Assessment of microbial communities in a dairy farm from a food safety perspective.

Microbial communities associated with dairy farm operations have a significant influence on food safety, dairy product quality, and animal health. This study aimed to create a microbial mapping at a dairy farm to learn about their bacterial diversity, distribution, and potential dissemination pathways. The investigation included the detection of key zoonotic pathogens, enumeration of Staphylococcus aureus and Escherichia coli as indicators of typical bacterial loads in a dairy production environment, and a microbiome analysis using metagenomics. A total of 160 samples (environmental, udder swabs, feed, feces, raw milk, and water) were collected during winter (N = 80) and spring (N = 80). In winter, Cronobacter spp. were detected in four feed and two water samples; L. monocytogenes was identified in two samples, one from feces and one from a cattle mat; E. coli O157:H7 was found in two feed samples. On the other hand, during spring, Cronobacter spp. were present in four feed samples and one hallway drain, with only one feed sample testing positive for E. coli O157:H7, while L. monocytogenes was absent during the spring season. Regarding microbial counts, there was no significant difference between the two seasons (p = 0.068) for S. aureus; however, a significant difference (p = 0.025) was observed for E. coli. Environmental microbiome analysis showed the presence of Proteobacteria (46.0 %) and Firmicutes (27.2 %) as the dominant phyla during both seasons. Moraxellaceae (11.8 %) and Pseudomonadaceae (10.62 %) were notable during winter, while Lactobacillaceae (13.0 %) and Enterobacteriaceae (12.6 %) were prominent during spring. These findings offer valuable insights into microbial distribution within a dairy farm and potential risks to animal and human health through environmental cross-contamination.

Effect of Tryptic Digestion on Sensitivity and Specificity in MALDI-TOF-Based Molecular Diagnostics through Machine Learning.

The digestion of protein into peptide fragments reduces the size and complexity of protein molecules. Peptide fragments can be analyzed with higher sensitivity (often > 102 fold) and resolution using MALDI-TOF mass spectrometers, leading to improved pattern recognition by common machine learning algorithms. In turn, enhanced sensitivity and specificity for bacterial sorting and/or disease diagnosis may be obtained. To test this hypothesis, four exemplar case studies have been pursued in which samples are sorted into dichotomous groups by machine learning (ML) software based on MALDI-TOF spectra. Samples were analyzed in 'intact' mode in which the proteins present in the sample were not digested with protease prior to MALDI-TOF analysis and separately after the standard overnight tryptic digestion of the same samples. For each case, sensitivity (sens), specificity (spc), and the Youdin index (J) were used to assess the ML model performance. The proteolytic digestion of samples prior to MALDI-TOF analysis substantially enhanced the sensitivity and specificity of dichotomous sorting. Two exceptions were when substantial differences in chemical composition between the samples were present and, in such cases, both 'intact' and 'digested' protocols performed similarly. The results suggest proteolytic digestion prior to analysis can improve sorting in MALDI/ML-based workflows and may enable improved biomarker discovery. However, when samples are easily distinguishable protein digestion is not necessary to obtain useful diagnostic results.

First Known Report of mcr-Harboring Enterobacteriaceae in the Dominican Republic.

Colistin is a last-resort antibiotic used to treat infections caused by multidrug-resistant Gram-negative bacteria. People with a history of travel to the Dominican Republic have become sick with pathogenic bacteria carrying the mobile colistin resistance gene, mcr-1, during and after traveling. This investigation aimed to identify mcr genes in Enterobacteriaceae isolated from food animal sources in the Dominican Republic. Three hundred and eleven samples were tested, from which 1354 bacterial isolates were obtained. Real-time PCR tests showed that 70.7% (220 out of 311) of the samples and 3.2% (44 out of 1354) of the isolates tested positive for the mcr gene. All RT-PCR presumptive mcr-positive isolates (n = 44) and a subset (n = 133) of RT-PCR presumptive mcr-negative isolates were subjected to whole-genome sequencing. WGS analysis showed that 39 isolates carried the mcr gene, with 37 confirmed as positive through RT-PCR and two as negative. Further, all of the mcr-positive genomes were identified as Escherichia coli and all contained a IncX4 plasmid replicon. Resistant determinants for other antibiotics important for human health were found in almost all isolates carrying mcr genes.