NSF Postdoctoral Fellowship in Biology: Developmental Determinacy as an Adaptation to a Stable Environment: Characterizing Distinct Life History Strategies in the Monkeyflowers

这一行动为2022财年NSF植物基因组生物学博士后研究奖学金提供了资金。该奖学金支持在东道主实验室为该研究员制定的研究和培训计划，该研究员还提出了扩大生物学参与度的计划。JesúS Martínez-Gómez研究金的研究和培训计划的标题是“适应稳定环境的发展决定性：表征猴子花的独特生活史战略”。该奖学金的主办机构是加州大学伯克利分校，赞助科学家是本杰明·布莱克曼博士和宋云博士。从肿瘤中的癌细胞到泥盆纪晚期的巨型真菌，所有级别的生物谱系都不断受到进化力量的冲击。预测自然种群对这些力量的反应是一个根本性的挑战，无论是对于扩大我们对生命原理的理解，还是对于许多应用生物学研究项目来说都是如此。进化生物学的推论为制定管理计划提供了信息，以保护受威胁的物种，以应对人为引起的气候变化。推论也有能力识别植物复杂作物性状背后的因果基因组变异。该项目将利用种群遗传学和基因组测序中的新方法，利用自然种群和实验进化的种群，了解基因组结构的变化如何通过进化导致两种不同形式的年度生活史之间的过渡。更广泛的影响包括指导和培训本科生，以及与加州大学伯克利分校植物园合作，开发一种通过花园的自我指导之旅，以及一门新的基于发现的本科研究课程的课程，该课程将向学生介绍专注于植物-传粉者相互作用的基于观察的研究。培训目标包括获得计算方法、建模以及生态学和定量基因组学方面的专业知识。这个项目将集中在两种数量上截然不同的年度生活史策略上：机会性一年生植物，在资源允许的情况下，新花发育和果实成熟；以及确定性一年生植物，所有的花和果实同步发展。假设前者更适合于更不可预测的环境，而后者更好地适应更可预测的环境。为了提供一个预测人口对气候变化反应的框架，该项目将采取两种互补的方法。首先，人口基因组学中基于机器学习的新方法将被应用于联合推断历史人口参数和选择目标。这项工作将利用从不同生活史策略的种群中采样的数百个个体的基因组重新测序数据，以确定这种表型变异背后的基因组假定区域。第二个目标将采用“进化和重新排序”的方法，通过选择开花早和果实成熟快的温室环境中的机会主义年度策略来“重新进化”确定性的年度策略。每一代中选定的个体将被测序，序列数据将用于验证基于机器学习的种群基因组模型。所有项目成果将通过长期公共储存库提供。关键词：Mimulus、实验进化、种群基因组学、生活史该奖项反映了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。

This action funds an NSF Plant Genome Postdoctoral Research Fellowship in Biology for FY 2022. 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 Jesús Martínez-Gómez is “Developmental Determinacy as an Adaptation to a Stable Environment: Characterizing Distinct Life History Strategies in the Monkeyflowers”. The host institution for the fellowship is the University of California, Berkeley and the sponsoring scientists are Drs. Benjamin Blackman and Yun Song.Biological lineages at all levels, from cancer cells in a tumor to the giant fungi of the late Devonian, are continuously buffeted by evolutionary forces. Predicting how natural populations respond to these forces is a fundamental challenge both for extending our understanding of the principles of life as well as for many applied biological research programs. Inferences from evolutionary biology inform development of management plans to conserve threatened species in the face of anthropogenic induced climate change. Inferences also have the power to identify causal genomic variants that underlie complex crop traits in plants. This project will employ both natural populations and experimentally evolved populations of the genus Mimulus (monkeyflowers) with novel methods in population genetics and genome sequencing to understand how changes to genomic architecture resulted in transitions between two distinct forms of the annual life history through evolution. Broader impacts include mentoring and training undergraduate students as well as partnering with the UC Botanical Garden of Berkeley to develop a self-guided tour through the Garden as well as curricula for a new discovery-based undergraduate research course that will introduce students to observation-based research focused on plant-pollinator interactions. Training objectives include obtaining expertise in computational methods, modeling, and ecological and quantitative genomics. This project will focus on two quantitatively distinct types of the annual life history strategy in the genus Mimulus: opportunistic annuals, where new flowers develop and fruits matures while resources allow, and deterministic annuals, where all flower and fruit develop in tandem. The former is hypothesized to be better suited in more unpredictable environments, and the latter to be better adapted to more predictable environments. To provide a framework for predicting population responses to climate change, the project will take two complementary approaches. First, novel machine-learning based approaches in population genomics will be applied to jointly infer historic demographic parameters and targets of selection. This work will leverage genome resequencing data obtained for several hundred individuals sampled from populations that differ between these life history strategies to identify putative regions of the genome that underlie this phenotypic variation. The second aim will take an ‘evolve and resequence’ approach to ‘re-evolve’ the deterministic annual strategy from the opportunistic annual strategy in a greenhouse setting by selecting for both earlier flowering and faster fruit maturation. Selected individuals in each generation will be sequenced, and sequence data will be used to validate machine learning based population genomic models. All project outcomes will be made available through long-term public repositories. Keywords: Mimulus, experimental evolution, population genomics, life historyThis 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.