Hugo Vaysset's scientific contributions

Transposase genes are ubiquitous in all domains of life and provide a rich reservoir for the evolution of novel protein functions. Here we report deep evolutionary links between bacterial IS110 transposases, which catalyze RNA-guided DNA recombination using bridge RNAs, and archaeal/eukaryotic Nop5-family proteins, which promote RNA-guided RNA 2’-O-methylation using C/D-box snoRNAs. Based on conservation in the protein primary sequence, domain architecture, and three-dimensional structure, as well as common architectural features of the non-coding RNA components, we propose that programmable RNA modification emerged via exaptation of components derived from IS110-like transposons. Alongside recent studies highlighting the origins of CRISPR-Cas9 and Cas12 in IS605-family transposons, these findings underscore how recurrent domestication events of transposable elements gave rise to complex RNA-guided biological mechanisms.

In recent years, a vast number of novel antiphage defense mechanisms were uncovered. To facilitate the exploration of mechanistic, ecological, and evolutionary aspects related to antiphage defense systems, we released DefenseFinder in 2021 (Tesson et al., 2022). DefenseFinder is a bioinformatic program designed for the systematic identification of all known antiphage defense mechanisms. The initial release of DefenseFinder v1.0.0 included 60 systems. Over the past three years, the number of antiphage systems incorporated into DefenseFinder has grown to 152. The increasing number of known systems makes it a challenge to enter the field and makes the interpretation of detections of antiphage systems difficult. Moreover, the rapid development of sequence-based predictions of structures offers novel possibilities of analysis and should be easily available. To overcome these challenges, we present a hub of resources on defense systems including 1) a search tool (DefenseFinder as a web service) 2) a knowledge base, and 3) precomputed databases (results of DefenseFinder ran on RefSeq database, predicted structure database computed with AlphaFold). These pages can be freely accessed for users as a starting point on their journey to better understand a given system. We anticipate that these resources will foster the use of bioinformatics in the study of antiphage systems and will serve the community of researchers who study antiphage systems. This resource is available at: https://defensefinder.mdmlab.fr .

Predicting how phages can selectively infect specific bacterial strains holds promise for developing novel approaches to combat bacterial infections and better understanding microbial ecology. Experimental studies on phage-bacteria interactions have been mostly focusing on a few model organisms to understand the molecular mechanisms which makes a particular bacterial strain susceptible to a given phage. However, both bacteria and phages are extremely diverse in natural contexts. How well the concepts learned from well-established experimental models generalize to a broad diversity of what is encountered in the wild is currently unknown. Recent advances in genomics allow to identify traits involved in phage-host specificity, implying that these traits could be utilized for the prediction of such interactions. Here, we show that we could predict outcomes of most phage-bacteria interactions at the strain level in Escherichia natural isolates based solely on genomic data. First, we established a dataset of experimental outcomes of phage-bacteria interactions of 403 natural, phylogenetically diverse, Escherichia strains to 96 bacteriophages matched with fully sequenced and genomically characterized strains and phages. To predict these interactions, we set out to define genomic traits with predictive power. We show that most interactions in our dataset can be explained by adsorption factors as opposed to antiphage systems which play a marginal role. We then trained predictive algorithms to pinpoint which interactions could be accurately predicted and where future research should focus on. 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 and show higher efficiency of tailored cocktails on a collection of 100 pathogenic E. coli isolates. Altogether, this work provides quantitative insights into understanding phage-host specificity at the strain level and paves the way for the use of predictive algorithms in phage therapy.