Pipeline for Antimicrobial Resistance Gene Quantification from Host Tissue

Antibiotics are frequently used in food production animals to control disease and improve productivity, but this promotes the development of antimicrobial resistance (AMR) and subsequent broader spread of AMR bacteria throughout food chain, endangering the well-being and health of both animals and humans. In humans, the gut microbiome harbors a diverse range of AMR bacteria, known as the resistome. To effectively mitigate AMR in food animals requires first determining the expression and abundance of AMR-related genes in the gut resistome. Currently, such knowledge in regard to food animals is largely lacking. Gut tissue RNA sequencing (GTRS) can capture metabolically active transcripts from both the host and the microbes attached to the gut epithelium. Ideally, AMR genes can be quantified using GTRS data, making it possible to study the relationship between host and microbe. For the majority of these GTRS studies, only host transcriptome changes have been reported, while the microbial AMR remains largely unexamined, mainly due to the lack of easily implementable bioinformatics tools. Here we present a straightforward workflow to accomplish that using common command-line bioinformatics tools. With this pipeline, the host is considered noise, and host data are filtered out from the microbial reads. Transcript quantification of the AMR genes is then performed. The pipeline then continues through AMR transcript quantification, differential gene expression, and SNP analysis. Using open-source tools, we made this analytical pipeline easy to implement and able to generate results ready to be incorporated into publishable reports. Published 2025. This article is a U.S. Government work and is in the public domain in the USA. Basic Protocol: Running the gene quantification pipeline Support Protocol 1: Downloading FASTQ files from the NCBI database Support Protocol 2: Building a genome reference index of the host Support Protocol 3: Differential gene expression analysis Support Protocol 4: Single-nucleotide polymorphism (SNP) analysis.