Predicting bacterial promoter function and evolution from random sequences

Elife. 2022 Jan 26;11:e64543. doi: 10.7554/eLife.64543.

Abstract

Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ70 binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10-20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ70-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought.

Keywords: E. coli; RNA polymerase; adaptive evolution; computational biology; evolutionary biology; gene regulation; genotype-phenotype map; promoter; systems biology.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Escherichia coli / genetics*
  • Escherichia coli / metabolism
  • Evolution, Molecular*
  • Gene Expression
  • Genome, Bacterial
  • Models, Theoretical
  • Mutation
  • Promoter Regions, Genetic*