Gut Microbiome and Metabolic and Immune Indices in Males with or without Evidence of Metabolic Dysregulation.

Background: The contributions of the gut microbiota to obesity and metabolic disease represent a potentially modifiable factor that may explain variation in risk between individuals. This study aimed to explore relationships among microbial composition and imputed functional attributes, a range of soluble metabolic and immune indices, and gene expression markers in males with or without evidence of metabolic dysregulation (MetDys). Methods: This case-control study included healthy males (n=15; 41.9±11.7 years; body mass index [BMI], 22.9±1.2 kg/m2) and males with evidence of MetDys (n=14; 46.6±10.0 years; BMI, 35.1±3.3 kg/m2) who provided blood and faecal samples for assessment of a range of metabolic and immune markers and microbial composition using 16S rRNA gene sequencing. Metagenomic functions were imputed from microbial sequence data for analysis. Results: In addition to elevated values in a range of traditional metabolic, adipokine and inflammatory indices in the MetDys group, 23 immunomodulatory genes were significantly altered in the MetDys group. Overall microbial diversity did not differ between groups; however, a trend for a higher relative abundance of the Bacteroidetes (P=0.06) and a lower relative abundance of the Verrucomicrobia (P=0.09) phyla was noted in the MetDys group. Using both family- and genera-level classifications, a partial least square discriminant analysis revealed unique microbial signatures between the groups. Conclusion: These findings confirm the need for ongoing investigations in human clinical cohorts to further resolve the relationships between the gut microbiota and metabolic and immune markers and risk for metabolic disease.

Effect of chronic restraint stress and western-diet feeding on colonic regulatory gene expression in mice.

BACKGROUND: Diet-induced obesity (DIO) and psychological stress are significant independent regulators of gastrointestinal physiology; however, our understanding of how these two disorders influence the host-microbe interface is still poorly characterized. The aim of this study was to assess the combined influences of diet-induced obesity and psychological stress on microbiome composition and colonic gene expression. METHODS: C57BL/6J mice (n = 48) were subject to a combination of 22 weeks of Western diet (WD) feeding and a chronic restraint stressor (CRS) for the last 4 weeks of feeding. At the end of the combined intervention, microbiome composition was determined from cecal contents, and colonic tissue gene expression was assessed by multiplex analysis using NanoString nCounter System and real-time qPCR. RESULTS: WD feeding induced a DIO phenotype with increased body weight, worsened metabolic markers, and alterations to microbiome composition. CRS reduced body weight in both dietary groups while having differential effects on glucose metabolism. CRS improved the Firmicutes/Bacteroidetes ratio in WD-fed animals while expanding the Proteobacteria phyla. Significantly lower expression of colonic Tlr4 (p = 0.008), Ocln (p = 0.004), and Cldn3 (p = 0.004) were noted in WD-fed animals compared to controls with no synergistic effects observed when combined with CRS. No changes to colonic expression of downstream inflammatory mediators were observed. Interestingly, higher levels of expression of Cldn2 (p = 0.04) and bile acid receptor Nr1h4 (p = 0.02) were seen in mice exposed to CRS. CONCLUSION: Differential but not synergistic effects of WD and CRS were noted at the host-microbe interface suggesting multifactorial responses that require further investigation.

The impacts of faecal subsampling on microbial compositional profiling.

OBJECTIVE: Despite the move to at-home, small-volume collection kits to facilitate large population-based studies of faecal microbial compositional profiling, there remains limited reporting on potential impacts of faecal subsampling approaches on compositional profiles. This study aimed to compare the microbial composition from faecal subsamples (< 5 g) collected from the beginning and end of a single bowel movement in ten otherwise healthy adults (6 female, 4 male; age: 24-55 years). Microbial composition was determined by V3-V4 16s rRNA sequencing and compared between subsamples. RESULTS: There were no significant differences in OTU count (p = 0.32) or Shannon diversity index (p = 0.29) between the subsamples. Comparison of relative abundance for identified taxa revealed very few differences between subsamples. At the lower levels of taxonomic classification differences in abundance of the Bacillales (p = 0.02) and the Eubacteriaceae family (p = 0.03), and the Eubacterium genera (p = 0.03) were noted. The observation of consistent microbial compositional profiles between faecal subsamples from the beginning and end of a single bowel movement is an important outcome for study designs employing this approach to faecal sample collection. These findings provide assurance that use of a faecal subsample for microbial composition profiling is generally representative of the gut luminal contents more broadly.

A YOLO based software for automated detection and analysis of rodent behaviour in the open field arena.

Rodent models are important in mechanistic studies of the physiological and pathophysiological determinants of behaviour. The Open Field Test (OFT) is one of the most commonly utilised tests to assess rodent behaviour in a novel open environment. The key variables assessed in an OFT are general locomotor activity and exploratory behaviours and can be assessed manually or by automated systems. Although several automated systems exist, they are often expensive, difficult to use, or limited in the type of video that can be analysed. Here we describe a machine-learning algorithm - dubbed Cosevare - that uses a trained YOLOv3 DNN to identify and track movement of mice in the open-field arena. We validated Cosevare's capacity to accurately track locomotive and exploratory behaviour in 10 videos, comparing outputs generated by Cosevare with analysis by 5 manual scorers. Behavioural differences between control mice and those with diet-induced obesity (DIO) were also documented. We found the YOLOv3 based tracker to be accurate at identifying and tracking the mice within the open-field arena and in instances with variable backgrounds. Additionally, kinematic and spatial-based analysis demonstrated highly consistent scoring of locomotion, centre square duration (CSD) and entries (CSE) between Cosevare and manual scorers. Automated analysis was also able to distinguish behavioural differences between healthy control and DIO mice. The study found that a YOLOv3 based tracker is able to easily track mouse behaviour in the open field arena and supports machine learning as a potential future alternative for the assessment of animal behaviour in a wide range of species in differing environments and behavioural tests.