Bioinformatic approaches to identify key genes involved in microbial metabolic control

Shepherd, Joseph (2021) Bioinformatic approaches to identify key genes involved in microbial metabolic control. Doctoral thesis, University of East Anglia.

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Abstract

This thesis combines biological experiments, computational analyses and software development in order to gain new knowledge of microorganisms that could be of benefit to industrial biotechnology processes. It has three linked components.

The main part involves the identification of genomic locations in a dataset of Whole Genome Sequenced (WGS) Saccharomyces cerevisae strains that correlate with furfural resistance, a chemical common in treated lignocellulosic waste biomass. The project comprises both experimental data gathering and computational analysis of the resulting datasets. Following an association analysis of the strains' phenotypes and genome-wide genotypes, directed evolution (DE) experiments are carried out to assess the impact on the strains' genomes. The sequence composition of the resulting strains is then compared to their states prior to the DE experiments in order to assess potential evolutionary paths, and to discover whether multi-strain resistance analysis is comparable to the directed evolution of select strains.

In the second part, diverse yeast strains are grown in YNB media, with subsequently obtained Nuclear Magnetic Resonance (NMR) spectra analysed computationally to quantitatively assess metabolite concentrations. Various Saccharomyces cerevisiae strains are also grown in malt extract media. The results of both analyses are examined and, where possible, compared in order to assess the relative potential of the strains in various industrial brewing processes. A Genome Wide Association Study on the malt datasets indicates genes potentially involved in metabolite quantity, that may taken forward in future research activities.

The final part of this thesis considers the computational prediction of specific cytochrome operons in all bacterial CDS genomes in the RefSeq database (2020). A new software program, ETMiner, is introduced and illustrated through its application to datasets with potentially interesting industrial profiles.
Github link for additional resources: https://github.com/Joenetics/PhD_Thesis.git

Item Type: Thesis (Doctoral)
Faculty \ School: Faculty of Science > School of Biological Sciences
Depositing User: Chris White
Date Deposited: 23 Aug 2022 08:23
Last Modified: 23 Aug 2022 08:23
URI: https://ueaeprints.uea.ac.uk/id/eprint/87556
DOI:

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