Exploring the antidepressant mechanisms of Guipi pill: A focus on gut-brain axis and immunity modulation

Abstract

BACKGROUND

Depression is a multifactorial neuropsychiatric disorder involving genetic, neuroendocrine, and environmental factors. Traditional Chinese medicine offers holistic treatments, such as Guipi pill (GPP), which tonifies the spleen, nourishes the heart, and calms the mind, aligning with patterns like heart-spleen deficiency. The gut-brain axis, modulated by immune dysregulation and inflammation, is implicated in depression. However, the molecular mechanisms of GPP’s antidepressant effects via this axis remain unclear. We hypothesize that GPP exerts antidepressant effects by regulating immune-related targets in the gut-brain axis.

AIM

To investigate the mechanism by which GPP exerts antidepressant effects via gut-brain axis regulation.

METHODS

This basic study integrated bioinformatics and animal experiments. Transcriptomic data from patients with inflammatory bowel disease and depression were obtained from the Gene Expression Omnibus database. Differentially expressed genes were identified, and shared genes defined gut-brain axis dysregulation. A chronic unpredictable mild stress rat model assessed depressive-like behaviors and transcriptomic changes. Expression quantitative trait loci analysis evaluated genetic associations at bulk and single-cell levels. Statistical methods included t-tests and odds ratio (OR) calculations.

RESULTS

GPP significantly ameliorated depressive-like behaviors in chronic unpredictable mild stress rats (P < 0.01). Transcriptomic analysis identified 1349 differentially expressed genes post-intervention. Intersection analysis revealed seven gut-brain axis-related targets, with granzyme A (GZMA) as the key gene associated with reduced depression risk (OR < 1, P < 0.05). At the single-cell level, GZMA expression positively correlated with CD8+ effector T cells (OR > 1, P < 0.05) and negatively with CD8+ naïve cells (OR < 1, P < 0.05). GPP modulated GZMA expression in these CD8+ T cell subsets.

CONCLUSION

GPP alleviates depression by modulating the gut-brain axis through GZMA regulation in CD8+ T cell subsets, highlighting an immune-mediated pathway for traditional Chinese medicine.

Key Words: Guipi pill; Depression; Gut-brain axis; Traditional Chinese medicine; Immune regulation; Transcriptomics; Granzyme A; Chronic unpredictable mild stress; Inflammatory bowel disease; Expression quantitative trait loci

Core Tip: This study demonstrates that the traditional formula Guipi pill (GPP) alleviates depression by modulating the gut-brain axis via immune regulation. GPP significantly improved depressive-like behaviors in a chronic unpredictable mild stress rat model and altered the expression of 1349 genes, with GZMA playing a key role. Mechanistically, GPP enhanced GZMA expression in CD8+ T-cell subsets, promoting effector T cells while reducing naïve T cells. These results suggest a novel immune-mediated pathway for GPP’s antidepressant effect.

INTRODUCTION

Depression is a multifactorial neuropsychiatric disorder involving intricate crosstalk between genetic vulnerability, neuroendocrine dysregulation, and environmental stressors[1]. While current treatments, such as antidepressants targeting monoamine neurotransmitters (e.g., serotonin), help many patients[2], nearly 50% of patients exhibit inadequate responses to first-line treatments, increasing risks of relapse and side effects[3]. This highlights an urgent need for innovative therapies that address depression’s multifaceted nature beyond just brain chemistry. Recent advances implicate the gut-brain axis - a two-way communication system between the gut and brain - as a pivotal mediator, where dysbiosis-driven systemic inflammation can disrupt neural circuits and contribute to depressive pathophysiology, including depressive symptoms like low mood and cognitive fog[4,5]. Concurrently, immune system dysregulation emerges as a critical bridge where gut inflammation triggers brain-related immune responses, leading to central nervous system dysfunction[6,7]. Despite these insights, therapies that effectively target this systemic gut-brain-immune network are scarce, leaving a critical gap in treatment options.

Traditional Chinese medicine (TCM) with its holistic approach to balancing body and mind, offers therapeutic outcomes that address both psychological and somatic symptoms of depression[8]. Unlike Western medicine's focus on isolated symptoms, TCM treats depression by addressing interconnected systems, often alleviating both emotional distress and physical issues like digestive problems[8]. Guipi pill (GPP), a classic TCM formula, is commonly prescribed for depression linked to “heart-spleen deficiency” - a pattern involving weakened digestive and emotional regulation - supported by its historical use for gastrointestinal complaints[9]. Modern pharmacological studies suggest GPP influences potential mechanisms, including neurotransmitter regulation, neuroendocrine modulation, and anti-inflammatory pathways[10-12]. However, the precise molecular targets and mechanisms through which GPP modulates the gut-brain axis and specific immune factors remain largely unexplored and poorly defined.

To address these gaps, our study uses an innovative, integrative approach combining genetic epidemiology with animal experiments. We hypothesize that GPP’s antidepressant effects stem from modulating key immune targets in the gut-brain axis. By analyzing human genetic data [including expression quantitative trait loci (eQTL) for gene regulation and single-cell eQTL for cell-specific effects] alongside transcriptomic profiles from a chronic unpredictable mild stress (CUMS) rat model - a validated mimic of human depression - we identified and tested immune modulators like granzyme A (GZMA), a protease enzyme involved in immune responses (Figure 1). This rigorous method allows us to pinpoint cell-level mechanisms and validate GPP’s effects in vivo, providing a novel framework for depression research and evidence to modernize TCM through cutting-edge tools. This study provides a novel framework for understanding depression etiology through the gut-brain axis and offers molecular evidence supporting the modernization of TCM formulae like GPP by leveraging contemporary genetic and bioinformatic tools.

Figure 1 Schematic illustration of the study flow. GEO: Gene Expression Omnibus; MDD: Major depressive disorder; IBD: Inflammatory bowel disease; DEGs: Differentially expressed genes; eQTL: Expression quantitative trait loci; PBMC: Peripheral blood mononuclear cell; LD: Linkage disequilibrium; CUMS: Chronic unpredictable mild stress; OFT: Open field test; WMT: Water maze test; IVW: Inverse-variance weighted; MR: Mendelian randomization; MR-PRESSO: MR-pleiotropy residual sum and outlier; GZMA: Granzyme A.

MATERIALS AND METHODS

Bioinformatics analysis of gut-brain axis-related target genes in depression

To identify shared molecular pathways between inflammatory bowel disease (IBD) and major depressive disorder (MDD), we conducted a comprehensive bioinformatics analysis. This approach was chosen because IBD and depression often co-occur, and genetic overlaps can reveal gut-brain axis dysregulation, supported by prior studies linking immune genes to both conditions[13].

Data acquisition

Transcriptomic datasets for IBD and MDD were obtained from the National Center for Biotechnology Information Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). The IBD datasets included GSE10616, GSE20881, and GSE179285, whereas the MDD datasets comprised GSE19738, GSE52790, and GSE98793. Detailed sample characteristics and experimental descriptions are summarized in Table 1.

Table 1 The detailed information of the platform.

Series number	Platform	Number of patients	Number of healthy controls	Sample type
GSE10616	GPL5760 (Affymetrix GeneChip Human Genome U133 Plus 2.0 Array)	42	16	Ileum and colon
GSE20881	GPL1708 (Agilent-012391 Whole Human Genome Oligo Microarray G4112A)	99	73	Ileum and colon
GSE179285	GPL6480 (Agilent-014850 Whole Human Genome Microarray 4x44K G4112F)	223	31	Ileum and colon
GSE19738	GPL6848 (Agilent-012391 Whole Human Genome Oligo Microarray G4112A)	34	33	Whole blood
GSE52790	GPL17976 (hGlue_3_0_v1 Affymetrix Human hGlue_3_0_v1 Array)	12	10	Whole blood
GSE98793	GPL570 (HG-U133_Plus_2 Affymetrix Human Genome U133 Plus 2.0 Array)	64	128	Whole blood

Data processing and DEG identification

Raw microarray data underwent preprocessing - including background adjustment, normalization, and log₂ transformation - using the affy package in R. When multiple probes mapped to the same gene, the mean expression value was retained. Batch effects and technical variations across datasets were corrected using the sva package. Differentially expressed genes (DEGs) were identified via the LIMMA package, applying a threshold of P < 0.05 and |log₂ (fold change)| > 0.5.

Identification of shared genes between IBD and depression

Common genes between IBD and MDD were identified using Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny) and were subsequently defined as candidate genes potentially involved in IBD-associated depression.

Animal experiment

To validate bioinformatics findings in vivo, we used a CUMS rat model, a validated mimic of human depression that induces behavioral and physiological changes through repeated stressors[14]. This model was selected for its translational relevance, as it replicates depression’s multifactorial nature without severe ethical concerns. All procedures adhered to the Guide for the Care and Use of Laboratory Animals (National Institutes of Health) and were approved by the Institutional Animal Care and Use Committee of Guangxi University of TCM, Protocol No. SCXK (gui) 2020-0032.

The animal protocol was designed to minimize pain or discomfort to the animals. The animals were acclimatized to laboratory conditions (22 ± 1 °C, 12 hours/12 hours light/dark, 52% ± 2% humidity, ad libitum access to food and water) for freely prior to experimentation. All animals were euthanized by pentobarbital sodium for tissue collection.

Preparation of drugs

The GPP used in this study were a commercially available, approved over-the-counter TCM product. Specifically, they were purchased from a local pharmacy in Nanning, China (the components were showed in Table 2). The pills were manufactured by Zhongjing Wanxi Pharmaceutical Co., Ltd. (Lot No. 220516). To administer the pills to rats, the pill coating was removed, and the inner contents were ground into a fine powder. This powder was then suspended in an appropriate volume of sterile distilled water at a concentration of 1 g/mL and vortexed thoroughly immediately before oral gavage administration. Fluoxetine hydrochloride capsules were used as the positive control drugs and purchased from Patheon France (specification 20 mg × 7 tablets, batch No. 220022).

Table 2 Constituents of Guipi pills.

Chinese name	English name	Botanical plant name	Family and plant parts used	Processing method	Content (g)
Ren Shen	Ginseng	Panax ginseng C.A.Meyer	Root	Dry	6
Huang Qi	Astragalus	Astragalus membranaceus (Fisch.) Bunge	Root	Dry	12
Suan Zao Ren	Sour Jujube Seed	Ziziphus jujuba Mill. var. spinosa (Bunge) Hu ex H. F. Chow	Fruit	Dry	12
Bai Zhu	White Atractylodes Rhizome	Atractylodes macrocephala Koidz	Root	Dry	9
Dang Gui	Dong Quai	Angelica sinensis (Oliv.) Diels	Root	Dry	9
Gan Cao	Licorice Root	Glycyrrhiza uralensis Fisch	Root	Dry	6
Yuan Zhi	Polygala Root	Polygala tenuifolia Willd	Root	Dry	3
Mu Xiang	Costus Root	Aucklandia lappa Decne	Root	Dry	6
LongYan Rou	Longan Fruit	Dimocarpus longan Lour	Fruit	Dry	12
Sheng Jiang	Ginger	Zingiber officinale Roscoe	Root	Dry	12
Fu Ling	Tuckahoe	Poria cocos (Schw.) Wolf	Sclerotium	Dry	9
Da Zao	Jujube	Ziziphus jujuba Mill	Fruit	Dry	12

Rats and treatments

Sprague-Dawley rats [12 weeks old, weighing 180-240 g, specific pathogen-free grade; Animal License No. SCXK (gui) 2020-0032] were obtained from Hunan Slyke Jingda Experimental Animal Co., Ltd. Following a one-week acclimation period under controlled conditions (12-hour light/dark cycle, temperature 22 ± 1 °C, relative humidity 52% ± 2%), the animals were provided food and water ad libitum except during specific experimental procedures. After acclimatization, the rats were randomly assigned to six groups (n = 8 per group): A control group (0.01 mL/g distilled water), a CUMS model group (0.01 mL/g distilled water), a positive control group (2.1 mg/kg fluoxetine hydrochloride), and three CUMS groups treated with GPP at doses of 3.67 g, 7.34 g, and 14.68 g crude drug/kg/day, respectively. The GPP doses were converted from clinical equivalents based on body surface area normalization.

The CUMS protocol (days 1-35) exposed groups to two randomly selected daily stressors (e.g., 24-hour food/water deprivation, 4-hour restraint, 5-minute ice-water swimming, reversed light cycle), avoiding consecutive-day repetition of any single stressor. Drug administration began 14 days post-CUMS initiation, delivered daily for 21 days (until day 35) using weekly-prepared 4 °C-stored doses. The normal group remained unstressed throughout. This design ensured systematic evaluation of stress responses and dose-dependent GPP effects through controlled randomization, standardized CUMS implementation (9 distinct stressors), and rigorous treatment protocols.

Behavioral tests

Behavioral evaluations, including the open field test (OFT) and Morris water maze (MWM), were performed at baseline day 0, day 28, and day 35 of the modeling period. Concurrently, the body weight of the animals was monitored on a weekly basis.

OFT: The test was conducted in a cubic arena (30 cm × 30 cm × 30 cm) subdivided into 16 equal quadrants. Under dim lighting conditions, each rat was first acclimatized to the arena for 2 minutes before a formal 10-minute test session. Their movements were recorded by an overhead camera. Parameters such as total ambulatory distance, duration of immobility, total quadrant crossings, as well as the number of entries and time spent in the central area were automatically quantified. The arena was thoroughly cleaned with an alcohol solution between trials to remove olfactory cues.

MWM test: The MWM apparatus consisted of a circular pool (160 cm in diameter, 50 cm in height) and a submerged hidden platform (12 cm in diameter). The experiment spanned four consecutive days, comprising a three-day navigation test followed by a spatial probe test on day four. Navigation test: Over the first three days, each rat underwent four daily trials from different starting points. The escape latency, defined as the time taken to locate and mount the hidden platform, was recorded. Animals that failed to find the platform within 60 seconds were guided to it and allowed to remain for 15 seconds, with their latency recorded as the maximum time. Spatial probe test: On the fourth day, the platform was removed. Each mouse was allowed to swim freely for 60 seconds, and the time spent in the target quadrant was analyzed as a measure of spatial memory. All behavioral data were acquired and processed using an automated video tracking system (Shanghai Xinfeng Information Technology Co., Ltd.).

Sample collection

On the morning following MWM testing (09:00 am to 11:00 am), overnight-fasted rats were deeply anesthetized with intraperitoneal pentobarbital sodium (50 mg/kg) and euthanized by exsanguination. Whole blood was collected through abdominal aorta blood withdrawal using anticoagulant-free tubes, and clotted at 4 °C for 30 minutes, centrifuged (3000 × g, 10 minutes, 4 °C), and the supernatant aliquoted for enzyme-linked immunosorbent assay before storage at -80 °C. Concurrently, decapitation was performed for acquisition of colon segments, and were snap-frozen in liquid nitrogen and preserved at -80 °C for subsequent processing.

Determination of cortisol and corticotropin-releasing hormone in serum

Serum was isolated by centrifugation and cryopreserved at -80 °C for subsequent assays. Serum cortisol (CORT) and corticotropin-releasing hormone (CRH) levels were quantified employing rat-specific enzyme-linked immunosorbent assay kits (Cat#: H167-1-2, A019-3-1, H205-1-2, and H288-1-2, respectively) procured from Nanjing Jiancheng Bioengineering Institute (Nanjing, Jiangsu Province, China).

Transcriptomics analysis of colon samples by RNA-seq

Total RNA was isolated from colon tissues with TRIzol reagent and quantified using an Agilent 2100 Bioanalyzer. After assessing total and effective library concentrations, indexed libraries were proportionally pooled based on effective concentration and target sequencing depth. The resulting pooled library was diluted to 2 nm and denatured under alkaline conditions, yielding single-stranded DNA.

The library was paired-end sequenced by Shanghai Personalbio Technol Co., Ltd using an Illumina system. The resulting raw sequencing reads underwent quality control processing to generate high-quality clean data, which were then mapped to the reference genome of Rattus norvegicus employing the Bowtie2 alignment tool. DEGs were identified with DESeq2, applying a threshold of employing the Bowtie2 alignment tool. DEGs were identified with DESeq2, applying a threshold of false discovery rate < 0.05 and |log2FC| > 1. Functional enrichment analysis of selected genes was performed using the Kyoto Encyclopedia of Genes and Genomes database to detect relevant metabolic and signaling pathways.

Identification and screening of key target genes underlying the antidepressant effects of GPP

Utilized Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/) to identify the overlapping genes between DEGs of GPP and targets of IBD-related depression, which were identified as therapeutic targets of GPP on IBD-related depression. This intersection highlighted potential mechanisms linking GPP’s effects to gut-brain pathways.

Integrative analysis of eQTL and Mendelian randomization revealed key targets of GPP

Mendelian randomization data sources: All cis-eQTL data used in this investigation were obtained from the eQTLGen consortium (https://www.eqtlgen.org/cis-eqtls.html), a resource comprising exclusively blood-derived cis-eQTLs. Instrumental variables (single nucleotide polymorphisms) were identified based on the following criteria: A genome-wide significance level of P < 5 × 10^-8, a clumping distance of 10000 kb, and a linkage disequilibrium (LD) threshold of r² < 0.1. Following this filtration process, cis-eQTL data corresponding to 15695 genes were retained for further examination. An intersection analysis between these genes and the genes involved in the antidepressant effects of GPP was subsequently conducted to identify GPP target genes harboring cis-eQTLs.

The MDD outcome dataset (F5_DEPRESSION_RECURRENT), encompassing a total of 245874 samples, was obtained from the FinnGen database (https://storage.googleapis.com/finngen-public-data-r9/summary_stats/finngen_R9_F5_DEPRESSION_RECURRENT.gz). To ensure ethnic homogeneity and reduce potential confounding, both the exposure and outcome Genome-Wide Association Study (GWAS) data were restricted to European ancestry. Given that the Mendelian randomization (MR) analysis used in this study relied solely on publicly accessible summary-level GWAS data, no further ethical clearance or individual consent was necessary.

Two-sample MR analysis: In this study, a two-sample MR analysis was performed using the TwoSample MR package. Cis-eQTLs associated with seven candidate GPP target genes were used as instrumental variables for exposure, with MDD defined as the outcome. To mitigate potential bias attributable to weak instruments, all genetic variants exhibiting an F-statistic lower than 10 were eliminated before conducting the main analysis. The inverse variance weighted (IVW) approach was employed as the principal method for MR, and any exposures for which IVW estimates could not be computed were subsequently discarded. To further strengthen methodological rigor, the following analytical steps were implemented: (1) Examination for potential horizontal pleiotropy using MR-Egger intercept tests; (2) Assessment of heterogeneity among instrumental variables via Cochran’s Q statistics; and (3) A leave-one-out sensitivity analysis to confirm the consistency of the main results.

Screening for and validation of key GPP targets: Following MR analysis, genes exhibiting substantial heterogeneity or evidence of pleiotropy were discarded. Genes that reached statistical significance (P < 0.05) were classified as either high-risk [odds ratio (OR) > 1.0] or low-risk (OR < 1.0) according to their respective ORs. A Venn diagram was subsequently generated using the “VennDiagram” package to visualize the overlap between key candidate genes and both up- and downregulated DEGs. Furthermore, strongly associated genes identified via both IVW and weighted median approaches were depicted in a forest plot generated using the “grid”, “readr”, and “forestploter” packages. Finally, the chromosomal positions of pivotal genes were represented in a circular genomic plot constructed with the “circlize” package.

Single-cell transcriptome-wide MR

Instrument selection and validation: In this MR analysis, genetic variants associated with plasma eQTLs served as instrumental variables. The cis-eQTL data were obtained from the OneK1K database (https://onek1k.org/). Conditionally independent cis-eQTLs showing an association with eGenes at a significance threshold of P < 0.005 were retained. To minimize LD, clumping was conducted using a European LD reference panel, applying a threshold of r² < 0.01. The robustness of each instrumental variable was assessed via F-statistics, and any variant with an F-value below 10, suggestive of possible weak instrument bias, was omitted from further MR and subsequent analyses.

Outcome data source for two-sample MR: The outcome GWAS summary statistics for recurrent MDD were sourced from the FinnGen database (dataset: F5_DEPRESSION_RECURRENT; https://storage.googleapis.com/finngen-public-data-r9/summary_stats/finngen_R9_F5_DEPRESSION_RECURRENT.gz). The dataset comprised 245874 individuals of European ancestry, including 17227 cases and 228647 controls.

MR analysis of single-cell eQTLs: This study conducted two-sample MR analyses using the TwoSampleMR package. The analysis employed cis-eQTLs associated with GPP-targets as exposures, with depression serving as the outcome. To minimize bias from weak instrumental variables, all variants with an F-statistic < 10 were excluded prior to analysis. The IVW approach served as the principal analytical method, and any exposure lacking a computable IVW result was discarded. Further sensitivity analyses were performed to validate the robustness of the findings, encompassing MR-Egger regression for the examination of horizontal pleiotropy, Cochran’s Q test to quantify heterogeneity across variants, and a leave-one-out analysis to determine the influence of individual variants on the overall effect estimate.

Statistical analysis

All statistical analyses were performed using R software (version 4.4.2; R Foundation for Statistical Computing, Vienna, Austria). Behavioral data and biochemical data were assessed for normality and homogeneity of variance. Depending on the data distribution and experimental design, appropriate parametric tests were employed. Specifically, comparisons between two groups were conducted using the unpaired Student’s t-test, while comparisons across multiple groups were performed using one-way or two-way analysis of variance followed by Tukey’s honest significant difference test. Data are presented as the mean ± SD. A P value of less than 0.05 was considered statistically significant.

RESULTS

Identification of DEGs in IBD and depression

We identified 590 DEGs in IBD patients and 881 DEGs in MDD patients (Figure 2A-D). A comparative analysis revealed 30 genes common to both conditions (Figure 2E, Table 3), suggesting potential shared molecular pathways.

Figure 2 Identification of differentially expressed genes in inflammatory bowel disease and depression patients by bioinformatics. A: Heatmap of the differentially expressed genes (DEGs) from inflammatory bowel disease (IBD) patients; B: Volcano plot of the DEGs from IBD patients; C: The heatmap of the DEGs from depression patients; D: Volcano plot of the DEGs from depression patients; E: Venny diagram analysis on the DEGs from IBD patients and depression patients. MDD: Major depressive disorder; IBD: Inflammatory bowel disease.

Table 3 Venny analysis of the differentially expressed genes between inflammatory bowel disease patients and depression patients.

Number	Gene list
1	SLC22A4
2	CHGA
3	MMP9
4	IL1R2
5	GZMA
6	LCN2
7	TMEM45B
8	KLRB1
9	GIMAP7
10	F13A1
11	TBX21
12	B3GNT8
13	S100A12
14	GPR171
15	GZMK
16	KCNJ2
17	HLA-DRA
18	VDR
19	LAD1
20	PRSS3
21	EMP1
22	CD24
23	SLAMF7
24	ITM2C
25	BATF2
26	TCN1
27	OASL
28	CTSG
29	DNASE1 L3
30	CCR7

Antidepressant efficacy of GPP in a CUMS-induced depression rat model

Using the CUMS model, which reliably induces depression-like behaviors, we evaluated the antidepressant potential of GPP. GPP treatment significantly improved behavioral deficits. Specifically, in the open field test, GPP administration enhanced exploratory behavior and reduced anxiety, as evidenced by increased total and central zone movement distances (Figure 3A-C). In the MWM test, GPP-treated rats showed improved spatial learning and memory (Figure 3D and E). The efficacy of GPP was comparable to that of fluoxetine, a standard antidepressant, indicating robust neurobehavioral recovery, particularly in hippocampal-dependent functions.

Figure 3 Animal behavioral test. A: Representative trajectory plots of rats in the open field test; B and C: Total distance and central distance traveled by rats in the open field test; D and E: Morris water maze experiment (spatial exploration experiment). ^aP < 0.05 vs control; ^bP < 0.05 vs chronic unpredictable mild stress model; ^cP < 0.01 vs chronic unpredictable mild stress model; ^dP < 0.01 vs control. CUMS: Chronic unpredictable mild stress; FH: Fluoxetine hydrochloride; GPP-L: Guipi pills-low dose; GPP-M: Guipi pills-medium dose; GPP-H: Guipi pills-high dose.

GPP reverses CUMS-induced elevations in serum CRH and CORT

As illustrated in Figure 4, CUMS led to significant increases in serum CRH and CORT levels. GPP treatment dose-dependently reduced these hormone levels, with the high-dose group showing effects similar to fluoxetine. These results confirm that GPP alleviates depression-associated neuroendocrine dysregulation.

Figure 4 Effects of Guipi pills on the serum corticosterone, corticotropin-releasing hormone, and neuropeptide Y in chronic unpredictable mild stress-induced depression rats. ^aP < 0.01 vs control; ^bP < 0.05 vs chronic unpredictable mild stress model; ^cP < 0.01 vs chronic unpredictable mild stress model. CORT: Corticosterone; CRH: Corticotropin-releasing hormone; NPY: Neuropeptide Y; CUMS: Chronic unpredictable mild stress; FH: Fluoxetine hydrochloride; GPP-L: Guipi pills-low dose; GPP-M: Guipi pills-medium dose; GPP-H: Guipi pills-high dose.

Pathway enrichment analysis of GPP-modulated genes

RNA-seq after GPP treatment identified 1349 DEGs (Figure 5A and B). Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that upregulated genes were involved in phosphatidylinositol 3-kinase-protein kinase B signaling, focal adhesion, neutrophil extracellular trap formation, and interleukin-17 signaling (Figure 5C), pathways linked to cell survival, inflammation, and immune regulation. Downregulated genes were enriched in cardiac muscle contraction, calcium signaling, and cardiomyocyte adrenergic signaling (Figure 5D), suggesting secondary effects on stress-responsive systems. These findings highlight GPP’s potential role in modulating immune-inflammatory processes and cellular resilience, which are relevant to gut-brain communication in depression.

Figure 5 Transcriptomics analysis of the colonic tissues from rats. A: The volcano plot of the differentially expressed genes (DEGs) from Guipi pills vs chronic unpredictable mild stress model group; B: The heatmap of the DEGs from Guipi pills vs chronic unpredictable mild stress model group; C: Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the up-regulated DEGs; D: Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the down-regulated DEGs. CUMS: Chronic unpredictable mild stress; GPP: Guipi pills; KEGG: Kyoto Encyclopedia of Genes and Genomes.

Seven overlapping genes as potential targets of GPP

By intersecting GPP-responsive DEGs with the common DEGs from IBD and depression datasets, we obtained seven overlapping genes: BATF2, CTSG, GZMA, GZMK, KCNJ2, LAD1, and LCN2 (Figure 6, Table 4). These genes represent candidate targets through which GPP may exert therapeutic effects on depression-IBD comorbidity.

Figure 6 Venny analysis of the differentially expressed genes from Guipi pills vs chronic unpredictable mild stress and differentially expressed genes from inflammatory bowel disease patients and depression patients. DEGs: Differentially expressed genes; GPP: Guipi pills.

Table 4 Venny analysis of the differentially expressed genes from Guipi pills vs chronic unpredictable mild stress and differentially expressed genes from inflammatory patients and depression patients.

Number	Gene list
1	BATF2
2	CTSG
3	GZMA
4	GZMK
5	KCNJ2
6	LAD1
7	LCN2

MR implicates GZMA in depression risk

Two-sample MR analysis was performed for the three genes (GZMA, GZMK, KCNJ2) with suitable genetic instruments. GZMA was significantly associated with depression risk (IVW P < 0.05), with higher expression conferring a protective effect (OR = 0.886, 95% confidence interval: 0.816-0.962; Figure 7, Table 5). Consistent with this, GPP upregulated GZMA expression in the colon tissue of CUMS rats (Figure 8), suggesting GZMA as a key mediator of GPP’s action along the gut-brain axis.

Figure 7 Forest plot of Mendelian randomization analysis between depression and GZMA, GZMK, and KCNJ2 genes. MR: Mendelian randomization; CI: Confidence interval; SNP: Single nucleotide polymorphism.

Figure 8 Mendelian randomization analysis results of GZMA and depression. A: Forest plot of Mendelian randomization (MR). The forest plot showed that most single nucleotide polymorphism effects were located to the left of the line of no effect, suggesting that up-regulation of GZMA reduced the risk of depression; B: Funnel plot of MR; C: Scatter plot of MR; D: Result of leave-one-out analysis. MR: Mendelian randomization; SNP: Single nucleotide polymorphism.

Table 5 Mendelian randomization analysis of the genes GZMA, GZMK, and KCNJ2.

Gene	NSNP	Method	B	SE	OR	OR_lci95	OR_uci95	P value
GZMA	7	MR Egger	-0.09239	0.078431	0.911751	0.781834	1.063256	0.291817
GZMA	7	Weighted median	-0.08458	0.053111	0.918895	0.82805	1.019706	0.111255
GZMA	7	Inverse variance weighted	-0.12065	0.041886	0.886347	0.816488	0.962183	0.003972
GZMA	7	Simple mode	-0.21042	0.087087	0.810241	0.683101	0.961046	0.052129
GZMA	7	Weighted mode	-0.07722	0.058268	0.925688	0.825784	1.037679	0.233323
GZMK	11	MR Egger	-0.02138	0.083978	0.978844	0.830291	1.153977	0.804737
GZMK	11	Weighted median	-0.01697	0.034472	0.983177	0.918942	1.051902	0.622597
GZMK	11	Inverse variance weighted	-0.00035	0.030074	0.999651	0.94243	1.060346	0.990735
GZMK	11	Simple mode	0.069455	0.064164	1.071923	0.945248	1.215574	0.304464
GZMK	11	Weighted mode	-0.02461	0.038771	0.975686	0.90429	1.052719	0.539766
KCNJ2	10	MR Egger	-0.0695	0.060344	0.932864	0.828805	1.049987	0.28271
KCNJ2	10	Weighted median	-0.05446	0.037879	0.946994	0.879234	1.019977	0.150487
KCNJ2	10	Inverse variance weighted	-0.05834	0.03037	0.943326	0.888812	1.001183	0.054721
KCNJ2	10	Simple mode	-0.04438	0.05821	0.956592	0.853448	1.072201	0.465342
KCNJ2	10	Weighted mode	-0.05748	0.042219	0.944141	0.869159	1.025592	0.206474

NSNP: Number of single nucleotide polymorphism; OR: Odds ratio.

Cell-type-specific effects of GZMA expression on depression across immune subsets

Using single-cell eQTL data from 14 immune cell types, we evaluated the effect of GZMA expression on depression risk. Significant associations were observed in seven cell types (Figure 9). For instance, elevated GZMA in CD8+ effector T cells was associated with increased depression risk, whereas expression in CD8+ naive cells was protective. Sensitivity analyses supported the robustness of these findings (Figures 8 and 9). These results underscore the cell-type-specific immune mechanisms through which GZMA may influence depression.

Figure 9 Mendelian randomization results for gene GZMA visualized in forest plot. CI: Confidence interval; OR: Odds ratio.

DISCUSSION

By integrating evidence from genetic epidemiology with experimental validation, this study elucidated a novel mechanism through which GPP alleviates depression via the gut-brain axis, centered on the modulation of GZMA expression within specific CD8+ T cell subsets. This approach precisely localized the effects of a complex TCM formula to specific immune cell populations, moving beyond a generalized multi-target model to a more defined, cell-level understanding. The molecular mechanism by which GPP exerts antidepressant effects through the gut-brain axis was illustrated in Figure 10.

Figure 10  The molecular mechanism by which Guipi pills exerted antidepressant effects through the gut-brain axis. Following Guipi pills intervention, it specifically upregulated GZMA expression in CD8+ NC cells by modulating the intestinal immune microenvironment. GZMA activated the cyclic adenosine monophosphate (cAMP)/protein kinase A/cAMP response element-binding protein signaling pathway by inhibiting phosphodiesterase 4B, thereby promoting GPX4 transcriptional upregulation and enhancing ferroptosis resistance. Concurrently, cAMP response element-binding protein pathway activation further facilitated neuroplasticity. These effects collectively alleviated neuroinflammation, protected neurons, and ultimately ameliorated depressive symptoms. GPP: Guipi pills; cAMP: Cyclic adenosine monophosphate; PKA: Protein kinase A; CREB: Cyclic adenosine monophosphate response element binding protein; PDE4B: Phosphodiesterase 4B.

GZMA, a serine protease, functions beyond its classical role in inducing apoptosis and has recently been implicated in inflammatory processes such as pyroptosis[15]. Our analyses indicated that GZMA expression was downregulated in patients with comorbid IBD and depression, suggesting its potential as a key molecular link between gut inflammation and mood disorders. MR analysis further established that a genetically predicted increase in GZMA expression was a protective factor against depression risk (OR = 0.886, 95% confidence interval: 0.818-0.959, P = 0.004).

Critically, single-cell MR analysis revealed the context-dependent functionality of GZMA: Its elevated expression in CD8+ terminal effector T cells was associated with an increased risk of depression, whereas its expression in CD8+ naive/central memory-like T (CD8+ NC) cells appeared to be protective. This heterogeneity suggested that the biological outcome of GZMA signaling is determined by the cellular microenvironment. The significant upregulation of GZMA in the colonic tissues of CUMS rats following GPP intervention pointed towards an immune-restorative mechanism. We hypothesize that GPP may preferentially enhance GZMA expression within the protective CD8+ NC cell subset, thereby recalibrating the immune homeostasis disrupted by chronic stress and facilitating beneficial gut-brain communication.

GPP, a classic TCM formula for treating “heart-spleen deficiency”, has documented clinical efficacy in improving both gastrointestinal symptoms and emotional disturbances, which aligns with our findings[16]. This study provides a modern scientific interpretation of the TCM principle of “treating the spleen to nourish the heart”. By converging the complex actions of GPP onto a specific target (GZMA) within a specific cellular “location” (CD8+ NC cells), our work offers a paradigm for TCM research: Integrating bioinformatic analyses to translate holistic therapeutic effects into quantifiable and localizable contemporary biological language. This not only deepens the understanding of GPP’s mechanism but also provides a methodological reference for the modernization of other complex TCM formulas.

The downstream mechanisms by which GZMA mediates its neuroprotective effects warrant further investigation. Evidence suggested that GZMA can suppress PDE4B activation in intestinal epithelial cells, triggering the cyclic adenosine monophosphate (cAMP)/protein kinase A/cAMP response element-binding protein signaling cascade and subsequently promoting the transcriptional activation of glutathione peroxidase 4, a key regulator of ferroptosis resistance. This pathway may confer protection to both intestinal and neuronal cells. Furthermore, the activation of the cAMP response element-binding protein pathway is intrinsically linked to neuroplasticity and antidepressant effects[17]. Future studies are needed to directly validate whether the GPP/GZMA axis ultimately influences central nervous system function via these or other signaling pathways within the gut-brain axis.

Several limitations should be acknowledged. Although eQTL and single-cell eQTL analyses indicated a genetic regulatory link, the causal relationship between GPP’s antidepressant effects and GZMA upregulation remained to be fully established. Further validation - for example, through GZMA knockdown experiments in animal models - would help clarify whether GPP’s efficacy depends specifically on this pathway. In addition, the study did not examine changes in the gut microbiome following GPP administration. Given the microbiome’s central role in gut-brain signaling, it is possible that GPP’s active constituents influenced immune responses, such as CD8+ T cell function, indirectly through microbial modulation. This represents an important direction for future investigation. Moreover, the specific components of the GPP formulation responsible for elevating GZMA expression have not yet been identified, highlighting the need to isolate and characterize the biologically active compounds. Finally, the mechanism by which intestinal GZMA expression communicates with the brain - whether through neural, humoral, or metabolic pathways - remains unclear. A more detailed understanding of this signaling axis will be essential for elucidating the gut-brain mechanism underlying GPP’s effects.

To our knowledge, this study was the first to propose a mechanistic model in which GPP alleviated depression by upregulating GZMA expression within intestinal CD8+ NC cells, thereby modulating immune homeostasis and gut-brain axis communication. This finding not only offers a scientific basis for the clinical use of GPP but also integrates the principles of TCM with modern immunology and genetics, paving the way for a more mechanistic understanding of traditional medicines.

CONCLUSION

In conclusion, this study systematically clarifies the mechanism through which GPP alleviates depression via the gut-brain axis, specifically by altering GZMA expression in CD8+ T cell subpopulations (CD8+ ET and CD8+ NC cells) and subsequently modifying the intestinal immune microenvironment. Here, we position GZMA as a novel target for gut-brain axis modulation in antidepressant therapy, highlighting the utility of pharmacological agents, including TCM formulations. The results offer new directions and methodological approaches for probing depression’s immune mechanisms and facilitating TCM modernization, while also supplying a theoretical basis for future immune-focused antidepressants targeting the gut-brain axis.