Identifying Mutations that Promote Microbial Evolvability

识别促进微生物进化的突变

• 批准号: 7644063
• 负责人: Jeffrey Evan Barrick
• 金额: $ 9万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-03-01 至 2011-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7644063
• 关键词: Affect; Bacteria; Biotechnology; Categories; Cell physiology; Chemistry; Chronic; Codon Nucleotides; Complex; Computers; Data; Development; Engineering; Environment; Escherichia coli; Evolution; Exhibits; Experimental Designs; Gene Deletion; Genes; Genetic; Genetic Models; Genome; Genotype; Industry; Infection; Knowledge; Libraries; Life; Measures; Medical Research; Medicine; Methods; Microbe; Modern Medicine; Mutation; Organism; Placebo Effect; Population; Population Genetics; Principal Investigator; Process; Property; Recording of previous events; Regulation; Relative (related person); Series; Techniques; Testing; Time; drug resistant microorganism; fitness; functional genomics; high throughput screening; microbial; microorganism; models and simulation; novel; novel therapeutics; overexpression; pathogen; practical application; pressure; prospective; research study; response; tool

DESCRIPTION (provided by applicant): Evolvability describes an organism's capacity for producing descendants that are better adapted to a given environment. Chronic infections of bacteria can become dominated by mutators over time because the elevated mutation rates of these strains make them more evolvable. Little is currently known about how mutations affecting other cellular processes impact microbial evolvability and about how evolvability changes at each mutational step during the prolonged adaptation of a microorganism to a novel environment. The proposed project consists of three integrated studies that aim to identify mutations that promote microbial evolvability. First, mutation accumulation lines will be used to establish baseline expectations for the effects of fitness changes on the relative evolvability of Escherichia coli strains. Next, the histories of mutations in a twenty-year E. coli long-term evolution experiment will be reconstructed to search for examples of mutations that were ultimately successful because they increased evolvability relative to competing genotypes. Finally, functional genomics techniques will be used to screen libraries of E. coli gene deletion and overexpression strains for genetic changes that increase evolvability with respect to different environments. At each step in this process, marker divergence experiments will provide a consistent measure of evolvability in terms of the local fitness landscape of a test genotype. Prospective experimental designs will be compared and data will be interpreted throughout these studies using a common set of population genetics models and simulations. Currently, I am investigating relationships between genetic regulation and evolvability in E. coli, the experimental evolution of E. coli with reduced genomes, and using pretermination codon avoidance to predict gene essentiality. I am also extending the Avida artificial life platform to enable studies of the evolvability of self-replicating computer progams exhibiting complex genetic regulation. Relevance: Understanding what kinds of mutations are likely to affect the evolvability of microorganisms has implications for holding the progression of chronic infections and the responses of pathogen populations to new therapeutic pressures in check. Methods for engineering more evolvable domesticated microbes would also be useful for green chemistry, renewable bioenergy, and biotechnology applications related to medicine.

描述（由申请人提供）：进化性描述了生物体产生更好地适应给定环境的后代的能力。随着时间的推移，细菌的慢性感染可能会被突变体所控制，因为这些菌株的突变率升高使它们更容易进化。目前很少有人知道如何影响其他细胞过程的突变影响微生物的进化性，以及如何进化性变化的微生物在一个新的环境的长期适应过程中的每个突变步骤。拟议的项目包括三项综合研究，旨在确定促进微生物进化的突变。首先，突变积累线将被用来建立基线预期的适应性变化对大肠杆菌菌株的相对进化能力的影响。其次，对20年的E.大肠杆菌长期进化实验将被重建，以寻找最终成功的突变的例子，因为它们相对于竞争基因型增加了进化能力。最后，利用功能基因组学技术筛选大肠杆菌文库。大肠杆菌基因缺失和过表达菌株的遗传变化，增加了进化相对于不同的环境。在这个过程中的每一步，标记分歧实验将提供一个一致的措施，在测试基因型的局部适应景观的进化。将比较前瞻性实验设计，并使用一组常见的群体遗传学模型和模拟在整个研究中解释数据。目前，我正在研究E. coli，E.大肠杆菌基因组减少，并使用前终止密码子回避预测基因的必要性。我还在扩展Avida人工生命平台，以研究展示复杂遗传调控的自我复制计算机程序的可进化性。 相关性：了解哪些类型的突变可能影响微生物的进化性，对于控制慢性感染的进展和病原体种群对新治疗压力的反应具有重要意义。用于工程化更可进化的驯化微生物的方法也可用于绿色化学、可再生生物能源和与医学相关的生物技术应用。