EAGER: Surveying impacts of community context on adaptive mutational landscapes

物种与其他物种在群落中进化。然而，在这些复杂的群体中追踪进化是很困难的。这个项目将研究群落环境如何改变物种进化。利用全基因组分析的新技术，这项研究将确定不同的群落如何改变整个基因组的适应性效应。本研究结合了生态学、进化论和微生物学等领域，为复杂群落的进化过程提供了新的视角。将通过这项研究开发和公开提供分析工具，所获得的见解可能与面临环境变化的生态系统的保护和管理有关。此外，这项研究将使研究生和本科生在实验室和计算技能方面得到训练。此外，研究人员将对当地高中进行抗生素耐药性的教育推广，这是一个微生物学和进化生物学交叉的话题。种群的适应能力依赖于突变输入。可能的突变定义了突变邻域。本研究将记录突变社区如何随着时间的推移随着社区背景而演变。利用微生物群落的实验进化和转座子插入测序，该项目将直接测量细菌中的突变邻域如何通过物种相互作用来预测。将探讨三种生态情景。它们包括捕食、竞争和捕食与竞争的结合。将分析祖先种群和进化种群中的转座子插入文库在不同情况下的频率变化。计算工具将开发计算突变邻域测量使用香农熵方法。这些社区将在不同的背景下进行比较。该项目将为社区环境中的进化过程提供新的见解，并产生新的分析工具。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。

Species evolve in communities with other species. However, tracking evolution in those complex communities is difficult. This project will examine how community context alters species evolution. Using new techniques in whole-genome analyses, this research will determine how different communities alter the fitness effects across the entire genome. This research bridges the fields of ecology, evolution, and microbiology to provide insight into how the evolutionary process may occur in the complex communities. Analysis tools will be developed and made publicly available through this research and the insights gained may be relevant for conservation and management of ecosystems facing environmental change. In addition, the research will enable training for graduate and undergraduate students in laboratory and computation skills. Moreover, the researchers will conduct educational outreach to local high schools on antibiotic resistance, a topic at the intersection of microbiology and evolutionary biology. The ability of a population to adapt is reliant on mutational input. Possible mutations define the mutational neighborhood. This research will document how the mutational neighborhood evolves with community context over time. Using experimental evolution in microbial communities and transposon-insertion sequencing, the project will directly measure how the mutational neighborhood in bacteria can be predicted by species interactions. Three ecological scenarios will be explored. They include predation, competition, and a combination of predation and competition. Transposon insertion libraries in ancestral and the evolved populations will be assayed for frequency changes across the scenarios. Computational tools will be developed that calculate mutational neighborhood measures using a Shannon entropy approach. These neighborhoods will be compared across contexts. This project will provide new insights into the evolutionary process in a community context and generate new analytic tools.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.