Abstract Methamphetamine (METH) abuse, a global public health concern, is closely linked to neuropsychiatric disorders such as depression. Although the central nervous system (CNS) damage induced by METH is well documented, the role of peripheral immune mechanisms remains underexplored. To investigate this, we establish a depressive-like mouse model in male mice using repeated intraperitoneal METH injections. Behavioral tests, flow cytometry, RNA sequencing and metabolomics reveal the underlying mechanisms. METH exposure increases the differentiation of CD4⁺ T cells into Th17 cells in the spleen, likely driven by mitochondrial dysfunction and impaired betaine metabolism. These Th17 cells secrete elevated IL-17 A, which binds to IL-17RA on hippocampal CA1 neurons, activates the p38 MAPK signaling pathway, and disrupts synaptic plasticity. Interventions targeting Th17 cells or IL-17 A signaling significantly reduce depressive behavior. These findings uncover a novel peripheral immune mechanism in METH-related depression, wherein CD4⁺ T cell-derived IL-17 A contributes to hippocampal dysfunction via IL-17RA/p38 MAPK signaling. Targeting Th17 cells or IL-17 A may represent a promising therapeutic strategy for METH-associated neuropsychiatric disorders. Supplementary Information The online version contains supplementary material available at 10.1186/s12974-025-03532-1. Keywords: Methamphetamine, Depressive behaviors, CD4⁺ T cells, Th17 cells, Betaine metabolism, Synaptic plasticity Introduction The global escalation of psychoactive substance abuse constitutes a mounting public health crisis. Among these substances, methamphetamine (METH) is distinguished by its potent addictive properties and profound neuropsychiatric consequences [[50]1–[51]3]. A considerable proportion of METH users engage in binge patterns of consumption—repeated high-dose exposures over short periods—particularly in recreational settings [[52]4]. This mode of use provokes acute central nervous system (CNS) stimulation, followed by a marked psychological crash [[53]5]. Depression emerges as one of the most prevalent and debilitating outcomes, reflecting dysregulation within neural circuits critical for emotional regulation [[54]6, [55]7]. Despite the clinical urgency, therapeutic strategies for METH-induced depression are limited, offering symptomatic relief without addressing underlying mechanisms [[56]8]. Identifying the biological pathways linking binge METH exposure to emotional dysfunction is thus essential. METH neurotoxicity arises from a cascade of pathological events, including hyperthermia, oxidative stress, mitochondrial dysfunction, excitotoxicity, and neuroinflammation, all of which converge to impair synaptic integrity [[57]9, [58]10]. Recently, increasing attention has been directed toward the role of immune dysregulation in the pathophysiology of psychiatric disorders [[59]11, [60]12]. Elevated levels of circulating cytokines and chemokines have been correlated with the severity of depressive symptoms, suggesting a link between peripheral immune activation, blood–brain barrier (BBB) disruption, and central immune responses [[61]13–[62]15]. Among immune components, CD4⁺ T lymphocytes have emerged as critical mediators of CNS dysfunction. Aberrant CD4⁺ T cell activity has been implicated in anxiety-like behaviors, as well as in the progression of disorders such as schizophrenia and Alzheimer’s disease [[63]16–[64]18]. Following immune activation, CD4⁺ T cells can differentiate into distinct subtypes. In particular, T helper 17 (Th17) cells have garnered significant attention for their high pathogenic potential in CNS diseases [[65]19, [66]20]. Expansion of Th17 cells and increased secretion of interleukin-17 A (IL-17 A) have been linked to depressive phenotypes in both preclinical models and clinical populations [[67]21–[68]24]. However, while Th17/IL-17 A dysregulation is implicated in depression, its specific involvement in METH-induced depressive-like behaviors remains undefined. Here, we established a rodent model of binge METH-induced depressive behavior in male mice to investigate the contribution of peripheral Th17 differentiation and IL-17 A secretion to hippocampal dysfunction and emotional dysregulation. By integrating behavioral, immunological, and molecular analyses, we aim to elucidate how immune-CNS interactions shape the neuropsychiatric consequences of METH abuse. Results CD4⁺ T lymphocytes mediate adaptive immune control of METH-induced depressive behaviors The experimental design for METH exposure and behavioral assessments is outlined in Fig. [69]1A. Mice were subjected to binge-dose METH administration under high ambient temperatures (28 °C) to model human patterns of use. In line with our previous study, this exposure paradigm induced a robust increase in core body temperature and elicited stereotypic behaviors (Fig. [70]1B), confirming effective pharmacological activation. Assessment of depressive behaviors revealed that METH-treated mice exhibited substantially increased immobility times in both the tail suspension test (TST; Fig. [71]1C) and the forced swimming test (FST; Fig. [72]1D), indicative of behavioral despair. Additionally, METH administration significantly reduced the sucrose preference index (Figure [73]S1A), without causing notable changes in locomotor activity (Figure S1B). To further characterize behavioral alterations induced by METH administration, we additionally conducted the three-chamber social interaction test. The results demonstrated that METH-treated mice exhibited significantly reduced interaction time with stranger mice compared to control mice (Figure S1C). These findings provide further evidence that METH exposure induces pronounced depressive-like behaviors in mice. Fig. 1. [74]Fig. 1 [75]Open in a new tab CD4⁺ T cells contribute to METH-induced depressive behavior. A Schematic diagram of drug treatment and experimental design. B Body temperature changes (n = 6) and stereotypic behavior scores (n = 10) in mice following METH administration. C Representative activity curve and immobility time in the tail suspension test (TST) 7 days after METH treatment (n = 10). D Representative activity curve and immobility time in the forced swimming test (FST) (n = 10). E Representative flow cytometry plots of immune cell subsets and immobility time in the TST and FST of wild-type (WT) and Rag^-/- mice (n = 8). F Immobility time in the TST and FST following neutralizing antibody treatment (n = 12). G Schematic diagram of CD4⁺ T cell adoptive transfer experiment. H Immobility time in the TST and FST after CD4⁺ T cell transfer without METH treatment (n = 8). I Immobility time in the TST and FST after CD4⁺ T cell transfer followed by METH administration (n = 8). Data are presented as mean ± SEM To determine whether adaptive immunity contributes to METH-induced depressive behaviors, we employed Rag1^-/- mice, which lack mature T lymphocytes (Figure S1C). Following binge-dose METH administration, Rag1^-/- mice failed to develop comparable immobility phenotypes (Fig. [76]1E), suggesting that adaptive immune components are required for METH-induced emotional dysregulation. To further delineate the responsible subsets, CD4⁺ or CD8⁺ T cells were selectively depleted in wild-type (WT) mice using neutralizing antibodies (Figure S1D). Behaviorally, CD4⁺ T cell depletion significantly reduced METH-induced immobility, whereas CD8⁺ T cell depletion had no appreciable effect (Fig. [77]1F). We adoptively transferred cells isolated from either saline or METH-treated donor mice into Rag1^-/- recipients to evaluate the sufficiency of CD4⁺ T cells in driving this behavioral phenotype (Fig. [78]1G, S1E). Under baseline conditions, recipient mice displayed no behavioral differences (Fig. [79]1H). However, upon METH exposure, both donor-derived CD4⁺ T cell groups exhibited significantly prolonged immobility times compared to PBS-injected controls (Fig. [80]1I). Together, these results establish CD4⁺ T lymphocytes as both necessary and sufficient for mediating METH-induced depressive behaviors, positioning them as key adaptive peripheral immune effectors in this pathological process. METH exposure promotes peripheral CD4^+ T cell differentiation toward Th17 lineage We then assessed splenic and peripheral blood CD4⁺/CD8⁺ T cell populations using flow cytometry and observed no significant alterations following METH exposure (Fig. [81]2A, B), suggesting that global T cell distribution remained unaltered. To probe functional changes in CD4⁺ T cells, bulk RNA sequencing was performed on purified splenic CD4⁺ T cells from METH-treated and control mice (Fig. [82]2C). Differential expression analysis identified 404 significantly altered genes, with 77 upregulated and 327 downregulated transcripts in the METH group (Fig. [83]2D, E). KEGG pathway enrichment analysis highlighted IL-17 signaling as one of the top altered pathways (Fig. [84]2F). Fig. 2. [85]Fig. 2 [86]Open in a new tab METH administration promotes differentiation of CD4⁺ T cells into Th17 cells. A Proportions of splenic CD4⁺ and CD8⁺ T cells after METH treatment (n = 8). B Proportions of peripheral blood CD4⁺ and CD8⁺ T cells after METH treatment (n = 8). C Schematic diagram of MACS-based CD4⁺ T cell isolation and bulk RNA sequencing. D Volcano plot of differentially expressed genes. E Heatmap of hierarchical clustering of differentially expressed genes. F Enriched upregulated pathways identified by KEGG analysis. G Proportion of Th17 cells in the spleen after METH treatment (n = 8). H Serum IL-17 A levels following METH administration (n = 8). I Correlation analysis between IL-17 A levels and immobility time in the TST and FST (n = 16). J Relative expression levels of Th17-related genes (n = 7). K Effects of SR1001 treatment on immobility time in the TST and FST (n = 12). L Immobility time in the TST and FST in IL-17 A^-/- and WT mice (n = 10). Data are presented as mean ± SEM Consistently, flow cytometry revealed a marked increase in splenic Th17 cell proportions in METH-treated mice (Fig. [87]2G, S2A), accompanied by elevated serum IL-17 A levels as determined by ELISA (Fig. [88]2H). To identify the source of IL-17 A, we examined γδ T cells, another major cellular source of IL-17 A. No significant changes were observed in the overall γδ T cell populations or in the IL-17 A-producing γδ T cells (γδ17 T cells) in the spleen following METH treatment (Figure S2B). In addition, given the close functional relationship between Treg and Th17 cells, we assessed the proportion of splenic Treg cells. The results showed no significant differences in Treg cell percentages among the groups (Figure S2C). Meanwhile, IL-10 levels remained unchanged following METH treatment, whereas the IL-17 A/IL-10 ratio was significantly elevated, suggesting a Th17/Treg imbalance skewed toward Th17 cells (Figure S2D). To assess the behavioral relevance of this immune signature, we conducted correlation analyses between serum IL-17 A levels and depressive behaviors. IL-17 A concentrations positively correlated with immobility time in both the TST and FST. Moreover, IL-17 A levels were also associated with the sucrose preference index and the preference for strangers. These results suggest that elevated IL-17 A levels are associated with METH-induced depressive-like behaviors (Fig. [89]2I, S2E). Given the established role of STAT3, RORC, RORγt, and IL-17 A in Th17 differentiation, we assessed the expression of these key genes by qRT-PCR. METH exposure significantly increased mRNA expression of all targets compared to saline controls (Fig. [90]2J). To functionally validate the role of Th17 signaling, we administered SR1001, a selective RORγt inverse agonist that blocks Th17 differentiation, prior to METH exposure. SR1001-treated mice exhibited significantly reduced immobility times compared to vehicle-treated controls in both behavioral paradigms (Fig. [91]2K). Finally, to genetically confirm the contribution of IL-17 A, gene deficient mice (IL17A^-/-) were subjected to METH treatment. Deletion of IL-17 A abolished METH-induced increases in immobility time in both TST and FST, confirming a causal role for IL-17 A in mediating METH-induced depressive behaviors (Figs. [92]2L). Collectively, these findings identify peripheral Th17 polarization and IL-17 A production as critical immune events linking METH exposure to the development of depressive behaviors. METH-induced mitochondrial dysfunction and disruption of betaine metabolism in CD4+ T cell promotes Th17 cells differentiation Metabolic reprogramming is closely linked to immune cell differentiation. To investigate metabolic alterations in CD4⁺ T cells following METH exposure, we performed untargeted metabolomic profiling on CD4⁺ T cells isolated from saline and METH-treated mice. Positive ion mode analysis identified 20 significantly dysregulated metabolites (Fig. [93]3A-C) while negative ion mode analysis detected 6 differential metabolites (Figure S3A-D). Among these, betaine was notably reduced in CD4⁺ T cells from METH-treated mice (Fig. [94]3D). Betaine can be synthesized from choline within mitochondria under the catalytic action of choline dehydrogenase (CHDH). Targeted metabolomic analysis further revealed a marked accumulation of choline (Fig. [95]3E, F). Correlation analysis demonstrated a significant association between choline and betaine levels (Fig. [96]3G), while ELISA results showed a significant reduction in CHDH expression in these cells (Fig. [97]3H). These findings suggest a potential impairment in the conversion of choline to betaine. Fig. 3. [98]Fig. 3 [99]Open in a new tab Mitochondrial dysfunction and altered betaine metabolism in CD4⁺ T cells. A–D Untargeted metabolomics in positive ion mode (n = 6). A Principal component analysis (PCA) of differential metabolites in CD4⁺ T cells. B Partial least squares discriminant analysis (PLS-DA). C Clustering heatmap of differential metabolites. D Z-score plot of differential metabolites. E Schematic diagram of the betaine synthesis pathway. F Quantification of betaine and choline levels in CD4⁺ T cells (n = 8). G Correlation analysis between choline and betaine levels (n = 16). H Expression of CHDH in CD4⁺ T cells (n = 5). I Representative transmission electron microscopy (TEM) images of CD4⁺ T cells following METH treatment. J–K Effects of METH treatment on mitochondrial membrane potential (n = 3). L Measurement of reactive oxygen species (ROS) levels (n = 5). M ATP production (n = 5). N Effect of betaine supplementation on the proportion of Th17 cells (n = 6). O Relative expression levels of STAT3 and RORγt after betaine treatment (n = 6). Data are presented as mean ± SEM Given that betaine synthesis predominantly occurs in mitochondria, we next examined mitochondrial morphology to explore the mechanism underlying betaine disruption. Transmission electron microscopy (TEM) revealed mitochondrial shrinkage, cristae loss, widened intracristal spaces, and increased matrix electron density in CD4⁺ T cells from METH-treated mice, indicative of severe mitochondrial structural damage (Fig. [100]3I). Functional assays showed a significant reduction in mitochondrial membrane potential, as evidenced by decreased JC-1 aggregates and increased monomers (Figs. [101]3J, K), along with elevated ROS production (Fig. [102]3L) and reduced ATP generation (Fig. [103]3M). Together, these data indicate that METH exposure impairs mitochondrial integrity and function in CD4⁺ T cells, leading to disrupted betaine metabolism. To evaluate whether exogenous betaine supplementation could rescue the aberrant Th17 phenotype, mice were treated with different doses of betaine following METH administration. While low-dose betaine (10 mg/kg) showed no effect, both 30 mg/kg and 100 mg/kg treatments significantly reduced Th17 cell proportions (Fig. [104]3N). Western blot analysis further confirmed both 30 mg/kg and 100 mg/kg betaine administration attenuated METH-induced upregulation of STAT3 and RORγt expression (Fig. [105]3O). These findings reveal that mitochondrial dysfunction in CD4⁺ T cells drives betaine metabolism disruption, promoting Th17 polarization, and that betaine supplementation can effectively reverse these immunometabolic abnormalities. Aberrant betaine metabolism influences Th17 cell differentiation via methylation modulation As a mitochondria-derived methyl donor, betaine plays a critical role in the epigenetic regulation of gene expression. Alterations in betaine availability can impact the concentration of S-adenosylmethionine (SAM), a key methyl donor in the one-carbon cycle (Fig. [106]4A). To assess whether reduced betaine levels affect SAM synthesis, we measured SAM concentrations in CD4⁺ T cells. SAM levels were significantly decreased in CD4⁺ T cells from METH-treated mice compared to controls, concurrently, homocysteine levels were markedly elevated (Fig. [107]4B), indicating disruption of methyl group metabolism. Fig. 4. [108]Fig. 4 [109]Open in a new tab Betaine attenuates Th17 cell differentiation by modulating DNA methylation. A Schematic diagram of betaine involvement in methyl metabolism. B S-adenosylmethionine (SAM) and homocysteine levels in CD4⁺ T cells (n = 8). C Differential distribution of methylation sites in the STAT3 gene. D Methylation status of CpG sites within the STAT3 promoter region in individual cells (avg indicates average methylation across sites). E Methylation levels across the STAT3 promoter region among different samples. F Differential distribution of methylation sites in the RORc gene. G Methylation status of CpG sites within the RORc promoter region in individual cells (avg indicates average methylation across sites). H Methylation levels across the RORc promoter region among different samples. *: P < 0.05, **: P < 0.01, ***: P < 0.001. I Schematic diagram of 5-Azacytidine (5-Aza)-mediated DNA methylation inhibition. J Effects of 5-Aza treatment on Th17 cell differentiation (n = 6). K Effects of 5-Aza on STAT3 and RORc expression levels (n = 6). Data are presented as mean ± SEM To examine whether impaired betaine metabolism affects the methylation of genes involved in Th17 cell differentiation, we performed DNA methylation sequencing on CD4⁺ T cells. The RORc gene encodes RORγt, a key transcription factor for Th17 cell differentiation, which can be activated by STAT3 signaling. METH treatment induced marked alterations in the CpG methylation pattern of the STAT3 promoter region in CD4⁺ T cells (Fig. [110]4C), accompanied by a significant reduction in average methylation levels (Fig. [111]4D) and overall promoter region methylation (Fig. [112]4E). Similarly, METH exposure led to pronounced changes in the CpG methylation pattern of the RORγt promoter region (Fig. [113]4F), with a significant decrease in methylation levels observed across individual CpG sites (Fig. [114]4G) and within the overall promoter region (Fig. [115]4H). To further elucidate the functional consequences of this epigenetic change, CD4⁺ T cells were treated in vitro with 5-Azacytosine (5-Aza), a DNA methyltransferase inhibitor (Fig. [116]4I). Treatment with 5-Aza significantly increased the proportion of Th17 cells (Fig. [117]4J). Consistent with this observation, western blot analysis showed that 5-Aza treatment upregulated STAT3 and RORγt expression (Fig. [118]4K), indicating enhanced Th17 differentiation. Therefore, the above results demonstrate that disrupted betaine metabolism impairs methyl group availability, reduces DNA methylation at key Th17-related loci, and thereby promotes Th17 polarization. Hippocampal neuronal IL-17RA mediates the effects of Th17-derived IL-17 A on METH-induced depressive behavior Accumulating evidence implicates the hippocampal CA1 region as a critical hub in depression pathophysiology. ELISA quantification revealed that IL-17 A levels in the hippocampal CA1 region were significantly elevated in METH-treated mice compared to controls, while Nissl staining indicated prominent neuronal damage in this area (Figure S4A, B). Notably, the METH-induced elevation of IL-17 A in the CA1 region was attenuated by either SR1001 pretreatment or IL-17 A genetic ablation (Fig. [119]5A). Given the potential of IL-17 A to compromise BBB integrity, we next assessed hippocampal BBB permeability using fluorescein sodium and Evans Blue tracer assays. METH exposure markedly increased hippocampal permeability to both tracers, whereas SR1001-pretreated and IL-17 A^-/- mice displayed comparable tracer extravasation despite METH challenge (Fig. [120]5B). Immunofluorescence colocalization with CD31 (an endothelial marker) confirmed augmented perivascular Evans Blue accumulation in the CA1 region of METH-exposed wild-type (WT), SR1001-treated, and IL-17 A^-/- mice (Figure S4C). Western blot analysis further demonstrated significant METH-induced reductions in the tight junction proteins Claudin-5 and Occludin within the CA1 region; notably, neither SR1001 pretreatment nor IL-17 A knockout rescued this BBB disruption (Fig. [121]5C). Fig. 5. [122]Fig. 5 [123]Open in a new tab IL-17 A crosses the damaged blood–brain barrier and acts on IL-17RA in the hippocampal CA1 region. A IL-17 A levels in the hippocampus (n = 6). B Permeability of sodium fluorescein and Evans blue (n = 6). C Expression levels of Occludin and Claudin-5 (n = 6). D Distribution of IL-17RA among different neuronal subtypes in the hippocampal CA1 region. E Schematic diagram of rAAV injection sites. F Effects of IL-17RA knockdown in the hippocampal CA1 region on immobility time in TST and FST (n = 8). Data are presented as mean ± SEM To delineate the cellular targets of IL-17 A signaling, triple immunofluorescence staining was performed to localize IL-17RA expression relative to neural cell markers. High colocalization coefficients were observed between IL-17RA and NeuN⁺ neurons, whereas minimal overlap was detected with Iba1⁺ microglia or GFAP⁺ astrocytes (Fig. [124]5D). To interrogate neuron-specific IL-17RA function, conditional IL-17RA knockout mice (IL-17RA^flox/flox) were generated. Stereotaxic delivery of Cre-expressing recombinant adeno-associated virus (rAAV-Cre) into the CA1 region of IL-17RA^flox/flox mice achieved selective neuronal IL-17RA deletion (Fig. [125]5E), as validated by western blotting (Figures S4D). Behavioral assessments revealed that METH-induced immobility prolongation in both the TST and FST was abolished in IL-17RA cKO mice, whereas vector controls exhibited typical despair-like behaviors (Fig. [126]5F). These data identified neuronal IL-17RA as the critical mediator of Th17-derived IL-17 A signaling in the hippocampus and highlight its essential role in linking peripheral immune activation to central depression-like phenotypes. IL-17 A activates the p38 MAPK pathway to impair synaptic plasticity of hippocampal CA1 neurons To investigate downstream signaling pathways including JNK, ERK and p38 MAPK potentially activated by IL-17/IL-17RA, we examined key molecular components of major IL-17RA-associated cascades. METH treatment significantly increased the expression of phosphorylated p38 (p-p38) in the hippocampal CA1 region, whereas IL-17RA knockout effectively abolished this METH-induced upregulation (Fig. [127]6A). To assess the functional relevance of p38 MAPK activation in METH-induced depressive behaviors, we microinjected the p38 MAPK inhibitor SB203580 into the CA1 region of the hippocampus via pre-implanted cannulas prior to METH administration (Fig. [128]6B, S4). Behavioral analyses on TST and FST revealed that SB203580 pretreatment prevented the development of METH-induced depressive behaviors (Fig. [129]6C), underscoring a pivotal role for the p38 MAPK pathway in mediating these effects. Fig. 6. [130]Fig. 6 [131]Open in a new tab IL-17 A activates p38 signaling via IL-17RA to induce synaptic plasticity impairment. A Effects of IL-17RA signaling on expression of JNK, ERK, and p38 pathway-related proteins (n = 6). B Schematic diagram of cannula implantation and injection procedure. C Effects of intra-CA1 injection of the p38 inhibitor SB203580 on immobility time in TST and FST (n = 6). D Representative diagram of dendritic spine morphology. E Quantification of dendritic spine density (n = 6). F Proportions of different types of dendritic spines. G Proportions of mushroom- and thin-type dendritic spines (n = 6). H Effects of p38 pathway inhibition on PSD95 and Synapsin-1 expression levels (n = 6). Data are presented as mean ± SEM Given the established involvement of p38 MAPK activation in impairments of synaptic plasticity, we next examined its impact using Golgi staining to visualize dendritic spines. METH exposure significantly reduced dendritic spine density in hippocampal neurons, whereas SB203580 pretreatment preserved spine density (Fig. [132]6D, E). Dendritic spines were further classified into four morphological types: thin, mushroom, stubby, and branched. Thin spines are typically considered immature, while mushroom spines represent mature synapses. METH-treated mice exhibited a pronounced decrease in mushroom spines and a concomitant increase in thin spines, whereas SB203580 administration effectively mitigated these alterations (Fig. [133]6F, G). To further explore the role of the p38 MAPK pathway in METH-induced synaptic dysfunction, we assessed the expression of key synaptic proteins. Western blot analysis showed that METH administration significantly downregulated PSD95 and Synapsin-1 levels in the hippocampus, whereas inhibition of p38 MAPK signaling with SB203580 attenuated this reduction (Fig. [134]6H), suggesting that p38 MAPK signaling mediates METH-induced synaptic deficits. Thus, IL-17 A impairs hippocampal synaptic plasticity by activating the p38 MAPK pathway, thereby contributing to the pathophysiology of METH-induced depressive behavior. Discussion METH abuse is a major public health concern, closely linked to the development of neuropsychiatric disorders, including depression. While previous research has largely focused on the neurotoxic effects of METH on dopaminergic and glutamatergic systems, the role of peripheral immune dysregulation in METH-induced affective disturbances has remained underexplored. Here, we identify a possible immunometabolism pathway whereby binge-dose METH exposure is associated with enhanced peripheral CD4⁺ T cell differentiation into the Th17 lineage and increased IL-17 A levels, which may affect hippocampal CA1 neurons and be involved in the development of synaptic and behavioral alterations. Importantly, we uncovered a previously unrecognized role for mitochondrial dysfunction and disrupted betaine metabolism in shaping CD4⁺ T cell fate, linking metabolic stress to epigenetic remodeling and pathogenic immune polarization. These findings bridge peripheral immune activation and central neuroplasticity, offering mechanistic insights with direct translational relevance. METH induces hyperthermia in a dose-dependent manner, which is closely linked to the resultant tissue damage [[135]25–[136]27]. Prolonged hyperthermia has been shown to aggravate METH-induced structural and functional abnormalities in the CNS, including cerebral edema and glial cell activation [[137]28–[138]30]. In real-world scenarios, drug users often consume METH in confined, poorly ventilated environments, which may further exacerbate METH-induced hyperthermia [[139]4]. It is important to note that environmental factors, particularly ambient temperature, can significantly exacerbate METH-induced hyperthermia, leading to more pronounced tissue injury [[140]31]. Our experimental design employed binge-dose METH administration at 28 °C to simulate conditions of acute recreational drug use in humans. This paradigm robustly induced depressive-like behaviors in mice, as evidenced by increased immobility in the TST and FST, reduced sucrose preference in SPT, and altered behaviors in the three-chamber social interaction test. In addition, the role of immune alterations induced by chronic METH exposure in the development of psychiatric disorders will be a key focus of our future investigations. While previous studies have shown that METH can disrupt the blood-brain barrier (BBB) and promote neuroinflammation, our data reveal that the peripheral immune compartment, particularly CD4⁺ T cells, plays an indispensable role in mediating METH’s behavioral effects [[141]32]. Immune-deficient Rag1^-/- mice were resistant to METH-induced depressive behaviors, and selective CD4⁺ T cell depletion attenuated these phenotypes. Notably, adoptive transfer experiments demonstrated that the presence of CD4⁺ T cells was necessary but not sufficient to induce depressive behavior in the absence of METH, suggesting that immune priming interacts with CNS vulnerabilities established by drug exposure and BBB damage. Transcriptomic analyses revealed that METH exposure preferentially skews CD4⁺ T cell differentiation toward the Th17 lineage, accompanied by elevated IL-17 A production. Th17 cells have emerged as key players in neuroimmune regulation and have been implicated in both human depression and animal models of mood disorders [[142]33–[143]35]. Consistent with clinical observations of increased peripheral Th17 markers in patients with major depressive disorder, we show that pharmacological inhibition of RORγt or genetic deletion of IL-17 A significantly ameliorated METH-induced depressive behaviors in mice. These findings position the Th17/IL-17 A axis as a critical immune effector pathway linking peripheral immune responses to mood regulation. Notably, a recent study by Zhu et al. demonstrated that opioid use promotes the expansion of fragile-like regulatory T cells (Tregs) in both patients and mice. These Tregs lose their suppressive function, secrete IFN-γ, and infiltrate the CNS through a disrupted blood-brain barrier, where they induce synaptic remodeling and contribute to withdrawal symptoms [[144]36]. Our study adds to this growing body of work by showing that METH, rather than driving Treg changes, promotes Th17 differentiation and IL-17 A secretion, leading to the occurrence of depressive behaviors. While the immune mechanisms differ, both studies highlight the pivotal role of peripheral immune cells in mediating drug-induced CNS dysfunction, suggesting that distinct classes of abused substances may exploit unique immune pathways to influence brain function and behavior. T cell metabolism plays a crucial role in regulating their differentiation and activation [[145]37–[146]39]. Previous studies have demonstrated that elevated intracellular levels of L-arginine in T cells can shift their metabolic profile from glycolysis to mitochondrial oxidative phosphorylation, thereby restraining T cell differentiation [[147]40]. In addition, the bile acid derivative 3-oxoLCA has been shown to inhibit Th17 cell differentiation by interacting with the RORγt [[148]41]. Notably, a major mechanistic insight of this study is the identification of mitochondrial dysfunction and betaine metabolic disruption as upstream regulators of Th17 polarization. We demonstrate that METH impairs mitochondrial integrity in CD4⁺ T cells, leading to reduced betaine synthesis and subsequent depletion of S-adenosylmethionine (SAM), a central methyl donor in the one-carbon cycle [[149]42]. Reduced SAM availability alters DNA methylation at key Th17-associated loci, including the promoters of STAT3 and RORγt, thereby facilitating pathogenic Th17 differentiation. In addition, betaine supplementation reversed these epigenetic and immunologic abnormalities, highlighting a potential metabolic intervention point. While mitochondrial metabolic reprogramming has been increasingly appreciated as a determinant of T cell fate, our findings establish a direct connection between drug abuse, immunometabolism, and depressiv behaviors. Within the CNS, we identify hippocampal CA1 neurons as principal targets of Th17-derived IL-17 A. Elevated IL-17 A levels in the hippocampus were accompanied by enhanced neuronal IL-17RA expression, activation of the p38 MAPK signaling pathway, reduced dendritic spine density, and decreased expression of key synaptic proteins. Selective deletion of IL-17RA in CA1 neurons or pharmacological inhibition of p38 MAPK effectively rescued METH-induced depressive phenotypes. Similarly, Massimiliano et al. have demonstrated that the activation of p38 MAPK signaling mediated by IL-17 A/IL-17RA contributes to cognitive impairment associated with multiple sclerosis [[150]43]. These results highlight a neuron-specific IL-17 A signaling axis that mediates synaptic dysfunction in response to peripheral immune activation. While METH-induced BBB disruption was observed, our data indicates that IL-17 A contributes minimally to BBB permeability changes, emphasizing the dominant role of METH itself or other mechanisms in compromising BBB integrity. From a translational perspective, these findings provide a compelling rationale for targeting peripheral immune circuits in METH-associated psychiatric disorders. Strategies such as Th17 cell inhibition, IL-17 A neutralization, betaine supplementation, and selective modulation of neuronal IL-17RA signaling hold therapeutic promise. Moreover, our results extend beyond METH addiction, suggesting that shared immunometabolism mechanisms may underline a broader range of neuropsychiatric conditions characterized by inflammation-induced synaptic remodeling. However, several limitations should be acknowledged. First, while our mouse model recapitulates key features of METH-induced affective disturbances, differences between rodent and human immune and CNS systems necessitate cautious extrapolation. Moreover, to eliminate potential confounding effects of hormonal fluctuations, only male mice were used in the current set of experiments. In future studies, we plan to validate these findings in female animals. Second, we primarily focused on Th17 cells derived from the spleen, while the potential roles of Th17 cells originating from other tissues, such as the colon, warrant further investigation. Third, the temporal dynamics of peripheral-to-central signaling remain to be elucidated, including the sequence of immune activation, BBB changes, and neuronal remodeling. Future studies employing longitudinal designs, single-cell profiling, and functional imaging in both preclinical models and human subjects will be essential to address these questions. Conclusion Our study delineates a mechanistic cascade that links METH-induced mitochondrial dysfunction and immunometabolic remodeling in peripheral CD4⁺ T cells to hippocampal synaptic deficits and depressive behaviors through the Th17/IL-17 A–IL-17RA axis. By uncovering how peripheral immune perturbations drive central neuroplasticity impairments, we advance the understanding of neuroimmune crosstalk in substance-induced mood disorders. These insights not only open new conceptual avenues for decoding the immune basis of addiction-related psychopathology but also highlight promising immunometabolic targets for therapeutic intervention across diverse neuropsychiatric conditions. Materials and methods Animals C57BL/6 mice were obtained from Beijing Vital River Laboratory Animal Technology Co., Ltd. Rag1^-/-, IL-17^-/-, and IL-17RA^Flox/Flox mice were purchased from Shanghai Model Organisms Center, Inc. All experiments were conducted using male mice. Animals were group-housed (4–5 mice per cage) under specific pathogen-free (SPF) conditions. Environmental parameters were strictly controlled, including a constant temperature of 22 °C, relative humidity of approximately 60%, and a 12-hour light/dark cycle. METH-induced depressive-like behavior model Mice were exposed to METH via intraperitoneal injection at a dosage of 10 mg/kg in a high ambient environment (28 °C). Each mouse received four injections per day at 2-hour intervals, with a single injection volume of 0.25 mL. Control mice received an equal volume of physiological saline under the same conditions. Stereotyped behaviors were observed 30 min after the first administration. Body temperature was measured at 1 h, 3 h, and 24 h following the initial injection. Behavioral tests were conducted starting on day 7 after METH administration. Stereotypic behavior scoring Stereotypic behaviors were scored using the Sams-Dodd stereotypy rating scale as follows: (0) stationary, little or no movement; (1) active, occasional to frequent movement; (2) active with episodes of repetitive forward head searching (the mouse walked forward in a stereotyped manner along the periphery of the arena without engaging in other behaviors); (3) continuous forward head searching; (4) frequent repetitive rearing, side-to-side weaving, or turning; (5) episodes of rapid, jerky side-to-side, circular, or dorsoventral head movements [[151]44]. Tail suspension test (TST) Mice was suspended by the distal 1/5 of the tail using medical adhesive tape fixed to a horizontal bar, allowing them to hang freely without escape for a duration of 6 min. The last 5 min of the session were recorded using a video camera. The recorded data were analyzed with Noldus EthoVision XT behavioral tracking software (Wageningen, Netherlands). Immobility time and highly active time were calculated, and longer immobility time is considered indicative of a higher level of depressive-like behavior. Forced swimming test (FST) FST apparatus consisted of a cylindrical transparent tank filled with water (depth: 15 cm; temperature: 25 ± 2 °C), and a camera positioned at the front for video recording. Mice were individually placed in the water for 6 min, and immobility time was recorded during the last 5 min. Locomotion test (LMT) LMT was assessed in an open-field box (40 cm × 40 cm × 40 cm, white acrylic, without a lid). At the start of the test, each mouse was gently placed in the center of the box and allowed to explore freely for 5 min. The total distance traveled within the 5-minute period was recorded. Sucrose preference test (SPT) Sucrose preference test was conducted using single-housed mice with two drinking bottles. During a 48-hour adaptation period, mice were given two bottles of 1.5% sucrose solution for the first 24 h, followed by one bottle of 1.5% sucrose and one bottle of water for the next 24 h, with bottle positions switched after 12 h to avoid location bias. After adaptation, mice were deprived of food and water for 24 h. The test phase lasted another 24 h, during which one bottle of 1.5% sucrose and one bottle of water were presented, with positions again switched at the 12-hour mark. Consumption was measured by weighing the bottles. Three-chamber social interaction test The three-chamber apparatus consisted of a central chamber connected to two identical side chambers, each equipped with a wire mesh cage. During the habituation phase, the test mouse was placed in the center chamber with the side chambers closed and allowed to acclimate for 10 min. In the second phase (exploration), the doors to the side chambers were opened, permitting the mouse to freely explore all three chambers for 10 min. In the third phase (social interaction), a novel conspecific mouse (stranger) was placed in the wire cage of one side chamber, while an inanimate object of similar size was placed in the opposite side. The test mouse was again allowed to explore freely for 10 min. Mouse behavior was recorded and analyzed using the Noldus EthoVision XT behavioral tracking software. Social Preference Index = Time in Stranger Chamber/(Time in Stranger Chamber + Time in Inanimate Chamber). Flow cytometric analysis Following completion of behavioral tests, blood and spleens were collected from mice. Spleen washed with PBS (Gibco, 10010023) and transferred into culture dishes containing RPMI-1640 medium (Gibco, 11-875-093), then mechanically dissociated. The resulting spleen tissue was passed through a 40 μm nylon mesh filter (Falcon, 352340) and rinsed with PBS. Cell suspensions or anticoagulated blood samples were treated with lysing buffer (BD, 555899) to remove erythrocytes and obtain single-cell suspensions. The cells were then resuspended in staining buffer (BD, 554656). For surface marker analysis, 1 × 10^6 cells were transferred into polystyrene test tubes (Falcon, 352054) and stained with PE-conjugated anti-mouse CD3 Antibody (eBioscience, 11-5711-82), FITC-conjugated anti-mouse CD4 antibody (BD, 553650), APC-conjugated anti-mouse CD8 antibody (TONBO Biosciences, 20–0081), FITC-conjugated anti-mouse TCR γ/δ Antibody (eBioscience, 11-5711-82) or APC-conjugated anti-mouse CD25 antibody (Invitrogen, 11-0041-82). After mixing, cells were incubated for 40 min at 4 °C in the dark. Unbound antibodies were washed off with staining buffer, and cells were resuspended in 300 µL PBS for flow cytometric analysis using a BD FACSCalibur™ flow cytometer (BD, 342975). For intracellular cytokine staining, following surface staining, cells were stimulated for 4–6 h using the BD Pharmingen™ Leukocyte Activation Cocktail, with BD GolgiPlug™ (BD, 550583). After stimulation, cells were fixed using IC Fixation Buffer (Invitrogen, 00-8222-49), vortexed, and incubated for 60 min in the dark. Subsequently, 2 mL of Permeabilization Buffer (Invitrogen, 00-8333-56) was added to permeabilize the cells, followed by staining with APC-conjugated anti-mouse IL-17 A antibody (Invitrogen, 17-7177-81) or PE-conjugated anti-mouse Foxp3 antibody (Invitrogen, 12-4774-42) at room temperature for 40 min in the dark. After washing, cells were resuspended in 300 µL PBS, vortexed, and subjected to flow cytometric analysis. Data were analyzed using FlowJo software (version 10.8.1). In vivo cell depletion To deplete CD4⁺ or CD8⁺ T cells, mice were administered 500 µg of anti-CD4 antibody (Leinco, C2838) or 500 µg of anti-CD8 antibody (Leinco, C2442) via tail vein injection every 3 days. Control mice received equal volumes of isotype control IgG (Leinco, R1371) via the same route and schedule. Depletion efficiency was verified by flow cytometry after two injections. To deplete Th17 cells, mice received intraperitoneal injections of 500 µg SR1001 (MCE, HY-13421) every 3 days. Control mice were intraperitoneally injected with an equal volume of solvent. Subsequent experiments were conducted after two administrations. Magnetic-activated cell sorting (MACS) Anti-mouse CD4 magnetic beads (BD, 551539) were added to the cell pellet at a ratio of 50 µL beads per 1 × 10⁷ cells. The mixture was incubated for 30 min at room temperature in the dark. After incubation, cells were resuspended in BD sorting buffer (BD, 552362) and adjusted to a concentration of 1 × 10⁷ cells/mL. The labeled cells were transferred to polystyrene test tubes and placed in the magnetic field of the BD IMagnet™ cell separation system (BD, 552311). After allowing sufficient time for magnetic separation, the magnetically labeled CD4⁺ T cells were collected and resuspended in PBS. Adoptive transfer CD4⁺ T cells were isolated by MACS and adoptively transferred into Rag1^-/- mice via tail vein injection. Each Rag1^-/- mouse received 1 × 10⁷ CD4⁺ T cells. Control mice were injected with an equal volume of PBS via the tail vein. One week after the transfer, the efficiency of CD4⁺ T cell engraftment was assessed by flow cytometric analysis. RNA sequencing Total RNA was extracted from the sorted CD4⁺ T cells. The integrity and concentration of the extracted RNA were assessed prior to library preparation. mRNA containing poly-A tails was enriched using magnetic beads and subsequently fragmented randomly. First-strand and second-strand complementary DNA (cDNA) were synthesized using the fragmented mRNA as a template and random oligonucleotides as primers, yielding purified double-stranded cDNA. The purified cDNA underwent end repair, A-tailing, adaptor ligation, and further purification to generate the sequencing library. Library quality was evaluated using the Agilent 2100 Bioanalyzer (Agilent Technologies, USA), and effective concentrations were quantified via qRT-PCR. Libraries that passed quality control were sequenced on the Illumina NovaSeq 6000 platform (Illumina, USA). Raw sequencing reads were processed to remove adaptor sequences, reads containing undetermined bases, and low-quality reads. Clean reads were aligned to the reference genome using HISAT2. Gene expression levels were quantified, and differential gene expression analysis was performed. Genes with adjusted p-values (padj) ≤ 0.05 and |log₂ fold change| ≥ 1 were considered significantly differentially expressed. Functional enrichment analysis of differentially expressed genes was conducted by comparison with the KEGG database. Real-Time quantitative reverse transcription PCR (qRT-PCR) Total RNA was extracted using TRIzol reagent (Invitrogen, 15596026CN) according to the manufacturer’s instructions. Complementary DNA (cDNA) was synthesized from total RNA using the PrimeScript™ RT reagent Kit with gDNA Eraser (TAKARA, RR047A). Quantitative PCR amplification was then performed using the TB Green^® Premix Ex Taq™ II kit (TAKARA, RR820A). Gene expression levels were normalized to GAPDH. The specific primers used for qRT-PCR are listed in Table [152]1. Table 1. Primer sequences used for qRT-PCR and methylation sequencing Gene Sequence (5’ to 3’) Application STAT3 Forward: ACCCAACAGCCGCCGTAG Reverse: CAGACTGGTTGTTTCCATTCAGAT qRT-PCR RORc Forward: TGGGCTCCAAGAGAAGAGGA Reverse: CAGGCTCCGGAGTTTTCCTT qRT-PCR RORγt Forward: GACCCACACCTCACAAATTGA Reverse: AGTAGGCCACATTACACTGCT qRT-PCR IL-17 A Forward: GAGCTTCATCTGTGTCTCTGA Reverse: GCCAAGGGAGTTAAAGACTTTG qRT-PCR GAPDH Forward: GGTGAAGGTCGGTGTGAACG Reverse: CTCGCTCCTGGAAGATGGTG qRT-PCR STAT3 Forward: GGAGGTTTTAGGGTGGTTTAGTTAG Reverse: CCCCATATAAATATCCATTCACAAC Methylation Sequencing RORc Forward: TGATTGAGAATTTGGTTTTTTGTTT Reverse: AAAACATCCCTACCCCTAATATCAC Methylation Sequencing [153]Open in a new tab Enzyme-Linked immunosorbent assay (ELISA) Mouse serum or CD4⁺ T cells were collected, and CD4⁺ T cells were lysed thoroughly using lysis buffer to obtain the supernatant. ELISA kits were used to measure the levels of IL-17 A(ABclonal, RK00039), CHDH (ZCiBio, ZC-56442), SAM (ABclonal, RK04519), and Homocysteine (ABclonal, RK09092) according to the manufacturer’s instructions. Absorbance was measured using a microplate reader (Tecan, Switzerland). Standard curves were generated using known concentrations of the standards, and sample concentrations were calculated using the four-parameter logistic fit based on the absorbance values. Untargeted metabolomics 300 µL of 80% methanol-water solution was added to the cell pellet. The sample was then frozen in liquid nitrogen for 5 min and thawed at 4 °C before being vortexed for 30 s and sonicated for 6 min. After sonication, the sample was centrifuged at 12,000 rpm for 5 min at 4 °C, and the supernatant was transferred to a new EP tube. The sample was lyophilized, and 10% methanol solution was added to reconstitute the lyophilized powder for instrumental analysis. Chromatographic separation was performed using a Hypesil Gold column (Thermo Fisher, Germany, 100 × 2.1 mm, 1.9 μm) with a Vanquish UHPLC system (Thermo Fisher, Germany), and mass spectrometry was conducted using a Orbitrap Q Exactive™ HF mass spectrometer (Thermo Fisher, Germany). Data were processed using Compound Discoverer 3.1 software (Thermo Fisher, Germany), and both qualitative and relative quantitative results of metabolites were obtained. After data quality control, metabolites were annotated using the KEGG, HMDB, and LIPIDMaps databases. Metabolite detection After behavioral testing, serum and cell samples were collected immediately. Methanol was added to serum, and the mixture was vortexed and centrifuged to precipitate proteins. For cells, 1 × 10⁶ cells were added to 1 mL of 60% methanol-water solution, frozen, and ground for 15 min. The samples were then sonicated in an ice-water bath for 10 min and centrifuged at 12,000 rpm for 10 min at 4 °C. The supernatants from both serum and cell samples were filtered through a 0.22 μm filter (Millipore) and subjected to instrumental analysis. High-performance liquid chromatography (HPLC) with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) was performed using a Vanquish UHPLC system (Thermo Fisher, Germany) and an AB SCIEX QTRAP 6500 + mass spectrometer (SCIEX, Netherlands). A 2 µL aliquot of the supernatant was injected onto an ACQUITY UPLC BEH HILIC column (Waters, USA, 1.7 μm, 2.1 × 100 mm), and elution was carried out with 30% solution A (0.01% formic acid in water) and 70% solution B (methanol) at a flow rate of 300 µL/min. The concentration of metabolites in the samples was calculated by constructing a standard curve based on known concentrations of standards. Transmission electron microscopy Cells were resuspended in 2 mL of electron microscopy fixative (Servicebio, G1102) and fixed at 4 °C for 3 h. The cells were then pre-embedded with 1% agarose (Sigma-Aldrich, A9539). After pre-embedding, the tissue was fixed in the dark with 1% osmium tetroxide (Ted Pella, 18456) for 2 h. The samples were subsequently dehydrated through a graded alcohol series. The samples were embedded in a mixture of acetone and 812 embedding medium (SPI, 90529-77-4) at various ratios under conditions of 37 °C, followed by embedding in pure 812 resin overnight. After embedding, ultra-thin sections were prepared using an ultramicrotome (Leica, UC7), and the sections were collected onto 150-mesh copper grids. The sections were stained using uranyl acetate and lead citrate, and subcellular structures were observed under a transmission electron microscope (hitachi, HT7800), with images captured. Mitochondrial membrane potential measurement The mitochondrial membrane potential was measured using the JC-1 assay kit (Beyotime, C2006). Cells were resuspended in RPMI 1640 medium. JC-1 working solution was added and the mixture was gently inverted to mix. The cells were incubated at 37 °C for 20 min. After incubation, the cells were centrifuged at 600 g for 5 min at 4 °C, and the supernatant was discarded. The cell pellet was resuspended in JC-1 staining buffer and centrifuged again at 600 g for 5 min at 4 °C. The washing step was repeated twice. After the final wash, the cells were resuspended in an appropriate volume of JC-1 staining buffer and observed using a fluorescence microscope (Leica, DMIL). Reactive oxygen species (ROS) detection ROS levels were measured using the ROS Detection Kit (Beyotime, S0033S). Cells were resuspended in DCFH-DA working solution. The cells were incubated in a 37 °C incubator for 20 min. After incubation, the cells were washed three times with RPMI 1640 medium and resuspended in PBS buffer. ROS levels were assessed using a flow cytometer. ATP measurement ATP levels were determined using an ATP Detection Kit (Beyotime, S0027). The ATP lysis buffer was added to lyse the cells; the supernatant was collected for further analysis. Simultaneously, ATP standard solutions were prepared by diluting ATP standards in the ATP lysis buffer. In a 96-well plate, 100 µL of ATP detection working solution was added and incubated at room temperature for 5 min to consume any background ATP. Subsequently, 20 µL of either sample or known concentration of standard solution was added to the plate, mixed thoroughly, and quickly read using a microplate reader (Tecan, Switzerland). Methylation sequencing Primers for methylation analysis were designed using EpiDesigner software. Genomic DNA samples were treated with NaHSO₃ followed by PCR amplification of the bisulfite-converted DNA and a shrimp alkaline phosphatase (SAP) reaction. T Cleavage transcription/RNase A digestion was then performed to generate amplification products with a T7 RNA polymerase promoter sequence. For the MALDI-TOF MS analysis, resin was evenly distributed into the wells of a 384/6 MG Dimple plate and allowed to dry for 10 min. Subsequently, 16 µL of water was added to each well of a 384-well sample plate. The sample plate was then gently inverted onto the Dimple plate, and light tapping was applied to allow the resin to transfer into each well of the sample plate. After centrifugation at room temperature shaking for 60 min, the samples were analyzed using the Sequenom^® MassARRAY system (Agena Bioscience, USA). The primer sequences used are listed in Table [154]1. 5-Azacytidine treatment Isolated CD4⁺ T cells were cultured at a density of 2 × 10⁵ cells per well in 96-well plates at 37 °C under a controlled atmosphere of 5% CO₂. The culture medium was supplemented with Recombinant Murine IL-6 (PeproTech, 112050, 40 ng/ml), Recombinant Murine IL-1β (PeproTech, 100947, 10 ng/ml), Recombinant Murine TGF-β1 (PeproTech, 0121354, 3 ng/ml). On day 3 of culture, 5-Azacytidine (TargetMol, 320-67-2) was added to the medium at a final concentration of 5 µM for 48 h. Cells were then collected for further analysis. Nissl staining Mouse brain sections were stained using a methylene blue-based Nissl staining kit (Solarbio, G1434) according to the manufacturer’s instructions. After staining, sections were differentiated for 3 min, treated with ammonium molybdate for 3 min, rinsed with distilled water, and then mounted on glass slides. Western blotting Tissue samples were homogenized in RIPA buffer (Solarbio, R0010) containing PMSF and a phosphatase inhibitor mixture (Ruipate Biotech, RW0103), and the supernatant was collected. Equal amounts of denatured protein were loaded onto polyacrylamide gels and separated by electrophoresis. The separated proteins were transferred to NC membranes (Millipore, HATF00010) and blocked with 5% BSA (Sigma-Aldrich, V900933). The membranes were incubated with the following antibodies: Rabbit Anti-STAT3 (Huaan, ET1607-38, 1:1000), Rabbit Anti-p-STAT3 (Huaan, ET1603-40, 1:1000), Rabbit Anti-RORγt (Abcam, ab207082, 1:1000), Mouse Anti-Claudin-5 (Invitrogen, 35-2500, 1:1000), Rabbit Anti-Occludin (Huaan, R1510-33, 1:1000), Rabbit Anti-IL-17RA (Abcam, ab180904, 1:1000), Rabbit Anti-p38 (Huaan, ET1702-65, 1:1000), Rabbit Anti-Phospho-p38 (Huaan, ER2001-52, 1:500), Rabbit Anti-JNK (Huaan, ET1601-28, 1:500), Rabbit Anti-p-JNK (Huaan, ET1609-42, 1:1000), Rabbit Anti-Erk (Servicebio, GB11560–100, 1:1000), Rabbit Anti-p-Erk (Huaan, ET1610-13, 1:1000), Rabbit Anti-PSD95 (Huaan, SR38–09, 1:2000), Rabbit Anti-Synapsin-1 (Cell Signaling, 5297, 1:5000), Rabbit Anti-β-actin (ABclonal, AC026, 1:100000), Mouse Anti-GAPDH (Abcam, ab8245, 1:5000), Rabbit Anti-GAPDH (ABclonal, AC001, 1:5000), or Rabbit Anti-β-Tubulin (Abcam, ab179513, 1:80000). Membranes were incubated overnight at 4 °C. After washing to remove unbound antibodies, the membranes were incubated with DyLight™ 680 Conjugated Mouse IgG Antibody (1:5000, 610-144-002, Rockland) or DyLight™ 680 Conjugated Rabbit IgG Antibody (1:5000, 611-144-002, Rockland) for 1 h at 37 °C. The membranes were washed with TBST (Solarbio, T1085). Protein bands were detected using the Odyssey Imaging System (LI-COR, USA), and the intensity was quantified using ImageJ software (NIH, USA). Blood–brain barrier permeability Mice were intraperitoneally injected with 2% fluorescein sodium (Sigma-Aldrich, 46960) at a dose of 2.5 mL/kg or intravenously injected with 0.5% Evans blue (Sigma-Aldrich, E2129) at a dose of 4 mL/kg. Mice were allowed free access for either 30 min–1 h, respectively. After CO₂ anesthesia, the mice were euthanized, and the brain was quickly removed. The meninges, cerebellum, and brainstem were carefully removed, and a portion of the brain was fixed with tissue fixative (Biosharp, BL539A) for later observation under a fluorescence microscope. The remaining hippocampal tissues were placed in 1.5 mL centrifuge tubes and homogenized in 50% trichloroacetic acid solution (Sigma-Aldrich, T0699). The samples were centrifuged at 1000 g for 5 min at 4 °C, and the supernatant was collected. The supernatant was diluted 4-fold with anhydrous ethanol, and 200 µL of the supernatant was placed into a 96-well plate for fluorescence detection. Fluorescence intensity was measured using a fluorometer with excitation/emission wavelengths of 440/525–550/620 nm. A standard curve was generated using known concentrations of fluorescein sodium or Evans blue, and the permeability of fluorescein sodium or Evans blue was calculated from the standard curve. Immunofluorescence After fixation, mouse brain tissues were sectioned into 30 μm thick slices along the coronal plane. The slices were blocked with goat serum and incubated at 37 °C for 40 min. After blocking, the sections were incubated with the following antibodies: Rabbit Anti-CD31 Antibody (1:500, GB11063-2, Servicebio), Mouse Anti-Claudin-5 Antibody (1:200, 35-2500, Invitrogen), Rabbit Anti-IL-17RA Antibody (1:50, ab180904, Abcam), Mouse Anti-Iba-1 Antibody (1:1000, GB12105, Servicebio), Mouse Anti-NeuN Antibody (1:200, [155]HA601111, Huaan, Zhejiang, China) or Mouse Anti-GFAP Antibody (1:500, sc-33673, Santa Cruz Biotechnology) at 4 °C overnight. After washing with PBS to remove unbound antibodies, the sections were incubated with Alexa Fluor™ 568 Goat Anti-Rabbit antibody (1:500) or Alexa Fluor™ 488 Goat Anti-Mouse antibody (1:500) for 40 min. The excess antibody was washed away, and 50 µL of PerLong^® Gold Antifade Reagent with DAPI (8961 S, Cell Signaling) was added to mount the coverslips. The sections were observed using a laser scanning confocal microscope (Leica, SP8). Viral injection Mice were anesthetized with isoflurane (RWD, R510-22-10) and maintained in an anesthetized state. They were then fixed on a stereotactic frame (NeuroStar, Germany), and the relative coordinates for the hippocampus were determined (AP: −1.95 mm; ML: ±1.3 mm; DV: 1.6 mm). Virus injections were performed at these coordinates. After injection, the skin was sutured, then returned to their home cages. This study used IL-17RA conditional knockout mice, and recombinant adeno-associated viruses (rAAVs) carrying Cyclization Recombination Enzyme (Cre) were used to regulate IL-17RA gene expression. The structure of the Cre virus was rAAV-hSyn-CRE-EGFP-WPRE-hGH pA with a titer of ≥ 2.00E + 12 vg/mL. The control virus vector structure was rAAV-hSyn-EGFP-WPRE-hGH pA, also with a titer of ≥ 2.00E + 12 vg/mL. Cannula infusion experiment The anesthesia and surgical procedure were the same as those used for the viral injection. The cannula was fixed to the skull. After suturing the skin, mice were treated with penicillin (MCE, HY-B1463, 1000 U/day) for 3 days post-surgery. After 7 days of recovery, drug administration was initiated. A microinjection was connected to the cannula via polyethylene tubing (depth: 1.3 mm; distance: 2.6 mm). The injection rate of SB203580 (Cayman CHEMICAL, 13344) was 0.1 µL/min, with a total volume of 0.5 µL injected on each side, and the dose was 5 µM. Golgi staining The Golgi staining procedure was performed using the Quick Golgi Staining Kit ([156]GMS80020.1, GENMED Medicine Technology, Shanghai, China). The whole mouse brain was collected, and 5 mm thick tissue blocks were cut from the target brain area. The tissue blocks were fixed for 24 h and then immersed in staining solution in the dark for 2 weeks. After the staining, the tissue blocks were transferred to a 30% sucrose solution for dehydration. The blocks were then sectioned into 40 μm thick slices using Vibrating Blade Microtome (Leica, VT1200). The brain slices were incubated with staining solution at room temperature for 30 min, followed by the addition of color development solution. When the brain slices turned dark brown, the color development was stopped immediately by removing the solution. The slices were washed, dehydrated, and cleared before being mounted with neutral gum (Beyotime, C0173). The sections were observed under a microscope as soon as possible. Statistical analysis All data are presented as means ± standard error of mean (SEM). GraphPad Prism 10.1.2 was used for data analysis. Analysis of variance (ANOVA), t test and Simple linear regression were used for the statistical analyses. Bonferroni’s post hoc test was performed to assess the differences between groups. The criterion for statistical significance was set at P < 0.05. The original data and statistical results are provided in Supplementary Material 2. Supplementary Information [157]Supplementary Material 1.^ (1.7MB, pdf) [158]Supplementary Material 2.^ (156.1KB, docx) [159]Supplementary Material 3.^ (3MB, docx) Authors’ contributions R.H. and J.X. performed the majority of the experiments and wrote the first draft of the manuscript. H.M. and T.F. performed molecular and histological assays. C.H. and X.W. performed the immunofluorescence and behavioral tests. M.J. and F.Y. conducted literature research, carried out statistical analyses, and assisted with key experiments. Y.S., B.X., and L.Z. critically reviewed and edited the manuscript. D.W., C.M. and B.C. provided essential resources and refined the final manuscript. Funding This research was funded by the Key Project of National Natural Science Foundation of China (82030057 to CM), the General Project of National Natural Science Foundation of China (82371899 to DW), the Hebei Medical University Postdoctoral Funding Project (30705010078 to RH), the High-level Talent Funding Project of Hebei Province, China (No. B20221015 to DW). Data availability The original data and statistical results are provided in Supplementary Material 2. Declarations Ethics approval and consent to participate The animal experiments were conducted following the guidelines outlined in the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Local Animal Use Committee of Hebei Medical University (approval no., IACUC-Hebmu-P2020072). Competing interests The authors declare no competing interests. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rongji Hui and Jiabao Xu contributed equally to this work. Contributor Information Bin Cong, Email: cong6406@hebmu.edu.cn. Chunling Ma, Email: chunlingma@hebmu.edu.cn. Di Wen, Email: wendi01125@hebmu.edu.cn. References