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Life sciences · Journal article
BMC Microbiology · August 27, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a large retrospective bioinformatic survey of aminoglycoside resistance determinants in over 110,000 global S. aureus genomes (2000–2025), demonstrating strong genotype-phenotype concordance and identifying declining prevalence of common resistance genes alongside emergence of rare veterinary-linked alleles. The findings support genomic prediction of gentamicin and amikacin resistance in well-characterized isolates but are observational and cannot establish causality or clinical impact; phenotypic confirmation remains essential.
Retrospective genomic and bioinformatic analysis with machine-learning prediction modelling. Staphylococcus aureus isolates from human clinical, animal, and environmental sources represented in publicly available genomic databases; no exclusion criteria specified.. n = 110,000. Global; geographic mapping and focal restriction analysis performed but specific number of countries or centres not stated..
Aminoglycoside resistance genes ant(6)-Ia, ant(9)-Ia, aph(3')-IIIa, sat4, aadD1, and aac(6')-Ie/aph(2'')-Ia occurred in 14–22% of isolates worldwide. ant(9)-Ia showed significant decline of −2.22 percentage points per year (p < 0.001) over 25 years. apmA exhibited non-significant decreasing trend in animal isolates.
No clinical outcome data (treatment efficacy, mortality, length of stay); phenotypic validation limited to publicly available data.
Clinicians and researchers may use these findings to anticipate resistance patterns and support genomic prediction of gentamicin and amikacin susceptibility; however, phenotypic testing remains essential for treatment decisions because prediction models depend on available phenotypic training data and co-resistance patterns.
A large retrospective genomic analysis with strong genotype-phenotype concordance and global scope, but uncontrolled observational design with limited phenotypic validation and no clinical outcome data.
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Quoted from the source exactly as published.
Clinicians and researchers may use these findings to anticipate resistance patterns and support genomic prediction of gentamicin and amikacin susceptibility; however, phenotypic testing remains essential for treatment decisions because prediction models depend on available phenotypic training data and co-resistance patterns.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
BACKGROUND: Aminoglycosides remain clinically valuable against Staphylococcus aureus. Aminoglycoside resistance in S. aureus represents a critical One Health concern and is primarily driven by aminoglycoside-modifying enzymes (AMEs), which are frequently plasmid-encoded. Although regional studies have provided valuable insights, the global epidemiology of aminoglycoside resistance determinants remains poorly characterized because comprehensive data integrating human, animal, and environmental reservoirs are still lacking. This study addresses this gap by analyzing over 110,000 S. aureus genomes (2000-2025) to map the global resistome, quantify temporal and host-specific trends, and assess the association between genetic determinants and phenotypic resistance. METHODS: and enrichment tests. Machine-learning models (elastic-net, random forests, XGBoost) were benchmarked for minimum inhibitory concentration (MIC) prediction via nested cross-validation, with performance evaluated by mean absolute error, RMSE, and SHAP-based feature importance. All analyses were conducted in R and Python using publicly available, de-identified genomic data. RESULTS: Aminoglycoside resistance-associated genes were dominated by modifying enzyme determinants, with ant(6)-Ia, ant(9)-Ia, aph(3')-IIIa, sat4, aadD1, and aac(6')-Ie/aph(2'')-Ia occurring in 14-22% of isolates worldwide. Temporal analysis revealed significant declines in several major determinants, most notably ant(9)-Ia (-2.22 percentage points per year, p < 0.001), whereas apmA exhibited a non-significant decreasing trend in animal isolates. Host structuring was marked: human clinical isolates concentrated common determinants, while animal and environmental isolates harbored rare alleles (apmA, spw, str, spd). Geographic mapping confirmed near-universal distribution of common genes but focal restriction of rare ones. Publicly available phenotypic data indicated strong activity of amikacin, whereas gentamicin showed a distinct resistant subpopulation that closely corresponded with AME gene carriage. Genotype-phenotype analyses demonstrated strong concordance, with gene-rich complements predicting resistant MIC strata and absence of determinants predicting susceptibility. Analysis across different gene classes revealed frequent co-occurrence of aminoglycoside resistance genes with determinants from other classes, such as mecA, blaZ, and MLS_B, embedding them within multidrug-resistant (MDR) genomic contexts. CONCLUSION: Over 25 years, the prevalence of aminoglycoside resistance-associated genes in S. aureus has declined for several common determinants, while rare veterinary-linked alleles are emerging in animal isolates. Strong genotype-phenotype concordance supports genomic prediction for gentamicin and amikacin, where MIC data are available, although phenotypic confirmation remains essential. The frequent co-occurrence of aminoglycoside resistance genes with other antimicrobial resistance determinants indicates their integration within co-occurrence patterns of MDR genes, defined here as clusters of co-occurring resistance genes often carried on shared mobile genetic elements. These patterns highlight the need for integrated One Health surveillance combining clinical, veterinary, and environmental monitoring with plasmid-context resolution to anticipate emerging threats.
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