Learning and forecasting shared evolutionary pathways to multi-drug resistance across global pathogens

Abstract

Infections with bacteria which have evolved multi-drug resistance (MDR) cause millions of deaths worldwide. Large-scale efforts are gathering genotypic and phenotypic data on MDR bacteria, but methods for learning the structure, diversity, and predictors of evolutionary pathways to MDR have yet to take full advantage of these data. Here, we use evolutionary accumulation modelling (EvAM), an emerging class of machine learning methods with roots in cancer progression, to infer these evolutionary pathways across ESKAPEE pathogens (seven bacterial species that dominate health burdens), using a database of over 635k genotyped phenotypic observations from around the world. We identify global patterns in MDR evolutionary pathways, remarkably shared across multiple ESKAPEE species. Species-specific deviations from these stereotypical pathways are connected with geographical and demographic covariates, facilitating predictions of future MDR evolution. We verify these predictions with several hundred new phenotypes from ESKAPEE samples spanning decades of clinical infections in sub-Saharan Africa, demonstrating the capacity to forecast future MDR evolution from these inferred shared pathways.

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BibTeX RIS

BibTeX

@misc{aga2026learning,
  title = {{Learning and forecasting shared evolutionary pathways to multi-drug resistance across global pathogens}},
  author = {O. Aga and S. Moyo and J. Ferno and J. Manyahi and U. Kibwana and I. Löhr and N. Langeland and B. Blomberg and I. Johnston},
  howpublished = {bioRxiv},
  year = {2026},
  month = aug,
  doi = {10.64898/2026.08.30.748110},
  url = {https://www.biorxiv.org/content/10.64898/2026.08.30.748110v1},
  note = {CC BY},
}

RIS

TY  - UNPB
TI  - Learning and forecasting shared evolutionary pathways to multi-drug resistance across global pathogens
AU  - O. Aga
AU  - S. Moyo
AU  - J. Ferno
AU  - J. Manyahi
AU  - U. Kibwana
AU  - I. Löhr
AU  - N. Langeland
AU  - B. Blomberg
AU  - I. Johnston
PY  - 2026
DA  - 2026/08/30
PB  - bioRxiv
DO  - 10.64898/2026.08.30.748110
UR  - https://www.biorxiv.org/content/10.64898/2026.08.30.748110v1
AB  - Infections with bacteria which have evolved multi-drug resistance (MDR) cause millions of deaths worldwide. Large-scale efforts are gathering genotypic and phenotypic data on MDR bacteria, but methods for learning the structure, diversity, and predictors of evolutionary pathways to MDR have yet to take full advantage of these data. Here, we use evolutionary accumulation modelling (EvAM), an emerging class of machine learning methods with roots in cancer progression, to infer these evolutionary pathways across ESKAPEE pathogens (seven bacterial species that dominate health burdens), using a database of over 635k genotyped phenotypic observations from around the world. We identify global patterns in MDR evolutionary pathways, remarkably shared across multiple ESKAPEE species. Species-specific deviations from these stereotypical pathways are connected with geographical and demographic covariates, facilitating predictions of future MDR evolution. We verify these predictions with several hundred new phenotypes from ESKAPEE samples spanning decades of clinical infections in sub-Saharan Africa, demonstrating the capacity to forecast future MDR evolution from these inferred shared pathways.
ER  -

Abstract

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