Post-transcriptional regulation of gene expression

Lead Research Organisation: University of Edinburgh

Abstract

In order to survive and grow, cells need to decode the information written in the genome. The "genetic code", which specifies how the information in the DNA is used to make different proteins, was solved in the 1960s. In contrast, the "regulatory code", which determines the amount of each protein produced, is still very poorly understood. If we can understand the regulatory code, we will be able to predict the effects of different DNA mutations on protein amounts in the cells. This information could be used, for example, to understand the mechanisms of various diseases, or to artificially manipulate the amounts of proteins made by genes.

We study the regulatory code, and how it differs between different organisms and different external conditions. We do that by measuring the amounts of proteins produced by thousands of mutant genes, and using computational methods to tell which mutations were most likely to produce an effect on protein production. We then confirm these statistical predictions experimentally. These experiments will increase our understanding of how the human genome works, and they may lead to practical applications in bio-medicine.

Technical Summary

A major goal of research in biology is to understand how DNA sequence mediates the regulation of gene expression. Historically, regulatory elements have been extensively studied in flanking regions of genes, but recent results point to an important role of coding sequences in regulation. We study the functional consequences of synonymous mutations in Eukaryotic genes. To characterize the influence of mutations on gene expression in human cells, we generate synthetic libraries of mutated genes, we use low- and high-throughput methods to measure the expression phenotypes of each mutant, and we apply bioinformatic analyses to disentangle the sequence features that influence gene expression at various stages. To study fitness effects of mutations, we mutagenize selected yeast genes and assay the fitness of each variant by deep sequencing the pooled mutants during competitive growth. This allows a comparison of fitness effects between synonymous and nonsynonymous mutations, and between specific classes of synonymous mutations.
In collaboration with other groups, we also use deep sequencing-based methods to investigate the interactions of regulatory proteins with RNA in yeast and human cells. We developed a new method for high-throughput mapping of RNA-RNA interactions, called CLASH. Our method is conceptually similar to the Chromosome Conformation Capture technique that has been widely used for the analysis of DNA structure. In collaboration with the Tollervey lab, we apply CLASH to the analysis of microRNA targets in human cells. This research will improve our understanding of gene regulation and molecular evolution, and may have practical applications in bio-medicine.

Publications

10 25 50
 
Description Membership of National Science Centre grant panel in Poland
Geographic Reach Europe 
Policy Influence Type Participation in a advisory committee
 
Description Wellcome Senior Research Fellowship in Basic Biomedical Science
Amount £1,416,787 (GBP)
Funding ID 207507/Z/17/Z 
Organisation Wellcome Trust 
Sector Charity/Non Profit
Country United Kingdom
Start 02/2018 
End 01/2023
 
Description ERC Advanced Grant with Laurence Hurst 
Organisation University of Bath
Department Department of Biology and Biochemistry
Country United Kingdom 
Sector Academic/University 
PI Contribution Experimental study of the effects of synonymous mutations on alternative splicing in mammalian cells
Collaborator Contribution Computational study of alternative splicing in mammalian cells
Impact N/A
Start Year 2015
 
Description Next-generation Gene Optimization 
Organisation Life Technologies
Country Global 
Sector Private 
PI Contribution We will measure the expression levels of a library of 50,000 synonymous mutants of the GFP gene in human cells. In collaboration with our partners, we will then study the sequence determinants of efficient gene expression, and design an improved version of the codon optimization algorithm, GeneOptimizer.
Collaborator Contribution Our collaborators will synthesize a library of 50,000 synonymous mutants of the GFP gene. In collaboration with us, they will study the sequence determinants of efficient gene expression, and design an improved version of the codon optimization algorithm, GeneOptimizer.
Impact N/A
Start Year 2016