Predictive phage therapy for Escherichia coli urinary tract infections: Cocktail selection for therapy based on machine learning models

• 成果类型: Article
• 署名作者: Keith, Marianne; de la Torriente, Alba Park; Chalka, Antonia; Vallejo-Trujillo, Adriana; Mcateer, Sean P.; Paterson, Gavin K.; Low, Alison S.; Gally, David L.
• 署名单位: UK Research & Innovation (UKRI); Biotechnology and Biological Sciences Research Council (BBSRC); Roslin Institute; University of Edinburgh; University of Edinburgh
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11395
• DOI: 10.1073/pnas.2313574121
• 发表日期: 2024-03-19
• 关键词: antimicrobial resistance; mechanisms; bacteria

摘要

This study supports the development of predictive bacteriophage (phage) therapy: the concept of phage cocktail selection to treat a bacterial infection based on machine learning (ML) models. For this purpose, ML models were trained on thousands of measured interactions between a panel of phage and sequenced bacterial isolates. The concept was applied to Escherichia coli associated with urinary tract infections. This is an important common infection in humans and companion animals from which multidrug- resistant (MDR) bloodstream infections can originate. The global threat of MDR infection has reinvigorated international efforts into alternatives to antibiotics including phage therapy. E. coli exhibit extensive genome-level variation due to horizontal gene transfer via phage and plasmids. Associated with this, phage selection for E. coli is difficult as individual isolates can exhibit considerable variation in phage susceptibility due to differences in factors important to phage infection including phage receptor profiles and resistance mechanisms. The activity of 31 phage was measured on 314 isolates with growth curves in artificial urine. Random Forest models were built for each phage from bacterial genome features, and the more generalist phage, acting on over 20% of the bacterial population, exhibited F1 scores of >0.6 and could be used to predict phage cocktails effective against previously untested strains. The study demonstrates the potential of predictive ML models which integrate bacterial genomics with phage activity datasets allowing their use on data derived from direct sequencing of clinical samples to inform rapid and effective phage therapy.