DS04 - Vie, santé et bien-être

Fast MS/MS identification of bacterial pathogens and antibiotic resistances – Phylopeptidomics

Submission summary

The early detection and diagnosis of infection by microbial pathogens is essential to establish an adapted therapy by identifying and characterizing the pathogens, ideally in the context of personalized medicine. A quicker detection without fastidious culturing steps of any virulent or opportunistic pathogen is of major interest for Public Health, and simultaneous identification of their antibiotic resistances will be a decisive breakthrough. Next-generation shotgun proteomics based on recent tandem mass spectrometers has the potential to identify any single or mixture of organisms in body tissues or body fluids and to describe their associated resistances. “Phylopeptidomics” consists in a new strategy to analyze peptide information for quick taxonomical identification: the proteins from a mixture of several organisms (pathogens & host) present in a given sample are analyzed by tandem mass spectrometry; the detected peptides are assigned to taxonomical data which are then deconvoluted to discriminate species and quantify them. Deconvolution of signals is based on specific organism signatures that have been calculated in terms of peptide sharing and patented by the consortium (patent n° EP2835751 A1). The software µorg.ID has been developed in-house in biopython to give the final answer within a few minutes for a 1h mass spectrometry measurement. This method has proved successful in identifying pathogens in complex biological matrices during several exercises organized by the Biotox-Piratox French national network. Recently, we have shown that enough shotgun peptidomic data can be recorded within 15 min by tandem mass spectrometry for allowing identification of a mixture of 24 different bacteria. We also obtained a quick overview of microbiota from feces within 60 min of mass spectrometry measurements. Our “without a priori” approach has been proved successful to identify bacteria and fungi from numerous samples. Even uncharacterized microorganisms can be classified in the most appropriate taxonomical groups. Moreover, the information obtained on the peptidome can be processed by bioinformatics to identify the bacterial resistances or toxins with appropriate database searches. Thus, phylopeptidomics represents a revolutionizing methodology for quick identification of any pathogen even present in mixtures and characterizing their antibiotic resistance arsenal and/or toxin production.
The objectives of the phylopeptidomics project are to further develop the approach for obtaining a fast MS/MS identification of bacterial pathogens and antibiotic resistances of representative medical samples. The project should exemplify the different possible medical applications of this new concept. The project will focus on sample preparation to deal with common medical matrices (blood, stools, urine, respiratory tract samples and broncho-alveolar lavages) by Partners 1 & 2. For this, sample preparation protocols will be adapted for removing as much as possible host proteins and mass spectrometry incompatible substances when necessary. Numerous experimental datasets will be acquired by tandem mass spectrometry data for adjusting acquisition and interpretation parameters and establishing the range of use of this breakthrough methodology. Partner 1 will be in charge of interfacing generalist databases comprising antibiotic resistance-associated proteins and toxins with the µorg.ID software and defining specific scoring metrics and customer-oriented interface. It is reasonable to say that the whole result could be obtained in less than 3h including sample preparation by the end of the project. We propose to exemplify phylopeptidomics in a routine medical diagnostic laboratory to identify and quantify any bacterial pathogens (including microorganisms difficult to cultivate such as Mycobacteria) present in medical samples even as mixtures, and establish the list of antibiotic resistances and toxins they produce.

Project coordination

Jean ARMENGAUD (Laboratoire Innovations technologiques pour la Détection et le Diagnostic)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.


CEA/DRF/JOLIOT/SPI/Li2D Laboratoire Innovations technologiques pour la Détection et le Diagnostic
Bactériologie-hygiène du CHRU de Lille

Help of the ANR 435,003 euros
Beginning and duration of the scientific project: - 36 Months

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