From genome to phenotype: advances, challenges and One Health perspectives in the genomic prediction of antimicrobial resistance
Year: 2026
Type: review
Abstract: Whole-genome sequencing (WGS) has substantially improved the detection and surveillance of antimicrobial resistance (AMR) by enabling the rapid identification of acquired resistance genes, resistance-associated point mutations, and mobile genetic elements. However, the presence of a resistance determinant does not necessarily establish its functional contribution to the antimicrobial phenotype. This mini-review argues that a central limitation of current genotype-to-phenotype prediction is representational: multidimensional resistance architectures are frequently reduced to binary presence-absence profiles. Such representations disregard gene integrity, expression state, promoter architecture, copy number, genomic localization, regulatory topology, strain background, and within-isolate population heterogeneity. Using functionally silent determinants, pseudogenized resistance genes, inducible and dosage-dependent resistance, altered permeability and efflux, heteroresistance, and genomic-context dependence as examples, we examine how this loss of biological information can generate both false-resistant and false-susceptible predictions. We further discuss why metagenomic ARG detection cannot be directly equated with phenotypic resistance and why prediction models trained on restricted clinical populations may not generalize across species, lineages, and One Health compartments. Moving from determinant detection toward context-aware and quantitative phenotype prediction will require the selective integration of long-read sequencing, transcriptomics, copy-number analysis, population-aware sequencing, standardized phenotypic metadata, and externally validated computational models. The relevant unit of AMR prediction is therefore not the resistance gene alone, but its functional state within a specific genomic, cellular, population, and environmental context.
Language: English
Subfield: Microbiology (medical)
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
Awards: Mímicos de ácidos nucleicos como uma nova ferramenta para o combate de salmonelose na indústria pecuária, ALICE - Laboratório Associado em Engenharia Química, G3262533447, G3326884410, G3537227798, Biossensor Rápido de Fluxo Lateral baseado em (RT)-RAA para Deteção On-Site de Escherichia coli Enterotoxigénica e Rotavírus na Diarreia Pós-Desmame de Leitões, Laboratório de Engenharia de Processos, Ambiente, Biotecnologia e Energia, G5088119131, G5538107981, Laboratório de Engenharia de Processos, Ambiente, Biotecnologia e Energia, G6701175450
OpenAlex ID: W7223121265