is possible that when grown on fumarate the cells uptake some fumarate for biosynthetic purposes but when grown on ferric citrate, the cells need to upregulate this enzyme to make up for the lack of fumarate available from the medium. Conversely the citrate ligand may be taken up under Fe(III) condition and fed into the TCA cycle.

5.2.3 Down-Regulated Orthologs

96 Table 5.13: Orthologs down-regulated by all four organisms when supplied with ferric citrate, versus fumarate.

Cluster Avg. Ratio Avg. Ratio Avg. Ratio Avg. Ratio ID Nodes Edges A2cp1 Ratio 1&2 Gbem Ratio 1&2 GSU Ratio 1&2 SO Ratio 1&2 0.52 1.00 0.35 1.04 64 6 15 A2cp1_0112 0.48* Gbem_0619 0.76 GSU3204 0.47 SO_3815 0.95 0.44 0.52 0.58 0.85 0.70 0.11 0.33 0.74 557 9 36 A2cp1_1171 0.76* Gbem_0970 0.79 GSU1139 0.32* SO_1315 0.94 0.81 1.46 0.31 1.14 0.60 0.83 0.68 0.78 698 9 36 A2cp1_1460 0.59* Gbem_0408 0.95 GSU3285 0.66* SO_4313 0.77* 0.58 1.07 0.64 0.75 0.78 1.08 0.47 0.48 905 9 36 A2cp1_2016 0.90 Gbem_0930 0.84 GSU2860 0.52* SO_0228 0.45* 1.01 0.60 0.57 0.42 1.21 0.22 0.62 0.47 922 9 36 A2cp1_2033 0.98 Gbem_0948 0.29* GSU2842 0.79 SO_0246 0.38* 0.76 0.37 0.97 0.28 0.77 0.10 0.56 1.13 928 9 36 A2cp1_2039 0.81* Gbem_0954 0.17* GSU2836 0.58* SO_2018 0.82 0.85 0.25 0.60 0.50 1.13 0.54 0.13 0.30 1303 9 36 A2cp1_2923 0.94 Gbem_3021 0.50* GSU1691 0.14* SO_3466 0.43 0.76 0.46 0.15 0.55

Seven clusters of orthologs were identified by the average ratios to be down-regulated by all four organisms when

supplied with ferric citrate, versus fumarate. In the data, shadings were given to ratios above 1, implying uncertain regulation.

The annotations of proteins mentioned in Table 5.13 are correspondingly listed below in Table 5.14. Annotations of genes that were unconfidently regulated were also shaded in Table 5.14.

97 Table 5.14: Annotations of orthologs down-regulated by all four organisms when supplied with ferric citrate, versus fumarate.

Cluster ID A2cp1 Gbem GSU SO putative nucleotide-binding protein of unknown function 64 nucleotide-binding protein nucleotide-binding protein protein DUF520 YajQ 557 tyrosyl-tRNA synthetase tyrosyl-tRNA synthetase tyrosyl-tRNA synthetase tyrosyl-tRNA synthetase TyrS hydroxymethylbilane 698 porphobilinogen deaminase porphobilinogen deaminase porphobilinogen deaminase synthase HemC translation elongation factor 905 elongation factor G elongation factor G elongation factor G G FusA 922 50S ribosomal protein L6 50S ribosomal protein L6 50S ribosomal protein L6 50S ribosomal protein L6 RplF 928 adenylate kinase adenylate kinase adenylate kinase adenylate kinase Adk 6,7-dimethyl-8-ribityllumazine 6,7-dimethyl-8- 6,7-dimethyl-8- riboflavin synthase beta 1303 synthase ribityllumazine synthase ribityllumazine synthase subunit RibE

Adenylate kinase (Table 5.14 cluster ID: 928)catalyzes the interconversion of adenine nucleotide: ATP + AMP = 2 ADP

(Goldberg, Tewari, and Bhat 2004). As the rate of oxidative phosphorylation can be controlled by the availability of ADP, adenylate is a factor affecting oxidative phosphorylation. Combining this with the fact that F0F1 type ATP synthase was up- regulated, as demonstrated in Section 5.2.2, it may suggest that the rate of oxidative phosphorylation was shifted. The roles of

F0F1 ATP synthase and adenylate kinase are still unclear.

98 Porphobilinogen deaminase (Table 5.14 cluster ID: 698) is an enzyme involved in the third step of biosynthesis of the heme group. This may be related to the differential expression of cytochromes to be discussed latter. YajQ in Shewanella oneidensis is a short protein only containing 161 amino acids. BLAST results suggest it likely to be also a nucleotide binding protein — which is consistent with the annotations for the orthologs in the other organisms. Though it was not confidently down-regulated in S. oneidensis and G. bemidjiensis, its down-regulation found in one of the two replicates of S. oneidensis and G. bemidjiensis as well as its significant down-regulation in other two species (greater than 2- fold down-regulation) may still suggest that a common transcriptional regulator is down regulated on iron versus fumarate.

It is interesting to find that 50s ribosomal protein L6 (Table 5.14 cluster ID: 922) was down-regulated while the 30s ribosomal protein S9 was found to be up-regulated. This contradictory change to a different type of ribosomal protein may suggest some change in ribosomal composition of the organisms when the electron acceptor was changed from organic fumarate to inorganic metal. Specific ribosomal proteins can be turned on to modulate ribosome activity.

6,7-dimethyl-8-ribityllumazine (Table 5.14 cluster ID: 1303) is a precursor for riboflavin, which is the central component of FAD (Flavin Adenine Dinucleotide) and FMN

(Flavin Mononucleotide). FAD and FMN are known to be universal electron carriers, involved in many pathways transporting electrons. FAD is an important cofactor of succinate dehydrogenase, in which it shuttles electrons to the flavoprotein subunit of the complex.

99 FMN is part of cytochrome c reductase. This down-regulation of the precursor may suggest a lower demand for FAD and FMN during iron reduction, compared to fumarate reduction.

5.3 Organism-Specific Analysis

5.3.1 Proteins in Important Pathways The identified proteins were assigned to biological pathways suggested by the KEGG

Pathway Database (http://www.genome.jp/kegg/pathway.html) (Kanehisa and Goto 2000) and Pathway Tools software (P. D. Karp, Paley, and Romero 2002). In the following analyses, the TCA cycle and oxidative phosphorylation are presented for each organism. Expression data for proteins involved in each step of these pathways are presented. One-sample.

Students’ t-tests were employed on the absolute value of the log2-transformed ratio to indicate the confidence that a protein ratio was not unity.

5.3.1.1 TCA Cycle Figures 5.13 to 5.16 below show proteins predicted to be involved in the TCA cycle of four species: A. dehalogenans, S. oneidensis, G. sulfurreducens and G. bemidjiensis, respectively, along with information such as protein abundance ratio and t-test p value.

Arrows in the Figures direct proteins to the steps they catalyze. The three numbers following the protein locus tags are Ratio_1, Ratio_2 and t-test p-value. Protein ratios used here were normal ratios without log2-transformation. Color shadings are used to present the regulation direction of each protein. Red was used on ratios above 1, meaning an up- regulation (proteins are more abundant under ferric citrate) in this replicate. Blue was used on ratios below 1, which represents a down-regulation (proteins were less abundant under

100 ferric citrate) in this replicate. The protein ID was shaded only if the results from the two replicates were consistent in their direction of regulation.

101

Figure 5.13: Detection and regulation status of proteins involved in each step of TCA Cycle (A. dehalogenans). Red color indicates an up- regulation while blue color indicates a down-regulation. P-values below 0.1 were denoted with “*”.

102 As shown in Figure 5.13, 31 proteins were predicted in be involved in TCA cycle of A. dehalogenans and 21 were detected. The majority of the enzymes of the TCA cycle of A. dehalogenans were generally more abundant when it was grown with ferric citrate but the enzyme converting succinate to fumarate was a little less abundant. A2cp1_3125 and

A2cp1_3126 (0.71 and 0.71 in average, respectively) are subunit C and subunit A of succinate dehydrogenase. Their expression ratios (0.85 and 0.57, 0.84 and 0.59, respectively) were pretty consistent in each replicate. This differential regulation condition compared to proteins in other steps of TCA pathways may suggest that the enzyme catalyzing this step of reaction may also be involved in side reactions. In cells grown with fumarate, succinate dehydrogenase donates electrons to the electron transport chain that terminally transports electrons to the electron acceptor, fumarate, reducing it to succinate.

High fumarate levels in these cells may shift the thermodynamics of this reaction away from favorability, then the expression of succinate dehydrogenase might be down-regulated

(repressed in expression). This may explain the higher abundance of succinate dehydrogenase in cells grown with Fe(III). The step transforming 2-oxo-glutarate to succinyl-

CoA is interesting. There are generally two pathways to achieve this transformation as shown in the Figure: through reaction EC 1.2.7.3 or through reaction EC 1.2.4.2, 1.8.1.4 and

EC 2.3.1.61. The protein ratio data shows that genes involved in reaction EC 1.2.7.3 were up- regulated while genes involved in the other pathway were down-regulated. The reason of this selective regulation is unclear.

103

Figure 5.14: Detection and regulation status of proteins involved in each step of TCA Cycle (S. oneidensis). Red color indicates an up- regulation while blue color indicates a down-regulation. P-values below 0.1 were denoted with “*”.

104 In TCA cycle of S. oneidensis, the step converting succinate to fumarate showed a different trend of that in A. dehalogenans. No obvious down-regulation of succinate dehydrogenase was found. Actually its subunit B (SO_1929) was found to be up-regulated greater than 2-fold and a p-value of 0.014. Whether succinate dehydrogenase conducts somewhat different physiological functions in S. oneidensis and A. dehalogenans remains unclear and may need further research. The generally differential regulation condition between TCA cycle in S. oneidensis cells and that in A. dehalogenans cells may be due to the different electron donors used with the two organisms (lactate for S. oneidensis and acetate for A. dehalogenans).

Malate dehydrogenase (NAD) (EC 1.1.1.37) (SO_0770) was highly up-regulated, with ratio of 7.680 and 6.677 in the two replicates, respectively. This enzyme was also found to be up-regulated in A. dehalogenans when grown under ferric citrate. High malate concentrations stimulate its activity (Mullinax et al. 1982). The up-regulation of fumarate hydratase (EC 4.2.1.2) in both A. dehalogenans and S. oneidensis may be an evidence of the higher concentration of malate under ferric citrate. Besides its involvement in the TCA cycle, this enzyme can also oxidize some other 2-hydroxydicarboxylic acids and contribute to the fermentation of acids, etc. This unusual up-regulation, compared to expression ratios of other proteins involved in the TCA cycle, may be worth additional research.

105

Figure 5.15: Detection and regulation status of proteins involved in each step of TCA Cycle (G. sulfurreducens). Red color indicates an up- regulation while blue color indicates a down-regulation. P-values below 0.1 were denoted with “*”.

106 G. sulfurreducens was reported to use the same enzyme frdCAB for both fumarate reduction and succinate oxidation in vivo (Butler et al. 2006). This bifunctional enzyme can serve as terminal fumarate reductase when fumarate is provided as electron acceptor. The succinate dehydrogenase subunit C (GSU1176) of G. sulfurreducens was highly up-regulated while subunit A (GSU1177) of the same enzyme was down-regulated. P-values of them were both below 0.05, demonstrating a fairly confident result. The different trends in abundances of proteins encoded by the same operon may be caused by inaccurate operon prediction, early termination of transcription, posttranscriptional regulation, and differences in proteins’ half-lives, not to mention variability in protein extraction efficiencies across different extractions. Whether one of the subunits carries another function other than in the

TCA cycle is unclear.

The down-regulation of malate dehydrogenase is not statistically significant, based on the high p-value 0.225. However, we can still cautiously conclude that the malate dehydrogenase was not significantly up regulated. This is showing an opposite result to the observation of A. dehalogenans and S. oneidensis. Citrate was reported to be able to both activate (when malate and NAD+ levels are high) and inhibit (when malate and NAD+ levels are low) the enzymatic activity of malate dehydrogenase (Gelpi et al. 1992), but whether the presence of citrate in the media can affect the expression of this gene is unclear.

107

Figure 5.16: Detection and regulation status of proteins involved in each step of TCA Cycle (G. bemidjiensis). Red color indicates an up- regulation while blue color indicates a down-regulation. P-values below 0.1 were denoted with “*”.

108 5.3.1.2 Oxidative Phosphorylation Oxidative phosphorylation is the key pathway in organisms to transfer electrons to terminal electron acceptors via an electron transport chain while simultaneously generating a proton motive force, which is then energetically-converted to ATP. Pathway maps from the KEGG site were used to reconstruct oxidative phosphorylation in the four organisms.

The Figures show the gene loci of all proteins predicted to be involved in the pathway, along with the replicate expression ratios and t-test p value for proteins that were detected in the proteome. The first and second cells following the locus tags are duplicate abundance ratios of the indicated protein. These ratios are normal ones without log2-transformation, which means a value of 1 indicates equal protein abundances under the two different growth conditions.

109

Figure 5.17: Detection and regulation status of proteins involved in each step of oxidative phosphorylation (A. dehalogenans). P-values below 0.1 were denoted with “*”.

110

Figure 5.18: Detection and regulation status of proteins involved in each step of oxidative phosphorylation (S. oneidensis). P-values below 0.1 were denoted with “*”.

111

Figure 5.19: Detection and regulation status of proteins involved in each step of oxidative phosphorylation (G. sulfurreducens). P-values below 0.1 were denoted with “*”.

112

Figure 5.20: Detection and regulation status of proteins involved in each step of oxidative phosphorylation (G. bemidjiensis). P-values below 0.1 were denoted with “*”.

113 Data from A. dehalogenans and G. sulfurreducens suggest an up-regulation of the

NADH dehydrogenase complex. Regulation of NADH dehydrogenase in S. oneidensis and G. bemidjiensis, is less clear given the limited number of proteins detected and relatively high p value. It is obvious that the expression of NADH dehydrogenase or at least several subunits of G. sulfurreducens was up-regulated. GSU_0339, subunit B of the complex, carried ratios of

11.2 and 13.8 with a p value of 0.036. This higher expression of one gene among others within the same operon warrants closer investigation, but membrane bound subunits may be highly susceptible to low extraction inefficiency during protein purification steps.

Succinate dehydrogenase involved here as Complex II is also linked to the TCA cycle.

The expression of it has already been discussed before in Section 5.3.1.1. Cytochrome c reductase, cytochrome bd complex, and cytochrome c oxidase (except one cbb3-type cytochrome c oxidase from A. dehalogenans) were not detected. This may due to the low detection efficiency of these membrane-bound proteins. Even for NADH dehydrogenase which had several subunits detected, those detected subunits were not the membrane- embedded subunits. This lack of information prevents us from determining the regulations of Complex III and Complex IV of oxidative phosphorylation. Additionally these complexes are typical for aerobic oxidative phosphorylation but not usually involved in anaerobic respiration.

As mentioned previously in Section 5.2.2, multiple subunits of the F0F1 type ATP synthase were found to be up-regulated across all four study organisms.

5.3.2 c-Type Cytochromes

114 Based on studies of Shewanella spp. and Geobacter spp., it is currently hypothesized that c-type cytochromes play an important role in electron transport and dissimilatory metal reduction by metal respiring Gram-negative microorganisms (Bird, Bonnefoy, and Newman

2011; Mikoulinskaia et al. 1999; Afkar and Fukumori 1999; Seeliger, Cord-Ruwisch, and

Schink 1998; Gaspard, Vazquez, and Holliger 1998; CHARLES R. Myers and Myers 1992;

Charles R. Myers and Myers 1997; Tsapin et al. 1996; Dobbin et al. 1999). Some c-type cytochromes have been identified as components of terminal reductase protein complexes for soluble terminal electron acceptors (Lancaster et al. 1999; Simon 2002; Zumft and

Kroneck 2006). C-type cytochromes can also be components of oxidoreductases in multi- protein electron-transport systems to transport electrons from the cytoplasmic membrane through the periplasm to the outer membrane where other enzymes shuttle the electrons to insoluble terminal electron acceptors (Shi et al. 2006; Shi et al. 2007; Weber, Achenbach, and Coates 2006). Table 5.15 shows the detected putative c-type cytochromes and their protein abundance ratios, predicted subcellular locations as well as t-test results. These predictions of c-type cytochromes were made by Nissen. et al (Nissen et al. 2012) based on the sequence similarities to other characterized c-type cytochromes and the presence of conserved heme-binding motifs such as CXXCH, CXXXCH and CXXXXCH (Wagner et al. 2012).

115 Table 5.15: c-Type cytochromes detected in A. dehalogenans. List of c-type cytochromes is suggested by Nissen et al. (Nissen et al. 2012)

Avg Ratio t-Test Locus_Tag Annotation Pfam PSORTb Ratio 1&2 (P) Cytochrome c554 and c-prime; Cytochrome Cytoplasmic 2.95 A2cp1_0127 hypothetical protein 2.76 0.022* b(N-terminal)/b6/petB Membrane 2.57 split soret cytochrome c Putative redox-active protein 0.20 A2cp1_0340 Unknown 0.15 0.052* precursor (C_GCAxxG_C_C) 0.10 0.16 A2cp1_0438 hypothetical protein Collagen triple helix repeat (20 copies) Extracellular 0.15 0.007* 0.14 0.42 A2cp1_0691 hypothetical protein 0 Extracellular 0.44 0.016* 0.46 bifunctional cbb3-type Cytochrome C oxidase, cbb3-type, subunit III ; 0.84 A2cp1_1333 cytochrome c oxidase subunit Cytochrome C oxidase, mono-heme Unknown 0.73 0.139 II/cytochrome c subunit/FixO 0.61 3.29 A2cp1_1731 multiheme cytochrome Cytochrome c7 Extracellular 5.58 0.083* 7.87 0.57 A2cp1_2170 hypothetical protein Seven times multi-haem cytochrome CxxCH Periplasmic 0.62 0.056* 0.67 0.25 A2cp1_2248 cytochrome c class III Cytochrome c7 Periplasmic 0.23 0.019* 0.21 0.23 A2cp1_2522 cytochrome c class I Cytochrome C oxidase, cbb3-type, subunit III Unknown 0.22 0.010* 0.21 Geobacter CxxxxCH...CXXCH motif 1.03 A2cp1_2997 cytochrome C family protein Unknown 0.97 0.350 (GSu_C4xC__C2xCH) 0.92 Geobacter CxxxxCH...CXXCH motif 0.64 A2cp1_2998 cytochrome C family protein Periplasmic 0.62 0.021* (GSu_C4xC__C2xCH) 0.60 0.29 A2cp1_3097 hypothetical protein Collagen triple helix repeat (20 copies) Unknown 0.26 0.026* 0.23 0.19 A2cp1_3188 multiheme cytochrome Doubled CXXCH motif (Paired_CXXCH_1) Periplasmic 0.15 0.044* 0.11 0.71 A2cp1_3257 cytochrome c family protein 0 Periplasmic 0.51 0.160 0.31

116 NHL repeat domain-containing 1.13 A2cp1_3258 0 Unknown 1.12 0.032* protein 1.11 0.64 A2cp1_3260 cytochrome C family protein Doubled CXXCH motif (Paired_CXXCH_1)) Periplasmic 0.58 0.52 0.058 0.67 A2cp1_3261 cytochrome C family protein Doubled CXXCH motif (Paired_CXXCH_1); Periplasmic 0.64 0.60 0.040* * Value 0 in Pfam column indicates that there is no pfam prediction of this protein. Students’ t-tests of the log2-transformed ratios were employed to provide a reference for the confidence of protein ratio data. P-values were shown in the last column and those below 0.1 were denoted with “*”.

117 A. dehalogenans is a versatile organism that can utilize a variety of organic or inorganic electron acceptors. A large portion of its predicted c-type cytochromes pool has not been functionally characterized (Nissen et al. 2012). Peptides from thirteen c-type cytochromes were detected under both ferric citrate and fumarate in this research (Table

5.15). In our experiment, no cytochromes had peptides detected under only one condition.

All but two c-type cytochromes had considerably different expression levels (3 were up- regulated and 8 were down-regulated on Fe(III)).

Two c-type cytochromes, A2cp1_0127 and A2cp1_1731, demonstrated higher expression levels in cells grown with ferric citrate than in cells grown with fumarate – with average ratios of 2.76 and 5.58, respectively. A2cp1_0127, annotated as a hypothetical protein in the genome, was later suggested to be a c-type cytochrome (Nissen et al. 2012). It was predicted to be located in the cytoplasmic membrane. It is in a SPOCS cluster including another two orthologs: GSU0274 (28% identity over 98% of the protein with a total BLAST score of 207) and Gbem_0095 (30% identity over 96% of the protein with a total BLAST score of 206), which are annotated as c-type cytochromes, without any further functional characterization. These two orthologs were not detected in our experiment with Geobacter strains, leaving us unclear about whether the up-regulation is consistent across the three species. A2cp1_0127 also shares 24% sequence identity with an MtrA protein in S. ondeidensis (SO_1777). It corresponded to 46% of the MtrA sequence (total BLAST score

87.8). MtrA is heavily involved in the extracellular iron oxide respiratory system of S. oneidensis. MtrA, MtrB MtrC and OmcA form an electron-transfer pathway reaching from the periplasm to the outer membrane (Richardson et al. 2012). The up-regulation of

118 A2cp1_0127 supports the hypothesis that it may play a similar role in metal respiration of A. dehalogenans 2cp-1 as MtrA plays in S. oneidensis MR-1. The putative decaheme cytochrome c A2cp1_1731 demonstrated the highest expression ratio of all cytochromes detected with both electron acceptors (5.58). It has an orthologous protein in the SPOCS cluster, Gbem_3371 (33% identity over 65% of the protein, total BLAST score = 450), which was not detected in our experiment. Its predicted subcellular location by PSORTb was extracellular, and it shared sequence similarities with outer-membrane cytochromes

OmcA/MtrC family. A BLASTP search revealed a 28% sequence identity with a decaheme c- type cytochrome SO_1779 of the OmcA/MtrC family found in S. oneidensis strain MR-1.

SO_1779 (OmcA) was reported to function as the terminal reductase in dissimilatory metal reduction of S. oneidensis (D. E. Ross, Brantley, and Tien 2009). This relatedness of

A2cp1_1731 and SO_1779 suggests that this decaheme c-type cytochrome A2cp1_1731 may be an outer-membrane protein involved in the electron transport to ferric citrate in A. dehalogenans 2cp-1. The high expression ratio observed in our study supports this hypothesis. SO_1777 and SO_1779 in S. oneidensis belong to a same operon, the simultaneous up-regulation of A2cp1_0127 and A2cp1_1731 may be an evidence of their involvement in dissimilatory iron reduction in roles analogous to MtrA and OmcA, respectively. A2cp1_1731 was suggested to be also highly enriched in a protein fraction with

Fe(III) reduction activity by Annie Otwell, Microbiology PhD student at Cornell University.

But its heterologous expression in E. coli was unsuccessful.

A2cp1_3097 and A2cp1_0438 shared 62% sequence identity, and one of them

(A2cp1_0438) was predicted to be extracellular. They were both predicted to be decaheme

119 c-type cytochromes belonging to OmcA/MtrC family. They shared 23% and 22% sequence with MtrF of S. oneidensis (SO_1780), respectively. MtrF is homologous to MtrC, and both are known to transfer electrons to soluble and solid metals. The reason why A2cp1_3097 and A2cp1_0438 were more abundant under the fumarate-grown condition is unclear and warrants further investigation. One hypothesis is that they are involved in electron transport to fumarate, but not iron.

A2cp1_0340 and A2cp1_0691 displayed 41% and 30% of amino acid sequence identity with a split-soret cytochrome c (Ddes_2150) from D. desulfuricans ATCC 27774. A study with D. desulfuricans suggested that this cytochrome was involved in nitrate respiration (Abreu et al. 2003). The higher abundance of these two hypothetical genes under the fumarate-grown condition may suggest more functions of the split-soret c-type cytochrome. Although the exact roles of these two genes in fumarate reduction remain unclear, our data suggest that they both are involved in fumarate reduction.

A2cp1_2248 shared 54% sequence identity with KN400_1004 over 97% of its sequence. KN400_1004 is known as PpcD, a component of pre-proposed electron transport pathway in model organism G. sulfurreducens. Shelobolina et al reported that elimination of

PpcD increased the rate of soluble Fe(III) reduction, without significantly affecting the rate of insoluble Fe(III) reduction (Shelobolina et al. 2007). This may imply that PpcD does not contribute to soluble Fe(III) reduction, which is consistent with our result: A2cp1_2248 was down-regulated in cells on ferric citrate (protein ratio = 0.23).

120 Table 5.16: c-Type cytochromes detected in S. oneidensis. List of c-type cytochromes is suggested by Nissen et al. (Nissen et al. 2012)

Avg Ratio t-Test Locus_Tag Annotation Pfam PSORTb Ratio 1&2 (P) ABC-type heme export system chaperone cytochrome c-type biogenesis Cytoplasmic 1.10 SO_0259 1.21 0.160 component CcmE protein CcmE ; Membrane 1.33 1.43 SO_0264 periplasmic monoheme cytochrome c5 ScyA cytochrome c ;cytochrome c Periplasmic 1.19 0.288 0.95 fumarate reductase flavoprotein 1.26 SO_0970 periplasmic fumarate reductase FccA Periplasmic 1.35 0.084* subunit precursor 1.44 extracelllular iron oxide respiratory system 1.22 SO_1777 periplasmic decaheme cytochrome c decaheme cytochrome c MtrA Unknown 1.60 0.178 component MtrA 1.97 extracellular iron oxide respiratory system 1.24 SO_1778 surface decaheme cytochrome c decaheme cytochrome c Unknown 1.19 0.085* component MtrC 1.14 extracelllular iron oxide respiratory system 1.30 SO_1779 surface decaheme cytochrome c 0 Unknown 1.14 0.273 0.98 component OmcA 1.21 SO_2178 diheme cytochrome c5 peroxidase CcpA cytochrome c551 peroxidase Periplasmic 0.92 0.413 0.63 1.61 SO_3420 monoheme cytochrome c' cytochrome c'; Periplasmic 1.78 0.065* 1.94 cytochrome c552; 2.81 SO_3980 ammonia-forming nitrite reductase NrfA Periplasmic 2.62 0.038* EC_number=1.7.2.2; 2.42 0.47 SO_4047 SoxA-like diheme cytochrome c cytochrome c family protein ; Periplasmic 0.56 0.062* 0.65 0.94 SO_4048 diheme cytochrome c4 cytochrome c family protein; Periplasmic 0.97 0.225 0.99 membrane anchored tetraheme Cytoplasmic 0.71 SO_4591 tetraheme cytochrome c ; 0.70 0.007* cytochrome c CymA Membrane 0.69 1.65 SO_4666 diheme cytochrome c4 CytcB cytochrome c Periplasmic 2.04 0.114 2.43

121 * Value 0 in Pfam column indicates that there is no pfam prediction of this protein. Students’ t-tests were employed to provide a reference for the confidence of protein ratio data. P-values were shown in the last column. P-values were shown in the last column and those below 0.1 were denoted with “*”.

122 Thirteen c-type cytochromes were detected in S. oneidensis cells grown in this study.

It is not surprising that SO_0970 (fccA) was up-regulated under the Fe(III)-grown condition.

It (fccA, flavocytochrome C flavin subunit) was previously described as the most abundant periplasmic protein in S. oneidensis strain MR-1 cells grown with ferric citrate (Schuetz et al.

2009). Our data support fccA’s proposed involvement in reduction of soluble metal species.

Only insignificant regulations of MtrA (SO_1777), MtrB(SO_1776) and

MtrC(SO_1778) were found in our data, which is surprising given their documented roles in reduction of Fe(III).

SO_3420 and SO_3980 were found to be up-regulated in Fe(III) culture. A BLASTP search of SO_3420 suggests its extensive existence in the genus of Shewanella. 16

Shewanella spp. were found to have c-type cytochromes that share more than 70% sequence with a 100% query coverage with SO_3420. SO_3980 (NrfA, formate-dependent nitrite reductase subunit A), which is located in the periplasm, was suggested to work with a cytoplasmic membrane-bound electron transport protein, cymA, to conduct nitrate reduction to ammonia (Gao et al. 2009). Our data showed an up-regulation of NrfA under the Fe(III)-grown condition. This implied that NrfA might be also involved in pathways conducting soluble iron reduction.

123 Table 5.17: c-Type cytochromes detected in G. sulfurreducens

Avg Ratio t-Test Locus_Tag Annotation Pfam PSORTb Ratio 1&2 (P) 0.63 GSU0357 cytochrome C cytochrome C Periplasmic 0.53 0.088* 0.44 0.19 GSU0592 cytochrome C cytochrome C Cytoplasmic 0.29 0.090* 0.40 2.08 GSU0594 cytochrome C cytochrome C Periplasmic 1.94 0.036* 1.79 GSU0618 6.63 cytochrome C cytochrome C Extracellular 3.98 0.203* (OmcE) 1.32 0.25 GSU1284 cytochrome C 0 Unknown 0.25 0.001* 0.25 1.04 GSU1397 cytochrome C cytochrome C Unknown 0.96 0.335 0.88 0.19 GSU1996 cytochrome C cytochrome C Periplasmic 0.23 0.040* 0.27 GSU2076 41.0 cytochrome C cytochrome C Extracellular 23.4 0.109 (OmcZ) 5.82 0.57 GSU2495 cytochrome C cytochrome C Periplasmic 0.74 0.189 0.90 GSU2504 0.23 cytochrome C 0 Extracellular 0.23 0.003* (OmcS) 0.24 0.33 GSU2645 cytochrome C cytochrome C Periplasmic 0.22 0.101 0.11 GSU2731 polyheme membrane-associated 4.90 0 Unknown 3.02 0.225 (OmcB) cytochrome C 1.13 polyheme membrane-associated 0.83 GSU2737 0 Unknown 0.68 0.160 cytochrome C 0.53 0.38 GSU2743 cytochrome C cytochrome C Unknown 0.42 0.033* 0.45 GSU2801 cytochrome C cytochrome C Unknown 0.43 0.51 0.065*

124 0.35 0.52 GSU2811 cytochrome C Hsc 0 Unknown 0.54 0.027* 0.57 0.18 GSU2937 cytochrome C cytochrome C Periplasmic 0.16 0.027* 0.13 * Value 0 in Pfam column indicates that there is no pfam prediction of this protein. Students’ t-tests were employed to provide a reference for the confidence of protein ratio data. P-values were shown in the last column. P-values were shown in the last column and those below 0.1 were denoted with “*”.

125 In G. sulfurreducens strain PCA, 17 predicted c-type cytochromes were detected.

Among them, GSU0618 (average ratio: 3.98), GSU2504 (average ratio: 0.234) and GSU2731

(average ratio: 3.016) shared sequence similarities with proteins of strain KN400 of G. sulfurreducens. GSU_0618 shared 94% sequence with KN400_0597 (OmcE) and GSU_2731 displayed 53% sequence identity with KN400_2677 (OmcB). They were also identified as orthologs by our SPOCS clustering analysis. In G. sulfurreducens KN400, a model bacterium for respiratory iron reduction, OmcS, OmcB, OmcE and OmpB form an outer-membrane complex to transfer electrons to iron (hydr)oxides as well as soluble iron (Shi et al. 2009). A protein located in the periplasm, PpcA, was demonstrated to transfer electrons from a quinol pool in the cytoplasmic membrane to the outer-membrane complex (Shi et al. 2009).

The up-regulation of GSU0618 and GSU2731 supports this suggested electron transport pathway. However, GSU2504, which shares 97% sequence with OmcS (KN400_2449) was significantly down-regulated under the Fe(III)-grown conditions in our study (average ratio of 0.23 and p-value of 0.003). PpcA of strain PCA (GSU_0612) was not presented in our protein ratio data. Only one peptide from it was detected (data not shown), and it showed no significant regulation on ferric citrate.

GSU2076 (average ratio: 23.414), which is known as omcZ (Methé et al. 2003), is a octaheme c-type cytochrome. OmcZ is located in the outer-membrane and can transfer electrons to a variety of potential extracellular electron acceptors in vitro, such as Fe(III) citrate, U(VI), Cr(VI), Au(III), Mn(IV) oxide, but not Fe(III) oxide. It is suggested to be well suited for promoting electron transfer in current-producing biofilms of G. sulfurreducens

(Inoue et al. 2010). The high expression ratio of 23.4 is an evidence of its involvement in

126 transferring electrons to ferric citrate in our cultures. However, a large difference between replicates resulted in a p-value above 0.1 (0.109).

GSU0594 (average ratio: 1.94) and GSU2645 (average ratio: 0.22) were both predicted to be located in the periplasm. GSU0594, which is 100% identical to KN400_0572, is a septaheme c-type cytochrome. KN400_0572 is subunit A of a menaquinol oxidoreductase complex Cbc5, as suggested by NCBI database. This complex was reported to be up-regulated when GSU was grown with Fe(III) oxide or Mn(IV) oxide by Aklujkar et al.(M.

Aklujkar et al. 2013) Possessing this function of extracting electrons from quinol pool, it is likely that GSU0594 can serve a similar function to ppcA, transferring electrons from a quinol pool to an outer-membrane complex, and it shares 26% sequence identity with ppcA in

KN400 (KN400_0591). GSU0594 has only one ortholog in our SPOCS data: Gbem_0676, which was not detected in our study. GSU2645 was down-regulated under Fe(III)-grown condition. A BLASTP search revealed a 24% sequence identity between GSU2645 and

KN400_2830, which is a 27-heme c-type cytochrome. It also shared 24% sequence with

MtrA (SO_1777) of S. oneidensis. This may imply its involvement in extracellular electron transfer pathway but its exact role is unclear.

127 Table 5.18: c-Type cytochromes detected in G. bemidjiensis

Avg Ratio t-Test Locus_Tag Annotation Pfam PSORTb Ratio 1&2 (P) 0.92 Gbem_2674 cytochrome c cytochrome c Unknown 0.65 0.223 0.37 0.45 Gbem_3379 lipoprotein cytochrome c lipoprotein cytochrome c Unknown 0.29 0.126 0.14 1.61 Gbem_3455 cytochrome c cytochrome c Periplasmic 1.57 0.017* 1.53 0.22 Gbem_3597 flavocytochrome c flavocytochrome c Periplasmic 0.16 0.066* 0.10 * Value 0 in Pfam column indicates that there is no pfam prediction of this protein. Students’ t-tests were employed to provide a reference for the confidence of protein ratio data. P-values were shown in the last column. P-values were shown in the last column and those below 0.1 were denoted with “*”.

128 Only 4 c-type cytochromes were detected in G. bemidjiensis cultures, and of those only one was up-regulated on iron at confidence p-value below 0.1 - protein Gbem_3455, which belongs to the cytochrome c3 superfamily. A BLASTP search implicated that it shared

39% sequence with OmcQ of G. sulfurreducens strain KN400 (KN400_0570). KN400_0570

(OmcQ/CbcC) is annotated to be subunit C of a menaquinol oxidoreductase complex Cbc5.

As mentioned before, Cbc5 may involve in the electron transfer pathways toward extracellular electron acceptors. Gbem_3455 may conduct similar function with ppcA, transferring electrons from the quinol pool to outer membrane-bound reductases. BLASTP revealed 23% identical sequence between Gbem_3597 and frdA (GSU_1177) of G. sulfurreducens PCA. FrdA is a bifunctional protein serving both as succinate dehydrogenase in TCA cycle and as a terminal fumarate reductase when fumarate is provided as electron acceptor (Butler et al. 2006). If Gbem_3597 can really conduct this function, the down- regulation of it is not surprising. This warrants further investigation.

5.3.3 Highly Up-Regulated Hypothetical Proteins Hypothetical proteins are proteins whose existence has been predicted by computational analysis of genomic DNA sequences, but for which there is no experimental evidence of its expression and/or activity in vivo (Gore and Chakule 2011). When the bioinformatics tools used to identify genes find an open reading frame without a characterized homologue in the protein database, the protein is annotated as “hypothetical protein” (Zarembinski et al. 1998). Different scientists have different procedures and practices for defining cutoffs to call genes homologous, so the annotation procedure is not consistent across genome annotations. Most microbial genomes contain a significant

129 number of hypothetical proteins. In order to prioritize hypothetical proteins for further bioinformatic and biochemical characterization, we determined those showing differential expression as a function of electron acceptor type.

Because of our particular interest in proteins involved in iron reduction, a focus was placed on hypothetical proteins that were up-regulated on ferric citrate. In our experiment,

246 out of 1430 (A.dehalogenans), 16 out of 455 (S. oneidensis), 18 out of 1028 (G. sulfurreducens), 167 out of 1086 (G. bemidjiensis) annotated “hypothetical proteins “were detected in all four cultures, demonstrating their existence in vivo. Follow-up database searches confirmed that they have not been functionally characterized since the original genome annotation was published.

To focus on the most highly regulated hypothetical proteins, those with normal expression ratio above 4 were arbitrarily selected from the pool. This information is shown in Table 5.19 for the four organisms of interest.

130 Table 5.19: Highly up-regulated hypothetical proteins in all four organisms

Avg Ratio t-Test Locus_Tag Pfam PSORTb Ratio 1&2 (P) 13.7 A2cp1_0528 0 Unknown 18.0 0.027 22.4 Stigma-specific protein, Stig1; 6.41 A2cp1_3748 Extracellular 7.86 0.029 Astacin (Peptidase family M12A) 9.31 5.16 A2cp1_2661 0 Cytoplasmic 4.40 0.038 3.64 44.8 SO_A0157 hypothetical protein Unknown 99.6 0.044 154.4 44.6 GSU2780 0 Unknown 36.6 0.020 28.6 4.91 GSU0977 0 Unknown 6.58 0.044 8.25 7.42 GSU0973 hypothetical protein Unknown 6.56 0.023 5.69 4.72 GSU0989 hypothetical protein Unknown 5.64 0.030 6.56 Outer 3.48 GSU2528 hypothetical protein 4.66 0.054 Membrane 5.83 14.5 Gbem_1123 hypothetical protein Cytoplasmic 14.0 0.004 13.6 5.04 Gbem_0318 0 Unknown 4.77 0.012 4.50

All proteins in Table 5.19 had p-values below 0.1, which suggests they were differentially regulated. A2cp1_0528 (average ratio: 18.0) was one of the highly up- regulated proteins when grown with ferric citrate. It displayed 29% identical amino acid sequence with SO_0675 which was identified by Clusters of Orthologous Groups (COG) as a

Mu-like prophage major head subunit gpT. Prophage is a phage genome inserted into a bacterial chromosome or existing as a plasmid. Its involvement in the iron reduction is unlikely. However it suggests that the prophage-like protein, A2cp1_0528, was being

activated in Fe(III) cultures, or just being expressed simultaneously with other genes in same

131 operon (A2cp1_0528, A2cp1_0529, A2cp1_0530). A2cp1_0529 is a hypothetical protein, which was detected at an average ratio of 2.40 (not shown in the Table).

The KEGG database suggested that A2cp1_3748 contains an integrin-like domain.

Integrin is a transmembrane receptor as the bridge of cell-extracellular interaction. When triggered, integrin can in turn trigger specific pathways of the cell through signal transduction. A change in chemical composition of the extracellular environment can result in a response such as the regulation of cell shape, cell cycles and motility (Hynes 2002). The

Pfam predicted it to contain an astacin domain. Astacin has wide range of functions including activation of growth factors (Rawlings and Barrett 1995). This protein is potentially a sensor for extracellular metals, and it can trigger the up-regulation of cellular pathways once the cell senses the presence of iron in the surroundings. Its predicted extracellular location supports this hypothesis.

There is little information on A2cp1_2661 besides its cytoplasmic location prediction.

Its top BLAST hit for a protein with functional description suggested D187_008362 from

Cystobacter fuscus DSM 2262, sharing 34% amino acid sequence with it. This protein is ambiguously annotated as a membrane protein involved in colicin up-take. Colicin can translocate to exert cytotoxic effect including depolarization of cytoplasmic membrane and

DNase/RNase activity (Cascales et al. 2007). The reason of its up-regulation is unclear but perhaps is a response to the up-regulation of the prophage-like A2cp1_0528.

None of the three above-mentioned hypothetical proteins has orthologs in other organisms, suggested by SPOCS.

132 In S. oneidensis, only one protein had an expression ratio above 4 — SO_A0157

(average ratio: 99.6), which was the most highly, up-regulated protein among detected ones. The “A” in the locus tag indicates that this gene is on a plasmid. This protein containing only 151 amino acid residues, shared 28% sequence with MBO_07373 of

Moraxella bovoculi Strain 237 described briefly as a signal peptide protein. Its exact function is unknown; it might be involved in signal transduction of metal iron sensing.

The highest up-regulated hypothetical protein in G. sulfurreducens culture is

GSU2780. Based on SPOCS analysis, it is an ortholog of Gbem_2375 which is also annotated as a hypothetical protein but was not detected in our G. bemidjiensis proteome. GSU2780 shares 22% amino acid sequence with D515_01041 of Grimontia sp. AK16, a putative ABC transporter which is reported by COG to be a part of a Fe(III) transport system. Although the low sequence identity rate of 22% cannot guarantee its similar function with D515_01041,

GSU2780 is still possibly involved in Fe(III) transport in G. sulfurreducens, which is supported by its high up-regulation.

A BLASTP search of GSU0977 provided no useful information but a gene from its operon, GSU0975, was predicted to be a phage tail sheath protein. This whole operon may have originally come from a phage.

GSU0973 was annotated by SEED (a genomic information database (Overbeek et al.

2005)) as a nitrogen regulation protein involved in ammonia assimilation. The relatedness between ammonia assimilation and the iron reduction system is still unclear and warrants further study.

133 GSU0989 contains an NHL repeat, as reported by SEED. NHL repeat domains exist widely in eukaryotic and prokaryotic proteins. Another protein containing an NHL repeat,

A2cp1_3258, was also up-regulated in our experiment, although slightly (Ratios of 1.130 and

1.105). The exact role of GSU0989 in iron reduction remains unknown.

A portion of GSU2528’s amino acid sequence contained a FecR family protein domain, as suggested by Pfam. FecR family proteins are known to be involved in iron transport in E. coli (Blattner et al. 1997), and that is likely GSU2528’s role in G. sulfurreducens.

Gbem_1123 is 86% identical to a PPIC-type PPIASE (Prolyl isomerase) protein found in Geobacter bremensis strain R1. PPIASE interconverts the cis and trans isomers of peptide bonds with proline. Its high expression ratio and low p-value (only 0.004) warrant its further investigation.

Information was too limited to provide any clue of possible function of Gbem_0318, but BLASTP search revealed that it shares 63% amino acid sequence with A2cp1_2071 of A. dehalogenans 2CP-1, also a hypothetical protein, which was not detected in our experiment.

5.3.4 Other Up-Regulated Proteins of Interest There were also some functionally characterized proteins that had interesting up- regulation in the study. They were analyzed and the reasons for their up-regulation are proposed.

134 Table 5.20: Other up-regulated proteins of interest in A. dehalogenans

Avg Ratio t-Test Locus_Tag Annotation Pfam PSORTb Ratio 1&2 (P) 3.31 A2cp1_0322 hypothetical protein Cupredoxin-like domain Periplasmic 3.28 0.003 3.24 acyl-CoA dehydrogenase Acyl-CoA dehydrogenase, C-terminal domain; 4.61 A2cp1_0390 Cytoplasmic 5.61 0.034 domain-containing protein N-terminal domain; middle domain 6.62 DNA mismatch repair protein, C-terminal 3.85 domain; MutL C terminal dimerisation A2cp1_2223 DNA mismatch repair protein Cytoplasmic 3.47 0.029 domain; Histidine kinase-, DNA gyrase B-, and 3.08 HSP90-like ATPase

Predicted to be located in the periplasm, hypothetical protein A2cp1_0322 was confidently up-regulated at a ratio of 3.28.

Pfam prediction suggests that it contains a cupredoxin-like domain. Cupredoxin is a protein containing type I copper centers

(T1Cu). One of its members, bacterial protein azurin, was reported to be involved in a cytochrome chain, exchanging electrons with cytochrome c551 (De Rienzo et al. 2000). Its predicted location, the periplasm, is consistent with this hypothetical function.

Whether A2cp1_0322 is performing this same function in iron reduction is unclear.

A2cp1_0390 contains acyl-CoA dehydrogenase domains. Acyl-CoA dehydrogenase functions at the initial step of each β- oxidation cycle, releasing two electrons from the fatty acid. Since we were feeding acetate as electron donor, the reason of up- regulation of this protein is unclear. In addition, COG suggests that A2cp1_0390 is presumably related to the alkylation response protein AidB, which was proposed to repair DNA or protect it from DNA alkylation caused by various exogenous and endogenous chemical agents (Mulrooney, Howard, and Hausinger 2011). The actual function of this protein and the reasons for its up-

135 regulation are unknown. Interestingly, A2cp1_2223, a DNA mismatch repair protein was also up-regulated under the Fe(III)- grown condition. These facts suggest a higher DNA mismatch rate of cells grown on ferric citrate than on fumarate. Whether this is resulting from the effect of ferric citrate on microorganism’s DNA warrants additional investigation.

Table 5.21: Other up-regulated proteins of interest in S. oneidensis

Avg Ratio t-Test Locus_Tag Annotation Pfam PSORTb Ratio 1&2 (P) periplasmic chaperone for outer 6.05 SO_1638 outer membrane protein OmpH ; Periplasmic 6.76 0.018 membrane proteins Skp 7.47 periplasmic nitrate reductase 4.28 SO_0848 molybdopterin-binding subunit nitrate reductase catalytic subunit Periplasmic 4.02 0.015 NapA 3.76

Skp (OmpH) gene (SO_1638) is a prefoldin-like chaperone that protects soluble and membrane proteins from aggregation

(Walton and Sousa 2004). Its high expression rate may imply that the Fe(III)-grown condition negatively affects folding of periplasmic proteins.

SO_0848 (NapA), like SO_3980 (NrfA) mentioned in Section 5.3.2, is also suggested to work with cytoplasmic membrane- bound electron transport protein cymA in the pathway catalyzing nitrate reduction to ammonia (Gao et al. 2009). The relatedness of nitrate reduction systems to iron reduction remains unknown.

136 Table 5.22: Other up-regulated proteins of Interest in G. sulfurreducens

Avg Ratio t-Test Locus_Tag Annotation Pfam PSORTb Ratio 1&2 (P) NADPH-dependent FMN NADPH-dependent FMN reductase domain- 13.2 GSU0772 reductase domain-containing Unknown 15.7 0.019 containing protein protein 18.3 ABC transporter ATP-binding Cytoplasmic 8.30 GSU1341 ABC transporter ATP-binding protein 11.2 0.035 protein Membrane 14.1 ABC transporter ATP-binding Cytoplasmic 12.9 GSU2413 ABC transporter ATP-binding protein ; 40.8 0.077 protein Membrane 68.8 3.25 GSU2525 nitroreductase nitroreductase ; Unknown 2.83 0.047 2.40

Considering its high expression ratio, GSU0772, a protein containing FMN reductase domain, is likely to be involved in an electron transport chain towards extracellular metal ions. Instead of pulling electrons from a quinol pool, as what ppcA does, a proposed role of GSU0772 is extracting electrons from NADPH, then transferring them to an outer membrane reductase complex. This protein is worth further investigation.

Although the p-value of GSU2413 is 0.077 (>0.05), the two high expression ratios (12.9 and 68.8) still make us confident in its up-regulation. The high expression ratios of protein GSU1341 and GSU2413, which are ABC transporter proteins, may suggest that they were potentially involved in a Fe(III) transport system. Another protein, GSU2780, annotated as hypothetical, which shares similarity with ABC transporter proteins, was up-regulated at an average ratio of 36.6 (as discussed previously). GSU2525 was annotated as a nitroreductase, while its homologous protein in strain KN400 of G. sulfurreducens, KN400_2470 was

137 predicted to be an NADPH oxidoreductases. SO_2708, which shares 27% amino acid sequence with GSU2525, was also annotated as a nitroreductase family protein. This potentially suggests GSU2525’s function of reducing nitrate with electrons from NADPH.

The up-regulation under Fe(III)-grown condition implies nitroreductase’s potential relatedness to iron reduction, consistent with the observation of NapA and NrfA of S. oneidensis.

Table 5.23: Other up-regulated proteins of interest in G. bemidjiensis

Avg Ratio t-Test Locus_Tag Annotation Pfam PSORTb Ratio 1&2 (P) alpha-crystallin/Hsp20 family alpha-crystallin/Hsp20 family ATP- 31.3 Gbem_3666 Cytoplasmic 25.5 0.023 ATP-independent chaperone independent chaperone ; 19.8

Gbem_3666’s up-regulation is interesting, since it belongs to the heat shock protein family Hsp20. Hsp, which was first described in relation to heat shock, was produced by cells in response to exposure to stressful environment to help refold denaturing proteins (Ritossa 1962). It would be interesting to investigate whether this up-regulation of Hsp is responding to the presence of ferric citrate, considering that up-regulation of stressor-responding proteins were also found in A. dehalogenans.

138 CHAPTER 6. CONCLUSIONS & SUGGESTIONS FOR FURTHER STUDY

6.1 Summary of Key Findings

• Proteome results of all four organisms are shown in Table 5.24 below.

Table 5.24: Proteome results of all four organisms

# of differentially expressed Genome- proteins predicted # of Detected proteins Organisms proteins (percentage) (greater than 2-fold and p<0.1)

A. dehalogenans 4515 1360 (30.12%) 165

S. oneidensis 4570 832 (18.21%) 152

G. sulfurreducens 3428 992 (28.94%) 312

G. bemidjiensis 4121 302 (7.33%) 56

There are 525 SPOCS cliques containing othologous proteins from all four organisms,

106 of them had proteins detected in all four organisms.

• F0F1 ATP synthase was found to be slightly up-regulated (when ferric citrate was

supplied as electron acceptor vs. fumarate) across all four species. This is possibly due

to the Fe(III)/Fe(II) couple possessing higher redox potential than the

fumarate/succinate couple, and therefore that more ATP was being harvested per

electron.

• Fumarate hydratase involved in TCA cycle was found to be up-regulated in the

presence of ferric citrate in all four species.

139 • Adenylate kinase was found to be down-regulated in the presence of ferric citrate in

all four species. Considering that the change of electron acceptors may shift the

reaction rate of oxidative phosphorylation, regulation of adenylate kinase is not

surprising.

• In the TCA cycle, the enzymes catalyzing the conversion of fumarate to malate

(A2cp1_1888, SO_2222, GSU0994 and Gbem_0889) were up-regulated across the

four organisms when supplied with ferric citrate vs. fumarate. This may relate to the

change of electron acceptor from fumarate to ferric citrate.

• In oxidative phosphorylation, NADH dehydrogenase seemed to be more active across

the four organisms when grown on ferric citrate. This may imply that ferric citrate is a

more energetic electron acceptor than fumarate, consistent with the fact that ATP

synthase was also more abundant in cells grown on ferric citrate.

• Some c-type cytochromes showed strong differential expression on iron. For G.

sulfurreducens and S. oneidensis, our results support previously reported trends. On

the other hand, for A. dehalogenans the results suggest which cytochromes

(A2cp1_0127 and A2cp1_1731) are directly involved in iron reduction in this versatile

and poorly studied delta-Proteobacterium. The significantly up-regulated c-type

cytochromes were A2cp1_0127, A2cp1_1731, SO_3980, SO_4666, GSU0618,

GSU2731, GSU2076, GSU0594, GSU2645, Gbem_3455. They were discussed and their

similarities to functionally characterized proteins were analyzed.

140 • Some genome-encoded hypothetical proteins were not only confirmed as proteins,

they also showed strong differential regulation. Some of these are even conserved

hypotheticals – meaning SPOCS identified orthologs in other organisms: A2cp1_0528,

A2cp1_3748, A2cp1_2661, AO_A0157, GSU2780, GSU0997, GSU0973, GSU0989,

GSU2528, Gbem_1123, Gbem_0318 were analyzed, and their mechanisms of up-

regulation were hypothesized. Further investigation is warranted.

• Other interestingly up-regulated proteins (A2cp1_0322, A2cp1_0390, A2cp1_2223,

SO_1638, SO_0848, GSU0772, GSU1341, GSU2413, GSU2525, and Gbem_3666) were

analyzed. Some of these have already been functionally characterized but the

connections between iron reduction and their annotated function remain unclear and

warrant further study.

6.2 Suggestions for Future Work

• Only 302 out of 4121 (7.33%) predicted proteins in G. bemidjiensis were detected in

this experiment, which limited our findings about iron reduction processes with this

organism. Another experiment of G. bemidjiensis may be designed to observe the

differential expression of proteins in cells grown on Fe(III) versus fumarate.

• F0F1-type ATPase and NADH dehydrogenase were found to be up-regulated by Fe(III)

across all four organisms. Ratios of house-keeping proteins such as RNA polymerase

can be used as references to normalize protein ratios to offset the effect of the

general shift in proteome caused by the change of electron acceptor. This can help to

explain whether the up-regulation of F0F1-type ATPase as well as NADH

141 dehydrogenase resulted from the fact that Fe(III) is a more energetic acceptor than

fumarate, thus help us identify proteins deeply involved in electron transport chain to

iron.

• Since a lack of genetic tools makes it ineffective to generate Anaeromyxobacter

deletion mutants, protein activity assays might be useful to determine if A2cp1_0127

and A2cp1_1731 carried functions of reducing Fe(III), as they were significantly up-

regulated on Fe(III) and they share similarities with MtrA and OmcA of S. oneidensis,

respectively.

• Our data suggest that SO_3980 (NrfA) may be also involved in transporting electrons

to iron. A deletion mutant of this gene is favored to determine if it is truly involved. If

the capability of iron reduction is reduced in mutant cells, its involvement in iron

reduction is suggested.

• Gbem_3455 is the only up-regulated c-type cytochrome detected in our study, and it

may serve a function of transporting electrons from a quinol pool to outer

membrane-bound reductases. This can be tested though deletion mutation of this

gene.

• Up-regulated hypothetical proteins mentioned in this thesis warrant further

investigation, both bioinformatically and biologically. High protein ratios implicated

their involvement in iron reduction.

142 APPENDICES

Appendix A. Non-normalized Log2-Transformed Peptide Intensities

The box plots below (Figure A1 to Figure A4) show the alignments of log2- transformed peptide intensities of four samples from each organism before the central tendency normalization.

Figure A1: Log2-transformed peptide intensities of A. dehalogenans (non-normalized). From left to right: FeC_1, Fum_1, FeC_2, Fum_2.

143

Figure A2: Log2-transformed peptide intensities of S. oneidensis (non-normalized). From left to right: FeC_1, Fum_1, FeC_2, Fum_2.

Figure A3: Log2-transformed peptide intensities of G. sulfurreducens (non-normalized). From left to right: FeC_1, Fum_1, FeC_2, Fum_2.

144

Figure A4: Log2-transformed peptide intensities of G. bemidjiensis (non-normalized). From left to right: FeC_1, Fum_1, FeC_2, Fum_2.

Appendix B. Normalized Log2-Transformed Peptide Intensities

Figures A5 to A8 below show the box plots of normalized log2-transformed peptide intensities after central tendency normalization was applied.

145

Figure A5: Log2-transformed peptide intensities of A. dehalogenans (normalized). From left to right: FeC_1, Fum_1, FeC_2, Fum_2.

Figure A6: Log2-transformed peptide intensities of S. oneidensis (normalized). From left to right: FeC_1, Fum_1, FeC_2, Fum_2.

146

Figure A7: Log2-transformed peptide intensities of G. sulfurreducens (normalized). From left to right: FeC_1, Fum_1, FeC_2, Fum_2.

Figure A8: Log2-transformed peptide intensities of G. bemidjiensis (normalized). From left to right: FeC_1, Fum_1, FeC_2, Fum_2. 147 Appendix C. SPOCS-identified Orthologous Proteins Detected in all Four Organisms (106*4)

Table C1: Locus tags of SPOCS -identified orthologous proteins detected in all four organisms (106*4)

Cluster Avg. t-Test Avg. t-Test Avg. t-Test Avg. t-Test ID Nodes Edges A2cp1 Ratio (P) Gbem Ratio (P) GSU Ratio (P) SO Ratio (P) 2 9 36 A2cp1_0002 1.11 0.120 Gbem_0002 1.05 0.380 GSU0001 0.83 0.132 SO_0009 1.52 0.008 9 9 36 A2cp1_0012 0.84 0.027 Gbem_3650 1.05 0.306 GSU3136 0.43 0.044 SO_3532 0.89 0.335 58 8 28 A2cp1_0102 1.23 0.151 Gbem_0277 1.75 0.335 GSU2921 0.68 0.101 SO_1030 0.61 0.154 64 6 15 A2cp1_0112 0.48 0.032 Gbem_0619 0.76 0.249 GSU3204 0.47 0.097 SO_3815 0.95 0.328 80 9 36 A2cp1_0134 0.44 0.032 Gbem_2761 0.35 0.134 GSU0668 1.89 0.205 SO_3927 1.26 0.198 81 9 36 A2cp1_0136 0.50 0.060 Gbem_2764 0.39 0.189 GSU0665 0.83 0.278 SO_3930 1.16 0.290 84 9 36 A2cp1_0139 0.87 0.076 Gbem_2768 0.39 0.132 GSU0661 1.23 0.457 SO_3837 1.93 0.027 139 9 36 A2cp1_0236 2.03 0.124 Gbem_3778 0.52 0.132 GSU3211 0.74 0.024 SO_1122 0.59 0.020 168 9 36 A2cp1_0295 0.78 0.119 Gbem_2756 0.17 0.010 GSU1921 1.38 0.212 SO_1629 0.47 0.055 174 9 36 A2cp1_0302 1.06 0.328 Gbem_2754 0.53 0.109 GSU1919 1.06 0.167 SO_1631 3.55 0.173 175 9 36 A2cp1_0303 1.14 0.196 Gbem_2753 1.16 0.390 GSU1918 0.41 0.044 SO_1632 0.80 0.126 210 9 36 A2cp1_0394 0.76 0.082 Gbem_3707 1.39 0.420 GSU3102 0.91 0.138 SO_3948 1.63 0.023 221 9 36 A2cp1_0407 1.32 0.036 Gbem_3415 0.70 0.261 GSU1317 0.56 0.014 SO_3653 1.05 0.446 223 8 28 A2cp1_0414 0.61 0.061 Gbem_0570 4.94 0.157 GSU0658 0.96 0.390 SO_3577 0.52 0.101 234 4 6 A2cp1_3997 0.74 0.181 Gbem_0243 4.75 0.274 GSU0451 0.78 0.230 SO_4428 0.93 0.380 305 5 10 A2cp1_0610 0.64 0.047 Gbem_0351 2.19 0.096 GSU1372 0.82 0.137 SO_2771 4.02 0.242 351 9 36 A2cp1_0682 0.71 0.069 Gbem_2055 0.50 0.198 GSU1752 1.22 0.145 SO_2328 1.17 0.105 377 9 36 A2cp1_0726 1.31 0.171 Gbem_3713 0.95 0.395 GSU3108 1.16 0.341 SO_0405 0.89 0.228 447 7 21 A2cp1_0869 1.57 0.043 Gbem_2866 1.30 0.243 GSU3450 1.60 0.017 SO_1325 0.47 0.022 490 6 15 A2cp1_0980 2.03 0.073 Gbem_1015 1.92 0.198 GSU1183 1.11 0.381 SO_1095 1.09 0.496 557 9 36 A2cp1_1171 0.76 0.084 Gbem_0970 0.79 0.305 GSU1139 0.32 0.010 SO_1315 0.94 0.382 579 9 36 A2cp1_1206 1.02 0.388 Gbem_0836 1.58 0.148 GSU2271 1.07 0.486 SO_0992 0.74 0.125 588 5 10 A2cp1_1216 0.67 0.035 Gbem_0016 0.22 0.129 GSU3453 0.38 0.082 SO_0435 1.57 0.159 599 9 36 A2cp1_1228 1.33 0.096 Gbem_1300 1.48 0.274 GSU1586 1.07 0.499 SO_1203 1.04 0.213

148 601 9 35 A2cp1_1230 0.71 0.081 Gbem_1302 0.62 0.195 GSU1588 0.88 0.136 SO_1204 1.17 0.117 605 9 36 A2cp1_1234 1.89 0.003 Gbem_1307 0.67 0.104 GSU1592 0.23 0.059 SO_1207 0.91 0.342 606 9 36 A2cp1_1235 1.56 0.005 Gbem_1308 0.97 0.417 GSU1593 1.47 0.065 SO_1209 1.61 0.057 610 7 21 A2cp1_1240 1.12 0.004 Gbem_2511 0.28 0.047 GSU2091 0.43 0.001 SO_4066 0.78 0.226 641 6 15 A2cp1_1341 2.00 0.025 Gbem_1294 0.92 0.359 GSU1660 0.96 0.346 SO_0432 3.93 0.063 646 9 36 A2cp1_1350 0.72 0.004 Gbem_3799 0.48 0.161 GSU3366 0.65 0.025 SO_1786 1.52 0.028 678 4 6 A2cp1_4233 1.58 0.030 Gbem_3311 1.37 0.185 GSU2637 0.84 0.336 SO_0640 0.94 0.353 679 9 36 A2cp1_1418 1.24 0.236 Gbem_2903 1.08 0.419 GSU1463 0.52 0.085 SO_2433 1.14 0.460 696 7 21 A2cp1_1456 0.64 0.111 Gbem_0404 0.71 0.261 GSU3281 0.54 0.016 SO_0406 2.15 0.111 698 9 36 A2cp1_1460 0.59 0.009 Gbem_0408 0.95 0.358 GSU3285 0.66 0.022 SO_4313 0.77 0.023 704 9 36 A2cp1_1487 0.82 0.000 Gbem_3458 1.64 0.005 GSU0609 0.86 0.021 SO_0442 1.91 0.081 742 9 36 A2cp1_1569 1.28 0.053 Gbem_1531 0.49 0.062 GSU2045 0.91 0.155 SO_3424 0.74 0.047 766 9 36 A2cp1_1615 1.01 0.203 Gbem_0764 0.78 0.310 GSU2195 1.05 0.449 SO_3293 2.82 0.193 767 9 36 A2cp1_1616 0.97 0.313 Gbem_0765 0.24 0.063 GSU2194 0.64 0.116 SO_3292 1.01 0.473 798 4 6 A2cp1_1721 1.27 0.034 Gbem_2901 0.54 0.043 GSU1465 1.11 0.024 SO_2629 3.08 0.041 850 9 36 A2cp1_1886 0.99 0.184 Gbem_0908 1.20 0.455 GSU2879 1.40 0.025 SO_4235 1.00 0.499 851 6 15 A2cp1_1888 1.46 0.101 Gbem_0889 1.33 0.200 GSU0994 1.54 0.070 SO_2222 1.76 0.029 867 8 28 A2cp1_1934 0.61 0.032 Gbem_1567 0.39 0.081 GSU2013 1.03 0.441 SO_2336 1.01 0.205 877 8 28 A2cp1_1975 1.85 0.094 Gbem_0933 0.78 0.308 GSU2857 2.58 0.043 SO_0231 0.70 0.088 885 9 36 A2cp1_1986 1.09 0.129 Gbem_2745 1.88 0.256 GSU1910 0.81 0.095 SO_2278 0.77 0.298 890 9 36 A2cp1_1991 1.13 0.026 Gbem_1994 0.64 0.141 GSU1519 0.83 0.049 SO_2085 0.59 0.128 891 9 36 A2cp1_1992 1.12 0.138 Gbem_1995 1.68 0.236 GSU1520 0.78 0.227 SO_2086 0.51 0.123 892 5 10 A2cp1_1994 0.68 0.085 Gbem_1996 1.10 0.470 GSU1521 0.40 0.035 SO_2087 1.57 0.166 904 9 36 A2cp1_2015 1.46 0.165 Gbem_0929 0.34 0.220 GSU2861 1.94 0.267 SO_0227 1.29 0.279 905 9 36 A2cp1_2016 0.90 0.262 Gbem_0930 0.84 0.297 GSU2860 0.52 0.047 SO_0228 0.45 0.030 907 9 36 A2cp1_2018 0.72 0.022 Gbem_0932 1.24 0.390 GSU2858 2.20 0.129 SO_0230 1.94 0.165 908 9 36 A2cp1_2019 1.54 0.174 Gbem_0934 0.31 0.062 GSU2856 0.87 0.326 SO_0232 0.40 0.045 910 9 36 A2cp1_2021 1.78 0.064 Gbem_0936 0.98 0.356 GSU2854 4.95 0.012 SO_0234 0.92 0.377 911 9 36 A2cp1_2022 1.34 0.009 Gbem_0937 1.17 0.393 GSU2853 1.55 0.091 SO_0235 1.66 0.114 913 9 36 A2cp1_2024 1.25 0.128 Gbem_0939 0.62 0.261 GSU2851 0.63 0.055 SO_0237 0.68 0.020 917 9 36 A2cp1_2028 1.56 0.095 Gbem_0943 0.53 0.097 GSU2847 0.94 0.359 SO_0241 0.29 0.061 149 919 9 36 A2cp1_2030 1.16 0.452 Gbem_0945 0.34 0.170 GSU2845 0.99 0.464 SO_0243 0.31 0.039 921 9 36 A2cp1_2032 1.50 0.071 Gbem_0947 0.52 0.155 GSU2843 0.48 0.052 SO_0245 0.93 0.381 922 9 36 A2cp1_2033 0.98 0.442 Gbem_0948 0.29 0.062 GSU2842 0.79 0.227 SO_0246 0.38 0.082 924 9 36 A2cp1_2035 1.39 0.125 Gbem_0950 0.66 0.079 GSU2840 0.35 0.038 SO_0248 1.05 0.334 928 9 36 A2cp1_2039 0.81 0.075 Gbem_0954 0.17 0.075 GSU2836 0.58 0.022 SO_2018 0.82 0.305 933 9 36 A2cp1_2044 1.69 0.056 Gbem_0959 1.51 0.004 GSU2832 2.80 0.009 SO_0255 0.34 0.124 934 9 36 A2cp1_2045 1.39 0.079 Gbem_0960 1.39 0.401 GSU2831 1.26 0.054 SO_0256 1.04 0.444 940 9 36 A2cp1_2052 1.21 0.258 Gbem_3426 0.37 0.155 GSU0648 0.87 0.103 SO_1360 0.72 0.022 980 4 6 A2cp1_2196 0.88 0.103 Gbem_1723 2.61 0.346 GSU1346 0.75 0.082 SO_4652 0.03 0.008 988 8 28 A2cp1_2208 1.38 0.028 Gbem_1900 1.49 0.050 GSU1276 0.63 0.018 SO_1142 1.14 0.293 1030 9 36 A2cp1_2305 1.31 0.059 Gbem_0996 3.22 0.084 GSU2286 0.97 0.235 SO_3440 1.58 0.064 1037 7 21 A2cp1_2319 1.60 0.025 Gbem_1654 0.87 0.346 GSU1110 0.75 0.180 SO_2274 1.73 0.027 1055 9 35 A2cp1_2359 1.26 0.135 Gbem_0926 1.67 0.153 GSU2863 1.14 0.080 SO_0224 0.85 0.104 1056 9 36 A2cp1_2360 0.88 0.270 Gbem_0925 2.06 0.242 GSU2864 1.97 0.177 SO_0223 1.20 0.409 1057 9 36 A2cp1_2361 1.22 0.214 Gbem_0924 1.07 0.406 GSU2865 1.77 0.105 SO_0222 0.69 0.119 1058 9 36 A2cp1_2362 0.72 0.122 Gbem_0923 1.30 0.500 GSU2866 0.90 0.256 SO_0221 1.05 0.253 1063 8 28 A2cp1_4483 1.05 0.345 Gbem_3505 1.86 0.170 GSU0033 1.03 0.205 SO_1126 1.13 0.335 1084 8 28 A2cp1_2424 0.94 0.211 Gbem_2337 0.95 0.401 GSU1628 0.43 0.002 SO_0932 1.42 0.106 1091 7 21 A2cp1_2435 1.02 0.478 Gbem_3255 0.81 0.102 GSU2603 1.85 0.062 SO_2402 0.74 0.154 1105 6 15 A2cp1_2455 0.97 0.425 Gbem_2634 0.63 0.024 GSU1804 2.01 0.065 SO_1351 1.67 0.060 1189 9 36 A2cp1_2650 1.50 0.025 Gbem_0642 1.39 0.116 GSU0579 1.04 0.496 SO_0014 0.29 0.101 1207 9 36 A2cp1_2683 0.77 0.127 Gbem_3800 1.15 0.396 GSU3365 1.01 0.490 SO_1791 0.87 0.263 1211 9 36 A2cp1_2691 0.82 0.089 Gbem_0552 0.47 0.062 GSU3308 0.77 0.189 SO_3937 1.83 0.075 1217 9 36 A2cp1_2702 1.08 0.356 Gbem_2402 0.37 0.009 GSU1758 0.70 0.174 SO_2760 0.91 0.113 1245 9 36 A2cp1_2788 1.08 0.351 Gbem_3214 0.80 0.252 GSU2239 0.75 0.256 SO_4257 0.68 0.039 1284 8 28 A2cp1_2885 0.88 0.087 Gbem_2711 1.72 0.150 GSU2549 1.00 0.465 SO_2705 1.43 0.060 1297 9 36 A2cp1_2909 1.30 0.238 Gbem_0756 1.01 0.395 GSU2206 1.08 0.090 SO_3537 0.79 0.157 1303 9 36 A2cp1_2923 0.94 0.384 Gbem_3021 0.50 0.035 GSU1691 0.14 0.010 SO_3466 0.43 0.106 1308 9 36 A2cp1_2928 0.85 0.023 Gbem_3102 0.53 0.040 GSU1607 0.76 0.031 SO_3471 1.42 0.034 1417 9 36 A2cp1_3272 0.90 0.170 Gbem_3417 0.45 0.086 GSU1311 2.25 0.035 SO_3547 1.20 0.110 1478 9 36 A2cp1_3500 0.83 0.108 Gbem_1271 1.36 0.134 GSU1793 1.77 0.067 SO_1793 0.87 0.181 150 1485 9 36 A2cp1_3517 2.18 0.026 Gbem_0913 4.70 0.098 GSU2875 1.39 0.036 SO_3939 1.54 0.046 1508 7 21 A2cp1_3577 0.72 0.040 Gbem_0572 3.50 0.187 GSU0656 0.93 0.230 SO_0340 1.85 0.040 1544 9 36 A2cp1_3706 1.08 0.336 Gbem_1874 3.25 0.039 GSU1827 1.47 0.079 SO_1341 0.45 0.073 1556 9 36 A2cp1_3734 1.43 0.035 Gbem_0248 2.55 0.181 GSU3339 2.55 0.022 SO_0703 1.03 0.468 1557 9 36 A2cp1_3735 0.82 0.142 Gbem_0249 6.51 0.122 GSU3340 2.87 0.023 SO_0704 0.94 0.273 1587 5 10 A2cp1_3811 2.48 0.010 Gbem_1652 0.88 0.352 GSU1106 1.61 0.049 SO_1926 3.94 0.039 1627 8 28 A2cp1_3906 0.68 0.014 Gbem_0486 0.31 0.103 GSU3074 0.64 0.182 SO_4224 1.09 0.459 1684 8 28 A2cp1_4090 0.90 0.184 Gbem_2891 1.74 0.038 GSU1460 0.74 0.038 SO_3154 1.31 0.069 1694 9 36 A2cp1_4108 1.09 0.042 Gbem_3630 0.66 0.228 GSU0145 0.51 0.062 SO_3430 0.72 0.247 1741 6 15 A2cp1_4216 1.24 0.038 Gbem_4053 1.48 0.364 GSU0160 0.71 0.182 SO_1140 1.16 0.088 1742 8 28 A2cp1_4217 1.88 0.068 Gbem_4054 0.63 0.189 GSU0159 1.19 0.039 SO_1879 1.60 0.019 1744 9 36 A2cp1_4220 1.32 0.154 Gbem_3641 0.86 0.250 GSU0156 0.27 0.028 SO_0279 0.80 0.014 1768 6 15 A2cp1_4319 0.24 0.032 Gbem_0870 2.85 0.236 GSU1886 1.09 0.493 SO_3962 0.69 0.173 1777 9 36 A2cp1_4334 2.01 0.023 Gbem_0163 0.24 0.070 GSU3235 4.63 0.029 SO_3651 0.29 0.138 1799 7 21 A2cp1_4390 1.07 0.396 Gbem_1182 1.85 0.104 GSU3331 0.70 0.258 SO_2491 2.82 0.003 1803 5 10 A2cp1_4408 1.97 0.080 Gbem_3077 1.69 0.249 GSU1835 2.79 0.074 SO_4410 2.34 0.065 1838 9 36 A2cp1_4499 1.06 0.209 Gbem_3927 0.94 0.392 GSU0337 0.58 0.064 SO_1300 0.87 0.269 1841 9 36 A2cp1_4503 1.25 0.015 Gbem_3950 6.05 0.136 GSU0113 9.86 0.018 SO_4747 1.58 0.092 1842 9 36 A2cp1_4504 1.15 0.349 Gbem_3951 1.40 0.461 GSU0112 11.50 0.039 SO_4748 1.80 0.065 1843 9 36 A2cp1_4505 1.01 0.373 Gbem_3952 1.13 0.365 GSU0111 16.00 0.009 SO_4749 2.23 0.038

151 Table C2: Annotations of SPOCS -identified orthologous proteins detected in all four organisms (106*4)

Cluster Annotations ID A2cp1 Gbem GSU SO DNA polymerase III subunit DNA polymerase III subunit DNA polymerase III subunit DNA polymerase III beta 2 beta beta beta subunit DnaN 9 isoleucyl-tRNA synthetase isoleucyl-tRNA synthetase isoleucyl-tRNA synthetase isoleucyl-tRNA synthetase IleS B12-dependent 5- 5-methyltetrahydrofolate-- 5-methyltetrahydrofolate- methyltetrahydrofolate-- 58 methionine synthase homocysteine homocysteine homocysteine methyltransferase methyltransferase, truncation methyltransferase MetH putative nucleotide-binding protein of unknown function 64 nucleotide-binding protein nucleotide-binding protein protein DUF520 YajQ 80 50S ribosomal protein L9 50S ribosomal protein L9 50S ribosomal protein L9 50S ribosomal protein L9 RplI 81 30S ribosomal protein S6 30S ribosomal protein S6 30S ribosomal protein S6 30S ribosomal protein S6 RpsF ribose-phosphate ribose-5-phosphate 1- ribose-phosphate ribose-phosphate 84 pyrophosphokinase pyrophosphokinase pyrophosphokinase pyrophosphokinase PrsA gamma-glutamyl phosphate gamma-glutamyl phosphate gamma-glutamyl phosphate gamma-glutamyl phosphate 139 reductase reductase reductase reductase ProA 168 30S ribosomal protein S2 30S ribosomal protein S2 30S ribosomal protein S2 30S ribosomal protein S2 RpsB 174 uridylate kinase uridylate kinase uridylate kinase uridylate kinase PyrH 175 ribosome recycling factor ribosome recycling factor ribosome recycling factor ribosome recycling factor Frr UDP-N-acetylglucosamine 1- UDP-N-acetylglucosamine 1- UDP-N-acetylglucosamine 1- UDP-N-acetylglucosamine 1- 210 carboxyvinyltransferase carboxyvinyltransferase carboxyvinyltransferase carboxyvinyltransferase MurA octaprenyl diphosphate octaprenyl-diphosphate octaprenyl-diphosphate 221 polyprenyl synthetase synthase synthase synthase IspB stress-induced multi- ATP-dependent chaperone ATP-dependent chaperone 223 ClpB protein chaperone system component ClpB ClpB ClpB winged helix family two two component signal DNA-binding response 234 component transcriptional transcriptional regulator transduction system response regulator regulator regulator OmpR family

152 6-phosphogluconate glyoxalate/succinic 3-hydroxyisobutyrate 2-hydroxy-3-oxopropionate 305 dehydrogenase semialdehyde reductase dehydrogenase reductase GarR translation elongation factor P 351 translation elongation factor P elongation factor P elongation factor P Efp transcription termination transcription termination transcription termination transcription termination 377 factor Rho factor Rho factor Rho factor Rho ferredoxin-dependent glutamate synthase glutamate synthase-related NADPH-dependent glutamate 447 glutamate synthase large (ferredoxin) protein synthase large subunit GltB subunit O-acetylhomoserine/O- O-acetyl-L-homoserine O-acetyl-L-homoserine O-acetylhomoserine (thiol)- 490 acetylserine sulfhydrylase sulfhydrylase sulfhydrylase lyase MetY 557 tyrosyl-tRNA synthetase tyrosyl-tRNA synthetase tyrosyl-tRNA synthetase tyrosyl-tRNA synthetase TyrS 579 lysyl-tRNA synthetase lysyl-tRNA synthetase lysyl-tRNA synthetase lysyl-tRNA synthetase LysS uroporphyrinogen uroporphyrinogen uroporphyrinogen uroporphyrinogen 588 decarboxylase decarboxylase decarboxylase decarboxylase HemE transcription elongation factor transcription elongation factor N utilization substance protein 599 NusA antitermination factor NusA NusA A NusA translation initiation factor IF-2 601 translation initiation factor IF-2 translation initiation factor IF-2 translation initiation factor IF-2 InfB 30S ribosomal protein S15 605 30S ribosomal protein S15 30S ribosomal protein S15 30S ribosomal protein S15 RpsO polynucleotide polynucleotide polynucleotide polyribonucleotide 606 phosphorylase/polyadenylase phosphorylase/polyadenylase phosphorylase/polyadenylase nucleotidyltransferase PnpA phosphoribosylaminoimidazole phosphoribosylaminoimidazole phosphoribosylaminoimidazole phosphoribosylaminoimidazole 610 -succinocarboxamide synthase succinocarboxamide synthase -succinocarboxamide synthase -succinocarboxamide synthase PurC bifunctional aconitate bifunctional aconitate bifunctional aconitate 641 hydratase 2/2-methylisocitrate hydratase 2/2-methylisocitrate hydratase 2/2-methylisocitrate aconitate hydratase AcnB dehydratase dehydratase dehydratase glutaminyl-tRNA synthetase 646 glutaminyl-tRNA synthetase glutaminyl-tRNA synthetase glutaminyl-tRNA synthetase GlnS NAD(P)H quinone zinc-dependent alcohol dehydrogenase, zinc- putative NAD(P)H quinone 678 oxidoreductase oxidoreductase containing oxidoreductase PIG3 family

153 679 aspartyl-tRNA synthetase aspartyl-tRNA synthetase aspartyl-tRNA synthetase aspartyl-tRNA synthetase AspS 696 thioredoxin thioredoxin thioredoxin thioredoxin 1 TrxA hydroxymethylbilane synthase 698 porphobilinogen deaminase porphobilinogen deaminase porphobilinogen deaminase HemC bifunctional bifunctional bifunctional bifunctional IMP phosphoribosylaminoimidazole phosphoribosylaminoimidazole phosphoribosylaminoimidazole cyclohydrolase/phosphoribosyl 704 carboxamide carboxamide carboxamide aminoimidazolecarboxamide formyltransferase/IMP formyltransferase/IMP formyltransferase/IMP formyltransferase PurH cyclohydrolase cyclohydrolase cyclohydrolase 742 valyl-tRNA synthetase valyl-tRNA synthetase valyl-tRNA synthetase valyl-tRNA synthetase ValS inosine-5prime- inosine-5'-monophosphate inosine-5'-monophosphate inosine-5-monophosphate 766 monophosphate dehydrogenase dehydrogenase dehydrogenase GuaB dehydrogenase GMP synthase (glutamine- 767 GMP synthase GMP synthase GMP synthase hydrolysing) GuaA isocitrate dehydrogenase, isocitrate dehydrogenase, isocitrate dehydrogenase 798 isocitrate dehydrogenase NADP-dependent NADP-dependent NADP-dependent Icd 3-isopropylmalate 3-isopropylmalate 3-isopropylmalate 3-isopropylmalate 850 dehydrogenase dehydrogenase dehydrogenase dehydrogenase LeuB Fe-S type, tartrate/fumarate anaerobic fumarate hydratase 851 subfamily hydro-lyase subunit class I fumarate hydratase fumarate hydratase, class I FumB alpha phosphoglucomutase alpha-D- phosphoglucomutase/phospho phosphoglucomutase/phospho 867 phosphoglucomutase glucose phosphate-specific mannomutase family protein mannomutase Pgm 877 50S ribosomal protein L3 50S ribosomal protein L3 50S ribosomal protein L3 50S ribosomal protein L3 RplC acetolactate synthase small acetolactate synthase 3 acetolactate synthase 3 acetolactate synthase III small 885 subunit regulatory subunit regulatory subunit subunit IlvH phenylalanyl-tRNA synthetase phenylalanyl-tRNA synthetase phenylalanyl-tRNA synthetase phenylalanyl-tRNA synthetase 890 subunit alpha subunit alpha subunit alpha alpha subunit PheS phenylalanyl-tRNA synthetase phenylalanyl-tRNA synthetase phenylalanyl-tRNA synthetase phenylalanyl-tRNA synthetase 891 subunit beta subunit beta subunit beta beta subunit PheT

154 integration host factor subunit integration host factor subunit integration host factor subunit integration host factor alpha 892 alpha alpha alpha subunit IhfA 904 30S ribosomal protein S7 30S ribosomal protein S7 30S ribosomal protein S7 30S ribosomal protein S7 RpsG translation elongation factor G 905 elongation factor G elongation factor G elongation factor G FusA 907 30S ribosomal protein S10 30S ribosomal protein S10 30S ribosomal protein S10 30S ribosomal protein S10 RpsJ 908 50S ribosomal protein L4 50S ribosomal protein L4 50S ribosomal protein L4 50S ribosomal protein L4 RplD 910 50S ribosomal protein L2 50S ribosomal protein L2 50S ribosomal protein L2 50S ribosomal protein L2 RplB 30S ribosomal protein S19 911 30S ribosomal protein S19 30S ribosomal protein S19 30S ribosomal protein S19 RpsS 913 30S ribosomal protein S3 30S ribosomal protein S3 30S ribosomal protein S3 30S ribosomal protein S3 RpsC 50S ribosomal protein L14 917 50S ribosomal protein L14 50S ribosomal protein L14 50S ribosomal protein L14 RplN 919 50S ribosomal protein L5 50S ribosomal protein L5 50S ribosomal protein L5 50S ribosomal protein L5 RplE 921 30S ribosomal protein S8 30S ribosomal protein S8 30S ribosomal protein S8 30S ribosomal protein S8 RpsH 922 50S ribosomal protein L6 50S ribosomal protein L6 50S ribosomal protein L6 50S ribosomal protein L6 RplF 924 30S ribosomal protein S5 30S ribosomal protein S5 30S ribosomal protein S5 30S ribosomal protein S5 RpsE 928 adenylate kinase adenylate kinase adenylate kinase adenylate kinase Adk 933 30S ribosomal protein S4 30S ribosomal protein S4 30S ribosomal protein S4 30S ribosomal protein S4 RpsD DNA-directed RNA polymerase DNA-directed RNA polymerase DNA-directed RNA polymerase DNA-directed RNA polymerase 934 subunit alpha subunit alpha subunit alpha alpha subunit RpoA 940 50S ribosomal protein L19 50S ribosomal protein L19 50S ribosomal protein L19 50S ribosomal protein L19 RplS sulfate ABC transporter sulfate ABC transporter ABC-type sulfate/thiosulfate sulfate ABC transporter 980 periplasmic sulfate-binding periplasmic sulfate-binding uptakesystem substrate- substrate-binding protein protein protein binding subunit Sbp carbamoyl phosphate synthase carbamoyl-phosphate synthase carbamoyl-phosphate synthase carbamoyl-phosphate synthase 988 large subunit large subunit lipoprotein large subunit large subunit CarB 1030 phosphopyruvate hydratase phosphopyruvate hydratase phosphopyruvate hydratase enolase Eno nucleoside diphosphate kinase 1037 nucleoside diphosphate kinase nucleoside diphosphate kinase nucleoside diphosphate kinase Ndk DNA-directed RNA polymerase DNA-directed RNA polymerase DNA-directed RNA polymerase DNA-directed RNA polymerase 1055 subunit beta subunit beta subunit beta beta subunit RpoB

155 50S ribosomal protein L7/L12 1056 50S ribosomal protein L7/L12 50S ribosomal protein L7/L12 50S ribosomal protein L7/L12 RplL 1057 50S ribosomal protein L10 50S ribosomal protein L10 50S ribosomal protein L10 50S ribosomal protein L10 RplJ 1058 50S ribosomal protein L1 50S ribosomal protein L1 50S ribosomal protein L1 50S ribosomal protein L1 RplA 1063 molecular chaperone DnaK molecular chaperone DnaK molecular chaperone DnaK chaperone protein DnaK bifunctional phosphoglycerate 1084 phosphoglycerate kinase phosphoglycerate kinase kinase/triosephosphate phosphoglycerate kinase Pgk isomerase 1091 30S ribosomal protein S1 30S ribosomal protein S1 30S ribosomal protein S1 ribosomal protein S1 RpsA pyridoxine 5'-phosphate pyridoxine 5'-phosphate pyridoxine 5prime-phosphate pyridoxal phosphate synthase 1105 synthase synthase synthase PdxJ glycyl-tRNA synthetase subunit glycyl-tRNA synthetase subunit glycyl-tRNA synthetase subunit glycyl-tRNA synthetase beta 1189 beta beta beta subunit GlyS 1207 cysteinyl-tRNA synthetase cysteinyl-tRNA synthetase cysteinyl-tRNA synthetase cysteinyl-tRNA synthetase CysS adenylosuccinate synthetase 1211 adenylosuccinate synthetase adenylosuccinate synthetase adenylosuccinate synthetase PurA phosphoribosylaminoimidazole phosphoribosylaminoimidazole phosphoribosylaminoimidazole phosphoribosylformylglycinami 1217 synthetase synthetase synthetase dine cyclo-ligase PurM protein of unknown function 1245 hypothetical protein hypothetical protein hypothetical protein TIGR00255 1284 DNA topoisomerase I DNA topoisomerase I DNA topoisomerase I DNA topoisomerase I TopA 30S ribosomal protein S20 1297 30S ribosomal protein S20 30S ribosomal protein S20 30S ribosomal protein S20 RpsT 6,7-dimethyl-8-ribityllumazine 6,7-dimethyl-8-ribityllumazine 6,7-dimethyl-8-ribityllumazine riboflavin synthase beta 1303 synthase synthase synthase subunit RibE serine serine serine serine 1308 hydroxymethyltransferase hydroxymethyltransferase hydroxymethyltransferase hydroxymethyltransferase GlyA glucose-6-phosphate glucose-6-phosphate glucose-6-phosphate glucose-6-phosphate 1417 isomerase isomerase isomerase isomerase Pgi FKBP-type peptidylprolyl cis- trigger factor peptidyl-prolyl 1478 trigger factor trigger factor trans isomerase cis-trans isomerase Tig 1485 30S ribosomal protein S9 30S ribosomal protein S9 30S ribosomal protein S9 30S ribosomal protein S9 RpsI

156 branched-chain amino acid branched-chain amino acid branched-chain amino acid branched-chain-amino-acid 1508 aminotransferase aminotransferase aminotransferase transaminase IlvE L-aspartate oxidase 1544 L-aspartate oxidase L-aspartate oxidase L-aspartate oxidase component of quinolinate synthetase NadB 1556 co-chaperonin GroES chaperonin GroES co-chaperonin GroES 10 kDa chaperonin GroES 1557 chaperonin GroEL chaperonin GroEL chaperonin GroEL 60 kDa chaperonin GroEL 1587 type I citrate synthase type I citrate synthase type I citrate synthase citrate synthase GltA UDP-N-acetylmuramoylalanyl- UDP-N-acetylmuramoylalanyl- UDP-N-acetylmuramoylalanyl- UDP-N-acetylmuramyl 1627 D-glutamate--2,6- D-glutamate--2,6- D-glutamate--2,6- tripeptide synthetase diaminopimelate ligase diaminopimelate ligase diaminopimelate ligase MurE 1684 prolyl-tRNA synthetase prolyl-tRNA synthetase prolyl-tRNA synthetase prolyl-tRNA synthetase ProS 1694 recA protein recombinase A recombinase A recombinase A RecA dihydrodipicolinate reductase 1741 dihydrodipicolinate reductase dihydrodipicolinate reductase dihydrodipicolinate reductase DapB dihydrodipicolinate synthase 1742 dihydrodipicolinate synthase dihydrodipicolinate synthase dihydrodipicolinate synthase DapA 1744 argininosuccinate lyase argininosuccinate lyase argininosuccinate lyase argininosuccinate lyase ArgH sigma 54 modulation ribosomal subunit interface- sigma-54 modulation ribosomal subunit interface 1768 protein/30S ribosomal protein associated sigma-54 protein/ribosome-associated protein S30EA modulation protein factor YhbH 50S ribosomal protein L27 1777 50S ribosomal protein L27 50S ribosomal protein L27 50S ribosomal protein L27 RpmA 1799 pyruvate kinase pyruvate kinase pyruvate kinase pyruvate kinase II PykA 1803 glutamine synthetase, type I type I glutamine synthetase glutamine synthetase, type I glutamine synthetase GlnA glutamate-1-semialdehyde glutamate-1-semialdehyde glutamate-1-semialdehyde glutamate-1-semialdehyde-21- 1838 aminotransferase aminotransferase aminotransferase aminomutase HemL F0F1 ATP synthase subunit F0F1 ATP synthase subunit F0F1 ATP synthase subunit ATP synthase F1 beta subunit 1841 beta beta beta AtpD F0F1 ATP synthase subunit F0F1 ATP synthase subunit F0F1 ATP synthase subunit ATP synthase F1 gamma 1842 gamma gamma gamma subunit AtpG F0F1 ATP synthase subunit F0F1 ATP synthase subunit F0F1 ATP synthase subunit ATP synthase F1 alpha subunit 1843 alpha alpha alpha AtpA 157 Appendix D. Top 20 Up-regulated and Down-regulated Proteins of Four organisms

Table D1: Top 20 up-regulated proteins in A. dehalogenans

Count of Avg. t-Test SPOCS Locus Tag Peptides ratio Ratio1 Ratio2 (p) ClusterID* Annotations A2cp1_0528 7 18.01 13.66 22.36 0.027 N/A hypothetical protein A2cp1_2201 10 8.03 8.22 7.83 0.004 983 sulfate adenylyltransferase, large subunit A2cp1_3748 2 7.86 6.41 9.31 0.029 N/A hypothetical protein A2cp1_2202 4 5.99 3.10 8.87 0.098 984 sulfate adenylyltransferase subunit 2 nitrite/sulfite reductase hemoprotein beta-component A2cp1_3346 10 5.94 3.79 8.10 0.070 N/A ferrodoxin domain-containing protein A2cp1_0390 15 5.61 4.61 6.62 0.034 208 acyl-CoA dehydrogenase domain-containing protein A2cp1_1731 5 5.58 3.29 7.87 0.083 643 multiheme cytochrome A2cp1_2661 3 4.40 5.16 3.64 0.038 N/A hypothetical protein A2cp1_0274 3 3.92 2.39 5.46 0.099 159 2-nitropropane dioxygenase A2cp1_0681 2 3.77 5.10 2.43 0.091 350 hypothetical protein A2cp1_1465 2 3.47 3.10 3.84 0.027 N/A hypothetical protein A2cp1_2223 3 3.47 3.08 3.85 0.029 996 DNA mismatch repair protein A2cp1_2660 6 3.42 3.17 3.67 0.019 N/A hypothetical protein A2cp1_2046 5 3.38 3.76 3.01 0.029 935 50S ribosomal protein L17 A2cp1_0322 5 3.27 3.31 3.24 0.003 N/A hypothetical protein A2cp1_2002 17 3.20 3.00 3.41 0.017 N/A 2-oxoglutarate ferredoxin oxidoreductase subunit beta A2cp1_3215 20 3.19 3.36 3.01 0.015 1394 pyruvate carboxylase A2cp1_2335 4 3.16 2.68 3.65 0.043 N/A vitamin B12-dependent ribonucleotide reductase A2cp1_3741 3 3.11 2.80 3.42 0.028 N/A P pilus assembly protein chaperone PapD-like protein A2cp1_0172 8 2.99 2.99 3.00 0.000 104 S-adenosylmethionine synthetase *N/A: no SPOCS cluster identified with other studied organisms.

158 Table D2: Top 20 down-regulated proteins in A. dehalogenans

Count of Avg. t-Test SPOCS Locus Tag Peptides ratio Ratio1 Ratio2 (p) ClusterID Annotations A2cp1_2177 29 0.007 0.006 0.008 0.010 N/A aspartate ammonia-lyase A2cp1_1797 2 0.067 0.082 0.052 0.027 273 4Fe-4S ferredoxin A2cp1_1869 5 0.085 0.109 0.061 0.037 N/A 4Fe-4S ferredoxin A2cp1_0368 3 0.110 0.166 0.055 0.074 200 hypothetical protein fumarate reductase/succinate dehydrogenase flavoprotein A2cp1_3684 3 0.139 0.150 0.128 0.013 N/A domain-containing protein A2cp1_3188 7 0.150 0.188 0.111 0.044 N/A multiheme cytochrome A2cp1_0438 46 0.150 0.157 0.144 0.007 N/A hypothetical protein A2cp1_0340 3 0.153 0.201 0.106 0.052 191 split soret cytochrome c precursor A2cp1_1883 10 0.188 0.213 0.163 0.025 848 hypothetical protein A2cp1_3545 17 0.189 0.188 0.190 0.001 847 FKBP-type peptidylprolyl isomerase A2cp1_1287 2 0.193 0.225 0.162 0.031 N/A hypothetical protein A2cp1_1696 2 0.201 0.182 0.221 0.019 N/A ECF subfamily RNA polymerase sigma-24 subunit A2cp1_2522 4 0.217 0.228 0.206 0.010 N/A cytochrome c class I phosphonate ABC transporter periplasmic phosphonate-binding A2cp1_0486 8 0.218 0.215 0.221 0.003 259 protein A2cp1_1868 30 0.222 0.262 0.182 0.038 N/A formate dehydrogenase subunit alpha A2cp1_2248 6 0.225 0.245 0.205 0.019 N/A cytochrome c class III A2cp1_4319 9 0.237 0.271 0.203 0.032 1768 sigma 54 modulation protein/30S ribosomal protein S30EA peptidase S9 prolyl oligopeptidase active site domain-containing A2cp1_2619 7 0.238 0.255 0.221 0.016 1173 protein A2cp1_0657 7 0.244 0.271 0.217 0.025 334 twitching motility protein A2cp1_0467 3 0.255 0.345 0.165 0.080 250 NosL family protein

159 Table D3: Top 20 up-regulated proteins in S. oneidensis

Count of Avg. t-Test SPOCS Locus Tag Peptides ratio Ratio1 Ratio2 (p) ClusterID Annotations SO_A0157 5 99.57 44.75 154.39 0.044 N/A hypothetical protein SO_3236 2 11.34 7.16 15.51 0.052 2338 uncharacterized flagella locus protein FlaG SO_4476 12 11.30 8.12 14.48 0.038 3796 periplasmic stress adaptor protein CpxP NAD-dependent malate dehydrogenase (oxaloacetate- SO_3855 6 8.70 10.07 7.34 0.023 1355 decarboxylating) SfcA SO_0162 6 7.52 8.84 6.21 0.028 3756 phosphoenolpyruvate carboxykinase PckA SO_0335 4 7.43 4.20 10.65 0.076 N/A septal ring assembly factor ZapB SO_0770 12 7.13 7.58 6.68 0.010 2481 NAD dependent malate dehydrogenase Mdh SO_0360 3 6.99 5.09 8.88 0.046 1555 DNA-directed RNA polymerase omega subunit RpoZ SO_1190 2 6.77 9.55 4.00 0.075 N/A putative periplasmic CbiK superfamily protein SO_1638 10 6.76 6.05 7.47 0.018 N/A periplasmic chaperone for outer membrane proteins Skp SO_4619 4 6.76 3.27 10.24 0.100 N/A iron-sulfur cluster biogenesis scaffold protein NfuA SO_2907 17 6.41 5.64 7.18 0.021 N/A ArgR-regulated TonB-dependent receptor SO_4640 4 6.22 2.01 10.43 0.158 N/A antioxidant AhpC/Tsa family SO_1679 3 5.96 7.18 4.74 0.037 1018 2-methylbutanoyl-CoA dehydrogenase IvdC SO_3099 2 5.78 6.06 5.50 0.009 21 outer membrane long-chain fatty acid receptor FadL family SO_0097 2 5.66 4.04 7.28 0.055 1099 urocanate hydratase HutU SO_3238 5 5.27 7.99 2.55 0.115 1170 flagellin FliC bifunctional acetylornithine aminotransferase/succinyl- SO_0617 13 5.19 3.99 6.40 0.046 1279 diaminopimelate aminotransferase/succinylornithine transaminase ArgD SO_4134 4 4.60 2.82 6.37 0.088 N/A protein of unknown function DUF465 SO_1677 9 4.44 4.33 4.55 0.005 207 3-ketoacyl-CoA thiolase IvdA

160 Table D4: Top 20 down-regulated proteins in S. oneidensis

Count of Avg. t-Test SPOCS Locus Tag Peptides ratio Ratio1 Ratio2 (p) ClusterID Annotations ABC-type sulfate/thiosulfate uptake system substrate-binding SO_4652 20 0.025 0.023 0.028 0.008 980 subunit Sbp SO_3727 2 0.074 0.061 0.087 0.022 984 sulfate adenylyltransferase small subunit CysD ABC-type sulfate/thiosulfate uptake system substrate-binding SO_3599 15 0.139 0.181 0.096 0.049 N/A component CysP SO_2882 20 0.144 0.187 0.101 0.049 1230 serine protein kinase PrkA SO_3233 2 0.149 0.151 0.148 0.001 1172 chaperone for FliC flagellin FliS SO_3906 3 0.155 0.250 0.061 0.104 2683 protein of unknown function DUF2333 SO_3726 4 0.156 0.194 0.118 0.042 983 sulfate adenylyltransferase large subunit CysN B12-independent 5-methyltetrahydropteroyltriglutamate-- SO_0818 5 0.165 0.164 0.166 0.001 3549 homocysteine methyltransferase MetE SO_0988 2 0.189 0.309 0.068 0.119 3398 molybdopterin-binding oxidoreductase SO_1198 2 0.192 0.290 0.094 0.097 1108 dihydropteroate synthase FolP SO_0344 8 0.227 0.239 0.215 0.011 1587 2-methylcitrate synthase PrpC SO_1301 7 0.230 0.270 0.190 0.038 N/A aspartate carbamoyltransferase PyrB SO_2916 24 0.237 0.302 0.172 0.060 1355 phosphate acetyltransferase Pta SO_1112 6 0.240 0.278 0.202 0.035 N/A bacterioferritin subunit 1 Bfr1 SO_1111 2 0.254 0.294 0.214 0.037 N/A bacterioferritin subunit 2 Bfr2 SO_3738 12 0.254 0.363 0.145 0.096 N/A sulfite reductase (NADPH) flavoprotein subunit CysJ SO_0226 3 0.264 0.498 0.031 0.187 903 30S ribosomal protein S12p RpsL SO_3743 2 0.271 0.470 0.072 0.161 N/A transcriptional regulator TetR family 16S rRNA (adenine1518-N6/adenine1519-N6)- SO_3639 2 0.275 0.384 0.166 0.094 1594 dimethyltransferase RsmA SO_3651 2 0.286 0.455 0.118 0.138 1777 50S ribosomal protein L27 RpmA

161 Table D5: Top 20 up-regulated proteins in G. sulfurreducens

Count of Avg. t-Test SPOCS Locus Tag Peptides ratio Ratio1 Ratio2 (p) ClusterID Annotations GSU2413 2 40.80 12.85 68.75 0.077 581 ABC transporter ATP-binding protein GSU2780 3 36.62 44.62 28.62 0.020 3767 hypothetical protein GSU2076 4 23.41 41.01 5.82 0.109 3993 cytochrome C GSU0111 18 16.00 17.32 14.69 0.009 1843 F0F1 ATP synthase subunit alpha GSU0772 3 15.74 13.22 18.25 0.019 1987 NADPH-dependent FMN reductase domain-containing protein GSU0085 2 14.11 6.13 22.08 0.081 228 heterodisulfide reductase, cytochrome reductase subunit GSU1467 2 13.89 15.68 12.11 0.016 895 iron-sulfur cluster-binding protein GSU0339 5 12.52 11.21 13.83 0.013 657 NADH dehydrogenase I subunit B GSU0112 8 11.50 8.18 14.81 0.039 1842 F0F1 ATP synthase subunit gamma GSU1341 2 11.20 8.30 14.11 0.035 581 ABC transporter ATP-binding protein GSU1330 4 9.88 10.47 9.28 0.008 1758 metal ion efflux outer membrane protein family protein GSU0113 14 9.86 8.58 11.15 0.018 1841 F0F1 ATP synthase subunit beta GSU1137 3 8.61 4.91 12.30 0.070 2488 phosphodiesterase GSU1331 3 8.49 9.00 7.98 0.009 1757 RND family efflux transporter MFP subunit GSU2196 12 7.93 8.40 7.46 0.009 3762 hydrolase GSU3304 12 7.57 6.76 8.39 0.017 3646 LamB porin family protein GSU2012 6 7.37 6.22 8.53 0.025 312 NifU family protein GSU1176 2 7.22 7.94 6.51 0.016 1364 fumarate reductase, cytochrome b subunit GSU0771 6 7.11 5.87 8.34 0.029 678 alcohol dehydrogenase, zinc-containing GSU0490 9 6.85 11.11 2.59 0.130 516 acetyl-CoA hydrolase/transferase

162 Table D6: Top 20 down-regulated proteins in G. sulfurreducens

Count of Avg. t-Test SPOCS Locus Tag Peptides ratio Ratio1 Ratio2 (p) ClusterID Annotations GSU0077 2 0.037 0.050 0.023 0.037 3755 hypothetical protein GSU3271 5 0.058 0.094 0.022 0.074 4070 hypothetical protein GSU0785 14 0.072 0.067 0.076 0.008 271 nickel-dependent hydrogenase large subunit GSU0786 4 0.082 0.050 0.114 0.051 270 hydrogenase maturation protease GSU3270 5 0.111 0.119 0.103 0.010 1453 feoA family protein GSU2980 2 0.111 0.180 0.041 0.093 280 nickel responsive regulator branched-chain amino acid ABC transporter periplasmic amino GSU2005 5 0.115 0.110 0.120 0.007 730 acid-binding protein formate dehydrogenase, major subunit, selenocysteine- GSU0777 12 0.118 0.091 0.145 0.034 1621 containing GSU1768 2 0.123 0.168 0.077 0.056 3810 ParA family protein GSU1382 2 0.126 0.036 0.217 0.113 1070 iron-dependent repressor GSU1691 7 0.139 0.130 0.148 0.010 1303 6,7-dimethyl-8-ribityllumazine synthase GSU3266 14 0.145 0.179 0.110 0.039 2080 DNA helicase II amino acid ABC transporter periplasmic amino acid-binding GSU3406 19 0.147 0.223 0.071 0.086 1877 protein GSU0913 3 0.148 0.160 0.136 0.014 820 ABC transporter ATP-binding protein GSU0644 5 0.149 0.204 0.093 0.062 937 KH domain-containing protein GSU1961 4 0.152 0.202 0.102 0.055 N/A glycosyl transferase, group 2 family protein GSU2937 2 0.156 0.181 0.132 0.027 2423 cytochrome C GSU2235 4 0.158 0.151 0.166 0.008 668 endoribonuclease L-PSP GSU3132 16 0.159 0.182 0.136 0.025 892 DNA-binding protein HU GSU2876 4 0.164 0.233 0.094 0.075 1486 50S ribosomal protein L13

163 Table D7: Top 20 up-regulated proteins in G. bemidjiensis

Count of Avg. t-Test SPOCS Locus Tag Peptides ratio Ratio1 Ratio2 (p) ClusterID Annotations Gbem_3666 3 25.53 31.32 19.75 0.023 1845 alpha-crystallin/Hsp20 family ATP-independent chaperone Gbem_1123 2 14.03 14.48 13.58 0.004 1411 hypothetical protein Gbem_0249 22 6.51 2.70 10.33 0.122 1557 chaperonin GroEL Gbem_0763 2 6.21 9.31 3.11 0.100 3762 glutamyl aminopeptidase M42 Gbem_3950 2 6.05 9.76 2.34 0.136 1841 F0F1 ATP synthase subunit beta Gbem_1706 2 5.50 8.02 2.99 0.096 N/A bacterioferritin-like domain-containing protein Gbem_1888 4 5.50 9.44 1.55 0.189 N/A malic enzyme NAD-dependent nucleoside diphosphate-sugar Gbem_2286 2 5.28 7.50 3.05 0.089 899 epimerase/dehydratase Gbem_0570 4 4.94 8.00 1.88 0.157 223 ATP-dependent chaperone ClpB Gbem_0727 5 4.78 4.01 5.56 0.033 209 ATP phosphoribosyltransferase Gbem_0318 2 4.77 5.04 4.50 0.012 948 hypothetical protein Gbem_0644 14 4.75 4.81 4.70 0.002 103 pyruvate phosphate dikinase Gbem_0243 2 4.75 8.65 0.85 0.274 234 transcriptional regulator Gbem_0913 2 4.70 6.72 2.68 0.098 1485 30S ribosomal protein S9 Gbem_1962 5 4.40 4.20 4.60 0.010 835 phospho-2-dehydro-3-deoxyheptonate aldolase isoprenoid biosynthesis protein with amidotransferase-like Gbem_0550 4 3.94 6.42 1.47 0.185 1002 domain Gbem_2767 3 3.58 5.76 1.39 0.191 83 50S ribosomal protein L25 Gbem_0572 6 3.50 5.60 1.41 0.187 1508 branched-chain amino acid aminotransferase nicotinate-nucleotide--dimethylbenzimidazole Gbem_3813 4 3.30 5.15 1.45 0.179 1978 phosphoribosyltransferase Gbem_2703 3 3.30 3.86 2.73 0.046 3625 hypothetical protein

164 Table D8: Top 20 down-regulated proteins in G. bemidjiensis

Count of Avg. t-Test SPOCS Locus Tag Peptides ratio Ratio1 Ratio2 (p) ClusterID Annotations Gbem_0645 2 0.124 0.102 0.145 0.026 158 cold shock DNA/RNA-binding protein Gbem_3597 8 0.159 0.220 0.098 0.066 4061 flavocytochrome c Gbem_2756 5 0.173 0.164 0.183 0.010 168 30S ribosomal protein S2 Gbem_0954 3 0.175 0.103 0.247 0.075 928 adenylate kinase Gbem_0554 2 0.176 0.292 0.060 0.119 N/A hypothetical protein Gbem_2518 2 0.184 0.140 0.228 0.045 692 PpiC-type peptidylprolyl cis-trans isomerase Gbem_0088 3 0.191 0.252 0.130 0.061 488 laccase family multicopper oxidase Gbem_1650 2 0.212 0.158 0.266 0.052 3848 AMP-binding domain-containing protein Gbem_0016 2 0.218 0.077 0.358 0.129 588 uroporphyrinogen decarboxylase Gbem_3905 3 0.219 0.224 0.214 0.005 N/A type I citrate synthase Gbem_0765 3 0.239 0.170 0.308 0.063 767 GMP synthase Gbem_0163 2 0.239 0.163 0.315 0.070 1777 50S ribosomal protein L27 Gbem_2410 4 0.242 0.344 0.139 0.092 2380 fructose-1,6-bisphosphatase Gbem_1693 4 0.274 0.267 0.281 0.006 2309 tungstate ABC transporter periplasmic tungstate-binding protein Gbem_2511 4 0.280 0.332 0.227 0.047 610 phosphoribosylaminoimidazole-succinocarboxamide synthase Gbem_3559 2 0.281 0.344 0.217 0.056 1234 FKBP-type peptidylprolyl cis-trans isomerase Gbem_3965 3 0.281 0.292 0.271 0.009 1025 cyclophilin type peptidylprolyl cis-trans isomerase polar amino acid/opine ABC transporter periplasmic amino acid- Gbem_1930 2 0.289 0.440 0.137 0.125 1877 binding protein Gbem_3350 2 0.291 0.179 0.404 0.095 1733 tryptophan synthase subunit alpha Gbem_3379 2 0.293 0.446 0.139 0.126 4037 lipoprotein cytochrome c

165 REFERENCES

Abreu, Isabel A., Alexandra I. Lourenco, Antonio V. Xavier, Jean LeGall, Ana V. Coelho, Pedro M. Matias, David M. Pinto, Maria Armenia Carrondo, Miguel Teixeira, and Lígia M. Saraiva. 2003. “A Novel Iron Centre in the Split-Soret Cytochrome c from Desulfovibrio Desulfuricans ATCC 27774.” JBIC Journal of Biological Inorganic Chemistry 8 (3): 360–70. Afkar, Eman, and Yoshihiro Fukumori. 1999. “Purification and Characterization of Triheme Cytochrome c7 from the Metal-Reducing Bacterium, Geobacter Metallireducens.” FEMS Microbiology Letters 175 (2): 205–10. Aklujkar, M., M. V. Coppi, C. Leang, B. C. Kim, M. A. Chavan, L. A. Perpetua, L. Giloteaux, A. Liu, and D. E. Holmes. 2013. “Proteins Involved in Electron Transfer to Fe(III) and Mn(IV) Oxides by Geobacter Sulfurreducens and Geobacter Uraniireducens.” Microbiology (Reading, England) 159 (Pt 3): 515–35. doi:10.1099/mic.0.064089-0. Aklujkar, Muktak, Nelson D. Young, Dawn Holmes, Milind Chavan, Carla Risso, Hajnalka E. Kiss, Cliff S. Han, Miriam L. Land, and Derek R. Lovley. 2010. “The Genome of Geobacter Bemidjiensis, Exemplar for the Subsurface Clade of Geobacter Species That Predominate in Fe (III)-Reducing Subsurface Environments.” BMC Genomics 11 (1): 490. Alexander J. B., Zehnder. n.d. Biology of Anaerobic Microorganisms. 99th ed. Wiley- Interscience. Altschul, Stephen F., Warren Gish, Webb Miller, Eugene W. Myers, and David J. Lipman. 1990. “Basic Local Alignment Search Tool.” Journal of Molecular Biology 215 (3): 403–10. Alves, Pedro, Randy J. Arnold, Milos V. Novotny, Predrag Radivojac, James P. Reilly, and Haixu Tang. 2007. “Advancement in Protein Inference from Shotgun Proteomics Using Peptide Detectability.” In Pacific Symposium on Biocomputing, 12:409–20. http://psb.stanford.edu/psb-online/proceedings/psb07/alves.pdf. Anderson, N. Leigh, and Norman G. Anderson. 1998. “Proteome and Proteomics: New Technologies, New Concepts, and New Words.” Electrophoresis 19 (11): 1853–61. Anderson, R. F., A. P. LeHuray, M. Q. Fleisher, and J. W. Murray. 1989. “Uranium Deposition in Saanich Inlet Sediments, Vancouver Island.” Geochimica et Cosmochimica Acta 53 (9): 2205–13. Anderson, Robert T., Helen A. Vrionis, Irene Ortiz-Bernad, Charles T. Resch, Philip E. Long, Richard Dayvault, Ken Karp, et al. 2003. “Stimulating the in Situ Activity of Geobacter Species to Remove Uranium from the Groundwater of a Uranium-Contaminated Aquifer.” Applied and Environmental Microbiology 69 (10): 5884–91. Benson, Dennis A., Mark S. Boguski, David J. Lipman, James Ostell, B. F. Ouellette, Barbara A. Rapp, and David L. Wheeler. 1999. “GenBank.” Nucleic Acids Research 27 (1): 12. Bird, Lina J., Violaine Bonnefoy, and Dianne K. Newman. 2011. “Bioenergetic Challenges of Microbial Iron Metabolisms.” Trends in Microbiology 19 (7): 330–40.

166 Blackstock, Walter P., and Malcolm P. Weir. 1999. “Proteomics: Quantitative and Physical Mapping of Cellular Proteins.” Trends in Biotechnology 17 (3): 121–27. Blattner, Frederick R., Guy Plunkett, Craig A. Bloch, Nicole T. Perna, Valerie Burland, Monica Riley, Julio Collado-Vides, et al. 1997. “The Complete Genome Sequence of Escherichia Coli K-12.” Science 277 (5331): 1453–62. Boehm, Andreas M., Stephanie Pütz, Daniela Altenhöfer, Albert Sickmann, and Michael Falk. 2007. “Precise Protein Quantification Based on Peptide Quantification Using iTRAQTM.” BMC Bioinformatics 8 (1): 214. Butler, Jessica E., Richard H. Glaven, Abraham Esteve-Núnez, Cinthia Núnez, Evgenya S. Shelobolina, Daniel R. Bond, and Derek R. Lovley. 2006. “Genetic Characterization of a Single Bifunctional Enzyme for Fumarate Reduction and Succinate Oxidation in Geobacter Sulfurreducens and Engineering of Fumarate Reduction in Geobacter Metallireducens.” Journal of Bacteriology 188 (2): 450–55. Caccavo, F., Debra J. Lonergan, Derek R. Lovley, Mark Davis, John F. Stolz, and Michael J. McInerney. 1994. “Geobacter Sulfurreducens Sp. Nov., a Hydrogen-and Acetate- Oxidizing Dissimilatory Metal-Reducing Microorganism.” Applied and Environmental Microbiology 60 (10): 3752–59. Callister, Stephen J., Richard C. Barry, Joshua N. Adkins, Ethan T. Johnson, Wei-jun Qian, Bobbie- Jo M. Webb-Robertson, Richard D. Smith, and Mary S. Lipton. 2006. “Normalization Approaches for Removing Systematic Biases Associated with Mass Spectrometry and Label-Free Proteomics.” Journal of Proteome Research 5 (2): 277–86. Cascales, Eric, Susan K. Buchanan, Denis Duché, Colin Kleanthous, Roland Lloubes, Kathleen Postle, Margaret Riley, Stephen Slatin, and Daniele Cavard. 2007. “Colicin Biology.” Microbiology and Molecular Biology Reviews 71 (1): 158–229. Coker, V. S., J. M. Byrne, N. D. Telling, G. Van der Laan, J. R. Lloyd, A. P. Hitchcock, J. Wang, and R. A. D. Pattrick. 2012. “Characterisation of the Dissimilatory Reduction of Fe (III)- Oxyhydroxide at the Microbe–mineral Interface: The Application of STXM–XMCD.” Geobiology 10 (4): 347–54. Cravatt, Benjamin F., Gabriel M. Simon, and John R. Yates Iii. 2007. “The Biological Impact of Mass-Spectrometry-Based Proteomics.” Nature 450 (7172): 991–1000. Cupples, Alison M., Robert A. Sanford, and Gerald K. Sims. 2005. “Dehalogenation of the Herbicides Bromoxynil (3,5-Dibromo-4-Hydroxybenzonitrile) and Ioxynil (3,5-Diiodino-4- Hydroxybenzonitrile) by Desulfitobacterium Chlororespirans.” Applied and Environmental Microbiology 71 (7): 3741–46. doi:10.1128/AEM.71.7.3741-3746.2005. Curtis, Darren S., Aaron R. Phillips, Stephen J. Callister, Sean Conlan, and Lee Ann McCue. 2013. “SPOCS: Software for Predicting and Visualizing Orthology/paralogy Relationships among Genomes.” Bioinformatics, btt454. Deckers-Hebestreit, Gabriele, and Karlheinz Altendorf. 1996. “The F0F1-Type ATP Synthases of Bacteria: Structure and Function of the F0 Complex.” Annual Reviews in Microbiology 50 (1): 791–824. De Rienzo, F., R.r. Gabdoulline, M. C. Menziani, and R.c. Wade. 2000. “Blue Copper Proteins: A Comparative Analysis of Their Molecular Interaction Properties.” Protein Science 9 (8): 1439–54. doi:10.1110/ps.9.8.1439.

167 Dimroth, PETER, GEORG Kaim, and ULRICH Matthey. 2000. “Crucial Role of the Membrane Potential for ATP Synthesis by F (1) F (o) ATP Synthases.” Journal of Experimental Biology 203 (1): 51–59. Dobbin, P., J. Butt, A. Powell, G. Reid, and D. Richardson. 1999. “Characterization of a Flavocytochrome That Is Induced during the Anaerobic Respiration of Fe3+ by Shewanella Frigidimarina NCIMB400.” Biochem. J 342: 439–48. Duhamel, Melanie, and Elizabeth A. Edwards. 2006. “Microbial Composition of Chlorinated Ethene-Degrading Cultures Dominated by Dehalococcoides.” FEMS Microbiology Ecology 58 (3): 538–49. Du, Xin, Benjaporn Boonchayaanant, Wei-Min Wu, Scott Fendorf, John Bargar, and Craig S. Criddle. 2011. “Reduction of uranium(VI) by Soluble iron(II) Conforms with Thermodynamic Predictions.” Environmental Science & Technology 45 (11): 4718–25. doi:10.1021/es2006012. Eddy, Sean R. 1998. “Profile Hidden Markov Models.” Bioinformatics 14 (9): 755–63. Emanuelsson, Olof. 2002. “Predicting Protein Subcellular Localisation from Amino Acid Sequence Information.” Briefings in Bioinformatics 3 (4): 361–76. Finn, Robert D., Alex Bateman, Jody Clements, Penelope Coggill, Ruth Y. Eberhardt, Sean R. Eddy, Andreas Heger, et al. 2014. “Pfam: The Protein Families Database.” Nucleic Acids Research 42 (D1): D222–30. doi:10.1093/nar/gkt1223. Fournier, Marjorie L., Joshua M. Gilmore, Skylar A. Martin-Brown, and Michael P. Washburn. 2007. “Multidimensional Separations-Based Shotgun Proteomics.” Chemical Reviews 107 (8): 3654–86. Gafken, Philip R., and Paul D. Lampe. 2006. “Methodologies for Characterizing Phosphoproteins by Mass Spectrometry.” Cell Communication & Adhesion 13 (5-6): 249–62. Gao, Haichun, Zamin K. Yang, Soumitra Barua, Samantha B. Reed, Margaret F. Romine, Kenneth H. Nealson, James K. Fredrickson, James M. Tiedje, and Jizhong Zhou. 2009. “Reduction of Nitrate in Shewanella Oneidensis Depends on Atypical NAP and NRF Systems with NapB as a Preferred Electron Transport Protein from CymA to NapA.” The ISME Journal 3 (8): 966–76. doi:10.1038/ismej.2009.40. Gardy, Jennifer L., Matthew R. Laird, Fei Chen, Sébastien Rey, C. J. Walsh, Martin Ester, and Fiona SL Brinkman. 2005. “PSORTb v. 2.0: Expanded Prediction of Bacterial Protein Subcellular Localization and Insights Gained from Comparative Proteome Analysis.” Bioinformatics 21 (5): 617–23. Gardy, Jennifer L., Cory Spencer, Ke Wang, Martin Ester, Gabor E. Tusnady, István Simon, Sujun Hua, et al. 2003. “PSORT-B: Improving Protein Subcellular Localization Prediction for Gram-Negative Bacteria.” Nucleic Acids Research 31 (13): 3613–17. Gaspard, Sarra, Francisco Vazquez, and Christof Holliger. 1998. “Localization and Solubilization of the Iron (III) Reductase of Geobacter Sulfurreducens.” Applied and Environmental Microbiology 64 (9): 3188–94. Gelpi, J. Li, Albert Dordal, Joan Montserrat, Adela Mazo, and Antonio Cortes. 1992. “Kinetic Studies of the Regulation of Mitochondrial Malate Dehydrogenase by Citrate.” Biochem. J 283: 289–97.

168 Goldberg, Robert N., Yadu B. Tewari, and Talapady N. Bhat. 2004. “Thermodynamics of Enzyme- Catalyzed Reactions—a Database for Quantitative Biochemistry.” Bioinformatics 20 (16): 2874–77. Gorby, Yuri A., and Derek R. Lovley. 1992. “Enzymic Uranium Precipitation.” Environmental Science & Technology 26 (1): 205–7. Gorby, Yuri A., Svetlana Yanina, Jeffrey S. McLean, Kevin M. Rosso, Dianne Moyles, Alice Dohnalkova, Terry J. Beveridge, et al. 2006. “Electrically Conductive Bacterial Nanowires Produced by Shewanella Oneidensis Strain MR-1 and Other Microorganisms.” Proceedings of the National Academy of Sciences 103 (30): 11358–63. Gore, Dilip, and Ashish Chakule. 2011. “Homology Modeling and Function Prediction in Uncharacterized Proteins of Pseudoxanthomonas Spadix.” Biocompx 1: 23–32. Haynes, Winston. 2013. “Student’s T-Test.” In Encyclopedia of Systems Biology, 2023–25. Springer. http://link.springer.com/10.1007/978-1-4419-9863-7_1184. Haywood-Farmer, Eric, Sarah P. Otto, and J. Huelsenbeck. 2003. “The Evolution of Genomic Base Composition in Bacteria.” Evolution 57 (8): 1783–92. Heidelberg, John F., Ian T. Paulsen, Karen E. Nelson, Eric J. Gaidos, William C. Nelson, Timothy D. Read, Jonathan A. Eisen, et al. 2002. “Genome Sequence of the Dissimilatory Metal Ion–reducing Bacterium Shewanella Oneidensis.” Nature Biotechnology 20 (11): 1118– 23. He, Qiang, and Robert A. Sanford. 2003. “Characterization of Fe (III) Reduction by Chlororespiring Anaeromxyobacter Dehalogenans.” Applied and Environmental Microbiology 69 (5): 2712–18. ———. 2004. “Acetate Threshold Concentrations Suggest Varying Energy Requirements during Anaerobic Respiration by Anaeromyxobacter Dehalogenans.” Applied and Environmental Microbiology 70 (11): 6940–43. Holmes, Dawn E., Kelly P. Nevin, and Derek R. Lovley. 2004. “Comparison of 16S rRNA, nifD, recA, gyrB, rpoB and fusA Genes within the Family Geobacteraceae Fam. Nov.” International Journal of Systematic and Evolutionary Microbiology 54 (5): 1591–99. Hostetler, P. B., and R. M. Garrels. 1962. “Transportation and Precipitation of Uranium and Vanadium at Low Temperatures, with Special Reference to Sandstone-Type Uranium Deposits.” Economic Geology 57 (2): 137–67. Hu, Lianghai, Mingliang Ye, Xiaogang Jiang, Shun Feng, and Hanfa Zou. 2007. “Advances in Hyphenated Analytical Techniques for Shotgun Proteome and Peptidome Analysis—a Review.” Analytica Chimica Acta 598 (2): 193–204. Hynes, Richard O. 2002. “Integrins: Bidirectional, Allosteric Signaling Machines.” Cell 110 (6): 673–87. Inoue, Kengo, Xinlei Qian, Leonor Morgado, Byoung-Chan Kim, Tünde Mester, Mounir Izallalen, Carlos A. Salgueiro, and Derek R. Lovley. 2010. “Purification and Characterization of OmcZ, an Outer-Surface, Octaheme c-Type Cytochrome Essential for Optimal Current Production by Geobacter Sulfurreducens.” Applied and Environmental Microbiology 76 (12): 3999–4007. doi:10.1128/AEM.00027-10. James, Peter. 1997. “Protein Identification in the Post-Genome Era: The Rapid Rise of Proteomics.” Quarterly Reviews of Biophysics 30 (04): 279–331.

169 Jensen, Mead LeRoy. 1958. “Sulfur Isotopes and the Origin of Sandstone-Type Uranium Deposits [Colorado Plateau and Wyoming].” Economic Geology 53 (5): 598–616. Kanehisa, Minoru, and Susumu Goto. 2000. “KEGG: Kyoto Encyclopedia of Genes and Genomes.” Nucleic Acids Research 28 (1): 27–30. Karp, Peter D., Suzanne Paley, and Pedro Romero. 2002. “The Pathway Tools Software.” Bioinformatics 18 (suppl 1): S225–32. Keller, Martin, and Robert Hettich. 2009. “Environmental Proteomics: A Paradigm Shift in Characterizing Microbial Activities at the Molecular Level.” Microbiology and Molecular Biology Reviews 73 (1): 62–70. Klinkhammer, G. P., and M. R. Palmer. 1991. “Uranium in the Oceans: Where It Goes and Why.” Geochimica et Cosmochimica Acta 55 (7): 1799–1806. Koebnik, R., K. P. Locher, and P. Van Gelder. 2000. “Structure and Function of Bacterial Outer Membrane Proteins: Barrels in a Nutshell.” Molecular Microbiology 37 (2): 239–53. Krogh, Anders, Michael Brown, I. Saira Mian, Kimmen Sjölander, and David Haussler. 1994. “Hidden Markov Models in Computational Biology: Applications to Protein Modeling.” Journal of Molecular Biology 235 (5): 1501–31. Lancaster, C. Roy D., Achim Kröger, Manfred Auer, and Hartmut Michel. 1999. “Structure of Fumarate Reductase from Wolinella Succinogenes at 2.2 \AA Resolution.” Nature 402 (6760): 377–85. Langmuir, Donald. 1978. “Uranium Solution-Mineral Equilibria at Low Temperatures with Applications to Sedimentary Ore Deposits.” Geochimica et Cosmochimica Acta 42 (6): 547–69. Liaw, Shwu-Huey, Ichun Kuo, and David Eisenberg. 1995. “Discovery of the Ammonium Substrate Site on Glutamine Synthetase, a Third Cation Binding Site.” Protein Science 4 (11): 2358–65. Livesay, Eric A., Keqi Tang, Beverley K. Taylor, Michael A. Buschbach, Derek F. Hopkins, Brian L. LaMarche, Rui Zhao, et al. 2008. “Fully Automated Four-Column Capillary LC-MS System for Maximizing Throughput in Proteomic Analyses.” Analytical Chemistry 80 (1): 294– 302. Lovley, Derek R. 1993. “Dissimilatory Metal Reduction.” Annual Reviews in Microbiology 47 (1): 263–90. ———. 2006. “Microbial Energizers: Fuel Cells That Keep on Going.” Microbe-American Society for Microbiology 1 (7): 323. Lovley, DEREK R., KELLY P. Nevin, J. D. Wall, C. S. Harwood, A. Demain, and others. 2008. “Electricity Production with Electricigens.” In Bioenergy, 295–306. ASM Press. http://www.cabdirect.org/abstracts/20083156313.html. Lovley, Derek R., and E. J. Phillips. 1992a. “Reduction of Uranium by Desulfovibrio Desulfuricans.” Applied and Environmental Microbiology 58 (3): 850–56. Lovley, Derek R., and Elizabeth JP Phillips. 1988. “Novel Mode of Microbial Energy Metabolism: Organic Carbon Oxidation Coupled to Dissimilatory Reduction of Iron or Manganese.” Applied and Environmental Microbiology 54 (6): 1472–80. ———. 1992b. “Bioremediation of Uranium Contamination with Enzymatic Uranium Reduction.” Environmental Science & Technology 26 (11): 2228–34.

170 Lovley, Derek R., Elizabeth JP Phillips, Yuri A. Gorby, and Edward R. Landa. 1991. “Microbial Reduction of Uranium.” Nature 350 (6317): 413–16. Lower, Brian H., Liang Shi, Ruchirej Yongsunthon, Timothy C. Droubay, David E. McCready, and Steven K. Lower. 2007. “Specific Bonds between an Iron Oxide Surface and Outer Membrane Cytochromes MtrC and OmcA from Shewanella Oneidensis MR-1.” Journal of Bacteriology 189 (13): 4944–52. Markich, Scott J. 2002. “Uranium Speciation and Bioavailability in Aquatic Systems: An Overview.” The Scientific World Journal 2: 707–29. McCullough, Julie, Terry Hazen, and Sally Benson. 1999. “Bioremediation of Metals and Radionuclides: What It Is and How It Works.” Lawrence Berkeley National Laboratory. http://escholarship.org/uc/item/8s57v0cs.pdf. McLean, J. S., G. E. Pinchuk, O. V. Geydebrekht, C. L. Bilskis, B. A. Zakrajsek, E. A. Hill, D. A. Saffarini, et al. 2008. “Oxygen-Dependent Autoaggregation in Shewanella Oneidensis MR-1.” Environmental Microbiology 10 (7): 1861–76. doi:10.1111/j.1462- 2920.2008.01608.x. Merkley, Eric D., Kelly C. Wrighton, Cindy J. Castelle, Brian J. Anderson, Michael J. Wilkins, Vega Shah, Tyler Arbour, et al. 2015. “Changes in Protein Expression Across Laboratory and Field Experiments in Geobacter Bemidjiensis.” Journal of Proteome Research 14 (3): 1361–75. doi:10.1021/pr500983v. Methé, B. A., K. E. Nelson, J. A. Eisen, I. T. Paulsen, W. Nelson, J. F. Heidelberg, D. Wu, et al. 2003. “Genome of Geobacter Sulfurreducens: Metal Reduction in Subsurface Environments.” Science (New York, N.Y.) 302 (5652): 1967–69. doi:10.1126/science.1088727. Mikoulinskaia, Oxana, Vasily Akimenko, Alexander Galouchko, Rudolf K. Thauer, and Reiner Hedderich. 1999. “Cytochrome c-Dependent Methacrylate Reductase from Geobacter Sulfurreducens AM-1.” European Journal of Biochemistry 263 (2): 346–52. Mitchell, Peter, and Jennifer Moyle. 1967. “Chemiosmotic Hypothesis of Oxidative Phosphorylation.” Nature 213: 137–39. Mullinax, Thomas R., J. N. Mock, A. J. McEvily, and J. H. Harrison. 1982. “Regulation of Mitochondrial Malate Dehydrogenase. Evidence for an Allosteric Citrate-Binding Site.” Journal of Biological Chemistry 257 (22): 13233–39. Mulrooney, Scott B., Michael J. Howard, and Robert P. Hausinger. 2011. “The Escherichia Coli Alkylation Response Protein AidB Is a Redox Partner of Flavodoxin and Binds RNA and Acyl Carrier Protein.” Archives of Biochemistry and Biophysics 513 (2): 81–86. doi:10.1016/j.abb.2011.07.004. Myers, CHARLES R., and JUDITH M. Myers. 1992. “Localization of Cytochromes to the Outer Membrane of Anaerobically Grown Shewanella Putrefaciens MR-1.” Journal of Bacteriology 174 (11): 3429–38. Myers, Charles R., and Judith M. Myers. 1997. “Outer Membrane Cytochromes of Shewanella Putrefaciens MR-1: Spectral Analysis, and Purification of the 83-kDa c-Type Cytochrome.” Biochimica et Biophysica Acta (BBA)-Biomembranes 1326 (2): 307–18. Myers, Charles R., and Kenneth H. Nealson. 1988. “Bacterial Manganese Reduction and Growth with Manganese Oxide as the Sole Electron Acceptor.” Science 240. http://www.researchgate.net/profile/Charles_Myers2/publication/6024266_Bacterial_ 171 manganese_reduction_and_growth_with_manganese_oxide_as_the_sole_electron_acc eptor/links/00b7d529ca93be8c2f000000.pdf. Nakai, K. 2000. “Protein Sorting Signals and Prediction of Subcellular Localization.” Advances in Protein Chemistry 54: 277–344. Nakai, Kenta, Paul Horton, Kenta Nakai, and Paul Horton. 1999. “PSORT: A Program for Detecting Sorting Signals in Proteins and Predicting Their Subcellular Localization.” Trends in Biochemical Sciences 24 (1): 34–35. doi:10.1016/S0968-0004(98)01336-X. Nancy, Y. Yu, James R. Wagner, Matthew R. Laird, Gabor Melli, Sébastien Rey, Raymond Lo, Phuong Dao, et al. 2010. “PSORTb 3.0: Improved Protein Subcellular Localization Prediction with Refined Localization Subcategories and Predictive Capabilities for All Prokaryotes.” Bioinformatics 26 (13): 1608–15. Nan, Jie, Erik Brostromer, Xiang-Yu Liu, Ole Kristensen, and Xiao-Dong Su. 2009. “Bioinformatics and Structural Characterization of a Hypothetical Protein from Streptococcus Mutans: Implication of Antibiotic Resistance.” PloS One 4 (10): e7245. Nealson, Kenneth H., and Daad Saffarini. 1994. “Iron and Manganese in Anaerobic Respiration: Environmental Significance, Physiology, and Regulation.” Annual Reviews in Microbiology 48 (1): 311–43. Nesvizhskii, Alexey I. 2007. “Protein Identification by Tandem Mass Spectrometry and Sequence Database Searching.” In Mass Spectrometry Data Analysis in Proteomics, 87–119. Springer. http://link.springer.com/10.1385/1-59745-275-0:87. Nevin, Kelly P., Dawn E. Holmes, Trevor L. Woodard, Erich S. Hinlein, David W. Ostendorf, and Derek R. Lovley. 2005. “Geobacter Bemidjiensis Sp. Nov. and Geobacter Psychrophilus Sp. Nov., Two Novel Fe (III)-Reducing Subsurface Isolates.” International Journal of Systematic and Evolutionary Microbiology 55 (4): 1667–74. Nissen, Silke, Xiaoxin Liu, Karuna Chourey, RobertL Hettich, DarleneD Wagner, SusanM Pfiffner, and Frank E. Löffler. 2012. “Comparative c-Type Cytochrome Expression Analysis in Shewanella Oneidensis Strain MR-1 and Anaeromyxobacter Dehalogenans Strain 2CP-C Grown with Soluble and Insoluble Oxidized Metal Electron Acceptors.” Biochemical Society Transactions 40 (6): 1204–10. North, Nadia N., Sherry L. Dollhopf, Lainie Petrie, Jonathan D. Istok, David L. Balkwill, and Joel E. Kostka. 2004. “Change in Bacterial Community Structure during in Situ Biostimulation of Subsurface Sediment Cocontaminated with Uranium and Nitrate.” Applied and Environmental Microbiology 70 (8): 4911–20. Overbeek, Ross, Tadhg Begley, Ralph M. Butler, Jomuna V. Choudhuri, Han-Yu Chuang, Matthew Cohoon, Valérie de Crécy-Lagard, et al. 2005. “The Subsystems Approach to Genome Annotation and Its Use in the Project to Annotate 1000 Genomes.” Nucleic Acids Research 33 (17): 5691–5702. doi:10.1093/nar/gki866. Ow, Saw Yen, Malinda Salim, Josselin Noirel, Caroline Evans, Ishtiaq Rehman, and Phillip C. Wright. 2009. “iTRAQ Underestimation in Simple and Complex Mixtures:‘the Good, the Bad and the Ugly.’” Journal of Proteome Research 8 (11): 5347–55. Polpitiya, Ashoka D., Wei-Jun Qian, Navdeep Jaitly, Vladislav A. Petyuk, Joshua N. Adkins, David G. Camp, Gordon A. Anderson, and Richard D. Smith. 2008. “DAnTE: A Statistical Tool for Quantitative Analysis of-Omics Data.” Bioinformatics 24 (13): 1556–58.

172 Rawlings, Neil D., and Alan J. Barrett. 1995. “[13] Evolutionary Families of Metallopeptidases.” Methods in Enzymology 248: 183–228. Reguera, Gemma, Kevin D. McCarthy, Teena Mehta, Julie S. Nicoll, Mark T. Tuominen, and Derek R. Lovley. 2005. “Extracellular Electron Transfer via Microbial Nanowires.” Nature 435 (7045): 1098–1101. Remm, Maido, Christian EV Storm, and Erik LL Sonnhammer. 2001. “Automatic Clustering of Orthologs and in-Paralogs from Pairwise Species Comparisons.” Journal of Molecular Biology 314 (5): 1041–52. Richardson, David J., Marcus J. Edwards, Gaye F. White, Nanakow Baiden, Robert S. Hartshorne, James K. Fredrickson, Liang Shi, et al. 2012. “Exploring the Biochemistry at the Extracellular Redox Frontier of Bacterial Mineral Fe (III) Respiration.” http://digitalcommons.unl.edu/usdoepub/190/. Ritossa, Ferruccio. 1962. “A New Puffing Pattern Induced by Temperature Shock and DNP in Drosophila.” Experientia 18 (12): 571–73. Ross, Daniel E., Susan L. Brantley, and Ming Tien. 2009. “Kinetic Characterization of OmcA and MtrC, Terminal Reductases Involved in Respiratory Electron Transfer for Dissimilatory Iron Reduction in Shewanella Oneidensis MR-1.” Applied and Environmental Microbiology 75 (16): 5218–26. Ross, Philip L., Yulin N. Huang, Jason N. Marchese, Brian Williamson, Kenneth Parker, Stephen Hattan, Nikita Khainovski, et al. 2004. “Multiplexed Protein Quantitation in Saccharomyces Cerevisiae Using Amine-Reactive Isobaric Tagging Reagents.” Molecular & Cellular Proteomics 3 (12): 1154–69. Sadowski, Pawel G., Tom PJ Dunkley, Ian P. Shadforth, Paul Dupree, Conrad Bessant, Julian L. Griffin, and Kathryn S. Lilley. 2006. “Quantitative Proteomic Approach to Study Subcellular Localization of Membrane Proteins.” Nature Protocols 1 (4): 1778–89. doi:10.1038/nprot.2006.254. Sanford, Robert A., James R. Cole, and James M. Tiedje. 2002. “Characterization and Description of Anaeromyxobacter Dehalogenans Gen. Nov., Sp. Nov., an Aryl-Halorespiring Facultative Anaerobic Myxobacterium.” Applied and Environmental Microbiology 68 (2): 893–900. Schuetz, Bjoern, Marcus Schicklberger, Johannes Kuermann, Alfred M. Spormann, and Johannes Gescher. 2009. “Periplasmic Electron Transfer via the c-Type Cytochromes MtrA and FccA of Shewanella Oneidensis MR-1.” Applied and Environmental Microbiology 75 (24): 7789–96. Seeliger, Sabine, Ralf Cord-Ruwisch, and Bernhard Schink. 1998. “A Periplasmic and Extracellular c-Type Cytochrome ofGeobacter Sulfurreducens Acts as a Ferric Iron Reductase and as an Electron Carrier to Other Acceptors or to Partner Bacteria.” Journal of Bacteriology 180 (14): 3686–91. Shelobolina, Evgenya S., Maddalena V. Coppi, Anton A. Korenevsky, Laurie N. DiDonato, Sara A. Sullivan, Hiromi Konishi, Huifang Xu, et al. 2007. “Importance of c-Type Cytochromes for U (VI) Reduction by Geobacter Sulfurreducens.” BMC Microbiology 7 (1): 16. Shi, Liang, Baowei Chen, Zheming Wang, Dwayne A. Elias, M. Uljana Mayer, Yuri A. Gorby, Shuison Ni, et al. 2006. “Isolation of a High-Affinity Functional Protein Complex between

173 OmcA and MtrC: Two Outer Membrane Decaheme c-Type Cytochromes of Shewanella Oneidensis MR-1.” Journal of Bacteriology 188 (13): 4705–14. Shi, Liang, David J. Richardson, Zheming Wang, Sebastien N. Kerisit, Kevin M. Rosso, John M. Zachara, and James K. Fredrickson. 2009. “The Roles of Outer Membrane Cytochromes of Shewanella and Geobacter in Extracellular Electron Transfer.” Environmental Microbiology Reports 1 (4): 220–27. Shi, Liang, Thomas C. Squier, John M. Zachara, and James K. Fredrickson. 2007. “Respiration of Metal (hydr) Oxides by Shewanella and Geobacter: A Key Role for Multihaem c-Type Cytochromes.” Molecular Microbiology 65 (1): 12–20. Simon, Jörg. 2002. “Enzymology and Bioenergetics of Respiratory Nitrite Ammonification.” FEMS Microbiology Reviews 26 (3): 285–309. Singleton, Paul, and others. 2004. Bacteria in Biology, Biotechnology and Medicine. Ed. 6. John Wiley & Sons. http://www.cabdirect.org/abstracts/20043152583.html. Sonnhammer, E. L., S. R. Eddy, and R. Durbin. 1997. “Pfam: A Comprehensive Database of Protein Domain Families Based on Seed Alignments.” Proteins 28 (3): 405–20. Stolz, John F., and Ronald S. Oremland. 2011. Microbial Metal and Metalloid Metabolism: Advances and Applications. American Society for Microbiology Press. https://books.google.com/books?hl=en&lr=&id=ngKsBAAAQBAJ&oi=fnd&pg=PT9&dq= Microbial+Metal+and+Metalloid+Metabolism+- +Advances+and+Applications&ots=c8BNWvIVjE&sig=X6g-5lZrQ5b7CrTpitlu-4r_bQ0. Strycharz, Sarah M., Sarah M. Gannon, Amber R. Boles, Ashley E. Franks, Kelly P. Nevin, and Derek R. Lovley. 2010. “Reductive Dechlorination of 2-Chlorophenol by Anaeromyxobacter Dehalogenans with an Electrode Serving as the Electron Donor.” Environmental Microbiology Reports 2 (2): 289–94. Sung, Youlboong, Kelly E. Fletcher, Kirsti M. Ritalahti, Robert P. Apkarian, Natalia Ramos- Hernández, Robert A. Sanford, Noha M. Mesbah, and Frank E. Löffler. 2006. “Geobacter Lovleyi Sp. Nov. Strain SZ, a Novel Metal-Reducing and Tetrachloroethene- Dechlorinating Bacterium.” Applied and Environmental Microbiology 72 (4): 2775–82. Taverner, Tom, Yuliya V. Karpievitch, Ashoka D. Polpitiya, Joseph N. Brown, Alan R. Dabney, Gordon A. Anderson, and Richard D. Smith. 2012. “DanteR: An Extensible R-Based Tool for Quantitative Analysis of-Omics Data.” Bioinformatics 28 (18): 2404–6. Tebo, Bradley M., and Anna Ya Obraztsova. 1998. “Sulfate-Reducing Bacterium Grows with Cr (VI), U (VI), Mn (IV), and Fe (III) as Electron Acceptors.” FEMS Microbiology Letters 162 (1): 193–98. Thomas, Sara H., Ryan D. Wagner, Adrian K. Arakaki, Jeffrey Skolnick, John R. Kirby, Lawrence J. Shimkets, Robert A. Sanford, and Frank E. Löffler. 2008. “The Mosaic Genome of Anaeromyxobacter Dehalogenans Strain 2CP-C Suggests an Aerobic Common Ancestor to the Delta-Proteobacteria.” PLoS One 3 (5): e2103. Torres, César I., Andrew Kato Marcus, and Bruce E. Rittmann. 2008. “Proton Transport inside the Biofilm Limits Electrical Current Generation by Anode-Respiring Bacteria.” Biotechnology and Bioengineering 100 (5): 872–81. Tsapin, A. I., K. H. Nealson, T. Meyers, M. A. Cusanovich, J. Van Beuumen, L. D. Crosby, B. A. Feinberg, and C. Zhang. 1996. “Purification and Properties of a Low-Redox-Potential

174 Tetraheme Cytochrome c3 from Shewanella Putrefaciens.” Journal of Bacteriology 178 (21): 6386–88. Venkateswaran, Kasthuri, Duane P. Moser, Michael E. Dollhopf, Douglas P. Lies, Daad A. Saffarini, Barbara J. MacGregor, David B. Ringelberg, et al. 1999. “Polyphasic Taxonomy of the Genus Shewanella and Description of Shewanella Oneidensis Sp. Nov.” International Journal of Systematic Bacteriology 49 (2): 705–24. Vrionis, Helen A., Robert T. Anderson, Irene Ortiz-Bernad, Kathleen R. O’Neill, Charles T. Resch, Aaron D. Peacock, Richard Dayvault, David C. White, Philip E. Long, and Derek R. Lovley. 2005. “Microbiological and Geochemical Heterogeneity in an in Situ Uranium Bioremediation Field Site.” Applied and Environmental Microbiology 71 (10): 6308–18. Wagner, Darlene D., Laura A. Hug, Janet K. Hatt, Melissa R. Spitzmiller, Elizabeth Padilla-Crespo, Kirsti M. Ritalahti, Elizabeth A. Edwards, Konstantinos T. Konstantinidis, and Frank E. Löffler. 2012. “Genomic Determinants of Organohalide-Respiration in Geobacter Lovleyi, an Unusual Member of the Geobacteraceae.” BMC Genomics 13 (1): 200. Wall, Judy D., and Lee R. Krumholz. 2006. “Uranium Reduction.” Annu. Rev. Microbiol. 60: 149– 66. Walton, Troy A., and Marcelo C. Sousa. 2004. “Crystal Structure of Skp, a Prefoldin-like Chaperone That Protects Soluble and Membrane Proteins from Aggregation.” Molecular Cell 15 (3): 367–74. Warneke, Thorsten, Ian W. Croudace, Phillip E. Warwick, and Rex N. Taylor. 2002. “A New Ground-Level Fallout Record of Uranium and Plutonium Isotopes for Northern Temperate Latitudes.” Earth and Planetary Science Letters 203 (3): 1047–57. Washburn, Michael P., Dirk Wolters, and John R. Yates. 2001. “Large-Scale Analysis of the Yeast Proteome by Multidimensional Protein Identification Technology.” Nature Biotechnology 19 (3): 242–47. Weber, Karrie A., Laurie A. Achenbach, and John D. Coates. 2006. “Microorganisms Pumping Iron: Anaerobic Microbial Iron Oxidation and Reduction.” Nature Reviews Microbiology 4 (10): 752–64. Wiatrowski, Heather A., Paula Marie Ward, and Tamar Barkay. 2006. “Novel Reduction of Mercury (II) by Mercury-Sensitive Dissimilatory Metal Reducing Bacteria.” Environmental Science & Technology 40 (21): 6690–96. Wolters, Dirk A., Michael P. Washburn, and John R. Yates. 2001. “An Automated Multidimensional Protein Identification Technology for Shotgun Proteomics.” Analytical Chemistry 73 (23): 5683–90. Wrighton, K. C., J. C. Thrash, R. A. Melnyk, J. P. Bigi, K. G. Byrne-Bailey, J. P. Remis, D. Schichnes, M. Auer, C. J. Chang, and J. D. Coates. 2011. “Evidence for Direct Electron Transfer by a Gram-Positive Bacterium Isolated from a Microbial Fuel Cell.” Applied and Environmental Microbiology 77 (21): 7633–39. Wu, Christine C., and Michael J. MacCoss. 2002. “Shotgun Proteomics: Tools for the Analysis of Complex Biological Systems.” Curr Opin Mol Ther 4 (3): 242–50. Yamagata, Shuzo. 1989. “Roles of O-Acetyl-L-Homoserine Sulfhydrylases in Microorganisms.” Biochimie 71 (11): 1125–43.

175 Zachara, John M., James K. Fredrickson, Shu-Mei Li, David W. Kennedy, Steven C. Smith, and Paul L. Gassman. 1998. “Bacterial Reduction of Crystalline Fe^ 3^+ Oxides in Single Phase Suspensions and Subsurface Materials.” American Mineralogist 83: 1426–43. Zarembinski, Thomas I., Li-Wei Hung, Hans-Joachim Mueller-Dieckmann, Kyeong-Kyu Kim, Hisao Yokota, Rosalind Kim, and Sung-Hou Kim. 1998. “Structure-Based Assignment of the Biochemical Function of a Hypothetical Protein: A Test Case of Structural Genomics.” Proceedings of the National Academy of Sciences 95 (26): 15189–93. Zieske, Lynn R. 2006. “A Perspective on the Use of iTRAQTM Reagent Technology for Protein Complex and Profiling Studies.” Journal of Experimental Botany 57 (7): 1501–8. Zumft, Walter G., and Peter MH Kroneck. 2006. “Respiratory Transformation of Nitrous Oxide (N 2 O) to Dinitrogen by Bacteria and Archaea.” Advances in Microbial Physiology 52: 107–227.

176