Evolutionary mechanisms of gene regulation in dynamic environments

Introduction

Living systems are the product of evolution in a dynamically changing world; they endlessly adapt to new challenges imposed by their environment. Yet, our understanding of evolutionary mechanisms involved in adaptation to dynamic environments is very sparse.

Gene Regulation and Adaptation

Even though gene regulation is well-known to endow cells with the ability to adjust their phenotype according to environmental fluctuations, we know little about genetic changes that fuel regulatory evolution or about the benefits and costs of regulatory changes. This lack of knowledge impacts several fields in biology:

• The biomedical challenge of cancer plasticity, where evolved gene regulations allow cancer cells to escape treatment.
• The challenge of synthetic biology, where gene circuits must be controlled despite environmental fluctuations.

Project Goals

This project builds on genomic advances to determine how the evolution of gene regulation depends on:

1. Mutational effects – “what can happen?”
2. The selective advantage of regulatory changes in dynamic environments – “who can survive?”

These fundamental questions will be addressed at the transcriptomic scale by combining innovative experimental and computational approaches in a powerful model organism: the yeast Saccharomyces cerevisiae.

Methodology

Using a novel high-throughput method by which the transcriptomes of thousands of genotypes can be profiled in different environments, we will determine how random mutations potentiate or constrain regulatory evolution and we will identify genetic variants altering gene regulation.

By competing random mutants and performing functional assays under diverse regimes of selection, we will determine when and for which genes the evolution of expression regulation is beneficial.

Expected Outcomes

This work will advance our understanding of the genetic mechanisms underlying regulatory differences and of their adaptive value in dynamic environments, providing an empirical foundation for the development of predictive models of regulatory evolution.