Modulation of Active Gut Microbiota by Lactobacillus rhamnosus GG in a Diet Induced Obesity Murine Model

• Published in: Frontiers in microbiology Vol. 9; p. 710
• Main Authors: Ji, Yosep, Park, Soyoung, Park, Haryung, Hwang, Eunchong, Shin, Hyeunkil, Pot, Bruno, Holzapfel, Wilhelm H
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 10-04-2018
• Subjects: 16S rDNA analysis; 16S rRNA analysis; gut microbiota; LGG; Microbiology; obesity; 16S rRNA analysis; 16S rDNA analysis; LGG; gut microbiota; obesity
• ISSN: 1664-302X; 1664-302X
• DOI: 10.3389/fmicb.2018.00710

Gut microbiota play a key role in the development of metabolic disorders. Defining and correlating structural shifts in gut microbial assemblages with conditions related to metabolic syndrome have, however, been proven difficult. Results from 16S genomic DNA and 16S ribosomal RNA analyses of fecal samples may differ widely, leading to controversial information on the whole microbial community and metabolically active microbiota. Using a C57BL/6J murine model, we compared data from 16S genomic DNA and ribosomal RNA of the fecal microbiota. The study included three groups of experimental animals comprising two groups with high fat diet induced obesity (DIO) while a third group (control) received a low fat diet. One of the DIO groups was treated with the probiotic
GG (LGG). Compared to the data obtained by DNA analysis, a significantly higher abundance of OTUs was accounted for by RNA analysis. Moreover, rRNA based analysis showed a modulation of the active gut microbial population in the DIO group receiving LGG, thus reflecting a change in the induced obesity status of the host. As one of the most widely studied probiotics the functionality of LGG has been linked to the alleviation of metabolic syndrome, and, in some cases, to an impact on the microbiome. Yet, it appears that no study has reported thus far on modulation of the active microbiota by LGG treatment. It is postulated that the resulting impact on calorie consumption affects weight gain concomitantly with modulation of the functional structure of the gut microbial population. Using the 16S rRNA based approach therefore decisively increased the precision of gut microbiota metagenome analysis.