A systematic study for evolution of bacterial drug resistance: phenotype to genotype

Bacterial drug resistance is a worldwide problem threatening millions of lives. Several studies showed that bacteria develop direct resistance against an antibiotic compound used throughout treatment. However, recent studies demonstrated that resistance to one antibiotic can pleiotropically lead to resistance to other antibiotics, a concept known as cross-resistance, imposing serious limitations for combating against infectious diseases. Therefore, slowing down evolution of cross-resistance is critical and important task for developing effective antibiotic therapies. Despite its importance, mechanisms behind crossresistance are not well understood due to lack of systematic studies. Here in this systematic study, we aim to provide a better understanding of evolution of antibiotic resistance using state of the art genetic tools. In this study, we evolved 88 initially isogenic Escherichia coli populations against 22 different antibiotics for 21 days. For each drug, two populations were evolved under strong selection and two populations were evolved under mild selection. Representative clones from each evolved population were phenotyped against all 22 drugs we used in our experiments and their resistance levels were carefully quantified. Furthermore, these clones were genotyped by Illumina whole genome sequencing and resistance-conferring mutations were identified. Bacterial populations evolved under strong selection acquired stronger resistance against higher number of antibiotics compared to populations evolved under mild selection. Strongly selected populations also acquired higher number of mutations compared mildly selected populations and there mutations were found to be more pathway specific among strongly selected populations. Finally, populations evolved against aminoglycosides were found to develop hypersensitivity against several other antibiotic classes due to mutations in trkH gene, coding for a membrane protein. Our study provides a thorough understanding for phenotype to genotype in the context of antibiotic resistance and demonstrates that selection strength is an important parameter contributing to the complexity of evolution of antibiotic resistance

Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution

Revealing the genetic changes responsible for antibiotic resistance can be critical for developing novel antibiotic therapies. However, systematic studies correlating genotype to phenotype in the context of antibiotic resistance have been missing. In order to fill in this gap, we evolved 88 isogenic Escherichia coli populations against 22 antibiotics for 3 weeks. For every drug, two populations were evolved under strong selection and two populations were evolved under mild selection. By quantifying evolved populations' resistances against all 22 drugs, we constructed two separate cross-resistance networks for strongly and mildly selected populations. Subsequently, we sequenced representative colonies isolated from evolved populations for revealing the genetic basis for novel phenotypes. Bacterial populations that evolved resistance against antibiotics under strong selection acquired high levels of cross-resistance against several antibiotics, whereas other bacterial populations evolved under milder selection acquired relatively weaker cross-resistance. In addition, we found that strongly selected strains against aminoglycosides became more susceptible to five other drug classes compared with their wild-type ancestor as a result of a point mutation on TrkH, an ion transporter protein. Our findings suggest that selection strength is an important parameter contributing to the complexity of antibiotic resistance problem and use of high doses of antibiotics to clear infections has the potential to promote increase of cross-resistance in clinics

Large-Scale Identification and Analysis of Suppressive Drug Interactions

One drug may suppress the effects of another. Although knowledge of drug suppression is vital to avoid efficacy-reducing drug interactions or discover countermeasures for chemical toxins, drug-drug suppression relationships have not been systematically mapped. Here, we analyze the growth response of Saccharomyces cerevisiae to anti-fungal compound ("drug") pairs. Among 440 ordered drug pairs, we identified 94 suppressive drug interactions. Using only pairs not selected on the basis of their suppression behavior, we provide an estimate of the prevalence of suppressive interactions between anti-fungal compounds as 17%. Analysis of the drug suppression network suggested that Bromopyruvate is a frequently suppressive drug and Staurosporine is a frequently suppressed drug. We investigated potential explanations for suppressive drug interactions, including chemogenomic analysis, coaggregation, and pH effects, allowing us to explain the interaction tendencies of Bromopyruvate