Gene Expression Variability Underlies Adaptive Resistance in Phenotypically Heterogeneous Bacterial Populations

Abstract

The root cause of the antibiotic resistance crisis is the ability of bacteria to evolve resistance to a multitude of antibiotics and other environmental toxins. The regulation of adaptation is difficult to pinpoint due to extensive phenotypic heterogeneity arising during evolution. Here, we investigate the mechanisms underlying general bacterial adaptation by evolving wild-type Escherichia coli populations to dissimilar chemical toxins. We demonstrate the presence of extensive inter- and intrapopulation phenotypic heterogeneity across adapted populations in multiple traits, including minimum inhibitory concentration, growth rate, and lag time. To search for a common response across the heterogeneous adapted populations, we measured gene expression in three stress-response networks: the mar regulon, the general stress response, and the SOS response. While few genes were differentially expressed, clustering revealed that interpopulation gene expression variability in adapted populations was distinct from that of unadapted populations. Notably, we observed both increases and decreases in gene expression variability upon adaptation. Sequencing select genes revealed that the observed gene expression trends are not necessarily attributable to genetic changes. To further explore the connection between gene expression variability and adaptation, we propagated single-gene knockout and CRISPR (clustered regularly interspaced short palindromic repeats) interference strains and quantified impact on adaptation to antibiotics. We identified significant correlations that suggest genes with low expression variability have greater impact on adaptation. This study provides evidence that gene expression variability can be used as an indicator of bacterial adaptive resistance, even in the face of the pervasive phenotypic heterogeneity underlying adaptation.

Potentiating antibiotic treatment using fitness-neutral gene expression perturbations

“…These genes were chosen due to existing evidence of their association with stress response and/or adaptation processes, which are promising candidates for potentiating antibiotic activity (20). We previously quantified the behavior of certain SOS response (recA, polB, dinB, dam), general stress response (rpoS, hfq, cyoA, mutS), and mar regulon (marA, rob, soxS, acrA, tolC) genes during adaptation (18). Several genes were also found to impact adaptive resistance in a transcriptome-level analysis of adapted versus unadapted strains (fiu, tar, wzc, yjjZ were differentially expressed while ybjG, ydhY, ydiV, and yehS were differentially variable) (19).…”

“…Their existence also enables rapid screening of how analogous gene knockdowns will impact antibiotic efficacy. We focused on thirty stress-response gene knockouts that we identified to be differentially expressed during antibiotic exposure in our previous works (18,19). We systematically characterized how each of the strains responded to growth in sub-minimum inhibitory concentrations (MICs) of nine commonly used antibiotics representing a broad spectrum of functional classes.…”

Adaptive resistance to antibiotics in bacteria: a systems biology perspective

“…This observation, plus the fact that slow‐growing cells can be identified in a clonal population even before the antibiotic is applied, supports the idea that simple mechanisms such as random fluctuations in gene expression patterns can generate a wide range of selectable phenotypes which in turn increase the overall chances for the population to survive antibiotic inductions. As a matter of fact, mathematical models of this phenotype switching mechanisms, have showed that populations living in nonuniform environments have a greater fitness advantage when they are phenotypically heterogeneous . Another relevant observation related to phenotypic switching is that cells have a way of correlating these seemingly random phenotypic fluctuations .…”

Design of a de novo aggregating antimicrobial peptide and bacterial conjugation delivery system

“…Other modified bacteriophages deliver CRISPR systems to inactivate antibiotic resistance genes in pathogenic bacteria. In addition, CRISPR libraries have been used to study how antibiotic resistance evolves by manipulating gene expression under antibiotic stress conditions (5, 6). Other examples include rearrangement of domains within polyketide synthase complexes to enable the production of new polyketide antimicrobials (7).…”