BioMolecular Networks in Antibiotic Resistance Evolution

抗生素耐药性进化中的生物分子网络

• 批准号: 7215720
• 负责人: RYAN T GILL
• 金额: $ 14.09万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-06-01 至 2009-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7215720
• 关键词: Affinity; Alleles; Antibiotic Resistance; Antibiotics; Bioinformatics; Biological; Biotin; Calculi; Ciprofloxacin; Clinical; Clinical Microbiology; Condition; Costs and Benefits; DNA; Discriminant Analysis; Disease; Disruption; Endopeptidases; Engineering; Environment; Evolution; Facility Construction Funding Category; Foundations; Future; Gene Chips; Gene Duplication; Gene Expression; Genes; Genome; Genomic Library; Genomics; Growth; Health; In Vitro; Label; Laboratories; Libraries; Maps; Measures; Mentors; Metabolic; Methods; Mind; Molecular; Molecular Evolution; Mutate; Mutation; Nucleotides; Numbers; Nutrient; Organism; Outcome; Pathogenesis; Peptide Hydrolases; Pharmaceutical Preparations; Phenotype; Phospholipase C; Phylogenetic Analysis; Plasmids; Polymerase Chain Reaction; Process; Program Development; Protein Overexpression; Proteins; Pseudomonas aeruginosa; Relative (related person); Research; Research Training; Resistance; Resolution; Sampling; Series; Site; Structure; Techniques; Testing; Training; Vertebral column; Virulence; Work; career; clinical phenotype; clinically relevant; cost; duplicate genes; fitness; fluoroquinolone resistance; improved; microbial; mutant; pyoverdin; research study; resistance mechanism; size; theories; tool; trait

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).

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：i）接受细菌发病机制的教学培训，ii）获得临床微生物学，分子进化和微生物进化研究的指导，iii）为未来使用，开发和联合收割机和生物信息学工具的研究工作奠定基础，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。 研究描述。已知氟喹诺酮耐药性以逐步方式发生，通常涉及药物蓄积或靶向亲和力降低的组合。铜绿假单胞菌中异常快速的适应性和令人惊讶的高数量的转运相关基因使得理解该生物体中氟喹诺酮耐药性的出现的努力变得复杂。例如，铜绿假单胞菌中有88个基因具有已知或推定的外排功能。这些基因和其他潜在的隐性耐药基因与氟喹诺酮耐药演变的相关性尚不清楚。本研究的第一个主要目的是使用无偏倚的全基因组方法识别并表征铜绿假单胞菌中隐藏的氟喹诺酮类耐药基因，以及与每个基因相关的生物适应性和毒力的相对成本和效益（目的1-2）。使用网络理论，我们假设，具有大量生物相互作用的基因中的抗性突变将比具有少量生物相互作用的基因中的突变产生更高的成本。本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，使我们能够验证这一假设（目标3-4）。