Investigation of MALDI-TOF Mass Spectrometry for Assessing the Molecular Diversity of Campylobacter jejuni and Comparison with MLST and cgMLST: A Luxembourg One-Health Study

Quadruplex qPCR for detection and discrimination of C. Coli,C. fetus, and C. Jejuni from other Campylobacter species in chicken and sheep meat

Campylobacter is gram-negative bacteria considered the predominant genera isolated from poultry samples and associated with gastroenteritis. Due to the problems in conventional cultural methods of time-consuming and technically demanding requirements, a rapid and feasible method for their identification and discrimination of the closely related spp. Including Campylobacter coli, Campylobacter fetus, and Campylobacter jejuni is needed. This study analyzes the chicken and sheep meats samples (n = 125) using culture and pre-enrichment-based Quadraplex real-time PCR by targeting OrfA, CstA, HipO, and 16 S rRNA genes of C. coli, C. fetus, C. jejuni and Campylobacter spp. Respectively. The analysis of 125 chicken and sheep meat samples by culture and real-time PCR showed high concordance between the results of the two methods. The present study show high prevalence of Campylobacter species (35% and 32% from chicken and meat respectively) of which C. jejuni were the most abundant. Reaction efficiencies were between 90 and 110%, and detect as low as 8.9 fg in C. jejuni. The need for quick detection and discrimination methods in sheep and chicken meat can be met using the described Quadraplex real-time PCR methodology.

Overview of Ecology and Aspects of Antibiotic Resistance in Campylobacter spp. Isolated from Free-Grazing Chicken Tissues in Rural Households

Rural households all over the world rear backyard chicken mainly for their own consumption and, to a lesser extent, for barter trade. These chickens represent a staple dish with numerous culinary variations and a cheap source of protein. Although some Campylobacter species, and particularly Campylobacter jejuni and Campylobacter coli, have been associated with industrial poultry carcasses, studies concerning the ecology of this genus in rural households do not exist. To assess the prevalence of Campylobacter species in the tissues of backyard chickens, samples were collected from birds Gallus domesticus bred in households in the rural area of Epirus (Greece), and Campylobacter strains were isolated by quantitative methods at 37 °C and 42 °C. In total, 256 strains were identified, belonging to 17 Campylobacter species, with C. jejuni and C. coli being the most prevalent. From the four ecological parameters studied (size of the flock, presence of small ruminants in the same household, presence of other poultry species in the same household, and feeding leftovers of the household), the size of the flock and the presence of small ruminants and/or pigs in the same household mostly affected the distribution of these strains. To study the phenotypical resistance against 14 antibiotics, 215 strains were selected. The results showed a high prevalence of multidrug-resistance (MDR) strains extending to all classes of antibiotics. Further genome analysis revealed the presence of genes coding resistance (blaOxA-61, tet(O), tet(A) cmeA, cmeB, cmeC, and gyrA (Thr-86-Ile mutation)), with the efflux pump CmeABC being the most prevalent. All antimicrobial resistance-encoded genes co-circulated, except for blaOXA-61, which moved independently. The minimum inhibitory concentration (MIC) values of two out of three antibiotics (representing different classes) were reduced when the strains tested were exposed to carbonyl cyanide 3-chlorophenylhydrazone (CCCP), a known efflux pump inhibitor. The same result was obtained with the addition of CCCP to the MIC values of bile salts. These results lead to the conclusion that Campylobacter species are present in an impressive diversity in backyard chicken tissues and that they exert a significant resistance to antibiotics, raising a potential danger for public health.

Machine learning model of imipenem-resistant Klebsiella pneumoniae based on MALDI-TOF-MS platform: An observational study

Background and Aim

Machine learning is an important branch and supporting technology of artificial intelligence, we established four machine learning model for the drug sensitivity of Klebsiella pneumoniae to imipenem based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and compared their diagnostic effect.

Methods

The data of MALDI-TOF-MS and imipenem sensitivity of 174 cases of K. pneumoniae isolated from clinical specimens in the laboratory of microbiology department of Tianjin Haihe Hospital from 2019 January to 2020 December were collected. The mass spectrometry and imipenem sensitivity of 70 cases of imipenem-sensitive and 70 resistant cases were randomly selected to establish the training set model, 17 cases of sensitive and 17 cases of resistant cases were randomly selected to establish the test set model. Mass spectral peak data were subjected to orthogonal partial least squares discriminant analysis (OPLS-DA), the training set data model was established by machine learning least absolute shrinkage and selection operator (LASSO) algorithm, logistic regression (LR) algorithm, support vector machines (SVM) algorithm, neural network (NN) algorithm, the area under the curve (AUC) and confusion matrix of training set and test set model were calculated and selected by Grid search and 3-fold Cross-validation respectively, the accuracy of the prediction model was verified by test set confusion matrix.

Results

The R²Y and Q² of OPLS-DA were 0.546 and 0.0178. The AUC of the best training set and test set models were 0.9726 and 0.9100, 1.0000 and 0.8581, 0.8462 and 0.6263, 1.0000 and 0.7180 evaluated by LASSO, LR, SVM and NN model respectively. The accuracy of the LASSO, LR, SVM and NN model were 87%, 79%, 62%, and 68% in test set, respectively.

Conclusion

The LASSO prediction model of K. pneumoniae sensitivity to imipenem established in this study has a high accuracy rate and has potential clinical decision support ability.

Comparative Analysis of Campylobacter jejuni and C. coli Isolated from Livestock Animals to C. jejuni and C. coli Isolated from Surface Water Using DNA Sequencing and MALDI-TOF

This study evaluated the contribution of cattle, sheep, poultry and pigs to the contamination of surface water from rivers by Campylobacter jejuni and C. coli using MLST, cgMLST and considered MALDI-TOF MS as an alternative technique. The 263 strains isolated from cattle (n = 61), sheep (n = 42), poultry (n = 65), pigs (n = 60) and surface water (n = 35) were distributed across 115 sequence types (STs), 49 for C. jejuni and 66 for C. coli. Considering MLST data, 14.2%, 11.4% and 2.8% of the surface water strains could be attributed to cattle, poultry and sheep, respectively, none to pigs, and 85.7% were non-attributed. Analysis of cg-MLST data with STRUCTURE indicated that C. jejuni strains from water were predominantly attributed to poultry (93.5%), weakly to sheep (<1%) and 6.3% non-attributed, and that conversely, C. coli strains from water were predominantly non-attributed (94.3%) and 5.7% attributed to poultry. Considering the protein profiles with a threshold of 94% and 97% of similarity, respectively, strains from surface water could be attributed to poultry (31.4% and 17.1%), and to cattle (17.1% and 5.7%); 54.1% and 77.1% were non-attributed. This study confirmed these livestock animals might contribute to the contamination of surface water, with a level of contribution depending on the typing technique and the method of analysis. MALDI-TOF could potentially be an alternative approach for source attribution.

Overview of methodologies for the culturing, recovery and detection of Campylobacter

Campylobacter species are responsible for human gastroenteritis with diverse clinical spectra, ranging from acute watery or bloody diarrhea to life-threatening autoimmune disorders. Given the importance of Campylobacter in causing human illness, this article has reviewed the transmission and attribution sources as well as methodologies for the detection and virulence characterization of campylobacteria. The recovery and detection of Campylobacter from clinical, food and environmental samples has been achieved by the combinatorial use of selective enrichment and culturing methods. Biochemical, immunological, and nucleic acid-based methodologies have enabled the detection and differentiation of closely related Campylobacter isolates in foodborne outbreak investigations and have assessed the diversity and phylogenetic relationships of these bacterial pathogens. Analyses of motility, adherence, and invasiveness in host cells have assessed the pathogenic potential of campylobacteria. Further examination of determinants conferring antimicrobial resistance in Campylobacter have supported the growing need to closely monitor antimicrobials use in clinical and agricultural sectors.

Proteotyping of Campylobacter jejuni by MALDI-TOF MS and Strain Solution Version 2 Software

Identification of microorganisms by MALDI-TOF MS has become a popular method in the past 20 years. Strain Solution ver. 2 software appended with MALDI-TOF MS enables accurate discrimination of serotypes and strains beyond the genus and species level by creating a theoretical mass-based database. In this study, we constructed a theoretical mass database with the validated biomarkers to proteotype Campylobacter jejuni. Using 10 strains belonging to Campylobacter spp. available from culture collections and 41 Campylobacter jejuni strains isolated from humans and foods, the ribosomal protein subunits L36, L32, S14, L24, L23, L7/L12, and S11 could be selected as the effective biomarkers for the proteotyping of C. jejuni at MALDI-TOF MS. An accurate database of their theoretical mass-based values was constructed by matching these gene DNA sequences and the observed mass peaks. We attempted to automatically classify 41 strains isolated from nature using this database and Strain Solution ver. 2 software, and 38 strains (93%) were correctly classified into the intended group based on the theoretical mass-based values. Thus, the seven biomarkers found in this study and Strain Solution ver. 2 are promising for the proteotyping of C. jejuni by MALDI-TOF MS.

MALDI Mass Spectrometry Imaging: A Potential Game-Changer in a Modern Microbiology

Nowadays, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) is routinely implemented as the reference method for the swift and straightforward identification of microorganisms. However, this method is not flawless and there is a need to upgrade the current methodology in order to free the routine lab from incubation time and shift from a culture-dependent to an even faster independent culture system. Over the last two decades, mass spectrometry imaging (MSI) gained tremendous popularity in life sciences, including microbiology, due to its ability to simultaneously detect biomolecules, as well as their spatial distribution, in complex samples. Through this literature review, we summarize the latest applications of MALDI-MSI in microbiology. In addition, we discuss the challenges and avenues of exploration for applying MSI to solve current MALDI-TOF MS limits in routine and research laboratories.

Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry in veterinary medicine: Recent advances (2019-present)

Matrix-assisted laser desorption ionization-time-of-flight (MALDI-TOF) mass spectrometry (MS) has become a valuable laboratory tool for rapid diagnostics, research, and exploration in veterinary medicine. While instrument acquisition costs are high for the technology, cost per sample is very low, the method requires minimal sample preparation, and analysis is easily conducted by end-users requiring minimal training. Matrix-assisted laser desorption ionization-time-of-flight MS has found widespread application for the rapid identification of microorganisms, diagnosis of dermatophytes and parasites, protein/lipid profiling, molecular diagnostics, and the technique demonstrates significant promise for 2D chemical mapping of tissue sections collected postmortem. In this review, an overview of the MALDI-TOF technique will be reported and manuscripts outlining current uses of the technology for veterinary science since 2019 will be summarized. The article concludes by discussing gaps in knowledge and areas of future growth.

Combination of MALDI-TOF Mass Spectrometry and Machine Learning for Rapid Antimicrobial Resistance Screening: The Case of Campylobacter spp

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.