ArticleNature communications2023
Global pathogenomic analysis identifies known and candidate genetic antimicrobial resistance determinants in twelve species.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 14 citations in OpenAlex.
- Article
- Genomic and socioeconomic drivers of antimicrobial resistance forecast to 2050.Cell genomics · 2026Article
- BaGPipe: an automated, reproducible, and flexible pipeline for bacterial genome-wide association studies.BMC microbiology · 2026Article
- StaLAENet: A stacked LSTM-nested deep-autoencoder network for identification of antimicrobial resistance of nosocomial pathogens.Journal of biosciences · 2026Article
- Predicting Strain-Specific Metabolic Capabilities in the GenusComputational and structural biotechnology journal · 2026Article
- Hitchhiking genes and boundaries of AmpC amplicons during amoxicillin resistance development in E. coli.BMC microbiology · 2025Article
- Bacterial Systematic Genetics and Integrated Multi-Omics: Beyond Static Genomics Toward Predictive Models.International journal of molecular sciences · 2025Review
- Integrating sequencing methods with machine learning for antimicrobial susceptibility testing in pediatric infections: current advances and future insights.Frontiers in microbiology · 2025Review
- Review
- Efflux pump inhibitor chlorpromazine effectively increases the susceptibility ofFuture microbiology · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
Abstract
Surveillance programs for managing antimicrobial resistance (AMR) have yielded thousands of genomes suited for data-driven mechanism discovery. We present a workflow integrating pangenomics, gene annotation, and machine learning to identify AMR genes at scale. When applied to 12 species, 27,155 genomes, and 69 drugs, we 1) find AMR gene transfer mostly confined within related species, with 925 genes in multiple species but just eight in multiple phylogenetic classes, 2) demonstrate that discovery-oriented support vector machines outperform contemporary methods at recovering known AMR genes, recovering 263 genes compared to 145 by Pyseer, and 3) identify 142 AMR gene candidates. Validation of two candidates in E. coli BW25113 reveals cases of conditional resistance: ΔcycA confers ciprofloxacin resistance in minimal media with D-serine, and frdD V111D confers ampicillin resistance in the presence of ampC by modifying the overlapping promoter. We expect this approach to be adaptable to other species and phenotypes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.