The evolution, evolvability and engineering of gene regulatory DNA

Eeshit Dhaval Vaishnav*, Carl G. de Boer*, Jennifer Molinet, Moran Yassour, Lin Fan, Xian Adiconis, Dawn A. Thompson, Joshua Z. Levin, Francisco A. Cubillos, Aviv Regev*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

88 Scopus citations

Abstract

Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal phenotype and fitness1–3. Constructing complete fitness landscapes, in which DNA sequences are mapped to fitness, is a long-standing goal in biology, but has remained elusive because it is challenging to generalize reliably to vast sequence spaces4–6. Here we build sequence-to-expression models that capture fitness landscapes and use them to decipher principles of regulatory evolution. Using millions of randomly sampled promoter DNA sequences and their measured expression levels in the yeast Saccharomyces cerevisiae, we learn deep neural network models that generalize with excellent prediction performance, and enable sequence design for expression engineering. Using our models, we study expression divergence under genetic drift and strong-selection weak-mutation regimes to find that regulatory evolution is rapid and subject to diminishing returns epistasis; that conflicting expression objectives in different environments constrain expression adaptation; and that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for using such models to detect signatures of selection on expression from natural variation in regulatory sequences and use it to discover an instance of convergent regulatory evolution. We assess mutational robustness, finding that regulatory mutation effect sizes follow a power law, characterize regulatory evolvability, visualize promoter fitness landscapes, discover evolvability archetypes and illustrate the mutational robustness of natural regulatory sequence populations. Our work provides a general framework for designing regulatory sequences and addressing fundamental questions in regulatory evolution.

Original languageAmerican English
Pages (from-to)455-463
Number of pages9
JournalNature
Volume603
Issue number7901
DOIs
StatePublished - 17 Mar 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature Limited.

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