Male C57BL/6 mice, aged 6 to 8 weeks, were procured from SLAC Laboratory in Shanghai, China, and were randomly assigned to three groups (1): Uninfected + Vehicle group, (2) Infected + Vehicle group, and (3) Infected + Dex group (n = 6 per group). All animal experiments were conducted in accordance with the National Institutes of Health’s Guidelines for the Care and Use of Laboratory Animals, and this study was approved by the Animal Ethics Committee of Naval Medical University (access number SCXK [Shanghai] 2022-0009). To establish a mouse model of schistosomiasis, mice were percutaneously exposed to 25 S. japonicum cercariae released from laboratory infected Oncomelania hupensis snails, which were sourced from the National Institute of Parasitic Disease at the Chinese Center for Disease Control and Prevention (Shanghai, China). From days 21 to 42 post-infection, mice in the dexamethasone group were intraperitoneally injected with 1 mg/kg dexamethasone (MK-125, Selleck, Shanghai, China) every other day, while the vehicle-treated groups were administered an equivalent volume of the corresponding vehicle. Mice were euthanized at the end of the treatment period for further analysis (Supplementary Figure S1).

Naive CD4⁺ T cells were purified from C57BL/6 mice and cultured for T-helper (Th) cell differentiation as described in earlier studies (31, 32). Briefly, CD4⁺ T cells were isolated from the spleens of C57BL/6 mice using a Naive CD4⁺ T Cell Isolation Kit (#130-117-043, Miltenyi Biotec, Bergisch Gladbach, Germany) according to the manufacturer’s instructions. Next, naive CD4⁺ T cells (1×10⁶) were plated in a 24-well plate precoated with 1 µg/mL anti-CD3e antibody (Cat# 100340, Biolegend) and 1 µg/mL anti-CD28 antibody (Cat# 102116, Biolegend). Th2 cell differentiation was induced by culturing cells in the presence of 20 ng/mL murine IL-2 (Cat# 714604, Biolegend), 10 ng/mL murine IL-4 (404-ML, R&D Systems), 10 µg/mL anti-IL-12 (Cat# 505308, Biolegend), and 10 µg/mL anti-interferon (IFN)-γ (Cat# 505834, Biolegend) for 3 days. Afterward, 2 µM dexamethasone was added, and the cells were incubated for 24 hours before cytokine expression was assessed.

Total RNA was extracted using TRIzol^® reagent (Invitrogen^® 15596018) following the manufacturer’s protocol. Following RNA extraction and purification, RNA-seq libraries were constructed using the Illumina Paired-End Sample Prep kit and sequenced on the Illumina NGS platform. The obtained high-quality reads were aligned to the Mus musculus GRCm38 reference genome using STAR. Based on the alignment results, gene-level read counts were determined using Kallisto, and differentially expressed genes (DEGs) were identified using the DESeq2 algorithm with the following criteria: |log2FC| > 1 and P-value < 0.05. DEGs were selected for further functional analysis, including pathway enrichment, protein-protein interaction, and regulatory network analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the R package clusterProfiler. Protein-protein interaction (PPI) networks were constructed using STRING (https://string-db.org). Transcription factors were identified based on the transcriptional regulatory relationships documented in the TRRUST database (https://www.grnpedia.org/trrust/).

In the present study, the public single-cell RNA-seq dataset (GSE220286) was downloaded from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/). Raw gene expression matrices were imported into R (version 4.2.2) and converted to Seurat objects using the Seurat R package (version 5.0.1) (33). The percentage of mitochondrial genes was calculated using the PercentageFeatureSet function, and the data were normalized using the NormalizeData function. Highly variable genes were identified using the FindVariableFeatures function. Principal Component Analysis (PCA) was performed using the RunPCA function, and the top 30 principal components were utilized to facilitate dimensionality reduction and clustering analysis using Uniform Manifold Approximation and Projection (UMAP). Clusters were annotated based on the expression of known marker genes and visualized using the integrated tools within the Seurat package. SCENIC analysis was performed to infer transcription factor regulatory networks from the single-cell RNA-seq data, utilizing the cisTarget databases via the RcisTarget package (34). Cytoscape software was used to visualize the transcription factor-target gene regulatory networks (35).

For immunohistochemical analysis, tissue sections were deparaffinized and rehydrated. Endogenous peroxidase activity was then blocked using a 3% H2O2 solution. Antigen retrieval was performed with citrate buffer. To reduce nonspecific binding, sections were incubated with 10% goat serum. Subsequently, they were incubated overnight at 4°C with primary antibodies, including anti-GATA3 (ab199428, Abcam,1:500) and anti-PPARG (WL01800, WanLeiBio, 1:200). The next day, sections were treated with secondary antibodies(ab6721, Abcam, 1:1000), followed by visualization with a DAB staining kit(Abcam), counterstaining with hematoxylin, and sealing with neutral gum.

Protein expression levels were determined by Western blot analysis. Briefly, total protein from cells was extracted using RIPA lysis buffer supplemented with protease inhibitors and phosphatase inhibitors to prevent protein degradation and dephosphorylation(Solarbio). Protein concentration was measured using the BCA Protein Assay Kit(Pierce). Next, Equal amounts of protein (20μg) for each sample was loaded onto a 10% SDS-PAGE gel and separated. The proteins were then transferred to polyvinylidene difluoride (PVDF) membranes. The membranes were blocked for 1 hour at room temperature with 5% non-fat milk in Tris-buffered saline containing 0.1% Tween-20 (TBST). Following this, the membrane was incubated overnight at 4°C with primary antibodies: anti-GATA3 (ab199428, Abcam, 1:1000), anti-PPARG (WL01800, WanLeiBio, 1:500), or anti-GAPDH (Abcam, ab181602, 1:10000). The secondary antibody used was goat anti-rabbit IgG (Bioss, bs-0295G, 1:1000).

cDNA synthesis was performed using the HiScript II 1st Strand cDNA Synthesis Kit (R211-01, Vazyme Biotech Co.). Quantitative PCR (qPCR) was performed with primers specific for IL-4, IL-5, IL-13, Gata3, Pparg, and Gapdh, which were synthesized by the Beijing Genomics Institute (BGI) (see Supplementary Table S1). ChamQ Universal SYBR qPCR Master Mix (Q711-02, Vazyme Biotech Co.) was employed for the qPCR assays. Relative gene expression levels were calculated using the 2^−ΔΔCt method, with GAPDH serving as the internal control for normalization.

To assess the size of hepatic egg granulomas, liver tissue sections were stained with Mayer’s hematoxylin and eosin (H&E). The maximum (longest) and minimum (shortest) diameters of each egg granuloma were measured under a microscope, and the number of granulomas was counted per section. The area of each granuloma was approximated as the product of the maximum and minimum diameters (Area = maximum diameter × minimum diameter). The total granuloma area for each liver sample was calculated as the sum of all granuloma areas, and the average granuloma area per sample was determined by dividing the total area by the number of granulomas.

For fibrosis evaluation, liver sections were stained with Masson’s trichrome. The degree of fibrosis was quantified by calculating the fibrosis score based on the color intensity and area of collagen fibers in the section. Both the color intensity and area of collagen fibers were graded on a scale from 1 to 4. The final fibrosis score for each sample was calculated by multiplying the color grade by the area grade.

All pathological features were evaluated by two pathologists in a blinded manner. The assessors were unaware of the sample group assignments, minimizing potential bias.

Single-cell suspensions were prepared and adjusted to a concentration of 1×10⁶ cells in 100 µL. For surface staining, cells were first incubated on ice with anti-CD16/CD32 antibody (Cat# 100340, Biolegend) to block Fc receptors, followed by staining with APC-conjugated anti-mouse CD4 antibody (Cat# 100516, Biolegend). For intracellular cytokine staining, cells were stimulated for 4 hours using a cell activation cocktail containing Brefeldin A (Biolegend) to inhibit cytokine secretion. After stimulation, they were fixed with Fixation Buffer (Biolegend) and permeabilized using Permeabilization Wash Buffer (Biolegend). Intracellular IL-4 and IFN-γ was stained using PE-conjugated anti-mouse IL-4 antibody (Cat# 504104, Biolegend) and PE-conjugated anti-interferon (IFN)-γ (Cat# 505834, Biolegend). Data acquisition was performed on a FACS Calibur flow cytometer, and analysis was conducted using FlowJo software.

GraphPad Prism software version 9.3.1 was used for statistical analysis. Data were expressed as the mean ± standard deviation (SD). Student’s t-test was used to assess differences between two groups, and one-way ANOVA followed by Tukey post-test was used for comparisons among three or more groups. P-values less than 0.05 were considered statistically significant.

To investigate the effect of GR activation on the hepatic Th2 response in schistosomiasis, dexamethasone was administered to mice infected with Schistosoma. Egg counting, HE staining, Masson staining, and qPCR were employed to assess the degree of liver fibrosis and the levels of Th2 cytokines in schistosomiasis. As anticipated, the egg burden was comparable between the Infected + Vehicle group and the Infected + Dex group (Figure 1B). HE staining revealed that, compared to the Uninfected + Vehicle group, the number of egg granulomas was significantly higher in the liver of the Infected + Vehicle group. In contrast, the average granuloma size in the liver was significantly lower in the Infected + Dex group. At the same time, Masson staining illustrated extensive collagen deposition within egg granulomas, with fibrosis scores significantly higher in the Infected + Vehicle group compared to controls, whereas these scores were markedly lower in the Infected + Dex group (Figure 1A). Additionally, qPCR results indicated that the expression levels of type 2 effector cytokines, including IL-4, IL-5, and IL-13, were significantly higher in the Infected + Vehicle group compared to the control group and significantly lower in the Infected + Dex group (Figure 1C). Taken together, these findings suggest that GR activation can suppress the Th2 immune response and mitigate schistosomiasis-associated liver fibrosis.

Th2 cells play an essential role in type 2 immune responses. To further investigate the role of GR in Th2 cells, splenic CD4⁺ naive T cells were isolated and induced to differentiate into Th2 cells in vitro, followed by treatment with dexamethasone. Flow cytometry analysis demonstrated that the purity of the extracted CD4⁺ T cells reached 99% (Figure 2A). After in vitro induction, the number of IL-4-positive cells significantly increased, indicating the successful polarization of Th2 cells (Supplementary Figure S4). Following dexamethasone treatment, qPCR analysis exposed that the mRNA expression levels of type 2 cytokines, including IL-4, IL-5, and IL-13, were decreased in the Dex-treated group (Figure 2D). Additionally, the flow cytometry result confirmed a significant reduction in both the proportion of IL-4-positive cells and the mean fluorescence intensity of IL-4 compared to the control group (Figures 2B, C). These findings collectively suggest that GR activation significantly inhibits Th2 cell function, leading to a reduction in the translation and secretion of type 2 cytokines.

To identify key regulatory genes influenced by GR activation, global gene expression profiling was carried out using RNA-seq on dexamethasone-treated Th2 cells. Differential expression analysis yielded a total of 2,272 DEGs compared to the control group, with 1,226 upregulated and 1,046 downregulated genes (Figure 3A). GO analysis indicated significant enrichment in biological processes such as regulation of leukocyte differentiation, regulation of inflammatory response, negative regulation of cytokine production, positive regulation of response to external stimulus, and negative regulation of the immune system process (Figure 3B). Meanwhile, KEGG pathway analysis highlighted the involvement of several key signaling pathways, including the NF-kappa B signaling pathway, MAPK signaling pathway, Rap1 signaling pathway, and TNF signaling pathway (Figure 3C). Afterward, a protein-protein interaction (PPI) network was constructed using the STRING database, and MCODE analysis identified three significant gene modules. Specifically, module 1 was enriched for genes involved in lymphocyte differentiation, while modules 2 and 3 were associated with ribosome biogenesis and phospholipid metabolism, respectively (Figure 3D, Supplementary Figures S2A-C). Based on the TRRUST database, 15 transcription factors that may regulate the expression of genes in Module 1 were identified, and a TF-mRNA regulatory network was generated (Figure 3E).

To examine transcription factors that drive Th2 differentiation in response to schistosome egg granulomas, cell-type annotation was conducted, and the SCENIC software package was used to identify key regulons from single-cell RNA-seq data (Supplementary Figures S3A-E). Our analysis revealed three distinct cell populations corresponding to Th1, Th2, and Th17 cells (Figure 4A). The SCENIC workflow comprises three primary stages, namely coexpression analysis, motif enrichment for target genes, and regulon activity scoring. Utilizing this methodology, five key regulons associated with the transcription factors Gata3, Pparg, Nfe2l1, Runx2, and Klf7 were identified (Figure 4B). Based on these findings, a transcriptional regulatory network that illustrates the interactions among these transcription factors and their corresponding target genes was constructed, thereby providing insight into the regulatory mechanisms that govern Th2 cell polarization (Figure 4C).

To investigate the specific transcription factors that inhibit egg-induced Th2 responses upon GR activation, single-cell RNA-seq data were integrated with transcriptome sequencing. This method identified two important transcription factors, namely Gata3 and Pparg (Figure 5A). To validate their roles, their expression was detected in Th2 cells in vitro. The results showed that their expression levels were significantly lower in the Dex-treated group compared to the control group (Figures 5B, C). Further examination of the liver tissue in vivo displayed that, compared with the Uninfected + Vehicle group, the expression levels of Gata3 and Pparg were significantly higher in the Infected + Vehicle group but were markedly lower in the Infected + Dex group (Figure 5E). Similarly, compared with the Uninfected + Vehicle group, the number of Gata3 and Pparg-positive cells was significantly higher in the Infected + Vehicle group, while the number of positive cells was significantly lower in the Infected + Dex group (Figure 5D). These results conjointly indicate that GR activation significantly inhibits Th2 cell function by down-regulating the expression of Gata3 and Pparg, thereby inhibiting the Th2 responses in schistosomiasis.