Although many mutations contributing to antibiotic resistance have been identified, the relationship between the mutations and the related phenotypic changes responsible for the resistance has yet to be fully elucidated. To better characterize phenotype–genotype mapping for drug resistance, here we analyse phenotypic and genotypic changes of antibiotic-resistant Escherichia coli strains obtained by laboratory evolution. We demonstrate that the resistances can be quantitatively predicted by the expression changes of a small number of genes. Several candidate mutations contributing to the resistances are identified, while phenotype–genotype mapping is suggested to be complex and includes various mutations that cause similar phenotypic changes. The integration of transcriptome and genome data enables us to extract essential phenotypic changes for drug resistances.

BackgroundUnderstanding ethanol tolerance in microorganisms is important for the improvement of bioethanol production. Hence, we performed parallel-evolution experiments using Escherichia coli cells under ethanol stress to determine the phenotypic changes necessary for ethanol tolerance.ResultsAfter cultivation of 1,000 generations under 5% ethanol stress, we obtained 6 ethanol-tolerant strains that showed an approximately 2-fold increase in their specific growth rate in comparison with their ancestor. Expression analysis using microarrays revealed that common expression changes occurred during the adaptive evolution to the ethanol stress environment. Biosynthetic pathways of amino acids, including tryptophan, histidine, and branched-chain amino acids, were commonly up-regulated in the tolerant strains, suggesting that activating these pathways is involved in the development of ethanol tolerance. In support of this hypothesis, supplementation of isoleucine, tryptophan, and histidine to the culture medium increased the specific growth rate under ethanol stress. Furthermore, genes related to iron ion metabolism were commonly up-regulated in the tolerant strains, which suggests the change in intracellular redox state during adaptive evolution.ConclusionsThe common phenotypic changes in the ethanol-tolerant strains we identified could provide a fundamental basis for designing ethanol-tolerant strains for industrial purposes.

To be able to predict antibiotic resistance in bacteria from fast label-free microscopic observations would benefit a broad range of applications in the biological and biomedical fields. Here, we demonstrate the utility of label-free Raman spectroscopy in monitoring the type of resistance and the mode of action of acquired resistance in a bacterial population of Escherichia coli, in the absence of antibiotics. Our findings are reproducible. Moreover, we identified spectral regions that best predicted the modes of action and explored whether the Raman signatures could be linked to the genetic basis of acquired resistance. Spectral peak intensities significantly correlated (False Discovery Rate, p < 0.05) with the gene expression of some genes contributing to antibiotic resistance genes. These results suggest that the acquisition of antibiotic resistance leads to broad metabolic effects reflected through Raman spectral signatures and gene expression changes, hinting at a possible relation between these two layers of complementary information.

Understanding the constraints that shape the evolution of antibiotic resistance is critical for predicting and controlling drug resistance. Despite its importance, however, a systematic 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. Utilizing 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 identification of trade-off relationships associated with drug resistance. We also report a decelerated 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.

Multi-drug strategies have been attempted to prolong the efficacy of existing antibiotics, but with limited success. Here we show that the evolution of multi-drug-resistant Escherichia coli can be manipulated in vitro by administering pairs of antibiotics and switching between them in ON/OFF manner. Using a multiplexed cell culture system, we find that switching between certain combinations of antibiotics completely suppresses the development of resistance to one of the antibiotics. Using this data, we develop a simple deterministic model, which allows us to predict the fate of multi-drug evolution in this system. Furthermore, we are able to reverse established drug resistance based on the model prediction by modulating antibiotic selection stresses. Our results support the idea that the development of antibiotic resistance may be potentially controlled via continuous switching of drugs.