ArticleScience advances2021
Functions predict horizontal gene transfer and the emergence of antibiotic resistance.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
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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
43 citing papers in PubMed.
- Cobalt and nickel ion synergy promotes gene amplification and stable metal resistance inApplied and environmental microbiology · 2026Article
- Evolutionary trajectory of microbial sulfur oxidation pathways recapitulates Earth's oxygenation history.Nature communications · 2026Article
- SEVA: structural and evolutionary feature integration for predicting virulence factors and antibiotic resistance genes.Microbiome · 2026Article
- Antibiotic-Driven Gut Microbiome Dysbiosis: Resistome Dynamics, Metabolic Disruption, and Paths to Restoration.Antibiotics (Basel, Switzerland) · 2026Review
- Nitrate-reducing bacteria bridge nitrogen cycling and antibiotic resistance in river ecosystems.Nature communications · 2026Article
- Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.Infection · 2026Review
- Targeting metabolism to combat anticancer and antibacterial drug resistance.Trends in pharmacological sciences · 2026Review
- Antimicrobial resistance dissemination via horizontal gene transfer is constrained in stratified waters.Communications biology · 2026Article
- Therapeutic advances in the fight against microbial resistance: innovative strategies and future challenges.Folia microbiologica · 2026Review
- Information storage across a microbial community using universal RNA barcoding.Nature biotechnology · 2026Article
- Exploring cooperation among social pathogens: a computational perspective.FEMS microbiology reviews · 2026Review
- Spread potential and architectural variant of a multiple-drug resistantFrontiers in microbiology · 2026Article
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- Targeting Horizontal Gene Transfer to Combat Antimicrobial Resistance: A Review of Mechanisms, Drivers, and Multi-Omics Strategies.Infection and drug resistance · 2026Review
- Genomic data representations for horizontal gene transfer detection.NAR genomics and bioinformatics · 2025Article
- Genomic Analysis of Antibiotic Resistance and Virulence Profiles inVeterinary sciences · 2025Article
- Transmission of the human respiratory microbiome and antibiotic resistance genes in healthy populations.Microbiome · 2025Article
- Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gut.Nature communications · 2025Article
- Functional Divergence of Plant-Derived Thaumatin-Like Protein Genes in Two Closely Related Whitefly Species.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Genetic compatibility and ecological connectivity drive the dissemination of antibiotic resistance genes.Nature communications · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Phylogenetic distance, shared ecology, and genomic constraints are often cited as key drivers governing horizontal gene transfer (HGT), although their relative contributions are unclear. Here, we apply machine learning algorithms to a curated set of diverse bacterial genomes to tease apart the importance of specific functional traits on recent HGT events. We find that functional content accurately predicts the HGT network [area under the receiver operating characteristic curve (AUROC) = 0.983], and performance improves further (AUROC = 0.990) for transfers involving antibiotic resistance genes (ARGs), highlighting the importance of HGT machinery, niche-specific, and metabolic functions. We find that high-probability not-yet detected ARG transfer events are almost exclusive to human-associated bacteria. Our approach is robust at predicting the HGT networks of pathogens, including
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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.