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

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.