The effects of a semen cuscutae flavonoids-based antidepressant treatment on microbiome and metabolome in mice

Abstract

Background: Depression is a prevalent psychiatric disorder and one of the leading causes of disability worldwide. Previous studies have shown that Semen Cuscutae flavonoids (SCFs) exert antidepressant effects by modulating the microbiota-neuroinflammation axis and ameliorating hippocampal metabolic disturbances. However, the impact of SCFs on gut microbiota and related metabolomics remains largely undefined. Given that the gut microbiota has been proven to play a significant role in the etiology of depression and serves as a promising target for its treatment in humans, this study aims to elucidate the antidepressant effects of SCFs and to investigate how they modulate microbial and metabolic pathways to alleviate depressive symptoms.

Materials and methods: Chronic unpredictable mild stress (CUMS)-induced mice were used as a depression model. The normal mice and CUMS-induced mice were treated with either vehicle or with SCFs. A range of standardized behavioral assays and physiological indicators were employed to evaluate the antidepressant effects of SCFs. Upon the confirmation of the effectiveness of the SCFs treatment, the composition, richness, and diversity of the fecal microbiota were assessed using 16S rRNA gene sequencing. Additionally, fecal metabolic profiling was analyzed using UHPLC-MS/MS-based metabolomics. Multivariate data analysis was subsequently performed to identify differential metabolites and characterize alterations in fecal metabolites. Furthermore, a correlation analysis between differential metabolites and key microbiota was conducted.

Results: SCFs significantly ameliorated depressive behaviors and the dysregulated diversity of fecal microbiota induced by CUMS. SCFs enhanced the gut microbiota structure in the CUMS group by increasing the Firmicutes/Bacteroidota ratio, significantly elevating the abundance of Firmicutes, Lactobacillus, Limosilactobacillus, and Actinobacteria while reducing the abundance of Bacteroidota and Bacteroides in CUMS-treated mice. Fecal metabolomics analyses revealed that SCFs could modulate metabolic pathways, including aldosterone synthesis and secretion, arachidonic acid metabolism, and primary bile acid biosynthesis.

Conclusions: Mice with depression induced by CUMS exhibited disturbances in both their gut microbiota and fecal metabolism. However, SCFs restored the balance of the microbial community and corrected metabolic disturbances in feces, exerting antidepressant effects through a multifaceted mechanism.

Keywords: CUMS; SCFs; depression; feces; metabolites; microbiota.

Figure 1

Experimental design flow.

Figure 2

SCFs relieved depression-like behaviors and physiological indicators in mice exposed to CUMS. (A) Weekly body weight changes of the mice during 28 days. (B) Weekly food intake changes during 28 days. (C) Weekly rectal temperature changes during 28 days. (D) Weekly the sucrose preference rate changes during 28 days. (E) Total distance in OFT on the day 29. (F) The time spent in the central area in the OFT on the day 29. (G) The immobility time in the TST on the day 30. (H) The immobility time in the FST on the day 31. Compared to the CON group, ^ΔP < 0.05, ^ΔΔΔP < 0.001; compared to the CUMS group, *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 3

(A) Alpha-phylogenetic diversity analysis showed that depressive-like mice induced by CUMS had lower microbial diversity and richness in six indexes relative to the CON (P < 0.01, respectively); these indexes had increased when administrated with H-SCFs. (B) Beta diversity analysis. PCoA revealed that the fecal microbiome composition in CUMS mice was markedly distinct from that of CON, and the fecal microbial composition was restored after being treated with H-SCFs. *P < 0.05, **P < 0.01.

Figure 4

(A) The relative abundance of the top 10 gut microbiota at the phylum level. (B) The metastat analysis on the top 10 differential species at the phylum between the CON and CUMS groups. (C) The metastat analysis on the top 10 differential species at the phylum between the CUMS and H-SCFs groups. (D) Column chart of linear discriminant analysis (LDA). Linear discriminant analysis effect size (LEfSe) analysis was conducted with a threshold of LDA > 3.5. The results were visualized using a histogram and showed 27 responsible for discriminating in CON, CUMS, and H-SCFs groups.

Figure 5

The metastat analysis of the dominant bacteria among the CON, CUMS, and H-SCFs groups. Compared to the CON group, ^ΔP < 0.05, ^ΔΔP < 0.01, ^ΔΔΔP < 0.001; compared to the CUMS group, *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 6

Effects of H-SCFs on the metabolic profile of feces in CUMS mice. (A) PCA score plot of fecal samples from CON, CUMS, and H-SCFs Groups. (B) Fecal OPLS-DA score of CUMS and CON groups. (C) Fecal OPLS-DA score of H-SCFs and CUMS groups. (D, E) OPLS-DA replacement test (200 times) for CON vs. CUMS and H-SCFs vs. CUMS group. (F, G) S-plot for CON vs. CUMS and H-SCFs vs. CUMS in the fecal metabolites.

Figure 7

The perturbed major metabolic pathways of the fecal samples. Metabolites are annotated to indicate significant changes between groups. The green upward (↑) and downward (↓) arrows represent metabolites increased or decreased in the CUMS group compared with the CON group, respectively. The blue upward (↑) and downward (↓) arrows represent metabolites increased or decreased in the H-SCFs group compared with the CUMS group, respectively. The metabolic pathways highlighted the effects of CUMS and the therapeutic impact of SCFs on gut microbiota-associated metabolites in the fecal samples.

Figure 8

Spearman correlations analysis illustrating the relationships among gut microbiota, fecal metabolites, and depression-related indicators. Heatmap of Spearman correlations among microbial taxa, metabolic pathways, and depression-related indicators. Colors indicate correlation direction and strength (blue: positive, red: negative), with square sizes reflecting magnitude. Labels represent microbial taxa (blue), pathways (brown), and phenotypes (black). Significant correlations are annotated. Hierarchical clustering highlights related patterns.