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，使候选人能够将其代谢工程的定量背景集中在健康和疾病的研究问题上。具体目标是1)接受细菌发病机制方面的教学培训；2)获得临床微生物学、分子进化和微生物进化研究方面的指导；3)为未来的研究工作奠定基础，利用、开发和结合基因组学和生物信息学工具，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。

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).