Description du projet :
The emergence and rapid dissemination of antibiotic resistance worldwide threatens medical progress. As a consequence, medicine might face a return to the pre-antibiotic era in a near future. The paucity of potential new anti-infectives in the pipeline of pharmaceutical industries urges the need for alternatives to fight this public health problem. Phage therapy might represent such an alternative. This re-emerging therapy uses viruses that specifically infect and kill bacteria during their life cycle to reduce/eliminate bacterial load and cure infections. These viruses, called bacteriophages or phages, have been coevolving with bacteria for billions of years, controlling bacterial populations and epidemics, and contributing to their genetic exchanges. With the advantage of having low impact on the commensal flora, as they are highly strain specific, some phages might, nevertheless, harbour virulence factors and drive horizontal gene transfer mediating dissemination of pathogenic traits including antibiotic resistance, calling for careful selection before their therapeutic use (see below ''Phage lifestyle inside the bacteria'')'.
The success of phage therapy mainly relies on the exact matching between both the target pathogenic bacteria and the therapeutic phage. Therefore, having access to a fully characterized phage library is necessary, although not sufficient, to start with phage therapy. An essential, and obligate, second step to conceive personalized phage therapy treatments is the capacity to predict the interactions between the target pathogen and its potential phage. The long term goal of the proposed research is, therefore, to develop quantitative and predictive 'in silico models of phage-bacteria infection networks. These models will describe the interactions between phage and bacteria and will serve to fasten the selection of effective phages to propose phage therapy in a personalized fashion.
To efficiently predict successful phage-bacteria interactions suitable for phage therapy, we will develop a novel 'in silico methodology that will, ultimately, enable the selection of phage candidates from an existing phage library to target a given pathogenic bacteria. To achieve this, we will combine genomic information with state-of-the-art bioinformatic and machine learning techniques, taking advantage of the growing amount of interaction data already available as well as of our own data to keep uncovering new phage families. We will ensure that our methodology brings explanatory power along, thereby shedding light on the relevant genomic features underscoring the interactions. To challenge our approach, we will, eventually, validate prospectively our methodology using paradigmatic pathogens (Pendleton et al. 2013). For this, we will construct the phage-bacterium infection networks around those pathogens, to identify single phages and/or phage cocktails with extended bactericidal activities, as assessed in different models of infections, including a 'Galleria melonnella model of infection and a rat endocarditis model. We expect 'our methodology to drive a paradigm shift in phage therapy, by offering a time-sparing and easy-to-use way to accurately select phages for each individual patient. The methodology will be made available online and several future developments of this project are already envisioned.
Equipe de recherche au sein de la HES-SO:
Barreto Sanz Miguel Arturo
, Pena Carlos Andrés
, Leite Carvalho Diogo
, Brochet Xavier
, Rodriguez Zaloña Oscar
Partenaires académiques: IICT
Durée du projet:
01.07.2016 - 30.06.2020
Montant global du projet: 519'961 CHF