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.

进化是许多重要的社会问题和解决方案的根源。传染病会对药物产生抗药性或逃避疫苗接种。肿瘤的进化是为了利用宿主的身体并耐受化疗。农业害虫会对杀虫剂产生抗药性。受威胁的物种必须适应环境的变化和土地利用的变化，否则就有灭绝的危险。为了解决这些植根于进化的问题，生物学家需要能够预测未来的进化变化。尽管生物学家对导致适应性进化的力量有深刻的理解，但进化预测仍然是一个重大挑战。在实验室里，如果我们让最初相似的种群承受相同的环境压力，他们通常会使用相同的基因进化出相同的解决方案，这表明预测是可能的。然而，有时实验进化会导致完全不同的结果。为什么进化在某些情况下是可以预测的，而在另一些情况下则不是？一种假设认为，对于由简单遗传网络(很少相互作用的基因)构建的特征，进化比复杂的遗传网络(许多基因协同工作)更具可预测性。这个项目试图检验这一假设。作为生态恢复的一部分，研究小组将本地刺鱼重新引入阿拉斯加的8个最近没有鱼的湖泊，开始了迄今在自然环境中尝试的最大规模的进化实验。追踪未来几代人在这些湖泊中的进化，将让研究人员测试遗传网络是否会进化，以及更简单的遗传网络是否会更具预测性地进化。这项实验的结果将为使用基因网络数据预测进化变化提供新的工具，然后可以应用于公共利益的进化问题。为了实现这一目标，该团队还必须开发计算机科学和统计学方面的新工具，以分析网络如何随时间变化。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。2019年，研究人员将本土刺鱼重新引入8个被入侵物种灭绝的湖泊。该团队将跟踪这些实验创建的种群在适应新栖息地时的演变，每年监测每个来源和实验种群的饮食、形态、寄生虫和微生物区系、免疫、基因型和转录本。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，以测量时间变化的网络结构。使用这些新工具，他们将测试重点人群中遗传、转录和生态网络的可塑性和进化变化。通过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，以及它与不断变化的生态网络的关系。这一融合的研究将生物学(遗传学、进化论、生态学、微生物学)与计算机科学和统计学深度结合，推动生命科学和数据科学接口的新进展。为了教育公众，该团队将与大型生物学播客合作，制作六个关于网络数据科学、进化、遗传学和保护交叉的节目。每个播客将补充K-12生物、数学和计算机科学课程的虚拟实地旅行视频、访谈和教案。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。