A multi-omics approach to investigate characteristics of gut microbiota and metabolites in hypertension and diabetic nephropathy SPF rat models

Front Microbiol. 2024 Apr 29:15:1356176. doi: 10.3389/fmicb.2024.1356176. eCollection 2024.

Abstract

Background: Imbalance in intestinal microbiota caused by microbial species and proportions or metabolites derived from microbes are associated with hypertension, as well as diabetic nephropathy. However, the involvement of the intestinal microbiota and metabolites in hypertension and diabetic nephropathy comorbidities (HDN) remains to be elucidated.

Methods: We investigated the effects of intestinal microbiota on HDN in a rat model and determined the abundance of the intestinal microbiota using 16S rRNA sequencing. Changes in fecal and serum metabolites were analyzed using ultra-high-performance liquid chromatography-mass spectrometry.

Results: The results showed abundance of Proteobacteria and Verrucomicrobia was substantially higher, whereas that of Bacteroidetes was significant lower in the HDN group than in the sham group. Akkermansia, Bacteroides, Blautia, Turicibacter, Lactobacillus, Romboutsia, and Fusicatenibacter were the most abundant, and Prevotella, Lachnospiraceae_NK4A136_group, and Prevotella_9 were the least abundant in the HDN group. Further analysis with bile acid metabolites in serum showed that Blautia was negatively correlated with taurochenodeoxycholic acid, taurocholic acid, positively correlated with cholic acid and glycocholic acid in serum.

Conclusions: These findings suggest that the gut microbiota and metabolites in feces and serum substantially differed between the HDN and sham groups. The F/B ratio was higher in the HDN group than in the sham group. Blautia is potentially associated with HDN that correlated with differentially expressed bile acid metabolites, which might regulate the pathogenesis of HDN via the microorganism-gut-metabolite axis.

Keywords: diabetic nephropathy; hypertension; intestinal microbiota; metabolites; multi-omics.

Associated data

  • figshare/10.6084/m9.figshare.25623453

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by Supporting the Liaoning Province Applied Basic Research Program (no. 2022JH2/101500060), High Quality Development Science and Technology Fund Project of China Medical University (2023020779-JH2/202), Liaoning Province Education Science Planning Project (no. JG21DB544), and 345 Talent Project of Shengjing Hospital of China Medical University (no. M0681).