Alterations in common marmoset gut microbiome associated with duodenal strictures

Common dietary emulsifiers promote metabolic disorders and intestinal microbiota dysbiosis in mice

Dietary emulsifiers are linked to various diseases. The recent discovery of the role of gut microbiota-host interactions on health and disease warrants the safety reassessment of dietary emulsifiers through the lens of gut microbiota. Lecithin, sucrose fatty acid esters, carboxymethylcellulose (CMC), and mono- and diglycerides (MDG) emulsifiers are common dietary emulsifiers with high exposure levels in the population. This study demonstrates that sucrose fatty acid esters and carboxymethylcellulose induce hyperglycemia and hyperinsulinemia in a mouse model. Lecithin, sucrose fatty acid esters, and CMC disrupt glucose homeostasis in the in vitro insulin-resistance model. MDG impairs circulating lipid and glucose metabolism. All emulsifiers change the intestinal microbiota diversity and induce gut microbiota dysbiosis. Lecithin, sucrose fatty acid esters, and CMC do not impact mucus-bacterial interactions, whereas MDG tends to cause bacterial encroachment into the inner mucus layer and enhance inflammation potential by raising circulating lipopolysaccharide. Our findings demonstrate the safety concerns associated with using dietary emulsifiers, suggesting that they could lead to metabolic syndromes.

Gastric emphysema and pneumatosis intestinalis in two common marmosets with duodenal dilation syndrome

BACKGROUND

Common marmosets (Callithrix jacchus) are widely used as primate experimental models in biomedical research. Duodenal dilation with chronic vomiting in captive common marmosets is a recently described life-threatening syndrome that is problematic for health control. However, the pathogenesis and cause of death are not fully understood.

CASE PRESENTATION

We report two novel necropsy cases in which captive common marmosets were histopathologically diagnosed with gastric emphysema (GE) and pneumatosis intestinalis (PI). Marmoset duodenal dilation syndrome was confirmed in each case by clinical observation of chronic vomiting and by gross necropsy findings showing a dilated, gas-filled and fluid-filled descending duodenum that adhered to the ascending colon. A diagnosis of GE and PI was made on the basis of the bubble-like morphology of the gastric and intestinal mucosa, with histological examination revealing numerous vacuoles diffused throughout the lamina propria mucosae and submucosa. Immunostaining for prospero homeobox 1 and CD31 distinguished gas cysts from blood and lymph vessels. The presence of hepatic portal venous gas in case 1 and possible secondary bacteremia-related septic shock in case 2 were suggested to be acute life-threatening abdominal processes resulting from gastric emphysema and pneumatosis intestinalis.

CONCLUSIONS

In both cases, the gross and histopathological findings of gas cysts in the GI tract walls matched the features of human GE and PI. These findings contribute to clarifying the cause of death in captive marmosets that have died of gastrointestinal diseases.

Microbiome-based classification models for fresh produce safety and quality evaluation

Small sample sizes and loss of sequencing reads during the microbiome data preprocessing can limit the statistical power of differentiating fresh produce phenotypes and prevent the detection of important bacterial species associated with produce contamination or quality reduction. Here, we explored a machine learning-based k-mer hash analysis strategy to identify DNA signatures predictive of produce safety (PS) and produce quality (PQ) and compared it against the amplicon sequence variant (ASV) strategy that uses a typical denoising step and ASV-based taxonomy strategy. Random forest-based classifiers for PS and PQ using 7-mer hash data sets had significantly higher classification accuracy than those using the ASV data sets. We also demonstrated that the proposed combination of integrating multiple data sets and leveraging a 7-mer hash strategy leads to better classification performance for PS and PQ compared to the ASV method but presents lower PS classification accuracy compared to the feature-selected ASV-based taxonomy strategy. Due to the current limitation of generating taxonomy using the 7-mer hash strategy, the ASV-based taxonomy strategy with remarkably less computing time and memory usage is more efficient for PS and PQ classification and applicable for important taxa identification. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing data sets for the application of machine learning in the area of microbial safety and quality of food.

IMPORTANCE

Identification of generalizable indicators for produce safety (PS) and produce quality (PQ) improves the detection of produce contamination and quality decline. However, effective sequencing read loss during microbiome data preprocessing and the limited sample size of individual studies restrain statistical power to identify important features contributing to differentiating PS and PQ phenotypes. We applied machine learning-based models using individual and integrated k-mer hash and amplicon sequence variant (ASV) data sets for PS and PQ classification and evaluated their classification performance and found that random forest (RF)-based models using integrated 7-mer hash data sets achieved significantly higher PS and PQ classification accuracy. Due to the limitation of taxonomic analysis for the 7-mer hash, we also developed RF-based models using feature-selected ASV-based taxonomic data sets, which performed better PS classification than those using the integrated 7-mer hash data set. The RF feature selection method identified 480 PS indicators and 263 PQ indicators with a positive contribution to the PS and PQ classification.

A novel approach for exploring the regional features of vaginal fluids based on microbial relative abundance and alpha diversity

Vaginal fluids are one of the most common biological samples in forensic sexual assault cases, and their characterization is vital to narrow the scope of investigation. Presently, approaches for identifying vaginal fluids in different regions are not only rare but also have certain limitations. However, the microbiome has shown the potential to identify the source of body fluids and reveal the characteristics of individuals. In this study, 16S rRNA gene high-throughput sequencing was used to characterize the vaginal microbial community from three regions, Sichuan, Hainan and Hunan. In addition, data on relative abundance and alpha diversity were used to construct a random forest model. The results revealed that the dominant genera in the three regions were Lactobacillus, followed by Gardnerella. In addition, Ureaplasma, Nitrospira, Nocardiodes, Veillonella and g-norank-f-Vicinamibacteraceae were significantly enriched genera in Sichuan, llumatobacter was enriched in Hainan, and Pseudomonas was enriched in Hunan. The random forest classifier based on combined data on relative abundance and alpha diversity had a good ability to distinguish vaginal fluids with similar dominant microbial compositions in the three regions. The study suggests that combining high-throughput sequencing data with machine learning models has good potential for application in the biogeographic inference of vaginal fluids.

Review of Environmental and Health Factors Impacting Captive Common Marmoset Welfare in the Biomedical Research Setting

As a small-bodied neotropical nonhuman primate species, common marmosets have unique requirements for adequate husbandry and veterinary care to ensure proper maintenance and to promote good animal welfare in a biomedical research setting. Environmental conditions, as well as medical and research-related manipulations, can impact marmoset welfare. Research focus areas, including basic neuroscience, transgenics, and aging, involve additional implications for marmoset welfare. This manuscript provides a comprehensive review of factors that should be considered and mitigated as needed by clinical and research staff working with marmosets in biomedical research facilities to optimize the welfare of captive marmosets.

Individual variations and effects of birth facilities on the fecal microbiome of laboratory-bred marmosets (Callithrix jacchus) assessed by a longitudinal study

Laboratory animals are used for scientific research in various fields. In recent years, there has been a concern that the gut microbiota may differ among laboratory animals, which may yield different results in different laboratories where in-vivo experiments are performed. Our knowledge of the gut microbiota of laboratory-reared common marmosets (Callithrix jacchus) is limited; thus, in this study, we analyzed the daily changes in fecal microbiome composition, individual variations, and effects of the birth facility in healthy female laboratory-reared marmosets, supplied by three vendors. We showed that the marmoset fecal microbiome varied among animals from the same vendor and among animals from different vendors (birth facility), with daily changes of approximately 37%. The fecal microbiome per vendor is characterized by alpha diversity and specific bacteria, with Bifidobacterium for vendor A, Phascolarctobacterium for vendor B, and Megamonas for vendor C. Furthermore, we found that plasma progesterone concentrations and estrous cycles were not correlated with daily fecal microbiome changes. In contrast, animals with an anovulatory cycle lacked Megamonas and Desulfovibrio bacteria compared to normal estrous females. This study suggests that the source of the animal, such as breeding and housing facilities, is important for in-vivo experiments on the marmoset gut microbiota.