Tuberculosis antigen specific host biomarker and kit for detecting tuberculosis infection

Abstract

The invention relates to a tuberculosis antigen specific host biomarker for detecting tuberculosis infection and a kit, and belongs to the field of biological medicines. The biomarker comprises IFN gamma, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22 and MMP1 (Metallocin Protein Protein 1). According to the present invention, the specific RD1 region antigen or peptide pool from mycobacterium tuberculosis and the specific RD2 region antigen or peptide pool from mycobacterium tuberculosis are jointly used as the antigen irritants to stimulate peripheral whole blood or peripheral blood mononuclear cells (PBMCs) of an individual to be detected, and then the expression quantity change of the host biomarker before and after the stimulation is detected; the kit can effectively distinguish tuberculosis infected patients from non-tuberculosis infected individuals, can identify and diagnose active tuberculosis and latent tuberculosis infected patients, solves the defects of insufficient sensitivity and limited specificity of the existing diagnostic technology, and has high diagnostic efficiency.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine, specifically relating to a tuberculosis antigen-specific host biomarker and kit for detecting tuberculosis infection, and particularly to a tuberculosis antigen-specific host biomarker based on peripheral blood samples. Background Technology

[0002] Tuberculosis (TB) is caused by Mycobacterium tuberculosis (Mycobacterium tuberculosis). Mycobacterium tuberculosis , M.tb Respiratory infectious diseases caused by tuberculosis infection (TB) remain the leading cause of infectious disease death worldwide. The spectrum of tuberculosis disease (TBD) caused by tuberculosis infection (TBI) is complex and diverse, including latent TB, asymptomatic TB, and active TB (including sputum-positive and sputum-negative pulmonary TB and extrapulmonary TB). Current diagnostic techniques have significant limitations: etiological detection, such as smear microscopy and culture, has a positive rate of only about 40-60%, with even lower detection rates for sputum-negative patients and children with pulmonary and extrapulmonary TB; the culture cycle is as long as 4 weeks or more, making it difficult to meet the needs of rapid diagnosis; in molecular diagnostics, although molecular diagnostics such as Xpert MTB / RIF have high sensitivity for pulmonary TB, the sensitivity for extrapulmonary TB is only 14%-50%, and they rely on expensive equipment, making them difficult to implement in resource-limited areas. In immunological testing, interferon-gamma release assays (IGRAs), such as the tuberculous infection of T cells spot test (T-SPOT.TB), are limited by antigenic epitopes, and their sensitivity fluctuates between 60% and 95% in areas with a high tuberculosis burden, resulting in a high risk of missed diagnoses. Regarding host biomarkers, existing biomarkers are mostly based on blood samples, which are easily affected by inflammation or other infections and lack specificity.

[0003] Furthermore, latent tuberculosis infection (LTBI) affects approximately 25% of the global population. Of these individuals, 5-10% may experience reactivation of their LTBI during their lifetime due to factors such as suppressed or damaged immune systems (e.g., co-infection with HIV, diabetes, or use of immunosuppressive drugs), developing into active tuberculosis. Therefore, TB is listed as one of my country's major infectious diseases. Early detection, diagnosis, and isolation are crucial for effectively controlling the spread and prevalence of TB. Sensitive and specific diagnostic methods are required for diagnosing tuberculosis infection (including active tuberculosis and LTBI) and for identifying patients with active tuberculosis (including active pulmonary tuberculosis and extrapulmonary tuberculosis) from among infected individuals.

[0004] The WHO emphasizes the need to develop novel diagnostic technologies that combine pathogen and host biomarkers, requiring high sensitivity, specificity, and low cost. This invention addresses these shortcomings by combining tuberculosis-specific antigen stimulation with peripheral blood transcriptome analysis to screen and identify pathogens... M.tb High-level expression of host cell transcriptome markers generated after stimulation by specific antigens will eventually lead to the establishment of novel diagnostic technologies based on reverse transcription quantitative polymerase chain reaction (RT-qPCR) at the mRNA expression level or ELISPOT, ELISA, high-throughput microarrays, or mass spectrometry at the protein expression level. These technologies can improve the sensitivity and specificity of screening for tuberculosis infection and also have the ability to differentiate between active tuberculosis and latent tuberculosis infection. Summary of the Invention

[0005] This invention uses a combination of RD1 region antigen or any peptide of RD1 region-encoded antigen and RD2 region-encoded antigen as antigen stimulants to stimulate peripheral blood mononuclear cells (PBMCs). The stimulated PBMCs are then subjected to whole-genome transcriptome sequencing to screen and identify tuberculosis antigen-specific host transcriptome biomarkers. This aims to improve diagnostic sensitivity while simultaneously satisfying the goal of using host biomarkers to differentiate between different patients.

[0006] According to a first aspect of the present invention, a tuberculosis antigen-specific host biomarker for detecting tuberculosis infection is provided, said biomarker being at least one of the following genes: IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1; said tuberculosis infection includes active tuberculosis and latent tuberculosis infection; The base sequences of IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1 are shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, and SEQ ID NO:12, respectively.

[0007] Preferably, at least one of the RHOV, IL-22, and MMP1 genes can further differentiate between active tuberculosis and latent tuberculosis infection.

[0008] Preferably, the active tuberculosis is active pulmonary tuberculosis or extrapulmonary tuberculosis.

[0009] Preferably, the active pulmonary tuberculosis is primary pulmonary tuberculosis, secondary pulmonary tuberculosis, or hematogenous disseminated pulmonary tuberculosis.

[0010] According to another aspect of the present invention, a reverse transcription quantitative polymerase chain reaction (RT-PCR) assay kit for detecting tuberculosis infection is provided, comprising any antigen encoded by the RD1 region of Mycobacterium tuberculosis or any peptide segment encoded by the RD1 region of Mycobacterium tuberculosis, and further comprising any antigen encoded by the RD2 region of Mycobacterium tuberculosis or any peptide segment encoded by the RD2 region of Mycobacterium tuberculosis, wherein the peptide segment is an effective T-cell epitope of the corresponding region-encoded antigen. It also includes primers for detecting at least one of the following genes: IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1. The base sequences of IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1 are shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, and SEQ ID NO:12, respectively.

[0011] According to another aspect of the present invention, an enzyme-linked immunospot assay kit for detecting tuberculosis infection is provided, comprising any antigen encoded by the RD1 region of Mycobacterium tuberculosis or any peptide segment encoded by the RD1 region of Mycobacterium tuberculosis, and further comprising any antigen encoded by the RD2 region of Mycobacterium tuberculosis or any peptide segment encoded by the RD2 region of Mycobacterium tuberculosis, wherein the peptide segment is an effective T-cell epitope of the corresponding region encoded by the antigen. It also includes a capture antibody coated on a solid-phase carrier, a biotin-labeled detection antibody, a streptavidin-enzyme complex, and an enzyme substrate chromogenic solution. The capture antibody is used to specifically bind to the protein encoded by the gene; the detection antibody can specifically bind to the protein encoded by the gene; the gene is: IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1; The base sequences of IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1 are shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, and SEQ ID NO:12, respectively.

[0012] According to another aspect of the present invention, the application of the aforementioned reverse transcription quantitative polymerase chain reaction (RT-PCR) assay kit in the preparation of reagents for detecting tuberculosis infection is provided.

[0013] Preferably, the application specifically involves: stimulating peripheral whole blood or peripheral blood mononuclear cells (PBMCs) with an antigenic stimulus, wherein the stimulus includes an RD1 region antigen or any peptide segment encoding an RD1 region antigen, and also includes an RD2 region antigen or any peptide segment encoding an RD2 region antigen; extracting mRNA from the stimulated peripheral whole blood or mRNA from the stimulated PBMCs, reverse transcribing the mRNA, and then using primers for the corresponding gene to perform real-time quantitative polymerase chain reaction (qPCR) to detect the gene; if high expression of at least one of the gene mRNAs produced after stimulation is detected, the sample is determined to be infected with tuberculosis.

[0014] According to another aspect of the present invention, the application of the enzyme-linked immunospot assay kit is provided in the preparation of reagents for detecting tuberculosis infection.

[0015] Preferably, the application specifically involves stimulating peripheral blood mononuclear cells (PBMCs) with an antigenic stimulant, wherein the stimulant includes an RD1 region antigen or any peptide segment encoding an RD1 region antigen, and also includes an RD2 region antigen or any peptide segment encoding an RD2 region antigen; the PBMCs after stimulation are then analyzed using an enzyme-linked immunospot assay kit, and if the T cells in the PBMCs produce at least one of the expressed proteins, the sample is determined to be infected with tuberculosis.

[0016] In summary, compared with the prior art, the above-described technical solutions conceived by this invention mainly possess the following technical advantages: (1) This invention establishes for the first time the mechanism of antigen-specific stimulation combined with peripheral blood transcriptome, and screens and identifies tuberculosis antigen-specific host biomarkers. Existing technologies rely on unstimulated or non-specific verification biomarkers, making it difficult to distinguish tuberculosis from inflammatory diseases caused by other pathogens. Based on the scientific principle that after tuberculosis infection, T cells in the local lymph nodes are sensitized by tuberculosis antigens and then return to the site of infection via systemic blood circulation, this invention stimulates PBMCs with any antigen in the RD1 region or any peptide of the antigen encoded by the RD1 region, and any antigen in the RD2 region or any peptide of the antigen encoded by the RD2 region as antigen stimulants to reveal the host T cell immune memory response induced by tuberculosis antigens; at the same time, by amplifying the signal through antigen stimulation, highly specific host immune memory response biomarkers such as homing T cells are screened to reduce false positive interference from host biomarkers. Subsequently, the expression changes of biomarkers at the mRNA or protein level are detected using techniques such as quantitative real-time PCR and ELISPOT. Since healthy individuals or individuals without tuberculosis infection do not have T cells sensitized by tuberculosis antigens, this invention can achieve accurate diagnosis of tuberculosis infection.

[0017] (2) Both active tuberculosis and latent tuberculosis infection belong to tuberculosis infection and both lead to T cells sensitized by tuberculosis antigen. Stimulation of these T cells with tuberculosis antigen in vitro will induce an immune recall response, such as the production of high levels of IFN-γ. Therefore, the existing IGRA technology cannot distinguish between active tuberculosis and latent tuberculosis infection. This invention innovatively uses RD1 and RD2 region-specific antigens in combination, which can cover a wider range of different tuberculosis-specific T cell epitopes. The transcriptome markers produced by stimulation change the expression levels of genes related to T cell activation, metabolism, proliferation and secretion, resulting in differential responses between active tuberculosis and latent tuberculosis infection. It is suitable for the differential diagnosis of active tuberculosis and latent tuberculosis infection. Attached Figure Description

[0018] Figure 1 This is a flowchart of the screening and validation study of diagnostic biomarkers for tuberculosis patients (TBD) and tuberculosis-infected individuals (TBI) in this invention; wherein: A is the study design process, which involves transcriptome sequencing of PBMCs stimulated with tuberculosis-specific antigens in the TBD group, TBI group, and non-tuberculosis-infected healthy control group (HC); B is the core gene identification process, which involves screening and identifying core genes of TBD and TBI through differentially expressed gene analysis; C is the diagnostic value validation process, which identifies biomarkers that can screen for TBI and distinguish between TBD and TBI.

[0019] Figure 2 This invention presents differentially expressed genes in patients with different disease spectrum types following tuberculosis infection after stimulation with different tuberculosis-specific antigens. Figure A shows the number of differentially expressed genes in the RD1 and RD2 antigen-stimulated tuberculosis patients (TBD), tuberculosis-infected individuals (TBI), and healthy controls (HC); Figure B shows a volcano plot of differentially expressed genes in the RD1 and RD2 antigen-stimulated tuberculosis patients (TBD), tuberculosis-infected individuals (TBI), and healthy controls (HC); Figure C shows the intersection of differentially expressed genes among the TBD, TBI, and HC groups. Pink represents differentially expressed genes unique to the TBD group, green represents differentially expressed genes unique to the HC group, orange represents differentially expressed genes unique to the TBI group, and black represents the number of overlapping differentially expressed genes.

[0020] Figure 3 This is the cluster analysis result of differentially expressed genes in patients with different disease spectrum types after tuberculosis infection, after stimulation by different tuberculosis-specific antigens.

[0021] Figure 4 This figure shows the functional enrichment results of differentially expressed genes with |log2FC(TBD / HC)|>1.0 in the TBD group and |log2FC(TBI / HC)|>1.0 in the TBI group after stimulation with RD1 and RD2, respectively. Figure A shows the functional enrichment analysis of GO and KEGG differentially expressed genes with |log2FC(TBD / HC)|>1.0 in the TBD group stimulated by RD1 and RD2, respectively; Figure B shows the functional enrichment analysis of GO and KEGG differentially expressed genes with |log2FC(TBI / HC)|>1.0 in the TBI group stimulated by RD1 and RD2, respectively. The size of the bubbles represents the number of genes, and the horizontal axis represents the number of genes. p Value, functional pathway enrichment based on |log2Fold Change|>1.5, p DEGs analysis using the <0.05 standard screening.

[0022] Figure 5This invention presents the functional enrichment and heatmap analysis of core genes related to T-cell immunity in the TBD and TBI groups. Figure A shows the GO and KEGG functional enrichment analysis of differentially expressed genes related to T-cell immunity with |log2FC(TBD / HC)|>1.0 generated by RD1 and RD2 stimulation in the TBD group, respectively; Figure B shows the GO and KEGG functional enrichment analysis of differentially expressed genes related to T-cell immunity with |log2FC(TBD / TBI)|>1.0 generated by RD1 and RD2 stimulation in the TBI group, respectively; Figure C shows the heatmap of DEGs-hub genes with |log2FC(TBD / TBI)|>1.0 in the TBD group; and Figure D shows the heatmap of DEGs-hub genes with |log2FC(TBD / TBI)|>1.0 in the TBI group.

[0023] Figure 6 This study validates the diagnostic value of the core gene in active tuberculosis and latent tuberculosis infection. Figure A shows the bar chart and ROC curve of the relative expression levels of the core gene in the RD1-stimulated TBD group (n=5) and TBI group (n=5); Figure B shows the ROC curve of RD1 stimulation for differential diagnosis between the TBD group (n=5) and TBI group (n=5).

[0024] Figure 7 This study validates the diagnostic value of the core gene in differentiating between active tuberculosis and latent tuberculosis infection. Figure A shows the bar chart and ROC curve of the relative expression levels of the core gene in the RD2-stimulated TBD group (n=5) and TBI group (n=5); Figure B shows the ROC curve of the RD2-stimulated differential diagnosis between the TBD group (n=5) and the TBI group (n=5). Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0026] To improve the sensitivity and specificity of tuberculosis infection diagnosis, this invention utilizes a highly specific diagnostic reagent from our team's previous research (patent application number: 202310704861.7, patent title: A Highly Specific Diagnostic Reagent and Kit for Mycobacterium tuberculosis Infection) as an antigenic stimulus. This reagent is a fusion protein rEC composed of ESAT6 and CFP10 encoded by the RD1 region, and a fusion protein rCM composed of CFP21 and MPT64 encoded by the RD2 region. This invention combines the RD1 (rEC) and RD2 (rCM) recombinant proteins as antigenic stimuli to stimulate peripheral blood mononuclear cells (PBMCs) of patients with Mycobacterium tuberculosis and those infected with Mycobacterium tuberculosis. The PBMCs stimulated with the tuberculosis-specific antigen are then subjected to whole-transcriptome sequencing. By combining the advantages of both pathogen and host biomarkers, characteristic host transcriptome biomarkers of tuberculosis-infected patients are screened and identified.

[0027] Specifically, this application analyzes differentially expressed genes in different groups of samples, including active tuberculosis samples, latent tuberculosis infection samples, and uninfected healthy control samples. For these differentially expressed genes, cluster analysis, functional enrichment analysis, and differential expression level comparison are used to identify the host core gene set of RD1 (rEC) and RD2 (rCM) antigen heterogeneity in TBD and TBI patients. At the same time, the differentially expressed genes are compared with those in the HC control group to remove non-specific core genes, and finally a set of tuberculosis antigen-specific host transcriptome markers is screened.

[0028] Preferably, the tuberculosis is an infectious disease caused by the presence of the pathogen Mycobacterium tuberculosis, including but not limited to drug-resistant tuberculosis, non-drug-resistant tuberculosis, multidrug-resistant tuberculosis, multidrug-resistant tuberculosis, and extensively drug-resistant tuberculosis.

[0029] Preferably, the tuberculosis includes primary pulmonary tuberculosis, secondary pulmonary tuberculosis, hematogenous disseminated pulmonary tuberculosis, extrapulmonary tuberculosis, etc.

[0030] Preferably, the Mycobacterium tuberculosis infection includes: primary infection, secondary infection, latent infection, and extrapulmonary infection.

[0031] This invention provides a method for diagnosing infectious diseases caused by the presence of the pathogen Mycobacterium tuberculosis by combining RD1 and RD2 as antigenic stimuli, and identifies host transcriptome biomarkers stimulated by this method. The biomarkers include the following genes: IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1. The full names of these 12 genes are all provided by the NCBI (National Center for Biotechnology Information (nih.gov) platform, as follows: IFNγ: Interferon gamma (SEQ ID NO:1) (NCBI Registration No.: NM_000619.3) CXCL10: CXC motif chemokine ligand 10 (SEQ ID NO:2) (NCBI Registration No.: NM_001565.4) IL-2: Interleukin-2 (SEQ ID NO:3) (NCBI Registration No.: NM_000586.4) IL-3: Interleukin-3 (SEQ ID NO:4) (NCBI Registration No.: NM_000588.4) GBP6: Guanylate binding protein family member 6 (SEQ ID NO:5) (NCBI Registration No.: NM_001320257.2) ULBP2: UL16 binding protein 2 (SEQ ID NO:6) (NCBI Registration No.: NM_025217.4) CXCL11: CXC motif chemokine ligand 11 (SEQ ID NO:7) (NCBI Registration No.: NM_001302123.2) ATP13A3: ATPase-13A3 (SEQ ID NO:8) (NCBI Registration No.: NM_001367549.1) RHOV: Ras homolog family member V (SEQ ID NO:9) (NCBI Registration No.: NM_133639.4) ATP2B1: ATPase plasma membrane Ca 2+ transporting 1 (SEQ ID NO:10) (NCBI Registration No.: NM_001001323.2) IL-22: Interleukin-22 (SEQ ID NO:11) (NCBI Registration No.: NM_020525.5) MMP1: Matrix metallopeptidase 1 (SEQ ID NO:12) (NCBI Registration No.: NM_001145938.2) The biomarkers provided by this invention are a set of genes, including the aforementioned 12 genes. This gene set was obtained based on transcriptome sequencing, cluster analysis, and differentially expressed gene analysis of PBMCs from patients with active tuberculosis and those with latent tuberculosis infection. These 12 genes can specifically distinguish between tuberculosis infection status and non-tuberculosis infection status, and can be used in combination for the differential diagnosis of patients with active tuberculosis and those with latent tuberculosis infection. In practice, it can be used more quickly and accurately for tuberculosis infection screening and disease diagnosis, while simultaneously differentiating between patients with active tuberculosis and those with latent tuberculosis infection, providing strong technical support for tuberculosis epidemic control.

[0032] This invention provides a reagent for the above-mentioned biomarkers in the preparation of products for diagnosing pulmonary tuberculosis and extrapulmonary tuberculosis caused by Mycobacterium tuberculosis infection. After the biological sample to be tested is stimulated by the Mycobacterium tuberculosis-specific antigen composition RD1 (rEC) and RD2 (rCM), the change in the expression level of the above-mentioned biomarkers specific to tuberculosis antigen is detected.

[0033] Specifically, the reagent includes substances that detect one or more of the following (1) to (3): 1. IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1 genes; 2. IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, mRNA transcribed from the RHOV, ATP2B1, IL-22, and MMP1 genes; 3. IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, Proteins encoded by the RHOV, ATP2B1, IL-22, and MMP1 genes.

[0034] In some embodiments, the diagnostic product includes a kit for diagnosing tuberculosis caused by Mycobacterium tuberculosis infection. Accordingly, the diagnostic kit includes a reverse transcription quantitative PCR assay kit and an enzyme-linked immunosorbent assay (ELISA) kit.

[0035] In some embodiments, the Mycobacterium tuberculosis-specific antigen composition includes any antigen encoded by the Mycobacterium tuberculosis RD1 region or any peptide of an antigen encoded by the Mycobacterium tuberculosis RD1 region, and further includes any antigen encoded by the Mycobacterium tuberculosis RD2 region or any peptide of an antigen encoded by the RD2 region, wherein the peptide is an effective T-cell epitope of the corresponding region-encoded antigen; all antigens encoded by the Mycobacterium tuberculosis RD1 region are Rv3871, Rv3872, Rv3873, CFP10 (Rv3874), ESAT6 (Rv3875), Rv3876, etc. Rv3877, Rv3878, and Rv3879c; all antigens encoded by the RD2 region of Mycobacterium tuberculosis, namely Rv1978, RvI979c, MPT64(Rv1980c)1, Rv1981c, Rv1982c, Rv1982a, Rv1983, CFP21(Rv1984c), Rv1985c, Rv1986, RV1987, and Rv1988, and a mixed peptide pool consisting of peptides of the antigens encoded by the above-mentioned RD1 and RD2 regions, wherein the peptides constitute the effective T-cell epitopes of the antigens encoded by this region. The antigen composition can specifically induce changes in the expression of the host biomarkers, thereby screening for Mycobacterium tuberculosis infection.

[0036] In some embodiments, the reagent includes different combinations of genes that detect the biomarkers for the diagnosis of tuberculosis and latent tuberculosis infection, including but not limited to combinations of one or more genes selected from IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1 for differential diagnosis.

[0037] Example 1: Screening of host transcriptome biomarkers specific to tuberculosis antigen 1.1 Case Collection This embodiment included 5 confirmed tuberculosis (TBD) patients, 5 patients with latent tuberculosis infection (TBI), and 5 healthy controls. Following the Chinese health industry standard "Diagnosis of Pulmonary Tuberculosis" (WS 288-2017), the inclusion criteria for the volunteer cases involved in this invention were as follows, based on clinical and laboratory examination results: 1) Patients diagnosed with tuberculosis: (1) Etiological evidence: Acid-fast bacilli were detected or cultured in sputum, pleural effusion or pleural tissue specimens. M.tb Or a positive XpertMTB / RIF result; (2) Molecular pathological evidence: Mycobacterium tuberculosis-specific nucleic acid was detected in the biopsy tissue by molecular biological detection methods such as PCR, in situ hybridization or gene sequencing; (3) Clinical comprehensive diagnostic criteria: tuberculosis-related clinical symptoms such as cough, weight loss, fever and imaging showing typical tuberculosis features; and at the same time, having a moderate or strong positive tuberculin skin test, a positive result of gamma-interferon release test or a positive result of Mycobacterium tuberculosis antibody.

[0038] (4) The patient responds positively to anti-tuberculosis drug treatment, while other possible causes of infection are ruled out.

[0039] 2) Patients with latent tuberculosis infection: (1) There is no clinical or radiological evidence of active tuberculosis infection; (2) Meets the above requirements and has immunological evidence of tuberculosis infection, such as a positive or strongly positive tuberculin skin test, a positive result of gamma-interferon release test or Mycobacterium tuberculosis antibody, and has not received any anti-tuberculosis treatment.

[0040] 3) Health control (HC) volunteers (1) No known contact with patients with active tuberculosis; (2) No symptoms of tuberculosis infection; (3) The tuberculosis-specific IGRA test was negative at the time of sample collection; (4) No history of autoimmune diseases, infectious diseases, malignant tumors or immunosuppressive therapy.

[0041] 1.2 Preparation of sequencing samples 1.2.1 Separation of PBMCs: 1) Reagent preparation: RPMI l640 complete medium: Add FBS (inactivated at 56℃ for 30 min, filtered through a 0.22 μm filter, final concentration 10%) and penicillin-streptomycin solution (100×, final concentration 1%) to RPMI l640 medium and store at 4℃.

[0042] 2) Collection of peripheral blood samples: Collect 5 mL of peripheral blood, transport at room temperature, store at 4°C for a short period of time, and ensure sterile conditions throughout the process.

[0043] 3) Density gradient centrifugation to separate PBMCs: After centrifuging peripheral blood at 800 g for 8 min at room temperature, discard the supernatant. Gently resuspend the precipitate in RPMI 1640 medium. Carefully add the cell suspension at a 1:1 ratio using a Pasteur pipette along the wall of a 15 mL centrifuge tube onto the lymphocyte separation medium, being gentle and slow, ensuring a clear interface. Tighten the cap. Centrifuge at 800 g for 22 min at room temperature (increase speed 2, decrease speed 1). After centrifugation, three layers are obtained: the upper layer is serum and RPMI 1640 medium; the lower layer mainly consists of erythrocytes and granulocytes; the middle layer is the lymphocyte separation medium. At the interface between the upper and middle layers, there is a white, cloudy layer mainly composed of mononuclear cells. Carefully aspirate the cells from this cloudy layer into a new centrifuge tube; this is the peripheral blood mononuclear cell (PBMC) mixture. Avoid aspirating Ficoll separation medium as much as possible.

[0044] 4) Cell washing: Add RPMI 1640 medium to 10-14 mL in a centrifuge tube to wash the cells. Invert the tube until the cell pellet is mixed. Centrifuge at 800 g for 10 min at room temperature (acceleration speed 6, deceleration speed 4). After centrifugation, discard the supernatant and add 0.5-1 mL of RPMI 1640 complete medium containing 5% serum preheated at 37℃. Gently pipette to resuspend the PBMCs.

[0045] 5) Cell Counting: PBMC suspension was added to 0.08% trypan blue staining solution at a 1:1 ratio and thoroughly vortexed. Immediately, 10 μL of the mixture was added to a cell counter for counting. The viable cell rate and the number of cells per mL were calculated. PBMCs were then diluted to their final working concentrations using RPMI 1640 complete medium according to cell viability for subsequent experiments.

[0046] 1.2.2 Antigen stimulation of PBMCs: Three culture wells were prepared for each sequencing sample: negative control wells (RPMI 1640 complete medium), RD1 (rEC) antigen stimulation wells, and RD2 (rCM) antigen stimulation wells. PBMCs were diluted to 5 × 10⁻⁶ in complete medium. 6 For each cell / mL cell suspension, 1 mL of the cell suspension is seeded into a 12-well culture plate pre-added with 500 μL of the corresponding stimulant. The stimulant is gently mixed with the cells, and the cell plate is incubated at 37°C in a 5% CO2 incubator for 12-16 h.

[0047] like Figure 1 As shown in A in the present invention, 5 cases of TBD, 5 cases of TBI and 5 cases of HC were selected. Each sequencing sample included a negative control, RD1 and RD2 antigen stimulation wells, for a total of 45 samples.

[0048] 1.2.3 Sample collection: On the second day, the cell culture medium was transferred to a 2 mL sterile enzyme-free EP tube and centrifuged at 1000 g for 5 min at room temperature. The supernatant was discarded. 1 mL of sterile PBS solution was added to the cell culture plate to wash the residual cells. The cells were then transferred to the EP tube again to wash the cells. The cells were centrifuged at 1000 g for 5 min at room temperature. The supernatant was discarded and the cell pellet was collected. 1 mL of Trizol lysis buffer was added to each tube and the cells were repeatedly pipetted to ensure complete lysis. The samples were sealed with sealing film and immediately transferred to a -80℃ freezer for storage. They were then transported to the sequencing company on dry ice for subsequent transcriptome sequencing experiments.

[0049] 1.3 cDNA library construction and transcriptome sequencing Forty-five sequencing samples that passed quality testing (analyzed with an Agilent 2100 Bioanalyzer) were used to construct cDNA libraries. After passing quality testing, the libraries were sequenced using Illumina NovaSeq 6000 paired-end sequencing (PE150 mode). The raw data were then subjected to Fastp quality control and compared with the human reference genome using HISAT2 (Hierarchical Indexing for Spliced ​​Alignment Transcript 2).

[0050] 1.4 Screening of tuberculosis antigen-specific host transcriptome biomarkers 1.4.1 Differentially expressed gene analysis In this embodiment of the invention, the DESeq2 algorithm was used to screen differentially expressed genes (DEGs), with a significance threshold set as |log2Fold Change| ≥ 1.0 and p Value < 0.05. FoldChange is defined as the ratio of gene expression levels in the experimental group (RD1 / RD2) to the control group (Blank), reflecting the differences in gene expression between the groups before and after antigen stimulation. In this embodiment of the invention, volcano plots are drawn using the ggplot2 package to display the differentially expressed genes between the groups.

[0051] like Figure 2As shown in the embodiments of the present invention, RD1 stimulation of TBD patients induced 494 DEGs (231 upregulated, 263 downregulated), while RD2 stimulation produced 452 DEGs (200 upregulated, 252 downregulated). RD1 stimulation of TBI patients induced 2230 DEGs (1252 upregulated, 978 downregulated), while RD2 stimulation produced 2256 DEGs (1316 upregulated, 940 downregulated). The volcano plot shows the distribution of these DEGs and marks differentially expressed genes in some high-fold expression regions. Meanwhile, the healthy control group also showed significant DEGs upon antigen stimulation (RD1: 984, RD2: 1137). To further screen host biomarkers that can distinguish between TBD / TBI patients and HC controls, this study analyzed the overlap of DEGs among the three groups. 196 DEGs from the RD1-stimulated TBD group and 183 DEGs from the RD2-stimulated group were found to be TBD-specific genes and could serve as candidate biomarkers for tuberculosis. Conversely, 1606 DEGs from the RD1-stimulated TBI group and 1604 DEGs from the RD2-stimulated group were found to be TBI-specific genes and hold promise as candidate biomarkers for tuberculosis infection.

[0052] 1.4.2 Cluster analysis of differentially expressed genes To systematically understand the effect of tuberculosis-specific antigen stimulation on the transcriptional regulation of PBMCs in tuberculosis-infected patients, this embodiment of the invention uses the K-means algorithm to analyze the gene expression clustering among different groups, analyzes genes with similar expression patterns in different patient transcriptomes, uses the R package ClusterGVis (v0.1.1) to visualize the gene expression profiles of each group, and displays the changes in gene expression after Z-score normalization.

[0053] like Figure 3 As shown, RD1 and RD2 stimulation significantly altered the transcriptomic characteristics of PBMCs in this embodiment of the invention. Notably, compared with the HC group, the TBD patient antigen-stimulated specific expression genes were concentrated in module C1 (n=291) (mean Z-score > 1.0), which was significantly enriched in T cell activation regulation, the JAK-STAT signaling pathway, and tuberculosis-related pathways. The TBI patient antigen-stimulated specific expression genes were concentrated in module C4 (n=1218) (mean Z-score > 1.0), which was significantly enriched in leukocyte activation and chemotaxis-related pathways.

[0054] 1.4.3 Functional and pathway enrichment analysis of differentially expressed genes To further analyze the characteristics of host T cell immune memory responses induced by RD1 and RD2 stimulation, functional and pathway enrichment analyses were performed in this embodiment of the invention. Gene ontology (GO) is a database describing gene function, comprising three parts: biological process (BP); cell component (CC); and molecular function (MF). The Kyoto encyclopedia of genes and genomes (KEGG) is a database integrating genomic, chemical, and systemic functional information. Functional and pathway enrichment of differentially expressed genes in the TBD and TBI groups was performed using these two databases. Enrichment analyses of GO and KEGG were performed using clusterProfiler (3.8.1) software, and the results were corrected using the Benjamini Hochberg method. p Values ​​less than 0.05 are considered to be significantly enriched entries.

[0055] like Figure 4 As shown in Table 1, in the GO analysis of the embodiments of the present invention, the differentially expressed genes (DEGs) generated by RD1 stimulation of TBD patients with |log2FC(TBD / HC)|>1.0 were significantly enriched in CD4. + The KEGG pathway analysis revealed that RD1-induced DEGs mainly involve cytokine-receptor interactions, the JAK-STAT signaling pathway, and Th17 cell differentiation. For example, [the KEGG pathway is described here]. Figure 4 As shown in Table 2, RD2-stimulated DEGs in TBD patients were also enriched in T cell-related functions, and the main biological processes in the GO analysis included CD4+. + The KEGG pathway is enriched primarily in pathways related to T cell differentiation, glucose and lipid metabolism, and Th2 cell differentiation and immune responses, including protein digestion and absorption, complement cascade reactions, and tuberculosis-related pathways. In summary, DEGs generated by RD1 and RD2 stimulation in TBD patients were significantly enriched in T cell immune-related pathways. These pathways are highly consistent with the known mechanisms of tuberculosis antigen-specific T cell memory responses, further confirming that the DEGs screened in this invention are T cell specific. Similarly, as... Figure 4 As shown in Tables 3 and 4, the DEGs generated by differentially expressed genes with |log2FC(TBI / HC)|>1.0 in TBI patients stimulated by RD1 and RD2 were significantly enriched in pathways such as lipopolysaccharide response, inflammatory response, and leukocyte activation, which are associated with known mechanisms of primary tuberculosis infection response.

[0056] Table 1

[0057] Table 2

[0058] Table 3

[0059] Table 4

[0060] 1.4.4 Screening of core genes based on T cell immune function like Figure 1 B and Figure 5 As shown in the embodiments of the present invention, by screening differentially expressed genes closely related to T-cell immune responses in each group of DEGs, 94 genes related to T-cell immune responses generated by RD1 and RD2 stimulation of the TBD group and 373 genes related to T-cell immune responses generated by RD1 and RD2 stimulation of the TBI group were found. Further functional pathway enrichment was performed on these T-cell immune response-related genes, and the TBD group was enriched with genes related to the regulation of adaptive immune responses (GO: 0002819) and CD4+. + The TBI group showed enrichment in pathways such as α-β T cell differentiation (GO: 0043370), cytokine-cytokine receptor interaction, and JAK-STAT signaling pathways (hsa04060, hsa04630), consistent with antigen-specific memory T cell responses. The TBI group, on the other hand, was enriched in pathways such as lipopolysaccharide response (GO: 0032496), leukocyte chemotaxis (GO: 0030595), cytokine-cytokine receptor interaction, and JAK-STAT signaling pathways (hsa04060, hsa04630), some of which were similar to those enriched in the TBD group, while others showed significant differences. By comparing the expression changes of these T cell immune response-related genes between the two groups, a set of tuberculosis-specific host biomarkers (DEGs-hub genes) was finally screened based on differential analysis.

[0061] On the one hand, the common hub genes for expression level changes greater than 2-fold in TBD compared to TBI stimulation by RD1 and RD2 are IFNγ and MS4A3; however, the independent differential hub genes for expression level changes greater than 2-fold in TBD stimulation by RD1 include RNASE2, CAMK2B, PLSCR3, IL-2, CXCL11, MPO, PPP1R1B, MEOX1, DPYSL3, FOXP3, ULBP2, GBP6, and OLIG2, and the independent differential hub genes for expression level changes greater than 2-fold in TBD stimulation by RD2 include ARHGEF16, MUC4, FITM1, CILP, TCN1, PAQR5, KCNG2, A3GALT2, GAL3ST1, KLHL41, ADAMTSL2, GCSH, and ADAM11. Among the genes mentioned above, RNASE2, TCN1, MPO, PPP1R1B, MS4A3, DPYSL3, PAQR5, KLHL41, OLIG2, GCSH, and ADAM11 are downregulated differentially expressed genes, while the rest are upregulated differentially expressed genes.

[0062] On the other hand, the common hub genes with expression level changes greater than 20-fold compared to TBD resulting from RD1 and RD2 stimulation of TBI were VSTM1, ATOH8, IL-23A, IL-20, ACE, GJB6, CPLX2, OXTR, MMP1, ROS1, ATP2B1, CEBPE, SERPINA10, SCN9A, COL8A2, SERPIND1, LPAR4, and TNFSF15. However, the independent differential hub genes with expression level changes greater than 20-fold compared to TBD resulting from RD1 stimulation of TBI included VEGFC, SLC27A2, and AREG, while the independent differential hub genes with expression level changes greater than 20-fold compared to TBD resulting from RD2 stimulation of TBI included IL-5RA, LGR5, ITGA1, CXCL8, NRN1, ATP13A3, and WFDC5. Of the genes mentioned above, IL-5RA, VSTM1, ACE, NRN1, CPLX2, CEBPE, and COL8A2 were downregulated differentially, while the rest were upregulated differentially. Furthermore, CSF2, COL17A1, IL-22, RHOV, KCNK5, BATF3, and IL-15RA were upregulated in both the TBD and TBI groups, with expression levels in the TBI group being more than twice that in the TBD group. These two core gene sets, while exhibiting significant differential expression, play a crucial role in screening for tuberculosis infection and also possess potential value as diagnostic biomarkers distinguishing between TBD and TBI patients. Finally, the upregulated genes from the above groups were selected as MUC4, FITM1, CAMK2B, PLSCR3, CILP, IL2, CXCL11, IL3, ARHGEF16, KCNG2, A3GALT2, FOXP3, GAL3ST1, ULBP2, ADAMTSL2, GBP6, IFNG, CXCL8, AREG, ATP13A3, MMP1, ATP2B1, WFDC5, TNFSF15, IL23A, IL20, ITGA1, CFS2, COL17A1, IL-22, RHOV, KCNK5, BATF3, and IL-15RA. The IFNγ precursor gene CXCL10 was also added, for a total of 35 genes for subsequent preliminary screening experiments.

[0063] Example 2: Validation of the tuberculosis antigen-specific host transcriptome biomarker of the present invention Through transcriptome analysis, this invention screens a set of host transcriptome genes that can distinguish between patients with tuberculosis and those with latent tuberculosis infection. Based on this, an RT-qPCR real-time quantitative analysis system for clinical detection is established, and the reliability of the transcriptome data and the diagnostic performance of the candidate host biomarkers are verified using clinically diagnosed samples.

[0064] 2.1 Case Collection In this embodiment of the invention, a total of 14 clinically diagnosed TBD patients, 12 TBI patients, and 12 healthy controls (HC) were included for the verification of host transcriptome biomarkers by fluorescence quantitative experiments. The inclusion criteria were the same as in 1.1.

[0065] 2.2 RT-qPCR detection 2.2.1 RNA Extraction (1) Cell lysis: The antigen stimulation and cell lysis procedures for PBMCs are the same as in 1.2.

[0066] (2) RNA extraction: Add 200 μL of RNA extraction aid (1-bromo-3-chloropropane) to each 1 mL of cell lysis sample, vortex thoroughly, let stand for 5 min, strictly follow the SteadyPure RNA extraction kit (AG21024) operation, and the final eluted RNA can be used directly or stored at -80℃.

[0067] (3) RNA concentration determination: Take 1-2 μL of RNA solution and use a NanoDrop spectrophotometer to determine its concentration. An OD260 / OD280 ratio of 1.8-2.1 indicates that the extracted RNA sample is qualified and can be used for subsequent experiments.

[0068] 2.2.2 Reverse Transcription: Genomic DNA removal and reverse transcription were performed using the Takara PrimeScript™ FAST RT Reverse Transcription Kit, strictly following the kit instructions. Specific procedures are as follows: (1) Removal of gDNA reaction: Prepare the following reaction system on ice, in which 2 μL of 8×gDNA Eraser Premix is ​​added to a 16 μL reaction system, a maximum of 1 μg of RNA sample can be used, and RNase Free H2O is added to a total of 16 μL. After preparing the reaction mixture, transfer it to 42℃ for 2 min or room temperature for 5 min.

[0069] (2) Reverse transcription reaction: Add 4 μL of 5× RT Premix to the reaction solution in (1) on ice, mix gently, and then perform reverse transcription reaction at 37℃ for 10 min. Then transfer to a metal bath at 85℃ for 5 s. The obtained cDNA can be stored at -20℃ or at low temperature.

[0070] 2.2.3 Primer Design and Synthesis: Primers for the target gene were designed using the NCBI Pick Primer tool, and primer specificity was verified using Primer-BlAST. The primer sequences were synthesized by Qingke Biotechnology Co., Ltd., and the specific sequences are shown in Table 5.

[0071] Table 5

[0072] 2.2.4 RT-qPCR reaction (1) Reaction system preparation: The RT-qPCR reaction solution was prepared using Takara's TB Green® Premix Ex Taq™ II kit. The reaction solution was prepared on ice. The total system volume was 25 μL, containing 12.5 μL of TB Green Premix Ex Taq II (Tli RNaseH Plus) (2×), 1 μL of PCR Forward Primer (10 μM), 1 μL of PCR Reverse Primer (10 μM), 2 μL of DNA template (<100 ng), and 8.5 μL of Easy Dilution II. To reduce errors in the reaction solution preparation process, the premix solution was prepared first, thoroughly mixed, and then aliquoted into PCR plates for subsequent experiments.

[0073] (2) RT-qPCR detection: Place the PCR plate into the real-time PCR instrument and use the two-step PCR amplification program. First, pre-denature at 95℃ for 30 s; perform 40 cycles of reaction at 95℃ for 5 s and 60℃ for 30 s. The melting curve is set to 95℃ for 10 s and 65℃ for 50 s. At the same time, the temperature is increased from 65℃ to 95℃, and the fluorescence signal is detected when the temperature increases by 0.5℃ each time.

[0074] 2.2.5 Results Analysis: For example... Figure 6 and Figure 7 Gene expression levels were analyzed using the 2-ΔΔCt relative quantification method, with ACTB as an internal reference gene. The relative expression level of the gene was calculated, ΔCt = Ct. （目的基因） -Ct （内参基因） ,ΔΔCt=ΔCt （抗原刺激组） -ΔCt （对照组）One-way ANOVA was used for intergroup comparisons. Diagnostic efficacy was assessed using receiver operating characteristic (ROC) curves to determine the optimal cutoff value and area under the ROC curve (AUC). Confidence intervals were estimated using bootstrap analysis (1000 iterations, random seed = 978) to ensure the repeatability of threshold selection and correct for bias caused by small samples. All analyses were performed using GraphPad Prism 10.1.2, MedCalc 23.1.5, and R 3.5.0 (ggplot2 package) for statistical analysis and visualization of the data.

[0075] 2.3 Validation of the diagnostic performance of tuberculosis antigen-specific host biomarkers like Figure 1 As shown in C, to evaluate the diagnostic performance of candidate genes, the relative gene expression levels of TBD patients (n=5), TBI patients (n=5), and HC patients (n=5) under the same antigen stimulation were measured. Similarly, antigen-stimulated and unstimulated samples were set up separately, using 2... -ΔΔCt The algorithm calculated the relative gene expression levels in the TPE and Non-TPE groups and performed inter-group difference analysis using a One-Way ANOVA test. For the 35 genes mentioned above that underwent subsequent initial screening experiments, after removing three genes with undetectable low expression levels (including CAMK2B, ARHGEF16, and IL20), the remaining 32 core genes were validated in 5 TBD patients, 5 TBI patients, and 5 HC controls. Twelve genes were then identified as having clinical diagnostic value. Figure 6 As shown in Tables 6, 7, and 8, in this embodiment of the invention, under RD1 antigen stimulation, the relative expression levels of the seven host gene markers in the TBD group were significantly higher than those in the HC group. p <0.05, which can be used to differentiate tuberculosis patients from healthy individuals. Among them, IFNγ, IL-2, GBP6, IL-3, and CXCL10 achieved an AUC of 1.00 when differentiating tuberculosis patients from healthy individuals. The relative expression levels of host gene markers ATP13A3, ATP2B1, IL22, and MMP1 were significantly higher in the TBI group than in the HC group. p<0.05), which can be used to differentiate between latent tuberculosis infected individuals and healthy individuals. Among them, ATP13A3 and ATP2B1 achieved an AUC of 1.00 when differentiating between latent tuberculosis infected individuals and healthy individuals. It is noteworthy that IL22 and MMP1 achieved an AUC of 1.00 when differentiating between tuberculosis patients and latent tuberculosis infected individuals, indicating their strong diagnostic ability to exclude different states of tuberculosis infection. In the RD2 stimulation system, such as Figure 6 As shown in Tables 6, 7, and 8, the relative expression level of IFNγ was significantly higher in the HC group only in the group identifying tuberculosis patients. p While the AUC of ATP13A3 and ATP2B1 was <0.05, they also reached 1.00 in differentiating latent tuberculosis infected individuals from healthy individuals. Furthermore, the relative gene expression levels of ATP2B1 and MMP1 under RD2 antigen stimulation showed more significant changes in the TBI group compared to RD1 stimulation, and RHOV gene stimulation demonstrated the ability to differentiate tuberculosis patients from latent tuberculosis infected individuals in the TBD group (AUC=1.0). Based on the results of this validation, it can be concluded that, when examining the changes in the expression levels of the above 12 genes before and after stimulation in the same patient, the trend of changes in TBD patients stimulated with RD1 antigen is that the expression levels of IFNγ, IL-2, GBP6, IL-3, CXCL10, ULBP2, and CXCL11 are increased, while the expression levels of ATP13A3, ATP2B1, IL-22, and MMP1 show no significant changes compared to the healthy control group; the trend of changes in TBI patients is that the expression levels of IFNγ, ATP13A3, ATP2B1, IL-22, and MMP1 are increased, while the expression levels of CXCL10, IL-2, IL-3, GBP6, ULBP2, and CXCL11 show no significant changes compared to the healthy control group; among them, the gene expression levels of RHOV, IL-22, and MMP1 show significant differences after TBD and TBI stimulation. p <0.05). The trend in TBD patients stimulated with RD2 antigen showed increased expression of IFNγ, GBP6, ULBP2, and RHOV, while the expression of IL-2, IL-3, CXCL10, CXCL11, ATP13A3, ATP2B1, and MMP1 showed no significant change compared to the healthy control group. The trend in TBI patients showed increased expression of ATP13A3, ATP2B1, RHOV, and MMP1, while the expression of IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, IL-22, and CXCL11 showed no significant change compared to the healthy control group. Among these, RHOV and MMP1 showed significant differences in gene expression levels after TBD and TBI stimulation. p <0.05). This means that after different antigen stimulations, the two types of patients exhibited different gene expression changes in these 12 gene combinations.

[0076] Table 6

[0077] Table 7

[0078] Table 8

[0079] Through the above verification, the embodiments of the present invention have verified that the simultaneous detection of 12 tuberculosis antigen-specific host transcriptome markers can be used as indicators for screening tuberculosis infection, and can also be used to distinguish between tuberculosis patients, latent tuberculosis infection patients and healthy individuals through multi-gene combinations, with solid reliability.

[0080] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A tuberculosis antigen-specific host biomarker for detecting tuberculosis infection, characterized in that, The biomarker is at least one of the following genes: IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1; the tuberculosis infection includes active tuberculosis and latent tuberculosis infection; The base sequences of IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1 are shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, and SEQ ID NO:12, respectively.

2. The tuberculosis antigen-specific host biomarker for detecting tuberculosis infection as described in claim 1, characterized in that, At least one of the RHOV, IL-22, and MMP1 genes can further differentiate between active tuberculosis and latent tuberculosis infection.

3. The tuberculosis antigen-specific host biomarker for detecting tuberculosis infection as described in claim 1, characterized in that, The active tuberculosis referred to is active pulmonary tuberculosis or extrapulmonary tuberculosis.

4. The tuberculosis antigen-specific host biomarker for detecting tuberculosis infection as described in claim 3, characterized in that, The active pulmonary tuberculosis refers to primary pulmonary tuberculosis, secondary pulmonary tuberculosis, or hematogenous disseminated pulmonary tuberculosis.

5. A reverse transcription quantitative polymerase chain reaction (RT-PCR) assay kit for detecting tuberculosis infection, characterized in that, It includes any antigen encoded by the RD1 region of Mycobacterium tuberculosis or any peptide segment of an antigen encoded by the RD1 region of Mycobacterium tuberculosis, and also includes any antigen encoded by the RD2 region of Mycobacterium tuberculosis or any peptide segment of an antigen encoded by the RD2 region, wherein the peptide segment is an effective T cell epitope of the corresponding region-encoded antigen. It also includes primers for detecting at least one of the following genes: IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1. The base sequences of IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1 are shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, and SEQ ID NO:12, respectively.

6. An enzyme-linked immunospot assay kit for detecting tuberculosis infection, characterized in that, It includes any antigen encoded by the RD1 region of Mycobacterium tuberculosis or any peptide segment of an antigen encoded by the RD1 region of Mycobacterium tuberculosis, and also includes any antigen encoded by the RD2 region of Mycobacterium tuberculosis or any peptide segment of an antigen encoded by the RD2 region, wherein the peptide segment is an effective T cell epitope of the corresponding region-encoded antigen. It also includes a capture antibody coated on a solid-phase carrier, a biotin-labeled detection antibody, a streptavidin-enzyme complex, and an enzyme substrate chromogenic solution. The capture antibody is used to specifically bind to the protein encoded by the gene; the detection antibody can specifically bind to the protein encoded by the gene; the gene is: IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1; The base sequences of IFNγ, CXCL10, IL-2, IL-3, GBP6, ULBP2, CXCL11, ATP13A3, RHOV, ATP2B1, IL-22, and MMP1 are shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO:10, SEQ ID NO:11, and SEQ ID NO:12, respectively.

7. The application of the reverse transcription quantitative polymerase chain reaction (RT-PCR) assay kit as described in claim 5 in the preparation of reagents for detecting tuberculosis infection.

8. The application as described in claim 7, characterized in that, The specific application involves stimulating peripheral whole blood or peripheral blood mononuclear cells (PBMCs) with an antigenic stimulus, wherein the stimulus includes an RD1 region antigen or any peptide segment encoding an RD1 region antigen, and also includes an RD2 region antigen or any peptide segment encoding an RD2 region antigen; extracting mRNA from the stimulated peripheral whole blood or mRNA from the stimulated PBMCs, reverse transcribing the mRNA, and then using primers for the corresponding gene to perform real-time quantitative polymerase chain reaction to detect the gene; if high expression of at least one of the gene mRNAs produced after stimulation is detected, the sample is determined to be infected with tuberculosis.

9. The application of the enzyme-linked immunospot assay kit as described in claim 6 in the preparation of reagents for detecting tuberculosis infection.

10. The application as described in claim 9, characterized in that, The specific application involves stimulating peripheral blood mononuclear cells (PBMCs) with an antigenic stimulant, wherein the stimulant includes an RD1 region antigen or any peptide segment encoding an RD1 region antigen, and also includes an RD2 region antigen or any peptide segment encoding an RD2 region antigen; the PBMCs after stimulation are then analyzed using an enzyme-linked immunospot assay kit, and if the T cells in the PBMCs produce at least one of the expressed proteins of the aforementioned genes, the sample is determined to be infected with tuberculosis.

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