Rapid Identification of Bacterial Pathogens by Top-Down Proteomics for Clinical Diagnostics – PathoTOP

Rapid and accurate identification of microorganisms is a prerequisite for appropriate patient care and infection control. In the last decade, Mass Spectrometry (MS) has revolutionized the field of clinical microbiology with the introduction of MALDI-TOF for rapid microbial identification. The principle is simple. Ionization of intact proteins from a cultured colony leads to a spectral profile, which is compared to reference spectra present in a library. The approach is now used in many hospitals for routine bacterial identification, as it is faster, more accurate and less expensive than conventional phenotypic or genotypic methods.
However, MALDI-TOF MS suffers from important limitations. Some bacteria remain difficult to identify, either because they do not give a specific profile or because the database lacks the appropriate reference. In addition, the discriminatory power of the technique is often insufficient for reliably differentiating sub-species within species or clones within sub-species. More importantly, virulence or resistance determinants cannot be characterized, which is a severe obstacle for appropriate patient care and antibiotics prescription in hospitals.
Genomic methods (either targeted or large-scale) also present substantial drawbacks. In addition to their complexity, these approaches do not take into account the fact that some proteins may not be expressed although the corresponding gene is present. Proteomics approaches, that directly target expressed proteins, and thus gene products, are closer to phenotypes.
Top-down proteomics is an emerging technology based on the analysis of intact proteins using very high-resolution mass spectrometry. The major advantage of top-down proteomics, in comparison to the more usual bottom-up approach, is its ability to address protein variations and characterize proteoforms arising from alternative splicing, allelic variation, or post-translational modification. Top-down proteomics also negates the problem of protein inference, which is a major issue in bottom-up proteomics.
The aim of the PathoTOP project is to develop innovative top-down proteomics approaches to identify and deeply characterize bacterial pathogens on a very short time scale after sample collection in the hospital. This characterization includes resistance and virulence factors.
J. Chamot-Rooke, coordinator of the project, has gained in past years an internationally recognized expertise for the analysis of bacterial proteins using top-down MS and she has recently set up a unique top-down proteomics platform in France at the Institut Pasteur (IP). The project will also benefit from the outstanding expertise of P. Trieu-Cuot (head of the Unit of Biology of Gram Positive Pathogens at IP), D. Clermont at the IP microbe Collection (the CIP gathers more than 10,000 bacterial strains) and three eminent medical scientists (MD-PhD) heads of clinical and research microbiology laboratories in hospitals: C. Poyart (Cochin, Paris), MC. Ploy (Limoges Hospital) and I. Podglajen (HEGP, Paris).
The project will focus on two pathogens. The first one is Staphylococcus aureus, for which a top-down method allowing both the identification of the species and the detection of biomarkers of methicillin-resistance will be developed. The second is Group B Streptococcus for which a specific approach allowing the detection of proteins signing the sequence type ST17, considered to be closely associated with bacterial invasiveness, together with the detection of the MLSB resistance phenotype will be set up. The methods developed for these pathogens will pave the way to applications to others.
This project, which aims at introducing state-of-the-art proteomics approaches in the field of clinical microbiology, represents a major breakthrough in the field. It will contribute to the development of better diagnostic tools and prognostic markers for the management of infections in clinical settings including early detection of epidemics.