The quantity of microbes was relatively low in the fracture fluids used as the inoculum as well as in the microcosms during the enrichment, which challenged the microbial community detection. The microbial community structure was first roughly characterized with DGGE community fingerprinting and traditional Sanger sequencing of the common phylotypes. Amplicon sequencing produced a reasonable amount of sequences, keeping in mind the low concentration of biomass of the samples. While amplicon sequencing gave a more detailed presentation of the bacterial community, the changes in the communities in the microcosms during the incubation were visible with the DGGE fingerprints. The use of nested PCR for the DGGE in order to produce enough material for the fingerprinting method is a possible source of bias (Yu et al., 2015). However, independent of the community characterization method, the microbial community structure was comparable at the order level. Especially phylotypes belonging to Alphaproteobacteria were detected in the microcosms with both of the community characterization methods. On the other hand, Desulfurella -affiliating phylotype was frequently detected with DGGE in the bacterial communities of the microcosms, but was absent in the amplicon sequencing results. In addition, sequences affiliating with Mollicutes were more frequently detected with DGGE than with amplicon sequencing. Similarly, Mollicutes were detected from the fracture fluids at the same depth characterized with DGGE using the same primers without nested PCR (Purkamo et al., 2013), as well as in amplicon sequencing of the intrinsic bacterial community of the fracture with amplicon sequencing, using fD1 and p2 –primers (Purkamo et al., 2016). Hence, we presume that the primers used in this study for amplicon sequencing may exclude Mollicutes to some level. A relatively large amount of sequences affiliating with chloroplasts in the fracture fluid could be a result of PCR bias. The low amount of template material is known to generate random fluctuations in priming efficiency and lead to variable microbial community fingerprints (Chandler et al., 1997). In addition, GC-rich template sequences have been shown to have higher affinity to amplification primers (Polz and Cavanaugh, 1998), but whether the amplicon sequencing primers used in this study have higher affinity to chloroplast 16S rRNA gene is not known.

# CONCLUSION

The deep subsurface is considered to be a fairly stable environment over long periods of time (Hoehler and Jørgensen, 2013), thus deep continental biosphere is providing a habitat for recalcitrant microbial life. In this study we used microcosms to study the prospective changes in the microbial community structure when abundant carbon sources, electron donors and electron acceptors were introduced. Based on the results obtained in this study, organic carbon is useful for the heterotrophic microbial groups in deep biosphere in Outokumpu. However, according to our results it appears that heterotrophic sulfate reducers or aceticlastic methanogens did not benefit from the acetate addition. Sulfate addition did not have a major effect on the number of sulfate reducers, although a minor increase in SRB marker gene copy numbers was detected. Our results show that microbial communities in the deep terrestrial crystalline bedrock subsurface are subject to transformation. The remaining bacteria in the communities toward the end of the experiment in this study are likely to ferment the organic matter for energy production as well as have the ability to use proteinaceous substrates. Overall, it is likely that the deep terrestrial subsurface microbial communities are continuously changing, depending on the available substrates and dominating metabolic processes at each time point. These changes can be cyclic, i.e., heterotrophic and fermenting microbes can produce substrates to other microbial groups that can take over when viruses or substrate limitation will decrease the numbers of heterotrophs. However, we can only detect a snapshot of these microbial communities and may miss some of the changes with only 68 days incubation period.

This work provides a basis for further studies for detecting the predominant carbon sources and cycling mechanisms in deep crystalline rock fractures. The data gathered here provides guidance for detailed studies exploring the microbial community changes through time, detecting the major carbon assimilation mechanisms and identifying the populations using specific energy and carbon metabolisms. As this study setup does not provide information about the functionality of the microbes, testing the physiological responses and activity of these microbial communities to different carbon sources will further elucidate carbon cycling in the deep Fennoscandian biosphere in the future.

#### AUTHOR CONTRIBUTIONS

fmicb-08-00232 February 16, 2017 Time: 16:38 # 12

LP, MB, and MN designed the experiments and collected the samples. All analyses were carried out by LP. LA assisted with sample collection and provided the geochemical metadata. IK provided funding and access to Outokumpu Deep Drill Hole. MI provided funding and assistance in experimental design. All authors contributed to the discussion of the results. Manuscript was written by LP with inputs from other authors.

#### ACKNOWLEDGMENTS

The authors acknowledge Mirva Pyrhönen from VTT Technical Research Centre of Finland for her skillful work with laboratory analyses and Arto Pullinen from Geological Survey of Finland for aiding in sampling at the Outokumpu Deep Scientific Drill Hole. Eetu Aalto and coworkers from Lapela Ltd. are acknowledged for assisting sampling at Outokumpu. Riikka Kietäväinen from Geological Survey of Finland and Maija Nuppunen-Puputti from

#### REFERENCES

VTT Technical Research Centre of Finland also contributed to the study. Dr. Mark Fox-Powell is thanked for the English language editing.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2017.00232/full#supplementary-material

FIGURE S1 | Phylogenetic affiliations of the partial 16S rRNA gene sequences retrieved from DGGE community fingerprinting analysis in relation to cultured reference strains and 16S rRNA gene sequences of uncultured environmental bacteria. Bootstrap support values were calculated from 1000 random repeats. Nodes with >40% bootstrap support are indicated. Scale bar shows the percent difference.

FIGURE S2 | UPGMA dendrograms of the similarity of bacterial community structure in microcosms with different treatments, (A) acetate amended, (B) acetate + SO2− 4 amended, (C) only with SO4-amended and (D) without any amendments, based on amplicon sequencing results. Clustering was calculated using Dice similarity index.

subsurface water of the South Coast of Korea. J. Microbiol. Biotechnol. 17, 1875–1879.

terrestrial subsurface. Phys. Chem. Earth, Parts A/B/C 58–60, 28–33. doi: 10. 1016/j.pce.2013.04.007

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Purkamo, Bomberg, Nyyssönen, Ahonen, Kukkonen and Itävaara. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Guar Gum Stimulates Biogenic Sulfide Production at Elevated Pressures: Implications for Shale Gas Extraction

Sophie L. Nixon1 \*, Leanne Walker2 , Matthew D. T. Streets2 , Bob Eden2 , Christopher Boothman1 , Kevin G. Taylor1 and Jonathan R. Lloyd1

1 School of Earth and Environmental Sciences, University of Manchester, Manchester, UK, 2 Rawwater Engineering Company Limited, Culcheth, UK

#### Edited by:

Cody Sheik, University of Minnesota Duluth, USA

#### Reviewed by:

Bradley Stevenson, University of Oklahoma, USA Kathleen Duncan, University of Oklahoma, USA Christopher Neil Lyles, Northwestern State University, USA

\*Correspondence: Sophie L. Nixon<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Microbiological Chemistry and Geomicrobiology, a section of the journal Frontiers in Microbiology

Received: 10 October 2016 Accepted: 03 April 2017 Published: 19 April 2017

#### Citation:

Nixon SL, Walker L, Streets MDT, Eden B, Boothman C, Taylor KG and Lloyd JR (2017) Guar Gum Stimulates Biogenic Sulfide Production at Elevated Pressures: Implications for Shale Gas Extraction. Front. Microbiol. 8:679. doi: 10.3389/fmicb.2017.00679 Biogenic sulfide production is a common problem in the oil industry, and can lead to costly hydrocarbon processing and corrosion of extraction infrastructure. The same phenomenon has recently been identified in shale gas extraction by hydraulic fracturing, and organic additives in fracturing fluid have been hypothesized to stimulate this process. Constraining the relative effects of the numerous organic additives on microbial metabolism in situ is, however, extremely challenging. Using a bespoke bioreactor system we sought to assess the potential for guar gum, the most commonly used gelling agent in fracturing fluids, to stimulate biogenic sulfide production by sulfatereducing microorganisms at elevated pressure. Two pressurized bioreactors were fed with either sulfate-amended freshwater medium, or low-sulfate natural surface water, in addition to guar gum (0.05 w/v%) and an inoculum of sulfate-reducing bacteria for a period of 77 days. Sulfide production was observed in both bioreactors, even when the sulfate concentration was low. Analysis of 16S rRNA gene sequences indicate that heterotrophic bacteria closely associated with the genera Brevundimonas and Acinetobacter became enriched early in the bioreactor experiments, followed by an increase in relative abundance of 16S rRNA genes associated with sulfate-reducing bacteria (Desulfosporosinus and Desulfobacteraceae) at later time points. Results demonstrate that guar gum can stimulate acid- and sulfide-producing microorganisms at elevated pressure, and may have implications for the potential role in microbially induced corrosion during hydraulic fracturing operations. Key differences between experimental and in situ conditions are discussed, as well as additional sources of carbon and energy for biogenic sulfide production during shale gas extraction. Our laboratory approach can be tailored to better simulate deep subsurface conditions in order to probe the role of other fracturing fluid additives and downhole parameters on microbial metabolisms observed in these systems. Such baseline studies will prove essential for effective future development of shale gas worldwide.

Keywords: sulfate-reducing bacteria, guar gum, bioreactor, hydraulic fracturing, organic carbon

# INTRODUCTION

fmicb-08-00679 April 15, 2017 Time: 15:24 # 2

The recent development of shale gas in the United States has dramatically increased domestic gas supply, and the UK and other European countries are planning to follow suit in the coming decades and exploit their own shale gas reserves (Andrews, 2013; Weijermars, 2013). The lead up to this development offers the opportunity to learn from and avoid problems encountered in the US, among them the biogenic production of hydrogen sulfide. This process, known as souring, causes pitting and cracking of susceptible materials (increasing the risk of leaks), can form pyrophoric films on steel in gas lines (posing a fire risk), and necessitates costly removal from produced gas. These issues can potentially lead to environmental and reputational damage, and can ultimately shorten the life span of a shale gas well (Eden et al., 1993). Biogenic hydrogen sulfide has been reported at a number of shale gas wells in the Barnett Shale (Fichter et al., 2008, 2009), and several studies have since demonstrated the presence of sulfidogenic bacteria in produced waters from this and other shale gas plays (Davis et al., 2012; Kirk et al., 2012; Struchtemeyer and Elshahed, 2012; Mohan et al., 2013a,b; Cluff et al., 2014; Akob et al., 2015).

Shale gas exists in unconnected or poorly-connected submicron scale spaces within very low permeability shale. It is therefore necessary to artificially fracture the formation in order to liberate shale gas. This is achieved through hydraulic fracturing, in which water-based fluids are pumped down the well at high pressure in order to overcome confining pressures and induce fractures in the formation. Wells are first drilled vertically to the required depth in the shale formation, then horizontally to increase contact with the gas-bearing rock. The fractures generated in this process are held open by a proppant, typically sand, to allow free flow of natural gas to the well. The fluid used to fracture the host rock is water-based, and includes a host of additives in addition to sand. Each serves a particular function, and the mix of additives varies from one operation to another, depending primarily on the characteristics of the source formation, though many such additives are organic (Elsner and Hoelzer, 2016). Here we address the hypothesis that organic fracturing fluid additives stimulate biogenic sulfide production.

A number of organic fracturing fluid additives are already known to be bioavailable to microorganisms. For example, ethylene glycol is widely used as a surfactant to inhibit scale formation during shale gas extraction (Elsner and Hoelzer, 2016), but is readily degraded and used for growth by soil and sewage microorganisms (Haines and Alexander, 1975; Watson and Jones, 1977; McGahey and Bouwer, 1992). Citric acid, used as a complexing agent in almost a third of disclosed hydraulic fracturing operations to date (Elsner and Hoelzer, 2016), is readily fermented by strains of Clostridium, yielding further bioavailable by-products such as acetate (Walther et al., 1977; Schink, 1984). Even the most conservative fracturing fluids contain an organic polymer, which serves as a gelling agent to increase viscosity and keep the proppant in suspension. The most widely used gelling agent is guar gum (Elsner and Hoelzer, 2016), a polysaccharide also common in the food industry, and fermented by intestinal bacteria (Tomlin et al., 1986; Crociani et al., 1994). It is therefore highly likely that fracturing fluid additives will stimulate microbial metabolism in shale gas extraction operations, and are in part responsible for the observed biogenic sulfide production in these systems (Fichter et al., 2008, 2009).

Tracking the metabolism of such organic amendments and their possible coupling to sulfate reduction in situ is, however, extremely challenging. Here we report on a laboratory-based procedure to test the potential for the widely used gelling agent, guar gum, to stimulate biogenic sulfide production in pressurized semi-continuous bioreactor experiments. We present evidence that guar gum stimulates sulfide production. The implications for biofouling in future shale gas extraction operations are discussed. This study employs an ex situ bioreactor approach and represents a first step in understanding the potential microbiological implications in shale gas extraction. This approach is more appropriate than conventional microcosm-based batch tests conducted in serum vials, and can be tailored to simulate the deep terrestrial subsurface. This method could therefore serve as a blueprint for similar "baseline" studies on, for example, the microbial metabolism of fracking additives, required to support effective large-scale shale gas extraction both in the United States, and more widely in regions considering exploitation of this resource.

# MATERIALS AND METHODS

# Microbial Enrichment

A sulfate-reducing enrichment culture was initiated using sediment-laden water from a drinking water reservoir near Buxton, Derbyshire in the UK. Sediment-laden water was added to 90 ml sterile (121◦C 20 min) Postgate B medium (described in Tanner, 1989) amended with sodium acetate as the electron donor (31 mM), pH 7, in a sterile 100 ml serum vial. The enrichment was incubated at 30◦C in the dark, and was considered positive for sulfate reduction upon the accumulation of a black precipitate (iron sulfide). Sulfate reduction was evidenced by the appearance of black precipitates (presumed iron sulfide) and was used as an inoculum for bioreactor experiments after a 17 day incubation period.

## Bioreactor Design

The pressurized bioreactors are bespoke, designed and manufactured by Rawwater Engineering Company Limited. Each bioreactor comprised a 5 cm outside diameter steel tube, lined with a unplasticized polyvinyl chloride liner, grouted in place either end prior to welding end-caps in place. Within the center of each end-cap was a weldable 0.635 cm Swagelok tube fitting. The inlet of the bioreactor was connected to a Perkins-Elmer HPLC series 100 pump, supplied by a nitrogen-pressurized aspirator at 2 barg. The outlet was connected to a union-T fitting, connected to a 0–3000 psig (0–206.8 bar) pressure gauge and a 1000 psig (68.9 bar) pressure release valve, from which samples were collected. All fittings were of 316L stainless steel.

# Bioreactor Experimental Setup

fmicb-08-00679 April 15, 2017 Time: 15:24 # 3

Bioreactor 1 was packed with glass beads, and was fed with an influent of sulfate-amended freshwater medium. Prior to injection medium was purged with nitrogen gas to drive off dissolved oxygen. The basal medium (pH 7) contained (in grams per liter deionized water): NaHCO3 (2.0), NH4Cl (0.25), NaH2PO4.H2O (0.06), KCl (0.1), MgSO4.7H2O (2.0), CaSO4 (1.0), and 10 ml mineral mix. The mineral contained (in grams per liter deionized water): nitrilotriacetic acid (1.5), MgSO4.7H2O (6.0), NaCl (1.0), FeSO4.7H2O (0.1), CaCl2 (0.076), CoCl2 (0.054), ZnCl2 (0.13), CuSO4.5H2O (0.01), AlK(SO4)2.12H2O (0.01), H3BO3 (0.01), MaNoO4.2H2O (0.294), and NiCl2.6H2O (0.024). All constituents of the medium were added prior to autoclaving for 20 min at 121◦C. Total concentration of sulfate in this medium was 15.7 mM.

Bioreactor 2 was packed with low-iron sand (Fisher Scientific, UK), and was fed with deoxygenated surface water collected from a seasonal pond on private land in Culcheth, Cheshire, UK (53◦ 370 44.0400N, 2◦ 290 44.0500W). This land was chosen as a source of readily available fresh surface water relevant to potential future hydraulic fracturing scenarios in the North of England. Formate, acetate, propionate, and butyrate concentrations were measured with ion chromatography (IC; Metrohm 930 Compact IC Flex, Daresbury, UK) and found to be below the detection limit of 0.1 mg/l. Sulfate, nitrate, and phosphate concentrations in this surface water were similarly measured with IC (Dionex ICS5000 Dual Channel, Hemel Hempstead, UK), and found to be 79.6 (0.83 mM), 0.19, and 0.1 mg/l, respectively. Bioreactor 2 therefore served as a "low sulfate" comparison to Bioreactor 1. The influent was purged with oxygen-free nitrogen gas for 24 h prior to injection.

The bioreactors were run at room temperature (15–25◦C) and 1000 psi (68.9 bar) under batch conditions. Injections were made at a flow rate of 3 ml/min for a 30 min batch injection twice weekly (on days 1 and 4 of each week) until day 28, then once weekly, for a total of 77 days. For each bioreactor, the weekly influent injection volume was equal to the pore volume of the bioreactor, quantified with a fluorescein tracer (Sigma-Aldrich, UK) prior to initiating the experiments. The pore volume of Bioreactor 1 was 376 ml, and 562 ml for Bioreactor 2. Both bioreactors were flushed with 6× pore volumes of anaerobic reverse-osmosis water after fluorescein tracer tests, and before the first experimental injection. A 1% (v/v) inoculum from the sulfate-reducing enrichment culture was added to the influent of both bioreactors from the start of the experiment until day 28. Guar gum (0.05% w/v) was added to the influent of both bioreactors from the start of the experiment until day 63. Samples were collected for analysis from the effluent generated at each injection point.

## Analytical Methods

Total sulfide concentrations in the effluent were measured using the methylene blue assay. Samples were collected directly onto zinc acetate crystals, which reacted with the sulfide in solution to form a zinc sulfide precipitate, preserving the aqueous sulfide concentration. The sulfide was then regenerated in acid for a methylene blue colorimetric test (Fonselius et al., 1999). The methylene blue test was calibrated using the standard iodometric determination of sulfide titration method (limit of detection 0/5 mg/l total sulfide). The pH of the effluent was measured using pH indicator strips (pH range 4–10, Fisher Scientific).

## Bacterial Community Composition

Bacterial community composition was examined by extraction of DNA from 10 ml samples of the enrichment culture influent (2 days prior to the start of experiments) and effluent using the MoBio PowerLyzerTM PowerSoil DNA Isolation Kit (MoBio Laboratories, Inc., Carlsbad, CA, USA). Sequencing of polymerase chain reaction (PCR) amplicons of 16S rRNA was conducted with the Illumina MiSeq platform (Illumina, San Diego, CA, USA) targeting the V4 hyper variable regions (forward primer, 515F, 50 -GTGY CAGCMGCCGCGGTAA-30 ; reverse primer, 806R, 50 -GGA CTACHVGGGTWTCTAAT-30 ) for 2 × 150-bp paired-end sequencing (Illumina) (Caporaso et al., 2011, 2012). PCR amplification was performed using Roche FastStart High Fidelity PCR System (Roche Diagnostics Ltd, Burgess Hill, UK) in 50 µl reactions under the following conditions: initial denaturation at 95◦C for 2 min, followed by 36 cycles of 95◦C for 30 s, 55◦C for 30 s, 72◦C for 1 min, and a final extension step of 5 min at 72◦C. The PCR products were purified and normalized to ∼20 ng each using the SequalPrep Normalization Kit (Fisher Scientific, Loughborough, UK). A negative PCR control was conducted in parallel to bioreactor effluent and inoculum samples, and found to be devoid of DNA. The PCR amplicons from all samples were pooled in equimolar ratios. The run was performed using a 4 pM sample library spiked with 4 pM PhiX to a final concentration of 10% following the method of Kozich et al. (2013). Raw sequences were divided into samples by barcodes (up to one mismatch was permitted) using a sequencing pipeline. Quality control and trimming was performed using Cutadapt (Martin, 2011), FastQC1 , and Sickle (Joshi and Fass, 2011). MiSeq error correction was performed using SPADes (Nurk et al., 2013). Forward and reverse reads were incorporated into full-length sequences with Pandaseq (Masella et al., 2012). Chimeras were removed using ChimeraSlayer (Haas et al., 2011) and operational taxonomic units (OTUs) were generated with UPARSE (Edgar, 2013). OTUs were classified by Usearch (Edgar, 2010) at the 97% similarity level, and singletons were removed. Rarefaction analysis was conducted using the original detected OTUs in Qiime (Caporaso et al., 2010). Taxonomic assignment was performed by the Ribosomal Database Project (RDP) Classifier using an 80% confidence limit (Wang et al., 2007). Raw sequencing data have been submitted to NCBI Sequence Read Archive2 with the project accession number SRP093359.

1http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ 2http://www.ncbi.nlm.nih.gov/sra/

# RESULTS

#### Bioreactor Experiments

fmicb-08-00679 April 15, 2017 Time: 15:24 # 4

Results from bioreactor experiments are summarized in **Figure 1**. Sulfide production was detected in both bioreactors. Much higher concentrations of sulfide were measured in bioreactor 1 (**Figure 1A**) compared to bioreactor 2 (**Figure 1B**). In both cases, the concentration of total sulfide was highest after inoculations had stopped, but before guar injection had ceased (**Figure 1**, points 1 and 2, respectively). In bioreactor 1, the average concentration of sulfide from day 0 to day 21 was 5.3 ± 1.6 mg/l. The average sulfide concentration for the same period in bioreactor 2 was 2.5 ± 1.4 mg/l. In both bioreactors the maximum total sulfide concentrations were recorded on day 63 (118.6 mg/l in bioreactor 1 and 12.6 mg/l in bioreactor 2). Sulfide concentrations decreased in both bioreactors after guar injections ceased. The average pH measured in bioreactors 1 and 2 was 8.3 and 7.6, respectively.

## Microbial Community Analysis

Microbial community composition was analyzed using 16S rRNA gene sequencing. A total of 281 OTUs were detected in the sulfate-reducing enrichment culture that was inoculated into both bioreactors (the inoculum). In bioreactor 1 samples, 599 OTUs were detected by day 4, 347 by day 49, and 420 by day 77. In bioreactor 2, 105 OTUs were observed by day 4, 135 by day 49, and 117 by day 77. Genus-level diversity of the inoculum and bioreactor samples is shown in **Figure 2**.

The inoculum was dominated by Firmicutes (64.0%, 78 OTUs), of which 58% of obtained sequences were affiliated to the class Clostridium (68 OTUs). The remainder of the sequences were affiliated with the phyla Proteobacteria (30.3%, 29 OTUs) and Bacteroidetes (5.5%, 9 OTUs). Other phyla accounted for less than 0.2% of the sequences from this sample. The most abundant genera detected in the inoculum were Clostridium (24.1%, 16 OTUs), Klebsiella (20.0%, 1 OTU) and Sedimentibacter (8.8%, 6 OTUs). Sequences of unidentified genera affiliated with the families Ruminococcaceae (7.8%, 13 OTUs), Enterobacteriaceae (5.9%, 1 OTU), Sphingobacteriaceae (4.0%, 1 OTU), and Clostridiaceae (2.4%, 2 OTUs) were also prominent. Sulfate-reducing genera detected in the inoculum include Desulfovibrio (2.2%, 1 OTU), Desulfosporosinus (1.5%, 1 OTU), and Desulfitobacterium (0.7%, 1 OTU), and 0.1% of sequences were assigned to the sulfur-reducing species Geobacter sulfurreducens (1 OTU), which is also a well known Fe(III) reducing bacterium.

Sequences obtained from bioreactor 1 on day 4 were dominated by Proteobacteria (77.9%, 114 OTUs), most of which were affiliated to the class Alphaproteobacteria (50.1%, 28 OTUs), in addition to Gammaproteobacteria (15.2%, 20 OTUs) and Betaproteobacteria (10.1%, 46 OTUs). The most abundant genus in this sample was Brevundimonas, accounting for 32.2% of sequences (2 OTUs). Other prominent bacteria in this sample included members of the Acinetobacter (6.1%, 3 OTUs) and Pseudomonas (3.6%, 7 OTUs) genera. In contrast to the inoculum, sequences assigned to the Clostridium genus only accounted for 1.8% (7 OTUs) of the community at this time point.

By day 49, the bioreactor 1 community was dominated by Proteobacteria (83.3%, 102 OTUs). The dominant genera were Acinetobacter (63.2%, 3 OTUs), Pseudomonas (10.7%, 6 OTUs), and Brevundimonas (4.6%, 2 OTUs). By day 77, diversity of the community had substantially increased (see **Figure 2**). Roughly two-thirds of sequences were affiliated with Firmicutes (36.0%, 92 OTUs) and Proteobacteria (35.3%, 112 OTUs). The majority of sequences assigned to the former were identified as unknown members of the Defluviitaleaceae family, and 8.1% were assigned to the sulfate-reducing genus Desulfosporosinus (4 OTUs).

Bioreactor 2 was dominated with Proteobacteria throughout the experiment, accounting for 71.1% (41 OTUs), 85.6% (51 OTUs), and 80.7% (41 OTUs) of sequences by day 4, 49, and 77, respectively. Actinobacteria accounted for 18.5% of sequences at day 4 (11 OTUs), but less than 1% at the later time points. Firmicutes were less abundant than in the inoculum (64.0% 78 OTUs), but relatively stable throughout (9.4% at day

4, 18 OTUs; 7.3% at day 49, 29 OTUs; 9.8% at day 77, 29 OTUs). Brevundimonas was the dominant genus at every time point of the bioreactor 2 experiment, representing 65.0% of day 4 sequences (5 OTUs), 44.8% of those obtained by day 49 (4 OTUs), and 45.0% at day 77 (6 OTUs). Organisms affiliated with Georgenia species were also prominent by day 4, accounting for 18.2% of sequences (2 OTUs). Unknown members of the Clostridiaceae family represented 7.0% of sequences at the same time point (4 OTUs), but were not detected at later time points. Organisms most closely affiliated with Acinetobacter was the second-most abundant genus by day 49 (31.9%, 3 OTUs) and 77 (33.0%, 2 OTUs).

The dominant genera identified throughout both bioreactor experiments (representatives of Brevundimonas, Acinetobacter) were detected in the inoculum, albeit each accounted for less than 1% of the inoculum community. Pseudomonas, found to be relatively prominent in bioreactor 1, was also detected in the inoculum, although Georgenia (prominent in bioreactor 2) was not. The bioreactors were not sterile prior to use in these experiments, so may represent an additional source of microorganisms.

# Presence of Sulfidogenic Lineages

The presence and abundance of genera associated with sulfide production are summarized in **Figures 3**, **4**. Only assigned taxa that include known bacterial strains capable of sulfide production have been included in this analysis, however we acknowledge that other microorganisms may have contributed to sulfide production. The acetate oxidizing sulfate-reducing enrichment used to inoculate the bioreactors appeared to select for organisms most closely related to Desulfovibrio and Desulfosporosinus species, though only the latter appeared to increase in relative abundance in the bioreactor experiments (see **Figure 2**).

The most abundant sulfate-reducing genus in the inoculum was Desulfovibrio, although it only accounted for 2.4% of sequences (1 OTU) obtained from this sample, and less than 1% of the community in both bioreactor experiments at every time point. Close relatives of known Desulfosporosinus species were also present in the inoculum (1.6%, 3 OTUs), although less abundant than Desulfovibrio species. The relative abundance of Desulfosporosinus species increased in the later stages of the bioreactor 1 experiment, representing 10.2% (4 OTUs) of the day

77 community (see **Figures 2**, **3**). The thiosulfate-reducing genus Dethiosulfatibacter was also detected in bioreactor 1 samples (2.7% of day 77 community, 1 OTU), but was not detected in the inoculum, presumably due to very low abundance, though its presence in the bioreactor prior to inoculation cannot be ruled out.

In bioreactor 2, Desulfosporosinus species were enriched by day 77, representing 4.1% of the community (1 OTU). Desulfitobacterium species were not detected in any samples from bioreactor 2. Organisms most closely affiliated with Dethiosulfatibacter and Desulfobulbus genera were both detected at day 4, but each accounted for less than 0.1% of the community, and were not detected in the inoculum or at later time points, either due to their low abundance in the inoculum or because they were already present in the bioreactor.

# DISCUSSION

A number of organic additives are used in hydraulic fracturing fluid, and whether these additives stimulate deleterious microbial activity is poorly understood. The results from this study demonstrate that guar gum, a widely used gelling agent in disclosed hydraulic fracturing operations, can stimulate heterotrophic microorganisms including sulfate-reducing bacteria under conditions relevant to the terrestrial subsurface, even with low sulfate concentrations typical of surface water sources, ultimately fuelling biogenic sulfide production.

The enrichment of heterotrophic taxa in both bioreactor experiments (**Figure 2**) suggests that guar gum is readily bioavailable for microbial metabolism. Consistent with this was the detection of high concentrations of volatile fatty acids in both bioreactors at day 28 (principally acetate and propionate at 182.6 and 75.4 mg/l, respectively in bioreactor 1, and 169.4 mg/l acetate and 109.8 mg/l propionate in bioreactor 2), which depleted over the course of the experiment (data not shown). Brevundimonas, the most abundant genus detected early in the bioreactor 1 experiment and throughout the bioreactor 2 experiment (**Figure 2**), is a non-fermentative Gram-negative genus in the alpha subdivision of the Proteobacteria, able to draw upon a wide range of organic compounds for nonfermentative respiration and is known to produce organic acids as by-products (Segers et al., 1994). At least one strain of Brevundimonas is known to metabolize mannose and galactose, the two sugars that make up the guar gum galactomannan polysaccharide (Segers et al., 1994). It therefore seems likely that strains of Brevundimonas were able to metabolize guar gum and its potential degradation products in these experiments. This hypothesis warrants further investigation, including culturing experiments with and without guar. Acinetobacter was also prominent in later stages of both bioreactor experiments. Strains of this genus are similarly non-fermentative and metabolically versatile, capable of using an array of organic compounds as sole energy and carbon sources (Doughari et al., 2011). A large number of strains have been successfully isolated using acetate

as the sole source of carbon and energy (Warskow and Juni, 1972), which may be present as a guar degradation product in the bioreactors. It is therefore feasible that strains of Acinetobacter are also able to use guar gum, directly or via its degradation products, in the experiments. Both genera were detected in the inoculum, and are assumed to originate from the reservoir water used to initiate the sulfate-reducing enrichment culture.

Unsurprisingly, sulfidogenic genera were more numerous and abundant in samples taken from bioreactor 1 (injected with sulfate-amended medium) compared with bioreactor 2 (injected with low-sulfate surface water). The most abundant sulfate-reducing genera in bioreactor 1 were Desulfosporosinus, Dethiosulfatibacter and unidentified members of the Desulfobacteraceae family (**Figures 2**, **3**). Of these, only Desulfosporosinus was detected in the inoculum. It is possible that these other taxa were in fact present but in such small numbers that they were not detected during sequencing. Interestingly, Dethiosulfatibacter species are unable to reduce sulfate to sulfide, and instead utilize thiosulfate and elemental sulfur as electron acceptors (Takii et al., 2007), neither of which were supplied to the bioreactor. It is therefore likely that Dethiosulfatibacter strains were not contributing directly to sulfide production, and were instead operating a fermentative metabolism (Takii et al., 2007), contributing to the breakdown of guar gum. Some sulfate-reducing genera that were enriched in the inoculum did not prosper in the bioreactor experiment, such as Desulfitobacterium and Desulfovibrio (**Figure 3**). The data therefore suggest that Desulfosporosinus and members of the sulfate-reducing family Desulfobacteraceae were responsible for sulfide production measured in the bioreactor 1 experiment. The sulfide production measured in bioreactor 2 (**Figure 1**) can most likely be attributed to Desulfosporosinus, the only sulfidogenic taxon that increased in abundance throughout the course of the experiment (**Figure 4**). In both cases it is possible that taxa other than those discussed here may have contributed to sulfide production.

The sulfide production measured in both bioreactors does not appear to be limited by the amount of sulfate available. Based on a 1:1 ratio of sulfate reduced to sulfide produced (whether direct or via intermediate sulfur species), the maximum amount of sulfide that could be produced is equal to the concentration of

sulfate available in each bioreactor, assuming the supply electron donors is not limiting. Therefore, 15.7 mM sulfate added to bioreactor 1 has the potential liberate 15.7 mM (519 mg/l) sulfide, yet the maximum concentration measured was almost five times lower. Similarly, only 12.6 mg/l total sulfide was measured in bioreactor 2, more than six times lower than the maximum concentration that could be produced from the direct reduction of 79.6 mg/l sulfate measured in the pond water. In both cases it therefore appears that the supply of electron donors was limiting.

### Results Compared to In Situ Studies of Shale Gas Plays

A number of other studies have identified sulfidogenic microorganisms obtained from active shale gas plays in the US. Struchtemeyer and Elshahed (2012) conducted 16S rRNA diversity analysis on flowback fluids from shale gas wells in the Barnett shale, and identified sequences affiliated with the sulfate-reducing genera Desulfosporosinus, Desulfotomaculum, as well as thiosulfate- and sulfur-reducing genera Dethiosulfovibrio, Thermotoga, Petrotoga, Thermovirga, and Halanaerobium. Davis et al. (2012) monitored the change in microbial communities in post-fracturing fluids stored in tanks in the Barnett shale formation over a 6-month period using 16S rRNA gene sequencing, and found the number of sequences affiliated with Desulfovibrio increased over time. However, their results indicate that the thiosulfate-reducing Halanaerobium genus was more abundant, and likely contributed to the biogenic sulfide production reported in this area (Fichter et al., 2008, 2009). Indeed, it is common that microorganisms other than those traditionally thought of as sulfate-reducing bacteria are the dominant sulfide-producers in conventional hydrocarbon reservoirs, especially at elevated temperature and pressure (Gittel et al., 2009; Stevenson et al., 2011) Desulfovibrio was found to be abundant in flowback fluids from one well in the Antrim shale gas play (Kirk et al., 2012), consistent with results reported by Davis et al. (2012). Desulfobacter halotolerans was abundant in a flowback impoundment in the Marcellus shale, despite treatment with biocide, though Halanaerobium congolense accounted for more than half of sequences obtained from 16S rRNA analysis (Mohan et al., 2013b). Halanaerobium is a genus of halophilic bacteria, capable of fermentation of wide array of organics as well as thiosulfate- and sulfur-reduction (Zeikus et al., 1983), and was also found to dominate sequences from produced waters in other studies of the same shale gas play (Mohan et al., 2013a; Cluff et al., 2014; Daly et al., 2016). Strains of the same genus in flowback fluids were later shown to be viable (Akob et al., 2015), and, more recently, capable of degrading guar gum (Liang et al., 2016).

It is clear that sulfidogenic taxa are common in flowback and produced fluids from active shale gas plays, though the overlap with those identified in our experiments is limited to Desulfosporosinus species (Struchtemeyer and Elshahed, 2012). In addition, a number of studies have identified the prominence of non-sulfate-reducing sulfidogenic taxa, especially stains of Halanaerobium. This genus was absent from our bioreactor samples, in part owing to the lack of thiosulfate and high salt concentrations in the systems.

# Bioreactor Compared with In Situ Conditions

In this study, we sought to simulate conditions more relevant to the deep terrestrial subsurface compared with conventional serum bottle substrate utilization tests. In particular, bioreactor experiments were run at high pressure (1000 psi, 68.9 bar, 6.89 MPa), and bioreactor 2 was fed with terrestrial surface water which represents a plausible source of fracturing fluid water in future UK shale gas extraction (CIWEM, 2016). Furthermore, the sulfate-reducing enrichment culture was initiated with water from a drinking water reservoir, a similarly plausible source of water for UK hydraulic fracturing operations, and thus the microbial community introduced to the bioreactor experiments could be considered highly appropriate.

A number of parameters at play in the hydraulic fracturing of shale formations were not, however, represented in the experiments reported here. These parameters are likely to impact on bacterial community composition, and can account for the major differences in our study compared with in situ studies. For example, hydraulic fracturing leads to significant changes in fracturing fluid composition that were not reflected in the bioreactor experiments. During shale gas extraction, input fluids are subjected to temperatures above 50◦C, and pressures greater than 30 MPa (Fichter et al., 2012; Picard and Daniel, 2013). Upon contact with freshly fractured shale, these fluids are influenced by the chemical composition of the formation, observed in flowback and produced waters as high concentrations of total dissolved solids, dissolved organic carbon, naturally-occurring radioactive minerals, lower pH, and salinities that can reach several times that of seawater (Struchtemeyer and Elshahed, 2012; Mohan et al., 2013a; Akob et al., 2015; Liang et al., 2016). Furthermore, the increased temperatures encountered by hydraulic fracturing fluids at depth may alter the properties (and hence the bioavailability) of organic additives, including guar gum. Significant changes in microbial diversity can be seen as a result of these well-documented chemical changes, from a typical freshwater aerobic community in input fluids, to a less diverse anaerobic community in flowback fluids. The predominance of halotolerant and halophilic taxa in flowback waters indicates that salinity is a significant contributing factor to changes in microbial ecology (Struchtemeyer and Elshahed, 2012; Mohan et al., 2013a; Cluff et al., 2014; Akob et al., 2015; Daly et al., 2016), and most likely accounts for the lack of halophilic sulfidogens, such as Halanaerobium (Zeikus et al., 1983), in our experiments. It is worth noting, however, that relatively few shale gas plays have been assessed with regard to microbial activity, and it remains unclear to what extent the predominance of certain taxa over others are controlled by factors such as the choice of source water used for fracturing fluids, the characteristics of the shale formations, the mix of additive used, and operational parameters chosen. These factors may change significantly in shale gas extraction development outside of the US. The experiments reported here thus serve as a blueprint for an ex situ method

superior to simple microcosm tests. This methodology can be applied extensively to better constrain the factors that contribute to souring and other deleterious microbial activities during shale gas extraction operations.

# Sources of Carbon and Energy for Biogenic Sulfide Production during Shale Gas Extraction

In this study, we have demonstrated that guar gum, a commonly used gelling agent added to fracturing fluids, has the potential to stimulate biogenic sulfide production. However guar gum is not the only potential electron donor for microbial activity during shale gas extraction. An alternative gelling agent to guar is cellulose, readily fermented by heterotrophic bacteria (e.g., Weimer and Zeikus, 1977), the by-products of which could serve as electron donors for sulfidogenesis. Acetate is a well-known electron donor for microbial sulfate reduction (e.g., Laanbroek and Pfennig, 1981), and is added to fracturing fluids to control pH (Elsner and Hoelzer, 2016). It is also plausible that other fracturing fluid additives known to be bioavailable to microorganisms, such as polyacrylamide (Nakamiya and Kinoshita, 1995; Wen et al., 2010) and ethylene glycol (Haines and Alexander, 1975; Watson and Jones, 1977; McGahey and Bouwer, 1992), would be broken down into directly available electron donors during hydraulic fracturing.

Another source of organic carbon and energy is the formation itself. Shale is organic rich, and whilst its nanodarcy permeability is thought to render it uninhabitable, the organic compounds they host are thought to support microbial communities at the interface of more permeable strata (Frederickson et al., 1997; Krumholz et al., 2002). It is clear from the organic chemistry of flowback and produced fluids that hydraulic fracturing liberates hydrocarbons from the formation (Strong et al., 2014). These hydrocarbons could conceivably serve as an indirect source of electron donors after undergoing fermentation by heterotrophic bacteria (e.g., species of Halanaerobium, Zeikus et al., 1983). Additionally, highly oxidizing peroxide compounds are added to fracturing fluids to reduce fluid viscosity and allow for fluid recovery prior to gas flow, and these compounds may render otherwise recalcitrant kerogen more bioavailable through oxidation.

Struchtemeyer et al. (2011) demonstrated that organic polymers in drilling waters were degraded by a community of anaerobic microorganisms present in drilling muds. Barite (BaSO4) and sulfonates, added to drilling mud to add weight and reduce viscosity, respectively, were also found to stimulate sulfate-reducing microorganisms and biogenic sulfide production (Struchtemeyer et al., 2011). The act of drilling into shale formations prior to hydraulic fracturing can therefore introduce additional carbon and energy sources for sulfidogenesis before organic-rich fracturing fluids are injected.

Additional sources of sulfur compounds can also be found in injection fluids, though to a much lesser extent than organic carbon. In a thorough review of disclosed fracturing fluid additives, Elsner and Hoelzer (2016) report the use of organo sulfonates as surfactants in 4% of operations, sodium sulfate to control ionic strength in 2.4% of operations, ammonium sulfate as a friction reducer in 1.1%, and ferric sulfate as a crosslinker in less than 1%. The source of water used to make up fracturing fluid may also harbor sulfate concentrations sufficient to lead to detectable biogenic sulfide production, though this will vary with operation location. In addition, it is plausible that oxidizing agents added to fracturing fluids to reduce viscosity for fluid recovery (breakers) could oxidize any reduced sulfur native to the formation [either as pyrite (Raiswell and Berner, 1986; Gross et al., 2015) or organo-sulfur compounds (Tissot and Welte, 1978; Sinninghe Damsté and de Leeuw, 1989)], thereby rendering it available to support sulfidogenesis through sulfate-, thiosulfate-, or sulfur-reduction.

# CONCLUSION

Using bespoke high-pressure bioreactor systems, we have demonstrated that guar gum, the most commonly used gelling agent in hydraulic fracturing fluids, can serve as a carbon and electron donor source for a sulfate-reducing microbial community, leading to biogenic sulfide production. These results have important implications for our understanding of the critical factors controlling souring during shale gas extraction, and highlight the value of adopting a bespoke bioreactor methodology to study these constraints in isolation. Future research should seek to accurately simulate the elevated pressures, temperatures and aqueous geochemistry encountered during shale gas extraction. This approach could be used to assess the potential for other additives to stimulate sulfidogenesis and other microbial processes, for example, biofilm formation and clogging, using the pressurized bioreactor model employed here, in order to better define the least problematic fracturing fluid composition, and to identify the microorganisms that need to be controlled prior to re-use of flowback fluids. The methodology adopted in this study offers the opportunity to probe the microbial metabolisms observed recently to be active in hydraulic fracturing produced waters in more detail (Daly et al., 2016). In particular, our approach allows for the relative influence of fracturing fluid additives on the activity of these metabolisms to be constrained beyond what is possible through in situ studies.

# AUTHOR CONTRIBUTIONS

SN, BE, KT, and JL designed the experiments. SN, LW, and MS conducted the experiments. CB carried out DNA extractions and sequencing. SN carried out data analysis, prepared figures, and wrote the manuscript with contributions from all authors.

# FUNDING

SN was supported through NERC Impact Acceleration Account NE/L012723/1.

# REFERENCES

fmicb-08-00679 April 15, 2017 Time: 15:24 # 10

sequence, northwestern New Mexico. Geomicrobiol. J. 14, 183–202. doi: 10.1080/01490459709378043

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Nixon, Walker, Streets, Eden, Boothman, Taylor and Lloyd. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Serpentinization-Influenced Groundwater Harbors Extremely Low Diversity Microbial Communities Adapted to High pH

Katrina I. Twing1,2 \*, William J. Brazelton2 , Michael D. Y. Kubo3 , Alex J. Hyer2 , Dawn Cardace4 , Tori M. Hoehler5 , Tom M. McCollom6 and Matthew O. Schrenk1

1 Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, USA, 2 Department of Biology, University of Utah, Salt Lake City, UT, USA, 3 SETI Institute, Mountain View, CA, USA, 4 Department of Geosciences, University of Rhode Island, Kingston, RI, USA, 5 Exobiology Branch, NASA Ames Research Center, Moffett Field, CA, USA, 6 Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, CO, USA

Edited by: Cody Sheik, University of Minnesota Duluth, USA

#### Reviewed by:

Thomas L. Kieft, New Mexico Institute of Mining and Technology, USA Wesley Douglas Swingley, Northern Illinois University, USA

\*Correspondence:

Katrina I. Twing<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Microbiological Chemistry and Geomicrobiology, a section of the journal Frontiers in Microbiology

Received: 14 January 2017 Accepted: 14 February 2017 Published: 01 March 2017

#### Citation:

Twing KI, Brazelton WJ, Kubo MDY, Hyer AJ, Cardace D, Hoehler TM, McCollom TM and Schrenk MO (2017) Serpentinization-Influenced Groundwater Harbors Extremely Low Diversity Microbial Communities Adapted to High pH. Front. Microbiol. 8:308. doi: 10.3389/fmicb.2017.00308 Serpentinization is a widespread geochemical process associated with aqueous alteration of ultramafic rocks that produces abundant reductants (H2 and CH4) for life to exploit, but also potentially challenging conditions, including high pH, limited availability of terminal electron acceptors, and low concentrations of inorganic carbon. As a consequence, past studies of serpentinites have reported low cellular abundances and limited microbial diversity. Establishment of the Coast Range Ophiolite Microbial Observatory (California, U.S.A.) allowed a comparison of microbial communities and physicochemical parameters directly within serpentinization-influenced subsurface aquifers. Samples collected from seven wells were subjected to a range of analyses, including solute and gas chemistry, microbial diversity by 16S rRNA gene sequencing, and metabolic potential by shotgun metagenomics, in an attempt to elucidate what factors drive microbial activities in serpentinite habitats. This study describes the first comprehensive interdisciplinary analysis of microbial communities in hyperalkaline groundwater directly accessed by boreholes into serpentinite rocks. Several environmental factors, including pH, methane, and carbon monoxide, were strongly associated with the predominant subsurface microbial communities. A single operational taxonomic unit (OTU) of Betaproteobacteria and a few OTUs of Clostridia were the almost exclusive inhabitants of fluids exhibiting the most serpentinized character. Metagenomes from these extreme samples contained abundant sequences encoding proteins associated with hydrogen metabolism, carbon monoxide oxidation, carbon fixation, and acetogenesis. Metabolic pathways encoded by Clostridia and Betaproteobacteria, in particular, are likely to play important roles in the ecosystems of serpentinizing groundwater. These data provide a basis for further biogeochemical studies of key processes in serpentinite subsurface environments.

Keywords: serpentinization, alkaliphile, extremophile, groundwater, borehole

# INTRODUCTION

fmicb-08-00308 February 27, 2017 Time: 16:48 # 2

The Earth's subsurface is predicted to be an expansive habitat for microorganisms (Whitman et al., 1998; Edwards et al., 2012; Kallmeyer et al., 2012). Unlike surface biomes, the subsurface is largely decoupled from photosynthetic primary production; instead, many subsurface ecosystems are influenced by carbon and energy liberated from the Earth's mantle and crust. However, given the inherent lack of accessibility, direct sampling of subsurface, rock-hosted environments has been limited. In continental settings, researchers have used caves (Northrup and Lavoie, 2001; Engel et al., 2004), mines (Onstott et al., 2003; Chivian et al., 2008), springs (Brazelton et al., 2012, 2013; Suzuki et al., 2013; Cardace et al., 2015), and isolated boreholes (Stevens and McKinley, 1995; Itävaara et al., 2011) as windows into the subsurface environment. These features grant access to an otherwise inaccessible environment, but they represent opportunistic sampling locations. In the present study, a series of wells were drilled directly into serpentinizationinfluenced aquifers of the Coast Range Ophiolite, a portion of ancient seafloor in northern California, USA, to sample microbial communities in serpentinizing rocks and groundwater. This observatory represents the first opportunity to investigate microbial communities with direct access to the range of conditions in the serpentinizing subsurface (Cardace et al., 2013).

Serpentinization is a widespread geochemical process involving the aqueous alteration of peridotite to serpentine minerals, resulting in an abundance of potential reductants, in the form of hydrogen, methane, and small organic molecules (McCollom and Seewald, 2007; Proskurowski et al., 2008; Schrenk et al., 2013). Serpentinization also releases hydroxyl ions, which creates extremely high pH fluids (pH > 10). At high pH, bicarbonate and carbonate are the dominant species of dissolved inorganic carbon (DIC), and the latter can precipitate out of solution as carbonate minerals when in the presence of divalent cations, such as Ca2+ and Mg2+ commonly found in serpentinite fluids. Thus, fluids associated with serpentinization are characteristically low in DIC, particularly dissolved CO2. Compared to the abundance of reductants in these systems, there is a lack of corresponding oxidants, which likely limits the range of potential microbial metabolisms. Thus subsurface serpentinite environments are characterized by unusual challenges to life, such as extreme pH (>10), limited availability of dissolved carbon, and a lack of potential terminal electron acceptors.

The best-characterized serpentinite-hosted microbial ecosystem to date is the Lost City Hydrothermal Field, located 15 km from the Mid-Atlantic Ridge (Kelley et al., 2005). The tall carbonate chimneys at Lost City are dominated by methanecycling archaea in the anoxic chimney interiors (Schrenk et al., 2004) and by methanotrophic and sulfur-oxidizing bacteria in the chimney exteriors (Brazelton et al., 2006). More recently, researchers have started exploring life within continental serpentinite environments by using natural springs, such as the Tablelands Ophiolite in Newfoundland, Canada (Brazelton et al., 2012, 2013) and The Cedars site in northern California (Suzuki et al., 2013, 2014), or previously established wells, such as at the Cabeço de Vide Aquifer (CVA) in Portugal (Tiago and Veríssimo, 2013). In these studies of continental serpentinite sites, microbial communities were dominated by clades of Betaproteobacteria and Firmicutes (Schrenk et al., 2013).

Surveys of the Tablelands Ophiolite suggest that subsurface serpentinite communities are dominated by Erysipelotrichia, a class within the phylum Firmicutes, in the deep, anoxic sourcewaters and microaerophilic H2-oxidizing Betaproteobacteria at the shallow, oxic/anoxic interface (Brazelton et al., 2013). Microcosm experiments from the Coast Range Ophiolite Microbial Observatory (CROMO), the location of this study, have indicated that Betaproteobacteria closely related to Hydrogenophaga pseudoflava and Clostridia (phylum Firmicutes) closely related to Dethiobacter alkaliphilus, are stimulated by small organic molecules that are expected to be available in the serpentinite environment (Crespo-Medina et al., 2014). Furthermore, recently published genomes of cultivated isolates of the proposed genus Serpentinomonas, which are most closely related to the genus Hydrogenophaga, are consistent with a role for these organisms at oxic/anoxic interfaces in serpentinizing systems (Suzuki et al., 2014).

While these previous studies suggest that distinct microbial communities inhabit different physico-chemical regimes in serpentinizing groundwater, these relationships have not yet been studied directly. Furthermore, little genomic or metagenomic data for organisms other than Serpentinomonas are available from serpentinizing environments. This study combines environmental 16S rRNA gene sequencing, shotgun metagenomic analyses, and geochemical monitoring across a range of conditions in order to relate patterns in microbial diversity and metabolic potential to underlying geochemical processes in serpentinite subsurface environments. This work improves our understanding of the physiology and ecology of the dominant bacteria in these ubiquitous ecosystems, and it will facilitate our integration of these systems into models of carbon cycling.

#### MATERIALS AND METHODS

#### Site Description and Sample Collection

The Coast Range Ophiolite is a 155–170 million year old ophiolite located in northern California, containing numerous calciumhydroxide rich springs, indicating serpentinizing activity below the surface (Barnes and O'Neil, 1971). The CROMO, which is located at the UC-Davis McLaughlin Natural Reserve in Lower Lake, CA and was established in August 2011 and using clean drilling techniques to enable subsequent monitoring of the microbial communities and associated geochemistry within the serpentinite subsurface (Cardace et al., 2013). CROMO consists of two sets of wells located 1.4 km apart: the Core Shed Wells (CSW), and the Quarry Valley wells (QV). CSW consists of five wells, drilled to depths between 9 and 31 m. QV consists of three wells, drilled to depths between 15 and 46 m.

Preliminary lithostratigraphic interpretations of CROMO cores indicate that both sites (CSW and QV) are characterized

by intercalated serpentine-rich units with variable contributions of other clay minerals; lizardite and magnetite are common in serpentine-rich units (Cardace et al., 2013). At specific intervals, minerals indicative of altered mafic rocks (e.g., albite, chlorite, quartz, rarely calcite) co-occur with serpentine minerals, such as at ∼28 m depth at the primary CSW site (CSW1.1), and at 18–22 m and 34–36 m depth at the primary QV site (QV1.1). Very thin serpentine-rich soil cover exists at the QV1.1 site (<1 m), while ∼4 m of soil cover occurs at CSW1.1 (Cardace et al., 2013). Taken together, these data indicate that CROMO scientific monitoring wells sample fluids interacting with tectonically reworked ultramafic units very near the surface, with some entrainment of altered mafic materials from adjacent units of the Coast Range Ophiolite.

The samples described here were collected from seven wells at CROMO in August 2012. For the current study, well QV1.3 was not sampled due to complications with sediments clogging the filters. Well fluids were collected using positive displacement Teflon bladder pumps (Geotech Environmental Equipment, Denver, CO, USA) and pumped through a YSI 3059 flow cell fitted with a YSI 556 multiprobe (Yellowsprings, OH, USA), which measured water temperature, specific conductance, pH, dissolved oxygen (DO) and oxidation-reduction potential (ORP) once the DO measurement stabilized at a minimum value. Samples were collected for dissolved gas analyses (CH4, CO, and H2) and aqueous phase species (DIC and organic acids), as previously described Crespo-Medina et al. (2014).

For DNA analyses, fluids were filtered through a 0.22 µm Sterivex filter cartridge (Millipore, Billerica, MA, USA) using a Masterflex E/S peristaltic pump (Cole Parmer, Vernon Hills, IL, USA). Field replicate samples, ranging between two to eight filters per well, were collected in succession (labeled A, B, C, etc.). Sterivex filter cartridges were flash frozen with liquid nitrogen and stored at −80◦C until DNA extraction. For microbial cell quantification, replicate samples of 45 mL of fluids were preserved at a final concentration of 3.7% formaldehyde and stored at 4◦C. All publicly available data generated from this project can be found1 .

# Geochemistry

Dissolved gasses (H2, CH4, and CO) were extracted into an inert (N2) gas phase of known volume and analyzed for CH4 via a SRI 8610C GC-FID and dissolved H2 and CO with a Trace Analytical RGA3 Reduced Gas Analyzer. DIC was measured by acidifying a known volume of well fluid within a sealed vial, and analyzing the concentration of liberated CO2 in the headspace by GC-FID (SRI 8610) following passage through a "methanizer," which catalyzes the in-line conversion of CO and CO2 to methane in the presence of H2 over a heated Ni catalyst, thus allowing sensitive detection of these species by flame ionization detector following their separation by gas chromatography. Organic acid samples were analyzed by HPLC with UV/VIS detection, following derivatization with 2-nitrophenylhydrazide (Albert and Martens, 1997). All sample vials were analyzed with duplicate injections.

# Microbial Cell Counts

Fluids preserved for cell counts were filtered through 0.2 µm black polycarbonate filters (Millipore, Billerica, MA, USA). The cells were stained with 1 µg/ml of 40 ,6-diamidino-2-phenylindole (DAPI) and were counted by epifluorescence microscopy using appropriate filter sets according to previously published protocols (Hobbie et al., 1977; Schrenk et al., 2003).

# DNA Extraction

DNA extractions from Sterivex filter cartridges were performed by lysis via freeze/thaw cycles and lysozyme/Proteinase K treatment and purified with phenol-chloroform extractions, precipitation in ethanol, and further purification with QiaAmp (Qiagen, Hilden, Germany) columns according to the manufacturer's instructions for purification of genomic DNA, as described previously by Brazelton et al. (2013).

# 16S rRNA Gene Amplicon Sequencing and Data Analysis

Samples were submitted to the DOE Joint Genome Institute (JGI) for 16S rRNA amplicon sequencing of the V4 region on an Illumina MiSeq instrument, as described by Caporaso et al. (2011). Briefly, the amplification reaction contained 5 PRIME's HotMasterMix, custom V4 16S rRNA gene primers, and Illumina sequencing adapters and unique barcodes. The individual amplicon libraries were quantified, normalized, and pooled. The pooled multiplex reactions were then quantified using KAPA Biosystem's next-generation sequencing library qPCR kit and run on a Roche LightCycler 480 real-time PCR instrument. The quantified, multiplexed amplicon pool was then loaded on an Illumina MiSeq instrument utilizing the v3 reagent mix and a 2 × 300 indexed recipe mix.

Sequence reads were aligned to the SILVA SSURef alignment (v119), and taxonomic classifications were assigned using mothur (Pruesse et al., 2007; Schloss et al., 2009). Sequences were clustered into operational taxonomic units (OTUs) at the 3% distance threshold using the cluster.split command and the average-neighbor clustering algorithm in mothur (Schloss and Westcott, 2011). Prior to calculating measures of diversity, data were subsampled to the sample with the fewest sequences (77,580). Beta (between sample) diversity of the microbial communities was assessed by calculation of the Bray–Curtis index and displayed in a multi-dimension scaling (MDS) plot with geochemical data overlay in Primer-6 (Clarke, 1993; Clarke and Gorley, 2006). Alpha (within sample) diversity was assessed with the Inverse Simpson diversity index and rarefaction analysis. Sequence identification of reads belonging to the top OTUs compared with 16S rRNA sequences from other serpentinite studies (Brazelton et al., 2013; Suzuki et al., 2013; Tiago and Veríssimo, 2013) was performed using MatGAT with the default settings (Campanella et al., 2003). The 16S rRNA sequence data are publicly available in the NCBI Sequence Read Archive under the accession number SRA280854.

1https://nai.nasa.gov/research/data-sharing/

# Metagenomic Sequencing and Data Analysis

fmicb-08-00308 February 27, 2017 Time: 16:48 # 4

Samples were submitted to JGI for metagenomic sequencing on an Illumina HiSeq2000 instrument, as described by Hawley et al. (2014). Briefly, 200 ng of DNA was used for each sample and sheared to 270 bp fragments via a Corvaris LE220 focusedultrasonicator and size selected by SPRI. Fragments were then end-repaired, A-tailed, and ligated with Illumina-compatible adapters with barcodes unique for each library. Libraries were quantified with KAPA Biosystem's next-generation sequencing library qPCR kit and run on a Roche LightCycler 280 realtime PCR instrument. Quantified libraries were combined into 10-library pools and prepared for sequencing on the Illumina instrument in one lane each, using the TruSeq paired-end cluster kit (v3) and Illumina's cBot instrument to generate clustered flowcells, which were sequenced on the Illumina HiSeq2000 sequencer using TruSeq SBS sequencing kit v3 and a 2 × 150 indexed run recipe.

Metagenomic assembly was conducted by JGI as described by Hawley et al. (2014) and briefly described again here. Raw reads were trimmed with a minimum quality score cutoff of 10, and the trimmed paired-end reads were assembled with SOAPdenovo v1.05, with the default settings and a variety of kmers (i.e., 81, 85, 89, 93, 97, 101; Luo et al., 2012; Hawley et al., 2014). Contigs were sorted into pools based on length: contigs < 1800 bp were further assembled by Newbler (Life Technologies, Carlsbad, CA, USA) and contigs > 1800 bp, including those produced from Newbler run, were combined using minimus 2 (flags: −D MINID = 89 –D OVERLAP = 80; Sommer et al., 2007). BWA was used to estimate read depth, based on mapping of trimmed, screened, paired-end Illumina reads to assembled contigs (Li and Durbin, 2009). These data are publicly available in the JGI IMG/M database2 under the project IDs: 1021918, 1021921, 1021924, and 1021927; and in the MG-RAST database (Meyer et al., 2008) under the following sample IDs: 4569549.3, 4569550.3, 4569551.3, and 4569552.3.

The Prokka pipeline (Seeman, 2014) was used for gene prediction and functional annotation of contigs. The arguments –metagenome and –proteins were used with Prokka v.1.12 to indicate that genes should be predicted with the implementation of Prodigal v.2.6.2 (Hyatt et al., 2010) optimized for metagenomes. Predicted protein-coding sequences were aligned to the last free version (2011) of the Kyoto Encyclopedia of Genes and Genomes (Ogata et al., 1999) using BLASTP v2.3, and any coding sequences that remained un-annotated were then aligned to Prokka's default databases. Predicted protein abundances (in units of reads per kilobase) were calculated with HTSeq v.0.6.1 (Anders et al., 2014), and the final normalized coverage was calculated by normalizing to the total number of bases in the smallest metagenome.

To assign taxonomy to specific contigs of interest, a consensus taxonomic classification was manually determined by examining the taxonomic classifications associated with the best BLAST hits for each predicted gene. Specifically, for each contig-ofinterest, all (Prokka-predicted) coding sequences (CDSs) on the contig were aligned against the NCBI NR database (v. 2016-10-01), and the taxonomy of the best hit was taken to be the taxonomy of the CDS. The taxonomy of the whole contig was manually determined by identifying the lowest common ancestor on the NCBI Taxonomy Tree where half of all CDS taxonomic assignments agreed (Hanson et al., 2016). The full list of CDS taxonomic assignments can be found in Supplementary Dataset S1.

#### Statistical Analyses

Correlation network analyses were constructed from statistically significant pairwise Pearson's correlations among environmental variables and sequence data (Fuhrman and Steele, 2008) and visualized in Cytoscape v 2.8.3 (Shannon et al., 2003). A matrix containing environmental data and relative OTU (97% similarity) abundance for each sample was used as input for pairwise Pearson's correlation analysis computed with the rcor.test function in the R package lmt (Rizopoulos, 2006). The falsediscovery rate (q-value) was computed for the distribution of Pearson's p-values to account for multiple tests. Pairwise correlations with both p- and q-values of <0.05 were considered significant and included in network analyses. Network models of significant correlations were created using Cytoscape v2.8.3 (Shannon et al., 2003).

The ANOSIM test using a Bray–Curtis resemblance matrix, with sequence data subsampled to 77,580 sequences (the size of the smallest 16S rRNA amplicon library), was used to test whether individual environmental parameter categories had significant effects on the community composition of samples (Clarke, 1993). To statistically determine which combinations of numerical environmental variables best described the community composition variation within the dataset, the BEST analysis was performed in PRIMER-6 (Clarke, 1993; Clarke and Gorley, 2006).

# RESULTS

## Sampling Site and Geochemistry

Fluids were collected from seven wells within the CROMO, which were drilled for the purpose of monitoring biogeochemistry and microbial community dynamics with high temporal and spatial resolution (Cardace et al., 2013). To identify which bacterial taxa are most influenced by geochemical indicators of serpentinization, geochemical and microbiological data from these seven wells were compared.

Geochemical data associated with the well fluids from August 2012 are summarized in **Table 1**. Samples from wells CSW1.1 and QV1.1 are characterized by extremely high pH (12.2 and 11.5, respectively) and generally reducing character (**Table 1**). These wells are depleted in DIC, containing one to two orders of magnitude less DIC than a nearby well with circumneutral pH, CSW1.4 (**Table 1**). CSW1.1 also had higher concentrations of H2 and organic acids, relative to the other wells (**Table 1**). QV1.1, the deepest well, contained the highest cell abundance and exhibited the lowest dissolved O2 measurement. Wells CSW1.4 and QV1.2 exhibited circumneutral pH, low conductivity and higher DIC (**Table 1**) than the other wells. These circumneutral pH wells

2www.img.jgi.doe.gov

TABLE 1 | Environmental and geochemical parameters associated with samples collected in August 2012.

Values reported are averages of replicate measurements taken throughout sampling. Specific parameter values associated with specific field replicate samples can be found in Supplementary Figure S2.

also had elevated concentrations of H2 and CO comparable to the wells with higher pH. CSW1.2 (pH 9.3) had the highest concentration of methane (1.6 mM; **Table 1**).

Unsurprisingly, many of the environmental parameters of the system (**Table 1**) were correlated with one another (Supplementary Table S1). Higher pH was correlated with more negative ORP, and lower concentrations of carbon monoxide (CO), DO, and DIC, and was positively correlated with depth and organic acid concentration (Supplementary Table S1). The concentrations of the organic acids acetate, formate, propionate, and butyrate were all positively correlated with one another and were negatively correlated with ORP (Supplementary Table S1). CO concentration was positively correlated with ORP (i.e., positively correlated with a more positive ORP value) and DO, and was negatively correlated with conductivity and H2 concentration (Supplementary Table S1). The concentration of methane was not significantly correlated with any other environmental parameters.

### 16S rRNA Gene Diversity and Community Composition

Bacterial diversity was assessed in fluids collected in August 2012 from the seven CROMO wells. Environmental sequences of 16S rRNA gene amplicons were obtained with an Illumina MiSeq platform, yielding between 78,000 and 179,000 merged paired-end sequences per sample, for a total of 2,528,572 16S rRNA sequences in this study. These sequences were clustered into 11,454 OTUs at a 97% sequence similarity threshold, and only 30 of these OTUs comprised greater than 1% of the sequences in any of the samples analyzed. All diversity analyses in this study were conducted with OTUs, instead of relying solely on taxonomic annotations, in order to avoid the biases and limitations inherent to database-dependent classifications that are magnified when studying poorly characterized microbial communities. Field replicates of samples were collected and analyzed in parallel and were statistically indistinguishable from one another, as determined by a SIMPROF test of community similarities among all samples (**Supplementary Figure S1**). The community compositions of samples from different wells were clearly distinct from each other (ANOSIM, R = 0.96, p-value = 0.001).

Alpha diversity of the samples, as measured by the Inverse Simpson diversity index, decreased with increasing pH (**Figure 1**). The wells with the highest pH, CSW1.1 and QV1.1, exhibited extremely low diversity, containing almost exclusively Betaproteobacteria and Firmicutes (**Figure 1**). Well CSW1.1 was dominated by a single betaproteobacterial OTU (OTU001), classified as a member of family Comamonadaceae with 100% sequence identity over 250 bp of the 16S rRNA gene's V4 region (calculated with MatGAT) to strain B1 from the proposed genus Serpentinomonas isolated from The Cedars serpentinite site (Suzuki et al., 2014; **Table 2**). The second most abundant OTU in CSW1.1 (OTU018), which comprised 12.5 ± 5.8% of the sequences from that well, was classified as Thermoanaerobacterales SRB-2, and exhibited 99% sequence identity to a Clostridia clone from a well in Cabeço de Vide (CVA) in Portugal (Tiago and Veríssimo, 2013; **Table 2**). The third most-abundant OTU detected in CSW1.1 (OTU002), which accounted for only 1% of the sequences from that well, was classified as Dethiobacter, and shared 100% sequence identity to a clone from CVA (Tiago and Veríssimo, 2013; **Table 2**), enriched in a microcosm from CROMO (Crespo-Medina et al., 2014), and a clone from the deep groundwater site at The Cedars (Suzuki et al., 2013). The remaining 29.0 ± 5.3% of the CSW1.1 microbial community was made up of rare species, defined as OTUs comprising less than 1% of the total sequences in any sample.

Well QV1.1 was dominated by three Clostridia OTUs (OTU003, OTU002, and OTU007) that together accounted for 47.1 ± 13.4% of the bacterial community (**Figure 1**).

Both OTU003 (classified as Thermoanaerobacterales SRB-2) and OTU002 (classified as Dethiobacter) exhibited 99–100% sequence identity to clones from CVA in Portugal (Tiago and Veríssimo, 2013; **Table 2**). The same betaproteobacterial OTU from CSW1.1 (OTU001) made up 28.5 ± 12.9% of the QV1.1 community, and the remaining bacterial taxa were rare, accounting for 24.3 ± 1.6% of the community (**Figure 1**). A time-series analysis of samples collected quarterly from QV1.1 (data not shown) indicated that community composition within wells is relatively constant over time, with no significant difference between time points (ANOSIM, R = 0.2, p-value = 0.902).

While Betaproteobacteria made up a large proportion of all samples above neutral pH, the diversity and composition of the Betaproteobacteria shifted with pH (**Figure 1**). As expressed above, OTU001 made up 42.7 ± 17.6% of the extremely high pH wells. However, in samples with pH ≤ 10, OTU001 was replaced by OTU004 (classified as Comamonadaceae and 100% identical to Alicycliphilus denitrificans; **Table 2**) and OTU008 (classified as Azonexus hydrophilus; **Table 2**) as the dominant betaproteobacterial taxa.

Clostridia, which accounted for up to 64% of the bacteria in the highest pH fluids, were also found in the moderately high pH wells (defined here as wells with pH 8.5–10). Dethiobacter OTUs made up 13.9 ± 10.1% of samples with a pH 9.5– 11.0. Erysipelotrichia (another class of the phylum Firmicutes) made up 8 and 2% of CSW1.5 (pH 9.7) and CSW1.2 (pH 9.3), respectively, but was not detected in any other CROMO samples. OTUs classified as Thermoanaerobacterales SRB-2 were detected in QV1.2, as well as CSW1.1 and QV1.1. No Firmicutes OTUs were detected in the wells with pH less than 9 (**Figure 1**). In addition to Betaproteobacteria and Firmicutes, the moderately high pH (pH 8.5–10) wells contained Bacteroidetes as well as Alpha-, Delta-, and Gammaproteobacteria (**Figure 1**). The circumneutral pH wells contained a greater complement of rare taxa and many taxa that were not present in the high pH wells (**Figure 1**).

No archaea were detected in any of the 16S rRNA amplicon libraries, which were created with the universal primers targeting the V4 region of the 16S rRNA gene used by the DOE Joint Genome Institute (Caporaso et al., 2011). In

†Percent identity, as determined

 by MatGAT (Campanella

 et al., 2003).

addition to 16S rRNA amplicon sequencing, a subset of the samples (CSW1.1AC, CSW1.3A, QV1.1A, and QV1.2A) underwent shotgun metagenomic sequencing. To further investigate the potential presence of archaea in CROMO fluids the relative abundance of archaea was also assessed by counting archaeal sequences in the metagenomic datasets. The number of metagenomic sequences classified as archaea by MG-RAST (Meyer et al., 2008) did not exceed 1% of the total sequences in any sample (**Table 3**). Furthermore, none of these archaeal metagenomic reads included a 16S rRNA gene. The bacterial diversity of the metagenomes exhibited similar abundances of Betaproteobacteria, but expressed a lower abundance of Clostridia and greater diversity of other taxa, compared to the 16S rRNA amplicon data (**Figures 1**, **2**).

#### Biogeochemical Relationships

One of the main goals of this study was to identify the geochemical drivers of microbial community composition within serpentinite subsurface environments. Several physical and chemical factors are overlain on a multi-dimensional scaling (MDS) plot of community composition (as measured by shared 16S rRNA OTUs; **Figure 3**). The figure highlights

TABLE 3 | Microbial communities at CROMO are dominated by Bacteria.

Relative abundance is expressed as percentage of metagenomic sequence reads assigned to different domains of life via the M5NR database in MG-RAST.

the geochemical variability among the wells and visually displays the correlated parameters (**Figure 3** and Supplementary Table S1). Community-level differences in the samples can be seen, such as a differentiation between high pH and moderate/circumneutral pH samples (**Figure 3**). A combination of pH, CO, and CH4 concentrations best explain the bacterial community composition variability across wells, as determined by the multivariate BEST test in Primer-6 ((R = 0.83, p-value = 0.001); Clarke, 1993; Clarke and Gorley, 2006). Therefore, pairwise Pearson's correlations among these three environmental parameters and the relative abundances of all associated OTUs were visualized with a correlation network diagram (**Figure 4**).

Fluid pH was positively correlated with the two most abundant OTUs in CSW1.1 (OTU001 and OTU018, classified as Comamonadaceae and Thermoanaerobacterales SRB-2, respectively) and negatively correlated with OTU008 (classified as Azonexus hydrophilus), the dominant betaproteobacterial OTU in wells with a pH below 10 (**Table 2** and **Figure 4**). The top OTUs from CSW1.1, OTU001 (classified as Comamonadaceae) and OTU018 (classified as Thermoanaerobacterales SRB-2), were negatively correlated with CO concentration. Except for the betaproteobacterial OTU033, all other OTUs that were positively correlated with CO concentration belonged to the alphaproteobacterial order Sphingomonadaceae (**Figure 4**). Among the OTUs positively correlated with the abundance of methane were Betaproteobacteria OTU004, most abundant in CSW1.2, and Clostridia OTU006, most abundant in CSW1.5 (**Table 2** and **Figure 4**). Additionally, five Gammaproteobacteria OTUs and three Alphaproteobacteria OTUs, two classes known to contain aerobic methanotrophs, were also positively correlated with methane concentration (**Figure 4**). It should be stressed, however, that correlation does not necessarily indicate utilization or production of this compound by the microorganisms.

The most abundant OTUs in CSW1.1, OTU001 (classified as Comamonadaceae) and OTU018 (classified as Thermoanaerobacterales SRB-2), were positively correlated with conductivity, organic acid concentrations, and H2 concentrations, and were negatively correlated with ORP (**Table 2**). Two of the most abundant Clostridia OTUs (OTU002 and OTU003), both dominant in QV1.1, were significantly correlated only with well depth (**Table 4**). While **Table 2** and the discussion above denote the sample in which each OTU was most abundant, it should be mentioned that many of those abundant OTUs were found in multiple samples, though at lower abundances (**Supplementary Figure S2**).

## Metabolic Potential

To elucidate whether microbes within the serpentinite subsurface environments are capable of metabolizing the geochemical products of serpentinization (specifically hydrogen, methane, acetate, and carbon monoxide) and other environmentally relevant compounds (specifically carbon dioxide and sulfur compounds), assembled and annotated metagenomes from four of the wells were searched for sequences predicted to encode proteins potentially diagnostic of specific metabolic pathways of interest (**Figure 5**). The metagenomes came from samples CSW1.1AC (pH 12.2), QV1.1A (pH 11.5), CSW1.3A (pH 10.1), and QV1.2A (pH 7.9). A summary of the metagenomic assembly statistics can be found in Supplementary Table S2.

To assess the potential for H2 metabolism, genes encoding [FeFe]-hydrogenase (KEGG ID: K00533) and [NiFe] hydrogenases (KEGG ID: K0628, K00436) were sought in the assembled metagenomes. [FeFe]-hydrogenases, often found

in fermentative Clostridia species, are typically involved in the production of H2 (Vignais et al., 2001; Vignais and Billoud, 2007). [NiFe]-hydrogenases are divided into functional groups: Group 1 includes membrane-bound uptake hydrogenases; Group 2 includes H2-sensing hydrogenases; Group 3 includes cytoplasmic hydrogenases catalyzing reversible hydrogenoxidation; and Group 4 includes archaeal H2 production (Vignais et al., 2001; Vignais and Billoud, 2007). The [NiFe] hydrogenases targeted in this analysis are hyaB (K06281), which is a Group 1 (H2-oxidizing) hydrogenase, and hoxH (K00436), which is a Group 3d (cytoplasmic) hydrogenase.

The [FeFe]-hydrogenase gene was detected in all four metagenomes, but was most abundant in QV1.1A (**Figure 5**). All of the contigs containing the [FeFe]-hydrogenase sequences were classified as Clostridia (**Table 4**). The Group 1 [NiFe] hydrogenase (hyaB) was only detected in the moderate pH CSW1.3A and QV1.2A metagenomes (**Figure 5**), and the contigs that contained the gene were classified as Clostridia, Flavobacteria, Cyanobacteria, and Proteobacteria (Alpha-, and Beta-; **Table 4**). The Group 3d [NiFe]-hydrogenase (hoxH), on the other hand, was detected in all four metagenomes (**Figure 5**) and was found on contigs belonging to Betaproteobacteria, Clostridia, and Alphaproteobacteria. The betaproteobacterial contigs it was present on were identified as belonging to Serpentinomonas in CSW1.1AC, QV1.1A, and CSW1.3A (**Table 4**).

The metagenomic assemblies were searched for representative genes of bacterial aerobic methane oxidation (particulate methane monooxygenase (pmoA; K10944) and methanol dehydrogenase (mxaF; K14028) and for a representative gene of archaeal methanogenesis (mcrA; K00399). No pmoA or mcrA genes were identified in any of the metagenomes. CSW1.3A (pH 10.1) contained mxaF genes (**Figure 5**) on contigs belonging to members of the Alphaproteobacteria (**Table 4**), likely of the family Methylobacteriaceae, a group of methylotrophs (Lau et al., 2013) found in low abundance in samples from CSW1.3 (**Figure 1**).

The acetyl-coA synthase gene (acsB; K14138), suggestive of acetogenesis via the Wood/Ljungdahl pathway (or reductive acetyl-coA pathway; Ragsdale and Pierce, 2008), was only detected in QV1.1A (acetate = 11.2 µM; **Table 1**) and CSW1.3A (acetate < 1.55 µM; **Table 1** and **Figure 5**). All instances of the contigs containing the gene were classified as being closely related to the clostridium Dethiobacter alkaliphilus (**Table 4**), which makes up 11 and 25% of the QV1.1A and CSW1.3A communities, respectively (**Figure 1**).

The high pH of the serpentinite environment limits the availability of CO2, potentially leading to the use of CO as both an electron donor and an inorganic carbon source. To assess CO metabolism at CROMO, two forms of CO dehydrogenase were sought in the metagenomes; coxL (K03520) and cooS (K00198) were used to identify aerobic and anaerobic CO oxidation,

respectively. Neither gene was detected in CSW1.1AC, and both were most abundant in QV1.1A (CO = 0.142 µM; **Table 1** and **Figure 5**). All of the cooS genes detected were on contigs that belonged to Clostridia, while the coxL genes were more diverse, belonging to contigs identified as Betaproteobacteria and Alphaproteobacteria in the moderate to circumneutral wells, and to the Betaproteobacteria closely related to Serpentinomonas strain A1 in the high pH wells (**Table 4**; Suzuki et al., 2014).

Sequences encoding the rbcL gene (K01601) of the RuBisCo enzyme used in the Calvin–Benson–Bassham cycle were detected in all four metagenomes (**Figure 5**). In CSW1.1AC and QV1.1A, rbcL genes were found on contigs with high similarity to Serpentinomonas strain B1 (**Table 4**; Suzuki et al., 2014). The sequences detected in CSW1.3A also belonged to Betaproteobacteria, but of the family Burkholderiaceae (unlike the Comamonadaceae found in the higher pH wells), and the rbcL sequences from QV1.2A were more diverse, identified on contigs as coming from Sphingobacteria and Bacilli, as well as Betaproteobacteria (**Table 4**).

The potential metabolism of sulfur compounds was assessed by searching for dissimilatory sulfite reductase (dsrA; K11180) and dissimilatory adenosine-50 -phosphosulfate reductase (aprA; K00394). Both genes were detected at high abundance in QV1.1A and at low abundance in CSW1.3A (**Figure 5**). In both QV1.1A and CSW1.3A the contigs containing aprA and dsrA were classified as Clostridia (**Table 4**), and both of these samples contained a relatively high abundance of Dethiobacter sp. and SRB-2 (**Figure 1**). In contrast, neither gene was detected in wells CSW1.1AC or QV1.2A.

#### DISCUSSION

#### Serpentinite Environment at CROMO

The geochemical variability among multiple wells at CROMO allowed us to distinguish groundwater with varying degrees of influence from subsurface serpentinization processes. For example, the shallow CSW1.4 and QV1.2 wells produced pH ∼8 water that had comparatively high levels of DO, DIC, and ORP (**Table 1**), which is consistent with oxygenated water that was recently exposed to the surface and was not noticeably affected by serpentinization. In contrast, the CSW1.1 well (pH 12.2) exhibited high conductivity and high concentrations of H2 and small organic molecules (**Table 1**), consistent with water heavily influenced by subsurface serpentinizationassociated reactions. Furthermore, CSW1.1 has remarkably low bacterial diversity, suggesting that it represents the most extreme window into the serpentinite subsurface. Other wells have weaker serpentinization signatures, such as that of CSW1.3, which is almost as deep as CSW1.1 and has almost as much H2, but its water is pH ∼10, suggesting a dilution of serpentinization-influenced water with shallower, surfaceinfluenced water.

It should be noted that all of the wells, save CSW1.1 and QV1.1, were cased with PVC pipe upon drilling, essentially isolating the flow of water into the wells (Cardace et al., 2013). The larger diameter CSW1.1 and QV1.1 wells remained uncased,

fmicb-08-00308 February 27, 2017 Time: 16:48 # 11

and therefore, could experience some fluid input from above the drilled depth, which suggests that true serpentinite end-member fluids could be even more extreme than those measured here.

Bacterial diversity trends across the CROMO wells are remarkably consistent with the geochemistry of the wells. The overall bacterial community compositions of water from the most serpentinization-influenced (CSW1.1 and QV1.1) wells are tightly correlated with pH, organic acid concentrations, and low ORP values (**Figure 3**). Conversely, the community compositions of shallow, pH 7.8–9 wells (CSW1.2, CSW1.4 and QV1.2) are more tightly coupled to higher ORP, DO, and DIC, which is consistent with the interpretation of geochemistry data that these wells represent mixing of surface waters and serpentinizationinfluenced groundwater.

The distributions of individual genes, as measured by metagenomic sequencing, are also consistent with these biogeochemical trends. For example, genes involved in CO oxidation were completely absent in the well (CSW1.1) with the lowest concentration of CO, and these genes were present in each of the other three metagenomes (**Table 1** and **Figure 5**). Genes associated with methane metabolism were surprisingly rare in the CROMO metagenomes, but one gene involved in bacterial methane oxidation (mxaF) was found in CSW1.3, which contains higher methane concentrations than the other three wells with metagenomes (**Table 1** and **Figure 5**).

#### Betaproteobacteria

Serpentinomonas is a newly proposed genus within family Comamonadaceae currently represented by three strains isolated from the Cedars, another site of subsurface serpentinization in northern California (Suzuki et al., 2014). An OTU with 100% sequence identity to Serpentinomonas B1 comprised over 50% of environmental 16S rRNA gene sequences from CSW1.1 (pH 12.2) and over 25% of sequences from QV1.1 (pH 11.5) and has been detected in CROMO wells at other time points (Crespo-Medina et al., 2014). The abundance of this OTU was positively correlated with pH, H2, conductivity, and organic acids and negatively correlated with ORP and CO (**Figure 1** and **Table 2**).

Each strain of Serpentinomonas (A1, B1, and H1) from the Cedars contains slightly different genes with regards to nitrate reduction, hydrogen oxidation, and carbon fixation (Suzuki et al., 2014). All strains are capable of CO2-fixation via the Calvin–Benson–Bassham cycle and contain the rbcL gene coding for the RuBisCo enzyme, which was detected in all four metagenomes from CROMO wells and was most abundant in CSW1.1 (**Figure 5**). The coxL gene (involved in aerobic CO oxidation) is found only in Serpentinomonas A1 and was detected in QV1.1, but not CSW1.1 in this study, suggesting different phylotypes of the abundant organism in different wells (**Figure 5** and **Table 4**). Only the H1 strain of Serpentinomonas contains the Group 1 [NiFe]-hydrogenase hydrogen-oxidation gene hyaB, which was not detected in either of the wells with extreme pH (**Figure 5**). However, Group 3d [NiFe]-hydrogenases, which are cytoplasmic hydrogenases capable of reversible H2-oxidation, are found in all three strains of Serpentinomonas (Suzuki et al., 2014) and were detected in all four CROMO wells (**Figure 5**). Serpentinomonas strains containing Group 3d (and not Group 1) [NiFe]-hydrogenase genes were experimentally shown to oxidize hydrogen to support autotrophic growth, so it has been speculated that Group 3d [NiFe]-hydrogenase genes, such as

hoxH, might be the key to H2 metabolism in these organisms (Suzuki et al., 2014). None of the Serpentinomonas strains contain ACS, cooS, aprA, or dsrA (Suzuki et al., 2014), and none of those genes were detected in CSW1.1, where Serpentinomonas dominated the community (**Figures 1**, **5**). These data support interpretations (Schrenk et al., 2013) that Serpentinomonaslike organisms are hydrogen-oxidizing, carbon-fixing members of serpentinite-hosted ecosystems, able to persist at extreme pH and thrive on the chemical disequilibrium of the mixing zone between anoxic end-member and oxygenated surface fluids.

The most abundant betaproteobacterial OTU found in QV1.2 (pH 7.9) was OTU008, which made up 33.1 ± 6.8% of the total community and exhibited 100% sequence identity to Azonexus hydrophilus (family Rhodocyclaceae; **Table 2** and **Figure 1**). This organism is a motile, non-spore-forming aerobe isolated from freshwater springs in Taiwan and Korea (Chou et al., 2008), and it has a circumneutral optimal growth pH. In this study, OTU004 exhibited a negative correlation with pH, which is consistent with a circumneutral optimal growth pH. It has been detected in sewage treatment facilities (Auguet et al., 2015; Yan et al., 2015) and coal bed methane environments (Guo et al., 2012), as well as in the deep subsurface (Ise et al., 2016; Jangir et al., 2016). In QV1.2A, Group 1 [NiFe]-hydrogenase gene hyaB and the rbcL gene of RuBisCo were detected on contigs identified as Azonexus hydrophilus (Supplementary Dataset S1), suggesting that these Betaproteobacteria are involved in H2-oxidation and carbon fixation in the moderate wells.

Alicycliphilus denitrificans made up 32.9% of CSW1.2 (pH 9.3), making it the most abundant betaproteobacterial OTU in the sample (**Figure 1** and **Table 2**). This organism is a facultative denitrifying bacterium that can use acetate as a carbon source (Mechichi et al., 2003). OTU004 (classified as A. denitrificans) was positively correlated with methane (**Table 2**), but this organism is not known to be involved in methane cycling.

#### Clostridia

Clostridia have been detected at sites of continental serpentinization around the world (Brazelton et al., 2012, 2013; Suzuki et al., 2013; Tiago and Veríssimo, 2013; Woycheese et al., 2015), where they are thought to inhabit the anoxic endmember serpentinite fluids (Schrenk et al., 2013). An abundance of [FeFe]-hydrogenases belonging to Clostridia in metagenomic data from serpentinite springs (Brazelton et al., 2012) suggests that these abundant organisms are producing H2, but additional clues to their physiology and ecology are lacking.

Operational taxonomic units classified as Clostridia were very abundant in the high pH wells at CROMO. The clostridial OTUs at CROMO were either classified as Dethiobacter, Thermoanaerobacterales SRB-2, or unclassified Clostridia. The most extreme well, CSW1.1 (pH 12.2), contained 13.9 ± 6.5% Clostridia, mostly represented by an OTU classified as SRB-2. QV1.1 (pH 11.5) displayed a greater abundance and diversity of Clostridia, as well as a shift in clostridial community composition between field replicates (**Figure 1**). The deeper QV1.1 samples (field replicates A/B) had more SRB-2 and fewer Dethiobacter than in waters naturally found higher up in the well (i.e., filtered later as water was drawn down).

Dethiobacter alkaliphilus is an anaerobic alkaliphile that was originally isolated from a Mongolian soda lake; it utilizes H2 as an electron donor, sulfur compounds (S◦ , thiosulfate, polysulfide) as electron acceptors, and acetate as a carbon source (Sorokin et al., 2008). Dethiobacter has been detected at sites of continental serpentinization and is hypothesized to live in the deep, anoxic serpentinite end-member fluids (Brazelton et al., 2012; Suzuki et al., 2013; Tiago and Veríssimo, 2013; Crespo-Medina et al., 2014; Woycheese et al., 2015). Crespo-Medina et al. (2014) detected growth in CROMO microcosms dominated by D. alkaliphilus with H2-headspace and the addition of thiosulfate with either acetate or methane as a carbon source. It should be noted that since there is no known mechanism for Clostridia to consume methane, it is believed there was a cryptic organism within the microcosms making methanederived carbon bioavailable to Dethiobacter (Crespo-Medina et al., 2014). In the QV1.1A metagenome, the genes attributed to Dethiobacter are hoxH (H2 sensing), FeFe-hydrogenase (H2 production), acsB (acetogenesis), and cooS (anaerobic COoxidation) (**Table 4**). These data suggest that the abundant Dethiobacter encode several metabolic pathways that might allow them to adapt to changing conditions in the subsurface or in the well water.

Members of the family Thermoanaerobacterales, identified as belonging to the SRB-2 lineage, were abundant in CSW1.1 and QV1.1 (**Figure 1**). The SRB-2 OTUs share high sequence identity with clones from CVA fluids and the Cedars (**Table 2**; Suzuki et al., 2013; Tiago and Veríssimo, 2013). Not much is known about this uncultivated group, but the Thermoanaerobacterales include organisms capable of sulfate reduction (Pereira et al., 2011), fermentative acetate oxidation (Oehler et al., 2012), fermentative hydrogen production (Rittmann and Herwig, 2012), and homoacetogenesis (Ljungdahl, 1994). The [FeFe] hydrogenase and acsB genes detected in the metagenomes could not be classified at the family level (with some exceptions, see **Table 4**) but they were all classified as Clostridia, consistent with SRB-2 and perhaps other unclassified Clostridia as potentially acetogenic, sulfur-reducing alkaliphiles in the subsurface serpentinite fluids.

Sequences belonging to Erysipelotrichia (another class of the phylum Firmicutes) were inferred to represent the most likely inhabitants of serpentinite end-member fluids exiting from springs at the Tablelands, Newfoundland (Brazelton et al., 2013). An OTU classified as genus Erysipelothrix was found in three moderately high pH CROMO wells, but in none of the other wells (**Figure 1**). None of the protein-coding genes in the CROMO metagenomes were classified as Erysipelothrix (**Table 4**).

#### Other Community Members

All of the OTUs in CSW1.1 and QV1.1 that made up greater than 1% of sequences in either well belonged to the Betaproteobacteria or Clostridia. Furthermore, all of the metagenomic contigs containing key genes of interest from these two wells were classified as one of these two classes of bacteria (**Table 4**). The

moderately high pH and circumneutral wells exhibited slightly higher diversity in not only 16S rRNA genes (**Figure 1**), but also in the variety and taxonomic classification of protein-encoding genes (**Figure 5** and **Table 4**). Additional bacterial taxa in these wells included Alphaproteobacteria, Deltaproteobacteria, and Bacteroidetes. These results suggest that a breadth of organisms are capable of using products of serpentinization, but might be restricted by the extreme pH conditions in wells containing a greater degree of end-member fluids.

It is notable that archaea were absent from all CROMO 16S rRNA sequences and were very rare (1% or less of sequences) in the metagenomic data (**Table 3**). Studies of other sites of continental serpentinization, in contrast, have detected methanogenic Euryarchaeota (Suzuki et al., 2013; Tiago and Veríssimo, 2013). Methane isotopologue analyses have suggested that the methane at CROMO displays a thermogenic signature, while methane from the nearby Cedars site (from which Serpentinomonas was isolated) has a more microbial signature (Wang et al., 2015). These isotope geochemistry results are consistent with the lack of methanogens in CROMO wells. Additionally, OD-1 and Chloroflexi, both of which are bacteria that Suzuki et al. (2013) hypothesized to be endemic to end-member serpentinite fluids, were not detected in any of the CROMO fluids. Therefore, the subsurface fluids in CROMO wells appear to be an unfavorable environment for several microorganisms that are abundant in other sites of serpentinization.

## CONCLUSION

By studying groundwater with a range of geochemical characteristics, we were able to identify the bacterial taxa with the strongest correlations to the environmental variables (such as pH, CO, and CH4) indicative of subsurface serpentinization processes. These data suggest that various Clostridia taxa are potentially capable of anaerobic CO-oxidation, acetogenesis, and the reduction of sulfur compounds in extremely high pH, anoxic subsurface fluids heavily influenced by serpentinization, while microaerophilic Serpentinomonas (Betaproteobacteria) are capable of H2-oxidation and CO2-fixation (and in some cases, aerobic CO-oxidation) in mixing zones where deep, anoxic fluids interact with oxygenated surface waters. These data provide tractable targets for further biogeochemical and microbiological analyses of serpentinite-hosted microbial ecosystems at CROMO and elsewhere.

The results of this study support those of other recent studies of serpentinite-hosted ecosystems that have identified a few key bacterial taxa that are common to such systems. Furthermore, our results significantly expand our current understanding of the microbial ecology of subsurface, serpentinite-hosted ecosystems. Previously, studies of the microbiology of continental serpentinites have focused on opportunistic sampling from a small number of surface-exposed sites (Brazelton et al., 2012; Suzuki et al., 2013; Tiago and Veríssimo, 2013). Because these studies must sample the surface expression of subsurface processes, identifications of taxa that are truly endemic to the subsurface must be indirect inferences that rely on assumptions of hydrology and sampling methodology. By directly accessing the serpentinite subsurface using wells at CROMO, we were able to observe subsurface microbial communities that had not directly experienced the surface processes that influence the composition of microbial communities sampled from springs. Furthermore, access to the groundwater revealed the absence of several taxa that were identified as key members of other serpentinite-hosted ecosystems, which highlights the remarkably low diversity of organisms apparently capable of thriving in these high pH subsurface fluids.

# AUTHOR CONTRIBUTIONS

KT, WB, and MS designed the study. WB, MK, and DC performed field sampling. KT, WB, and AH performed the bioinformatic analyses and interpretations. KT and WB performed statistical analyses. MK, DC, TH, and TM contributed to the geochemical analyses and interpretations. KT, WB, MK, AH, DC, TH, TM, and MS wrote the manuscript.

# FUNDING

The establishment of CROMO was funded by the NASA Astrobiology Institute and the research conducted was funded by the Alfred P. Sloan Foundation's Deep Carbon Observatory (2011-12-01). All of the sequencing was performed by the DOE Joint Genome Institute (supported by the Office of Science of the U.S. Department of Energy under the contract DE-AC02- 05CH11231).

# ACKNOWLEDGMENTS

We thank C. Koehler and P. Aigner at the UC-Davis McLaughlin Nature Reserve and S. Moore and Homestake Mining Company for support in the establishment of CROMO. Thank you to D. Carnevale for help with field sampling, C. Thornton for data analysis assistance, and D. Morgan-Smith, M. Crespo-Medina, and D. Wang for insightful discussions.

# SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb.2017. 00308/full#supplementary-material

FIGURE S1 | Microbial community structure. (A) Rarefaction analysis of 16S rRNA amplicon sequences. Multiple samples from the same well represent field replicates. (B) Community similarity dendrogram calculated from Bray-Curtis index. Samples connected by red lines are not distinguishable from one another by a SIMPROF test and ANOSIM analysis indicated that there is a significant difference in community composition between wells (R = 0.9, p-value < 0.05).

FIGURE S2 | Heatmap of most abundant OTUs and geochemical parameters across all samples. Dendrogram at the top represents community similarity between samples and red lines indicate no statistical difference between field replicates, as determined by SIMPROF (Clarke, 1993).

# REFERENCES

fmicb-08-00308 February 27, 2017 Time: 16:48 # 15

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Twing, Brazelton, Kubo, Hyer, Cardace, Hoehler, McCollom and Schrenk. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Methane Dynamics in a Tropical Serpentinizing Environment: The Santa Elena Ophiolite, Costa Rica

Melitza Crespo-Medina1 , Katrina I. Twing2 , Ricardo Sánchez-Murillo3 , William J. Brazelton2 , Thomas M. McCollom4 and Matthew O. Schrenk 5 \*

1 Center for Education, Conservation and Research, Inter-American University of Puerto Rico, San Juan, PR, United States, 2 Department of Biology, University of Utah, Salt Lake City, UT, United States, 3 Stable Isotope Research Group, School of Chemistry, National University of Costa Rica, Heredia, Costa Rica, 4 Laboratory for Atmospheric and Space Physics, Center for Astrobiology, University of Colorado Boulder, Boulder, CO, United States, 5 Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI, United States

#### Edited by:

Cody Sheik, University of Minnesota Duluth, United States

#### Reviewed by:

Maggie Lau, Princeton University, United States D'Arcy Renee Meyer-Dombard, University of Illinois at Chicago, United States

> \*Correspondence: Matthew O. Schrenk<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Microbiological Chemistry and Geomicrobiology, a section of the journal Frontiers in Microbiology

Received: 17 January 2017 Accepted: 04 May 2017 Published: 23 May 2017

#### Citation:

Crespo-Medina M, Twing KI, Sánchez-Murillo R, Brazelton WJ, McCollom TM and Schrenk MO (2017) Methane Dynamics in a Tropical Serpentinizing Environment: The Santa Elena Ophiolite, Costa Rica. Front. Microbiol. 8:916. doi: 10.3389/fmicb.2017.00916

Uplifted ultramafic rocks represent an important vector for the transfer of carbon and reducing power from the deep subsurface into the biosphere and potentially support microbial life through serpentinization. This process has a strong influence upon the production of hydrogen and methane, which can be subsequently consumed by microbial communities. The Santa Elena Ophiolite (SEO) on the northwestern Pacific coast of Costa Rica comprises ∼250 km2 of ultramafic rocks and mafic associations. The climatic conditions, consisting of strongly contrasting wet and dry seasons, make the SEO a unique hydrogeological setting, where water-rock reactions are enhanced by large storm events (up to 200 mm in a single storm). Previous work on hyperalkaline spring fluids collected within the SEO has identified the presence of microorganisms potentially involved in hydrogen, methane, and methanol oxidation (such as Hydrogenophaga, Methylobacterium, and Methylibium spp., respectively), as well as the presence of methanogenic Archaea (such as Methanobacterium). Similar organisms have also been documented at other serpentinizing sites, however their functions have not been confirmed. SEO's hyperalkaline springs have elevated methane concentrations, ranging from 145 to 900 µM, in comparison to the background concentrations (<0.3 µM). The presence and potential activity of microorganisms involved in methane cycling in serpentinization-influenced fluids from different sites within the SEO were investigated using molecular, geochemical, and modeling approaches. These results were combined to elucidate the bioenergetically favorable methane production and/or oxidation reactions in this tropical serpentinizing environment. The hyperalkaline springs at SEO contain a greater proportion of Archaea and methanogens than has been detected in any terrestrial serpentinizing system. Archaea involved in methanogenesis and anaerobic methane oxidation accounted from 40 to 90% of total archaeal sequences. Genes involved in methanogenic metabolisms were detected from the metagenome of one of the alkaline springs. Methanogenic activities are likely to be facilitated by the movement of nutrients, including dissolved inorganic carbon (DIC), from surface water and their infiltration into serpentinizing groundwater. These data provide new insight into methane cycle in tropical serpentinizing environments.

Keywords: tropical serpentinization, methane, methanogenesis, metagenomics, bioenergetics

# INTRODUCTION

Serpentinization is the aqueous alteration of low-silica ultramafic rocks, mainly olivine and pyroxenes, into serpentinite, brucite, magnetite, and other minerals (Moody, 1976). This reaction produces hydrogen gas (H2) and the reducing conditions that favor the abiogenic synthesis of methane and higher molecular weight hydrocarbons through Fischer-Tropsch-type reactions (e.g., McCollom and Seewald, 2007; Proskurowski et al., 2008). Serpentinization also produces favorable conditions for the potential biogenic formation of methane through the activity of chemolithoautotrophic microorganisms, however the occurrence and relative importance of abiogenic vs. biogenic methanogenesis processes is enigmatic (Wang et al., 2015; Etiope, 2016; Kohl et al., 2016; Miller et al., 2016).

The relationship between serpentinization and life has been relatively well studied in submarine hydrothermal systems (Schrenk et al., 2004; Kelley et al., 2005; Brazelton et al., 2006; Quéméneur et al., 2014). The study of the microbiology and geochemistry of terrestrial serpentinites has accelerated in the past several years at numerous locations globally, where meteoric water infiltrates and interacts with obducted ultramafic rock and mixes with serpentinizing fluids.

More recently, a terrestrial serpentinization site was described in a tropical setting at the Santa Elena Ophiolite (SEO), which comprises over 250 km2 of ultramafic rocks and mafic associations along the northwestern Pacific coast of Costa Rica (Sánchez-Murillo et al., 2014; **Figure 1**). This system is characterized by warm air temperature (up to 38◦C during the dry season months) and extreme variation in precipitation conditions between wet (May–November) and dry (December–April) seasons, resulting in a distinctive hydrogeological setting (Sánchez-Murillo et al., 2014). These dynamic hydroclimatic conditions make the hyperalkaline springs at SEO unique, and potentially enhance rock weathering processes and the delivery of nutrients and oxidants likely stimulating subsurface microbial activity.

Microorganisms involved in the methane cycle are abundant in marine hydrothermal serpentinizing systems (Brazelton et al., 2006). At terrestrial serpentinization sites, however, even though there is abundant methane (Wang et al., 2015), there is less evidence of the presence and activity of methanogenic and methanotrophic microorganisms. Sequences related to the anaerobic methanotrophic archaea ANME-1a have been previously detected from the springs in Cabeço de Vide aquifer in Portugal (Tiago and Veríssimo, 2013). Methanogenic taxa have

FIGURE 1 | Map of the study area. The bold-black line denotes the Santa Elena Ophiolite boundary, dominated by ultramafic mantle rocks based on Gazel et al. (2006). Sample locations are color-coded: alkaline spring systems within Murciélago and Potrero Grande watersheds are identified by green and yellow symbols, respectively; black triangles denote groundwater wells; pink square stands for a control surface water site. Blue and orange lines are the main perennial rivers and faults, respectively.

been detected in springs from the Voltri Massif, Italy (Brazelton et al., 2017) and wells from the Samail Ophiolite in the Sultanate of Oman (Miller et al., 2016) and from carbonate samples collected from serpentinizing seeps in Manleluag Spring National Park, Philippines (Woycheese et al., 2015). Microcosm studies using 13C-labeled substrates demonstrated that native microbial communities from The Cedars (California, USA) spring water and sediments were capable of methanogenesis and acetogenesis (Kohl et al., 2016). Previous work in SEO detected the presence of microorganisms involved in the methane cycle, such as methanotrophic bacteria from the families Methylococcaceae, Methylobacteriaceae, and Methylocystaceae, and methanogenic archaea from the orders Methanobacteriales, Methanocellales, and Methanomicrobiales (Sánchez-Murillo et al., 2014). Here we investigate in more detail the presence of these organisms and their metabolic potential in fluids collected from different sites within this tropical serpentinizing setting, using molecular, geochemical, and modeling approaches. The results are discussed in light of the unique precipitation and hydrological processes within the SEO.

#### Hydroclimatic Conditions at SEO

Four regional air circulation processes predominantly control the climate of the SEO: NE trade winds, the latitudinal migration of the Intertropical Convergence Zone (ITCZ), cold continental outbreaks, and sporadic influence of tropical cyclones (Waylen et al., 1996; Sáenz and Durán-Quesada, 2015). These circulation processes produce two predominant rainfall maxima, one in May and June and the second one in August-September-October, which are interrupted by a relative minimum in July known as the Mid-Summer Drought (Magana et al., 1999; Maldonado et al., 2013). In addition to these circulation processes, the continental divide (i.e., a mountainous range that extends from NW to SE) also influences precipitation patterns across the country, dividing the territory into the Caribbean and Pacific slopes. The SEO (located on the northwestern Pacific slope) receives on average 1,464 mm/year of rainfall (based on 10 years of historical records at the Santa Rosa climatological station, Sánchez-Murillo et al., 2014). This region is highly dependent on the variations of the El Niño/Southern Oscillation cycles. For instance, La Niña (cold and wet phase) produces a considerable rainfall increase up to 3,000 mm, while El Niño (warm and dry phase) years are characterized by annual precipitation below 1,200 mm and a dry period usually covering 5–7 months. Seasonal temperature variation is low; mean annual maximum and minimum ambient temperatures are 31 and 23◦C, respectively. During the dry period, maximum temperatures can reach up to 38◦C (Sánchez-Murillo et al., 2014). The northwestern Pacific region of Costa Rica has estimated regional groundwater recharge rates of <300 mm/year (Mulligan and Burke, 2005). Baseflow recession starts in November and reaches its minimum in late April. Most of the groundwater recharge in the SEO occurs between May and October. Commonly, terrestrial serpentinization and alkaline spring studies have been conducted in temperate regions (e.g., Tablelands, Canada; California Coast Range, USA; Gruppo di Voltri, Italy; Othrys, Greece) and at subtropical sites (e.g., Samail, Oman). In these regions precipitation is mostly composed of snow and rainfall events, meteoric recharge occurs in a short time during spring runoff or in relatively slow snowmelt rates. Coastal temperate sites might experience intermittent rainfall at relatively low intensities. Furthermore, in arid and semiarid areas where precipitation events are scarce and isolated, infiltration is limited due to large evaporation losses. Contrary to the previous scenarios, in tropical environments like the SEO (Sánchez-Murillo et al., 2015), the Zambales Ophiolite and the Palawan Ophiolite (Abrajano et al., 1988; Cardace et al., 2015; Woycheese et al., 2015) precipitation amounts and rainfall intensities are usually greater and occur throughout several months, which facilitate infiltration and deep percolation, and thus, water-rock reactions, may be enhanced. The SEO differs from the Philippines serpentinites in that rainfall and recharge is punctuated into a short (few-month) interval.

# MATERIALS AND METHODS

#### Site Description and Sample Collection

During a field sampling campaign in February 2014 (dry season), fluid and gas samples were collected from three hyperalkaline springs: Spring 9 and Spring 8, located at the Murciélago river watershed, and Quebrada Danta (Q. Danta) located in the Potrero Grande watershed (**Figure 1**). Upstream locations to the springs were sampled (Murciélago Upstream and Q. Danta Upstream, respectively) for comparison purposes. Three private and municipal wells located nearby the SEO were also sampled (30–70 m depth): Pozo Murciélago (P. Murciélago), Pozo Nuevo (P. Nuevo), and Pozo Aguas Calientes (P. Aguas Calientes), as well as one control location at Río Calera (R. Calera) (**Figure 1**). Although the groundwater wells are located outside SEO, their recharge and flow paths originate within the ophiolite complex (Sánchez-Murillo et al., 2014).

Potrero Grande watershed (**Figure 1**) is characterized by a 10.3 km long floodplain with very steep tributaries (∼33% slope), such as Q. Danta. Active erosion processes resulted in greater peridotite exposure among all watersheds in the SEO. Vegetation (i.e., deciduous trees) is mostly located in riparian areas, whereas tropical dry forest grass is common on the upper part of the stream canyons. Carbon and nutrient contributions from the topsoil layer are negligible during baseflow periods, and it solely represents subsurface conditions. The hyperalkaline system (Q. Danta) within Potrero Grande is located in the headwaters about 121 m a.s.l. Murciélago River is characterized by a relatively narrow valley and a greater presence of riparian vegetation. The hyperalkaline springs within the Murciélago watershed are located at the bottom portion of the catchment (78 m a.s.l.). Overall, field evidence suggests that hyperalkaline seepages are numerous and are active late in the baseflow period of perennial streams (December to April). Hyperalkaline springs often form shallow pools characterized by moderate turbidity, thin white films of carbonate precipitates, and extensive yellow-brown carbonate deposits.

Surface waters were exclusively collected at the flowing sections of streams to avoid stagnant ponds with biased evaporative signals (Sánchez-Murillo and Birkel, 2016). Groundwater samples were collected using Teflon sterile and Crespo-Medina et al. Methane Cycle in Tropical Serpentinizing Fluids

disposable geobailers (Geotech Environmental Equipment, Colorado, USA) and automated pumping (P. Murciélago). Spring samples were collected according to following criteria: evidence of continuous water flow from the rock, close to bubbling zones, and near the most reductive point. The sampling campaign was designed to target baseflow conditions. Baseflow is described as the cumulative outflow from all upstream riparian aquifers during rainless periods (Brutsaert, 2005), therefore, it represents the most average geochemical characteristics of any particular watershed or aquifer (Sánchez-Murillo et al., 2015).

Fluid and gas samples were collected from all sites following methods in the subsections below. Standard physicochemical measurements (i.e., water temperature, pH, Eh, electrical conductivity (EC), total dissolved solids, and salinity) were recorded using handheld probes (Oakton PC Testr 35 and Oakton Testr 10), which were calibrated twice a day using standard solutions.

## Gas and Aqueous Geochemical Characterization

Water samples for dissolved methane concentration and isotopic composition analyses were collected from the spring source using a bubble-free 60 mL syringe with Tygon tubing attached. The samples were carefully transferred to a He-purged 160 mL serum bottle and crimp sealed immediately. For H2, a 10 mL sample was collected following the same protocol and transferred to a 14 mL serum vial. For dissolved inorganic carbon (DIC), a 14 mL serum vial, containing one NaOH pellet, was filled to the top, capped with a butyl rubber stopper, and microbial activity was arrested using saturated ZnCl2 (80 µM final conc.). All samples were collected in triplicate and were sent for analysis as a contracted service to JBL Analytical Services (http://www. joyeresearchgroup.uga.edu; University of Georgia, GA, USA).

The filtrate from the 0.2 µm Sterivex filter cartridges (EMD Millipore, Billerica, MA) used for molecular sampling (see methods below) was collected into an acid-cleaned 30 mL Nalgene bottle (Tupas et al., 1994; Joye et al., 2010) for dissolved organic carbon (DOC) and nitrogen and phosphorous species: total dissolved nitrogen (TDN), ammonium (NH+ 4 ), nitrite (NO− 2 ), nitrite + nitrate (NO− 2 + NO− 3 ), total dissolved phosphorous (TDP), and orthophosphate (PO−3 4 ). Samples were transported in cold bags from the field and were frozen at −20◦C immediately upon arrival to the field station (2–6 h after sampling). Samples were sent for analysis to JBL Analytical Services (University of Georgia, GA, USA).

Samples for water stable isotope analysis were collected in 30 mL glass E-C borosilicate bottles with tetrafluoroethylene (TFE)-lined caps (Wheaton Science Products, USA). Bottles were filled completely, leaving no head space, covered with parafilm (Thermo Scientific, USA) avoiding exchange with atmospheric moisture, and stored upside down at 5◦C until analysis. Water stable isotope analyses were conducted at the Stable Isotope Research Group facilities of the National University (Heredia, Costa Rica) using a Cavity Ring Down Spectroscopy (CRDS) water isotope analyzer L2120-i (Picarro, USA). The secondary standards were: Moscow Tap Water, MTW (δ 2H = −131.4‰, δ 18O = −17.0‰), Deep Ocean Water, DOW (δ 2H = −1.7‰, δ 18O = −0.2‰), and Commercial Bottled Water, CAS (δ 2H = −64.3‰, δ 18O = −8.3‰). MTW and DOW standards were used to normalize the results to the VSMOW2-SLAP2 scale, while CAS was used as a quality control and drift control standard. The analytical long-term precision was: ±0.5‰ (1 s) for δ 2H and ± 0.1‰ (1 s) for δ 18O.

# Cell Counts

Fluids were preserved in 3.7% (v/v) formaldehyde (final concentration) for enumeration of cell abundances and stored at 4◦C until processing. The preserved fluids were filtered through a 0.2 µm black polycarbonate filters (Whatman; PA, USA), and cells were stained with 1 µg/ml of 4′ ,6-diamidino-2-phenylindole (DAPI) and counted by epifluorescence microscopy using appropriate filter sets according to previously published protocols (Hobbie et al., 1977; Schrenk et al., 2003).

#### DNA Extraction

For molecular characterization of the microbial communities, between one to three liters of fluids were filtered in the field though 0.22 µm SterivexTM –GV filter units (EMD Millipore, MA, USA) using a Masterflex R E/S portable sampler (Cole-Parmer, IL, USA). Filters were covered with ice during the filtration process, then capped and flash frozen in liquid nitrogen, and stored at −80◦C until processing. Extraction of total genomic DNA followed previously described protocols (Huber et al., 2002; Sogin et al., 2006). DNA extracts were purified with the DNA Clean and ConcentratorTM-5 kit (Zymo Research, CA, USA) according to the manufacturer's instructions. DNA extracts were quantified with a Qubit R dsDNA High Sensitivity Assay kit in a Qubit R 2.0 Fluorometer (Life Technologies, NY, USA).

## 16S rRNA Gene Enumeration

Quantification of 16S rRNA gene copies were determined by quantitative polymerase chain reaction (q-PCR) on a BioRad C-1000 thermo-cycler with a q-PCR module using the SsoAdvanced SybrGreen Assay (BioRad; CA, USA). Domain-specific primers, 958F and 1048R for Archaea and 967F and 1064R for Bacteria, targeted the V6 hypervariable region of the 16S rRNA gene (Sogin et al., 2006). Gene copy numbers were calculated using standard curves generated by amplification of DNA from Methanocaldococcus jannaschii for Archaea and Escherichia coli for Bacteria. Amplification efficiencies were 70% for the Archaeal and 93% for the Bacteria qPCR reactions, respectively.

# 16S rRNA Gene Tag Sequencing and Data Analysis

Purified DNA was submitted to the Josephine Bay Paul Center, Marine Biological Laboratory (http://www.mbl.edu/jbpc/) for amplicon sequencing of archaeal and bacterial 16S rRNA genes with an Illumina MiSeq instrument with domain-specific primers targeting the V4-V5 hypervariable regions of the 16S rRNA gene (518F-926R for bacteria and 517F-958R for archaea), following the methods described by Nelson et al. (2014). Quality-filtering of the sequences was conducted via the Visualization and Analysis of Microbial Population Structures (VAMPS) pipeline (Huse et al., 2014). These data are publicly available at https://vamps. mbl.edu/ under the project code DCO\_BRZ and the sample code Serp\_SEO as well as the NCBI SRA database under SRP096759 and Bioproject accession number PRJNA361138.