Evolution of new regulatory functions on biophysically realistic fitness landscapes

Tamar Friedlander,Nicholas H Barton,Gašper Tkačik,Roshan Prizak

doi:10.1038/s41467-017-00238-8

Tamar Friedlander, Nicholas H Barton + Show 2 more

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https://doi.org/10.1038/s41467-017-00238-8

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Abstract

Gene expression is controlled by networks of regulatory proteins that interact specifically with external signals and DNA regulatory sequences. These interactions force the network components to co-evolve so as to continually maintain function. Yet, existing models of evolution mostly focus on isolated genetic elements. In contrast, we study the essential process by which regulatory networks grow: the duplication and subsequent specialization of network components. We synthesize a biophysical model of molecular interactions with the evolutionary framework to find the conditions and pathways by which new regulatory functions emerge. We show that specialization of new network components is usually slow, but can be drastically accelerated in the presence of regulatory crosstalk and mutations that promote promiscuous interactions between network components.

Highlights

Gene expression is controlled by networks of regulatory proteins that interact with external signals and DNA regulatory sequences
Examples range from repressors involved in bacterial carbon metabolism that arose from the same ancestor via a series of duplication–divergence events[25], and ancestral transcription factors (TFs) Lys[14] in the metabolism of Saccharomyces cerevisiae, which diverged into three different TFs regulating different subsets of genes in Candida albicans[26], to many variants of Lim and Pou-homeobox genes involved in neural development across different organisms[27]
A biophysical model of this set-up gives rise to complex fitness landscapes that are markedly different from simple forms considered previously; in what follows, we show that realistic landscapes exert a major influence over the evolutionary outcomes and dynamics

Summary

Introduction

Gene expression is controlled by networks of regulatory proteins that interact with external signals and DNA regulatory sequences. When cis-regulatory mutations that control the expression of the duplicated gene are included[31,32,33,34], this is done in a simplified fashion, e.g., by a small number of discrete alleles that represent TF-binding sites appearing and disappearing at fixed rates[33, 34]. Because this approach ignores the essentials of molecular recognition, it cannot model co-evolution between TFs and their binding sites—the topic of our interest

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