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Genomic and enzymatic evidence for acetogenesis
among multiple lineages of the archaeal phylum
Bathyarchaeota widespread in marine sediments
Y. He1,2†,M.Li
3†, V. Perumal1, X. Feng1, J. Fang1, J. Xie1, S. M. Sievert4and F. Wang1,2*
Members of the archaeal phylum Bathyarchaeota are widespread and abundant in the energy-deficient marine subsurface
sediments. However, their life strategies have remained largely elusive. Here, we provide genetic evidence that some
lineages of Bathyarchaeota are acetogens, being capable of homoacetogenesis, a metabolism so far restricted to the
domain Bacteria. Metabolic reconstruction based on genomic bins assembled from the metagenome of deep-sea
subsurface sediments shows that the metabolism of some lineages of Bathyarchaeota is similar to that of bona fide
bacterial homoacetogens, by having pathways for acetogenesis and for the fermentative utilization of a variety of organic
substrates. Heterologous expression and activity assay of the acetate kinase gene ack from Bathyarchaeota, demonstrate
further the capability of these Bathyarchaeota to grow as acetogens. The presence and expression of bathyarchaeotal
genes indicative of active acetogenesis was also confirmed in Peru Margin subsurface sediments where Bathyarchaeota
are abundant. The analyses reveal that this ubiquitous and abundant subsurface archaeal group has adopted a versatile
life strategy to make a living under energy-limiting conditions. These findings further expand the metabolic potential of
Archaea and argue for a revision of the role of Archaea in the carbon cycle of marine sediments.
Alarge number of bacteria and archaea reside in energy-
deficient subsurface sediments at depths greater than one
metre below the seafloor (mbsf) and thus represent a con-
siderable portion of the Earth’s biomass1. Most of these microorgan-
isms do not have cultivated representatives, and this poses a major
challenge when elucidating the mechanisms allowing life to exist
under the extreme energy-limiting conditions prevailing in the
deep biosphere2. It has been proposed that these subsurface
microbes are mainly fuelled by recalcitrant organic carbon buried
in the marine sediments3. However, it remains unexplained how
these subsurface microorganisms subsist under conditions where
the available energy flux appears to be 1,000-fold lower than the
minimum value for maintenance energy estimated from laboratory
cultures2,4. It is estimated that ∼90% of the cells reside below the
sulfate methane transition zone (SMTZ) of continental shelf sedi-
ments, where fermentation and methanogenesis are assumed to
occur5. Although acetogenesis has been found to co-occur with
sulfate reduction and methanogenesis in terrestrial groundwater
and subseafloor sediments6,7, the microbes associated with this
remain unknown.
Members of Archaea, the third domain of life distinct from
Bacteria and Eukarya, constitute a major component of subsurface
microbial communities4,8. Globally, members of the Miscellaneous
Crenarchaeota Group (MCG, recently assigned to a novel phylum
Bathyarchaeota) often dominate marine subsurface archaeal com-
munities9,10 and have been identified to be one of the most active
microbial groups in the deep marine biosphere based on the esti-
mation of chemical rates11. Bathyarchaeota have been found to
account for between 10 and 100% of all archaea (on average
30–60%) in the deep marine biosphere9. Assuming that bacteria
and archaea are present in equal abundance in the deep biosphere12
and that 50% of the deep biomass (in total 2.9 ×10
29
cells globally1)
occurs at continental margins (at water depths of <3,500 m), with
approximately 90% below the SMTZ5, then Bathyarchaeota cells
would account for between 2 ×10
28
and 3.9 ×10
28
cells globally,
making it one of the most abundant microbial groups on the
planet. Bathyarchaeota were initially considered to be heterotrophs4,
which has been partially confirmed by single-cell genomic and
metagenomic analyses10,13, as well as stable isotope probing exper-
iments14. However, the phylum Bathyarchaeota is composed of a
large number of diverse lineages, most probably also with a high
metabolic diversity9,15. Recently, members of two lineages of
Bathyarchaeota were suggested to be capable of methane meta-
bolism based on two genomes obtained from metagenomic
analysis16. However, it is the paucity of genomes across this huge,
diverse phylum10,15,16 that currently impedes a comprehensive under-
standing of the metabolic capabilities and potential biogeochemical
roles of this phylum9.
Results and discussion
We conducted shotgun metagenomic sequencing of a marine
sediment sample collected from the Guaymas Basin (GB) in the
Gulf of California. Quantitative-PCR (q-PCR) analyses of the 16S
rRNA gene demonstrated that the sediment sample contained
∼2.8 ×10
8
archaeal cells per gram of sediment, of which 74% were
Bathyarchaeota (Supplementary Fig. 1). De novo assembly of metage-
nomic reads and binning by tetranucleotide signatures (for details see
Methods) revealed six Bathyarchaeota genomic bins (Supplementary
Fig. 2). The bin size of the obtained Bathyarchaeota ranged from
∼0.9 Mb to ∼2.0 Mb, with coverage ranging from ∼10×to 28×
(Table 1 and Supplementary Table 1). Genome completeness
within each bin was between 60 and 94% based on the presence of
1State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. 2State Key
Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, China. 3Institute for Advanced Study, Shenzhen University, Shenzhen,
Guangdong, China. 4Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, USA. †These authors contributed
equally to this work. *e-mail: fengpingw@sjtu.edu.cn
ARTICLES
PUBLISHED: 4 APRIL 2016 | ARTICLE NUMBER: 16035 | DOI: 10.1038/NMICROBIOL.2016.35
NATURE MICROBIOLOGY |www.nature.com/naturemicrobiology 1
© 2016 Macmillan Publishers Limited. All rights reserved
single-copy genes (Table 1), and 148 genes were shared by the ident-
ified Bathyarchaeota genomic bins (Supplementary Table 2).
The phylogeny based on 53 concatenated archaeal conserved
single-copy genes17 confirmed the placement of Bathyarchaeota
into a novel archaeal phylum (Fig. 1a and Supplementary
Table 3), which is consistent with previous results13. A separate
analysis with 16S rRNA genes from all publicly available
Bathyarchaeota genomes15,16 indicated that they represent separate
groups that diverge widely within Bathyarchaoeta: B23 belongs to
subgroup MCG-15; B26-1 and B26-2 belong to MCG-16; and B24
and B25 represent members of unclassified novel groups (Fig. 1b).
Metabolic reconstruction showed that the Bathyarchaeota ident-
ified in the present study (if not all the sublineages within it15,16)
have the genetic potential for inorganic carbon fixation via the reduc-
tive acetyl-CoA (Wood–Ljungdahl, WL) pathway (Fig. 2). Genes of
all five subunits of the carbon monoxide dehydrogenase/acetyl-CoA
synthase complex (Codh/Acs), the key enzyme for the WL
pathway, were found in five of the genomic bins (but not in B23,
Supplementary Table 4). In addition, most of the genes that encode
enzymes for the methyl branch of the WL pathway in archaea,
through which CO
2
is reduced to methyltetrahydromethanopterin,
were found (Fig. 2 and Supplementary Table 4). The enzyme formyl-
methanofuran dehydrogenase (FwdA-F), which is involved in the
reduction of CO
2
, was detected in all six Bathyarchaeota genomic
bins. Genes encoding for enzymes catalysing the subsequent
reactions, that is, formylmethanofuran:tetrahydromethanopterin
formyltransferase (Ftr), N5,N10-methenyltetrahydromethanopterin
cyclohydrolase (Mch), methylenetetrahydromethanopterin dehy-
drogenase (Mtd) and N5,N10-methylenetetrahydromethanopterin
reductase (Mer), were all identified in three genomic bins (B24,
B26_1 and B26_2). B63 included all of these genes except mer
(Supplementary Table 4). The complete set of genes encoding the
membrane-associated methyltransferase (MtrA-H) was only found
in B25, and was absent in all the other five bins. Similarly, only
B25 contained the gene coding for methanol coenzyme M methyl-
transferase (MtaA)(Supplementary Table 4). These results suggest
that most members of the Bathyarchaeota possess the key
enzymes required for a functional WL pathway. Interestingly,
none of these six Bathyarchaeota genomic bins (which represent
at least four Bathy-subgroups within the Bathyarchaeota) contains
genes coding for McrABG (Supplementary Table 4), the key gene
for methane metabolism, indicating that only a few groups of
Bathyarchaeota have the potential for methane metabolism16.
The present data further provide evidence that members of the
Bathyarchaeota are able to metabolize acetate. Phosphate acetyltrans-
ferase (Pta) and acetate kinase (Ack) were found in three of the six
Bathyarchaeota genomic bins (Supplementary Table 4). The Pta-Ack
pathway for acetate production/assimilation is widely distributed in
bacteria, whereas archaea usually use the acetyl-CoA synthetase (ade-
nosine diphosphate (ADP)-forming)(ACD),includingthepotentially
acetogenic Bathyarchaeota lineages reported previously15.ACDwas,
until recently, regarded as being specificforArchaea,whenitwas
also found in a few bacterial genomes, such as the propionic acid-pro-
ducing bacteria Propionibacterium acidipropionici18,19.Inarchaea,the
Pta-Ack pathway has so far only been identified in the methanogenic
genus Methanosarcina,whichiscapableofproducingmethanefrom
acetate20.Thus,thepresentstudyisthefirst to identify genes coding
for Pta-Ack in non-methanogenic archaea. The pta-ack genes in
Methanosarcina are assumed to have been acquired by a single hori-
zontal gene transfer event, probably from a clade of cellulolytic
Clostridia21.However,phylogeneticanalysisofthetranslatedack and
pta sequences of Bathyarchaeota indicated that these genes form a
monophylogenetic clade that is distinct from all other known
sequences (Fig. 3a,b). Thus, the Pta-Ack pathway in Bathyarchaeota
appears to have evolved independently from that of Bacteria.
Bona fide homoacetogenesis, that is, the production of acetate solely
from hydrogen and CO
2
,istraditionallyseenasametabolismrestricted
to Bacteria22,althoughtwoarchaealspecies—the methanogen
Methanosarcina acetivorans and the sulfate-reducing archaeon
Archaeoglobus fulgidus—have been shown to produce acetate from
CO in addition to formate under certain growth conditions20,23.In
support of the acetogenic lifestyle of Bathyarchaeota suggested by the
genomic data presented here, we were able to clone and express the
bathyarchaeotal ack gene in Escherichia coli (Supplementary Fig. 3).
The purified enzyme showed catalysing activity for the reversible phos-
phorylation of acetate to acetyl phosphate. The bathy-Ack was approxi-
mately 15-fold more active (higher catalytic efficiency) in the reaction
of acetate synthesis than the reverse reaction, and it showed a much
higher affinity to acetyl phosphate (K
m
=0.48mM) than to acetate
(K
m
=3.9mM)(SupplementaryTable5).
The Bathyarchaeota analysed in the present study also demon-
strated the potential to use a number of complex organic compounds
as growth substrates, including proteins, cellulose, chitin and aromatic
compounds, in line with previous findings10,13,15. This flexibility
suggests that these widespread archaea9,10 are heterotrophs with
extensive abilities to efficiently use organic matter, a strategy well
suited to make a living in carbon-starved deep marine sediments2.
Genes encoding glycolysis/gluconeogenesis and beta-oxidation path-
ways were present in all genomic bins (Supplementary Tables 6 and 7),
highlighting the heterotrophic lifestyle of Bathyarchaeota. A
partial tricarboxylic acid (TCA) cycle only missing citrate synthase
(Cit) was identified in genomic bins, similarly to what has been
Table 1 | Overview of Bathyarchaeota genomic bins.
Bathyarchaeota bin B23 B24 B25 B26-1 B26-2 B63
Number of contigs 108 37 92 136 84 158
Bin size (Mb) 0.898 1.73 0.939 1.384 1.969 1.22
Number of predicted genes 1,093 1,791 1,079 1,454 1,948 1,391
N50 value (kb) 8,536 64,843 12,095 12,086 33,188 8,025
Number of genes annotated by Pfam 812 1,437 867 1,051 1,452 1,008
Number of CSCGs* 97 152 114 119 124 95
Expected complete genome size based on CSCGs (Pfam, in Mb)* 1.5 1.84 1.334 1.884 2.572 2.08
Achieved genome coverage (Pfam)* 59.90% 93.80% 70.40% 73.50% 76.60% 58.70%
Number of genes annotated by COG 836 1,482 890 1,083 1,463 1,037
Number of CSCGs
†
31 42 38 36 43 29
Expected complete genome size based on CSCGs (COG, in Mb)
†
1.275 1.81 1.087 1.692 2.015 1.851
Achieved genome coverage (COG)
†
70.40% 95.50% 86.40% 81.80% 97.70% 65.90%
Completeness
‡
60.9% 96.7% 74.3% 90.7% 93.0% 59.8%
Contamination
‡
0.0% 2.8% 1.3% 6.1% 3.8% 0.9%
Estimation of genome size wasconducted with two separateapproaches: *with162 identified archaea conserved single copy genes (CSCGs) annotated by Pfam as in ref. 53;
†
for 44 CSCGs annotatedby COG as
in ref. 10.
‡
Completeness and contamination were estimated by CheckM (ref. 46).
ARTICLES NATURE MICROBIOLOGY DOI: 10.1038/NMICROBIOL.2016.35
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0.1
Bathyarchaeota
Aarhus Bay, Denmark10
White Oak River, USA15
Surat Basin, Australia16
Guaymas Basin, Mexico
Nanoarchaeota
Woesearchaeota
Pacearchaeota
Diapherotrites
Aenigmarchaeota
Nanohaloarchaeota
Parvarchaeota
Euryarchaeota
Lokiarchaeota
Korarchaeota
Geoarchaeota
Crenarchaeota
B25
B24
SMTZ-80
B63
B26-1
B26-2
BA1
BA2
SG8-32-1-MCG1
SG-32-3-MCG1
AD8-1-MCG6
SMTZ1-55
B23
DG-45-MCG15
E09
Thaumarchaeota
Aigarchaeota
0.1
Subgroup 15
Subgroup 17
Subgroup 16
Subgroup 3
Subgroup 6
Subgroup 8
Aigarchaeota
Thaumarchaeota
B25
B24
AB177267
FJ404038
AB362547
EU385859
E09
DQ363811
AM697965
DQ641883 (BLAST: DG-45-MCG15)
GU567019
B23
SMTZ80
FJ536696
FJ649521
AM942165
EU713901
FJ264803 (BLAST: SMTZ1-55)
AY354118
AY396672
AY592009
FJ484299
DQ641807
Subgroup 13
AB237758
B26-1
AB355130
EF367568
FJ649531
FJ902705
AM942159
GQ848427
AY354121
Subgroup 14
Subgroup 12
Subgroup 11
Subgroup 1
Subgroup 7
Subgroup 5a
Subgroup 5b
AY501700
AB161338
AY464796
EU155996 (BLAST: AD8-1-MCG6)
AB161343
AB538512
AB183856
FJ902830
AY454600
BA1
EF367492
FJ649517
EF680209
BA2
FJ484638
FJ901676
AY592006
EF367607
FJ485506
FJ853479
GU972264
Subgroup 10
Subgroup 4
Subgroup 9
Subgroup 2
a
b
Figure 1 | Phylogenetic trees showing the placement of genomic bins in the archaeal phylum Bathyarchaeota. a, Maximum-likelihood phylogeny of
concatenated alignments constituting 53 conserved archaeal single-copy genes (Supplementary Table 3), rooted with the DPANN superphyla
(Diapherotrites, Parvarchaeota, Aenigmarchaeota, Nanoarchaeota and Nanohaloarchaeota). b, Maximum-likelihood 16S rRNA gene tree showing the
placement of bathyarchaeotal representatives from all publicly available Bathyarchaeota genomic and environmental sequences, with the previously defined
classification9. Sequences from Aigarchaeota and Thaumarchaeota are selected as outgroups. Bootstrap values are based on 1,000 replicates, and
percentages are shown with open (≥80%) and filled (≥90%) circles. Phylotypes identified in the present study are named by their genomic bin index and
indicated by red circles, while phylotypes reported in the studies described in refs 10, 15 and 16 are indicated by blue stars, green squares and orange
triangles, respectively.
NATURE MICROBIOLOGY DOI: 10.1038/NMICROBIOL.2016.35 ARTICLES
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found in autotrophic Methanobacteriales24. The presence of both
an archaeal-type phosphoenolpyruvate carboxylase (Pepc) and
phosphoenolpyruvate carboxykinase (Pck) in the Bathyarchaeota
genomic bins suggests that phosphoenolpyruvate (PEP) might
serve a central role in carbon metabolism by maintaining the con-
tinuity of the TCA cycle and also by connecting glycolysis to the
TCA cycle24 (Fig. 2). As previously identified by single-cell
genome analysis10, Bathyarchaeota are well equipped to degrade
proteins, as 17 different families of extracellular peptidases, such
as C1A, C25 and S8A, could be identified (Supplementary
Table 8). Diverse genes for carbohydrate-activating enzymes were
also identified, such as those for amylase, cellulase, chitinase, ester-
ase, fucosidase galactosidase, hexosaminidase, lipase and xylanase
(Supplementary Table 9). The presence of genes for benzoyl degra-
dation confirmed that members of Bathyarchaeota are capable of
degrading aromatic compounds (Supplementary Table 10). The
presence of a wide variety of transporters of organic compounds
(Supplementary Table 11) further supports the versatile hetero-
trophic metabolism of Bathyarchaeota (Fig. 2), a strategy well
suited to make a living in carbon-starved deep marine sediments2.
The analysed Bathyarchaeota appear to be capable of using a
flavin-based electron bifurcation involving heterodisulfide reductase
(HdrABC) and methyl-viologen-reducing hydrogenase (MvhADG),
which are proposed to be used repeatedly in different contexts. In
Bathyarchaeota genomic bins, genes of HdrABC and MvhADG
were located adjacent to one another (no Mvh in B23 and B63,
see Supplementary Table 12 for details). They might also be
involved in methylenetetrahydrofolate reduction, as proposed for
Moorella thermoacetica25, which will require further investigation.
In addition, Fpo subunits (F
420
H
2
dehydrogenase, missing in
B26_1 and B63) were also present (Fig. 2 and Supplementary
Table 12). Genes for the hydrogenase maturation enzyme, such as
HypA-F, were identified in all genomic bins. B25 had the complete
gene cluster for the synthesis of F
420
/F
420
H
2
(the oxidized/reduced
forms of a 5-deazaflavin derivative), and part of this gene cluster
was also found in the other genomic bins (Supplementary
Table 12). The beta subunit of F
420
-reducing NiFe-hydrogenase
(FrhB) was found to be present in four out of six genomic bins,
while the other two subunits, FrhA and FrhG, were not present in
any of the genomic bins. The proton-translocating ATPase was
present in three genomic bins (B23, B25 and B63; Fig. 2 and
Supplementary Table 12). The available genomic data for
Bathyarchaeota suggest that H
2
serves as the electron donor, and
reduced F
420
serves as the potential electron acceptor for the
reduction of ferredoxin. However, different groups of
Bathyarchaeota may use different pathways, and further analyses
TCA
Oxaloacetate
WL pathway
Beta
oxidation
ABC transporter
Glycolysis
ABC transporter
Cit
Fdox
Fdred
Cu2+
CopFeo
Fe2+
Trk
K+
Hem
Fe3+
Pst
Pi
ATPase
ATPADP
H+
H+
Aromatic
compounds
ABC transporter
PEP
Acetyl-CoA
CO2
CO2
CHO-MFR
CHO-THMPT
CH≡THMPT
CH2=THMPT
CH3-THMPT
Ftr
Mch
Fwd
Mtd Mer
CO2
CO
Codh
Fdred
Fdred
Fdox
Fdox
Acs
Pfor
Fpo
F420
F420H2
Lipids
Fatty acids
Pyruvate
Malate
Citrate
Pck/
Pepk
Ps/Pk
Acetyl-
phosphate
Acetate Ack
Acd
Pta
Acetogenesis
Peptide
degradation
Proteins
Extracellular
peptidases
Oligo/dipeptides
Amino acids
Amyloses Hemicelluloses CellulosesChitins
ABC transporter
Mono/
oligosaccharides
Amino
acids
F420H2
F420H2
F420
F420
Hydrogenase
Hydrogenase
Hydrogenase
Hydrogenase
Hydrogenase
B24
B26-1
B26-2
B24
B26-1
B26-2
Figure 2 | Overview of pathways reconstructed in Bathyarchaeota. Protein abbreviations are described, with full entry information, in Supplementary Tables
4–12. Grey solid lines refer to genes found in fewer than three Bathyarchaeota genomic bins, and solid lines in other colours refer to genes identified in the
majority (that is, at least three of six) of the Bathyarchaeota genomic bins: green solid lines, acetogenesis (including the WL pathway); purple, TCA cycle;
orange, organic carbon degradation; black, transporters. Grey dotted lines refer to genes not identified in any genome. Orange dashed lines refer to processes
where multiple steps are involved. Each step is fully described in the Supplementary Tables 4–12.
ARTICLES NATURE MICROBIOLOGY DOI: 10.1038/NMICROBIOL.2016.35
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are required to fully understand the mechanisms of electron
bifurcation and energy production in this phylum.
In all Bathyarchaeota genomic bins, no potential enzymes were
detected that would be indicative of the use of external electron accep-
tors, such as oxygen, sulfate and nitrate (nitrite). Collectively, the
available data suggest that some lineages of Bathyarchaeota (such as
MCG-16 and some novel groups to which B24, B26_1 and B26_2
belong) are archaeal acetogens that generate energy by fermenting
various organic carbon compounds and/or acetogenesis: they have
the WL pathway and genes for acetate production/consumption
(mainly through the Pta-Ack pathway) and they also possess pyruvate
ferredoxin oxidoreductase (Pfor, Supplementary Table 4 and
Supplementary Fig. 4), a key genethatlinkstheheterotrophic
metabolism to the WL pathway in canonical acetogenic bacteria26.
Interestingly, the acetogenic Bathyarchaeota identified in the present
study appear to be a hybrid using an archaeal-type methyl-transferring
pathway via tetrahydromethanopterin (H4MPT) as carrier and a
unique ‘bacterial’-type ACK-PTA system for acetate production from
acetyl-CoA. This finding has significant implications considering the
evolution of bacteria and archaea. Methanogenesis and acetogenesis
are regarded as the most ancient biochemical reactions, mainly as
they use the simple WL pathway for both CO
2
-fixation and adenosine
triphosphate (ATP) generation27.Hence,acetogensandmethanogens
without cytochromes and quinones have been considered founders
of bacteria and archaea, respectively28.Methanogenesishasbeen
traced back to about 3.5 billion years ago, soon after the origin of
life29.Acetoclasticmethanogensareproposedtohaveevolvedaround
250–300 million years ago by obtaining the bacterial ack-pta,
7; Actinobacteria
4; Bacteroidetes
9; Epsilonproteobacteria
6; Deltaproteobacteria
6; Planctomycetia
Bathyarchaeota
Bathyarchaeota
Spirochaetia
9; Mollicutes
9; Erysipelotrichia
5; Bacilli
13; Cyanobacteria
15; Alpha/Betaproteobacteria
7; Chlorobi
5; Acidobacteria
8; Gammaproteobacteria
4; Deinococci
10; Thermotogae
5; Spirochaetia
5; Bacteroidetes
10; Synergistia
4; Clostridia
4; Methanosarcinales
9; Negativicutes
10; Fusobacteriia
100
99
37
98
97
97
86
78
100
100
99
99
93
89
97
57
100
99
99
99
59
100
93
100
99
98
85
86
99
100
100
99
51
95
100
87
53
85
99
84
92
53
98
88
0.2
0.2
B24
B26-1
B26-2
7; Fusobacteriia
8; Negativicutes
B26-1
B26-2
B24
Spirochaeta thermophila DSM 6578
Treponema primitia ZAS-2
Treponema denticola SP23
Treponema denticola SP32
Treponema denticola US-Trep
Treponema maltophilum ATCC 51939
9; Alpha/Beta/Gammaproteobacteria
6; Erysipelotrichia
13; Bacilli
8; Epsilonproteobacteria
8; Oscillatoriophycideae
10; Deltaproteobacteria
8; Planctomycetia
5; Actinobacteria
3; Betaproteobacteria
7; Gammaproteobacteria
8; Alpha/Betaproteobacteria
9; Mollicutes
5; Deltaproteobacteria
8; Bacteroidetes
Lachnoclostridium phytofermentans ISDg
Caldicellulosiruptor saccharolyticus DSM 8903
Methanosarcina barkeri str. Fusaro
Methanosarcina acetivorans C2A
Methanosarcina mazei Tuc01
Ruminiclostridium thermocellum ATCC 27405
Clostridium cellulolyticum H10
Clostridium papyrosolvens C7
Clostridium cellulovorans 743B
Clostridium perfringens SM101
Clostridium sp. 7_2_43FAA
Clostridium sartagoforme AAU1
97
97
99
98
100
65
100
47
98
99
60
68
99
93
99
98
100
93
98
97
84
77
99
99
95
100
84
99
78
93
97
96
91
97
100
100
90
100
99
30
97
64
99
99
95
100
79
91
92
75
Methanosarcinales
Clostridia
Clostridia
ab
Figure 3 | Phylogenies of key genes known to be involved in acetogenesis. a,b, Maximum-likelihood phylogenies of the alignment of 407 amino-acid
residues of Ack homologues (a) and 331 amino-acid residues of Pta homologues (b) identified in Bathyarchaeota and all other archaea and bacteria.
Bootstrap values are based on 1,000 replicates, and percentages are shown at the nodes. Genes identified in this study are named by their genomic bin
index. Numbers of genomes in each collapsed clade are displayed before the clade name.
NATURE MICROBIOLOGY DOI: 10.1038/NMICROBIOL.2016.35 ARTICLES
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supposedly contributing to the mass extinction at that time30.Thus,the
finding of acetogenic archaea with the pta-ack system described in the
present study, combined with the previous report of methanogenesis in
the same phylum16,raisesinterestingquestionsandopensanew
window for investigating the evolution of early life.
In the subsurface sediments, evidence is accumulating suggesting
that acetogenesis is playing an important role in organic carbon
cycling in the deep biosphere7,31,32. Our results suggest that
Bathyarchaeota, which are ubiquitous and dominant members of
the deep biosphere, have the potential to grow as acetogens and
thus could potentially account for the observed acetogenic activities.
Acetogens are well adapted to low-energy environments, using
the simplest CO
2
-fixing pathway—the reductive acetyl CoA
pathway—for both energy production and biosynthesis.
Acetogenesis is thermodynamically favourable over a wide range
of organic substrates, as calculated for typical deep subsurface con-
ditions32. Furthermore, acetogenic archaea might have an energetic
advantage over acetogenic bacteria, as they do not have to invest
ATP to activate formate22, which could be a competitive advantage
under the energy-limiting conditions prevailing in the deep subsur-
face. Screening the publicly available metagenomes33 and metatran-
scriptomes34 from the Peru Margin deep subsurface sediments for
key genes associated with inorganic carbon oxidation and acetate
turnover lends further support to this hypothesis, as a high pro-
portion of the genes previously considered to be from methanogens
probably originate from Bathyarchaeota, as they display the highest
identity to bathyarchaeotal sequences (Supplementary Tables 13
and 14 and Supplementary Fig. 5a,b). The discovery that some
lineages of Bathyarchaeota are acetogens can also explain previous
paradoxical observations, such as the simultaneous evidence for het-
erotrophy10,13,14 and
13
C-depletion in the signature lipid biomarkers
of Bathyarchaeota35. Similar to acetogenic bacteria, bathyarchaeal
acetogens appear to use a wide range of carbon compounds as
energy substrates, such as CO
2
/H
2
, CO, fatty acids, alcohols, alde-
hydes, aromatic compounds and complex organic polymers36. This
suggests that acetogenic archaea may play key roles in organic
carbon transformations in deep marine sediments, degrading
complex organic carbon and producing acetate, fueling the subsur-
face ecosystem with substrates for heterotrophy and acetoclastic
methanogenesis, and changing our view of the roles of archaea in
the carbon cycle of marine sediments (Fig. 4). It should be noted
that the metabolic capabilities and ecological roles of
Bathyarchaeota are still far from being fully understood, as there
appears to be a high metabolic diversity among members of this
new archaeal phylum, as evidenced by the recent discovery of dissim-
ilatory nitrite reduction to ammonium and methane metabolism15,16.
In conclusion, our work demonstrates that some lineages of
Bathyarchaeota are likely to be acetogens with a versatile metab-
olism, which expands the metabolic repertoire of Archaea. By
being able to fix inorganic carbon and to use a wide range of
organic substrates, the metabolism of the acetogenic
Bathyarchaeota is similar to canonical homoacetogenic bacteria,
but with the added advantage of using a more energy-efficient
pathway, making them key players in the carbon cycle of deep
marine sediments. Both acetogenesis and methanogenesis are
thought to represent ancient metabolic pathways. Thus, the identi-
fication of both pathways in the Bathyarchaeota phylum also has
important implications for the evolution of life on Earth.
Methods
Sample description. The sediment sample used in this study was collected by push
cores at a deep-sea vent site at Guaymas Basin (Gulf of California, Mexico) in
October 2007 during cruise AT15-25 on Alvin dive 4,358 (latitude 27° 0.71238 N,
longitude 111° 24.3237 W, depth 2,016 m). The sediment was oily, and the sediment
surface was covered witha white microbial mat. The temperature was measured withthe
Acetoclastic
methanogens
Heterotrophic
bacteria
Detrital
organic
matter
Depth
CH4
Bathyarchaeota
Refractory
compounds Acetogenic archaea
Fermentation
WL pathway
Aromatic
compounds
Chitin
Cellulose
Protein
CO2
CO2
CO2
H2
H2
B24, subgroup 16
Acetate
Subgroups 1,6,15,7/17
Hydrolysis
n
OO
CH3
OO
HO NH
O
OH
OCH3
NH
OH
HO
O
n
C
N
H
R
O
n
O
O
O
HO
OH
OH
OH
OH
O
OH
H
Figure 4 | Proposed roles of acetogenic archaea in the deep biosphere. Ovals represent acetogenic archaea (Bathyarchaeota) and different colours (purple,
blue and orange) refer to Bathyarchaeota that differ in their abilities to use refractory compounds, such as aromatic compounds, extracellular protein, chitin
and cellulose. Both heterotrophic bacteria and acetoclastic methanogens may consume acetate produced by acetogenic Bathyarchaeota (B24 and MCG-16
from this study, highlighted in red; 1, 6, 15 and 7/17 from the study reported in ref. 15 highlighted in green; and others to be identified).
ARTICLES NATURE MICROBIOLOGY DOI: 10.1038/NMICROBIOL.2016.35
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© 2016 Macmillan Publishers Limited. All rights reserved
Alvin temperature probe and increased from 5 °C at the surface to 44 °C at a sediment
depth of 10 cm. Sediment from the upper 10 cm of the push core was transferred to a
Pyrex bottle with a butyl rubber stopper, gassed with an anaerobic N
2
/CO
2
gas mixture,
and stored anaerobically at 4 °C until analyses were conducted in 2014.
SDS-based DNA extraction. Sediment (300 mg) was mixed with an equal weight of
0.1 mm glass beads and 670 µl extraction buffer consisting of 100 mM Tris-HCl
(pH 8.0), 100 mM sodium EDTA (pH 8.0), 100 mM sodium phosphate (pH 8.0),
1.5 m NaCl and 1% hexadecyltrimethylammonium bromide. The sediment was
mixed with the extraction buffer using a low-speed vortex for 5 min. The mixture
was homogenized with a tissue lyser for 2 ×30 s at 30 Hz with a2 min interval. After
homogenization, 50 µl lysozyme (20 mg ml
–1
) and proteinase-K (20 mg ml
–1
) were
added, and the mixture was incubated for 30 min at 37 °C. Then, 70 µl of 20% SDS
was added and incubated at 65 °C for 2 h with a 10 min mingling interval.
The supernatant was collected after centrifuging at 1,000gfor 10 min at room
temperature and then transferred into a 2 ml microcentrifuge tube. The sediment
pellets were extracted again by adding 500 µl extraction buffer and repeating the
previous steps, with the only difference being that the incubation at 65 °C was
carried out for only 1 h. The supernatants from the two cycles of extraction were
combined and mixed with equal volumes of phenol:chloroform:iso-amyl alcohol
(24:25:1). The aqueous phase was recovered by centrifugation and precipitated
overnight 4 °C at with a 0.6 volume of ice-cold propyl alcohol and 0.3 M sodium
acetate (pH 5.2). The nucleic acid was pelleted by centrifuging at 16,000gfor 20 min
at room temperature. The supernatant was removed and the nucleic acid pellet was
washed with 70% ice-cold ethanol, then resuspended in distilled water. The nucleic
acids from three sets of independent extractions were combined for further
downstream applications.
Archaeal and bacterial 16S rRNA gene quantification. Copy numbers of archaeal
and bacterial small subunit ribosomal genes were quantified by qPCR with an Applied
Biosystems 7500 Real Time system. Archaeal 16S rRNA gene copies were quantified
with the universal primer Uni 519F (5′-CAGCMGCCGCGGTAA-3′)37 and the
archaeal-specific primer Arch 908R (5′-CCCGCCAATTCCTTTAAGTT-3′)38.
The qPCR conditions included SYBR Green Premix Ex Taq II (TAKARA), 0.8 µM
each of forward and reverse primers and 1 µl template DNA. The qPCR, with a 25 µl
reaction volume for each sample together with standard series and negative control,
was run in triplicate. The thermal cycling program was 15 min at 95 °C and
40 cycles of 95 °C for 30 s, 60 °C for 30 s and 72 °C for 45 s. The standard curve
contained a series of diluted known linearized plasmids containing between 10
and 1 ×10
7
copies of 16S rRNA genes µl
–1
. Bacterial genomic DNA was used as a
negative control. The amplification efficiency was 96%, the R
2
values of the standard
curve were 0.99, and the slope value was −3.26. Bacterial 16S rRNA gene copies
were quantified using the same SYBR Green Premix Ex Taq II mix, bacterial
specific primer Bac431F (5′-CCTACGGGWGGCWGCA-3′)38 and prokaryotic 519R
(5′-TTACCGCGGCKGCTG-3′)37. The standard curve consisted of a dilution series
ranging from 1 ×10
2
to 1 ×10
7
copies µl
–1
of a known amount of linearized plasmid
containing bacterial 16S rRNA genes. Pyrococcus yayanosii CH1 genomic DNA was
used as a negative control. The estimated amplification efficiency was 100%, the R
2
value of the standard curve was 0.99, and the slope value was −3.03. The thermal cycle
program was 15 min at 95 °C, and 35 cycles at 95 °C for 30 s and 72 °C for 30 s.
Tag sequencing of archaeal V4 regions of 16S rRNA genes. The archaeal
hypervariable V4 regions of 16S rRNA genes were amplified with Uni519F
(5′-XXXXXXXXXXXY MGCCRCGGKAAHACC-3′)39 and Arc806R
(5′-GGACTACNSGGGTMTCTAAT-3′)39 primer sets fused to variable
11-nucleotide key tags for multiplexing, where the xregion represents various key
tags for each sample. An initial denaturation step of 3 min at 95 °C was followed
by 35 cycles at 94 °C for 40 s, 56 °C for 1 min and 72 °C for 1 min. The final
extension step was 72 °C for 10 min. The bacterial V4 region was amplified by 520F
(5′-XXXXXXXXAYTGGGYDTAAAGNG-3′)39 with 8-nucleotide key tags for each
sample and 802R (5′-TACNVGGGTATCTAATCC-3′)39. The amplification mix
contained 1 µl each of forward and reverse primers, 1 µl template genomic DNA,
5 µl 10×Ex Taq buffer, 0.25 µl Ex Taq (TAKARA), 4 µl 2.5 mM dNTP mix and 5 µl
BSA (25 mg ml
–1
). An initial denaturation step of 3 min at 95 °C was followed by
35 cycles at 94 °C for 40 s, 56 °C for 40 s and 72 °C for 2 min. The final extension step
was 72 °C for 10 min. The amplified 290 bp PCR products were excised from 1.8%
agarose gel and purified using a gel extraction kit (EZNA-Omega) and sequenced with
the Illumina Miseq platform. Sequence demultiplexing, trimming, clustering and
classification were performed in mothur40. In brief, sequence reads were initially
denoised using the shhh.flows command to prevent mismatches of barcodes or
primers, and reads <200 bp were removed. Putative chimeric sequences were
identified and removed by the chimera.uchime command using the most abundant
reads in the respective sequence data sets as references. Finally, the sequence reads
were classified according to the Silva taxonomy41 using the classify.seqs command.
Metagenome library construction and sequencing. Between 2.5 and 5 µg of DNA
were initially fragmented by Covaris and then tested by electrophoresis using 1%
agarose gel. Fragmented DNA was combined with an end repair mix and incubated
at 20 °C for 30 min. The end-repaired DNA was then purified with a QIAquick PCR
Purification Kit (Qiagen). Subsequently, the A-tailing mix was added and the mixture
was incubated at 37 °C for 30 min. The purified 3′ends adenylate DNA with adapter
and ligation mix were incubated for the ligation reaction at 20 °C for 15 min. After
this process, the adapter-ligated DNA was selected by running a 2% agarose gel to
recover the target fragments. The gel was purified with a QIAquick Gel Extraction kit
(Qiagen) and several rounds of PCR amplification with a PCR primer cocktail and
PCR master mix were performed to enrich the adapter-ligated DNA fragments. The
PCR products were then selected by running another 2% agarose gel to recover the
target fragments. Gel purification was achieved using a QIAquick Gel Extraction kit
(Qiagen) and the final libraries were validated by theaverage molecule length using an
Agilent 2100 Bioanalyzer instrument (Agilent DNA 1000 Reagents) and by real-time
q-PCR (TaqMan Probe). The qualified libraries (with a 350 bp insert size) were
amplified on cBot to generate a cluster on the flowcell (TruSeq PE Cluster Kit
V3-cBot-HS, Illumina). Pair-end sequencing of the amplified flow cell was performed
using a HiSeq 2000 System (TruSeq SBS KIT-HS V3, Illumina, at BGI-Shenzhen).
De novo assembly and binning of metagenomic sequences. The raw shotgun
sequencing metagenomic reads were dereplicated (100% identity over 100% lengths)
and trimmed with Sickle (https://github.com/najoshi/sickle) using the ‘se’option
and default setting. The dereplicated, trimmed and paired-end DNA reads were
assembled using IDBA-UD42 with the following parameters: pre_correction, mink
52, maxk 92, step 8 and seed kmer 52. Binning of assembled metagenomic sequences
was initially performed using tetranucleotide frequencies in ESOM using 4 to 8 K as
fragment cutoffs43. The tetranucleotide frequencies of each DNA fragment of
assemble scaffold and reference genomes were determined using a custom Perl
script. DNA fragments were then clustered by tetranucleotide frequency using Data
bionics ESOM tools. An assembled scaffold was designed in a specific genomic bin if
all fragments of the assembled sequence were placed into this genomic bin during
the ESOM binning process. Initially, five Bathyarchaeota genomic bins were
identified from ESOM binning. The completeness of the genomic bins was then
estimated by counting universal single-copy genes10. One initial Bathyarchaeota
genomic bin (B26) was shown to contain a least two closely related genomes based
on the number of single copy genes. These scaffolds were further separated by
plotting the differential guanine and cytosine content and assembled sequence
coverage within the bin. The coverage of each sequence was determined by recruiting
original DNA short reads to scaffolds using the Burrows–Wheeler Aligner44. After
this step, B26 was divided into two bins (B26_1 and B26_2). In the end, we obtained
six Bathyarchaeota genomic bins (Table 1 and Supplementary Table 1). To rule out
contamination, all identified genomic bins were rebinned with the method described
by Wrighton and colleagues45 a binning method considering both sequence
composition and sequencing coverage. After this step, some reads were identified
as potential contamination from bacteria and other archaea and thus removed
from identified Bathyarchaeota. Detailed estimates of genome contamination
(shown as ‘Completeness’and ‘Contamination’in Table 1) were assessed based
on lineage-specific marker sets with CheckM46.
Metagenome annotation. Protein coding sequences (CDS) were determined using
MetaGene47 with the default settings. Ribosomal RNA (rRNA) genes were called
with RNAmmer48, and the archaeal model was used to identify all three rRNA
subunits. For each predicted CDS, functional information was collected by a
sequence-similarity search against the NCBI NR database using BLASTP with
E-values of <1 ×10
–5
. The sequences with reliable hits in the NR database were also
compared against KEGG, COG and Pfam with E-values <1 ×10
–5
. The metagenome
was also annotated using IMG-M (https://img.jgi.doe.gov/cgi-bin/m/main.cgi). All
of the predicted CDS and functional annotations were subject to manual inspection
because the annotations identified by more than one source database were
considered for the assigned functions. For genes encoding peptidases and
carbohydrate-active enzymes, all of the annotated genes in the Bathyarchaeota bins
were searched against public databases of peptidases (MEROPS)49 and the CAZy
database50 with E-values <1 ×10
–10
by BLASTP. All of the hits were compared
against the NCBI NR protein database, but only top hits for peptidases and
carbohydrate-active enzymes were considered. The extracellular peptidases were
further confirmed based on the identification of extracellular transport signals using
SignalP51. Genes related to carbohydrate-active enzymes were further classified into
different groups according to the CAZy predictions. Genes involved in the lipid
metabolic pathway of Bathyarchaeota were identified using similar procedures as
well as with the NCBI NR protein database and LIPID Metabolites and Pathways
Strategy (LIPID MAPS) database52.
Estimation of the completeness of genomic bins. The completeness of each
Bathyarchaeota genomic bin was estimated based on a conserved single copy gene
(CSCG) analysis similar to that carried out in previous work53. Two different archaeal
CSCG data sets were used in this study to provide information on the estimated
genome size. We collected 44 CSCGs annotated by the COG database as previously
used by Lloyd and colleagues10 (COG0016, COG0018, COG0049, COG0052, COG
0072, COG0080, COG0081, COG0087, COG0088, COG0089, COG0090, COG0091,
COG0092, COG0093, COG0094, COG0096, COG0097, COG0098, COG0099, COG
0102, COG0103, COG0185, COG0197, COG0202, COG0231, COG0250, COG0256,
COG1093, COG1094, COG1095, COG1358, COG1471, COG1537, COG1631, COG
NATURE MICROBIOLOGY DOI: 10.1038/NMICROBIOL.2016.35 ARTICLES
NATURE MICROBIOLOGY |www.nature.com/naturemicrobiology 7
© 2016 Macmillan Publishers Limited. All rights reserved
1632, COG1727, COG1976, COG2007, COG2092, COG2097, COG2125, COG2139,
COG2147 and COG5257) and 162 CSCGs (annotated Pfam) as previously applied by
Rinke and colleagues53 (for details see Supplementary Table 1). The ratios of the
numbers of CSCGs present in the metagenome and the number of total archaeal
CSCGs were then used to estimate the bin size. No corrections were made for a
decreasing number of CSCGs or for biases caused by the clustering of CSCGs.
Metabolic pathway identification. The similarity of genes with confirmed
annotations was identified using the KEGG database. A match was counted if the
similarity resulted in an E-value <1 ×10
−5
. All KO (KEGG Orthology) numbers were
mapped against KEGG pathway functional hierarchies. Similarly, all predicted
open reading frames were also identified using the COG database with an
E-value <1 ×10
−5
. Certain pathways were reconstructed from the literature
and are discussed in the main text of this work. To determine the potential origin of
genes from the Peru Margin metagenome33 and metatranscriptome34, a best-
reciprocal BLASTN approach was used. Genes identified previously in the Peru
Margin were searched against the NCBI NR database and our data set. Genes in the
Peru Margin with best hits from our data set were considered to be
bathyarchaeotal genes.
Phylogenetic tree construction. To identify relevant gene homologues of
bathyarchaeotal genes, 113 complete archaeal genomes (available in January 2015 at
NCBI) were included in this analysis. In total, 252 archaeal genomes, including all
publicly available Bathyarchaeota genomic bins and some partial/uncultured
archaeal genomes, were included in the phylogenetic analysis wherever applicable
(Supplementary Table 15). As stated above, functional genes were annotated against
Pfam, COG, KEGG and NCBI NR. Archaeal CSCGs were used to infer the
phylogenetic affiliation of inspected Bathyarchaeota genomic bins. Previously, 58
prokaryotic genes were considered to undergo no horizontal gene transfer events17.
A total of 53 of 58 archaeal CSCGs were found to be present in most of the archaeal
genomes, and in all six Bathyarchaeota genomic bins. Homologue sequences were
individually aligned with ClustalW54. For the phylogenetic analysis, deletions and
highly variable regions were removed using a 30% positional conservatory filter, and
alignments were concatenated for the final alignment whenever required. Short
alignments containing fewer than 20 amino-acid residues were discarded. The
individual alignments of proteins were concatenated to a ‘supermatrix’alignment for
phylogenetic inference. To construct phylogenetic trees of the 16S rRNA gene,
aligned sequences were subjected to a maximum-likelihood analysis in FastTree55
using the generalized time-reversible model with the CAT approximation. For the
phylogenies of other functional genes, a maximum-likelihood-based approach with
FastTree was performed using the Jones–Taylor–Thornton (JTT) model with the
CAT approximation.
Expression and purification of Ack. The gene coding for Ack was synthesized by
Generay Biotech and subsequently cloned into the vector pET28a between sites
NdeI/BamHI. E. coli BL21 (DE3) obtained from TransGen Biotech was used as the
expression strain. All chemicals were reagent grade and were purchased from Sigma.
Gene coding for Ack was overexpressed in E. coli strain BL21 (DE3) with 0.5 mM
isopropyl-β-D-thiogalactoside in a total volume of 400 ml. Cells were collected by
centrifugation at 5,000 r.p.m. for 5 min, resuspended in lysis buffer (20 mM Tris-
HCl pH 8.0, 300 mM NaCl, 5 mM imidazole, 10% glycerol) and then lysed by
sonication. The cell lysate was centrifuged at 8,000 r.p.m. for 45 min to discard the
debris. The remaining supernatant was applied to a Ni-NTA column. After washing
with the buffer (20 mM Tris-HCl, pH 8.0, 300 mM NaCl, 20 mM imidazole and
10% glycerol), the fusion protein was eluted with the same washing buffer and then
dialysed to storage buffer (20 mM Tris-HCl, pH 8.0, 300 mM NaCl, 50% glycerol).
An empty pET28a vector was used as control.
Enzyme assays. The activityof Ack was determined at 25 °C by (1) measuring the rate
of ADP and acetyl phosphate formation by couplingthe enzymes pyruvate kinase (PK)
and lactate dehydrogenase (LDH), and (2) monitoring the formation of acetate and
ATP using a hexokinase (HK)-glucose 6-phosphate dehydrogenase (G6PDH)
enzyme-linked assay. For reaction (1), the assay mixtures contained 100 mM Hepes
(pH 7.5), 0.25 mM NADH, 5 mM PEP, 2 U of PK, 2 U of LDH, 5 mM MgCl
2
,
1 mM ATP, 20 mM acetate K, 2.12 µg Ack. For reaction (2), the assay mixtures
contained 100 mM Tris (pH 8.5), 0.25 mM NADP, 5 mM glucose, 2 U HK, 2 U
G6PDH, 10 mM MgCl
2
, 1 mM ADP, 20 mM acetyl phosphate, 2.12 µg Ack. Kinetic
parameters were determined at different substrate concentrations and calculated by
fitting the data of initial rates to the Michaelis–Menten equation by nonlinear
regression. The measurements were performed a minimum of three times.
Accession codes. This sediment metagenome has been deposited at JGI IMG-MER
(https://img.jgi.doe.gov/cgi-bin/m/main.cgi) with ID no. 3300003332. The genomes
supporting the results have been deposited at DDBJ/ENA/GenBank under the
BioProjectID PRJNA312413 (accession numbers from LUCA00000000 to
LUCF00000000).
Received 10 December 2015; accepted 24 February 2016;
published 4 April 2016
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Acknowledgements
The authors thank R. Thauer for critical reading of the manuscript, chief scientist
C. Vetriani for logistical support, as well as the of ficers, crewand pilots of R/V Atlantis and
DSV Alvin for their expert help at sea. This work has been financially supported by the
Natural Science Foundation of China (grant numbers 91228201, 31290232, 41525011 and
41506163), the China Ocean Mineral Resources R&D Association (grant DY125-15-T-04),
the Natural Science Foundation of Guangdong and Shenzhen of China (grant
numbers 2014A030310056 and JCY20140828163633985) and the US National Science
Foundation (grant numbers MCB-0456689 and MCB-0702677 to S.M.S.).
Author contributions
Y.H., M.L., S.M.S. and F.W. designed the experiment and analysis, and interpreted the data.
S.M.S. carried out sampling and preservation. V.P. prepared samples for nucleic acid
extractions and sequencing, and performed quantitative PCR. Y.H., M.L., X.F. and J.F.
performed the bioinformatics analyses. J.X. conducted the protein expression, purification
and enzyme assay. Y.H., M.L., S.M.S. and F.W. wrote the manuscript, in consultation with
all other authors.
Additional information
Supplementary information is available online.
Reprints and permissions information is
available online at www.nature.com/reprints. Correspondence and requestsfor materials should
be addressed to F.W.
Competing interests
The authors declare no competing financial interests.
NATURE MICROBIOLOGY DOI: 10.1038/NMICROBIOL.2016.35 ARTICLES
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