Application of CD93+DC / IL-10 as target in screening medicine for inhibiting immunological rejection and tolerance after liver transplantation

Abstract

The invention belongs to the technical field of biological medicine, and provides application of CD93 + DC / IL-10 as a target in screening of drugs for inhibiting immunological rejection and tolerance after liver transplantation. The CD93 < + > DC inhibits CD8 < + > T cells by secreting IL-10, inhibits immunological rejection after liver transplantation and enhances tolerance. The result shows that compared with the non-rejection group, the acute rejection group has the advantages that the CD8 + T lymphocytes are increased, and the CD93 + DC is reduced. A mouse liver transplantation model experiment result shows that the number of CD8 + T lymphocytes in blood and liver of a rejection group is higher, and the CD93 + DC level is reduced. In-vitro experiments prove that the CD93 + DC can inhibit the proliferation, migration and secretion functions of CD8 + T lymphocytes. After the anti-IL-10 antibody is added, the inhibition effect of CD93 + DC on CD8 + T lymphocytes is weakened. CD93 + DC inhibits CD8 + T lymphocytes through IL-10, and plays a key role in acute rejection of liver transplantation.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and specifically relates to the use of CD93+DC / IL-10 as a target in screening drugs for inhibiting immune rejection and tolerance after liver transplantation. Background Art

[0002] Liver transplantation is an effective treatment for end-stage liver disease. However, post-transplant immune rejection remains a key factor affecting patient prognosis. Although oral immunosuppressants can alleviate rejection to some extent, long-term use may increase the risk of infection, metabolic syndrome, and malignancy, and reduce the quality of life and long-term survival of transplant recipients. Therefore, elucidating the molecular mechanisms of liver transplant rejection, identifying effective intervention targets, and developing postoperative treatment strategies are of great significance for liver transplant patients.

[0003] From an immunological perspective, recipient immune cells and resident donor immune cells converge after transplantation, reshaping the immune microenvironment of the transplanted liver. Immune cells from different sources trigger a cascade of rejection reactions, profoundly impacting the survival and function of the transplanted liver. These cellular processes and molecular regulatory mechanisms are complex, involving multiple immune cell responses, signaling pathways, and multi-level regulatory mechanisms. T cell-mediated rejection (TCMR) is a common type of acute rejection, typically occurring within 6 weeks after transplantation. In TCMR, recipient T cells recognize donor major histocompatibility complex (MHC) antigens, prompting T cell activation, differentiation, and cytokine secretion, which in turn activates macrophages, neutrophils, and natural killer (NK) cells to mediate rejection. The proliferation and function of CD8+ T lymphocytes support post-transplant survival. CD8+ T lymphocytes are processed by antigen-presenting cells (APCs) and activated by MHC class I antigens, thereby mediating tissue damage.

[0004] APCs play a pivotal role in alloantigen rejection. Dendritic cells (DCs), B lymphocytes, and macrophages can all serve as APCs, with DCs being the most potent and key regulators of the liver immune system. Under steady-state conditions, immature liver DCs have minimal activation of CD8+ T lymphocytes. However, after liver transplantation, DCs gradually mature under the influence of inflammatory cytokines such as TNF-α, triggering acute rejection. Notably, DC maturation is closely linked to immune rejection / tolerance. Single-cell sequencing of the NGDC genome sequence archive (https: / / ngdc.cncb.cn / gsa-human / ) identified a novel CD93+ DC. However, the impact of CD93+ DCs on CD8+ T lymphocytes remains unclear, and further investigation is needed into their role in transplant rejection or tolerance. Summary of the Invention

[0005] This study provides the use of CD93+ DCs / IL-10 as targets for screening drugs to inhibit immune rejection and tolerance after liver transplantation. The goal is to elucidate the critical role of transplanted CD8+ T lymphocytes in acute rejection in humans and mice and to identify the specific DC phenotype responsible for antigen delivery to CD8+ T lymphocytes. We attempt to demonstrate, at both the animal (mice) and cellular levels, how these DCs regulate CD8+ T lymphocyte levels, thereby influencing immune rejection and tolerance, providing a new theoretical basis for immune rejection and tolerance.

[0006] The present invention is achieved by the following technical solution: using CD93+DC / IL-10 as a target in screening drugs for inhibiting immune rejection and tolerance after liver transplantation.

[0007] Furthermore, the CD93+DCs suppress CD8+T cells by secreting IL-10, thereby inhibiting immune rejection after liver transplantation and enhancing tolerance.

[0008] Furthermore, the CD93+DCs are used as a marker of immature DCs in screening drugs for inhibiting immune rejection and tolerance after liver transplantation.

[0009] The present invention, based on liver pathological examinations of patients with rejection, confirmed an elevated proportion of CD8+ T lymphocytes in the rejecting livers. Single-cell sequencing analysis revealed differences in the levels of CD93+ DCs between the rejecting and non-rejecting groups. Subsequent validation demonstrated that CD93+ DCs in the blood and livers of patients and mice can enhance tolerance by suppressing CD8+ T lymphocytes, providing a new theoretical basis for transplant immunology. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 Figure 3: CD8+ T cells and DCs are associated with liver transplant rejection. Figure: A: HE staining of human liver tissue (200×); B: Immunohistochemical expression levels of CD4, CD8, and CD11c in human liver tissue of the normal control group (NC, n=5), the acute rejection group (R, n=5), and the non-rejection group (NR, n=5); C: Comparison of liver rejection activity index (RAI) scores between the acute rejection group (R, n=5) and the non-rejection group (NR, n=5); D: Comparison of liver function (ALT, AST, TBIL) among the three groups. Data are expressed as mean ± standard error. P<0.01, P < 0.001, ns: not statistically significant; Figure 2Single-cell analysis shows that CD93+DCs may be associated with liver transplant rejection; Figure: A: Clustering and cell annotation of 4883 myeloid cells; B: Using two DCs marker genes to label DCs and compare the IL-10 expression levels of each group; C: Expression of differentially expressed genes in DCs; D: Gene Ontology (GO) enrichment analysis of DC_2 differential genes; E: Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DC_2 differential genes; F: GO enrichment analysis of DC_4 differential genes; G: KEGG enrichment analysis of DC_4 differential genes; H: Signal pathway bubble diagram analysis results. Each dot represents a communication network of a signal pathway, and the dot size is proportional to the communication probability; I: Dot plot showing the interaction between DC_2, DC_4 and CD8Tc, CD8Tpro, and CD8Tex in liver transplantation; J: Bulk RNA sequencing expression of non-rejection group and rejection group; Figure 3 CD93+DCs are associated with immune tolerance in liver transplantation. Figure: A: Immunohistochemical detection of CD93 expression levels in human liver tissues of normal control group (NC, n=5), acute rejection group (R, n=5), and non-rejection group (NR, n=5); B: Multiplex immunofluorescence (mIF) staining of human liver tissues showing CD93+DCs marker combination (CD11c-purple, CD80-yellow, CD93-green) and DAPI (blue); C: Proportion of CD93+DCs in peripheral blood (gated by CD11c+CD11b+DCs); Data are expressed as mean ± standard error. P < 0.001, ns: not statistically significant; Figure 4 Figure 3: The spatial relationship between CD93+ DCs and CD8+ T cells in different groups. Figure A: Multiplex immunofluorescence was used to detect the distribution of CD93+ DCs and CD8+ T cells in human liver tissue. Paraffin-embedded tissue sections were labeled with CD11c (purple), CD80 (yellow), CD93 (green), CD8 (red), and DAPI (blue). Figure B: The spatial relationship between CD93+ DCs and CD8+ T lymphocytes in different groups was determined based on the labeling results (CD93+ DCs were labeled as CD11c+ CD80+ CD93+). Figure 5To investigate the potential role of CD93+ DCs in influencing CD8+ T lymphocytes through IL-10, a non-rejection group (NR, n = 5) and a rejection group (R, n = 5) of liver transplantation mice were established. Figures: A: HE staining of liver samples from the normal control group (NC, n = 5) and the liver transplantation model mice; B: Comparison of liver rejection activity index (RAI) scores between the rejection and non-rejection groups; C: Proportions of CD93+ DCs in spleen, peripheral blood, and liver of the NC, NR, and R groups; D: Proportion of CD8+ T lymphocytes (gated on CD3+ T cells) in the liver; E: Correlation analysis between the proportions of CD93+ DCs and CD8+ T lymphocytes in the transplanted liver; F: Flow cytometry analysis of IL-10 expression in the liver; G: qRT-PCR analysis of IL-10 expression in the liver of liver transplanted mice. Data are expressed as mean ± standard error. P<0.05, P<0.01, P < 0.001, ns: the difference was not statistically significant; Figure 6 CD93+ DCs inhibit CD8+ T lymphocyte function through IL-10; Figure: A: Flowchart of the co-culture experiment of CD8+ T lymphocytes and CD93+ DCs; B: Proliferation of CD8+ T lymphocytes after 3 days of co-culture; C: Transwell migration assay results showing the number of CD8+ T lymphocytes in the two groups; D: Flow cytometry detection of Granzyme B expression; E: Flow cytometry detection of IFN-γ expression; Data are expressed as mean ± standard error. P<0.01, P<0.001, ns: the difference was not statistically significant. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0012] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, and the disclosure and materials cited therein are hereby incorporated by reference.

[0013] Technical equivalents to the specific embodiments described that are apparent to those skilled in the art using no more than routine experimentation are intended to be encompassed by this application.

[0014] The experimental methods in the following examples, unless otherwise specified, are all conventional methods. The instruments and equipment used in the following examples, unless otherwise specified, are all conventional laboratory instruments and equipment; the experimental materials used in the following examples, unless otherwise specified, are all purchased from conventional biochemical reagent stores.

[0015] 1. Materials and Methods 1. Study Subjects and Grouping: This study included 10 patients who underwent liver biopsy at the First Hospital of Shanxi Medical University between June and December 2023 (Table 1). Based on the pathological findings of the liver biopsy, the patients were divided into an acute rejection group (R group = 5) and a non-rejection group (NR group = 5). Five donor liver samples served as a normal control group (NC group = 5). Patients with severe biliary complications or postoperative infections were excluded. This study was approved by the Ethics Committee of our hospital (No. KYLL-2023-143). All participants provided written informed consent for the use of their liver tissue, blood samples, and clinical data for research and publication.

[0016] Table 1: Clinical characteristics of participants 2. Hematoxylin and eosin (HE) staining: Liver tissue was fixed in formalin, embedded in paraffin, and sectioned (2 μm) for HE staining. Histological grading was performed according to the Banff scheme based on the HE staining results. The rejection activity index (RAI) was scored based on the degree of venous endothelial inflammation, bile duct inflammation, and portal vein inflammation, with a total score of 9. An RAI score of >3 was considered acute rejection.

[0017] 3. Immunohistochemistry (IHC) Analysis: Paraffin-embedded liver tissue sections (2 μm) were deparaffinized and hydrated, followed by high-pressure antigen retrieval in sodium citrate buffer (pH 6.0) for 2 minutes and cooling to room temperature. Following treatment with 3% H₂O₂ for 10 minutes, the sections were incubated overnight at 4°C with primary antibodies against CD4 (1:100, ab288724, Abcam, UK), CD8α (1:100, ab245118, Abcam, UK), CD11C (1:100, ab52632, Abcam, UK), and CD93 (1:100, ab198854, Abcam, UK). Secondary antibody working solution was then added, and color was developed using a DAB kit (ZLI-9017; Zhongshan Jinqiao, Shanghai, China).

[0018] 4. Multiplex Immunofluorescence (mIF): Paraffin-embedded tissue sections were deparaffinized and hydrated after baking at 60°C for 2 hours. Antigen retrieval was performed at 95°C for 20 minutes in pH 6.0 buffer. Immunofluorescence staining was performed sequentially with primary antibodies against CD11c (1:500, ab52632, Abcam, UK) (room temperature for 30 minutes), CD93 (1:300, YT5597, Immunoway, USA) (overnight at 4°C), CD8 (1:400, 85336, Cst, USA) (37°C for 60 minutes), and CD80 (1:1000, ab134120, Abcam, UK) (room temperature for 60 minutes). Enzyme-conjugated anti-rabbit IgG polymer (Opal IHC Detection Kit; Akoya Biosciences) was used as a secondary antibody, and development was performed with TSA colorimetric working solution. Sections were mounted with DAPI anti-quenching mounting medium (PO131, Beyotime, China) and stored at 4°C in the dark. Images were acquired using the VectraPolaris multispectral imaging platform (Akoya Biosciences), and image analysis was performed using Inform 2.4.8 software (Akoya Biosciences).

[0019] 5. Establishment of a Mouse Liver Transplantation Model: 7-8 week-old SPF-grade C3H and C57 male mice were used (purchased from Spef (Beijing) Biotechnology Co., Ltd., license number: SYXK (Beijing) 20170010). In the non-rejection group (NR group), C3H mice served as both donors and recipients. In the rejection group (R group), C57 mice served as donors and C3H mice served as recipients. The mouse liver transplantation model was established using the "double-cuff" technique. All mice were housed in the SPF animal facility of the Experimental Animal Center of Shanxi Medical University. Tissue specimens were collected 14 days after surgery.

[0020] 6. Cell Preparation: Bone marrow-derived dendritic cells (DCs) and lymph node cells were isolated from C3H mice, and erythrocytes were removed using erythrocyte lysis buffer. Bone marrow DCs were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum, IL-4, and GM-CSF. The medium was changed on days 3 and 5, and adherent cells were harvested on day 8. CD93+ DCs and CD93- DCs were sorted using a BD FACSAria™ III flow cytometer (BD Biosciences, USA). CD8+ T lymphocytes were isolated from lymph node cells using the Mouse CD8a+ T Cell Isolation Kit (130-104-075, Miltenyi Biotec, Germany). CD93+ DCs and CD93- DCs were co-cultured with CD8+ T lymphocytes in RPMI 1640 medium supplemented with 10% fetal bovine serum for 3 days. After that, CD8+ T lymphocytes were harvested and analyzed by flow cytometry to assess their proliferation, migration, and secretory functions.

[0021] 7. Flow Cytometry: Detection was performed using a BD FACSAria™ III flow cytometer (BD Biosciences, USA). DCs were identified using FITC-conjugated anti-CD45, APC-conjugated anti-CD11b, AF700-conjugated anti-CD11c, BV421-conjugated anti-CD80, and PE / CY7-conjugated anti-CD93 antibodies. PE-conjugated IL-10 antibody was used to assess DC secretory function. CD8+ T lymphocytes were identified using PerCP / Cyanine 5.5-conjugated mouse anti-CD3, FITC-conjugated mouse anti-CD4, and AF700-conjugated mouse anti-CD8a. CD8+ T lymphocyte proliferation was assessed using FITC-conjugated CFSE antibody. Secretory function was assessed using APC-conjugated IFN-γ antibody and PE-conjugated granzyme B antibody. Dead cells were identified using the Live / Dead™ Fixable Aqua Dead Cell Staining Kit.

[0022] 8. In vitro migration assay: CD8+ T lymphocytes co-cultured for 3 days were sorted using a mouse CD8a+ T cell sorting kit (130-104-075, Miltenyi Biotec, Germany) and added to the upper layer of a Transwell chamber (NEST, Wuxi). After 2 hours, the lower layer of the fluid was collected to count the migrated CD8+ T lymphocytes.

[0023] 9. Real-time quantitative PCR: Total RNA was extracted using Trizol reagent, and 2 μg of RNA was reverse transcribed into cDNA. A 20 μl reaction was performed using 2× M5 HiPer Real-Time Fluorescence Quantitation Premix (Low Rox) and IL-10 primers (mouse, IL-10-F: GGTTGCCAAGCCTTATCGGAAATG, IL-10-R: GCCGCATCCTGAGGGTCTTC, Shanghai Bioengineering). β-actin was used as an internal control. Relative mRNA expression was calculated using the 2-ΔΔCt method, where ΔCt = Ct (target gene) - Ct (reference gene).

[0024] 10. Single-cell sequencing analysis A. Single-cell gene expression quantification and subpopulation classification: Based on single-cell data from previous studies [1], data were imported using the Seurat R package (version 4.3.0) [2]. Strict quality control was performed (cells with <501 expressed genes, <1001 UMIs, or >25% mitochondrial gene counts were excluded), and the default parameters were used for standardization and normalization. Highly variable feature genes were screened using "FindVariableFeatures". Principal component analysis (PCA) was performed on the scaled data. Dimensionality reduction and clustering were performed using "FindNeighbors" and "FindClusters" (resolution = 0.5) based on the first 10 principal components. tSNE nonlinear dimensionality reduction was used for visualization.

[0025] B. Cell type identification: The "FindMarkers" and "FindAllMarkers" functions were used to identify differentially expressed characteristic genes, and cell type annotation was performed in combination with classic markers from the CellMarker database (http: / / bio-bigdata.hrbmu.edu.cn / CellMarker / ), supplemented by verification using the SingleR package (version 2.0.0) [3].

[0026] C. Functional enrichment analysis: GO and KEGG pathway analysis was performed on the differentially expressed genes using the clusterProfiler package (version 3.17.0) [4] and the org.Hs.eg.db package (version 3.11.4) (P < 0.05 was considered significant), and the top 10 significant items were selected and displayed using bar charts / bubble charts.

[0027] D. Intercellular communication analysis: The CellChat package (version 1.6.1) [5] was used to analyze ligand-receptor pairs based on the KEGG signaling pathway database and the latest experimental research. The probability of intercellular communication was assessed by identifying differentially expressed signaling genes and calculating the overall average expression level.

[0028] E. Bulk RNA sequencing data processing: Bulk RNA sequencing data of liver transplant biopsy samples were obtained from the GEO database (accession number GSE145780) [6]. Differential analysis was performed using the limma package (version 3.54.0) and visualization was performed using the ggplot2 package (version 3.4.2).

[0029] 11. Statistical Analysis: GraphPad Prism 9 software was used for analysis. Continuous data were expressed as mean ± standard deviation. Two-group comparisons were performed using the t-test, and multiple-group comparisons were performed using one-way analysis of variance (ANOVA) followed by Tukey's post hoc test. Pearson correlation analysis was used to assess the correlation between CD93+ DCs and CD8+ T lymphocyte counts. Statistical significance was set at P < 0.05.

[0030] 2. Experimental Results 1. CD8+ T lymphocytes and DCs participate in liver transplant rejection: HE staining of human liver tissue was performed to observe the level of inflammatory cell infiltration in the portal tract of the liver in the three groups. The results showed that compared with the NR group and the NC group, the portal tract of the R group showed significant inflammatory cell infiltration, ballooning degeneration, necrosis, hepatocyte edema, bile duct damage and endothelial damage ( Figure 1 A). The liver rejection activity index (RAI) was scored by HE staining of the three groups of livers using the Banff standard. The results showed that the score of the R group was significantly higher than that of the NR group ( Figure 1 B) The immune rejection reaction of the liver in the rejection group was significantly stronger than that in the tolerance group and the normal control group. Since HE staining cannot specifically identify inflammatory cell types, we used IHC staining to evaluate the infiltration of inflammatory cells in liver samples. CD4, CD8, and CD11c markers were used to distinguish T lymphocytes from DCs in liver tissue. IHC results showed that compared with the NC and NR groups, the infiltration of CD8+ T lymphocytes and DCs in the R group was significantly increased ( Figure 1 C). In addition, by analyzing the clinical liver function data of the three groups, the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total bilirubin (TBIL) in group R were higher than those in group NC and group NR. The immune rejection reaction of the liver in group R was more severe than that in group NC and group NR. Figure 1 D). These results indicate that CD8+ T lymphocytes and DCs are associated with liver transplant rejection.

[0031] 2. Single-cell sequencing revealed that CD93+ DCs may be involved in liver transplant rejection: By analyzing published human liver transplant single-cell sequencing data, DCs were divided into five categories (DC_1-DC_5) using two DCs marker genes (CLEC9A and CD1C) ( Figure 2 A, 2B). Analysis of IL-10 expression levels in various cell types revealed that DC_2 and DC_4 expressed higher levels of IL-10 ( Figure 2 B). Through screening, we obtained 12 key genes highly expressed in DCs, among which CD93 was highly expressed in DC_2 and DC_4 ( Figure 2 C). GO and KEGG analyses revealed that differentially expressed genes (DEGs) in the DC_2 group were mainly related to antigen processing and cell adhesion molecules ( Figure 2 D, 2E), and the DC_4 group is involved in gene regulation and antigen processing in the apoptosis signaling pathway ( Figure 2 F, 2G). Scatter plot analysis showed that the output interaction between CD8Tc, CD8Tpro and CD8Tex was significant, and the input interaction between DC_2 and CD8Tc and CD8Tex was significant ( Figure 2H), there are input and output interactions between DC_2, DC_4 and CD8Tc, CD8Tpro, and CD8Tex. KEGG analysis showed that members of the NECTIN2-TIGIT pathway are expressed in DC_2 and CD8+ T cells ( Figure 2 I). Bulk RNA sequencing analysis showed that the abundance of CD68, CD11B, and IDO1 was significantly increased in the rejection group ( Figure 2 J). These results suggest that CD93+ DCs may play an important role in liver transplant rejection.

[0032] 3. The proportion of CD93+DCs in non-rejecting patients increased: The expression level of CD93 in human liver was detected by IHC, and it was found that the expression of CD93 in R group and NR group was lower than that in NC group ( Figure 3 A). Further co-localization of CD93+DCs in the liver by multiple immunofluorescence analysis revealed that the infiltration of CD11c+CD80+CD93+DCs in the NR group was increased compared with the NC and R groups ( Figure 3 B). Flow cytometry was used to detect the peripheral blood of the three groups. The analysis results showed that the infiltration of CD93+DCs in the NR group was also significantly higher than that in the NC and R groups ( Figure 3 C). These results indicate that the proportion of CD93+ DCs is upregulated in non-rejecting patients.

[0033] 4. Spatial interaction between CD93+DCs and CD8+T lymphocytes: To explore the relationship between CD93+DCs and CD8+T lymphocytes, multiple immunofluorescence staining analysis was performed on the liver tissues of the three groups of people. The results showed that the spatial location of CD93+DCs and CD8+T lymphocytes was close ( Figure 4 A). If we statistically analyze the spatial position of CD8+ T lymphocytes with CD93+ DCs as the center and 100 μm as the radius, we can see that the spatial distance between the two is shortened after liver transplantation, and the distance between the two cells is the closest in the R group ( Figure 4 B). This indicates that CD93+ DCs and CD8+ T lymphocytes closely interact after liver transplantation.

[0034] 5. CD93+ DCs may regulate CD8+ T lymphocytes through IL-10: To explore the mechanism of interaction between CD93+ DCs and CD8+ T cells, a non-rejection group (NR group = 5) and a rejection group (R group = 5) of mouse liver transplantation were established. 14 days after liver transplantation, mice were killed and samples were collected. HE staining analysis of the liver tissues of the three groups of mice showed that the R group had significant inflammatory cell infiltration, liver microstructural damage, and liver cell necrosis compared with the NC and NR groups. Figure 5 A), RAI score is higher ( Figure 5B), the immune rejection reaction in liver tissue was more severe. Flow cytometry analysis showed that the infiltration of CD93+DCs in the peripheral blood and liver of the NR group was significantly increased compared with the R group, which was consistent with the results of human samples ( Figure 5 C), but there was no significant difference in the proportion of CD93+DCs in the spleen ( Figure 5 C). The proportion of CD8+T lymphocytes in the liver of mice in the R group was also significantly higher than that in the NC and NR groups ( Figure 5 D), which is consistent with the results of liver HE staining. The immune rejection reaction in the liver of group R was more severe. Correlation analysis showed that the proportion of CD93+DCs in the liver transplantation model was negatively correlated with the proportion of CD8+T lymphocytes ( Figure 5 E). The expression of anti-inflammatory factor IL-10 in DCs of NR group was higher than that of R group ( Figure 5 F), Real-time quantitative PCR (qRT-PCR) detection confirmed that the IL-10 mRNA level in the transplanted liver of the NR group was significantly increased ( Figure 5 G). This suggests that CD93+ DCs may affect CD8+ T lymphocytes through IL-10.

[0035] 6. CD93+ DCs inhibit CD8+ T lymphocyte function through the IL-10 pathway: In order to verify the role of the IL-10 pathway between CD93+ DCs and CD8+ T lymphocytes, an in vitro cell co-culture experiment was performed ( Figure 6 A). The experimental results showed that the proliferation capacity of CD8+ T lymphocytes was significantly restricted when co-cultured with CD93+ DCs ( Figure 6 B). Transwell migration assay showed that CD93+ DCs inhibited the migration function of CD8+ T lymphocytes ( Figure 6 C). Flow cytometry revealed that CD93+ DCs significantly reduced the secretion levels of granzyme B and IFN-γ by CD8+ T lymphocytes. IL-10 enhanced the inhibitory effect of CD93+ DCs on CD8+ T lymphocytes, while anti-IL-10 blocked this effect ( Figure 6 These results confirm that CD93+ DCs can inhibit CD8+ T lymphocyte function through the IL-10 pathway.

[0036] The present invention, based on liver pathological examinations of patients with rejection, confirmed an elevated proportion of CD8+ T lymphocytes in the rejecting livers. Single-cell sequencing analysis revealed differences in the levels of CD93+ DCs between the rejecting and non-rejecting groups. Subsequent validation demonstrated that CD93+ DCs in the blood and livers of patients and mice can enhance tolerance by suppressing CD8+ T lymphocytes, providing a new theoretical basis for transplant immunology.

[0037] Multiple cell types exist in the liver transplant microenvironment. Although single-cell sequencing can analyze cell distribution and number and confirm the role of CD8+ T cells in liver transplantation, the mechanisms of interaction between immune cells remain unclear. In human and mouse rejecting livers, we observed prominent infiltration of neutrophils, DCs, and lymphocytes in the portal tract, a finding consistent with the typical histopathological features of acute rejection.

[0038] It is worth noting that a large number of CD8+T cells can be seen gathering in the portal vein area during acute rejection, indicating that the level of CD8+T cells has important clinical significance for the severity of acute rejection and immune rejection after liver transplantation. Previous studies have shown that the number of allogeneic reactive CD8+T cells associated with acute rejection in liver transplantation is mainly affected by cell apoptosis and proliferation, and inhibiting CD8+T cell proliferation can significantly prolong graft survival. In addition, enhanced CD8+T cell function is a key factor directly affecting acute rejection after liver transplantation. The latest studies emphasize that autophagy participates in the occurrence of acute rejection after liver transplantation by inhibiting CD8+T cell apoptosis and promoting its proliferation and function. Although the mechanism of action of CD8+T cells after liver transplantation has been widely studied, the complexity of upstream cells and related pathways has not been fully elucidated.

[0039] In liver transplantation, dendritic cells (DCs) are the most powerful antigen-presenting cells and are considered key regulators of the liver immune system. CD8+ T cells can promote or inhibit allogeneic responses to DCs with different phenotypes and functions through various pathways. Studies have shown that mature DCs can activate CD8+ T cells to cause acute rejection, while immature DCs can inhibit CD8+ T cells and promote immune tolerance. Of particular note, in a liver transplant model, DCs with high PD-L1 expression are associated with CD8+ T cell exhaustion and immune tolerance induction. Liver DCs exhibit a more immature phenotype and function than DCs in secondary lymphoid organs, resulting in a weaker ability to activate allogeneic naive T cells.

[0040] Single-cell sequencing data in this study showed that CD93 expression was higher in the NR group than in the R group, suggesting that CD93+ DCs may serve as a marker for immature DCs. CD93 is a transmembrane glycoprotein belonging to the C-type lectin family, type 14 transmembrane glycoprotein, and is involved in angiogenesis, inflammation, and cell adhesion. Although CD93 is primarily expressed in endothelial cells, it is also present in monocytes, neutrophils, B cells, and natural killer cells, indicating its important role in immunity. Studies have shown that CD93 expression in peritoneal macrophages does not participate in C1q-mediated phagocytosis but contributes to the clearance of dead cells. In terms of disease association, CD93 has been identified as a prognostic marker for a variety of malignant tumors, and targeting the CD93 pathway may provide a new strategy for cancer immunotherapy. Single-cell sequencing and bioinformatics analysis showed that CD93 is expressed in all six immune cell types in hepatocellular carcinoma, involving multiple immune-related signaling pathways and immune responses. This study focuses on the regulatory effects of CD93+ DCs on CD8+ T cells and their role in immune tolerance.

[0041] Current research has found a negative correlation between CD8+ T cell and CD93+ DC levels in both rejecting and non-rejecting liver transplantation groups, suggesting that CD93+ DCs may induce immune tolerance by suppressing CD8+ T cells. However, the specific mechanism by which CD93+ DCs suppress CD8+ T cells remains unclear. In addition to the traditional pathway by which DCs present antigens to CD8+ T cells via MHC class I molecules, the present study observed elevated IL-10 levels in the non-rejecting group, suggesting that DCs may also influence the differentiation of naive T cells into different effector T cell subsets by modulating the cytokine milieu (e.g., IL-10, IL-12, IL-17, IL-22, IL-23, IFN-I, and TNF). Furthermore, the present study investigated the migration patterns of DCs. Compared with the rejecting group, the proportion of CD93+ DCs in the spleen of the non-rejecting group was decreased, while it was increased in the peripheral blood and liver, confirming that DCs migrate from central lymphoid organs to the transplanted liver via the peripheral blood.

[0042] CD8+ T cells and DCs are associated with acute rejection after liver transplantation. In vitro experiments have shown that CD93+ DCs regulate CD8+ T cell function by secreting IL-10. Based on the results of this study, the results suggest that intrahepatic CD93+ DCs may participate in immune rejection after liver transplantation by regulating CD8+ T cells.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

[0044] References: [1]Li [2] Hao Y, Stuart T, Kowalski MH, Choudhary S, Hoffman P, Hartman A,et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol. 2023. doi: 10.1038 / s41587-023-01767-y. [3] Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, et al. Reference-based analysis of lung single-cell sequencing reveals a transitionalprofibrotic macrophage. Nat Immunol. 2019;20:163-172. doi: 10.1038 / s41590-018-0276-y. [4]Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler4.0: A universal enrichment tool for interpreting omics data. Innovation(Camb). 2021;2:100141. doi: 10.1016 / j.xinn.2021.100141. [5]Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH, etal. Inference and analysis of cell-cell communication using CellChat. NatCommun. 2021;12:1088. doi: 10.1038 / s41467-021-21246-9. [6]Madill-Thomsen K, Abouljoud M, Bhati C, Ciszek M, Durlik M, FengS, et al. The molecular diagnosis of rejection in liver transplant biopsies:First results of the INTERLIVER study. Am J Transplant. 2020;20:2156-2172.doi: 10.1111 / ajt.15828。

Claims

1. Application of CD93+DC / IL-10 as a target in screening drugs to inhibit immune rejection and tolerance after liver transplantation.

2. The use according to claim 1, characterized in that: The CD93+DC suppresses CD8+T cells by secreting IL-10, thereby inhibiting immune rejection after liver transplantation and enhancing tolerance.

3. The use according to claim 1, characterized in that: The CD93+DCs are used as a marker of immature DCs in screening drugs for inhibiting immune rejection and tolerance after liver transplantation.

Images