A staging method for immune reconstitution after liver transplantation and an early warning marker for acute rejection

Abstract

The present invention relates to a method for staging immune reconstitution after liver transplantation and an early warning marker for acute rejection. By mapping the dynamic changes in the peripheral immune microenvironment of non-rejecting and rejecting patients under tacrolimus treatment after liver transplantation, the present invention reveals a four-step recovery phase of immune reconstitution indicated by immune characteristics and clinical parameters, and discovers that CD14 + RNASE2 + Monocytes, serum RNASE2 (EDN), FOS + Monocytes and inflammatory NK cells can predict acute rejection after liver transplantation in the early stage of recovery after liver transplantation, suggesting that they can be used as early warning biomarkers of acute rejection after liver transplantation. CD8 + The cytotoxic function of T cells can be used as an indicator for adjusting tacrolimus dosage. This invention provides a new entry point for the development of new treatment strategies and biomarkers for acute rejection reactions, and can also provide a reference for precise postoperative medication planning and prognosis judgment for patients after liver transplantation.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine technology, and in particular to a method for staging immune reconstruction after liver transplantation and an early warning marker for acute rejection. Background Art

[0002] Although the liver is known as an “immune-privileged organ,” immunosuppressants are still needed to prevent liver transplant (LT) rejection. Importantly, studies have shown that the inter-patient dose variation of tacrolimus anti-rejection therapy can be greater than 20-fold. Therapeutic drug monitoring (TDM) is used clinically to adjust the tacrolimus dose, but changes in tacrolimus blood concentrations only represent the absorption and metabolism of the immunosuppressant in the body and can only indirectly reflect the immunosuppressive status of the patient after transplantation. The lack of direct indicators to indicate the immune status of LT patients under tacrolimus treatment makes it difficult to achieve precise management of immunosuppressants. Therefore, a comprehensive understanding of the immune status reflected by the dynamic composition and function of immune cells after transplantation will greatly facilitate the use of immunosuppressants and thus improve patient prognosis. However, little is currently known about the dynamic process of immune reconstitution after LT.

[0003] Both liver biopsy and peripheral blood mononuclear cells (PBMCs) can reflect the status of immune cells after liver transplantation. Analysis of PBMCs is a minimally invasive, rapid, convenient method that can dynamically reflect the systemic immune status. It can make up for the shortcomings of TDM and more accurately assess the patient's immune status.

[0004] Post-transplant rejection is primarily categorized as hyperacute rejection, acute rejection, and chronic rejection. Acute rejection is the most common type of immune rejection after liver transplantation and a serious early complication after liver transplantation. It is also the most important risk factor for chronic rejection and loss of transplant liver function. Therefore, developing biomarkers that can predict acute rejection after liver transplantation is crucial.

[0005] Chinese patent CN110408691A, published on November 5, 2019, discloses a marker EGR-1 related to predicting rejection reaction after liver transplantation and its application. Specifically, it relates to the marker EGR-1 for predicting rejection reaction after liver transplantation and its application. The marker of this invention can be used to warn the occurrence of acute rejection reaction after liver transplantation, which has good guiding significance for the early clinical diagnosis and treatment of acute rejection reaction.

[0006] However, there are no reports on the method for staging immune reconstitution after liver transplantation and the early warning marker for acute rejection according to the present invention. Summary of the Invention

[0007] The purpose of the present invention is to address the deficiencies in the prior art and provide a method for staging immune reconstitution after liver transplantation and an early warning marker for acute rejection.

[0008] In one aspect, the present invention provides a method for staging immune reconstitution after liver transplantation, comprising the following steps:

[0009] 1) Obtain blood samples from patients after liver transplantation;

[0010] 2) Dynamic monitoring using scRNA-seq, CyTOF, and high-throughput cytokine quantification;

[0011] 3) The postoperative recovery stage of liver transplantation in the non-rejection group was divided into four stages based on immunological characteristics and clinical parameters.

[0012] As a preferred example, the patients after liver transplantation in the above step 1) all used a tacrolimus-based immunosuppressive regimen.

[0013] More preferably, the immunological characteristics in the above step 3) include the proportion and function of immune cells, and the clinical parameters are clinical indicator parameters such as liver function, blood routine, and drug concentration; the four stages are: stage 1: 7 days before liver transplantation, stage 2: 7 to 15 days after liver transplantation, stage 3: 15 to 90 days after liver transplantation, and stage 4: 90 to 365 days after liver transplantation.

[0014] In a second aspect, the application of any of the above-mentioned methods in the postoperative management of patients is provided, and the postoperative management includes but is not limited to precise medication, staged patient management, and tolerance induction.

[0015] As a preferred example, the reagent or kit can predict the early incidence of acute rejection after liver transplantation

[0016] In a third aspect, a biomarker for early warning of acute rejection during the recovery phase after liver transplantation is provided, wherein the biomarker is selected from CD14 + RNASE2 + Monocytes, serum RNASE2 (EDN), FOS + One or more of monocytes and inflammatory NK cells.

[0017] As a preferred example, the above-mentioned CD14 + RNASE2 + Monocytes are CD14 in recovery phase 1 + RNASE2 + Monocyte CD14 + CD16 - RNASE2+ ; the above FOS + Monocytes are FOS in recovery phase 2 + Monocyte subset CD14 + CD16 - c-Fos + The above-mentioned inflammatory NK cells are inflammatory NK cell subsets CD56 before transplantation and during recovery phase 2. dim CD16 + STAT1 + ; CD14 in the above recovery phase 1 + RNASE2 + The decrease in the proportion of monocyte subsets in peripheral blood immune cells is a warning indicator for acute rejection after liver transplantation; the decrease in peripheral serum RNASE2 (EDN) content in patients in the above recovery stage 1 is a warning indicator for acute rejection after liver transplantation; the decrease in FOS in the above recovery stage 2 is a warning indicator for acute rejection after liver transplantation. + The increase in the proportion of monocytes in peripheral blood is a warning indicator of acute rejection after liver transplantation; the above-mentioned inflammatory NK cell subsets before transplantation and in the recovery phase 2 are CD56 in peripheral blood. dim CD16 + Increased proportion of NK cells is a warning indicator of acute rejection after liver transplantation

[0018] In a fourth aspect, the present invention provides the use of the above-mentioned biomarkers in the preparation of a detection reagent or kit for acute rejection reaction after liver transplantation.

[0019] In a fifth aspect, a kit for early prediction of acute rejection after liver transplantation is provided, wherein the kit comprises any of the above-mentioned biomarkers or reagents for detecting the markers.

[0020] More preferably, the kit comprises the amino acid sequence of RNASE2 or an amino acid sequence having no less than 70% homology with the above amino acid sequence; the amino acid sequence of RNASE2 is shown in SEQ ID NO: 1.

[0021] In the sixth aspect, CD14 is provided. + RNASE2 + Monocytes, serum RNASE2 (EDN), FOS + The application of monocytes and inflammatory NK cells in the preparation of drugs for preventing acute rejection is characterized in that the CD14 + RNASE2 + Monocytes are CD14 in recovery phase 1 + RNASE2 + Monocyte subset CD14 + CD16- RNASE2 + ; The FOS + Monocytes are FOS in recovery phase 2 + Monocyte subset CD14 + CD16 - c-Fos + The inflammatory NK cells are CD56 inflammatory NK cell subsets before transplantation and during recovery phase 2. dim CD16 + STAT1 + .

[0022] In the seventh aspect, the present invention provides an application of the cytotoxic function of early T cells after liver transplantation in the prognosis of liver transplant patients, wherein the T cells are CD8 + T cells; the patient is a patient undergoing non-acute rejection liver transplantation; the cytotoxic function can be used as an indicator for adjusting tacrolimus medication.

[0023] The advantages of the present invention are:

[0024] 1. Monitoring the changes in immune cells at different stages after liver transplantation can help accurately manage immunosuppressants, prevent graft rejection, and reduce drug toxicity.

[0025] 2. Gain a deeper understanding of the dynamic changes in the distribution and function of peripheral immune cells, thereby constructing a high-resolution dynamic single-cell map of the immune system throughout the LT recovery process, and further identifying the key cell populations that regulate individual differences in immune status, providing a reference for precise medication planning and prognosis judgment after LT patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Attachment Figure 1 Overview of the study and changes in cell type abundance in liver transplant patients; a: Schematic diagram of sample collection and experimental route; b: UMAP plot of all patient samples combined, colored by the nine major cell types identified by subpopulation gene markers; c: Fold change in absolute abundance of each cell type based on scRNA-seq data relative to baseline in the non-ACR group; d: Fold change in absolute abundance of each cell type based on scRNA-seq data relative to baseline in the non-ACR group;

[0027] Attachment Figure 2To determine the "four-step recovery period" after liver transplantation through dynamic changes in immune and liver function; a: Volcano plot showing the genes upregulated and downregulated in the non-ACR group at all time points in the gene differential expression analysis. Points with P values ​​< 0.05 and absolute values ​​avg_log2FC > 0.25 are indicated in red, and others are indicated in black. The gray numbers at the top and bottom indicate the number of upregulated and downregulated genes, respectively: b: Enrichment dot plot of time point label genes with relative functions in the non-ACR group. The dot color corresponds to the adjusted p value, and the dot size indicates the proportion of enriched genes. The top 10 enriched terms at each time point are visualized; c: Unsupervised cluster analysis of immune-related gene expression profiles revealed four distinguishable stages after liver transplantation (left). Unsupervised cluster analysis of GSVA results (right); d: Principal component analysis (PCA) of all samples based on immune-related gene expression (left) or immune cell distribution (right). Samples are colored according to their phase and sample ID; e: Volcano plot showing the number of upregulated (red) and downregulated (blue) genes between each phase and healthy volunteers. Points with P value < 0.05 and avg_log2FC > 0.25 are indicated in red, while points with P value < 0.05 and avg_log2FC < (-0.25) are indicated in blue. Others are indicated in gray; f: Box plot showing the dynamic changes of clinical parameters in each phase. *p < 0.05, **p < 0.01, ***p < 0.001 in Wilcoxon test. ALT, alanine aminotransferase; AST, aspartate aminotransferase; TB, total bilirubin; DB, direct bilirubin; PA, prealbumin; PT-INR, prothrombin time (international normalized ratio); g: Fuzzy c-means clustering identified 9 different temporal patterns of gene expression. The x-axis represents the phase. The y-axis represents the log2-transformed normalized intensity ratio in each phase (left). The right figure shows the heat map of longitudinal DEGs in each stage of liver transplantation, in which selected genes and pathways are displayed.

[0028] Attachment Figure 3 Figure 2 shows the dynamic changes of T cell subsets after transplantation; a: UMAP graph shows the 7 T cell subsets of all samples, and the percentage of each cell type in PBMC is marked; b: Dot plot shows the proportional expression levels of subset-specific genes in non-ACR, ACR and healthy groups. The size of the dots indicates the percentage of expressing cells, and the color indicates the expression level; c: Line graph showing the longitudinal changes in the relative proportions of the 7 T cell subsets in the non-ACR group (green) and the ACR group (red); d: Line graph showing the longitudinal changes in CD8+ T cells based on CyTOF data; e: Line graph showing the terminal effector CD8 + Line graph of temporal changes in cytotoxicity-related gene scores in T cells; f: terminal effector CD8 + Line graph of longitudinal changes in expression of cytotoxicity-related genes in T cells.

[0029] Attachment Figure 4 Figure 3 shows the heterogeneity of NK cells during the LT recovery phase; a: UMAP plots of two NK cell subsets for all samples are shown, with the percentage of each cell type annotated; b: Dot plot showing the proportional expression levels of NK cell type-specific genes in the non-ACR, ACR, and healthy groups. The size of the dots indicates the percentage of expressing cells, and the color indicates the expression level. c: Bar graph showing the frequency of NK cell subsets in different groups during liver transplantation; d: Bar graph showing CD56 dim CD16 + Line graph showing temporal changes in relative function-specific gene set scores in NK cells; e: UMAP graph showing NK cell subset distribution information; f: UMAP graph depicting CD56 identified by transcriptional profiles dim CD16 + Six subpopulations of NK cells (left), showing cell trajectories (upper, right) and group distribution (lower, right) identified by Monocle3; g: Dot plots showing the six subpopulations CD56 dim CD16 + The average expression levels of the top 10 specific genes in NK cell subsets. The size of the dots indicates the percentage of expressing cells, and the color indicates the expression level; h: shows the expression of each CD56 dim CD16 + Box plots showing the distribution of cell type-specific gene set scores for NK cell subsets; i: Box plots showing the distribution of signaling and metabolic gene set scores in cluster 2 (“terminally differentiated NK”) and cluster 5 (“mature NK”). Wilcoxon test *p<0.05, **p<0.01, ***p<0.001. j: Shows the CD56 expression in different groups at different stages of liver transplantation. dim CD16 + Bar graph showing the relative abundance of NK cell subsets; k: CD56 in non-ACR and ACR patients dim CD16 + Inflammatory NK cells (CD56 dim CD16 + STAT1 + ) percentages by flow cytometry analysis; l: Bar graph showing the percentages of CD56 in non-ACR (n=11) and ACR (n=9) patients dim CD16 + The proportion of inflammatory NK cells in NK cells (right). Student's t test *p<0.05, **p<0.01, ***p<0.001.

[0030] Attachment Figure 5 is the flow cytometry gating strategy for inflammatory NK cells; a: Inflammatory NK cells (CD56dim CD16 + STAT1 + )'s flow gating strategy.

[0031] Attachment Figure 6 Figure 2 Dynamic changes of monocyte subsets in LT; a: UMAP graph showing 7 monocyte subsets of all samples, with the percentage of each cell type annotated; b: Dot plot showing proportional expression levels of monocyte type-specific genes in non-ACR, ACR, and healthy groups. The size of the dots indicates the percentage of expressing cells, and the color indicates the expression level; c: UMAP graph showing all merged monocytes colored by group (top) and stage (bottom); d: Line graph showing longitudinal changes in the relative proportions of 7 monocyte subsets in the non-ACR group (green) and the ACR group (red); e: FOS-expressing monocytes (CD14 + CD16 - c-Fos + ) flow cytometry gating strategy; f: Histogram showing the distribution of FOS-expressing monocytes in CD14 + CD16 - The proportion of monocytes; g: Line graph of RNASE2 expression in peripheral blood at different time points detected by qPCR; h: Flow cytometry analysis of CD14 in PBMC samples of patients treated with or without Golgi stop. + RNASE2 + Percentage of monocytes. The left panel shows representative flow cytometry images, and the right panel shows quantitative data for the two groups (n = 10 per group); i: Boxplot showing changes in plasma RNASE2 (EDN) concentrations at different time points after LT (ELISA assay). Paired t-test within samples. *p < 0.05, **p < 0.01, ***p < 0.001; j: Same as K, but unpaired Wilcoxon test was performed to compare the ACR group with the non-ACR group. *p < 0.05, **p < 0.01, ***p < 0.001; k: Receiver operating characteristic curve reflecting RNASE2 plasma concentrations at different time points after LT and acute rejection.

[0032] It should be noted that the above drawings have all been processed in grayscale. DETAILED DESCRIPTION

[0033] Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiments are only used to illustrate the present invention and are not used in limiting the scope of the present invention.In addition, should be understood that after reading the content of the present invention record, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall equally within the scope limited by the application's appended claims.

[0034] Example 1 Clinical Analysis

[0035] 1 Case data

[0036] Study cohort: From April 2020 to January 2022, Xiamen University Xiang'an Hospital recruited 6 patients with hepatitis B cirrhosis. All patients underwent liver transplantation and no infection occurred after the operation. Post-transplant medication regimen: oral tacrolimus twice a day, with an initial dose of 0.06 mg / kg / d. Only 500 mg of methylprednisolone was intravenously injected before the portal vein was reopened during surgery, and then routine therapeutic drug monitoring (TDM) was performed. Starting from the first day after LT, patients took mycophenolate mofetil 0.5 mg orally twice a day. And 20 mg of basiliximab was given on the first and fourth days after surgery respectively. All patients underwent routine blood tests and liver function tests before and after surgery, and liver biopsy pathological examination was performed when rejection was suspected.

[0037] Validation cohort: An additional 19 patients similar to cohort 1 were recruited as the validation cohort, of whom 9 patients developed acute cellular rejection (ACR) after LT.

[0038] Informed consent was obtained from all patients and their relatives, and the research was approved by the Ethics Committee of Xiang'an Hospital Affiliated to Xiamen University. The experiments were performed in accordance with the Declaration of Helsinki and its later amendments or comparable ethical standards.

[0039] 2 Methods

[0040] 2.1 Sample collection and pretreatment

[0041] Samples were collected from liver transplant patients during hospitalization and outpatient follow-up and monitored by scRNA-seq, CyTOF, and high-throughput cytokine quantification ( Figure 1 a). The collection schedule was: day 0 (before LT), day 1, day 3, day 5, day 7, day 11, day 15, day 19, day 23, day 60, day 90, day 180, day 365 or later. If the patient experienced acute rejection, additional samples were collected at the time of rejection and after rejection. A 10ml peripheral blood sample was collected from each subject. Of this, 2ml of the sample was subjected to single-cell sequencing within 2 hours, and the supernatant of the remaining 8ml sample was collected and PBMCs were obtained using the standard Ficoll-Paque Plus protocol. These samples were stored at -80°C overnight and then transferred to liquid nitrogen for storage until further processing.

[0042] 2.2 Cytokine detection

[0043] Serum samples were thawed on the day of the experiment. The target protein detected was RNASE2 (EDN), whose amino acid sequence is "MVPKLFTSQICLLLLLGLLAVEGSLHVKPPQFTWAQWFETQHINMTSQQC TNAMQVINNYQRRCKNQNTFLLTTFANVVNVCGNPNMTCPSNKTRKNCH HSGSQVPLIHCNLTTPSPQNISNCRYAQTPANMFYIVACDNRDQRRDPPQYP VVPVHLDRII" (SEQ ID NO: 1). Serum EDN was quantified by ELISA using a human serum EDN ELISA kit (MBLCo., Japan). Experimental procedures were performed strictly according to the kit instructions.

[0044] 2.3 Single-cell sequencing

[0045] Single cells were sorted into 384-well plates using a flow cytometer. Each well contained 2 μL of lysis solution using primers for bar-coded poly(T) reverse transcription (RT). Four wells of each 384-well plate were left empty as a cell-free control. After sorting, each plate was quickly centrifuged to ensure that the cells were fully immersed in the lysis solution. The plates were then frozen in liquid nitrogen and stored at -80°C.

[0046] Single-cell libraries were constructed using a massively parallel single-cell RNA-seq method [1]. Briefly, mRNA in each well was reverse transcribed into cDNA using a reverse transcription reaction mixture and barcoded reverse transcription primers. After reverse transcription, the complementary DNA (cDNA) was barcoded and pooled into low-DNA binding Eppendorf tubes. The aggregated cDNA was transcribed in vitro into amplified RNA (aRNA). The barcoded aRNA was chemically fragmented. The library was sequenced on an Illumina Novaseq6000.

[0047] 2.4 Single-cell data analysis

[0048] The gene barcode matrix of UMI counts was analyzed using the R package Seurat (v4.0.3). Briefly, genes expressed in fewer than three cells were excluded, and cells with fewer than 200 or more than 2000 detected genes were removed. The merged objects were normalized and scaled, and then principal component analysis (PCA) was calculated using the top 2000 variable genes. Cluster identification was performed using the standard k-nearest neighbor method with a 50-dimensional 2-resolution. Subpopulation annotation was performed using the human immune reference dataset [2] using SingleR (v1.6.1) to first identify the major cell types. We then annotated the minor immune cell types based on classical immune cell markers.

[0049] 2.5 Differentially expressed genes (DEGs) analysis and pathway enrichment

[0050] We used the 'FindMarkers' function in the Seurat R package to perform differential gene expression analysis using the MAST method [3]. Feature genes were screened based on adjusted p < 0.01 and fold change > 0.2. Gene ontology (GO) analysis of the feature genes was performed using the compareCluster function in the clusterProfiler R package (v4.0.3) with an FDR < 0.05. Gene variant analysis (GSVA) ​​[4] was performed on the corresponding gene sets to investigate the functions of each subcluster.

[0051] 2.6 Quasi-timing analysis

[0052] NK cell trajectory analysis was performed using Monocle3 (v.1.0.0) [6]. Briefly, the Seurat object was converted to a Monocle3 object using the cell_data_set function, and the trajectories were calculated using the “learn_graph” function in Monocle3.

[0053] 2.7 Gene set scoring for specific biological processes

[0054] Seurat function “AddModuleScore” was used to score different gene sets. These gene sets were collected from the MSigDB (v7.4.1) [8] database or previous studies.

[0055] 2.8 Flow cytometry

[0056] Before staining, PBMCs were treated with 5% Fc receptor blocking solution (absin) at 4°C in the dark for 15 minutes. The blocked cells were then incubated with the antibody labeled with the target antibody for 20 minutes. Intracellular staining was performed using the transcription factor staining buffer kit (BD Pharmingen). TM; USA), and the operation was strictly in accordance with the manufacturer's instructions. For unconjugated antibodies, PBMCs were incubated with the primary antibody at 4°C for 30 minutes. The incubated cells were then washed twice and incubated with a fluorescently conjugated goat anti-rabbit IgG secondary antibody at 4°C for 20 minutes. The stained cells were detected by Cytoflex LX (Beckman Coulter, USA). Flow cytometry data were analyzed by FlowJov10 (FlowJo LLC, Beckton Dickinson, Ashland, Oregon, US) and Graphpad Prism9 (Graphpad Software, San Diego, California USA). The results are shown as the percentage of positive cells or mean fluorescence intensity (MFI). The antibodies used are shown below.

[0057] For inflammatory NK cells:

[0058] CD3-BV650, CD14-BV786, CD56-BV510, CD16-BV421, CD45-PECY5, STAT1-PE, MOUSEIgG2a-PE, Dye-eFluor 780.

[0059] For monocytes expressing FOS: CD14-PE, CD16-BV421, CD45-PECY5, FOS-FITC, Rabbit IgG-FITC.

[0060] For CD14 + RNASE2 + Monocytes:

[0061] CD14-PE, CD16-FITC, CD45-PECY5, RNASE2, goat anti-rabbit IgG- 647, DyeeFluor 780.

[0062] 2.9 RNA extraction and qPCR analysis

[0063] Total RNA was extracted from PBMCs using TRIzol reagent. qPCR reverse transcription was performed using HyperScripTIII RTSuperMix (EnzyArtisan, China). Quantitative real-time PCR was performed in a LightCycler 96 Real-Time System (Roche) according to the manufacturer's instructions. Relative gene expression was calculated using the comparative cycle method (2-ΔΔCt) and normalized to the housekeeping gene GAPDH. The primers used were as follows: RNASE2: forward primer: AGCGCGGAGACTGGGAAAC (SEQ ID NO: 2) and reverse primer: ATTGCATTGGTGCATTGCTGG (SEQ ID NO: 3)

[0064] 2.10 Mass cytometry experiments

[0065] A total of 42 metal-conjugated antibodies were used to characterize each immune cell. Antibody conjugation was performed using the Maxpar Antibody Labeling Kit, following the protocol provided by Fluidigm (South San Francisco, California). The treated antibodies were stored in PBS-based antibody stabilization solution (Candor Biosciences, Constance, Germany) supplemented with 0.05% NaN3 at 4°C.

[0066] All immune cells were defined using CD45 antibodies, and live cells were isolated using cisplatin (Fluidigm, Kentucky, USA). The cells were washed once with 1xPBS and then stained with 100 μL 250nM cisplatin (Fluidigm) on ice for 5 minutes to exclude dead cells, then incubated in Fc receptor blocking solution and then stained with surface antibodies on ice for 30 minutes. The cells were washed twice with FACS buffer (1xPBS+0.5% BSA) and fixed overnight in 200 μL intercalation solution (fixation permeabilization solution containing 250nM 191 / 193Ir). After fixation, the cells were washed once with FACS buffer, then stained with staining buffer (eBioscience) and stained on ice with an intracellular antibody cocktail for 30 minutes. The cells were washed and resuspended in deionized water and detected on the HeliosCyTOF system (Fluidigm).

[0067] 2.11 Mass cytometry data analysis

[0068] The raw data for each marker were normalized using the Bead-based Normalizer (PMID: 23512433). All markers were used for clustering and visualization. The phenotypic algorithm used the R package R Phenograph [9] to cluster cells. 10,000 cells were randomly sampled from each sample and analyzed by t-SNE using the R package ImmunoCluster

[10] . Major cell types were annotated using classical markers, and subtypes were annotated using specific markers.

[0069] 3 Statistical analysis

[0070] All statistical analyses were performed using the R statistical software (v4.1.2). Groups were compared using the Wilcoxon-Mann-Whitney test or the Student's T test. Receiver operating characteristic (ROC) curve analysis was performed using the R package "pROC." All statistical tests were two-sided, and p < 0.05 was considered statistically significant.

[0071] 4 Results

[0072] Rhythmic and sustained transcriptional changes in peripheral blood mononuclear cells in LT patients

[0073] PBMCs were collected from 6 LT patients at multiple time points (including 0, 1, 3, 5, 7, 11, 15, 19, 23, 60, 90, 180, and 365 days after admission). Figure 1 a). Two of the patients experienced acute rejection, as indicated by ALT / AST levels and confirmed by pathological puncture, with additional samples collected during and after the rejection episode. Ultimately, nine major circulating immune cell types were identified by the most differentially expressed genes ( Figure 1 b) These include monocytes, CD4 + T cells, CD8 + T cells, B cells, NK cells, etc.

[0074] To gain a deeper understanding of the immune status during LT recovery, we then examined the immune cell composition of the non-ACR and ACR groups at different time points before and after liver transplantation ( Figure 1 c and 1d). For the non-ACR group, significant changes occurred before day 7, and the abundance of immune cell populations gradually approached the baseline from day 7 to day 19. The abundance of immune cell populations fluctuated almost at the baseline level from day 19 to day 90. Compared with healthy volunteers, the trend of immune cell abundance after day 90 was almost normalized, but there were still differences from healthy people. Therefore, persistent and rhythmic changes in cell composition were observed in the non-ACR group, indicating that there may be conserved immune processes in non-ACR patients ( Figure 1c). In contrast, the longitudinal changes in the abundance of immune cell populations in the ACR group lacked regularity ( Figure 1 d).

[0075] In conclusion, these transcriptomic analyses suggest persistent and progressive alterations in circulating immune cell populations after LT, particularly in the non-ACR group.

[0076] 4.2 Determining the Four-Step Recovery Period of LT by Immunological Characteristics and Liver Function Changes

[0077] To fully understand the immune response during LT, we then investigated the dynamics of the conservative recovery trajectory after LT over time. We compared the transcriptional profiles at each time point with those at other time points to identify temporal signature genes (TFGs) that were differentially expressed at each time point, with p-value < 0.05 and avg_log2FC > 0.25 ( Figure 2 a) The number of TFGs increased dynamically on day 1 after LT and remained high for the first 7 days, followed by a first wave of decline until day 15. From day 19 to day 90, TFG numbers underwent a second wave of decline and remained at a low level. A second increase in TFG numbers occurred on day 90 and remained high until day 365. Collectively, these observations suggest that the patient's peripheral immune composition may undergo four continuous remodeling processes.

[0078] We performed gene ontology (GO) analysis to understand the functional changes at multiple time points after LT in the non-ACR group. We found that the functional stage changes after LT ( Figure 2 b) In particular, pathways enriched on days 90, 180, and 365, respectively, showed strong similarities with those in healthy volunteers. Furthermore, many pathways enriched on day 90 post-LT were similar to those in healthy volunteers, suggesting that the immune phase at day 90 post-LT may be a potential time point for reaching a stable state. In summary, the immune response after LT undergoes multiple stages, ultimately reaching a stable state similar to that in healthy volunteers.

[0079] Then, using unsupervised hierarchical clustering gene expression analysis of immune-related genes, we successfully identified four distinct stages after LT ( Figure 2 c, left). Unsupervised hierarchical clustering using GSVA scores also revealed a roughly similar segmentation pattern ( Figure 2 c, right). Principal component analysis (PCA) further showed that the functional expression and composition of the above-identified immune cells were different at different stages and showed rhythmic changes ( Figure 2 d). Importantly, the overall composition and characteristics of immune cells from stages 1 to 4 in the non-ACR group gradually approached those in healthy volunteers, which was also confirmed by the gradual decrease in the counts of differentially expressed genes between LT patients and healthy volunteers ( Figure 2 e), indicating that the immune status of patients in the non-rejection group after transplantation gradually approaches that of healthy people.

[0080] To evaluate the clinical significance of these four recovery stages after LT, we retrospectively analyzed the changes in liver function indicators in these four stages ( Figure 2 f). Interestingly, these parameters showed comparable and consistent changes as liver recovery progressed. Specifically, acute liver damage was observed in stage 1, and liver function rapidly recovered in stage 2, although parameters remained above normal reference values. A slow recovery toward normal values ​​was observed in stage 3, and liver function parameters ultimately returned to normal reference ranges in stage 4.

[0081] In summary, scRNA-seq data suggest that there are four key periods after LT that are consistent with the recovery of clinical indicators after liver transplantation, namely, stage 1: the first 7 days, stage 2: 7 to 15 days, stage 3: 15 to 90 days, and stage 4: 90 to 365 days after LT.

[0082] 4.3 Different immune states after LT represented by the four recovery stages

[0083] To gain a preliminary understanding of the immune responses corresponding to these four phases, we used Mfuzz

[11] to specifically elucidate the time-dependent transcriptional programs related to immunity after LT. Nine different expression patterns were successfully clustered ( Figure 2 g). Genes involved in cellular oxidant detoxification (e.g., GPX1, NCF2) and Toll-like receptor binding (e.g., TLR8, S100A12) were highly expressed in phase 1 (cluster 6), likely reflecting an early response to traumatic injury-induced stress. Transcripts involved in responding to type I interferons (IFNs) (e.g., ISG15, MX1), also known as interferon-stimulated genes (ISGs), increased significantly in phase 2 (cluster 3). Following phase 1, genes associated with protein ubiquitination are upregulated to disassemble stress granules formed by acute injury, thereby restoring cellular activity. Collectively, these results reflect that immune cells primarily participate in alloimmune responses and post-traumatic stress repair responses in phase 2.

[0084] Moderate increases in RNA splicing (e.g., SF3A3) and chromatin remodeling-related transcripts (e.g., UPF1, CHD2) were observed in stages 2 and 3 (cluster 2). Similarly, cell cycle-related transcripts (e.g., CDK4, TLK1) were also moderately increased in these two stages, peaking in stage 3 (cluster 8). These results suggest that immune cells from stage 2 to stage 3 are successively activated and recovered from the stress-induced transcriptional repression that occurs in stage 1

[12] . Transcripts encoding ribosomal protein structural components (e.g., RPL4, CD5), natural killer cell activation (e.g., IL2RB, GZMB), T cell activation (e.g., CD8A, SLAM7), and cytokine receptor activity (e.g., IL6ST, IL18RAP) increased and peaked in stage 4 (cluster 5). Notably, protein binding-related genes (e.g., TGFB1, NOTCH1) were extensively repressed in the LT recovery stage (cluster 1) compared with healthy volunteers. Genes encoding molecules involved in cell adhesion (which are involved in regulating inflammation and immune responses) also followed this pattern. These results suggest that the immune composition and function of LT patients have been partially restored by stage 4, but differences between LT patients and healthy volunteers still exist.

[0085] 4.4 In the early post-liver transplantation period, activation of CD8+ T cell cytotoxicity is positively correlated with a controllable cascade reaction network in the T cell population

[0086] By analyzing scRNA-seq in long-term follow-up LT patients, we conducted a detailed analysis of the dynamics of T cells under tacrolimus-based immunosuppression. Clustering of gene expression profiles revealed a total of seven T cell subsets ( Figure 3 a and 3b), including naive CD4 + T cells (CD4, CCR7, LEF1), central memory CD4 + T cells (CD4, IL7R, CCR7-), regulatory T cells (CD4, FOXP3, PIK3IP1), naive CD8 + T cells (CD8A, LEF1, CCR7), disabled CD8 + T cells (CD8, LAG3, TIGIT), effector memory CD8 + T cells (CD8, IL7R, GZMK) and terminal effector CD8 + T cells (CD8, GZMK, CX3CR1).

[0087] Detailed analysis showed that there was a clear trend of different T cell immune responses between the ACR and non-ACR groups ( Figure 3 c) CD4 + T cell subsets and CD8+ The proportion of T cell subsets decreased rapidly in stage 1, except for the anergic CD8 + T cells and T regulatory cells. Interestingly, all reduced T cell subsets recovered in stage 2 and continued to increase from stage 2 to stage 4. The above observations were confirmed by CyTOF analysis, and the proportion of CD8+ T cells continued to increase after LT ( Figure 3 d) Based on the above observations, the function of effector or memory T cell subsets that increase in proportion after stage 2 may be strictly controlled to produce immune homeostasis in the non-ACR group. Then we turn our attention to CD8 + The cytotoxic function of T cells, which is one of the main functions leading to transplant rejection, was observed during the recovery period of LT in the non-ACR group. + The overall cytotoxic function of T cells gradually decreased, while when rejection occurred, the overall cytotoxic function of cells in the ACR group increased ( Figure 3 e). Detailed analysis of genes associated with cytotoxicity showed that GNLY and GZMB decreased continuously, while PRF1 and KLRC2 gradually increased in the early stages (stages 1 and 2) and then decreased ( Figure 3 f). These results showed that CD8 + The cytotoxicity of T cells was limited. In the non-ACR group, PRF1 and KLRC2-mediated CD8 + The transient enhancement of early T cell cytotoxicity indicates that moderate T cell activation is beneficial for graft recognition and tolerance.

[0088] These data suggest that transient activation of cytotoxicity at an early stage may be a response to alloantigens and is beneficial for reestablishing immune homeostasis.

[0089] 4.5CD56 dim CD16 + NK cell heterogeneity determines the magnitude of IFN-γ responses in ACR and non-ACR patients

[0090] Two NK cell clusters were identified based on specific gene expression, including CD56 bright CD16 - NK cells and CD56 dim CD16 + NK cells ( Figure 4 a and Figure 4 b). The results showed that these two NK cell subsets decreased rapidly in all patients in the first period after LT, after which the proportions of NK cell subsets gradually recovered in both the non-ACR and ACR groups ( Figure 4 c) These results indicate that CD56 dim CD16 +NK cells may mainly regulate the immune response in the late stage of LT (after stage 3). Then, the analysis of related gene expression profiles revealed that CD56 dim CD16 + Functional dynamics of NK cells ( Figure 4 d) We found that although the cytotoxicity and proliferation were generally similar between the ACR and non-ACR groups, CD56 dim CD16 + The IFN-γ response of NK cells was highly activated and lasted longer in the ACR group, indicating a strong and persistent inflammatory response of NK cells in ACR patients.

[0091] By focusing on CD56 dim CD16 + NK cells, we found different characteristics of NK subsets in non-ACR patients, ACR patients and healthy volunteers ( Figure 4 e) CD56 dim CD16 + NK cells were re-clustered and CD56 dim CD16 + NK cells are divided into 6 subpopulations ( Figure 4 f) Using Monocle3 to detect CD56 dim CD16 + Trajectory analysis of NK cells showed that the main path along the pseudo-time started from healthy human NK cells, then the ACR group, and finally ended with the non-ACR group ( Figure 4 f). Comparative transcriptional analysis was performed to elucidate the phenotypes of these NK cell subsets by comparing our NK subsets with newly published NK subsets. We identified cluster 0 as adaptive NK cells, cluster 1 as similar to inflammatory NK cells expressing type I IFN response markers such as STAT1 and XAF1, clusters 2 and 5 as "terminally differentiated NK" and "mature NK," respectively, cluster 3 as resting NK cells primarily in the early developmental stages, and cluster 4 as erythroid NK cells due to high expression of erythrocyte-related genes, which may be caused by the adhesion of erythrocyte fragments generated during the sorting process ( Figure 4 g、 Figure 4 h and Figure 4 i).

[0092] Significant heterogeneity in NK cell subsets along the LT recovery trajectory was observed between the ACR and non-ACR groups. Importantly, patients in the ACR group had more inflammatory NK cells compared with the non-ACR group, particularly in stage 3 ( Figure 4 j). However, this difference disappeared after the rejection reaction was relieved (hormone pulse therapy). Flow cytometry analysis further confirmed that inflammatory NK cells were more enriched in the ACR group than in the non-ACR group at baseline and stage 3 ( Figure 4 k and Figure 4 l, Figure 5 a), indicating that NK cell heterogeneity between ACR and non-ACR persists from the outset.

[0093] These results together indicate that CD56 dim CD16 + NK cells promote post-LT transplant rejection by differentiating into inflammatory NK cell subsets, possibly through immune responses regulated by IFN signaling.

[0094] 4.6 The fate of monocyte subsets changes dynamically with changes in LT

[0095] scRNA-seq analysis identified a total of seven mononuclear cell clusters based on specific gene expression ( Figure 6 a and Figure 6 b), which includes CD14 + Monocytes (CD14, LYZ, VCAN), CD14 + RNASE2 + Monocytes (RNASE2, CD14, LYZ, VCAN), CD14 + AP-1 + Monocytes (FOSB, JUN, CD14, LYZ, VCAN), CD14 + FOS + Monocytes (FOS, CD14, LYZ, VCAN), etc. In addition, high heterogeneity was observed among monocyte subsets between groups and at different recovery stages ( Figure 6 c).

[0096] The proportion of monocytes and subpopulations changes dynamically with the progression of LT ( Figure 6 d) In stage 1, CD14 + Monocytes and CD14 + RNASE2 + Monocytes increased significantly and became the two most abundant populations in this stage, and then gradually decreased. + Monocytes and CD14 + RNASE2 + The proportion of monocytes decreased synchronously and remained stable. + Interestingly, the proportion of monocytes expressing FOS (including CD14 + FOS + Monocytes and CD14 + AP-1 +The increase in FOS-expressing monocytes in ACR patients was also confirmed by flow cytometry in a larger cohort ( Figure 6 e and Figure 6 f). These results suggest that FOS-expressing monocytes may play an important role in regulating acute liver transplant rejection.

[0097] Given that CD14 + RNASE2 + Monocytes were a common feature between ACR and non-ACR groups and were mainly enriched in the non-ACR group in stage 1. CD14 + RNASE2 + Monocytes highly express a molecule called RNASE2, which is often referred to as eosinophil-derived neurotoxin (EDN). We further validated the increased expression of RNASE2 during the first phase in an independent cohort ( Figure 6 g). As expected, intracellular flow cytometry experiments showed that RNASE2 could be secreted from monocytes, which was confirmed by ELISA analysis ( Figure 6 h) Our cohort analysis further confirmed that the changes in serum EDN levels at different time points after LT were associated with the expression of CD14 + RNASE2 + The changes in monocytes were consistent, with EDN increasing in phase 1, especially on day 1, and gradually decreasing thereafter ( Figure 6 i). In addition, significant differences between the ACR and non-ACR groups were observed at multiple time points ( Figure 6 j). Based on the above observations, we performed receiver operating characteristic (ROC) curve analysis and found that the areas under the ROC curve (AUC-ROC) of serum EDN levels on day 1 and day 3 were 0.889 and 0.8 ( Figure 6 k). Therefore, the first stage CD14 + RNASE2 + Monocyte and serum RNASE2 (EDN) levels may be potential early warning indicators for the occurrence of ACR.

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[0112] The above is only a preferred embodiment of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and supplements without departing from the method of the present invention. These improvements and supplements should also be regarded as the scope of protection of the present invention.

Claims

1. Use of a biomarker in the preparation of a detection reagent or kit for acute rejection after liver transplantation, characterized in that: The marker is CD14 + RNASE2 + Monocytes, serum RNASE2, FOS + It is composed of monocytes and inflammatory NK cells.

2. The use according to claim 1, characterized in that The CD14 + RNASE2 + Monocytes are CD14 in recovery phase 1 + RNASE2 + Monocyte subset CD14 + CD16 - RNASE2 + ; The FOS + Monocytes are FOS in recovery phase 2 + Monocyte subset CD14 + CD16 - c-Fos + The inflammatory NK cells are CD56 inflammatory NK cell subsets before transplantation and during recovery phase 2. dim CD16 + STAT1 + ; CD14 in the recovery phase 1 + RNASE2 + The decrease in the proportion of monocytes in peripheral blood immune cells is a warning indicator for acute rejection after liver transplantation; the decrease in the peripheral serum RNASE2 content of patients in the recovery stage 1 is a warning indicator for acute rejection after liver transplantation; the decrease in FOS in the recovery stage 2 is a warning indicator for acute rejection after liver transplantation. + The increase in the proportion of monocytes in peripheral blood is a warning indicator of acute rejection after liver transplantation; the inflammatory NK cell subsets before transplantation and in the recovery phase 2 are CD56 in peripheral blood. dim CD16 + An increased proportion of NK cells is a warning indicator of acute rejection after liver transplantation; recovery stage 1 refers to the first 7 days after liver transplantation, and recovery stage 2 refers to 7 to 15 days after liver transplantation.

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