Campylobacter jejuni is the leading cause of bacterial gastroenteritis, which has motivated the monitoring of genetic profiles circulating in Luxembourg since 13 years. From our integrated surveillance using a genotyping strategy based on an extended MLST scheme including gyrA and porA markers, an unexpected endemic pattern was discovered in the temporal distribution of genotypes. We aimed to test the hypothesis of stable lineages occurrence by implementing whole genome sequencing (WGS) associated with comprehensive and internationally validated schemes. This pilot study assessed four WGS-based typing schemes to classify a panel of 108 strains previously identified as recurrent or sporadic profiles using this in-house typing system. The strain collection included four common lineages in human infection (N = 67) initially identified from recurrent combination of ST-gyrA-porA alleles also detected in non-human samples: veterinary (N = 19), food (N = 20), and environmental (N = 2) sources. An additional set of 19 strains belonging to sporadic profiles completed the tested panel. All the strains were processed by WGS by using Illumina technologies and by applying stringent criteria for filtering sequencing data; we ensure robustness in our genomic comparison. Four typing schemes were applied to classify the strains: (i) the cgMLST SeqSphere+ scheme of 637 loci, (ii) the cgMLST Oxford scheme of 1,343 loci, (iii) the cgMLST INNUENDO scheme of 678 loci, and (iv) the wgMLST INNUENDO scheme of 2,795 loci. A high concordance between the typing schemes was determined by comparing the calculated adjusted Wallace coefficients. After quality control and analyses with these four typing schemes, 60 strains were confirmed as members of the four recurrent lineages regardless of the method used (N = 32, 12, 7, and 9, respectively). Our results indicate that, regardless of the typing scheme used, epidemic or endemic signals were detected as reflected by lineage B (ST2254-gyrA9-porA1) in 2014 or lineage A (ST19-gyrA8-porA7), respectively. These findings support the clonal expansion of stable genomes in Campylobacter population exhibiting a multi-host profile and accounting for the majority of clinical strains isolated over a decade. Such recurring genotypes suggest persistence in reservoirs, sources or environment, emphasizing the need to investigate their survival strategy in greater depth.

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.

There is a need for active molecular surveillance of human and veterinary Campylobacter infections. However, sequencing of all isolates is associated with high costs and a considerable workload. Thus, there is a need for a straightforward complementary tool to prioritize isolates to sequence. In this study, we proposed to investigate the ability of MALDI-TOF MS to pre-screen C. jejuni genetic diversity in comparison to MLST and cgMLST. A panel of 126 isolates, with 10 clonal complexes (CC), 21 sequence types (ST) and 42 different complex types (CT) determined by the SeqSphere+ cgMLST, were analysed by a MALDI Biotyper, resulting into one average spectra per isolate. Concordance and discriminating ability were evaluated based on protein profiles and different cut-offs. A random forest algorithm was trained to predict STs. With a 94% similarity cut-off, an AWC of 1.000, 0.933 and 0.851 was obtained for MLSTCC, MLSTST and cgMLST profile, respectively. The random forest classifier showed a sensitivity and specificity up to 97.5% to predict four different STs. Protein profiles allowed to predict C. jejuni CCs, STs and CTs at 100%, 93% and 85%, respectively. Machine learning and MALDI-TOF MS could be a fast and inexpensive complementary tool to give an early signal of recurrent C. jejuni on a routine basis.

Campylobacter jejuni is a leading cause of foodborne illnesses worldwide. Although considered fragile, this microaerophilic bacterium is able to survive in various challenging environments, which subsequently constitutes multiple sources of transmission for human infection. To test the assumption of acquiring specific features for adaptation and survival, we established a workflow of phenotypic tests related to the survival and the persistence of recurrent and sporadic strains. A representative collection of 83 strains isolated over 13 years from human, mammal, poultry, and environmental sources in Luxembourg, representing different spreading patterns (endemic, epidemic, and sporadic), was screened for survival to oxidative stresses, for acclimating to aerobic conditions (AC), and for persistence on abiotic surfaces. Using the cgMLST Oxford typing scheme for WGS data, the collection was classified into genomic lineages corresponding to host-generalist strains (lineages A and D, CC ST-21), host-specific strains (lineage B, CC ST-257 and lineage C, CC ST-464) and sporadic strains. We established that when a strain survives concentrations beyond 0.25 mM superoxide stress, it is six times more likely to survive hyperoxide stress and that a highly adherent strain is 14 times more likely to develop a biofilm. Surprisingly, more than half of the strains could acclimate to AC but this capacity does not explain the difference between recurrent genomic lineages and sporadic strains and the survival to oxidative stresses, while recurrent strains have a significantly higher adhesion/biofilm formation capacity than sporadic ones. From this work, the genomic lineages with more stable genomes could be characterized by a specific combination of phenotypes, called metaphenotypes. From the functional genomic analyses, the presence of a potentially functional T6SS in the strains of lineage D might explain the propensity of these strains to be strong biofilm producers. Our findings support the hypothesis that phenotypical abilities contribute to the spatio-temporal adaptation and survival of stable genomic lineages. It suggests a selection of better-adapted and persistent strains in challenging stress environments, which could explain the prevalence of these lineages in human infections.