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：
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
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/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

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为启发的基因预测和控制多基因健康特征编辑项目摘要：新突变是适应性进化新颖性的来源，但也会引起遗传疾病和癌症。虽然我们现在可以使用CRISPR/CAS9技术纠正有害突变，但DNA修改可以具有通过看似不可预测的认识和环境互动，意想不到的后果，好吧，这是CCR5中假定的抗HIV抗性突变的情况。更高真核生物，健身或健康特征（例如适应性或疾病易感性）似乎由许多共同作用的突变 - 它们是所谓的多基因或复杂的特征。这样的突变可能在某些环境中，即使在某些环境中有益于明显的定义，但也称为拮抗剂多效性。拟议工作的主要目标是将多功能模型植物拟南芥用于增强突变的多基因和拮抗作用效应的可预测性和控制。该项目的结果将提供普遍的原则，以加深我们对复杂人类通用的理解疾病并告知未来有害突变的安全纠正或避免。具体而言，我将追求以下目的：1）预测跨越多基因健身效应环境，2）通过使用有害和有益的突变来改善健身多路复用基因组编辑和突变器等位基因。拟南芥是教导分开的理想模型由于其对工程突变的较高的锻造性，突变在复杂环境中的适应性影响及其广泛的社区和资源。 1001拟南芥基因组项目和全基因组敲除（KO）收集允许量化数千种公共可用自然和人工突变的适应性跨环境。建立了拟南芥研究人员的全球网络，我们已经开始了一个实验在45个位置具有相同的天然菌株，我将用来量化与环境相关的突变效应。将其与相关KO线的信息集成在一起，我将基于我以前的预测模型来理解突变对环境跨环境的健身的影响以及使其有害的特征。这样的对突变效应的深刻理解最终将使我们能够以可预测的方式改变健身。我会测试这个通过两种方式：首先，使用多重CRISPR基础编辑，我将用探测器代替有益突变。其次，为了研究积累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器和抗突变器等位基因的植物。这些等位基因，与DNA修复机械相关癌症的敏感性，可以提高或降低塔利亚纳曲霉的突变率，帮助我们探索突变在许多哺乳动物中，积累高达致命水平。总体而言，我的研究将提供基本的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的方式铺平了道路风险预测并安全探测多基因疗法的极限。