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Vol.:(0123456789)1 3
Marine Life Science & Technology (2020) 2:73–86
https://doi.org/10.1007/s42995-019-00007-0
RESEARCH PAPER
Microbial diversity of sediments from an inactive
hydrothermal vent field, Southwest Indian Ridge
Zhifeng Yang1 · Xiang Xiao2,3 ·
Yu Zhang1,2
Received: 5 August 2019 / Accepted: 1 September 2019 / Published
online: 18 October 2019 © Ocean University of China 2019
AbstractThe Southwest Indian Ridge, which is the
slowest-spreading of the main ridges, separates the African and
Antarctic plates. The slow expanding rate is associated with less
density of hydrothermal vent fields, shorter longevity of
hydrothermal activ-ity, cold mantle temperatures and thick
lithosphere. However, the microbial communities adapting to such
specific charac-teristics of this area have remained largely
unexplored. To study the microbial diversity at the Southwest
Indian Ridge, we sampled three sediment cores in a newly found
inactive vent field, the Tianzuo field, and used high-throughput
sequencing of 16S rRNA genes to reveal the microbial composition.
Microbial communities of three sampling sites were very similar at
the surface, and underwent a gradient change along depth.
Gammaproteobacteria, namely Alteromonadaceae, Nitroso-coccus and
the JTB255 marine benthic group, were the most dominant bacterial
taxa. Marine Group I was the dominant archaeal taxon in our
samples. In addition, microbial populations capable of ammonia
oxidation, nitrite oxidation, sulfur oxidation and manganese
oxidation were detected to be the main chemolithoautotrophs. The
enrichment of sulfur-oxidizing and manganese-oxidizing bacteria was
observed in deep layers. When compared with other vent fields along
different ocean ridges, the Tianzuo field showed distinct
composition in both archaeal and bacterial communities. These
results provide the first view of microbial communities of the
Tianzuo field at the Southwest Indian Ridge, and give a better
understanding of metabolic potential possessed by the microbial
populations.
Keywords Hydrothermal · Microbial diversity ·
High-throughput sequencing · Deep-sea sediments ·
Ultraslow-spreading ridge
Introduction
Deep-sea hydrothermal vents are commonly distributed near the
mid-ocean ridge, ocean floor subduction zones, back-arc basins, and
ocean volcanoes. The seawater infiltrated
through the seabed cracks is gradually mixed with the man-tle
material, generating the hydrothermal fluid bursting from the vent.
Reduced materials rich in the hydrothermal fluid, such as Fe(II),
sulfur, and methane, support the growth of autotrophic
microorganisms, which are the main drivers of the hydrothermal vent
ecosystem (Fisher et al. 2007). Since the discovery of
hydrothermal vents in the late 1970s, the bloom and distribution of
the microbial communities under the influence of hydrothermal
activity have been a long-standing research hotspot.
Microbial populations of a diverse range of taxa are widely
distributed in various ecological niches in sediments near
hydrothermal vents (Fisher et al. 2007). In sediments of an
inactive hydrothermal vent field, the bacterial com-munities were
dominated by Proteobacteria, Bacteroidetes, Actinobacteria and
Firmicutes whereas Thaumarchaeota and Euryarchaeota represented the
most dominant archaeal taxa (Zhang et al. 2016). In addition,
the cosmopolitan OTUs in all sediments of two vent fields along
Mid-Atlantic Ridge were affiliated with the bacterial clades
JTB255, Sh765B-TzT-29,
Edited by Chengchao Chen.
Electronic supplementary material The online version of this
article (https ://doi.org/10.1007/s4299 5-019-00007 -0) contains
supplementary material, which is available to authorized users.
* Yu Zhang [email protected]
1 School of Oceanography, Shanghai Jiao Tong University,
Shanghai 200240, China
2 State Key Laboratory of Ocean Engineering, Shanghai Jiao
Tong University, Shanghai 200240, China
3 State Key Laboratory of Microbial Metabolism,
and School of Life Sciences and Biotechnology,
Shanghai Jiao Tong University, Shanghai 200240, China
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Rhodospirillaceae and the OCS155 marine group and with the
archaeal Marine Group I (Cerqueira et al. 2017). Another study
of a vent field along the Mid-Atlantic Ridge revealed that specific
mesophilic, thermophilic and hyperthermophilic archaeal (e.g.,
Archaeoglobus, ANME-1) and bacterial (e.g., Caldithrix,
Thermodesulfobacteria) taxa were highly abun-dant near the vent
chimney (Cerqueira et al. 2015). Epsilon-proteobacteria
belonging to the mesophilic chemolithoauto-trophic genera
Sulfurovum and Sulfurimonas was the most abundant group in
shallow-water hydrothermal sediments (Wang et al. 2015).
Nevertheless, in a research on hydrother-mal sediments covering a
wide range of temperatures and depths, more than 80% of all
detected OTUs were shared among different temperature realms and
sediment depths, sug-gesting a connectivity between distinct
hydrothermal habitats (Meyer et al. 2013). The environmental
gradients generated by hydrothermal activities give rise to the
specific populations inhabiting hydrothermal sediments and make the
deep-sea hydrothermal field a highly diverse ecosystem.
Ultraslow oceanic spreading ridges cover over 15,000 km of
divergent plate boundaries, and an assessment of their role in
participating global biogeochemical cycle requires an intensive
survey of the microbial community. The Southwest Indian Ridge
(SWIR) was the slowest expanding edge in the world, spreading at a
full rate of 14 mm year−1 (German et al. 1998). Also, the SWIR
produced more sparse hydrothermal vents as a result of the slow
spreading rate (Baker 2017). Besides, the slow expanding rate was
associated with shorter longevity of hydrothermal activity, cold
mantle temperatures and a thick lithosphere at individual vent
fields (Copley et al. 2016; German et al. 1998).
Microbial communities surviv-ing there should have shaped specific
structures to adapt to these traits of SWIR. There were 26 vent
fields found in SWIR whereas only one, named Longqi, was confirmed
with activity according to the InterRidge Database (http://vents
-data.inter ridge .org). Studies on microbial communities in SWIR
revealed the diversity and abundance of microbial communities
inhabiting the vent chimney (Ding et al. 2017), ridge-flank
(Sinha et al. 2019), seamounts (Djurhuus et al. 2017),
hydrothermal plume (Li et al. 2015) and sediments (Chen
et al. 2016). Further work on microbial diversity in SWIR will
enhance our apprehension of the ecosystem in this largely
unexplored area.
During the investigation of deep-sea hydrothermal fields in the
Southwest Indian Ocean as part of the Chi-nese DY115-20th
expedition, abnormal values of methane, hydrogen sulfide, reduction
potential and temperature were detected and associated with
hydrothermal activity in the Tianzuo hydrothermal field (63.541°E,
27.951°S) (Chen et al. 2018; Tao et al. 2009). Subsequent
investigations have revealed remarkable long lasting, i.e., over
50,000 years, hydrothermal activity, which is caused by the
slow spread-ing rate and a weak thermal budget in the area, being
based
on geochemical and mineral analyses (Sauter et al. 2013;
Chen et al. 2018; Münch et al. 2001). In this study, we
tried to understand the ecological features, which have been
impacted by the thousands of years’ of hydrothermal activ-ity.
Therefore, the microbial communities as well as their metabolic
potential in the Tianzuo field have been analyzed and compared with
those of other vents located along ultra-slow-spreading and
slow-spreading ocean ridges.
Results
Overview of the sampling sites
The Tianzuo hydrothermal field (63.541°E, 27.951°S) is located
at the easternmost ultraslow-spreading Southwest Indian Ridge. It
is an inactive sulfide field, which is hosted by ultramafic rocks
and controlled by detachment faults, and is covered by thick
sediments, indicating that the ancient hydrothermal activity has
ceased for a long time (Chen et al. 2018). Three sediment
columns were sampled at south (63.53909°E, 27.9535°S), west
(63.53422°E, 27.9466°S) and north (63.53896°E, 27.9391°S) of
Tianzuo, which were divided into several layers as described in the
Methods. The location of the Tianzuo field and the sampling sites
as well as two adjacent hydrothermal vents have been shown in Fig.
S1. The water depth of the sampling sites ranged from 3618.83 to
3759.47 m whereas the temperature range was from 1.52 to
1.54 °C. In addition, the salinity was 34.7‰ for all three
sites.
Alpha diversity of bacterial and archaeal
communities
The bacterial and archaeal sequencing quality data have been
included in Tables S1 and S2. 13,417 OTUs were identified from a
total of 468,205 bacterial 16S rRNA gene sequences. In addition,
13,540 OTUs were identified from a total of 743,580 archaeal 16S
rRNA gene sequences. The rarefac-tion curve of bacterial and
archaeal sequences suggested that the sequencing depth was close to
saturation. Therefore, the sequences could recover most species in
the original com-munities (Fig. S2). The reads of all samples were
rarified to an even depth (bacteria: 19,453, archaea: 21,215) for
alpha and beta diversity analyses. The richness of bacterial
com-munities (the number of total OTUs in each bacterial
com-munity) was similar at the surface among three sampling sites
(south, west and north) and continued to decrease until the depth
of 15 cm at the south sample (Fig. 1a). Similarly, the
evenness of bacterial communities (opposed to the Gini unevenness
index) was close at the same depth among three sampling sites, and
rapidly decreased in the south sample until 13 cm
(Fig. 1b). Conversely, the number of total OTUs
http://vents-data.interridge.orghttp://vents-data.interridge.org
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and evenness of the archaeal communities varied greatly at three
locations, and decreased until depth of 7 cm and 13 cm,
respectively (Fig. 1c, d). The Shannon diversity index,
Pielou’s evenness index and Chao1 diversity index showed similar
patterns (Tables S3, S4).
Bacterial and archaeal community structure
The relative abundance of the bacterial class with average
percentages over 1% has been summarized in Fig. 2a.
Gener-ally, the surface bacterial composition was relatively
simi-lar across south, west and north sampling sites, whereas a
greater variation was detected along the depth of the south
sediment. The most abundant bacterial class retrieved from all
sites was Gammaproteobacteria with an average percent-age of 22.2%.
Actinobacteria, representing 11.0% of the whole bacterial
communities, was the second abundant class. A clear decrease of
Gammaproteobacteria (33.5%–4.5%) from sediment surface to deep
layers was observed in the south sediment. Actinobacteria were
distributed mostly in
the deep layer, reaching a peak abundance (56.9%) in the
24–26 cm layer of the south sediment. Similarly, the rela-tive
abundance of bacilli increased from 1.3% to 20.6% with increasing
depth of layers. Furthermore, Alphaproteobacte-ria consisted a
relatively stable part of the total communities with a mean
abundance of 9.7% and a standard deviation of 2.3% (Fig. 2a).
In addition, the bacterial classes with less than 1% relative
abundance have been shown in Fig. S3. The surface communities
showed similar composition for these classes whereas the
communities of deep layers were rela-tively variable.
Zetaproteobacteria were found at up to 0.4% in surface layers but
hardly detected at all in deep layers. In contrast, Marinimicrobia
(SAR406 clade) was distributed mostly in the deep layers, with a
peak percentage of 0.3% in the south 10–12 cm layer (Fig.
S3).
The archaeal community structure was much simpler than that of
bacteria. Thus, the relative abundance of archaeal members at the
level of classes has been included in Fig. 2b. Marine Group I,
which included mainly ammonia-oxidizing archaea, was dominant at
all vent sites with percentages
Fig. 1 Alpha diversity of bacterial and archaeal communities
along depth at three sites in the Tianzuo hydrothermal fields. (a)
Bacterial richness estimated by total number of OTUs; (b) bacterial
unevenness estimated by Gini index.; (c) archaeal richness; (d)
archaeal unevenness
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from 87.7% to 98.6%. Woesearchaeota uncultured group represented
the second most abundant archaea, accounting for 0.9%–9.4% of all
recovered archaeal sequences (Fig. 2b).
Beta diversity of bacterial and archaeal
communities
To see the gradient change of the community composition (beta
diversity) among all samples, a non-metric multidi-mensional
scaling (NMDS) plot was applied to express the community variation
into two orthogonal directions, NMDS1 and NMDS2 (Fig. 3). The
distance of two sam-ples at the NMDS plot represents the
dissimilarity of their community composition. Thus, the bacterial
community structure was similar at the surface of three sampling
sites, and showed a gradient change within 15 cm depth, which
is along the direction of NMDS1 (Fig. 3). At a depth of
over
15 cm, the bacterial community showed a random change along
the NMDS1 direction, but a gradient change along the NMDS2
direction. Furthermore, the main microbial clades were joined at
the NMDS plot to show the distribution of clades among samples. Any
clade that lies close to a sam-ple point is more likely to be found
in that sample. At the bacterial phylum level, the composition of
bacterial com-munities in the NMDS1 direction changed from
Proteobac-teria, Acidobacteria and Planctomycetes to Bacteroidetes,
Gemmatimonadetes, Nitrospirae and then Actinobacteria
(Fig. 3a). Specially, in the main phylum Proteobacteria,
Zetaproteobacteria was shifted to Gammaproteobacteria,
Deltaproteobactera, JTB23, SPOTSOCT00m83 and then
Alphaproteobacteria, Betaproteobacteria and Epsilonpro-teobacteria
in the NMDS1 direction. In the NMDS2 direc-tion,
Epsilonproteobacteria replaced other classes (Fig. 3b). Also,
the archaeal community structure was similar at the
Fig. 2 Relative abundance of bacterial (a) and archaeal (b)
classes in each sample. The bacterial classes with percentages over
1% were shown while the other minor populations below 1% were
summed as
“Others” at the plot. The sample name describes the sampling
loca-tion (S: south, W: west, N: north) and its layer depth
(cm)
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surface of three sampling sites. Moreover, the archaeal
com-munity showed a depth-dependent change in the NMDS1 direction
above 17 cm depth. Being different from the bac-teria, a
random change occurred in the NMDS2 direction (Fig. 3c). At
the phylum level, Euryarchaeota, Thaumar-chaeota and Woesearchaeota
changed to the Miscellaneous Crenarchaeotic Group in the NMDS1
direction (Fig. 3c).
The Venn plot of microbial communities of the sur-face layer
(south: 0–2 cm, west: 0–2 cm, north: 0–5 cm) showed
that there were 883 bacterial OTUs and 440
archaeal OTUs shared by three sites (Fig. S4). Moreover, these
shared bacterial OTUs accounted for 30%–34% of total bacterial OTUs
whereas the shared archaeal OTUs accounted for 22%–30% of total
archaeal OTUs at each site (Fig. S4). Also, the shared bacterial
OTUs made up 78%–82% reads of each bacterial community whereas the
shared archaeal OTUs made up 85%–91% reads of each archaeal
community. Furthermore, these results confirmed the similarity
between the surface communities.
Fig. 3 NMDS plot of bacterial communities and archaeal
commu-nities along depth at three sites. (a), (b) Beta diversity of
bacterial community which shows shift of phylum or classes in
Proteobacte-ria. (c) Beta diversity of archaeal community which
shows shift of phylum. The number indicates the depth of each
community while its color differs for three sampling sites. The red
text is the acronym of taxonomy: Proteobacteria: PRO,
Acidobacteria: ACI, Actinobacte-
ria: ACT, Chloroflexi: CHL, Gemmatimonadetes: GEM,
Planctomy-cetes: PLA, Firmicutes: FIR, Bacteroidetes: BAC,
Nitrospirae: NIT. Euryarchaeota: EUR, Miscellaneous Crenarchaeotic
Group: MCG, Thaumarchaeota: THA, Woesearchaeota: WOE,
Alphaproteobacte-ria: ALP, Betaproteobacteria: BET,
Deltaproteobacteria: DEL, Epsi-lonproteobacteria: EPS,
Gammaproteobacteria: GAM, JTB23: JTB, SPOTSOCT00m83: SPO,
Zetaproteobacteria: ZET
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Bacterial metabolic potentials
According to confirmed characteristics of isolated and cul-tured
bacteria, the relative abundance of bacteria capable of hydrogen
oxidation, sulfur compound oxidation, manga-nese oxidation, ammonia
oxidation, nitrite oxidation, and methane oxidation was estimated
at sediment in the south sample using Functional Annotation of
Prokaryotic Taxa (FAPROTAX) (Fig. 4a). Among 13,417 bacterial
OTUs, 2870 OTUs were assigned to 67 functional groups, account-ing
for 24% ± 5% reads in each sample. Among all types of bacteria,
bacteria with functions of ammonia oxidation and nitrite oxidation
were the most abundant chemoautotrophic types over 20 cm.
These two types of bacteria had the highest relative abundance in
the shallow layer of 5–7 cm, accounting for 7.8% and 6.4% of
all bacteria, respectively. Also, there was a local peak at the
depth of 21–23 cm, which accounted for 1.9% and 3.9% of all
bacteria, respectively. Sulfur-compound-oxidizing bacteria
comprised the third most common autotrophic bacteria, showing a
maximum abundance of 4.9% at 21 cm and a minimum of 0.4%.
Man-ganese-oxidizing bacteria were relatively low in abundance, but
reached peaks of 0.25% at 19 cm. The relative abundance of
methane-oxidizing bacteria and hydrogen-oxidizing bac-teria was
less than 0.03%, and they were not detected in nearly half of the
layers (Fig. 4a).
In addition, we applied Phylogenetic Investigation of
Communities by Reconstruction of Unobserved States (PIC-RUSt) to
predict the average functional gene copies per 16S rRNA gene in the
samples (Fig. 4b). Functional genes pmoA/amoA, narG/nxrA,
soxB, mnxG, mmoX and hyaB gene were used to quantify the bacteria
capable of methane/ammonium oxidation (Holmes et al. 1995),
nitrite reduction/oxidation
(Attard et al. 2010; Henry et al. 2006), sulfur
oxidation (Tourna et al. 2014), manganese oxidation (Dick
et al. 2008), methane oxidation (Horz et al. 2001) and H2
metabolism (H2 producing or H2 oxidation) (Voordouw 1992). mnxG and
mmoX were not detected in any sample. The pmoA/amoA and narG/nxrA
genes, which are phylogenically similar, are unable to be
distinguished by PICRUSt. The average copies of four detected
functional genes fluctuated along all depths, whereas the narG/nxrA
gene was the most abun-dant. The narG/nxrA gene showed the highest
copies per 16S rRNA gene at the 25 cm layer (Fig. 4b).
Due to the small number of isolates but much more genomic
information on archaea, which is insufficiently recognized by
FAPRO-TAX, we applied only PICRUSt rather than both methods to the
archaeal communities. As Thaumarchaeota is the main archaeal group,
the pmoA/amoA gene copies per 16S rRNA gene were calculated for
archaeal communities which indicates the ammonia oxidizing
potential (Fig. S5). The archaeal communities were predicted to
harbor 0.44–1.61 copies pmoA/amoA gene per 16S rRNA gene. However,
the weighted nearest sequenced taxon index (NSTI) (bacteria:
0.10–0.37; archaea: 0.17–0.22) for PICRUSt indicated long
phylogenic distances between our OTUs with the reference
genomes.
Specificity of microbial communities
in the Tianzuo field
The microbial communities in the Tianzuo field were com-pared
with those in hydrothermal vent fields along the Mid-Atlantic Ridge
(Menez Gwen, Lucky Strike and Rainbow vents) (Cerqueira et al.
2017), Arctic Mid-Ocean Ridge (Loki’s Castle and Soria Moria vents)
(Dahle et al. 2015)
Fig. 4 Function potential of bacterial communities. Relative
abun-dance of the bacteria able to oxidize hydrogen, sulfur
compound, nitrite, ammonium, Mn and methane along depth of the
south sedi-
ment (a) and functional gene copies per 16S rRNA gene of
bacterial communities along depth of the south sediment (b)
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and another inactive hydrothermal vent field of west SWIR (Zhang
et al. 2016). Sediments from Lucky Strike and Rain-bow vents
were far from active vents whereas sediments of Menez Gwen were
close to an active vent. Biofilms of Loki’s Castle and Soria Moria
were located at the surface of active chimneys. Also, sediments
from another inactive hydrother-mal vent field of west SWIR were
far away from active vents. The research area that we selected was
either from ultraslow-spreading (West SWIR, Loki’s Castle and Soria
Moria vents) or slow-spreading ridges (Menez Gwen, Lucky Strike and
Rainbow vents). The microbial communities of the Tianzuo field are
significantly different from other five hydrothermal
fields according to the NMDS plot (Fig. 5). The Analysis of
Similarities (ANOSIM) test for bacterial communities is 0.991 (P =
0.001) and the ANOSIM test for archaeal com-munities is 0.943 (P =
0.001). Moreover, the microbial com-munities of the Tianzuo field
were more similar to Lucky Strike and Rainbow as well as another
inactive hydrothermal vent of west SWIR. Actinobacteria and
Phycisphaerae were the second and eighth most abundant bacterial
classes in the Tianzuo field whereas they were hardly detected at
all in other fields (Fig. 6a). Besides, Marine Group I was the
main archaeal class of sediments from Tianzuo and other three vent
fields away from hydrothermal activity (Fig. 6b).
Fig. 5 NMDS plot of bacterial communities (a) and archaeal
commu-nities (b) at different hydrothermal vent fields. Arctic
represents sedi-ments from Loki’s Castle and Soria Moria at the
Arctic Mid-Ocean Ridge; Menez Gwen, Lucky Strike and Rainbow
represents sediments
sampled from these three vent fields at the Mid-Atlantic Ridge;
West SWIR represents sediments (50.9277°E, 37.6251°S and 50.9643°E,
37.6174°S) sampled from a vent field at west SWIR
Fig. 6 Relative abundance of bacterial (a) and archaeal classes
(b) with percentages over 1% in different vent fields. The relative
abundance of classes in each vent field was the average relative
abundance of classes in samples belonging to that vent field
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In contrast, although biofilms from Arctic Mid-Ocean Ridge and
sediments from Menez Gwen were both influenced by intensive
hydrothermal activity, their community similarity is extremely low
(Fig. 6).
Discussion
In this article, we present the first report of microbial
diver-sity at three sediments in the Tianzuo field of SWIR, which
was recently determined to be an inactive hydrothermal vent field.
Based on the results of alpha diversity and community structure
(Figs. 1, 2, 3, Figs. S3, S4), the microbial commu-nities of
top layers at different sites were very similar. This result
indicated that no strong environmental gradient drove the turnover
of microbial communities in our sampling area. It is realized that
temperature (Meyer et al. 2013; Nunoura et al. 2010),
organic concentration (Mahmoudi et al. 2015), metal
composition (Cerqueira et al. 2017) and reduction potential
(Edlund et al. 2008) are the common environmen-tal factors
driving community change in hydrothermal sedi-ments. Considering
the low density of hydrothermal vents and weak hydrothermal
activities caused by the ultraslow spreading rate of SWIR, it is
not surprising that the plume or fluids from distant active vents
(about 40 km) (Fig. S1) could not build sufficiently strong
environmental gradients to influence overall communities. Moreover,
the inactive state of the Tianzuo vent field may have lasted long
enough to eliminate the environmental gradient caused by ancient
hydrothermal activities.
Dominant taxa and their ecological functions
In the sediment samples examined in this study, we found a
series of bacterial populations associated with hydrothermal
activities, including Epsilonproteobacteria (0.27% ± 0.43%)
(Fig. 3b) and bacteria capable of hydrogen oxidation, sulfur
compound oxidation, and manganese oxidation (Fig. 4a). Similar
populations taking part in N cycling, S oxidation, metal oxidation
and methane oxidation were detected in limited abundance in another
inactive vent of SWIR (Zhang et al. 2016). As a common group
of hydrothermal vent communities, Epsilonproteobacteria is widely
distributed in sulfide deposits (Flores et al. 2012) and
diffuse flow (Campbell et al. 2013), where this group is
involved in sul-fur metabolism and associated with a high
concentration of hydrogen sulfide. As hydrothermal plumes could
spread out over a few hundred kilometers away from the vent source
(German 2010), the reduced compound in deposits from the plume may
fuel these autotrophic bacteria to a limited abun-dance (Flores
et al. 2012). Within 40 km from the Tianzuo field, the
newly discovered Tiancheng hydrothermal field was identified as an
active low-temperature diffuse flow field
with potential high-temperature vents nearby (Chen et al.
2018). This could be the source of some sulfur compounds for the
Tianzuo field (Fig. S1).
The enrichment of autotrophic species may serve also as an
indicator for past hydrothermal activity. There is a potential that
reduced materials from past active vents may be able to sustain
these microbial groups to exist even in lower populations. In
addition, iron-oxidizing and sulfur-oxidizing bacteria of
hydrothermal sediments were enriched in incubation without adding
organic matter (Handley et al. 2010) showing that autotrophic
microorganisms may be adapted to oligotrophic conditions and
survive for a long time. The sudden enrichment of sulfur-oxidizing
bacteria at the depth of 21 cm indicates a sulfur-rich layer
(Fig. 4a). Similarly, although the population of
manganese-oxidizing bacteria was quite small, its abundance was
much higher at 19 cm (0.25%) relative to the surface layer.
The enrichment of sulfur-oxidizing and manganese-oxidizing bacteria
in the adjacent deep layers may be attributed to supplementation of
sulfur and Mn(II) by metalliferous sediments from past hydrothermal
plumes of Tianzuo rather than distant vents. By contrast,
iron-oxidizing bacteria were not found in the sediments. In
comparison with iron-oxidizing bacteria, most manganese-oxidizing
bacteria are heterotrophic (Bohu et al. 2016; Dick et al.
2006; Tebo et al. 2005). This heterotrophic trait makes it
possible for manganese-oxidizing bacteria to survive for a long
time without Mn(II) (Sabirova et al. 2008).
In addition to Epsilonproteobacteria, chemoautotrophic
Gammaproteobacteria is the predominant primary producer of both
free-living and symbiotic microbial communities in deep-sea
hydrothermal fields (Yamamoto and Takai 2011). However, most
Gammaproteobacteria in our sediments are not attributed to
sulfur-oxidizing species, which were merely detected in our
sediments (Fig. 4a). Instead, they are attrib-uted to species
of the genera Alteromonadaceae, Nitroso-coccus and the JTB255
marine benthic group; these three groups are commonly distributed
in diverse marine environ-ments (Ivanova and Mikhailov 2001; Koops
and Pommeren-ing-Röser 2015; Mussmann et al. 2017). A
metagenomic analysis revealed that the JTB255 marine benthic group
is heterogeneous and covers a broad physiological spectrum, ranging
from facultative sulfur- and hydrogen-based chemo-lithoautotrophy
to obligate chemorganoheterotrophy (Muss-mann et al. 2017).
However, it is not clear if the JTB255 marine benthic group in our
sediments was able to oxidize sulfur, because of the lack of both
isolates and metagen-omic analysis in our study. The
ammonia-oxidizing bacteria genus Nitrosococcus represented 3.1% in
total communi-ties and accounted for most bacterial ammonia
oxidizers in our sediments (3.5%), whereas this genus comprised
4.7% of populations in bathyal plain sediments from the Menez Gwen
hydrothermal vent system of the Mid-Atlantic Ridge (Cerqueira
et al. 2015). In contrast, the ammonia-oxidizing
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archaea Marine Group I remained as a relatively stable
pop-ulation among all sites (Fig. 2b). Despite the competition
over ammonia, there was not any significant negative cor-relation
between the abundances of these two groups (Pear-son’s r =
− 0.12, p value = 0.6392) because of the complex effect of
multiple environmental factors—the interaction of ammonia
concentration, pH, organic matter, and temperature (Liu 2017).
Besides, the fluctuation of pmoA/amoA gene copies per 16S rRNA gene
of archaea suggested a shift of subgroups occupying individual
niches (Fig. S5).
The microbial communities of Tianzuo harbored a high proportion
of bacteria, which were mainly attributed to the class Nitrospira,
being able to oxidize nitrite (Fig. 4a). Com-pared with
Thaumarchaeota, nitrite oxidizers of this group were important
contributors to global dark carbon fixation with a greater
metabolic efficiency, owing to their less costly carbon fixation
through the rTCA cycle (Pachiadaki et al. 2017). In addition
to its ability of oxidizing nitrite, Nitro-spira exhibited diverse
metabolic potential, such as nitrate reduction, ureolysis, ammonia
oxidation and sulfide oxida-tion, giving rise to its worldwide
success (Daims et al. 2015; Füssel et al. 2017; Koch
et al. 2015). Although the data-base of FAPROTAX assigns
Nitrospira nitrosa, Nitrospira nitrificans and Nitrospira inopinata
as ammonia-oxidizers, OTUs in our samples did not belong to these
three species. Another metagenomic analysis of microbial
communities in Rainbow hydrothermal vent fields verified this group
as a potential nitrite-oxidizer, as suggested by the presence of
genes encoding enzymes of the nitrification process (Cer-queira
et al. 2018). Similarly, the sequences retrieved from the vent
chimney of SWIR included diverse populations tak-ing part in the N
cycle, and included Nitrosococcus, Nitro-spira, Nitratifractor,
Nitrosomonas, and Rhizobiales (Ding et al. 2017). So, our
results confirmed the importance of nitrification in the
hydrothermal vent field of SWIR. Moreo-ver, it was interesting to
find that the narG/nxrA genes were much higher in deep layers,
indicating a bloom of denitrifiers therein (Fig. 4b). In
contrast to our sediments samples, the communities inhabiting the
chimney were mainly governed by sulfur oxidizers other than ammonia
oxidizers (Ding et al. 2017). For lack of sufficient sulfur
flux from the active vent, the easily available nitrogen compounds
would be a better choice for chemolithotrophic bacteria in our
sediments.
Spatial patterns of the microbial communities
Organic flux to the sea floor broadly co-varies with the
sedi-mentation rate. As an example, at very low sediment
accu-mulation rate of SWIR (1 g/cm2 per thousand years), most
organic matter is consumed at or near the sea floor (D’Hondt
et al. 2015; Jahnke 1996). Thus, the intense change of
micro-bial communities along quite a short depth of 26 cm in
our sediments (Fig. 3a, c) could result from a decreasing
flux
rate of organic carbon along depth. After a certain depth, the
species gradually adapts to the local environmental stress,
commonly coupled with a relatively stable alpha diversity in
sediments (Mahmoudi et al. 2015; Vuillemin et al. 2018).
Although similar stable states of microbial communities were
observed in our sediments, the fluctuation of alpha and beta
diversity was somewhat larger than other samples (Figs. 1, 3).
Specially, it was the class Actinobacteria that mainly contributed
to the community variation in deep lay-ers, and was even more
abundant in deep layers, unlike most clades (Fig. 2a).
Besides, this class appeared to be a special group when compared
with other vents (Fig. 6a). The strong adaptation ability of
Actinobacteria to the harsh environ-ment of deep layers may be
attributed to several reasons. For example, the genomic islands in
its genome comprise gene clusters that could produce secondary
metabolites to increase adaptation ability. The gene clusters are
dynamic entities that are readily acquired, rearranged and
fragmented in the context of genomic islands (Penn et al.
2009). As a result, fitness or niche utilization could be formed
quickly through shifting the ability of producing diverse natural
products. In addition, most of isolated Actinobacteria from the
sediments of Southwest Indian Ocean showed the activ-ity of using
refractory organic carbon, which could fuel this group in
energy-limited condition (Chen et al. 2016). In our work,
sequences assigned to Actinobacteria were mostly affiliated with
the order Corynebacteriales. This order was also the dominant
Actinobacteria in sediments from an active hydrothermal vent field
of SWIR, which may benefit from its ability in degrading polycyclic
aromatic hydrocar-bons, a commonly found substrate in hydrothermal
vents (Chen et al. 2016). For the rare class (less than 1% of
the total), Marinimicrobia (SAR406 clade) contributed a lot to the
variation of communities (Fig. S3). This group was reported to
possess variable energy metabolism and con-servation strategies
including utilization of nitrogen oxide, sulfur compounds, and
methanol (Hawley et al. 2017), which may account for its
enrichment in deep layers of our sediments.
Microbial communities in the Tianzuo field had shaped specific
microbial community structure compared with hydrothermal vent
fields in the Mid-Atlantic Ridge, Arctic Mid-Ocean Ridge and west
SWIR (Fig. 5). Although both Arctic Mid-Ocean Ridge and SWIR
are ultraslow-spreading ridges, their microbial communities were
not more similar compared with vent fields in the Mid-Atlantic
Ridge, which is a slow-spreading ridge. This result suggested no
over-whelming impact of spreading speed on microbial communi-ties
whereas local environmental characteristics should have played a
more important role in shaping specific community structure.
Similarly, microbial communities in the Juan de Fuca Ridge showed a
distinct structure among six vents while Epsilonproteobacteria was
important in distinguishing
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82 Marine Life Science & Technology (2020) 2:73–86
1 3
the community structure (Opatkiewicz et al. 2009). Besides,
the distinct pattern revealed by both functional genes and 16S rRNA
genes was observed in Atlantic hydrothermal systems whereas the
similar pattern of most vents was the detection of abundant
populations participating in H2 and methane oxidation (Roussel
et al. 2011). The global distribu-tion and comparation of
microbial communities along ocean ridges remained unclear.
Bachraty’s research on deep-sea hydrothermal vent faunas delineated
six major hydrother-mal provinces and possible dispersal pathways
(Bachraty et al. 2009), which provided a good research
direction for the global study on microbial communities in vent
fields. Also, our results identified Actinobacteria and
Phycisphaerae in distinguishing the microbial communities of the
Tianzuo field from other fields (Fig. 6a). Similar with
Actinobac-teria, isolates of Phycisphaerae have been shown to be
capable of degrading a variety of different polysaccharides
(Fukunaga et al. 2009; Kovaleva et al. 2015).
Additionally, Phycisphaerae was observed as one of the most
abundant classes in hydrothermal vent fields along the Valu Fa
Ridge in southwestern Pacific and iron-hydroxide deposits from the
Arctic Mid-Ocean Ridge (Storesund et al. 2018; Storesund and
Ovreas 2013). Our results suggested Phycisphaerae may be restricted
in distribution in hydrothermal systems, for which the reason
requires intensive research.
Limitation of methods
In our study, FAPROTAX supplied a fast and reliable method to
access the functional groups according to the confirmed
characteristics of the isolates. Compared with another widely used
function prediction method PICRUSt with genomes as reference
(Douglas et al. 2019; Langille et al. 2013), FAPROTAX was
more reliable and direct owing to experimental verification.
Furthermore, PICRUSt assumes the close relatives of published
genomes share partial genes, whereas FAPROTAX is more conserved to
assume the close relatives of published isolates share similar
functions only if all isolates in this taxon possess the function.
As a result of predicting the function of phy-logenic distant OTUs,
the high weighted NSTI of PIC-RUST for our samples suggested a low
accuracy. However, FAPROTAX is supposed to underestimate the
abundance of each group due to relatively fewer isolates compared
with the huge number of uncultured species. Indeed, our result
showed that only 2870 OTUs could be assigned to functional groups
out of a total of 13,417 OTUs. As an additional method for groups
with few isolates, PIC-RUSt could be applied to predict the
functional genes in archaea. It was quite difficult to calculate
the percentages of OTUs for each function using PICRUSt like
FAPRO-TAX. Instead, we calculated the functional gene copies per
16S rRNA gene in our samples, which provided an
indirect estimation of functional groups (Fig. 4b, Fig.
S5). So, an integrated method combining both characteristics of
cultured species and genome information is required for a more
detailed and reliable analysis based on the phylo-genic
information, which would be helpful in filling the gap between
phylogenic diversity and function diversity. In addition,
considering the continuous change of micro-bial community along
depth, our samples as well as those of other previous work in SWIR
(Ding et al. 2017; Peng et al. 2011; Zhang et al.
2016) were mostly restricted to the surface part of this area,
which were far from reveal-ing the great diversity below the sea
floor. In contrast, the Integrated Ocean Drilling Program (IODP)
Expedi-tion revealed that microbial communities buried deeply below
the sea floor differed markedly from shallower sub-seafloor
communities and instead resembled organo-trophic communities in
forest soils (Inagaki et al. 2015). As SWIR is associated with
cold mantle temperatures and thick lithosphere (German et al.
1998), the microorgan-isms may inhabit and bloom in deeper layers
than at the fast-spreading edge, due to the modest temperature
bound-ary of SWIR for microbes to survive. Therefore, a deeper
sampling depth below the sea floor and better annotation to the
retrieved sequences from samples may be required for a clear and
full comprehension on the role and diversity of microbial
communities in SWIR.
Conclusions
High-throughput sequencing revealed a total of 13,417 bacterial
OTUs and 13,540 archaeal OTUs from the sedi-ments of the Tianzuo
hydrothermal field. Both bacterial and archaeal compositions were
similar at the surface layer of three sampling sites around
Tianzuo, whereas the deep layers of the south sampling site showed
a gradient change along depth. Gammaproteobacteria and
Actinobacteria were the main bacteria classes, whereas Marine group
I accounted for at least 87.7% of all archaea among all samples.
The bacteria capable of ammonia oxidation and nitrite oxidation
were the most abundant chemolithoautotrophic groups. In addition,
sulfur-oxidizing bacteria, such as Epsilonproteo-bacteria and
Mn(II)-oxidizing bacteria, occurred as enriched populations in deep
layers of the sediments. Through com-parison of microbial
communities with other vent fields, the Tianzuo field showed a
significant distinct composition of microbial communities. In
addition, Actinobacteria and Phycisphaerae played an important role
in distinguishing microbial communities of Tianzuo from other
vents. To our knowledge, this provides the first view of microbial
diversity and the metabolic characteristics of sediments in the
Tianzuo hydrothermal vent field along the Southwest Indian
Ridge.
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83Marine Life Science & Technology (2020) 2:73–86
1 3
Materials and methods
Study site
In December, 2014 during the Dayang 35 cruise to the Tianzuo
field, three sediment cores were sampled at the south, north, west
of the Tianzuo hydrothermal field (63.541°E, 27.951°S) using the
manned submersible Jiao Long. Temperature and depth of the sampling
sites were measured using CTD. Soon after the sediments were
acquired on board, the sediment core of 26 cm depth sam-pled
at the south was immediately divided into 13 layers every
2 cm, and the sediment core of 8 cm depth sam-pled at the
west was immediately divided into 4 layers every 2 cm. The
sediment core of 5 cm depth sampled at the north was not
divided. Then, the sediments were fully mixed with 2 volumes of
RNAlater (Thermo Fisher, USA) and stored at 4 °C on board.
Finally, the material was transported and stored at -80 °C in
the laboratory until processing.
DNA extracting and sequencing
After being fully mixed, ~ 0.5 g sediments were used to
extract environmental DNA using the FastDNA SPIN Kit for Soil (MP
Biomedicals, USA) following manufacturer’s instructions. The DNA
concentration was then measured by a Nanodrop 2000 (Thermo
Scientific, USA). The V4 region of the bacterial 16S rRNA gene was
amplified using the primers 533F (5′-TGC CAG CAG CCG CGG TAA -3 and
Bact 806R (5′-G GAC TAC CAG GGT ATC TAA TCC TGT T-3′) (Klindworth
et al. 2013) and V4–V5 region of the archaeal 16S rRNA gene
was amplified using the primer Arch516F (5′-TGY CAG CCG CCG CGG TAA
HAC-CVGC-3′) and Arch855R (5′-TCC CCC GCC AAT TCC TTT AA-3′)
(Klindworth et al. 2013) with a 8 bp unique bar-code at
5′ end of forward primer. PCR was carried out in triplicate 50 μl
reactions using 10–50 ng of DNA. Ther-mal cycling conditions
for bacterial 16S rRNA gene con-sisted of initial denaturation at
94 °C for 5 min followed by 25 cycles of denaturation at
94 °C for 30 s, annealing at 58 °C for 40 s,
and extension at 72 °C for 30 s with a final extension at
72 °C for 10 min. The thermal cycling conditions for
archaeal 16S rRNA gene consisted of ini-tial denaturation at
94 °C for 5 min followed by 35 cycles of denaturation at
94 °C for 30 s, annealing at 58 °C for 40 s,
and extension at 72 °C for 45 s with a final exten-sion
at 72 °C for 10 min. PCR products were gel-purified using
an EZNA Gel Extraction Kit (Omega Bio-Tek, Inc., USA). The purified
DNA was sequenced on the Illumina MiSeq platform by Personalbio
Biotechnology company
(Shanghai, China). All sequences have been deposited in NCBI SRA
under the BioProject accession number PRJNA558519.
Sequence processing
The paired-end FASTQ reads were quality-filtered by sliding
windows of 5 bp with 1 bp per step, and the remaining
bases had an average quality of more than Q20 for each window.
Filtered reads over 150 bp remained, and no N (ambiguous base)
was included in the reads. FLASH was then used to merge the
paired-end reads with an overlap of more than 10 bp (Magoč and
Salzberg 2011). Then, we used QIIME (Caporaso et al. 2010) and
VSEARCH (Rognes et al. 2016) to eliminate chimeric sequences
and pick operational taxo-nomic units (OTUs) for 97% similarity
from the reads. Then, the OTUs were assigned a taxonomy according
to the SILVA database (123 release) (Quast et al. 2013).
Non-specific amplification of OTUs was eliminated from the reads.
The rarefaction curve of sequences was plotted using
alpha_rar-efaction.py script in QIIME. Then, we randomly sampled
19453 reads per sample for bacteria and 21215 reads per sample for
archaea to eliminate the effect of reads number when comparing
alpha diversity and beta diversity between samples.
Data analysis
Most data analysis and visualization was done using R lan-guage
(R Core Team 2019). The alpha diversity of archaeal and bacterial
communities is estimated by calculating the total number of OTUs
per sample, Shannon diversity index, Pielou’s evenness index, the
Gini unevenness index, and Chao1 diversity index (Chao and Shen
2003; Heip et al. 1998; Kemp and Aller 2004; Naeem 2009;
Ricotta and Avena 2003). Due to the high diversity of bacterial
classes, the relative abundance of bacterial classes in different
sam-ples was plotted to show the classes over 1% and below 1%,
respectively. For limited number of archaeal classes, we plotted
the relative abundance of all archaeal classes. The Bray–Curtis
distance was calculated to show the beta diversity among
communities through NMDS (Borcard et al. 2011).
The relative abundance of species possessing the meta-bolic
potential of oxidizing hydrogen, methane, nitrogen compounds,
sulfur compounds, and manganese in each sample was estimated using
FAPROTAX (version 1.1) (Louca et al. 2016). FAPROTAX is a
manually constructed database that maps prokaryotic taxa (e.g.,
genera or spe-cies) to metabolic or other ecologically relevant
functions (e.g., nitrification, denitrification or fermentation),
based on the literature on cultured representatives. For example,
if all cultured species within a bacterial genus (or more
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84 Marine Life Science & Technology (2020) 2:73–86
1 3
precisely, all type strains of species) have been identified as
denitrifiers, FAPROTAX assumes that all uncultured members of that
genus are also denitrifiers. And the data-base in FAPROTAX
contained 7820 functional annotations covering 4724 taxa, which
would facilitate our analysis of potential function and should be
more comprehensive than one-by-one annotation by ourselves. PICRUSt
(PICRUSt2: a new version) was also applied to predict the abundance
of functional genes in bacterial communities and archaeal
communities (Douglas et al. 2019). Considering that the
predicted read counts for each sample did not represent the
abundance of functional groups, we divided the predicted counts of
each functional gene by the total 16S rRNA gene counts. Thus, the
average copies per 16S rRNA gene of functional genes could be then
used to compare the weight of functional genes in different
communities.
To compare the community composition of Tianzuo field with other
hydrothermal vent fields, we downloaded the sequencing data of
biofilms or sediments in six hydro-thermal vents located at
Mid-Atlantic Ridge (Cerqueira et al. 2017), Arctic Mid-Ocean
Ridge (Dahle et al. 2015) and west SWIR (Zhang et al.
2016). The quality filter was processed following similar methods
for our sam-ples. Due to the difference of primer sets used in
different researches, when clustering the OTUs for 97% similar-ity,
we used SILVA database as the reference through the
pick_close_otus.py script in QIIME rather than clustering our
sequences against each other. The samples with over 500 reads
clustered into OTUs remained for later analysis. Then, we could
compare the OTUs between samples with enough reads and consistent
OTU names. Other steps fol-lowed the methods we used for our data.
NMDS plot was also applied to show the community variation between
different vent fields. Besides, ANOSIM was applied to test
statistically whether there is a significant difference between
communities of six locations (Clarke 1993). The relative abundance
of bacterial classes and archaeal classes over 1% was also plotted
to show if there were special populations in the Tianzuo field.
Acknowledgements This work was supported by the National
Sci-ence Foundation of China (41530967, 41776173, 41576129) and the
National Key Research and Development Program of China
(2016YFC03007). We acknowledge the crew of the Dayang 35th
cruise.
Author contributions XX and YZ designed the experiments and
collected the samples. ZY performed the experiments and analyzed
the data. ZY, XX and YZ wrote the paper. The final manuscript was
approved by all the authors
Compliance with ethical standards
Conflicts of interest The authors declare that they have no
conflict of interest.
Animal and human rights statement This article does not contain
any studies with human participants or animals performed by any of
the authors.
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https://www.R-project.org/
Microbial diversity of sediments from an inactive
hydrothermal vent field, Southwest Indian
RidgeAbstractIntroductionResultsOverview of the sampling
sitesAlpha diversity of bacterial and archaeal
communitiesBacterial and archaeal community structureBeta
diversity of bacterial and archaeal communitiesBacterial
metabolic potentialsSpecificity of microbial communities
in the Tianzuo field
DiscussionDominant taxa and their ecological
functionsSpatial patterns of the microbial
communitiesLimitation of methods
ConclusionsMaterials and methodsStudy siteDNA extracting
and sequencingSequence processingData analysis
Acknowledgements References
