Using machine learning to map transcriptomics to phenotype in bacteria and humans

Non UBCUnreviewedAuthor affiliation: Northwestern UniversityPostdoctora

Experimental evolution of diverse <i>Escherichia coli</i> metabolic mutants identifies genetic loci for convergent adaptation of growth rate

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

Experimental evolution of diverse <i>Escherichia coli</i> metabolic mutants identifies genetic loci for convergent adaptation of growth rate

<div><p>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 <i>Escherichia coli</i> 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.</p></div

Mapped genetic changes in 12 <i>Fast</i> strains evolved from wild type and 16 <i>sup</i> strains evolved from mutant backgrounds.

<p>Mapped genetic changes in 12 <i>Fast</i> strains evolved from wild type and 16 <i>sup</i> strains evolved from mutant backgrounds.</p

Growth rates of all <i>E</i>. <i>coli</i> strains assayed in M9G.

<p>(A) The colored bars indicate the mean growth rates for the WT strain (blue), five primary deletion strains (WT + Δ; pink), 16 fast-growth <i>sup</i> strains (WT + Δ + AE; orange), and the same 16 <i>sup</i> strains with the respective primary deletion knocked in (WT + AE; green). Each point marks the growth rate of an independent culture (6 replicates for each mutant strain, <i>sup</i> strain, and <i>sup</i> knock-in strain; 30 replicates for the WT strain). (B) Growth rates of fast growing adaptively evolved strains derived from a wild-type background presented as in (A). Fast strains are colored according to mutation class: strains with <i>rpoB</i> or <i>rpoC</i> mutations (dark orange) or with <i>pykF</i> mutations (light orange). Points represent 4 independent replicates for each strain. In (A) and (B), the error bars represent the standard deviation over all replicates. All growth rates are normalized by that of the WT strain in M9G.</p

Evaluation of the compensatory and restorative responses to each gene deletion.

<p>(A) Histogram of the Gene Change Score (GCS) exhibiting compensatory (negative) and restorative (positive) transcriptional changes for each <i>sup</i> strain (marked by different lines). Each case clearly shows a bias toward positive (restorative) changes. (B) List of genes exhibiting the strongest responses (GCS > 0.4 or < –0.2) across all <i>sup</i> strains associated with each deletion strain. Genes marked by an asterisk are discussed in more detail in the text.</p