Chalka, A. | bioRxiv Science

bioRxiv2026

Integrating bacterial and viral genomic information enhances and extends the machine learning predictions of bacteriophage activity against pathogenic Escherichia coli

The use of bacteriophage (phage) to treat bacterial infections is undergoing a resurgence due to the rise of antibiotic resistance. Pathogenic Escherichia coli, including extraintestinal pathotypes such as uropathogenic E. coli (UPEC), pose a substantial clinical burden with the challenge of multi-drug resistance strains, making it important to progress alternative treatment options such as those based on phage. One major hurdle is selecting effective phage combinations against an infecting strain while accounting for the multiple mechanisms that determine phage susceptibility and bacterial resistance. We previously analysed over 9000 interactions between 31 phage and 314 sequenced E. coli and built machine learning models to predict phage activity against "unseen" E. coli based on these scores and bacterial genome sequence data. However, this approach did not leverage phage gene content, limiting its ability to generalise across phage. Here, we advance this framework by combining bacterial and phage pangenomes into a single predictive model, allowing gene content across related phage as well as bacteria to be incorporated as machine learning features. This unified approach increased predictive accuracy relative to the single-phage models and produced a single model capable of predicting previously unseen phage-E. coli interactions. In leave-phage-out analyses, it also predicted activity for phages whose interaction data were entirely excluded from training. The genomic features most consistently contributing to prediction included bacterial determinants of surface recognition and anti-phage defence, alongside phage-associated features, indicating that this combined model, termed "PanPhage", captures genetic information relevant to multiple stages of the phage-host interaction.

microbiology↗

bioRxiv2023

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

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 models (MLM). For this purpose, MLM were trained on thousands of measured interactions between a panel of phage and sequenced bacterial isolates. The concept was applied to Escherichia coli (E. coli) associated with urinary tract infections. This is an important common infection in humans and companion animals from which multi-drug 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 were 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 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. Significance StatementWith the growing challenge of antimicrobial resistance there is an urgency for alternative treatments for common bacterial diseases including urinary tract infections (UTIs). Escherichia coli is the main causative agent of UTIs in both humans and companion animals with multidrug resistant strains such as the globally disseminated ST131 becoming more common. Bacteriophage (phage) are natural predators of bacteria and potentially an alternative therapy. However, a major barrier for phage therapy is the specificity of phage on target bacteria and therefore difficulty efficiently selecting the appropriate phage. Here, we demonstrate a genomics driven approach using machine learning prediction models combined with phage activity clustering to select phage cocktails based only on the genome sequence of the infecting bacterial strain.

microbiology↗