The Endolithic and Planktonic Subsurface Microbiome Within Zones of Active Low-Temperature Serpentinization in the Samail Ophiolite of Oman

Home , Defluviicoccus, Methanogen

THE ENDOLITHIC AND PLANKTONIC SUBSURFACE MICROBIOME

WITHIN ZONES OF ACTIVE LOW-TEMPERATURE SERPENTINIZATION IN THE

SAMAIL OPHIOLITE OF OMAN

by Emily Antoinette Kraus

A thesis submitted to the Faculty and Board of Trustees of the Colorado School of Mines

in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Civil and

Environmental Engineering).

Golden, Colorado

Date: ______

Signed: ______

Emily Antoinette Kraus

Signed: ______

Dr. John R. Spear Thesis Advisor

Golden, Colorado

Date: ______

Signed: ______

Dr. Junko Munakata Marr Professor and Department Head Department of Civil and Environmental Engineering

ABSTRACT

The rock-hosted microbial ecosystems within serpentinizing subsurface environments are

a prime target for future NASA missions searching for extant life in the solar system and

constitute a unique habitat within Earth’s subsurface that is relatively uncharacterized. The

water-rock interaction called serpentinization generates highly reduced and hyperalkaline fluids

replete with hydrogen (H2) and reduced carbon compounds such as methane, which are potent electron donors and carbon sources used in microbial metabolism. This dissertation investigates the microbiome within a low-temperature terrestrial serpentinizing subsurface in the Samail ophiolite, Sultanate of Oman, from biomass collected from peridotite- and gabbro-hosted aquifer waters and drilled serpentinite rock cores.

The metabolic pathways and key organisms of the Samail ophiolite’s microbial methane cycle and sulfur cycle are elucidated through multiple molecular methods with DNA and RNA sequencing and geochemical data. Results from this effort demonstrate a single active methanogen and methanotrophic populations in waters with hyperalkaline pH, representing an extension of the pH tolerance range of methanogens and suggesting biological involvement in the generation of unusual isotopic signatures of methane in the Samail ophiolite. The more complex subsurface microbial sulfur cycle is dominated by a few alkaliphilic sulfate reducing organisms and a single primary sulfur oxidizer. Biological sulfate reduction is a prominent metabolism in gabbro and hyperalkaline peridotite wells; polysulfide and elemental sulfur metabolism is important in moderately alkaline waters; and sulfur oxidation is limited to less alkaline gabbro waters.

In addition to identifying planktonic microorganisms within well fluids, genomic material iii of endolithic microbiota residing within serpentinite rock was recovered using a novel core

handling and DNA extraction protocol focused on contaminant minimization. Putative sulfate

reducers, syntrophic organisms, methanogens, iron-metabolizing organisms, and other

phylotypes constitute the endolithic microbiome based on small-subunit ribosomal RNA

sequencing. Comparison of organisms in planktonic and endolithic biomass indicates a distinct

rock-hosted community exists, potentially influenced by local fluid geochemistry, syntrophic

interactions, and adjacency to Fe-bearing minerals.

Taken together, this dissertation provides insight into the structure, distribution, and

metabolism of the resident planktonic and endolithic microbiota of the Samail ophiolite and the

habitability of deep subsurface environments of low temperature serpentinization. These results

help to inform on the biogeochemical cycles within modern ophiolites, aid future missions by

further constraining the habitability of other rocky subsurfaces such as on Mars, and refine

protocols to process low-biomass rock in the presence of abundant contaminants.

TABLE OF CONTENTS

ABSTRACT……………………………………………………………………………………...iii

LIST OF FIGURES………………………………………………………………………………xi

LIST OF TABLES……………………………………………………………………………...xiii

LIST OF EQUATIONS……………………………………………………………………...….xiv

LIST OF SYMBOLS………………………………………………………………………….…xv

LIST OF ABBREVIATIONS……………………………………………………………...…....xvi

ACKNOWLEDGEMENTS……………………………………………………………….…....xvii

CHAPTER 1 INTRODUCTION…………………………………………………………..…1

1.1 Serpentinization: a water – rock reaction to support subsurface life…………...………...1

1.2 Serpentinites and serpentinization: a target in astrobiology and geobiology………...…..3

1.3 Life in the deep subsurface…………………………………………………………...…..5

1.4 Life in terrestrial serpentinites………………………………………………………...... 8

1.5 The Samail Ophiolite: an accessible venue to modern water-rock alteration………...…..8

1.6 The use of high-throughput sequencing to study microbial life at extremes………...….10

1.7 Organization of thesis chapters…………...………………………………………....…..12

1.8 References……………………………………………………………………………….14

CHAPTER 2 MOLECULARE EVIDENCE FOR AN ACTIVE MICROBIAL METHANE CYCLE IN SUBSURFACE SERPENTINITE-HOSTED GROUNDWATERS IN THE SAMAIL OPHIOLITE, OMAN……...……....23

2.1 Importance……………...………………………………………………………...……..24

2.2 Introduction…………………………………………………………………...…………25

2.3 Materials and methods………………………………………………………...…...……29

2.3.1 Site description and geochemical characterization of subsurface waters…………29

2.3.2 Biomass collection, DNA/RNA extraction, quantification, and SSU rRNA gene and transcript sequencing……………………………………………………32

2.3.3 Metagenomic and metatranscriptomic library preparation and sequencing………………………………………………………………………...34

2.4 Results………………………………………………………………………………...….37

2.4.1 Geochemical characterization of subsurface fracture waters………………..……37

2.4.2 Diversity of 16S rRNA genes and transcripts in subsurface fracture waters………………………………………………………………………….….40

2.4.3 Metagenomic characterization of subsurface fracture water communities………………………………………………………………………42

2.4.4 Metatranscriptomic characterization of subsurface fracture water communities…………………………………………………………………...... 47

2.5 Discussion……………………………………………………………………..……..…..51

2.5.1 Active microbial methanogenesis in subsurface waters of the Samail ophiolite…………………………………………………………………………...51

2.5.2 Methanotrophy in subsurface waters of the Samail ophiolite…………………...... 55

2.5.3 A subsurface methane cycle impacted by microbial activity………………….….57

2.6 Data availability…………………...…………………………………………………….59

2.7 Acknowledgements……………………………………………………...…………...….59

2.8 References………………………………………………………………...……………..60

CHAPTER 3 SULFUR METABOLISM IN THE TERRESTRIAL SERPENTINITE SUBSURFACE OF THE SAMAILOPHIOLITE, OMAN………………..…..70

3.1 Introduction……………………………………………………………………...……....71

3.2 Methods……………………………………………………………………………...…..74

3.2.1 Metagenomic sequencing and generation of MAGs..………………………….….74

3.2.2 Metatranscriptomic sequencing, transcript assembly, and annotation……...……..75

3.2.3 Assessment of MAGs and transcription activity for sulfur metabolism……...…...76

3.2.4 16S SSU rRNA community comparisons and geochemistry……………..………77

3.3 Results……………………………...……………………………………………………79

3.3.1 Fluid type and geochemistry drives community dissimilarity…………..…...……79

3.3.2 Identification of putative sulfate reducers and oxidizers…………...……………..83

3.3.3 Relative expression of transcripts related to sulfate reduction…………...……….83

3.3.4 Metagenome-assembled genomes (MAGs) involved in the sulfur cycle...…...…..87

3.4 Discussion……………………………………...……………………………..…………91

3.4.1 Dissimilatory sulfate reduction and Thermodesulfovibrionales in the Samail ophiolite………………………………..…………………………..…...…94

3.4.2 Microbiota in the ophiolite may be phosphate limited and communities in the hyperalkaline fluids may be sulfate limited……………………….…...... 101

3.4.3 Assimilator sulfate reduction may be important at high pH……….……….....…102

3.4.4 Sulfide oxidation, metabolism of intermediate S compounds and Rhodocyclaceae………………..…………………………………………….…..102

3.4.5 Potential for sulfate reduction by a member of the Melioribacteraceae.…...……107

3.5 Conclusion.……………………………………………………...…………………..…108

3.7 References..………………………………………………………………...……….….109

CHAPTER 4 SYNTROPHY AND DISTINCT MICROBIAL COMMUNITIES DIFFERENTIATE ROCK-HOSTED AND FLUID-HOSTED LIFE IN A TERRESTRAIL SITE OF SERPENTINIZATION.……………….…..117

4.1 Introduction..………………………………………………………………...…………119

4.2 Methods…………...……………………………………………………………...…….123

4.2.1 Core collection, contaminant minimization, and contamination control sampling………………………………………………………………....123

4.2.2 Collection of post-drilling stabilized borehole fluids in 2020…………..…….…124

4.2.3 DNA extraction and sequencing of SSU rRNA……………………………….....125

4.2.4 Network Analyses…………………………………………..……..…………..…127 vii

4.2.5 Quantification of 16S rRNA copy number and sequence abundance normalization…………………………………………….....……..…………..…128

4.3 Results..……………………………………………………………………….……….129

4.3.1 Samail ophiolite cores have an estimated 102-103 cells g−1……………..…...…..132

4.3.2 Sequencing and contamination removal………………………………………....134

4.3.3 Correlations between samples indicate possible contamination sources……...... 137

4.3.4 Possible endemic organisms in serpentinites from the Samail ophiolite……...…137

4.3.5 Comparison to post-drilling stabilized BA1B, BA4A, and BA3A borehole fluids sampled in 2020………..………………………………...……..141

4.3.6 Network analysis suggests interactions between taxa………………………...…144

4.4 Discussion...………………………………………………………………………...….144

4.4.1 Contaminating DNA remains in low biomass samples despite efforts at minimization and removal ………………………………………………………144

4.4.2 Endolithic methanogens and sulfate reducers in terrestrial serpentinite rock.…..151

4.4.3 Syntrophic propionate oxidation and iron corrosion may be important metabolic methods in serpentinite rock…...……………………………….….…156

4.4.4 Fe-bearing minerals possibly influence the distribution of microbial life in serpentinites…………………………………………………………..…..158

4.4.5 Fluid geochemistry may influence rock-hosted microbial populations....…….....160

4.4.6 Rock-hosted life differs from planktonic life…………………………....…….....161

4.5 Conclusion……...…………………………………………………………….………..161

4.6 Acknowledgements..………………………………………………………….………..162

4.7 References……………………...……………………………..………………………..163

CHAPTER 5 CONCLUDING REMARKS………………..………………….……………175

5.1 Conclusions and implications of this work……...……………………………………..175

5.2 Future directions to study subsurface life in serpentinites.………………...……..……178

APPENDIX A SUPPLEMENTARY INFORMATION FOR CHAPTER TWO…………….180

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APPENDIX B SUPPLEMENTARY INFORMATION FOR CHAPTER THREE……..……185

APPENDIX C SUPPLEMENTARY INFORMATION FOR CHAPTER FOUR……………189

APPENDIX D MICROSCALE BIOSIGNATURES AND ABIOTIC MINERAL AUTHIGENESIS IN LITTLE HOT CREEK, CALIFORNIA………………192

D.1 Introduction……...……………………………………………………………….……193

D.2 Materials and methods……………...……………………………………………...….197

D.2.1 Site description and aqueous geochemistry…………...……………...…………197

D.2.2 Solid carbonate stable isotope analysis………………...………….…………….199

D.2.3 Petrography and microscopy of Little Hot Creek precipitates……...…………...199

D.2.4 DNA extraction and sequencing from Little Hot Creek precipitates……...….…200

D.3 Results……………...………………………………………………………………….203

D.3.1 Little Hot Creek water geochemistry ………………...…………………………203

D.3.2 Petrography…………………………………………………………………...…208

D.3.3 Microbial communities of Little Hot Creek precipitates……………..……...….208

D.4 Discussion……….…………………………………………………………….…...….212

D.4.1 A geochemical model for precipitate formation…………………………...……212

D.4.2 Biological controls on precipitate morphogenesis………………….……...……220

D.4.3 Endolithic microborings in the abiogenic calcite base………………………….222

D.5 Conclusions……….………………………………………………………...……...….224

D.6 Acknowledgements ………………………………………………………………...…225

D.7 Contributions and Funding………………………………………………………...….225

D.8 References……………………………………………………………………...……...226

APPENDIX E SUPPLEMENTARY INFORMATION FOR APPENDIX D……………..…234

APPENDIX F COPYRIGHT FOR PUBLISHED WORKS…………………………………240

F.1 Copyright permission for Chapter 2…………………………………………………...240

F.2 Co-author permission for Chapter 2…………………………………………………...241

F.3 Copyright permission for Appendix D………………………………………………...244

F.4 Co-author permission for Appendix D………………………………………………...244

LIST OF FIGURES

Figure 1.1 Subsurface environments in the Samail ophiolite and cored rock…………….…..7

Figure 1.2 Well and fluid types within the Samail ophiolite…………………………...... 10

Figure 2.1 Geological map of the Samail ophiolite and wells sampled for biomass to assess methane cycling in 2017…………………………..……………...……31

Figure 2.2A Mean relative abundances of methane cycling taxa………………………...…...43

Figure 2.2B Percentage of sequences assigned as methane cycling taxa from DNA and RNA extracts………………………...………………………….…….43

Figure 2.3A Abundances of key functional genes involved in methane metabolism…………46

Figure 2.3B Abundances of key functional genes involved in methane metabolism that are homologous to Methanobacterium protein sequences………….……….46

Figure 2.4 Transcript abundances for key methane cycling genes……………..……………49

Figure 3.1 Biological sulfur cycling reactions and genes targeted……...……………..….…78

Figure 3.2A Non-metric multidimensional scaling ordination of Bray-Curtis dissimilarity………………………………………………………………………81

Figure 3.2B Canonical analysis of principal components of Bray-Curtis dissimilarity….…...82

Figure 3.3A Relative abundance of putative sulfate and sulfur reducing organisms……..…...86

Figure 3.3B Relative abundance of putative sulfur oxidizing organisms…………….……...86

Figure 3.4 Normalized transcript abundances for sulfur cycling genes……………………89

Figure 3.5 Completion heatmap of targeted metabolic pathways in a subset of metagenome-assembled genomes…..………………………………………...... 93

Figure 3.6 Conceptual model of key sulfur metabolic reactions and taxa………….………99

Figure 4.1 Examples of serpentinite cores collected for biological study……………..…...122

Figure 4.2 Estimates of cell density based on qPCR results…………………………….…133

Figure 4.3 Alpha diversity of core and sulfidic fluid samples………………….……….…135

Figure 4.4 Sample network based on ASV co-occurrence…………………..………….....136

Figure 4.5A Normalized read counts for methanogens, sulfate reducers, and syntrophs in selected core, sulfidic fluid, and control samples…………….……..…….....142

Figure 4.5B Normalized read counts for methanogens, sulfate reducers, and syntrophs in 2020 borehole fluids…………………………………….…………………...143

Figure 4.6 Network of ASVs from rock samples………………………………..……..…..148

Figure A.1 Relative abundance of the top ten ASVs………………………………………180

Figure A.2 Rarefaction curves of observed species richness…………………………...…181

Figure A.3 Relative abundance of the top ten ASVs in controls…………………………..182

Figure C.1 Alpha diversity of all sample types collected in 2018 drilling operations…….190

Figure C.2 Sequencing read counts of methanogens, sulfate reducers, and syntrophs for all 2018 samples………….………………………………………………..191

Figure D.1 Little Hot Creek site and sampling locations………………………….………196

13 Figure D.2 Measured  C values of mineral precipitates and hot spring dissolved inorganic carbon…………………………………………………………….....205

Figure D.3 Petrographic photomicrographs of Little Hot Creek precipitates…………..…207

Figure D.4 Relative abundance of bacterial and archaeal phyla in Little Hot Creek samples……………………………………………………………………...…211

Figure D.5 Fractionation of dissolved inorganic carbon from Little Hot Creek water during degassing……………………………………………...…………214

13 Figure D.6 Model of the evolution of calcite saturation and CDIC for degassing of LHC waters…………………………………………………..……………..216 휹 Figure D.7 Model of the evolution of calcite and silica saturation during evaporation...... 217

Figure D.8 Schematic model of mineral precipitate formation in Little Hot Creek……….219

Figure E.1 Light microscopy and scanning electron microscopy images of precipitate samples…………………………………………..……...…………237

Figure E.2 Maximum likelihood phylogenetic tree of Cyanobacteria…………………….239

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LIST OF TABLES

Table 2.1 Geochemistry of well waters sampled for assessment of the biological methane cycle in 2017 ………………………………………………………...... 39

Table 2.2 All functional genes, descriptions, and transcript abundances (counts per million reads mapped) targeted in methane cycling analyses…………...... ……..50

Table 3.1 Selected geochemical features of Samail ophiolite wells sampled in 2017…...... 80

Table 4.1 Summary of rock core samples for DNA extraction………………...……...….130

Table 4.2 Quantified DNA and 16S copy number from serpentinite cores and contamination controls………………………………………………...……...... 131

Table 4.3 Taxonomic identities of potential endolithic ASVs………………………..…...139

Table A.1 Trace element concentrations for well waters………………………….…...….183

Table A.2 Sequencing information for SSU rRNA, metagenomes, and metatranscriptomes……………………………………………………………..184

Table B.1 Counts per million reads mapped of all transcripts assessed in Chapter 4…..…186

Table B.2 Sequencing information for metagenomes and metatranscriptomes…...……....188

Table D.1 Water chemistry along the flow path of Little Hot Creek…………….………..204

Table D.2 Selected metabolic gene abundances from Little Hot Creek metagenomes..…..212

Table E.1 Taxonomy of sequences assigned as Cyanobacteria...…………………………234

Table E.2 Sequence library information for Little Hot Creek metagenomes………….…..235

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LIST OF EQUATIONS

Equation 1 Simplified serpentinization equation……………………………………...…2, 120

Equation 2 Formula for 16S copy numbers from nanograms of a DNA amplicon measured via qPCR ………………………………………………………….…129

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LIST OF SYMBOLS

∑ : the sum of all species of an element.  : delta notation for isotopic composition Ω : saturation state

LIST OF ABBREVIATIONS

ASV: amplicon sequence variant OUT: operational taxonomic unit DIC: dissolved inorganic carbon LOQ: limit of quantification BLOQ: below limit of quantification LHC: Little Hot Creek DSR: dissimilatory sulfate reduction ASR: assimilatory sulfate reduction

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ACKNOWLEDGEMENTS

Writing a thesis is an arduous task under normal circumstances. I cannot recommend undertaking one in the middle of a pandemic, a community tragedy, a horrifically hateful political climate fueled by racism, misogyny, and xenophobia, and all of the other events that came along in 2020 and 2021. Thankfully, I have had the privilege to be surrounded by people who are kind, loving, and compassionate to their very cores. I am so grateful for everyone’s support and love.

To the people that shaped me into who I am: Thanks to my mom, Penny Cyr, for instilling me with an intense love of animals so strong that I spent most of my life preparing to be a zoologist or a dolphin trainer. Thank you to my dad, Dr. Dave Kraus, for waking a cranky 9- year-old me up at 4am many years ago to watch a Leonid meteor storm, which drove me into space science with its stunning brilliance. Many thanks to Davey and the Cyr and Kraus families for your support and jokes. I also want to say thank you to my grandparents, Lorne and Irene Cyr for teaching me to draw, letting me drive tractors, and to not take any ‘baloney’.

Thank you, many times over, to my advisor Dr. John Spear. As a grad student with you, it has been an absolutely wild ride from day two when I introduced a speaker at Normposium to getting lost in the back country of a foreign county half a world away. We’ve been literally stuck

(and still taking samples) between mating bison and a hot spring in Yellowstone, covered in alkaline salt on Mono Lake, had some encounters with Bedouin folks and sand spiders in the deserts of Oman, touched the endless deserts of the Empty Quarter, and so many more stories I can’t fit into this page. The many treasured memories of Spear Lab adventures in fieldwork are well worth the future 20% tax.

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I am incredibly grateful to Dr. Alexis Templeton for her guidance, fieldwork, and leadership in the astrobiology community and Rock Powered Life. I also need to thank my committee members Drs. Junko Munakata Marr, Josh Sharp, and Alexis Navarre-Sitchler; without your counsel and kindness, this dissertation would not have been remotely possible. All of my committee members are great role models and STEM could use many more folks like them. I feel especially fortunate to have been able to interact with my committee members often at the GEM lab, Mines, and CU, and share in the joy of ultimate frisbee with Junko and John.

Many thank yous to the whole GEM Lab! A more wonderful, thoughtful, and intelligent group of folks is hard to come by. Drs. Blake Stamps and Gary Vanzin have been excellent mentors in lab and data analyses over the last years, thank you both for all of the knowledge you’ve downloaded to me. It has been wonderful working and growing with all the students that have passed through the GEM lab including Dr. Chris Trivedi, Kalen Rasmussen, Patrick

Thieringer, Mariah Papac, Alex Honeyman, Mike Vega, Laura Leonard, Adam Brady, Duke

Douglas, and many others.

I would not be here writing this without the help of my undergraduate advisors. Thank you, Drs. John Tarduno, Rory Cottrell, and Bob Minckley. A small moment of Janet Fogg’s compassion and kindness kept me from leaving science and set me on the path toward astrobiology.

Thank you to all of the friends and instructors from the 2016 International Geobiology

Course. The course and the folks I met there transformed me into a better scientist and person. I treasure memories of the field work, doing puzzles, the Gastronome, jumping off the Catalina dock, and swimming in bioluminescence at night after working all day. I couldn’t imagine a better science family to join.

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I’m eternally thankful for my Clarence High School and University of Rochester friends for accepting the quiet, goofy, weird girl that I am. The U of R women’s and men’s ultimate teams and the inhabitants of 44 Congress will forever be my family. I’ve also met many amazing people during my time in Colorado and have loved every moment of playing ultimate and hanging out with the sweet, talented goofballs of All Jeeps, All Night, Jackwagon, and folks from Grass Roots Ultimate. And the last year would have been impossible without Tasha,

Tristan, Martha, Julia, Alysa, Luda, Briana, and the whole South Boulder pod.

Finally, I am perpetually grateful to and humbled by the women who have blazed a trail in science before me. They have made my path easier and more accessible, and I hope to do the same for all young scientists from all walks of life.

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“The ability to tell your own story, in words or images, is already a victory, already a revolt.”

-R. Solnit

CHAPTER 1

INTRODUCTION

1.1 Serpentinization: a water – rock reaction to support subsurface life

The search for life in the universe and life’s origins here on Earth has led researchers to a examine a variety of environments to define the limits and history of life on our planet. One of the most promising environments in this realm is the deep subsurface wherein spontaneous water-rock reactions generate chemical disequilibria that can support microbial life. Ultramafic rock is formed from solidifying magma, has low silica (< 45%), high iron (Fe) and magnesium

(Mg) content, and makes up much of the Earth’s mantle as well as the rock on other planets and moons in our solar system. When ultramafic rock interacts with liquid water at temperatures <

400 ℃ and pressures < 100 MPa, the rock is altered in a process called serpentinization (Evans,

1977; Klein et al., 2009; Mayhew et al., 2013; McCollom & Bach, 2009). Specifically, serpentinization is the hydration of olivine and/or pyroxene bearing ultramafic rocks, in which the oxidation of ferrous iron is coupled to the reduction of water, producing serpentine minerals, a variety of Fe(II/III) hydroxides and oxides (e.g. brucite, magnetite) and molecular hydrogen

(H2) (Moody, 1976; Sleep et al., 2004).

Serpentinization reactions on Earth (e.g. Equation 1) occur in hydrothermal environments beneath the seafloor and at low temperatures (<100 ℃) in terrestrial settings (Mayhew et al.,

2013). These terrestrial systems of serpentinization, called ophiolites, are pieces of Earth’s crust and mantle that have been tectonically emplaced on a continent (Mayhew et al., 2013; Neubeck

et al., 2014). In serpentinization systems, reaction progress results in elevated aqueous dissolved

H2 concentrations that can drive the reduction of inorganic carbon (CO2) to abiotically generate

− formate (HCOO ), carbon monoxide (CO), methane (CH4), and other reduced carbon

compounds to temperatures of at least 55 ℃ (Etiope & Whiticar, 2019; Mayhew et al., 2013;

Proskurowski et al., 2008; Seewald et al., 2006). Dissolved inorganic carbon (DIC) concentrations are often low in these environments as divalent cations (e.g., Mg2+, Ca2+) can also

react easily with CO2 to precipitate carbonate minerals from fluids (Kelemen et al., 2011;

Kelemen & Matter, 2008).

Mg1.8Fe0.2SiO4 + H2O → (Mg, Fe)3Si2O5(OH)4 + (Mg,Fe)(OH)2 + Fe3O4 + H2 (1.1) olivine serpentine brucite magnetite

Serpentinization-impacted waters in “closed” systems typically have very low oxidation-

reduction potentials (Eh), pH values of 8 to >12, and can have nM to mM concentrations of H2

and CH4 (Barnes & O’Neil, 1969; Miller et al., 2016; Rempfert et al., 2017). Unlike “closed” systems, “open” aquifers are those that interact with the atmosphere and are characterized by recharge with meteoric water. Thus, fluids collected from such systems are less alkaline (pH 8-

9), have varied, more positive redox potentials, greater DIC and oxidant concentrations, and are

− enriched in Mg and bicarbonate (HCO3 ) (Barnes & O’Neil, 1969). Barnes and O’Neil (1969) classified these “open” and “closed” waters as “Type I” and “Type II”, respectively. Zones of mixing occur between the fluid types, leading to a myriad of geochemical gradients and microenvironments within an ophiolite (Miller et al., 2016; Rempfert et al., 2017).

Cellular metabolism in the deep subsurface of the Earth must rely on chemical energy

generated from such water-rock reactions in the absence of photosynthetically derived organic

2+ carbon from the surface. In these habitats, H2 and reduced compounds (e.g. Fe , CH4) act as

2- - electron donors and oxidized species (e.g. CO2, SO4 , NO3 ) as electron acceptors for endemic

microorganisms (Brazelton et al., 2012; Canovas et al., 2017; Crespo-Medina et al., 2017; Kohl

et al., 2016; Miller et al., 2016; Rempfert et al., 2017; Russell et al., 2010; Spear et al., 2005;

Suzuki et al., 2017). DIC and reduced carbon compounds provide carbon sources for resident

microbial populations. Paradoxically, microbial life in serpentinites must contend with multiple

extreme conditions induced by serpentinization reaction progress including hyperalkaline, hyper-

reduced fluids often under DIC and oxidant limitation. Regardless, the serpentinization reaction

can supply the requirements of chemolithoautotrophic life in ultramafic rock, linking the abiotic

lithosphere to the biotic realm with this production of chemical energy and carbon compounds.

This connection of the geosphere and biosphere has given rise to hypotheses that serpentinizing

environments fostered the origin of life on Earth and possibly on other planets with liquid water

(Chapelle et al., 2002; Müntener, 2010; Russell et al., 2010; Sleep et al., 2011).

1.2 Serpentinites and serpentinization: a target in astrobiology and geobiology

Recent findings indicate serpentinization is ongoing or has occurred across the solar

system and Earth’s history, occurring wherever ultramafic rock interacts with liquid water (Holm

et al., 2015; Schrenk et al., 2013). The Martian subsurface shows signs of past and present

serpentinization through spectral imaging (Ojha et al., 2015; Quesnel et al., 2009) and mineral

assemblages associated with low-temperature (<100ºC) serpentinization have been observed on

the planet’s surface (Amador et al., 2018). Geophysical modeling indicates serpentinization

occurs beneath the crust of Europa (Vance et al., 2016) and Cassini’s flyby of Enceladus

estimated H2 comprised up to 1.4% of the sampled vapor plume, providing strong evidence of this water-rock interaction beneath the ice (Waite et al., 2017). This ubiquitous nature of serpentinization increases the possible habitable zones in the nearest rocky planets. To narrow the search for past and extant life in the solar system, targeted study of rock-hosted microbial life within low-temperature serpentinizing subsurface analogs on Earth is critical.

In addition to its astrobiological importance, an environment of active serpentinization may have harbored the earliest traces or origin of life on Earth. On early Earth, the pH and redox gradients generated from hydrothermal alkaline serpentinization-impacted fluids contacting acidic CO2-rich seawater at seafloor vents could have provided abundant geochemical energy to be harnessed by early cells (Schulte et al., 2006; Sleep et al., 2011). The synthesis of simple

carbon compounds from the abiotic reduction of CO2 could provide organic carbon to such early

life. These simple abiotic synthesis reactions have also been hypothesized to have evolved into

the first biosynthetic metabolic pathways based on the structural similarities between Fe-S

enzymatic cores and sulfide minerals at modern hydrothermal vents (Martin & Russell, 2007;

McCollom & Seewald, 2013). Mineral precipitate structures at vent sites have also been

theorized to have functioned as compartments that gradually morphed into an organic

membrane-like barrier of proto-cells (Baaske et al., 2007; Budin et al., 2009). Furthermore,

genomic reconstruction of the last universal common ancestor (LUCA) of Bacteria and Archaea

indicates LUCA was anaerobic, thermophilic, contained Fe-S enzymatic clusters, and had the

metabolic capability to fix CO2 with H2 using the ancient Wood-Ljungdahl pathway (Weiss et

al., 2016). These microbial traits are well matched to the context of an early anoxic hydrothermal

serpentinizing environment rich in H2 and Fe. Collectively, evidence and theory suggest

serpentinization may have harbored the origins of life or sustained life through the early,

unstable periods on the Earth surface such as the Late Heavy Bombardment (Abramov &

Mojzsis, 2009; Plümper et al., 2017). Therefore, understanding serpentinite biospheres is key to understanding the history of habitability on our planet from the earliest life to the modern age.

Since direct experimentation on other planets and in the deep mantle is incredibly limited and costly, analog environments are sought on Earth to study serpentinization’s role in the evolution of life. Modern day serpentinization can be observed at hydrothermal sites on and below the seafloor and in terrestrial serpentinites harboring low temperature alteration. Extreme geochemical gradients are exploited by microorganisms at these hydrothermal venting sites (such as the Lost City Hydrothermal Field in the mid-Atlantic Ocean) when serpentinization-impacted fluid contacts seawater (Kelley et al., 2005). The water-rock reactions at lower temperatures are slower and result in less intense geochemical gradients, making terrestrial serpentinites a distinctly lower-energy and lower-biomass environment. However, low temperature serpentinization may be more analogous to the subsurface of Mars or other rocky bodies lacking tectonic heating and such systems have great value as extraterrestrial analogs (Jones et al., 2018).

1.3 Life in the deep subsurface

Though life and habitability in the deep subsurface is largely unexplored, the subsurface

biosphere is estimated to contain approximately 15% of Earth’s biomass (Bar-On et al., 2018)

and 40 – 60% of all bacterial cells (McMahon & Parnell, 2014; Wilkins et al., 2014). The

paucity of observations on life from subsurface environments is due to the inherent challenges of

sampling deep fracture fluids and rocks. Both continental and seafloor drilling and deep pumping

operations are costly endeavors and core recovery percentages can be quite low in these

operations. Subsurface samples obtained in these efforts typically have low biomass densities

down to tens of cells per gram and are thus easily contaminated from a wide variety of sources

during drilling, collection, and all processing steps (Wilkins et al., 2014). Furthermore, the

limited number of sampling points cannot capture the vast extent of the deep biosphere and

subsurface habitability (Edwards et al., 2012). With this multitude of challenges, the global

diversity, metabolic pathways, and geophysical controls on distribution of organisms in the

subsurface remain largely unknown.

Most subsurface studies have used groundwater samples to assess the microbial

communities of the deep biosphere, however, evidence is increasing that the microbes within

subsurface groundwaters, on fracture surfaces, and in the bulk rock matrix reside in very

different environments and are distinct from one another (Figure 1.1). Sequencing of the 16S

rRNA gene has demonstrated that unique rock-hosted microbiomes are different than the

surrounding groundwater within Precambrian rocks of the Sanford Underground Research

Facility (SURF) (Momper et al., 2017)and within subsurface limestones and mudstones from

two sites in Germany (Lazar et al., 2019). Stark differences in community composition were also

observed between the more permeable fracture surfaces and the less permeable parent rock

matrix in the limestones/mudstones (Lazar et al., 2019). Additionally, biomass density is likely

much greater within subsurface fractures and fracture surfaces where biofilms can form.

Fractured or high porosity rock would be an ideal subsurface environment for microorganisms to

establish biofilms to take advantage of available physical space and to maximize nutrient and

water access (Escudero, Oggerin, et al., 2018; Escudero, Vera, et al., 2018), and biofilms within

fractured rock environments have been detected and described with microscopy and molecular techniques (Jägevall et al., 2011; Pfiffner et al., 2006; Wanger et al., 2006). For example, in

oceanic crust basalts, cell densities from fracture materials of 1010 cells cm-3 are far greater than the overlying sediments (102 cells cm-3) and formation fluids (104 cells cm-3) (Suzuki et al.,

2020). Active biofilms of Bacteria and Archaea have been detected even in deep, low porosity

subsurface rock of the Iberian Pyrite Belt with fluorescence in situ hybridization (FISH) methods

and confocal microscopy, suggesting biofilms are widespread in deep rock regardless of porosity

(Escudero, Vera, et al., 2018). These findings highlight the need for continued targeted study of

rock matrices and fracture surfaces to understand the habitability of the deep subsurface.

Figure 1.1. Diagram of different subsurface environments in the Samail ophiolite and cored rock collected for DNA extraction in this work.

1.4 Life in terrestrial serpentinites

The discovery of the Lost City Hydrothermal Field in the early 2000’s and the subsequent

interest in serpentinizing systems has led to intense scrutiny of serpentinites over the last several

decades (Kelley et al., 2001, 2005; McCollom & Seewald, 2013). Around the world, biological studies of ophiolites in Oman, the Philippines, Turkey, Norway, California, Canada, Italy,

Portugal, and other locales have been conducted to understand the diversity and distribution of life in these rocks. From 16S rRNA gene sequencing surveys, we know that waters from terrestrial serpentinites commonly contain a variety of anaerobic organisms, typically including hydrogenotrophic methanogens, H2 and CH4 oxidizers, fermenters, acetogens, sulfate reducers,

and iron metabolizing organisms (Brazelton et al., 2013; Daae et al., 2013; Meyer-Dombard et

al., 2018; Twing et al., 2017; Woycheese et al., 2015). The genomic potential for these

metabolic pathways to proceed has been confirmed in many locations via metagenomic

sequencing and genome binning (e.g., Brazelton et al., 2017; Sabuda et al., 2020). Furthermore,

the identities and isotopic compositions of lipid biomarkers revealed archaeal methanogenesis

and bacterial sulfate reduction under DIC limitation in the Chimera ophiolite of Turkey (Zwicker

et al., 2018). Members of candidate phyla with exceptionally small genomes, such as

Parcubacteria, have also been identified in hyperalkaline serpentinization-impacted waters

(Rempfert et al., 2017; Suzuki et al., 2017).

1.5 The Samail Ophiolite: an accessible venue to modern water-rock alteration

Ophiolites provide an accessible venue to study the interplay between serpentinization

and life in systems undergoing modern water-rock reactions (Mayhew et al., 2013; Miller et al.,

2016). Our field site, the Samail ophiolite, Sultanate of Oman, is the best exposed and largest

8 piece of mantle peridotite (~15,000 km3) emplaced on a continent, with much of it currently undergoing serpentinization and carbonation (Kelemen & Matter, 2008; Nicolas et al., 2000).

Boreholes drilled by both the Omani government and the Oman Drilling Project in the active alteration zones of the mantle peridotite section have facilitated the study of subsurface aquifer fluids impacted by serpentinization (Kelemen et al., 2020; Oman Drilling Project | Oman

Drilling Project, n.d.). Subsurface fracture waters have been geochemically characterized across several years with this expansive multi-borehole observatory (Kelemen et al., 2011; Kraus et al.,

2021; Miller et al., 2016; Neal & Stanger, 1983; Nothaft et al., 2020; Rempfert et al., 2017). In addition to the Type I or II fluid classification, Rempfert et al. (2017) further distinguishes fluid types in the Samail ophiolite by characterizing waters by the borehole mineralogy (peridotite, gabbro, transition) (Figure 1.2). Fluid mixing also occurs between types and meteoric inputs, generating varied geochemical gradients across the ophiolite.

Microorganisms in the Samail ophiolite aquifer waters have been observed over several years, vary by fluid type and pH, and include a diverse assemblage of organisms (Fones et al.,

2019; Kraus et al., 2021; Miller et al., 2016; Rempfert et al., 2017). Initial work detected high abundances of members of the genus Meiothermus, family Thermodesulfovibrionaceae,

Firmicutes, Proteobacteria, and Chloroflexi in hyperalkaline fluids (pH > 10) from wells

NSHQ14 and NSHQ04 (Miller et al., 2016; Rempfert et al., 2017). Taxonomy of recovered sequences suggests methanogenesis, methanotrophy, acetogenesis, fermentation, and H2 and CH4 oxidation is common within the ophiolite, and hyperalkaline fluids from peridotite zones also display lower microbial diversity than alkaline peridotite and gabbro-hosted wells (Rempfert et al., 2017). Lipid biomarkers collected from travertines, carbonate veins, and host rock in the mantle portion of the Samail ophiolite support past microbial activity of methanogenesis,

methanotrophy, nitrification, and sulfate reduction (Newman et al., 2020). Additionally, the

microbial CH4 cycle in the Samail ophiolite has received scrutiny for the unusual isotopic compositions and high concentrations of CH4 (Miller et al., 2016).

Figure. 1.2 Peridotite, contact, and gabbro-hosted wells intersect fluids with varied geochemistry in the Samail ophiolite. Adapted from Rempfert et al. (2017).

1.6 The use of high-throughput sequencing to study microbial life at extremes

The vast majority of microorganisms are not yet able to be cultured with standard techniques in laboratory settings due to difficulties in replicating diverse environmental conditions (Rappé & Giovannoni, 2003; Vartoukian et al., 2010). This inability to bring organisms into culture is amplified when conditions for habitability are not well-defined. Thus, organisms residing in the subsurface can be highly adapted to survive elevated temperatures and pressures that increase with depth, and these conditions combined with highly alkaline and

10 reduced fluid conditions of serpentinites are difficult to replicate and maintain in the laboratory.

The low biomass density within subsurface rock and fluid ecosystems further complicates cultivation-based experiments due to the risk of microbial contamination compromising cultivars or enrichments (Wilkins et al., 2014). These challenges high-light the need for cultivation- independent techniques to study subsurface biospheres.

High-throughput DNA and RNA sequencing of genomic material is one technique that has facilitated exploration of the uncultivatable diversity of microbial life (Daly et al., 2016,

2018; Pace, 1997; Rappé & Giovannoni, 2003). Comparisons of DNA and transcribed RNA recovered from sequencing of environmental samples can provide information on the identities and relative activity of microbes, respectively (Jia et al., 2019). With these techniques, metagenomes from environmental DNA can inform on metabolic functional capabilities, and metatranscriptomes reveal cellular metabolic activity at the step of DNA transcription to RNA.

Throughout this thesis, I use molecular methods to examine the identity, functional capability, and activity of endemic microorganisms within the anoxic, highly reduced, alkaline, and oligotrophic fluids and rocks of the Samail Ophiolite.

Polymerase chain reaction (PCR) amplicon surveys of small subunit ribosomal RNA genes (SSU rRNA) are a powerful and inexpensive tool for assessing microbial community diversity, structure, and abundance. The 16S rRNA gene is the most common phylogenetic target in modern environmental microbiology due to its (1) requirement for proper ribosome functionality so the gene is highly conserved across distantly related organisms, a so-called

‘central-housekeeping’ gene, and (2) it contains hypervariable regions wherein sequence mutations can be used to measure relatedness between organisms (Janda & Abbott, 2007). To cover the three domains of Bacteria, Archaea, and Eukarya as evenly as possible, the most

universal primer set developed by Parada et al. (2016) was used in this work. The 515F-Y M13

and 926R primers evenly amplify the V4 and V5 hypervariable regions of the 16S (for

Bacteria/Archaea) and 18S (for Eukarya) rRNA genes (Parada et al., 2016).

While amplicon surveys are invaluable to examine microbial distribution over larger

numbers of samples, metagenomics can provide much more resolved phylogenetic and

functional gene information to the genome level. Random “shotgun” metagenomic sequencing

can elucidate community-wide metabolic capabilities within a sample to target genetic functional

potential.

Metagenomic sequences from a community can also be binned to reconstruct individual

microbial genomes (called metagenome-assembled genomes or MAGs). Thus, metagenomic methods offer a broad picture of microbial community metabolic structures across variable environmental conditions as well as highly detailed, organism-specific information on metabolic capabilities. Akin to metagenomics, metatranscriptomic sequencing targets all RNA molecules contained within a sample at the time of its collection. However, instead of functional potential with metagenomics, sequencing of the RNA fraction captures a snapshot of microbial transcriptional activity that represents both present and active metabolisms occurring at the time of collection. Collectively, metagenomics and metatranscriptomics provide a detailed view into microbial metabolism when cultivation techniques are difficult.

1.7 Organization of thesis chapters

Within this thesis on microbial life within terrestrial serpentinites, I have 3 chapters and a

related appendix chapter. In Chapter 2, I characterize the microbial methane cycle within waters

of the Samail Ophiolite to understand methanogenic and methanotrophic activity across different

geochemical regimes in aquifers with low temperature serpentinization. Potential implications

for the methane production and isotopic composition are discussed. This chapter was published

with the title, “Molecular evidence for an active microbial methane cycle in subsurface

serpentinite-hosted groundwaters in the Samail Ophiolite, Oman” in “Applied and

Environmental Microbiology.”

Chapter 3 details the rock-hosted microbiome within serpentinites to examine possible

physical and geochemical controls on the distribution of microbial life in the deep subsurface.

The taxonomy of endolithic life, possible environmental controls on microbial distribution, and

comparisons to Samail aquifer planktonic life are discussed. The challenges of contamination

during sampling and processing of ultra-low biomass rock are also outlined as this contaminant

’noise’ can mask true signals of endemic life. Distinct endolithic and planktonic communities are observed through DNA sequencing. Microbial interactions within endolith communities and the potential importance of iron-bearing minerals for subsurface microbiota is outlined. This chapter is submitted to the journal Geobiology and is currently in review.

Chapter 4 discusses the microbial sulfur cycle within the Samail ophiolite by leveraging

metagenome assembled genomes and metatranscriptomes from the aquifer water communities.

Metabolism of sulfate, sulfide, and intermediate sulfur compounds such as sulfite, thiosulfate,

polysulfides, and elemental sulfur is characterized over the varied fluid types found in the Samail

ophiolite. In addition to an overview of the biological sulfur cycle across varied fluid

geochemistry, a detailed investigation of the pathways used by microorganisms to metabolize

sulfur is discussed. High quality metagenome-assembled genomes for several key alkaliphilic

lithotrophic sulfur cycling organisms are produced and analyzed, including genomes of

Thermodesulfovibrionales, Desulfonatronum, Desulforudis, Dethiobacteria, and

Rhodocyclaceae. This chapter is being prepared for submission as a manuscript to the journal

“Environmental Microbiology.”

Appendix D, entitled “Microscale biosignatures and abiotic mineral authigenesis in Little

Hot Creek, California,” and was published in Frontiers in Microbiology. This work stemmed

from a research project I participated in as part of the 2016 International Geobiology Course, an

intensive summer training program for graduate students to explore the developing field of

geobiology. This paper examined the abiotic and biotic influences on mineral formation and

alteration of calcite/silica structures in a hot spring system called Little Hot Creek. The role of

endolithic organisms and microbe-mineral interactions in biosignature generation is explored and

a model for the development of laminated mineral biosignatures from hot springs was developed.

Furthermore, the tools and techniques learned in the Geobiology Course have been highly applicable to the work of this thesis and my understanding of interactions between microbes and rock.

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CHAPTER 2

MOLECULAR EVIDENCE FOR AN ACTIVE MICROBIAL METHANE CYCLE IN

SUBSURFACE SERPENTINITE-HOSTED GROUNDWATERS IN THE SAMAIL

OPHIOLITE, OMAN

A paper published in Applied and Environmental Microbiology1

Emily A. Kraus2, Daniel Nothaft3, Blake W. Stamps4, Kaitlin R. Rempfert3, Eric T. Ellison3, Juerg M. Matter5, Alexis S. Templeton3, Eric S. Boyd6, and John R. Spear2

Abstract

Serpentinization can generate highly reduced fluids replete with hydrogen (H2) and methane (CH4), potent reductants capable of driving microbial methanogenesis and

1Kraus, E. A., Nothaft, D., Stamps, B. W., Rempfert, K. R., Ellison, E. T., Matter, J. M., Templeton, A. S., Boyd, E. S., & Spear, J. R. (2021). Molecular Evidence for an Active Microbial Methane Cycle in Subsurface Serpentinite-Hosted Groundwaters in the Samail Ophiolite, Oman. Applied and Environmental Microbiology, 87(2), 1–18. doi: 10.1128/aem.02068-20 2Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, Colorado, USA 3Department of Geological Sciences, University of Colorado, Boulder, Boulder, Colorado, USA 4UES Inc., Dayton, Ohio, USA 5Department of Ocean and Earth Science, University of Southampton-Waterfront, Southampton, UK 6Department of Microbiology and Immunology, Montana State University, Bozeman, Montana, USA

methanotrophy, respectively. However, CH4 in serpentinized waters is thought to be primarily abiogenic, raising key questions about the relative importance of methanogens and methanotrophs in the production and consumption of CH4 in these systems. Herein, we apply molecular approaches to examine the functional capability and activity of microbial CH4 cycling

in serpentinization-impacted subsurface waters intersecting multiple rock and water types within

the Samail Ophiolite of Oman. Abundant 16S rRNA genes and transcripts affiliated with the

methanogenic genus, Methanobacterium, were recovered from the most alkaline (pH > 10), H2-

and CH4-rich subsurface waters. Additionally, 16S rRNA genes and transcripts associated with

the aerobic methanotrophic genus, Methylococcus, were detected in wells that spanned varied

fluid geochemistry. Metagenomic sequencing yielded genes encoding homologs of proteins

involved in the hydrogenotrophic pathway of microbial CH4 production and in microbial CH4

oxidation. Transcripts of several key genes encoding methanogenesis/methanotrophy enzymes

were identified, predominantly in communities from the most hyperalkaline waters. These results

indicate active methanogenic and methanotrophic populations in waters with hyperalkaline pH in

the Samail Ophiolite thereby supporting a role for biological CH4 cycling in aquifers that

undergo low temperature serpentinization.

2.1 Importance

Serpentinization of ultramafic rock can generate conditions favorable for microbial

methane (CH4) cycling, including the abiotic production of H2 and possibly CH4. Systems of

low-temperature serpentinization are geobiological targets due to their potential to harbor

microbial life and ubiquity throughout Earth’s history. Biomass in fracture waters collected from

the Samail Ophiolite of Oman, a system undergoing modern serpentinization, yielded DNA and

RNA signatures indicative of active microbial methanogenesis and methanotrophy. Intriguingly,

transcripts for proteins involved in methanogenesis were most abundant in the most highly-

reacted waters that have hyperalkaline pH and elevated concentrations of H2 and CH4. These findings suggest active biological methane cycling in serpentinite-hosted aquifers, even under extreme conditions of high pH and carbon limitation. These observations underscore the potential for microbial activity to influence the isotopic composition of CH4 in these systems,

information that could help in identifying biosignatures of microbial activity on other planets.

2.2 Introduction

Life in deep subsurface environments is dependent on lithosphere-derived nutrients to

drive metabolism and biosynthesis (i.e., chemosynthesis). Water-rock interactions are one

potential source of nutrients that can be used by biological systems to generate chemical energy.

During the hydration of olivine and/or pyroxene in ultramafic rocks, the oxidation of ferrous iron

coupled to the reduction of water can generate molecular hydrogen (H2) through the geological

process of serpentinization (Schulte et al., 2006; Sleep et al., 2004). Elevated dissolved H2

− concentrations can drive the reduction of inorganic carbon (CO2) to generate formate (HCOO )

and carbon monoxide (CO) (Seewald et al., 2006), as well as methane (CH4) and additional light

hydrocarbons through abiotic reactions at low temperature (<100 ºC) (G. Etiope & Whiticar,

2019; Proskurowski et al., 2008). Serpentinization-impacted waters often have very low

oxidation-reduction potentials, have pH values of 8 to greater than 12, and can have nM to mM

concentrations of H2 and CH4 that can serve as electron donors to fuel microbial metabolism

(Brazelton et al., 2012; Canovas et al., 2017; Crespo-Medina et al., 2017; Kohl et al., 2016;

Miller et al., 2016; Rempfert et al., 2017; Suzuki et al., 2017). Zones of active, low-temperature

25 serpentinization exist beneath the water table within ophiolites, portions of oceanic crust and upper mantle that have been tectonically emplaced onto a continent. Ophiolites, such as the

Samail Ophiolite in the Sultanate of Oman, provide an accessible venue to study the subsurface biosphere in bedrock environments undergoing serpentinization (Mayhew et al., 2013; Miller et al., 2016).

Current data suggests CH4 in ophiolites is generated abiotically at low temperatures or is primarily relict from early high temperature water/rock reactions that trapped fluids and gases in fluid inclusions, which are later released during weathering (G. Etiope & Whiticar, 2019;

Grozeva et al., 2020). The abiotic sources of CH4 in these systems are inferred by studies of

13 stable isotope compositions showing CH4 enriched in C (G. Etiope & Whiticar, 2019).

Alternatively, several types of microorganisms can produce CH4, including methanogenic

Archaea that can generate CH4 from a variety of substrates, including H2/CO2, CO, formate, a variety of methylated substrates, and acetate (Thauer et al., 2010), many of which have been detected in waters that have been subjected to serpentinization (Brazelton et al., 2006, 2017;

Crespo-Medina et al., 2017; Miller et al., 2016; Tiago & Veríssimo, 2013). Although methanogens differ in their substrate use, all require the methyl-coenzyme M reductase (MCR) enzyme complex (encoded by mcrABG) for the terminal step of methanogenesis. Some H2- dependent methanogens can also use formate as a source of electrons to reduce CO2 instead of

H2 via the activity of formate dehydrogenase (encoded by fdhAB) (Shuber et al., 1986).

Conversely, anaerobic methanotrophs oxidize CH4 with a variety of terminal electron acceptors

2− − − including sulfate (SO4 ), nitrate (NO3 ), nitrite (NO2 ), and metals, likely via the reverse methanogenesis pathway (Cui et al., 2015). In addition to anaerobes, aerobic methanotrophs catalyze the oxidation of CH4 (to methanol) using the particulate or soluble methane

monooxygenase enzymes, encoded by the pmoABC and mmoXYZ genes, respectively (Csáki et

al., 2003; Stainthorpe et al., 1990). In the second step of CH4 oxidation, methanol

dehydrogenases (MDH) oxidize methanol to formaldehyde. The mxaF gene encodes for the large subunit of the NAD-independent MDH known to proteobacterial methanotrophs (Lau et al., 2013). These genes, therefore, can serve as key putative markers of methanogenesis and methanotrophy in natural systems. The presence of genes that encode for [NiFe]-hydrogenases and carbon cycling processes can provide further insight into the specific electron donors capable of fueling these cells in an environment.

Several environments impacted by the process of serpentinization show evidence of microbial methanogenesis and methanotrophy. For example, the detection of key genes required for methanogenesis and/or methanotrophy from the Voltri Massif (Italy), the Samail Ophiolite

(Oman), and the Santa Elena Ophiolite (Costa Rica) suggests that these processes are active in these system (Brazelton et al., 2017; Crespo-Medina et al., 2017; Fones et al., 2019) . The case for the presence of these organisms in ophiolites is bolstered by detection of 16S ribosomal RNA genes affiliated with known CH4 cycling organisms (Crespo-Medina et al., 2017; Miller et al.,

2016; Rempfert et al., 2017; Sánchez-Murillo et al., 2014). Methanotrophic ANME-1 archaea

have been detected via high-throughput sequencing methods in the Voltri Massif and Cabeço de

Vide aquifers of Italy and Portugal (Brazelton et al., 2017; Tiago & Veríssimo, 2013). Further,

the composition and 13C enrichment of archaeal lipids from the Chimaera ophiolite of Turkey

provides evidence of archaeal methanogenesis under inorganic carbon limitation at that site

(Zwicker et al., 2018). Organisms collected from the Samail Ophiolite and the Cedars

14 13 (California) show CH4 production when amended with C- or C-labeled substrates in activity

assays, respectively (Fones et al., 2019; Kohl et al., 2016), and the incubations conducted with

organisms from the Samail Ophiolite also show labeled substrate assimilation into biomass

(Fones et al., 2019). Additionally, an enrichment culture of a methanogen of the genus

Methanobacterium grown from alkaline waters of the Samail ophiolite was active over a pH

− range of 6.9 – 10.1 and showed an ability to use HCO3 and CaCO3 as a C source (Miller et al.,

2018). Yet, other sites, including the Coast Range Ophiolite Microbial Observatory (CROMO)

(California) and the Tablelands ophiolite (Newfoundland, Canada), show no evidence of

microbial methanogenesis, suggesting the presence of unknown factors that limit the distribution

of CH4 metabolisms at these sites (Morrill et al., 2014; Twing et al., 2017). Therefore, while

incubation and cultivation studies show microbial activity when amended with substrate, they

may not be representative of activity in the modern subsurface of ophiolites. Consequently, the

environmental conditions conducive to methanogenic activity requires further investigation.

The Samail Ophiolite is the largest (approximately 15,000 km3) and best exposed on

Earth, with zones in the mantle peridotite section currently undergoing serpentinization largely

below 60 ºC (Kelemen et al., 2011; Kelemen & Matter, 2008; Miller et al., 2016; C. Neal &

Stanger, 1983; Nicolas et al., 2000; Paukert et al., 2012). In 1983 – 1985 and 2004 – 2005, the

Sultanate of Oman drilled several wells into the ophiolite, making it an accessible location to sample subsurface fracture waters and investigate the microbial contribution to CH4 cycling in a

low-temperature continental serpentinizing environment. Previous work has detected µM to mM

concentrations of dissolved H2 and CH4 in aquifer waters, with the CH4 in hyperalkaline waters

displaying unusually high 13C values (up to +3‰ VPDB) that do not fall within typical ranges

of microbial CH4 (Giuseppe Etiope, 2017; Miller et al., 2016, 2018; C. Neal & Stanger, 1983).

This suggests either an abiotic origin for CH4 or extensive biological production and/or

13 consumption of CH4 that is already enriched in  C. Here, we apply genomic and transcriptomic

sequencing approaches to biomass collected from these same sites to better define the

distribution and putative activity of microbial methanogens and methanotrophs within the Samail

Ophiolite.

2.3 Materials and Methods

2.3.1 Site Description and Geochemical Characterization of Subsurface Waters

Five pre-existing water wells drilled in the mantle section of the Samail ophiolite by the

Oman Ministry of Regional Municipalities and Water Resources were sampled in February of

2017. Well waters were collected for geochemistry and cellular biomass from borehole NSHQ14 at 50m (NSHQ14B) and 85m (NSHQ14C), and boreholes WAB188, WAB71, and WAB55 using a Grundfos SQ2-85 submersible pump (Grundfos Pumps Corp., Denmark, Netherlands) and a splitting manifold with field-washed Tygon tubing. Borehole NSHQ04 was sampled with a small Typhoon® pump (Proactive Env. Products, Bradenton, FL).

At each well, the pump, manifold, tubing, and filter housing were field washed by running the pump for 20-30 minutes (approximately ≥ 100L throughput). Well waters were then passed through a 0.22µm polycarbonate filter (MilliporeSigma, Burlington, MA) and collected in

15 ml Falcon™ tubes (Corning Inc., Corning, NY) for analyses of anion and cation concentrations, with the latter acidified with nitric acid (for a solution pH < 2) in the field at the time of collection. Cations and anions were quantified using inductively coupled plasma atomic emission spectroscopy (ICP-AES; Optima 5300, Perkin-Elmer, Fremont, CA) and ion chromatography (IC; ICS-90, Dionex, Sunnyvale, CA), respectively, at the Colorado School of

Mines. For DIC analyses, 6 ml aliquots of water were transferred from the sample collection vials (blue butyl-stoppered borosilicate glass) to 12.0 ml helium purged Labco Exetainer® tubes.

To convert DIC species to CO2 for analysis, 0.5 ml of boiled 85% H3PO4 were added to the

samples while still hot. Standards were made by weighing out CaCO3 in varying amounts to

Exetainer® tubes, which were subsequently flushed with He and injected with 6 ml of boiled

MilliQ water while still hot.

Acidification was performed at the same time and using the same methods for standards and samples. Standards and samples were centrifuged and then mixed on a shaker table for 12-18 hours to homogenize and equilibrate CO2. Headspace CO2 was then introduced via a Thermo

Fisher GasBench II to a Thermo Delta V Plus isotope ratio mass spectrometer for analysis.

Gas sampling was conducted using the bubble strip method (modified from (Kampbell et al., 1998)). Details on bubble strip gas sampling are available at http://dx.doi.org/10.17504/protocols.io.2x5gfq6. Gas concentrations were measured using an SRI

8610C gas chromatograph (GC) with N2 as the carrier gas. H2, CO, CH4, and CO2 were separated with a 2 m by 1 mm ID micropacked ShinCarbon ST column. Peak intensities were measured concurrently using a thermal conductivity detector (TCD) and a flame ionization detector (FID) and calibrated with standard gas mixes (Supelco Analytical, Bellefonte, PA, USA, accuracy ±2

%). Measurement repeatability expressed as relative standard deviation is 5 % over most of the calibrated range. We define the limit of quantitation as the signal at which the relative standard deviation increases to 20 %.

Figure 2.1. Geological map of a portion of the Samail Ophiolite (after Nicolas et al., 2000) showing sampling locations in the Wadi Tayin massif (adapted from Nothaft et al., 2020) (Nothaft et al., 2020).

2.3.2 Biomass Collection, DNA/RNA Extraction, Quantification, and SSU rRNA Gene and Transcript Sequencing.

Biomass was collected onto 0.22µm polycarbonate filters. Once filters began to clog or appeared to hold particulates, they were removed from the housing and suspended in bead tubes with DNA/RNA Shield Lysis/Stabilization Solution (Zymo Research, Inc., Irvine, CA), which stabilizes nucleic acids at room temperature over several weeks. Samples were shipped to the

Colorado School of Mines where cells were lysed by bead beating for a total of 5 minutes with rests (in intervals of 1 min. lysis, 1 min. rest) to cool the sample tubes to prevent RNA degradation. DNA and RNA were extracted in parallel using the Zymo Research Microbiomics

Soil/Fecal DNA MiniPrep Extraction kit (Zymo Research, Inc.) following the manufacturer’s protocol. DNA was quantified post-extraction by QubitTM dsDNA HS assay (ThermoFisher

Scientific, Waltham, MA). Recovered DNA and RNA were stored at -80ºC.

A portion of the extracted RNA from each sample was converted to complementary DNA

(cDNA) via reverse-transcription polymerase chain reaction (RT-PCR) with the qScript™ XLT

One-Step RT-PCR Kit (Quanta Biosciences, Beverly, MA). Each 25µl PCR reaction contained

One-Step HiFi PCR ToughMix (1x concentration), One-Step RT master mix (1x), 200nM of the forward primer and 200nM of the reverse primer (described below), nuclease-free water, and

10µl of RNA sample template. A reaction in which no One-Step RT master mix was added served as a negative control for the activity of the reverse transcriptase to ensure no extraneous

DNA was being amplified. Reactions were run in a thermocycler with the lid pre-heated to 105

ºC, two initial steps of 48 ºC for 20 minutes and 94 ºC for 3 minutes, followed by 30 cycles of 94

ºC for 45 seconds, 50 ºC for 45 seconds, and 68 ºC for 90 seconds, ending with a final extension of 68 ºC for 5 minutes and a hold at 4 ºC until removal from the thermocycler.

SSU rRNA genes were amplified from each DNA and cDNA sample via PCR with primers that span the V4 and V5 hypervariable regions of the 16S rRNA to produce gene fragments of ~400 bp and ~600 bp for Bacteria/Archaea and Eukarya, respectively. The 515-Y

M13 and 926R primer set (modified from (Parada et al., 2016)) most evenly amplifies this region of SSU rRNA from all three domains of life. The primers and PCR conditions used in this study are described previously (Kraus et al., 2018). Technical replicate reactions for each sample, five extraction negative controls, and three negative PCR controls (no sample added) were amplified as well. Technical replicates were pooled and purified using Kapa Pure Beads (Kapa Biosystems,

Wilmington, MA) at a 1.0x ratio of beads to sample volume to retain any fragments ≥ 250bp in length. Barcoding of sequences was carried out on the purified PCR products using a limited 6- cycle PCR (Kraus et al., 2018). Replicate barcode reactions were pooled and purified with Kapa

Beads before quantification with the QubitTM dsDNA HS assay. Final products were pooled in equimolar amounts before being concentrated to a final volume of 80µl on an Ultracel-30K membrane (Millipore Sigma, Billerica, MA) within an Amicon Ultra 0.5 ml centrifugal filter

(Millipore Sigma). Extraction blanks (no sample added) and negative PCR control reactions (no template added) were included in this sequenced pool. The prepared DNA/cDNA library was sequenced on an Illumina MiSeq (Illumina Inc., San Diego, CA) at the Duke Center for Genomic and Computational Biology (https://www.genome.duke.edu) using V2 PE250 chemistry.

Sequences produced from this effort are available on the Short Read Archive (NCBI) database under accession PRJNA560313.

Resultant FASTQ sequence files were demultiplexed and trimmed with Cutadapt

(Martin, 2011). Reads were filtered by error rates, amplicon sequence variants (ASVs) were identified, and read pairs merged to construct a sequence table with DADA2 in R (Callahan et

al., 2016; R Core Team, 2013). Chimeric sequences were removed before taxonomic assignment

against the SILVA r138 database (Yilmaz et al., 2014). The protocol and resultant files for this

effort are available at https://github.com/danote/Samail_16S_compilation as “OM17”. The

phyloseq and ggplot2 software packages were used in analysis and visualization of the sequence

table (McMurdie & Holmes, 2013; Wickham, 2016).

2.3.3 Metagenomic and Metatranscriptomic Library Preparation and Sequencing

Metagenomic and metatranscriptomic libraries were prepared from six DNA samples from the five wells examined: WAB55, WAB188, WAB71, NSHQ04, and NSHQ14, with separate libraries made for two different depths (50 m and 85m) of NSHQ14. Metagenomic library preparation was conducted using the NexteraXT library preparation kit (Illumina Inc.) according to manufacturer’s instructions with 1 ng template DNA as input. Metatranscriptomic libraries were generated by first incubating 10µl of RNA templates in a reaction mix of 12.5µl qScriptTM XLT One-Step RT-qPCR ToughMix (1x final concentration) (QIAGEN, Beverly

MA), 200nM of random hexamer primers, 1µl of 25x qScript XLT One-Step reverse transcriptase (RT), and 1.25µl of nuclease free water. A RT-negative control was run with the same components but an additional 1µl of nuclease-free water was added instead of the RT enzyme. Technical replicate reactions of 25ul were produced for each sample and RT-negative control and pooled after reverse transcription and amplification in separate thermocyclers with pre-heated bonnets. The reactions underwent 20 minutes at 48 ºC, 3 minutes at 94 ºC, followed by 30 cycles of 94 ºC for 45 seconds, 50 ºC for 45 seconds, and 68 ºC for 90 seconds, ending with a final extension of 5 minutes at 68 ºC and a short 4 ºC hold. Library amplification and fragment size distribution were confirmed on an Agilent 2100 Bioanalyzer with the Agilent

DNA 7500 assay (Agilent Technologies, Santa Clara CA) for all libraries. Libraries were then

pooled at an equimolar ratio and sequenced on an Illumina HiSeq 2500 using V2 PE250 Rapid

Run chemistry at the Duke Center for Genomic and Computational Biology. Metagenomic

sequences are available in the MG-RAST database under accession numbers mgm4795805.3 –

mgm4795809.3 and mgm4795811.3.

Raw metagenomic sequence read adapters were removed using PEAT (Y. L. Li et al.,

2015) and individual samples were assembled using MEGAHIT (D. Li et al., 2015) with a

maximum kmer of 141. This work used the Extreme Science and Engineering Discovery

Environment (XSEDE) (Towns et al., 2014) resource Comet at the San Diego Supercomputer

Center through allocation TG-BIO180010. Protein coding regions were identified and annotated

with Prokka v1.12 and Prodigal v.2.6.3 with an e-value threshold of 1 × 10-6 (Hyatt et al., 2010;

Seemann, 2014) and the output translated protein files were also annotated with GHOSTX

(Suzuki et al., 2014) to search for homologs involved in various methanogenesis and methanotrophic pathways (described in detail below). Protein sequences with homology to those involved in methanogenesis and methanotrophic pathways were subjected to reciprocal BLASTp analysis (Altschul et al., 1997) to check annotation accuracy and homology to known

methanogens/methanotrophs. Homology was determined if the query amino acid sequence was

>30% identical over >50% of its length, with an e-value below 1 × 10-6. Homolog counts were normalized by exon length and sequencing depth to fragments per kilobase of exon per million reads (FPKM) to ensure adequate coverage indicating a non-contaminant.

Metatranscriptomic sequences were trimmed and de novo co-assembled into transcripts

with Trinity v2.8.6 (Bolger et al., 2014; Grabherr et al., 2011; Haas et al., 2013). Sequence reads

from each sample were aligned to the assembled transcripts with RSEM and counted to generate

an expression matrix (B. Li & Dewey, 2011). Coding regions of transcripts were identified with

Transdecoder and annotated with Trinotate against the NCBI and SwissProt databases and

against the Pfam database using HMMER v3.3 (hmmer.org) using default parameters and an e-

value threshold of 1 × 10-6. (Agarwala et al., 2018; Bryant et al., 2017; Haas et al., 2013; The

UniProt Consortium, 2019). Top hits were used as transcript identities. Exploratory analyses of

transcript expression used R with the “edgeR” and “limma” packages (R Core Team, 2013;

Ritchie et al., 2015; M. Robinson et al., 2010). Transcript counts were normalized by the

trimmed-mean of M-values method (TMM). Transcripts with less than or equal to 10 counts per

million (CPM) were removed and transcript counts were renormalized by the TMM method for

comparison of counts across samples (M. D. Robinson & Oshlack, 2010). Metatranscriptomic

sequences are available at the Short Read Archive (NCBI) database under accession

PRJNA560313 (SRR11431188 – SRR11431193).

Metagenome and metatranscriptomes were queried for key genes associated with

methanogenesis and methanotrophic pathways including genes for methyl coenzyme M

reductases (mcrABGCD, mcrIIABGCD) (Reeve et al., 1997), the subunits of [NiFe]- hydrogenases (Thauer et al., 2010), formate dehydrogenase (fdhAB) (Shuber et al., 1986), acetate kinase and phosphate acetyltransferase (ackA, pta) for acetoclastic methanogenesis (Fournier &

Gogarten, 2008), methylotrophic methanogenesis genes (mtaA, mtbB, mtmB, mttB, mtsA)

(Crespo-Medina et al., 2017; Paul et al., 2000), particulate and soluble methane monooxygenases

(pmoABC, mmoXYZ, respectively) (Csáki et al., 2003; Stainthorpe et al., 1990), and the large

subunit of methanol dehydrogenase (mxaF) (Lau et al., 2013). Transcripts of genes potentially

involved in producing alternative inorganic carbon sources for methanogens including carbonic

anhydrases (can, cynT, cah) (Smith et al., 1999) and carbon monoxide dehydrogenases (cooS, cdhAB, coxL, cutL) (Fones et al., 2019) were also examined.

2.4 Results

2.4.1 Geochemical Characterization of Subsurface Fracture Waters

Fracture waters from five pre-existing wells in the Samail Ophiolite, Oman (Figure 2.1), were sampled in February of 2017 for planktonic biomass for use in geochemical (Table 1) and DNA- and RNA-based-analyses. Well NSHQ14 was sampled at a depth of 50m (NSHQ14B) and 85m

(NSHQ14C). Waters recovered from wells drilled in peridotite bedrock (NSHQ14 and WAB71) had hyperalkaline pH (pH > 10), with waters from NSHQ14C at 85 meters depth exhibiting the highest measured pH (11.3) and H2 concentration (253 µM) of any of the sampled wells. The waters recovered from wells drilled near the “contact” or subsurface faulted boundary between gabbro and peridotite bedrock (NSHQ04, WAB55) had alkaline pH, with values of 10 for

NSHQ04 and 9.2 for WAB55. The pH of gabbro-hosted well WAB188 was not measured in

2017 but previous observations recorded values of 8.7 and 7.6 in 2015 and 2016, respectively

(Rempfert et al., 2017).

CH4 was detected in every well, with the highest concentration (483 µM) measured in contact well NSHQ04. Dissolved inorganic carbon (DIC) was detected in low (<0.2 mM) concentrations in the hyperalkaline wells and in greater concentrations (up to 3 mM) in WAB188 and WAB55. Peridotite wells had lower concentrations of potential electron acceptors (e.g.,

2− − − SO4 , NO3 , NO2 ) relative to the contact wells (Table 1). Trace metal and non-metal elemental

concentrations for all wells are in Table S1.

Table 2.1. Geochemical composition of waters sampled from wells that intersect peridotites or that lie at the boundary of peridotites and gabbros in the Samail Ophiolite in 2017. For comparison, data is presented on the geochemical composition of a single sample of rainwater collected from the Samail Ophiolite in 2017. ∑ indicates the value is the sum of all species of an element. Values below the limit of quantification (BLOQ) are indicated and dashes (-) indicate 2- that measurements were not taken. Concentrations of PO4 was below the limit of quantification in all samples. The pH of NSHQ04 was taken with a colorimetric indicator strip. Measurement of the pH of WAB188 was not possible in 2017 and previous measurements recorded in 2015 and 2016 were 8.7 and 7.6, respectively (Rempfert et al., 2017).

NSHQ14 WAB71 NSHQ04 WAB55 WAB188 Rain LOQ Well type Peridotite Peridotite Contact Contact Contact

Pump 50 85 70 5.8 30 78 depth (m)

pH 11.05 11.28 10.59 10 9.22 NA -

Temp. °C 34.4 36.3 - - - - -

Eh (mV) −415 −253 −133 - - 214 -

H2 (µM) 32.5 253 0.59 BLOQ BLOQ 0.99 - 0.05

CH4 (µM) 53.5 106 14.8 483 0.106 1.83 - 0.015

DIC (mM) 0.05 0.13 0.12 0.04 2.90 3.00 - 0.02

CO (µM) BLOQ BLOQ BLOQ BLOQ BLOQ BLOQ - 0.28

∑ Na (mM) 8.35 10.21 4.95 10.40 4.12 3.49 0.25 5.85x10−3

∑ Ca (mM) 3.66 4.34 4.07 7.79 0.05 1.33 0.52 1.6x10−4

∑ Mg(mM) 0.01 0.02 BLOQ 0.02 2.75 1.44 0.15 5.9x10−5

∑ Mn (µM) 7.83x10−3 3.3x10−2 BLOQ 0.03 0.02 0.14 1.0x10−4 7.38x10−4

∑ Al (mM) BLOQ 2.0x10−3 1.8x10−3 2.0x10−3 BLOQ BLOQ 1.0x10−3 7.6x10−4

−4 −3 −4 −4 −3 −4 −4 −6 ∑ Fe (mM) 1.5x10 1.8x10 1.6x10 8.2x10 2.5x10 3.8 x10 3.6x10 6x10

−3 −3 −2 −2 −3 −4 ∑ Si (mM) 8x10 6x10 2.1x10 3.6x10 3 x10 0.37 0.08 4x10

∑ K (mM) 0.20 0.25 0.25 0.29 0.21 3.9x10−2 7.1x10−2 8.3x10−4

+ NH4 (µM) 14.2 13.0 100.0 55.5 BLOQ BLOQ - 1.0

2- −3 −3 SO4 (mM) 0.13 2x10 0.04 0.68 0.88 1.13 0.17 1.04x10

- −3 NO3 (mM) BLOQ BLOQ BLOQ BLOQ 0.14 0.12 0.26 1.61x10

Cl- (mM) 14.28 16.20 11.59 BLOQ 7.24 5.04 0.40 2.82x10−3

Br- (mM) 0.02 2.5x10−2 1.2x10−2 2.7 x10−2 5 x10−3 2 x10−3 BLOQ 1.79x10−4

Previous studies grouped the waters in each of the wells sampled herein as either Type I,

Type II, or crust/mantle “contact” waters based on water geochemistry, specifically water pH

and concentrations of Ca and Mg (Barnes & O’Neil, 1969; Chavagnac et al., 2013; Drever,

1985; Kelemen & Matter, 2008; Colin Neal & Stanger, 1985; Paukert et al., 2012). In agreement

with prior classifications, NSHQ14, WAB71, and NSHQ04 were dominated by Ca/OH− waters

typical of closed-system serpentinization (termed Type II waters), WAB55 intersects

− Mg/HCO3 waters more typical of open-system serpentinization (termed Type I waters), and

gabbro-hosted WAB188 contains waters typical of a contact well near the crust/mantle

boundary.

2.4.2 Diversity of 16S rRNA Genes and Transcripts in Subsurface Fracture Waters

Biomass was concentrated from subsurface Type I and II waters by filtration (0.22 µm)

and processed for DNA and RNA; the latter of which was converted to cDNA. The V4/5

hypervariable region of both 16S rRNA genes and their transcripts (cDNA) were amplified,

sequenced and clustered into amplicon sequence variants (ASVs). An overview of the most

abundant ASVs from each well (Figure S1), sequencing metrics (Table S2), and rarefaction

curves of observed species richness (Figure S2) are in the supplemental information. DNA/RNA

extraction, PCR, and the RT-negative controls produced low numbers of sequence reads and did

not resemble sampled well community 16S rRNA gene compositions (Figure S3). Eukaryotic

18S rRNA sequence counts were low (0.11% of all sequences from all wells) and these

sequences were deemed contaminants in this investigation due to the low read counts and

compositional similarity to laboratory controls.

Numerous sequences were detected with close affiliation to known CH4-producing and

consuming organisms. Based on homology to cultivars, the most abundant sequences

corresponding to methanogenic taxa were those affiliated with the genus Methanobacterium, a methanogen within the Euryarchaeota phylum (David R. Boone, 2015). This Methanobacterium

16S rRNA gene ASV was one of the most abundant sequences detected in both Type I and Type

II well waters (Figure S1). Other lesser abundant ASVs showed homology to other characterized methanogenic archaea. The most abundant sequences related to methanotrophs were those showing homology to Methylococcus, an aerobic methanotroph within the Gammaproteobacteria

(Bowman, 2015). Less abundant ASVs affiliated with putative anaerobic CH4 oxidizing taxa were also detected in well water communities including ANME-1 (Cui et al., 2015) and

Candidatus Methylomirabilis of the NC10 group (Ettwig et al., 2010).

Stark differences in ASV abundance were apparent between the DNA and cDNA fractions within samples. The mean abundance of 16S rRNA genes (DNA) and transcripts

(cDNA) of two taxa putatively involved in CH4 cycling is shown in Figure 2.2. The mean percentage of sequences of ASVs affiliated with the most abundant methanogen,

Methanobacterium, was greater in the cDNA fraction for NSHQ14 (Figure 2.2A). At the 85m depth for NSHQ14, 47.1% of the cDNA reads were attributed to Methanobacterium compared to

13.3% of the DNA reads. Similarly, Methanobacterium constituted 34.5% of the cDNA and

10.7% of the DNA reads at the 50m depth. The proportion of Methanobacterium reads in

NSHQ14 varied more widely in the cDNA (19.9 – 62.9%) than in the DNA (6.9 – 18.6 %)

(Figure 2.2B). In contrast to NSHQ14, the Methanobacterium ASVs represented less than 5% of the reads in any sample from other wells. Similarly, the Methylococcus affiliated ASV comprised a greater mean percentage of the cDNA (6.6 %, 2.8 %) when compared to the DNA (1.0 %, 0.4

%) in NSHQ14 at 50m and 85m. In hyperalkaline contact well NSHQ04, Methylococcus was

41.4% of the cDNA and 17.4% of the DNA. The anaerobic methanotroph affiliated ASVs

(ANME-1, C. Methylomirabilis) had low read abundances (< 0.5%) in both the DNA fractions in hyperalkaline peridotite water samples.

2.4.3 Metagenomic Characterization of Subsurface Fracture Water Communities

Key genes encoding proteins involved in methanogenesis were detected in assembled metagenomic sequences from the Type II hyperalkaline waters of the peridotite-hosted wells

NSHQ14 and WAB71 (Figure 2.3A). Metagenomic assemblies from both depth intervals at

NSHQ14 harbored genes coding for MCR (mcrABCDG) and tetrahydromethanopterin S- methyltransferase (MTR; mtrABCDEFGH) operons. The MCR and MTR homologs were co- localized on the same contigs recovered from both 50m and 85m depth intervals in NSHQ14 and the McrA sequences were most closely affiliated with McrA from a cultivated

Methanobacterium sp. (GenBank: TMS43336.1). Genes for various [NiFe]-hydrogenases involved in supplying reductant or balancing osmotic potential in methanogens (Thauer et al.,

2010) were also identified. These include active site subunits of membrane-bound, ion translocating [NiFe]-hydrogenases (mbhJL), putative bifurcating hydrogenases (mvhADG), and

F420 or cytochrome reducing hydrogenases (frhABG, vhcD, respectively) (Peters et al., 2015).

Like MCR and MTR, these homologs were detected in assemblies from both depths of NSHQ14 and showed close homology to various cultivated Methanobacterium sp. CH4 cycling genes identified in metagenomes with homology to proteins from Methanobacterium sp. (with an e- value of < 1 x 10-6, > 30% amino acid identity over > 50% of the length) are shown in Figure

2.3B.

Figure 2.2. (A) Mean relative abundance of the most abundant taxa putatively involved in CH4 production and consumption in the cDNA and DNA of biomass collected from well waters of the Samail Ophiolite. (B) Percentage of the top putative methanogenic and methanotrophic organisms inferred by SSU rRNA sequence abundance of ASVs with homology to Methanobacterium (red) and Methylococcus (blue). Each point represents a biological replicate.

In metagenomic assemblies from the peridotite-hosted well WAB71, only an amino acid

sequence for mcrG was detected and it exhibited homology to mcrG of a Methanophagales sp. of the ANME-1 group (Genbank: RZN33282.1). Among the communities in Type I well waters, those from WAB188 showed the greatest capability for hydrogenotrophic methanogenesis

(Figure 2.3A). The operons encoding MCR (mcrABCDG) and MTR (mtrABCDEFGH) were co- localized on a single contig, while the MCR II (mrtBDGA) operon was found on a separate contig, and homologs of [NiFe]-hydrogenases (mvhDG, frhABG, vhcG, and mbhJL) were detected. Homologs of each protein were closely related to cultivated Methanobacterium sp.

(mcrA: Genbank WP_048081846.1, mrtA: Genbank AXV36901.1). In comparison, metagenomic assemblies from WAB55 (Type I) were found to only contain homologs of mcrCG

(related to Methanobacterium, Genbank WP_048081846.1) and assemblies from NSHQ04 only encoded mtbB homologs most closely related to halophilic methanogenic taxa

(Methanonatronarchaeum thermophilium, Genbank OUJ19070; and Methanohalophilus sp.,

Genbank OBZ35607.1). These findings point to methanogens being less abundant in WAB55

and NSHQ04, as compared to WAB188 and NSHQ14, thereby leading to incomplete

representation of pathways involved in their energy metabolism in metagenomic assemblies.

Homologs of fdhAB encoding the subunits of the formate dehydrogenase enzyme were

detected in all well metagenomes, with some sequences homologous to Methanobacterium

strains (Figure 2.3). Homologs of carbonic anhydrase (CA) genes (can, cynT, cah) were detected

in all metagenomes. Most CA sequences were identified most closely to non-

methanogenic/methanotrophic organisms but CA sequences from NSHQ14 and WAB188 were

found to be most closely related to methanogens and methanotrophs, including

Methanobacterium and Methylococcus strains.

Figure 2.3. (A): Fragments per kilobase of exon per million reads (FPKM) of key functional genes of interest for CH4 cycling metabolisms. The methyl-coenzyme M I (mcrABGCD), methyl-coenzyme M II (mrtABGD), formate dehydrogenase(fdhAB), carbonic anhydrase (CA), particulate methane monooxygenase (pmoABC), and methanogenic [NiFe]-hydrogenases (frh, mvh, mbh, vhc) enzymes from assembled metagenomes are shown. Notably, FPKM values are comparable within each sample (shown by color) but not across samples. Wells are ordered by decreasing fluid pH. (B) FPKM of CH4 cycling genes that are homologous to proteins from Methanobacterium sp. (e-value of < 1 x 10-6, > 30% amino acid identity over > 50% of the length).

Homologs of protein coding genes associated with methanotrophy were detected in metagenomic sequences, consistent with 16S rRNA gene and transcript data suggesting the potential importance of methanotrophy in the Samail Ophiolite. Homologs of pmoABC were detected in every well (Figure 2.3A), often with one or more slightly divergent copies. These homologs were most closely related to those identified in previously characterized aerobic

Methylococcus sp. Homologs of protein coding genes affiliated with soluble methane monooxygenases (mmoXYZ) or of the large subunit of methanol dehydrogenase (mxaF) were not detected in any metagenome from the subsurface water communities.

2.4.4 Metatranscriptomic Characterization of Subsurface Fracture Water Communities

Transcripts of genes involved in various CH4-cycling processes were detected in subsurface waters from the Samail ophiolite. Transcript abundances were normalized to counts per million reads (CPM) and lowly expressed transcripts (<1 CPM in all samples) were removed as possible contaminants. The results for all transcripts investigated are in Table 2.

The abundance of transcripts in CPM for MCR (mcrABG), [NiFe]-hydrogenases

(mvhADG, frhABG, vhcADG, vhuADGU, mbhJL), pmoABC, CA, and fdhAB are shown in

Figure 2.4. MCR was expressed in NSHQ14, WAB188, and WAB71. NSHQ14 extracts contained the greatest expression of MCR transcripts, with 510.6 CPM at the 50m depth interval.

To further evaluate the energy metabolism of putative methanogens in the Samail Ophiolite, the abundance of transcripts affiliated with [NiFe]-hydrogenases was examined. After filtering out transcripts with low normalized expression (<1 CPM), homologs of [NiFe]-hydrogenases common to characterized methanogens were detected primarily in NSHQ14C and NSHQ14B with CPM of 21.7 and 16.1, respectively (Table 2, Figure 2.4). No transcription of energy-

converting [NiFe]-hydrogenases was detected. Transcripts for formate dehydrogenases (fdhAB) were most abundant in NSHQ04 (103.7 CPM) and NSHQ14C (55 CPM). Transcripts for fdhAB and for [NiFe]-hydrogenases were not highly expressed relative to MCR in NSHQ14.

The carbon monoxide dehydrogenase (CODH) and carbonic anhydrase (CA) enzymes could generate inorganic carbon from CO or carbonates as another source of CO2 for H2- dependent methanogens and autotrophs under carbon limitation in serpentinizing environments.

Transcripts affiliated with Ni-containing CODH and Mo-containing CODH homologs were detected throughout the ophiolite waters. NSHQ14C contained the largest CPM of both Ni-

CODH and Mo-CODH with 32.8 and 29.7 CPM, respectively. CA transcripts were similarly observed across all well extracts, with the greatest expression at the 85m depth interval (757

CPM) and 50m depth interval (363 CPM) of NSHQ14.

Aligned with the presence of genes encoding the three subunits of particulate methane monooxygenases (pmoABC) in metagenomic assemblies from all wells examined, transcripts for

pmoABC were detected in extracts from all wells with the greatest expression in NSHQ04

(1,650.9 CPM) and NSHQ14C (72.2 CPM). Transcripts for mxaF were similarly expressed with

513.1 CPM in NSHQ04 and 18.3 CPM in NSHQ14C. No transcripts of soluble CH4

monooxygenase subunit genes (mmoXYZ) were detected in transcriptomes from any well.

Figure 2.4: Transcript counts per million reads (CPM) for genes of interest in CH4-cycling normalized by the TMM method. Reads with homology to transcripts for methyl coenzyme M reductase (mcrABGCD) and the co-localized tetrahydromethanopterin S-methyltransferase subunits (mtrACDEH), particulate CH4 monooxygenase (pmoABC), formate dehydrogenase alpha subunit (fdhA), and carbonic anhydrase (CA) are shown. A negative control wherein no reverse transcriptase was added to the PCR reaction had no transcripts for any gene within this subset.

Table 2.2. Genes targeted to investigate CH4-cycling metabolisms in the Samail ophiolite. Transcript counts per million reads (CPM) from metatranscriptomes are given for each gene and well. CPM Enzymatic function / Pathway in Reference NSHQ14C NSHQ14B WAB71 NSHQ04 WAB55 WAB188 NoRT Gene Target Name: CH4 cycling mcrABG, Catalyzes the final step in Reeve et al. Methyl-coenzyme M reductase I and II subunits 236.3 510.6 0.2 0 0 9.4 0 mrtABG methanogenesis 1997 mvhADG F420-non-reducing hydrogenase subunits 0.5 0 0 4.1 0 0 0 frhABG F -reducing hydrogenase subunits 10.1 6.3 0.1 0 0 0.3 0.2 420 Cytoplasmic [NiFe]-hydrogenase vhcADG F420-non-reducing hydrogenase vhc subunits 5.2 1.5 0.8 4.4 0 3.4 0 vhuADGU F420-non-reducing hydrogenase vhu subunits 0 5.1 0 0 0 2.7 0 Energy-converting [NiFe]-hydrogenase mbh mbhJL 6.0 3.2 0 0 0 0 0 subunits Thauer et al. 2010 ehaNO Energy-converting [NiFe]-hydrogenase eha subunits NA NA NA NA NA NA NA Membrane-associated energy- converting [NiFe]-hydrogenase echCE Energy-converting [NiFe]-hydrogenase ech subunits NA NA NA NA NA NA NA ebhMN Energy-converting [NiFe]-hydrogenase ebh subunits NA NA NA NA NA NA NA

Catalyzes the oxidation of formate to Shuber et al. fdhAB Formate dehydrogenase subunits 55.0 41.7 6.9 103.7 3.3 24.7 4.5 reduce CO2 to CH4 1986 ackA Acetate kinase Fournier et al. 3.1 1.5 3.0 305.2 7.1 4.4 4.2 Acetoclastic methanogenesis pta Phosphate acetyltransferase 2008 2.7 8.7 14.3 15.8 17.1 18.8 12.7 mtaA Methylcobamide:CoM methyltransferase 0 0 0 0 0 5.4 0 Crespo-Medina mtbB Dimethylamine methyltransferase 0 0 0 0 0 0 0 et al. 2017 mtmB Monomethylamine methyltransferase Methylotrophic methanogenesis 0 0 0 0 0 0 0 mttB Trimethylamine methyltransferase Paul et al. 0 0 0 14.6 0 1.7 1.8 mtsA Methylated-thiol-coenzyme M methyltransferase 2000 NA NA NA NA NA NA NA Stainthorpe et pmoABC Particulate CH4 monoxygenase subunits 72.2 37.2 1.8 1650.9 14.2 38.4 0 Catalyzes the oxidation of CH4 to al. 1990 methanol Csaki et al. mmoXYZ Soluble CH monooxygenase subunits NA NA NA NA NA NA NA 4 2003 Catalyzes the oxidation of methanol to Lau et al. mxaF Methanol dehydrogenase alpha subunit 18.3 5.3 5.0 513.1 8.4 6.6 3.3 formaldehyde 2013 − cynT, cah, Catalyzes the interconversion of HCO3 Smith et al. Carbonic anhydrase + 757.3 363.1 16.0 8.1 0.8 12.5 10.6 can + H to CO2 + H2O 1999 cdhAB Ni-containing CO dehydrogenase subunits NiCODH, catalyzes the reversible 32.8 9.6 0.9 25.2 0 3.0 0 cooS Ni-containing CO dehydrogenase oxidation of CO to CO2 Fones et al. cutSML Mo-containing CO dehydrogenase subunits MoCODH, catalyzes the reversible 2019 29.7 16.7 13.1 16.6 7.6 26.8 4.5 coxSML Mo-containing CO dehydrogenase subunits oxidation of CO to CO2

2.5 Discussion

2.5.1 Active Microbial Methanogenesis in Subsurface Waters of the Samail Ophiolite

The enrichment of Methanobacterium in the cDNA fraction relative to the DNA fraction

of 16S rRNA in extracts from NSHQ14 suggests this organism is active. This finding agrees with

14 14 previous studies of planktonic communities from this well showing CH4 production from C-

labeled bicarbonate (Fones et al., 2019) and the detection of Methanobacterium affiliated 16S

rRNA genes. (Miller et al., 2016; Rempfert et al., 2017). However, cDNA and DNA

comparisons can be impacted by differences in sequencing library sizes, affecting the ratio of

Methanobacterium-affiliated SSU rRNA genes. In addition to SSU rRNA, genes for the

biosynthesis of MCR were detected on single contigs assembled from NSHQ14 and WAB188

metagenomes that share close homology to Methanobacterium sp. The homology of this MCR to

Methanobacterium sp. was previously reported by Fones et al. 2019, where the authors found

low homolog counts per Mbp assembled for MCR. In this work, these single contigs of

Methanobacterium MCR have > 2,000 fragments per kilobase of exon per million reads (FPKM)

indicating adequate sequencing coverage and Methanobacterium as the primary methanogenic strain. Metagenomic sequences can offer evidence of functional capability but not cellular activity as these genes may be from dormant or dead organisms. Microbial methanogenic activity was evidenced by detection of MCR transcripts in NSHQ14. Transcription of mcr genes has been correlated with methanogenic activity in other environments and incubation experiments containing Methanobacterium (Freitag & Prosser, 2009; Morgan et al., 1997; Munk et al., 2012).

Collectively, these molecular observations suggest Methanobacterium to be an active organism

in the hyperalkaline (pH 11.3) waters of NSHQ14 and thus represents an important extension of

the pH spectrum (4.5 – 10.2) (Taubner et al., 2015) where methanogenesis is commonly observed.

SSU rRNA gene sequencing from other deep subsurface ecosystems (e.g., (Blank et al.,

2009; Brazelton et al., 2017; Moser et al., 2005; Rempfert et al., 2017; Woycheese et al., 2015))

that have geochemical similarity to NSHQ14 and WAB188 suggests Methanobacterium is a cosmopolitan organism in other highly reduced, high pH environments. For example, in the pH

9.1, H2- and CH4-containing waters of a deep fault within the Driefontein Mine of South Africa,

Methanobacterium co-dominate the microbial community with a sulfate-reducing bacterium

(Moser et al., 2005). SSU rRNA sequences affiliated with members of the family

Methanobacteriaceae (of which Methanobacterium belongs to) have also been identified in pH

11.5 springs of the Santa Elena ophiolite, yet ANME–1 organisms dominate the archaeal communities in these waters (Crespo-Medina et al., 2017). Likewise, 16S rRNA gene and metagenomic data suggest the presence of Methanobacterium in hyperalkaline waters (pH 11.8

– 12.3) in the Voltri Massif of Italy; however, subsequent incubation experiments did not confirm methanogenic activity (Brazelton et al., 2017). The transcriptional evidence presented herein confirms methanogenic metabolic activity at pH 11.3, a unit above the pH of 10.2 at which methanogenesis is typically observed (Taubner et al., 2015). In the context of previous

− observations of biological reduction of HCO3 to CH4 in incubations from NSHQ14C (Fones et al., 2019), these data strongly point to a microbial contribution to CH4 in waters impacted by serpentinization in the Samail Ophiolite, in particular in hyperalkaline, highly reacted waters with dissolved inorganic carbon levels below detection.

Genes and transcripts of the energy-converting [NiFe]-hydrogenases Eha/Ehb typical of

Methnobacterium sp. were not detected. However, genes for another energy-converting [NiFe]- 52 hydrogenase (mbhJL) and a subtype of the hydrogenase Mvh (vhcD) displayed homology to

Methanobacterium sp. though these genes are not found in this cytochrome-lacking methanogen

(Thauer et al., 2010). This may indicate an annotation mischaracterization for Mbh and Vhc in our study. Interestingly, while [NiFe]-hydrogenase genes were identified in metagenomic assemblies in Samail Ophiolite waters, transcripts corresponding to these genes were not in high abundance. [NiFe]-hydrogenases are requisite for hydrogenotrophic methanogenesis (Thauer et al., 2010) and thus the evidence for their low transcription CPM is surprising. It is possible that this observation is attributable to limitations and/or sequencing biases imposed by the low biomass associated with these samples or that [NiFe]-hydrogenase transcripts are inherently less stable and thus degrade rapidly.

A subset of methanogens can use the formate dehydrogenase enzyme (encoded by fdhAB) to use formate as a methanogenic substrate as an alternative to using hydrogenases to activate H2 as a source of electrons for methanogenesis (Shuber et al., 1986). Furthermore, Fones et al.

(Fones et al., 2019) found that life in alkaline Samail ophiolite waters is likely carbon limited rather than energy limited, and formate may be a favored carbon source in alkaline waters. The hydrogenotrophic Methanobacterium sp. may therefore use formate instead of H2/CO2 as a methanogenic substrate under carbon limitation and abundant H2 in this environment. Transcripts of fdhAB had higher CPM than [NiFe]-hydrogenases in hyperalkaline NSHQ14 but were not highly expressed relative to MCR transcripts in this well. This finding suggests formate marginally augments the energy metabolism of methanogens in NSHQ14 and may point to a different and yet to be defined mechanism of generating reductant in methanogens (D. R. Boone

& Castenholz, 2001).

Other compounds such as CO or mineral carbonates might be a source of inorganic

carbon for autotrophs in serpentinizing environments. Homologs of CODH were detected in

NSHQ14 metagenomes previously (Fones et al., 2019), but low abundances of transcripts for these genes in the communities examined indicate that CO is not a predominant source of carbon or energy for the microbial populations. Alternatively, mineral carbonates may provide a source of carbon for autotrophs. Mineral carbonate dissolution in seawater has been correlated to activity of microbial carbonic anhydrase (CA) (Subhas et al., 2017), an enzyme that catalyzes the

− + interconversion of carbonic acid with HCO3 and H (Capasso & Supuran, 2015). CA may therefore play a role in liberating inorganic carbon from mineral carbonates to make it

− bioavailable by shifting the equilibrium dissolution of carbonate toward HCO3 . Of the three (α,

β, γ) classes of CA, genes for the β and γ types have been identified in strains of the hydrogenotrophic methanogen Methanobacterium thermoautotrophicum and acetoclastic methanogen Methanosarcina thermophilia, respectively (Smith et al., 1999). In M. thermoautotrophicum, where CO2 is in demand for hydrogenotrophic methanogenesis, the β CA

− could be used to interconvert HCO3 and CO2 and/or concentrate CO2 near the

formylmethanofuran dehydrogenase for the first step of methanogenesis, analogous to β CA and

the CO2-fixing enzyme in photosynthetic organism (Smith & Ferry, 1999).

In NSHQ14, the CPM of CA transcripts increased with increasing depth and pH,

indicating CA may be generating the carbon needed by autotrophic methanogens as the water

becomes more alkaline. Consistent with a potential for CA in mitigating inorganic carbon

limitation for autotrophs in serpentinizing systems, homologs of this gene were detected in

genomes of Serpentinomonas strains isolated from a hyperalkaline serpentinization site in The

Cedars, California (Suzuki et al., 2014). Indeed, this strain was shown to be capable of 54

autotrophic growth on solid calcium carbonate (CaCO3), H2, and O2 (Suzuki et al., 2014).

Furthermore, Miller et al. (Miller et al., 2018) observed a Methanobacterium strain cultured from the Samail ophiolite to be capable of growth on H2 and solid CaCO3 as the sole carbon source. Collectively, these observations and CA transcription suggest that dissolution of carbonates may be a source of inorganic carbon for methanogens in high pH, carbon-limited environments such as NSHQ14.

Alternatively, carbonic anhydrase activity may be driven by microorganisms scavenging bicarbonate introduced into NSHQ14 via limited mixing of Type I and II waters within the aquifer or small amounts of atmospheric CO2 introduced during sample collection. Zwicker et al.

(Zwicker et al., 2018) describes this scenario where a mixture of Type I and II fluids with

minimal CO2 is the carbon source for methanogens in the Chimaera ophiolite of Turkey.

Furthermore, the isotopic composition of archaeal lipid biomarkers from the C-limited conditions

of the Chimaera ophiolite show enrichment in 13C, and the authors conclude that microbially-

produced CH4 may contribute to the abiotic CH4 pool in that system (Zwicker et al., 2018).

Similarly, the high H2 concentrations in NSHQ14 likely make scavenging any available

bicarbonate via CA favorable for autotrophs and methanogens.

2.5.2 Methanotrophy in Subsurface Waters of the Samail Ophiolite

All well waters contained SSU rRNA gene sequences affiliated with the aerobic bacterial

methanotroph Methylococcus, with the greatest relative abundance observed in the CH4-rich

groundwaters of NSHQ04. This is consistent with detection of pmo genes and their transcripts in

NSHQ04 and, to a lesser extent, NSHQ14. Notably, NSHQ04 was sampled shallowly at 6m due

to blockage within the well. Waters sampled from this depth might have been infused with 55

atmospheric oxygen to a greater extent than waters from other wells that were sampled much

deeper, and this may have led to the increased relative proportions of aerobic methanotrophs

relative to methanogens in samples collected in NSHQ04. Consistent with this hypothesis,

previous sequencing work on 16S rRNA genes recovered from deeper within this well, prior to

the blockage, detected sequences affiliated with Methanobacterium (Miller et al., 2016, 2018).

Thus, aerobic methanotrophs are components of communities in subsurface waters of Oman;

most notably in more oxidizing waters near zones of mixing between aquifer waters and/or the

atmosphere.

In addition to aerobic methanotrophy, DNA sequencing suggests the possibility of

anaerobic CH4 oxidizing organisms in several of the communities sampled from the Samail

Ophiolite. Several sequences with homology to the ANME–1 group of Archaea, organisms putatively involved in anaerobic oxidation of CH4 (AOM) (Cui et al., 2015), were detected in the hyperalkaline wells WAB71 and NSHQ14. Likewise, mcrG sequences with homology to

ANME–1 were also detected in WAB71. The lack of a full complement of MCR homologs for putative ANME–1 organisms in these wells is likely attributable to their low abundance, as gauged by 16S rRNA gene and transcript sequencing, which likely limited their representation in our metagenomic sequences. Nonetheless, the presence of ANME in serpentinization-impacted waters is consistent with previous reports of the evidence of these guilds in Chimaera, Santa

Elena, and Cabeço de Vide serpentinizing environments (Crespo-Medina et al., 2017; Tiago &

Veríssimo, 2013; Zwicker et al., 2018). However, the lack of detected transcripts affiliated with

these organisms and their genes and their limited representation in metagenomic sequences,

together, suggest that AOM is of minimal importance in the waters in the Samail ophiolite.

2.5.3 A Subsurface CH4 Cycle Impacted by Microbial Activity

13 Dissolved CH4 in ophiolite waters is often enriched in C and this has been used to suggest that this CH4 is primarily abiogenic (Giuseppe Etiope et al., 2013; Milkov & Etiope,

13 2018). However, methanogens can produce CH4 enriched in C under inorganic C limited conditions. For example, a Methanobacterium strain isolated from NSHQ04 (pH 10) was shown

13 to generate CH4 that was markedly enriched in C (−28‰ VPDB) when using calcium carbonate mineral (−0.1‰ VPDB) as the sole C source, in particular when cultivated in medium with pH values greater than 9 (Miller et al., 2018). Previous CH4 isotopic measurements of

NSHQ14 waters reported a δ13Cof up to +3‰ VPDB (Miller et al., 2016) and our study found transcription of carbonic anhydrase specific to this well. The dominance of Methanobacterium in our analysis of SSU rRNA genes and MCR transcripts in NSHQ14 (pH 11.3) indicates that methanogenesis is occurring at high environmental pH values in this system and it is possible that these cells are using C liberated from carbonate minerals as a carbon source. The use of carbon liberated from carbonate minerals by methanogens under hyperalkaline, DIC-limited conditions may thus be contributing to the environmental CH4 pool, yet their contribution may be obscured by the unusual isotopic signatures associated with this environment.

The opposing process of microbial methanotrophy can impact the C isotopic composition

12 13 of CH4 by preferentially using CH4 leading to C enrichment (Whiticar, 1999). Like the Samail

13 Ophiolite waters, those of the Santa Elena ophiolite host CH4 that is unusually enriched in C and contain methanogenic and methanotrophic microorganisms, including ANME–1 and

13 Methanobacterium sp. (Crespo-Medina et al., 2017). The detection of CH4 that is enriched in C combined with evidence for potential methanogens/methanotrophs in geographically distinct

ophiolites warrants further studies focused on the interplay between organisms involved in CH4

13 cycling and their effect on the δ C of CH4 in environments influenced by serpentinization.

Additional work is also needed to better understand potential electron donors that fuel methanogenesis in environments that are impacted by the process of serpentinization. The high concentration of H2 in some serpentinizing environments has been used to suggest these systems are not only conducive to hosting robust communities of hydrogenotrophic methanogens but may have also been prime environments for the origin of this process (Boyd et al., 2020). This

argument was based on the extremely low reduction potentials associated with waters in active

serpentinizing systems, a feature that should allow for the facile reduction of low potential

ferredoxin (Fd) with H2. Reduced, low potential Fd is required during the reduction of CO2 to formylmethanofuran during the first step of autotrophic methanogenesis (Thauer et al., 2008).

However, transcripts for [NiFe]-hydrogenases that can catalyze reduction of Fd with H2 in

Methanobacterium (group 4 Eha/Ehb or group 3c Mvh) (Peters et al., 2015)) were detected in

low abundance in our analysis of the NSHQ14 RNA pool, pointing toward the potential

importance of other electron donors (e.g., formate, CO) capable of fueling methanogenesis in

environments impacted by serpentinization.

The process of serpentinization creates additional challenges for autotrophs, including

methanogens. Serpentinization generates waters with high pH and Ca, which leads to low

aqueous DIC in systems closed to atmospheric CO2. Nonetheless, data presented here indicate

that autotrophic Methanobacterium are active in such conditions, which indirectly shows that they are meeting demands for cytoplasmic CO2 in a yet to be defined mechanism. Carbonic

anhydrase transcripts within NSHQ14 indicate that cells may be capable of interconverting

bicarbonate introduced from fluid mixing or liberated from dissolution of carbonate minerals to 58

meet CO2 demands. However, the source of bicarbonate remains unknown and it is unclear

whether carbonate dissolution rates are sufficient to meet this CO2 demand through equilibration

or if cells actively promote dissolution. These possibilities are likely to have an influence on the

isotopic composition of CH4 produced during methanogenesis. Additional physiological studies of these organisms and their mechanisms of acquiring cytoplasmic CO2 need to be conducted.

2.6 Data Availability

Unprocessed demultiplexed sequences produced for the SSU rRNA and metatranscriptomic analyses are available at the Short Read Archive (NCBI) database under

BioProject accession PRJNA560313 (https://www.ncbi.nlm.nih.gov/sra/PRJNA560313). SSU

rRNA sequences are accessions SRR12495563 – SRR12495576 and metatranscriptomic

sequences are accessions SRR12816294 – SRR12816299 in the SRA. Metagenomic sequences

are available in the MG-RAST database under accession numbers mgm4795805.3 –

mgm4795809.3 and mgm4795811.3 (https://www.mg-rast.org/linkin.cgi?project=mgp85625).

2.7 Acknowledgements

The authors thank the Ministry of Regional Municipalities and Water Resources in the

Sultanate of Oman (particularly Engineer Said Al-Habsi, Dr. Rashid Al-Abri, Engineer Salim

Al-Khanbashi, Abdullah Al-Kasbi and Said Al Mangji), and the Oman 2017 BIO Team for

insightful discussion, collaboration, and sample collection. This work was supported by the

NASA Astrobiology Institute “Rock-Powered Life” NAI (NNA15BB02A). The funding agency

had no role in study design, data collection and interpretation, or the decision to submit the work for publication. 59

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CHAPTER 3

SULFUR METABOLISM IN THE TERRESTRIAL SERPENTINITE SUBSURFACE OF THE

SAMAIL OPHIOLITE, OMAN

A paper in preparation for submission to Environmental Microbiology

Emily A. Kraus1, Blake W. Stamps2, Lauren M. Seyler3, Alexis S. Templeton4, John R. Spear1

Abstract

Sulfur-cycling microorganisms in alkaline serpentinizing subsurface environments use oxidized and reduced sulfur compounds for energy generation in metabolism. However, subsurface biogeochemical cycles mediated by microorganisms in ophiolites are relatively unknown with only a small number of observational studies. We used metatranscriptomics, metagenome-assembled genomes, and small subunit ribosomal RNA amplicon sequence data to investigate the microbial sulfur cycle in the Samail ophiolite of Oman. Results from this effort demonstrate that sulfate reduction is an important and predominant metabolism throughout the

1Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, Colorado, USA 2UES Inc., Dayton, Ohio, USA 3Department of Biology, Stockton University, Galloway Township, New Jersey, USA 4Department of Geological Sciences, University of Colorado, Boulder, Boulder, Colorado, USA

Samail ophiolite; and is performed by a few key organisms including Thermodesulfovibrionales,

Desulfonatronum, and Desulforudis. Thermodesulfovibrionales in particular is the most abundant and widespread sulfate reducer and likely couples hydrogen oxidation to the reduction of sulfate in this ecosystem. Other sulfur reducing reactions involving compounds with intermediate redox states, such as elemental sulfur and polysulfide reduction, also occur in the ophiolite in moderately alkaline fluids. Biological oxidation of sulfur species occurs in the gabbro-hosted waters of WAB188 where Rhodocyclaceae are the primary sulfur-oxidizing organisms. Reconstructed genomes suggest Rhodocyclaceae use the dissimilatory sulfate reduction pathway in the oxidative direction to transform hydrogen sulfide or sulfite to sulfate, though they are also capable of using intermediate sulfur species in their metabolism. Transcript abundances and assembled genomes indicate organisms within the ophiolite may be experiencing phosphate and/or sulfate limiting conditions. Collectively, our results describe a subsurface sulfur cycle dominated by a few alkaliphilic lithoautotrophs and a single primary sulfur-oxidizing organism. Furthermore, sulfate reduction is a dominant metabolism in gabbro and hyperalkaline (pH >10) peridotite wells; polysulfide and elemental sulfur metabolism is important in moderately alkaline (pH 8 – 10) peridotite wells; and sulfur oxidation occurs in gabbro waters. With this work, we identify an active biological sulfur cycle in the Samail ophiolite aquifer waters that helps to further inform on serpentinite habitability.

3.1 Introduction

Inorganic sulfur (S) cycling between the most oxidized (sulfate, S+6) and the most

reduced (sulfide, S−2) sulfur species involves a variety of intermediate sulfur compounds and

redox transition pathways that are exploited by microorganisms for energy generation (Wasmund 71

et al., 2017). Sulfate-reducing microorganisms (SRMs) use oxidized S compounds as electron

acceptors in energy generation whereas sulfur-oxidizing microorganisms (SOM) use reduced S

compounds as electron donors for their metabolism. In anoxic, reducing conditions found in the

2− deep serpentinizing subsurface, sulfate (SO4 ) is an important electron acceptor for organisms capable of sulfate reduction using hydrogen (H2) or organic acids as an electron donor (Lang et

al., 2018; Sabuda et al., 2020). These organisms and the metabolic pathway genes involved in

microbially-mediated sulfate reduction, polysulfide reduction, and sulfide oxidation have been

detected previously in serpentinization environments using molecular methods (Brazelton et al.,

2006; Lang et al., 2018). However, abiotic S-cycling redox reactions and production of S compounds (e.g., sulfate mineral dissolution) also occur in ophiolites, making it difficult to distinguish the roles of abiotic geochemical processes from biological activity in the deep subsurface S cycle within serpentinites (Sabuda et al., 2020).

Evidence of microbially-mediated sulfate reduction and sulfur cycling in the serpentinizing environment of the Samail ophiolite in the Sultanate of Oman is derived from geochemical, molecular, and experimental work on biomass from well fluids and serpentinite cores. Sulfate is bioavailable in the ophiolite fluids at concentrations ranging from 2 to 3830 µM, with the lowest concentrations found in highly alkaline waters (Kraus et al., 2021). Sequencing of the 16S small subunit ribosomal RNA (SSU rRNA) gene from biomass collected from the ophiolite aquifers shows the presence of putative sulfate reducers such as Thermodesulfovibrio,

Desulfonatronum, and Desulfotomaculum sp., as well as possible sulfur oxidizers like

Sulfuritalea and Thiobacillus in the subsurface (Kraus et al., 2021; Miller et al., 2016; Rempfert et al., 2017). Furthermore, biological sulfate reduction was demonstrated with assays of biomass

35 2- and aquifer waters amended with S-labeled SO4 . In the fluids, sulfate reduction ranged from a 72

high of 2.1 pmol mL−1 day−1 to the detection limit of 0.5 fmol mL−1 day−1, with the lowest rates

of activity in fluids above pH 10.5 (Glombitza et al. 2021). Low rates of sulfate reduction ranging from 4 – 1000 fmol cm−3 day−1 have also been recorded with rock-hosted biomass

recovered from Samail ophiolite serpentinites, though many of the experimental abiotic controls

showed similar rates up to 50 fmol cm−3 day−1 (Templeton et al., 2021).

However, despite evidence of sulfate reducers, the availability of sulfate, and its value as

an oxidant across the ophiolite, sulfate reduction does not appear to be ubiquitous based on

results from the 35S- labeled activity assays (Glombitza et al. 2021). Furthermore, the production

2− 2− and recycling of intermediate S compounds such as thiosulfate (S2O3 ), polysulfides (SX ),

2− 0 sulfite (SO3 ), and elemental S (S ) make it difficult to precisely characterize the biotic and

abiotic S cycle in serpentinizing systems with geochemical methods alone. Hyperalkaline

conditions found in ophiolite aquifers also impact the distribution of S compounds. For example,

sulfide and elemental S can react to form polysulfides and thiosulfate which are chemically

stable at elevated pH (Sabuda et al., 2020; Van Den Bosch et al., 2008). Therefore, the

geochemical conditions that control the ability of microbes to reduce or oxidize S in the Samail

ophiolite and the microbial metabolic mechanisms for mediating biological S oxidation and

reduction reactions are undefined. To address this void, we examine how the genomic capability

for various biological oxidation/reduction reactions of sulfur compounds is distributed across

geochemical regimes within the Samail ophiolite. We identify the dominant microbial sulfur-

cycling pathways, key taxa, and possible environmental conditions dictating sulfur metabolisms

in the ophiolite. Ultimately, this work better informs upon subsurface sulfur metabolism in

serpentinizing environments and the biogeochemical cycling of sulfur by subsurface microbiota.

3.2 Methods

3.2.1. Metagenomic sequencing and generation of MAGs

DNA and RNA were extracted from biomass collected from seven wells in Wadi

Lawayni in the Samail ophiolite of Oman. All field sampling and DNA/RNA extraction

protocols are described in Kraus et al., (2021) and Chapter 2. Metagenomic libraries were prepared from eight DNA samples from seven wells examined: WAB105, WAB104, WAB55,

WAB188, WAB71, NSHQ04, NSHQ14, with separate libraries made for two different depths

(50 m and 85m) of NSHQ 14. Library preparation was carried out using the NexteraXT library preparation kit (Illumina Inc.) according to manufacturer’s instructions, using 1 ng template

DNA as input. Library amplification and fragment size distribution were confirmed on an

Agilent 2100 Bioanalyzer with the Agilent DNA 7500 assay (Agilent Technologies, Santa Clara

CA). Libraries were then pooled at an equimolar ratio and sequenced on an Illumina HiSeq 2500 using V2 PE250 Rapid Run chemistry at the Duke Center for Genomic and Computational

Biology (https://genome.duke.edu).

After sequencing, adapters were removed with PEAT (Y. L. Li et al., 2015), individual

samples libraries were assembled with MEGAHIT (D. Li et al., 2015) with a maximum kmer of

141. A co-assembly of the concatenated sample libraries was generated with the same

parameters. Prior to metagenomic binning, raw reads were mapped to the co-assembly with

Bowtie2 (Langmead & Salzberg, 2012) to generate coverage information needed for downstream

analyses. An Anvi’o (Eren et al., 2015) contig database was constructed from the co-assembly

and mapping information. Contigs were binned with CONCOCT (Alneberg et al., 2014) and

manually refined within the Anvi’o interface. This work used the Extreme Science and

Engineering Discovery Environment (XSEDE) resource Comet at the San Diego Supercomputer 74

Center (Towns et al., 2014) through allocation TG-BIO180010. All bins were annotated for

taxonomic identity and functional genes using the MetaSanity package and pipeline (Aramaki et

al., 2020; Neely et al., 2020; Parks et al., 2018; Seemann, 2014). KEGG and Prokka annotations

were used for gene identification. Based on the CheckM (Parks et al., 2015) results, contig bins

with an estimated completion ≥ 50% and redundancy of ≤ 10% were included in our analysis as

putative metagenome-assembled genomes (MAGs).

3.2.2. Metatranscriptomic sequencing, transcript assembly, and annotation

Generation and processing of reads produced from RNA sequencing, including transcript assembly, is described in Kraus et al. (2021). Briefly, RNA libraries were processed in parallel to

DNA samples from the seven wells listed above. Metatranscriptomic reads were used to produce a co-assembly of transcripts with Trinity v2.8.6 (Bolger et al., 2014; Grabherr et al., 2011; Haas et al., 2013). Alignment and counting of metatranscriptomic reads to the assembled transcripts was completed with the RSEM package (B. Li & Dewey, 2011) to generate an expression

(counts) matrix for the seven wells and a control that was untreated with reverse transcriptase

(No RT) to assess residual double-stranded DNA contamination in RNA extracts.

Coding regions of transcripts were identified via Transdecoder (Bryant et al., 2017).

Regions were annotated with Trinotate via a NCBI Blast search against the SwissProt/Uniprot

database with an e-value threshold of 1 × 10-6 and default input parameters, and annotation top

hits were used as transcript identities (Agarwala et al., 2018; Bryant et al., 2017; Haas et al.,

2013; The UniProt Consortium, 2019). Relative transcript expression differences were analyzed

with the “edgeR” and “limma” packages in R (R Core Team, 2013; Ritchie et al., 2015; M.

Robinson et al., 2010). Counts of transcripts were normalized by the trimmed-mean of M-values 75

method (TMM) and transformed to counts per million reads mapped (CPM) to allow for inter-

sample comparison of target gene expression. Transcripts with ≤ 1 CPM were removed as

probable contaminants and counts were renormalized by the TMM method for the final

comparison of genes across samples (M. D. Robinson & Oshlack, 2010). The metatranscriptomic

sequences produced in this effort are available at the Short Read Archive (NCBI) database under

accession PRJNA560313 (SRR11431188 – SRR11431193) (Kraus et al., 2021).

3.2.3. Assessment of MAGs and transcription activity for sulfur metabolism

Sulfur cycling genes were sought in metagenome-assembled genomes (MAGs) and

assembled transcripts to elucidate the biotic S cycle in the Samail ophiolite (Figure 3.1). Sulfur-

reducing reactions of the dissimilatory sulfate reduction (DSR) and assimilatory sulfate reduction

(ASR) pathways were identified. The DSR pathway includes the genes encoding for sulfate

adenylyltransferase (sat), the alpha and beta subunits of adenylylsulfate reductase (aprAB), and subunits of the dissimilatory sulfite reductase (dsrAB). The ASR pathway also contains a sulfate adenylyltransferase encoded by either the sat or cysND gene, followed by adenylylsulfate kinase

(cysC), and phosphoadenosine phophosulfate reductase (cysH) genes to transform sulfate to sulfite. This sulfite is transformed to sulfide in the last step of ASR via either the ferredoxin- dependent sulfite reductase encoded by sir or by the NADPH-dependent sulfite reductase encoded by cysJI. Genes for sulfate/thiosulfate transporters (cysAWUP, cysZ, and sulP)

(Marietou et al., 2018) were also detected in MAGs and transcriptomes.

The reduction of polysulfide and elemental S was assessed in addition to sulfate reduction. The sudAB genes encode the alpha and beta subunits for the sulfide dehydrogenase enzyme which uses NADPH as an electron donor to catalyze the reduction of elemental sulfur or 76 polysulfide compounds to hydrogen sulfide (Ma & Adams, 1994). Additionally, polysulfide reduction is investigated via the presence of the psrA gene encoding the polysulfide reductase enzyme (Jormakka et al., 2008).

Evidence of thiosulfate disproportionation (quinone) is represented by the phsABC genes for thiosulfate reductase to reduce thiosulfate to hydrogen sulfide and sulfite. Thiosulfate disproportionation (rhodanese) differs in that a S is transferred from thiosulfate to produce thiocyanate and sulfite, and this reaction is indicated by the sseA and glpE genes for the 3- mercaptopyruvate sulfurtransferase and thiosulfate sulfurtransferase enzymes, respectively.

Thiosulfate can also be oxidized to sulfate by the SOX enzyme complex (soxAXYZBC) or to the polysulfide tetrathionate via one of two thiosulfate dehydrogenases (tsdA or doxA). To examine the biological oxidation of sulfide to elemental S, the sulfide-quinone reductase (sqr) and sulfide dehydrogenase (fccAB) genes encoding for separate enzymes were sought in the MAGs and transcriptomes.

3.2.4. 16S SSU rRNA community comparisons and geochemistry

Geochemical constituents and sequencing data from biomass collected from seven wells generated and processed in Kraus et al. (2021) was used for this analysis. The “phyloseq” package (McMurdie & Holmes, 2013) was used to filter sequences to retain amplicon sequence variants (ASVs) seen ≥ 5 times in at least 10% of samples and samples with > 250 total reads.

Differences between sample communities were assessed with a non-metric multidimensional scaling (NMDS) ordination of the Bray-Curtis dissimilarity between samples (Bray & Curtis,

1957).

Figure 3.1. Overview of biological sulfur reactions targeted in the Samail ophiolite. Sulfur species are labeled with oxidation numbers in brackets. Genes encoding for enzymes of interest in the dissimilatory and assimilatory sulfate reduction pathways (DSR, ASR, in blue), polysulfide reduction (purple), thiosulfate disproportionation (green), thiosulfate oxidation (red), sulfide oxidation (gold), and other metabolic reactions (grey) are indicated next to the reaction arrows. Figure adapted from Sabuda et al. (2020).

Analysis of similarities (ANOSIM) in the “vegan” package (Oksanen et al., 2018) was

performed to test the significance of differences between microbial communities based on well

2− − water type. To test if specific geochemical constituents (SO4 , nitrate (NO3 ), phosphorus (P),

2+ 2+ magnesium (Mg ), ferrous iron (Fe ), H2 , and methane (CH4)) were associated with sample

clustering based on the Bray-Curtis distance, a canonical analysis of principal coordinates (CAP)

and analysis of variance (ANOVA) were performed (Anderson & Willis, 2003). All analyses

were conducted in R and visualized with the “ggplot2” package (Wickham, 2016).

3.3. Results

3.3.1 Fluid type and geochemistry drives community dissimilarity

Microbial communities grouped by water type (alkaline Type I, hyperalkaline Type II,

gabbro) based on Bray-Curtis dissimilarity with an ANOSIM R statistic of 0.927, p < 0.001

2− − 2+ 2+ (Figure 3.2 A). The aqueous concentrations of SO4 , NO3 , P, Mg , Fe , H2, and CH4 were

significantly correlated with microbial community clustering (ANOVA p < 0.001) (Figure 3.2

B). The concentration of sulfate varies throughout the ophiolite with the greatest sulfate of 1.13

mM in WAB188 at 78m and lowest of 0.002 mM at 85m depth in NSHQ14C (Table 3.1, Table

2.1). The aqueous concentrations of intermediate S species such as thiosulfate were not able to

be measured in this study and have not been previously measured in the Samail ophiolite.

Table 3.1. Selected geochemical features of the Samail ophiolite wells sampled in 2017. Well types of peridotite (P), contact (C), and gabbro (G) are indicated after the well name. LQ indicates a value below the limit of quantification. In agreement with prior classifications (Rempfert et al., 2017), NSHQ14, WAB71, and NSHQ04 were dominated by Ca/OH− waters typical of closed-system serpentinization (termed Type II waters) while WAB55, WAB104, and − WAB105 intersect Mg/HCO3 waters more typical of open-system serpentinization (termed Type I waters). WAB188 is considered an alkaline gabbro well, consistent with Nothaft et al. (2020). * indicates a value measured in 2018.

NSHQ14 WAB71 NSHQ04 WAB55 WAB104 WAB105 WAB188 (P) (P) (C) (C) (P) (P) (G) Water II II II II I I I I type Depth 50 85 70 6 30 70 50 78 pH 11.1 11.3 10.6 10 9.2 8.5 8.4 8 Eh (mV) −415 −253 −133 −174* 269 133* 162* 214 2- SO4 0.13 2E-3 4E-2 0.68 0.88 0.48 0.29 1.13 (mM) H2 (µM) 32.5 253 0.59 LQ LQ NA NA 0.99 2- NO3 LQ LQ LQ LQ 0.14 0.12 2.4 0.12 (mM)

Figure 3.2. (A) Non-metric multidimensional scaling (NMDS) ordination of the Bray-Curtis dissimilarity between samples. Color denotes water type and shapes represent the well lithology. (B) A canonical analysis of principal coordinates of the Bray-Curtis dissimilarity of sample communities with vectors showing the influence of significant geochemical parameters (CH4, 2 H2, Fe , Mg, NO3, SO4 and P concentrations) on clustering.

Figure 3.2 continued

3.3.2. Identification of putative sulfur reducers and oxidizers

Putative sulfate reducers comprise significant fractions of the microbial communities in

hyperalkaline peridotite (WAB71, NSHQ14), hyperalkaline contact (NSHQ04), alkaline contact

(WAB55), and gabbro (WAB188) wells (Figure 3.3 A). Members of the Class

Thermodesulfovibrionia are predominant and widespread, and other sulfate reducers including

Desulfanatronum and Desulforudis are less abundant and more specific to individual well waters. Dethiobacter (Dethiobacteria) are more widespread in the moderately alkaline fluids and

abundant in WAB71, NSHQ04, and WAB105. Putative S oxidizers are primarily found in

WAB188. The Rhodocyclaceae family constitutes most of the S-oxidizing organisms and within

this group Sulfuritalea and Thiobacillus genera are 7 – 13% of the microbial community within

gabbro well WAB188 (Figure 3.3 B).

3.3.3. Relative expression of transcripts related to sulfate reduction

Transcript expression suggests sulfate reduction is more widespread than sulfur oxidation

pathways (Figure 3.4, Appendix B, Table B.1). Transcript expression differences across wells for

the dissimilatory sulfate reduction (DSR) pathway suggests DSR is an important metabolic

2- process in WAB188 (a pH 8 gabbro type water with 1.1 mM SO4 , 1 µM H2), and the

2- hyperalkaline wells NSHQ04 (pH 10, 0.7 mM SO4 ) and NSHQ14 (pH 11.3, with 0.002 mM

2- SO4 , 253 µM H2). Overall, DSR does not appear to be an important metabolic pathway in Type

I waters (WAB55, WAB104, WAB105). Transcription of genes of the assimilatory sulfate

reduction (ASR) pathway is greatest in NSHQ04 followed by WAB188, NSHQ14C, and

WAB71. The shallower 50m sample from NSHQ14B and the Type I waters have minimal

transcription associated with ASR. Additionally, transcripts of the cysZ gene, encoding for a 83

putative transmembrane sulfate uptake protein CysZ, solely occurs in NSHQ04. The sulP sulfate

transporters (sulP) are found on transcripts from all samples. For sulfate/thiosulfate import into a cell, transcription of cysAWUP which encodes the CysAWTP complex is greatest in NSHQ04,

NSHQ14C, and WAB71.

Transcripts involved in sulfide oxidation (sqr and fccB) were recovered from well waters

with pH ≥ 10 as well as WAB188. NSHQ04 had the highest counts per million reads mapped

(CPM) associated with sulfide oxidation followed by WAB188. No transcript identified as fccA

was annotated. The SOX enzyme complex involved in thiosulfate oxidation, encoded by

soxAXBCYZ, was incompletely detected such that only soxA and soxYZ were observed in

assembled transcripts. Thiosulfate dehydrogenases (tsdA) needed to oxidize thiosulfate to

tetrathionate was highest in WAB71, but overall tsdA is less prevalent and expressed than soxYZ.

No transcripts with the doxA gene for a thiosulfate dehydrogenase were identified. Thiosulfate

disproportionation (rhodanese) genes sseA and glpE, are expressed throughout the biomass

collected and expression is highest in NSHQ04. The phsAB genes for an alternative pathway of

thiosulfate disproportionation (quinone) are incompletely detected, with only phsA detected in

the wells with pH ≥ 10 and WAB104.

Transcripts associated with the sudAB or psrA genes for polysulfide or elemental S

reduction are noted in all wells. The psrA gene is most expressed in NSHQ14 whereas greater

CPM of sudAB is detected in the other wells. Transcripts of the membrane-bound sulfite

dehydrogenase Soe (soeA gene) are observed in NSHQ04 and WAB188. Lastly transcripts

containing genes for with cellular response to sulfate limitation (ssuD, tauD) (Appendix B, Table

B.1) and phosphate limitation (phoAB, phoPR) are expressed throughout the RNA extracts from

the Samail ophiolite. 84

Figure 3.3. Relative abundance of (A) putative sulfate / sulfur-reducing and (B) sulfur-oxidizing organisms. Wells are ordered from highest pH (NSHQ14C, pH 11.3) at the top to the lowest (WAB188, pH 8) at the bottom. Desulfonatronum spp. belong to the Desulfovibrionia (light orange) and Desulforudis spp. belong to the Desulfotomaculia (dark red) in panel A.

3.3.4. Metagenome-assembled genomes (MAGs) involved in the sulfur cycle

Eight metagenomes were generated from shotgun genomic sequencing of microbial

communities of seven wells in the Samail ophiolite, producing a total of 93.67 Gbp and 373.18

million reads (Appendix B, Table B.2). Co-assembly of all metagenomes with a minimum contig

length of 1,500 bp generated 157,576 contigs. The assembled contigs were binned by differential

coverage and manually refined within Anvi’o v.3 to 368 bins representing 99.4% of contig

nucleotides. Of the 368 bins, 106 were ≥50% complete and ≤10% redundant (Appendix B, Table

B.3). Three hundred and thirty bins were classified as Bacteria, 22 as Archaea, and 13 were

unclassified (Appendix B, Table B.4). The taxonomic assignments spanned 33 different phyla,

including Proteobacteria, Desulfobacterota, Crenarchaeota, Nitrospirota, Omnitrophota,

Patescibacteria, Methylomirabilota, Riflebacteria, Plantomycetota (Appendix B, Table B.4).

Eight MAGs contained the full complement of genes for the reversible pathway of

dissimilatory sulfate reduction (DSR). These MAGs belonged to the Thermodesulfovibrionales order, Desulfomonilaceae family, Desulfonatronum genus, Desulforudis genus (Firmicutes),

Melioribacteraceae family, and three MAGs to the Rhodocyclaceae family (Figure 3.5). Other

MAGs including those belonging to Thermodesulfovibrionales, Desulfobacterota, Crenarchaeota

(Nitrososphaerales), Acidobacteriota, and Proteobacteria harbor partial DSR pathways. No MAG

contained the full assimilatory sulfate reduction pathway. Members of the Dethiobacteriacaea

family were identified however, these MAGs contained no genes involved in the S metabolic

processes targeted, despite > 70% completions and low redundancy of the bins.

Figure 3.4. Transcript counts per million reads mapped (CPM) for S-cycling genes. Size of the circles represents the CPM value of a transcript containing each gene of interest on the y-axis from each well on the x-axis. Colors of circles denote the metabolic pathway or reaction of the associated genes. Blue for sulfate reduction; purple for polysulfide or elemental sulfur reduction; yellow for hydrogen sulfide oxidation; red for thiosulfate oxidation; dark red for sulfite oxidation; green for thiosulfate disproportionation; and grey for the cellular response to phosphate limitation. Importantly, CPM is not normalized by feature length so within-sample comparisons of different genes are not always valid.

The most complete Thermodesulfovibrionales genome recovered, Bin 69-1, harbored the

DSR pathway as well as genes for sulfite oxidation to sulfate by the sulfite dehydrogenase Soe

(soeABC) and sulfide oxidation to elemental S via sulfide-quinone reductase (sqr). Bin 69-1 contains genes for a periplasmic [NiFe] hydrogenase (hydAB) and NADP-reducing hydrogenase

(hndABC), a periplasmic nitrate reductase (napA), nitrogenase (nifBDEHK), transporters for phosphate, ammonia, cobalt, and Fe(II), and has a partial gene for the O2-tolerant [NiFe] hydrogenase hyd-1. For carbon fixation, the Thermodesulfovibrionales Bin 69-1,

Desulfomonilaceae, and Desulfobacterota MAGs have 83% of the Wood-Ljungdahl pathway genes for CO2 fixation.

In addition to the genes for the DSR pathway, the Rhodocyclaceae genomes also contain the capabilities to oxidize sulfide to elemental S via the sulfide-quinone reductase (sqr) or the flavoprotein chain of sulfide dehydrogenase (fccB); transform sulfite to sulfate via sulfite dehydrogenase Soe and Sor (soeABC, sor); and they contain genes for the SOX enzyme complex

(soxAXYZB) to oxidize thiosulfate to sulfate. The Rhodocyclaceae also display genes indicative of the abilities for dissimilatory nitrate reduction to ammonia (napAB, nirBD), nitrogen fixation

(nifDHK), and partial denitrification (napAB, nirS, norBC) though one genome has nosZ for the full pathway. All three genomes have NAD-reducing hydrogenase, RuBisCo, and transporters for ammonia, phosphate, copper (copA), ferrous iron (feoB), and ferric iron (afuA). Other MAGs capable of oxidizing S compounds included the Proteobacteria genus Elioraea, other

Proteobacteria (e.g., Burkholderia, Acetobacteraceae), and Nitrospirota. Additionally, the sqr gene was widespread among the MAG bins assembled.

Many MAGs including those of Thermodesulfovibrionales, Desulfomonilaceae,

Desulfonatronum, Rhodocyclaceae, Dethiobacterales, and Proteobacteria contain genes for 90

alkaline phosphatase (phoAB) and transcriptional regulators (phoPR) involved in the cellular response to phosphate limitation. Of the subset of MAGs assessed for S-cycling genes (in Figure

3.5), genes involved in the metabolic response to sulfate limitation were minimally detected such that no taurine dioxygenase (tauD) was identified and only Bin 43-1, an Acetobacteraceae, encoded copies of ssuD for alkanesulfonate monooxygenase.

3.4. Discussion

2− − 2+ 2+ Key components of the aqueous geochemistry (SO4 , NO3 , P, Mg , Fe , H2, and CH4) were significantly correlated with microbial community composition and communities clustered by well water type, which is consistent with findings reported in Rempfert et al. (2017) of previous samples of Samail ophiolite fluids. These geochemical factors or a subset of the components tested exert some control over the community composition of microbiota within the

Samail ophiolite. Sulfate varies throughout the ophiolite and is relatively low compared to ~28

2− 2− mM SO4 in modern seawater SO4 . WAB188 contained the greatest concentrations of 1.13

2− mM and NSHQ14C held the lowest of 0.002 mM SO4 (Table 3.1, Table 2.1). Sulfate is the

2− − dominant S species of the SO4 / HS redox couple within the waters observed, as all had pH values > 8 and temperatures of 34℃ - 36℃ indicating little aqueous HS− is available.

Figure 3.5. Heatmap of the completion of various S cycling and related pathways within targeted metagenome-assembled genomes. Metabolic pathways are defined by KEGG modules and color intensity denotes the percentage of genes present in a MAG from each module. Genes for sulfate/thiosulfate transporters (cysAWUP and cysZ) were identified with Prokka annotations. All other annotations are from KEGG Orthology (KO) assignments from MetaSanity.

3.4.1. Dissimilatory sulfate reduction and Thermodesulfovibrionales in the Samail ophiolite

Microbial sulfate reduction is more widespread than sulfur oxidation pathways based on

both transcript expression across wells and greater abundances of putative sulfate and S reducers

compared to S oxidizers from SSU rRNA sequencing (Figure 3.6). Dissimilatory sulfate

reduction (DSR) is an important metabolic process for microbiota in the gabbro-hosted, alkaline

fluids of WAB188, the hyperalkaline NSHQ04 near the peridotite-gabbro contact, and in the

hyperalkaline peridotite well NSHQ14. Analysis of transcript abundance suggests DSR is not a

prominent metabolic pathway in alkaline Type I waters within wells WAB55, WAB104,

WAB105. Sulfate reduction was previously suggested to be an important metabolism in the

Samail ophiolite based on identification of putative sulfate reducing organisms via SSU rRNA

sequencing (Rempfert et al., 2017). In agreement with these findings, members of the

Thermodesulfovibrionales are the most abundant and widespread of the sulfate reducers across varied fluid types and several metagenome-assembled genomes (MAGs) are identified as belonging to this group and contain complete DSR pathways. High abundances of > 20% of the anaerobic Thermodesulfovibrionales were previously identified in hyperalkaline fluids in the

Samail ophiolite via 16S amplicon surveys over several years (Kraus et al., 2021; Miller et al.,

2016; Rempfert et al., 2017) but are absent or observed in low abundances in other ophiolites

(e.g., Woycheese et al., 2015). Additionally, the sulfate reducing organisms

Thermodesulfovibrionales, Desulfonatronum, and Desulforudis identified within the SSU rRNA

and MAGs all have known alkaliphilic members identified from deep subsurface genomic or

cultivation studies suggesting they may be common in alkaline subsurface waters (Bell et al.,

2020; Chivian et al., 2008; Frank et al., 2016; Karnachuk et al., 2019; Sabuda et al., 2020).

Members of Thermodesulfovibrionales couple the reduction of sulfate or thiosulfate with electrons donated from either H2 or C1-C3 organic acids for chemolithoautotrophic or chemoorganotrophic growth, respectively, and some types can replace sulfate with nitrate, sulfite, or ferric iron as the electron acceptor (Henry et al., 1994; Sekiguchi et al., 2008). A facultatively alkaliphilic strain of Thermodesulfovibrio was previously isolated from a deep subsurface aquifer beneath Siberia, Russia. This isolate required acetate as a C source, could use

H2, formate, lactate, pyruvate as electron donors; use sulfate, sulfite, thiosulfate, and Fe(III) as electron acceptors; and contained genes for a polysulfide reductase (psrAB) and an incomplete

Wood-Ljungdahl pathway (Frank et al., 2016). Consistent with the isolate description, the most complete bin of Thermodesulfovibrionales in our analysis (Bin 69-1) has the capability for DSR with H2 as an electron donor, as this MAG encodes a periplasmic [NiFe] hydrogenase and

NADP-reducing hydrogenase characterized from other sulfate reducers (De Luca et al., 1998;

Rousset et al., 1998). Use of acetate as a carbon source is evidenced by genes for acetyl- coenzyme A synthetase (acsA) and acetate/cation symporter (actP) in the same genome region, as was also observed in the alkaliphilic Thermodesulfovibrio isolate. Bin 69-1 also has polysulfide reductase (psrA) genes indicating it can use thiosulfate as an electron acceptor and nitrogenase genes (nifBDEHK) for nitrogen fixation in the subsurface.

There may be two primary types of Thermodesulfovibrionia in the Samail ophiolite.

Rempfert et al., (2017) observed a Thermodesulfovibrionaceae phylotype in hyperalkaline fluids and a different one, affiliated with the genus LCP6, in less alkaline waters at pH 8.8. In this study, the contigs constituting the Thermodesulfovibrionales MAGs are predominantly recruited from alkaline gabbro well WAB188, except for one MAG (Bin 26_4). Though Bin 26_4 has a low completion (18.9%) it is identified as a Thermodesulfovibrionales and the bin contigs are 95

predominantly recruited from the more hyperalkaline waters of NSHQ04 (data not shown). The

use of a co-assembly of sequences to generate MAGs in this study may be making it difficult to distinguish and accurately bin the more alkaline-tolerant Thermodesulfovibrionales phylotype from the WAB188-sourced strains. Thus, Thermodesulfovibrionales MAGs generated from individual sample assemblies may be more useful to better distinguish between the features of these phylotypes. Prior to the completion of this study, MAGs will be generated from individual sample assemblies to determine if strains of Thermodesulfovibrionales are adapted to hyperalkaline conditions. Adaptation of a microbial strain to hyperalkalinity in Samail ophiolite fluids has previously been described in the methanogenic genus Methanobacterium, another

highly abundant member of the hyperalkaline NSHQ14 community (Fones et al., 2020), so it is

feasible that Thermodesulfovibrionales may exhibit similar adaptive partitioning in the ophiolite

fluids.

Genomes of the genera Desulforudis and Desulfonatronum were recovered from the

Samail ophiolite in addition to Thermodesulfovibrionales. The Desulforudis genus was initially

discovered in a deep gold mine within South Africa where it was the only microbe detected,

forming a single-species ecosystem dependent on H2 in the subsurface (Chivian et al., 2008).

Isolation of Desulforudis audaxviator further revealed the subsurface organism reduces sulfate

with H2 and other electron donors in the presence of elemental iron within alkaline (pH 8) media

(Karnachuk et al., 2019). ASVs of Desulforudis (of the Desulfotomaculia class) are

predominately found in the pH 8 waters of WAB188, consistent with prior observations that this

taxon prefers a moderately alkaline pH. Members of the genus Desulfonatronum are alkaliphilic lithotrophic bacteria that have been isolated from soda lakes, can grow heterotrophically or autotrophically, require high concentrations of carbonate, and can use H2 and formate as electron 96

donors coupled to sulfate, sulfite, or thiosulfate as electron acceptors (Pikuta et al., 2003; Zhilina

et al., 2005). The Desulfonatronum spp. in our study are observed in NSHQ04 in pH 10 waters,

consistent with the taxon’s obligate alkaliphilic lifestyle. The Desulfonatronum Bin 20-2 and

Desulforudis Bin 64-4 can use the DSR pathway but lack genes for formate dehydrogenase and

thiosulfate metabolism, indicating both taxa are hydrogenotrophic sulfate reducers in the Samail

ophiolite. In addition to Desulforudis in WAB188 and Desulfonatronum in NSHQ04, sulfate- reducing taxa appear to be specific to individual wells except for the widespread

Thermodesulfovibrionales, suggesting highly specialized niches for sulfate reducers dependent on geochemistry of aquifer waters.

Assembled genomes of the class Dethiobacteria displayed no S metabolizing genes targeted in this study. Dethiobacteria couple H2 oxidation to the reduction of elemental S, polysulfides, or thiosulfate to produce sulfide, and are represented by an alkaliphilic chemolithoautotrophic isolate of Dethiobacter and genomes collected from soda lakes (Melton et al., 2017; Sorokin et al., 2008). Like the other S reducers identified in the Samail ophiolite,

Dethiobacteria have been observed as key S cycling taxa in other subsurface sites of serpentinization such as the Coast Range Ophiolite Microbial Observatory (CROMO) in

California, where they are likely disproportionating thiosulfate and reducing elemental S but not sulfate (Sabuda et al., 2020). The reduction of elemental S or polysulfides in NSHQ04, WAB71,

WAB104, and WAB105 is evidenced by expression of sulfide dehydrogenase (sudAB) and these wells contained the most Dethiobacteria in our 16S analysis. Therefore, Dethiobacteria in

WAB71, WAB105, NSHQ04 and WAB104 may be reducing elemental S or polysulfides within a variety of fluid geochemistries in the moderately to highly alkaline zones of the Samail ophiolite. 97

Figure 3.6. Conceptual model of key S cycling metabolic reactions and associated taxa in the Samail ophiolite subsurface. Sulfur species are highlighted with red text. Sulfate- or sulfur- reducing organisms are represented with light blue ovals and S oxidizing organisms with orange. Roughly, hyperalkaline Type II waters are shown on the far left (NSHQ14 and WAB71), moving to less alkaline Type I waters (WAB105, WAB104, WAB55) and Type II waters near the gabbro-peridotite boundary (NSHQ04) in the middle, then gabbro-type waters on the far right (WAB188). Organisms are placed near the water types they are observed in, with the exception of Thermodesulfovibrionales which are detected throughout the ophiolite.

Sulfate-reducing bacteria can use different transporters for sulfate or thiosulfate import

into a cell, including the sulfate/thiosulfate import protein CysAWTP encoded by cysAWUP, the sulfate permease encoded by sulP, and the sulfate-specific importer encoded by cysZ (Marietou

et al., 2018). In our study, transcription for the CysAWTP complex is in the greatest abundance

in NSHQ04, NSHQ14C, WAB71, and WAB188, which supports the assertation that sulfate

reduction is occurring in these wells.

Sulfate reduction to sulfide in marine sediments is followed by the concomitant

oxidization of that sulfide by other oxidants such as Fe(III) (Jørgensen et al., 2019). The

concurrent reoxidation of sulfide generated from sulfate is called the “cryptic sulfur cycle” due to

the inability to detect this cycling reaction by geochemical measurements alone (Holmkvist et al., 2014; Treude et al., 2005). Instead of other chemical oxidants reacting with sulfide

abiotically, microorganisms can mediate sulfide oxidation for energy generation in the cryptic

sulfur cycle (Callbeck et al., 2018; Jørgensen et al., 2019). The alkaline, reducing fluid

conditions in the Samail ophiolite are favorable for the reoxidation of hydrogen sulfide back to

sulfate, indicating there is a cryptic sulfur cycle in this environment. Our SSU rRNA analysis

demonstrates a lack of sulfur-oxidizing organisms in all peridotite wells, which suggests this

cryptic sulfur cycle is catalyzed abiotically. Additionally, a cryptic sulfur cycle could help

explain the low microbial sulfate reduction rates in Samail ophiolite fluids and rocks that were

35 35 2- calculated by measuring H2 S produced in assays of biomass amended with SO4 , (Glombitza

et al., 2021; Templeton et al., 2021).

100

3.4.2. Microbiota in the ophiolite may be phosphate limited and communities in hyperalkaline fluids may be sulfate limited

Biomass from wells with the lowest aqueous concentrations of phosphorus (P) and S

displayed the highest relative transcript expression for genes associated with microbial responses

to P and S starvation. The genes for taurine dioxygenase (tauD) and alkanesulfonate

monooxygenase (ssuD) are expressed in E. coli only under sulfate or cysteine starvation

conditions. These genes encode proteins for the desulfonation of taurine or alkanesulfonates to

use these compounds as an alternative S source when sulfate is not available (Eichhorn et al.,

2000). Transcript CPM of tauD and ssuD are greatest in WAB71 (total 15.5 CPM) followed by

NSHQ14C (12.4 CPM) and WAB105 (7.5 CPM) (Appendix B, Table B.1); the lowest sulfate

concentrations in the fluids were measured in NSHQ14C (2 µM), WAB71 (40 µM), and

WAB105 (290 µM). This association between low sulfate concentration and tauD/ssuD

transcription suggests that communities within these three wells may be under sulfate limiting

conditions. Consistent with sulfate limitation in the Samail ophiolite, electron acceptors appear to

be limiting in Samail ophiolite but electron donors (e.g., H2) are not (Glombitza et al., 2021).

Alkaline phosphatase and a two-component transcriptional signal regulatory system

encoded by phoAB and phoPR, respectively, are involved in the acquisition of inorganic

2− phosphate (PO4 ) in the cellular response to phosphate starvation (Antelmann et al., 2000;

Huang et al., 1998). The pho genes are expressed in all well water biomass samples and have the greatest relative expression in NSHQ04, NSHQ14C, and WAB71. Though phosphate could not be measured, P is detected in low concentrations (< 0.9 µM) across the fluids and is below detection level in NSHQ14C, where pho transcript abundance is high. Additionally, many MAGs harbored these pho genes, indicating the capability to respond to phosphate stress is widespread

101 in ophiolite microbiota. Collectively, this evidence suggests sulfate or phosphate limitation may be dictating the use of S metabolic pathways in the serpentinizing subsurface in addition to geochemical controls such as pH.

3.4.3. Assimilatory sulfate reduction may be important at high pH

Transcript analyses suggest assimilatory sulfate reduction (ASR) may be an important metabolic strategy in waters above pH 10 where the lowest concentrations of sulfate are measured. Among the wells, ASR associated transcripts are most prevalent in NSHQ04,

WAB71, and NSHQ14C. The high CPM in NSHQ04 for ASR and all other pathways may be the result increased oxidant availability due to fluid mixing at the shallow sampling depth. However, hyperalkaline peridotite wells WAB 71 and NSHQ14C have the lowest sulfate concentrations and may be under sulfate-limited conditions as discussed above. Thus, it is reasonable that organisms within the most hyperalkaline waters may need to assimilate more of the available S from sulfate to adapt to limiting concentrations.

3.4.4. Sulfide oxidation, metabolism of intermediate S compounds, and Rhodocyclaceae

Biological sulfide oxidation via the sulfide-quinone reductase SQR (encoded by sqr) is feasible in wells with pH ≥ 10 and WAB188 based on transcript abundances. The sqr gene was also the most widespread of any S-cycle gene among the MAGs analyzed in this study.

Organisms in these waters may be oxidizing hydrogen sulfide produced by microbial sulfate reduction in tightly coupled partnerships or possibly within acidic microenvironments.

Metabolism of sulfide with the SQR enzyme would produce elemental sulfur and polysulfides

(Thorup et al., 2017; Wasmund et al., 2017) in the hyperalkaline and gabbro-hosted zones of the 102

ophiolite. However, in alkaline, Type I waters, sulfide oxidation transcripts were not expressed,

indicating sulfide oxidation is not a metabolism employed by microbial communities inhabiting

these waters.

Thiosulfate oxidation and disproportionation reactions are important pieces of the

subsurface S cycle within the Samail ophiolite. Though measurements of intermediate S

compounds were not possible in this study, it is likely that intermediate compounds are available

in the ophiolite aquifers since thiosulfate and polysulfides are thermodynamically stable at high

pH (Van Den Bosch et al., 2008). Thiosulfate disproportionation may be occurring in the ophiolite, particularly in waters with pH ≥ 10 where transcripts of thiosulfate sulfurtransferase

(glpE) and thiosulfate reductase (phsA) are most abundant. The 3-mercaptopyruvate

sulfurtransferase (sseA) required with glpE for thiosulfate disproportionation (rhodanese) is

lowly expressed in the ophiolite biomass, indicating this pathway is more likely used instead of

thiosulfate disproportionation (quinone) due to the absence of phsB from transcripts.

Thiosulfate oxidation to tetrathionate via thiosulfate dehydrogenase (tsdA) and thiosulfate

oxidation with the SOX enzyme complex are active S cycling metabolisms in the subsurface.

Expression of thiosulfate dehydrogenase transcripts is greatest in WAB71 suggesting thiosulfate

oxidation is occurring in WAB71 communities at high pH. Similarly, in the CROMO ophiolite,

genes for both thiosulfate disproportionation and oxidation pathways were identified in

metagenomes, metatranscriptomes, and MAGs (Sabuda et al., 2020). Calculation of Gibbs free

energies for reactions at CROMO also demonstrated that thiosulfate disproportionation,

reduction, and oxidation reactions were energetically favorable across a six-order magnitude

range of hypothetical thiosulfate concentrations (Sabuda et al., 2020). Therefore, though we do

not know the thiosulfate concentrations within the Samail ophiolite wells, it is reasonable to 103

assume thiosulfate metabolism is occurring within microbial communities within hyperalkaline

waters.

The sulfide dehydrogenase enzyme encoded by sudAB couples the oxidation of NADPH to the reduction of elemental sulfur or polysulfides to produce hydrogen sulfide (Ma & Adams,

1994). Sulfide dehydrogenase genes are expressed in the greatest CPM in NSHQ04, WAB71,

WAB104, and WAB188, suggesting elemental S or polysulfides are important substrates for energy generation in these wells over a variety of fluid and host-rock types. Similarly, the polysulfide reductase encoded by psrA can reduce polysulfide to hydrogen sulfide, but cannot act on elemental S (Jormakka et al., 2008). The polysulfide reductase gene psrA is primarily expressed in NSHQ14 at both depths, indicating polysulfide reduction may be more favorable than elemental S reduction in the most hyperalkaline waters. The idea that reduction of polysulfides is favored over elemental S in high pH waters is aligned with evidence that polysulfides are chemically stable and abiotically generated from sulfide and elemental S in hyperalkaline conditions (Van Den Bosch et al., 2008). Additionally, the PsrABC polysulfide reductase has been identified as an important enzyme in deep seafloor vent ecosystems and it may function as an energy conserving enzyme by proton translocation across a cell membrane

(Jormakka et al., 2008).

Metagenome-assembled genomes demonstrate several organisms have the genomic capability to metabolize sulfide and S intermediates, including three high quality genomes of

Rhodocyclaceae. The Rhodocyclaceae family includes members capable of chemolithoautotrophic sulfur oxidation, nitrogen fixation, and methylotrophy, and can use various electron acceptors including nitrate, perchlorate, and chlorate (Oren, 2014).

Rhodocyclaceae have previously been observed in the Samail ophiolite via 16S amplicon 104 sequencing and were most abundant in a moderately alkaline well at pH 8.8 (Rempfert et al.,

2017). Outside of the Samail ophiolite, Rhodocyclaceae have been detected as up to 33% of the community within slightly alkaline (pH 7.9) waters from the ophiolite under CROMO in

California (Twing et al., 2017). The contigs constituting the Rhodocyclaceae MAGs generated in our study are primarily recruited from WAB188 sequences, where the SSU rRNA data indicates the genus Sulfuritalea is the most abundant S-oxidizing taxon. The genus Sulfuritalea belongs to the Rhodocyclaceae family (Oren, 2014) and a BLAST search of the translated amino acid sequence of dsrA from these MAGs identify them as closely related to dsrA proteins from

Sulfuritalea strains (NCBI accession MBK7024449.1, 81-83% identities, e-values of 0). The sole isolate of autotrophic Sulfuritalea hydrogenivorans demonstrated the genus was capable of oxidizing thiosulfate, elemental sulfur, and hydrogen for growth under circumneutral conditions

(Kojima & Fukui, 2011).

As putative S-oxidizers based on their taxonomy, the DSR pathways of the three

Rhodocyclaceae MAGs likely run in the oxidative direction taking hydrogen sulfide to sulfate. In support of this idea, metatranscriptomic, proteomic, and metagenomic analyses of

Rhodocyclaeceae genomes recovered from a geologic repository in the deep subsurface of

Finland suggested these organisms oxidize sulfite to sulfate via the DSR pathway or with the cytoplasmic sulfite dehydrogenase SoeABC (Bell et al., 2020). In addition to the DSR pathway, two of the Samail ophiolite Rhodocyclaceae genomes encode soeABC and soxYZ genes for the cytoplasmic sulfite oxidizing enzyme and the periplasmic sulfur substrate-binding protein

SoxYZ. SoxYZ is critical for sulfite metabolism with SoeABC, where it is theorized to act as a sulfite-binding protein and/or importer for the Soe enzyme (Dahl et al., 2013). While no genes for the Sor sulfite dehydrogenase were detected in assembled transcripts, this coupling of SoxYZ 105

and SoeABC indicates sulfite oxidation by Rhodocyclaceae in WAB188 and perhaps NSHQ04 is

feasible. Furthermore, the Rhodocyclaceae can oxidize thiosulfate to sulfate via the SOX enzyme

complex by their soxAXYZB genes, suggesting these organisms may be an important source of sulfate production from multiple intermediate S compounds in the Samail ophiolite.

The Rhodocyclaceae genomes also show a range of metabolic capabilities beyond the metabolism of S. The MAGs contain genes encoding a NAD-reducing hydrogenase, a partial

[NiFe]-hydrogenase (Hyd-1), C fixation by RuBisCo, and transporters for ammonia, phosphate, copper, ferrous iron, and ferric iron. Two-thirds of the MAGs show the genomic potential for dissimilatory nitrate reduction to ammonium, nitrite and nitric oxide reduction, suggesting involvement in the nitrogen cycle within the Samail ophiolite subsurface as well.

Rhodocyclaceae from other serpentinizing terrestrial subsurfaces have shown similarly complex and varied metabolic capabilities. Metagenomic sequences of the [NiFe]-hydrogenase gene hyaB and RuBisCo rbcL were associated with Rhodocyclaceae at CROMO, indicating these organisms may oxidize H2 and autotrophically fix C (Twing et al., 2017). In a separate study, metagenomic analyses indicated Rhodocyclaceae at CROMO are capable of generating formate by oxidizing formaldehyde produced from methane oxidation via the tetrahydrofolate-dependent and glutathione-dependent pathways (Seyler et al., 2020). Microbial methanogenesis and methane oxidation are active metabolisms in the Samail ophiolite (Kraus et al., 2021), thus it is

feasible that Rhodocyclaceae in Oman could be generating formate from formaldehyde produced

during methane oxidation. This formate might then be used by other community members

including the hydrogenotrophic methanogens themselves (Fones et al., 2020), as formate is both

an important electron donor and C source in Oman’s hyperalkaline fluids (Fones et al., 2019).

Further analysis of the metabolic flexibility of subsurface Rhodocyclaceae and its potential 106

adaptive advantages is required to distinguish this taxon’s complex role in the S, N, and C cycles

within the Samail ophiolite.

3.4.5. Potential for sulfate reduction by a member of the Melioribacteraceae

A MAG of the Melioribacteraceae family is observed to have a full complement of genes

for the DSR pathway in our study. The lone cultured representative of this family, Melioribacter

roseus, represents a deeply branching group in the Bacteriodetes/Chlorobi, has been recovered from deep subsurface aquifers in Siberia, and is a slightly alkaliphilic, halotolerant, and thermophilic organism able to use nitrite, arsenate, and Fe(III) oxides as electron acceptors

(Gavrilov et al., 2017; Kublanov & Podosokorskaya, 2018). Though Melioribacter roseus is not

known to have the ability to use the DSR pathway, genomic evidence indicates S-cycling

capabilities in other members of the Ignavibacteria class, which contains the Melioribacteraceae

family. For example, Anantharaman and colleagues (2018) reported a phylogenetically diverse

set of organisms with the ability for sulfate/sulfite reduction via DSR, greatly expanding the

number of microbial groups involved in dissimilatory sulfur cycling. The authors identify five

Ignavibacteria genomes recovered from the subsurface of Rifle, Colorado and a site in Japan

with the genomic ability for sulfate/sulfite reduction (Anantharaman et al., 2018). Furthermore, a

MAG of the genus Melioribacter was identified as a minor part of a bioreactor microbiome associated with the production of zero-valent S during the dissimilatory reduction of sulfate, though the Melioribacter MAG did not contain DSR genes (Fang et al., 2019). Thus, our identification of DSR pathway genes within a Melioribacteraceae representative may represent an expansion of the abilities of this group, though this hypothesis requires further testing.

107

3.5. Conclusion

Our description of the biological sulfur cycle within the Samail ophiolite using a

combination of genomic, metatranscriptomic, and geochemical methods confirms active sulfate

reduction by a small number of organisms in hyperalkaline and alkaline fluids.

Thermodesulfovibironales, Desulfonatronum, and Desulforudis are the dominant sulfate- reducing organisms and likely use the H2 as an electron donor, which is abundant in zones of the ophiolite. Additionally, a cryptic sulfur cycle may be present in which any sulfide produced from the microbial reduction of sulfate is immediately abiotically oxidized to sulfate. In peridotite- hosted wells with pH 8.4 – 10.6 waters, the reduction of elemental sulfur and/or polysulfides appears to be more important than sulfate reduction and is likely facilitated by members of the

Dethiobacteria. The reduction of polysulfide may be favored over elemental sulfur in the most hyperalkaline waters of NSHQ14. Transcript abundances further indicate thiosulfate oxidation is occurring in other hyperalkaline zones such as WAB71. In alkaline gabbro waters, Sulfuritalea of the Rhodocyclaceae family facilitates the oxidation of S compounds coupled to the reduction of nitrate. WAB188 also harbors sulfate reduction transcript activity, as Desulforudis reduces

sulfate or sulfite with electrons from hydrogen. Genomic and transcriptomic evidence also

suggests microbiota in the Samail ophiolite are under phosphate and potentially sulfate

limitation. Collectively, our findings characterize the Samail ophiolite subsurface S cycle as

dominated by a few S-reducing alkaliphilic lithoautotrophs and highlight the metabolic

importance of sulfate and intermediate S compounds in high pH ecosystems. These results help

to further characterize the subsurface biosphere within modern serpentinites undergoing active

low-temperature water-rock alteration.

108

3.6 References

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CHAPTER 4

SYNTROPHY AND DISTINCT MICROBIAL COMMUNITIES DIFFERENTIATE ROCK-

HOSTED AND FLUID-HOSTED LIFE IN A TERRESTRIAL SITE OF SERPENTINIZATION

A paper in preparation for submission to Geobiology

Emily A. Kraus1, Kaitlin R. Rempfert2, Patrick Thieringer1, Mariah Papac1, Alexis S. Templeton2, Oman Drilling Project Phase II Science Party, John R. Spear1*.

Abstract

The rock-hosted microbiome within serpentinites is a unique and relatively uncharacterized ecosystem on Earth’s and are a prime target for future NASA missions searching for extraterrestrial life in the solar system. Serpentinite rocks undergoing continued low temperature hydration and oxidation can support microorganisms that utilize the chemical products of water-rock interactions. However, the microbial community structure, potential metabolisms, and constraints on the distribution of the endolithic biosphere within serpentinite rock requires further investigation. Here, rock core samples obtained from zones of active

1 Civil and Environmental Engineering, Colorado School of Mines, Golden CO, USA 2 Geological Sciences, University of Colorado, Boulder CO, USA * Corresponding author: Department of Civil and Environmental Engineering, Colorado School of Mines, Golden CO 80401. [email protected] 117

alteration during the Oman Drilling Project in the Samail ophiolite are investigated with multiple

genomic techniques to identify life residing in serpentinite rock. The microbial biomass

introduced during drilling and core processing is also characterized from extensive

contamination controls. Despite abundant precaution, contamination is detected in these ultra-

low biomass samples. Rock cores contained an estimated 102 to 103 cells g−1 inferred by quantification of 16S gene copy numbers. Two years after the boreholes were drilled and cores collected, fluids were pumped to collect planktonic biomass from the boreholes after the subsurface had stabilized after drilling. Borehole fluids harbored an estimated 104 – 106 cells

mL−1 based on 16S copy numbers recovered. Potential endolithic organisms are identified in the

cores via small sub-unit (SSU) rRNA amplicon sequencing and contaminant sequences were

removed based on their taxonomy by using reports of common reagent contaminants. Inferred

metabolisms include putative methanogens, sulfate-reducers, syntrophs, and iron metabolizing

organisms. Comparison of phylotypes between planktonic and endolithic biomass indicates a

distinct rock-hosted community exists, potentially influenced by local fluid geochemistry.

Taxonomic identities, distribution of recovered sequences, and network analysis suggest that

syntrophic partnerships are important metabolic interactions in some deep serpentinites. The

detection of taxa capable of Fe(II) oxidation, Fe(III) reduction, or use of metallic Fe points to Fe-

bearing minerals (such as brucite or awaruite) as influential to shaping the serpentinite biosphere.

These results provide insight into the structure and habitability of the serpentinizing subterranean

biosphere and outline protocols to process low-biomass rock in the presence of abundant

contaminants.

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4.1 Introduction

The deep subsurface biosphere is estimated to hold approximately 15% of Earth’s

biomass (Bar-On et al., 2018) and 40 – 60% of all bacterial cells on the planet (McMahon &

Parnell, 2014; Wilkins et al., 2014), but subsurface habitability and endemic life is largely unexplored (Edwards et al., 2012). Microbial life has been detected in diverse deep rock types and contributes to subsurface biogeochemical cycles despite intense conditions of limited pore space and energy sources, as well as elevated temperatures and pressures (Edwards et al., 2012;

Kieft, 2016). The biosphere of the deep subsurface is supported by chemical energy generated by abiogenic processes. For instance, hydrogen (H2) is a potent reductant derived from multiple abiotic sources in the subsurface including the radiolysis of pore water (Blair et al., 2007; Lin et al., 2005), the reduction of water coupled to the oxidation of iron in silicate minerals in basalt- hosted aquifers (Stevens & McKinley, 1995), and water-rock interactions such as serpentinization (McCollom & Seewald, 2013; Schrenk et al., 2013). Serpentinization (Equation

1) can generate H2 at temperatures as low as 55℃ (Mayhew et al., 2013; Miller et al., 2016) and that H2 can in turn reduce dissolved inorganic carbon (DIC) to carbon compounds such as

− methane (CH4), formate (HCOO ), and carbon monoxide (CO) (McCollom & Seewald, 2007;

2+ − Seewald et al., 2006). In these systems, H2 and reduced compounds (e.g. Fe , HCOO , CH4) act

2− − as electron donors and oxidized species (e.g. CO2, SO4 , NO3 ) as electron acceptors for

endemic microorganisms (Brazelton et al., 2012; Canovas et al., 2017; Crespo-Medina et al.,

2017; Kohl et al., 2016; Miller et al., 2016; Rempfert et al., 2017; Russell et al., 2010; Spear et

al., 2005; S. Suzuki et al., 2017). The serpentinization reaction can therefore supply the electron-

donor requirements of chemolithoautotrophic life in ultramafic rock, linking the abiotic

lithosphere to the biotic realm with this production of chemical energy and carbon compounds, 119

thus giving rise to hypotheses that serpentinites (hydrated ultramafic rock) fostered the origin of

life on Earth and possibly on other planets (Chapelle et al., 2002; Müntener, 2010; Russell et al.,

2010; Sleep et al., 2011).

Mg1.8Fe0.2SiO4 + H2O → (Mg, Fe)3Si2O5(OH)4 + (Mg,Fe)(OH)2 + Fe3O4 + H2 (3.1)

Though most studies of subsurface serpentinites have used deep fracture fluids to

examine subsurface planktonic microbial diversity, community structure, and geochemical

controls on distribution (e.g. Brazelton et al., 2006; Crespo-Medina et al., 2017; Frouin et al.,

2018; Kraus et al., 2021; Meyer-Dombard et al., 2015; Miller et al., 2016; Morrill et al., 2013;

Rempfert et al., 2017; Seyler et al., 2020; S. Suzuki et al., 2013, 2017; Tiago & Veríssimo, 2013;

Twing et al., 2017), estimates place up to 80% of subsurface organisms as surface-attached to rock or mineral-filled fractures (Flemming & Wuertz, 2019). A few studies have investigated the rock-hosted (endolithic) microbial populations of subseafloor hydrothermal serpentinites at the

Atlantis Massif (Motamedi et al., 2020), submarine basalts (Dutta et al., 2019; Mason et al.,

2009; Santelli et al., 2008) and other terrestrial rock types (e.g. Momper et al., 2017a; Sahl et al.,

2008; Walker et al., 2005) with varied success despite challenges in sample acquisition, low biomass densities of rock, and abundant microbial contamination during drilling and processing

(Sheik et al., 2018; Wilkins et al., 2014). However, a molecular survey of endolithic life harbored within a low temperature terrestrial serpentinite ecosystem has yet to be completed. As the largest and best exposed ophiolite on Earth, the Samail ophiolite in the Sultanate of Oman contains serpentinites and zones of active water-rock alteration (Kelemen et al., 2013; Miller et al., 2016; Nicolas et al., 2000). A new suite of boreholes was drilled in the ophiolite at sites of 120

low temperature active alteration (<50℃) by The Oman Drilling Project in 2018 (Kelemen et al.,

2020), and ~1km total of intact core was recovered from three sites. From these cores, one hundred 50cm subsections of serpentinite cores were carefully collected for biological study

(Templeton et al., 2021).

In our investigation of endolithic life within the Samail Ophiolite, we focused on several questions: What organisms reside within serpentinites from a site of ongoing low temperature serpentinization? Can we distinguish endogenous microbiota from organisms introduced during drilling and sample handling? Can we infer the effects of local mineralogy or water geochemistry on microbial distribution from 16S abundance data? In this study, rock core samples from the surface to 300m depth along with a comprehensive suite of contamination controls from the

Samail Ophiolite were investigated with sequencing of the small subunit ribosomal RNA genes to characterize life residing in ultramafic rock. We suggest possible physical and geochemical constraints on the microbial populations within serpentinites and potential metabolic strategies employed by these microbes to inhabit this unique subsurface environment. Additionally, many studies of low biomass rock systems struggle to handle abundant contamination during sample drilling, collection, processing, sequencing, and analysis. Contamination in such ultra-low biomass samples has resulted in mischaracterization of rock-hosted life within literature and public data repositories (Sheik et al., 2018). With better consideration of contamination, we show improvement on the recovery, extraction, and analysis of DNA from endemic microbial life from subsurface rock core.

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Figure 4.1. Section of core with veining collected for biological study from (A) BA4A 250, (B) BA4A 300, and (C) BA4A 120m after sub-sectioning for various biological assays including DNA extraction. Photographers: John Spear and Daniel Nothaft. 122

4.2 Methods

4.2.1 Core collection, contaminant minimization, and contamination control sampling

Whole round cores (6.35 cm diameter) were obtained by diamond wire-line coring at

sites of active water-rock alteration of highly serpentinized dunite and harzburgite rocks. The

boreholes: BA1B, BA3A, and BA4A, in Wadi Lawayni of the Samail ophiolite, were drilled

from January - March of 2018 as part of the Oman Drilling Project (Kelemen et al., 2020;

Templeton et al., 2021). From every 10m interval drilled, a 10cm continuous piece of core was preserved for genomic work in an on-site mobile laboratory, with 100 total samples processed over the three boreholes. Handling of all core for biological study was conducted carefully to

reduce field- and human-associated contaminants. Core samples scrubbed with sterile deionized

water and wiped down with ethyl alcohol wipes, rinsed again with sterile deionized water,

wrapped in sterile foil, placed in a mylar bag, and sealed under an N2 headspace for anaerobic transport to the United States. Extensive sampling of contamination sources on-site and in the mobile lab was conducted to identify major contamination sources in the downstream analysis.

Possible contamination sources were sampled for each core collected and included the drilling fluid, drill barrel grease, air, local water, core wash and rock saw water, site dust, core scanner, and the core tray. Additionally, a non-reactive 0.25-0.45µm UV-fluorescent paint used as a contamination tracer (Friese et al., 2017) was added to the drilling fluid regularly. Subsequently, tracer concentrations were quantified in fixed subsamples of every core collected as a metric of core contamination (Templeton et al., 2021).

In the laboratory, whole core rounds were processed as aseptically as possible to extract

DNA from rock-associated biomass while minimizing contamination. Cores were kept at −80℃ until processing. Approximately 1-2 cm of rock surface were removed with a brick saw so that 123 all outer edges of the core whole round were removed. This exterior core material was removed with a diamond blade cleaned with 70% ethanol and methanol between samples, and the saw was modified to run with unrecycled deionized water that was sterilized by 0.2µM filtration and UV light exposure (i.e., MilliQ water). This “exterior” sample material and the remaining “interior” rock were processed in parallel for each core. Exterior (Ext) and interior (Int) core pieces were manually crushed to a powder in a steel mortar and pestle (Humboldt Manufacturing, Part No.

H-17270) that was sterilized with ethanol and a flame before use. All crushing work was performed within an ethanol-cleaned clean laminar flow hood and rock powders were used for

DNA extraction. Comparison of the interior and exterior core sequences serves as an additional check against microbial contamination, in addition to extraction and sequencing of contamination controls (e.g. French et al., 2015).

4.2.2 Collection of post-drilling stabilized borehole fluids in 2020

Water samples from boreholes BA1B, BA4A, and BA3A were collected with a submersible pump in February 2020 to assess the microbial community composition of the aquifer waters after re-equilibration from drilling of cores in 2018. Prior to sampling, each borehole was subjected to a preliminary flush to remove any residual drilling fluids or other contaminants. Each borehole was pumped for at least 30 minutes until physical chemistry measurements (temperature, pH, DO, conductivity, and redox potential) stabilized. The submersible pump (Grundfos SQ2-85, Grundfos Pumps Corp., Denmark, Netherlands) and sampling manifold were field washed for an additional 20-30 minutes prior to sample collection.

Biomass was collected by passing 3-5 liters of sampled water through a 0.22 µm polycarbonate in-line filter or until the filter began to clog or contained particulates. Filters were transferred to 124

bead tubes containing DNA/RNA Shield (Zymo Research, Irvine, CA) to stabilize nucleic acids

until transportation back to the laboratory. Additional samples were collected from well BA3A at

discrete depth intervals of 50, 100, 200, and 275 m using a 1L air-tight gas sampler with biomass

concentrated onto 0.1 µm syringe filters. Each syringe filter collected biomass from ~500 mL of

water before clogging and was similarly preserved in a DNA/RNA Shield within a bead tube

until extraction.

4.2.3 DNA Extraction and Sequencing of SSU rRNA

After testing protocols from Lever et al.(2015), commercial DNA extraction kits, and

other options to separate biomass from rock, an extraction method modified from Daly et al.,

(2018) was developed. Ten milliliters of DNA/RNA Shield, 10-15 mL of diethyl pyrocarbonate

(DEPC)-treated water, and 0.3M guanidine-HCl was mixed with approximately 30 – 35 g of rock

powder to form a crushed rock slurry in sterile 50mL conical tubes. The slurry was vortexed on

high speed for 10 minutes to lyse any cells from the rock powder and stabilize DNA to prevent it from binding to mineral surfaces. The slurry then underwent a phenol:chloroform:isoamyl alcohol (25:24:1, v/v) extraction wherein 15 mL of the phenol solution was added to the slurry, mixed by inversion, and centrifuged at 10,000 G for 10 minutes to separate the rock, organic, and aqueous phases. The aqueous phase was carefully pipetted off, placed in a new tube and washed with 15 mL of chloroform:isoamyl alcohol (24:1, v/v). The chloroform:isoamyl alcohol wash was mixed by inversion, centrifuged at 10,000G for 10 minutes to separate the phases and the supernatant was recovered in a clean tube. The chloroform:isoamyl wash step was repeated a second time and the aqueous phase was carefully moved to a sterile 50 mL conical tube. 0.2 volumes of 5M NaCl was added to the recovered supernatant, followed by 2.5 volumes of 100% 125

ethanol to precipitate DNA. The precipitation reaction was incubated overnight at 4℃ before

precipitated DNA was pelleted by centrifugation at 12,000G. Some core samples produced a gel-

like substance with the pellet at this step and the pellet or pellet / gel were resuspended in 50 –

100 µl of nuclease free water. The gel settled to the bottom of the resuspension mixture but did

not seem to impact quantification.

Extracted DNA from each sample was quantified with a Qubit™ 2.0 fluorometer and

dsDNA HS assay (ThermoFisher Scientific, Waltham, MA). Purity of the DNA extracts was

assessed with a Nanodrop™ spectrophotometer (ThermoFisher Scientific, Waltham, MA). All

samples, including those below the limit of quantification, underwent polymerase chain reaction

(PCR) to amplify a portion of the V3-V4 hypervariable region of the 16S small subunit

ribosomal RNA gene (SSU rRNA) common to all life. The reaction set up, thermocycler

conditions, use of the 515F-Y-M13 and 926R primers, bead cleaning, barcoding, and

pooling/normalization protocols are described elsewhere (Kraus et al., 2021). The pooled

amplicon library was sequenced on an Illumina MiSeq with V2 250bp PE chemistry at the Duke

Center for Genomic Sequencing (https://www.genome.duke.edu).

A full sequence analysis pipeline used in this work is described in Kraus et al. (2021).

Briefly, forward and reverse fastq files were demultiplexed and the barcodes, linker-primer sequence containing the M13, and primers were removed using Cutadapt (Martin, 2011). The demultiplexed sequences were input into DADA2 (Callahan et al., 2016) to construct an amplicon sequence variant (ASV) table. Chimeric sequences were removed from the ASV table and ASV taxonomy was assigned against the Silva database (v.138) (Quast et al., 2013). A phyloseq object (McMurdie & Holmes, 2013) was generated and mitochondrial and chloroplast sequences were removed from the dataset. 126

Contamination sequences from laboratory reagent controls were removed manually from

all samples by taxonomy using lists of common molecular lab reagent contaminants previously

described (Eisenhofer et al., 2019a; Salter et al., 2014; Weyrich et al., 2019). The full list of removed sequences and taxonomic assignments can be found in Table C.1. Taxa marked as common contaminants that could possibly be endemic to an anaerobic subsurface were retained.

Removal of contaminating sequences was assessed with other methods (e.g. the decontam package (Davis et al., 2018)), however these methods are confounded by very low sequence counts combined with possible backward contamination (true samples contaminating controls in extraction and sequencing). Removal of sequences attributed to Methanobacterium, a phylotype endemic to the Samail ophiolite waters (Kraus et al., 2021; Miller et al., 2018; Rempfert et al.,

2017), from fluid samples indicated these methods were insufficient to distinguish contaminants based on prevalence.

4.2.4 Network Analyses

Co-occurrences of retained ASVs between samples were used to construct an association network of all borehole and control samples. The table of ASVs was binarized to 0’s and 1’s (1 indicating presence of an ASV; 0 an absence of ASV) and input into the Anets algorithm developed by Karpinets et al. (2018). The Anets tool identified edges and calculated Spearman correlations and p-values for each edge. The produced graph was filtered to edges with p-values

< 0.05 and visualized with Cytoscape v.3.8.2 (Shannon et al., 2003).

Network analyses were conducted on the retained ASV sequence counts after contamination filtering to reconstruct microbial interactions and groupings within the subsurface.

ASVs found within rock samples were used to construct a graph with the SpiecEasi package 127

(Kurtz et al., 2015) with the Neighborhood selection (MB) method and a lamba min ratio of

0.01. A weighted adjacency matrix was extracted from the SpiecEasi output and the resultant

graph was visualized in Cytoscape v.3.8.2 with the yFiles Organic Layout (Shannon et al., 2003).

The graph was filtered to remove negative edges and edges with low frequency (representing low

reproducibility) < 0.25. Clustering of ASV nodes was performed with the Community cluster

(GLay) algorithm in the clusterMaker application of Cytoscape (Morris et al., 2011).

4.2.5 Quantification of 16S rRNA copy number and sequence abundance normalization

Bacterial and archaeal 16S rRNA gene counts were quantified via a TaqMan® probe assay (BactQuant) developed to provide broad phylogenetic coverage of a portion of the V3-V4 region (Liu et al., 2012). Samples of DNA from core exteriors and interiors, no-template controls, and a fluorescent control (no-master mix added) were assayed in technical triplicate reactions on a QuantStudio 3 quantitative PCR (qPCR) instrument (Applied Biosystems,

Waltham, MA) in Fast mode. The no-master mix control was used to detect fluorescent contaminants in the PCR possibly introduced through drilling or other sources. A five-point standard curve was produced using Bacterial DNA Standards (Zymobiomics, Irvine, CA).

Assays of 20 µL reactions contained 1X of TaqMan Fast Advanced Master Mix (Applied

Biosystems, Waltham, MA), 225 nM of the BactQuant TaqMan probe, 1.8 µL of each primer, and 2 µl of template DNA. The PCR cycling conditions consisted of 2 minutes at 50℃, 2 minutes at 95℃, and 45 cycles of 1 second at 95℃ followed by 20 seconds at 60℃.

Copy numbers of the 16S target region were estimated using the mean quantified amount of ng of DNA for each sample, the amplicon product length (466 bp) (Liu et al., 2012), and the average weight of 660 Daltons (Da) for a basepair of double-stranded DNA (Equation 2). 128

Estimates of cells per gram of rock, per mL of fluid filtered, or per hour of air filtered were

calculated assuming an average copy number of 3.61 previously reported for bacteria/archaea

(Sun et al., 2013).

Read counts produced in the SSU rRNA sequencing were transformed to relative

abundances prior to contamination filtering. The relative abundances were multiplied by the

mean 16S copy number calculated for each core sample to produce a biomass-normalized and

material-normalized abundance estimate for each ASV in the rocks.

3.2) ( ( ) (. /) Copy number = ( )( /) ( /)

4.3 Results

Thirteen cores were cut and processed from BA3A, BA4A, and BA1B (e.g., Figure 4.1,

Table 4.1). Samples are referred to by well, depth in meters (m), and interior (Int) or exterior

(Ext) portion (e.g., BA4A 120 Ext). A sulfidic black fluid was released during paring of exterior core for several samples from BA1B and BA4A, but not from any BA3A cores. A detailed description of the borehole sites and the black sulfidic fluids can be found in Templeton et al.

2021.

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Table 4.1. Summary of rock core samples in this analysis. Values for pH and Eh were measured after boreholes were drilled in 2018.

Depth (m) pH Eh Mineralogy 50 10.9 −650 Harzburgite with carbonate veins 110 10.8 −650 Harzburgite with some veins BA3A 180 10.6 −650 Harzburgite with some veins 200 10.5 −675 Harzburgite and pyroxenite 300 10.5 −675 Harzburgite, few veins 30 9.7 −450 Dunite 120 10.2 −600 Dunite BA4A 180 10.5 −550 Harzburgite 230 10.3 −575 Harzburgite 37 9.3 150 Black sulfidic fluid 50 9.5 150 Black harzburgite BA1B 120 10.6 −25 Black harzburgite and dunite 150 10.8 −150 Black harzburgite and dunite 260 9.7 −350 Black harzburgite and dunite

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Table 4.2. Amount of material used in extraction, mean quantified DNA, and mean 16S copy number for core, black fluid, air, and water samples. *The air sample was collected from 4 hours of air filtration via a vacuum pump at chest level in the field sampling trailer.

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4.3.1 Samail ophiolite cores have an estimated 102-103 cells g−1

For each sample, the mean and standard deviation of DNA quantified was used to estimate 16S copy numbers and cells per gram or mL of material used in each extraction (Table

4.2). Rock samples generally had between 6.4 x 102 and 4.5 x 103 cells g−1, though four samples

(BA3A 50 Int and Ext, BA3A 200 Ext, and BA4A 120 Int; where Int refers to pared, interior core and Ext, exterior parings) showed unusually high (1010 -1011 cells g−1) estimates (Figure

4.2). The lowest estimates of cells/gram rock that were detectable were in BA4A 30 Ext and

BA1B 50 Ext with 6 x 102 and 9 x 102 cells g−1 respectively. Excluding the high cells g−1

outliers, all rocks with detectable DNA had a mean of 2.6 x 103 cells g−1. From this group,

interior and exterior core pieces averaged 3.8 x 103 and 1.8 x 103 cells g−1, respectively.

Based on qPCR quantification of 16S copy numbers, the sulfidic black fluid collected

during drilling at 37m in BA1B had an approximate range of 7 x 105 to 2 x106 cells mL−1.

Similarly, the aquifer fluids collected in 2020 from the stabilized BA3A, BA4A, and BA1B

boreholes showed similar values in the 104 – 106 cells mL−1 range with qPCR data. BA3A

generally contained 104 cells mL−1 consistently as depth increased.

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Figure 4.2. Estimates of cells g−1 or cells mL−1 calculated from quantified 16S copy numbers. The BA1B 120 air sample (not plotted) had an estimated 8 x 104 (± 1 x 104) cells per hour of air filtered.

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4.3.2 Sequencing & contamination removal

DNA extracted from the exterior and interior rock of 4 cores from BA1B, 5 from BA3A,

4 from BA4A, (26 total core samples) and 72 total lab and drilling contamination controls were

sequenced. Some samples with no detectable 16S copies in qPCR did produce reads during

sequencing. Demultiplexing resulted in variable numbers of sequences assigned to all samples, though some samples had no reads remaining after denoising and chimera removal. After mitochondrial and chloroplast sequences were removed from the amplicon sequence variants

(ASVs) to retain bacterial and archaeal ASVs, 4,576 taxa in 68 control and core samples remained in our analysis. Removal of common lab and reagent contaminants resulted in 2,715 taxa and 66 samples retained, representing a mix of subsurface-derived and contamination control-derived sequences.

Rock core interior and exterior samples displayed a difference in alpha diversity after contamination filtering based on the number of observed ASVs within sample types (Figure 4.3).

Core interiors from each borehole averaged fewer observed ASVs than the corresponding exteriors, with the lowest average observed ASVs in hyperalkaline BA3A (Table 4.1).

Furthermore, the average alpha diversity of the sulfidic black fluid sampled in BA1B is nearly the same as the exterior cores from BA1B. The alpha diversity of all sample types after contamination filtering can be found in Appendix C, Figure C.1.

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Figure 4.3. Alpha diversity by number of observed amplicon sequence variants (ASVs) of interior and exterior cores and sulfidic fluid from BA1B, BA3A, and BA4A boreholes.

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Figure 4.4. Sample network made with ASV co-occurrences via the method of Karpinets et al., (2018). Rectangular nodes represent biological samples. The greyscale color of the connecting edges indicates the Spearman correlation between two nodes. Nodes are colored by borehole (BA3A = yellow, BA4A = green, BA1B = blue, extraction controls = grey) and field contamination controls from boreholes are lighter in color.

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4.3.3 Correlations between samples indicate possible contamination sources

Despite abundant caution in sample preparation and sequence analysis, contaminant sequences likely remain in the dataset, particularly from field site sources that may not be represented in the list of lab reagent contamination sequences gleaned from publications. As an important check on possible contamination sources, network analysis was used to identify similarities between samples based on ASVs. Network analyses found groupings of samples and

ASVs after lab contaminant ASVs were removed from the dataset. An association network of the biological samples was constructed with ASV co-occurrences (presence/absence) between samples (Figure 4.4). Most of the correlated samples consisted of field and laboratory controls with low Spearman correlations (<0.5, p < 0.05). Drilling mud and core wash water were correlated across drilling sites based on ASV co-occurrence. A sample of black sulfidic fluid from BA1B 37m and core BA3A 300 Ext were associated with an air sample collected from the mobile lab at the BA1B drill site: BA1B 120 air, with Spearman correlations < 0.3 (p values <

0.05). Additionally, the ASVs of the BA3A 300 Int core piece were significantly correlated

(Spearman correlation of 0.82, p < 0.001) to a swab of the core scanner taken while drilling at

BA4A.

4.3.4 Possible endemic organisms in serpentinites from the Samail ophiolite

Anaerobic organisms potentially endemic to the deep subsurface are identified in the

ASVs and network analysis. Members of multiple phyla including Desulfobacterota,

Euryarchaeota, Crenarchaota, Patescibacteria, Caldisericota, Chloroflexi, Planctomycetes, and others are detected solely in rock core extracts. Phyla specific to contamination controls samples

137 includes ASVs of Acetothermia, Methylomirabilota, Nanoarchaeota, and Vertebrata among others.

Organisms involved in sulfur (S) and iron (Fe) cycling metabolisms, methanogenesis, syntrophic partnerships, and alkaliphilic microorganisms are notable among the deep subsurface candidates (see Table C.2 for all taxa remaining after contaminant filtering). Sulfate-reducing microorganisms are prominent in several core samples, indicating they are an important part of the endolithic community. ASVs identified as putative sulfate-reducing bacteria (SRB) include

Desulfovibrio, Desulfomonas, and Desulfomonile (Table 4.3, Figure 4.5A). Methanogens including Methanosaeta, Methanospirillum, and Methanolinea are found in cores, and an ASV of Methanobacterium spp. was detected in black sulfidic fluid samples from BA1B 37m.

Additionally, sequences identified as Smithella, Desulfovibrio, and Syntrophus are detected in several core samples. When detected, these organisms are often co-occurring in samples from cores BA3A 180 Int, BA3A 300 Ext, and BA4A 120 Ext (Figure 4.5A).

Microorganisms that produce propionate, acetate, H2, CO2, and/or other products through fermentation are distributed in both rock and control samples. Some, such as Macellibacteroides spp. and the propionate producing Propionivibrio spp. in BA4A 120 Ext, are associated with specific core samples. Other fermenting organisms such as Alishewanella are detected exclusively in field controls from drill sites. However, most fermenters are observed in multiple rock and control samples. Examples of this last grouping include Lactivibrio spp. in BA4A 120

Ext and BA3A 300 Ext, multiple members of the Lachnospiraceae family in BA3A 180 Int, and

Tepidimicrobium spp. in BA4A 180 Int.

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Table 4.3. Possible endemic ASV sequences inferred by taxonomic identity and previous reports of its presence subsurface environments.

Group ASV affiliation Methanogens Methanobacterium, Methanospirillum, Methanosaeta, Methanolinea

Syntrophs Smithella, Syntrophus, Desulfovibrio, Geobacter, Desulfuromonas

Sulfate reducers Desulfovibrio, Desulfomonile, Dethiosulfovibrio, Desulfosporosinus,

S reducers Trichloromonas, Geotoga, Desulfuromonas

Sulfuritalea, Thiobacillus, Thiocapsa, Thiovirga, Thiothrix, Sulfurisoma, S oxidizers Sulfurifustis, Elioraea, Arcobacter, Thauera

Fe(III)-reducers Geobacter, Tepidimicrobium, Acidibacter, Shewanella, Desulfuromonas

Fe(II)-oxidizers Ferrovibrio, Denitromonas, Rhodobacter, Ferritrophicum

Fe0 metabolizers Prolixibacter, Geobacter

H2 oxidizers Hydrogenophaga, Sulfurisoma

Propionivibrio, Geotoga, Micropruina, Lactivibrio, Lachnospiraceae Fermenters family, Macellibacteroides, Butyrivibrio, Alkalibacterium, etc.

Lithotrophs MND1, Candidatus Omnitrophus, Thauera, Rhodobacter

Candidatus Nomurabacteria, Candidatus Gottesmanbacteria, Candidatus Candidate phyla Peregrinibacteria, Candidatus Daviesbacteria, Candidatus Shapirobacteria, Candidatus Peribacteria, Absconditabacterales Ammonia Nitrosomonadaecae, Nitrososphaeraceae oxidizers Denitromonas, Thauera, Denitratisoma, Dokdonella, Dechloromonas, Nitrate reducers Defluviicoccus, Candidatus Solibacter, Sulfuritalea Nitrogen fixers Azomonas, Desulfovibrio

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Other organisms potentially endemic to the Samail ophiolite subsurface are detected in low sequence counts. Fe-oxidizing organisms (Ferrovibrio, Denitromonas, etc.) are detected in

BA3A 180 Int, BA4A 180 Int, BA1B black fluids, and several controls. Fe(III)-reducers

(Geobacter, Acidibacter, Shewanella) are found within at least one core from each hole and the

BA1B sulfidic fluid. For nitrogen cycling, ammonia oxidizing bacteria from the family

Nitrosomonadaecae (e.g. the genus MND1) are noted in BA1B 120 Int and Ext, BA4A 230 Int, and BA3A 300 Ext; and an ammonia-oxidizing archaeon (“Candidataus Nitrososphaera”) is observed solely in BA3A 180 Int. Sequences identified as nitrate-reducing organisms (e.g.

Thauera, Denitromonas spp.) are in BA1B 120 Ext and sulfidic fluid, BA4A 120 Ext, 180 Int, and 230 Int, and BA3A 180 Int and 300 Ext. Additionally, a nitrogen-fixing bacterium under aerobic conditions, Azomonas spp., was detected solely in BA4A 120 Ext, BA1B 150 Int, and

BA1B sulfidic fluid. ASVs from the genus “Candidatus Omnitrophus” are detected in BA4A

120 Ext; the members of which are chemolithoautotrophs and in some cases magnetotactic.

Additionally, members of the Patescibacteria phylum are observed in our study, several of which are found only in core extracts. These include the orders Candidatus Peregrinibacteria in BA1B

120 Ext, Cand. Shapirobacteria and Cand. Gottesmanbacteria in BA4A 120 Ext, Cand.

Normurabacteria in BA3A 180 Int, and Cand. Peribacteria in BA1B 120 Int.

Like several orders of the Patescibacteria above, some organisms highlighted here are found solely in rock cores while others are detected in sequences from both cores and contamination control samples. For example, Desulfovibrio is observed in BA3A 100 Int and the rocks listed above, as well as in 7 total air, saw, “blue truck water”, and drilling mud controls and

2 out of 7 extraction controls. This distribution makes it difficult to assess the origin of

Desulfovibrio. However, at the individual ASV level, the most abundant Desulfovirio ASV is 140 found in the cores and controls mentioned above while the other 10 ASVs show more specificity to the BA3A 180, 300, and BA4A 120 cores.

4.3.5 Comparison to post-drilling stabilized BA1B, BA4A, and BA3A borehole fluids sampled in 2020

The distribution of key taxa differs between the rock cores and post-drilling stabilized borehole fluids in both presence/absence and approximate abundance analysis (Figure 4.5A,

4.5B). For example, the hydrogenotrophic methanogen Methanobacterium is exclusively found in the stabilized 2020 waters and the black sulfidic fluids we sampled in 2018, and

Methanobacterium is the only methanogen detected in the fluids in this study. Other methanogens including Methanosaeta and Methanospirillum, are found in several cores and controls but not in the higher biomass fluids. Similarly, the syntrophic Smithella and Syntrophus genera are within biomass extracted from several rocks but are not detected in any fluid sample.

The types of sulfate reducing organisms in the fluids and core are variable, but stabilized borehole fluids generally contain the class Thermodesulfovibrionia, Desulfonatronum,

Desulfomicrobium, and “Candidatus Desulforudis” whereas the rocks contain greater counts of

Desulfovibrio and other sulfate-reducers. Similarly, other taxa found in rock cores are not within the stabilized borehole water samples including ASVs of the genera Isoptericola, Geobacter, and

Shewanella.

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Figure 4.5A. Read counts of methanogens, sulfate reducers, and syntrophic organisms for 2018 samples normalized by mean copy number from qPCR. Samples BA3A 110 Int, BA4A 180 Int, and BA4A 230 Int had no quantifiable DNA during qPCR, though the samples contained reads attributed to Desulfovibrio, Desulfuromonas, and Smithella respectively. See the Figure C.2 (Appendix C) of unadjusted read counts for those samples. 4.5B. Distribution of methanogens, sulfate reducers, and syntrophic organisms from fluids collected after boreholes had stabilized post-drilling in 2020. Counts are normalized by mean copy number.

A. Rock

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Figure 4.5 continued

B. Stabilized fluids

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4.3.6 Network analysis suggests interactions between taxa

Several groupings of ASVs observed in the network graph suggests interaction between

specific phylotypes in the serpentinite rock-hosted subsurface. The most striking of these

groupings, the largest cluster, includes edges linking syntrophic organisms, methanogens, sulfate

and sulfur reducing organisms, fermenters, and other possible subsurface organisms capable of

metabolizing Fe and S (Figure 4.6). In this cluster, an ASV of Smithella adjoins

Methanospirillum, Kapabacteriales, and Methanosaeta, which are connected to the cluster through a Spirochaetales, Desulfuromonas, and Lactivibrio, a fermenter (bottom right panel

Figure 4.6). This large cluster and other groups (top right panel Figure 4.6) contain taxa known to have lithoautotrophic members, such as “Candidatus Omnitrophus”, Prolixibacter,

Desulfomonile, Thauera, and MND1 spp. Information on the node identities, edges, and clusters

is available in Table C.3 and C.4.

4.4 Discussion

4.4.1 Contaminating DNA remains in low biomass samples despite efforts at minimization and removal

Though care was taken to minimize contamination of cores with field site and laboratory

biomass, contaminating DNA constituted a portion of sequencing results for all samples.

Methods for removal of contaminant DNA that rely on sequence relative abundance differences

across control vs. true samples, such as the decontam and sourcetracker2 packages, were

ineffective due to low biomass of the target environment and likely backward (sample to control)

contamination. A simpler approach was used to filter known lab reagent contaminants that

commonly impact low biomass studies (Eisenhofer et al., 2019b; Salter et al., 2014; Sheik et al.,

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2018; Weyrich et al., 2019). Many of these contaminants are aerobic organisms which are

unlikely to reside within the anaerobic or suboxic subsurface of serpentinite rock. Out of an

abundance of caution, the initial 4,576 taxa were trimmed to 2,715 taxa after filtering in this

study to better understand actual subsurface microbiota in the Samail serpentinite ecosystem,

however, some retained taxa are likely to be contaminants from drilling. Thus, removal of

documented aerobic lab reagent contaminants by taxonomic identity was employed and is

effective in reducing total contaminating sequences where other statistical methods were

questionably accurate in this low biomass environment.

This taxonomy-based removal method does not account well for field-site-specific

contaminants however, and the presence of ASVs from field controls and other aerobic taxa in

rock samples indicates contaminant DNA remains within the data set. To assess if core samples

were significantly impacted by field controls, we used a network analysis detailed in Karpinets et

al. (2018) that uses the presence/absence of each ASV across all samples instead of relative sequence abundance comparisons. Samples that both contain an ASV were therefore more correlated than samples that did not both share the presence of that sequence. The “Anets” algorithm (Karpinets et al., 2018) then calculated a Spearman correlation coefficient for each pairing of samples to produce the network graph (Figure 4.4) and visualize significantly correlated rock core and field or laboratory control samples. Since this method does not determine an ASV’s origin, sequences were not removed. This analysis does provide a better idea of which specific rock samples are more associated with, and therefore likely impacted by, contamination. Therefore, results from BA3A 300 Int and Ext and a replicate of the BA1B sulfidic goo sample should be examined with caution. Contamination will continue to be a challenge for molecular studies of deep subsurface ophiolite-hosted life due to the low biomass 145

nature of serpentinite rocks and the impossibility of fully aseptic drilling in real-world

conditions. These challenges high-light the continual need for low biomass studies to carefully

plan and sample for all possible contamination sources.

Microbiota in laboratory air is a primary source of contamination in this study. A field

control of the mobile laboratory air from BA1B was correlated to a variety of other sample types including a BA3A core, a sulfidic fluid sample, and eight other controls. A similar finding was reported by Motamedi and colleagues in which lab air appeared to be a predominant source of contamination during sequencing of low biomass serpentinite core samples from the Atlantis

Massif subseafloor (Motamedi et al., 2020). Additionally, the interior of BA3A 300 was also significantly associated with a swab of the core scanner collected at the BA4A site suggesting that both the interior and exterior of BA3A 300 contain some contamination from these sources.

Importantly, the majority of cores were not significantly correlated with other cores or controls.

This may be due to a lack of sufficient data among co-occurring ASVs needed to produce a statistically meaningful relationship. However, an alternative hypothesis for this lack of correlation is that the core microbial communities recovered are different and truly not correlated to other samples. Subsurface microbiota hosted within the rock matrix or on fracture surfaces are likely to be highly specific to the local mineral surface composition (Jones & Bennett, 2017), which may explain such differences between rock samples from the Samail ophiolite.

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Figure 4.6. Clusters of associated ASVs generated in network analysis (left). Nodes represent ASVs. Edges are colored based on the edge frequency (indicating reproducibility) such that darker red indicates a greater frequency (and thus reproducibility). Two highlighted interaction graphs among ASVs showing potential relationships between methanogens, sulfate reducers, syntrophs, lithotrophs, and fermentative organisms in the subsurface (right).

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148

Consistent with the idea that rock is a low biomass environment, cell densities in serpentinite rock samples, as estimated by qPCR, ranged from an estimated 102 to 103 cells g−1 with a mean of 2.6 x 103 when the outlying high counts are excluded. These cell densities are well aligned with direct cell counts of 101 to 103 cells g−1 from interior cores of BA1B, BA3A, and BA4A used for sulfate reduction assays, and are below the 103 to 107 cells g−1 detected in fixed core materials dominated by vein and fracture material (Templeton et al., 2021). Our estimates of 102 to 103 cells g−1 are also consistent with previous reports of low direct cell counts of 10s to 1000s of cells cm−3 in subseafloor serpentinite rocks at Atlantis Massif (Früh-Green et al., 2018). Though reports of cell count estimates from serpentinite rocks are minimal, measurements using qPCR methods have been reported for other igneous rock types such as basalt. Studies of the continental basaltic subsurface have indicated 105 cells g−1 rock within the

Deccan traps of India (Dutta et al., 2018), 104 cells g−1 in oceanic basaltic crustal rocks

(Jørgensen & Zhao, 2016), and typically 104 to 106 cells g−1 in basalts worldwide (Magnabosco et al., 2018). Contaminating microbes are likely within our DNA extracts as well as in those of other published studies (Sheik et al., 2018), indicating the endemic biomass of rock is lower than we report here. Another important factor when considering these cell estimates is that we do not correct our estimates to account for DNA extraction efficiency, which is less than 100% as some biomass is lost in sample processing. Therefore, our 102 to 103 cells g−1 estimates for serpentinites may be lower than the true endogenous biomass. For the stabilized borehole fluids, our qPCR-based cell density estimates range from 104 – 106 cell mL−1 which is concordant with direct cell counts of 105 cells mL−1 on Samail ophiolite aquifer fluids sampled from nearby boreholes in 2017 (Fones et al., 2019).

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The cell density estimates for four samples (BA3A 50 Int and Ext, BA3A 200 Ext, and

BA4A 120 Int) are oddly higher than aquifer fluids sampled in previous years. These estimates

are each derived from one of three successful replicate reactions during qPCR, indicating these

outliers may be caused by a protocol issue in plate or quantification on the QuantStudio

instrument. However, two of the samples with high DNA quantities were from the interior and

exterior of the same core, BA3A 50, and the mean copy numbers per gram rock measured were similar with 5.7 x1011 for the exterior and 6.9 x 1011 for the interior, suggesting a non-random

error. Compared to the planktonic and rock matrix biomass, higher cell densities are observed on

rock fracture surfaces up to 109 cells g-1 (Suzuki et al., 2020; Wanger et al., 2006), indicating

higher cell densities are possible in the deep subsurface on fracture surfaces. Therefore, we opted

to include these observations.

We hypothesized that exterior core pieces would contain greater cell densities than

interiors due to contamination from drilling-induced fractures and fluid influx into the rock pore

space and any fracture zones. Interior core within this study had a slightly higher mean estimate

of cells g−1 than exteriors. However, a greater number of the interior pieces showed no detectable

amplification via quantitative PCR indicating the interior mean estimates may not be fully

accurate due to a paucity of data. In addition, counts of observed ASVs demonstrated interior

cores have lower average alpha diversity when compared to corresponding exterior samples

within boreholes. This finding implies that the core exteriors may be more impacted than the

interiors as drilling and core handling contributed contaminant ASVs to cause this increase.

Consistent with this explanation, the average alpha diversity of exterior core from BA1B was

nearly the same as the BA1B sulfidic fluids, indicating the sulfidic fluids and exterior cores have

approximately the same number of organisms represented in our sequence data. These

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observations collectively point to some contamination of core exteriors from drilling and

formation fluids, which is confirmed by enumeration of the fluorescent tracer particles added to

the drilling fluids, as discussed in Templeton et al. (2021).

4.4.2 Endolithic methanogens and sulfate reducers in terrestrial serpentinite rock

Organisms were identified as potentially endemic to the deep subsurface of the Samail

ophiolite based on belonging to a phylotype associated with subsurface metabolism as indicated by 16S taxonomy assignment despite the potential presence of contaminant microbiota in our samples. Chemolithoautotrophs, methanogens, S- and Fe- reducers and oxidizers, and syntrophs are likely candidates to be endemic to the Samail ophiolite subsurface rock. Our findings are aligned with organisms observed in other deep subsurface systems, but differ from microbial communities previously identified in subseafloor serpentinite rocks wherein no methanogens or sulfate-reducing organisms were observed (Motamedi et al., 2020). Many of the genera detected within cores of this study are commonly found within the Census for Deep Life and considered endemic to the deep subsurface (Sheik et al., 2018), including Desulfovibrio, Methanosaeta,

Syntrophus, Ferritrophicum, Caldisericum, Desulfuromonas, Hydrogenophaga, and

Methylotenera among others.

Methanogens are commonly observed in deep subsurface environments and have been previously observed in serpentinizing systems (Brazelton et al., 2017; Crespo-Medina et al.,

2017; Kohl et al., 2016; Morrill et al., 2013; Sánchez-Murillo et al., 2014; Zwicker et al., 2018), including in Samail ophiolite waters. For example, Methanobacterium has been consistently observed in the most alkaline waters of the Samail ophiolite aquifer over several years (Fones et al., 2020; Kraus et al., 2021; Miller et al., 2016; Rempfert et al., 2017) and is found within our

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BA1B sulfidic fluid sample, indicating a successful extraction protocol by its recovery.

Interestingly, other methanogens are detected in rock but not fluid samples despite the higher

biomass density of the fluids. These rock-hosted methanogens include two H2- and formate-

using organisms, Methanolinea in BA1B 120 and Methanospirullum in BA3A 180, 300, and

BA4A 120, and the aceticlastic Methanosaeta in BA3A and BA4A and an air control sample.

The aceticlastic Methanosaeta may be consuming available acetate or working with syntrophic partners such as Smithella or Desulfovibrio to reside in the serpentinite rock of BA3A and BA4A

(Embree et al., 2015). This indicates these methanogens may specialize to reside on or within

rock surfaces/pore spaces whereas Methanobacterium is adapted to the alkaline ophiolite waters.

Sulfate reducing organisms are also observed in the Samail ophiolite waters and other

rock studies (Miller et al., 2016; Rempfert et al., 2017). Members of the families

Thermodesulfovibrionaceae and Desulforudaceae and the genus Desulfonatronum have been

observed in alkaline waters of the Samail ophiolite (Rempfert et al., 2017; Nothaft et al., in

review), yet Desulfovibrio, Desulfuromonas, and Desulfomonile spp. are the most prominent

sulfate and sulfur reducers in the rock-derived biomass. The most widely distributed, the sulfate-

reducing Desulfovibrio, is found within cores from BA3A and BA4A as well as sulfidic fluids

from BA1B and an air contamination control sample, making it difficult to discern the physical

origin of this strain. However, Desulfovibrio is one of the most abundant and most commonly

detected genera identified as likely endemic to the subsurface within the Census for Deep Life

dataset (Sheik et al., 2018). Due to the amount of drill core processed onsite, endemic organisms,

such as Desulfovibrio could have become ’contaminants’ despite efforts to keep things as clean

as possible. Members of the genus Desulfovibrio are aerotolerant and can use a variety of

electron donors including H2 and formate to reduce sulfate to sulfide, with some members also

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able to use sulfite and thiosulfate as electron acceptors (Kuever, 2014). Additionally,

Desulfovibrio strains are capable of syntrophic growth with methanogens through interspecies electron transfer via H2 or formate (Dolfing et al., 2008; Embree et al., 2015). Sulfate reducers and some Desulfovibrio spp. are also involved in microbial Fe cycling via corrosion through direct electron uptake and hydrogen sulfide (H2S) generation (Enning & Garrelfs, 2014). These metabolic capabilities of sulfate reducers make them good candidates as endolithic taxa in the

Samail ophiolite where the use of H2, formate, syntrophic relationships, and Fe minerals would be beneficial adaptations. Additionally, Thermodesulfovibrionaceae, a sulfate-reducing family found in the Samail ophiolite fluids over several years (Miller et al., 2016; Rempfert et al., 2017;

Kraus et al., 2021, Nothaft et al., in review) and in the stabilized borehole waters, is not detected in the serpentinite sequences, suggesting these organisms have a planktonic lifestyle in the subsurface.

ASVs from the orders Kapabacteriales and Spirochaetales are observed to be associated with the sulfate reducers and methanogens highlighted in Figure 4.6. Kapabacteriales are part of the Bacteroidetes/Chlorobi/Ignavibacteria superphylum and metagenome-assembled genomes

(MAGs) representing this lineage have been recovered from a deep subsurface aquifer in

Western Siberia and from biomass associated with an algal bloom (Al-Saud et al., 2020;

Kadnikov et al., 2020). These MAGs demonstrated Kapabacteriales strains are likely chemolithotrophic and have the capability for the reversible pathway of dissimilatory sulfate reduction, sulfur assimilation, and the reduction of nitrite and nitrous oxide (Al-Saud et al., 2020;

Kadnikov et al., 2020). Fermentative Spirochaetales have also been detected in deep subsurface environments including the Western Siberian aquifer and Homestake Gold Mine (Karnachuk et al., 2020; Momper et al., 2020). Sequences of Spirochaetales were also recovered from the

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Deccan Traps basaltic subsurface and were associated with methanogens, sulfate reducers, and

other subsurface organisms (Dutta et al., 2018). Therefore, Kapabacteriales, Spirochaetales, and

many others in our SSU rRNA and network analyses are likely part of the endolithic and/or

planktonic microbiome in the Samail subsurface.

Organisms known for their streamlined genomes and unusual characteristics are detected

in core extracts. Sequences from five orders of the Patescibacteria were detected only in rock

core biomass extracts from BA1B 120, BA4A 120 Ext, and BA3A 180 Int; and two other orders

were noted in both rock and control samples. Patescibacteria are common in groundwater

environments, have reduced genomes, small cell volumes, simplified membranes, and are

thought to be either symbiotic or primitive, ancestral fermenters due to their limited genomic

capabilities (Beam et al., 2020; Brown et al., 2015; Rinke et al., 2013; Tian et al., 2020). These traits may be advantageous in deep subsurface aquifers where nutrient (C, S, P, N, etc.) concentrations are likely low and limiting (Fones et al., 2019; Tian et al., 2020) or in rock pore spaces where both nutrients and space are restricted. Our study cannot distinguish whether these

Patescibacteria are planktonic or associated with rock fracture surfaces or the rock matrix. Lazar et al. (2019) suggested Patescibacteria prefer a planktonic existence based on their relative abundance in groundwater and as < 1% of the rock matrix community in subsurface limestone and mudstone. However, we observed some Patescibacteria orders to be specific to rock core samples despite abundant contamination sampling and three of the five rock-specific orders were not detected in the 2020 borehole fluids that had stabilized after drilling. Therefore, these subtypes of Patescibacteria (Candidatus Peribacteria, Candidatus Normurabacteria, and

Candidatus Shapirobacteria) may be capable of an endolithic lifestyle.

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Other ASVs in this study were identified as chemolithoautotrophic organisms and

mineral associated phylotypes. For example, “Candidatus Omnitrophus” in BA4A 120 Ext is a

chemolithoautotrophic genus of which one species is magnetotactic (Kolinko et al., 2016) and is

within our network graph (Figure 4.6) alongside other endemic ASVs such as the Fe0-

metabolizing Prolixibacter (Iino et al., 2015). Though some ASVs appear specific to the core

samples, some are difficult to claim as contaminant or endemic. For example, Pantoea is

observed throughout both core and controls samples in our study and members of this genus can

couple acetate oxidation to dissimilatory reduction of a variety of metals like Fe(III), Mn(IV),

and Cr(VI) (Francis et al., 2000). However, Pantoea spp. are well known to be associated with

human diseases as a pathogen (Cruz et al., 2007), making it difficult to know if this ASV came

from the subsurface (wherein metal reduction is a plausible metabolism) or if it was introduced

to the extract during sample processing.

Ammonia-oxidizing bacteria from the Nitrosomonadaecae (e.g., the genus MND1) were

observed in BA1B 120, BA4A 230 Int, and BA3A 300 Ext and ammonia-oxidizing archaea

“Candidatus Nitrososphaera” was observed in BA3A 180 Int. Both groups were in very low

relative abundance (< 0.03%) in stabilized borehole fluids from BA1B, BA4A, and BA3A.

Nitrosomonadaecae are lithotrophic ammonia-oxidizing organisms and have been noted in basalt

rock from the Deccan Traps subsurface (Dutta et al., 2018; Prosser et al., 2014). The Archaean family Nitrososphaeraceae (of which “C. Nitrososphaera” is a member) is commonly found in soil and contains organisms capable of chemolithoautotrophic growth by aerobic ammonia oxidation to nitrite and CO2 fixation (Kerou et al., 2018). Sequences of Nitrososphaeraceae have been recovered from alkaline Type I and gabbro fluids from the Samail ophiolite (Rempfert et al., 2017) and the serpentinizing subsurface at the Prony Bay Hydrothermal field (Postec et al.,

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2015). In addition to ammonia oxidizers, bacteria that reduce nitrate to nitrite or N2 such as

Thauera and Denitromonas are in cores from all three boreholes, and an aerobic nitrogen-fixing

bacterium, Azomonas, was detected solely in the sulfidic fluid and two cores from BA4A and

BA1B. Strains of Desulfovibrio are also capable of nitrogen fixation (Delmont et al., 2018;

Sayavedra et al., 2020). Organisms that oxidize nitrate to nitrate such as Nitrospira have been previously detected in Samail aquifer fluids, however, these organisms were not found in the rock cores form BA1B, BA3A, or BA4A. Collectively, these organisms appear to be involved in the nitrogen cycle within the rock at sites of serpentinization, however further investigation focused on microbial nitrogen cycling and fixation in rock environments is necessary.

4.4.3 Syntrophic propionate oxidation and iron corrosion may be important metabolic methods in serpentinite rock

The broad role and pervasiveness of syntrophic partnerships in the subsurface biosphere remains an open question (Colman et al., 2017). Syntrophic interactions among anaerobic methane oxidizers (ANME), methanogens, and sulfate reducers have been observed in deep fracture fluids from the terrestrial subsurface of the Witwatersrand Basin of South Africa using molecular sequencing (Lau et al., 2016) and in shallow marine sediments (Prokopenko et al.,

2013). However, it is unknown if microbial syntrophy is a widespread metabolic strategy in the subsurface or in serpentinites.

Associations identified between syntrophs, methanogens, sulfate reducers, and fermenters point to syntrophy as a key metabolic strategy in a serpentinite rock-hosted ecosystem. Several organisms capable of syntrophic growth within the cores are Smithella, Syntrophus,

Desulfovibrio, and Geobacter spp. (Sieber et al., 2012), and many are associated in the largest

cluster of our network graph and detected in BA3A and BA4A. Syntrophic growth in co-culture

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has been demonstrated for Smithella spp. with Methanosaeta, Methanospirillum, and

Desulfovibrio; the last of which has strains that are also capable of syntrophic growth (De Bok et

al., 2001; Embree et al., 2015; Sieber et al., 2012). Methanogenic enrichment cultures of

Smithella¸ Methanosaeta, and hydrogenotrophic methanogens syntrophically oxidized

propionate to acetate, H2, and CO2, which in turn sustained the acetoclastic Methanosaeta and a

variety of hydrogenotrphic methanogen genera (Puengrang et al., 2020). A different study

examined an enrichment from a hydrocarbon contaminated soil wherein Smithella syntrophically

degraded hexadecane to H2, formate, and acetate in partnership with Methanosaeta,

Desulfovibrio, and hydrogenotrophic methanogens (Embree et al., 2015). The primary carbon source for these reactions is unknown but carbon may be sourced from buried hydrocarbons or other organic matter in the subsurface near or below the peridotite sections of the ophiolite.

Degradation of buried organic matter by many fermentative organisms also found in our network and analyses, including Spirochaetales, Propionivibrio, Lactivibrio, and members of the

Clostridia, can produce organic acids (e.g., propionate, formate, acetate) used in syntrophic reactions and other metabolisms.

However, in these laboratory studies the removal of H2 by the second syntrophic partner is key to keeping the initial partner’s metabolic reaction thermodynamically favorable. In the

Samail ophiolite, many of the most alkaline aquifer zones contain abundant H2 and little dissolved inorganic carbon (DIC) and oxidants due to the highly reducing nature of the waters

(Fones et al., 2019; Kraus et al., 2021; Rempfert et al., 2017). Since H2 is abundant and available in the alkaline fluids for a methanogen or other syntrophic partner to exploit, this close coupling of reactions and syntrophic removal of H2 does not seem thermodynamically favorable.

Therefore, syntrophic reactions in this alkaline, H2-rich subsurface might hinge on the removal

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of some other intermediate compound such as formate, acetate, or dissolved CO2. Dissolved inorganic carbon is limiting for the sole methanogen in the most reduced, hyperalkaline, H2-rich waters (Fones et al., 2020) and low weight organic acids (formate, acetate, lactate, propionate, butyrate) are detected in µM concentrations in well fluids (Rempfert et al., 2017; Clemens

Glombitza, unpublished data), suggesting availability of these compounds is more metabolically limiting than H2 availability. Additional cultivation experiments, metagenomic, and metatranscriptomic sequencing work is required to assess the syntrophic interactions in hyperalkaline H2-rich environments. Regardless of the mechanisms, syntrophic oxidation of propionate, alkanes, or other organics are processes that operate near the thermodynamic limit of life (Jackson & McInerney, 2002), potentially sustaining microbes in an alkaline, highly reduced deep rock serpentinite ecosystem containing little biomass and low DIC.

4.4.4 Fe-bearing minerals possibly influence the distribution of microbial life in serpentinites

The presence of iron-bearing minerals may be an important substrate for microbes in

serpentinite rock suggested by the diversity and distribution of organisms detected that can use

Fe(II), Fe(III), or Fe0 in Oman serpentinites. Putative Fe(III) reducers are found in BA1B 120 Int

and Ext, BA4A 120 Ext, BA3A 180 and 300 Ext, and the sulfidic fluids. The sulfidic fluids and

BA1B 120 Ext contain only Geobacter sequences, Acidibacter is more prevalent in controls, and

Shewanella, Tepidimicrobium, and the other putative Fe(III) reducers listed in Table 4.3 are

found in cores and controls. Organisms like Geobacter spp. can oxidize a variety of organic

molecules to CO2 by using Fe(III) oxides as an electron acceptor in the subsurface, and some

strains have adapted to do this chemolithoautotrophically (Zhang et al., 2020). Cultivation and

sequencing experiments have demonstrated evidence of Fe(III)-metabolizing taxa in serpentinite

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aquifer fluids, though not of the types identified here (Rowe et al., 2017; S. Suzuki et al., 2013).

Previous thermodynamic modeling in ophiolites in the Philippines indicated that, depending on the ratio of aqueous Fe2+ / Fe3+, both Fe(III) reduction and Fe(II) oxidation are energetically favorable metabolisms in terrestrial serpentinizing systems (Cardace et al., 2015), supporting the potential importance of Fe-bearing minerals to subsurface serpentinite-hosted life. Notably, the favorability of such metabolisms must also depend on the localized availability of strong reductants or oxidants.

Putative Fe(II) oxidizers (e.g. Ferrovibrio, Denitromonas) are observed across boreholes and control samples. Fe(II)-brucite has been proposed to be a potent reductant for microbial metabolism in reducing conditions generated by serpentinization reactions and may be fueling these organisms (A. S. Templeton & Ellison, 2020) and bacterial types capable of ferrous iron oxidation have been detected by sequencing methods in other ophiolite systems such as the

Philippines and the Lake ophiolite of Norway (Daae et al., 2013; Woycheese et al., 2015). Fe(II) oxidizing taxa are detected in samples BA4A 120, 180, and 230 but not at the 30m depth. BA4A

–60 at these depths is fully anoxic (O2 ~ 10 mol/L), indicating a different oxidant is used in this metabolism. Two of these organisms, Ferrovibrio and Denitromonas, can couple the anaerobic oxidation of Fe(II) to nitrate reduction (Laufer et al., 2016; Sorokina et al., 2012). In 2020 sampling of BA4A fluids, nitrate was measured to be 6.3 µM and 5.3 µM at the 50m and 80m depths, respectively (Patrick Thieringer, unpublished data). This availability of nitrate in the

BA4A fluids suggests Fe(II)-oxidizing organisms identified here could be using nitrate as

electron acceptor within serpentinite rock. Quantitative x-ray diffraction (QXRD) analyses

measured brucite as 3.5 −6.3% of the bulk mineralogy in the depth intervals where Fe(II)

oxidizers are observed in BA4A but minimal brucite near 30m in BA4A (Templeton et al.,

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2021). However, no such phylotypes are found at BA3A 300 where brucite is higher (6.7%)

(Templeton et al., 2021), highlighting the need for additional study to understand the role of brucite or other Fe-bearing minerals in the microbiome of serpentinizing systems.

Organisms capable of using metallic iron (Fe0) as an electron donor were observed in

core DNA, including Prolixibacter and Geobacter strains, indicating microbes may be directly

using Fe0 surfaces for metabolic processes (Iino et al., 2015; Tang et al., 2019). Furthermore,

some methanogens and sulfate reducers, including Methanobacterium and Desulfovibrio, can

0 gain electrons from Fe faster than from H2 (Dinh et al., 2004) via extracellular electron transfer

from Fe0 (Kato, 2016). Thus, though iron cycling organisms have been identified in deep

subsurface ecosystems previously, our work sheds light on the potential importance of Fe (as

Fe0, Fe(II), Fe(III)) and Fe-bearing minerals to metabolic functioning in serpentinite endolithic

communities.

4.4.5 Fluid geochemistry may influence rock-hosted microbial populations

The distribution of methanogens within borehole cores hints at possible geochemical

controls on microbial distribution in serpentinite rocks. The greatest abundance of methanogens

is in BA3A samples and stabilized borehole waters of BA3A are the most alkaline at pH 11.5

(BA4A is pH 9.7 and BA1B is pH 8.9) (Templeton et al., 2021). This increase in methanogen

abundance with increasingly alkaline pH is in agreement with previous work demonstrating

methanogens as most prevalent in the most alkaline waters of the Samail ophiolite (Kraus et al.,

2021). Additionally, the redox potential (Eh) of BA3A and BA4A is much lower (near −600mV)

than BA1B at 120m (near −50mV) (Templeton et al., 2021). Therefore, low redox potential and

alkaline pH of surrounding waters may make hydrogenotrophic or formatotrophic

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methanogenesis more favorable in BA3A and BA4A. Previous work has shown the Samail

aquifer methanogen, Methanobacterium, capable of diversification and adaptation into similar geochemical conditions (Fones et al., 2020). Local aquifer water geochemistry is likely to exert some control on the distribution of serpentinite endolithic life, though further study in needed to confirm this hypothesis.

4.4.6 Rock-hosted life differs from planktonic life

An important takeaway from this work is that serpentinite endolithic microbial life in the

Samail ophiolite is distinct from life residing planktonically within the aquifer waters. This is evidenced by the differing distribution of specific methanogens and sulfate reducers between the serpentinite rocks and aquifer waters, as well as the specificity of syntrophic and some lithotrophic organisms to the rocks within this study. This distinction is observed between

Atlantis Massif subseafloor serpentinite endoliths and the ocean water column organisms above

(Motamedi et al., 2020), and between basalt-hosted and seawater communities at the East Pacific

Rise (Mason et al., 2009). Similarly, endolithic communities in metamorphosed Precambrian rock were markedly different in taxonomic composition than the microbial inhabitants of the

formation pore fluid in the former Homestake Gold Mine in South Dakota (Momper et al.,

2017b). Thus, this distinction and specificity of rock and water associated microbial inhabitants

appears to be a widespread feature of subsurface ecosystems.

4.5 Conclusions

Terrestrial serpentinites from the Samail Ophiolite of Oman contain low concentrations

of DNA and a variety of microbial phylotypes that are distinct from planktonic communities,

161 including sulfate reducers, syntrophs, methanogens, and Fe-cycling organisms. A focused approach to minimize contamination from drilling and core handling allowed identification of these endemic taxa despite contaminants. To further assess habitability of these systems, and the broader subsurface, syntrophy and Fe-mineral utilization should be targeted in future work as potential metabolic strategies of endolithic organisms. Enrichment cultivar studies targeting the syntrophic consortia within the serpentinite rocks of the Samail ophiolite could use molecular

(metatranscriptomes and metagenomes) and geochemical measurements to assess the interplay between endemic organisms and syntrophic intermediate compounds. Similarly, cultivation and subsequent sequencing of organisms that couple the oxidation of Fe(II) from brucite to nitrate reduction and/or use Fe0 surfaces for direct electron transfer should be prioritized to investigate the potential for subsurface endolithic microbiota to use Fe-minerals in their metabolism.

4.6 Acknowledgments

The authors would like to thank the Oman Drilling Project Science teams, drillers, and leadership especially Jude Coggon, Peter Kelemen, and Juerg Matter. We are very grateful to the

Ministry of Regional Municipal and Water Resources of the Sultanate of Oman. This research would not have been possible without many hours of work from the Oman BIO team, including

Eric Ellison, Daniel Nothaft, Eric Boyd, Libby Fones, Clemens Glombitza, Kalen Rasmussen, and Dawn Cardace. We also thank the science team that worked on the 2020 field season that pumped borehole fluids.

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CHAPTER 5

CONCLUDING REMARKS

5.1 Conclusions and implications of this work

Serpentinites constitute a unique habitat that can provide potent electron donors and reduced carbon compounds but paradoxically harbor multiple “extreme” conditions for resident microorganisms within Earth’s relatively unexplored subsurface environment. Endolithic and planktonic microorganisms use the chemical products of water-rock interactions in these rock environments with a history of low-temperature serpentinization. However, the endogenous microorganisms and their metabolic capabilities in these habitats are understudied. To help fill this information void, the research of this dissertation has characterized the microbiomes of waters and rocks from the high pH serpentinite-hosted aquifers of the Samail ophiolite of Oman.

Active microbial metabolic cycling of methane was evidenced with DNA and RNA sequencing as well as gene and transcript reconstruction for methanogenesis and methane oxidation pathways. A single methanogenic genus, Methanobacterium, and a primary aerobic methanotrophic genus, Methylococccus, were determined to play key roles in the biological production and consumption of methane at hyperalkaline pH values of at least 11.3. These findings represent an extension of the pH range (previously < 10) known to be amenable to methanogens. Additionally, evidence of an active biological methane at high pH and low

dissolved inorganic carbon concentrations supports the idea that life may be involved in the

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generation of methane with an unusual carbon isotope composition observed in Samail ophiolite fluids previously.

Transcript expression and metagenome-assembled genomes also demonstrated a complex biological sulfur cycle mediated by several key organisms throughout the ophiolite ecosystem.

The microbial reduction of sulfate, elemental sulfur, and polysulfides is widespread in the ophiolite subsurface in both peridotite and gabbro wells and across a fluid pH range of 8 to 11.3.

Conversely, sulfur compound oxidation is limited to the less alkaline, gabbro-hosted waters

which contain greater concentrations of electron acceptors such as nitrate. Multiple near-

complete metagenome-assembled genomes for the predominant sulfur cycling organisms are

assembled and analyzed from the Samail ophiolite. These genomes represent members of the

Thermodesulfovibrionales, Desulfonatronum, Desulforudis, Dethiobacteria, Rhodocyclaceae,

and Melioribacteraceae, all of which have known alkaliphilic and lithotrophic strains consistent

with adaptive traits needed for residence in the Samail ophiolite. Expression of transcripts

involved in the cellular response to phosphate and sulfate starvation conditions suggests

endogenous microorganisms must contend with limiting concentrations of phosphate and sulfate

in the subsurface. Collectively, our analyses detect a broad sulfur cycle likely dependent on

sulfate, polysulfides, and elemental sulfur in the alkaline waters of the ophiolite. A potentially

hyperalkaline-adapted strain of Thermodesulfovibrionales, represented by a genome with

sequences recovered from hyperalkaline fluids, will be investigated further to look for adaptive

traits that allow this taxon to be widespread over all fluid types.

Endolithic microbiota within serpentinites was identified with DNA sequencing after a

specialized collection and extraction protocol focused on minimizing contaminant biomass. Rock

core samples and contamination controls were collected from the Samail ophiolite during the

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Oman Drilling Project to identify life residing in ultramafic serpentinized rock. Care was taken to overcome the challenges of contamination to ultra-low biomass rock including the removal of sequences known to be common lab reagent contaminants from our dataset. Results demonstrate distinct differences between the rock-hosted and fluid-hosted microbial communities and sulfate reducing Desulfovibrio as a common taxon in the rock. Specific strains and types of methanogens, sulfate reducers, and syntrophic organisms are identified in rock biomass while other strains are found primarily in fluid biomass or control samples. Network analysis between

ASVs recovered from the ophiolite suggest syntrophic interactions between methanogens,

Smithella, fermentative organisms and Desulfovibrio. Other microbes able to use iron for metabolism, such as Prolixibacter and Ferrovibrio, were identified and potentially related to iron mineral (brucite) abundance. Additional taxa with putative lithotrophic or subsurface-associated members were identified including the unusual Patescibacteria known for their reduced genomes.

Collectively, our results indicate endolithic communities are distinct from planktonic communities; syntrophy between organisms and the use of iron-bearing minerals may be important metabolic strategies in serpentinite rock; and contamination can be controlled to some degree but remains a large challenge in searching for life in deep subsurface rock ecosystems.

This dissertation has helped provide deep understanding of ecological roles of specific taxa in hyperalkaline fluids and rock-hosted environments within the Samail ophiolite. The results herein provide insight into the structure of Earth’s subterranean serpentinite biosphere which can be compared globally to make inferences about the distribution of life and habitability in the modern subsurface and in Earth’s past. A serpentinizing subsurface environment may have fostered the origin and evolution of early life on Earth and importantly, environments of active low-temperature serpentinization are a high-priority target for future NASA missions searching

177 for extant life on Mars and in the solar system. Therefore, the additional insights garnered from this research on the habitability of the deep serpentinite biosphere may someday aid in narrowing the search for life in our solar system. Constraining possible habitable zones in the nearest rocky planets and moons is vital to the selection of sampling sites for future Mars’ and Europa missions tasked with extant life detection or sample return.

This research has also produced refined protocols to process low-biomass rock in the presence of abundant contaminants during drilling, sample handling, DNA extraction, and molecular analyses. These molecular protocols for contamination minimization are directly applicable to exploration missions with a sample return or subsurface drilling component, such as the Integrated Ocean Drilling Program and Mars Exploration Program.

5.2 Future directions to study subsurface life in serpentinites

In completing this work, I have found many outstanding questions and avenues for future inquiry. Additional research on distinctions between organisms residing with planktonic communities versus the rock matrix or rock fracture surfaces is necessary. Fluids, rock pore spaces, and mineralized fracture surfaces represent unique habitats within the subsurface in serpentinites and other rock types. With the core processing and contamination protocols developed in this dissertation work, additional molecular work should continue with the aim to recover genomes from endolithic organisms. Cultivation studies focused on the enrichment of endolithic organisms using serpentinite rock material or mineral substrates could aid in the study of these microbes and their metabolic abilities. Genomes would be a useful tool to inform cultivation strategies for subsurface microbes under unusual conditions, which are often difficult to enrich for in laboratory settings.

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Microbe-mineral interactions and microbial community dynamics at high pH should continue to be examined in the Samail and other ophiolites. High pH ecosystems are relatively underexplored compared to circumneutral and acidic systems. Yet unknown metabolic mechanisms likely exist in hyperalkaline biospheres. Uncovering these metabolisms could have broad implications for past and present planetary habitability or even be of value to commercial or industrial technologies akin to the discovery of Taq polymerase.

Investigation of syntrophic relationships and direct microbe-mineral interactions in endolithic communities and serpentinites under multiple extreme conditions should be a priority to understand these mechanisms as possible adaptive traits in both serpentinites and the broader subsurface of Earth. Furthermore, this system is interesting from an evolutionary biology perspective because the microbial interactions, metabolism, community structure, and niche partitioning in the Samail ophiolite is occurring under highly reducing, hyperalkaline conditions with limited electron acceptors and dissolved inorganic carbon, and potentially limited phosphate and sulfate. Geobiologists seeking to understand how life adapts to extreme conditions, or multiple extreme conditions in this case, should pursue syntrophy and direct microbe-mineral interactions in serpentinites.

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APPENDIX A

SUPPLEMENTARY INFORMATION FOR CHAPTER 2: MOLECULAR EVIDENCE FOR

AN ACTIVE MICROBIAL METHANE CYCLE IN SUBSURFACE SERPENTINITE-

HOSTED GROUNDWATERS IN THE SAMAIL OPHIOLITE, OMAN

Figure A.1. Relative abundance of the top 10 ASVs from DNA and cDNA amplicon sequencing of well samples. Bars are colored by Class. Numbers following well samples on x axis denote biological replicates.

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Figure A.2. Rarefaction curves of observed species richness (observed ASV counts) against sequence library size plateau indicating SSU rRNA libraries were sufficiently large to capture the majority of ASVs within each well water type.

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Figure A.3: Relative abundance of the top 100 ASVs identified in extraction, PCR amplification, and negative reverse-transcription (RT) controls.

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Table A.1. Trace metal and non-metal elemental concentrations for well waters sampled in 2017. ∑ indicates the value is the sum of all species of an element. LOQ indicates the limit of quantification.

NSHQ14 WAB71 NSHQ04 WAB55 WAB188 Rain LOQ

Pump depth (m) 50 85 70 5.8 30 78 ∑ As (µM) 0.17 0.14 0.17 0.21 BLOQ 0.13 9.1x10−2 4x10−4 ∑ B (µM) 5.48 6.39 BLOQ BLOQ 11.70 18.33 5.21 4.25 ∑ Ba (µM) 0.11 0.11 4.6x10−2 0.81 2.85x10−2 0.05 5.6x10−2 1x10−3 ∑ Cd (µM) BLOQ BLOQ 1.9x10−3 BLOQ BLOQ BLOQ BLOQ 1x10−4 ∑ Cr (µM) BLOQ 0.01 BLOQ BLOQ 0.10 2.4x10−2 4.7x10−2 9x10−4 ∑ Cu (µM) 1.8x10−2 1.8x10−2 BLOQ BLOQ BLOQ BLOQ 4.3x10−2 2x10−4 −4 −4 ∑ Li (mM) BLOQ BLOQ 4.59E-04 6.85E-04 7.49x10 3.39x10 BLOQ 1.99x10−4 −2 ∑ Mo (µM) BLOQ BLOQ BLOQ BLOQ BLOQ 2.9x10 BLOQ 1.1x10−2 ∑ Ni (µM) 9x10−3 7.5x10−2 1.76x10−2 3.7x10−2 BLOQ 7.23x10−2 4.5x10−2 2x10−4 ∑ P (µM) 0.63 BLOQ 0.53 0.72 0.52 0.89 3.63 3.41x10−1 ∑ S (mM) 0.23 0.12 0.16 0.90 0.88 1.16 0.19 8.6x10−4 ∑ Sr (µM) 14.19 16.21 7.91 24.84 3.09 4.22 1.97 1x10−3 −2 ∑ V (µM) BLOQ BLOQ BLOQ BLOQ BLOQ 0.09 2.4x10 1.6x10−2 ∑ Zn (µM) 0.26 0.37 0.14 0.27 0.87 0.49 0.11 5x10−3

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Table A.2. Sequencing information for SSU rRNA, metagenomes, and metatranscriptomes of communities sampled from fracture waters collected from five wells. PF indicates reads that passed the sequencing center quality control filter.

Neg. NSHQ14B NSHQ14C WAB71 No RT NSHQ04 WAB55 WAB188 PCR (50m) (85m) (50m) control control SSU rRNA (DNA) No. reads 93,243 29,262 46,409 14,280 30,208 14,811 42 - SSU rRNA (cDNA) No. reads 23,556 14,206 7,330 7,078 18,965 5,896 - 0 Metagenomes Total Gbp, PF 18.24 17.46 8.62 16.53 5.90 14.35 - - Total paired reads, 72.69 69.56 34.36 65.86 23.51 57.16 - - PF (millions) Q 30% 83.89 85.58 85.08 84.07 84.22 86.31 - - Avg. quality score 34.64 35.05 34.98 34.69 34.76 35.29 - - Metatranscriptomes: Total Gbp, PF 3.50 4.31 0.29 5.49 0.18 0.63 - 0.18 Total paired reads, 13.93 17.16 1.16 21.89 0.72 2.51 - 0.72 PF (millions) Q 30% 85.66 83.44 65.23 84.93 84.12 80.18 - 79.79 Avg. quality score 35.14 34.59 30.43 34.98 34.77 33.84 - 33.77

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APPENDIX B

SUPPLEMENTARY INFORMATION FOR CHAPTER 3: SULFUR METABOLISM IN THE

TERRESTRIAL SERPENTINITE SUBSURFACE OF THE SAMAIL OPHIOLITE, OMAN

Electronic files too large to be added as in-line tables:

Table B.3: Metagenome-assembled genome bin quality from MetaSanity

Table B.4 Metagenome-assembled genome bin taxonomy assignments from MetaSanity

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Table B.1: Counts per million reads mapped (CPM) of all transcripts assessed in Chapter 4. NA indicates a transcript was not detected with that gene annotation.

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Table B.2. Sequencing statistics of metagenomes and metatranscriptomes sequenced.

Sample Total PF bp Total reads Average (Gbp) (millions) quality score NSHQ14B 18.24 72.69 34.64 NSHQ14C 17.46 69.56 35.05 WAB188 14.35 57.16 35.29 WAB71 8.62 34.36 34.98 WAB55 5.90 23.51 34.76 WAB105 4.94 19.66 35.20 WAB104 7.62 30.37 35.18 NSHQ04 16.53 65.86 34.69 RNA NSHQ14B 3.50 13.93 35.14 RNA NSHQ14C 4.31 17.16 34.59 RNA WAB188 0.63 2.51 33.84 RNA WAB71 0.29 1.16 30.43 RNA NSHQ04 5.49 21.89 34.98 RNA WAB55 0.18 0.72 34.77 RNA WAB105 0.16 0.64 34.09 RNA WAB104 5.25 20.92 34.32 No RT control 0.18 0.72 33.77

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APPENDIX C

SUPPLEMENTARY INFORMATION FOR CHAPTER 4: SYNTROPHY AND DISTINCT

MICROBIAL COMMUNITIES DIFFERENTIATE ROCK-HOSTED AND FLUID-HOSTED

LIFE IN A TERRESTRIAL SITE OF SERPENTINIZATION

Electronic files too large to be added as in-line tables:

Table C.1. Taxonomy table with Sequence IDs of ASVs removed as contamination.

Table C.2. Taxonomy table with Sequence IDs of ASVs retained after contamination filtering.

Table C.3. ASV taxonomy and cluster information from Cytoscape.

Table C.4. Edges and edge frequencies between ASV nodes from Cytoscape.

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Figure C.1. Alpha diversity of sample types collected in 2018 drilling operations.

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Figure C.2. Sequencing read counts of methanogens, sulfate reducers, and syntrophic organisms for 2018 samples.

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APPENDIX D

MICROSCALE BIOSIGNATURES AND ABIOTIC MINERAL AUTHIGENESIS IN LITTLE

HOT CREEK, CALIFORNIA

A paper published in Frontiers in Microbiology1

Emily A. Kraus2, Scott R. Beeler3, R. Agustin Mors4, James G. Floyd5, GeoBiology 20166, Blake W. Stamps2, Heather S. Nunn5, Bradley S. Stevenson5, Hope A. Johnson7, Russell S. Shapiro8, Sean J. Loyd9, John R. Spear2, and Frank A. Corsetti6

Abstract

Hot spring environments can create physical and chemical gradients favorable for unique

microbial life. They can also include authigenic mineral precipitates that may preserve signs of

1 Kraus, E. A., Beeler, S. R., Mors, R. A., Floyd, J. G., Stamps, B. W., Nunn, H. S., Stevenson, B. S., Johnson, H. A., Shapiro, R. S., Loyd, S. J., Spear, J. R., & Corsetti, F. A. (2018). Microscale biosignatures and abiotic mineral authigenesis in Little Hot Creek, California. Frontiers in Microbiology, 9(MAY). https://doi.org/10.3389/fmicb.2018.00997 2 Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, Colorado, U.S.A. 3 Washington University in St. Louis, St. Louis, Missouri, 63130, U.S.A 4 LaPGE - Laboratorio de Paleobiología y Geomicrobiología Experimental, CICTERRA - Centro de Investigaciones en Ciencias de la Tierra (CONICET-UNC), X5016GCA, Córdoba, Argentina. 5 Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, 73019, U.S.A. 6 Department of Earth Sciences, University of Southern California, Los Angeles, California, U.S.A. 7 Department of Biological Science, California State University, Fullerton, Fullerton, California, 92831, U.S.A. 8 Geological and Environmental Sciences, California State University, Chico, Chico, California 95929, U.S.A. 9 Department of Geological Sciences, California State University, Fullerton, Fullerton, California, 92831, U.S.A.

192 biological activity on Earth and possibly other planets. The abiogenic or biogenic origins of such precipitates can be difficult to discern, therefore a better understanding of mineral formation processes is critical for the accurate interpretation of biosignatures from hot springs. Little Hot

Creek (LHC) is a hot spring complex located in the Long Valley Caldera, California, that contains mineral precipitates composed of a carbonate base (largely submerged) topped by amorphous silica (largely emergent). The precipitates occur in close association with microbial mats and biofilms. Geological, geochemical, and microbiological data are consistent with mineral formation via degassing and evaporation rather than direct microbial involvement.

However, the microfabric of the silica portion is stromatolitic in nature (i.e., wavy and finely laminated), suggesting that abiogenic mineralization has the potential to preserve textural biosignatures. Although geochemical and petrographic evidence suggests the calcite base was precipitated via abiogenic processes, endolithic microbial communities modified the structure of the calcite crystals, producing a textural biosignature. Our results reveal that even when mineral precipitation is largely abiogenic, the potential to preserve biosignatures in hot spring settings is high. The features found in the LHC structures may provide insight into the biogenicity of ancient Earth and extraterrestrial rocks.

D.1 Introduction

Differentiating morphological biosignatures from abiogenic mineral assemblages remains a problem in the interpretation of the evolution of life in the geologic rock record and for the search for life on other planets. The environments of early Earth and Mars are thought to have been similar, containing aqueous geothermal activity, which could provide the potential to preserve traces of life (McKay & Stoker 1989, Wordsworth 2016). Detection of surface silica

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deposits associated with hydrothermal features on Mars (Squyres et al., 2008) and their

resemblance to biotically-influenced silica structures on Earth (Ruff & Farmer 2016) highlights

the need for further study of active modern hot springs with silica precipitation. The discovery of

hot-spring-associated biosignatures on Mars has been a goal of astrobiological research for

decades, largely guided by insight gained from Earth analog systems (NASA 1995, Cady et al.,

2003).

Earth’s modern hot springs exhibit strong thermal and chemical gradients that generate energetically favorable redox conditions for microbial life (Spear et al., 2005, Meyer-Dombard et al., 2005, Meyer-Dombard et al., 2011, Inskeep et al., 2010). Rapid mineral precipitation

tends to occur in these systems as inorganic carbon- and silica-containing waters reach the

surface and interact with the atmosphere. When these waters reach the surface, rapid physico-

chemical changes occur due to degassing, cooling, evaporation, and water mixing, which can

drive carbonate and silica precipitation abiotically (Pentecost, 2005, Fouke et al., 2000, Fouke,

2011, Konhauser et al., 2004). Due to rapid mineralization, microorganisms and/or traces of their

activity can be preserved in the rock record (e.g., Cady & Farmer 1996). Recent findings have

demonstrated the ability of similar silica-rich deposits to preserve some of the earliest signs of

life on Earth (Djokic et al., 2017), but abiotically-synthesized microstructures resembling

biological morphologies have been shown to self-assemble in an experimental setting, casting

doubt on solely using morphological features as biogenic indicators (García-Ruiz et al., 2003).

Microorganisms can induce mineral precipitation by changing local chemical

microenvironments on micrometer scales through metabolic activity. For example, metabolic

reactions such as photosynthesis, sulfate reduction, and denitrification have been shown to drive

carbonate mineral precipitation by increasing relative alkalinity, thus increasing the saturation

194 state of calcite (Dupraz & Visscher 2005, Visscher & Stolz 2005, Baumgartner et al., 2006,

Chrachri et al., 2018). In addition, microbes can control mineral nucleation by the production of negatively charged surfaces, like exopolymeric substances (EPS), providing a surface for mineral nucleation, which has been observed in silica precipitation in hot springs (Konhauser & Ferris

1996, Farmer 1999, Phoenix et al., 2000, Benning et al., 2004). Thus, several processes can influence carbonate precipitation and silicification in different ways. Microbial control can be direct, as in carbonates (Dupraz & Visscher, 2005), or marginal to absent, as in silicification, by imparting an influence on the resulting mineral textures (Konhauser et al., 2004).

Disentangling the main physio-chemical and microbiological controls in mineral precipitation is necessary to better understand biosignature preservation in the sedimentary record; thus, the study of modern active hot spring systems yields valuable insight into Earth’s history and beyond. Little Hot Creek (LHC) is a hot spring system in the Long Valley Caldera of

California, containing mineral precipitates with a carbonate base and an amorphous silica/carbonate top. The precipitates occur in close association with microbial mats and biofilms in the spring. The phage community structure, geochemistry, isotopic characteristics, mineralogy, microbial community of LHC sediments, and microbial mat structures have been described in some detail previously (Breitbart et al., 2004, Vick et al., 2010, Bradley et al., 2017,

Wilmeth et al., 2018). The mineral precipitate microbial communities, physical structure, and mineralogy have not been characterized, affording an opportunity to investigate the generation of silica- and carbonate-based biosignatures. Here, we investigate the biogenicity of LHC precipitates using geochemical modeling, petrography, microscopy, high-throughput DNA sequencing, and isotopic data to better inform how the structures formed.

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Figure D.1. A photomosaic of Little Hot Creek 1. Sampling sites are indicated by yellow dots. Dashed red circles show the precipitate structures that were removed from LHC for sampling.

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D.2 Materials and Methods

D.2.1 Site Description and Aqueous Geochemistry

Little Hot Creek 1 (LHC) (Vick et al., 2010) is a 28-meter-long channelized hot spring in the hydrothermally-active Long Valley Caldera of California at 37°41'26.1"N 118°50'39.9"W.

Samples for water geochemistry and four mineralized structures within the hot spring were collected for biological, isotopic, and petrographic sampling under a U.S. Forest Service

Research Permit (#MLD15053) (Figure 1). All structures were rooted in the spring bottom or side and ranged in size, with the largest structure approximately 15 cm long, 10 cm wide, and 9 cm thick. The above-water mineral surfaces were covered in biofilms of varying colors and/or white grainy material. Spring water was collected several centimeters directly upstream of the chosen mineralized structures via syringe and passed through a 0.45 µm filter to fill 15 ml

Falcon® tubes with no headspace for triplicate measurements of anions and cations. Samples for cation analysis were acidified with drops of nitric acid and triplicate samples of four milliliters of

0.45 µm filtered water was injected into pre-evacuated Exetainer vials (Labco Limited,

Lampeter, Wales U.K.) for dissolved inorganic carbon (DIC) measurements. Temperature and pH were measured at the point of water collection with a Mettler Toledo SevenGo Duo™ pH meter (Mettler Toledo, Columbus, OH) at each site.

To empirically assess the isotopic evolution of dissolved inorganic carbon downstream at

LHC, one source fluid degassing experiment was performed on-site contemporaneously with sample collection. This experiment was conducted on spring source waters to remove potential impacts from spring channel biology and mineral precipitates. One and a half liters of water was collected from the source of LHC in an open container and placed on a stir plate to mimic fluid turbulence exhibited along the flow path (the complexity of LHC outflow morphology produces

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local variability in turbulence; stirring was meant to mimic but not perfectly reconstruct

turbulence). At 30-second intervals 4 ml of water was removed, passed through a sterile 0.45µm

filter, and injected into an evacuated Exetainer vial to analyze for DIC concentration and

isotopic composition. Temperature and pH were recorded concurrently at each sampling time

point. The entire duration of the source fluid experiment lasted 15 min.

Cation and anion concentrations were analyzed using inductively coupled plasma atomic

emission spectroscopy (ICP-AES; Optima 5300, Perkin-Elmer, Fremont, CA) and ion

chromatography (IC; ICS-90, Dionex, Sunnyvale, CA), respectively. The maximum allowable

sodium concentration in the ICP-AES instrument is 500 mg/L and the maximum allowable

chloride concentration in the IC instrument is 300 mg/L; therefore, samples were diluted

accordingly using ultrapure water and then acidified to a pH of less than 2 using nitric acid for

13 ICP only. Dissolved inorganic carbon (DIC) concentrations and  C values were determined on

waters from LHC and the degassing experiment using a cavity ringdown spectrometer ((CRDS),

2121-i, Picarro, Santa Clara, CA). Samples were acidified using an AutoMate carbonate

preparation device (AutoMate FX, Inc., Bushnell, FL) to quantitatively convert DIC to CO2(g).

CO2(g) is passed via ultra-high purity N2 carrier gas to the CRDS. The CRDS provides

simultaneous DIC concentration and 13C data. DIC concentration and 13C replicates are generally better than +/- 0.3 mM and 0.5 ‰, respectively.

Calculation of saturation indices and geochemical modeling were performed with

PHREEQC-I version 3.3.7 (Parkhurst and Apello, 2013). PHREEQC-I enables modeling of saturation state with respect to temperature and elemental activities. This allows for the ability to assess changes in saturation state as these parameters change during physico-chemical processes

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such as evaporation, cooling, and degassing. Measured source water values were used as the

initial parameters for all geochemical modeling performed.

D.2.2 Solid carbonate stable isotope analysis

Solid samples were powdered using either a rotary tool (Dremel Co., Racine, WI) fitted

with a 1.5 mm carbide drill bit or mortar and pestle. Powdered samples (5-7 mg) were placed

into evacuated Exetainer vials in triplicate. Carbonate powders were then dissolved in 3 ml of

10% phosphoric acid overnight. Acid-produced CO2(g) was passed via ultra-high purity N2 carrier gas to the CRDS. The CRDS provides simultaneous total inorganic carbon (TIC) content and 13C data. TIC concentration and 13C replicates are generally better than +/- 0.2 wt% and

0.1 ‰, respectively.

D.2.3 Petrography and microscopy of LHC precipitates

Large (51x75 mm) and small (27 x 46 mm) format thin sections were made from several of the samples. Photomosaics of select thin sections were produced on a Zeiss Axioscope petrographic microscope (Carl Zeiss Microscopy, LLC, Thornwood, NY) in the petrography lab at the University of Southern California. Samples of LHC mineral precipitates and associated biofilms were processed for biologic microscopy. Partially lithified precipitate material was homogenized by finely grinding approximately 1 gram of material with a mortar and pestle with sterile phosphate buffered saline. The homogenized material was placed onto glass slides and imaged on an Olympus BX41 (Olympus Corporation, Shinjuku, Tokyo, Japan). Images were collected under 400 X using brightfield, fluorescent green light (540 – 560 nm) to detect the presence of photosynthetic organisms, and DAPI (4’6’-diamidino-2-phenylindole) stain under

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UV light to highlight DNA in organisms associated with mineral precipitates. Environmental

scanning electron microscopy (ESEM) was done on mineral precipitates from Sites 3 and 4

(Figure 1). Small sections from the top mineral precipitate (approx. 3 x 3 x 3 mm) were prepared

without chemical fixation, sputter coating, or dehydration steps. ESEM was performed on a

Hitachi TM-1000 Tabletop SEM (Hitachi Global, Tokyo, Japan) equipped with a back-scatter

electron (BSE) detector, an accelerating voltage of 15 kV, and a working distance of 8.4 – 8.7

mm.

D.2.4 DNA extraction and sequencing from LHC precipitates

Sterile spatulas were used to scrape partially lithified, carbonaceous material and

overlying green/brown biofilm from the upstream, submerged edge of each of the four

mineralized structures. Three technical replicates were collected from each. Three technical

replicate samples were also taken from the above-water, precipitate-top portions of the structures

at Sites 1, 2, and 4 (Figure 1). Three samples for metagenomic sequencing were collected from

the leading, submerged edge of the mineral structure at Site 4. Samples were immediately

suspended in 750 µL XpeditionTM Lysis/Stabilization Solution (Zymo Research Co., Irvine, CA), and homogenized for 1 min on-site using a custom designed lysis head attached to a reciprocating saw. Samples in the field were then maintained at room temperature (~25º) stably in the Lysis/Stabilization Solution for several hours and stored at -20C in the laboratory until completion of nucleic acid extraction. Within 2-3 days, DNA was extracted from these preserved samples using the Zymo Research Xpedition Soil/Fecal DNA MiniPrep Extraction kit (Zymo

Research Co.) following manufacturer’s instructions.

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Libraries of partial bacterial, archaeal, and eukaryotic small subunit (SSU) rRNA genes

were amplified from each DNA extraction using PCR with primers that spanned the V4 and V5

hypervariable regions of the 16S ribosomal RNA gene (16S rRNA gene) between position 515

and 926 (E. coli numbering), producing a ~400 bp fragment for Bacteria and Archaea, and a 600 bp fragment for the Eukarya 18S rRNA gene. These primers evenly amplify a broad distribution of SSU rRNA genes from all three domains of life (Parada et al., 2015). The forward primer

515F-Y (GTA AAA CGA CGG CCA G CCG TGY CAG CMG CCG CGG TAA-3’) contains

the M13 forward primer (in bold) fused to the gene-specific forward primer (underlined), while

the reverse primer 926R (5’-CCG YCA ATT YMT TTR AGT TT-3’) was unmodified from

Parada et al. (2015). 5 PRIME HotMasterMix (Quanta Biosciences, Beverly, MA) was used for

all reactions at a final reaction volume of 50 μL. Reactions were purified using Agencourt

Ampure XP paramagnetic beads (Beckman Coulter Inc., Indianapolis, IN) at an 0.8x final concentration. After purification, 4 μL of PCR product was used in a barcoding reaction to attach a unique 12 bp barcode to each library in duplicate 50 μL reactions. Duplicate reactions were pooled, purified using AmpureXP beads to a final volume of 40 μL, quantified using the QubitTM

dsDNA HS assay kit (Thermo Fisher Scientific Inc., Waltham, MA), and pooled in equimolar

amounts before concentration using an Amicon Ultra 0.5 ml centrifugal filter unit with

Ultracel-30K membrane (Millipore Sigma, Billerica, MA) to a final volume of 80 μL. To

mitigate the effects of reagent contamination (Salter et al., 2014), triplicate extraction blanks

(DNA extraction with no sample addition) and negative PCR controls (PCR with no template

DNA added) were sequenced. The pooled, prepared library was then submitted for sequencing

on the Illumina MiSeq (Illumina Inc., San Diego, CA) using V2 PE250 chemistry at the

Oklahoma Medical Research Foundation (OMRF) Clinical Genomics Center (https://omrf.org/).

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Small sub-unit (SSU) rRNA gene analyses were carried out within QIIME version 1.9.1

(Caporaso et al., 2010). Briefly, paired-end reads were joined using PEAR (Zhang et al., 2014), barcodes were extracted, and de-multiplexed prior to operational taxonomic unit (OTU) clustering. Chimeras were filtered prior to clustering using usearch61 (Edgar, 2010) and representative sequences for each OTU were assigned a taxonomic identity by mothur (Schloss et al., 2009) against the SILVA r128 database (Yilmaz et al., 2013) clustered to a 97% similarity.

Finally, sequences were aligned with PyNAST (Caporaso et al., 2010, Wang et al., 2007) against the SILVA r128 database and FastTree (Price et al., 2010) was used to produce a phylogenetic tree to generate a weighted UniFrac distance matrix (Lozupone & Knight, 2005). A BIOM file

(McDonald et al., 2012) was generated and used to generate bar charts of the relative abundances of OTUs in each sample. A phylogenetic tree of OTUs belonging to the Cyanobacteria phylum with greater than 100 sequence counts (40 OTUs total) was constructed. Evolutionary history was inferred by using the Maximum Likelihood method based on the Tamura-Nei model

(Tamura & Nei, 1993). Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value. A total of a thousand trees were produced to generate bootstrap values. All positions containing gaps and missing data were eliminated. A total of 417 positions were in the final dataset. Evolutionary analyses were conducted in MEGA7 (Kumar et al., 2016).

The metagenomic libraries were prepared from three samples of precipitate material collected from the upstream edge of the Site 4 mineral structure using the NexteraXT library preparation kit (Illumina Inc., San Diego CA) following manufacturer’s instructions. Briefly,

DNA from each sample was normalized to a total amount of 1 ng as input into the tagmentation

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reaction. After limited-cycle amplification, samples were cleaned, normalized using a bead-

based method (Illumina Inc.), pooled at an equimolar ratio, and sequenced on the Illumina

NextSeq 500 using high output PE150 chemistry at the OMRF Clinical Genomics Center.

Metagenomic sequencing reads were demultiplexed and had barcodes removed prior to being

uploaded to MG-RAST (Meyer et al., 2008, Wilke et al., 2015). The MG-RAST pipeline

removed adapters and completed a quality control step before analysis and annotated using the

KEGG Orthology database (Kanehisa, 2002) for functionality and against the RefSeq database

(Pruitt et al., 2007) for taxonomic assignments.

D.3 Results

D.3.1 Little Hot Creek water geochemistry

The water chemistry of Little Hot Creek exhibits substantial variability from source to

outflow (Table 1). Waters near the hot spring source are hot and mildly acidic (81.2 ºC, and pH=6.7 at the sampling location closest to source). Waters become cooler and more alkaline as they move downstream over a 28 m transect (70.7 ºC, and pH=7.45 at the final sampling

location). The concentration of DIC decreases from 15.7 mM closest to the source to 12.8 mM at

the output. All other measured geochemical parameters display little variability along the flow

path (Table 1). Data collected in 2015 from LHC show a decrease in the dissolved silica

concentration from 1.49 mM near the source to 1.36 mM at the output (2015 Agouron

International Geobiology Course, unpublished data). LHC waters are undersaturated with respect

to amorphous silica at all sampling locations.

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Table D.1. Water chemistry values along the flow path of Little Hot Creek. Site 1 is closest to the hot spring source and Site 4 is closest to the output.

+ + 2+ - - 2- T DIC Na K Ca F Cl SO4 Site pH Calcite (˚C) (mM) (mM) (mM) (mM) (mM) (mM) (mM)

1 6.70 81.2 2.1 15.7 16.8 0.7 1.3 0.5 5.0 1.1

2 6.80 79.6 1.5 14.9 17.5 0.7 0.7 0.5 5.3 1.2

3 7.07 76.7 2.1 13.7 17.5 0.7 0.7 0.5 4.9 1.1

4 7.45 70.7 7.2 12.8 17.3 0.8 1.0 0.5 5.1 1.1

The stable carbon isotopic composition of the carbonate portion of the precipitates

13 13 ( CCARB), the measured DIC isotopic values from LHC water ( CDIC), and the expected

13 isotopic values for DIC of water from which a precipitate formed ( CEXP), given equilibrium fractionation and a calcite-bicarbonate enrichment factor of 1.0‰ (Romanek et al., 1992), are

13 shown in Figure 2.  CCARB varies between −2.2 and −0.2‰ with a mean value of −1.2‰. No

13 clear pattern is observed in  CCARB from top to bottom within individual precipitates or between

13 precipitates along the flow path.  CEXP varies between −3.2‰ and −1.2‰ with a mean value of

13 13 −2.2‰.  CDIC is depleted relative to  CEXP with values ranging from −4.8‰ and −3.1‰ and a

13 13 mean value of −3.9‰. However,  CDIC increases towards the average  CEXP moving

13 downstream and is comparable to the most depleted  CEXP value at the final sampling location.

The degassing experiment fluids exhibited a progressive decrease in DIC in association with 13C enrichment. The fluid DIC contents decreased from 14.7 mM to 13.5 mM and 13C values increased from −5.2‰ to −3.3‰. Carbon contents and isotope composition were strongly correlated aside from the first six samples.

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13 Figure D.2. Measured  C values of mineral precipitates (closed boxes) and DIC from hot spring waters (blue lines). Open boxes show the expected isotopic values of the water from which a mineral structure precipitated under equilibrium conditions. Mineral precipitate values are plotted in order from top to bottom of sampled structure. No data was collected from the mineral precipitate of Site 4 due to the loss of the sample during transport.

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Figure D.3. Transmitted-light petrographic photomicrographs of LHC precipitates in thin section. (A) Plane-polarized transmitted light photomosaic of LHC precipitate (inset box enlarged in C). Light-colored area towards the bottom of the image is calcite and wavy- laminated, darker area towards the top is amorphous silica. (B) Cross-polarized transmitted light photomosaic revealing bladed calcite crystals in lower portion and amorphous silica in the upper portion (same field of view as A). Amorphous silica is opaque under cross-polarized light, so a minor amount of reflected light was cast across the thin section to enhance the visibility of the upper silica portion. (C) Inset image from A. Arrows point to several curved, downward dipping surfaces that indicate progressive growth of the bladed calcite from left to right. (D) Crossed- polarized photomicrograph of bladed calcite from the lower portion of the precipitate indicating growth from left to right. (E) Plane-polarized transmitted light image of bladed calcite crystals with cloudy appearance caused by pervasive endolithic activity (blue areas represent pore space). (F) and (G) Plane-polarized transmitted light photomicrographs of endoliths within calcite crystals.

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D.3.2 Petrography

The precipitate sample from Site 1 near the hot spring source was used for petrographic analysis (Figure 3A). The precipitate was 19.5 cm long by 9 cm wide, protruded ~ 2-3 cm above water surface, and extended ~ 4-5 cm below the water surface. The precipitate consists of an upper, subaerially exposed, partially lithified portion and a lower, subaqueous highly lithified portion (Figure 3B). The top portion has a white crenulated fabric with a light-pink tinge in the center. Beneath the white domal crenulations, dark green mats were encapsulated. Along the upstream margin, the edges of the upper layer are smooth and light yellow to red in color. The lower subaqueous portion is very well lithified and black in color with a rough surface.

In thin section, two different zones were recognized, aligning with those observed in the hand sample (Figure 3A-B). The upper zone is laminated and consists of mixed amorphous silica and micritic calcite. The laminae are domal, wavy, and characterized by a low degree of inheritance (i.e., stromatolitic in appearance). The lower zone consists of bladed calcite crystal fans that grew perpendicular to or slightly downward from the surface (Figure 3C-D). Curved, downward dipping surfaces spaced approximately 100 µm apart are visible within the lower carbonate portion (Figure 3C). Within this zone, the bladed calcite crystals appear cloudy under lower magnification (Figure 3D, E). Higher magnification (400x) reveals a network of tubular structures (up to 2 µm in diameter and 20 µm in length) that extend into the calcite. (Figure 3F,

G).

D.3.3 Microbial communities of LHC precipitates

Biofilm and lithified material were collected from leading edges and silica/calcite tops from four precipitates at four locations in LHC. SSU rRNA gene libraries were sequenced to

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identify microorganisms present on precipitate leading edges, tops, and interior (Accession

SRX3004381). The relative abundances of bacterial and archaeal OTUs are shown at the phylum level in Figure 4. Eukarya were found to constitute a low percentage (0 – 0.7%) of the microbial community in both SSU rRNA and metagenomic data and were not investigated further for this study (SI Table 1). The precipitate top communities (sampled at Sites 1 and 2) contained higher relative abundances of Cyanobacteria than the adjacent leading edges. And while no precipitate tops were sampled at Site 3 or 4, Cyanobacteria relative abundance increased at leading edges.

Of the Cyanobacteria at Site 4, the metagenomic data identified the orders Chroococcales

(average of 85.4% of sequences), Nostocales (6.8%), Oscillatoriales (4.6%), Prochlorales (0.8%),

Gloeobacterales (1.8%) and unclassified reads (0.6%) (SI Table 1). The genus Synechococcus

comprised 90.2% of the reads attributed to Chroococcales. A maximum likelihood tree of

Cyanobacteria OTUs (with greater than 100 sequences in the dataset) showed most OTUs to be

most closely related to Synechococcus sp. JA-3-3Ab(CP000239) (SI Figure 2), a photosynthetic

cyanobacterium previously isolated from microbial mats in Yellowstone National Park’s

Octopus Spring (Allewalt et al, 2006, Steunou et al., 2006).

Microscopy of precipitate tops indicated the presence of photosynthetic cyanobacteria populations (SI Figure 1). Fluorescence microscopy revealed the most abundant cellular morphology to be rod-shaped and approximately 3µm in length. A lesser number of coccoidal organisms was noted as well, which is the one known morphology of Synechococcus, the most abundant Cyanobacteria identified in sequence data. It was difficult to detect organisms entrained within the mineral grains, but when grains were examined under green light (550 nm), organisms capable of photosynthesis became more evident as they fluoresced red. Samples were stained with DAPI (4’6’-diamidino-2-phenylindole) and exposed to ultraviolet light to fluoresce

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DNA allowing for easier imaging of organisms associated with mineral grains. Images of

mineral precipitates were taken at low magnification (200x) on an ESEM that showed

filamentous organisms, potentially cyanobacteria, in close association with mineral grains on

LHC precipitate tops (SI Figure 1E). Under higher magnification (1200x) an amorphous matrix,

potentially exopolymeric substance (EPS), appeared to be in close association with the mineral

grains as well (SI Figure 1F).

In addition to SSU rRNA sequencing, metagenomic libraries were generated from the

leading edge of the Site 4 precipitate (Accessions mgm4709842.3, mgm4709411.3,

mgm4709423.3). A total of 4,868,811 sequences were produced from 788,297,300 base pairs (SI

Table 2). The relative abundances of metagenomic 16S rRNA sequences associated with taxa

were in general agreement with the SSU rRNA sequencing results for the sampling site (Figure

4, SI Table 1). The metagenomic reads were run against the KEGG Orthologues database within

MG-RAST to identify genes associated with photosynthesis, which could affect carbonate

precipitation through the biological consumption of CO2 (Zhu & Dittrich, 2016). Photosynthesis and phycobilisome-associated genes were a major constituent of the metabolic makeup at Site 4, accounting for 21.6%, 33.2%, and 2.7% of all energy metabolism-associated sequences in samples (Table 2).

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Figure D.4. Relative abundances of bacterial and archaeal phyla in LHC precipitates. S1, S2, S3, S4 indicates Site number where a sample was taken, followed by the type of sample taken: top, leading edge (LE), precipitate interior (IN), and leading-edge metagenome (LE MG). Each bar represents the microbial community of a biological replicate. Cyanobacteria are most abundant in the precipitate top portions and at the leading edge of the Site 4 precipitate. Aquificae is heavily represented in the warmest parts of the hot spring closer to the source and Thermotogae dominates the interior mineral material.

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Table D.2. Metabolic gene annotations from the KEGG Orthology database and MG-RAST. Genes associated with photosynthetic metabolism (photosynthesis, photosynthesis antenna proteins, and carbon fixation in photosynthetic organisms) constitute 21.6%, 33.2&, and 2.7% of the metabolic annotations in the three samples from Site 4.

Percentage of annotated genes Energy Metabolism MG 4A MG 4B MG 4C Oxidative phosphorylation 54.6 50.5 78.6 Photosynthesis 13.5 20.9 1.6 Photosynthesis antenna proteins 7.1 10.5 0.7 C fixation in photosynthetic 1.0 1.7 0.4 organisms C fixation in prokaryotes 5.2 3.3 8.8 Methane metabolism 10.9 6.3 6.1 Nitrogen metabolism 2.3 1.6 2.1 Sulfur metabolism 5.4 5.1 1.7

D.4 Discussion

D.4.1 A Geochemical Model for Precipitate Formation

Metabolic reactions carried out by microorganisms have been shown to impact aqueous geochemistry and drive mineral precipitation in a number of environments (Dupraz & Visscher,

2005; Visscher & Stolz, 2005; Baumgartner et al., 2006). Thus, the intimate association of microorganisms with the studied mineral precipitates at Little Hot Creek suggests the possibility of a biological role in their formation. Indeed, photosynthesis can increase the saturation state of calcite, and genes associated with this metabolism were observed in metagenomic libraries from the Site 4 precipitate. However, the rapid physico-chemical changes that occur when the geothermal waters emerge at the surface and equilibrate with the atmosphere could also drive mineral formation. Geochemical modeling was performed to assess the effects of physico- chemical processes on the saturation states of calcite and amorphous silica, which were identified

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as the primary mineral phases within the precipitates at LHC from petrographic analysis.

Measured geochemical values from sampling Site 1 were used as the starting parameters for all

models.

Figure 5 demonstrates the progressive DIC loss and 13C-enrichment of LHC and source fluid experiment waters follow a Rayleigh-type evolutionary trend as has been observed in other hot springs (Usdowski et al., 1979; Michaelis et al., 1985; Fouke et al., 2000). Similar systems produce a predictable relationship between the isotope composition of the residual DIC and the

13 fraction of DIC remaining (f). Linear regression of  CDIC versus ln(f) yields a slope that is

approximately equal to the fractionation (in ‰) between the lost product and reactant DIC (P-

DIC). The fractionation associated with LHC DIC evolution (  −7.80) is similar to that

measured in the degassing experiment (CO2-DIC  −8.67). Both fractionations conform to CO2

degassing, a common phenomenon impacting hot springs (Renaut & Jones, 2000). This suggests

that other factors impacting LHC (biological and mineral precipitation related) do not yield

significant isotope fractionation of spring DIC (recall that the fluid experiment was conducted

outside of the spring, in the absence of spring biota and mineral precipitates). However, CO2

degassing can promote mineral formation by increasing pH and thus the saturation state of

carbonate minerals as discussed below.

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Figure D.5. Comparison of the Rayleigh fractionation from the degassing experiment and measured values from LHC1. The slope of a regression line of the natural log of the fraction of 13 DIC remaining in the system (lnf) and its isotopic composition ( CDIC) is approximately equal to the fractionation factor of the process. Open squares are the first six sampling points of the degassing experiment when mixing was likely incomplete, thus this휹 data was not included in the regression analysis.

Because of the similarity between experimentally determined isotopic fractionations, the effect of degassing on calcite formation at LHC was modeled. Degassing was modeled by decreasing the concentration of DIC while allowing pH to vary to keep alkalinity constant as degassing removes DIC without affecting alkalinity. Model results indicate that degassing can drive an increase in the saturation state (Ω) of calcite, indicating that calcite precipitation becomes more thermodynamically favorable as CO2 is degassed (Figure 6). A Rayleigh fractionation model for carbon isotopic fractionation during degassing predicts a concurrent

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isotopic enrichment in carbon during the modeled rise in saturation state (Usdowski et al., 1979;

Michaelis et al., 1985; Fouke et al., 2000). Intriguingly, the modeled isotopic values during the

greatest increase in saturation state match those expected for waters in equilibrium with calcite

13 measured from the precipitates ( CEXP). Further, the modeled saturation values are comparable to those measured at the final sampling location of LHC. The agreement between isotope compositions predicted from the model and measured values indicate that degassing alone can drive mineral formation and generate the 13C values of the LHC precipitates.

While degassing can explain the precipitation of calcite, it has less of an effect on the saturation state of amorphous silica. Primarily, silica saturation is controlled by the concentration of dissolved silica and temperature; therefore evaporation and/or cooling of waters could potentially increase silica saturation at LHC (Berelson et al., 2011). Modeling was performed to assess the effects of these processes on silica saturation at LHC (Figure 7). Reducing temperature from the source temperature of 81.2 ºC to 25 ºC alone is incapable of increasing the saturation state of silica to values where precipitation is thermodynamically favorable. Evaporation alone can drive silica to supersaturation when around 70% of water has been evaporated. However, evaporation also drives an increase of calcite saturation to values much higher than that of silica implying that calcite precipitation would be more quantitatively important in this scenario. In contrast, the combined effects of cooling and evaporation increase the saturation state of both mineral phases nearly equally, indicating that formation of each mineral would be equally favorable. This result is consistent with mixed calcite and silica layers observed petrographically and suggests that evaporation and cooling may be responsible for the precipitation of silica at

LHC. Additionally, this result explains silica precipitation abiotically without any influence from microorganisms.

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13 Figure D.6. Model results for the evolution of calcite saturation state (Ω) and CDIC for 13 degassing of LHC waters. The measured isotopic values from the precipitates ( CCARB) occur at the greatest slope in the modelled increase of calcite saturation state. 휹 휹

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Figure D.7. Model results for evolution of the saturation state of calcite (blue lines) and amorphous silica (red lines) during evaporation at 81.2 ˚C and (solid lines) and 25 ˚C (dashed lines). The saturation state of both minerals begins to increase when ~70% of water has been evaporated. At high temperature calcite precipitation is more thermodynamically favorable than silica precipitation. However, at cooler temperatures both minerals are equally favorable.

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Based on the results of geochemical modeling, a multi-step growth model was developed describing mineral precipitate formation at LHC through physico-chemical processes alone

(Figure 8). In this model, degassing increases the saturation state of calcite moving along the flow path promoting carbonate precipitation by nucleating at a point along the edge of the stream. Sequential precipitation leads to continued calcite formation and growth of the precipitate extending into the stream, as indicated by the curved, down-dipping growth lines observed in thin section (Figure 3C). As the precipitate grows and extends further into the stream, it forms a platform at the air water interface where water begins to splash onto its surface, which cools and evaporates leading to the precipitation of the upper layer of amorphous silica. Water splashing onto surfaces near hot spring environments leading to silica precipitation has been observed in other hot springs (Mountain et al., 2003; Cady & Farmer, 1996). This model indicates carbonate and silica precipitation can be fully explained through degassing and evaporation alone and is consistent with the results of geochemical modeling as well as field measurements and observations. Thus, from a thermodynamic standpoint, mineral precipitate formation at LHC can be explained through wholly abiotic processes, and microbial metabolic processes are not needed to generate substantial mineral deposition in LHC although they cannot be ruled out as an additional contributor.

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Figure D.8. Schematic model of mineral precipitate formation at a cross-section of Little Hot Creek. (A) CO2 degasses from water increasing the saturation state for calcite. (B) When sufficient CO2 has degassed calcite begins to precipitate. (C) As degassing continues calcite continues to precipitate leading the mineral structure to extend perpendicularly into the stream. (D) When the structure extends far enough out into the stream water begins to splash onto its surface. (E) Water that has splashed onto the precipitate evaporates and cools. (F) Silica precipitates once the water cools and evaporates sufficiently.

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D.4.2 Biological Controls on Precipitate Morphogenesis

The removal of CO2 through physio-chemical or biological processes can shift carbonate

equilibrium to favor the precipitation of CaCO3 (e.g. Dupraz et al., 2009). Photosynthesis may

drive carbonate precipitation through the biological removal of aqueous CO2 and the

concomitant increase in alkalinity. This removal and subsequent carbon fixation process can be

− 2− + represented by the simplified equation: 106 CO2 + 16 NO3 + HPO4 +122 H2O +18H 

C106N16H263O110P +138 O2 (Bradley et al. 2017). Metagenomic functional annotations

demonstrated photosynthetic genes present in the three sampled depths at Site 4 and SSU rRNA

data shows Cyanobacteria are found on precipitate tops and leading edges. Active cyanobacteria

populations in LHC may be altering geochemical gradients on micrometer scales to promote

localized precipitation via photosynthesis. However, LHC fluids degas CO2 readily as they reach the surface and equilibrate with the atmosphere along the stream’s length. Modeling results indicate carbonate and silica precipitation can be fully explained through degassing and evaporation alone, and metabolic processes are not needed to generate substantial mineral deposition. The bladed morphology of the calcite crystals is also suggestive of abiogenic precipitation (e.g., Grotzinger and Knoll, 1999; Riding, 2008), consistent with the geochemical results.

Microscale biogenic morphological features such as altered mineral structure, low inheritance between mineral layers, asymmetric laminae couples, micritization, and the inclusion of benthic microfossils can be collectively diagnostic of biotic influence. Inheritance and micritization are two features that can be utilized in determining biogenicity of a deposit.

Inheritance describes the relationship between laminations. High inheritance, where the overlying lamination mimics the underlying lamination, indicates predominantly abiogenic

220 precipitation. Low inheritance, where the overlying lamination differs from the underlying lamination, is typically a property of microbial mats (e.g., Grotzinger & Rothman, 1996;

Grotzinger & Knoll, 1999; Frantz et al., 2014; Petryshyn et al., 2016). Micritization is the formation of micrite brought on by the alteration of minerals by endolithic microorganisms via boring and is observable as darkened granular material in petrographic thin section (Bathurst,

1966). Additionally, other trace fossils and mineral alterations can be utilized to this end, and morphological evidence of biological involvement can be highly variable across different geochemical environments. Thus, microorganisms can not only influence the precipitate morphological features during mineral formation, but also after deposition.

Petrographic and microscopic analyses suggest a microbial role in shaping the emergent silica/calcite layer of the LHC precipitates. The LHC silica/calcite layer contained domed and wavy fabrics with micritization and a low degree of inheritance that is most parsimoniously explained as the result of precipitation occurring on an irregular surface formed by a microbial mat on the precipitates. As evaporation and decreasing temperature drive precipitation of dissolved silica, it will adsorb and precipitate onto cells of microbial biofilms regardless of microbial activities (Spear & Corsetti, 2013, Konhauser et al., 2004). Additionally, ESEM images from this layer show microbial filaments intimately associated with minerals implying they impart some impact on mineral formation.

The presence of filamentous cells, the genomic identification of photosynthesis and

Cyanobacteria on the precipitate tops and leading edges, and previous studies of hot spring microbialites indicates Cyanobacteria likely play a role in shaping the emergent precipitate, even if they did not actively play a role in mineral precipitation via metabolism or cell wall/sheath interaction. Previous studies have noted this phyla in mats on the surface of microbialites and

221 several mm into the surface, likely due to the above layer providing protection from predation and UV light filtration through mineral grains (Walker et al., 2005). Cyanobacteria have been implicated as ‘builders’ of stromatolitic microfabrics in microbialites recovered from Obsidian

Pool Prime in Yellowstone National Park, producing very finely-laminated structures with high inheritance between layers (Berelson et al., 2011, Pepe-Ranney et al., 2012, Mata et al., 2012;

Spear & Corsetti, 2013), and genesis of similar microfabrics in hot spring environments due to cyanobacterial colonization have been seen elsewhere (e.g. Konhauser et al., 2001). Many of the most abundant Cyanobacteria OTUs identified in LHC are most closely related to a

Synechococcus from Octopus Spring, another alkaline siliceous hydrothermal spring. Though environmental conditions of mineral formation between LHC and these other hot springs differ, preventing direct comparison, but instead provide insight into the variance of biogenic textures that can be produced with differing geochemistry. We postulate that cyanobacteria are involved in producing the mineral textures seen in LHC but cannot determine the mechanisms of formation. The differentiation and variability in textural clues of biogenicity highlights the need for geochemical context and environmental parameters in biosignature identification.

D.4.3 Endolithic microborings in the abiogenic calcite base

In contrast to the mineral texture of the silica/calcite layer, the lower calcite layer is comprised of crystal fans that are typically interpreted as abiotic fabrics (Riding, 2008).

However, the tubular dark structures observed in this abiogenic calcite layer (Figure 3F, 3G) resemble microborings made by euendoliths. Euendoliths are a type of endolithic organisms that actively bore into rock, leaving tube cavities (Golubic et al., 1981). Textural clues such as microboring distribution, size, directionality, and relation to external surfaces and fluid paths are

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key to determine the biogenicity of these structures (McLoughlin et al., 2007). Besides endolithic

organisms, other explanations have been put forth for tunneling features in carbonates such as

organic matter within rocks acting as a dissolution agent or high fluid pressures pushing harder

mineral grains through a mineral precipitate (producing ambient inclusion trails) (McLoughlin et al., 2007). However, abiotic mineral dissolution at heterogeneities and ambient inclusion trails would not leave borings of the morphology we see in precipitates from LHC (see McLoughlin et al., 2010 for a review). The tube structures appear to have well-defined, circular cross-sections with similar diameters and varied lengths up to 20µm, and the tubes propagate into the calcite from external surfaces of the crystals as if a microbe were tunneling inward.

Euendolithic cyanobacteria are commonly found in microbialites and other carbonate surfaces. It’s been suggested that these phototrophic endoliths actively transport Ca2+ away from the microboring front to lower the saturation state of CaCO3 and utilize the mineral as a carbon source in times of aqueous DIC limitation (Garcia-Pichel et al., 2010, Ramírez-Reinat & Garcia-

Pichel, 2012). This mechanism allows carbonate dissolution to occur despite photosynthetic activity acting to promote carbonate precipitation and provides an explanation of the behavior as evolutionarily adaptive (Garcia-Pichel, 2006). Additionally, some lineages of Cyanobacteria

(including the genus Synechococcus) are capable of generating intracellular carbonate inclusions, and this ability is not rare (Benzerera et al. 2014). Internal carbonate biomineralization could lower the alkalinity generated during photosynthesis, thereby decreasing

CaCO3 saturation on the microscale (Benzerera et al. 2014). These metabolic interactions highlight the complexities and feedbacks in mineral-microbe relationships and how difficult it can be to tease apart such reactions.

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We cannot directly demonstrate that Cyanobacteria are the euendolithic microorganisms responsible for the borings in LHC, but believe the microboring structures preserved in the calcite can act as a trace fossil of microbial activity. These endolithic signatures do not require rapid mineralization for preservation like the silica/calcite morphological biosignatures. These observations suggest that even in the absence of a direct influence of microbial activity on mineral formation, microorganisms may secondarily alter microfabrics and create biosignatures under similar environmental conditions.

D.5 Conclusions

Examination of the biogenicity of mineral precipitates from Little Hot Creek revealed that, while abiotic processes can explain their formation thermodynamically, their morphogenesis suggests a microbial contribution, highlighting the need for interdisciplinary approaches (e.g., geochemistry, modeling, and petrography) when investigating the role of life in the formation of mineralized structures. Genomic and microscopic evidence of the microbial communities associated with the mineral precipitates indicated that Cyanobacteria were likely involved in the generation of stromatolitic microfabrics on LHC precipitate tops, while thermophiles, typical of other hot springs, reside within precipitate interiors and leading edges.

This study underscores the necessity of utilizing multiple analytical tools and fields of expertise to understand the formation of geologically and astrobiologically important structures in the field

of geobiology. At Little Hot Creek, geochemical investigation alone would not indicate life was

involved with the formation of the precipitates. However, the addition of the microscopic

textural investigation reveals a biological role in the formation and modification of the

precipitates. Interpretation of potentially microbially influenced minerals from the geologic

224 record or other planets requires a broad suite of techniques to avoid misinterpretation of the biogenicity of the structures.

D.6 Acknowledgements

We would like to thank student participants and instructors from the 2016 International

Geobiology Course, who assisted with sample collection and laboratory analyses, and insightful discussion of the results. We would also like to thank the students of the 2014 and 2015

International Geobiology Courses who helped lay the groundwork for this study. A permit was granted to J.R.S. from the U.S. Forest Service (Permit #MLD15053) to conduct fieldwork and sample the LHC system.

D.7 Contributions and Funding

JRS, FAC, WMB, HAJ, SJL, BWS, HSN, and BSS led the design of the study. Fieldwork and laboratory analyses were conducted by all authors. All authors interpreted results. EAK wrote the manuscript with contributions from all other authors. SRB wrote the geochemical results and discussion.

This research was funded by the Agouron Institute, the Center for Dark Energy Biosphere

Investigations (C-DEBI) at the University of Southern California (USC), and the USC Wrigley

Institute. JRS was supported by the Zink Sunnyside Family Fund. EAK was supported by the

NASA Astrobiology Institute Rock-Powered Life grant.

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APPENDIX E

SUPPLEMENTARY INFORMATION FOR APPENDIX D: MISCROSCALE

BIOSIGNATURES AND ABIOTIC MINERAL AUTHIGENESIS IN LITTLE HOT CREEK,

CALIFORNIA

Table E.1. Taxonomic classifications of metagenomic (MG 4A – C) and SSU rRNA sequences at Kingdom level and for those sequences identified within the Phylum Cyanobacteria. SSU rRNA sequences identified most of the Cyanobacteria sequences as “Other”.

SSU rRNA, all MG 4A MG 4B MG 4C samples Archaea 4.5 1.2 1.8 4.2 Bacteria 94.8 98.2 97.8 95.0 Eukaryota 0.7 0.6 0.5 0.0 0.2 to Other* Cyanobacteria by Order Chroococcales 90.4 91.0 74.8 Nostocales 4.6 4.3 11.4 Oscillatoriales 3.4 3.2 7.3 Prochlorales 0.3 0.3 1.9 Gloeobacterales 1.0 0.9 3.5 Unclassified 0.3 0.3 1.1 Total no. sequences 189,219 628,813 21,940

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Table E.2. Sequence library metrics for three metagenomic samples from the Site 4 precipitate. Information was taken from the MG-RAST pipeline for QC and analysis.

Library metric MG 4A MG 4B MG 4C Sequences 1,190,690 2,431,302 1,246,819 bp 193,326,738 393,009,937 201,960,625 avg length in bp 162 162 162 % passed QC 86.1 77.9 87.0 Of those that passed QC: % ribosomal RNA 9.0 11.0 13.0 % predicted proteins w/ known function 50.5 55.5 45.4 % predicted proteins with unknown 40.5 33.3 42.0 function Post QC GC% 56 +/- 8 57 +/- 8 54 +/- 11

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Figure E.1. Light microscopy of mineral precipitate samples from LHC. (A) Brightfield image from site 3-2 indicating the primary cellular morphologies to be rod-shaped. (B) DAPI-stained microorganisms from the mineral precipitates. (C) Brightfield image of mineral grains from LHC Site 4 (D) and corresponding green light image from site 4 showing photosynthetic organisms in the mineral grains. (E) ESEM image of the Site 3 silica/carbonate portion of the mineral precipitate taken at low magnification (200x) showing potential filamentous organisms in close association with mineral grains. (F) ESEM image of the Site 4 silica/carbonate precipitate portion at 1200x. An amorphous matrix, potentially exopolymeric substance (EPS), appears in close association with the mineral grains.

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Figure E.2. Maximum likelihood phylogenetic tree of LHC Cyanobacteria OTUs represented by greater than 100 sequences in the SSU rRNA dataset. The tree with the highest log likelihood (- 3543.30) is shown. The percentage of trees in which the associated taxa clustered together is shown above the branches.

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APPENDIX F

F.1 Open Access copyright permission for published article in Chapter 2. https://journals.asm.org/content/statement-author-rights

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F.4 Permission from all co-authors to reprint the article in Appendix D for this dissertation.

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