Taxonomic profile of microbial communities on the Deception
Island volcano
Through the annotation of reads, we observed that the taxonomic
composition in the 98 oC fumarole was distinct in
comparison with other fumaroles and glaciers. Archaea were dominant in
samples from the 98 oC fumarole (relative abundance
between 31.5 and 87.3%), with the most abundant archaeal phyla
classified as Crenarchaeota (23.8-79.3%), followed by Euryarchaeota
(2.5-7.5%) and Korarchaeota (0.1-0.4%). Firmicutes (3.1-22.4%),
Bacteroidetes (0.6-15.3%), Aquificae (0.3-4.6%), and Thermotogae
(0.3-1.0%) were also detected in minor proportions in the 98oC fumarole. Looking at the class level, Thermoprotei,
Thermococci, Methanococci, Archaeoglobi, Methanobacteria, Methanopyri,
and Methanomicrobia represented the most abundant archaeal classes
(>0.1%) in the 98 oC site, and Bacilli,
Gammaproteobacteria, Betaproteobacteria, Fusobacteria, Flavobacteria,
Aquificae (order Aquificales) and Thermotogae (order Thermotogales) were
the dominant classes within Bacteria (Figure 2a).
Archaea were less dominant in the other samples, with a relative
abundance of 0.7-2% in fumaroles <80 oC and
0.4-0.6% in glaciers. Although some dominant phyla were common between
<80 oC fumaroles and glaciers (e.g.
Bacteroidetes, Proteobacteria, and Firmicutes), less dominant phyla were
uniquely distributed according to temperature. For example,
Thaumarchaeota was predominantly found in <80oC fumaroles (0.8-1% for Whalers Bay and 0.2-0.3% in
Fumarole Bay). Verrucomicrobia and Acidobacteria were only detected in
glaciers (1.2-3.1% and 1-1.6%, respectively) (Figure 2a). The main
classes affiliated within the Bacteroidetes phylum were Cytophagia,
Flavobacteria and Sphingobacteria, whereas Gamma- and
Alphaproteobacteria were the most represented classes within
Proteobacteria, followed by Beta-, Delta- and Epsilonbacteria (Figure
2b). Solibacteres was the abundant class within Acidobacteria, and
Verrucumicrobiaea within Verrucomicrobia. Thaumarchaeota assignments
were not classified at the class level using reads annotation in
MG-RAST. The taxonomic annotation of contigs through the IMG/M system
showed similar patterns when compared to reads annotation (Supplementary
Figure 1).
We then used co-occurrence network analysis to explore the complexity of
interactions within the microbial communities in each treatment (Figure
2c). For this, we calculated SparCC correlations between microbial taxa
at the genus level based on metagenome reads annotated in MG-RAST. In
general, the complexity of the community increased with the temperature.
We also noted that communities of Fumarole Bay were more complex than
Whalers Bay. The FBA (98 ºC) site showed the highest level of complexity
and a modular structure, whereas the WBC (0 ºC) site had the least
complex network. Interestingly, the proportion of positive/negative
correlations also changed according to the temperature; at higher
temperatures, the proportion is even, while in lower temperatures there
was an increase in the number of positive correlations.