Natural product biosynthetic gene cluster engineering using computational and synthetic biology

Abstract

This thesis focuses on the development and application of methods for natural product discovery in microbes. In particular, it describes the application of computational and synthetic biology methods for finding natural product pathways in the genome (in the form of biosynthetic gene clusters), expressing them in an amenable host, discovering BGC-associated metabolites, and engineering pathways to improve the production of said metabolites. First, a novel genome-mining pipeline (ModuleMapper) is described for the inference of natural product biosynthetic gene clusters from genome sequence. After this, a bespoke workflow for defining biosynthetic gene cluster boundaries is presented, and used to collate a small collection of verified biosynthetic gene clusters with defined boundaries. The performance of ModuleMapper is assessed on this small biosynthetic gene cluster collection, and avenues for further improvement suggested. Following this, biosynthetic strategies of the well-characterised, clinically relevant pbt cluster are assessed, with particular focus placed on genome editing approaches for the improvement of GE2270A product titre. Finally, the tools developed in this thesis are collected with recently published heterologous expression methods to define a novel heterologous expression workflow, which has the potential for extension according to the classic Design-Build-Test-Learn paradigm.

Date of Award	21 Mar 2024
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Rainer Breitling (Co Supervisor) & Eriko Takano (Main Supervisor)