Collaborative Research: Assessing the connections between genetic interactions, environments, and phenotypes in Arabidopsis thaliana

合作研究：评估拟南芥遗传相互作用、环境和表型之间的联系

• 批准号: 2210432
• 负责人: Patrick Krysan
• 金额: $ 53.5万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210432&HistoricalAwards=false
• 关键词: Collaborative; Research; Assessing; connections; between

Organismal complexity is due in large part to genes working not in isolation but with each other. Knowledge of such interactions will facilitate improving plant productivity and resilience to increasingly extreme conditions. However, studying the impacts of gene interactions on plant traits is challenging for two reasons. First, there can be millions of possible interactions to sieve through. Second, both nature (i.e., genes and gene interactions) and nurture (i.e., the environment) are important. Even when a gene interaction is identified as being important, its relevance is frequently known only for one environment. This project will address these challenges by investigating the question of how nature and nurture jointly impact plant traits. Specifically, interactions between hundreds of pairs of genes in the model plant Arabidopsis will be examined by measuring survival traits under different temperatures. Artificial intelligence-based approaches will be used to measure traits and to build models that predict gene interactions under different environments. These prediction models will also incorporate existing knowledge of interactions among similar genes from non-plant species. The predictions will be tested experimentally and will provide insight into how nature and nurture jointly influence plant survival and fitness. Such insight will facilitate better predictions of gene functions in both model and crop plants and provide candidate genes for engineering productive and resilient plants. Findings from this project will serve as examples illustrating to the scientific community and the public the benefits of integrating experimental and computational approaches. Advances in genetics and genomics have led to an unprecedented understanding of how genotypes connect with phenotypes and the roles of genetic interactions and the environment in controlling phenotype. However, the environmental dependency of gene × gene interactions is frequently not considered, particularly in multicellular species. The goal of this project is to better understand the connection between genotypes and phenotype by assessing the impact of environmental perturbation on genetic interactions and by identifying the genetic components underlying this plasticity in the model plant Arabidopsis thaliana using protein kinase genes as examples. This will be accomplished through phenotyping experiments coupled with computational modeling. First, models predicting genetic interactions specific to an environmental context will be generated through multi-omics data integration and the use of existing genetic interaction data from Arabidopsis and other model species (e.g., yeast and worm) and new experimental data generated from 150–200 pairs of single and double kinase mutants grown in 3–5 different environmental contexts (i.e., temperature regimes), yielding multiple trait values, which will be used to calculate quantitative measures of genetic interactions between gene pairs and the environment. Next, model predictions will be validated using the experimental data, and the results will be used to further refine the models. The refined models will be dissected using model interpretation methods to reveal the molecular features important for specifying context-specific genetic interactions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

生物体的复杂性在很大程度上是由于基因不是孤立而是彼此之间的。对这种相互作用的了解将有助于提高植物生产力和弹性，以提高极端条件。但是，研究基因相互作用对植物特征的影响是有两个原因的。首先，可能会有数百万个可能的互动来筛选。其次，自然（即基因和基因相互作用）和护士（即环境）都很重要。即使将基因相互作用确定为重要，它的相关性也通常仅在一个环境中知道。该项目将通过调查自然和护士如何共同影响植物特征的问题来应对这些挑战。具体而言，将通过测量不同温度下的生存特征来检查模型植物拟南芥中数百对基因之间的相互作用。基于人工智能的方法将用于测量特质并构建预测不同环境下基因相互作用的模型。这些预测模型还将结合非植物物种相似基因之间相互作用的现有知识。这些预测将进行实验测试，并将提供有关自然和护士如何共同影响植物生存和适应性的洞察力。这种见解将促进对模型和作物植物中基因功能的更好预测，并为工程产品和弹性植物提供候选基因。该项目的发现将作为向科学界和公众说明整合实验和计算方法的好处的例子。遗传学和基因组学的进步导致对基因型如何与表型的联系以及遗传相互作用的作用以及环境控制表型的作用有前所未有的理解。但是，通常不考虑基因×基因相互作用的环境依赖性，尤其是在多细胞物种中。该项目的目的是通过评估环境扰动对遗传相互作用的影响，并通过使用蛋白激酶基因的模型植物拟南芥中这种可塑性来更好地了解基因型和表型之间的联系。这将通过表型实验与计算建模相结合来实现。首先，将通过多组数据集成以及从拟南芥和其他模型物种（例如酵母和蠕虫）（例如，酵母和蠕虫）以及由150-200对产生的新的实验数据来生成预测环境环境特定遗传相互作用的模型，并使用3–5个不同环境环境（即均为温度），从而产生的150-200对生长的150-200对生长的实验数据。基因对与环境之间遗传相互作用的定量度量。接下来，将使用实验数据验证模型预测，结果将用于进一步完善模型。精制模型将使用模型解释方法进行解剖，以揭示对于指定上下文特异性遗传相互作用很重要的分子特征。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响来审查标准，认为通过评估通过评估而被视为珍贵的支持。