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

进化是许多社会重要问题和解决方案的根源。传染病会产生抗药性或逃避疫苗。肿瘤进化到利用宿主的身体并耐受化疗。农业害虫进化出对杀虫剂的抗药性。受威胁的物种必须适应环境的变化和土地使用的变化，否则就有灭绝的危险。为了解决这些源于进化的问题，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着深刻的理解，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经历相同的环境压力，它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预测的，而在另一些情况下却不可预测？一种假说认为，进化对于由简单遗传网络（很少有相互作用的基因）构建的性状比复杂遗传网络（许多基因协同工作）更可预测。本项目旨在验证这一假设。作为生态恢复的一部分，研究小组将本地棘鱼重新引入阿拉斯加8个最近无鱼的湖泊，开始了迄今为止在自然环境中尝试的最大规模的进化实验。跟踪这些湖泊未来几代的进化将使研究人员能够测试遗传网络是否进化，以及更简单的遗传网络是否更可预测地进化。这项实验的结果将产生新的工具，用于预测进化的变化，使用基因网络数据，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，以分析网络如何随着时间的推移而变化。这些新的计算工具将具有广泛的适用性，可以测量任何类型的网络变化对国家健康和安全的影响。2019年，研究人员将原生棘鱼重新引入8个湖泊，这些湖泊已经被入侵物种灭绝。该团队将跟踪这些实验创造的种群在适应新栖息地时的进化，每年监测每个来源和实验种群的饮食，形态，寄生虫和微生物群，免疫力，基因型和转录组。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，以测量随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、转录组和生态网络的可塑性和进化变化。比较实验种群之间的网络变化，他们可以测试网络结构演变的可预测性，以及它与变化的生态网络的关系。这种融合研究将生物学（遗传学，进化，生态学，微生物学）与计算机科学和统计学深度融合，以促进生命和数据科学界面的新进展。为了教育公众，该团队将与Big Biology Podcast合作，制作六个关于网络数据科学，进化，遗传学和保护的交叉点的节目。每个播客将补充“虚拟实地考察”的视频，采访，并为K-12生物，数学和计算机科学课程的课程计划。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.