The use of 16S rRNA gene metagenetic monitoring of refrigerated food products for understanding the kinetics of microbial subpopulations at different storage temperatures: the example of white pudding

Evaluation of Enzymatic Cleaning on Food Processing Installations and Food Products Bacterial Microflora

Biofilms are a permanent source of contamination in food industries and could harbor various types of microorganisms, such as spoiling bacteria. New strategies, such as enzymatic cleaning, have been proposed to eradicate them. The purpose of this study was to evaluate the impact of enzymatic cleaning on the microbial flora of installations in a processing food industry and of the final food product throughout its shelf life. A total of 189 samples were analyzed by classical microbiology and 16S rDNA metagenetics, including surface samples, cleaning-in-place (CIP) systems, and food products (at D₀, D_{end of the shelf life}, and D_{end of the shelf life +7 days}). Some surfaces were highly contaminated with spoiling bacteria during conventional cleaning while the concentration of the total flora decreased during enzymatic cleaning. Although the closed circuits were cleaned with conventional cleaning before enzymatic cleaning, there was a significant release of microorganisms from some parts of the installations during enzymatic treatment. A significant difference in the total flora in the food products at the beginning of the shelf life was observed during enzymatic cleaning compared to the conventional cleaning, with a reduction of up to 2 log CFU/g. Metagenetic analysis of the food samples at the end of their shelf life showed significant differences in bacterial flora between conventional and enzymatic cleaning, with a decrease of spoiling bacteria (Leuconostoc sp.). Enzymatic cleaning has improved the hygiene of the food processing instillations and the microbial quality of the food throughout the shelf life. Although enzymatic cleaning is not yet commonly used in the food industry, it should be considered in combination with conventional sanitizing methods to improve plant hygiene.

Modeling the Growth and Interaction Between Brochothrix thermosphacta, Pseudomonas spp., and Leuconostoc gelidum in Minced Pork Samples

The aim of this study was to obtain the growth parameters of specific spoilage micro-organisms previously isolated in minced pork (MP) samples and to develop a three-spoilage species interaction model under different storage conditions. Naturally contaminated samples were used to validate this approach by considering the effect of the food microbiota. Three groups of bacteria were inoculated on irradiated samples, in mono- and in co-culture experiments (n = 1152): Brochothrix thermosphacta, Leuconostoc gelidum, and Pseudomonas spp. (Pseudomonas fluorescens and Pseudomonas fragi). Samples were stored in two food packaging [food wrap and modified atmosphere packaging (CO2 30%/O2 70%)] at three isothermal conditions (4, 8, and 12°C). Analysis was carried out by using both 16S rRNA gene amplicon sequencing and classical microbiology in order to estimate bacterial counts during the storage period. Growth parameters were obtained by fitting primary (Baranyi) and secondary (square root) models. The food packaging shows the highest impact on bacterial growth rates, which in turn have the strongest influence on the shelf life of food products. Based on these results, a three-spoilage species interaction model was developed by using the modified Jameson-effect model and the Lotka Volterra (prey-predator) model. The modified Jameson-effect model showed slightly better performances, with 40-86% out of the observed counts falling into the Acceptable Simulation Zone (ASZ). It only concerns 14-48% for the prey-predator approach. These results can be explained by the fact that the dynamics of experimental and validation datasets seems to follow a Jameson behavior. On the other hand, the Lotka Volterra model is based on complex interaction factors, which are included in highly variable intervals. More datasets are probably needed to obtained reliable factors, and so better model fittings, especially for three- or more-spoilage species interaction models. Further studies are also needed to better understand the interaction of spoilage bacteria between them and in the presence of natural microbiota.

Assessment of Spoilage Bacterial Communities in Food Wrap and Modified Atmospheres-Packed Minced Pork Meat Samples by 16S rDNA Metagenetic Analysis

Although several studies have focused on the dynamics of bacterial food community, little is known about the variability of batch production and microbial changes that occur during storage. The aim of the study was to characterize the microbial spoilage community of minced pork meat samples, among different food production and storage, using both 16S rRNA gene sequencing and classical microbiology. Three batches of samples were obtained from four local Belgian facilities (A–D) and stored until shelf life under food wrap (FW) and modified atmosphere packaging (MAP, CO₂ 30%/O₂ 70%), at constant and dynamic temperature. Analysis of 288 samples were performed by 16S rRNA gene sequencing in combination with counts of psychrotrophic and lactic acid bacteria at 22°C. At the first day of storage, different psychrotrophic counts were observed between the four food companies (Kruskal-Wallist test, p-value < 0.05). Results shown that lowest microbial counts were observed at the first day for industries D and A (4.2 ± 0.4 and 5.6 ± 0.1 log CFU/g, respectively), whereas industries B and C showed the highest results (7.5 ± 0.4 and 7.2 ± 0.4 log CFU/g). At the end of the shelf life, psychrotrophic counts for all food companies was over 7.0 log CFU/g. With metagenetics, 48 OTUs were assigned. At the first day, the genus Photobacterium (86.7 and 19.9% for food industries A and C, respectively) and Pseudomonas (38.7 and 25.7% for food companies B and D, respectively) were dominant. During the storage, a total of 12 dominant genera (>5% in relative abundance) were identified in MAP and 7 in FW. Pseudomonas was more present in FW and this genus was potentially replaced by Brochothrix in MAP (two-sided Welch’s t-test, p-value < 0.05). Also, a high Bray-Curtis dissimilarity in genus relative abundance was observed between food companies and batches. Although the bacteria consistently dominated the microbiota in our samples are known, results indicated that bacterial diversity needs to be addressed on the level of food companies, batches variation and food storage conditions. Present data illustrate that the combined approach provides complementary results on microbial dynamics in minced pork meat samples, considering batches and packaging variations.

Integrating Microbiome Network: Establishing Linkages Between Plants, Microbes and Human Health

The trillions of microbes that colonize and live around us govern the health of both plants and animals through a cascade of direct and indirect mechanisms. Understanding of this enormous and largely untapped microbial diversity has been the focus of microbial research from the past few decades or so. Amidst the advancements in sequencing technologies, significant progress has been made to taxonomically and functionally catalogue these microbes and also to establish their exact role in the health and disease state. In comparison to the human microbiome, plants are also surrounded by a vast diversity of microbes that form complex ecological communities that affect plant growth and health through collective metabolic activities and interactions. This plant microbiome has a substantial influence on human health and environment via its passage through the nasal route and digestive tract and is responsible for changing our gut microbiome. This review primarily focused on the advances and challenges in microbiome research at the interface of plant and human, and role of microbiome at different compartments of the body’s ecosystems along with their correlation to health and diseases. This review also highlighted the potential therapies in modulating the gut microbiota and technologies for studying the microbiome.

Assessment of food microbiological indicators applied on poultry carcasses by culture combined MALDI-TOF MS identification and 16S rRNA amplicon sequencing

Examination of the bacterial contamination on food products is still largely performed by standardized culture methods, though culture-independent methods are suggested as a more reliable approach. Knowledge of the diversity of bacteria isolated from food as well as the impact of the plate incubation conditions applied are still understudied. The impact of incubation at 7 °C and 30 °C on total aerobic bacterial count and diversity, and the performance of ISO methods generally applied in microbiological quality examination were assessed by culture combined MALDI-TOF MS identification and 16S rRNA amplicon sequencing. Examining breast skin of 16 chicken carcasses, no significant impact of the incubation temperature on the total aerobic bacteria level and diversity was detected, limiting the usefulness of additional psychrophilic examination. Bacteria phenotypically similar to Pseudomonas, were identified on selective CFC plates, and on MRS agar plates for lactic acid bacteria, Escherichia coli and Staphylococcus were commonly present. Application of 16S rRNA amplicon sequencing revealed a higher bacterial diversity, but the impact of the DNA extraction kit applied, and the detection of non-viable bacteria should be taken into account to interpret the final outcome.

The impact of human activities and lifestyles on the interlinked microbiota and health of humans and of ecosystems

Plants, animals and humans, are colonized by microorganisms (microbiota) and transiently exposed to countless others. The microbiota affects the development and function of essentially all organ systems, and contributes to adaptation and evolution, while protecting against pathogenic microorganisms and toxins. Genetics and lifestyle factors, including diet, antibiotics and other drugs, and exposure to the natural environment, affect the composition of the microbiota, which influences host health through modulation of interrelated physiological systems. These include immune system development and regulation, metabolic and endocrine pathways, brain function and epigenetic modification of the genome. Importantly, parental microbiotas have transgenerational impacts on the health of progeny. Humans, animals and plants share similar relationships with microbes. Research paradigms from humans and other mammals, amphibians, insects, planktonic crustaceans and plants demonstrate the influence of environmental microbial ecosystems on the microbiota and health of organisms, and indicate links between environmental and internal microbial diversity and good health. Therefore, overlapping compositions, and interconnected roles of microbes in human, animal and plant health should be considered within the broader context of terrestrial and aquatic microbial ecosystems that are challenged by the human lifestyle and by agricultural and industrial activities. Here, we propose research priorities and organizational, educational and administrative measures that will help to identify safe microbe-associated health-promoting modalities and practices. In the spirit of an expanding version of "One health" that includes environmental health and its relation to human cultures and habits (EcoHealth), we urge that the lifestyle-microbiota-human health nexus be taken into account in societal decision making.

The fecal bacterial microbiota of bats; Slovenia

Methods

Fecal samples were collected from 92 bats in Slovenia, consisting of 12 different species, and the bacterial microbiota was assessed via next generation sequencing of the 16S rRNA gene V4 region.

Results

Sequences were assigned to 28 different phyla, but only Proteobacteria, Firmicutes, Bacteroidetes and Actinobacteria accounted for ≥1% of sequences. One phylum (Proteobacteria), one class (Gammaproteobacteria), three orders (Pseudomonadales, Lactobacillales, Bacillales), four families (Enterobacteriaceae, Pseudomonadaceae, Staphylococcaceae, Carnobacteriaceae), and five genera (Pseudomonas, Staphylococcus, Carnobacterium, an unclassified Enterobacteriaceae, Acinetobacter) accounted for 50% of sequences. There were no significant differences in the relative abundances of any phyla between bat species, but various differences were noted at lower taxonomic levels, such as Enterobacteriaceae (P = 0.007, most abundant in M. blythii), Pseudomonadaceae (P = 0.007, most abundant in Rhinolophus hipposideros) and Chlamydiaceae (P = 0.04, most abundant in Myotis myotis). There were significant differences in richness between species in both adults and juveniles/subadults, but there was no impact of sex on any alpha diversity index. When only adults are considered, there were significant differences in community membership between M. blythii and M. emarginatus (P = 0.011), and M. blythii and R. hipposideros (P = 0.004). There were also significant differences in community structure between M. blythii and M. emarginatus (P = 0.025), and M. blythii and R. hipposideros (P = 0.026). When adults of the four main species were compared, 14 OTUs were identified as differentially abundant using LEfSe. Only one difference was identified when comparing R. hipposideros adults and juvenile/subadults, with Klebsiella over-represented in the younger bats.

Conclusions

Bats have a complex and diverse microbiota with a high relative abundance of Proteobacteria. The relevance of this difference is unclear and requires further study. Differences in the microbiota were observed between bat species, perhaps reflecting different diets and environmental exposures.

Structure of association networks in food bacterial communities

The structure of microbial association networks was investigated for seventeen studies on food bacterial communities using the CoNet app. The results were compared with those for host and environmental microbiomes. Microbial association networks of food bacterial communities shared several properties with those of host microbiomes, although they were less complex and lacked a scale-free, small world structure that is characteristic of environmental microbial communities. This may depend on both the initial contamination pattern, whose main source is the raw material microbiome, and on the copiotrophic nature of food environments, with lack of well defined, specific niches. The selective factors which are characteristic of fermentation and spoilage drastically simplified microbial association networks and showed the emergence of negative hubs. Co-presence and mutual exclusion networks had a radically different structure, with high clustering coefficient in the first and high heterogeneity in the latter. Node properties (degree, positive degree, betweenness centrality, abundance) can be combined in plots, which allow a rapid identification of hub species. The combined use of three network inference tools (CoNet, SparCC, and SPIEC-EASI) confirmed that microbial association network detection is method specific, but several coherent copresence or mutual exclusion relationships were detected by at least two different methods.