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Large-scale comparative genomics and machine learning identify over 5,000 novel genetic sequences associated with antimicrobial resistance and demonstrate that horizontal gene transfer and co-harbored multi-drug resistance mechanisms significantly shape AMR profiles across diverse bacterial pathogens. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

June 20, 2026Open Access

Large-scale comparative genomics and structure–function analysis enables characterization of known and novel genetic determinants of antimicrobial resistance in bacterial pathogens

AMAnthony MannionMIT Lincoln Laboratory DHDaniel HooksLos Alamos National Laboratory ACArianna ComendulMIT Lincoln Laboratory

Key Points

This study aims to characterize known and novel genetic determinants of antimicrobial resistance in bacterial pathogens through large-scale comparative genomics.
Analyzed over 70,000 genomes representing 39 pathogen-antibiotic combinations.
Employed a kmer-based GWAS approach to identify resistance determinants statistically enriched in resistant strains.
Developed a Random Forest classifier using protein structure and molecular features to predict interactions.
Identified over 7,000 unique sequences among resistant genomes, with 1,925 homologous to known AMR genes.
Discovered over 5,000 sequences lacking homology, indicating novel AMR-associated genes.
Detected significant associations in multi-drug resistance mechanisms and potential new interaction proteins with antibiotics.

Abstract

Introduction Antibiotics are crucial for preventing infection-induced complications, but their widespread overuse has spurred the evolution of antimicrobial resistance (AMR) mechanisms in pathogens. Data-driven biosurveillance approaches utilizing whole genome sequencing data and computational approaches have the potential to improve the detection and characterization of known and emerging AMR profiles, especially in high-priority ESKAPE, enteric, and sexually-transmitted pathogens. Methods In this study, a large-scale analysis of over 70,000 genomes representing 39 pathogen-antibiotic combinations was performed to identify resistance determinants statistically enriched in antibiotic resistant strains. Results Using a kmer-based GWAS approach, over 7,000 unique sequences were identified among all resistant genomes. Of these, 1,925 sequences were homologous to known AMR genes, while over 5,000 sequences lacked homology, suggesting novel AMR-associated genes. In addition to identifying the predominant AMR genes for specific pathogen-antibiotic combinations, the findings for this study suggest that horizontal gene transfer mechanisms may influence AMR gene profiles between phylogenetically similar pathogens and antibiotic classes. Likewise, significant associations in co-harbored, multi-drug resistance mechanisms were identified in select pathogens. Protein domains analysis frequently detected efflux/membrane structure and antibiotic-associated metabolism domains in novel AMR-associated proteins, suggesting additional mechanisms potentiate resistance phenotypes. Furthermore, a Random Forest classifier using protein structure, molecular features, and binding affinity profiles to predict protein-antibiotic interactions was developed, identifying several novel proteins that may interact with antibiotics. Discussion This study demonstrates the potential of large-scale comparative genomics coupled with AI/ML-based modeling to advance the understanding of AMR threats, thereby enhancing biosurveillance efforts and promoting new strategies to counteract emerging pathogens.

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