MIF Regulates the Th17/Treg Ratio to Exacerbate Kidney Injury in Chronic Kidney Disease

Abstract

Chronic kidney disease (CKD) is characterized by gradual and progressive deterioration of renal function over time, and its pathogenesis is strongly associated with immune cell imbalance, Th cell diversification, and oxidative stress. This study aimed to investigate the impact of alterations in the Th17/Treg ratio mediated by the secretory protein macrophage migration inhibitory factor (MIF) on renal injury in CKD patients. Single-cell sequencing was performed on peripheral blood mononuclear cell (PBMC) from 15 CKD patients and 20 healthy individuals to compare the characteristics of T-cell subsets, followed by differential signaling pathway analysis. Peripheral blood samples from 20 CKD patients and 10 healthy donors were analyzed using flow cytometry to quantify CD4 + T cell subsets and MIF expression. A 5/6 nephrectomy mouse model and blood CD4 + T cells were extracted to verify the effects of MIF-related proteins on the Th17/Treg ratio and progression of CKD. Besides, AAV-MIF was locally administered into the renal tissue to achieve kidney-specific MIF suppression in CKD model. Single-cell sequencing revealed significant correlation between MIF levels and Th17/Treg ratios, identifying MIF-(CD74 + CXCR4) pathway involvement. Flow cytometry analysis of the clinical cohort demonstrated an elevated Th17/Treg ratio (p < 0.01) in CKD patients compared to normal controls. MIF levels also correlated with renal parameters (Crea, p < 0.001; BUN, p < 0.0001; eGFR, p < 0.0001). In vitro experiments confirmed a significant increase (p < 0.05) in Th17/Treg cells after MIF protein or serum from CKD patients, and these changes were partially reversed by an MIF inhibitor (ISO-1) (p < 0.001). Moreover, renal injury in CKD mice in the AAV-MIF group was significantly reversed. This study revealed that MIF promotes the progression of CKD by regulating the Th17/Treg ratio and that MIF serves as a potential target to prevent CKD.

Graphical Abstract

MIF regulates the Th17/Treg ratio to exacerbate kidney injury in chronic kidney disease

Supplementary Information

The online version contains supplementary material available at 10.1007/s10753-025-02418-x.

Introduction

Chronic kidney disease (CKD) is a noninfectious inflammatory state characterized by a long-term, progressive decline in renal function. Approximately 843.6 million people worldwide are affected by stage 1–5 CKD, according to the Global Prevalence of Chronic Kidney Disease 2019 study [1]. According to statistics provided by the WHO (2020), renal insufficiency is currently the seventh leading risk factor for death. CKD is a growing public health problem due to its increasing prevalence, resulting in premature deaths and significant pressure on global health funding for the treatment of advanced disease. Thus, elucidating the mechanisms underlying the progression of CKD is vital. The complex mechanisms by which CKD leads to chronic inflammation include glomerular and tubular injury, interstitial fibrosis, and activation of the immune system [2].

Macrophage migration inhibitory factor (MIF), a pleiotropic cytokine with proinflammatory properties, is widely expressed in macrophages, monocytes, and vascular smooth muscle cells [3]. MIF initially engages with CD74 and subsequently recruits CXCR or CD44 receptors to create a complex that regulates downstream signaling in both autocrine and paracrine manners. Serving as an upstream mediator within the intricate inflammatory cascade, MIF plays a pivotal role in modulating the expression of a diverse array of inflammatory mediators, including IL-1, MCP-1, IL-6, TNF-α, and INF-γ, and it activates macrophages, T cells, and neutrophils [4]. Given its broad proinflammatory activity, MIF is related to a variety of inflammatory diseases, including CKD [5], cardiovascular disease [6], sepsis [7], and autoimmune disease [8]. MIF has shown potential in predicting the progression or prognosis of various renal diseases. The serum MIF concentration is elevated in acute kidney injury (AKI) patients at the onset or early stages of the disease [9]. Kinga Musiał et al. revealed that the serum and urinary concentrations of MIF were significantly elevated in patients with CKD via ELISA. MIF continues to increase with the progression of CKD and is correlated with fractional excretion [10]. MIF was also found to be a crucial major contributor to the development and prognosis of CKD [10] by regulating oxidative stress and endothelial activation [5].

As a major pathological feature of CKD, the renal inflammatory state is closely linked to T-cell subset differentiation. Naïve CD4 + T cells are differentiated into T-cell subsets, including helper T cells (Th17) and regulatory T cells (Tregs). Several studies have shown that Th17 cells are major renal infiltrative inflammatory factors involved in kidney injury caused by ureteral obstruction [11], acute kidney injury [12], and CKD. However, Foxp3-expressing Tregs inhibited Th17-mediated inflammatory responses through specific immunomodulatory functions and promoted inhibitory cytokine secretion, ultimately protecting against renal injury [13].

Previous single-cell sequencing results suggest that MIF may be closely associated with CKD progression due to chronic inflammation and that the MIF-CD74/CXCR4 pathway may mediate the Th17/Treg imbalance associated with CKD progression [14]. This study aimed to investigate the role of MIF in CKD disease progression.

Methods

Single-cell RNA-seq and Analysis

Single-cell sequence data were obtained from the GEO database (GSE183276), including 15 CKD patients and 20 healthy individuals [15]. In parallel, single-cell RNA sequencing data from unilateral ureteral obstruction (UUO) mouse models were utilized for independent validation (GSE175412). Before analysis, genes detected in fewer than 5 cells were removed, and the cells were subjected to a filtering process based on the following criteria: (1) percentage of mitochondrial genes less than 50%; and (2) number of characterized genes less than 500 or greater than 5000. Two-dimensional uniform manifold approximation and projection (UMAP) plots were employed to visually represent the similarity in gene expression profiles across all cells. To identify differentially expressed genes (DEGs) between distinct clusters, the Wilcoxon test was utilized [14]. To annotate our transcriptome-defined cell clusters, we applied “SingleR” for peripheral blood immune cells and T-cell subpopulations separately. The annotation of clusters was performed using marker genes obtained through Seurat’s FindAllMarkers function as well as a literature survey. Cell-cell communication analysis was conducted using “CellChat” (version 1.6.1) to determine the signaling pathways between Th17 and Treg. Pseudotime trajectory analysis was conducted using “Monocle2” (version 2.14.0) to order cells along disease progression based on differentially expressed genes. Besides, functional annotation of the differentially expressed proteins was conducted through GO classification, KEGG, and Reactome pathway enrichment analyses to elucidate the underlying pathogenic mechanisms. pySCENIC (version 0.12.1) was used to infer upstream transcription factors.

Clinical Data Collection

The samples in this study were obtained from patients in the Department of Nephrology, the Second Hospital of Shanxi Medical University (n = 20), and healthy volunteers from the medical check-up center (n = 10). Inclusion criteria: (1) Diagnosis of end-stage renal disease (CKD-5); (2) Receiving arteriovenous fistula hemodialysis for 5–10 years; (3) Primary etiology of chronic glomerulonephritis or diabetic kidney disease; (4) Discontinuation of immunosuppressants for ≥ 1 year. Exclusion criteria: (1) Active infections (e.g., viral hepatitis, HIV); (2) Current or history of malignancy within 5 years; (3) Autoimmune diseases (e.g., systemic lupus erythematosus, rheumatoid arthritis); (4) Vaccination or biologic therapy within 3 months. This study followed the principles of the Declaration of Helsinki and good clinical practice, as well as institutional regulatory guidelines. Blood was collected intravenously in the fasting state, and laboratory tests such as renal function tests were performed. The quantification of lymphocyte subsets was performed according to previous methods [14]. CKD was defined according to the Kidney Disease Outcome Quality Initiative (KDOQI) [16].

MIF Assay

ELISA for MIF in patient serum (Beyotime, PM715) and mouse serum (Epizyme, HJ402) was conducted, and the manufacturers’ instructions were strictly followed.

Chronic Kidney Disease Model

Wild-type male C57BL/6 mice, aged 6–8 weeks and weighing 18–25 g, were purchased from Nanjing Junke Bioengineering Co., Ltd. (Nanjing, China). The animals were maintained in a specific pathogen-free facility under controlled conditions of 21 ± 1 °C, 60 ± 5% humidity, and a 12-hour light/dark cycle. The experiments were performed after acclimatization for 1 week. All experiments were performed following the Animal Health and Use Policy.

5/6 nephrectomy was used to create a CKD model. The mice in the model group were anesthetized with isoflurane, and 1/3 of each of the upper and lower pole tissues of the left kidney were removed to reset the kidneys. One week later, the entire right kidney was removed via 2-stage surgery, and the kidney was harvested after 4 or 8 weeks of rearing. The other 12 mice were used as the sham operation group. The experimental groups were the sham group (n = 6), the CKD-4 W group (n = 18), and the CKD-8 W group (n = 6). Some CKD mice were pre-transfected with AAV for subsequent experiments.

Knockdown of MIF in CKD Mice

The shRNA sequence targeting the mouse MIF gene (5’-GGGTCTACATCAACTATTACG-3’) was cloned into the AAV9 vector under the U6 promoter to generate the MIF-AAV construct. In vivo delivery of the MIF-AAV construct was achieved using AAV9, while scrambled siRNA (5’-TTCTCCGAACGTGTCACGT-3’) served as the negative control (NC), synthesized by Hanheng Co. Ltd., Shanghai, China. The wild-type (WT) capsid AAV9 or MIF-mutant AAV9 (10¹⁰ vg in 50 µL saline per mouse) was administered via renal pelvis injection to ensure targeted kidney transduction. Renal tissues were collected 4 weeks post-injection to assess MIF knockdown efficiency and functional outcomes.

Detection of Serum Renal Function Indices in Mice

The mice were anesthetized, blood was obtained via eyeball removal, and the blood was placed in yellow procoagulant vessels to promote coagulation. The blood was centrifuged at 3000 rpm for 10 min to obtain the upper layer of serum, which was stored at -80 °C for long-term storage. The creatinine level in mouse serum was measured via a creatinine oxidase assay, and the urea nitrogen level was measured via a urease assay. Details are provided in the instruction manual of the Nanjing Jiancheng Bioengineering Institute Creatinine Detection Kit (C011-2-1) and Urea Nitrogen Detection Kit (C013-2-1).

Pathological Staining

Kidney tissues were immersed in 10% paraformaldehyde fixative at 4 °C for 24 h, followed by paraffin embedding. The tissues were stained with hematoxylin‒eosin (HE) or Masson’s reagent following the routine methods of commercial reagent instructions.

Immunofluorescence Staining

Paraffin sections of mouse kidneys were deparaffinized and blocked after antigen repair for 30 min. Primary antibodies (CD4 (Servicebio, GB15064), CD74 (immunoway, YT5464), and FOXP3 (Servicebio, GB112325)) were added dropwise, and the samples were incubated overnight at 4 °C. Then, secondary antibodies (HRP-conjugated goat anti-rabbit IgG (Servicebio, GB23303)) were added, and the samples were incubated for 50 min at room temperature in the dark. The iF488-tyramide and iF555-tyramide were added dropwise to the slide and incubated for 10 min at room temperature away from light, after which it was washed with TBST. All the sections were incubated with DAPI (Boster Biotech, AR1176) at room temperature for 5 min. The plates were sealed by adding an anti-fluorescence quenching sealer. The sections were observed under a fluorescence confocal microscope (ZEISS LSM 880, Germany) and photographed.

Western Blotting

Mouse kidney tissues were placed in enhanced RIPA lysis buffer and thoroughly ground using a tissue crusher. Quantification was performed via a BCA protein concentration assay kit (Boster Biotech, Wuhan, China) according to the manufacturer’s instructions. Total protein was separated by SDS‒PAGE. The proteins were subsequently transferred to a PVDF membrane. The membrane was blocked with TBST buffer containing 5% skim milk powder at room temperature. Following transfer, the membrane was incubated with a primary antibody at 4 °C overnight to ensure optimal binding. The membrane was subsequently exposed to the corresponding secondary antibody for 1 h at room temperature, facilitating detection and signal amplification. Ultimately, protein signals were visualized in the ChemiDoc system through the employment of enhanced chemiluminescence (ECL) luminescent solutions. Protein expression levels were analyzed via ImageJ software. The target proteins were normalized to the internal reference proteins. The antibodies used in this study were as follows: β-actin (Servicebio, GB15001), CD74 (Servicebio, GB151179), CXCR4 (proteintech, 4B5E4), IL-17 (Servicebio, GB11110), FOXP3 (Servicebio, GB112325), and MIF (Servicebio, GB111354).

CD4 + T-cell Isolation

Human peripheral blood mononuclear cells (PBMCs) were isolated via Ficoll‒Hypaque density gradient centrifugation (Ficoll‒Paque). The extracted human peripheral blood PBMCs were resuspended in a suitable volume of EasySep™ Buffer (STEMCELL, Catalog #20144) to ensure a final cell concentration of at least 5 × 10⁷ cells/mL, and the cell suspension was pipetted into a 5-mL polystyrene round-bottomed tube, to which 50 µL/mL was added. The isolation cocktail (STEMCELL, Catalog #17952 C) was mixed well and incubated at room temperature for 5 min, the Dextran RapidSpheres (STEMCELL, Catalog #50103) were shaken for 30 s, the mixture was added to the cell suspension at a dose of 50 µL/mL, and EasySep™ Buffer was used to mix the suspension. The volume of the suspension was fixed to 2.5 mL via EasySep™ Buffer, and the CD4 cell suspension was purified via a magnetic pole (STEMCELL, Catalog #18000).

Detection of Cell Activity Via Trypan Blue Staining

A total of 10 µL of the mixed liquid was aspirated and placed in a cell counting plate (Invitrogen, C10228), 10 µL of the cell suspension was aspirated and mixed with 10 µL of 0.4% Trypan Blue stain (Invitrogen, T10282), and a Countess™ 3 Automatic Cell Counter (Invitrogen, AMQAX2000) was inserted into the plate. The cells were counted, and viability was calculated according to the manufacturer’s instructions.

Flow Cytometry

The treated naïve CD4 + T cells were washed and resuspended in 100 µL of flow-staining buffer (Invitrogen, 00-4222-26), and 5 µL of FITC-conjugated anti-human CD4 antibody (BioLegend, 317408) was added and incubated for 30 min at 4 °C in the dark. The cells were washed twice with 2 mL of flow-through staining buffer and centrifuged at 500 × g for 5 min at room temperature, after which the supernatant fraction was subsequently discarded. The cells were resuspended in 100 µL of flow-through staining buffer, and 1 mL of FOXP3 fixation/membrane-breaking working solution (Invitrogen, 00-5523-00) was added to each tube and incubated for 45 min at 4 °C in the dark. Two milliliters of 1X Membrane Breaking Solution (Invitrogen, 00-5523-00) was added to each tube, and the samples were centrifuged at 500 × g for 5 min at room temperature. The precipitate was resuspended in the remaining volume of 1X membrane-breaking solution, 5 µL of APC-conjugated anti-human IL-17 antibody (BioLegend, 512334) and PE-conjugated anti-human FOXP3 antibody (BioLegend, 320108) were added, and the mixture was incubated for 30 min at 4 °C in the dark. Following staining, 2 mL of 1× membrane-disrupting solution was added to each tube. The samples were then centrifuged at 500 × g for 5 min at room temperature, the resulting supernatant was discarded, and the procedure was repeated twice. The cells were resuspended in 500 µL of flow cytometry staining solution, and the cells were analyzed via flow cytometry. The results were analyzed via FlowJo software version 10.0.7 (BD Biosciences). Besides, MIF protein (MedChemExpress, HY-P7387) and ISO-1 (MedChemExpress, HY-16692) were added to CD4 + T cells.

Statistical Analysis

The data are presented as the means ± SDs. Normality was determined, and t tests/ANOVAs or Mann‒Whitney U tests were subsequently applied. Tukey’s test or Dunn’s test was used for group comparisons. Spearman’s test was performed for correlation analysis, and Prism 9 (GraphPad software) was used for data visualization. P < 0.05 was considered statistically significant. *P < 0.05, **P < 0.01, **P < 0.01, ***P < 0.001, and ****P < 0.0001 indicate significant differences between the two groups.

Results

Bioinformatics Reveals Significantly Elevated MIF Levels and Th17/Treg Ratios in CKD Patients

Following rigorous screening to exclude ineligible cells and genes, the final dataset comprised 73,963 cells, with 52,314 cells belonging to the CKD group and 21,649 cells in the control group. Using the unsupervised clustering algorithm and labeled genes in each group, the cells were annotated as proximal tubule cells, principal cells, mesangial cells, intercalated cells, endothelial cells, loop of Henle cells, distal tubule cells, T cells, Bs-intercalated cells, connecting tubule cells, NK cells, podocytes, macrophages, B cells, and monocytes via the “SingleR” R package (Fig. 1a). We focused on T cells and identified 7 subpopulations based on marker genes, including CD4_naïve, TFH, CD8, Treg, Th17, Th2, and Th1 (Fig. 1b). Further analysis revealed a significantly increased Th17 cell count, slightly increased Treg cell count, and reduced CD4 naïve cell number in CKD renal tissues (Fig. 1c). The remodeling of T-cell subsets in patients with CKD has attracted our attention. Furthermore, we examined the expression of MIF-related proteins in the renal tissues of CKD patients and found that MIF and CD74 were significantly upregulated (Fig. 1d and e). Specifically, the expression levels of MIF, CD74, and CXCR4 in T cell populations between CKD and normal control represented significant differences (Supplementary Fig. 1a-c). In addition, the MIF-(CD74 + CXCR4) signaling pathway was demonstrated to act in intercellular communication between Treg and Th17 populations (Fig. 1f, h). Furthermore, GO analysis revealed upregulation of pathways including NF-κB signaling pathway and regulation of T cell activation in T cell populations from CKD patients (Fig. 1g). Additionally, pseudotime analysis delineated the differentiation trajectory of CD4 naïve T cells into Th17 and Treg subsets (Fig. 1i). Additionally, upstream transcription factor detection across various cell populations revealed the activation of RELB and CREL in Treg cells, indicating that the NF-κB signaling pathway serves as an upstream hub driving Treg/Th17 imbalance (Supplementary Fig. 2).

Fig. 1.

Identification of cell type interactions in CKD patients and normal controls. (a) Two-dimensional uniform mobility approximation projection (UMAP) plots of single-cell sequence data-sourced cells, colored by cell type; (b) UMAP plots of single-cell sequence data-derived UMAP plots of T-cell subsets; (c) proportions of T-cell subsets in CKD patients and normal controls; (d-e) expression levels of MIF and CD74 in CKD patients and CD74; (f, h) Bubble plots depict ligand-receptor pairs for intercellular communication between Th17 and Treg cells; (g) GO analysis reveals pathways enriched in highly expressed genes of renal T cells from CKD patients; (i) Pseudotime trajectory analysis.

Additionally, we conducted supplementary analysis of the unilateral ureteral obstruction (UUO) mouse model (GSE175412). After two-stage clustering annotation, we identified key immune cell subsets including CD4 + naïve T cells, CD8 + T cells, Th1, Th17 and Treg cells (Supplementary Fig. 3a-f). KEGG pathway analysis revealed that highly variable genes in Treg cells from CKD patients were significantly associated with the Th17 cell differentiation pathway and NF-κB signaling pathway (Supplementary Fig. 3g). Similarly, Reactome pathway analysis confirmed predominant activation of NF-κB signaling in both Th17 and Treg cells from CKD samples, suggesting this pathway may drive the Th17/Treg imbalance observed in CKD patients (Supplementary Fig. 3h). Cell-cell communication analysis revealed that Th17 cells could function as ligand-secreting cells, producing MIF to mediate crosstalk with Treg cells (Supplementary Fig. 3i). Pseudotime trajectory analysis further demonstrated that the differentiation of CD4 + naïve T cells into Th17 and Treg subsets represents a complex dynamic process (Supplementary Fig. 3j).

The Increased Expression of MIF in the Peripheral Blood of CKD Patients Is Correlated with the Th17/Treg Ratio

A total of 20 patients with chronic kidney disease (CKD) and 10 healthy individuals serving as controls were enrolled in this study. Renal function tests revealed a significant increase in the blood urea nitrogen (BUN) and creatinine (Crea) and a decrease in the estimated glomerular filtration rate (eGFR) in patients with CKD (Fig. 2a-c). ELISA revealed elevated serum MIF levels in patients with CKD (Fig. 2d). Spearman’s correlation analysis revealed a statistically significant linear relationship between MIF levels and eGFR (r=-0.7097, p < 0.0001), BUN (r = 0.6987, p < 0.0001), and Crea (r = 0.5939, p < 0.001). In clinical samples, lymphocyte subsets revealed that although total cells (T lymphocytes, B lymphocytes, and NK cells) (%) were not significantly altered in CKD patients, total T lymphocytes (CD3 + CD19-) and Th cells (CD3 + CD4+) (%) were increased (Fig. 2e-g). Specifically, the absolute counts and relative values of Th17 cells were increased (Fig. 2h-i), while those of Treg cells were decreased (Fig. 2j-k), and the Th17/Treg ratio was increased (Fig. 2l). The Th17/Treg ratio was linearly correlated with the levels of Crea, BUN, and MIF (Fig. 2m-o). In addition, other subtypes of Th cells also significantly differed between CKD patients and normal controls (Supplementary Fig. 4).

Fig. 2.

Correlation analysis of immune cell imbalance and kidney dysfunction in CKD patients. (a-c) Renal function laboratory test indices in CKD patients and healthy individuals; (d) ELISA results of serum MIF levels in CKD patients and healthy subjects; (e-k) total cells (%), total T lymphocytes (%), Th cells (%), Th17 and Treg counts and ratios, and Th17/Treg ratios in CKD patients and normal controls; (l) Th17/Treg ratio in CKD patients and normal controls; (m-o) Spearman correlation analysis.

The Th17/Treg Ratio Is Elevated in CKD Mice and Correlates with the Extent of Kidney Injury

For the 5/6 nephrectomized CKD mouse model, kidneys were harvested at 4 and 8 weeks after modeling for testing (Fig. 3a). With the progression of CKD, the decline in renal function levels in mice worsened, and Crea, BUN, and MIF levels were significantly increased (Fig. 3b). Renal pathological staining revealed irregularly shaped glomeruli in the CKD group, with tubular dilatation and vacuolization of tubular epithelial cells, accompanied by renal interstitial inflammatory cell infiltration in the late stage, a significant increase in collagen fibers, and obvious fibrous tissue hyperplasia (Fig. 3c). The expression of CD74, CXCR4, IL-17, and FOXP3 in the renal tissues of the mice in the 5/6 nephrectomy group was significantly greater than that in the control group (Fig. 3d-e).

Fig. 3.

The Th17/Treg ratio is elevated in CKD mice and is correlated with the degree of kidney injury. (a) Schematic diagram of CKD animal model establishment and sampling; (b-c) renal function and pathological changes in CKD mice; (d-e) expression of MIF pathway proteins and expression of IL-17 and FOXP3 in sham, CKD-4 W, and CKD-8 W mice.

MIF Inhibition Reduces the Renal Th17/Treg Ratio and Ameliorates Kidney Injury in CKD Mice

The CKD mouse model was injected with either AAV-NC or AAV-MIF via renal pelvis injection, and all samples were collected after 4 weeks. Western blot analysis confirmed that AAV-MIF injection effectively knocked down MIF expression levels in the kidney (Supplementary Fig. 5). Compared to the Sham group, serum MIF levels, BUN, and Crea values were significantly elevated in the CKD group. This change was alleviated in the CKD + AAV-MIF group, but no significant change was observed in the CKD + AAV-NC group (Fig. 4a-c). HE and Masson staining revealed differences in renal pathological injury across the groups, with collagen fiber area reflecting the degree of fibrosis (Fig. 4d-e). In addition, co-localization of CD4 + T cells with CD74 was relatively higher in the CKD group, and CD4 + FOXP3 + T cells were downregulated in the CKD group, whereas AAV-MIF reversed this change (Fig. 4f-g), as confirmed by WB results (Fig. 4h-k). These findings collectively suggest that MIF knockdown can modulate the Th17/Treg ratio and reverse renal functional damage.

Fig. 4.

MIF inhibitors reduce the renal Th17/Treg ratio and ameliorate renal injury in CKD mice. (a-c) Changes in MIF levels and renal function in the Sham, CKD, CKD + AAV NC, and CKD + AAV MIF groups of mice; (d-e) Pathological changes in the kidneys of the Sham, CKD, CKD + AAV NC, and CKD + AAV MIF groups of mice; (f-k) Immunofluorescence and WB showing the expression of MIF pathway proteins and the expression of IL-17 and FOXP3.

Serum from CKD Patients Induced Altered Th17/Treg Ratios

CD4 + T cells were sorted from peripheral blood PBMCs with normal cell activity. The peripheral blood PBMC viability was determined to be 95% by Trypan blue staining, of which the proportion of CD4 + cells was 51%; the viability of CD4 + T cells sorted by the immunomagnetic bead assay was 96%, and the proportion of CD4 + cells was approximately 97% (Supplementary Fig. 6). The gating strategy was shown (Supplementary Fig. 7). Th17 cells (1.22%), Treg cells (2.90%), and the Th17/Treg ratio (42.07%) were detected. After 6 h of serum stimulation from CKD patients, the differentiation of CD4 naïve T cells to subset cells changed, with the following percentages found: Th17 cells (1.74%), Treg cells (3.4%), and Th17/Treg cells (51.18%). In addition, after 6 h of serum stimulation in CKD patients + 5 µM ISO-1, the Th17/Treg ratio decreased to 26.15% (Fig. 5).

Fig. 5.

Detection of the MIF-regulated Th17/Treg ratio via flow cytometry. (a) Phenotypic characterization of Th17 cells. (b) Phenotypic characterization of subset-T cells, including Th17(%) (c), Treg (%) (d), and the Treg/Th17 ratio (e).

Inhibition of the MIF Pathway Reduced the Th17/Treg Ratio

For CD4 + T cells obtained from peripheral blood PBMC sorting, the addition of 200 ng/mL recombinant MIF protein for 6 h resulted in an increase in Th17 cells from 1.22% to 2.89%, Treg cells from 2.90% to 6.07%, and Th17/Treg cells from 42.07% to 47.61%. In addition, 5 µM ISO-1 stimulation for 6 h decreased the Th17/Treg ratio to 23.97%, which reversed the increase in Th17/Treg ratio in CKD patients to some extent (Fig. 5).

Discussion

In this study, based on single-cell sequencing results, MIF was closely related to the progression of CKD. For the first time, we revealed that the progression of renal injury was correlated with the MIF-CD74/CXCR4 pathway and the Th17/Treg ratio in a 5/6 nephrectomized CKD mouse model and that renal impairment could be partially reversed by MIF-AAV. In addition, we extracted healthy human peripheral blood CD4 + T cells for in vitro experiments and confirmed that MIF could induce a Th17/Treg ratio imbalance, which provides an important therapeutic approach to delay the development of CKD.

MIF is a highly conserved secreted protein expressed in glomerular podocytes, tethered cells, endothelial cells, tubular cells, fibroblasts, vascular smooth muscle cells, and leukocytes [8]. MIF binds to the receptor CD74 and recruits the CD44, CXCR2, CXCR4, and CXCR7 receptors to initiate the downstream ERK1/2, AMPK, and AKT signaling pathways, thus participating in the regulation of the stress response, immune cell activation, and proliferation. MIF serum levels range from 2 to 6 ng/mL under physiological conditions. Presynthesized MIF is stored in intracellular pools, with increased release in response to stimulation by bacterial lipopolysaccharide (LPS), tumor necrosis factor (TNF), or interferon-γ (IFN-γ) to drive various types of chronic inflammation, including atherosclerosis [17], sepsis [18], systemic lupus erythematosus, rheumatoid arthritis [8], malignancies, autoimmune diseases, and organ fibrosis. MIF is involved in a wide range of renal diseases. In acute kidney injury (AKI), MIF mediates the interaction of NLRP3 with waveform proteins to activate and assemble inflammatory vesicles [19]. In a mouse model of glomerulonephritis, MIF/CD74/CD44 signaling promoted the pathological proliferation of tethered membranes and mural epithelial cells [20]. In addition, MIF immune-neutralization or gene deletion has an ameliorative effect on experimental lupus nephritis. In MIF(-/-) MRL/lpr mice, there is a significant reduction in renal macrophage recruitment, as well as the levels of MCP-1, TNF, IL-6, and IL-12 [21, 22]. In addition, glomerular and tubulointerstitial CD74 mRNA in patients with type 2 diabetes mellitus and diabetic nephropathy is increased, and immunohistochemical quantification in a rat model of streptozotocin (STZ)-induced diabetic nephropathy (DN) revealed a significant increase in glomerular CD74 staining [23]. MIF also increases the expression of TNF-associated apoptosis-inducing ligand (TRAIL) and monocyte chemotactic protein 1 (MCP-1) in podocytes and tubular cells, thereby leading to diabetic kidney injury [23]. Several researchers have demonstrated that MIF affects CKD progression by regulating oxidative stress markers (8-OH-dG) and endothelial activation markers (ICAM-1) [5]. In this study, ELISA results revealed elevated serum MIF levels in CKD patients and revealed that MIF levels increased with the progression of CKD.

The role of MIF in the regulation of inflammation, the immune response, and fibrosis is complex. For example, MIF elicits enhanced activation of the PU.1 transcription factor, culminating in the upregulation of Toll-like receptor 4 (TLR4), which is involved in the natural immune response against infection [24]. MIF inhibits cortisol-induced expression of IκB, an inhibitor of NFκB, which leads to an increase in the transcription of NFκB-associated inflammatory genes and inhibits the expression of MAPK phosphatase 1 (MKP-1). Subsequently, dephosphorylation and inactivation of the inflammatory ERK1/2, JNK and p38MAP kinase signaling pathways are reduced [1]. In addition, MIF promotes leukocyte rolling, adhesion, and recruitment by promoting the expression of endothelial cell adhesion molecules (E-selectin, ICAM-1, and VCAM-1) [25]. However, in a mouse model of unilateral ureteral obstruction, overall, MIF deficiency or MIF inhibition exacerbates fibrosis. MIF exerts a protective effect on the kidney by counteracting renal tubular cell cycle arrest and promoting tubular cell proliferation [26]. In this study, single-cell RNA sequencing analysis indicates that certain signaling pathways (particularly NF-κB) might play a role in the regulation of Th17/Treg ratio by MIF, which will be further investigated and refined in subsequent studies.

Specifically, MIF and T-cell-mediated immune injury are mutually regulated. MIF can be produced by activated T cells; however, MIF promotes T-cell proliferation, activation, and migration by inducing the upregulation of adhesion molecules and chemokines or inducing delayed-type hypersensitivity (DTH) [4]. Genetic variation in the MIF promoter regulates the secretion of Th17-spectrum cytokines in PBMCs [27]. MIF activates the NF-κB signaling pathway and promotes Th17 cell differentiation in Hashimoto’s thyroiditis [28]. Furthermore, the visceral adipose tissue of MIF (-/-) mice contains more Treg cells [28]. However, little research has been conducted on the interrelationship between MIF and the Th17/Treg ratio. Ruqing Xu et al. revealed that MIF was involved in modulating the altered Th17/Treg ratio in hepatitis B (HBV) patients treated with radicicic acid sulfur (SFN) [29], which is consistent with our findings.

As major subpopulations of CD4 + T cells, Th17 and Treg cells are associated with and antagonize each other. Th17 cells express IL-17 and C-C-motif chemokine receptors and can recruit lymphocytes, dendritic cells, monocytes, and neutrophils while activating various downstream pathways, including the nuclear factor κB (NF-κB) and mitogen-activated protein kinase pathways, which can lead to kidney injury. ROR-γt cooperates with other transcription factors to induce IL-17 expression, and this process can be inhibited by Treg-expressed Foxp3. Tregs promote the secretion of inhibitory cytokines (IL-10 and TGF-β), which suppress the inflammatory response to the processes of kidney injury, regeneration, and fibrosis [30]. Gilbert R Kinsey et al. demonstrated that Treg-deficient mice had more severe inflammatory infiltrates and renal injury after renal IRI than mice containing Tregs [31].

The Th17/Treg ratio is elevated during the progression of several renal diseases and is strongly associated with the progression and prognosis of CKD [32], allogeneic kidney transplant rejection [33], lupus nephropathy [34], and acute kidney injury [35]. Demp or acarbose reverses T-cell imbalance by inhibiting the SGK1 pathway, thereby delaying the progression of diabetic kidney disease (DKD) in db/db mice [36]. Human placental mesenchymal stem cells (PMSCs) reversed the Th17/Treg balance in rats by increasing PD-1 and downregulating programmed death ligand 1 (PD-L1) expression in PMSCs via cell‒cell contact, thereby alleviating STZ-induced DKD. The Th17/Treg ratio can be used as an important tool for identifying and monitoring renal involvement and disease remission. It may be an important therapeutic target for delaying CKD progression. In addition, multiple signaling factors, such as epigenetic modifications, metabolic pathways (including enhanced glycolysis, lipid synthesis, glutamine catabolism, and highly activated mTOR), and the microbiota, may lead to the transdifferentiation of Tregs into Th17 cells [34]. In fact, the potential transdifferentiation of Treg cells into Th17 cells remains controversial. On one hand, some studies suggest that Treg cells may undergo transdifferentiation into Th17 cells under specific conditions. For instance, myeloid-derived suppressor cells (MDSCs) can promote this conversion by secreting various cytokines, including prostaglandin E2 (PGE2), interleukin-6 (IL-6), and transforming growth factor-beta (TGF-β) [37]. On the other hand, while Treg cells can transform into IL-17 + Foxp3 + double-positive cells, it remains unconfirmed whether these cells can fully differentiate into Th17 cells [38]. Besides, the specific initiating factors that induce alterations in the Th17/Treg ratio in CKD are unclear.

Currently, MIF inhibitors are widely used in clinical research, mainly for anti-tumor treatment. For instance, both imalumab (BAX69) (NCT01765790) and PG1094 (NCT06212076) have entered clinical trial phases. Besides, ISO-1 has shown excellent performance in studies targeting chronic kidney disease in the preclinical stage, demonstrate promising clinical translation potential. For the first time, in this study, ISO-1 was applied to a 5/6 nephrectomized CKD mouse model and in vitro CD4 + T cells, and its protective effect against renal injury and its remodeling effect on CD4 + T cells were demonstrated. ISO-1 was identified as a potent inhibitor that binds to the active site of MIF interconjugative isomerase and blocks the interaction between MIF and CD74, thereby inhibiting leukocyte infiltration, the expression of proinflammatory cytokines and chemokines, and progressive renal injury in which CD74/CXCR4 is involved. Histological parameters of glomerular injury and interstitial inflammation were improved after ISO-1 treatment in mice with lupus nephritis [39]. Hyperglycemia, urinary albumin, and macrophage infiltration were partially reversed after ISO-1 treatment in type 2 diabetic db/db mice [40]. Notably, caution must be exercised as anti-MIF therapy may exert multifaceted effects, including increased infection risks and metabolic dysregulation. MIF deficiency/inhibition increases infection risk by impairing macrophage activation and Th1 responses (reduced IFN-γ/IL-12), elevating IL-10/cortisol, and decreasing bacterial clearance [41–43]. Meanwhile, anti-MIF therapy disrupts the homeostatic balance between its pro-insulin secretory (metabolic positive regulation) and anti-inflammatory (β-cell protective) functions [44, 45]. Rigorous preclinical and clinical trials are required to provide definitive evidence.

A limitation of this study is the limited clinical sample size, which limits the generalizability and replication of the findings to a certain extent, and the inclusion of a larger number of patient samples could make the results more realistic and reliable. Second, exploration of the detailed mechanism of MIF regulation of T-cell lineage plasticity via genetically engineered mice and genealogical tracing is lacking, which will be our future research direction. Third, in vitro CD4 + T cell manipulation yielded statistically significant but numerically modest Th17 proportion changes, this likely reflects that Th17 cells normally constitute an extremely low proportion of CD4 + T cells in peripheral blood, and differences in detection methods and marker selection across studies may influence result accuracy. In subsequent assays, we will optimize the flow cytometry gating strategy and increase the number of experimental replicates.

Conclusion

The results of this study confirm that MIF induces a Th17/Treg imbalance in circulating and renal tissues to mediate CKD progression, during which NF-κB signaling pathway could exert regulatory influence. The elucidation of these mechanisms provides important insights into the pathogenesis of CKD and highlights the therapeutic value of targeting MIF for the treatment of kidney disease.

Supplementary Information

Below is the link to the electronic supplementary material.

Author Contributions

Ruijing Zhang and Keyi Fan performed the experiments, acquired the data, and drafted the manuscript; Heng Wang, Jinshan Chen performed the experiments; Chuanlong Lu, Lizheng Li, Runze Chang, Maolin Qiao, Yuhang Zhang, Hongjiu Zhang, Yaling Li, Jie Zhang, and Yijie Ning conducted data visualization. Honglin Dong designed the study and supervised the study. Honglin Dong and Ruijing Zhang, obtained funding. All authors approved the final version of the manuscript.

Funding

This work was supported by the Basic Research Program of Shanxi Province (No.202103021223438); The Scientific Research Program of the Health Commission of Shanxi Province (No.2021066), and the Science Technology Support Project for Overseas Scholars in Shanxi Province (No.2022045); The Regional Cooperation Program of Shanxi Province, China (Grant No. 202204041101038); The Leading Talent Team Building Program of Shanxi Province, China (Grant No. 202204051002010); Construction and demonstration of molecular diagnosis and treatment platform for vascular diseases in Shanxi Province, China (Grant No. SCP-2023-17); Translational Medicine Engineering Research Center for Vascular Diseases of Shanxi Province, China (Grant No. 2022017); The Shanxi Provincial Science and Technology Department Centralized Guided Local Projects (Grant No. YDZJSX2021C026); The National Natural Science Foundation of China (Grant No. 82400883, 81770695, 81870354); The Shanxi Province Graduate Education Innovation Program (Grant No. 2024SJ169, 2023SJ145).

Data Availability

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors. Single-cell RNA sequencing data are accessible in Gene Expression Omnibus under the accession number GSE183276, GSE175412.

Declarations

Ethics Approval and Consent to Participate

All animal experiments were conducted in compliance with the National Institutes of Health guidelines and approved by the Ethics Committee of the Second Hospital of Shanxi Medical University (DW2023053). This study involving human samples was conducted in accordance with the Declaration of Helsinki and were approved by the Ethics Committee of the Second Hospital of Shanxi Medical University (No.2023098). All subjects participating in the study gave informed consent.

Consent to Publish

The authors affirm that human research participants provided informed consent for publication of the images in Figs. 2 and 5 and Supplementary Fig. 1.

Competing interests

The authors declare no competing interests.

Clinical Trial Number

Not applicable.