Limits...
Microbial metabolic networks in a complex electrogenic biofilm recovered from a stimulus-induced metatranscriptomics approach.

Ishii S, Suzuki S, Tenney A, Norden-Krichmar TM, Nealson KH, Bretschger O - Sci Rep (2015)

Bottom Line: Potential metabolic switches between eleven dominant members were mainly observed for acetate, hydrogen, and ethanol metabolisms.These results have enabled the estimation of a multi-species metabolic network and the associated short-term responses to EET stimuli that induce changes to metabolic flow and cooperative or competitive microbial interactions.This systematic meta-omics approach represents a next step towards understanding complex microbial roles within a community and how community members respond to specific environmental stimuli.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbial and Environmental Genomics, J. Craig Venter Institute, La Jolla, CA 92037, USA.

ABSTRACT
Microorganisms almost always exist as mixed communities in nature. While the significance of microbial community activities is well appreciated, a thorough understanding about how microbial communities respond to environmental perturbations has not yet been achieved. Here we have used a combination of metagenomic, genome binning, and stimulus-induced metatranscriptomic approaches to estimate the metabolic network and stimuli-induced metabolic switches existing in a complex microbial biofilm that was producing electrical current via extracellular electron transfer (EET) to a solid electrode surface. Two stimuli were employed: to increase EET and to stop EET. An analysis of cell activity marker genes after stimuli exposure revealed that only two strains within eleven binned genomes had strong transcriptional responses to increased EET rates, with one responding positively and the other responding negatively. Potential metabolic switches between eleven dominant members were mainly observed for acetate, hydrogen, and ethanol metabolisms. These results have enabled the estimation of a multi-species metabolic network and the associated short-term responses to EET stimuli that induce changes to metabolic flow and cooperative or competitive microbial interactions. This systematic meta-omics approach represents a next step towards understanding complex microbial roles within a community and how community members respond to specific environmental stimuli.

No MeSH data available.


Related in: MedlinePlus

Single-copy housekeeping gene-based analysis to profile microbial diversity using metagenomic analyses and Bin-genome clustering.(Panel A) shows principal component analysis (PCA) diagram for 107 single-copy bacterial housekeeping genes based on existence in each Bin-genome (Supplementary Table S3). Genes determined as suitable for the community analyses are clustered within gray area. Names of sixteen core-genes used for microbial community population analysis are described in red/orange colors. (Panel B) shows the taxonomic composition of the microbial community based on core-gene frequencies of each taxon and Bin-genome (bars inside). (Panel C) shows comparison of microbial community compositions between three different methods of metagenomic analyses and 16S rRNA clone analyses separately conducted for domains Bacteria and Archaea.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4595844&req=5

f2: Single-copy housekeeping gene-based analysis to profile microbial diversity using metagenomic analyses and Bin-genome clustering.(Panel A) shows principal component analysis (PCA) diagram for 107 single-copy bacterial housekeeping genes based on existence in each Bin-genome (Supplementary Table S3). Genes determined as suitable for the community analyses are clustered within gray area. Names of sixteen core-genes used for microbial community population analysis are described in red/orange colors. (Panel B) shows the taxonomic composition of the microbial community based on core-gene frequencies of each taxon and Bin-genome (bars inside). (Panel C) shows comparison of microbial community compositions between three different methods of metagenomic analyses and 16S rRNA clone analyses separately conducted for domains Bacteria and Archaea.

Mentions: Bin-genome frequencies within the community are essential for normalizing and calculating mRNA/DNA ratios, which is the quantitative value used to determine gene expression levels. Metagenomics-based community composition analysis was performed by using prokaryotic single copy housekeeping genes (Fig. 2). Within 107 reported housekeeping genes21, we selected sixteen core genes that are present in all eleven Bin-genomes (Fig. 2A). The relative frequencies of each Bin-genome were determined based on coverage of the selected core genes for each Bin-genome (Fig. 2B); then the community composition was compared using different methods (Fig. 2C). The results showed that 52% of the microbial population was occupied by the eleven dominant Bin-genomes (Table 1). This approach revealed a more accurate view of community composition relative to other methods including 16S rRNA gene-based clone analysis21 and raw read frequencies of each taxon (Supplementary Table S6–S7). The detailed discussion for the improved community composition analysis can be found in Supplementary Discussion.


Microbial metabolic networks in a complex electrogenic biofilm recovered from a stimulus-induced metatranscriptomics approach.

Ishii S, Suzuki S, Tenney A, Norden-Krichmar TM, Nealson KH, Bretschger O - Sci Rep (2015)

Single-copy housekeeping gene-based analysis to profile microbial diversity using metagenomic analyses and Bin-genome clustering.(Panel A) shows principal component analysis (PCA) diagram for 107 single-copy bacterial housekeeping genes based on existence in each Bin-genome (Supplementary Table S3). Genes determined as suitable for the community analyses are clustered within gray area. Names of sixteen core-genes used for microbial community population analysis are described in red/orange colors. (Panel B) shows the taxonomic composition of the microbial community based on core-gene frequencies of each taxon and Bin-genome (bars inside). (Panel C) shows comparison of microbial community compositions between three different methods of metagenomic analyses and 16S rRNA clone analyses separately conducted for domains Bacteria and Archaea.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4595844&req=5

f2: Single-copy housekeeping gene-based analysis to profile microbial diversity using metagenomic analyses and Bin-genome clustering.(Panel A) shows principal component analysis (PCA) diagram for 107 single-copy bacterial housekeeping genes based on existence in each Bin-genome (Supplementary Table S3). Genes determined as suitable for the community analyses are clustered within gray area. Names of sixteen core-genes used for microbial community population analysis are described in red/orange colors. (Panel B) shows the taxonomic composition of the microbial community based on core-gene frequencies of each taxon and Bin-genome (bars inside). (Panel C) shows comparison of microbial community compositions between three different methods of metagenomic analyses and 16S rRNA clone analyses separately conducted for domains Bacteria and Archaea.
Mentions: Bin-genome frequencies within the community are essential for normalizing and calculating mRNA/DNA ratios, which is the quantitative value used to determine gene expression levels. Metagenomics-based community composition analysis was performed by using prokaryotic single copy housekeeping genes (Fig. 2). Within 107 reported housekeeping genes21, we selected sixteen core genes that are present in all eleven Bin-genomes (Fig. 2A). The relative frequencies of each Bin-genome were determined based on coverage of the selected core genes for each Bin-genome (Fig. 2B); then the community composition was compared using different methods (Fig. 2C). The results showed that 52% of the microbial population was occupied by the eleven dominant Bin-genomes (Table 1). This approach revealed a more accurate view of community composition relative to other methods including 16S rRNA gene-based clone analysis21 and raw read frequencies of each taxon (Supplementary Table S6–S7). The detailed discussion for the improved community composition analysis can be found in Supplementary Discussion.

Bottom Line: Potential metabolic switches between eleven dominant members were mainly observed for acetate, hydrogen, and ethanol metabolisms.These results have enabled the estimation of a multi-species metabolic network and the associated short-term responses to EET stimuli that induce changes to metabolic flow and cooperative or competitive microbial interactions.This systematic meta-omics approach represents a next step towards understanding complex microbial roles within a community and how community members respond to specific environmental stimuli.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbial and Environmental Genomics, J. Craig Venter Institute, La Jolla, CA 92037, USA.

ABSTRACT
Microorganisms almost always exist as mixed communities in nature. While the significance of microbial community activities is well appreciated, a thorough understanding about how microbial communities respond to environmental perturbations has not yet been achieved. Here we have used a combination of metagenomic, genome binning, and stimulus-induced metatranscriptomic approaches to estimate the metabolic network and stimuli-induced metabolic switches existing in a complex microbial biofilm that was producing electrical current via extracellular electron transfer (EET) to a solid electrode surface. Two stimuli were employed: to increase EET and to stop EET. An analysis of cell activity marker genes after stimuli exposure revealed that only two strains within eleven binned genomes had strong transcriptional responses to increased EET rates, with one responding positively and the other responding negatively. Potential metabolic switches between eleven dominant members were mainly observed for acetate, hydrogen, and ethanol metabolisms. These results have enabled the estimation of a multi-species metabolic network and the associated short-term responses to EET stimuli that induce changes to metabolic flow and cooperative or competitive microbial interactions. This systematic meta-omics approach represents a next step towards understanding complex microbial roles within a community and how community members respond to specific environmental stimuli.

No MeSH data available.


Related in: MedlinePlus