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project . 2014 - 2019 . Closed

Undestanding microbial communities through in situ environmental 'omic data synthesis

UK Research and Innovation
Funder: UK Research and InnovationProject code: NE/L011956/1
Funded under: NERC Funder Contribution: 425,506 GBP
Status: Closed
01 Nov 2014 (Started) 31 Oct 2019 (Ended)
Description

The purpose of this research is to integrate different sources of 'omics data in environmental science for microbial community analysis. The computational based comparative analysis of DNA sequences may provide information about genome structure, gene function, metabolic and regulatory pathways and how microbial genomes evolve. However, to fully delineate microbial activity and its response to environmental factors, it is necessary to include all levels of gene products, mRNA, protein, metabolites, as well as their interactions. I propose to use large-scale whole genome metagenomic sequencing for assessment of taxonomic and functional diversity of microbial communities. The data generated by metagenomic experiments are both enormous and inherently noisy, containing fragmented DNA sequences representing as many as thousands of microbial species. After using pre-filtering steps, including removal of redundant, low quality sequences, the short DNA sequences are assembled together into longer contigs of overlapping reads, and these contigs may then be scaffolded into full genomes in a bottom-up approach. Having obtained the assembled contigs, the obvious next step is to use publically available databases to annotate the coding regions in these contigs. This will tell us WHAT functionality is available and provide information on WHO is there, the metagenomic sequences are binned, i.e., by associating a particular sequence with an organism. This can be done by either searching for phylogenetic markers or by looking for similar sequences in existing public databases. The end result is the community profile of different samples in terms of organismal abundances within each sample. Whilst metagenomic analysis gives a profile of the microbial community at a specific place or time, and their potential functional, it does not reveal which genes are actually being transcribed. I thus propose to integrate sequencing-based metatranscriptomics in which total RNA (a proxy for gene activity) is extracted from microbial community, converted to cDNA and sequenced without the need for cloning. This will provide information on the regulation and expression profiles of complex communities by enabling quantitative measurements of dynamic expression of RNA molecules and their variation between different states reflecting the genes that are being actively expressed at any given time. However, the story is still far from complete, as we do not have direct evidence of the metabolism within a cell. To give a more complete picture of living organisms, I will integrate metabolomics which will provide unique chemical fingerprints that are a function of specific cellular activity. In particular, the focus will be on identifying habitat-specific endogenous and exogenous metabolites along distinct geochemical conditions. These metabolites will be detected using two-dimensional gas chromatography coupled with mass spectrometry. They will be related to the expression levels from transcriptomes using information on metabolic pathways readily available from annotating metagenomic sequences. In this way we will integrate all three sources of information, mapping the metatranscriptome onto the assembled annotated metagenomes and reconciling the reconstructed metabolic pathways with observations on metabolite concentrations and fluxes. From this we will be able to predict the metabolic function of the entire community not simply who is there.

Data Management Plans
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