Congrats to the leading authors Baptiste Gaborieau,
Hugo Vaysset, and @ftesson.bsky.social and to all the great team.
www.biorxiv.org/content/10.1...

Finally, we show the application of such predictions by establishing a pipeline to recommend tailored phage cocktails to target pathogenic strains from their genomes only🧬💻.

We show higher efficiency of tailored cocktails on a collection of 100 pathogenic E. coli isolates.

We then trained predictive algorithms to figure out which interactions we can accurately predict. We got an overall AUROC of 86%. We provide a roadmap for the future as it will allow us to focus on the 3,379 false negatives and 2,922 false positives that are not well predicted.

On the phage side, the strain the phage was isolated on accounts for 53% of the variance (!). We show this is linked to the phage ability to adsorb which is driven by its Receptor Binding Proteins. Side note: Only ⅓ of out phages were able to infect Coli K-12

Then came the team effort 🤩. We infected everyone against everyone generating a matrix >38000 interactions. This was done in triplicates with 3 concentrations. The matrix of interactions reveals complex (and beautiful !) patterns. How much of these do we understand ?

We needed a dataset, diverse and at scale. We chose the Escherichia genus. We established 2 collections of 403 natural, diverse, Escherichia strains and 96 bacteriophages. We looked in their genomes 💻🧬 for traits related to infection (receptors, capsules, defense systems…).