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Coordinating environmental genomics and geochemistry reveals metabolic transitions in a hot spring ecosystem.

Swingley WD, Meyer-Dombard DR, Shock EL, Alsop EB, Falenski HD, Havig JR, Raymond J - PLoS ONE (2012)

Bottom Line: We improved automated annotation of the BP environmental genomes using BLAST-based Markov clustering.We show that changes in environmental conditions and energy availability are associated with dramatic shifts in microbial communities and metabolic function.The complementary analysis of biogeochemical and environmental genomic data from BP has allowed us to build ecosystem-based conceptual models for this hot spring, reconstructing whole metabolic networks in order to illuminate community roles in shaping and responding to geochemical variability.

View Article: PubMed Central - PubMed

Affiliation: School of Natural Sciences, University of California Merced, Merced, California, United States of America.

ABSTRACT
We have constructed a conceptual model of biogeochemical cycles and metabolic and microbial community shifts within a hot spring ecosystem via coordinated analysis of the "Bison Pool" (BP) Environmental Genome and a complementary contextual geochemical dataset of ~75 geochemical parameters. 2,321 16S rRNA clones and 470 megabases of environmental sequence data were produced from biofilms at five sites along the outflow of BP, an alkaline hot spring in Sentinel Meadow (Lower Geyser Basin) of Yellowstone National Park. This channel acts as a >22 m gradient of decreasing temperature, increasing dissolved oxygen, and changing availability of biologically important chemical species, such as those containing nitrogen and sulfur. Microbial life at BP transitions from a 92 °C chemotrophic streamer biofilm community in the BP source pool to a 56 °C phototrophic mat community. We improved automated annotation of the BP environmental genomes using BLAST-based Markov clustering. We have also assigned environmental genome sequences to individual microbial community members by complementing traditional homology-based assignment with nucleotide word-usage algorithms, allowing more than 70% of all reads to be assigned to source organisms. This assignment yields high genome coverage in dominant community members, facilitating reconstruction of nearly complete metabolic profiles and in-depth analysis of the relation between geochemical and metabolic changes along the outflow. We show that changes in environmental conditions and energy availability are associated with dramatic shifts in microbial communities and metabolic function. We have also identified an organism constituting a novel phylum in a metabolic "transition" community, located physically between the chemotroph- and phototroph-dominated sites. The complementary analysis of biogeochemical and environmental genomic data from BP has allowed us to build ecosystem-based conceptual models for this hot spring, reconstructing whole metabolic networks in order to illuminate community roles in shaping and responding to geochemical variability.

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Distribution of Markov protein families in “Bison Pool”.5-way Venn diagram of the overlap between protein families containing at least one BPEG sequence.
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pone-0038108-g005: Distribution of Markov protein families in “Bison Pool”.5-way Venn diagram of the overlap between protein families containing at least one BPEG sequence.

Mentions: Markov clustering of protein families yielded 38,352 clusters containing more than a single protein, though 14,232 clusters were composed entirely of KEGG database sequences. The conservation of protein families between sites is shown in Figure 5. The two largest overlaps occur in the chemotrophic site 1 & 2 pair (2,816 protein families) and the phototrophic sites 4 & 5 (7,567 protein families), with the latter being nearly 3-fold larger than the former. This disparity likely stems from the larger protein library in the relatively large Chloroflexi/Cyanobacteria genomes in the phototrophic sites versus the smaller Aquificae/Crenarchaeota genomes in chemotrophic sites.


Coordinating environmental genomics and geochemistry reveals metabolic transitions in a hot spring ecosystem.

Swingley WD, Meyer-Dombard DR, Shock EL, Alsop EB, Falenski HD, Havig JR, Raymond J - PLoS ONE (2012)

Distribution of Markov protein families in “Bison Pool”.5-way Venn diagram of the overlap between protein families containing at least one BPEG sequence.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3367023&req=5

pone-0038108-g005: Distribution of Markov protein families in “Bison Pool”.5-way Venn diagram of the overlap between protein families containing at least one BPEG sequence.
Mentions: Markov clustering of protein families yielded 38,352 clusters containing more than a single protein, though 14,232 clusters were composed entirely of KEGG database sequences. The conservation of protein families between sites is shown in Figure 5. The two largest overlaps occur in the chemotrophic site 1 & 2 pair (2,816 protein families) and the phototrophic sites 4 & 5 (7,567 protein families), with the latter being nearly 3-fold larger than the former. This disparity likely stems from the larger protein library in the relatively large Chloroflexi/Cyanobacteria genomes in the phototrophic sites versus the smaller Aquificae/Crenarchaeota genomes in chemotrophic sites.

Bottom Line: We improved automated annotation of the BP environmental genomes using BLAST-based Markov clustering.We show that changes in environmental conditions and energy availability are associated with dramatic shifts in microbial communities and metabolic function.The complementary analysis of biogeochemical and environmental genomic data from BP has allowed us to build ecosystem-based conceptual models for this hot spring, reconstructing whole metabolic networks in order to illuminate community roles in shaping and responding to geochemical variability.

View Article: PubMed Central - PubMed

Affiliation: School of Natural Sciences, University of California Merced, Merced, California, United States of America.

ABSTRACT
We have constructed a conceptual model of biogeochemical cycles and metabolic and microbial community shifts within a hot spring ecosystem via coordinated analysis of the "Bison Pool" (BP) Environmental Genome and a complementary contextual geochemical dataset of ~75 geochemical parameters. 2,321 16S rRNA clones and 470 megabases of environmental sequence data were produced from biofilms at five sites along the outflow of BP, an alkaline hot spring in Sentinel Meadow (Lower Geyser Basin) of Yellowstone National Park. This channel acts as a >22 m gradient of decreasing temperature, increasing dissolved oxygen, and changing availability of biologically important chemical species, such as those containing nitrogen and sulfur. Microbial life at BP transitions from a 92 °C chemotrophic streamer biofilm community in the BP source pool to a 56 °C phototrophic mat community. We improved automated annotation of the BP environmental genomes using BLAST-based Markov clustering. We have also assigned environmental genome sequences to individual microbial community members by complementing traditional homology-based assignment with nucleotide word-usage algorithms, allowing more than 70% of all reads to be assigned to source organisms. This assignment yields high genome coverage in dominant community members, facilitating reconstruction of nearly complete metabolic profiles and in-depth analysis of the relation between geochemical and metabolic changes along the outflow. We show that changes in environmental conditions and energy availability are associated with dramatic shifts in microbial communities and metabolic function. We have also identified an organism constituting a novel phylum in a metabolic "transition" community, located physically between the chemotroph- and phototroph-dominated sites. The complementary analysis of biogeochemical and environmental genomic data from BP has allowed us to build ecosystem-based conceptual models for this hot spring, reconstructing whole metabolic networks in order to illuminate community roles in shaping and responding to geochemical variability.

Show MeSH