Application of NETs / STING / IRF3 / MHCII pathway in liver transplantation immune tolerance

Abstract

The invention belongs to the technical field of biological medicines, and particularly relates to application of an NETs / STING / IRF3 / MHCII pathway in liver transplantation immune tolerance. The NETs / STING / IRF3 / MHCII pathway is used as a target to be applied to preparation and / or screening of drugs for preventing or treating liver transplantation immune tolerance. The NETs regulates and controls the expression of the dendritic cell MHCII through an STING / IRF3 pathway, further regulates and controls the maturation of the dendritic cell, and aggravates the occurrence of immunological rejection after liver transplantation. A clinical specimen of a liver transplantation patient and a liver transplantation mouse model are utilized to clarify the characteristics of NETs in different immune states after liver transplantation, and functional regulation and control and specific action pathways of NETs on dendritic cells are explored through in-vitro experiments, so that theoretical support is provided for research on immune microenvironment change after liver transplantation, and the application prospect is wide. And an opinion is provided for clinical diagnosis, treatment and prevention of liver transplantation immunological rejection.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and specifically relates to the application of the NETs / STING / IRF3 / MHCII pathway in liver transplantation immune tolerance. Background Art

[0002] Severe liver structural abnormalities or dysfunction can lead to irreversible liver damage. When liver disease reaches its end stage, liver transplantation is the definitive treatment. Overactivation of various immune cells in the liver after transplantation can lead to immune rejection and damage graft function. Therefore, exploring the pathological mechanisms of immune rejection after liver transplantation will provide a deeper understanding of the disease and provide a theoretical basis for the treatment of immune rejection after liver transplantation.

[0003] The liver has a unique structure and complex functions. When multiple factors lead to irreversible liver damage, liver transplantation becomes the ultimate treatment. The occurrence of post-transplant immune rejection puts the scarce graft donors at risk of dysfunction. Clinically, immunosuppressants are routinely used after liver transplantation. While prolonging graft survival, they also increase the risk of infection, metabolic diseases, and other diseases. However, the specific mechanisms of post-liver transplant immune rejection are not yet fully understood. Therefore, an accurate understanding of the changes in immune status after liver transplantation will enable more targeted diagnosis, treatment, and prevention of post-liver transplant immune rejection.

[0004] Neutrophils are our first line of defense against microbial pathogens. Neutrophil extracellular traps (NETs), one of their pathogen-eliminating mechanisms, are network-like structures composed of decondensed chromatin and antimicrobial proteins (such as histones, myeloperoxidase, and neutrophil elastase). Their production is regulated by reactive oxygen species (ROS). Chromatin in NETs is released into the periphery by the cells, forming cfDNA. Donor-derived cfDNA can be detected in transplant recipients. Previous studies have shown that NETs are associated with liver transplant rejection, regulating M1 polarization of Kupffer cells and promoting liver immune rejection. However, little research has examined the interactions between these structures and various immune cells under different immune states after liver transplantation.

[0005] Dendritic cells are professional antigen-presenting cells that are crucial for the initiation and coordination of immune responses. In the immune microenvironment after liver transplantation, studies have shown that immature liver dendritic cells express low levels of CD80, but after maturation, their expression increases and activates T cells. Further experiments have confirmed that NETs fragments can promote the expression of CD80, CD86, and MHCII on dendritic cells, thereby promoting the activation of CD4 T cells. +T cell proliferation prevents lung transplant immune tolerance. Few studies have focused on the effects of NETs on dendritic cells in the rejection and non-rejection states after liver transplantation.

[0006] The cGAS-STING pathway, a double-stranded DNA (dsDNA) sensor, plays an essential role in inflammation and tumor immunity. Reports suggest that the STING / TBK1 / IRF3 signaling pathway can promote the maturation of bone marrow-derived dendritic cells following Mycobacterium bovis infection. However, whether NETs generated after liver transplantation can affect dendritic cell function through STING-related pathways remains undetermined. Summary of the Invention

[0007] The present invention provides application of NETs / STING / IRF3 / MHCII pathway in liver transplantation immune tolerance.

[0008] The present invention is achieved by the following technical solutions: application of the NETs / STING / IRF3 / MHCII pathway in liver transplant immune tolerance, and application of the NETs / STING / IRF3 / MHCII pathway as a target in the preparation and / or screening of drugs for preventing or treating liver transplant immune tolerance.

[0009] Furthermore, the NETs regulate the expression of MHCII in dendritic cells through the STING / IRF3 pathway, thereby regulating the maturation of dendritic cells and aggravating the occurrence of immune rejection reactions after liver transplantation.

[0010] The drug for treating liver transplantation immune tolerance is a drug that inhibits or reduces NETs formation, a STING inhibitor, or a dendritic cell MHCII expression inhibitor. Further, the STING inhibitor is H-151.

[0011] The present invention also provides the use of the NETs / STING / IRF3 / MHCII pathway in liver transplant immune tolerance, and the use of the NETs / STING / IRF3 / MHCII pathway in the preparation and / or screening of products for identifying or assisting in the identification of liver transplant immune tolerance. Such products include kits, drugs, test strips, or detection platforms.

[0012] The present invention utilizes the public RNA-Seq database of liver transplant patients, clinical samples, and a mouse liver transplant model. Through GSEA (Gene set enrichment analysis), KEGG (Kyoto Encyclopedia of Genes and Genomes), and GO (Gene Ontology) analysis, and adopts experimental techniques such as hematoxylin-eosin staining (H&E), multiple immunofluorescence, flow cytometry, real-time polymerase chain reaction (Real-time PCR), and protein immunoblotting, an in vitro cell co-culture experiment is conducted to clarify the regulatory effect of NETs on dendritic cells in the immune rejection state after liver transplantation.

[0013] Differential gene expression analysis of RNA-Seq data showed that the NETs formation pathway was enriched in patients with immune rejection after liver transplantation, with increased expression of genes related to this pathway, and enriched in pathways related to its inducing factor, reactive oxygen species (ROS). Patients with immune rejection after liver transplantation had increased levels of in situ NETs in the liver and increased cell-free DNA (cf-DNA) in peripheral plasma, which was positively correlated with disease severity. In mice with immune rejection after liver transplantation, increased in situ NETs formation was associated with elevated levels of ROS, the inducing factor, including ROS and mitochondrial reactive oxygen species (mtROS), and increased release of cf-DNA from bone marrow neutrophils, which was enriched in mitochondrial DNA (mtDNA). NETs in mice with immune rejection after liver transplantation more effectively promoted the maturation of dendritic cells. NETs regulated dendritic cell maturation through the STING / IRF3 pathway, which may in turn aggravate the occurrence of immune rejection after liver transplantation.

[0014] This study uses clinical specimens from liver transplant patients and a liver transplant mouse model to elucidate the characteristics of NETs in different immune states after liver transplantation. In vitro experiments are used to explore the functional regulation and specific pathways of NETs on dendritic cells, aiming to provide theoretical support for studying changes in the immune microenvironment after liver transplantation and to provide insights into the clinical diagnosis, treatment, and prevention of liver transplant immune rejection reactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 RNA-Seq results of patients with immune rejection after liver transplantation; Figure: A is the KEGG analysis bubble chart of RNA sequencing results; B is the heat map of genes related to NETs formation; Figure 2The NETs formation pathway is upregulated in patients with immune rejection after liver transplantation; Figure: A. GSEA diagram of NETs formation pathway; B. GO analysis bubble diagram; Figure 3 Pathological results of liver transplant patients with different immune status; Figure: A is H&E staining of liver sections of liver transplant patients (NR = non-rejection, R = rejection, magnification 200 times); B is liver pathological score of liver transplant patients (n = 8, *** p <0.001); Figure 4 Figure 1: Transplant liver function damage in immune rejection after liver transplantation. Figure 1: ALT level in liver transplant patients; B: AST level in liver transplant patients. Figure 1: Correlation between ALT, AST and Banff score (n=8, ** p <0.01); Figure 5 Increased in situ NET formation in the liver of patients with immune rejection after liver transplantation; Figure: A is a representative image of in situ multiple immunofluorescence of the liver (N = normal control, NR = non-rejection, R = rejection, magnification 200x); B is the quantitative statistical results of immunofluorescence colocalization; C is the correlation between immunofluorescence colocalization and Banff score (n = 3, *** p <0.001, ns indicates no significant difference); Figure 6 Increased peripheral NETs formation in patients with liver transplant rejection; Figure: A is the plasma cf-DNA level in liver transplant patients; B is the correlation between plasma cf-DNA level and Banff score (n=8, * p <0.05,** p <0.01, ns indicates no significant difference); Figure 7 Establishment of liver transplant rejection and non-rejection mouse models; Figure: A is a schematic diagram of liver transplantation mouse model; B is H&E staining of mouse liver (N = normal control, NR = non-rejection, R = rejection, magnification 200x); C is liver pathology score of liver transplantation mouse model (n = 3; * p <0.05); Figure 8 Neutrophil infiltration increases during liver transplant rejection; Figure: A is a representative image of in situ immunofluorescence in mouse liver (N = normal control, NR = non-rejection, R = rejection, magnification 400 times); B is the quantitative statistical results of immunofluorescence colocalization (n = 3, *** p <0.001, **** p <0.0001); Figure 9Increased in situ NET formation in the livers of mice rejecting liver transplants; Figure: A is a representative image of mouse liver multiple immunofluorescence (N = normal control, NR = non-rejecting, R = rejecting, magnification 400x); B is the quantitative statistical results of immunofluorescence colocalization; C is the correlation between immunofluorescence colocalization and Banff score (n = 3, ** p <0.01,*** p <0.001, ns indicates no significant difference); Figure 10 Increased peripheral NETs formation in mice with liver transplant rejection; Figure: A is a representative immunofluorescence image of NETs induced by mouse bone marrow neutrophils (N = normal control, NR = non-rejection, R = rejection, magnification 400 times); B is the quantitative statistical results of NETs formation (n = 3, ** p <0.01,*** p <0.001, ns indicates no significant difference); Figure 11 Figure 2: NETs induced by bone marrow neutrophils in a liver transplant mouse model. Figure A shows the cf-DNA level in the supernatant of cells after NETs were induced by neutrophils in mice (N = normal control, NR = non-rejecting, R = rejecting). Figure B shows the correlation between the cf-DNA level in the supernatant of cells after NETs were induced by neutrophils in mice and the Banff score (n = 3, * p <0.05,** p <0.01, ns indicates no significant difference); Figure 12 Figure 2: ROS and mtROS levels in liver neutrophils of a mouse liver transplant model. Figure A shows the gating strategy for neutrophils in mouse liver using flow cytometry. Figure B shows the mean fluorescence intensity of neutrophil ROS and mtROS by flow cytometry (N = normal control, NR = non-rejecting, R = rejecting). Figure 13 Increased ROS and mtROS levels in mice with immune rejection after liver transplantation; Figure: A shows the statistical results of the mean fluorescence intensity of H2DCFDA in mouse liver; B shows the correlation between the mean fluorescence intensity of H2DCFDA in mouse liver and NETs formation; C shows the statistical results of the mean fluorescence intensity of MitoSOX in mouse liver; D shows the correlation between the mean fluorescence intensity of MitoSOX in mouse liver and NETs formation (N = normal control, NR = non-rejection, R = rejection, n = 3, * p <0.05, ns indicates no significant difference); Figure 14The positive correlation between ROS and mtROS and the severity of liver transplant rejection in mice; Figure: A shows the correlation between ROS and mtROS and the severity of liver transplant rejection in mice; B shows the mitochondrial DNA / genomic DNA level in peripheral NETs in the liver transplant mouse model (NR = non-rejection, R = rejection); C shows the correlation between 16S / 18S and the severity of liver transplant rejection (n = 3, * p <0.05); Figure 15 NETs promote an increase in the proportion of dendritic cells and cytokine secretion after liver transplantation. Figure: A. Schematic diagram of co-culture of NETs and bone marrow-derived dendritic cells; B. Dendritic cell flow cytometry gating strategy; C. Representative diagram of changes in the proportion of bone marrow-derived dendritic cells under different stimuli (BLANK, LPS, NR-NETs, R-NETs); D. Statistical results of changes in the proportion of bone marrow-derived dendritic cells under different stimuli; E. IL-6 secretion levels after co-culture of dendritic cells with NETs from different sources (BLANK = blank control, LPS = lipopolysaccharide, N = normal control, NR = non-rejecting, R = rejecting, n = 3, * p <0.05,** p <0.01,*** p <0.001, ns indicates no significant difference); Figure 16 NETs more effectively promote dendritic cell maturation in the immune rejection state after liver transplantation; Figure: A. Representative graph of the mean fluorescence intensity of MHCⅡ, CD80, and CD86 on bone marrow-derived dendritic cells under different stimulation factors; B. Statistical results of the mean fluorescence intensity of MHCⅡ on dendritic cells; D. Statistical results of the mean fluorescence intensity of CD86 on dendritic cells; Statistical results of the mean fluorescence intensity of MHCⅡ on dendritic cells (BLANK = blank control, LPS = lipopolysaccharide, N = normal control, NR = non-rejecting, R = rejecting, n = 3, * p <0.05,** p <0.01,*** p <0.001, **** p <0.0001, ns indicates no significant difference); Figure 17 NETs promote the expression of STING-related pathways in bone marrow-derived dendritic cells; Figure: A. Representative immunoblot images of STING-related pathway proteins in bone marrow-derived dendritic cells under different stimulation factors; B.-D. Represent the grayscale statistical results of the relative expression of p-STING, STING, p-IRF3, and IRF3, respectively (LPS = lipopolysaccharide, N = normal control, NR = non-rejection, R = rejection, n = 3, * p <0.05,** p <0.01,***p <0.001, **** p <0.0001, ns indicates no significant difference); Figure 18 H-151 inhibits the effect of NETs on dendritic cell maturation after liver transplantation; Figure: A. Representative graph of the mean fluorescence intensity of MHCⅡ expression on bone marrow-derived dendritic cells after administration of NETs or NETs + H-151; B. Statistical results of the change in mean fluorescence intensity of MHCⅡ on dendritic cells (NR = non-rejection, R = rejection, n = 3, ** p <0.01,*** p <0.001). DETAILED DESCRIPTION

[0016] 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.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art in the art to which the invention belongs, and the materials cited herein and those cited by them will be incorporated by reference. Equivalent technologies of the specific embodiments described that can be understood through routine experiments recognized by those skilled in the art will be included in this application. The experimental methods in the following examples are all conventional methods unless otherwise specified. The instruments and equipment used in the following examples are all conventional laboratory instruments and equipment unless otherwise specified; the experimental materials used in the following examples are all purchased from conventional biochemical reagent stores unless otherwise specified. The reagents used in the present invention are shown in Table 1.

[0018] Table 1: Reagents 1. Experimental Methods 1. RNA-Seq data analysis: Gene expression profiles and clinical data of liver transplant biopsies with T cell-mediated rejection (TCMR, n=37) and normal samples (n=129) were obtained from the Gene Expression Omnibus (GEO) database (GSE145780). The raw data were processed and normalized using the limma software package, with |log2(Fold Change)|>0.263 and pA threshold of 0.05 was used to identify differentially expressed genes (DEGs). Gene set enrichment analysis (GSEA) was performed using the clusterProfiler package. Genes enriched in the pathway were displayed in a heatmap after normalization, accompanied by group-specific annotations. Functional enrichment analysis, including KEGG and Gene Ontology (GO), was also performed using clusterProfiler.

[0019] 2. Experimental Design: Sixteen patients undergoing liver biopsy were recruited. Based on the pathological results of the liver biopsy, the patients were divided into two groups: a rejection group (R = 3) and a non-rejection group (NR = 3). The liver control group consisted of five donor liver samples (normal group, N = 3). Patients with severe biliary complications or postoperative infection were excluded from the study. Consent was obtained from all participants and / or their legal guardians for the use of liver tissue, blood samples, and clinical data for research and publication.

[0020] 3. Liver Transplantation Mouse Model Establishment: 7-8-week-old SPF-rated C57BL / 6 and C3H male mice were purchased from Beijing Weitonglihua Laboratory Animal Technology Co., Ltd. Orthotopic liver transplantation was performed using the double-cuff technique. C57BL / 6 and C3H mice served as liver donors, and C3H mice served as recipients to establish liver transplant rejection and non-rejection models. Postoperatively, mice were housed in an SPF-rated animal facility at the Laboratory Animal Center. Samples were collected 14 days after surgery.

[0021] 4. Hematoxylin-eosin staining: Follow the kit instructions. Briefly, the following steps are as follows: After baking paraffin sections at 70°C for 1 hour, dewax them in xylene twice for 5-10 minutes each time. Rehydrate them in a gradient of ethanol (100%, 95%, 85%, 75%) for 3 minutes each step, and soak them in distilled water for 2 minutes. Stain with hematoxylin for 10 minutes, then rinse with distilled water to remove any excess stain. Differentiate in differentiation solution for 30 seconds, then rinse twice in tap water for 3 minutes each time. Stain with eosin for 40 seconds, remove excess stain, and rapidly dehydrate. Wash in a gradient of ethanol (75%, 85%, 95%, and 100%) for 2-3 seconds each, then rinse in 100% ethanol for 50 seconds. Clear the sections in xylene twice for 1 minute each time, and mount in neutral gum.

[0022] 5. Isolation of Mouse Liver Non-Parenchymal Cells: Dissociate the mouse liver, cut the bile duct, remove the liver, wash it with PBS, and mince it. Place it in a Miltenyi Gentle MACS C tube and add 10 ml of digestive enzyme (containing 1 mg type IV collagenase, 50 μl fetal bovine serum (FBS), and 0.1 mg deoxyribonuclease I (DNase I)). Preliminary digestion of the liver tissue was performed twice using a GentleMACS automated tissue processor, followed by slow digestion at 37°C on a shaker for 30 minutes. The liver tissue was then dissociated again using a GentleMACS automated tissue processor. Digestion was terminated by adding 10 ml of MACS buffer to the C tube and mixing. The cell suspension was filtered through a 70 μm cell sieve and centrifuged at 500 g for 5 minutes at 4°C. The supernatant was discarded, and the pellet was resuspended in 3 ml of 30% Percoll working solution and centrifuged at 500 g for 5 minutes at room temperature. The supernatant was discarded to obtain mouse liver non-parenchymal cells.

[0023] 6. Isolation of Mouse Bone Marrow Cells and Neutrophils: Dissect the femur and tibia by separating the tendon attachments. Cut the femur and tibia at both ends. Use a 1 mL syringe to draw an appropriate amount of PBS buffer and blow the bone marrow cavity until the cavity turns from red to white. Collect the cell suspension, filter it through a 70 μm cell sieve, and centrifuge it at 500 g at 4°C for 5 minutes. Discard the supernatant. Resuspend the cells in 1 mL of red blood cell lysis buffer, lyse the red blood cells at 4°C for 5 minutes, and then terminate the cycle with 10 mL of PBS buffer. Centrifuge at 500 g at 4°C for 5 minutes to obtain mouse bone marrow cells.

[0024] After preparing the Percoll working solution (Percoll stock solution: 10X PBS buffer 9:1), prepare 3 mL each of 72%, 60%, and 52% gradient Percoll solutions. Gently and evenly layer the 72% and 60% Percoll solutions in the same 15 mL centrifuge tube. Resuspend the bone marrow cell pellet with 3 mL of 52% Percoll and gently layer it on top of the 60% Percoll solution. Centrifuge horizontally at 1100g for 30 minutes at room temperature without braking. Gently aspirate the cell buffy coat layer between 72% and 60% into a new centrifuge tube. Wash the cells twice with an appropriate amount of PBS buffer and centrifuge at 500g for 5 minutes at 4°C. Discard the supernatant to obtain neutrophils.

[0025] 7. Induction of NETs in mouse bone marrow neutrophils: 5×10 5Mouse bone marrow neutrophils were resuspended in 1640+10% FBS+1% penicillin-streptomycin, 100 nM PMA was added, and the cells were plated in a 24-well plate at 500 μL / well. After stimulation and culture for 24 hours, the supernatant was aspirated to obtain mouse bone marrow neutrophil-induced NETs, which were stored at -80°C for subsequent cell slides. The cell slides were fixed with 4% paraformaldehyde.

[0026] 8. Induction of mouse bone marrow dendritic cells: Take 1.5×10 6 Mouse bone marrow cells were resuspended in 1640 medium with 10% FBS, 1% penicillin-streptomycin, GM-CSF (20 μg / mL), and IL-4 (20 μg / mL). The cells were plated in a 6-well plate at a rate of 4 mL / well. The medium was changed halfway on days 3 and 5. On day 7, the cells were harvested by pipetting the plate and centrifuged at 500 g for 5 minutes at 4°C to obtain mouse bone marrow-derived dendritic cells.

[0027] 9. Stimulation of mouse bone marrow-derived dendritic cells: Collect the mouse bone marrow-induced dendritic cells on the 7th day, and take 5×10 5 The cells were resuspended in 1640 medium + 10% FBS + 1% penicillin-streptomycin and cultured in groups according to the following stimulation factors: blank (1640 medium), LPS (2 μg / mL), neutrophil culture supernatant NETs (0.2 μg / mL), neutrophil culture supernatant NETs (0.2 μg / mL) + H-151 (10 mM), and plated in a 24-well plate at 500 μL / well. After 24 hours of stimulation culture, the cells were collected by pipetting the plate.

[0028] 10. Flow cytometry: Flow cytometry was performed using a BD FACSAria™ III. Cells were harvested and centrifuged at 500g for 5 minutes at 4°C. The cell pellet was washed once with PBS buffer and centrifuged again. The cell pellet was blocked with a pre-mixed CD16 / CD32 blocking buffer (1:500) for 20 minutes at room temperature. The centrifugation was terminated with PBS buffer. A pre-mixed staining solution of a dead-alive dye (1:1000) and a surface antibody (1:200) was added. The cells were stained in the dark for 20 minutes at room temperature. The cells were then quenched with PBS buffer and centrifuged. The cells were resuspended in an appropriate amount of PBS and analyzed by flow cytometry.

[0029] 11. Purification of NETs Induced by Mouse Bone Marrow Neutrophils: NETs induced by bone marrow neutrophils were purified using a plasmid extraction kit. Briefly, the following steps were performed: 500 μL of P4 solution and 100 μL of NETs were mixed, gently inverted 6-8 times, allowed to stand at room temperature for 10 minutes, and centrifuged at 13,400 g for 10 minutes. The supernatant was aspirated and applied to a CS column, which was centrifuged at 13,400 g for 2 minutes. The filtrate was collected in a new 1.5 mL centrifuge tube, 0.3 volumes of isopropanol were added, and the mixture was mixed by inversion. The filtrate was then transferred to an adsorption column CP4 (which was placed in a collection tube). The adsorption column was centrifuged at 13,400 g for 1 minute at room temperature. The waste liquid from the collection tube was discarded and the adsorption column was returned to the collection tube. 500 μL of deproteinized solution PD was added to the adsorption column CP4, and the column was centrifuged at 13,400 g for 1 minute. The waste liquid from the collection tube was discarded and the adsorption column was returned to the collection tube. Add 600 μL of rinse buffer PW to the adsorption column and centrifuge at 13,400 g for 1 minute. Discard the waste liquid from the collection tube and return the adsorption column to the collection tube. Repeat this step once. Return the empty adsorption column to the collection tube and centrifuge at 13,400 g for 2 minutes. Remove the adsorption column and place it in a clean 1.5 mL centrifuge tube. Add 100-300 μL of elution buffer TB dropwise to the center of the adsorption membrane. Incubate at room temperature for 2 minutes. Centrifuge at 13,400 g for 1 minute to obtain purified NETs. Measure the ds-DNA concentration using a UV-visible spectrophotometer.

[0030] 12. Real-time Quantitative PCR: RT-qPCR was performed on 6 ng of purified NETs in a 20 μL system. The real-time quantitative PCR system settings were as follows: 95°C pre-denaturation for 30 seconds, 95°C denaturation for 15 seconds, 55°C annealing for 15 seconds, and 72°C extension for 60 seconds, for 40 cycles. PCR primers are listed in Table 2.

[0031] Table 2: Primer sequences 13. Multiplex immunofluorescence Tissue fluorescence: Paraffin sections were baked at 70°C for 1 hour and then deparaffinized and rehydrated using the following reagent concentration gradient: pure xylene (2 times, 5 minutes each), xylene:anhydrous ethanol (1:1) (3 minutes), pure anhydrous ethanol (2 times, 5 minutes each), 95% anhydrous ethanol (5 minutes), 90% anhydrous ethanol (5 minutes), 85% anhydrous ethanol (2 minutes), 80% anhydrous ethanol (2 minutes), 70% anhydrous ethanol (2 minutes), and 50% anhydrous ethanol (2 minutes). Sections were then washed three times on a shaker with PBS buffer (5 minutes each). Sections were then placed in boiling antigen retrieval solution (1.21 g Tris + 0.39 g EDTA, made up to 1000 mL) and autoclaved for 2 minutes before being allowed to cool. Sections were then washed three times on a shaker with PBS buffer (5 minutes each). Catalase blocking agent was added dropwise and the sections were heated at room temperature for 10 minutes. Sections were then washed three times on a shaker with PBS buffer (3 minutes each). Add a drop of 0.3% Triton-X100 in PBS to cover the tissue, permeabilize at room temperature for 20 minutes, and then wash three times with PBS buffer on a shaker for 15 minutes each. Add a drop of 5% BSA in PBS to cover the tissue and block at room temperature for 30 minutes. Gently remove the BSA and add a drop of primary antibody prepared in 3% BSA in PBS to cover the tissue and incubate at 4°C overnight. On the second day, after rewarming for 1 hour, wash three times with PBS buffer on a shaker for 15 minutes each. Add a drop of fluorescent secondary antibody prepared in 3% BSA in PBS to cover the tissue and incubate at room temperature in the dark for 1 hour. Wash three times with PBS on a shaker for 15 minutes each. Add a drop of DAPI in the dark for 10 minutes, then wash three times with PBS on a shaker for 10 minutes each. Add an anti-fluorescence fade agent to seal the slide.

[0032] Cytofluorescence: Aspirate and discard the 4% paraformaldehyde from the cell slide. Add 1 mL of PBS buffer and soak the slide for 5 minutes. Aspirate the liquid and repeat three times. Permeabilize the slide with 0.1% Triton-X100 in PBS at room temperature for 3 minutes. Aspirate the liquid and soak the slide three times in PBS for 1 minute each. Discard the liquid and block the slide with 5% BSA in PBS at 37°C for 1 hour. Aspirate the liquid and soak the slide on ice for 3 minutes. Add the primary antibody prepared in 1% BSA in PBS and incubate overnight at 4°C. Aspirate the liquid and soak the slide three times in 1% BSA in PBS for 5 minutes each. Discard the liquid. Add the secondary antibody prepared in 1% BSA in PBS and incubate at 37°C in the dark for 1 hour. Immerse the slide three times in 1% BSA in PBS for 5 minutes each. Discard the liquid and add DAPI. Block the slide for 10 minutes at room temperature in the dark. Immerse the slide three times in PBS for 10 minutes each. Add anti-fluorescence attenuation mounting medium on the slide, take out the cell slide, and place the mounting medium with the cells facing the mounting medium.

[0033] 14. Determination of cell-free DNA (cf-DNA): Quantify using the Quant-iT™ PicoGreen™ dsDNA Quantification Kit and dsDNA reagent. A brief summary is as follows: Prepare the various working solutions according to the manufacturer's instructions. Dilute the neutrophil-induced NETs in the prepared diluent at a ratio of 1:4. Add 50 μL of the diluted NETs and standard to an opaque 96-well plate. Add 50 μL of the dye, agitate thoroughly, and incubate in the dark for 5 minutes. Measure the fluorescence intensity using a microplate reader. Construct a standard curve. Substitute the measured values into the standard curve to calculate the sample concentration. Multiply the calculated concentration by 8 to obtain the final sample concentration.

[0034] 15. Quantification of Neutrophils and NETs: In tissue sections or cell slides, photograph random fields of view at 0°, 120°, and 240°. Colocalization of DAPI and Ly-6G indicates tissue neutrophils; colocalization of DAPI, Ly-6G, and H3Cit indicates the formation of NETs in tissue neutrophils; and colocalization of DAPI, MPO, and H3Cit indicates the formation of NETs in bone marrow cultured neutrophils. In tissues, the number of colocalized cells within the field of view is counted to indicate the number of neutrophils and NET formation. In cells, the percentage of NET formation is calculated as the ratio of the number of colocalized cells within the field of view to the number of cells with DAPI alone.

[0035] 16. Western Blot: After harvesting cells, wash with PBS and lyse with lysis buffer (RIPA + 1% PMSF + 1% phosphatase inhibitor) on ice for 10 minutes. Vortex for 15 seconds, repeat four times. Centrifuge at 14,000g at 4°C for 10 minutes. Collect the supernatant and determine protein concentration using a BCA kit. Add 5X protein loading buffer (1 / 4 the volume of protein) and denature by boiling. Store at -20°C. Prepare a 10% gel using a PAGE kit. Electrophoresis is performed at 80V for 40 minutes, followed by 120V for 2 hours. Cut a PVDF membrane to the appropriate size based on the intended purpose and the location of the internal control. Form a "sandwich" of membrane, gel, filter paper, and sponge in the correct order. Place the membrane in ice-free rapid transfer buffer and transfer at 400mA for 30 minutes. Remove the PVDF membrane and rinse it with TBST for 1 minute, then block it in protein-free rapid blocking buffer for 40 minutes at room temperature. Gently rinse the PVDF membrane with TBST and incubate with the primary antibody overnight at 4°C. The membrane was washed three times with TBST (5 minutes each time) and incubated with secondary antibodies at room temperature for 1 hour. The membrane was washed five times with TBST (5 minutes each time). The membrane was developed with a drop of luminescent solution. The antibody ratios were as follows: p-STING (1:1000), STING (1:1000), p-IRF3 (1:1000), IRF3 (1:1000), and Vinculin (1:1000).

[0036] 17. Statistical Methods: Data are presented as mean ± standard deviation (SD) and analyzed using GraphPad Prim 9.5. Data from two groups conforming to a Gaussian distribution were compared using the unpaired t-test or the unpaired t-test with Welch correction, depending on whether the variances were uniform. Correlation analysis was performed using the Pearson test. Data from two groups conforming to a non-Gaussian distribution were compared using the Mann-Whitney test, and correlation analysis was performed using the Spearman test. For multiple data sets conforming to a Gaussian distribution, the standard one-way analysis of variance (Tukey's multiple comparisons test) and the Brown-Forsythe and Welch T3 multiple comparisons test were used. For multiple data sets conforming to a non-Gaussian distribution, the Kruskal-Wallis test (Dunn's multiple comparisons test) was used. p Values < 0.05 were considered statistically significant. Asterisks in the figures indicate the following: * p <0.05,** p <0.01,*** p <0.001, **** p <0.0001, ns indicates no significant difference. Statistical results are from ≥3 independent experiments.

[0037] 2. Experimental Results 1. Enrichment of NETs formation pathways in patients with immune rejection after liver transplantation: To study the changes in the immune microenvironment after liver transplantation, gene expression profiles and clinical data of liver transplant biopsies of T cell-mediated rejection (TCMR, n=37) and normal samples (n=129) were obtained from the Gene Expression Omnibus (GEO) database (GSE145780). KEGG analysis of differentially expressed genes in RNA-Seq results revealed that pathways such as antigen presentation, circulating DNA sensing, and NETs formation were enriched in patients with immune rejection ( Figure 1 A). NETs pathway-related genes are upregulated in immune rejection patients ( Figure 1 B).

[0038] At the same time, GSEA analysis was performed, and the results were as follows Figure 2 As shown in A, the results showed that the NETs formation pathway showed an up-regulation trend in immune rejection patients. ROS is one of the most widely reported inducing factors of NETs. GO analysis suggested that the differentially expressed genes were enriched in the ROS metabolic process and its related pathways ( Figure 2 B). This prompted us to explore the role of NETs in the immune microenvironment after liver transplantation.

[0039] 2. Increased in situ and peripheral NET formation in liver transplant patients with immune rejection: To verify the results of the big data analysis, we first used H&E staining to rigorously evaluate patients after clinical liver transplantation, and screened samples that met the diagnosis for experimental verification. It was observed that immune rejection patients showed more severe inflammatory cell infiltration in the portal area, bile duct, and venous endothelial cells than non-rejection patients ( Figure 3 A) Banff liver transplant rejection diagnostic criteria quantitative scoring was used to evaluate the rejection of patients with mild to moderate acute rejection, while non-rejection patients showed borderline or no rejection ( Figure 3 B).

[0040] Analysis of clinical routine liver function test results showed that rejection patients had higher alanine aminotransferase ( Figure 4 A), aspartate aminotransferase ( Figure 4 B) levels and both levels are positively correlated with disease severity ( Figure 4 C, D). This indicates liver transplant function damage under immune rejection.

[0041] Next, we further verified the formation of NETs in liver transplant patients. We used multiple immunofluorescence to detect liver biopsy tissues of liver transplant patients. We can see that there is more co-localization of DAPI (blue), MPO (red), and H3Cit (green) in patients after liver transplantation ( Figure 5 A), indicating increased NETs formation. Random visual fields were selected for statistical analysis, and the results showed that the NETs level in rejection patients was higher, with significant differences ( Figure 5 B). Correlation analysis between NETs formation level and Banff score showed that NETs formation was positively correlated with the severity of liver transplant rejection ( Figure 5 C).

[0042] The formation of peripheral NETs was detected simultaneously. Plasma cf-DNA was measured and it was found that the peripheral cf-DNA level of rejection patients was significantly increased ( Figure 6 A), suggesting that peripheral NETs release increases and is positively correlated with disease severity ( Figure 6 B). The above results indicate that after liver transplantation, the formation of NETs in situ and peripherally in the liver of patients with immune rejection increases, which is positively correlated with the severity of the disease.

[0043] 3. Increased neutrophil infiltration during liver transplant rejection: Considering the particularity of clinical liver transplant patients, a liver transplant mouse model was constructed to further explore the changes in the immune microenvironment after liver transplantation. C57 and C3H mice were used as liver donors and C3H mice as recipients to establish liver transplant rejection and non-rejection models ( Figure 7A). H&E staining of the liver revealed inflammatory cell infiltration in the portal area, bile duct, and vein of mice in the model group, and the infiltration was more severe in mice in the rejection group ( Figure 7 B). There are differences in the quantitative scoring of the Banff liver transplant rejection diagnostic criteria ( Figure 7 C), which is consistent with the clinical results, indicating that the modeling was successful.

[0044] Considering that the liver is an immune privileged organ, it has fewer resident immune cells. First, immunofluorescence was used to confirm the co-localization of Ly-6G (red) and DAPI (blue) in transplanted mice, confirming the presence of neutrophils in situ in the liver ( Figure 8 A). Then, quantitative analysis of neutrophils in different model mice revealed an increase in neutrophil infiltration in the rejection group mice ( Figure 8 B). This further prompted the exploration of the role of NETs in liver transplant immunity.

[0045] 4. More NETs are produced in situ and peripherally in the liver of mice with immune rejection after liver transplantation: The phenotypic characteristics of NETs in immune rejection were further verified using a liver transplantation mouse model. By performing multiple immunofluorescence staining on mouse liver tissue, it can be seen that in random fields of view, DAPI (blue), Ly-6G (red), and H3Cit (green) co-localized in the liver of mice in the surgery group ( Figure 9 A). Quantitative analysis revealed that more colocalization was observed in the mice in the rejection group ( Figure 9 B) suggests that more NETs are produced in situ in the liver of immune rejection mice after liver transplantation, and this is positively correlated with the severity of the disease ( Figure 9 C), which is consistent with the clinical phenomenon.

[0046] To explore the formation characteristics of NETs at a more detailed level, neutrophils from mouse bone marrow were extracted and induced with PMA to form NETs. The changes in the cells were observed. It was found that the mice in the surgical group showed more obvious fibrous reticular structures and co-localization of DAPI (blue), MPO (red), and H3Cit (green). Figure 10 A). Quantification results showed that the neutrophils in the rejection group mice ( Figure 10 B) showed a significant increase in NETs formation, and analysis found that this result was positively correlated with the Banff liver transplant rejection diagnostic criteria quantitative score assessment ( Figure 10 C).

[0047] Due to the limited availability of mouse plasma samples, in order to echo the detection of peripheral NETs levels in clinical patients, the culture supernatant of mouse bone marrow neutrophils after NETs induction was measured, and it was found that the rejection group mice had higher cf-DNA levels ( Figure 11 A). In-depth analysis found that this detection index was positively correlated with the degree of transplant rejection in mice ( Figure 11B). This suggests that peripheral neutrophils in mice after liver transplantation respond more quickly to PMA and are more likely to form NETs.

[0048] 5. Increased ROS accumulation and increased mtDNA content in NETs during liver transplant rejection: Based on the positive correlation between NETs and liver transplant immune rejection, we continue to explore the phenotypic characteristics of NETs. GO analysis of clinical patients suggested an increase in ROS metabolism and related pathways, which was verified using a mouse model. Flow cytometry was used to detect the ROS and mtROS content of mouse liver neutrophils. CD11b + Ly-6G + Double-positive cells represent neutrophils ( Figure 12 A). H2DCFDA is a cell-permeable probe used to detect intracellular reactive oxygen species (ROS). It is a cell-permeable non-fluorescent probe that can be hydrolyzed by intracellular esterases to DCFH. When oxidized by intracellular ROS, it forms fluorescent DCF, producing green fluorescence. MitoSOX is a live cell fluorescent probe that specifically targets mitochondria. It has cell membrane permeability. After entering the mitochondria, MitoSOX will be oxidized by superoxide, but will not be oxidized by other ROS or RNS generating systems. The oxidized MitoSOX then binds to nucleic acids in the mitochondria / nucleus, producing strong red fluorescence ( Figure 12 B).

[0049] By quantitatively analyzing the average fluorescence intensity values of H2DCFDA and MitoSOX in different model mice, it was found that the rejection group mice had no significant difference in ROS ( Figure 13 A) or mtROS ( Figure 13 C) levels increased and were positively correlated with NETs formation ( Figure 13 B, D).

[0050] To promote clinical translation more accurately, the correlation between the above fluorescence intensity and the severity of mouse liver transplant immune rejection was analyzed, and it was found that the average fluorescence intensity of mouse ROS was positively correlated with the severity of transplant immune rejection ( Figure 14 A left); The mean fluorescence intensity of mtROS in mice was also positively correlated with the severity of transplant immune rejection ( Figure 14 A right). Since ROS can cause DNA damage, the DNA components in the purified NETs were detected and it was found that the rejection group mice had higher 16S / 18S levels ( Figure 14 B), suggesting that it is rich in mtDNA. And the analysis found that this phenomenon is positively correlated with the severity of the disease ( Figure 14 C). The above results suggest that ROS accumulation increases and the mtDNA content in NETs increases in mice under liver transplant rejection.

[0051] 6. NETs after liver transplantation promote dendritic cell maturation: Mitochondrial DNA has stronger pro-inflammatory properties and can promote the maturation of dendritic cells. Based on our discovery that the structural composition of DNA in NETs changes under different mouse liver transplant immune states, we further explored the effect of NETs on dendritic cell function under different immune states after liver transplantation. Neutrophils from the bone marrow of model mice were taken, and NETs were induced by PMA and the cell supernatant was collected. At the same time, bone marrow cells from normal mice were taken, and dendritic cells were induced with GM-CSF and IL-4. After the two were co-cultured for 24 hours, the phenotypic characteristics of dendritic cells were detected ( Figure 15 A). Flow cytometry using CD45 + CD11c + After double positive positioning of dendritic cells ( Figure 15 B), detect its number ( Figure 15 C) It was found that NETs in the surgical group promoted an increase in the proportion of dendritic cells, which was no different from the positive control LPS ( Figure 15 D). NETs in the rejection group promoted dendritic cells to secrete inflammatory cytokine IL-6 ( Figure 15 E).

[0052] Considering the potent antigen-presenting function of mature dendritic cells, their maturation promotes CD8 + T cell function, which may further aggravate immune rejection. The expression of co-stimulatory molecules on dendritic cells was also detected simultaneously. It can be observed that after stimulation with NETs from different sources, the mean fluorescence intensity levels of MHCⅡ, CD80, and CD86 on dendritic cells changed ( Figure 16 A). Further quantitative analysis revealed that NETs in the rejection group more effectively promoted the expression of MHCⅡ in dendritic cells than those in the non-rejection group. Figure 16 B), CD80 ( Figure 16 C), CD86 ( Figure 16 D) expression. The above results suggest that NETs more effectively promote the maturation of dendritic cells in immune rejection mice after liver transplantation.

[0053] 7. NETs regulate dendritic cell function through STING-related pathways after liver transplantation: STING is a DNA damage sensing pathway. KEGG analysis has suggested that circulating DNA sensing pathways are enriched in liver transplant rejection patients. Considering that NETs contain DNA components, it is speculated that they may promote dendritic cell function through STING-related pathways. To verify this hypothesis, after NETs stimulation of bone marrow-induced dendritic cells, STING-related pathway proteins were detected by protein immunoblotting. It can be seen that after NETs stimulation of the rejection group mice, the expression of dendritic cells p-STING and p-IRF3 increased ( Figure 17 A), with statistically significant differences ( Figure 17 B, D), STING ( Figure 17 C) and IRF3 ( Figure 17 E) No significant difference was found.

[0054] To further clarify the effect of STING-related pathways on dendritic cells, the function of dendritic cells was detected after administration of STING inhibitor H-151 ( Figure 18 A). It was found that H-151 inhibited the promoting effect of NETs on the expression of MHCⅡ in dendritic cells after liver transplantation ( Figure 18 B, C). These results suggest that NETs regulate dendritic cell function through the STING-related pathway after liver transplantation.

[0055] In this study, using clinical samples, we confirmed that NETs differ between patients who reject and those who do not reject liver transplants, positively correlating with disease severity. Animal and cell-based experiments confirmed that NETs levels increase in mice that reject liver transplants, are enriched in mitochondrial DNA, and promote dendritic cell maturation through the STING pathway. This NET-promoting effect on liver transplant rejection provides a potential target for clinical diagnosis and treatment of this disease.

[0056] NETosis is an inflammatory cell death process of neutrophils, initially described as a process in which neutrophils eliminate pathogens through suicide, accompanied by the release of NETs. NETs released through NETosis contain a variety of proteins and DNA and have been implicated in various diseases, including liver transplantation. Previous studies have demonstrated that NETs regulate HMGB1 translocation and Kupffer cell M1 polarization in a rat liver transplant rejection model, exacerbating liver injury. Other studies have suggested that diphenylhydrogen iodide can ameliorate acute liver rejection during transplantation by inhibiting NET formation in vivo. Considering potential interspecies differences and to more accurately reflect clinical observations, we first performed KEGG and GSEA analyses on RNA-Seq data from clinical liver transplant patients. We found upregulation of the NET formation pathway, which was confirmed in clinical samples, and that NET levels were positively correlated with disease severity. We then established mouse models of liver transplant rejection and non-rejection to dynamically observe changes in the immune microenvironment and explore their specific roles. We found that compared with normal mice, model mice had increased NET formation both in situ and in the periphery of the liver, consistent with previous studies, with significantly higher levels in the rejection group. The cf-DNA component of NETs is released into the periphery with the cells, and previous studies have shown that donor-derived cf-DNA can be detected in transplant recipients. Our animal model revealed increased cf-DNA release from the bone marrow of rejection-treated mice after NET formation, consistent with clinical findings. This suggests that measuring NET and cf-DNA levels can be used to predict the development of post-transplant immune rejection.

[0057] Reactive oxygen species (ROS) are key mediators of ischemia-reperfusion injury. Liver transplantation involves a process of ischemia-reperfusion, leading to an imbalance in ROS production. ROS are one of the factors that trigger the release of NETs. Gene oncology analysis of differentially expressed genes in clinical liver transplant samples suggests the expression of ROS-related pathways. Using flow cytometry, we demonstrated increased ROS and mitochondrial ROS in mice with liver transplant rejection. Reactive oxygen species (ROS) are a group of short-lived, highly reactive oxygen-containing molecules that can induce DNA damage and affect the DNA damage response (DDR), and mitochondrial ROS can cause mitochondrial DNA damage. To investigate the effects of ROS on DNA in NETs, we examined the DNA content of NETs and found that NETs are enriched in mitochondrial DNA. Studies have shown that mitochondrial DNA-enriched NETs can cause autoimmune diseases. Therefore, we further investigated whether NETs under different immune states after liver transplantation have different biological effects on immune cells.

[0058] Given the antigen-presenting function of dendritic cells, their expression of costimulatory molecules can activate T cells and trigger cellular immunity. Reports indicate that NETs can regulate HMGB1 translocation, which increases the expression of CD80, CD86, and MHC class II molecules on bone marrow-derived DCs. Others suggest that mitochondrial DNA drives non-canonical inflammatory activation via the cGAS-STING signaling pathway in retinal microvascular endothelial cells. We hypothesized that NETs may also directly activate dendritic cells and promote their maturation due to altered DNA composition. To validate this hypothesis, we co-cultured NETs with dendritic cells from different mouse models and assayed their function. We found that, at the same stimulatory concentration, NETs in an immune-rejecting state more effectively promoted the expression of CD80, CD86, and MHC class II on dendritic cells, confirming our hypothesis.

[0059] NETs are complexes composed of proteins and chromatin, and the STING pathway can sense DNA changes. Western blotting experiments using dendritic cells stimulated with NETs in different immune states after liver transplantation confirmed increased expression of STING pathway-related proteins (p-STING and p-IRF3) in dendritic cells. Furthermore, administration of the STING inhibitor H-151, in addition to NET stimulation, reduced MHCII expression in dendritic cells and inhibited dendritic cell maturation, further demonstrating that NETs indeed affect dendritic cell maturation through STING.

[0060] In general, the present invention clarifies that NETs formation increases after immune rejection occurs in clinical liver transplant patients and mice, that there are differences in DNA structure between rejection and non-rejection NETs, and that dendritic cell maturation is promoted through the STING-related pathway. It is clear that NETs formation increases in patients with immune rejection after liver transplantation, and is positively correlated with the severity of the disease. By constructing a mouse liver transplant rejection and non-rejection model, it was verified that NETs formation increases after liver transplant rejection occurs in mice. It is proposed that there are differences in DNA structure of NETs under different immune states after liver transplantation, and that dendritic cell maturation is promoted through the STING-related pathway, which may further aggravate liver transplant rejection and damage graft function. It provides a new perspective for the clinical diagnosis, treatment and prevention of liver transplant immune rejection.

[0061] 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.

Claims

1. Application of the NETs / STING / IRF3 / MHCII pathway in liver transplantation immune tolerance, characterized by: The use of the NETs / STING / IRF3 / MHCII pathway as a target in the preparation and / or screening of drugs for preventing or treating liver transplantation immune tolerance.

2. The use according to claim 1, characterized in that: The NETs regulate the expression of MHCII in dendritic cells through the STING / IRF3 pathway, thereby regulating the maturation of dendritic cells and aggravating the occurrence of immune rejection reactions after liver transplantation.

3. The use according to claim 1, characterized in that: The drug for treating liver transplantation immune tolerance is a drug that inhibits or reduces the formation of NETs, a STING inhibitor or a dendritic cell MHCII expression inhibitor.

4. The use according to claim 3, characterized in that: The STING inhibitor is H-151.

5. Application of the NETs / STING / IRF3 / MHCII pathway in liver transplantation immune tolerance, characterized by: Application of the NETs / STING / IRF3 / MHCII pathway in the preparation and / or screening of products for identifying or assisting in the identification of liver transplant immune tolerance.

6. The use according to claim 5, characterized in that: The products include test kits, drugs, test strips or detection platforms.

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