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Escherichia Coli ARTICLE https://doi.org/10.1038/s41467-020-19713-w OPEN High-throughput laboratory evolution reveals evolutionary constraints in Escherichia coli ✉ Tomoya Maeda 1,4 , Junichiro Iwasawa 2,4, Hazuki Kotani1, Natsue Sakata1, Masako Kawada1, ✉ Takaaki Horinouchi1, Aki Sakai1, Kumi Tanabe1 & Chikara Furusawa 1,2,3 Understanding the constraints that shape the evolution of antibiotic resistance is critical for predicting and controlling drug resistance. Despite its importance, however, a systematic 1234567890():,; investigation of evolutionary constraints is lacking. Here, we perform a high-throughput laboratory evolution of Escherichia coli under the addition of 95 antibacterial chemicals and quantified the transcriptome, resistance, and genomic profiles for the evolved strains. Uti- lizing machine learning techniques, we analyze the phenotype–genotype data and identified low dimensional phenotypic states among the evolved strains. Further analysis reveals the underlying biological processes responsible for these distinct states, leading to the identifi- cation of trade-off relationships associated with drug resistance. We also report a deceler- ated evolution of β-lactam resistance, a phenomenon experienced by certain strains under various stresses resulting in higher acquired resistance to β-lactams compared to strains directly selected by β-lactams. These findings bridge the genotypic, gene expression, and drug resistance gap, while contributing to a better understanding of evolutionary constraints for antibiotic resistance. 1 RIKEN Center for Biosystems Dynamics Research, 6-2-3 Furuedai, Suita, Osaka 565-0874, Japan. 2 Department of Physics, The University of Tokyo, 7-3-1 Hongo, Tokyo 113-0033, Japan. 3 Universal Biology Institute, The University of Tokyo, 7-3-1 Hongo, Tokyo 113-0033, Japan. 4These authors contributed ✉ equally: Tomoya Maeda, Junichiro Iwasawa. email: [email protected]; [email protected] NATURE COMMUNICATIONS | (2020) 11:5970 | https://doi.org/10.1038/s41467-020-19713-w | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-19713-w he emergence of antibiotic resistance and multidrug- for 27 daily passages corresponding to ~250–280 generations. Tresistant bacteria is a growing global health concern1–4, Figure 1a shows examples of the time course of half-maximal and alternative strategies for suppressing the emergence of inhibitory concentrations (IC50s) during laboratory evolution, resistant bacteria are actively being sought. Various mechanisms while all-time courses of IC50s are shown in Supplementary Fig. 1. for antibiotic resistance have been identified, including the acti- Among the 95 stressors, a significant increase in IC50 was vation of efflux pumps, modifications of specific drug targets, and observed for 89 stressors (Mann–Whitney U test, false discovery shifts in metabolic activities5–10. Quantitative studies of resistance rate (FDR) < 5%). For further phenotypic and genotypic analyses, evolution showed that these mechanisms for resistance are tightly we selected 192 evolved strains, i.e., four evolved strains isolated interconnected, as demonstrated by the complicated networks of from 47 stressors plus a control without any stress. These cross-resistance and collateral sensitivity among drugs11–15, 47 stressors were selected from the initial 95 stress environments, which is the phenomena whereby the acquisition of resistance to due to the limitation of experimental capacity. Selections were a certain drug is accompanied by resistance or sensitivity to made based on the degree of increased IC50 values; to ensure a another drug. Such interactions among resistance mechanisms variety of stressors with different action mechanisms, and based result in constraints on accessible phenotypes in evolution16–18. on the predicted novelty of the expected results. For further For example, the cyclic or simultaneous use of two drugs with analysis, we selected the top four independent culture lines collateral sensitivity, to which pathogens did not easily acquire showing higher IC50 values among the six. resistance simultaneously, were demonstrated to suppress resis- tance evolution19,20. Thus, elucidating evolutionary constraints Phenotypic and genotypic changes in evolved strains.To are crucial for predicting and controlling the evolution of anti- explore phenotypic changes in the 192 evolved strains, we first biotic resistance; however, despite its importance, a systematic quantified changes in the stress resistance profiles by measuring investigation of evolutionary constraints for antibiotic resistance the IC of all 47 chemicals for each evolved strain (9024 mea- evolution is still lacking. 50 surements in total), relative to the parent strain (Supplementary Laboratory evolution associated with genotype sequencing and Data 2 and “Methods”). The resistance profile measurements phenotyping is an effective approach to investigate constraints in – allowed us to study how common cross-resistance, a phenom- adaptive evolution21 23. Here, we perform high-throughput enon where an evolved strain in certain stress gains resistance to laboratory evolution of Escherichia coli under 95 heterogeneous another stress, occurs. Here, cross-resistance refers to both cross- stressors (Supplementary Data 1). To analyze the expanded cross- genetic resistance and cross-heteroresistance. By comparing the resistance/collateral sensitivity network, including both antibiotic four evolved replicates and the parent strain, we found that 336 and non-antibiotic stressors, while elucidating the molecular and 157 pairs of stressors exhibited cross-resistance and collateral mechanisms associated with resistance acquisition, we choose a sensitivity, respectively, within the possible 2162 combinations variety of antibacterial chemicals, including antibiotics with var- (Mann–Whitney U test, FDR < 5%, Supplementary Fig. 2). ious mechanisms of action, and non-antibiotic toxic chemicals, To investigate genetic alterations underlying the observed against E. coli. We quantify changes in the transcriptome, geno- resistance, we performed genome resequencing analysis of the mic sequence, and resistance profile in the evolved strains, pro- 192 evolved strains (Supplementary Data 3). Although some of ducing a multiscale dataset for analyzing stress resistance. By these strains carried more than 20 mutations, 147/192 evolved analyzing the gene expression-resistance map through machine- strains (76.6%) harbored fewer than five. To show the variation in learning techniques, we show the emergence of low dimensional the number of mutations among strains evolved in the same phenotypic states in the evolved strains, indicating the existence environment, the mean number and standard deviation of of evolutionary constraints. We then analyze the underlying mutations for the four strains are shown in Supplementary Fig. 3. biological processes corresponding to each state by introducing Among the 47 stressors, the highest number of mutations was the representative mutations to the parent strain. To examine observed in glutamic acid γ-hydrazide (GAH) evolved strains whether the whole population or only a subset of cells are phe- carrying 157 ± 67 mutations (Supplementary Fig. 3), which was notypically resistant, we conduct a population analysis profile significantly more than the number observed in evolved strains (PAP). Heteroresistance is a common phenomenon for several against known mutagens (e.g., 4-nitroquinoline-1-oxide (NQO, bacterial species, and antibiotic classes, in which a subpopulation – 23±5 mutations) and mitomycin C (MMC, 27 ± 4 mutations)), among susceptible cells exhibits increased resistance24 27.We indicating a high mutagenic activity of GAH. Excluding strains identify many heteroresistance-conferring mutations, as well as evolved in GAH, NQO, and MMC, which harbored more than 18 known repressors for multidrug efflux pumps. We also report mutations on average per stressor, 21 and 307 mutations were decelerated evolution, in which the resistance of the evolved identified as synonymous and nonsynonymous mutations, strains in a certain stressor is overtaken by strains evolved in a respectively (Fig. 1c). For strains evolved in stresses other than different stressor. Herein, we demonstrate how our experimental GAH, NQO, and MMC, the ratio of nonsynonymous to system could provide a quantitative understanding of evolu- synonymous mutations per site was 5.26, implying that tionary constraints in adaptive evolution, leading to the basis for approximately 80% of the nonsynonymous mutations were predicting and controlling antibiotic resistance. beneficial29,30. Results Supervised principal component analysis (PCA) reveals mod- Laboratory evolution of E. coli under 95 stress conditions.To ular phenotypic states. Phenotypic changes of the evolved strains systematically investigate drug-resistant phenotypes, we per- were quantified by transcriptome analysis to examine gene formed high-throughput laboratory evolution using an auto- expression levels responsible for stress resistance (Supplementary mated culture system (Fig. 1a)28 for 95 stressors covering a wide Data 4). In the transcriptome analysis, all evolved strains were range of action mechanisms (Fig. 1b and Supplementary Data 1). cultured without the addition of stressors to standardize the To evaluate the reproducibility of the evolutionary dynamics, six culture condition. To explore the relationship between gene independent culture lines were propagated in
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