A biomarker for predicting or diagnosing a chronic tuberculosis immune suppression state and application thereof

Abstract

This invention belongs to the field of biotechnology, specifically relating to a biomarker for diagnosing immunosuppressive status in chronic tuberculosis and its application. The biomarker is arginase-1. This invention demonstrates that serum Arginase-1 levels are significantly elevated in the progression of chronic tuberculosis, and are significantly positively correlated with the enrichment ratio of myeloid-derived suppressor cells and the expression levels of T cell exhaustion markers. As a key effector molecule of MDSCs, high expression of Arginase-1 reflects abnormal arginine metabolism and T cell exhaustion caused by MDSC amplification, making it an ideal indicator for assessing the immunosuppressive microenvironment in chronic tuberculosis. Using the biomarker of this invention, serum samples can be detected by simple methods such as ELISA, enabling rapid diagnosis, stratification, and dynamic monitoring of the immune status of patients with chronic tuberculosis.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology, specifically relating to a biomarker for predicting or diagnosing immunosuppressive status in chronic tuberculosis and its application. Background Technology

[0003] Traditional clinical anti-tuberculosis chemotherapy primarily targets Mycobacterium tuberculosis; however, such treatment regimens typically require long-term medication for at least 6 months, accompanied by significant drug toxicity, posing a major challenge to patient adherence and thus promoting the occurrence and spread of drug-resistant and persistent tuberculosis. Host-guided therapy ( host-directed therapies HDTs (high-dose chemotherapy) are considered an important direction to overcome the limitations of traditional anti-tuberculosis chemotherapy. However, their application in tuberculosis faces significant uncertainties, mainly due to the lack of precise stratification and dynamic assessment methods for patients' immune status, especially the degree of immunosuppression.

[0004] In tuberculosis, T cell depletion and myeloid-derived suppressor cells ( myeloid-derived suppressor cells Abnormal amplification of MDSCs (tumor mitochondrial stem cells) is a key mechanism driving immunosuppression, tuberculosis persistence, and granulomatous pathological transformation. However, currently, there are no simple, reproducible, and dynamically monitorable indicators to reflect the overall state of this immunosuppressive axis. Furthermore, current assessments of T cell exhaustion and MDSCs mainly rely on flow cytometry, which is insufficient to meet the clinical need for real-time, low-cost, and standardized monitoring of immune status during long-term tuberculosis treatment. This technological bottleneck directly limits the rational selection of high-dose therapeutic techniques (HDT), the control of treatment timing, and the evaluation of efficacy, and also restricts the precise application of immunomodulatory HDT in clinical practice.

[0005] Therefore, it is urgently necessary to develop a blood biomarker that can comprehensively reflect the immunosuppressive state mediated by MDSC amplification and T cell depletion. This type of biomarker can not only be used for immunophenotyping and disease progression risk assessment in patients with chronic tuberculosis, but also serve as a key tool for screening suitable candidates for HDT, determining the treatment window, and dynamically monitoring efficacy, providing important assurance for the safety, effectiveness, and personalized implementation of host-guided therapy for tuberculosis. Summary of the Invention

[0006] In view of the problems existing in the prior art, the purpose of this invention is to design and provide a technical solution for predicting or diagnosing the immunosuppressive state of chronic tuberculosis and its application.

[0007] The present invention is specifically implemented using the following technical solutions: The first aspect of the present invention provides a biomarker for predicting or diagnosing immunosuppressive status in chronic tuberculosis, said biomarker being arginase-1 (ARG1).

[0008] A second aspect of the present invention provides a kit for predicting or diagnosing immunosuppressive status in chronic tuberculosis, the kit comprising reagents for detecting a biomarker, said biomarker being arginase-1. Furthermore, the reagent contains an antibody or ligand that specifically binds to arginase-1.

[0009] A third aspect of the present invention provides a system for predicting or diagnosing immunosuppressive status in chronic tuberculosis, the system comprising a data analysis module for analyzing the detection values ​​of a biomarker in a sample of a patient to be tested, the biomarker being arginase-1.

[0010] Furthermore, the data analysis module determines whether the patient under test is in a state of chronic tuberculosis immunosuppression based on the detection values ​​of biomarkers in the sample and reference values. The determination condition is: when the detection value is significantly higher than the reference value, the subject is determined to be in a state of chronic tuberculosis immunosuppression.

[0011] Furthermore, the sample from the patient to be tested is serum or blood.

[0012] The fourth aspect of this invention provides the use of the above-mentioned biomarkers in the preparation of reagents for predicting or diagnosing immunosuppressive states in chronic tuberculosis.

[0013] The present invention has the following beneficial effects: (1) The biomarker Arginase-1 discovered in this invention is directly associated with the core mechanism of tuberculosis immune escape (MDSCs-T cell exhaustion axis) and can accurately reflect the degree of immunosuppression.

[0014] (2) This invention requires only a small amount of peripheral blood (serum / plasma) for detection. Compared with traditional flow cytometry (which requires the separation of PBMCs, is complex and difficult to standardize), it has the advantages of simple operation, low cost, minimal trauma and easy dynamic monitoring.

[0015] (3) This invention can be used for immunophenotyping and disease progression risk assessment of patients with chronic tuberculosis, as well as to guide the precise use and efficacy evaluation of immunomodulators (such as MDSCs inhibitors or immune checkpoint inhibitors). Attached Figure Description

[0016] Figure 1The results are as follows: (A) Principal component analysis (PCA) showed significant clustering differences among the groups; (B) Volcano plot showed differentially expressed proteins between the two-infection group and the control group: red dots represent upregulated proteins, blue dots represent downregulated proteins, and gray dots represent proteins with no significant changes; (C) Heatmap showed hierarchical clustering of representative immune-related differentially expressed proteins between the control group and the two-infection group; (D) Quantitative analysis of arginase-1 (ARG1) protein levels. Note: Statistical significance was determined using one-way ANOVA and corresponding post-hoc tests. *p <0.05, **p <0.005.

[0017] Figure 2 The results are as follows: (A) Principal component analysis (PCA) of the data showed a clear separation between the two groups; (B) KEGG pathway enrichment analysis of differentially expressed metabolites showed significant enrichment in the arginine biosynthesis pathway; (C) Volcano plot analysis of differentially expressed metabolites among the groups, with red dots indicating significantly upregulated metabolites, blue dots indicating significantly downregulated metabolites, and gray dots indicating metabolites with no significant difference, with L-arginine highlighted; (D) Quantitative analysis of serum L-arginine levels in the control and model groups.

[0018] Figure 3 Association analysis of mouse serum Arginase-1; (A) Pearson correlation analysis of mouse serum Arginase-1 and the proportion of MDSCs in spleen; (B) Mouse serum Arginase-1 and PD-1 in lung cells. + Pearson correlation analysis of the population; (C) Serum Arginase-1 and Tim-3 in lung cells of mice + Pearson correlation analysis of the population; (D) Mouse serum Arginase-1 and PD-1 in lung cells + Tim-3 + Pearson correlation analysis of the population.

[0019] Figure 4 Serum arginase levels in patients with chronic tuberculosis.

[0020] Figure 5 (A) Flow cytometry dot plot of MDSCs in PBMCs of patients with chronic tuberculosis; (B) Flow cytometry dot plot of MDSCs in PBMCs of healthy individuals; (C) CD11b in PBMCs. + CD33 + The proportion of HLA-DR- MDSCs; (D) CD11b in PBMCs + CD33 +The proportion of HLA-DR-MDSCs.

[0021] Figure 6 (A) Flow cytometry dot plot of exhausted T cells in PBMCs of patients with chronic tuberculosis; (B) Flow cytometry dot plot of exhausted T cells in PBMCs of healthy individuals; (C) PD-1 in PBMCs. + T cell percentage; (D) PD-1 in PBMCs + Tim-3 + T cell ratio.

[0022] Figure 7 Association analysis of serum Arginase-1 in patients with chronic tuberculosis; (A) Pearson correlation analysis of serum Arginase-1 and the proportion of splenic MDSCs in patients with chronic tuberculosis; (B) Association analysis of serum Arginase-1 and PD-1 in PBMCs in patients with chronic tuberculosis. + Pearson correlation analysis of the population; (C) Serum Arginase-1 and Tim-3 in PBMCs of patients with chronic tuberculosis + Pearson correlation analysis of the population; (D) Serum Arginase-1 and PD-1 in PBMCs of patients with chronic tuberculosis + Tim-3 + Pearson correlation analysis of the population.

[0023] Figure 8 Serum Arginase-1 and CD3 + PD-1 + Tim-3 + ROC curve of T cell diagnostic efficacy; (A) ROC curve of serum Arginase-1 concentration evaluating the condition of patients with chronic tuberculosis; (B) CD3 + PD-1 + Tim-3 + The ROC curve of the proportion of exhausted T cells was used to evaluate the condition of patients with chronic tuberculosis. Detailed Implementation

[0024] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features can be combined with each other. Unless otherwise specified, the methods used in the embodiments of the present invention are conventional methods, and the reagents used are commercially available.

[0025] Example 1: Screening of immunosuppressive markers for chronic tuberculosis This embodiment aims to establish a rabbit model of chronic tuberculosis and, by combining serum proteomic and metabolomic data, screen for immunosuppressive biomarkers of chronic tuberculosis.

[0026] The specific steps are as follows: 1. Establish a rabbit model of chronic tuberculosis. (1) Experimental animals: female New Zealand white rabbits.

[0027] (2) The method for constructing the rabbit chronic tuberculosis model used in this embodiment: The rabbit intradermal Mycobacterium bovis BCG infection model was established according to the method described in the previous literature (NPJVaccines. 2024 Dec 19;9(1):248. doi: 10.1038 / s41541-024-01049-x). The brief steps are as follows: Two injection points were selected on each side of the abdomen of each rabbit, and 100 μL of Mycobacterium bovis BCG suspension (containing 5×10) was injected intradermally into each injection point. 6 (CFU), with injection sites spaced 2-3 cm apart, a total of 4 sites per animal. Six weeks after the first infection, when the primary nodules have completely disappeared, a second intradermal challenge with Mycobacterium bovis BCG (BCG) is performed at a site 2 cm away from the initial injection site to simulate tuberculosis reinfection, thereby establishing a chronic persistent tuberculosis infection model.

[0028] (3) Results showed that after the primary infection model, severe granulomatous lesions appeared at the infection site, with the largest granuloma volume being 401.48±51.91 cubic millimeters. Liquefaction began on day 10 post-infection, ulceration appeared on day 12, the peak of liquefaction occurred on day 16, and complete healing occurred on day 35 post-infection. Compared with the primary infection, the progression of granulomas in the reinfection was generally more rapid, with the largest volume being 467.19±27.11 cubic millimeters. Liquefaction began on day 6 post-infection, ulceration occurred on day 9, and the peak of liquefaction occurred on day 13. The immunopathological features presented by these experimental results are consistent with the classic immunological mechanism in the Koch reaction that "the cellular immune response formed by the primary tuberculosis infection can antagonize the reinfection," clearly indicating that the rabbit skin primary / secondary tuberculosis infection model established in this study has good scientific validity and applicability.

[0029] 2. Blood collection and serum separation M. bovis Following secondary infection, at the peak of liquefaction at the infection site, 5 ml of peripheral blood was collected from the marginal ear vein of each rabbit. The blood samples were centrifuged at 4000 rpm for 10 minutes to obtain serum for subsequent proteomic and metabolomic analyses.

[0030] 3. Serum proteomic detection and result analysis (1) Protein enrichment and preparation. 100 μL of rabbit serum and 4 μL of pre-washed magnetic beads were incubated on a magnetic rack for 2 hours. After incubation, high-abundance proteins were removed, and the magnetic beads were washed five times with UA lysis buffer (8M urea, 150 mM Tris-HCl, pH 8.0) for 5 minutes each time. The bound proteins were then lysed in the lysis buffer, reduced with 20 mM DTT at 37°C for 60 minutes, and alkylated with 50 mM iodoacetamide for 30 minutes at room temperature in the dark. The sample was diluted with 50 mM NH4HCO3 to reduce the urea concentration to <1.5 M, then centrifuged, and the supernatant was digested with trypsin at a ratio of 1:50 (w / w) at 37°C overnight. The peptides were desalted by C18 solid-phase extraction column, dried under vacuum, redissolved with 20 μL of 0.1% formic acid, and then quantified based on UV at 280 nm, and iRT peptides were added.

[0031] (2) Mass Spectrometry Acquisition. Peptide samples were analyzed using a Thermo Scientific Vanquish Neo ultra-high performance liquid chromatography system with an Orbitrap™ Astral™ mass spectrometer equipped with an Easy-Spray ion source. Chromatographic separations were performed on an ES906C18 column (1.9 μm, 150 μm × 15 cm) using mobile phase A (0.1% aqueous formic acid) and mobile phase B (80% acetonitrile plus 0.1% formic acid). MS1 spectra were acquired at a resolution of 240,000 (200 m / z) in the mass range of 380 to 980 m / z, with a normalized AGC target of 500% and a maximum injection time of 5 ms. MS2 spectra were collected in data-independent acquisition (DIA) mode using 299 isolation windows (each 2 m / z), an HCD collision energy of 25 eV, a normalized AGC target of 500%, and a maximum injection time of 3 ms.

[0032] (3) DIA Data Processing. Raw mass spectrometry data were processed using DIA-NN (version 1.8.1). Trypsin was designated as the digestive enzyme, with a maximum of one erroneous cleavage allowed. Cysteine ​​carbamate methylation was set as a fixed modification, while oxidation (M) and N-terminal acetylation were included as variable modifications. Protein and peptide identification was performed with a false discovery rate (FDR) ≤1%, corresponding to a 99% confidence level. Subsequently, downstream statistical analyses, including differential expression profiling and pathway enrichment analysis, were performed on the quantified protein matrix.

[0033] (4) Results show: Figure 1 Significant differences were observed in the expression of immune-related proteins, as well as metabolic and inflammation-related proteins, between the normal control group and the M. bovis secondary infection model group. Notably, arginase-1 (a key immunosuppressive enzyme and a typical functional marker of myeloid-derived suppressor cells) was significantly elevated in the model group compared to the control group. p <0.05). This finding suggests that arginase-1 may be related to the immunosuppressive state of chronic tuberculosis, especially to the enrichment of myeloid-derived suppressor cells.

[0034] 4. Serum non-target metabolomics detection and result analysis (1) Sample preparation. Rabbit serum samples were thawed stepwise at 4°C, and then an appropriate amount of each sample was mixed with pre-cooled methanol / acetonitrile / water (2:2:1, v / v) and shaken thoroughly. The mixture was incubated at -20°C for 10 min, followed by centrifugation at 14000×g for 20 min at 4°C. The resulting supernatant was collected and evaporated to dryness under vacuum. Before LC-MS analysis, the dried extract was redissolved in 100 μL of acetonitrile / water (1:1, v / v), shaken, and centrifuged again at 14000×g for 15 min at 4°C. The clarified supernatant was transferred to an autosampler. All analyses were performed using a Vanquish UHPLC system coupled to an Orbitrap Exploris™ 480 mass spectrometer (Thermo Fisher Scientific), and the experiments were conducted by Shanghai Applied Protein Technology Co., Ltd.

[0035] (2) Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS / MS) analysis. Hydrophilic interaction liquid chromatography (HILIC) analysis was performed using an ACQUITY UPLC BEH aromatic amine column (2.1 × 100 mm, 1.7 μm; Waters, Ireland). Samples were analyzed in electrospray ionization (ESI) mode. The mobile phase consisted of solvent A (an aqueous solution containing 25 mmol ammonium acetate and 25 mmol ammonium hydroxide) and solvent B (acetonitrile). The chromatographic gradient was set as follows: 0–0.5 min, solvent B concentration 95%; 0.5–5.0 min, gradually reduced to 65% B; 5.0–5.5 min, reduced to 40% B; 5.5–6.0 min, restored to 95% B; 6.0–8.0 min, equilibration at 95% B. The Orbitrap Exploris 480 was operated in high-resolution full scan and data-dependent MS / MS detection mode to ensure sensitive and accurate detection of components.

[0036] (3) Data Processing and Compound Identification. The raw mass spectrometry data were converted to mzXML format using ProteoWizard MSConvert and processed using the XCMS software package. Peak detection, retention time alignment, and total ion current normalization were performed to generate the processed data matrix. Statistical analysis was performed using the R package ropls. Univariate significance was tested using the independent samples Student's test, while simultaneously meeting VIP 1.0 and... p Metabolites <0.05 were considered to be significantly altered.

[0037] (4) Results showed that 93 differentially expressed metabolites were identified between the model group and the control group, including 48 upregulated metabolites and 45 downregulated metabolites. Notably, L-arginine levels also changed significantly between the model group and the control group. Quantitative analysis of serum L-arginine levels further showed that the L-arginine level in the model group was significantly lower than that in the control group. p <0.05). This is consistent with the finding in serum proteomics results that showed a significant increase in the expression of arginase-1 in the model group.

[0038] like Figure 2 Compared with the control group, the serum L-arginine level in the model group was significantly lower. Data are presented as box plots, showing the median and interquartile range. Statistical analysis was performed using one-way ANOVA, followed by post-hoc multiple comparison tests. *p <0.05, **p <0.005.

[0039] Example 2: Association analysis of serum Arginase-1 with enrichment of MDSCs in chronic tuberculosis and degree of T cell exhaustion This embodiment aims to construct a mouse model of chronic tuberculosis, detect serum Arginase-1 concentration, enrichment of myeloid-derived suppressor cells (MDSCs), and T cell depletion, and analyze their correlation.

[0040] The specific steps are as follows: 1. Establishing a mouse model of chronic tuberculosis (1) Experimental animals: female C57BL / 6 mice.

[0041] (2) Method for constructing the mouse chronic tuberculosis model used in this embodiment: In order to simulate the continuous antigen stimulation experienced by patients with chronic tuberculosis, a mouse model of M. bovis BCG three-time infection was established. For the three-time infection model, mice were given 1×10⁻⁶ BCG via tail vein at weeks 0, 4 and 10. 7 Intravenous injection of M. bovis BCG of CFU. For the two-infection model, injections were given at weeks 4 and 10, while for the single-infection model, a single injection was given at week 10.

[0042] 2. Flow cytometry detection of MDSC enrichment and T cell exhaustion. (1) Single-cell isolation from mouse lungs. Mice infected three times were euthanized four weeks after the last infection, and lung tissue was aseptically collected and prepared into a single-cell suspension (1×10⁻⁶ cells / mL). 6 Cells / mL). Use red blood cell lysis buffer ( Proteintech GroupRed blood cells were lysed by [Company Name], followed by washing with pre-cooled PBS and centrifugation to collect the pellet. The cell pellet was then resuspended in flow cytometry buffer (FACS buffer).

[0043] (2) Incubate the cells with anti-mouse CD16 / CD32 antibody at room temperature for 30 minutes to block the Fc receptor.

[0044] (3) After blocking, the cells were washed and stained with a combination of fluorescently labeled antibodies against exhausted T cells and myeloid-derived suppressor cells (MDSCs). The antibody combination for exhausted T cells included FITC-labeled CD3 antibody, PE-labeled PD-1 antibody (all purchased from Proteintech Group, Inc.), and APC-labeled Tim-3 antibody (purchased from Elabscience Biotechnology Co., Ltd.); the antibody combination for MDSCs included APC-labeled CD11b antibody, PE-labeled Gr-1 antibody, and APC-labeled PD-L1 antibody (all purchased from Proteintech Group, Inc.). The staining process was carried out at room temperature in the dark for 30 minutes.

[0045] (4) After staining, the cells were washed by centrifugation (350×g, 10 min), resuspended in 300 μL of flow cytometry buffer (FACS buffer), and immediately analyzed using a CytoFlex flow cytometer (Beckman Coulter). Data were collected and analyzed using CytExpert software, and a gating strategy was used to analyze exhausted T cells (CD3+). + PD-1 + Tim-3 + ) and MDSCs (CD11b + Gr-1 + PD-L1 + ) to be quantified.

[0046] (5) Results showed that we first analyzed the proportion of exhausted T cells in the lungs of mice after multiple infections. T cells were identified based on CD3 expression, and the exhausted T cell subset was defined as PD-1. + Tim-3 + and PD-1 + Tim-3 + Population. Compared with normal control mice, mice that were infected once or twice had higher levels of PD-1 in their lungs. + Tim-3 + and PD-1 + Tim-3 +The proportion of T cells showed an increasing trend, and this trend became more pronounced with the increase in the number of repeated infections; PD-1 was found in the lungs of mice infected three times. + Tim-3 + and PD-1 + Tim-3 + The proportion of T cells was significantly higher in mice than in the normal control group ( p <0.05 (Figure 3). We also examined MDSCs (Gr-1) in the lungs of mice with recurrent infections. + CD11b + The frequency of MDSCs in the lungs of mice infected twice was significantly higher than that of mice infected only once. p <0.05; compared with mice infected twice, mice infected three times had a further increased proportion of MDSCs ( p <0.05 (Figure 4). More importantly, the concentration of arginase-1 in mouse serum increased with the number of infections and time, consistent with the trend of MDSC recruitment and exhaustion of T cells.

[0047] 3. Association analysis of serum Arginase-1 with MDSC enrichment and T cell exhaustion (1) Serum Arginase-1 detection: Peripheral blood was collected from the orbital cavity of mice that were infected three times, and serum was separated 4 weeks after the last infection. The Arginase-1 content was detected by ELISA.

[0048] (2) Statistical analysis was performed using SPSS 26.0 software. Pearson correlation analysis was used to explore the relationship between serum Arginase-1 concentration and the proportion of MDSCs and PD-1. + Tim-3 + and PD-1 + Tim-3 + The correlation between T cells was plotted as a scatter plot and the correlation coefficient (r) was calculated. P A correlation value <0.05 indicates statistical significance.

[0049] (3) Results showed that Pearson correlation analysis indicated that the concentration of Arginase-1 in mouse serum was correlated with the concentration of MDSCs (CD11b) in the spleen. + Gr-1 + PD-L1 + The enrichment ratio showed a significant positive correlation (r=0.8187). P< 0.01), with increasing serum Arginase-1 concentration, the proportion of splenic MDSCs showed a significant upward trend. Further analysis of the association between serum Arginase-1 and various subsets of exhausted T cells revealed a positive correlation between serum Arginase-1 concentration and the proportion of PD-1⁺ T cells (r = 0.6656, P <0.05), and was positively correlated with the proportion of Tim-3⁺ T cells (r=0.6807, P <0.05); and compared with PD-1, which has a higher degree of functional exhaustion. + Tim-3 + The correlation was most significant among double-positive T cell subsets (r=0.7454). P <0.01).

[0050] The above data confirm that, in the course of chronic tuberculosis, the increase in serum Arginase-1 concentration is closely related to the enrichment of MDSCs and the aggravation of T cell exhaustion, suggesting that Arginase-1 can serve as a marker for predicting immunosuppressive cells MDSCs and exhausted T cells.

[0051] Example 3: Application of Arginase-1 in evaluating MDSC enrichment and T cell exhaustion in clinical chronic tuberculosis patients This embodiment aims to clarify the application value of serum Arginase-1 concentration in the clinical evaluation of the enrichment of myeloid-derived suppressor cells (MDSCs) and the level of T cell depletion in patients with chronic tuberculosis, and to provide a novel biomarker and detection basis for the assessment of chronic tuberculosis and the monitoring of immune status.

[0052] The specific steps are as follows: 1. Research subjects: This study included 40 participants, comprising 20 patients with secondary pulmonary tuberculosis and 20 healthy controls. All participants were recruited from Lishui Traditional Chinese Medicine Hospital, Zhejiang Province. Patients in the pulmonary tuberculosis group were diagnosed with active pulmonary tuberculosis based on clinical manifestations, chest imaging, and positive sputum smears for Mycobacterium tuberculosis. Individuals with concurrent HIV infection, autoimmune diseases, malignancies, or those currently receiving immunosuppressive therapy were excluded. Healthy controls had no history of tuberculosis infection, no chronic inflammation or infectious diseases, and negative tuberculosis screening results.

[0053] 2. Serum Arginase-1 concentration detection (1) Sample collection: 5 mL of venous blood was collected from all subjects in the morning on an empty stomach. The blood was placed in a vacuum blood collection tube without anticoagulant and left to stand at room temperature for 30 min. Then, the upper serum was separated by centrifugation at 3500×g for 10 min. The serum was aliquoted into EP tubes and stored at -80℃ for later use, avoiding repeated freeze-thaw cycles.

[0054] (2) Enzyme-linked immunosorbent assay (ELISA) kit for detecting serum Arginase-1. The simplified steps are as follows: All reagents were equilibrated to room temperature before use; standard wells, sample wells (three replicates per sample), and blank wells were set up; serially diluted standards (concentration range 1.56~100 ng / mL) were added to the standard wells, and 100 μL of serum sample was added to the sample wells; the plate was incubated at 37°C in the dark for 1.5 hours, followed by washing; then biotinylated detection antibody and streptavidin-HRP were added sequentially, with washing after each incubation; substrate solution was added for color development for 15 minutes, followed by stop solution to terminate the reaction; the absorbance (OD value) of each well was measured at 450 nm using an ELISA reader. After subtracting the OD value of the blank wells, the concentration of Arginase-1 in the sample was calculated based on a standard curve plotted using known concentration standards.

[0055] 3. Detection of MDSC enrichment and T cell exhaustion in peripheral blood PBMCs (1) Isolation of peripheral blood mononuclear cells (PBMCs): 5 mL of venous blood was collected from all subjects and placed in a vacuum blood collection tube containing EDTA anticoagulant. The tube was gently inverted to mix. PBMCs were isolated using density gradient centrifugation: the anticoagulated blood was diluted with an equal volume of PBS buffer and slowly stacked onto the top layer of Ficoll-Paque Plus separation solution. The mixture was centrifuged at 2000×g for 20 min. After centrifugation, the liquid separated into four layers. The middle white PBMC layer was aspirated and placed in a new centrifuge tube. The tube was washed twice with an appropriate amount of PBS buffer (centrifuged at 1500×g for 10 min each time). Finally, the PBMCs were resuspended in flow cytometry buffer (FACS buffer) and the cell concentration was adjusted to 1×10⁻⁶. 6 Cells / mL, for later use.

[0056] (2) Flow cytometry was used to detect the phenotypes of exhausted T cells and myeloid-derived suppressor cells (MDSCs): Fluorescently labeled monoclonal antibodies used for exhaustive T cell phenotype analysis included FITC-labeled CD3 antibody, PE-labeled PD-1 antibody, and ABflo647-labeled Tim-3 antibody (all purchased from ABclonal Biotechnology Co., Ltd.). The prepared PBMCs suspension was added to the above antibody combination and incubated at room temperature in the dark for 30 min. After incubation, the cells were washed twice with FACS buffer (centrifuged at 350×g for 10 min) to remove unbound antibodies. The cell pellet was resuspended in 300 μL of FACS buffer, and data was immediately acquired using a CytoFLEX flow cytometer (Beckman Coulter). Data were analyzed using CytExpert software according to the standard gating strategy to quantify the exhausted T cell subset (CD3+). + PD-1 in T cells + Tim-3 + and PD-1 + Tim-3 + Cells) and MDSC subsets (CD11b) + CD33 + Cells, CD11b + CD33 + The proportion of HLA-DR cells.

[0057] 4. Statistical Analysis Data were statistically analyzed using SPSS 26.0 software. Data conformed to a normal distribution and were expressed as mean ± standard deviation (x ± s). Independent samples t-tests were used for comparisons between two groups. Pearson correlation analysis was used to explore the correlation between serum Arginase-1 concentration and the proportion of MDSCs and the proportions of various exhausted T cell subsets. Receiver operating characteristic (ROC) curves were plotted to analyze the efficacy of serum Arginase-1 concentration in evaluating MDSC enrichment and T cell exhaustion in patients with chronic tuberculosis, and the area under the curve (AUC), optimal cutoff value, sensitivity, and specificity were calculated. P <0.05 indicates that the difference or correlation is statistically significant.

[0058] 5. Results (1) Comparison of serum Arginase-1 concentration, MDSC ratio, and T cell exhaustion-related indicators between the two groups. The results showed that, compared with the normal control group, the serum Arginase-1 concentration of patients with chronic tuberculosis (experimental group) was significantly higher ( P <0.01); the proportion of MDSCs (CD11b+CD33+HLA-DR-) in PBMCs was significantly increased ( P<0.01); compared with healthy controls, the expression of exhaustion-related markers (including PD-1) on T cells was significantly increased in patients with chronic tuberculosis (<0.01). P <0.05); and the proportion of PD-1+Tim-3+ double-positive exhausted T cells was also significantly increased ( P <0.05). In summary, these results indicate that patients with chronic tuberculosis are in a state of systemic immunosuppression, characterized by the simultaneous amplification of MDSCs and depletion of T cells in peripheral blood.

[0059] like Figure 4 Data are presented as violin plots, showing the median and interquartile range. Statistical analysis was performed using one-way ANOVA with post-hoc multiple comparison tests. *p <0.05, **p <0.005.

[0060] like Figure 5 Compared with healthy individuals, the proportion of MDSCs in PBMCs of patients with chronic tuberculosis was significantly higher. Statistical analysis was performed using one-way ANOVA, followed by post-hoc multiple comparison tests. *p <0.05, **p <0.005.

[0061] like Figure 6 Compared with healthy individuals, PBMCs of patients with chronic tuberculosis have higher levels of Tim-3. + The proportion of T cells was significantly increased. Statistical analysis was performed using one-way ANOVA, followed by post-hoc multiple comparison tests. *p <0.05, **p <0.005.

[0062] (2) Correlation analysis of serum Arginase-1 concentration with MDSCs and T cell exhaustion indicators in patients with chronic tuberculosis Correlation analysis showed that serum Arginase-1 concentration in patients with chronic tuberculosis was related to MDSCs (CD11b) in PBMCs. + CD33 + HLA-DR - The proportion of ) showed a significant positive correlation (r=0.9110, P <0.001); and CD3 + PD-1 + CD3 + Tim-3 + The proportions were both positively correlated (r values ​​were 0.9705 and 0.9499, respectively). P <0.001); and CD3+ PD-1 + Tim-3 + The positive correlation was strongest with the proportion of double-positive T cells (r=0.9670). P <0.001). The scatter plot results clearly show the upward trend of the above indicators with increasing Arginase-1 concentration.

[0063] like Figure 7 For A, the result shows r=0.9110. p <0.001, indicating a significant positive correlation between the two. For example... Figure 7 The B result shows r=0.9705. p <0.001, indicating a significant positive correlation between the two. For example... Figure 7 The C result shows r = 0.96709499. p <0.001, indicating a significant positive correlation between the two. For example... Figure 7 The D result shows r=0.9670. p <0.001, indicating a significant positive correlation between the two. Statistical analysis was performed using Pearson correlation analysis. Pearson Correlation ). *p <0.05, **p <0.005.

[0064] (3) Efficacy analysis of serum Arginase-1 and T cell depletion levels in evaluating the condition of patients with chronic tuberculosis ROC curve analysis showed that the AUC for serum Arginase-1 concentration in evaluating the severity of chronic tuberculosis was 0.8000 (95% CI: 0.5987-1.000), with a sensitivity of 100% and a specificity of 50%. The AUC for T cell exhaustion level in evaluating the severity of chronic tuberculosis was 0.7563 (95% CI: 0.5275-0.9850), with an optimal cutoff value of 9.615%, at which point the sensitivity was 60% and the specificity was 87.5%. These results indicate that serum Arginase-1 concentration and T cell exhaustion level have good evaluative efficacy for the severity of chronic tuberculosis. Figure 8 The area under the A curve (AUC) is 0.8000, as... Figure 8 The area under the B curve (AUC) is 0.7563.

Claims

1. A biomarker for predicting or diagnosing a state of immune suppression in chronic tuberculosis, characterized by, The biomarker is arginase-1.

2. A kit for predicting or diagnosing a state of immunosuppression in chronic tuberculosis, characterized in that, The kit contains reagents for detecting a biomarker, namely arginase-1.

3. The kit according to claim 2, characterized in that, The reagent contains an antibody or ligand that specifically binds to arginase-1.

4. A system for predicting or diagnosing immunosuppressive status in chronic tuberculosis, characterized in that, The system includes a data analysis module, which is used to analyze the detection values ​​of biomarkers in the samples of the patients to be tested, wherein the biomarker is arginase-1.

5. A system for predicting or diagnosing immunosuppressive status in chronic tuberculosis as described in claim 4, characterized in that, The data analysis module determines whether the patient under test is in a state of chronic tuberculosis immunosuppression based on the detection values ​​of biomarkers in the sample and reference values. The determination condition is: when the detection value is significantly higher than the reference value, the subject is determined to be in a state of chronic tuberculosis immunosuppression.

6. The system for predicting or diagnosing immunosuppressive status in chronic tuberculosis as described in claim 4, characterized in that, The sample from the patient to be tested is serum or blood.

7. The use of the biomarker as described in claim 1 in the preparation of reagents for predicting or diagnosing immunosuppressive status in chronic tuberculosis.

Images