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Effects of genetic background on adaptive evolution

遗传背景对适应性进化的影响

基本信息

批准号：
10615639
负责人：
Peter Andolfatto
金额：
$ 32.96万
依托单位：
COLUMBIA UNIV NEW YORK MORNINGSIDE
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10615639
关键词：
ATP phosphohydrolase Address Amino Acid Substitution Amino Acids Animals Behavior Biology CRISPR/Cas technology Cardiac Glycosides Chromosome Mapping Cre lox recombination system Data Dependence Development Disease Drosophila genus Drosophila melanogaster Proteins Drug Targeting Eating Engineering Evolution Exhibits Fertility Gene Expression Profiling Genes Genetic Genetic Determinism Genetic Drift Genetic Engineering Genetic Epistasis Genetic Variation Genome Genome engineering Genomics Glycosides Homeostasis Human Human Biology In Vitro Individual Insecta K ATPase Learning Link Maps Modeling Molecular Molecular Genetics Mutation Natural Selections Nature Neurologic Organism Outcome Phenotype Physiological Plants Population Population Genetics Process Property Proteins Quantitative Trait Loci Recurrence Resistance Role Shapes Site Steroids System Technology Time Toxic effect Toxin Variant Vertebrates Work comparative genomics experience fitness genetic analysis genetic architecture genome editing genome wide association study human disease in vivo inhibitor innovation insight model organism novel pathogenic bacteria pathogenic virus permissiveness protein function tool trait whole genome

项目摘要

Project Summary To what extent is adaptive evolution predictable? Despite its importance in understanding the biology of human diseases, including the evolution of viral and bacterial pathogens, the dynamics of genetic adaptation are still poorly understood. In particular, few examples yet exist that have been dissected to the molecular level. Examples of adaptations from natural systems and the use of model organisms are powerful tools that can be combined to make progress on this difficult question. We have employed instances of “parallel evolution”, involving assemblages of species experiencing a common regime of natural selection, to evaluate multiple outcomes of the process of adaptation. With this information, we can learn about recurrent features of adaptation and infer constraints and regularities in the adaptive process. Our work focused on the biomedically-important interaction between Na+,K+-ATPases and their regulatory steroidal-glycosides that a large variety of plants and animals use as toxins to defend themselves from being eaten. Using a diverse set of animals that have independently evolved resistance to steroidal-glycoside toxicity, including insects and vertebrates, we discovered these diverse species most often evolve resistance via a small number of possible options (i.e. involving only three of 41 possible sites in the protein that could be modified to confer resistance). These findings suggest that adaptive evolution is often path-dependent, implying that the individual components of an adaptation must evolve in a prescribed, and ultimately predictable, order. They also raise numerous questions about the nature of this path dependency, including the extent to which it emerges from interactions among residues within a protein, or from the genomic background of the species; whether it similarly constrains adaptation over short and longer time scales, and how generally it applies in adaptive protein evolution. Here we propose three aims that address these questions, by combining approaches from evolutionary genomics and molecular genetics. In Aim 1, we will use genome engineering in Drosophila to elucidate the path dependence of the steroidal-glycoside resistance adaptation both at the level of its primary target (Na+,K+-ATPase) and at the level of the whole genome. In Aim 2, we will determine which and how many genomic factors contribute to naturally occurring variation within Drosophila populations. This information will reveal the relationship between within- population and between-species genetic variation underlying the same trait, connecting short- and long-term dynamics of the adaptive process. In Aim 3, we will use principles learned from molecular adaptation at Na+,K+-ATPase to computationally predict and use genome engineering to functionally validate path dependent adaptation dynamics in Drosophila for a diverse group of proteins, many of which (like Na+,K+-ATPase) have important roles in neurological development and homeostasis. Together this work will greatly increase our understanding of the constraints on adaptive protein evolution and the predictability of the genetic changes by which novel phenotypes emerge.

项目总结