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Comparisons of the composition and biogeographic distribution of the bacterial communities occupying South African thermal springs with those inhabiting deep subsurface fracture water.

Magnabosco C, Tekere M, Lau MC, Linage B, Kuloyo O, Erasmus M, Cason E, van Heerden E, Borgonie G, Kieft TL, Olivier J, Onstott TC - Front Microbiol (2014)

Bottom Line: Proteobacteria were identified as the dominant phylum within both subsurface and thermal spring environments, but only one genera, Rheinheimera, was identified among all samples.Using Morisita similarity indices as a metric for pairwise comparisons between sites, we found that the communities of thermal springs are highly distinct from subsurface datasets.Although the Limpopo thermal springs do not appear to provide a new window for viewing subsurface bacterial communities, we report that the taxonomic compositions of the subsurface sites studied are more similar than previous results would indicate and provide evidence that the microbial communities sampled at depth are more correlated to subsurface conditions than geographical distance.

View Article: PubMed Central - PubMed

Affiliation: Department of Geosciences, Princeton University Princeton, NJ, USA.

ABSTRACT
South Africa has numerous thermal springs that represent topographically driven meteoric water migrating along major fracture zones. The temperature (40-70°C) and pH (8-9) of the thermal springs in the Limpopo Province are very similar to those of the low salinity fracture water encountered in the South African mines at depths ranging from 1.0 to 3.1 km. The major cation and anion composition of these thermal springs are very similar to that of the deep fracture water with the exception of the dissolved inorganic carbon and dissolved O2, both of which are typically higher in the springs than in the deep fracture water. The in situ biological relatedness of such thermal springs and the subsurface fracture fluids that feed them has not previously been evaluated. In this study, we evaluated the microbial diversity of six thermal spring and six subsurface sites in South Africa using high-throughput sequencing of 16S rRNA gene hypervariable regions. Proteobacteria were identified as the dominant phylum within both subsurface and thermal spring environments, but only one genera, Rheinheimera, was identified among all samples. Using Morisita similarity indices as a metric for pairwise comparisons between sites, we found that the communities of thermal springs are highly distinct from subsurface datasets. Although the Limpopo thermal springs do not appear to provide a new window for viewing subsurface bacterial communities, we report that the taxonomic compositions of the subsurface sites studied are more similar than previous results would indicate and provide evidence that the microbial communities sampled at depth are more correlated to subsurface conditions than geographical distance.

No MeSH data available.


Related in: MedlinePlus

Sørensen similarity index in relation to geographical distance. The OTU0.03 Sørensen similarity index (y-axis) of subsurface sites is shown in relationship to the geographical distance between the two sites used to calculate the Sørensen index (x-axis). The V6 data used in this study is displayed as both the Sørensen indices of the total subsurface OTU0.03 dataset (V6 Data, red circle) and a rarefied OTU0.03 Sørensen Index (V6 Data*, blue square). The rarefied OTU0.03 Sørensen index was calculated by averaging the result of 100 iterations of subsampling the OTU0.03 community data table to 85% of the smallest sample. A third set of OTU0.03 Sørensen similarity indices and distances (Previous Data, green triangle) was calculated from previous South African subsurface studies (Takai et al., 2001; Moser et al., 2003, 2005; Kieft et al., 2005; Gihring et al., 2006; Lin et al., 2006a,b; Borgonie et al., 2011; Chehoud, 2011).
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Figure 6: Sørensen similarity index in relation to geographical distance. The OTU0.03 Sørensen similarity index (y-axis) of subsurface sites is shown in relationship to the geographical distance between the two sites used to calculate the Sørensen index (x-axis). The V6 data used in this study is displayed as both the Sørensen indices of the total subsurface OTU0.03 dataset (V6 Data, red circle) and a rarefied OTU0.03 Sørensen Index (V6 Data*, blue square). The rarefied OTU0.03 Sørensen index was calculated by averaging the result of 100 iterations of subsampling the OTU0.03 community data table to 85% of the smallest sample. A third set of OTU0.03 Sørensen similarity indices and distances (Previous Data, green triangle) was calculated from previous South African subsurface studies (Takai et al., 2001; Moser et al., 2003, 2005; Kieft et al., 2005; Gihring et al., 2006; Lin et al., 2006a,b; Borgonie et al., 2011; Chehoud, 2011).

Mentions: All seven subsurface samples shared 220 genera and 1410 V6 sequences. Shared V6 sequences were present at similar relative abundances within each subsurface site (Supplement Figure 7). NO14 and MM51940 shared the most genera (n = 551) and exhibited the highest Sørensen indices (0.85) (Figures 5A,B, respectively). At the OTU0.03 level, Sørensen similarity indices ranged from 0.59 to 0.69 between sites—a range higher than previously reported in the South African subsurface (Figure 6). When singleton genera were removed, Sørensen indices increased slightly (0.70–0.85 to 0.80–0.88), while Morisita dissimilarity indices remain unchanged (Figures 5B,D; Supplement Figure 1B,D).


Comparisons of the composition and biogeographic distribution of the bacterial communities occupying South African thermal springs with those inhabiting deep subsurface fracture water.

Magnabosco C, Tekere M, Lau MC, Linage B, Kuloyo O, Erasmus M, Cason E, van Heerden E, Borgonie G, Kieft TL, Olivier J, Onstott TC - Front Microbiol (2014)

Sørensen similarity index in relation to geographical distance. The OTU0.03 Sørensen similarity index (y-axis) of subsurface sites is shown in relationship to the geographical distance between the two sites used to calculate the Sørensen index (x-axis). The V6 data used in this study is displayed as both the Sørensen indices of the total subsurface OTU0.03 dataset (V6 Data, red circle) and a rarefied OTU0.03 Sørensen Index (V6 Data*, blue square). The rarefied OTU0.03 Sørensen index was calculated by averaging the result of 100 iterations of subsampling the OTU0.03 community data table to 85% of the smallest sample. A third set of OTU0.03 Sørensen similarity indices and distances (Previous Data, green triangle) was calculated from previous South African subsurface studies (Takai et al., 2001; Moser et al., 2003, 2005; Kieft et al., 2005; Gihring et al., 2006; Lin et al., 2006a,b; Borgonie et al., 2011; Chehoud, 2011).
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4269199&req=5

Figure 6: Sørensen similarity index in relation to geographical distance. The OTU0.03 Sørensen similarity index (y-axis) of subsurface sites is shown in relationship to the geographical distance between the two sites used to calculate the Sørensen index (x-axis). The V6 data used in this study is displayed as both the Sørensen indices of the total subsurface OTU0.03 dataset (V6 Data, red circle) and a rarefied OTU0.03 Sørensen Index (V6 Data*, blue square). The rarefied OTU0.03 Sørensen index was calculated by averaging the result of 100 iterations of subsampling the OTU0.03 community data table to 85% of the smallest sample. A third set of OTU0.03 Sørensen similarity indices and distances (Previous Data, green triangle) was calculated from previous South African subsurface studies (Takai et al., 2001; Moser et al., 2003, 2005; Kieft et al., 2005; Gihring et al., 2006; Lin et al., 2006a,b; Borgonie et al., 2011; Chehoud, 2011).
Mentions: All seven subsurface samples shared 220 genera and 1410 V6 sequences. Shared V6 sequences were present at similar relative abundances within each subsurface site (Supplement Figure 7). NO14 and MM51940 shared the most genera (n = 551) and exhibited the highest Sørensen indices (0.85) (Figures 5A,B, respectively). At the OTU0.03 level, Sørensen similarity indices ranged from 0.59 to 0.69 between sites—a range higher than previously reported in the South African subsurface (Figure 6). When singleton genera were removed, Sørensen indices increased slightly (0.70–0.85 to 0.80–0.88), while Morisita dissimilarity indices remain unchanged (Figures 5B,D; Supplement Figure 1B,D).

Bottom Line: Proteobacteria were identified as the dominant phylum within both subsurface and thermal spring environments, but only one genera, Rheinheimera, was identified among all samples.Using Morisita similarity indices as a metric for pairwise comparisons between sites, we found that the communities of thermal springs are highly distinct from subsurface datasets.Although the Limpopo thermal springs do not appear to provide a new window for viewing subsurface bacterial communities, we report that the taxonomic compositions of the subsurface sites studied are more similar than previous results would indicate and provide evidence that the microbial communities sampled at depth are more correlated to subsurface conditions than geographical distance.

View Article: PubMed Central - PubMed

Affiliation: Department of Geosciences, Princeton University Princeton, NJ, USA.

ABSTRACT
South Africa has numerous thermal springs that represent topographically driven meteoric water migrating along major fracture zones. The temperature (40-70°C) and pH (8-9) of the thermal springs in the Limpopo Province are very similar to those of the low salinity fracture water encountered in the South African mines at depths ranging from 1.0 to 3.1 km. The major cation and anion composition of these thermal springs are very similar to that of the deep fracture water with the exception of the dissolved inorganic carbon and dissolved O2, both of which are typically higher in the springs than in the deep fracture water. The in situ biological relatedness of such thermal springs and the subsurface fracture fluids that feed them has not previously been evaluated. In this study, we evaluated the microbial diversity of six thermal spring and six subsurface sites in South Africa using high-throughput sequencing of 16S rRNA gene hypervariable regions. Proteobacteria were identified as the dominant phylum within both subsurface and thermal spring environments, but only one genera, Rheinheimera, was identified among all samples. Using Morisita similarity indices as a metric for pairwise comparisons between sites, we found that the communities of thermal springs are highly distinct from subsurface datasets. Although the Limpopo thermal springs do not appear to provide a new window for viewing subsurface bacterial communities, we report that the taxonomic compositions of the subsurface sites studied are more similar than previous results would indicate and provide evidence that the microbial communities sampled at depth are more correlated to subsurface conditions than geographical distance.

No MeSH data available.


Related in: MedlinePlus