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

这一行动为NSF 2022财年生物学博士后研究奖学金提供了资金，综合研究调查了基因组、环境和表型之间相互作用的生命规则。该奖学金支持研究员的研究和培训，这些研究员将以创新的方式为生活规则领域做出贡献。微生物无处不在，参与了大量的生物过程，如生产和降解抗生素，以及帮助宿主新陈代谢。由于大多数微生物可能尚未被发现，对这些微生物的持续调查将有助于更好地了解微生物的生态重要性和技术潜力。最近在微生物学领域的许多发现都依赖于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，帮助恢复微生物群落中的数据，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，以提高从本科生到博士后水平的生物学家的计算能力。该项目的总体目标是改进元基因组和元翻译的基因注释、丰度估计和功能表征。该项目将首先扩展现有的依赖组装图的方法来实现基因组测序数据中的直系同源水平注释和丰度估计。组装图包含基因组库中的所有序列，因此不会遭受与当前分析技术相同的数据损失。考虑到来自特定环境的少量概况群落，染色体组测序实验也经常缺乏动力。转移学习克服了这一问题，方法是首先在大型微生物数据集上训练模型，然后重复使用这些学习到的信息来预测较小数据集中的模式。这个项目将建立一个转移学习模型，该模型将根据公开可用的细菌分离RNA测序数据进行训练，并将用于发现后转录本中的模式。这些方法将用于研究猪肠道微生物群中的抗菌素耐药性。虽然抗菌素耐药性通常可以通过测序来检测，但抗菌素耐药基因的出现可能被低估了，因为这些序列在许多基因组中是共享的，这使得得到的测序数据变得复杂。该项目中开发的工具将改进复杂序列的检测，如抗菌素耐药基因。该项目还将调查饲养环境和抗生素暴露等宿主特征如何影响猪肠道微生物在暴露于各种抗生素时的抗药性的发生和出现。这项研究中开发的工具将改进对不同微生物生态系统的注释和功能表征，在其他系统中利用基因组测序数据产生预测价值。最后，这项提议旨在利用开放式科学原则来开发和传播研究产品，并通过促进进入数据密集型生物学领域的培训努力来扩大对科学的参与。这一奖项反映了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。

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.