Esketamine alleviates depressive-like behavior in mice via modulation of the microbiota-gut-brain axis and amino acid metabolism

Abstract

Background: The microbiota‒gut‒brain axis is increasingly recognized as a critical pathway in the pathogenesis of depression and the response to antidepressant treatments. Esketamine(S-Ket), a noncompetitive glutamatergic N-methyl-D-aspartate receptor (NMDAR) antagonist, has shown a rapid and long-lasting antidepressant effects. However, the precise mechanisms underlying the antidepressant actions of S-Ket remain unclear.

Methods: In this study, we explore the role of gut microbiota and metabolites in the antidepressant effects of esketamine in lipopolysaccharide (LPS)-induced mouse model of depression. Behavioral tests, including the open-field test, forced swimming test, tail suspension test, and sucrose preference test, were conducted to evaluate the antidepressant efficacy of S-Ket. Additionally,16S rRNA sequencing and untargeted metabolomics were performed to characterize the gut microbiota and metabolome profiles in fecal and hippocampal tissues of LPS-induced mice treated with S-Ket. Bioinformatics analysis was employed to identify key changes. Spearman's rank correlation analyses were used to explore associations between depression-like behaviors (DLBs), differential gut microbes, and metabolites.

Results: S-Ket significantly alleviated DLBs in LPS-induced mice, partially restored the disrupted gut microbiota composition (β-diversity), and improved metabolic dysfunction. Pathway analysis revealed that four key amino acid metabolism pathways were significantly altered in both fecal and hippocampal samples, including ‘Glutathione metabolism’. ‘Alanine, aspartate, and glutamate metabolism’, ‘Arginine biosynthesis’, and ‘Arginine and proline metabolism’. Further analysis indicated that the genus Rikenella was significantly correlated with DLBs and host amino acids (e.g., glutamic acid and pyro glutamic acid).

Conclusions: This study demonstrates that a single dose of S-Ket rapidly alleviates depression-like behaviors in LPS-induced mice, and its mechanisms are associated with regulating both the composition of gut microbiota and associated metabolites in fecal and hippocampal tissues, particularly the alteration of host amino acid metabolism. These results highlight the potential role of the gut–microbiome–amino acid metabolism axis in esketamine’s antidepressant effects and suggest that targeting this axis may offer therapeutic benefits for depression. Further research is needed to fully elucidate these mechanisms.

Graphical Abstract:

Supplementary Information: The online version contains supplementary material available at 10.1186/s12866-025-04181-3.

Keywords: Depression; Esketamine; Gut microbiota; Lipopolysaccharide; Metabolomics; Microbiota-Gut-Brain Axis.

Fig. 1

Antidepressant effects of Esketamine in the LPS-induced depression-like mice. A Schematic timeline of the experimental procedure. B-C Open field test, total distance total distance (F (2, 38) = 3.191, p = 0.052), central distance (F (2, 38) = 7.051, p = 0.002). D Forced swim test, F (2, 38) = 6.959, p = 0.003). E Tail suspension test (F (2, 38) = 8.065, p = 0.001). F Sucrose preference test F (2, 38) = 18.930, p < 0.001. G Water consumption. H Body weight (Two-way ANOVA, day: F (10,450) = 10.263, p < 0.01, group: F(2,450) = 81.566, p < 0.01, day*group: F(20,450) = 2.825, p < 0.01). n = 14, 14, 13 mice for Con, LPS, LPS + S-Ket; *p < 0.05,** p < 0.01, ***p < 0.001; n.s., not significant, one-way ANOVA

Fig. 2

Analyses of the effects of Esketamine on the overall structure of the gut microbiota. A Venn diagram depicting ASV richness and overlap in microbial communities. B Principal coordinate analysis (PCoA) plot showing beta diversity among the three groups at the ASV level (ANOSIM, R = 0.2315, p = 0.001). C-D Bar plots showing the relative abundance of the microbiota in the three groups at the phylum and genus levels. E Differential microbiota at different taxa levels among the different groups. n = 14, 14, and 13 mice for Con, LPS, and S-Ke; *p < 0.05, ** p < 0.01, Kruskal‒Wallis H test

Fig. 3

Relative abundances and functional predictions of differential microbiota. A Differential gut microbes at the genus level correlated with DLB. PICRUSt prediction based on the KEGG annotation at level 2 (B) and level 3 (C) (the top 10 terms are listed and sorted by the p-value). DLB, depression-like behavior; TST, tail suspension test; FST, forced swim test; SPT, sucrose preference test; CD, center distance. n = 14, 14, and 13 mice for Con, LPS, and S-Ke; *p < 0.05, ** p < 0.01, Kruskal‒Wallis H test

Fig. 4

Metabolites in the gut-brain axis. PLS-DA score plot of the feces (A) and hippocampus (B); Volcano plot of differentially abundant metabolites in the feces (C) and hippocampus (D) between the LPS and Con groups; Volcano plot of differentially abundant metabolites in the feces (E) and hippocampus (F) between the LPS + S-Ket and LPS groups. Metabolites with a VIP > 1.0 and a p-value < 0.05 were considered significantly different. The FC was calculated from the average mass response ratio (FC = LPS/Con or LPS + S-Ket/LPS). VIP, variable importance in projection; FC, fold change

Fig. 5

KEGG pathway enrichment analysis of differentially abundant metabolites. A Overview of pathway enrichment (top 10) in the feces and hippocampus. The circle color indicates the p-value, and the size of the circle is proportional to the number of differentially abundant metabolites involved in pathway enrichment. B Heatmap of the key differentially abundant metabolites from the hippocampus and feces. C Heatmap of correlations between the key differentially abundant metabolites and DLB among the three groups. n = 14, 14, and 13 mice for Con, LPS, and S-Ke; *p < 0.05, ** p < 0.01

Fig. 6

Correlations among key differentially abundant metabolites from the hippocampus and feces, differential gut microbes at the genus level, and DLB. A Heatmap displaying the correlation coefficients among differential gut microbiomes and metabolites and DLBs, *p < 0.05, **p < 0.01, ***p < 0.001. B The integrative network shows the correlations (Spearman correlation analysis, absolute value of correlation coefficient > 0.5, p < 0.05) among differential gut microbiomes and metabolites and DLBs. Red connections indicate positive correlations, blue connections indicate negative correlations, and thicker connection lines indicate larger correlation coefficients. DLBs, depression-like behaviors

Fig. 7

A simplified schematic diagram of metabolic changes induced by Esketamine. The red boxes indicate upregulation, the gray boxes indicate no significant change, and the blue boxes indicate downregulation in the LPS + S-Ket group compared with the LPS group. S-Ket, Esketamine; LPS, lipopolysaccharide