Construction of CHAOS gene perturbation library

To explore artificial induction of negative epistasis, we constructed a library of Escherichia coli strains harboring dCas930 or dCas9-ω26 devices to inhibit or activate mRNA production respectively. Each strain hosts a unique array of one or more single guide RNAs (sgRNAs) to direct the CRISPR enzymes to particular genomic loci (Fig. 2a, b).

Fig. 2 Negative fitness from multiple perturbations of gene expression. a Multiplexed CRISPR activation of stress response genes using aTc-inducible dCas9-ω engineered to bind upstream ~80–100 nt upstream of the +1 transcription start site. b Multiplexed CRISPR inhibition of conserved genes using aTc-inducible dCas9 engineered to bind overlapping the +1 transcription start site. c, d Fitness relative to control strain (C mcherry ) after combinatorially increasing expression of stress response genes (c) or decreasing expression of conserved genes (d) during exposure to 0.005 µg mL−1 ciprofloxacin and 10 ng mL−1 aTc in LB medium. Relative fitness is listed below each strain name, followed by the standard deviation (n = 8). Asterisks to the left of strain names indicate significant fitness differences in relation to strain control grown under the same conditions and exhibiting a competitive fitness of 0.99 ± 0.07 (P < 0.01, two-tailed type II t-test). No significant differences were observed between strain control and another control strain CCCC harboring four nonsense gene perturbations and exhibiting a competitive fitness of 0.98 ± 0.14. e, f Fitness relative to control strain (C mcherry ) from combinatorially increasing expression of stress response genes (e) or decreasing expression of conserved genes (f) during exposure to no ciprofloxacin and 10 ng mL−1 aTc in LB medium. Relative fitness is listed below each strain name, followed by the standard deviation (n = 3 for conserved gene strains, n = 4 for stress response gene strains). No strain was significantly different (at the P < 0.01 level) than the control strain CCCC with four nonsense gene perturbations grown in the same conditions and exhibiting average fitness of 0.93 ± 0.23 (n = 3, significance calculated using two-tailed type II t-test). g, h Growth of strains harboring stress response perturbations (g) or conserved gene perturbations (h) either individually or all at once during exposure to 10 ng mL−1 aTc in LB medium. Error bars represent standard deviation of five biological replicates Full size image

We designed CHAOS strains targeting two gene sets. The first set included activation of four universal stress response genes important for adaptation27,28: mutS (DNA mismatch repair), soxS (SOX pathway regulator), tolC (multidrug efflux pump), and recA (SOS response activator) (Fig. 2a). These four genes were determined in our previous work to be particularly important during bacterial adaptation to a broad range of antibiotics and other stressors, and we reasoned that upregulating these genes would serve as the most likely avenue influencing adaptation22,27,28. Furthermore, we hypothesized that activating these genes would serve as the worst-case scenario, as we expect broad upregulation of stress response to provide multiple avenues of adaptive escape and encourage positive epistasis. The second set included inhibition of four genes found to be universally conserved across a broad set of bacteria central to distinct cellular pathways (Fig. 2b). We began with a set of 174 genes conserved across diverse bacterial genomes31, from which we selected genes that were not present in operons to minimize the number of genes directly perturbed by each CRISPR construct (Supplementary Table 1). From this, we selected well-characterized genes that were involved in diverse cellular processes in order to maximize potential epistatic effects. This led us to four promising conserved gene targets: dfp (synthesizes essential coenzyme A), topA (an essential supercoiling-relaxing enzyme), zwf (a key glycolysis enzyme), and frr (essential for ribosome recycling). These genes are monocistronic (except tolC and recA) and possess little direct interactions between one another (except mutS/recA and soxS/tolC) (Supplementary Figure 1).

We analyzed the STRING Database to determine the direct protein-protein interactions known for each gene and gene combination32. Multiplexing perturbation of conserved genes was predicted to cause more cascading impacts than multiplexing perturbation of stress response genes (Supplementary Figures 2–3). We predicted that perturbations within the conserved gene set would thus have a greater impact on fitness, as the cascading effects of the primary gene perturbation would affect more downstream partners in more diverse pathways.

We created CHAOS constructs with all possible single, double, triple and quadruple combinations of gene perturbations for each of these sets (Supplementary Figures 4–5 and Supplementary Table 2). A two-plasmid system was utilized to induce gene expression perturbation; the first plasmid encoded sgRNA target sequence(s), while the second plasmid encoded an anhydrotetracycline (aTc) inducible dCas9 or dCas9-ω for gene inhibition or activation respectively. Activation sgRNAs targeted ≈80–100 nt upstream of the +1 transcription start site of each gene26. Inhibition sgRNAs targeted the +1 transcription start site to inhibit transcriptional read-through via roadblock mechanism30. We engineered CRISPR perturbations to cause an approximately 10-fold range of under-expression or over-expression and verified the degree of perturbation using RT-qPCR (Supplementary Figure 6). The only unexpected result involved topA perturbation, which exhibited a likely Fis-dependent response (explored further in Supplementary Figure 6b).

We constructed two control strains targeting nonsense perturbation of the red fluorescent protein (rfp) gene, which was absent in all strains. These strains harbored one or four copies of rfp targeting sgRNAs (hereafter referred to as control and CCCC, respectively). Another control strain including constitutively expressed mCherry and one copy of rfp perturbation (C mcherry , see Methods) was created to enable tracking of the control population during strain competition. Finally, two control strains harboring either one or four sgRNAs to inhibit lacZ were constructed and used to demonstrate that no differences arose from targeting our control to a nonsense perturbation (rfp) or a gene irrelevant to fitness (β-galactosidase encoding lacZ) (Supplementary Figure 7). All subsequent experiments use nonsense rfp perturbations as controls.

Combining gene perturbations reduces competitive fitness

We evaluated the fitness impacts caused by each CHAOS construct by competing these experimental strains with the fluorescent control strain C mcherry during exposure to 0.005 µg mL−1 ciprofloxacin, a sub-minimal inhibitory concentration (MIC) allowing for moderate growth while still imparting selective pressure. We chose ciprofloxacin as it is a clinically-relevant antibiotic treatment which selects for resistant populations at very low concentrations33 via specific mutations in the gyrA gene34, thus allowing us to assess adaptation of strains. The CHAOS and controls strains were competed for one day followed by plating on solid media. Fitness impacts were quantified by measuring the relative changes in colony forming units of both control (C mcherry —red colonies), and the competed CHAOS strain before and after exposure to sub-MIC of ciprofloxacin using fluorescence imaging (see methods). No significant differences were observed between either control strains control or CCCC (exhibiting fitness of 0.99 ± 0.07 and 0.98 ± 0.14, respectively).

Individual gene perturbations of stress response genes either had no statistically significant impact (soxS, tolC) or increased fitness (mutS, recA) in relation to the control (Fig. 2c). A striking trend emerged as we multiplexed these four perturbations. While the average fitness of individual perturbations was improved (1.22 ± 0.30, P = 0.003), their benefits were abated when combined in pairs (0.98 ± 0.27, P = 0.88), triplets (0.78 ± 0.23, P = 0.002), and all at once (0.87 ± 0.23, P = 0.10) (Fig. 2c). Only one double perturbation was beneficial (mutS-recA), while another two (soxS-tolC and tolC-recA) resulted in significant fitness losses. Furthermore, half of the triple perturbations reduced fitness (mutS-tolC-recA and soxS-tolC-recA).

Perturbation of conserved genes resulted in a similar but even more drastic trend. Three of these perturbations significantly improved fitness (zwf, topA, and frr), with inhibition of topA providing a strong fitness benefit (Fig. 2d). As before, while the average fitness of these four perturbations was significantly improved (1.49 ± 0.44, P = 8 × 10−5), fitness decreased when these were combined in pairs (1.02 ± 0.34, P = 0.71) or three at a time (0.88 ± 0.38, P = 0.29). Combining all four perturbations resulted in a particularly severe diminishment of fitness (0.48 ± 0.13, P = 1 × 10−11), suggesting drastic epistatic effects (Fig. 2d). Collectively, these data support our hypothesis and demonstrate that fitness loss results from combinations of gene perturbations during antibiotic exposure.

We investigated if the inclusion of ciprofloxacin impacted these results by repeating the experiment without ciprofloxacin (Fig. 2e, f). The overall impact of perturbations was diminished under these conditions, and no strain exhibited a competitive fitness significantly different than the control (CCCC, 0.93 ± 0.23). These strains did exhibit an apparent trend towards lower fitness upon multiplexing; the average fitness of individual stress response gene perturbations (1.10 ± 0.24) decreased upon combining perturbation in pairs (1.04 ± 0.16), triplets (1.02 ± 0.23), and all at once (0.91 ± 0.18) (Fig. 2e). The same was true for multiplexing individual conserved gene perturbations (1.05 ± 0.20) into pairs (0.90 ± 0.27), triplets (0.91 ± 0.10), and all at once (0.93 ± 0.14) (Fig. 2f).

Growth curves of individual and four gene perturbations were also examined in the absence of ciprofloxacin (Fig. 2g, h). No growth changes were observed for any of the stress response gene perturbations in the presence or absence of aTc, while growth of strain mstr did diminish upon induction (Fig. 2g). Similarly, only minor impacts on growth emerged due to perturbation of conserved genes individually, while significant growth defects were present during induction of strain dzTf (Fig. 2h). All strains grew to similar maximum optical densities (ODs) by the end of 24 h. The growth impacts observed in the absence of aTc is likely due to leaky expression from the tet-promoter driving dCas9 and dCas9-ω expression. We quantified a ~100-fold increase in dCas9 expression upon aTc induction, with maximum induction occurring around 3.125 ng mL−1 (Supplementary Figure 8), less than the 10 ng mL−1 used in competition experiments.

Strain growth was analyzed in M9 minimal media to exacerbate potential growth impacts caused by gene perturbations. We varied concentrations from 0 to 50 ng mL−1 to parse the dCas9 response to induction. At high concentrations of aTc, we observed slight growth rate impacts on control strains, as would be expected for growth in minimal media with two antibiotics for maintaining two plasmids (Supplementary Figure 9). Again, no significant growth impacts were observed for any of the stress response gene perturbations (Supplementary Figure 10). Slight growth defects were observed due to conserved gene perturbations of topA at high aTc concentrations (Supplementary Figure 11). Perturbations of strains mstr and dzTf demonstrated significant growth defects at high aTc concentrations that were maintained up to the end 24 h of growth (Supplementary Figure 11). This resulted in significant reductions in growth rates at aTc concentrations used during competition (Supplementary Figure 12).

We also investigated whether growth in microplate cultures impacted our overall experimental outcomes, as growth in such conditions has frequently been correlated with oxidative stress due to poor oxygenation35. We found that batch growth competition resulted in similar conclusions as observed in microplate growth (Supplementary Figure 13).

Finally, we investigated whether inhibition or activation of gene expression had any impact on the phenomenon observed in Fig. 2c, d. For this, we created another four-perturbation strain activating expression of mutS and soxS while inhibiting expression of topA and frr. We tested the competitive fitness of this strain during ciprofloxacin exposure. While three of these perturbations significantly improved fitness, perturbation of all four simultaneously resulted in neutral fitness (1.09 ± 0.10) (Supplementary Figure 14). This strain also grew slower than the control strain, corroborating the notion that these perturbations interacted detrimentally upon multiplexing.

Taken together, these results demonstrate that individual gene perturbations were not detrimental to the fitness of E. coli. Only upon multiplexing these perturbations did significant growth and fitness impacts emerge, which were markedly more pronounced during exposure to ciprofloxacin. Thus, a clear trend towards lower fitness emerged upon multiplexing of gene perturbations.

Negative epistasis emerges from combining gene perturbations

We next quantified epistasis between simultaneous perturbations by calculating deviations between the measured fitness of multiple perturbation strains, and their expected fitness based upon single perturbation (see methods). Comparing these fitness values, no deviation between expected and actual fitness of strain CCCC was observed, while multiple perturbation strains clearly exhibited lower fitness than was expected (Fig. 3a). This trend correlated into significant negative epistasis in half of the double perturbation strains and all but one of triple and quadruple gene perturbation strains (Fig. 3b). Notably, the only gene pairs known to interact (mutS-recA and soxS-tolC) did not demonstrate significant epistasis, indicating that direct interaction is not required to produce negative epistatic effects. The degree of negative epistasis also appeared to increase as more genes were perturbed. Inhibition of conserved genes resulted in statistically greater levels of negative epistasis than activation of stress response genes for triple perturbation constructs (P = 0.01). The high degree of negative epistasis across both sets appears compounded by sign-epistasis, wherein individually beneficial perturbations become deleterious once combined. Raw epistasis values and significance are presented in the Supplementary Data 1.

Fig. 3 Epistasis resulting from two or more gene perturbations. a The relationship between expected and actual relative fitness of each strain harboring multiple gene perturbations. Centroid of each group (based on the number of genes perturbed) is shown by the larger transparent symbol. The dashed diagonal line indicates theoretical results if no epistasis was present. b Calculated epistasis of each strain (abbreviated as in Fig. 2). Error bars indicate standard deviation (n = 8). Raw expected fitness and epistasis values are presented in the Supplementary Data 1. Asterisks indicate significant negative epistasis in relation to the null hypothesis of zero epistasis (P < 0.01, Student’s t-test). c Counts of proteins directly impacted by gene perturbations, separated by functional classes as annotated by the Kyoto Encyclopedia of Genes and Genomes (KEGG). Only statistically significant (P < 0.05) over-represented KEGG pathways are reported. d Relationship between epistasis and the cumulative amount of direct protein interactions disturbed by each perturbation set. A linear fit is included, with Pearson correlation coefficient (r) and its significance (P). e The metabolic activity of all strains after sub-MIC ciprofloxacin exposure in LB medium quantified using fluorescence change from Resazurin dye. Fluorescence was normalized to final ODs (580 nm) of the respective replicate. Data represent the average of at least three biological replicates. A linear fit of the metabolic rate’s relationship to epistasis was performed using ANOVA associated r and P values presented Full size image

We investigated the functional processes influencing epistasis. We started by identifying all proteins known to directly interact with the gene(s) disrupted by CRISPR perturbation(s) as characterized by the STRING database32. Functional differences between the affected pathways from each set could explain these differences, as conserved gene perturbations disrupted pathways more central to cell survival. Exploring the functionality of each gene within the impacted networks, we observed that all simultaneous perturbation constructs impacted pathways related to DNA maintenance, RNA production, and protein synthesis, but metabolism and membrane transport were unique to conserved gene perturbations and stress response perturbations respectively (Fig. 3c). As expected, introducing more primary CRISPR perturbations introduced more cascading downstream impacts on protein partners within the E. coli. Interestingly, we observed a strong correlation (P = 6 × 10−4) between the total amount of affected partners and the degree of negative epistasis exhibited by each CHAOS strain (Fig. 3d). This suggests that as more interactions are disturbed from homeostasis, the greater the impact of epistasis on the cell’s overall health, likely due to increased disruption of homeostasis.

As central metabolic pathways were impacted only by conserved gene perturbations, we quantified metabolic rates of each strain after 20 h of exposure to sub-MIC levels of ciprofloxacin (Fig. 3e). We observed greater metabolic activity within the conserved set of perturbations (P = 8 × 10−7), and a significant correlation between metabolic activity and the degree of epistasis (r = −0.48, P = 0.02). A potential mechanism for this could be reduced efficacy of resource dedication towards surviving antibiotic exposure. This is supported by previous work correlating increased metabolic rates to potentiation of bactericidal antibiotics36 and another study correlating antibiotic efficacy to the presence of metabolites37. Furthermore, 13 of the affected metabolic genes in strain dzTf are essential. Adapting bacteria exhibit less stochastic expression in essential genes22, and negative epistasis has been proposed as a mechanism constraining this heterogeneity24 due to essential genes exhibiting stronger overall genetic interactions38. Indeed, genetic interactions are statistically greater in the conserved gene set than stress response set (Supplementary Figure 15, P = 7 × 10−10). Collectively, these studies appear to corroborate our finding that accumulating gene expression deviations in diverse cellular pathways has a fundamental tendency to influence fitness detrimentally in an epistatic fashion.

Multiplexed perturbations slow bacterial adaptation rates

To test the hypothesis that induced negative epistasis can restrict the adaptive potential of bacteria, we exposed single and quadruple perturbation strains to ciprofloxacin over three days (D1–3) and quantified changes in MIC (Fig. 4, see methods). The trajectory of MIC change was quantified using Pearson correlation coefficients of linear fits over time (r, see methods). Statistical differences in fits between the control and CHAOS strains were estimated using F-tests (reported as P > F values).

Fig. 4 Perturbation of multiple genes slows bacterial adaptation. Change in the MIC of ciprofloxacin towards adapting populations at the end of each day of exposure for a strains harboring individual stress response gene perturbation constructs for mutS, soxS, tolC, and recA activation, b strains harboring individual conserved gene perturbation constructs for dfp, zwf, topA, and frr inhibition, c control strains, and d strains with simultaneous perturbation of four genes corresponding to stress response genes only (mstr), combination of stress response and conserved genes (msTf), and conserved genes only (dzTf). Each box-plot (n = 22, individual data points shown) includes a linear fit with associated Pearson correlation coefficient (r) and its significance (P). F-tests were performed for strain’s linear fit against the linear fit of strain control during the same experiment, and the resulting significance is reported as P > F. Raw MIC values are presented in the Supplementary Data 1. Asterisks indicate significantly different average MICs in relation to strain control during the same day and experimental run (P < 0.01, two-tailed type II t-test) Full size image

Most single perturbation strains and the controls adapted similarly, with MICs increasing over time. recA activation resulted in a significant increase in MIC on D1 (P = 2 × 10−5) and D3 (P = 2 × 10−3), and the rate of this increase was significantly faster than the control (P > F = 9 × 10−5) (Fig. 4a). While topA perturbation lowered MIC on D1 (P = 5 × 10−15), the strain quickly adjusted back to control levels resulting in a statistically faster rate of increase than the control (P > F = 5 × 10−7) (Fig. 4b). The discrepancy between fitness and initial MIC impacts of individual topA perturbation may be due to the aforementioned gene expression dependency on cell phase. As expected, no differences were observed between single and four gene control perturbation strains (Fig. 4c).

Experimental strains of quadruple perturbations exhibited striking differences in adaptive trends. Strain mstr (mutS, soxS, tolC, and recA) exhibited a positive correlation that was weaker than each individual perturbation (Fig. 4d). Strain msTf (mutS, soxS, topA, and frr) exhibited a completely flat MIC trajectory, and always survived statistically lower ciprofloxacin concentrations than the control. Strain dzTf (dfp, zwf, topA, and frr) presented the most striking results, as not only were MICs statistically lower than the control, average MIC actually decreased over time (r = −0.37, P = 3 × 10−3). This was primarily due to death of fifteen replicates by the end of adaptation. Nevertheless, excluding replicates that died during the course of the experiment showed a neutral trajectory of MIC over time (r = −0.09, P = 0.56), indicating that the living population was adapting slower (Supplementary Figures 16–17). The failure of strain dzTf to adapt to ciprofloxacin exposure was reproducible across multiple experimental runs (r = −0.42, P > F = 7 × 10−15) (Supplementary Figure 18). Strains exhibiting greater negative epistasis (dzTf and msTf) also adapted at slower rates, suggesting a correlation between the degree of epistasis and the rate of adaptation. Raw MIC values for all strains discussed above are presented in the Supplementary Data 1. These results demonstrate the potential of employing CHAOS to introduce epistasis into an organism and constrain rates of bacterial adaptation to antibiotics over time.

To determine if differences in MICs translated to changes at the genetic level, we sequenced each strain at the end of three days of ciprofloxacin exposure. We focused specifically on gyrA, as mutations in this gene (S83L and D87Y) have been found as the first genetic changes during the evolution of ciprofloxacin resistance34. The vast majority of strains exhibited no gyrA mutations (Supplementary Table 3). In total, only four isolates exhibited any gyrA mutations. These included one replicate of the control, and one replicate each of strains with individual perturbation of mutS, soxS, and topA. Increased perturbations did not appear to bias cells to mutate more, as no replicates of mstr, msTf, and dzTf exhibited mutations in gyrA. Mutation fluctuation assays corroborate this, revealing similar mutation rates across all strains with the exception of individual mutS and soxS perturbation (Supplementary Figure 19). Due to similar mutation rates, differences in adaptive trajectories across strains appear to be driven primarily by changes in gene expression.

We further explored strain adaptability by looking at the sustainability of MIC changes. We again adapted each of the individual conserved gene perturbations, as well as strains dzTf and the control, for three days (Fig. 5a). We found similar results as before, with strain dzTf adapting particularly poorly. After three days of exposure, all strains were removed from ciprofloxacin exposure for two days to reset the phenotypic state, after which they were re-exposed to ciprofloxacin gradients. Despite growth in the absence of ciprofloxacin for two days, every strain survived to the same levels of ciprofloxacin it had on day three. This suggests that the bacteria exhibited sustainable adaptive changes in response to ciprofloxacin, except for the bacteria exposed to multiplexed perturbations inducing negative epistasis.

Fig. 5 The impact of multiplexed conserved gene perturbation is maintained in the absence of ciprofloxacin and for longer periods of adaptation. a Change in the MIC of ciprofloxacin towards adapting populations of strains harboring the noted perturbation constructs Each box-plot (n = 22, individual data points shown) includes a three-day linear fit with associated Pearson correlation coefficient (r) and its significance (P). F-tests were performed for strain’s linear fit against the linear fit of strain control during the same experiment, and the resulting significance is reported as P > F. At the end of the third day (3a), samples were inoculated into LB with induction and grown for two days in the absence of ciprofloxacin. The MICs of these samples were measured again after one day (1b) of ciprofloxacin exposure. b The experiment was continued for six replicates of each strain at the end of three days of exposure (3a) for day four to day 14. The first six replicates for all strains besides dzTf, which continued the best six replicates reaching the highest MICs. Stars are overlain on each graph to show the average MIC of the control strain. All asterisks (*) and hashtags (#) indicate statistical difference from the control strain on the same day of the experiment (P < 0.01, two-tailed type II t-test) Full size image

Finally, we also examined the adaptive potential of these strains in a longer, clinical relevant timeframe of two weeks (Fig. 5b). Six replicates of each of the six strains from the above experiment were taken at the end of day three, and continued for another 11 days of adaptation to ciprofloxacin. We chose the first six replicates of each of the individually perturbed strains to continue with, and biased our experiment by picking the six best replicates of strain dzTf to continue with adaptation. This was done to see if a subset of the dzTf population was able to escape the epistatic effect and evolve resistance to ciprofloxacin. Despite this bias, strain dzTf never managed to reach similar levels as the control strain throughout the entire experiment. The population did begin surviving higher ciprofloxacin levels around day eight of the experiment, suggesting that adaptation was eventually possible. But the speed at which this strain evolved resistance was clearly diminished, suggesting that CHAOS restriction of adaptation is applicable in clinically relevant timeframes.

PNA perturbations increase susceptibility of MDR E. coli

To demonstrate the therapeutic potential of CHAOS we multiplexed gene perturbations against a clinical isolate of carbapenem-resistant Enterobacteriaceae (CRE) E. coli, a bacterial pathogen recently designated as priority 1 critical class by the World Health Organization39. Characterization of this isolate using the 2016–2017 Clinical & Laboratory Standards Institute (CLSI) sensitive/resistant breakpoint values40 showed resistance to at least eight classes of antibiotics41,42. For this study, we tested the response of this strain to chloramphenicol as it exhibited greater than 8-fold higher MIC (>256 µg mL−1) than the corresponding CLSI breakpoint of 32 µg mL−1 (Supplementary Table 4). We targeted four new genes, allowing us to confirm if CHAOS is generalizable. Based on the success of CHAOS strain dzTf, we chose to target essential genes representing diverse cellular pathways. These genes included folC, an H2-folate synthetase involved in folate biosynthesis43, ffh encoding a signal recognition particle protein gene essential for protein translocation44, gyrB encoding gyrase subunit B important for transcription, and an essential noncoding small RNA fnrS.

As delivery of CRISPR systems into bacteria is still difficult to achieve therapeutically, we employed an alternative gene expression repression strategy based on 12-mer peptide nucleic acids (PNAs, abbreviated as α-gene). These PNAs target and bind to the translation start codon of these genes, thus inhibiting translation of these genes’ mRNAs into protein45 (see Methods). We chose concentrations of PNA at 2.5 µM at which individual perturbations had minimal to no effect on cell growth of standard MG1655 E. coli. These PNAs were conjugated via an O-linker to a cell-penetrating peptide (CPP) motif for direct intracellular delivery. We exposed the clinically isolated CRE E. coli to these PNAs individually and in combination for 24 h, before plating in both the presence and absence of chloramphenicol (Fig. 6a).

Fig. 6 CHAOS increases the antibiotic susceptibility of clinically isolated CRE E. coli. A CRE isolate of E. coli exhibiting resistance to at least 11 antibiotics above CLSI breakpoint levels was isolated from a clinical infection (Supplementary Table 4). We focused on applying CHAOS induced epistasis to re-sensitize this isolate to chloramphenicol. a A new set of four universally conserved bacterial genes were perturbed using PNA to demonstrate applicability outside of CRISPR interference and towards clinically relevant infections. PNA structure consists of a peptide backbone connecting nucleosides analogous to DNA and linked to a cell penetrating peptide. These molecules are able to enter bacteria and anneal tightly to analogous mRNA sequences, allowing for targeted blockage of protein translation. Chloramphenicol-resistant CRE E. coli was exposed to 2.5 µM of four unique PNAs either individually or in combination (for a total concentration of 10 µM PNA) for 24 h, after which cells were plated on both plain LB agar, as well as clinically-relevant levels of chloramphenicol to determine viable cells. b CFU analysis of CRE E. coli after exposure to PNA treatment demonstrates CHAOS’s effectiveness. Exposure to PNA ffh resulted in a ~16-fold reduction in viable cells with respect to no PNA treatment, while the remaining PNAs exhibited largely no effect under both conditions. Combination of all 4 PNAs exacerbated chloramphenicol’s toxicity and gave rise to ~110-fold reduction in viable cells with respect to no PNA treatment in an apparently epistatic fashion even at sub-resistance levels. P values were calculated using two-tailed type II t-test Full size image

We observed that in absence of chloramphenicol, the total amount of viable cells of the isolate was affected only by α-ffh (16-fold decrease with respect to no PNA treatment), with no apparent exacerbation of α-ffh’s toxic effect upon multiplexing with other PNAs (Fig. 6b). When these same cultures were plated in the presence of 16 µg mL−1 of chloramphenicol, an intermediate CLSI-breakpoint level, similar results were observed for all of the individual PNA treatments. However, multiplexing of all four PNAs significantly reduced the viability of the clinical isolate by nearly 110-fold with respect to no PNA treatment and resulted in a significant reduction (P = 0.002) compared to the best individual perturbation. We note that overall PNA concentration was increased under this condition, leading to the potential of slightly increased antibiotic permeability (Supplementary Figure 20). However, such impacts were relatively small, and disappeared after 15 h of growth. Taken together, this data provides evidence that CHAOS can be used to increase the susceptibility of highly drug-resistant clinical isolates in an apparently epistatic fashion.