Metagenomics and Modelling of Synthetic and Bioaugmented Microbial Communities for Bioremediation and Animal Health

Microorganisms and their communities are fundamental to natural environmental processes, human and animal health, and tackling the challenges of industrial contamination, waste treatment and sustainable energy generation. However, many of the relevant microorganisms are unculturable or yet to be characterised. State-of-the-art next- and third-generation DNA sequencing approaches allow the assembly of complete or near-complete genome sequences from unculturable microorganisms isolated from diverse environments. These Metagenome-Assembled Genomes (MAGs) allow prediction of both general and specialist functions for the novel organisms concerned, and this knowledge in turn aids in the design of synthetic or bioaugmented microbial communities based on these organisms, which can be employed in diverse applications including bioremediation, waste treatment and animal health (Free et al., 2018). Predictive modelling using microbial functional groups defined by information obtained from MAGs and cultured species can be used to assist in such synthetic community design, which can then be tested experimentally.

In this project, the student will generate and analyse Illumina and Nanopore metagenomic datasets from a diverse range of samples including oil-contaminated soils (Assil et al., 2021), acid mine drainage, wastewater treatment, pharmaceutical-degrading enrichments and sheep GI tracts. MAGs will be assembled from these datasets, and the functional information derived from these will be used to determine individual species and predicted community function, and to suggest culturing or enrichment strategies where applicable. Microbial community modelling using microPop (Kettle et al., 2017) will be used to compare community functionalities and predict the functional consequences of bioaugmentation with specific species or enrichment cultures. Resulting synthetic assemblies with biotechnological potential will be tested experimentally in small-scale laboratory microcosm systems.

The project will allow the student to develop skills in generation of next-generation DNA sequence data, analysis of large metagenomic datasets and assembly of MAGs using bioinformatics tools, functional prediction and modelling in R, as well as traditional microbiology techniques such as pure culture, enrichment culture and microcosm mixed culture. Given the range of samples and existing datasets available, there will be scope for the student to pursue his or her own particular interests, as well as the most promising avenues for general understanding and specific application, as the project progresses.