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CRISPR perturbation of gene expression alters bacterial fitness under stress and reveals underlying epistatic constraints Peter Britton Otoupal, Keesha E Erickson, Antoni Escalas Bordoy, and Anushree Chatterjee ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.6b00050 • Publication Date (Web): 16 Aug 2016 Downloaded from http://pubs.acs.org on August 17, 2016

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CRISPR perturbation of gene expression alters bacterial fitness under stress 4 5 6

and reveals underlying epistatic constraints 7 8 9 10 1 12

Peter B. Otoupal1, Keesha E. Erickson1, Antoni Escalas-Bordoy1 and Anushree Chatterjee1,2* 13 14 15 16 17 18

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Department of Chemical and Biological Engineering, University of Colorado at Boulder, Boulder, CO,

USA. 21 2 23

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BioFrontiers Institute, University of Colorado at Boulder, Boulder, Colorado, USA.

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Classification: Synthetic Biology & Biotechnology, Transcription, Evolution 31 32 3

Key words: CRISPR / Transcriptome / Adaptive Evolution / Epistasis / Antibiotic Resistance 34 35 36 37 38 39

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Corresponding author:

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Anushree Chatterjee 4 45

3415 Colorado Avenue, 596 UCB, University of Colorado Boulder, CO 80303 46 47

Email: [email protected] 48 49 50 51

Phone: (303) 735-6586 Fax: (303) 492-8425

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ABSTRACT

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The evolution of antibiotic resistance has engendered an impending global health crisis that

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necessitates a greater understanding of how resistance emerges. The impact of non-genetic factors and

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how they influence the evolution of resistance is a largely unexplored area of research. Here we present a

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novel application of CRISPR-Cas9 technology for investigating how gene expression governs the

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adaptive pathways available to bacteria during the evolution of resistance. We examine the impact of gene

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expression changes on bacterial adaptation by constructing a library of deactivated CRISPR-Cas9

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synthetic devices to tune the expression of a set of stress-response genes in Escherichia coli. We show

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that artificially inducing perturbations in gene expression imparts significant synthetic control over fitness

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and growth during stress exposure. We present evidence that these impacts are reversible; strains with

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synthetically perturbed gene expression regained wild-type growth phenotypes upon stress removal, while

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maintaining divergent growth characteristics under stress. Furthermore, we demonstrate a prevailing trend

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towards negative epistatic interactions when multiple gene perturbations are combined simultaneously,

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thereby posing an intrinsic constraint on gene expression underlying adaptive trajectories. Together, these

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results emphasize how CRISPR-Cas9 can be employed to engineer gene expression changes which shape

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bacterial adaptation, and present a novel approach to synthetically control the evolution of antimicrobial

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resistance.

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INTRODUCTION

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As bacteria continue to demonstrate their ability to adapt to a broad range of antibiotics1 and other

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antimicrobials2, a dearth of effective treatments for life-threatening pathogenic infections has become a

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prominent concern3. Although genomic divergences (i.e. mutations) have been the focus of conventional

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adaptive evolutionary research, the impact of variations in gene expression on microbial evolution during

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stress exposure4–6 is a relatively unexplored field. Heterogeneous gene expression has been shown to

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enable bacterial bet-hedging7 strategies to create diversity in order to dynamically respond to sudden

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environmental stressors6. This mutation-independent process, known as adaptive resistance8, could

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expedite the evolution of antimicrobial resistance. Supporting this notion is the observance of distinct

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changes in bacterial transcriptomes during exposure to antibiotics9 and disinfectants10, as well as

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significant heterogeneity in inter-population gene expression during the first hundred or so generations of

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adapting bacterial populations11–13.

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In this study, we take inspiration from these adaptive strategies found in nature, and hypothesize

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that synthetically inducing small perturbations in gene expression can enable artificial control over both

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positive and negative fitness phenotypes in adapting strains. Assuming that gene expression is normally

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distributed around basal levels in a bacterial population, we hypothesize small changes in the distribution

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of gene expression could exacerbate the pre-existing growth and fitness phenotypes of sub-populations

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with altered gene expression (Fig 1A). Further, we hypothesize that the simultaneous perturbation of

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multiple genes can induce unique phenotypic responses via epistatic interactions. Negative epistatic

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interactions, where the combined fitness benefits of simultaneous mutations are less than expected, have

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been shown to either overshadow positive epistasis during adaptation14,15 to environmental conditions or

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impact long-term evolvability16. While it has been suggested that the epigenetic epistatic interactions of

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gene expression ultimately constrain long-term evolution17, very little is understood regarding how these

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interactions might impact the early stages of adaptive resistance.

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To investigate our hypotheses, we engineered deactivated CRISPR (Clustered Regularly

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Interspaced Short Palindromic Repeats)-associated protein 9 (dCas9) based genomic devices to

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synthetically induce small perturbations in the transcriptome of Escherichia coli (E. coli). This presents a

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novel application of CRISPR technology as we employ it to explore the impact of subtle gene expression

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changes on bacterial fitness in the presence of sub lethal levels of stressors, and to the best of our

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knowledge is the first of its kind18. dCas9 and dCas9 constructs fused with the ω-subunit of RNA

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polymerase (dCas9-ω) have been shown to controllably inhibit19 or activate20 gene expression

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respectively. When combined in vivo with small guide RNAs (sgRNAs), these devices exhibit highly

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specific and localized control over the transcription rates of individual genes. Moreover, these CRISPR

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devices are able to perturb expression of multiple genes simultaneously, thereby allowing for the

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investigation of combinatorial effects19 of targeted gene control and the subsequent interactions this

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induces.

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We chose to investigate seven stress-response genes, whose functions are outlined in

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Supplementary Table S1. These include the global transcriptional regulators marA, soxS and recA. MarA

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(multiple antibiotic resistance) activates expression of the mar operon to increase efflux activity, decrease

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porin expression, and regulate other biochemical processes to confer tolerance to solvents and drugs21.

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SoxS (superoxide stress response) shares 49% homology in binding sites with MarA, and regulates

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similar genes to promote antimicrobial tolerance22. RecA activates the SOS response, wherein DNA

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repair occurs and cell growth is arrested23. The remaining four genes we chose to examine were

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downstream genes of these global regulators: mutS, dinB, acrA and tolC. MutS functions in DNA

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mismatch repair pathways (thereby decreasing mutation rates)24, while DinB acts as an error-prone

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polymerase lacking proofreading capacity (thereby increasing mutation rates)25. Finally, TolC and AcrA

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work in tandem to construct an efflux pump to channel toxic materials outside of the cell26.

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engineered CRISPR devices to systematically inhibit and activate the expression of these stress-response

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genes in E. coli during short-term (72 hour) exposure to various stress conditions, including antibiotics

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(tetracycline and rifampicin), disinfectants (bleach and hydrogen peroxide) and glucose limitation. We

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monitored the resulting growth and fitness impacts during the early stages of adaptation, as well as the

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epistatic interactions induced by simultaneous gene perturbation.

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We

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Corroborating our hypothesis, we observe that CRISPR-Cas9 based synthetic devices enable

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small perturbations in gene expression that are sufficient to significantly influence native bacterial

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adaptive responses to stress by altering growth rates, lag times, and overall fitness. We show that these

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impacts are reversible upon stress removal, indicating their non-genetic nature. We demonstrate that

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simultaneous perturbations predominately induce negative epistasis, extending mutation-based epistasis

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concepts to the gene expression landscape. This work builds upon landmark gene knockout27, plasmid

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over-expression28, network rewiring29 and long-term evolution30 studies by outlining a novel synthetic

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biology approach for engineering control over bacterial adaptation via exogenously regulating gene

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expression profiles. Our study also helps to elucidate the early adaptive response preceding genome

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modifications, and serves as a paradigm shift in the field of antibiotic resistance research away from

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investigating downstream adaptations and towards pathways bacteria utilize for adaptation.

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Results

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Design and characterization of single target gene perturbation devices

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To accomplish controlled gene expression perturbation, we designed and synthesized (see

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Methods) a set of 14 Type II CRISPR sgRNA plasmid constructs to inhibit or activate transcription of

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seven stress-response genes in E. coli, chosen for their known influence on adaptation21–26 (Fig 1B and

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Supplementary Fig S1). The sgRNA constructs were named pPO-genei or pPO-genea for inhibition and

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activation respectively of each given gene (Supplementary Table S2), and were co-transformed alongside

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a separate plasmid containing anhydrotetracycline (aTc) inducible dCas9 or dCas9-ω into E. coli strain

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MG1655. This produced 14 unique experimental perturbation strains, designated MG1655-genei or

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MG1655-genea. Two control strains harboring dCas9 or dCas9-ω plasmids, as well as the control sgRNA

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construct sgRNA-RFPi (targeting the rfp coding sequence not present in MG1655) were also created

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(Supplementary Table S3). All sgRNAs utilized common promoter and scaffolding elements, but differed

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in their unique 20 nucleotide (nt) sequence-specific DNA-binding domain. Inhibition and activation

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sgRNAs were coupled in vivo with dCas9 or dCas9-ω respectively to form the final protein-RNA hybrid

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construct with inherent DNA-binding affinity for the 20 nt sequences of each sgRNA, allowing for

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specific control of gene expression (Fig 1C). Activation sgRNAs targeting ≈80-110 nt upstream of the +1

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transcription start site of each gene provided optimal spacing for RNA polymerase to bind to the promoter

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and increase gene expression20. Inhibition sgRNAs targeted within the first ≈50 nt of the genes’ open

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reading frame (ORF) to inhibit transcriptional read-through via a roadblock mechanism19. Each CRISPR

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target sequence was flanked by an “NGG” Protospacer-Adjacent-Motif (PAM) on the 3’end for proper

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binding of the protein-RNA complex with the target DNA19. The impact of a subset of these constructs on

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neighboring genes’ expression was quantified and was found to be either absent or minimal

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(Supplementary Fig S2). It is expected that perturbing each of these genes may induce changes in

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expression of downstream genes as governed by the connections through respective gene regulatory

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networks within E. coli.

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The ability of bacteria to evolve resistance depends on the accessibility of higher-fitness states

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within a hypothetical “adaptive landscape”, which can be visualized as a multi-dimensional space

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comprised of the variable expression states of n-by-n genes (analogous to similar adaptive landscapes

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based on gene mutations)31–33 (Fig 1A). Cloning our library of synthetic CRISPR devices into MG1655

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enabled us to engineer a set of strains in which this adaptive landscape was perturbed. By inhibiting or

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activating individual genes, these strains enabled exploration of the impact that gene expression has on

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stress response. An advantage of using CRISPR devices is that this approach does not directly modify the

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wild-type genome, allowing for investigation of adaptive pathways in their natural state without the need

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to create a unique genome for each gene studied as done in canonical gene knockout studies, and thereby

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provides a unique insight.

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To measure the effects of gene perturbation, we utilized RT-qPCR to quantify the gene

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expression of each of these strains relative to wild-type MG1655. Our results indicate that the strains’

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expression profiles were indeed perturbed as intended, with a range of 32-fold reduction to 8-fold increase

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in gene expression (Fig 1D). Optimization of expression perturbation was influenced by native gene

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orientation; for instance, binding of dCas9-ω upstream of the +1 soxS transcription start site necessitated

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overlap with the ORF of soxR, an activator of soxS. Growth tests were also performed to analyze the

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viability of these strains. No loss of viability that is not intrinsic to growth with two plasmids was

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observed (Supplementary Fig S3). Since MG1655-rfpi and MG1655-rfpa strains demonstrated similar

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growth characteristics, we used MG1655-rfpa as the control strain in subsequent stress-exposure

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experiments (referred to hereafter as the MG1655-Control).

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Perturbation of gene expression influences bacterial growth characteristics during stress exposure

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We sought to examine the growth of strains harboring the CRISPR constructs under various

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environmental stresses to which infectious bacteria are commonly exposed, to determine whether

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artificial perturbation of gene expression enabled control over bacterial growth (and thus adaptive

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potential). To achieve this, five stress conditions were selected representing oxidizing agents (household

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bleach34 and hydrogen peroxide35), antibiotics (tetracycline36 and rifampicin37), and nutrient limitation

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(M9 minimal media supplemented with 0.4% glucose). The Minimum Inhibitory Concentration (MIC)

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was determined using MG1655-Control to estimate the appropriate starting concentrations for growth

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under each stress condition (Supplementary Fig S4). The sub-MIC levels were used as starting points for

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stress exposure experiments (Fig 2A, see Methods). We exposed E. coli strains harboring the CRISPR

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constructs to each stress over a course of 72 hours (Fig 2B), transferring biological triplicates every 24

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hours into fresh media supplemented with aTc and antibiotics to maintain plasmid selection (see

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Methods). During this time, optical densities were monitored to track changes in growth rate (µ) and

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inverse lag phase (τ-1) on each day of the experiment (Extended Dataset). These data was normalized to

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MG1655-Control by dividing µ and τ-1 by the average performance of biological triplicates of MG1655-

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Control from the experimental day (creating µnorm and τ-1norm). Normalized data was averaged over three

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experimental days. Adapting bacterial populations have been shown to exhibit significant heterogeneity in

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growth rates38 and lag times39, and thus these serve as useful metrics to quantitatively compare adaptive

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trends between strains. We chose to keep lag times in their reciprocal format, as larger lag times (smaller

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inverse lag time, τ-1 norm