IMAGiNE: Testing multi-level controls on an aridity tolerance phenotype over time through physio-genomic data integration

IMAGiNE：通过生理基因组数据集成，随着时间的推移测试对干旱耐受表型的多级控制

• 批准号: 2107975
• 负责人: Dale DeNardo
• 金额: $ 86.94万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107975&HistoricalAwards=false
• 关键词: IMAGiNE; Testing; multi; level; controls

When faced with environmental challenges, animal populations have adjusted to better cope with local conditions, using mechanisms that range from permanent to highly dynamic. While changes to the inherited genetic material are permanent, dynamic adjustments include alterations in behavior, including the timing and duration of activity, the rate of gene expression off of DNA (i.e., RNA transcription) and non-permanent but stable and heritable alterations that affect gene expression (epigenetic modifications). These mechanisms operate on different timescales, and currently, we do not know the relative importance of these mechanisms in enabling animal populations to adjust to changing environmental conditions. DeNardo and colleagues will compare the ability of different populations of rattlesnakes to tolerate prolonged periods of limited water availability, examining three different population pairs that diverged at different times in the past. The team will examine differences in the genomic DNA, behavior, physiology, gene expression, and epigenetic modifications between populations pairs as well as seasonally among individuals in each population. The ability to integrate different types of information is critical for big data analysis which is a critical skill in science and industry, and the research group will develop undergraduate and graduate teaching modules for use in innovative courses. To extend this training out to K-12 learners, they are partnering with the Ask a Biologist program at Arizona State University to design and implement an online educational and interactive drought survival game that will enable gamers to explore the impact of genomic and behavioral strategies for rattlesnakes during extreme drought conditions. Adaptive phenotypes are ubiquitous in nature, but such phenotypes can come about in different ways, including deterministic mechanisms such as coding and regulatory genomic changes, as well as plastic responses such as epigenomic, transcriptomic, and behavioral modifications. These mechanisms not only operate on different timescales, but their relative importance and how they interplay to produce adaptive phenotypes is largely unknown. Understanding these relationships and how they vary based on divergence time of the phenotype is critical to understanding how organisms respond to novel environmental conditions and therefore accurately predict their ability to tolerate the impacts of rapidly changing environmental conditions. Here, DeNardo et al. will integrate whole-genome, epigenome, and transcriptome sequencing with behavioral and physiological data to quantify their direct and indirect contributions to tolerance of prolonged water limitation in three rattlesnake lineages of differing divergence times. Integrative statistical analysis using structural equation modeling will enable them to compare the relative controls across the different lineages, among populations within lineages, and within individuals across seasons and hydric states. To improve teaching integrative statistics at different educational levels, the PIs will develop modules that progressively use these datasets in undergraduate and graduate online and in-person courses. In coordination with Ask a Biologist, they will also coordinate the design, implementation, and assessment of an online education and interactive drought survival game that, at differing complexity levels, will require the gamer to assign genomic and behavioral strategies to rattlesnakes as they enter a time of drought.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.

当面临环境挑战时，动物种群会利用从永久到高度动态的各种机制进行调整，以更好地应对当地条件。虽然遗传遗传物质的变化是永久性的，但动态调整包括行为的改变，包括活动的时间和持续时间，DNA的基因表达率（即RNA转录）和影响基因表达的非永久性但稳定和可遗传的改变（表观遗传修饰）。这些机制在不同的时间尺度上运作，目前，我们还不知道这些机制在使动物种群适应不断变化的环境条件方面的相对重要性。德纳尔多和他的同事们将比较不同种群的响尾蛇忍受长时间有限水的能力，研究过去不同时期分化的三对不同种群。研究小组将检查基因组DNA、行为、生理、基因表达和表观遗传修饰在种群间的差异，以及每个种群中个体之间的季节性差异。整合不同类型信息的能力对于大数据分析至关重要，而大数据分析是科学和工业的一项关键技能，研究小组将开发用于创新课程的本科和研究生教学模块。为了将这种培训扩展到K-12的学习者，他们正在与亚利桑那州立大学的“问生物学家”项目合作，设计并实施一种在线教育和互动的干旱生存游戏，使玩家能够探索在极端干旱条件下响尾蛇的基因组和行为策略的影响。适应性表型在自然界中无处不在，但这种表型可以以不同的方式产生，包括确定性机制，如编码和调控基因组变化，以及可塑性反应，如表观基因组、转录组和行为改变。这些机制不仅在不同的时间尺度上运作，而且它们的相对重要性以及它们如何相互作用以产生适应性表型在很大程度上是未知的。了解这些关系以及它们如何根据表型的分化时间而变化，对于理解生物体如何对新环境条件作出反应，从而准确预测它们耐受快速变化的环境条件影响的能力至关重要。在这里，DeNardo等人将整合全基因组、表观基因组和转录组测序与行为和生理数据，量化它们对三种不同分化时间的响尾蛇谱系长时间水分限制耐受性的直接和间接贡献。使用结构方程模型的综合统计分析将使他们能够比较不同谱系之间的相对控制，谱系内的种群之间，以及不同季节和水分状态的个体之间。为了改善不同教育水平的综合统计教学，pi将开发模块，逐步在本科和研究生的在线和面对面课程中使用这些数据集。在与Ask a biology的合作下，他们还将协调设计、实施和评估一个在线教育和交互式干旱生存游戏，在不同的复杂程度上，这将要求玩家在响尾蛇进入干旱时期时分配基因组和行为策略。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。