Current diagnostic methods for detecting foodborne pathogens are time-consuming, require sophisticated equipment, and have a low specificity and sensitivity. Magnetic nanoparticles (MNPs) and plasmonic/colorimetric biosensors like gold nanoparticles (GNPs) are cost-effective, high-throughput, precise, and rapid. This study aimed to validate the use of MNPs and GNPs for the early detection of Escherichia coli O157:H7, Salmonella enterica spp., Campylobacter jejuni, and Listeria monocytogenes in bovine fecal samples. The capture efficiency (CE) of the MNPs was determined by using Salmonella Typhimurium (ATCC_13311) adjusted at an original concentration of 1.5 × 10⁸ CFU/mL. One (1) mL of this bacterial suspension was spiked into bovine fecal suspension (1 g of fecal sample in 9 mL PBS) and serially diluted ten-fold. DNA was extracted from Salmonella Typhimurium to determine the analytical specificity and sensitivity/LOD of the GNPs. The results showed that the CE of the MNPs ranged from 99% to 100% and could capture as little as 1 CFU/mL. The LOD of the GNPs biosensor was 2.9 µg/µL. The GNPs biosensor was also tested on DNA from 38 naturally obtained bovine fecal samples. Out of the 38 fecal samples tested, 81.6% (31/38) were positive for Salmonella enterica spp., 65.8% (25/38) for C. jejuni, 55.3% (21/38) for L. monocytogenes, and 50% (19/38) for E. coli O157:H7. We have demonstrated that MNP and GNP biosensors can detect pathogens or their DNA at low concentrations. Ensuring food safety throughout the supply chain is paramount, given that these pathogens may be present in cattle feces and contaminate beef during slaughter.

Salmonellosis in humans is a public health threat and cattle are important reservoirs for the pathogen. Cattle products such as ground beef have been linked to human salmonellosis outbreaks, and some disease investigations have been traced back to infected animal herds on farms and animal markets as the origin of infection. It is now common to isolate Salmonella from many cattle operations as the pathogen once introduced onto a farm can establish an undesirable endemic condition among herds. It is, therefore, essential to adopt measures to mitigate or prevent the introduction and spread of zoonotic disease agents like Salmonella in animal populations. With this background, the potential sources and risks of Salmonella infection in cattle, the control of already established infection, and other preventative measures are discussed in this article. We conclude that a holistic approach involving all stakeholders in cattle production is needed to safeguard public health, eventually forestalling human salmonellosis from cattle sources. In achieving this, it will be essential to consider the farm as a critical control point in preventing the introduction of Salmonella into the food chain.

Salmonella Dublin (S. Dublin) and Salmonella Typhimurium (S. Typhimurium) are commonly linked to bovine salmonellosis. S. Dublin is, however, considered a bovine-adapted serovar for primarily infecting and thriving in cattle. Using S. Typhimurium (a generalist serovar) as a benchmark, this study investigates genomic factors contributing to S. Dublin’s adaptation to cattle hosts in the U.S. A total of 1337 S. Dublin and 787 S. Typhimurium whole-genome sequences from bovine sources were analyzed with CARD (version 4.0.0), ARG-NOTT (version 6), and AMRfinderPlus (version 4.0.3) for antimicrobial resistance (AMR) genes; VFDB and AMRfinderPlus for virulence genes; AMRFinderPlus for stress genes; and Plasmidfinder for plasmids. Existing clonal groups among isolates of the two serovars were also investigated using the Hierarchical Clustering of Core Genome Multi-Locus Sequence Typing (HierCC-cgMLST) model. The results revealed minimal genomic variation among S. Dublin isolates. Comparatively, the IncX1 plasmid was somewhat exclusively identified in S. Dublin isolates and each carried an average of four plasmids (p-value < 0.05). Furthermore, S. Dublin isolates exhibited a higher prevalence of AMR genes against key antimicrobials, including aminoglycosides, beta-lactams, tetracyclines, and sulfonamides, commonly used in U.S. cattle production. Additionally, Type VI secretion system genes tssJKLM and hcp2/tssD2, essential for colonization, were found exclusively in S. Dublin isolates with over 50% of these isolates possessing genes that confer resistance to heavy metal stressors, like mercury. These findings suggest that S. Dublin’s adaptation to bovine hosts in the U.S. is supported by a conserved genetic makeup enriched with AMR genes, virulence factors, and stress-related genes, enabling it to colonize and persist in the bovine gut.

Listeria monocytogenes is a major foodborne pathogen associated with increasing global public health concern due to numerous outbreaks. Rapid pathogen detection is critical for reducing both the incidence and severity of foodborne illnesses. Recent advances in nanotechnology are transforming analytical methods, particularly for detecting foodborne pathogens. Magnetic nanoparticles (MNPs) and gold nanoparticles (GNPs) are among the most widely used nanomaterials in this field. This study investigated the potential use of MNPs and GNPs for the rapid and specific isolation of L. monocytogenes from fresh salad, deli meat, and frozen vegetables. L. monocytogenes (ATCC 19117) served as the model organism for biosensing and target capture. Results showed that the limits of detection (LoDs) for the GNP-based plasmonic/colorimetric biosensor and the MNP-based biosensor were 2.5 ng/µL DNA and 1.5 CFU/mL, respectively. Both GNPs and MNPs specifically detected L. monocytogenes even in the presence of closely related pathogens. Integration of MNPs and GNPs significantly enhanced the sensitivity of L. monocytogenes detection. Within one hour, naturally contaminated pre-packaged salad samples demonstrated clear evidence of effective direct capture by MNPs and specific identification by GNPs. This combined approach enables rapid and accurate on-site detection of L. monocytogenes, facilitating timely intervention and reducing the risk of contaminated foods reaching consumers.

Campylobacter spp. are the causative agents of campylobacteriosis, a major foodborne illness globally, with millions of cases reported annually. These pathogens pose significant risks to both human and animal health. Conventional culture-based diagnostic methods are labor-intensive and time-consuming, underscoring the need for more efficient molecular detection strategies. This study employed a pangenomic analysis to identify novel gene-specific markers for pathogenic Campylobacter species and subspecies, laying the groundwork for their application in diverse diagnostic assays. A curated dataset of 105 high-quality genomes, representing 33 species and 9 subspecies, was analyzed using the Roary ILP Bacterial Annotation Pipeline. The results revealed substantial genomic diversity within the genus, with core gene counts varying across different nucleotide identity thresholds. Ribosomal genes such as rpsL, rpsJ, rpsS, rpmA, rpsK, rpsU, rpsG, rpmH, and rpsZ were consistently identified in the core genome, whereas accessory genes exhibited marked variability. This study uncovered novel and highly specific genetic markers for various Campylobacter species, including petB, clpX, and carB for C. coli; hypothetical proteins for C. jejuni and C. fetus; porA2 for C. lari; and mdtJ for C. upsaliensis. These markers demonstrated a specificity of at least 90% with minimal cross-reactivity with non-target organisms. The findings underscore the genomic heterogeneity within Campylobacter and provide essential genetic targets for the enhanced molecular detection of its pathogenic species, subspecies, and biovars.

Shiga toxin-producing Escherichia coli (STEC) are genetically diverse foodborne pathogens of major global public health concerns. Serogroup-level identification is critical for effective surveillance and outbreak control; however, it is often challenged by STEC’s genome plasticity and frequent recombination. In this study, we employed a standardized pangenomic pipeline integrating Roary ILP Bacterial Core Annotation Pipeline (RIBAP) and Panaroo to analyze 160 complete, high-quality STEC genomes representing eight major serogroups at a 95% sequence identity threshold. Candidate serogroup-specific markers were identified using gene presence/absence profiles from RIBAP and Panaroo. Our analysis revealed several high-confidence markers, including metabolic genes (dgcE, fcl_2, dmsA, hisC) and surface polysaccharide-related genes (capD, rfbX, wzzB). Comparative pangenomic evaluation showed that RIBAP predicted a larger pangenome size than Panaroo. Additionally, some genomes from the O104:H1, O145:H28, and O45:H2 serotypes clustered outside their expected clades, indicating sporadic serotype misplacements in phylogenetic reconstructions. Functional annotation suggested that most candidate markers are involved in critical processes such as glucose metabolism, lipopolysaccharide biosynthesis, and cell surface assembly. Notably, approximately 22.9% of the identified proteins were annotated as hypothetical. Overall, this study highlights the utility of pangenomic analysis for potential identification of clinically relevant STEC serogroups markers and phylogenetic interpretation. We also note that pangenome analysis could guide the development of more accurate diagnostic and surveillance tools.