Monitoring antimicrobial resistance trends from global genomics data: amr.watch

作者：Sophia David, Julio Diaz Caballero, Natacha Couto, Khalil Abudahab, Nabil-Fareed Alikhan, Corin Yeats, Anthony Underwood, Andrew Molloy, Diana Connor, Heather M. Shane, Philip Ashton, Hajo Grundmann, Matthew T. G. Holden, Edward J. Feil, Sonia Sia, Pilar Donado-Godoy, Ravikumar Kadahalli Lingegowda, Iruka N. Okeke, Silvia Argimón, David M. Aanensen · 发表于：PLOS Global Public Health · 年份：2025 · DOI：10.1371/journal.pgph.0005256 · 被引用次数：8 · 研究领域：Antibiotic Use and Resistance、Antibiotic Resistance in Bacteria、Genomics and Phylogenetic Studies

Whole genome sequencing (WGS) is increasingly supporting routine pathogen surveillance at local and national levels, providing comparable data that can inform on the emergence and spread of antimicrobial resistance (AMR) globally. However, the potential for shared WGS data to guide interventions around AMR remains under-exploited, in part due to challenges in collating and transforming the growing volumes of data into timely insights. We present an interactive platform, amr.watch (https://amr.watch), that enables interrogation of AMR trends from public WGS data on an ongoing basis to support research and policy. The amr.watch platform incorporates, analyses and visualises high-quality WGS data from WHO-defined priority bacterial pathogens. Analytics are performed using community-standard methods with bespoke species-specific curation of AMR mechanisms. By 31 March 2025, the platform included data from 620,700 pathogen genomes with geotemporal information, with highly variable representation of different species and geographic regions. By integrating WGS data with sampling information, amr.watch enables users to assess geotemporal trends among genotypic variants (e.g., sequence types) and AMR mechanisms, with implications for interventions including antimicrobial prescribing and drug and vaccine development. While metadata inconsistencies demand future attention we focus on the collation of high quality genomic data allied with geotemporal distribution. In conclusion, amr.watc...