Indole derivatives ameliorated the methamphetamine-induced depression and anxiety via aryl hydrocarbon receptor along "microbiota-brain" axis

Abstract

In addition to the high neurotoxicity, depression, and anxiety are the most prominent characteristics of methamphetamine (Meth) withdrawal. Studies to date on the issue of Meth-associated depression and anxiety are focused on the brain, however, whether peripheral homeostasis, especially the "microbiota-gut" axis participates in these adverse outcomes, remains poorly understood. In the current study, with the fecal microbiota transplantation (FMT) assay, the mice received microbiota from Meth withdrawal mice displayed marked depression and anxiety behaviors. The 16S rRNA sequencing results showed that Meth withdrawal contributed to a striking reduction of Akkermansia, Bacteroides, Faecalibaculum, Desulfovibrio, and Anaerostipes, which are known to be associated with tryptophan (TRP) metabolism. Noteworthily, the substantial decreases of the indole derivatives from the TRP metabolic pathway, including IAA, IPA, ILA, IET, IArA, IAld, and TRM were observed in the serum of both Meth abusing humans and mice during Meth withdrawal with the UHPLC-MS/MS analysis. Combining the high and low TRP diet mouse model, the mice with high TRP diet obviously impeded Meth-associated depression and anxiety behaviors, and these results were further strengthened by the evidence that administration of IPA, IAA, and indole dramatically ameliorated the Meth induced aberrant behaviors. Importantly, these protective effects were remarkably counteracted in aryl hydrocarbon receptor knockout (AhR KO) mice, underlining the key roles of microbiota-indoles-AhR signaling in Meth-associated depression and anxiety. Collectively, the important contribution of the present work is that we provide the first evidence that peripheral gut homeostasis disturbance but not limited to the brain, plays a key role in driving the Meth-induced depression and anxiety in the periods of withdrawal, especially the microbiota and the indole metabolic disturbance. Therefore, targeting AhR may provide novel insight into the therapeutic strategies for Meth-associated psychological disorders.

Keywords: Depression and anxiety; aryl hydrocarbon receptor; indole derivatives; methamphetamine.

Figure 1.

Effects of Meth on mouse behaviors and gut microbiota. (a) Experimental design and timeline. (b) Body weight change (n=8–10 per group). (c,d) Immobility time (%) in the FST and TST; time spent in the open arms (%) in the EPM test; time spent in the center (s) and total track length (m) in the OFT (n=8 per group). (e,f) The representative trajectory diagrams in the OFT and EPM. (g)Ace, Chao, and Shannon index of α diversity. (h) Principal coordinate analysis of β diversity based on Bray-Curtis distances among the control, saline, WD 0d, and WD 14d groups. (i) Stacked chart of gut microbiota composition at the phylum level. (j) The ratio of phylum Firmicutes/Bacteroidetes. (k) Genomic functional prediction of gut microbiota community by Tax4Fun. (l) Random forest analysis of gut microbiota importance at the genus level on WD 14d and control, saline groups. Each dot indicates an individual mouse. Data are expressed as mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.001.

Figure 2.

Indole metabolism in Meth withdrawal mice and humans. (a) Schematic representation of the TRP microbial metabolic pathway, including IAA, IPA, ILA, IArA, IAM, IPYA, IAld, TRM and IET. (b) Analysis of indole derivatives in feces of WD 14d mice compared to control mice (n = 8 per group). (c) Analysis of indole derivatives in serum samples in mice. (d) Schematic of sample collection, pre-treatment, and mass spectrometry detection and analysis in healthy controls and Meth abusers. (e) Comparison of indole derivatives in the serum of Meth abusers versus healthy controls (HC) (n = 78–80 per group). (f) ROC curve for distinguishing Meth abusers from healthy controls based on indole derivative levels. (g) Relative abundance of gut microbiota genera in WD 14d and control mice. Each dot indicates an individual mouse. Data are expressed as mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Figure 3.

Roles of the microbiota in mediating Meth-induced anxiety and depression-like behavior in mice. (a) Experimental design and timeline. (b,c) Time spent in the open arms (%) in the EPM test; and time spent in the center (s) and total track length (m) in the OFT; immobility time (%) in the FST and TST. (d)The representative trajectory diagrams in the OFT and EPM. (e,f) Levels of indole derivatives in the serum and feces of FMT recipient mice. (g-h) H&E staining of the colons (scale bar 100 μm) and the histological score analysis. (i) Villus length analysis. Data are expressed as mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.001,****p < 0.0001; ^#p < 0.05, ^##p < 0.01, ^###p < 0.001, ^####p < 0.0001.

Figure 4.

Effects of dietary TRP supplementation on Meth-induced behavioral changes and levels of indole derivatives in mice. (a) Experimental design and timeline. (b) Time spent in the open arms (%) in the EPM test; time spent in the center (s) and total track length (m) in the OFT; immobility time (%) in the FST and TST. (c) Levels of indole derivatives in fecal samples from different groups. Data are expressed as mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001; ^#p < 0.05, ^##p < 0.01.

Figure 5.

Effects of Meth on microglial morphology and neurogenesis in the hippocampus. (a) Representative immunofluorescence images of IBA1⁺ microglia (green) in the hippocampus of control, saline, WD 0d, and WD 14d groups. DAPI (blue) was used for nuclei staining (scale bar 50 μm). (b) 3D reconstructions of microglial cells from the hippocampal regions of each group. (c) Sholl analysis quantifies the number of microglial intersections as a function of the distance from the cell soma. (d-i) Quantitative analyses of microglial morphology: (d) area under the curve (AUC) from Sholl analysis, (e) number of IBA1⁺ cells, (f) volume of IBA1⁺ cells, (g) total process length, (h) number of terminal points, and (i) number of branch points. (j) Flow cytometry gating strategy for identifying microglial cells (CD11b⁺ CD45^mid) in the hippocampus. (k) Flow cytometry results quantifying the percentage of CD11b⁺ CD45^mid microglia in the control, saline, and WD 14d groups. (l) Western blot analysis of IBA1 protein expression in hippocampal lysates. (m) Immunofluorescence staining for doublecortin (DCX, green) in the hippocampus (scale bar 100 μm), with zoomed-in images highlighting neurogenesis. Data are expressed as mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Figure 6.

Indole derivatives and AhR agonist Ficz alleviated Meth-induced anxiety and depression-like behaviors. (a) Schematic diagram of the MST experimental design to measure the binding of IPA, IAA, and IND to AhR. (b) The binding affinity of IPA, IND, and IAA to AhR. (c) Experimental design and timeline. (d,e) Time spent in the open arms (%) in the EPM test; time spent in the center (s) and total track length (m) in the OFT; immobility time (%) in the FST and TST (n=5 per group). (f,g) the representative trajectory diagrams in the OFT and EPM. (h) mRNA expression of AhR pathway-related genes (Ahr, Cyp1a1, and Cyp1b1) in the hippocampus. Data are expressed as mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Figure 7.

Protective effects of indole derivatives and Ficz on Meth-induced microglial morphological changes and neurogenesis defects in the hippocampus. (a) Representative immunofluorescence images of IBA1⁺ microglia (green) in the hippocampus of control, saline, WD 14d, WD 14d+Ficz, and WD 14d+indoles groups. DAPI (blue) was used for nuclei staining (scale bar 50 μm). (b) 3D reconstructions of microglial cells in the hippocampus. (c) Sholl analysis quantifies the number of microglial intersections as a function of the distance from the cell soma. (d-i) quantitative analyses of microglial morphology: (d) AUC from Sholl analysis, (E) number of IBA1⁺ cells, (f) volume of IBA1⁺ cells, (g) total process length, (h) number of terminal points, and (i) number of branch points. (j) Flow cytometry quantifies the percentage of CD11b⁺ CD45^mid microglia in each group. (k) Immunofluorescence staining for DCX (green) in the hippocampus of each group, with zoomed-in images highlighting neurogenesis (scale bar 100 μm). Data are expressed as mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Figure 8.

Roles of AhR KO in Meth-induced depressive and anxiety-like behaviors. (a) Experimental design and timeline. (b) Gel image showing PCR results confirming the AhR KO in mice. (c) Immobility time (%) in the FST and TST. (d,e) The representative trajectory diagrams in the OFT and EPM. (f) Time spent in the open arms (%) in the EPM test; time spent in the center (s) and total track length (m) in the OFT. (g) mRNA expression of AhR-related gene (Ahr, Cyp1a1, and Cyp1b1) in the hippocampus. Data are expressed as mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001; ^#p < 0.05, ^###p < 0.001.

Figure 9.

AhR KO alleviated the protective effects of indole derivatives against Meth-induced microglial dystrophy and neurogenesis defects. (a) Representative immunofluorescence images of IBA1⁺ microglia (green) in the hippocampus. DAPI (blue) was used for nuclei staining (scale bar 50 μm). (b) 3D reconstructions of microglial cells from the hippocampal regions of each group. (c) Sholl analysis quantifies the number of microglial intersections as a function of the distance from the cell soma. (d–i) Quantitative analyses of microglial morphology: (d) area under the curve (AUC) from Sholl analysis, (e) number of IBA1⁺ cells, (f) volume of IBA1⁺ cells, (g) total process length, (h) number of terminal points, and (i) number of branch points. (j) Immunofluorescence staining for DCX (green) in the hippocampus, with zoomed-in images highlighting neurogenesis (scale bar 100 μm). Data are expressed as mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Figure 10.

A schematic depicting the peripheral mechanism underlying Meth-induced anxiety and depression-like behaviors, particularly highlighting the role of microbiota and their indole metabolites. During Meth withdrawal, a reduction of indole metabolite-producing microbiota decreases the levels of indole metabolites, which impedes AhR expression and causes microglial dystrophy and neurogenesis defects and finalizes anxiety and depression-like behaviors in mice. Noteworthily, supplementing with indole metabolites, TRP diet, or targeting AhR could offer novel strategies for Meth-induced anxiety and depression.