NSF Postdoctoral Fellowship in Biology FY 2021: Mapping the Relationship Between Genetic Pleiotropy, Gene Function, and Adaptation

本行动资助2021财年美国国家科学基金会植物基因组生物学博士后研究奖学金。该奖学金支持奖学金获得者在主办实验室的研究和培训计划，该奖学金获得者还提出了扩大生物学参与的计划。这项奖学金的研究和培训计划的标题是“绘制基因多效性、基因功能和适应之间的关系”。该奖学金的主办机构是卡内基科学研究所，赞助科学家是卡耐基博士。Sue Rhee和Moises Exposito-Alonso。气候变化和人类消费对生态系统的影响一直是，而且预计将是对地球宜居的最大威胁。社会依靠植物不仅获得食物、物质产品和保健品，而且还吸收大气中的大部分碳和维持生命所需的氧气。从这些担忧中产生的一个主要问题是植物将如何应对气候变化。它们会迁移到一个新的栖息地，适应不断变化的气候，还是面临灭绝？遗传和性状数据越来越多地用于预测植物自然种群对气候变化的反应。在这个项目中，现代基因组方法和迄今为止最广泛的植物遗传和性状数据集将用于测量不同环境下突变效应和适应之间的关系。进化理论预测，影响多种性状的突变很可能会减缓适应，因为生理和遗传的限制会在性状之间产生权衡。确定大效应突变何时通过影响多个性状来限制适应，将使基因组编辑技术成功培育出植物性状，而不会产生可能导致生物在未来气候中不适应的意外后果。此外，更好地了解突变效应将增强预测适应性的能力，这对农业和保护生物学具有广泛的意义。培训目标包括在卡内基科学研究所获得比较功能基因组学和数量遗传学的新技能。通过这项奖学金的支持将为研究员提供新的机会，教育公众为什么植物科学如此重要，特别是在面对气候变化的情况下，以及社会可以做些什么来减少碳足迹。多效性，即基因突变可以影响多种性状的现象，通常被认为是适应的关键障碍。这个想法的起源可以追溯到20世纪初，遗传学家仍然使用Fisher的几何模型来解释生物体复杂性或衰老的代价。然而，根据酿酒酵母的实验数据构建基因型-表型图谱的最新进展表明，多效性仅限于功能相关表型的模块，可能并不总是不利于适应。我们对多效性在复杂生物如植物中的作用知之甚少。本项目将利用拟南芥的1001个基因组（1001genomes.org）和1001个表型（arapheno.1001genomes.org）研究其多效性，以了解其功能原因和对自然环境适应的最终后果。利用前所未有的公开可用的1850种表型数据集，涵盖多达1135个拟南芥个体的1000多万个snp，研究员将：1)设计量化多效效应及其在基因组和表型中的模块化的指标；2)利用公开的全基因组组蛋白修饰和胞嘧啶甲基化数据，以及细胞过程、分子功能和定位的基因注释数据集，研究多向突变的功能背景；3)在半自然的室外实验中，通过直接测量适应度来检验多效性变异是否有助于或限制适应。本研究过程中产生的所有数据、计算工具和资源将通过公共存储库免费提供给更广泛的研究社区。关键词：拟南芥，机器学习，序列分析，多效性，基因功能，基因注释该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This action funds an NSF Plant Genome Postdoctoral Research Fellowship in Biology for FY 2021. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The title of the research and training plan for this fellowship to Megan Ruffley is "Mapping the Relationship Between Genetic Pleiotropy, Gene Function, and Adaptation" The host institution for the fellowship is the Carnegie Institution for Science and the sponsoring scientists are Drs. Sue Rhee and Moises Exposito-Alonso.The impact of climate change and human consumption on ecosystems has been, and is predicted to be, the greatest threat to an inhabitable earth. Society relies on plants not only for food, material goods, and health products, but also for sequestration of a majority of carbon in the atmosphere and for the oxygen needed to sustain life. One major question arising from these concerns is how will plants respond to the changing climate. Will they migrate to a new suitable habitat, adapt to the changing climate, or face extinction? Genetic and trait data have increasingly been used to predict how natural populations of plants may respond to the changing climate. In this project, modern genomic approaches and the most extensive genetic and trait dataset to date for plants will be used to measure the relationship between mutational effect and adaptation in different environments. Evolutionary theory predicts that mutations affecting multiple traits will most likely slow down adaptation because physiological and genetic constraints create trade-offs between traits. Identifying when large effect mutations are constraining adaptation by affecting multiple traits will enable the success breeding of plant traits from genome editing technology without unintended consequences that could lead to maladapted organisms in future climates. Additionally, a better understanding of mutational effects will enhance the power to predict adaptation, which has broad implications for agriculture and conservation biology. Training objectives include acquiring new skills in comparative functional genomics and quantitative genetics at the Carnegie Institution for Science. Support through this fellowship will provide new opportunities for the Fellow to educate the public on why plant science is so important, especially in the face of climate change, and what society can all do to reduce its carbon footprint. Pleiotropy, the phenomenon that a genetic mutation can affect multiple traits, is often considered a critical barrier to adaptation. The origin of this idea dates back to the early 20th century and Fisher’s Geometric Model which is still used by geneticists to explain the cost of complexity of organisms or aging. However, recent advances in constructing a genotype-to-phenotype map from experimental data in Saccharomyces cerevisiae suggest that pleiotropy is limited within modules of functionally related phenotypes and may not always be detrimental to adaptation. Little is known about the role of pleiotropy in complex organisms such as plants in nature. This project will study pleiotropy with the 1001 Genomes (1001genomes.org) and Phenomes (arapheno.1001genomes.org) of Arabidopsis thaliana to understand its functional causes and ultimate consequences to adaptation in natural environments. Using an unprecedented publicly available dataset of 1850 phenotypes spanning over 10 million SNPs for up to 1135 individuals of the plant Arabidopsis thaliana, the Fellow will: 1) devise metrics to quantify pleiotropic effects and their modularity across the genome and phenome; 2) study the functional context of pleiotropic mutations using public genome-wide histone modification and cytosine methylation data along with curated datasets of gene annotations in cellular processes, molecular function, and localization; and, 3) test whether pleiotropic variants contribute to or constrain adaptation in semi-natural outdoor experiments with direct measures of fitness. All data and computational tools and resources generated during the course of this study will be freely available to the broader research community through public repositories. Keywords: Arabidopsis thaliana, machine learning, sequence analysis, pleiotropy, gene function, gene annotationThis 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.