Data Challenge: Gene emergence

Tracking the dissemination of emerging Resistant Genes (TrackR-G)

Antimicrobial resistance (AMR) represents an escalating global health challenge, particularly due to the spread of extended-spectrum beta-lactamase (ESBL)- and carbapenemase-producing Enterobacterales. Despite the recognized role of horizontal gene transfer (HGT) in driving resistance in Enterobacterales, the dynamics of how these genes emerge and spread across species remain poorly described. Moreover, with increased human global mobility facilitating microbial exchange, understanding how quickly resistance genes propagate from one country to another is critical for early detection and containment.
In response, we propose to explore available data from multinational AMR surveillance programs on the occurrence of resistance genes to analyse their prevalence and dynamics at the global scale. First, we will exploit resistance mechanisms in Enterobacterales available in the ATLAS and SMART databases, which cover a large number of countries, to identify spatial, bacterial, and temporal clusters of resistance genes using machine learning methods. Questions such as the dissemination speed of each specific gene will also be addressed. Then, we will develop a mechanistic model describing both gene transfers between species and gene diffusion across countries. Once calibrated to the surveillance data, this model will allow us to make worldwide projections of the presence of resistant genes in the future years for various bacterial species.
This analysis will enable us to provide a global view of historical trends in the dissemination of resistance genes over the world since 2004 by species and geographical regions, thereby improving ongoing efforts in global AMR surveillance and preparing a coordinated response in the case of new emergences. By informing future trends in the spread of resistance genes, our analysis will also help to refine global AMR priorities at the gene level.