Computational Discovery of Metabolic Gene Clusters in Yeast

Pyatt, Christopher (2019) Computational Discovery of Metabolic Gene Clusters in Yeast. Doctoral thesis, University of East Anglia.

Preview

PDF Download (6MB) | Preview

Abstract

Metabolic gene clusters are the genetic source of many natural products (NPs) that can be of use in a range of industries, from medicine and pharmaceuticals to food pro-duction, cosmetics, energy, and environmental remediation. These NPs are synthesised as secondary metabolites by organisms across the tree of life, generally to confer an ephemeral competitive advantage. Finding such gene clusters computationally, from ge-nomic sequence data, promises discovery of novel compounds without expensive and time consuming wet-lab screens. It also allows detection of ‘cryptic’ biosynthetic pathways that would not be found by such screens. The genome sequences of approximately 1,000 yeast strains from the National Collection of Yeast Cultures (NCYC) were searched for both known and unknown metabolic gene clusters, to assess the NP potential of the collection and investigate the evolution of gene clusters in yeast.

Variants of gene clusters encoding popular biosurfactants were found in tight-knit taxonomic groups. The mannosylerythritol lipid (MEL) gene cluster was found to be composed of unique genes and constrained to a small number of species, suggesting a period of substantial evolutionary change in its history. The cellobiose lipid (CBL) gene cluster, conversely, was found to be assembled from members of widespread gene families and is present in at least two widely separated lineages.

The yeast genome dataset was also searched for novel gene clusters using a com-bination of state-of-the-art software and ad hoc methods. A case study of pigmented Rhodotorula species suggested a substantial amount of untapped metabolic potential. One software pipeline, FindClusters, has been developed as a method for high-throughput gene cluster variant discovery in assembled genomes. Another, Flagdown, aims to predict gene cluster types missed by existing methods.

The NCYC genomes prove that gene clusters can be found in diverse yeasts, if we only look, offering hope for the discovery of useful compounds.

Item Type:	Thesis (Doctoral)
Faculty \ School:	Faculty of Science > School of Biological Sciences
Depositing User:	Chris White
Date Deposited:	11 Feb 2021 14:57
Last Modified:	11 Feb 2021 14:57
URI:	https://ueaeprints.uea.ac.uk/id/eprint/79225
DOI:

Downloads

Actions (login required)

View Item