PubMed · 8286818
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K J Madaras-Kelly, J C Rotschafer. 1993. Moving on.. https://doi.org/10.1177/106002809302701116
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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: We performed a retrospective One Health meta-analysis of 110,309 S. aureus genomes collected between 2000 and 2025 from 128 countries. Genomes were quality-filtered and aminoglycoside resistance determinants were identified using NCBI AMRFinderPlus (v4.0.23). Multilocus sequence typing and host-source harmonization (Human, Animal, Environment, Unknown) enabled clonal and reservoir stratification. Temporal trends in gene prevalence and resistance burden were modeled with robust regression. Geographic and host-associated structuring of key genes was assessed via χ2 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.
Natural oligosaccharides and aminoglycosides are important sources of new drug candidates, especially in the development of antibiotics. In the past, discovering novel saccharides has been time-consuming and costly. However, the rapid expansion of high-throughput data, including genomic and mass spectrometry data sets, has greatly increased opportunities for natural saccharide discovery. Yet, due to the complex biosynthesis pathways of saccharides, no existing method can predict their structures with high precision. To address this, we introduce Seq2Saccharide, a tool designed to automate saccharide natural product discovery by integrating both genomic and mass spectrometry data. To enhance accuracy, Seq2Saccharide predicts hundreds or thousands of putative structures for each gene cluster. The correct structure is then identified from these predictions using a mass spectral search. Benchmarks against saccharides in the MiBIG database show that Seq2Saccharide outperforms existing methods in predicting the structure of saccharides. Furthermore, mass spectrometry analysis indicates that the variable search module can correct mispredictions from genome mining. By searching genomic and mass spectrometry data of microbial strains, Seq2Saccharide correctly identified the biosynthetic gene cluster for the polysaccharide oligosaccharide trestatin B.
The antibiotics known as aminoglycosides are commonly used to treat severe infections caused by Gram-negative bacteria. Unfortunately, they often lead to acute renal failure after their accumulation in the lysosomes of renal cells, where an inhibition of the phospholipid catabolism is observed. The lipopeptidic antibiotic daptomycin has been shown to reduce the nephrotoxicity of aminoglycosides, but the exact mechanism of this protection is still unknown. In the present study, Fourier transform infrared spectroscopy (FTIR) has been used to monitor the hydrolysis of phosphatidylcholine by phospholipase A2 (PLA2) from Naja mocambique mocambique venom in the presence of various aminoglycosides and/or daptomycin. Gentamicin and amikacin inhibited the reaction in its early stage. Kanamycin A, tobramycin, and especially kanamycin B enhanced the initial enzyme activity by reducing the lag time. After the initiation period, the reaction proceeded at a much slower rate in the presence of gentamicin. On the other hand, daptomycin led to dramatic alterations of the hydrolysis profile: the initial latency period was eliminated, and the maximal extent of hydrolysis was reduced. When both daptomycin and any of the aminoglycosides were present, the latency period also disappeared, and the phospholipase activity was higher than with the lipopeptide alone. The most drastic change occurred with gentamicin, which was the most inhibitory aminoglycoside when used alone but worked synergistically with daptomycin to yield the most dramatic activation of PLA2.