Correction: Phenotypic heterogeneity promotes adaptive evolution

Bódi, Zoltán; Farkas, Zoltán; Nevozhay, Dmitry; Kalapis, Dorottya; Lázár, Viktória; Csörgő, Bálint; Nyerges, Ákos; Szamecz, Béla; Fekete, G.; Papp, Balázs; Araújo, Hugo; Oliveira, José Luís; Moura, Gabriela; Santos, Manuel A. S.; Szèkely, T.; Balázsi, Gábor; Pál, Csaba

doi:10.1371/journal.pbio.1002607

Cited by 5 publications

(8 citation statements)

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“…This heterogeneity has been observed across all the domains of life and arises from the inherent random nature of biochemical reactions ( Elowitz et al, 2002 ; Kaern et al, 2005 ; Lidstrom and Konopka, 2010 ). Phenotypic heterogeneity may allow some individual cells to survive shifts in the environmental conditions, and thus permitting the population to withstand fluctuating environments ( Balaban et al, 2004 ; Ackermann, 2015 ; Venturelli et al, 2015 ; Schreiber et al, 2016 ; Bódi et al, 2017 ). It has also been suggested that phenotypic heterogeneity can accelerate evolutionary adaptation to different environmental challenges ( Beaumont et al, 2009 ; New et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

The Culture Environment Influences Both Gene Regulation and Phenotypic Heterogeneity in Escherichia coli

et al. 2018

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Microorganisms shape the composition of the medium they are growing in, which in turn has profound consequences on the reprogramming of the population gene-expression profile. In this paper, we investigate the progressive changes in pH and sugar availability in the medium of a growing Escherichia coli (E. coli) culture. We show how these changes have an effect on both the cellular heterogeneity within the microbial community and the gene-expression profile of the microbial population. We measure the changes in gene-expression as E. coli moves from lag, to exponential, and finally into stationary phase. We found that pathways linked to the changes in the medium composition such as ribosomal, tricarboxylic acid cycle (TCA), transport, and metabolism pathways are strongly regulated during the different growth phases. In order to quantify the corresponding temporal changes in the population heterogeneity, we measure the fraction of E. coli persisters surviving different antibiotic treatments during the various phases of growth. We show that the composition of the medium in which β-lactams or quinolones, but not aminoglycosides, are dissolved strongly affects the measured phenotypic heterogeneity within the culture. Our findings contribute to a better understanding on how the composition of the culture medium influences both the reprogramming in the population gene-expression and the emergence of phenotypic variants.

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Section: Introductionmentioning

confidence: 99%

The Culture Environment Influences Both Gene Regulation and Phenotypic Heterogeneity in Escherichia coli

et al. 2018

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“…Synthetic gene networks have been engineered to regulate drug resistance and have been shown to serve as wellcharacterized models of natural stress response modules in evolution experiments (González et al, 2015;Bódi et al, 2017;Farquhar et al, 2019;Gouda et al, 2019). Nevozhay et al (2012) constructed a two-gene positive feedback network that enables bi-stable gene expression to control a Zeocin antibiotic resistance gene in S. cerevisiae.…”

Section: Synthetic Antimicrobial Resistance Gene Networkmentioning

confidence: 99%

“…The emerging paradigm is that drug resistance is a multi-stage process and that acute, non-genetic drug resistance can facilitate the evolution of permanent, genetic drug resistance ( Figure 1C). Non-genetic mechanisms are now thought to facilitate genetic resistance by increasing the population size under drug treatment and hence the chance of acquiring genetic mutations (Brock et al, 2009;Charlebois et al, 2011;Farquhar et al, 2019), and through synergism between adaptive mutations and non-genetic heterogeneity (Bódi et al, 2017;Salgia and Kulkarni, 2018). Furthermore, it is known that mutations in PDR1, a gene that regulates PDR5 in the pleiotropic drug resistance (PDR) network in yeast (Figure 2A), can cause full resistance to the antifungal drug fluconazole (Ferrari et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Advancing Antimicrobial Resistance Research Through Quantitative Modeling and Synthetic Biology

Farquhar

Flohr

Charlebois

2020

Front. Bioeng. Biotechnol.

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Antimicrobial resistance (AMR) is an emerging global health crisis that is undermining advances in modern medicine and, if unmitigated, threatens to kill 10 million people per year worldwide by 2050. Research over the last decade has demonstrated that the differences between genetically identical cells in the same environment can lead to drug resistance. Fluctuations in gene expression, modulated by gene regulatory networks, can lead to non-genetic heterogeneity that results in the fractional killing of microbial populations causing drug therapies to fail; this non-genetic drug resistance can enhance the probability of acquiring genetic drug resistance mutations. Mathematical models of gene networks can elucidate general principles underlying drug resistance, predict the evolution of resistance, and guide drug resistance experiments in the laboratory. Cells genetically engineered to carry synthetic gene networks regulating drug resistance genes allow for controlled, quantitative experiments on the role of nongenetic heterogeneity in the development of drug resistance. In this perspective article, we emphasize the contributions that mathematical, computational, and synthetic gene network models play in advancing our understanding of AMR to discover effective therapies against drug-resistant infections.

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“…If the novel phenotype has a 34 significantly higher fitness than the already encoded protein, both the phenotypic 35 mutation and the first point mutation will get fixed in the population. Once the 36 phenotypic mutation is fixed, Whitehead et al Whitehead calculated that the 37 probability for encoding the phenotypic alteration on DNA level is highly probable. 38 Accordingly, phenotypic mutations are suggested to enable exploration of protein 39 sequences, which enables quick assessment of viable phenotypes.…”

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confidence: 99%

“…Overall transcriptional heterogeneity has been found to be beneficial in stressful 291 November 12, 2019 7/20 condition [36,37], and suggested to be an adaptive trait under selection [38]. Also 292 protein synthesis has been found to be heterogeneous and noisy [8,26], but also 293 harmful [39].…”

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confidence: 99%

Translational readthrough goes unseen by natural selection

Kleppe

Bornberg‐Bauer

2019

Preprint

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Occasionally during protein synthesis, the ribosome bypasses the stop codon and continues translation to the next stop codon in frame. This error is called translational readthrough (TR). Earlier research suggest that TR is a relatively common error, in several taxa, yet the evolutionary relevance of this translational error is still unclear. By analysing ribosome profiling data, we have conducted species comparisons between yeasts to infer conservation of TR between orthologs. Moreover, we infer the evolutionary rate of error prone and canonically translated proteins to deduct differential selective pressure. We find that about 40% of error prone proteins in Schizosaccharomyces pombe do not have any orthologs in Saccharomyces cerevisiae, but that 60% of error prone proteins in S. pombe are undergoing canonical translation in S. cerevisiae. Error prone proteins tend to have a higher GC-content in the 3'-UTR, unlike their canonically translated ortholog. We do not find the same trends for GC-content of the CDS. We discuss the role of 3'-UTR and GC-content regarding translational readthrough. Moreover, we find that there is neither selective pressure against or for TR. We suggest that TR is a near-neutral error that goes unseen by natural selection. We speculate that TR yield neutral protein isoforms that are not being purged. We suggest that isoforms, yielded by TR, increase proteomic diversity in the cell, which is readily available upon sudden environmental shifts and which therefore may become adaptive.

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Correction: Phenotypic heterogeneity promotes adaptive evolution

Abstract: Technology (FCT) www.fct.pt and the European Structural funds (FEDER). Received by iBiMED and MASS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Cited by 5 publications

References 1 publication

The Culture Environment Influences Both Gene Regulation and Phenotypic Heterogeneity in Escherichia coli

The Culture Environment Influences Both Gene Regulation and Phenotypic Heterogeneity in Escherichia coli

Advancing Antimicrobial Resistance Research Through Quantitative Modeling and Synthetic Biology

Translational readthrough goes unseen by natural selection

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