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Primer-free FISH probes from metagenomics/metatranscriptomics data permit the study of uncharacterised taxa in complex microbial communities.

ABSTRACT: Methods for the study of member species in complex microbial communities remain a high priority, particularly for rare and/or novel member species that might play an important ecological role. Specifically, methods that link genomic information of member species with its spatial structure are lacking. This study adopts an integrative workflow that permits the characterisation of previously unclassified bacterial taxa from microbiomes through: (1) imaging of the spatial structure; (2) taxonomic classification and (3) genome recovery. Our study attempts to bridge the gaps between metagenomics/metatranscriptomics and high-resolution biomass imaging methods by developing new fluorescence in situ hybridisation (FISH) probes-termed as R-Probes-from shotgun reads that harbour hypervariable regions of the 16S rRNA gene. The sample-centric design of R-Probes means that probes can directly hybridise to OTUs as detected in shotgun sequencing surveys. The primer-free probe design captures larger microbial diversity as compared to canonical probes. R-Probes were designed from deep-sequenced RNA-Seq datasets for both FISH imaging and FISH-Fluorescence activated cell sorting (FISH-FACS). FISH-FACS was used for target enrichment of previously unclassified bacterial taxa prior to downstream multiple displacement amplification (MDA), genomic sequencing and genome recovery. After validation of the workflow on an axenic isolate of Thauera species, the techniques were applied to investigate two previously uncharacterised taxa from a tropical full-scale activated sludge community. In some instances, probe design on the hypervariable region allowed differentiation to the species level. Collectively, the workflow can be readily applied to microbiomes for which shotgun nucleic acid survey data is available.

SUBMITTER: Tan SM

PROVIDER: S-EPMC6592924 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Primer-free FISH probes from metagenomics/metatranscriptomics data permit the study of uncharacterised taxa in complex microbial communities.

Tan Shi Ming SM Yung Pui Yi Maria PYM Hutchinson Paul E PE Xie Chao C Teo Guo Hui GH Ismail Muhammad Hafiz MH Drautz-Moses Daniela I DI Little Peter F R PFR Williams Rohan B H RBH Cohen Yehuda Y

NPJ biofilms and microbiomes 20190625 1

Methods for the study of member species in complex microbial communities remain a high priority, particularly for rare and/or novel member species that might play an important ecological role. Specifically, methods that link genomic information of member species with its spatial structure are lacking. This study adopts an integrative workflow that permits the characterisation of previously unclassified bacterial taxa from microbiomes through: (1) imaging of the spatial structure; (2) taxonomic c ...[more]

PMID: 31263569

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Project description:BACKGROUND:The species composition of a microbial community is rarely fixed and often experiences fluctuations of varying degrees and at varying frequencies. These perturbations to a community's taxonomic profile naturally also alter the community's functional profile-the aggregate set of genes encoded by community members-ultimately altering the community's overall functional capacities. The magnitude of such functional changes and the specific shift that will occur in each function, however, are strongly dependent on how genes are distributed across community members' genomes. This gene distribution, in turn, is determined by the taxonomic composition of the community and would markedly differ, for example, between communities composed of species with similar genomic content vs. communities composed of species whose genomes encode relatively distinct gene sets. Combined, these observations suggest that community functional robustness to taxonomic perturbations could vary widely across communities with different compositions, yet, to date, a systematic study of the inherent link between community composition and robustness is lacking. RESULTS:In this study, we examined how a community's taxonomic composition influences the robustness of that community's functional profile to taxonomic perturbation (here termed taxa-function robustness) across a wide array of environments. Using a novel simulation-based computational model to quantify this taxa-function robustness in host-associated and non-host-associated communities, we find notable differences in robustness between communities inhabiting different body sites, including significantly higher robustness in gut communities compared to vaginal communities that cannot be attributed solely to differences in species richness. We additionally find between-site differences in the robustness of specific functions, some of which are potentially related to site-specific environmental conditions. These taxa-function robustness differences are most strongly associated with differences in overall functional redundancy, though other aspects of gene distribution also influence taxa-function robustness in certain body environments, and are sufficient to cluster communities by environment. Further analysis revealed a correspondence between our robustness estimates and taxonomic and functional shifts observed across human-associated communities. CONCLUSIONS:Our analysis approach revealed intriguing taxa-function robustness variation across environments and identified features of community and gene distribution that impact robustness. This approach could be further applied for estimating taxa-function robustness in novel communities and for informing the design of synthetic communities with specific robustness requirements.

Dataset Information

Primer-free FISH probes from metagenomics/metatranscriptomics data permit the study of uncharacterised taxa in complex microbial communities.

Publications

Primer-free FISH probes from metagenomics/metatranscriptomics data permit the study of uncharacterised taxa in complex microbial communities.

Similar Datasets

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