Bacteroides Microbial Source Tracking Markers Perform Poorly in Predicting Enterobacteriaceae and Enteric Pathogen Contamination of Cow Milk Products and Milk-Containing Infant Food

Consumption of microbiologically contaminated food is one of the leading causes of diarrheal diseases. Understanding the source of enteric pathogens in food is important to guide effective interventions. Enterobacteriaceae bacterial assays typically used to assess food safety do not shed light on the source. Source-specific Bacteroides microbial source tracking (MST) markers have been proposed as alternative indicators for water fecal contamination assessment but have not been evaluated as an alternative fecal indicator in animal-derived foods. This study tested various milk products collected from vendors in urban Kenyan communities and infant foods made with the milk (n = 394 pairs) using conventional culture methods and TaqMan qPCR for enteric pathogens and human and bovine-sourced MST markers. Detection profiles of various enteric pathogens and Bacteroides MST markers in milk products differed from that of milk-containing infant foods. MST markers were more frequently detected in infant food prepared by caregivers, indicating recent contamination events were more likely to occur during food preparation at home. However, Bacteroides MST markers had lower sensitivity in detecting enteric pathogens in food than traditional Enterobacteriaceae indicators. Bacteroides MST markers tested in this study were not associated with the detection of culturable Salmonella enterica and Shigella sonnei in milk products or milk-containing infant food. The findings show that while Bacteroides MST markers could provide valuable information about how foods become contaminated, they may not be suitable for predicting the origin of the enteric pathogen contamination sources.

Microbial source tracking and antimicrobial resistance in one river system of a rural community in Bahia, Brazil

Use of antibiotics inevitably leads to antimicrobial resistance. Selection for resistance occurs primarily within the gut of humans and animals as well as in the environment through natural resistance and residual antibiotics in streams and soil. We evaluated antimicrobial resistance in Gram negative bacteria from a river system in a rural community in Bahia, Brazil. Water was collected from the Jiquiriçá and Brejões rivers and the piped water supply. Additionally, stools were collected from a random sample of residents, cows, pigs and horses near the river. The samples were screened for bacteria resistant to ciprofloxacin, cefotaxime, and meropenem and identified biochemically at the genus and species levels. Microbial source tracking demonstrated that ruminant and human fecal contamination increased as the rivers neared the village center and decreased after the last residence. Antibiotic bacteria were identified from all samples (n = 32). No bacteria were resistant to carbapenems, but the majority of the enterobacteria were resistant to ciprofloxacin, even though this class of antibiotics is not commonly used in food animals in this region. Considering these facts, together with the pattern of human fecal contamination, a human source was considered most likely for these resistant isolates.

A Novel Method for Source Tracking of Chemical Gas Leakage: Outlier Mutation Optimization Algorithm

Chemical industrial parks, which act as critical infrastructures in many cities, need to be responsive to chemical gas leakage accidents. Once a chemical gas leakage accident occurs, risks of poisoning, fire, and explosion will follow. In order to meet the primary emergency response demands in chemical gas leakage accidents, source tracking technology of chemical gas leakage has been proposed and evolved. This paper proposes a novel method, Outlier Mutation Optimization (OMO) algorithm, aimed to quickly and accurately track the source of chemical gas leakage. The OMO algorithm introduces a random walk exploration mode and, based on Swarm Intelligence (SI), increases the probability of individual mutation. Compared with other optimization algorithms, the OMO algorithm has the advantages of a wider exploration range and more convergence modes. In the algorithm test session, a series of chemical gas leakage accident application examples with random parameters are first assumed based on the Gaussian plume model; next, the qualitative experiments and analysis of the OMO algorithm are conducted, based on the application example. The test results show that the OMO algorithm with default parameters has superior comprehensive performance, including the extremely high average calculation accuracy: the optimal value, which represents the error between the final objective function value obtained by the optimization algorithm and the ideal value, reaches 2.464e-15 when the number of sensors is 16; 2.356e-13 when the number of sensors is 9; and 5.694e-23 when the number of sensors is 4. There is a satisfactory calculation time: 12.743 s/50 times when the number of sensors is 16; 10.304 s/50 times when the number of sensors is 9; and 8.644 s/50 times when the number of sensors is 4. The analysis of the OMO algorithm’s characteristic parameters proves the flexibility and robustness of this method. In addition, compared with other algorithms, the OMO algorithm can obtain an excellent leakage source tracing result in the application examples of 16, 9 and 4 sensors, and the accuracy exceeds the direct search algorithm, evolutionary algorithm, and other swarm intelligence algorithms.

Molecular subtyping for source tracking of Escherichia coli using core genome multilocus sequence typing at a food manufacturing plant

When harmful bacteria are detected in the final product at a food manufacturing plant, it is necessary to identify and eliminate the source of contamination so that it does not occur again. In the current study, the source of contamination was tracked using core genome multilocus sequence typing (cgMLST) analysis in cases where Escherichia coli was detected in the final product at a food manufacturing plant. cgMLST analysis was performed on 40 strains of E. coli collected from the environment [floor (26 strains), drainage ditch (5 strains), container (4 strains), post-heating production line (1 strain)] and products [final product (3 strains) and intermediate product (1 strain)]. In total, 40 E. coli isolates were classified into 17 genogroups by cgMLST analysis. The 4 E. coli strains isolated from the intermediate and final products were classified into two genogroups (I and II). Certain isolates collected from the environment also belonged to those genogroups, it was possible to estimate the transmission of E. coli in the manufacturing plant. Thus, the dynamics of E. coli in the food manufacturing location were clarified by using cgMLST analysis. In conclusion, our results indicate that cgMLST analysis can be effectively used for hygiene management at food manufacturing locations.

Environmental sources along natural cave ripening shape the microbiome and metabolome of artisanal blue-veined cheeses

Microorganisms colonising processing environments can significantly impact food quality and safety. Here we describe a detailed longitudinal study assessing the impact of cave ripening on the microbial succession and quality markers across different producers of blue-veined cheese. Both the producer and cave in which cheeses were ripened significantly influenced the cheese microbiome and metabolome. The cheese microbiome was significantly determined by the microbiome of caves, which were a source of Brevibacterium, Corynebacterium, Staphylococcus, Tetragenococcus and Yaniella, among others, as demonstrated through source tracking and the characterization of 613 metagenome assembled genomes. Tetragenococcus koreensis and T. halophilus were detected at high abundance in cheese for the first time, associated with the occurrence of various metabolites, and showed high levels of horizontal gene transfer with other members of the cheese microbiome. Overall, we demonstrated that processing environments can be a source of non-starter microorganisms of relevance to ripening of artisanal fermented foods.