Wild primate microbiomes prevent weight gain in germ-free mice [journal]

Journal

Animal Microbiome - December 2020

Authors

Dimitrios N Sidiropoulos, Gabriel A Al-Ghalith, Robin R Shields-Cutler, Tonya L Ward, Abigail J Johnson, Pajau Vangay, Dan Knights (professor), Purna C Kashyap, Yibo Xian, Amanda E Ramer-Tait, Jonathan B Clayton

Abstract

Background

The gut microbiome harbors trillions of bacteria that play a major role in dietary nutrient extraction and host metabolism. Metabolic diseases such as obesity and diabetes are associated with shifts in microbiome composition and have been on the rise in Westernized or highly industrialized countries. At the same time, Westernized diets low in dietary fiber have been shown to cause loss of gut microbial diversity. However, the link between microbiome composition, loss of dietary fiber, and obesity has not been well defined.

Results

To study the interactions between gut microbiota, dietary fiber, and weight gain, we transplanted captive and wild douc gut microbiota into germ-free mice and then exposed them to either a high- or low-fiber diet. The group receiving captive douc microbiota gained significantly more weight, regardless of diet, while mice receiving a high-fiber diet and wild douc microbiota remained lean. In the presence of a low-fiber diet, the wild douc microbiota partially prevented weight gain. Using 16S rRNA gene amplicon sequencing we identified key bacterial taxa in each group, specifically a high relative abundance of Bacteroides and Akkermansiain captive douc FMT mice and a higher relative abundance of Lactobacillus and Clostridiumin the wild douc FMT mice.

Conclusions

In the context of our germ-free mouse experiment, wild douc microbiota could serve as a reservoir for microbes for cross-species transplants. Our results suggest that wild douc microbiota are tailored to diverse fiber diets and can prevent weight gain when exposed to a native diet.

Link to full paper

Wild primate microbiomes prevent weight gain in germ-free mice

Keywords

bioinformatics, computational biology