Guild-based analysis for understanding gut microbiome in human health and diseases

et al. 2021

Front. Microbiol.

More and more studies have indicated that gut microbiota takes part in the biosynthesis and metabolism of sex hormones. Inversely, sex hormones influence the composition of gut microbiota. However, whether microbiota in the gut and vagina is associated with estrus return of weaning sows is largely unknown. Here, using 16S rRNA gene sequencing in 158 fecal and 50 vaginal samples, we reported the shifts in the gut and vaginal microbiota between normal return and non-return sows. In fecal samples, Lactobacillus and S24-7 were enriched in normal return sows, while Streptococcus luteciae, Lachnospiraceae, Clostridium, and Mogibacterium had higher abundance in non-return sows. In vaginal swabs, the operational taxonomic units (OTUs) annotated to Clostridiales, Ruminoccaceae, and Oscillospira were enriched in normal return sows, while those OTUs annotated to Campylobacter, Anaerococcus, Parvimonas, Finegoldia, and Dorea had higher abundances in non-return sows. Co-abundance group (CAG) analysis repeated the identification of the bacterial taxa associated with the estrus return of weaning sows. The predicted functional capacities in both gut and vaginal microbiome were changed between normal return and non-return sows. Serum metabolome profiles were determined by non-targeted metabolome analysis in seven normal return and six non-return sows. The metabolite features having higher abundance in normal return sows were enriched in the pathways Steroid hormone biosynthesis, Starch and sucrose metabolism, Galactose metabolism, and Vitamin B6 metabolism, while the metabolite features belonging to organic acids and derivatives, indoles and derivatives, sulfoxides, and lignans and neolignans had significantly higher abundance in non-return sows. Correlation analysis found that the changes in gut microbiota were associated with the shifts of serum metabolites and suggested that certain bacteria might affect estrus return of weaning sow through serum metabolites. These findings may provide new insights for understanding the role of the gut and vaginal microbiota in sow return to estrus after weaning.

Section: Methodsmentioning

confidence: 99%

Gut and Vagina Microbiota Associated With Estrus Return of Weaning Sows and Its Correlation With the Changes in Serum Metabolites

Zhong

et al. 2021

Front. Microbiol.

“…Recent studies have introduced the concept of a ‘guild’ into the study of the gut microbiota, defining a group of microorganisms that utilize similar resources or perform the same biological function as the same guild to identify potential clusters associated with specific disease phenotypes and identify candidate gut microbes that may contribute to human health and disease[ 37 ]. Using a clinical study of genetically obese children, the researchers found that the abundance of one strain of E. coli increased sharply after 30 d of eating a diet rich in nondigested carbohydrates, while the abundance levels of the other four strains decreased[ 37 ]. Therefore, guilds, as an important form of intermember organization in ecology, can better reflect the changes in specific responses of strains.…”

Section: Association Of the Gut Microbiota With Obesitymentioning

confidence: 99%

Gut microbiota in obesity

Liang

et al. 2021

WJG

284

151

Obesity is a major global health problem determined by heredity and environment, and its incidence is increasing yearly. In recent years, increasing evidence linking obesity to the gut microbiota has been reported. Gut microbiota management has become a new method of obesity treatment. However, the complex interactions among genetics, environment, the gut microbiota, and obesity remain poorly understood. In this review, we summarize the characteristics of the gut microbiota in obesity, the mechanism of obesity induced by the gut microbiota, and the influence of genetic and environmental factors on the gut microbiota and obesity to provide support for understanding the complex relationship between obesity and microbiota. At the same time, the prospect of obesity research related to the gut microbiota is proposed.

“…The notion of eco-group (or guild), i.e., a set of bacterial taxa that perform similar functions, is very useful to understand microbial ecology [45, 46]. We divided the entire gut microbiota into three eco-groups: (1) 5 primary degraders of inulin; (2) 32 generic responders to inulin intervention; (3) non-responders.…”

Section: Resultsmentioning

confidence: 99%

Ecological dynamics of the gut microbiome in response to dietary fiber

Liao

Tang

et al. 2021

Preprint

Self Cite

Dietary fibers are generally thought to benefit intestinal health. Their impacts on the composition and metabolic function of the gut microbiome, however, vary greatly across individuals. Previous research showed that each individual’s response to fibers depends on their baseline gut microbiome, but the ecology driving microbiota remodeling during fiber intake remained unclear. Here, we studied the long-term dynamics of gut microbiome and short-chain fatty acids (SCFAs) in isogenic mice with distinct microbiota baselines fed with the fermentable fiber inulin compared to the non-fermentable fiber cellulose. We found that inulin produced generally rapid response followed by gradual stabilization to new equilibria, and those dynamics were baseline-dependent. We parameterized an ecology model from the timeseries data, which revealed a group of bacteria whose growth significantly increases in response to inulin. and whose baseline abundance and interspecies competition explains the baseline-dependence of microbiome density and community composition dynamics. Fecal levels of of SCFAs, such as propionate, is associated with the abundance of inulin responders, yet inter-individual variation of gut microbiome impedes the prediction of SCFAs by machine learning models. Finally, we showed that our methods and major findings are generalizable to dietary resistant starch. This study emphasizes the importance of ecological modeling to understand microbiome responses to dietary changes and the need for personalized interventions.