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How to build next-generation regulatory devices for engineering gene expression

Primary Supervisor: Professor John McCarthy, School of Life Sciences

Secondary supervisor: Dr Alex Darlington

PhD project title: How to build next-generation regulatory devices for engineering gene expression

University of Registration: University of Warwick

Project outline:

Introduction

Bioscientists are progressively learning how to engineer biological systems like cells and regulatory circuits in ways that give rise to predictable outcomes. Pursuing this fascinating path of research has two major types of benefit: 1. By trying to engineer biosystems we understand them better; 2. Engineered biosystems are useful in many areas, including medicine, agriculture, energy and the environment, and thus such work is important in industry.

It is often the case that we want to construct a genetic system that can be tightly regulated. For example, if we engineer a cluster of genes that encode the enzymes that catalyse the respective steps of a biosynthetic pathway, it can be useful to control exactly when that pathway is turned on or off. The most obvious reason for doing this is that when the pathway is operating it may be imposing a significant burden on cellular resources so that we need to constrain the overall impact on growth.

So what is the best way to regulate a synthetic biosystem such as a pathway? Up to now, this has largely been achieved using transcriptional components (promoters, transcription factors). Relying on circuitry built at this level in an important host organism like yeast limits our options because there are not many suitable transcriptional components available. In this project, you will examine whether an alternative route is possible, i.e. one that relies on translational regulatory devices built using synthetic mRNA 5’untranslated regions, aptamers, aptazymes and/or RNA-binding proteins (these are reviewed in McCarthy, 2021).

Experimental work

The first challenge will be to build and compare regulatory devices constructed using transcriptional and translational parts. One way of doing this would be to introduce the doxycycline-responsive TetON and TetOFF promoter devices (Belli et al, 1998) and the corresponding translation-based doxycycline aptamer devices (McCarthy, 2021) into Saccharomyces cerevisiae.  The characteristics of each type of regulatory device could initially be studied by coupling each one to the gene encoding the high-intensity ymNG fluorescent reporter. After this, the devices could be introduced upstream of a biosynthetic pathway such as that producing lycopene.

Cutting-edge quantitative methods involving flow cytometry, robotics and single-cell RNAseq will be used to characterise these systems in yeast cells growing on different media. This will yield an overview of the impact of regulation of gene expression directed by the respective genetic devices on cell physiology and growth, thus increasing our understanding of these systems and also providing data that inform their potential use in industrial processes. Novel combinations of the devices will also be considered.

Modelling

This project will also provide an exciting opportunity for the student to learn and to apply relatively straightforward computational techniques in order to model physiological processes, gene expression and growth in the host organism and how these are impacted by different regulatory expression systems.

Overall, this project will provide training in sought-after experimental and computational techniques, and also generate valuable new data on important genetic and physiological processes and relationships in living cells. There is a high probability of co-authorship on at least one research publication.

BBSRC Strategic Research Priority: Understanding the Rules of Life: Systems Biology

    Techniques that will be undertaken during the project:

    Techniques include:

    • Genome editing using CRISPR/Cas9,
    • Flow cytometry
    • Robotics
    • Single-cell RNAseq
    • Microscopy
    • Proteomics and computational modelling

    Contact: Professor John McCarthy, University of Warwick