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.

使用概率模型和进化启发基因预测和控制多基因健康特征编辑项目概要：新的突变是适应性进化新颖性的来源，但也可能导致遗传疾病和癌症。虽然我们现在可以使用 CRISPR/Cas9 技术纠正突变，但 DNA 修饰可以通过看似不可预测的上位和环境相互作用产生意想不到的后果，最近在人类中进行 CRISPR 编辑的 CCR5 中假定的 HIV 抗性突变就是这种情况。真核生物的适应性或疾病易感性等健康特征似乎是由许多突变协同作用——它们是所谓的多基因或复杂性状。甚至在某些环境中表现出有害，而在另一些环境中表现出有益，因此也称为拮抗拟议工作的主要目标是利用多功能模型植物拟南芥来实现多效性。增强突变的多基因和拮抗适应性效应的可预测性和控制。该项目的结果将为加深我们对复杂人类遗传学的理解提供普遍原则疾病并为将来安全纠正或避免有害突变提供信息。具体来说，我将追求以下目标：1）预测多基因适应性效应环境，2）通过控制有害和有益的突变来提高适应性多重基因组编辑和突变等位基因是梳理拟南芥的理想模型。由于其对工程突变的高度可塑性，复杂环境中突变的适应性效应及其广泛的社区和资源。1001 拟南芥基因组计划和全基因组敲除。 (KO) 集合允许量化数千个公开可用的自然和人工突变的适合度建立一个全球拟南芥研究人员网络，我们已经开始了一项实验。 45 个地点的相同自然菌株，我将用它来量化与环境相关的突变效应。将其与相关 KO 线的信息相结合，我将基于我之前的预测模型来理解突变对不同环境适应性的影响，以及使其有害的特征。对突变效应的深入理解最终将使我们能够以可预测的方式改变适应性，我将对此进行测试。有两种方式：首先，使用多重 CRISPR 碱基编辑，我将用有利的突变代替不利的突变。其次，为了研究累积突变如何影响适应性并学习如何纠正这一点，我将设计具有已知突变和抗突变等位基因的植物这些等位基因与 DNA 修复机制相关。和癌症易感性，可以增加或减少拟南芥的突变率，帮助我们探索突变总体而言，我的研究将为许多哺乳动物的累积达到致命水平提供基础见解。复杂健康特征的遗传控制，最终为改善个性化基因组疾病铺平道路风险预测并安全探索多基因疗法的局限性。