NSF Postdoctoral Fellowship in Biology FY 2022: Determining functional correlates to the emergence and maintenance of antibiotic resistance genes in host-associated microbiomes

该行动资助了2022财年的NSF生物学博士后研究奖学金，即调查基因组，环境和表型之间相互作用的生命规则的综合研究。该研究金支持研究员的研究和培训，以创新的方式为生活规则领域做出贡献。微生物无处不在，并参与广泛的生物过程，如生产和降解抗生素，并帮助宿主代谢。由于大多数微生物尚未被发现，对这些生物的持续研究将有助于更好地了解微生物的生态重要性和技术潜力。微生物学的许多新发现都依赖于DNA和RNA测序方法来识别新的微生物。虽然测序是一种很好的方法，但目前存在大量未使用的测序数据。该项目将开发工具来帮助恢复微生物群落中的这些数据，提高我们对微生物在不同生态系统中所起作用的理解。该研究员还将参与多项培训工作，以提高从本科到博士后水平的生物学家的计算素养。该项目的总体目标是改进宏基因组和元转录组的基因注释，丰度估计和功能表征。该项目将首先扩展目前依赖于组装图的方法，以实现元组学测序数据中的直系同源物水平注释和丰度估计。组装图包含元组学文库中的所有序列，因此不会遭受与当前分析技术相同的数据丢失。元组学测序实验也经常缺乏权力，因为来自给定环境的小数量的剖析社区。迁移学习首先在大型微生物数据集上训练模型，然后重新使用这些学习到的信息来预测较小数据集中的模式，从而克服了这个问题。该项目将建立一个迁移学习模型，该模型将在公开的细菌分离物RNA测序数据上进行训练，并将用于发现元转录组中的模式。这些方法将用于研究猪肠微生物组中的抗菌素耐药性。虽然抗菌素耐药性通常可以通过测序来检测，但抗菌素耐药性基因的出现可能被低估，因为这些序列在许多基因组中共享，这使得所得的测序数据复杂。该项目开发的工具将改善对复杂序列（如抗菌素耐药基因）的检测。该项目还将研究饲养环境和抗生素暴露等宿主性状如何影响猪肠微生物组在暴露于各种抗生素时抗菌素耐药性的发生和出现。本研究开发的工具将改善不同微生物生态系统的注释和功能表征，在其他系统中产生预测价值与元组学测序数据。最后，通过开放科学的原则，在研究成果的开发和传播中，通过开展有助于进入数据密集型生物学领域的培训活动，扩大科学参与度。该奖项体现了NSF的法定使命，通过基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2022, Integrative Research Investigating the Rules of Life Governing Interactions Between Genomes, Environment and Phenotypes. The fellowship supports research and training of the fellow that will contribute to the area of Rules of Life in innovative ways. Microbes are everywhere, and participate in broad number of biological processes such as producing and degrading antibiotics, and aiding in host metabolism. As most microbes have not yet likely been discovered, continued investigation of these organisms will lead to better understanding of the ecological importance and technological potential of microbes. Many recent discoveries in microbiology have relied on DNA and RNA sequencing approaches to identify new microbes. While sequencing is a excellent approach, there exists a large amount of sequencing data that is currently being unused. This project will develop tools to help recover this data in microbial communities, improving our understanding of the role microbes play in different ecosystems. The fellow will also engage in multiple training efforts to improve computational literacy of biologists from the undergraduate to post doctoral level.The overarching goal of this project is to improve gene annotation, abundance estimation, and functional characterization of metagenomes and metatranscriptomes. This project will first extend current approaches that rely on assembly graphs to achieve ortholog-level annotation and abundance estimation in meta-omic sequencing data. Assembly graphs contain all the sequences in a meta-omic library and thus do not suffer the same data loss as current analysis techniques. Meta-omic sequencing experiments also frequently lack power given small numbers of profiled communities from a given environment. Transfer learning overcomes this problem by first training a model on a large microbial dataset, then re-using this learned information to predict patterns in a smaller dataset. This project will build a transfer learning model that will be trained on publicly available bacterial isolate RNA sequencing data and will be used to discover patterns in metatranscriptomes. These approaches will be used to study antimicrobial resistance in pig gut microbiomes. While antimicrobial resistance can often be detected through sequencing, the occurrence of antimicrobial resistance genes is likely underestimated because these sequences are shared across many genomes which makes the resultant sequencing data complex. The tools developed in this project will improve the detection of complex sequences like antimicrobial resistance genes. The project will also investigate how host traits like rearing environment and antibiotic exposure impact the occurrence and emergence of antimicrobial resistance in pig gut microbiomes when exposed to various antibiotics. The tools developed in this study will improve annotation and functional characterization of diverse microbial ecosystems, generating predictive value in other systems with meta-omic sequencing data. Lastly, this proposal aims to broaden participation in science using open science principles for the development and dissemination of research products, and through training endeavors that facilitate entry into the field of data-intensive biology.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.