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Predicting and controlling polygenic health traits using probabilistic models and evolution-inspired gene editing

使用概率模型和进化启发的基因编辑来预测和控制多基因健康特征

基本信息

批准号：
10477409
负责人：
Moises Exposito-Alonso
金额：
$ 40.38万
依托单位：
CARNEGIE INSTITUTION OF WASHINGTON, D.C.
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-10 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10477409
关键词：
Address Alleles Arabidopsis Atlases Award Awareness Biological Models Biology Budgets CCR5 gene CRISPR/Cas technology Cell Culture Techniques Clustered Regularly Interspaced Short Palindromic Repeats Collaborations Collection Communities Complex Core Facility Country DNA DNA Modification Process DNA Repair Dangerousness Data Development Disease Disease susceptibility Doctor of Philosophy Ecology Education Engineering Ensure Environment Eukaryota European Event Evolution Faculty Foundations Funding Future Generations Genes Genetic Genetic Diseases Genetic Engineering Genome Genomics Geographic Locations Germany Goals HIV resistance Hand Health Heart Human Human Genetics Induced Mutation Institution International Intervention Investigation Knock-out Knowledge Laboratories Lead Learning Location Logistics Malignant Neoplasms Mammals Measures Mediating Mentors Modeling Molecular Biology Molecular Genetics Mouse-ear Cress Mutation Nonprofit Organizations Philosophy Plant Model Plants Positioning Attribute Postdoctoral Fellow Predisposition Research Research Personnel Resources Science Source Spain Statistical Models Students System Testing Training United Kingdom Universities Work Workload antagonist base base editing career collaborative environment disorder risk experience experimental study fitness fundamental research gene therapy genetic manipulation genome editing genome-wide graduate student human genomics improved insight loss of function member multidisciplinary predictive modeling professor programs recruit research vision resilience resistance mutation risk prediction statistics tool trait undergraduate student

项目摘要

Predicting and controlling polygenic health traits using probabilistic models and evolution-inspired gene editing PROJECT SUMMARY: New mutations are a source of adaptive evolutionary novelty but can also cause genetic diseases and cancer. While we can now correct detrimental mutations using CRISPR/Cas9 technologies, DNA modifications can have unintended consequences through seemingly unpredictable epistatic and environmental interactions, as could well be the case for the presumed HIV-resistance mutations in CCR5 recently CRISPRed into humans. In higher eukaryotes, fitness or health traits such as adaptability or disease susceptibility appear to be controlled by numerous mutations acting in concert – they are so-called polygenic or complex traits. Such mutations might even manifest detrimental in some environments while beneficial in others, therefore also called antagonistic pleiotropic. The main goal of the proposed work is to use the versatile model plant Arabidopsis thaliana to enhance the predictability and control of the polygenic and antagonistic fitness effects of mutations. Results from this project will provide universal principles to deepen our understanding of complex human genetic disease and inform the safe correction or avoidance of harmful mutations in the future. Specifically, I will pursue the following aims: 1) predicting polygenic fitness effects across environments, 2) improving fitness by controlling deleterious and beneficial mutations using multiplexed genome editing and mutator alleles. Arabidopsis thaliana is an ideal model to tease apart the fitness effects of mutations in complex environments due to its high malleability to engineered mutations, and its extensive community and resources. The 1001 Arabidopsis Genome Project and a genome-wide Knock-Out (KO) collection allow for quantifying fitness of thousands of publicly available natural and artificial mutations across environments. Building a global network of Arabidopsis researchers, we have started an experiment with the same natural strains in 45 locations, which I will use to quantify environment-associated mutation effects. Integrating this with information of relevant KO lines, I will build on my previous predictive models to understand the effects of mutations on fitness across environments, and the features that make them deleterious. Such a deep understanding of mutation effects will ultimately allow us to alter fitness in predictable ways. I will test this in two ways: First, using multiplexed CRISPR base-edits, I will substitute detrimental for beneficial mutations. Second, to study how accumulating mutations impact fitness and to learn how to correct this, I will engineer plants with known mutator and anti-mutator alleles. These alleles, associated with the DNA repair machinery and cancer susceptibility, can increase or decrease the mutation rate in A. thaliana, helping us explore mutation accumulations up to lethal levels in many mammals. Overall, my research will provide fundamental insights into the genetic control of complex fitness traits, ultimately paving the way to improving personalized genomic disease risk predictions and safely probing the limits of poly-gene therapies.

利用概率模型和进化启发基因预测和控制多基因健康性状