While MALDI-TOF mass spectrometry (MS) is widely considered as the reference
method for the rapid and inexpensive identification of microorganisms in routine
laboratories, less attention has been addressed to its ability for detection of antimicrobial
resistance (AMR). Recently, some studies assessed its potential application together
with machine learning for the detection of AMR in clinical pathogens. The scope of
this study was to investigate MALDI-TOF MS protein mass spectra combined with
a prediction approach as an AMR screening tool for relevant foodborne pathogens,
such as Campylobacter coli and Campylobacter jejuni. A One-Health panel of 224
C. jejuni and 116 C. coli strains was phenotypically tested for seven antimicrobial
resistances, i.e., ciprofloxacin, erythromycin, tetracycline, gentamycin, kanamycin,
streptomycin, and ampicillin, independently, and were submitted, after an on- and
off-plate protein extraction, to MALDI Biotyper analysis, which yielded one average
spectra per isolate and type of extraction. Overall, high performance was observed
for classifiers detecting susceptible as well as ciprofloxacin- and tetracycline-resistant
isolates. A maximum sensitivity and a precision of 92.3 and 81.2%, respectively, were
reached. No significant prediction performance differences were observed between on and off-plate types of protein extractions. Finally, three putative AMR biomarkers for
fluoroquinolones, tetracyclines, and aminoglycosides were identified during the current
study. Combination of MALDI-TOF MS and machine learning could be an efficient and
inexpensive tool to swiftly screen certain AMR in foodborne pathogens, which may
enable a rapid initiation of a precise, targeted antibiotic treatment