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MDM lab

@mdmlab.bsky.social

Molecular Diversity of Microbes Lab at Institut Pasteur Focusing on bacterial immunity 🦠🧬🧫 // We ♥️ sharing our science with everyone // PI Aude Bernheim

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MDM lab @mdmlab.bsky.social · 23/11/2023

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...

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MDM lab @mdmlab.bsky.social · 23/11/2023

So, can we predict phage-bacteria from their genomic data ? Yes, to a certain extent ! There is much more work to be done to improve these predictions, but our study demonstrates feasibility. We anticipate similar approaches could be applied to many bacterial species.

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MDM lab @mdmlab.bsky.social · 23/11/2023

We are amazed by the diversity of phage-bacteria interactions beyond laboratory models 🤩 . We hope that making available to the community 2 new collections & the accompanying interaction dataset will provide a starting point for mechanistic exploration of these interactions.

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MDM lab @mdmlab.bsky.social · 23/11/2023

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.

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MDM lab @mdmlab.bsky.social · 23/11/2023

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.

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MDM lab @mdmlab.bsky.social · 23/11/2023

On the bacterial side, we show that most interactions in our dataset can be explained by adsorption factors as opposed to antiphage systems which play a marginal role (more about antiphage systems in the manuscript).

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MDM lab @mdmlab.bsky.social · 23/11/2023

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

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MDM lab @mdmlab.bsky.social · 23/11/2023

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 ?

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MDM lab @mdmlab.bsky.social · 23/11/2023

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…).

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MDM lab @mdmlab.bsky.social · 23/11/2023

Recent advances in genomics enable the identification of traits linked to phage-host specificity, suggesting the potential use of these traits for predicting such interactions. So how well can we predict a range of phage-bacteria interactions exclusively from their genomic data?

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MDM lab @mdmlab.bsky.social · 23/11/2023

Predicting which phages infect specific strains would allow to better fight bacterial infections and understand microbial ecology . Many studies focused on model phage-bact couples, how concepts learns from these apply to the *super* diverse natural context remains uncertain.🤔

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MDM lab @mdmlab.bsky.social · 23/11/2023

💻🧫 New preprint: How accurately can we predict diverse phage bacteria-interactions from their genomes only ? We created a matrix of >38k phage-bacteria interactions to find out (=> AUROC 86%) & used our predictions to recommend tailored phage cocktails. www.biorxiv.org/content/10.1...

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