Mitigating the attachment of Salmonella Infantis on isolated poultry skin with cetylpyridinium chloride.

To provide the poultry industry with effective mitigation strategies, the effects of cetylpyridinium chloride (CPC) on the reduction of Salmonella Infantis, hilA expression, and chicken skin microbiota were evaluated. Chicken breast skins (4×4 cm; N = 100, n = 10, k = 5) were inoculated with Salmonella (Typhimurium or Infantis) at 4°C (30min) to obtain 108 CFU/g attachment. Skins were shaken (30s), with remaining bacteria being considered firmly attached. Treatments were applied as 30s dips in 50 mL: no inocula-no-treatment control (NINTC), no treatment control (NTC), tap water (TW), TW+600 ppm PAA (PAA), or TW+0.5% CPC (CPC). Excess fluid was shaken off (30s). Samples were homogenized in nBPW (1 min). Samples were discarded. Salmonella was enumerated and Log10 transformed. Reverse transcriptase-qPCR (rt-qPCR) was performed targeting hilA gene and normalized using the 2-ΔΔCt method. Data were analyzed using one-way ANOVA in RStudio with means separated by Tukey's HSD (P≤0.05). Genomic DNA of rinsates was extracted, 16S rRNA gene (V4) was sequenced (MiSeq), and data analyzed in QIIME2 (P≤0.05 and Q≤0.05). CPC and PAA affected Salmonella levels differently with CPC being effective against S. Infantis compared to TW (P<0.05). Treatment with CPC on S. Infantis-infected skin altered the hilA expression compared to TW (P<0.05). When inoculated with S. Typhimurium, there was no difference between the microbiota diversity of skins treated with PAA and CPC; however, when inoculated with S. Infantis, there was a difference in the Shannon's Entropy and Jaccard Dissimilarity between the two treatments (P<0.05). Using ANCOM at the genus level, Brochothrix was significant (W = 118) among skin inoculated with S. Typhimurium. Among S. Infantis inoculated, Yersiniaceae, Enterobacterales, Lachnospiraceae CHKCI001, Clostridia vadinBB60 group, Leuconostoc, Campylobacter, and bacteria were significant (408). CPC and PAA-treated skins had lowest relative abundance of the genera. In conclusion, CPC mitigated Salmonella Infantis, altered hilA expression, and influenced the chicken skin microbiota.

Practical opportunities for microbiome analyses and bioinformatics in poultry processing.

Poultry processing is undergoing changes both in operations as well as microbial methodologies. Traditionally, microbial data has been gathered through a series of culturing methods using liquid media and plating for isolation and enumeration. Both foodborne pathogens and nonpathogenic bacterial populations are estimated to assess food safety risks as well as the potential for spoilage. Bacterial loads from carcasses are important for estimating processing control and the effectiveness of antimicrobial applications. However, these culture-based approaches may only provide part of the microbial ecology landscape associated with chicken carcasses and the subsequent changes that occur in these populations during processing. Newer molecular-based approaches, such as 16S sequencing of the microbiota, offer a means to retrieve a more comprehensive microbial compositional profile. However, such approaches also result in large data sets which must be analyzed and interpreted. As more data is generated, this will require not only bioinformatic programs to process the data but appropriate educational forums to present the processed data to a broad audience.

Sample-sort simulated annealing.

A simulated annealing (SA) algorithm called Sample-Sort that is artificially extended across an array of samplers is proposed. The sequence of temperatures for a serial SA algorithm is replaced with an array of samplers operating at static temperatures and the single stochastic sampler is replaced with a set of samplers. The set of samplers uses a biased generator to sample the same distribution of a serial SA algorithm to maintain the same convergence property. Sample-Sort was compared to SA by applying both to a set of global optimization problems and found to be comparable if the number of iterations per sampler was sufficient. If the evaluation phase dominates the computational requirements, Sample-Sort could take advantage of parallel processing.