From Genomes to Ecosystems: Harnessing Bioinformatics, Metagenomics, and AI for One Health AMR Surveillance

Antimicrobial resistance (AMR) is now recognized as one of the greatest global health challenges of our time, threatening the effectiveness of antibiotics and other antimicrobial drugs in both human and veterinary medicine. The rapid spread of resistant bacteria and resistance genes across humans, animals, food systems, and the environment underscores the urgent need for integrated One Health strategies that consider the close connections between these ecosystems. In recent years, major advances in whole-genome sequencing (WGS), have transformed our ability to investigate resistance genes, mobile genetic elements, transmission routes, and the emergence of multidrug-resistant high-risk clones across diverse ecological settings.This Research Topic focuses on the use of One Health genomics, metagenomics, and advanced bioinformatics approaches to better understand antimicrobial resistance determinants circulating across interconnected ecosystems, including humans, livestock, companion animals, wildlife, food production systems, aquaculture, and environmental reservoirs. The collection aims to provide a multidisciplinary platform for studies investigating the genomic epidemiology, evolution, dissemination, and persistence of AMR within and across these interfaces.We particularly encourage submissions employing high-resolution genomic approaches such as WGS, comparative genomics, pan-genome analysis, resistome profiling, metagenomics, and phylogenomics to explore resistance mechanisms and transmission dynamics. Studies integrating genomic findings with epidemiological, clinical, environmental, and phenotypic data are especially welcomed, as they can provide deeper insights into the factors driving the emergence and spread of AMR across ecosystems.This Research Topic also aims to highlight the important role of mobile genetic elements (i.e. plasmids, integrons, transposons, bacteriophages, and genomic islands) in facilitating horizontal gene transfer and accelerating the dissemination of clinically important resistance determinants. Particular attention will be given to emerging resistance mechanisms, high-risk international clones, zoonotic and foodborne pathogens, environmental resistomes, and the influence of antimicrobial use practices on resistance evolution.In addition, the collection seeks to explore the growing contribution of artificial intelligence, machine learning, and data-driven bioinformatics to predictive AMR surveillance, genomic risk assessment, outbreak detection, and precision public health. The integration of AI-assisted analytical tools with large-scale genomic datasets offers powerful new opportunities for the early detection of emerging resistance threats and for strengthening global AMR surveillance systems.This Research Topic welcomes original research articles, reviews, mini-reviews, methodological papers, and perspective articles addressing, but not limited to, the following themes:- Genomic characterization of multidrug-resistant bacteria - One Health genomic surveillance of AMR pathogens - AMR dissemination across food production chains and environmental systems- Mobile genetic elements and horizontal gene transfer - Comparative genomics and phylogenomics of resistant pathogens- Cross-sectoral transmission of zoonotic and foodborne multidrug resistant pathogens - Emerging resistance genes and high-risk international clones- Metagenomic analysis of environmental and animal resistomes - AI and machine learning-based AMR prediction models - Precision surveillance and integrated genomic monitoring systems- Bioinformatics pipelines and computational tools for AMR detection- Evolutionary dynamics and ecology of antimicrobial resistance- Wastewater-based and environmental AMR surveillance Topic editors: Hazem Ramadan, Hazim O. Khalifa Source: https://www.frontiersin.org/research-topics/81847/from-genomes-to-ecosystems-harnessing-bioinformatics-metagenomics-and-ai-for-one-health-amr-surveillance