BioMolecular Networks in Antibiotic Resistance Evolution

描述(由申请人提供)：拟议的K25职业发展计划的总体目标是提供一段时间的指导教学和研究培训，使候选人能够将他在代谢工程方面的量化背景集中在健康和疾病的研究问题上。具体目标是i)接受细菌发病机制的教学培训，ii)在临床微生物学、分子进化和微生物进化研究方面获得指导指导，以及iii)为未来使用、开发和结合基因组学和生物信息学工具以更好地了解细菌发病机制和抗生素耐药性背景下的生物分子网络进化的研究工作奠定基础。 研究说明。众所周知，氟喹诺酮耐药是以循序渐进的方式发生的，通常涉及药物积累减少或靶向亲和力下降的组合。铜绿假单胞菌异常快速的适应性和令人惊讶的大量运输相关基因使理解这种微生物中出现氟喹诺酮耐药的努力变得复杂起来。例如，在铜绿假单胞菌中有88个基因具有已知或推测的外排功能。这些基因和其他潜在的隐匿耐药基因在氟喹诺酮类药物耐药进化中的相关性尚不清楚。这项研究的第一个主要目标是使用一种无偏见的全基因组方法来识别铜绿假单胞菌中隐匿的氟喹诺酮耐药基因，并就与每个基因相关的生物适合性和毒力的相对成本和好处(目标1-2)进行表征。利用网络理论，我们假设具有大量生物相互作用的基因的耐药性突变将比具有少量生物相互作用的基因突变带来更高的成本。这项研究的第二个主要目标是进一步开发几种基因组学/生物信息学工具，使我们能够检验这一假说(目标3-4)。

DESCRIPTION (provided by applicant): The overall objective of the proposed K25 career development program is to provide a period of mentored didactic and research training that will allow the candidate to focus his quantitative background in metabolic engineering on research questions of health and disease. The specific objectives are i) to receive didactic training in bacterial pathogenesis, ii) to obtain mentored guidance in clinical microbiology, molecular evolution, and microbial evolution research, and iii) to develop a foundation for future research endeavors that use, develop, and combine genomics and bioinformatics tools to better understand bio-molecular network evolution within the context of bacterial pathogenesis and antibiotic resistance. Research Description. Fluoroquinolone resistance is known to occur in a step wise fashion often involving a combination of decreased drug accumulation or target affinity. The unusually rapid adaptability and surprisingly high number of transport related genes in P. aeruginosa has complicated efforts to understand the emergence of fluoroquinolone resistance in this organism. For example, there are 88 genes in P. aeruginosa with known or putative efflux functions. The relevance of each of these and other potential cryptic resistance genes in the evolution of fluoroquinolone resistance is not known. The 1st major objective of this study is to identify, using an unbiased, genome-wide approach, and characterize cryptic fluoroquinolone resistance genes in P. aeruginosa with respect to the relative costs and benefits to biological fitness and virulence associated with each gene (Aims 1-2). Using network theory, we hypothesize that resistance mutations in genes with a large number biological interactions will impose a higher cost than mutations in genes with a small number of biological interactions. The 2nd major objective of this study is to further develop several genomics/bioinformatics tools that will allow us to test this hypothesis (Aims 3-4).