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

生物体的复杂性在很大程度上是由于基因不是独立工作的，而是相互作用的。了解这种相互作用将有助于提高植物的生产力和对日益极端的条件的适应能力。然而，研究基因互作对植物性状的影响具有挑战性，原因有两个。首先，可能有数百万种可能的互动需要筛选。其次，先天(即基因和基因相互作用)和后天(即环境)都很重要。即使当一个基因的相互作用被确定为重要时，它的相关性通常也只对一个环境已知。这个项目将通过调查先天和后天如何共同影响植物特性的问题来解决这些挑战。具体地说，将通过测量不同温度下的生存特征来检查模式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被用于测量特征并建立预测不同环境下基因交互作用的模型。这些预测模型还将纳入非植物物种相似基因之间相互作用的现有知识。这些预测将得到实验验证，并将提供关于先天和后天如何共同影响植物生存和健康的洞察力。这种洞察力将有助于更好地预测模式植物和农作物中的基因功能，并为设计高产和有弹性的植物提供候选基因。该项目的研究结果将作为例子，向科学界和公众说明将实验方法和计算方法结合起来的好处。遗传学和基因组学的进步使人们对基因与表型的联系以及遗传相互作用和环境在控制表型中的作用有了前所未有的了解。然而，通常没有考虑到基因×基因相互作用的环境依赖性，特别是在多细胞物种中。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，并以蛋白激酶基因为例，确定模式植物拟南芥中这种可塑性的遗传成分，从而更好地了解基因类型和表型之间的联系。这将通过表型实验和计算建模来实现。首先，预测特定于环境背景的遗传交互作用的模型将通过多组学数据集成和使用来自拟南芥和其他模式物种(例如酵母和蠕虫)的现有遗传交互作用数据以及从在3-5个不同环境背景(即温度制度)中生长的150-200对单和双激酶突变体产生的新的实验数据来产生，产生多个特性值，这些特性值将被用于计算基因对与环境之间的遗传交互的定量测量。接下来，将使用实验数据验证模型预测，并使用结果进一步完善模型。精细化的模型将使用模型解释方法进行剖析，以揭示对指定特定背景的遗传相互作用非常重要的分子特征。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.