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Hydrocarbon seepage in the deep seabed links
subsurface and seafloor biospheres
Anirban Chakraborty
a,1
, S. Emil Ruff
b,c
, Xiyang Dong
a,d,e
, Emily D. Ellefson
a,b
, Carmen Li
a
, James M. Brooks
f
,
Jayme McBee
f
, Bernie B. Bernard
f
, and Casey R. J. Hubert
a,1
a
Department of Biological Sciences, University of Calgary, Calgary, AB T2N 1N4, Canada;
b
Department of Geoscience, University of Calgary, Calgary, AB T2N
1N4, Canada;
c
Ecosystems Center and J. Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, MA
02543;
d
School of Marine Sciences, Sun Yat-Sen University, 519082 Zhuhai, People’s Republic of China;
e
Southern Marine Science and Engineering
Guangdong Laboratory (Zhuhai), 519000 Zhuhai, People’s Republic of China; and
f
TDI-Brooks International, College Station, TX 77845
Edited by David M. Karl, University of Hawaii at Manoa, Honolulu, HI, and approved March 26, 2020 (received for review February 6, 2020)
Marine cold seeps transmit fluids between the subseafloor and
seafloor biospheres through upward migration of hydrocarbons
that originate in deep sediment layers. It remains unclear how
geofluids influence the composition of the seabed microbiome and
if they transport deep subsurface life up to the surface. Here we
analyzed 172 marine surficial sediments from the deep-water East-
ern Gulf of Mexico to assess whether hydrocarbon fluid migration
is a mechanism for upward microbial dispersal. While 132 of these
sediments contained migrated liquid hydrocarbons, evidence of
continuous advective transport of thermogenic alkane gases was
observed in 11 sediments. Gas seeps harbored distinct microbial
communities featuring bacteria and archaea that are well-known
inhabitants of deep biosphere sediments. Specifically, 25 distinct
sequence variants within the uncultivated bacterial phyla Atribac-
teria and Aminicenantes and the archaeal order Thermoprofundales
occurred in significantly greater relative sequence abundance along
with well-known seep-colonizing members of the bacterial genus Sul-
furovum, in the gas-positive sediments. Metabolic predictions guided
by metagenome-assembled genomes suggested these organisms are
anaerobic heterotrophs capable of nonrespiratory breakdown of or-
ganic matter, likely enabling them to inhabit energy-limited deep sub-
seafloor ecosystems. These results point to petroleum geofluids as a
vector for the advection-assisted upward dispersal of deep biosphere
microbes from subsurface to surface environments, shaping the micro-
biome of cold seep sediments and providing a general mechanism for
the maintenance of microbial diversity in the deep sea.
deep biosphere
|
microbiome
|
dispersal
Deep-sea sediments found in greater than 1,000-m water
depth cover half of the Earth’s surface. Unlike at shallow
continental margins, abyssal sedimentary ecosystems are gener-
ally oligotrophic due to negligible local primary productivity,
extremely slow rates of sediment deposition, and limited influx of
organic matter (1). Despite these conditions, sediments in and
beneath the deep ocean floor harbor a diverse microbiome, with
microbial life detected in the deepest points in the ocean (2) as
well as down to kilometers below the seafloor (3, 4). Such sub-
seafloor habitats experience varying thermochemical gradients
across geological horizons (5) and remain connected to the
overlying oceans through unique geological features such as
marine cold seeps (6, 7), submarine mud volcanoes (8), and
midocean ridge-associated vents or seamounts (9). Organic-rich
geofluids from the warm subsurface emanate up and out of the
cold seafloor at these sites.
Because of their ability to constantly deliver organic substrates
and inorganic energy sources, marine cold seeps, mud volcanoes,
and hydrothermal vents create geochemical and biological oases
in the seabed (7, 9). Cold seeps are particularly diverse in terms
of the chemical composition of the seeping hydrocarbons, with
some expelling a mixture of thermogenic liquid and gaseous
hydrocarbons sourced from deep petroleum reservoirs and oth-
ers emitting biogenic methane produced in shallower sediment
layers (10). Emigrating geofluids support locally selected and
largely distinct microbial communities relative to the surround-
ing seabed microbiome, resulting in biodiversity hotspots (11,
12). Recent investigations focusing on the distribution of ther-
mophilic endospores in hydrocarbon-associated sediments (13)
and on microbial dispersal via mud volcanoes (14, 15) suggest
that upward migration of subsurface microbes into the seabed
occurs via ebullition. Hydrocarbons and other gases in mud
volcanic discharges allow for an overpressurized buoyancy-driven
transport of sediment being expelled to the surface through an
overlying crater (8). Cold seeps, which are much more wide-
spread in the global ocean than mud volcanoes (16), experience
similar buoyancy-driven geofluid flow through saturated porous
sediments but do not physically transport the same large volumes
of subsurface material that mud volcanoes do (17). It is therefore
less clear whether cold seeps can similarly achieve the upward
transportation of microbial cells, and to which extent gas-rich
fluids are a dominant vector for deep-to-shallow microbe dis-
persal in the marine environment.
The Gulf of Mexico (GoM) basin is well known for widespread
natural seepage of petroleum-derived thermogenic hydrocarbons
Significance
The marine subsurface is one of the largest habitats on Earth
composed exclusively of microorganisms and harboring on the
order of 10
29
microbial cells. It is unclear if deep subsurface life
impacts overlying seafloor diversity and biogeochemical cy-
cling in the deep ocean. We analyzed the microbial communi-
ties of 172 seafloor surface sediment samples, including gas
and oil seeps as well as sediments not subject to upward fluid
flow. A strong correlation between typical subsurface clades
and active geofluid seepage suggests that subsurface life is
injected into the deep ocean floor at hydrocarbon seeps, a
globally widespread hydrogeological phenomenon. This sup-
ply of subsurface-derived microbial populations, biomass, and
metabolic potential thus increases biodiversity and impacts
carbon cycling in the deep ocean.
Author contributions: A.C. and C.R.J.H. designed research; A.C., S.E.R., X.D., E.D.E., C.L.,
J.M.B., and B.B.B. performed research; A.C., S.E.R., X.D., J.M.B., J.M., and B.B.B. analyzed
data; and A.C., S.E.R., X.D., and C.R.J.H. wrote the paper.
The authors declare no competing interest.
This article is a PNAS Direct Submission.
This open access article is distributed under Creative Commons Attribution-NonCommercial-
NoDeriv atives Lic ense 4.0 (CC B Y-NC-ND) .
Data deposition: DNA sequences (amplicon and metagenome raw sequences) have been
deposited in NCBI’s Sequence Read Archive (SRA) under BioProject accession numbers
PRJNA511010 and PRJNA485648.
1
To whom correspondence may be addressed. Email: anirban.chakraborty@ucalgary.ca or
chubert@ucalgary.ca.
This article contains supporting information online at https://www.pnas.org/lookup/suppl/
doi:10.1073/pnas.2002289117/-/DCSupplemental.
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sourced from deeply buried oil and gas reservoirs. The compo-
sition and activity of cold-adapted microbial communities at
GoM seeps have been studied in detail in relation to the impact
of hydrocarbons on biodiversity (18) and hydrocarbon bio-
degradation (19). Efforts to distinguish between different seep
habitats in the GoM basin have largely focused on geochemical
assessments of migrating fluids and gases, with an emphasis on
readily accessible northern locations found in shallow water (6).
Cold seeps situated in the deep water Eastern GoM (EGoM)
have received much less attention regarding hydrocarbon geo-
chemistry and microbial diversity. Using sediments from 172
locations in the EGoM we investigated whether sediments ex-
periencing advective migration of gas- or oil-rich fluids harbor
distinct microbiomes, and whether subsurface-derived taxa can
be identified and correlated to migrated gas or oil. Detailed gas
and oil geochemistry were combined with rRNA gene-based
biodiversity surveillance and shotgun metagenomic sequencing,
enabling correlations between specific microbial taxa and mul-
tiple hydrocarbon parameters to be determined and interpreted
based on metabolic predictions from metagenome-assembled
genomes. This revealed evidence of deep-to-shallow microbial
dispersal significantly associated with seepage of gaseous hy-
drocarbons in these deep-sea sediments.
Results
Migrated Hydrocarbons in EGoM Sediments. Piston cores from 172
locations covered a wide range of water depths (132 to 3,395 m)
including 134 locations deeper than 2,000 m (Fig. 1 and SI Ap-
pendix, Table S1). Light hydrocarbon gases (C
1
–C
5
) and mi-
grated oil (C
6+
liquid hydrocarbons) were investigated at all
locations and are presented as the mean of individual mea-
surements obtained from three depth intervals within the sedi-
ment cores (Fig. 2). The major parameters used to monitor the
light hydrocarbon gases were the concentrations of total and C
2+
alkane gases (Fig. 2A). Total alkane gases showed a wide dis-
tribution of 1 to 135,448 parts per million by volume (ppmv)
across all locations. Only a small fraction of sediment cores (n=
11) contained >10,000 ppmv of light hydrocarbon gases and were
considered “gas-positive”in this study. Distribution of C
2+
al-
kane gases was strongly correlated with the total alkane gases
(adjusted R
2
=0.651, F
1,170
=320.6, P<2.2e
−16
) despite higher
alkane gases representing relatively minor fractions (0.01 to
2.01%) of the total gases. Comparing the ratio of methane to
combined ethane and propane concentrations [C
1
/(C
2
+C
3
)] and
the carbon isotopic compositions of methane (δ
13
C
1
) in EGoM
gas-positive samples (SI Appendix, Fig. S1A) further showed that
the C
1
/(C
2
+C
3
) ratio for most of the EGoM samples remained
in the >1,000 range while contributions of biogenic methane
were evident from δ
13
C
1
values being more negative than −60‰
(20). Historic hydrocarbon data obtained from elsewhere in
24°N
!
(
26°N
28°N
30°N
92°W 88°W 84°W
0 100 200
Kilometers
Gas positive locations
Gas negative locations
3000 m
2000 m
1000 m
200 m
Fig. 1. Location and bathymetric profile of the EGoM sediment cores. Map
of the Eastern Gulf of Mexico showing the 172 sampling locations and ba-
thymetry of the study area. Symbol colors indicate the gas-positive and gas-
negative locations. The Inset map shows the extent of the study area relative
to the entire GoM basin. Maps were drawn using ArcGIS Desktop 10.4.
5
10
15
20
25
TSF (max. intensity units x 104)
10 20 30 40 50
UCM (μg/g dry sediment)
B
10-1
100
101
102
103
C2+ alkane gases (ppmv)
101103105
A
Gas-positive
Gas-negative
Total alkane gases (ppmv)
Fig. 2. Hydrocarbon characterization of the EGoM sediment cores. Scat-
terplots showing the mean values (n=3 extracts per core) of the geo-
chemical parameters used for assessing the gaseous (A) and liquid (B)
hydrocarbon content in sediment cores. Symbol colors represent gaseous
hydrocarbon categories as determined by total alkane gas concentrations
greater than 10,000 ppmv. The major gas and oil parameters are also sum-
marized by marginal box-and-whisker plots (minimum, lower quartile, me-
dian, upper quartile, and maximum) beside the corresponding axes. The
light blue shaded area in Bhighlights thresholds of UCM (≥10 μg/g) and TSF
maximum intensity (≥50,000) for assessing the presence of oil in the EGoM
sediment cores.
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the GoM reveal that these 11 gas-positive sediments are
similar to many other gassy sites throughout this basin (SI
Appendix,Fig.S1B).
The presence of liquid hydrocarbons in the cores was assessed
by measuring the mass of the unresolved complex mixture
(UCM) and the total scanning fluorescence (TSF) intensity in all
172 sediments. UCM and TSF are often used as major indicators
of migrated oil (13, 21). The UCM represents a suite of bio-
degraded saturated hydrocarbons that are not easily separated
using gas chromatography, resulting in a hump-shaped baseline
with numerous smaller peaks representing individual com-
pounds. TSF intensities on the other hand are generally in-
dependent of microbial alteration of hydrocarbon compounds
and provide a measure of petroleum-related aromatic hydro-
carbon concentrations. When compared against each other, the
“thermogenicity trend”of these two parameters remains linear
over several orders of magnitude (21). Mean UCM and TSF
values generally ranged from 2 to 52 μg/g dry sediment and 1,800
to 244,668 intensity units, respectively. These two parameters
were positively correlated (adjusted R
2
=0.281, F
1,170
=66.5, P<
7.4e
−14
) among all locations (Fig. 2B). Minimum thresholds for
UCM (10 μg/g dry sediment) and TSF (50,000 intensity units)
were used to eliminate hydrocarbon signatures not sourced from
the subsurface (13). By these criteria, all 11 gas-positive sedi-
ments also qualified for unambiguous occurrence of migrated oil.
The presence of oil within EGoM sediments was more wide-
spread compared to the presence of gas, with a further 121 out of
the 161 gas-negative locations being qualified for oil.
Microbial Diversity and Community Structure in EGoM Sediments.
Genomic DNA extracted from near-surface sediments repre-
senting 0 to 20 centimeters below seafloor (cmbsf) was used to
construct bacterial and archaeal 16S rRNA gene amplicon li-
braries. Analysis of variance (ANOVA) confirmed that observed
and estimated (Chao1) richness of both bacterial and archaeal
communities within gas-positive and gas-negative locations were
significantly different (p.adj <0.05; SI Appendix, Fig. S2). Re-
latedness between bacterial and archaeal diversity was further
corroborated by a Mantel test (Mantel statistic r=0.85; P<
0.001) using Bray–Curtis dissimilarity indices. The top 10 bac-
terial phyla representing 84.5% of the bacterial sequences were
Proteobacteria (34.5%), Chloroflexi (20.4%), Acidobacteria
(6.5%), Nitrospirae (4.2%), Aminicenantes (formerly OP8; 4.1%),
Actinobacteria (4%), Aerophobetes (formerly CD12; 3.4%), Fir-
micutes (2.6%), Atribacteria (formerly JS1 and OP9; 2.6%), and
Nitrospinae (2.2%) (SI Appendix, Fig. S3A). Prominent archaeal
phyla that represented 95.7% of the archaeal sequences were
Thaumarchaeota (51.1%), Crenarchaeota (14.3%), Asgardaeota
(13.7%), Nanoarchaeota (13.2%), and Euryarchaeota (3.4%) (SI
Appendix, Fig. S3B).
Permutational multivariate analysis of variance coupled with
false discovery rate correction, and analysis of similarities con-
ducted with bacterial and archaeal communities showed that the
gas-positive locations harbored significantly dissimilar bacte-
rial (PERMANOVA pseudoF=3.596, p.adj =0.0009, 999 per-
mutations; ANOSIM R=0.36, P=0.001) and archaeal
(PERMANOVA pseudoF=1.895, p.adj =0.019, 999 permuta-
tions; ANOSIM R=0.17, P=0.034) communities (Fig. 3 Aand
B). Repeating this analysis with locations grouped based on the
presence of oil in the cores resulted in community dissimilarity
not being significant, for either bacteria (PERMANOVA pseu-
doF=1.605, p.adj =0.092, 999 permutations; ANOSIM R=
0.015, P=0.634) or archaea (PERMANOVA pseudoF=1.882,
p.adj =0.072, 999 permutations; ANOSIM R=0.077, P=0.479)
(Fig. 3 Cand D). Partial distance-based redundancy analysis
using total alkane gases, total C
2+
alkane gases, UCM, and TSF
as constraining variables further indicated that total alkane gases
best explains bacterial and archaeal community dissimilarity
between gas-positive and gas-negative locations (Fig. 3 Eand F).
Correlation analyses of the gaseous and liquid hydrocarbon pa-
rameters with community composition showed that both total
and C
2+
alkane gases had maximum correlation with bacterial
and archaeal communities among all possible subsets (Spear-
man’s rank correlations of 0.223 and 0.218, respectively), sug-
gesting that advection of gas-rich fluids exerts a strong influence
on community dissimilarity in gas-positive sediments.
Association of Subsurface Lineages with Gas Seepage. Members of
the bacterial phyla Atribacteria and Aminicenantes were present
in significantly greater abundance in the 11 gas-positive locations
(Fig. 4 Aand B), as were members of the genus Sulfurovum
A
Stress = 0.18
B
Stress = 0.16
CAP2 (27.5%)
CAP1 (43.1%)
TSF
UCM
Total alkane
gases
C
2+ alkane gases
Total alkane
gases
C
2+ alkane gases
UCM TSF
CAP1 (41.8%)
CAP2 (32.4%)
EF
Stress = 0.18 Stress = 0.16
CD
Oil-negative
Oil-positive
Gas-negative
Gas-positive
Gas-negative
Gas-positive
Fig. 3. Community similarity within the EGoM sediments. Similarity of mi-
crobial communities visualized by two-dimensional (2D) nonmetric multidi-
mensional scaling (NMDS) when samples are grouped based on the presence
of gas (Aand B) or oil (Cand D). Bacterial communities are visualized in A
and Cwhile archaeal communities are shown in Band D. A distance-based
redundancy analysis (db-RDA) showing bacterial (E) and archaeal (F) com-
munity similarity when total alkane gases, total C
2+
alkane gases, UCM, and
TSF were used as constraining variables and were fitted onto the ordination
as arrows. The length of each arrow indicates the multiple partial correlation
of the variable to RDA axes and can be interpreted as an indication of that
variable’s contribution to the explained community similarity. Significance of
RDA was tested by ANOVA for both bacterial (F=1.845, P<0.001) and
archaeal (F=1.898, P<0.001) communities. In A–D, each sample (dot) is
connected to the weighted averaged mean of the within-group distances
and the ellipses represent one SD of the weighted averaged mean. The
amplicon libraries were subsampled to 5,000 (bacteria) and 3,500 (archaea)
reads in order to account for uneven sequencing depth and to ensure
comparability of sample diversity.
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within the phylum Campylobacterota (formerly class Epsilonpro-
teobacteria) (22) and members of the archaeal order Thermo-
profundales (formerly Marine Benthic Group D) (23). This was
confirmed by differential abundance analysis at various taxo-
nomic levels using a negative binomial distribution model
implemented in the R package DESeq2 (24) (SI Appendix, Table
S2). Closely related Atribacteria,Aminicenantes, and Thermo-
profundales are frequently detected in deeper subsurface sedi-
ments (1, 25–28). Consistent with this, comparison of the relative
sequence abundances of these taxa at three sediment depth re-
gimes (0 to 20, 40 to 60, and 80 to 100 cmbsf) from one gas-
positive and three gas-negative sediment cores revealed that
these three lineages were more abundant in the deeper sedi-
ments, whereas Sulfurovum was detected only in the uppermost
section (Fig. 4 Cand D). Atribacteria,Aminicenantes, and Ther-
moprofundales had comparable abundances in the deeper sec-
tions of all four cores regardless of the presence of gas. Within
the topmost section, however, these lineages were severely di-
minished in the three gas-negative cores (Fig. 4D) relative to the
gas-positive core.
Atribacteria and Sulfurovum were represented by 65 and 12
amplicon sequence variants (ASVs), respectively, whereas the
other two groups had greater diversity with 289 Aminicenantes
ASVs and 160 Thermoprofundales ASVs, the majority occurring
in low relative sequence abundance. In order to investigate the
correlation of the relative sequence abundances of these ASVs
with the hydrocarbon parameters, we first removed the ASVs
occurring in fewer than 5 out of the 172 locations. Distributions
of relative sequence abundances of each of the remaining 91
ASVs were used to conduct pairwise Pearson’s correlation
analysis with the distribution of each of the four gas and oil
parameters. A total of 25 ASVs (Atribacteria =13, Aminicenantes =
6, Sulfurovum =2, and Thermoprofundales =4) were found to be
significantly correlated (P<0.05)withbothtotalandC
2+
alkane
gases (hereafter referred to as gas-associated ASVs; Fig. 4Eand
SI Appendix,TableS3). None of these ASVs showed similar
correlation with the oil parameters UCM and TSF. Pairwise co-
occurrence analysis of the 25 gas-associated ASVs further
revealed that 12 out of the 21 bacteria co-occurred with the
three most abundant archaea (Fig. 4F). Individually, the gas-
associated ASVs represented <2% mean relative sequence
abundance in the gas-positive sediment microbial communities
(SI Appendix,Fig.S4).
Relative sequence abundance (%)
Atribacteria
Aminicenantes
Sulfurovum
Thermoprofundales
0-20
40-60
80-100
Sediment depth (cmbsf)
0510 0 5 10 15
0
5
10
15
Relative sequence abundance (%)
Gas-negative
(n=161)
B
Gas-positive
(n=11)
A
Gas-positive
(n=1)
C
Gas-negative
(n=3)
D
Pearson's r
E
0.0
0.2
0.4
0.6
ASV357
ASV5
ASV562
ASV1011
ASV287
ASV260
ASV848
ASV172
ASV497
ASV127
ASV782
ASV616
ASV182
ASV96
ASV32
ASV108
ASV167
ASV958
ASV83
ASV63
ASV360
ASV92
ASV34
ASV67
ASV264
ASV96
ASV182
ASV616
ASV92
ASV108
ASV34
ASV63
ASV67
ASV83
ASV167
ASV360 ASV782
ASV264
ASV264
ASV172
F
Fig. 4. Distribution and co-occurrence of gas-associated lineages. Relative sequence abundances of Atribacteria,Aminicenantes,Sulfurovum, and Ther-
moprofundales in near-surface sediments within the gas-positive and the gas-negative locations (Aand B), visualized by kernel density distribution in violin
plots. Embedded black box and whiskers indicate minimum, lower quartile, upper quartile, and maximum values. The thick white horizontal line and the
white dot within each box represent the median and the mean of the distribution, respectively. Sequence abundances of the above four lineages in three
sediment depths within a gas-positive (C) and three gas-negative (D) sediment cores, respectively, are shown using horizontal bar plots. Pearson’s correlation
coefficients of each of the 25 gas-associated ASVs from these four lineages with total alkane gases are shown in E. Co-occurrence of 15 gas-associated ASVs
(Atribacteria =7, Aminicenantes =3, Thermoprofundales =3, and Sulfurovum =2) are shown in a network diagram (F) where each node represents an ASV
and a line joining two nodes represents co-occurrence of two ASVs. The size of the nodes indicates its degree of connectivity and the thickness of the lines
indicates strength of correlation. The blue, solid gray, and light gray colored lines indicate connections between bacterial and archaeal, within bacterial and
within archaeal ASVs, respectively. Symbol colors represent taxonomic affiliations of the four lineages.
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Phylogeny of Gas-Associated ASVs. Among these lineages, Atri-
bacteria showed the most significantly elevated sequence abun-
dance in the gas-positive locations (SI Appendix, Table S2). This
phylum also contained the largest number of gas-associated
ASVs (n=13) including 5 of the top 7 ASVs showing the
strongest correlation with the total alkane gases (Fig. 4E). To
investigate the phylogeny of Atribacteria ASVs and their close
relatives, a high-resolution phylogenetic tree was calculated
based on >1,100 near-full-length 16S rRNA gene sequences
from the SILVA nonredundant (NR) small subunit (SSU) ri-
bosomal database (release 132, December 2017) (29) and the
National Center for Biotechnology Information (NCBI) nucle-
otide database (as of June 2018). A consensus tree supported by
four separate phylogenetic reconstructions (Methods) demon-
strated that the phylum Atribacteria is comprised of two class-
level clades, corresponding to the OP9 and JS1 divisions (SI
Appendix, Fig. S5). Based on minimum sequence identity (MSI)
thresholds for taxonomy using the 16S rRNA gene (30), OP9
comprises one order-level clade (MSI cutoff: 83.6%) and JS1
comprises two order-level clades. Atribacteria contains at least
11 family-level clades (MSI cutoff: 87.7%) and a larger and
rapidly growing number of genus-level clades (MSI: 94.8%; not
shown) as this phylum continues to be the focus of new discov-
eries (28, 31, 32). The 13 gas-associated Atribacteria ASVs are all
affiliated with the same family within JS1 (SI Appendix, Fig. S5).
Fig. 5 shows that most of these cluster among three genus-level
clades consisting almost exclusively of sequences from deep
subseafloor habitats as well as seep and near-seep habitats im-
pacted by subseafloor geofluids. Similarly, gas-associated ASVs
belonging to Aminicenantes (n=6) and Thermoprofundales (n=4)
were also most closely related (98 to 100% identity) to sequences
detected in various subseafloor benthic sedimentary habitats in-
cluding gas-hydrate-bearing sediments and in other near-surface
habitats associated with hydrocarbon seepage (SI Appendix,Fig.
S6). Close relatives of two gas-associated Sulfurovum ASVs were
detected only in near-seafloor oxic habitats such as cold seeps and
hydrothermal vent fluids (SI Appendix,Fig.S6). This agrees with
Sulfurovum not being detected in the deeper core sections in our
study (Fig. 4 Cand D) and previous reports of Sulfurovum and
other Campylobacterota inhabiting geofluid-impacted surface
sediments (33) and not deeper sediment layers.
Metagenome-Assembled Genomes of Gas-Associated Lineages. Shot-
gun metagenome sequencing from three sediments including a
gas-positive location followed by coassembly and binning resulted
in the reconstruction of 14 metagenome-assembled genomes
(MAGs) affiliated with the four gas-associated lineages (Atribac-
teria =5, Aminicenantes =5, Sulfurovum =1, and Thermopro-
fundales =3; SI Appendix,TableS4). Together these 14 MAGs
corresponded to >25% of the total reads in the gas-positive sed-
iment metagenome (Fig. 6), consistent with the amplicon se-
quencing results indicating high abundance of these organisms
(Fig. 4A). Respiratory genes including cytochrome coxidase,
periplasmic nitrate reductase, sulfur oxidation, and sulfide-
quinone reductase were only detected in the Sulfurovum MAG,
not in the MAGs of the three other lineages. The Sulfurovum
MAG also contained a complete reductive tricarboxylic acid
(rTCA) cycle for autotrophic carbon fixation, consistent with the
presence of this pathway in all previously described autotrophic
members of Campylobacterota (22). This suggests a respiratory
lithoautotrophic lifestyle fueled by inorganic sulfur compounds. In
contrast to Sulfurovum, the 13 MAGs affiliated with Atribacteria,
Aminicenantes,andThermoprofundales showed evidence for fer-
mentative degradation of organic matter. Acetogenesis has been
postulated as an important process for energy metabolism in the
deep subsurface (34). In agreement with this, potential for ace-
togenesis was observed in all three lineages with three out of
the five Aminicenantes MAGs containing genes for the Wood–
Ljungdahl pathway (Fig. 6). A variety of predicted extracellular
and intracellular peptidases were observed in MAGs from all
three lineages. In particular, MAGs encoding extracellular pepti-
dases gingipain (Merops family C25) and clostripain (Merops
family C11) were encoded by Thermoprofundales and Atribacteria,
respectively. These extracellular enzyme families have been im-
plicated in detrital protein degradation in the subseafloor by these
lineages (27, 28). Hydrogen metabolism genes were also detected
with Aminicenantes,Thermoprofundales,andAtribacteria MAGs
encoding putative cytosolic group 3 [NiFe]-hydrogenases (Fig. 6).
These enzymes couple NAD(P)H reoxidation to fermentative
hydrogen production (35). Atribacteria MAGs also encoded pu-
tative groups 1 and 4 [NiFe]-hydrogenases consistent with fer-
mentative hydrogen production and previous reports that these
bacteria are anaerobic heterotrophs (36). Genes for acetogenesis,
hydrogen production, and peptide degradation are indicative of
fermentative lifestyles found in microbes inhabiting deep
subseafloor sediments.
5 %
Branch probability
supported by four trees
supported by three trees
supported by two trees
Sequence origin
Gas-associated ASV
Seep sediment
Subsurface sediment
Terrestrial or freshwater sediment
ODP subsurface sediment, AB805647
Yung-An Ridge subsurface sediment, JQ816843
Deformation Front subsurface sediment, JX001020
Gas-associated ASV848
Gas-associated ASV63, 67, 83, 167
Gas hydrate subsurface sediment, AM229211
Tainan Slope subsurface sediment, JQ818123
Amsterdam mud volcano sediment, HQ588613
Kazan mud volcano sediment, FJ712453
Gas-associated ASV360, 497, 1011
ODP Site 1226 subsurface sediment, JN676013
Santa Monica basin seep sediment, KU324288
Hydrothermal seep sediment, KP091097
South China Sea subsurface sediment, KM269672
ODP subsurface sediment, AB805087
Gas-associated ASV32
ODP subsurface sediment, AB805820
Hydrate Ridge sediment, KM356516
Deep Basin subsurface sediment, JQ816303
Ross Sea, Antarctica subsurface sediment, KY888019
Japan Trench seep sediment, JTB138, AB015269
Yung-An Ridge subsurface sediment, JQ816643
ODP Site 1226 subsurface sediment, JN676006
Good Weather Ridge subsurface sediment, JQ816972
Methane hydrate subsurface sediment, AB177134
Terrestrial mud volcano, AJ937694
Janssand tidal subsurface sediment, KR825158
Antarctic subsurface sediment, FN429787
Cadiz mud volcano sediment, FN820327
Hydrate Ridge sediment, KM356370
Gulf of Mexico sediment, AM746086
Gas-associated ASV34, 357, 782
Japan Trench seep sediment, AB189340
Okhotsk Sea sediment, FJ873257
East Pacific hydrothermal vent sediment, KM071718
Lei-Gong-Huo mud volcano, JQ407282
Hydrocarbon contaminated aquifer, JQ087147
Aquatic moss pillars, MPB1-147, AB630529
Lake Skallen O-lke sediment, LC124762
Anoxic lacustrine sediment, JX472297
Rifle aquifer background sediment, MEYG0100
Gulf of Mexico sediment, AM745209
Håkon Mosby mud volcano sediment, KX581156
Gas-associated ASV287
ODP subsurface sediment, AB805297
Santa Monica basin cold seep sediment, KU324297
Kazan mud volcano sediment, FJ712496
Hydrocarbon seep sediment, DQ004676
Tainan Ridge subsurface sediment, JN123585
Fig. 5. Phylogeny of Atribacteria based on full-length 16S rRNA gene sequences
from diverse environments. Gas-associated ASVs along with their closest relatives
(minimum sequence identity for inclusion 98%) are shown. This consensus tree is
based on four separate phylogenetic reconstructions (phyML, RAxML, RAxML8,
and neighbor-joining), with shaded nodes indicating the number of individual
trees that gave rise to the same branching pattern, i.e., black nodes signify mi-
crobial lineages that are very stable independent of the phylogenetic algorithm,
and that have high bootstrap support (typically >95%; not shown). An extended
tree (SI Appendix,Fig.S5) showing the phylogeny of all Atribacteria ASVs de-
tected in this study is included in SI Appendix.
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Discussion
Sedimentary deposits within the Gulf of Mexico basin are
characterized by enormous amounts of natural oil and gas
seepage, sourced from the underlying petroleum-bearing reser-
voirs (6). Hydrocarbon movement through porous sediment
layers is typically controlled by a multitude of factors including
fluid buoyancy, fracture stability, sediment morphology, and
pore pressure in the sediment column (21). Migrating geofluids
are highly variable in their composition, often containing deeply
sourced thermogenic oil and gas mixed with biogenic methane
produced in the overlying anoxic sedimentary layers (10). This
effect was observed in the 11 gas-positive sediments studied here,
which all contained >10,000 ppmv of light alkane gases. Plotting
the gas data from these sediments together with historic data from
sediments sampled similarly in the Central and Western GoM
basin shows that these characteristics are representative of >200
additional locations (SI Appendix, Fig. S1) demonstrating that
vigorous gas seepage is widespread in the Gulf of Mexico.
The presence of gaseous hydrocarbons greatly contributes to
the overall buoyant force of the migrating fluids which in turn is
crucial for maintaining a steady, pressure-driven upward flow
through the sediment layers (37). Only 11 cores showed evidence
of this continuous advection of gas and oil, which is a small
fraction compared to the large majority of the cores (n=121)
containing only thermogenic oil, suggesting weak, discontinuous
or even permanently ceased geofluid seepage in most locations.
Detecting thermogenic C
2+
gases in all 11 gas-positive locations
(Fig. 2A) further indicates that gas-derived buoyancy was present
at deeply buried petroleum accumulations where fluid migration
would have initiated. Entrainment of biogenic gases, whether
from the same deep layers or from overlying methanogenic
sediments likely increases the buoyancy and therefore the ad-
vective capacity of the geofluid flow.
Microbial community comparisons revealed that near-seafloor
sediments in the 11 gas-positive locations harbored a distinct
microbiome (Fig. 3). By contrast, the presence of migrated oil
did not significantly affect surface sediment microbial commu-
nity composition. Hydrocarbons advecting up from the sub-
surface together with distinct microbial communities in the near-
surface sediments point toward the development of a microbial
completeness (%)
proportion in gas-positive sediment (%)
Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7 Bin 8 Bin 9 Bin 10 Bin 11 Bin 12 Bin 13 Bin 14
60 70 80
Atribacteria Aminicenantes Sulfurovum Thermo-
profundales
Wood-Ljungdahl pathway
reverse TCA cycle
TCA cycle
glycolysis
succinate production
ethanol fermentation
acetate production
fermentation
carbon
fixation
central
metabolism
NiFe Group 4 evolving H2ase
NiFe Group 1 uptake H2ase
FeFe Group C H2ase
FeFe Group A H2ase
sulfur oxidation
sulfide-quinone reductase
periplasmic nitrate reductase
cytochrome c oxidase (cbb3-type)
respiration
hydrogen
metabolism
Zinc carboxypeptidase
Xaa-Pro aminopeptidase
Phosphoserine aminotransferase
Peptidase family M50
Peptidase family M28
Peptidase family C25
Oligoendopeptidase F
Lipoprotein signal peptidase
Leucyl aminopeptidase
Di- and tri-peptidases
D-aminopeptidase
Clostripain (C11) family
Aminopeptidase N
protein
degradation
0-1% of genes
Pathways
1-99% of genes 100% of genes
Absent
Genes
Present
NiFe Group 3 bidirectional H2ase
12
Fig. 6. Metagenome-assembled genomes affiliated with the gas-associated bacteria and archaea. Fourteen MAGs were affiliated with the four gas-
associated lineages. Heat maps show genome completeness, relative abundance in a gas-positive sediment (based on mapping against total unassembled
reads), and the relative proportions of different metabolic pathways involved in central metabolism, carbon fixation, and protein degradation detected in
each MAG. The presence or absence of additional genes of interest related to protein degradation, hydrogen metabolism, and respiration, are also indicated.
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ecosystem in these gas seeps influenced not only by energy-rich
geofluids but also by immigrating microbes traveling up from the
subsurface together with the fluids. Other indications of
subsurface-to-seafloor dispersal include recent observations of a
lineage of Atribacteria in surficial sediment at an active marine
mud volcano on the Norwegian continental slope (15), and an-
other lineage of Atribacteria in the bottom water overlying a mud
volcano offshore Japan (14). In the present study, anomalies of
multiple subsurface lineages of not only Atribacteria but also
Aminicenantes and archaeal Thermoprofundales (Fig. 4) across
multiple cold seeps demonstrate this phenomenon to be much
more widespread. Associations of subsurface microbial groups
with gaseous thermogenic hydrocarbons suggest deep-to-shallow
dispersal of diverse populations at cold seeps is a persistent
feature in the deep sea.
The deep biosphere provenance of these lineages is supported
by various community surveys (14, 25, 26, 28, 32). The majority
of the gas-associated ASVs identified in our study belonged to
Atribacteria that cluster within distinct clades of sequences al-
most exclusively derived from subseafloor habitats and hydro-
carbon seeps (Fig. 5). The updated high-resolution phylogeny
reported here reveals Atribacteria to be a relatively narrow clade,
with closely related members likely representing very similar
metabolic and ecological niches (SI Appendix, Fig. S5). It also
suggests that Atribacteria evolve slowly, possibly due to very long
generation times in the subsurface (38), where fewer stressors
may also contribute to lower rates of evolution (39). Atribacteria
also seem to be sensitive to oxygen as their populations have
been shown to disappear upon exposure to the oxygenated sea-
floor at a mud volcano (15), and increase in relative abundance
in anoxic layers of coastal sediment (32). Seabed oxygen pene-
tration at cold seeps tends to be very shallow, e.g., <1 to 2 cmbsf
(40), thereby establishing anoxic, reduced conditions similar to
deeper subsurface environments in near-surface sediment layers
(12). Due to the large depth interval (20 cm) of the core sections
in our study, we were unable to determine whether or not Atri-
bacteria were present precisely in oxic surficial sediment layers.
Indeed, differentiating between migrant cells arriving via upward
dispersal, and their descendants that may have proliferated
in situ, requires deeper cores sampled at higher resolution.
Geofluid-supplied cells from below together with maintenance of
anoxic subsurface-like conditions near to the surface by geofluid
migration likely allow migrant Atribacteria to survive and pro-
liferate in the gas-positive locations. Relative abundances of up
to 2% for gas-associated ASVs in these sediments support this
interpretation (SI Appendix, Fig. S4).
It is likely that different Atribacteria share a relatively large
proportion of genes and pathways such that the present number
of metagenome-assembled and single-cell genomes from the
classes OP9 and JS1 (28, 36, 41) may provide a comprehensive
picture of the ecological niches realized by members of this
phylum. Currently available Atribacteria genomes provide evi-
dence that these organisms are capable of fermentative degra-
dation of organic matter (36, 42). The metabolic potential
inferred from Atribacteria MAGs in this study is consistent with
those findings, showing no evidence for aerobic metabolism
(Fig. 6). Similar metabolic capabilities have been proposed for
the archaeal Thermoprofundales that are widespread in the ma-
rine subsurface (23). Single-cell genomic sequencing of a Ther-
moprofundales from the subsurface showed potential for
extracellular protein degradation using enzymes that are abun-
dant in anoxic deep marine sediments (27). The same families of
extracellular proteases were detected in all Thermoprofundales
MAGs retrieved here (Fig. 6). A lifestyle based on degradation of
detrital amino acids in subsurface sediments has also been pro-
posed for Aminicenantes (28) highlighted here as a deep biosphere
clade that can be detected in cold seep sediments. Genomics for
these lineages points to an anaerobic heterotrophic lifestyle which
is consistent with the prominence of these taxa in anoxic deep
sediments (28, 43), and in contrast to the metabolic requirements
for living at the sediment–water interface.
Two ASVs within the genus Sulfurovum also showed strong
correlation with the gas parameters (Fig. 4Eand SI Appendix,
Table S3). Unlike the three subsurface-derived lineages dis-
cussed above, Sulfurovum was not detected in the deeper core
sections (Fig. 4 Cand D). Members of this genus are well known
for their aerobic chemolithoautotrophic lifestyle in oxic sediment
layers where they oxidize high-energy electron donors. As
expected, the Sulfurovum MAG in our study contains respiratory
genes along with a complete carbon fixation pathway (Fig. 6).
The phylogeny of Sulfurovum ASVs and their close relatives (SI
Appendix, Fig. S6) confirms that they are not found in deeply
buried sediments but rather colonize hydrothermal vents and
sulfidic near-surface sediment horizons of hydrocarbon seeps
(33). In EGoM sediments, Sulfurovum may be present at higher
abundances at the sediment–water interface than the levels
revealed in these sequencing libraries, which represent an ag-
gregate signal for 0 to 20 cmbsf. Quantitative assays specific for
different lineages would need to be applied at finer depth res-
olution to reveal spatial variance in population densities of seep-
colonizing and subsurface-derived groups in settings like these.
Similar relative abundances at the surface for most gas-
associated Atribacteria,Aminicenantes,Thermoprofundales, and
Sulfurovum ASVs (SI Appendix, Fig. S4) suggest that all of these
groups may exhibit activity in the shallow sediment layers in situ,
although not necessarily at the same precise depths.
Coexistence of subseafloor lineages in near-surface EGoM
sediments, their association with hydrocarbon gas emission, their
elevated abundance at greater sediment depth, and their physi-
ological potential revealed by genomics suggest that Atribacteria,
Aminicenantes, and Thermoprofundales are dispersed from
deeper sediment layers by the upward migration of hydrocar-
bons. Continuous emission of geofluids could alter the sedi-
ment habitat near the sediment–water interface and maintain
subsurface-like conditions that allow migrant Atribacteria,Ami-
nicenantes, and Thermoprofundales to survive. Previous work in
the same study area presented evidence that thermophilic spores
passively migrate up to the surface from underlying warm
petroleum-bearing sediments via hydrocarbon seepage (13).
Whereas dormant endospores remain unaffected by selection
pressure from the near-surface conditions and could be corre-
lated to the presence of oil regardless of the continuity of active
buoyant seepage, the gas-associated Atribacteria,Aminicenantes,
and Thermoprofundales identified in this study likely play a
metabolic role in seep ecosystems owing to their consistently
high relative abundance in cold seep surface sediments where
conditions may more closely resemble the subsurface. This
points to hydrocarbon seepage as an important dispersal vector
and determinant of microbial biogeography and diversity in the
Gulf of Mexico, where gas seepage is widespread (SI Appendix,
Fig. S1B). Redistribution of subsurface microbes into the deep
sea enables their deposition in near-surface environments where
they may contribute to local biogeochemical cycling where con-
ditions permit, before reentering the deep biosphere via sedi-
mentation over longer timescales.
Methods
Sampling of Marine Sediments. Marine sediments were collected during
January–March 2011, aboard RV GeoExplorer as part of TDI-Brooks Inter-
national’s Surface Geochemical Exploration (SGE) program. Piston cores
penetrating 2.2 to 5.8 m below seafloor (mbsf) were collected and imme-
diately extruded into 20-cm sections. Sediments for microbiological analyses
were collected from the uppermost section, i.e., 0 to 20 cmbsf for all loca-
tions, whereas additional samples were collected from two deeper sections
(40 to 60 and 80 to 100 cmbsf) from four locations. Sediment samples were
sealed in sterile Whirl-Pak bags with minimal air exposure and kept frozen
at −20 °C. Sediment sections from the bottom half of each piston core were
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also frozen at −20 °C for hydrocarbon analysis (44). Portions of these bottom
sections were placed in 500-mL gas canisters containing 160 mL clean,
degassed, and sterilized seawater for analysis of interstitial light hydrocar-
bon gases. Canisters were sealed immediately after flushing the headspace
with purified nitrogen and stored at −20 °C.
Hydrocarbon Characterization. Concentrations of interstitial light hydrocar-
bon gases were measured from the sediment samples stored in gas canisters.
Frozen sediments in canisters were allowed to thaw and a silicone septum was
silicone glued to each lid, followed by canisters being placed at 40 °C for at
least 4 h. Light hydrocarbons dissolved in the interstitial water were sub-
sequently equilibrated with the nitrogen gas phase by vigorous agitation for
5 min using a high-speed shaker. Hydrocarbon gases in these equilibrated
samples were measured using gas chromatography coupled with flame
ionization detection. Responses for each analyte were compared to a cali-
bration curve (constructed using an external standard mixture of five hy-
drocarbon gases) to derive the concentration of each gas partitioned into
the canister’s headspace. Concentrations (in ppmv) of each gas originally in
the sediment were calculated from the volumes of sediment and headspace
in the canister and equilibrium partition coefficients for each compound.
Gas isotopic composition was determined using gas chromatography com-
bustion isotope ratio mass spectrometry (GC-C-IRMS; see SI Appendix for
details). Liquid hydrocarbon analysis was performed on solvent extracts
prepared from three equidistant segments within the bottom half of each
piston core (see SI Appendix,Supplementary Methods for additional details).
Frozen sediments were thawed, oven dried, and 15-g portions were solvent
extracted with hexane using an automated extraction apparatus (Dionex
ASE200). Oil parameters (UCM and TSF intensities) were subsequently
measured from these extracts.
16S rRNA Gene Amplicon Sequencing. Genomic DNA was extracted from 0.5 to
1.0 g of sediment from each sample using the DNeasy PowerLyzer PowerSoil
Kit (12855-100, QIAGEN) according to manufacturer protocol with minor
modifications for the step of homogenization and cell lysis, i.e., cells were
lysed by bead beating at 6 m s
−1
for 45 s using a Bead Ruptor 24 (OMNI).
Extraction blanks were performed alongside the samples to assess labora-
tory contamination during the extraction process. DNA concentrations were
assessed fluorometrically using a Qubit 2.0 fluorometer (Thermo Fisher Sci-
entific). The v3-4 region of the bacterial 16S rRNA gene and the v4-5 region
of the archaeal 16S rRNA gene were amplified using the primer pairs
SD-Bact-0341-bS17/SD-Bact-0785-aA21 and SD-Arch-0519-aS15/SD-Arch-0911-
aA20, respectively (45), followed by post-PCR cleanup and indexing. Indexed
amplicon samples were sequenced using Illumina’s v3 600-cycle (paired-end)
reagent kit on an Illumina MiSeq benchtop sequencer (Illumina Inc.) after all
DNA extraction blanks and PCR reagent blanks were confirmed for negative
amplification. Raw sequences were quality controlled and further processed to
construct an ASV table. Detailed methods for PCR conditions, amplicon
cleanup, indexing, sequence processing, ASV taxonomy assignment, estima-
tion of alpha and beta diversity, and associated statistical analyses are de-
scribed in SI Appendix.
Phylogenetic Analyses. Phylogenetic analyses were conducted based on 16S
rRNA gene sequences from the SILVA database SSU Ref NR 132 (29). For an
up-to-date phylogeny of Atribacteria, the NCBI database (as of June 2018)
was searched against selected Atribacteria full-length sequences using
blastn with a match/mismatch score of 1/−1 to allow for broad hits. The
nonredundant SILVA reference tree was subsequently updated by adding
sequences retrieved from NCBI. From the resulting 1,757 nonredundant se-
quences affiliated with Atribacteria, those that were nearly full length
(>1,350 nucleotides) and had an alignment quality score of >94 were
retained, resulting in 698 sequences. After retaining sequences from diverse
ecosystems, while removing close relatives from the same ecosystem, the
remaining 217 sequences were aligned using SINA (46). The alignment was
then manually curated based on ribosomal secondary structure and sub-
sequently used to calculate trees using four different algorithms (phyML,
RAxML, RAxML8, and neighbor-joining). For each tree we used the same
positional variability filter including only conserved positions in the align-
ment with a mutation rate of less than 3.1%. The final alignment included
1,126 valid columns encompassing Escherichia coli reference positions 1,784
to 42,529. Each tree was calculated with 100 iterations after which the most
robust tree was selected. Four resulting trees were used to calculate a con-
sensus tree using the tool as implemented in ARB (47). Finally, the short ASV
sequences from this study were added to the consensus tree using the same
positional variability filter without changing the overall tree topology.
Phylogeny construction of the other groups is described in SI Appendix.
Metagenome Library Preparation, Sequencing, and Analyses. For metagenome
library preparation, DNA was extracted from ∼10 g of sediment from three
different locations using the PowerMax Soil Kit (12988-10, QIAGEN)
according to the manufacturer’s protocol. Cells were lysed by bead beating
for 45 s at 5.5 m s
−1
, followed by fluorometric assessment of DNA concen-
trations. Metagenomic library preparation and DNA sequencing was con-
ducted at the Center for Health Genomics and Informatics in the Cumming
School of Medicine, University of Calgary. Genomic DNA was sheared using a
Covaris sonicator, and DNA fragment libraries were subsequently prepared
using a NEBNext Ultra II DNA Library Prep Kit for Illumina (New England
BioLabs). Metagenomic libraries were sequenced using a ∼40 Gb (i.e., 130 M
reads) midoutput 300 cycle (2 ×150 bp) sequencing kit on an Illumina
NextSeq 500 System. Detailed methods of raw read processing, assembly,
mapping, binning, and annotation are provided in SI Appendix.
ACKNOWLEDGMENTS. We wish to thank Jody Sandel as well as the crew of
R/V GeoExplorer for collection of piston cores, onboard core processing,
sample preservation, and shipment. Cynthia Kwan and Oliver Horanszky are
thanked for assistance with amplicon library preparation. We also wish to
thank Jayne Rattray, Daniel Gittins, and Marc Strous for valuable discussions
and suggestions, and Rhonda Clark for research support. Collaborations with
Andy Mort from the Geological Survey of Canada, and Richard Hatton from
Geoscience Wales are also gratefully acknowledged. This work was finan-
cially supported by a Mitacs Elevate Postdoctoral Fellowship awarded to
A.C.; an Alberta Innovates-Technology Futures/Eyes High Postdoctoral
Fellowship to S.E.R.; and a Natural Sciences and Engineering Research
Council Strategic Project Grant, a Genome Canada Genomics Applications
Partnership Program grant, a Canada Foundation for Innovation grant
(CFI-JELF 33752) for instrumentation, and Campus Alberta Innovates Pro-
gram Chair funding to C.R.J.H.
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