Reference : A model microbial community for Eco-Systems Biology
Scientific congresses, symposiums and conference proceedings : Poster
Life sciences : Environmental sciences & ecology
Life sciences : Microbiology
http://hdl.handle.net/10993/19407
A model microbial community for Eco-Systems Biology
English
Muller, Emilie mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Roume, Hugo [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Buschart, Anna mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Pinel, Nicolas [> >]
Laczny, Cedric Christian mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Satagopam, Venkata mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
May, Patrick mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Lebrun, Laura mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Wilmes, Paul mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
2013
No
International
2nd International Systems Biomedicine Symposium
from 21-09-2013 to 22-10-2013
[en] Objective
Microbial communities (MCs) play crucial roles in human health and disease. In-depth characterization of the vast organismal and functional diversity of MCs is now facilitated by high-resolution molecular approaches. Systematic measurements are key for meaningful data integration, analysis and modeling. Based on a model MC from a biological wastewater treatment plant, we have developed a new framework based on wet- and dry-lab methods for the integrated analyses of MCs at the population- as well as at the community-level.

Methods
The overall methodological framework first relies on a standardised wet-lab procedure for the isolation of concomitant biomolecules, i.e., DNA, RNA, proteins and metabolites, from single undivided samples. Purified biomolecular fractions then are subjected to high-resolution omic analyses including metagenomics, metatranscriptomics, metaproteomics and (meta-) metabolomics. The resulting data form the input for integrated bioinformatic analyses. Population-level integrated omic analyses rely on a newly developed binning and re-assembly method, which yields near-complete genome reconstructions for dominant populations. Community-level analyses involve the reconstruction of community-wide metabolic networks. Functional omic data is then mapped onto these reconstructions and contextualized.

Results
Application of the population-centric workflow has allowed us to reconstruct and identify 10 major populations within the model MC and has led to the identification of a key generalist population, Candidatus Microthrix spp., within the community. Analysis of the community-wide metabolic networks has allowed the identification of keystone genes involved in lipid and nitrogen metabolism within the MC.

Conclusions
Our new methodological framework offers exciting new prospects for elucidating the functional relevance of specific populations and genes within MCs. The established workflows are now being applied to samples of biomedical research interest such as human gastrointestinal tract-derived samples.
http://hdl.handle.net/10993/19407

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