URofL:EN: Does re-wilding lead to re-wiring of gene expression and species interaction networks?

URofL:EN：重新野化是否会导致基因表达和物种相互作用网络的重新连接？

• 批准号: 2133740
• 负责人: Daniel Bolnick
• 金额: $ 300万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133740&HistoricalAwards=false
• 关键词: URofL; EN; Does; re; wilding

Evolution is at the root of many socially important problems and solutions. Infectious diseases evolve resistance to drugs or escape vaccines. Tumors evolve to exploit their host’s body and tolerate chemotherapy. Agricultural pests evolve resistance to pesticides. Threatened species must adapt to environmental variability and land use change or else risk extinction. To solve such problems rooted in evolution, biologists need to be able to forecast future evolutionary change. Although biologists have a deep understanding of the forces that cause adaptive evolution, evolutionary forecasting remains a major challenge. In the laboratory, if we subject initially similar populations to the same environmental stress, they often evolve the same solutions using the same genes, demonstrating that forecasts are possible. However, sometimes experimental evolution leads to entirely different outcomes. Why is evolution predictable in some situations, but not others? One hypothesis proposes that evolution is more predictable for traits built by simple genetic networks (few interacting genes) than for complex genetic networks (many genes working synergistically). This project seeks to test this hypothesis. As part of an ecological restoration, the research team reintroduced native stickleback fish into 8 recently-fishless lakes in Alaska, beginning the largest evolution experiment yet attempted in a natural setting. Tracking evolution in these lakes in coming generations will let the investigators test whether genetic networks evolve, and whether simpler genetic networks evolve more predictably. The results of this experiment will yield new tools for forecasting evolutionary change using gene network data, which can then be applied to evolutionary problems of public interest. To achieve this aim the team must also develop new tools in computer science and statistics to analyze how networks change through time. These new computational tools will have broad applicability to measure changes in any type of network of concern to impacts national health and security.In 2019, researchers reintroduced native stickleback fish into 8 lakes where they had been extirpated by invasive species. The team will track the evolution of these experimentally-created populations as they adapt to their new habitats, annually monitoring diet, morphology, parasites and microbiota, immunity, genotypes, and transcriptomes in every source and experimental population in each year. To analyze these data they will develop new computational and mathematical models in network theory to measure temporally changing network structures. Using these new tools they will test for plastic and evolutionary changes in genetic, transcriptomic, and ecological networks in the focal populations. Comparing network changes between experimental populations they can test the predictability of network structure evolution, and its relation to changing ecological networks. This convergent research deeply integrates biology (genetics, evolution, ecology, microbiology) with computer science and statistics to promote new advances at the interface of life and data science. To educate the public, the team will work with the Big Biology Podcast to produce six shows about the intersection of network data science, evolution, genetics, and conservation. Each podcast will be supplemented with ‘virtual field trip” videos, interviews, and lesson plans for K-12 biology, math, and computer science classes.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.

进化是许多在社会上重要的问题和解决方案的根源。感染性疾病对药物的进化抗性或逃避疫苗。肿瘤的演变以利用宿主的身体并耐受化疗。农业害虫对农药的进化抗性。受威胁物种必须适应环境变异性和土地利用变化，否则风险扩大。为了解决植根于进化的问题，生物学家需要能够预测未来的进化，尽管生物学家对导致适应性进化的力有深刻的了解，但进化预测仍然是一个重大挑战。在实验室中，如果我们最初遭受相同的环境压力，他们通常会使用相同的基因发展相同的溶液，从而证明森林是可能的。但是，有时实验进化会导致完全不同的结果。为什么在某些情况下可以预测进化，而是其他情况下的？一个假设的提议是，进化对于​​由简单遗传网络（少数相互作用基因）构建的特征比复杂的遗传网络（许多基因协同起作用）更容易预测。该项目旨在检验这一假设。作为生态恢复的一部分，研究小组将本地的粘质鱼重新引入了阿拉斯加的8个最近无毛的湖泊，开始了在自然环境中尝试的最大进化实验。在未来几代的这些湖泊中跟踪演变将使研究人员测试遗传网络是否进化，以及更简单的遗传网络是否可以更可预测地发展。该实验的结果将产生新的工具，用于使用基因网络数据预测进化变化，然后可以将其应用于公共利益的进化问题。为了实现这一目标，团队还必须开发计算机科学和统计数据中的新工具，以分析网络如何随着时间的流逝而变化。这些新的计算工具将具有广泛的适用性，可以衡量任何类型的关注网络的变化，以影响国家健康和安全。在2019年，研究人员将本地粘稠的鱼类重新引入了8个湖泊中，在这些湖泊中，它们已被入侵物种消除。该团队将跟踪这些实验创建的人群的演变，因为它们适应了新的栖息地，年度监测饮食，形态，寄生虫和微生物群，免疫学，基因型和转录组中每年的每个来源和实验人群中的转录组。为了分析这些数据，他们将在网络理论中开发新的计算和数学模型，以衡量暂时改变网络结构。他们将使用这些新工具来测试局部种群中遗传，转录组和生态网络的塑性和进化变化。比较实验人群之间的网络变化，他们可以测试网络结构演变的可预测性及其与不断变化的生态网络的关系。这项收敛研究将生物学（遗传学，进化，生态学，微生物学）与计算机科学和统计学深入融合，以促进生活与数据科学界面的新进步。为了教育公众，团队将与大型生物学播客合作，制作有关网络数据科学，进化，遗传学和保护的六个节目。每个播客都将为K-12生物学，数学和计算机科学课程提供“虚拟实地考察”视频，访谈和课程计划。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的评估标准，认为通过评估来获得珍贵的支持。