Elucidating the factors governing microbial community composition


CALL: 2017

DOMAIN: SR - Sustainable Management and Valorization of Bioresources





HOST INSTITUTION: University of Luxembourg

KEYWORDS: Dynamics; integrated multi-omics; metabolomics; metagenomics; metatranscriptomics; modelling; systems ecology; time-series

START: 2018-01-01


WEBSITE: https://www.uni.lu

Submitted Abstract

Knowledge of the factors which govern microbial community structure and function is essential to predict community dynamics and to ultimately control microbial ecosystems. In biological wastewater treatment plants, predictive understanding of the dynamics of oleaginous mixed microbial communities (OMMCs) is required. Here, we will integrate, analyse and model time-resolved meta-omics data along with corresponding environmental and operational data to unravel the main biotic and abiotic factors governing OMMC dynamics, most notably of the dominant population of Microthrix parvicella. We will leverage a unique set of samples from a biological wastewater treatment plant collected on a weekly basis over the period of five years. The collection includes OMMCs sampled from the air-water interface, the underlying activated sludge biomass as well as influent wastewater. In addition, we have compiled a detailed record of corresponding environmental and operational data. Samples will be processed using a comprehensive biomolecular isolation protocol, which allows the isolation of concomitant DNA, RNA, proteins and metabolites. Following the systematic generation of metagenomic, metatranscriptomic and (meta-)metabolomic data, we will process, integrate and analyze the resulting data using methodologies recently developed in-house. More specifically, population-level genomes will be reconstructed de novo and used to integrate the multi-omic data from each timepoint. The resulting datasets will first be analyzed using appropriate time-series analysis methods to identify biotic and abiotic factors which govern community structure and function. Following the determination of the main factors, we aim to construct a community model that integrates the multi-omic measurements with the available metadata to predict the dynamics of M. parvicella. The model will be validated using additional samples to be collected during the course of the project. Overall, the project will allow us to ascertain which factors affect community composition and will lead to the development of strategies for the control of M. parvicella. Finally, the developed approaches will be generalisable to other microbial communities including those constituting the human microbiome.

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