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

• 批准号: 10005708
• 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
• 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/10005708
• 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; Vision; Work; Workload; base; 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; resilience; resistance mutation; statistics; tool; trait; undergraduate student

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 编辑 项目概要： 新的突变是适应性进化新奇性的来源，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技术纠正有害的突变，但DNA修饰可能会导致 通过看似不可预测的上位性和环境相互作用产生意想不到的后果， 最近，CRISPRed在人类中发现了假定的CCR 5抗艾滋病病毒突变。高等 真核生物，健身或健康性状，如适应性或疾病易感性似乎是由控制 许多突变协同作用-它们是所谓的多基因或复杂性状。这种突变可能 甚至在某些环境中表现为有害的，而在另一些环境中表现为有益的，因此也称为拮抗的 多效性的这项工作的主要目标是利用多功能模式植物拟南芥， 增强突变的多基因和拮抗适应性效应的可预测性和控制。 该项目的结果将提供普遍的原则，以加深我们对复杂的人类遗传学的理解。 疾病和通知安全纠正或避免有害的突变在未来。 具体来说，我将追求以下目标：1）预测多基因健身效果， 环境，2）通过控制有害和有益的突变来改善适应性， 多重基因组编辑和增变等位基因。拟南芥是一个理想的模型， 突变在复杂环境中的适应性效应，由于其对工程突变的高度延展性， 广泛的社区和资源。拟南芥1001基因组计划和全基因组敲除 (KO)收集允许量化数千个公开可用的自然和人工突变的适合度 跨环境。建立一个拟南芥研究人员的全球网络，我们已经开始了一项实验， 在45个地方的相同的自然菌株，我将使用它来量化环境相关的突变效应。 结合相关KO系的信息，我将建立在我以前的预测模型，以了解 突变对环境适应性的影响，以及使它们有害的特征。这样的 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。我来测试一下 通过两种方式：首先，使用多重CRISPR碱基编辑，我将用有害的突变代替有益的突变。 第二，为了研究累积的突变如何影响适应性，并学习如何纠正这种情况，我将设计 具有已知增变基因和反增变基因等位基因的植物。这些等位基因与DNA修复机制有关， 和癌症易感性，可以增加或减少A. thaliana帮助我们探索突变 在许多哺乳动物中积累到致命水平。总的来说，我的研究将提供基本的见解， 复杂适应性特征的遗传控制，最终为改善个性化基因组疾病铺平道路 风险预测和安全地探索多基因疗法的极限。