The global emergence of hybrid diarrheagenic E. coli strains incorporating genetic markers from different pathotypes is a public health concern. Hybrids of Shiga toxin-producing and enterotoxigenic E. coli (STEC/ETEC) are associated with diarrhea and hemolytic uremic syndrome (HUS) in humans. In this study, we identified and characterized STEC/ETEC hybrid strains isolated from livestock feces (cattle and pigs) and animal food sources (beef, pork, and meat patties) in South Korea between 2016 and 2020. The strains were positive for genes from STEC and ETEC, such as stx (encodes Shiga toxins, Stxs) and est (encodes heat-stable enterotoxins, ST), respectively. The strains belong to diverse serogroups (O100, O168, O8, O155, O2, O141, O148, and O174) and sequence types (ST446, ST1021, ST21, ST74, ST785, ST670, ST1780, ST1782, ST10, and ST726). Genome-wide phylogenetic analysis revealed that these hybrids were closely related to certain ETEC and STEC strains, implying the potential acquisition of Stx-phage and/or ETEC virulence genes during the emergence of STEC/ETEC hybrids. Particularly, STEC/ETEC strains isolated from livestock feces and animal source foods mostly exhibited close relatedness with ETEC strains. These findings allow further exploration of the pathogenicity and virulence of STEC/ETEC hybrid strains and may serve as a data source for future comparative studies in evolutionary biology.

Coagulase-positive Staphylococcus aureus is a foodborne pathogen considered one of the causes of food-related disease outbreaks. Like S. aureus, Staphylococcus capitis, Staphylococcus caprae, and S. epidermidis are opportunistic pathogens causing clinical infections and food contamination. The objective of our study was to develop a rapid, accurate, and monitoring technique to detect four Staphylococcus species in food. Four novel molecular targets (GntR family transcriptional regulator for S. aureus, phosphomannomutase for S. epidermidis, FAD-dependent urate hydroxylase for S. capitis, and Gram-positive signal peptide protein for S. caprae) were mined based on pan-genome analysis. Primers targeting molecular target genes showed 100% specificity for 100 non-target reference strains. The detection limit in pure cultures and artificially contaminated food samples was 10² colony-forming unit/mL for S. aureus, S. capitis, S. caprae, and S. epidermidis. Moreover, real-time polymerase chain reaction successfully detected strains isolated from various food matrices. Thus, our method allows an accurate and rapid monitoring of Staphylococcus species and may help control staphylococcal contamination of food.

Hybrid strains Escherichia coli acquires genetic characteristics from multiple pathotypes and is speculated to be more virulent; however, understanding their pathogenicity is elusive. Here, we performed genome-based characterization of the hybrid of enteropathogenic (EPEC) and enterotoxigenic E. coli (ETEC), the strains that cause diarrhea and mortality in children. The virulence genes in the strains isolated from different sources in the South Korea were identified, and their phylogenetic positions were analyzed. The EPEC/ETEC hybrid strains harbored eae and est encoding E. coli attaching and effacing lesions and heat-stable enterotoxins of EPEC and ETEC, respectively. Genome-wide phylogeny revealed that all hybrids (n = 6) were closely related to EPEC strains, implying the potential acquisition of ETEC virulence genes during ETEC/EPEC hybrid emergence. The hybrids represented diverse serotypes (O153:H19 (n = 3), O49:H10 (n = 2), and O71:H19 (n = 1)) and sequence types (ST546, n = 4; ST785, n = 2). Furthermore, heat-stable toxin-encoding plasmids possessing estA and various other virulence genes and transporters, including nleH2, hlyA, hlyB, hlyC, hlyD, espC, espP, phage endopeptidase Rz, and phage holin, were identified. These findings provide insights into understanding the pathogenicity of EPEC/ETEC hybrid strains and may aid in comparative studies, virulence characterization, and understanding evolutionary biology.

Rapid detection of Salmonella serovars is important for the effective control and monitoring of food industries. In this study, we evaluate the application of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for the rapid detection of three serovars, Enteritidis, Typhimurium, and Thompson, that are epidemiologically important in Korea. All strains were identified at the genus level, with a mean score of 2.319 using the BioTyper database, and their protein patterns were confirmed to be similar by principal component analysis and main spectrum profile dendrograms. Specific peaks for the three serovars were identified by analyzing 65 reference strains representing 56 different serovars. Specific mass peaks at 3018 ± 1 and 6037 ± 1, 7184 ± 1, and 4925 ± 1 m/z were uniquely found in the reference strains of serovars Enteritidis, Typhimurium, and Thompson, respectively, and they showed that the three serovars can be differentiated from each other and 53 other serovars. We verified the reproducibility of these mass peaks in 132 isolates, and serovar classification was achieved with 100% accuracy when compared with conventional serotyping through antisera agglutination. Our method can rapidly detect a large number of strains; hence, it will be useful for the high-throughput screening of Salmonella serovars.

Droplet digital polymerase chain reaction (ddPCR) is one of the newest and most promising tools providing absolute quantification of target DNA molecules. Despite its emerging applications in microorganisms, few studies reported its use for detecting lactic acid bacteria. This study evaluated the applicability of a ddPCR assay targeting molecular genes obtained from in silico analysis for detecting Lactiplantibacillus plantarum subsp. plantarum, a bacterium mainly used as a starter or responsible for fermentation in food. The performance characteristics of a ddPCR were compared to those of a quantitative real-time PCR (qPCR). To compare the linearity and sensitivity of a qPCR and ddPCR, the calibration curve for a qPCR and the regression curve for a ddPCR were obtained using genomic DNA [10²–10⁸ colony-forming units (CFU)/mL] extracted from a pure culture and spiked food sample. Both the qPCR and ddPCR assays exhibited good linearity with a high coefficient of determination in the pure culture and spiked food sample (R² ≥ 0.996). The ddPCR showed a 10-fold lower limit of detection, suggesting that a ddPCR is more sensitive than a qPCR. However, a ddPCR has limitations in the absolute quantitation of high bacterial concentrations (>10⁶ CFU/mL). In conclusion, a ddPCR can be a reliable method for detecting and quantifying lactic acid bacteria in food.

Although Weissella cibaria and W. confusa are essential food-fermenting bacteria, they are also opportunistic pathogens. Despite these species being commercially crucial, their taxonomy is still based on inaccurate identification methods. In this study, we present a novel approach for identifying two important Weissella species, W. cibaria and W. confusa, by combining matrix-assisted laser desorption/ionization and time-of-flight mass spectrometer (MALDI-TOF MS) data using machine-learning techniques. After on- and off-plate protein extraction, we observed that the BioTyper database misidentified or could not differentiate Weissella species. Although Weissella species exhibited very similar protein profiles, these species can be differentiated on the basis of the results of a statistical analysis. To classify W. cibaria, W. confusa, and non-target Weissella species, machine learning was used for 167 spectra, which led to the listing of potential species-specific mass-to-charge (m/z) loci. Machine-learning techniques including artificial neural networks, principal component analysis combined with the K-nearest neighbor, support vector machine (SVM), and random forest were used. The model that applied the Radial Basis Function kernel algorithm in SVM achieved classification accuracy of 1.0 for training and test sets. The combination of MALDI-TOF MS and machine learning can efficiently classify closely-related species, enabling accurate microbial identification.