RESEARCH-PGR: Illuminating the Plant Protein Interactome with Genome-wide Signatures of Coevolution

对植物基因组进行测序已经解锁了大量的基因目录，但我们理解每个基因的具体作用的能力远远落后，这意味着功能只被分配给了已知植物基因的一小部分。单独的基因很少单独起作用，而给定基因功能的一个关键方面是它的相互作用伙伴。破译整个植物基因组中的遗传互作将促进我们对大量未表征基因的功能的理解；然而，现有的大多数识别遗传互作的方法都是费力、昂贵的，并且难以规模化到整个基因组。该项目将开发可扩展的、高效的计算分析，以基于植物基因组中特定于基因的进化速度来识别遗传交互作用。这些计算工具将作为适用于任何植物组的资源，并用于生成几个具有科学和农业重要性的植物组的遗传相互作用数据库，植物遗传学家和分子生物学家可以搜索这些数据库，以确定感兴趣基因的新功能。这些结果还将被用于探索整个遗传相互作用网络的涌现特性，这将阐明植物基因组的更高水平的功能架构。通过为对使用这些工具感兴趣的研究人员提供培训，并通过与一年级本科生接触以扩大对计算生物学领域的参与，这些资源的影响将被最大化。遗传相互作用是基因功能的重要指标，但现有的用于识别植物全基因组相互作用组合(即相互作用组)的工具仅提供了部分观点。相互作用蛋白之间的进化速率协变(ERC)的系统发育特征已成功地应用于植物中的小基因集，以展示酶复合体中已知亚单位之间的共同进化，并在非植物谱系中的全基因组基础上发现新的相互作用伙伴。然而，ERC从未在基因组范围内应用于植物，这可能是由于植物中特别频繁的基因和基因组复制所造成的系统发育挑战。该项目将开发一种为植物基因组的ERC分析量身定做的新型管道，它将利用现有的理论来协调跨基因树复制的复杂历史。这条管道将用于在植物进化的多个水平上进行ERC分析，并将结果汇编成一个基于网络的植物互作组数据库，可以查询该数据库以寻找特定的遗传相互作用。此外，所得到的基于ERC的相互作用数据将与现有的结合分析和基于共表达的相互作用相结合，以从功能上验证ERC结果。最后，网络分析将确定相互作用的协同功能蛋白质的模块，并确定网络结构是如何由基因功能驱动的。这项工作将创建一个资源，使基因组生物学家能够为基因组注释工作添加所需的信息层，并为分子生物学家提供大量随时可以测试的功能假说。所有项目成果将通过CyVerse和GitHub和Dryad等长期资源库免费获取。该奖项反映了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。

Sequencing plant genomes has unlocked a massive catalog of genes and yet our ability to understand the specific role of each gene lags far behind, meaning functions have only been assigned to a fraction of known plant genes. Individual genes rarely work alone, and a critical aspect of a given gene's function is its interacting partners. Deciphering genetic interactions throughout plant genomes will advance our understanding of the function of a large number of uncharacterized genes; however, most existing methods for identifying genetic interactions are laborious, expensive, and difficult to scale to the entire genome. This project will develop scalable, efficient computational analyses tailored to identifying genetic interactions based on gene-specific rates of evolution across plant genomes. These computational tools will serve as resources applicable to any group of plants and be used to generate databases of genetic interactions for several scientifically and agriculturally important plant groups, which plant geneticists and molecular biologists can search to identify new functions for genes of interest. The results will also be used to probe the emergent properties of entire networks of genetic interactions, which will illuminate the higher-level functional architecture of plant genomes. The impact of these resources will be maximized by offering workshops that will provide training to researchers interested in using these tools and by engaging with first-year undergraduates to broaden participation in the field of computational biology.Genetic interactions are an important indicator of gene function but the existing tools for identifying the genome-wide assemblage of interactions (i.e., the interactome) in plants provide only a partial view. The phylogenetic signature of evolutionary rate covariation (ERC) between interacting proteins has been successfully applied on small sets of genes in plants to demonstrate coevolution between known subunits within enzyme complexes and on a genome-wide all-by-all basis in non-plant lineages to discover novel interaction partners. However, ERC has never been applied at genome-wide scale in plants, likely due to phylogenetic challenges caused by the especially frequent gene and genome duplication that occur in plants. This project will develop a novel pipeline tailored to ERC analyses of plant genomes, which will employ existing theory on reconciling complex histories of duplication across gene trees. This pipeline will be used to perform ERC analyses at multiple levels of plant evolution and compile the results to generate a web-based plant interactome database, which can be queried for specific genetic interactions. Further, the resulting ERC-based interaction data will be integrated with existing binding assay and coexpression-based interactions to functionally validate ERC results. Finally, network analytics will identify modules of interacting cofunctional proteins and determine how network structure is driven by gene function. This work will create a resource that will empower genome biologists to add a needed layer of information to genome annotation efforts and provide molecular biologists with a wealth of ready-to-test functional hypotheses. All project outcomes will be freely accessible through CyVerse and long-term repositories such as GitHub and Dryad.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.