Abstract

Cell growth is determined by substrate availability and the cell’s metabolic capacity to assimilate substrates into building blocks. Metabolic genes that determine growth rate may interact synergistically or antagonistically, and can accelerate or slow growth, depending on genetic background and environmental conditions. We evolved a diverse set of Escherichia coli single-gene deletion mutants with a spectrum of growth rates and identified mutations that generally increase growth rate. Despite the metabolic differences between parent strains, mutations that enhanced growth largely mapped to core transcription machinery, including the β and β’ subunits of RNA polymerase (RNAP) and the transcription elongation factor, NusA. The structural segments of RNAP that determine enhanced growth have been previously implicated in antibiotic resistance and in the control of transcription elongation and pausing. We further developed a computational framework to characterize how the transcriptional changes that occur upon acquisition of these mutations affect growth rate across strains. Our experimental and computational results provide evidence for cases in which RNAP mutations shift the competitive balance between active transcription and gene silencing. This study demonstrates that mutations in specific regions of RNAP are a convergent adaptive solution that can enhance the growth rate of cells from distinct metabolic states.

Highlights

  • Single-celled organisms such as Escherichia coli provide excellent models to investigate the genetic basis of evolution

  • Whether such gene pairs share a local chemical or regulatory relationship or interact via a non-local mechanism has implications for the co-evolution of genetic changes, development of alternatives to gene therapy, and the design of combination antimicrobial therapies that select against resistance

  • We present results of a laboratory evolution approach that has the potential to address both challenges, showing that mutations in specific regions of RNA polymerase enhance growth rates of distinct mutant strains of Escherichia coli with a spectrum of growth defects

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Summary

Introduction

Single-celled organisms such as Escherichia coli provide excellent models to investigate the genetic basis of evolution. Whereas experiments probing adaptive genetic changes of wild-type bacterial cells to a range of growth and stress conditions are prevalent [5,6,7,8,9,10,11,12,13,14], studies investigating adaptive evolutionary trajectories from different genetic starting states are less common [15,16,17,18]. Computational approaches have been developed to predict genetic interactions in the metabolic networks of microbial cells, and several of these interactions have been confirmed experimentally [2, 20,21,22,23,24]. Like other exhaustive methods, these approaches present combinatorial challenges for probing multigenic interactions

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