A natural history of AMR in Klebsiella pneumoniae: Global diversity, predictors, and predictions of evolutionary pathways

Abstract

Antimicrobial resistance (AMR) is a substantial and growing global health burden. Understanding, and predicting, its evolution in specific pathogens will help responses across scales from individual patient cases to large-scale policy. Here, we use global data on AMR features, predicted from 47k Klebsiella pneumoniae genomes, with hypercubic transition path sampling to infer the evolutionary pathways by which AMR features in K. pneumoniae (KpAMR) are acquired across 102 countries, territories and areas. We identify "globally consistent" evolutionary behaviours that hold across countries, and "globally divergent" behaviours including carbapenem and fluoroquinolone resistance that vary across countries. We show how these divergent dynamics covary both with public health superregion and drug use policy, and reveal competing evolutionary pathways within and between countries. Using newly-sequenced data across several decades from sub-Saharan Africa, we show that this inferred global roadmap of KpAMR evolution successfully predicts prospective evolutionary dynamics. Together, we hope that the ability to characterize and predict evolutionary dynamics of AMR acquisition, connected to socio-economic and drug policy predictors, will help strengthen our understanding of AMR evolution worldwide. SignificanceAntimicrobial resistance (AMR) occurs when microbial pathogens evolve resistance to the drugs we use to treat them. Our understanding of bacterial genomes and how they confer AMR is constantly expanding through beautiful and powerful work establishing large-scale global datasets. Here, we use emerging machine learning approaches with this genomic data to reveal the evolutionary dynamics that have generated AMR characters in a particular pathogen, Klebsiella pneumoniae (Kp), and how these dynamics are influenced by geography and drug use across the globe. This "natural history" of AMR in Kp makes predictions about which characters will evolve next for a given bacterium, and we validate these predictions with newly-sequenced data from clinical isolates from Africa, providing both past and prospective descriptions of AMR in Kp.