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Genet Mol Biol. 2020 Mar 16;43(1):e20190028. doi: 10.1590/1678-4685-GMB-2019-0028. eCollection 2020.

Metabarcoding reveals that a non-nutritive sweetener and sucrose yield similar gut microbiota patterns in Wistar rats.

Author information

1
Hospital de Clínicas de Porto Alegre (HCPA), Centro de Pesquisa Experimental, Núcleo de Bioinformática, Porto Alegre, RS, Brazil.
2
Universidade Federal do Rio Grande do Sul (UFRGS), Programa de Pós-Graduação em Medicina: Ciências Médicas, Porto Alegre, RS, Brazil.
3
Hospital de Clínicas de Porto Alegre (HCPA), Centro de Pesquisa Experimental, Unidade de Pesquisa Laboratorial, Porto Alegre, RS, Brazil.
4
Hospital de Clínicas de Porto Alegre (HCPA), Serviço de Endocrinologia, Porto Alegre, RS, Brazil.

Abstract

The effects of non-nutritive sweeteners (NNS) on the gut microbiota are an area of increasing research interest due to their potential influence on weight gain, insulin resistance, and inflammation. Studies have shown that mice and rats fed saccharin develop weight gain and metabolic alterations, possibly related to changes in gut microbiota. Here, we hypothesized that chronic exposure to a commercial NNS would change the gut microbiota composition in Wistar rats when compared to sucrose exposure. To test this hypothesis, Wistar rats were fed either NNS- or sucrose-supplemented yogurt for 17 weeks alongside standard chow (ad libitum). The gut microbiome was assessed by 16S rDNA deep sequencing. Assembly and quantification were conducted using the Brazilian Microbiome Project pipeline for Ion Torrent data with modifications. Statistical analyses were performed in the R software environment. We found that chronic feeding of a commercial NNS-sweetened yogurt to Wistar rats, within the recommended dose range, did not significantly modify gut microbiota composition in comparison to sucrose-sweetened yogurt. Our findings do not support the hypothesis that moderate exposure to NNS is associated with changes in gut microbiota pattern compared to sucrose, at least in this experimental model.

PMID:
32191789
DOI:
10.1590/1678-4685-GMB-2019-0028
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