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Gene regulation in Escherichia coli is commonly selected for both high plasticity and low noise

Abstract

Bacteria often respond to dynamically changing environments by regulating gene expression. Despite this regulation being critically important for growth and survival, little is known about how selection shapes gene regulation in natural populations. To better understand the role natural selection plays in shaping bacterial gene regulation, here we compare differences in the regulatory behaviour of naturally segregating promoter variants from Escherichia coli (which have been subject to natural selection) to randomly mutated promoter variants (which have never been exposed to natural selection). We quantify gene expression phenotypes (expression level, plasticity and noise) for hundreds of promoter variants across multiple environments and show that segregating promoter variants are enriched for mutations with minimal effects on expression level. In many promoters, we infer that there is strong selection to maintain high levels of plasticity, and direct selection to decrease or increase cell-to-cell variability in expression. Taken together, these results expand our knowledge of how gene regulation is affected by natural selection and highlight the power of comparing naturally segregating polymorphisms to de novo random mutations to quantify the action of selection.

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Fig. 1: Polymorphisms in IGRs and ORFs across 135 environmental isolates of E. coli and MG1655.
Fig. 2: Segregating genetic variation in promoters correlates with phenotypic variation in expression levels.
Fig. 3: The effects of random mutations on expression level are promoter- and environment-dependent.
Fig. 4: Selection acts against mutations with large effects on expression levels.
Fig. 5: Selection on plasticity.
Fig. 6: Selection on noise.

Data availability

The data files can be accessed through the Figshare repository with the identifier https://doi.org/10.6084/m9.figshare.c.5517228.v6

Code availability

All scripts with access to original data files are available through the Zenodo repository with the identifier https://doi.org/10.5281/zenodo.6494122

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Acknowledgements

We thank T. Cooper, A. Sajuthi and N. Freed for valuable comments on the final draft of this manuscript. We are also grateful to S. Pearless and B. Morampalli for sequencing several plasmid constructs. This work was supported by a Marsden Grant—Royal Society Te Apārangi MAU1703 awarded to O.K.S. The funder had no role in study design, data collection and interpretation or the decision to submit the work for publication.

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Contributions

M.V. and O.K.S. conceived the project and designed the experiments and analyses. O.K.S. supervised the project. M.V. performed all experiments and all analyses. M.V. wrote the paper with contributions from O.K.S.

Corresponding authors

Correspondence to Markéta Vlková or Olin K. Silander.

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The authors declare no competing interests.

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Nature Ecology & Evolution thanks Bianca Sclavi, Mo Siddiq and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Experimental design to assay the effects of segregating and random mutations on gene expression.

a) We isolated ten promoters (aldA, yhjX, mtr, aceB, lacZ, dctA, cdd, ptsG, purA, and tpiA) originating from MG1655. b) We then PCR amplified variants of these ten promoters segregating among environmental E. coli isolates from DNA pools. The average number of mutations across all segregating variants (as compared to MG1655) is 7.2 (ranging from 1 to 12.7 for individual promoters). c) We also performed PCR random mutagenesis using each of the ten MG1655 promoters with a target mutation rate of 1.5 mutations per promoter sequence. d) We cloned the resulting PCR amplicons (both segregating and random) into the pUA66 vector upstream of GFPmut264. We Sanger sequenced all the promoter variants to confirm the presence and location of mutations. From mutagenesis only the variants containing 1 to 3 SNPs were used for further phenotypic assays. e) We then cultured each of these individual promoter variants (1000 in total) in three different environments in triplicates, and f) quantified the modal population expression and modal coefficient of variation levels using flow cytometry.

Extended Data Fig. 2 Comparison of intergenic regions (IGR) genetic variation measures with and without flanking open reading frames (ORFs).

a) and b) Correlation in sequence variation between IGRs and promoters (IGRs with 100 bp of flanking ORF regions). a shows the alignment length normalized Watterson’s estimator θ and b displays the average pairwise nucleotide diversity π.

Extended Data Fig. 3 Segregating genetic variation in promoters correlates with variation in expression levels.

a) and b) Standard deviations in modal expression levels from segregating variants are correlated with the genetic variation of the promoter (IGR with 100 bp flanking regions). Panel a shows the correlation with π and panel b shows the correlation with the number of segregating promoter variants cloned and used for the phenotypic assay. For each promoter, the standard deviation of modal population expression was measured in three environments (three points per promoter, Table 2). The rho and p-values were calculated using Spearman’s correlation test (two-sided).

Extended Data Fig. 4 Comparison of expression levels from promoters in pairs of environments.

All x and y axes represent the modal population expression level in the particular environment noted on the axis label. The blue dotted lines indicate equal expression levels in both environments, that is, no phenotypic plasticity. The further from the line a promoter is, the higher the absolute difference is in expression from the promoter between the two environments (that is, the higher its phenotypic plasticity).

Extended Data Fig. 5 Fits of smoothing splines to modal population expression and modal coefficient of variation (mCV).

A smoothing spline was fitted to all variants (segregating and mutagenized) in each environment. The term “noise” is used for the vertical deviation of each variant, that is, deviation in the mCV from the fitted spline. The mCV is a measure analogous to the coefficient of variation. It was calculated as a standard deviation of log transformed expression levels (stdev) divided by modal population expression level (mode). We observe qualitatively similar patterns for most promoters and growth conditions, in which there were monotonic decreases in noise as expression increased. However, there were exceptions to this pattern, most notably for lacZ in glucose and galactose as well as dctA in L-malic acid; for both of these noise increased and expression increased. In lacZ this is likely due to the fact that in glucose (and galactose) for most promoters, all cells do not express above background fluorescence. However, for a small number of promoters, there are a few cells expressing above background. This increases noise while having little effect on modal expression levels. However, in dctA we speculate that there may in fact be selection for higher noise, resulting in the monotonically increasing relationship we observed and the widely divergent noise levels (Fig. 6).

Extended Data Fig. 6 Overall selection pressure.

We calculated a cumulative z-score for each promoter variant that was indicative of the deviation from the average promoter behaviour for all phenotypes (expression level, plasticity, and noise) between segregating and random variants. The numbers above each pair of segregating and random variants indicate the p-values for two-sided Wilcoxon rank-sum test to test for differences between the two groups. The numbers in bold indicate significant p-values. The horizontal black lines indicate the median values of cumulative z-scores of each group. The promoters are arranged in decreasing order of segregating genetic variation (θ). The MG1655 variant of the mtr promoter was omitted from calculation due to a SNP in GFP (Supplementary Note). The inset shows the full scale of cumulative z-scores on the y-axis for comparison of high values in aceB and purA promoters.

Extended Data Fig. 7 Cell gating strategy from flow cytometry data.

a) Raw data from the flow cytometer of one of the samples. Each point is an individual event recorded by the flow cytometer, the majority of which are expected to be cells. b) Identification of the highest kernel density of forward and side scatter values is displayed as the red cross. c) Removing events that are too far from the highest kernel density point. This ensured compactness of the final gating step. d) Final gating step. The function ellipsoidGate from the flowCore R package was used to isolate the densest homogenous population within the sample.

Supplementary information

Supplementary Information

Supplementary Note

Reporting Summary

Supplementary Tables

Supplementary Table 1: Genetic variability measures of all IGRs and ORFs present in at least 130 out of 153 environmental E. coli isolates. Supplementary Table 2: Characteristics of promoters selected for phenotypic assays. The functional groups of downstream genes were obtained using MultiFun. *For the full description of the assay environments see Supplementary Table 3. Supplementary Table 3: Promoter–environment combinations. All environments included M9 minimal salt media supplemented with MgSO4, CaCl2 and 50 µg ml−1 of kanamycin. Supplementary Table 4: Primers and oligos used in this work. Bold sequences indicate regions homologous to the ends of the PCR-amplified pUA66 vector used in DNA assembly. In the case of the lacZ and yhjX promoter, two versions of reverse primer exist differing by a single SNP in downstream lacZ gene (C69A) and two SNPs in upstream yhjY gene (G615A and G624A), respectively.

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Vlková, M., Silander, O.K. Gene regulation in Escherichia coli is commonly selected for both high plasticity and low noise. Nat Ecol Evol (2022). https://doi.org/10.1038/s41559-022-01783-2

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