FG-4592 relieves diabetic kidney disease severity by influencing metabolic profiles via gut microbiota reconstruction in both human and mouse models

Abstract

Objective: Diabetic kidney disease (DKD) is one of the most prevalent complications of diabetes mellitus (DM) and is highly associated with devastating outcomes. Hypoxia-inducible factor (HIF), the main transcription factor that regulates cellular responses to hypoxia, plays an important role in regulating erythropoietin (EPO) synthesis. FG-4592 is the HIF stabilizer that is widely used in patients with renal anemia. We investigated the effect of FG-4592 on DKD phenotypes and the pharmacologic mechanism from the perspective of gut microbiota and systemic metabolism. Design: We collected the clinical data of 73 participants, including 40 DKD patients with combined renal anemia treated with FG-4592, and 33 clinical index-matched DKD patients without FG-4592 treatment from The First Affiliated Hospital of Zhengzhou University at the beginning and after a 3-6-month follow-up period. We established DKD mouse models treated by FG-4592 and performed fecal microbiota transplantation from FG-4592-treated DKD mice to investigate the effects of FG-4592 on DKD and to understand this mechanism from a microbial perspective. Untargeted metabolome-microbiome combined analysis was implemented to globally delineate the mechanism of FG-4592 from both microbial and metabolomic aspects. Result: DKD phenotypes significantly improved after 3-6 months of FG-4592 treatment in DKD patients combined with renal anemia, including a decreased level of systolic blood pressure, serum creatinine, and increased estimated glomerular infiltration rate. Such effects were also achieved in the DKD mouse model treated with FG-4592 and can be also induced by FG-4592-influenced gut microbiota. Untargeted plasma metabolomics-gut microbiota analysis showed that FG-4592 dramatically altered both the microbial and metabolic profiles of DKD mice and relieved DKD phenotypes via upregulating beneficial gut microbiota-associated metabolites. Conclusion: FG-4592 can globally relieve the symptoms of DKD patients combined with renal anemia. In the animal experiment, FG-4592 can reconstruct the intestinal microbial profiles of DKD to further upregulate the production of gut-associated beneficial metabolites, subsequently improving DKD phenotypes.

Keywords: FG-4592; diabetic kidney disease; gut microbiota; mechanism; untargeted metabolomics analysis.

FIGURE 1

Experimental design and grouping process of animal experiment clinical phenotyping of DKD mouse models. Flow chart of the animal experiment (A). Scatter plots show blood glucose (B), body weight (C), and urine total protein to creatinine ratio (T/Cr) (D) at the end of the experiment (6 weeks). DKD, diabetic kidney disease; GF, germ free; STZ, streptozotocin; FMT, fecal microbiota transplantation; T/Cr, total urinary protein/urinary creatinine. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001. ns, no significance.

FIGURE 2

Bacterial diversity among Con (n = 7), DKD (n = 8), and DKD-FG-4592 groups (n = 8). α diversity: bacterial richness and diversity were evaluated by observed OTUs (B) and Shannon/Ace indices (A, C), respectively. Venn diagram (F) showed observed OTUs among the three groups. β diversity: PCoA (D, E) analysis was measured by unweighted UniFrac distance at the OTU level. Adonis revealed that unweighted analysis taking OTU abundance into account could better reflect the spatial differences among the three groups (R2 = 0.274, p < 0.001). PCoA, principal coordinate analysis; PC, principal component, PC1, PC2, and PC3; Adonis, permutational/non-parametric multivariate analysis of variance.

FIGURE 3

Composition of microbial communities at phylum (A) and genus (B) levels in Con, DKD, and DKD-FG groups. The Kruskal–Wallis rank-sum test was implemented to compare and identify significantly different bacteria at the phylum or genus level (C–G). *p < 0.05, **p < 0.01, ***p < 0.001.

FIGURE 4

Distribution of key OTUs and enrichment of KEGG functional pathways among Con, DKD, and DKD-FG groups. Through the Wilcoxon rank-sum test, 43 OTUs with p-value > 0.05 and abundance >0.003% were considered key lineages for DKD and displayed as a heatmap (A). Blue represents lower abundance; orange represents higher abundance. KEGG pathways indicated by LEfSe analysis with LDA score ≥3.0 and p ≤ 0.05 (B). OTU, operational taxonomic units; KEGG, Kyoto Encyclopedia of Genes and Genomes; LEfSe, linear discriminate analysis and effect size; LDA, linear discriminant analysis. *p < 0.05, **p < 0.01, ***p < 0.001.

FIGURE 5

Validation of plasma metabolite disparity among Con, DKD, and DKD-FG groups. PCA score plots with (A) or without (B) internal QC; PLS-DA score plot (C); (D) scatter plots of statistical validations obtained by 200s permutation tests. QC, quality control; PCA, principal component analysis; OPLS-DA, orthogonal partial least-squares discrimination analysis.

FIGURE 6

Advanced functional metabolic pathways analysis of Con, DKD, and DKD-FG groups. Top 20 metabolic pathways ranked by the number of important differential metabolites they contained (A). KEGG enrichment analysis of significantly enriched metabolic pathways (B). OS, organismal system; M, metabolism; HD, human disease; EIP, environmental information processing; CP, cellular processing.

FIGURE 7

Metabolic alterations of mice models based on untargeted metabolomic detection and Spearman’s correlation analysis between important differential metabolites and key OTUs. Boxplots showing the comparison of the relative expression level of eight important differential metabolites among three groups (A–H). Spearman’s correlation relationship between important differential metabolites and key OTUs presented as a heatmap (I). *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.