Abstract :
[en] For decades, antimicrobial resistance has been considered as a global long-lasting challenge. If no action is taken, antimicrobial resistance-related diseases could give a rise up to 10 million deaths each year by 2050 and 24 million people might end into extreme poverty. The ever-increasing spread and cross-transmission of drug-resistant foodborne pathogens such as Campylobacter spp. between reservoirs, such as human, animal and environment are of concern. Indeed, because of the over-exposition and overuse of antibiotics in food-producing animals, the latter could carry multidrug resistant Campylobacter that could be transmitted to humans via food sources or from direct animal contacts. One of the solutions to tackle antimicrobial resistances is the development of rapid diagnostics tests to swiftly detect resistances in routine laboratories. By detecting earlier AMR, adapted antibiotherapy might be administrated promptly shifting from empirical to evidence-based practices, conserving effectiveness of antimicrobials. The already implemented cost- and time-efficient MALDI-TOF MS in routine laboratories for the identification of microorganisms based on expressed protein profiles was successfully applied for bacterial typing and detection of specific AMR peak in a research context. In the line of developing rapid tests for diagnostics, MALDI-TOF MS appeared to be an ideal candidate for a powerful and promising “One fits-all” diagnostics tool. Therefore, the present study aimed to get more insights on the ability of MALDI-TOF MS-protein based signal to reflect the AMR and genetic diversity of Campylobacter spp.
The groundwork of this research consisted into the phenotypic and genotypic characterization of a One-Health Campylobacter collection. Then, isolates were submitted to protein extraction for MALDI-TOF MS analysis. Firstly, mass spectra were investigated to screen AMR to different classes of antibiotics and to retrieve putative biomarkers related to already known AMR mechanisms. The second part evaluated the ability of MALDI-TOF MS to cluster mass spectra according to the genetic relatedness of isolates and congruently compare it to reference genomic-based methods. MALDI-TOF MS protein profiles combined to machine learning displayed promising results for the prediction of the susceptibility and the ciprofloxacin and tetracycline Campylobacter’s resistances. Additionally, MALDI-TOF MS C. jejuni protein clusters were highly concordant to conventional DNA-based typing methods, such as MLST and cgMLST, when a similarity cut-off of 94% was applied. A similar discriminatory power between 2-20 kDa expressed protein and cgMLST profiles was underlined as well. Finally, putative biomarkers either linked to known or unknown AMR mechanisms, or genetic structural population of Campylobacter were identified.
Overall, a single spectrum based on bacterial expressed protein could be used for species identification, AMR screening and potentially as a complete pre-screening for daily surveillance, including genetic diversity and source attribution after further analysis.
