Microbiome analysis of raw honey reveals important factors influencing the bacterial and fungal communities.

Raw honeys contain diverse microbial communities. Previous studies have focused on isolating bacteria and fungi that are culturable, while missing a large proportion of the microbial community due to culture-based constraints. This study utilized next-generation sequencing (NGS) to analyze the composition of microorganisms in raw honey; these data can reveal environmental and physicochemical variables that are associated with different microbial communities. To examine the microbial composition (bacteria and fungi) of raw honey and analyze its association with physicochemical properties, four types of honey (monofloral, wildflower, manuka, and feral; n total = 36) were analyzed via amplicon metagenomics. The analyzed honey samples had relatively similar bacterial communities but more distinct and diverse fungal communities. Honey type was determined as a significant factor influencing alpha and beta diversity metrics of bacterial and fungal communities. For the bacterial communities, titratable acidity (TA) was associated with community richness and diversity. For the fungal communities, Brix, TA, and color were associated with community richness, while water activity and color were associated with community diversity. Additionally, important bacterial and fungal amplicon sequence variants (ASVs) that influenced the overall community were identified. Results from this study provide important insights into the microbial communities associated with different types of raw honey, which could improve our understanding of microbial dynamics in beehives, improve honey production, and prevent honeybee disease.

Implementation of ATP and Microbial Indicator Testing for Hygiene Monitoring in a Tofu Production Facility Improves Product Quality and Hygienic Conditions of Food Contact Surfaces: A Case Study.

Rapid ATP testing and microbiological enumeration are two common methods to monitor the effectiveness of cleaning and sanitation in the food industry. In this study, ATP testing and microbiological enumeration were implemented at a tofu production facility with the goal of improving cleaning practices and overall plant hygiene. Results from ATP monitoring were used to target areas of the production environment needing additional cleaning; ATP results were verified by microbiological enumeration of aerobic microorganisms, lactic acid bacteria, and yeasts and molds. Products from the production line were enumerated for the same microorganisms to determine if there was an impact on product quality. After the implementation of ATP monitoring and targeted cleaning, there was a statistically lower proportion of swabs that failed to meet established sanitary requirements for ATP, aerobic microorganisms, and lactic acid bacteria (p < 0.05), but not for yeasts and molds. ATP swabs and microbiological enumeration agreed on site hygiene 75.1% (72.3-77.7%, 95% CI) of the time. Product data indicated that unpasteurized finished products contained a statistically lower microbial load of the three groups of organisms following implementation of the practices (p < 0.05).ImportanceCleaning and sanitation are critical to maintaining safe and high-quality food production. Monitoring these activities is important to ensure proper execution of procedure and to assure compliance with regulatory guidelines. The results from monitoring activities can direct targeted cleaning of areas with higher risk of contamination from foodstuffs and microorganisms. The results of this study show that ATP monitoring and microbiological enumeration are useful tools to verify and improve the efficacy of cleaning and sanitation practices, which can have a positive impact on both plant hygiene and product quality. However, testing regimes and critical parameters will vary based on the product and facility.

Draft genome sequence of antimicrobial producing Paenibacillus alvei strain MP1 reveals putative novel antimicrobials.

OBJECTIVE: A Paenibacillus strain isolated in previous research exhibited antimicrobial activity against relevant human pathogens including Staphylococcus aureus and Listeria monocytogenes. In this study, the genome of the aforementioned strain, designated as MP1, was shotgun sequenced. The draft genome of strain MP1 was subject to multiple genomic analyses to taxonomically characterize it and identify the genes potentially responsible for its antimicrobial activity. RESULTS: Here we report the draft genome sequence of an antimicrobial producing Paenibacillus strain, MP1. Average Nucleotide Identity (ANI) analysis established strain MP1 as a new strain of the previously characterized Paenibacillus alvei. The genomic analysis identified several putative secondary metabolite clusters including seven Nonribosomal Peptide Synthetase clusters (NRPS) (> 10,000 nt), one bacteriocin or other unspecified Ribosomally Synthesized and Post-Translationally modified Peptide Product (RiPP), one lanthipeptide, and six hybrid clusters (NRPS-Type I Polyketide synthase (T1PKS) and NRPS-trans Amino Transferase Polyketide Synthase (AT-PKS)).

Inactivation Kinetics of Pathogens during Thermal Processing in Acidified Broth and Tomato Purée (pH 4.5).

Thermal inactivation kinetics for single strains of Shiga toxin-producing Escherichia coli (STEC), Listeria monocytogenes, and Salmonella enterica were measured in acidified tryptic soy broth (TSB; pH 4.5) heated at 54°C. Inactivation curves also were measured for single-pathogen five-strain cocktails of E. coli O157:H7, L. monocytogenes, and S. enterica heated in tomato purée (pH 4.5) at 52, 54, 56, and 58°C. Inactivation curves were fit using log-linear and nonlinear (Weibull) models. The Weibull model yields the time for a 5-log reduction (t*) and a curve shape parameter (ß). Decimal reduction times (D-values) and thermal resistance constants (z-values) from the two models were compared by defining t* = 5D* for the Weibull model. When the log-linear and Weibull models match at the 5-log reduction time, then t* = 5D* = 5D and D = D*. In 18 of 20 strains heated in acidified TSB, D and D* for the two models were not significantly different, although nonlinearity was observed in 35 of 60 trials. Similarly, in 51 of 52 trials for pathogen cocktails heated in tomato purée, D and D* were not significantly different, although nonlinearity was observed in 31% of trials. At a given temperature, D-values for S. enterica << L. monocytogenes < E. coli O157:H7 in tomato purée (pH 4.5). When using the two models, z-values calculated from the D-values were not significantly different for a given pathogen. Across all pathogens, z-values for E. coli O157:H7 and S. enterica were not different but were significantly lower than the z-values for L. monocytogenes. These results are useful for supporting process filings for tomato-based acidified food products with pH 4.5 and below and are relevant to small processors of tomato-based acidified canned foods who do not have the resources to conduct research on and validate pathogen lethality.