Comparative resistomics of Gram-negative bacterial pathogens

革兰氏阴性细菌病原体的比较耐药组学

• 批准号: 10548874
• 负责人: ANDREI L OSTERMAN
• 金额: $ 73.35万
• 依托单位: SANFORD BURNHAM PREBYS MEDICAL DISCOVERY INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-10 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10548874
• 关键词: Acinetobacter baumannii; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Antimicrobial Resistance; Automobile Driving; Bioinformatics; Bioreactors; Ciprofloxacin; Clinical; Clinical Data; Colistin; Collection; Communities; Comparative Study; Data; Development; Devices; Diagnostic; Drug Combinations; Drug resistance; Enhancement Technology; Escherichia coli; Evolution; Foundations; Frequencies; Genomics; Genotype; Gram-Negative Bacteria; Grant; Individual; Klebsiella pneumoniae; Knowledge; Laboratory Study; Maps; Meropenem; Methodology; Methods; Modeling; Modern Medicine; Multi-Drug Resistance; Mutation; Pharmaceutical Preparations; Phenotype; Population; Predisposition; Pseudomonas aeruginosa; Publishing; Regimen; Reporting; Research; Resistance; Sampling; Series; Standardization; Testing; Therapeutic; Time; Treatment Protocols; Validation; Variant; antimicrobial; comparative; comparative genomics; deep sequencing; driver mutation; drug candidate; fitness; genetic variant; genome sequencing; genome-wide; mutant; novel; novel therapeutics; pathogen; pathogenic Escherichia coli; pathogenic bacteria; predictive modeling; success; therapy development; tigecycline; translational impact; treatment optimization; web site

ABSTRACT Increasing antibiotic resistance necessitates expanding research into the mechanisms by which bacterial pathogens acquire and perpetuate drug resistance. Despite rapidly expanding genomic mapping of resistance-conferring mutations in clinical isolates and laboratory studies, our knowledge of dynamics and mechanisms underlying evolution of antimicrobial resistance is still insufficient. To fill-in this gap, the authors of this proposal combine experimental evolution in a continuous culturing device, morbidostat, with time- resolved ultradeep genomic sequencing of evolving bacterial cultures. The utility of the developed morbidostat-based workflow is supported by published and ongoing studies with established antimicrobials and experimental drug candidates. The preliminary results of comparative resistomics studies over a range of Gram-negative bacterial species provided initial support to a premise that evolution of drug resistance in morbidostat proceeds via a limited set of trajectories defined by a combination of resistance and fitness constrains approximating clinical evolution, which favors selection of low-frequency/high-fitness over high- frequency/low-fitness mutants. A comparative resistomics approach enables mapping of both universal and strain-specific mechanisms as demonstrated in a recent proof-of-concept study on experimental evolution of ciprofloxacin resistance in three Gram-negative bacteria. The proposed 5-year project will test the central hypothesis and extend exploration of antimicrobial resistome by pursuing the following specific aims: (i) in Aim 1, the established morbidostat-based workflow will be used to determine major mechanisms driving resistance to broad-spectrum clinical antibiotics, ciprofloxacin, colistin, tigecycline and meropenem, in four difficult-to-treat Gram-negative bacterial pathogens, Acinetobacter baumannii ATCC17978, P. aeruginosa ATCC27853, E. coli ATCC25922, and K. pneumoniae ATCC13883; (ii) in Aim 2, a representative panel of selected clones will be systematically characterized to assess the effects of individual mutations and combinations thereof on acquired resistance and fitness; (iii) Aim 3 will leverage a moribidostat-based workflow to make first steps toward experimental evolution of multidrug resistance focusing on A. baumannii and starting from clones selected in single-drug evolution studies. The results that will be obtained in all planned studies will be a subject of in-depth bioinformatics analysis (including comparison with public data for clinical isolates), predictive modeling, integration and sharing with broad research community via a specialized web-site on integrative Genomics of Evolution of Antimicrobial Resistance (iGEAR). The proposed study is expected to have translational impacts in advancing methodology to support rational optimization of antibiotic treatment regimens and development of new drugs with minimized resistibility.

摘要