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Potential for microbial anaerobic hydrocarbon degradation in naturally petroleum-associated deep-sea sediments

August 2018

DOI:10.1101/400804

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CC BY-NC-ND 4.0

Authors:

Chris Greening

Monash University (Australia)

Jayne E Rattray

University of Calgary

Anirban Chakraborty

Idaho State University

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The lack of cultured isolates and microbial genomes from the deep seabed means that very little is known about the ecology of this vast habitat. Here, we investigated energy and carbon acquisition strategies of microbial communities from three deep seabed petroleum seeps (3 km water depth) in the Eastern Gulf of Mexico. Shotgun metagenomic analysis revealed that each sediment harbored diverse communities of chemoheterotrophs and chemolithotrophs. We recovered 82 metagenome-assembled genomes affiliated with 21 different archaeal and bacterial phyla. Multiple genomes encoded enzymes for acetogenic fermentation of aliphatic and aromatic compounds, specifically of candidate phyla Aerophobetes, Aminicenantes, TA06 and Bathyarchaeota. Microbial interactions in these communities are predicted to be driven by acetate and molecular hydrogen, as indicated by a high abundance of fermentation, acetogenesis, and hydrogen utilization pathways. These findings are supported by sediment geochemistry, metabolomics and thermodynamic modelling of hydrocarbon degradation. Overall, we infer that deep-sea sediments experiencing thermogenic hydrocarbon inputs harbor phylogenetically and functionally diverse communities potentially sustained through anaerobic hydrocarbon, acetate and hydrogen metabolism.

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Potential for microbial anaerobic hydrocarbon degradation in naturally 1

petroleum-associated deep-sea sediments 2

Xiyang Dong 1, *, Chris Greening 2, Jayne E. Rattray 1, Anirban Chakraborty 1, 3

Maria Chuvochina2, 3, Daisuke Mayumi 1, 4, Carmen Li 1, James M. Brooks 5, 4

Bernie B. Bernard 5, Ryan A. Groves1, Ian A. Lewis 1, Casey R.J. Hubert 1, *

1 Department of Biological Sciences, University of Calgary, Calgary, T2N 1N4, Canada 6

2 School of Biological Sciences, Monash University, Clayton, VIC 3800, Australia 7

3 Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The 8

University of Queensland, QLD 4072, Australia 9

4 Institute for Geo-Resources and Environment, Geological Survey of Japan, National Institute of 10

Advanced Industrial Science and Technology (AIST), 1-1-1 Higashi, Tsukuba, 305-8567, Japan 11

5 TDI Brooks International, College Station, Texas, TX 77845, USA 12

* Corresponding authors. E-mail: xiyang.dong@ucalgary.ca (X. Dong), chubert@ucalgary.ca (C. 14

R.J. Hubert). 15

The authors declare no conflict of interest. 17

.CC-BY-NC-ND 4.0 International licensepeer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not. http://dx.doi.org/10.1101/400804doi: bioRxiv preprint first posted online Aug. 28, 2018;

Abstract 18

The lack of cultured isolates and microbial genomes from the deep seabed means that very little 19

is known about the ecology of this vast habitat. Here, we investigated energy and carbon 20

acquisition strategies of microbial communities from three deep seabed petroleum seeps (3 km 21

water depth) in the Eastern Gulf of Mexico. Shotgun metagenomic analysis revealed that each 22

sediment harbored diverse communities of chemoheterotrophs and chemolithotrophs. We 23

recovered 82 metagenome-assembled genomes affiliated with 21 different archaeal and bacterial 24

phyla. Multiple genomes encoded enzymes for acetogenic fermentation of aliphatic and aromatic 25

compounds, specifically those of candidate phyla Aerophobetes, Aminicenantes, TA06 and 26

Bathyarchaeota. Microbial interactions in these communities are predicted to be driven by 27

acetate and molecular hydrogen, as indicated by a high abundance of fermentation, acetogenesis, 28

and hydrogen utilization pathways. These findings are supported by sediment geochemistry, 29

metabolomics and thermodynamic modelling of hydrocarbon degradation. Overall, we infer that 30

deep-sea sediments experiencing thermogenic hydrocarbon inputs harbor phylogenetically and 31

functionally diverse communities potentially sustained through anaerobic hydrocarbon, acetate 32

and hydrogen metabolism. 33

Deep-sea sediments, generally understood to be those occurring in water depths greater than 34

~500 meters, represent one of the largest habitats on Earth. In recent years, culture-independent 35

16S rRNA gene surveys and metagenomic studies have revealed these sediments host a vast 36

abundance and diversity of bacteria and archaea 1-8. Cell numbers decrease with sediment depth 37

and age, from between 106 and 1010 cm-3 in the upper cm at the sediment-water interface to 38

below 104 cm-3 several kilometers below the ocean floor 9, 10. However, due to a lack of cultured 39

representatives and genomes recovered from deep-sea sediments, it remains largely unresolved 40

how microorganisms survive and function in these nutrient-limited ecosystems. Energy and 41

carbon sources are essential requirements that allow the buried microorganisms to persist. With 42

sunlight penetration not reaching the deep seabed, photosynthetic processes do not directly 43

support these communities 11. It has therefore been proposed that deep sea benthic and 44

subsurface microbes are primarily sustained by complex detrital organic matter, including 45

carbohydrates, proteinaceous compounds, and humic substances, derived from the overlying 46

water column via sedimentation 11-13. 47

Another important potential carbon and energy source in deep-sea sediments are petroleum 48

geofluids that migrate from subseafloor reservoirs up to the seafloor 14. Petroleum compounds 49

include smaller gaseous molecules, such as methane, propane and butane, and larger aliphatic 50

and aromatic liquids. Numerous studies have investigated the role of methane oxidation in 51

seabed sediments, which is mediated by anaerobic methanotrophic archaea (ANME), generally 52

in syntrophy with bacteria respiring sulfate or other electron acceptors 4, 6, 8, 15, 16. In contrast, 53

little is known about the degradation of larger alkanes or aromatic compounds by deep seabed 54

microorganisms. Vigneron et al. 2 performed a comparative gene-centric study of hydrocarbon 55

and methane seeps of the Gulf of Mexico, and suggested that microorganisms in deep cold seeps 56

(water depth ~1 km) can potentially utilize a range of non-methane hydrocarbons. However, due 57

to the absence of metagenome binning in that study, relevant metabolic functions were not 58

assigned to specific pathways or taxa. 59

In addition to organic carbon compounds, microbial life in deep-sea sediments is also supported 60

by inorganic electron donors. Some microorganisms have been isolated from deep sediments that 61

are able to sustain themselves by oxidizing elemental sulfur, hydrogen sulfide, carbon monoxide, 62

ammonia and molecular hydrogen (H2) 6, 8, 11. Of these, H2 is a particularly important energy 63

source given its production in large quantities by biological and geochemical processes. H2 can 64

be generated as a metabolic byproduct of fermentation, together with volatile fatty acids such as 65

acetate, during organic matter degradation 9, 17. H2 can also be produced abiotically via 66

serpentinization, radiolysis of water, or thermal alteration of sedimentary organic matter 18. For 67

example, the radiolysis of water by naturally occurring radionuclides (e.g. 40K and 238U) is 68

estimated to produce 1011 mol H2 per year 8, 19. Depending on the availability of electron 69

acceptors, H2 oxidation can be coupled to sulfate, nitrate, metal, and organohalide respiration, as 70

well as acetogenesis and methanogenesis 8, 11.

To develop understanding of the role of hydrocarbon substrates metabolic processes in 72

supporting microbial life in deep-sea sediments, we performed metagenomic, geochemical and 73

metabolomic analyses of three deep seabed sediments (water depth ~3km). The three sites 74

exhibited different levels of migrated thermogenic hydrocarbons. Metagenomes generated from 75

sediment samples of each site were assembled and binned to obtain metagenome-assembled 76

genomes (MAGs) and to reconstruct metabolic pathways for dominant members of the microbial 77

communities. Complementing this genome-resolved metagenomics, a gene-centric analysis was 78

performed by directly examining unassembled metagenomic data. Through the combination of 79

metagenomics with geochemistry and metabolomics, with supporting thermodynamic modeling, 80

we provide evidence that (1) deep-sea sediments harbor phylogenetically diverse heterotrophic 81

and lithotrophic microbial communities; (2) some members from the candidate phyla are 82

engaged in degradation of aliphatic and aromatic thermogenic hydrocarbons; and (3) microbial 83

community members are likely interconnected via acetate and hydrogen metabolism. 84

Results 85

Sediment geochemistry 86

This study tested three petroleum-associated near-surface sediments (referred to as Sites E26, 87

E29 and E44) sampled from the Eastern Gulf of Mexico 20. Petroleum content and other 88

geochemical characteristics were analyzed for each of the three sites (Table 1). All sites had high 89

concentrations of aromatic compounds and liquid alkanes; aromatic compounds were most 90

abundant at Site E26, while liquid alkanes were at 2.5-fold higher concentration at Sites E26 and 91

E29 than Site E44. Alkane gases were only abundant at Site E29 and were almost exclusively 92

methane (CH4). CH4 sources can be inferred from stable isotopic compositions of CH4 and molar 93

ratios of CH4 to higher hydrocarbons 15. Ratios of C1/(C2+C3) were greater than 1,000 and 13C 94

values of methane were more negative than -60‰, indicating that the CH4 in these sediments is 95

predominantly biogenic 15, 21. GC-MS revealed an unresolved complex mixture (UCM) of 96

saturated hydrocarbons in the C15+ range in all three sites. Such UCM signals correspond to 97

degraded petroleum hydrocarbons and may indicate the occurrence of oil biodegradation at these 98

sites 22. Signature metabolites for anaerobic biodegradation of alkanes and aromatic compounds 99

23 were also detected, including benzoate, toluate and methyl- or trimethylsilyl esters (Table S1). 100

High concentrations of sulfate (>20 mM) were detected at each of the three sites (Table 1), 101

consistent with sulfate being present in high concentrations in seawater and diffusing into the 102

sediments. H2 and acetate concentrations were both below limits of detection (0.015-0.1 nM and 103

2.5 µM, respectively); this is consistent with previous observations in deep-sea sediments 104

showing that H2 and acetate is present at extremely low steady-state concentrations due to tight 105

coupling between producers and consumers 3, 15. 106

Deep-sea sediments harbor phylogenetically diverse bacterial and archaeal communities 107

Illumina NextSeq sequencing of genomic DNA from deep-sea sediment communities produced 108

85,825,930, 148,908,270, and 138,795,692 quality-filtered reads for Sites E26, E29, and E44, 109

respectively (Table S2). The 16S rRNA gene amplicon sequencing results suggest the sediments 110

harbor diverse bacterial and archaeal communities, with Chao1 richness estimates of 359, 1375 111

and 360 amplicon sequence variants (ASVs) using bacterial-specific primers, and 195, 180 and 112

247 ASVs using archaeal-specific primers, for Sites E26, E29 and E44, respectively (Table S3 113

and Figure S1). Taxonomic profiling of these metagenomes using small subunit ribosomal RNA 114

(SSU rRNA) marker genes demonstrated that the most abundant phyla in the metagenomes were, 115

in decreasing order, Chloroflexi (mostly classes Dehalococcoidia and Anaerolineae), Candidatus 116

Atribacteria, Proteobacteria (mostly class Deltaproteobacteria), and Candidatus Bathyarchaeota 117

(Figure 1a). While the three sites share a broadly similar community composition, notable 118

differences were Ca. Bathyarchaeota and Proteobacteria being in higher relative abundance at 119

the sites with more hydrocarbons (E29 and E26; Table 1), whereas the inverse is true for 120

Actinobacteria, the Patescibacteria group, and Ca. Aerophobetes that are all present in higher 121

relative abundance at Site E44 where hydrocarbon levels are lower. Additional sampling is 122

required to determine whether these differences are due to the presence of hydrocarbons or other 123

factors. 124

Assembly and binning for the three metagenomes resulted in a total of 82 MAGs with >50% 125

completeness and <10% contamination based on CheckM analysis 24. Reconstructed MAGs 126

comprise taxonomically diverse members from a total of six archaeal and 15 bacterial phyla 127

(Figure 2 and Table S4). Within the domain Bacteria, members of the phylum Chloroflexi are 128

highly represented in each sample, especially from the classes Dehalococcoidia and 129

Anaerolineae. Within the domain Archaea, members of phylum Bathyarchaeota were recovered 130

from all three sites. Most other MAGs belong to poorly understood candidate phyla that lack 131

cultured representatives, including Aminicenantes (formerly OP8), Aerophobetes (formerly 132

CD12), Cloacimonas (formerly WWE1), Stahlbacteria (formerly WOR-3), Atribacteria 133

(formerly JS1 and OP9), TA06 and the Asgard superphylum including Lokiarchaeota, 134

Thorarchaeota, and Heimdallarchaeota. 135

Among those phyla, candidate phylum TA06 is the only one not yet given provisional names. 136

Also known as GN04 or AC1, it was originally discovered in a hypersaline microbial mat 25. 137

First genomic representatives of this phylum were recovered from estuarine sediments 26 with a 138

small number of other MAGs recently reported to belong to this lineage 27, 28. Due to the paucity 139

of available MAGs and misclassifications based on 16S rRNA gene sequences, members of 140

TA06 are often ‘confused’ with members of the phylum WOR-3 (Stahlbacteria) 28. In addition to 141

the phylogenetic inference here based on 43 concatenated protein marker genes (Figure 2), the 142

placement of two bins within the original TA06 phylum is further supported by genome 143

classification based on concatenation of 120 ubiquitous, single-copy marker genes 29 as well as 144

classification of 16S rRNA genes using the SILVA database 30 (Tables S4 and S5). 145

In summary, while there are considerable community-level differences between the three sample 146

locations, the recovered MAGs share common taxonomic affiliations at the phylum and class 147

levels. Guided by sediment geochemistry (Table 1), we subsequently analyzed the metabolic 148

potential of these MAGs to understand how bacterial and archaeal community members generate 149

energy and biomass in these natural petroleum-associated deep-sea environments. Hidden 150

Markov models (HMMs) and homology-based models were used to search for the presence of 151

different metabolic genes in both the recovered MAGs and unbinned metagenomes. Where 152

appropriate, findings were further validated through metabolomic analyses, phylogenetic 153

visualization, and analysis of gene context. 154

Capacity for detrital biomass and hydrocarbon degradation in sediment microbial 155

communities 156

In deep-sea marine sediments organic carbon is supplied either as detrital matter from the 157

overlying water column or as aliphatic and aromatic petroleum compounds that migrate upwards 158

from underlying petroleum-bearing sediments 11. With respect to detrital matter, genes involved 159

in carbon acquisition and breakdown were prevalent across both archaeal and bacterial MAGs. 160

These include genes encoding intracellular and extracellular carbohydrate-active enzymes and 161

peptidases, as well as relevant transporters and glycolysis enzymes (Figure 3 and Table S6). The 162

importance of these carbon acquisition mechanisms is supported by the detection of 163

corresponding intermediate metabolites, such as glucose and amino acids, in all three sediments 164

(Table S1). The ability to break down fatty acids and other organic acids via the beta-oxidation 165

pathway was identified in 13 MAGs, including members of Chloroflexi, Deltaproteobacteria, 166

Aerophobetes and Lokiarchaeota (Figure 3 and Table S6). These results align with many other 167

studies suggesting that the majority of seabed microorganisms are involved in recycling of 168

residual organic matter, including complex carbohydrates, proteins and lipids 13, 31, 32. 169

Unlike in other studies, the presence of petroleum hydrocarbons is a defining feature of the 170

sediments investigated here and thus a key goal of this study was to identify the potential for 171

microbial degradation of hydrocarbons as a source of energy and carbon. To this end, we focused 172

on functional marker genes encoding enzymes that catalyze the activation of mechanistically 173

sophisticated C-H bonds, to initiate hydrocarbon biodegradation 33. For anaerobic hydrocarbon 174

degradation, four oxygen-independent C-H activation reactions have been characterized: (1) 175

addition of fumarate by glycyl-radical enzymes, e.g. for activation of alkylbenzenes and 176

straight chain alkanes 34; (2) hydroxylation with water by molybdenum cofactor-containing 177

enzymes, e.g. for activation of ethylbenzene 33; (3) carboxylation catalyzed by UbiD-like 178

carboxylases, e.g. for activation of benzene and naphthalene 35; and (4) reverse methanogenesis 179

involving variants of methyl-coenzyme M reductase, e.g. for activation of methane and butane 180

36. Most of the evidence for mechanisms (1) – (3) has come from studies of hydrocarbon 181

contaminated aquifers, whereas mechanism (4) has been studied extensively in marine sediments 182

23, 37. 183

Evidence for glycyl-radical enzymes that catalyze fumarate addition was found in 15 out of the 184

82 MAGs based on identifying genes encoding alkylsuccinate synthase (AssA) (Figures 3 and 185

4a). The assA sequences identified, while phylogenetically distant from canonical fumarate-186

adding enzymes and pyruvate formate lyases (Pfl), form a common clade with Pfl-like AssA 187

from Archaeoglobus fulgidus VC-16 and Abyssivirga alkaniphila L81 (Figure 4a). Both of these 188

organisms have been shown experimentally to be capable of anaerobic alkane degradation 38, 39. 189

The putative assA genes identified here are present in all three samples regardless of 190

hydrocarbon concentrations. They belong to MAGs affiliated with the bacterial phyla 191

Aerophobetes, Aminicenantes and Chloroflexi as well as the archaeal phyla Bathyarchaeota, 192

Lokiarchaeota and Thorarchaeota. The highest relative abundance of putative assA sequences 193

was found in Site E29 as indicated by quality-filtered reads, which is consistent with this 194

sediment containing the highest concentration of aliphatic compounds (Tables 1 and S7). 195

Additional searching for other genes encoding fumarate-adding enzymes in the quality-filtered 196

reads (e.g. bssA, nmsA, and canonical assA) did not return significant counts (Figure 4 and Table 197

S7). Among the other three anaerobic hydrocarbon biodegradation mechanisms mentioned above, 198

a MAG classified as Dehalococcoidia (Chloroflexi E29_bin2) contained genes encoding putative 199

catalytic subunits of p-cymene dehydrogenase (Cmd) and alkane C2-methylene hydroxylase 200

(Ahy) (Figures 3 and S2), known to support p-cymene and alkane utilization 37. Genes encoding 201

enzymes catalyzing hydrocarbon carboxylation, reverse methanogenesis and aerobic 202

hydrocarbon degradation (e.g. alkB, nahC and nahG) were not detected (Table S6). The latter 203

result is expected due to the low concentrations of oxygen in the top 20 cm of organic rich 204

seabed sediments 11. 205

Considering the degradation of aromatic hydrocarbons, genes responsible for reduction of 206

benzoyl-CoA were detected in 12 MAGs (Figures 3 and 4b). Benzoyl-CoA is a universal 207

biomarker for anaerobic degradation of monoaromatic compounds as it is a common 208

intermediate to biochemical pathways catalyzing this process 40. Benzoyl-CoA reduction to 209

cyclohex-1,5-diene-1-carboxyl-CoA is performed by Class I ATP-dependent benzoyl-CoA 210

reductase (BCR; BcrABCD) in facultative anaerobes (e.g. Thauera aromatica) or Class II ATP-211

independent reductase (Bam; BamBCDEFGHI) in strict anaerobes like sulfate reducers 41. The 212

bcr genes detected are all Class I, and were found in bacterial MAGs (i.e., Dehalococcoidia, 213

Anaerolineae, Deltaproteobacteria, Aminicenantes and TA06) and archaeal MAGs (i.e., 214

Thermoplasmata and Bathyarchaeota) (Figures 3 and 4b). Genes for further transformation of 215

dienoyl-CoA to 3-hydroxypimelyl-CoA were also identified (Figures 3 and 4b), i.e., those 216

encoding 6-oxo-cyclohex-1-ene-carbonyl-CoA hydrolase (Oah), cyclohex-1,5-diencarbonyl-CoA 217

hydratase (Dch) and 6-hydroxycyclohex-1-ene-1-carbonyl-CoA dehydrogenases (Had) 42. 218

Together with the detection of 23 – 162 nM benzoate in these sediments (Table 1) these results 219

strongly suggest that the organisms represented by these MAGs mediate the typical downstream 220

degradation of aromatic compounds through the central benzoyl-CoA Bcr-Dch-Had-Oah 221

pathway. However, the upstream pathways resulting in benzoate production from degradation of 222

complex aromatic compounds were not resolved based on current data. 223

Widespread capacity for fermentative production and respiratory consumption of acetate 224

and hydrogen 225

Analysis of MAGs from these deep-sea hydrocarbon-associated sediments suggests that 226

fermentation, rather than respiration, is the primary mode of organic carbon turnover in these 227

environments. Most recovered MAGs with capacity for heterotrophic carbon degradation lacked 228

respiratory primary dehydrogenases and terminal reductases, with exceptions being several 229

Proteobacteria and one Chloroflexi (Table S6). In contrast, 6 and 14 MAGs contained genes 230

indicating the capability for fermentative production of ethanol and lactate, whereas some 69 231

MAGs contained genes for fermentative acetate production (Figure 3 and Table S6). These 232

findings are consistent with other studies emphasizing the importance of fermentation, including 233

acetate production, in deep-sea sediments 12, 43. 234

Acetate can also be produced by acetogenic CO2 reduction through the Wood-Ljungdahl 235

pathway using a range of substrates, including heterotrophic compounds 15. Partial or complete 236

sets of genes for the Wood-Ljungdahl pathway were found in 50 MAGs (Figures 3 and S3), 237

including those affiliated with phyla previously inferred to mediate acetogenesis in deep-sea 238

sediments through either the tetrahydrofolate-dependent bacterial pathway (e.g. Chloroflexi and 239

Aerophobetes) 7, 44 or the tetrahydromethanopterin-dependent archaeal variant (e.g. 240

Bathyarchaeota and Asgard group) 45, 46. In addition, the signature diagnostic gene for the Wood-241

Ljungdahl pathway (acsB; acetyl-CoA synthase) is in high relative abundance in the quality-242

filtered metagenome reads at all three sites (Table S7). These observations are in agreement with 243

mounting evidence that homoacetogens play a quantitatively important role in organic carbon 244

cycling in the marine deep biosphere 45, 47, 48. 245

Evidence for H2 metabolism was also found in MAGs from all three sites. We screened putative 246

hydrogenase genes from various subgroups in MAGs as well as unbinned metagenomic 247

sequences (Figures 1, 3 and Tables S6, S7). Surprisingly few H2 evolving-only hydrogenases 248

were detected, with only five Group A [FeFe]-hydrogenases and five Group 4 [NiFe]-249

hydrogenases detected across the bacterial and archaeal MAGs. Instead, the most abundant 250

hydrogenases within the MAGs and quality-filtered unassembled reads were the Group 3b, 3c, 251

and 3d [NiFe]-hydrogenases. Group 3b and 3d hydrogenases are physiologically reversible, but 252

generally support fermentation in anoxic environments by coupling NAD(P)H reoxidation to 253

fermentative H2 evolution 49-51. Group 3c hydrogenases mediate a central step in 254

hydrogenotrophic methanogenesis, bifurcating electrons from H2 to heterodisulfides and 255

ferredoxin 52; their functional role in Bacteria and non-methanogenic Archaea remains 256

unresolved 51 yet their corresponding genes co-occur with heterodisulfide reductases across 257

multiple archaeal and bacterial MAGs (Figure 3). Various Group 1 [NiFe]-hydrogenases were 258

also detected, which are known to support hydrogenotrophic respiration in conjunction with a 259

wide range of terminal reductases. This is consistent with previous studies in the Gulf of Mexico 260

that experimentally measured the potential for hydrogen oxidation catalyzed by hydrogenase 261

enzymes 53. 262

Given the genomic evidence for hydrogen and acetate production in these sediments, we 263

investigated whether any of the MAGs encoded terminal reductases to respire using these 264

compounds as electron donors. In agreement with the high sulfate concentrations (Table 1), the 265

key genes for dissimilatory sulfate reduction (dsrAB) were widespread across the metagenome 266

reads, particularly at Site E29 (Table S7). These genes were recovered from MAGs affiliated 267

with Deltaproteobacteria and Dehalococcoidia (Table S6). We also identified 31 novel reductive 268

dehalogenase (rdhA) genes across 22 MAGs, mainly from Aminicenantes and Bathyarchaeota 269

(Figure 3 and Table S6), suggesting that organohalides – that can be produced through abiotic 270

and biotic processes in marine ecosystems 54 – may be electron acceptors in these deep-sea 271

sediments. All MAGs corresponding to putative sulfate reducers and dehalorespirers encoded the 272

capacity to completely oxidize acetate and other organic acids to CO2 using either the reverse 273

Wood-Ljungdahl pathway or TCA cycle (Figure 3 and Table S6). Several of these MAGs also 274

harbored the capacity for hydrogenotrophic dehalorespiration via Group 1a and 1b [NiFe]-275

hydrogenases (Figure 3). In addition to these dominant uptake pathways, one MAG belonging to 276

the epsilonproteobacterial genus Sulfurovum (E29_bin29) included genes for the enzymes 277

needed to oxidize either H2 (group 1b [NiFe]-hydrogenase), elemental sulfur (SoxABXYZ), and 278

sulfide (Sqr), using nitrate as an electron acceptor (NapAGH); this MAG also has a complete set 279

of genes for autotrophic CO2 fixation via the reductive TCA cycle (Figure 3 and Table S6). ). In 280

contrast, the capacity for methanogenesis appears to be relatively low and none of the MAGs 281

contained mcrA genes. The genes for methanogenesis were detected in quality-filtered 282

unassembled reads in all three sediments (Figures 1d and S4) and were mainly affiliated with 283

acetoclastic methanogens at Site E29, and hydrogenotrophic methanogens at the other two sites 284

(Figures 1d and S4). Overall, the collectively weak mcrA signal in the metagenomes suggests 285

that the high levels of biogenic methane detected by geochemical analysis (Table 1) is primarily 286

due to methanogenesis in sediment layers deeper than the top 20 cm. 287

Thermodynamic modelling of hydrocarbon degradation 288

Both the geochemistry data and biomarker gene survey suggest that hydrocarbon degradation 289

occurs in the three deep-sea sediments sampled (Tables 1 and S1). Recreating the environmental 290

conditions for cultivating the organisms represented by the retrieved MAGs is a challenging 291

process, preventing further validation of the hydrocarbon degradation capabilities (and other 292

metabolisms) among the majority of the lineages represented by the MAGs retrieved here 48. 293

Instead, we provide theoretical evidence that hydrocarbon degradation is feasible in this 294

environment by modelling whether these processes are thermodynamically favorable in the 295

conditions typical of deep sea sediments, namely high pressure and low temperature. 296

As concluded from the genome analysis and supported by metabolomics (Table 1), it is likely 297

that most hydrocarbon oxidation occurs through fermentation rather than respiration. Taking 298

hydrogen production and the Wood-Ljungdahl pathway into consideration (Figures 3 and 4), we 299

compared the thermodynamic constraints on hydrocarbon biodegradation for two plausible 300

scenarios: (1) fermentation with production of hydrogen and acetate, and (2) fermentation with 301

production of acetate alone. Hexadecane and benzoate are used as representative aliphatic and 302

aromatic compounds, respectively, based on the geochemistry results (e.g. C2+ alkane detection) 303

and genomic analysis (e.g. bcr genes) 47, 55. The calculated results show that the threshold 304

concentrations of acetate that result in favorable energetics ( G < 0 kJ mol-1) for fermentative 305

co-generation of acetate and hydrogen require acetate to be extremely low in a hexadecane 306

degradation scenario (< 10-12 mM acetate) and acetate to be at moderate levels in a benzoate 307

degradation scenario (< 3.8 mM acetate) (Figure 5). By contrast, for fermentation leading to 308

production of only acetate, its concentration can be as high as 470 mM in a benzoate degradation 309

scenario and as high as 300 mM in a hexadecane degradation scenario (Figure 5). Fermentative 310

degradation of hexadecane to hydrogen and acetate in the deep seabed could therefore be less 311

favorable than acetate production alone via the Wood-Ljungdahl pathway Thus, if microbial 312

communities consume hexadecane or more complex hydrocarbons as carbon and energy sources, 313

it is likely that they employ the Wood-Ljungdahl pathway to produce acetate. However, other 314

reactions such as fermentation to H2 still cannot be excluded, e.g., for less complex hydrocarbons 315

such as benzoate and related compounds. 316

Discussion 317

In this study, metagenomics revealed that most of the Bacteria and Archaea in the deep-sea 318

sediment microbial communities sampled belong to candidate phyla that lack cultured 319

representatives and sequenced genomes (Figures 1 and 2). As a consequence, it is challenging to 320

link phylogenetic patterns with the microbial functional traits underpinning the biogeochemistry 321

of deep seabed habitats. Here, we were able to address this by combining de novo assembly and 322

binning of metagenomic data with geochemical and metabolomic analyses, and complementing 323

our observations with thermodynamic modeling. Pathway reconstruction from 82 MAGs 324

recovered from the three deep-sea near surface sediments revealed that many community 325

members were capable of anaerobic hydrocarbon degradation as well as acquiring and 326

hydrolyzing residual organic matter (Figure 3), whether supplied as detritus from the overlying 327

water column or as autochthonously produced necromass (Figure 6). Heterotrophic fermenters 328

and acetogens were in considerably higher relative abundance than heterotrophic respirers, 329

despite the abundance of sulfate in the sediments (Table 1). For example, while genomic 330

coverage of putative sulfate reducers is relatively low (< 1% of the communities), the most 331

abundant MAG at each site were all putative acetogenic heterotrophs, i.e. Dehalococcoidia 332

E26_bin16, Actinobacteria E44_bin5, and Aminicenantes E29_bin47 for Sites E26, E44 and E29 333

respectively (~3.3-4.5% relative abundance, Table S4). Therefore, in contrast with coastal 334

sediments 56, microbial communities in the deep seabed are likely influenced by the capacity to 335

utilize available electron donors more so than by the availability of oxidants. 336

In this context, multiple lines of evidence indicate degradation of aliphatic or aromatic petroleum 337

compounds as carbon and energy sources for anaerobic populations in these deep-sea 338

hydrocarbon seep environments (Table 1, Figures 3 - 5). Whereas capacity for detrital organic 339

matter degradation is a common feature in the genomes retrieved in this study, and from many 340

other environments 26, anaerobic hydrocarbon degradation is a more exclusive feature that was 341

detected in 23 out of 82 MAGs. Evidence of anaerobic alkane oxidation via fumarate addition 342

and hydroxylation pathways, as well as anaerobic aromatic compound degradation by the Class I 343

benzoyl-CoA reductase pathway, was found in all three sediments. The ability to utilize 344

hydrocarbons may explain the ecological dominance (high relative abundance) of certain 345

lineages of Bacteria and Archaea in these microbial communities (Figure 1a), as many of those 346

phyla have previously been found to be associated with hydrocarbons in various settings. For 347

example, Aerophobetes have been detected in other cold seep environments 7, Aminicenantes are 348

often found associated with fossil fuels 27, and Chloroflexi harboring genes for anaerobic 349

hydrocarbon degradation have been found in hydrothermal vent sediments 4. While Archaea 350

have been reported to mediate oxidation of methane and other short-chain alkanes in sediments 5,

351

36, few have been reported to anaerobically degrade larger hydrocarbons 37. The finding of 352

Bathyarchaeota and other archaeal phyla potentially capable of anaerobic hydrocarbon 353

degradation extends the potential hydrocarbon substrate spectrum for Archaea. More broadly, 354

these findings extend the breadth of bacterial and archaeal lineages that putatively degrade 355

hydrocarbons. Current knowledge of anaerobic hydrocarbon degradation remains limited, with 356

the majority of studies focused on environments subject to anthropogenic hydrocarbon 357

contamination, most notably groundwater aquifers 37. It is possible that microorganisms 358

inhabiting deep-sea sediments harbor novel mechanisms for anaerobic hydrocarbon degradation 359

that may be relevant for biotechnology and bioremediation in a variety of other settings, e.g., 360

other cold habitats. Future studies of genome-enabled hydrocarbon degradation using samples 361

such as the sediments studied here may elucidate this further. 362

Genomic analyses of 12 MAGs harboring genes for central benzoyl-CoA pathway reveal that 363

they are likely a mixture of obligate fermenters and sulfate reducers. The finding that these 364

organisms use the ATP-consuming class I, not the reversible class II, benzoyl-CoA reductase is 365

surprising. It is generally thought that strict anaerobes must use class II BCRs because the 366

amount of energy available from benzoate oxidation during sulfate reduction or fermentation is 367

not sufficient to support the substantial energetic requirement of the ATP-dependent class I BCR 368

reaction 42. However, acetogenic fermentation of hydrocarbons may explain how the Class I 369

reaction could be thermodynamically favorable, as shown in Figure 5. In agreement with this, 370

there are reported exceptions to the general Class I vs Class II observations, such as the 371

hyperthermophilic archaeon Ferroglobus placidus that couples benzoate degradation via the 372

Class I system with iron reduction 42, and fermentative deep-sea Chloroflexi strains DscP3 and 373

Dsc4 that contain genes for class I benzoyl-CoA reductases 44. Indeed, acetogens can utilize 374

many different substrates and have relatively high ATP yields, as well as thermodynamic 375

efficiencies toward heterotrophic substrates, which is consistent with the proposed importance of 376

acetogens in energy-limited seafloor ecosystems 45, 47. 377

Based on the evidence presented here, we propose that acetate and hydrogen are the central 378

intermediates underpinning community interactions and biogeochemical cycling in these deep-379

sea sediments (Figure 6). Maintaining low acetate and hydrogen concentrations in the 380

environment is important for promoting continuous fermentation of organic substrates, consistent 381

with thermodynamic constraints (Figure 5). Acetate and hydrogen in sediment porewater were 382

below detection limits, consistent with the high turnover rates of both compounds. This may 383

correspond with the genomic potential within these microbial communities for the coupling of 384

acetate consumption to sulfate reduction, organohalide respiration and acetoclastic 385

methanogenesis, as suggested in other studies 55, 57. Some community members also appear to be 386

capable of H2 consumption, including via putative heterodisulfide reductase-coupled 387

hydrogenases. In turn, hydrogen oxidation can support autotrophic carbon fixation and therefore 388

may provide a feedback loop for regeneration of organic carbon. Acetate- and hydrogen-389

oxidizing community members are likely to promote upstream fermentative degradation of 390

necromass and hydrocarbons (Figure 6). 391

Overall, this metagenome dataset extended the knowledge of metabolic potential of microbial 392

communities inhabited in deep-sea sediments that receive an input of thermogenic hydrocarbon. 393

They are mostly likely sustained through fermentation, acetogenesis and hydrogen metabolisms. 394

More importantly, as supported by geochemical data, metabolomic analysis, and thermodynamic 395

modelling, our findings expand the diversity of microbial lineages with the potential for 396

anaerobic hydrocarbon degradation through e.g. the activity of glycyl-radical enzymes. Together 397

with the recent discovery of anaerobic butane degradation in gas-rich hydrothermally-heated 398

sediments 36, it can be inferred that anaerobic degradation of hydrocarbons heavier than methane 399

might be more widespread than previously expected and may significantly contribute to energy 400

and carbon budgets in dark deep-sea sediments 58. 401

Methods 402

Sampling and geochemical measurements 403

The three marine sediment samples used in this study were collected from the near-surface (top 404

20 cm) of the seafloor in the Eastern Gulf of Mexico as part of a piston coring survey, as 405

described previously 20. Samples for hydrocarbon characterization were sectioned on board the 406

research vessel immediately following piston core retrieval, flushed with N2 and sealed in 407

hydrocarbon-free gas tight metal canisters then frozen until analysis. Interstitial gas analysis was 408

later performed on the headspace in the canisters using GC with Flame Ionization Detector (GC-409

FID). Sediment samples for gas/liquid chromatography and stable isotope analysis were frozen, 410

freeze-dried and homogenized then extracted using accelerated solvent extraction (ACE 200). 411

Extracts were subsequently analyzed using GC/FID, a Perkin-Elmer Model LS 50B fluorometer, 412

GC/MS and Finnigan MAT 252 isotope mass spectrometry as detailed elsewhere 59. 413

Sulfate and chloride concentrations were measured in a Dionex ICS-5000 reagent-free ion 414

chromatography system (Thermo Scientific, CA, USA) equipped with an anion-exchange 415

column (Dionex IonPac AS22; 4 × 250 mm; Thermo Scientific), an EGC-500 K2CO3 eluent 416

generator cartridge and a conductivity detector. Organic acids were analysed in the 0.2 µm 417

filtered sediment porewater using a Thermo RS3000 HPLC fitted with an Ultimate 3000 UV 418

detector. Separation was achieved over an Aminex HPX-87H organic acid column (Biorad, USA) 419

under isocratic conditions (0.05 mM H2SO4) at 60°C with a run time of 20 minutes. Organic 420

acids were compared to the retention time of known standards and the limit of detection for 421

acetate was determined to be 2.5 µM. 422

For the analysis of metabolites, sediment was spun down, the supernatant collected, diluted 1:1 423

in pure methanol, and filtered through 0.2 µm Teflon syringe filters. Extracts were separated 424

using Ultra High-Performance Liquid Chromatography (UHPLC) equipped with a hydrophilic 425

interaction liquid chromatography column (Syncronis HILIC, Thermo Fisher). A Thermo Fisher 426

Scientific Q-Exactive HF mass spectrometer in negative-mode electrospray ionization was used 427

to collect high-resolution full-scan MS data from 50-750 m/z at 240,000 resolution with an 428

automatic gain control (AGC) target of 3e6 and a maximum injection time of 200 ms. In addition, 429

benzoate ion (m/z 121.02943) was subjected to fragmentation using collision induced 430

dissociation (CID) with a collision energy of 10eV at 120,000 resolution (m/z 121.02943 > 431

77.03948). For CID experiments, an AGC target of 1e6 was used with a maximum injection time 432

of 100 ms. Metabolites were further identified using accurate mass and retention times of 433

standards using Thermo Xcalibur software and MAVEN freeware. Larger compound lists were 434

assigned identification using a combination of MAVEN and the KEGG database 60. 435

DNA extraction and sequencing 436

For the three sediment samples, DNA was extracted from 10 g of sediment using the PowerMax 437

Soil DNA Isolation Kit (12988-10, QIAGEN) according to the manufacturer’s protocol with 438

minor modifications for the step of homogenization and cell lysis i.e., cells were lysed in 439

PowerMax Bead Solution tubes for 45 s at 5.5 m s−1 using a Bead Ruptor 24 (OMNI 440

International). DNA concentrations were assessed using a Qubit 2.0 fluorometer (Thermo Fisher 441

Scientific, Canada). Metagenomic library preparation and DNA sequencing was conducted at the 442

Center for Health Genomics and Informatics in the Cumming School of Medicine, University of 443

Calgary. DNA fragment libraries were prepared by shearing genomic DNA using a Covaris 444

sonicator and the NEBNext Ultra II DNA library preparation kit (New England BioLabs). DNA 445

was sequenced on a ~40 Gb (i.e. 130 M reads) mid-output NextSeq 500 System (Illumina Inc.) 446

300 cycle (2 × 150 bp) sequencing run. 447

To provide a high-resolution microbial community profile, the three samples were also subjected 448

to 16S rRNA gene amplicon sequencing on a MiSeq benchtop sequencer (Illumina Inc.). DNA 449

was extracted from separate aliquots of the same sediment samples using the DNeasy 450

PowerLyzer PowerSoil kit (MO BIO Laboratories, a Qiagen Company, Carlsbad, CA, USA) and 451

used as the template for different PCR reactions. The v3-4 region of the bacterial 16S rRNA 452

gene and the v4-8 region of the archaeal 16S rRNA gene were amplified using the primer pairs 453

SD-Bact-0341-bS17/SD-Bact-0785-aA21 and SD-Arch-0519-aS15/SD-Arch-0911-aA20, 454

respectively 61 as described previously 20 on a ~15 Gb 600-cyce (2 × 300 bp) sequencing run (for 455

results see Figure S1). 456

Metagenomic assembly and binning 457

Raw reads were quality-controlled by (1) clipping off primers and adapters and (2) filtering out 458

artifacts and low-quality reads as described previously 62. Filtered reads were assembled using 459

metaSPAdes version 3.11.0 63 and short contigs (<500 bp) were removed. Sequence coverage 460

was determined by mapping filtered reads onto assembled contigs using BBmap version 36 461

(https://sourceforge.net/projects/bbmap/). Binning of metagenome contigs was performed using 462

MetaBAT version 2.12.1 (--minContig 1500) 64. Contaminated contigs in the produced bins were 463

further removed based on genomic properties (GC, tetranucleotide signatures, and coverage) and 464

taxonomic assignments using RefineM version 0.0.22 65. Resulting bins were further examined 465

for contamination and completeness using CheckM version 1.0.8 with the lineage-specific 466

workflow 24. 467

Annotation 468

For MAGs, genes were called by Prodigal (-p meta) 66. Metabolic pathways were predicted 469

against the KEGG GENES database using the GhostKOALA tool 67 and against the Pfam, 470

TIGRfam and custom HMM databases (https://github.com/banfieldlab/metabolic-hmms) using 471

MetaErg (https://sourceforge.net/projects/metaerg/). The dbCAN web server was used for 472

carbohydrate-active gene identification (cutoffs: coverage fraction: 0.40; e-value: 1e-18) 68. 473

Genes encoding proteases and peptidases were identified using BLASTp against the MEROPS 474

database release 12.0 (cutoffs: e-value, 1e-20; sequence identity, 30%) 69. Genes involved in 475

anaerobic hydrocarbon degradation were identified using BLASTp against a custom database 476

(Table S8) (cutoffs: e-value, 1e-20; sequence identity, 30%). Hydrogenases were identified and 477

classified using a web-based search using the hydrogenase classifier HydDB 70. 478

Full-length 16S rRNA genes were reconstructed from metagenomic reads using phyloFlash 479

version 3.1 (https://hrgv.github.io/phyloFlash/) together with the SILVA SSU 132 rRNA 480

database 30. Diversity calculations were based on separate 16S rRNA gene amplicon library 481

results 20. Functional and taxonomic McrA gpkgs were used to assess the diversity of 482

methanogens against the metagenomic reads using GraftM with default parameters 71. Genes 483

encoding the catalytic subunits of hydrogenases, dsrA, acsB, assA, nmsA and bssA were retrieved 484

from metagenomic reads through diamond BLASTx 72 queries against comprehensive custom 485

databases 46, 70, 73 (cutoffs: e-value, 1e-10; sequence identity, 70%). 486

Phylogenetic analyses 487

For taxonomic classification of each MAG, two methods were used to produce genome trees that 488

were then used to validate each other. In the first method the tree was constructed using 489

concatenated proteins of up to 16 syntenic ribosomal protein genes following procedures 490

reported elsewhere 74; the second tree was constructed using concatenated amino acid sequences 491

of up to 43 conserved single-copy genes following procedures described previously 75. Both trees 492

were calculated using FastTree version 2.1.9 (-lg -gamma) 76 and resulting phylogenies were 493

congruent. Reference genomes for relatives were accessed from NCBI GenBank, including 494

genomes selected from several recent studies representing the majority of candidate bacterial and 495

archaeal phylogenetic groups 4, 65, 77-80. The tree in Figure 2 was inferred based on concatenation 496

of 43 conserved single-copy genes (Database S1). Specifically, it was built using RAxML 497

version 8 81 implemented by the CIPRES Science Gateway 82 and it was called as follows: 498

raxmlHPC-HYBRID -f a -n result -s input -c 25 -N 100 -p 12345 -m PROTCATLG -x 12345. 499

The phylogeny resulting from RAxML is consistent with the taxonomic classification of MAGs 500

that resulted from FastTree. Interactive tree of life (iTOL) version 3 83 was used for tree 501

visualization and modification. 502

For phylogenetic placements of functional genes, sequences were aligned using the MUSCLE 503

algorithm 84 included in MEGA7 85. All positions with less than 95% site coverage were 504

eliminated. Maximum likelihood phylogenetic trees were constructed in MEGA7 using a general 505

time reversible substitution model and uniform rates among sites. These trees were bootstrapped 506

with 100 replicates. 507

Taxonomic classification of MAGs inferred to belong to candidate phylum TA06 after 508

phylogenetic analyses were additionally confirmed by performing classify workflow using 509

GTDB-Tk version 0.0.6+ (https://github.com/Ecogenomics/GtdbTk). 510

Thermodynamic calculations 511

The values of Gibbs free energy of formation for substances were taken from Madigan et al. 86 512

and Dolfing et al. 55. The pH used in all calculations was 8.0 as reported in a previous 513

thermodynamic study of deep marine sediments 47, partial pressure was 300 atm based on water 514

depths at the three sites (http://docs.bluerobotics.com/calc/pressure-depth/), and temperature was 515

set as 4°C to represent deep sea conditions 87. Calculations followed accepted protocols for 516

determining reaction kinetics and thermodynamics 88. 517

Acknowledgements 518

The work was supported by a Genome Canada Genomics Applied Partnership Program (GAPP) 519

grant to CRJH. I.A.L is supported by an Alberta Innovates Translational Health Chair. 520

Metabolomics data were acquired by R.A.G. at the Calgary Metabolomics Research Facility 521

(CMRF), which is supported by the International Microbiome Centre and the Canada Foundation 522

for Innovation (CFI-JELF 34986). We thank Xiaoli Dong and Marc Strous for establishing 523

bioinformatics workflow and pipelines, the Centre for Health Genomics and Informatics at 524

University of Calgary for NextSeq sequencing, Alexander Probst for providing the database of 525

16 syntenic ribosomal proteins, and Nina Dombrowski and Brett Baker for providing a custom 526

blast database for hydrocarbon degradation genes. 527

Data availability 528

DNA sequences (amplicon sequences, genomes and raw sequence reads) have been deposited in 529

the NCBI BioProject database with accession number PRJNA415828 and PRJNASUB3936075 530

(https://www.ncbi.nlm.nih.gov/bioproject/). The authors declare that all other data supporting the 531

findings of this study are available within the article and its supplementary information files, or 532

from the corresponding authors upon request. 533

References 534

1. Petro, C., Starnawski, P., Schramm, A. & Kjeldsen, K.U. Microbial community assembly 535

in marine sediments. Aquat Microb Ecol 79, 177-195 (2017). 536

2. Vigneron, A. et al. Comparative metagenomics of hydrocarbon and methane seeps of the 537

Gulf of Mexico. Sci Rep 7, 16015 (2017). 538

3. Marshall, I.P.G., Karst, S.M., Nielsen, P.H. & Jørgensen, B.B. Metagenomes from deep 539

Baltic Sea sediments reveal how past and present environmental conditions determine 540

microbial community composition. Mar Genomics 37, 58-68 (2018). 541

4. Dombrowski, N., Seitz, K.W., Teske, A.P. & Baker, B.J. Genomic insights into potential 542

interdependencies in microbial hydrocarbon and nutrient cycling in hydrothermal 543

sediments. Microbiome 5, 106 (2017). 544

5. Lloyd, K.G. et al. Predominant archaea in marine sediments degrade detrital proteins. 545

Nature 496, 215-218 (2013). 546

6. Orsi, W.D. Ecology and evolution of seafloor and subseafloor microbial communities. 547

Nat Rev Microbiol (2018). 548

7. Wang, Y. et al. Draft genome of an Aerophobetes bacterium reveals a facultative lifestyle 549

in deep-sea anaerobic sediments. Sci Bull 61, 1176-1186 (2016). 550

8. Orcutt, B.N., Sylvan, J.B., Knab, N.J. & Edwards, K.J. Microbial ecology of the dark 551

ocean above, at, and below the seafloor. Microbiol Mol Biol Rev 75, 361-422 (2011). 552

9. Inagaki, F. et al. Exploring deep microbial life in coal-bearing sediment down to ~2.5 km 553

below the ocean floor. Science 349, 420-424 (2015). 554

10. Jørgensen, B.B. & Marshall, I.P. Slow microbial life in the seabed. Ann Rev Mar Sci 8, 555

311-332 (2016). 556

11. Jørgensen, B.B. & Boetius, A. Feast and famine--microbial life in the deep-sea bed. Nat 557

Rev Microbiol 5, 770-781 (2007). 558

12. Arndt, S. et al. Quantifying the degradation of organic matter in marine sediments: A 559

review and synthesis. Earth-Sci Rev 123, 53-86 (2013). 560

13. Orsi, W.D., Richards, T.A. & Francis, W.R. Predicted microbial secretomes and their 561

target substrates in marine sediment. Nat Microbiol 3, 32-37 (2018). 562

14. Brooks, J.M. et al. Deep-sea hydrocarbon seep communities: evidence for energy and 563

nutritional carbon sources. Science 238, 1138-1142 (1987). 564

15. Ijiri, A. et al. Deep-biosphere methane production stimulated by geofluids in the Nankai 565

accretionary complex. Sci Adv 4, eaao4631 (2018). 566

16. Ruff, S.E. et al. Global dispersion and local diversification of the methane seep 567

microbiome. Proc Natl Acad Sci U S A 112, 4015-4020 (2015). 568

17. Dong, X. et al. Fermentative Spirochaetes mediate necromass recycling in anoxic 569

hydrocarbon-contaminated habitats. ISME J 12, 2039-2050 (2018). 570

18. Lin, Y.-S. et al. Towards constraining H2 concentration in subseafloor sediment: A 571

proposal for combined analysis by two distinct approaches. Geochim Cosmochim Acta 77, 572

186-201 (2012). 573

19. Sauvage, J., Flinders, A., Spivack, A. & D'Hondt, S. Global distribution of radiolytic H2 574

production in marine sediment and implications for subsurface life. AGU Fall Meeting 575

Abstracts (2017). 576

20. Chakraborty, A. et al. Thermophilic endospores associated with migrated thermogenic 577

hydrocarbons in deep Gulf of Mexico marine sediments. ISME J 12, 1895-1906 (2018). 578

21. Whiticar, M.J. Carbon and hydrogen isotope systematics of bacterial formation and 579

oxidation of methane. Chem Geol 161, 291-314 (1999). 580

22. Killops, S.D. & Aljuboori, M.A.H.A. Characterization of the Unresolved Complex 581

Mixture (UCM) in the gas chromatograms of biodegraded petroleums. Org Geochem 15, 582

147-160 (1990). 583

23. Gieg, L.M. & Toth, C.R.A. in Anaerobic Utilization of Hydrocarbons, Oils, and Lipids. 584

(ed. M. Boll) 1-30 (Springer International Publishing, Cham; 2017). 585

24. Parks, D.H., Imelfort, M., Skennerton, C.T., Hugenholtz, P. & Tyson, G.W. CheckM: 586

assessing the quality of microbial genomes recovered from isolates, single cells, and 587

metagenomes. Genome Res 25, 1043-1055 (2015). 588

25. Ley, R.E. et al. Unexpected diversity and complexity of the Guerrero Negro hypersaline 589

microbial mat. Appl Environ Microbiol 72, 3685-3695 (2006). 590

26. Baker, B.J., Lazar, C.S., Teske, A.P. & Dick, G.J. Genomic resolution of linkages in 591

carbon, nitrogen, and sulfur cycling among widespread estuary sediment bacteria. 592

Microbiome 3, 14 (2015). 593

27. Hu, P. et al. Genome-resolved metagenomic analysis reveals roles for candidate phyla 594

and other microbial community members in biogeochemical transformations in oil 595

reservoirs. MBio 7, e01669-01615 (2016). 596

28. Sieber, C.M.K. et al. Recovery of genomes from metagenomes via a dereplication, 597

aggregation and scoring strategy. Nat Microbiol 3, 836-843 (2018). 598

29. Parks, D.H. et al. A standardized bacterial taxonomy based on genome phylogeny 599

substantially revises the tree of life. Nat Biotechnol (2018). 600

30. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data 601

processing and web-based tools. Nucleic Acids Res 41, D590-596 (2013). 602

31. Bradley, J.A., Amend, J.P. & LaRowe, D.E. Necromass as a limited source of energy for 603

microorganisms in marine sediments. J Geophys Res-Biogeo 123, 577-590 (2018). 604

32. Lipp, J.S., Morono, Y., Inagaki, F. & Hinrichs, K.U. Significant contribution of Archaea 605

to extant biomass in marine subsurface sediments. Nature 454, 991-994 (2008). 606

33. Boll, M. & Heider, J. in Handbook of Hydrocarbon and Lipid Microbiology. (ed. K.N. 607

Timmis) 1011-1024 (Springer Berlin Heidelberg, Berlin, Heidelberg; 2010). 608

34. Tan, B. et al. Comparative analysis of metagenomes from three methanogenic 609

hydrocarbon-degrading enrichment cultures with 41 environmental samples. ISME J 9, 610

2028-2045 (2015). 611

35. Meckenstock, R.U. et al. Anaerobic degradation of benzene and polycyclic aromatic 612

hydrocarbons. J Mol Microbiol Biotechnol 26, 92-118 (2016). 613

36. Laso-Perez, R. et al. Thermophilic archaea activate butane via alkyl-coenzyme M 614

formation. Nature 539, 396-401 (2016). 615

37. Rabus, R. et al. Anaerobic Microbial Degradation of Hydrocarbons: From Enzymatic 616

Reactions to the Environment. J Mol Microbiol Biotechnol 26, 5-28 (2016). 617

38. Schouw, A. et al. Abyssivirga alkaniphila gen. nov., sp. nov., an alkane-degrading, 618

anaerobic bacterium from a deep-sea hydrothermal vent system, and emended 619

descriptions of Natranaerovirga pectinivora and Natranaerovirga hydrolytica. Int J Syst 620

Evol Microbiol 66, 1724-1734 (2016). 621

39. Khelifi, N. et al. Anaerobic oxidation of long-chain n-alkanes by the hyperthermophilic 622

sulfate-reducing archaeon, Archaeoglobus fulgidus. ISME J 8, 2153-2166 (2014). 623

40. Porter, A.W. & Young, L.Y. in Advances in Applied Microbiology, Vol. 88. (eds. S. 624

Sariaslani & G.M. Gadd) 167-203 (Academic Press, 2014). 625

41. Dong, X. et al. Reconstructing metabolic pathways of a member of the genus 626

Pelotomaculum suggesting its potential to oxidize benzene to carbon dioxide with direct 627

reduction of sulfate. FEMS Microbiol Ecol 93, fiw254-fiw254 (2017). 628

42. Holmes, D.E., Risso, C., Smith, J.A. & Lovley, D.R. Genome-scale analysis of anaerobic 629

benzoate and phenol metabolism in the hyperthermophilic archaeon Ferroglobus 630

placidus. ISME J 6, 146-157 (2012). 631

43. Beulig, F., Røy, H., Glombitza, C. & Jørgensen, B.B. Control on rate and pathway of 632

anaerobic organic carbon degradation in the seabed. Proc Natl Acad Sci U S A 115, 367-633

372 (2018). 634

44. Sewell, H.L., Kaster, A.K. & Spormann, A.M. Homoacetogenesis in Deep-Sea 635

Chloroflexi, as inferred by single-cell genomics, provides a link to reductive 636

dehalogenation in terrestrial Dehalococcoidetes. MBio 8 (2017). 637

45. He, Y. et al. Genomic and enzymatic evidence for acetogenesis among multiple lineages 638

of the archaeal phylum Bathyarchaeota widespread in marine sediments. Nat Microbiol 1, 639

16035 (2016). 640

46. Adam, P.S., Borrel, G. & Gribaldo, S. Evolutionary history of carbon monoxide 641

dehydrogenase/acetyl-CoA synthase, one of the oldest enzymatic complexes. Proc Natl 642

Acad Sci U S A 115, E1166-E1173 (2018). 643

47. Lever, M.A. Acetogenesis in the energy-starved deep biosphere - a paradox? Front 644

Microbiol 2, 284 (2011). 645

48. Yu, T. et al. Growth of sedimentary Bathyarchaeota on lignin as an energy source. Proc 646

Natl Acad Sci U S A 115, 6022-6027 (2018). 647

49. Berney, M., Greening, C., Conrad, R., Jacobs, W.R., Jr. & Cook, G.M. An obligately 648

aerobic soil bacterium activates fermentative hydrogen production to survive reductive 649

stress during hypoxia. Proc Natl Acad Sci U S A 111, 11479-11484 (2014).

650

50. Ma, K., Hao, Z. & Adams, M.W.W. Hydrogen production from pyruvate by enzymes 651

purified from the hyperthermophilic archaeon, Pyrococcus furiosus: A key role for 652

NADPH. FEMS Microbiol Lett 122, 245-250 (1994). 653

51. Greening, C. et al. Genomic and metagenomic surveys of hydrogenase distribution 654

indicate H2 is a widely utilised energy source for microbial growth and survival. ISME J 655

10, 761 (2015). 656

52. Wagner, T., Koch, J., Ermler, U. & Shima, S. Methanogenic heterodisulfide reductase 657

(HdrABC-MvhAGD) uses two noncubane [4Fe-4S] clusters for reduction. Science 357, 658

699-703 (2017). 659

53. Adhikari, R.R. et al. Hydrogen utilization potential in subsurface sediments. Front 660

Microbiol 7, 8 (2016). 661

54. Kawai, M. et al. High frequency of phylogenetically diverse reductive dehalogenase-662

homologous genes in deep subseafloor sedimentary metagenomes. Front Microbiol 5, 80 663

(2014). 664

55. Dolfing, J., Larter, S.R. & Head, I.M. Thermodynamic constraints on methanogenic 665

crude oil biodegradation. ISME J 2, 442-452 (2008). 666

56. Canfield, D.E., Thamdrup, B. & Hansen, J.W. The anaerobic degradation of organic 667

matter in Danish coastal sediments: iron reduction, manganese reduction, and sulfate 668

reduction. Geochim Cosmochim Acta 57, 3867-3883 (1993). 669

57. Liu, Y.F. et al. Metabolic capability and in situ activity of microorganisms in an oil 670

reservoir. Microbiome 6, 5 (2018). 671

58. Colman, D.R., Poudel, S., Stamps, B.W., Boyd, E.S. & Spear, J.R. The deep, hot 672

biosphere: Twenty-five years of retrospection. Proc Natl Acad Sci U S A 114, 6895-6903 673

(2017). 674

59. Bernard, B.B. et al. Surface geochemical exploration and heat flow surveys in fifteen (15) 675

frontier Indonesian basins. Thirty-Second Annual Convention & Exhibition May 2008 676

(2008). 677

60. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a 678

reference resource for gene and protein annotation. Nucleic Acids Res 44, D457-462 679

(2016). 680

61. Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for 681

classical and next-generation sequencing-based diversity studies. Nucleic Acids Research 682

41, e1-e1 (2013). 683

62. Kleiner, M. et al. Assessing species biomass contributions in microbial communities via 684

metaproteomics. Nat Commun 8, 1558 (2017). 685

63. Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P.A. metaSPAdes: a new versatile 686

metagenomic assembler. Genome Res 27, 824-834 (2017). 687

64. Kang, D.D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately 688

reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 689

(2015). 690

65. Parks, D.H. et al. Recovery of nearly 8,000 metagenome-assembled genomes 691

substantially expands the tree of life. Nat Microbiol 2, 1533-1542 (2017). 692

66. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site 693

identification. BMC Bioinformatics 11, 119 (2010). 694

67. Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P.M. & Henrissat, B. The 695

carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res 42, D490-495 696

(2014). 697

68. Yin, Y. et al. dbCAN: a web resource for automated carbohydrate-active enzyme 698

annotation. Nucleic Acids Res 40, W445-451 (2012). 699

69. Rawlings, N.D. et al. The MEROPS database of proteolytic enzymes, their substrates and 700

inhibitors in 2017 and a comparison with peptidases in the PANTHER database. Nucleic 701

Acids Res 46, D624-D632 (2018). 702

70. Søndergaard, D., Pedersen, C.N. & Greening, C. HydDB: A web tool for hydrogenase 703

classification and analysis. Sci Rep 6, 34212 (2016). 704

71. Boyd, J.A., Woodcroft, B.J. & Tyson, G.W. GraftM: a tool for scalable, phylogenetically 705

informed classification of genes within metagenomes. Nucleic Acids Res 46, e59 (2018). 706

72. Buchfink, B., Xie, C. & Huson, D.H. Fast and sensitive protein alignment using 707

DIAMOND. Nat Methods 12, 59 (2014). 708

73. Anantharaman, K. et al. Expanded diversity of microbial groups that shape the 709

dissimilatory sulfur cycle. ISME J 12, 1715-1728 (2018). 710

74. Tully, B.J., Sachdeva, R., Graham, E.D. & Heidelberg, J.F. 290 metagenome-assembled 711

genomes from the Mediterranean Sea: a resource for marine microbiology. PeerJ 5, 712

e3558 (2017). 713

75. Kato, S. et al. Genome-enabled metabolic reconstruction of dominant chemosynthetic 714

colonizers in deep-sea massive sulfide deposits. Environ Microbiol 20, 862-877 (2018). 715

76. Price, M.N., Dehal, P.S. & Arkin, A.P. FastTree 2--approximately maximum-likelihood 716

trees for large alignments. PLoS One 5, e9490 (2010). 717

77. Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected 718

biogeochemical processes in an aquifer system. Nat Commun 7, 13219 (2016). 719

78. Zaremba-Niedzwiedzka, K. et al. Asgard archaea illuminate the origin of eukaryotic 720

cellular complexity. Nature 541, 353-358 (2017). 721

79. Probst, A.J. et al. Differential depth distribution of microbial function and putative 722

symbionts through sediment-hosted aquifers in the deep terrestrial subsurface. Nat 723

Microbiol 3, 328-336 (2018). 724

80. Hug, L.A. et al. A new view of the tree of life. Nat Microbiol 1, 16048 (2016). 725

81. Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of 726

large phylogenies. Bioinformatics 30, 1312-1313 (2014). 727

82. Miller, M.A., Pfeiffer, W. & Schwartz, T. Creating the CIPRES Science Gateway for 728

inference of large phylogenetic trees. 2010 Gateway Computing Environments Workshop 729

(GCE), 1-8 (2010). 730

83. Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and 731

annotation of phylogenetic and other trees. Nucleic Acids Res 44, W242-245 (2016). 732

84. Edgar, R.C. MUSCLE: multiple sequence alignment with high accuracy and high 733

throughput. Nucleic Acids Res 32, 1792-1797 (2004). 734

85. Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular Evolutionary Genetics 735

Analysis Version 7.0 for Bigger Datasets. Mol Biol Evol 33, 1870-1874 (2016). 736

86. Madigan, M.T., Martinko, J.M. & Parker, J. Brock biology of microorganisms, Vol. 13. 737

(Pearson, 2017). 738

87. Xu, W.Y., Lowell, R.P. & Peltzer, E.T. Effect of seafloor temperature and pressure 739

variations on methane flux from a gas hydrate layer: Comparison between current and 740

late Paleocene climate conditions. J Geophys Res-Sol Ea 106, 26413-26423 (2001). 741

88. Dolfing, J. in Hydrocarbon and Lipid Microbiology Protocols. (eds. T.J. McGenity, K.N. 742

Timmis & B. Nogales Fernández) 155-163 (Springer Berlin Heidelberg, Berlin, 743

Heidelberg; 2015). 744

Tables and Figures 745

Table 1 Geochemical description of sediment samples from Sites E26, E29 and E44. TSF 746

Max: total scanning fluorescence maximum intensity. UCM: uncharacterized complex mixture. 747

n-Alk: sum of C15-C34 n-alkanes. Alk Gas: total alkane gases. C2+ Alk: sum of alkane gases 748

larger than methane. T/D: thermogenic/diagenetic n-alkane ratio. BDL: below detection limit. 749

NA: not analyzed. 750

Core ID Site

E26 Site

E29 Site

E44

Latitude (N) 26.59 27.43 26.28

Longitude (W) 87.51 86.01 86.81

Water depth (km) 2.8 3.2 3.0

Sulfate (mM) 20.01 33.73 31.72

Benzoate (nM) 93.6 22.6 161.7

Succinate (nM) 11.7 5.0 16.6

Acetate (µM) BDL BDL BDL

Chloride (g L-1) 21.04 20.15 21.05

Total Scanning Fluorescence MAX 57326.7 26738.3 13502.3

Unresolved Complex Mixture ( g g

1) 32 13 7.3

n-Alkanes (ng g-1) 2845.3 2527 1045

Thermogenic/Diagenetic Ratio 1.0 2.6 0.8

Alkane Gas (ppm) 9 36012 9.9

C2+ Alkanes (ppm) 0.3 17.5 0.5

C1/(C2+C3) NA 3974.2 NA

13CH4 (‰, vs. PDB) NA -85.1 NA

H2 (ppm) BDL BDL BDL

Figure 1 Relative frequency of metagenomic sequence reads for different marker genes at 751

Sites E26, E29 and E44. (a) Community composition based on reconstruction of full-length 16S 752

rRNA genes from the metagenomes. Eukaryotes and unassigned reads are not shown. (b) 753

Relative occurrences of hydrogenases with different metal cofactors. (c) Relative occurrences of 754

different subtypes of NiFe hydrogenases. (d) Relative occurrences of mcrA genes indicative of 755

different types of methanogenesis. 756

Figure 2 Phylogenetic placement of 82 reconstructed metagenome-assembled genomes. A 757

maximum-likelihood phylogenomic tree was built based on concatenated amino acid sequences 758

of 43 conserved single copy genes using RAxML with the PROTGAMMALG model. Sequences 759

of Altiarchaeales ex4484_43 were used as an outgroup. The scale bar represents 1 amino acid 760

substitution per sequence position. Bootstrap values > 70% are indicated. Blue for Site E26 761

(E26_binX), red for Site E29 (E29_binY), and green for Site E44 (E44_binZ). 762

Figure 3 Identification of functional genes or pathways present in MAGs. The presence of 763

genes or pathways are indicated by orange shaded boxes. Gene names: Aor, aldehyde:ferredoxin 764

oxidoreductase; Kor, 2-oxoglutarate/2-oxoacid ferredoxin oxidoreductase; Por, 765

pyruvate:ferredoxin oxidoreductase; Ior, indolepyruvate ferredoxin oxidoreductase; GHs, 766

glycoside hydrolases; AssA, catalytic subunit of alkylsuccinate synthase. CmdA, catalytic 767

subunit of p-cymene dehydrogenase; AhyA, catalytic subunit of alkane C2-methylene 768

hydroxylase; H2ase, hydrogenase; DsrAB, dissimilatory sulfite reductase. Pathways were 769

indicated as being present if at least five genes in the Embden-Meyerhof-Parnas pathway, three 770

genes in the beta-oxidation pathway, four genes in the Wood-Ljungdahl pathway, and six genes 771

in the TCA cycle were detected. Additional details for the central benzoyl-CoA degradation 772

pathway can be found in Figure 4. Lactate and ethanol fermentation are indicated if genes 773

encoding respective dehydrogenases were detected. More details about these functional genes 774

and pathways can be found in the text and in Table S6. 775

Figure 4 Evidence for anaerobic hydrocarbon degradation in MAGs. (a) Phylogenetic 776

relationship of identified genes in MAGs with currently known alkyl-arylalkylsuccinate 777

synthases based on the respective catalytic alpha-subunits. Gene names: Ass/Mas, n-alkanes (1-778

methylalkyl) succinate synthase; Nms, 2-naphthylmethyl succinate synthase; Bss, benzyl 779

succinate synthase; Ibs, 4-isopropylbenzyl succinate synthase; Hbs, 4-hydroxybenzyl succinate 780

synthase. Sequences of pyruvate formate lyase (Pfl) from E. coli were used as an outgroup. The 781

scale bar represents 0.1 amino acid substitutions per sequence position. Bootstrap values > 70% 782

are indicated. The full sequences can be found in Text S1. (b) Summary of identified enzymes 783

involved in central benzoyl-CoA processing in anaerobic aromatic hydrocarbon biodegradation. 784

The MAGs were shown only if it was at least partially complete (presence of at least three 785

subunits within one cluster for BcrABCD). Presence of genes or pathways are indicated by green 786

boxes. Gene names: Bcr, benzoyl-CoA reductase; Oah, 6-oxo-cyclohex-1-ene-carbonyl-CoA 787

hydrolase; Dch, cyclohex-1,5-diencarbonyl-CoA hydratase; Had, 6-hydroxycyclohex-1-ene-1-788

carbonyl-CoA dehydrogenases. 789

Figure 5 Thermodynamic constraints on anaerobic benzoate and hexadecane degradation 790

in deep sea sediments. Two possible scenarios are illustrated depending on the end products 791

based on metabolic predictions in Figure 3: (1) fermentation with production of hydrogen and 792

acetate, and (2) fermentation with production of acetate alone. Thermodynamics for each 793

reaction are indicated by a line in its corresponding color. If G < 0, the reaction is energetically 794

favorable (yellow-shaded area), and if G > 0 the reaction is assumed not to occur. The graph 795

shows that G for hexadecane fermentation to acetate alone (green reaction) will not reach 796

negative values unless the concentration for acetate is extremely low (far lower than the 797

detection limit for acetate of 2.5 µM in this study, see dash line) such that the other three 798

reactions are more realistic scenarios for anaerobic hydrocarbon degradation in the marine 799

sediments studied here. 800

Figure 6 Common potential organotrophic and hydrogenotrophic pathways in three 801

hydrocarbon-impacted microbial communities as inferred from metagenomics and 802

metabolomics. 803

Figure 1 804

805

Figure 2 806

807

Figure 3 808

809

Figure 4 810

811

Figure 5 812

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Figure 6 814

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Growth of sedimentary Bathyarchaeota on lignin as an energy source

Article

Full-text available

May 2018

Significance Marine sediment holds the largest organic carbon pool on earth, where microbial transformation of carbon is considered a key process of carbon cycling. Bathyarchaeota are among the most abundant and active groups of microorganisms in marine sediment. It has been suggested that Bathyarchaeota may play a globally important role in the carbon cycling in the marine environment through fermentation of complex organic substances, acetogenesis, and methanogenesis based on metagenome analysis. Here we provide several lines of converging evidence suggesting the bathyarchaeotal group Bathy-8 is able to grow with lignin as an energy source and bicarbonate as a carbon source. Consequently, members of the Bathyarchaeota are probably important, previously unrecognized degraders of lignin.

GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes

Article

Full-text available

Mar 2018
NUCLEIC ACIDS RES

Large-scale metagenomic datasets enable the recovery of hundreds of population genomes from environmental samples. However, these genomes do not typically represent the full diversity of complex microbial communities. Gene-centric approaches can be used to gain a comprehensive view of diversity by examining each read independently, but traditional pairwise comparison approaches typically over-classify taxonomy and scale poorly with increasing metagenome and database sizes. Here we introduce GraftM, a tool that uses gene specific packages to rapidly identify gene families in metagenomic data using hidden Markov models (HMMs) or DIAMOND databases, and classifies these sequences using placement into pre-constructed gene trees. The speed and accuracy of GraftM was benchmarked with in silico and in vitro mock communities using taxonomic markers, and was found to have higher accuracy at the family level with a processing time 2.0-3.7× faster than currently available software. Exploration of a wetland metagenome using 16S rRNA- and methyl-coenzyme M reductase (McrA)-specific gpkgs revealed taxonomic and functional shifts across a depth gradient. Analysis of the NCBI nr database using the McrA gpkg allowed the detection of novel sequences belonging to phylum-level lineages. A growing collection of gpkgs is available online (https://github.com/geronimp/graftM_gpkgs), where curated packages can be uploaded and exchanged.

Surface geochemical exploration and heat flow surveys in fifteen (15) frontier Indonesian Basins

Conference Paper

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May 2008

Bernie Boyd Bernard

Expanded diversity of microbial groups that shape the dissimilatory sulfur cycle

Article

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Feb 2018

A critical step in the biogeochemical cycle of sulfur on Earth is microbial sulfate reduction, yet organisms from relatively few lineages have been implicated in this process. Previous studies using functional marker genes have detected abundant, novel dissimilatory sulfite reductases (DsrAB) that could confer the capacity for microbial sulfite/sulfate reduction but were not affiliated with known organisms. Thus, the identity of a significant fraction of sulfate/sulfite-reducing microbes has remained elusive. Here we report the discovery of the capacity for sulfate/sulfite reduction in the genomes of organisms from 13 bacterial and archaeal phyla, thereby more than doubling the number of microbial phyla associated with this process. Eight of the 13 newly identified groups are candidate phyla that lack isolated representatives, a finding only possible given genomes from metagenomes. Organisms from Verrucomicrobia and two candidate phyla, Candidatus Rokubacteria and Candidatus Hydrothermarchaeota, contain some of the earliest evolved dsrAB genes. The capacity for sulfite reduction has been laterally transferred in multiple events within some phyla, and a key gene potentially capable of modulating sulfur metabolism in associated cells has been acquired by putatively symbiotic bacteria. We conclude that current functional predictions based on phylogeny significantly underestimate the extent of sulfate/sulfite reduction across Earth's ecosystems. Understanding the prevalence of this capacity is integral to interpreting the carbon cycle because sulfate reduction is often coupled to turnover of buried organic carbon. Our findings expand the diversity of microbial groups associated with sulfur transformations in the environment and motivate revision of biogeochemical process models based on microbial community composition.

Differential depth distribution of microbial function and putative symbionts through sediment-hosted aquifers in the deep terrestrial subsurface

Article

Full-text available

Mar 2018

An enormous diversity of previously unknown bacteria and archaea has been discovered recently, yet their functional capacities and distributions in the terrestrial subsurface remain uncertain. Here, we continually sampled a CO2-driven geyser (Colorado Plateau, Utah, USA) over its 5-day eruption cycle to test the hypothesis that stratified, sandstone-hosted aquifers sampled over three phases of the eruption cycle have microbial communities that differ both in membership and function. Genome-resolved metagenomics, single-cell genomics and geochemical analyses confirmed this hypothesis and linked microorganisms to groundwater compositions from different depths. Autotrophic Candidatus "Altiarchaeum sp." and phylogenetically deep-branching nanoarchaea dominate the deepest groundwater. A nanoarchaeon with limited metabolic capacity is inferred to be a potential symbiont of the Ca. "Altiarchaeum". Candidate Phyla Radiation bacteria are also present in the deepest groundwater and they are relatively abundant in water from intermediate depths. During the recovery phase of the geyser, microaerophilic Fe- and S-oxidizers have high in situ genome replication rates. Autotrophic Sulfurimonas sustained by aerobic sulfide oxidation and with the capacity for N2 fixation dominate the shallow aquifer. Overall, 104 different phylum-level lineages are present in water from these subsurface environments, with uncultivated archaea and bacteria partitioned to the deeper subsurface.

Metabolic capability and in situ activity of microorganisms in an oil reservoir

Article

Full-text available

Jan 2018

Background Microorganisms have long been associated with oxic and anoxic degradation of hydrocarbons in oil reservoirs and oil production facilities. While we can readily determine the abundance of microorganisms in the reservoir and study their activity in the laboratory, it has been challenging to resolve what microbes are actively participating in crude oil degradation in situ and to gain insight into what metabolic pathways they deploy. ResultsHere, we describe the metabolic potential and in situ activity of microbial communities obtained from the Jiangsu Oil Reservoir (China) by an integrated metagenomics and metatranscriptomics approach. Almost complete genome sequences obtained by differential binning highlight the distinct capability of different community members to degrade hydrocarbons under oxic or anoxic condition. Transcriptomic data delineate active members of the community and give insights that Acinetobacter species completely oxidize alkanes into carbon dioxide with the involvement of oxygen, and Archaeoglobus species mainly ferment alkanes to generate acetate which could be consumed by Methanosaeta species. Furthermore, nutritional requirements based on amino acid and vitamin auxotrophies suggest a complex network of interactions and dependencies among active community members that go beyond classical syntrophic exchanges; this network defines community composition and microbial ecology in oil reservoirs undergoing secondary recovery. Conclusion Our data expand current knowledge of the metabolic potential and role in hydrocarbon metabolism of individual members of thermophilic microbial communities from an oil reservoir. The study also reveals potential metabolic exchanges based on vitamin and amino acid auxotrophies indicating the presence of complex network of interactions between microbial taxa within the community.

The MEROPS database of proteolytic enzymes, their substrates and inhibitors in 2017 and a comparison with peptidases in the PANTHER database

Article

Full-text available

Nov 2017
NUCLEIC ACIDS RES

The MEROPS database (http://www.ebi.ac.uk/merops/) is an integrated source of information about peptidases, their substrates and inhibitors. The hierarchical classification is: protein-species, family, clan, with an identifier at each level. The MEROPS website moved to the EMBL-EBI in 2017, requiring refactoring of the code-base and services provided. The interface to sequence searching has changed and the MEROPS protein sequence libraries can be searched at the EMBL-EBI with HMMER, FastA and BLASTP. Cross-references have been established between MEROPS and the PANTHER database at both the family and protein-species level, which will help to improve curation and coverage between the resources. Because of the increasing size of the MEROPS sequence collection, in future only sequences of characterized proteins, and from completely sequenced genomes of organisms of evolutionary, medical or commercial significance will be added. As an example, peptidase homologues in four proteomes from the Asgard superphylum of Archaea have been identified and compared to other archaean, bacterial and eukaryote proteomes. This has given insights into the origins and evolution of peptidase families, including an expansion in the number of proteasome components in Asgard archaeotes and as organisms increase in complexity. Novel structures for proteasome complexes in archaea are postulated.

Assessing species biomass contributions in microbial communities via metaproteomics

Article

Full-text available

Nov 2017

Microbial community structure can be analyzed by quantifying cell numbers or by quantifying biomass for individual populations. Methods for quantifying cell numbers are already available (e.g., fluorescence in situ hybridization, 16-S rRNA gene amplicon sequencing), yet high-throughput methods for assessing community structure in terms of biomass are lacking. Here we present metaproteomics-based methods for assessing microbial community structure using protein abundance as a measure for biomass contributions of individual populations. We optimize the accuracy and sensitivity of the method using artificially assembled microbial communities and show that it is less prone to some of the biases found in sequencing-based methods. We apply the method to communities from two different environments, microbial mats from two alkaline soda lakes, and saliva from multiple individuals. We show that assessment of species biomass contributions adds an important dimension to the analysis of microbial community structure.

Genome‐enabled metabolic reconstruction of dominant chemosynthetic colonizers in deep‐sea massive sulfide deposits

Article

Jan 2018
ENVIRON MICROBIOL

Deep-sea massive sulfide deposits remaining after ceasing of hydrothermal activity potentially provide energy for a chemosynthetic ecosystem in the dark, cold marine environments. Although yet-uncultivated bacteria in the phylum Nitrospirae and the class Deltaproteobacteria are known to dominate the microbial communities of sulfide deposits at and below the seafloor, their metabolic capabilities remain largely elusive. Here, we reveal the metabolic potential of these yet-uncultivated bacteria in hydrothermally inactive sulfide deposits collected at the Southern Mariana Trough by seafloor drilling. Near-complete genomes of the predominant bacterial members were recovered from shotgun metagenomic sequences. The genomic capabilities for CO2 and N2 fixation suggest that these bacteria are primary producers in the microbial ecosystem. Their genomes also encode versatile chemolithotrophic energy metabolisms, such as the oxidation of H2, sulfide and intermediate sulfur species including thiosulfate, all of which can be supplied by chemical reactions between seawater and metal sulfides. Notably, the presence of genes involved in thiosulfate oxidation in Nitrospirae and Deltaproteobacteria genomes is unusual. Our study strongly support the presence of a chemosynthetic ecosystem fuelled by the Earth's internal energy in the deep-sea massive sulfide deposits, and illustrates the unexpected metabolic capability of known bacterial taxonomic groups.

Control on rate and pathway of anaerobic organic carbon degradation in the seabed

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Dec 2017

Significance The subsurface seabed is a great anaerobic bioreactor where organic matter deposited from the ocean’s water column is slowly being degraded by microorganisms. It is uncertain how rates of organic matter degradation progress through different geochemical zones, and the deep production of methane and CO 2 in the global seabed therefore remains poorly constrained. With highly depth-resolved analyses of geochemistry and microbial activities, we demonstrate that rates of organic matter degradation decrease continuously with sediment age, irrespective of the prevailing redox zonation and associated changes in the degradation pathway. Moreover, our results show that the microbial food chain leading to methane does not proceed directly through the general key intermediate acetate, but rather through an additional, as of yet unidentified, syntrophic step.

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Supplementary Material 9