A set of biomarkers for the diagnosis of tuberculosis

Abstract

The application belongs to the field of biomedicine and particularly relates to a group of biomarkers for diagnosing subclinical tuberculosis. The application discloses a group of biomarkers for diagnosing subclinical tuberculosis, and a group of plasma protein marker combinations for diagnosing subclinical tuberculosis are screened out by Olink technology. The biomarkers can significantly distinguish subclinical TB from healthy+LTBI samples, and have high diagnostic efficiency for subclinical tuberculosis alone or in combination, which is helpful to promote early intervention and treatment of tuberculosis. The AUC values of EN-RAGE and EN-RAGE for single diagnosis are both greater than 0.8, and the AUC value of the combination of EN-RAGE and MCP-3 for joint diagnosis is 0.896.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biomedicine, and particularly relates to a group of biomarkers for diagnosing subclinical tuberculosis. BACKGROUND

[0002] Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (M. tb) and mainly transmitted through the air, which seriously threatens global public health security. Although some progress has been made in the control of tuberculosis worldwide, its morbidity and mortality remain high. Human body will go through four stages from infection with Mycobacterium tuberculosis to disease, i.e. infection clearance, latent infection, subclinical tuberculosis and active tuberculosis (Li Meng, Gao Qian. Status and prospect of phase division of tuberculosis natural history and its diagnosis [J]. Chinese Journal of Tuberculosis Prevention and Control, 2021, 43(11): 1125-1131.). Early diagnosis and timely treatment of tuberculosis are crucial for controlling its transmission and reducing mortality.

[0003] Subclinical tuberculosis (S-TB) refers to the presence of tuberculosis infection that can be detected by imaging, laboratory tests and other means, but the infected person has no obvious clinical symptoms. Studies have shown that in terms of age distribution, the proportion of subclinical tuberculosis patients in the age group of ≥45 years old is relatively high (Wang Hanfei, Li Tao, Zhao Yanlin, et al. Analysis of treatment outcomes and influencing factors of subclinical tuberculosis in China, 2021-2022 [J]. Tropical Diseases and Parasitology, 2023, 21(02): 72-77+92.). Subclinical tuberculosis plays a key role in the transmission chain of tuberculosis, because its infected people are often not diagnosed and treated in time, thus becoming a hidden source of transmission. Although there have been many studies on biomarkers for diagnosing subclinical tuberculosis, no biomarker has been developed that can be widely used in the actual diagnosis of subclinical tuberculosis. Currently, there is no validated tool for diagnosing subclinical tuberculosis, and the diagnosis mainly relies on the auxiliary diagnosis of etiological means for diagnosing active tuberculosis, including chest X-ray, sputum acid-fast bacillus smear microscopy, mycobacterium culture and GeneXpert MTB / RIF, etc. However, due to the relatively low amount of subclinical tuberculosis bacteria, it is not easy to be found, and misdiagnosis often occurs (Wang Hanfei, Zhao Yanlin, Xu Caihong. Research progress of subclinical tuberculosis [J]. Chinese Journal of Tuberculosis Prevention and Control, 2023, 45(08): 808-813.).

[0004] Proteomics technology can provide important molecular markers for early diagnosis of diseases by large-scale analysis and identification of proteins and their modifications in organisms. In recent years, the application of proteomics technology in infectious disease research has gradually increased, and it has shown great potential in finding and verifying disease-related biomarkers. Olink technology is a high-throughput proteomics technology that combines protein amplification and probe-based immunoassay methods to achieve precise quantitative analysis of multiple proteins in plasma. Olink technology has the advantages of high sensitivity and high specificity, and can maintain good performance in the detection of low-abundance proteins, making it suitable for biomarker screening and verification. SUMMARY

[0005] Through Olink technology, the present application screens a combination of plasma protein markers for the diagnosis of subclinical tuberculosis. These markers show significant changes in the plasma of patients with subclinical tuberculosis and have strong diagnostic efficiency for the diagnosis of subclinical tuberculosis, which helps to promote early intervention and treatment of tuberculosis. Based on this, the present application is completed.

[0006] In a first aspect, the present application provides a combination of biomarkers for diagnosing subclinical tuberculosis, wherein the biomarkers are EN-RAGE and / or MCP-3.

[0007] Further, the biomarkers are from the blood of patients.

[0008] In a second aspect, the present application provides an application of biomarkers in the preparation of reagents for diagnosing subclinical tuberculosis, wherein the biomarkers are one or more of EN-RAGE and MCP-3, and the reagents are reagents that can detect the content of EN-RAGE and MCP-3 in the biological samples of patients; when the content of EN-RAGE and / or MCP-3 increases, it indicates subclinical tuberculosis.

[0009] Further, the biological samples are selected from the blood of patients.

[0010] In a third aspect, the present application provides a kit for diagnosing subclinical tuberculosis, wherein the kit comprises reagents for detecting the content of EN-RAGE and MCP-3 in the biological samples of patients; when the content of EN-RAGE and / or MCP-3 increases, it indicates subclinical tuberculosis.

[0011] Further, the kit can be one or more of an ELISA detection kit, a colloidal gold detection kit, an immunohistochemical kit, an immunofluorescence kit, and / or an in situ hybridization staining kit.

[0012] Further, the kit diagnostic method comprises one or more of a direct method, an indirect method, a double antibody sandwich method and / or a competition method.

[0013] Advantages

[0014] The biomarkers screened by the application can significantly distinguish subclinical TB and healthy+LTBI samples, and have high diagnostic efficiency for subclinical TB, alone or in combination. Among them, the AUC values of EN-RAGE and EN-RAGE alone in the screening cohort are both >0.8, and the AUC value of the combined diagnosis of EN-RAGE and MCP-3 is 0.896; the AUC values of EN-RAGE and EN-RAGE alone in the verification cohort are both >0.6, and the AUC value of the combined diagnosis of EN-RAGE and EN-RAGE is 0.838. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 It is a schematic diagram of blood stratification after centrifugation.

[0016] Figure 2 It is a principal component analysis (PCA) graph of protein expression levels in the screening cohort. Different colored points represent the proteomic data distribution of different populations. The horizontal axis is the first principal component (PC1), and the vertical axis is the second principal component (PC2).

[0017] Figure 3 It is the expression level of the differentially expressed protein in the screening cohort. Note: A. EN-RAGE expression level; B. MCP-3 expression level.

[0018] Figure 4 It is the diagnostic efficiency analysis of the differentially expressed protein in the screening cohort. Note: A. EN-RAGE diagnostic efficiency; B. MCP-3 diagnostic efficiency; C. EN-RAGE and MCP-3 combined diagnostic efficiency.

[0019] Figure 5 It is the expression level of the differentially expressed protein in the verification cohort. Note: A. EN-RAGE expression level; B. MCP-3 expression level.

[0020] Figure 6 It is the diagnostic efficiency of the differentially expressed protein in the verification cohort. Note: A. EN-RAGE diagnostic efficiency; B. MCP-3 diagnostic efficiency; C. EN-RAGE and MCP-3 combined diagnostic efficiency. DETAILED DESCRIPTION

[0021] The specific embodiments of the present application are further described below. It should be noted that the description of these embodiments is intended for purposes of illustration only and is not intended to be limiting. Furthermore, the features involved in the following described embodiments can be combined with each other as long as they do not conflict with each other.

[0022] The experimental methods in the following examples are all conventional methods unless otherwise specified. The test materials used in the following examples are all commercially available unless otherwise specified.

[0023] The "EN-RAGE" described in the present application is a new member of the calgranulin superfamily, also known as S100A12 and MRP6, which is mainly synthesized and secreted by granulocytes and plays a key role in regulating immune response and inflammatory process. As a pro-inflammatory factor, EN-RAGE can specifically bind to RAGE to activate downstream signaling pathways, including NF-KB and MAP kinase. Studies have shown that the expression level of S100A12 in the serum of RA and systemic lupus erythematosus patients is significantly higher than that of healthy controls. The same phenomenon also occurs in patients with chronic obstructive pulmonary disease (COPD).

[0024] The "MCP-3" described in the present application refers to monocyte chemoattractant protein-3, which is a kind of basic protein with multiple biological functions produced by cells, which can act on endothelial cells to promote cell proliferation and migration, and positively regulate angiogenesis. As a powerful chemoattractant for various cells, MCP-3 can bind to various chemokine receptors, such as CCR 1, CCR 2, CCR 3, and CCR 5 and CCR 10, and its binding can activate multiple signaling pathways to affect the occurrence and development of tumors.

[0025] Example 1 Screening of differentially expressed proteins in subclinical tuberculosis based on Olink proteomics technology

[0026] 1. Experimental subjects

[0027] The subjects were divided into a healthy elderly group, a Latent Tuberculosis Infection (LTBI) elderly group, and a subclinical elderly TB group.

[0028] According to the inclusion and exclusion criteria, 45 samples (15 healthy elderly, 15 LTBI elderly, and 15 subclinical elderly TB patients) were included in Guangxi Zhuang Autonomous Region Chest Hospital as a screening cohort, and the subjects signed the informed consent form.

[0029] 2. Inclusion criteria

[0030] Healthy older adults: age ≥ 60 years; negative IGRA; no other serious medical history.

[0031] LTBI in older adults: age ≥ 60 years; positive IGRA; no other serious medical history.

[0032] Subclinical elderly TB patients: age ≥60 years; positive IGRA; laboratory diagnosis of TB; mild disease; sputum present, but no clinical symptoms; no extrapulmonary tuberculosis; no other serious medical history.

[0033] Exclusion criteria: Individuals with HIV co-infection or other co-existing conditions, and anyone who presented with an indeterminate IGRA result.

[0034] 3. Sample collection

[0035] 1) Collect 5 mL of peripheral blood from the subject.

[0036] 2) Add 3-4 mL of mononuclear cell separation solution to a centrifuge tube, use a pipette to draw blood sample and add it to the surface of the separation solution, adjust the induction speed to 8 and the deduction speed to 6, and centrifuge at 600g for 25 min.

[0037] 3) After centrifugation, the centrifuge tube will now have four layers from top to bottom. Figure 1 The first layer is the plasma layer, the second layer is the ring-shaped milky white mononuclear cell layer, the third layer is the transparent separation fluid layer, and the fourth layer is the red blood cell layer.

[0038] 4) Aspirate the upper plasma layer, dispense it, and record the patient's name, hospital number, and date. Store the sample at -80℃.

[0039] 4. Identification of differentially expressed biomarkers using plasma Olink proteomics

[0040] The collected plasma samples were subjected to proteomic analysis using Olink technology to identify differentially expressed biomarkers associated with subclinical tuberculosis. The specific experimental steps are as follows:

[0041] 4.1 Sample Randomization

[0042] Use the RAND function in an Excel spreadsheet to generate random values ​​for each sample, then sort the values ​​in ascending order to randomly distribute the samples across the 96-well plate.

[0043] 4.2 Sample Sampling

[0044] Based on the randomized sample distribution table, 10-40 μL of each sample was placed in a 96-well plate.

[0045] External control: add 5 μL mixed plasma sample into holes A12 and B12 respectively; add 5 μL negative control (NC) into holes C12, D12 and E12 respectively; add 5 μL interplate control (IPC) into holes F12, G12 and H12 respectively.

[0046] 4.3 Hybridization incubation

[0047] Hybridization mixture preparation: take out the reagent from the refrigerator, vortex and centrifuge, then add Incubation Solution 280 μL, Incubation Stabilizer 40 μL, A-probes 40 μL and B-probes 40 μL into a nuclease-free centrifuge tube in sequence, vortex and centrifuge. Add 47 μL hybridization mixture into each well of the 8-well tube. Label the 96-well plate as the incubation plate, and use an 8-channel pipette to add 3 μL hybridization mixture into each well, and then add 1 μL sample into each well. Seal the plate with a sealing film, centrifuge at 400 g for 1 min, and place it in the PCR instrument, set 4℃∞, and incubate for 16-24 h.

[0048] 4.4 Extension and amplification

[0049] PCR program setting: 50℃ for 20 min, 95℃ for 5 min, 95℃ for 30 s, 54℃ for 1 min, 60℃ for 1 min for 17 cycles, and 10℃ for holding. Extension and amplification mixture preparation: add High Purity Water 9385 μL, PEA Solution 1100 μL, PEA Enzyme 55 μL and PCR Polymerase 22 μL into a centrifuge tube in sequence, vortex and centrifuge. Take out the incubation plate from the PCR instrument, centrifuge, add 96 μL extension and amplification mixture into each well, vortex and centrifuge after sealing the plate, and place it in the 50℃ preheated PCR instrument to resume the program.

[0050] 4.5 Chip pretreatment

[0051] Take out the chip, press the piston hole in the syringe, and push the liquid. Click the Target 96 option of the instrument, place the chip and cover plate, and click Start to perform chip pretreatment.

[0052] 4.6 On-machine operation

[0053] Preparation of detection solution: Detection Solution 550 μL, High Purity Water 230 μL, Detection Enzyme 7.8 μL, PCR Polymerase 3.1 μL were added into a centrifugal tube in turn, vortexed and centrifuged. 95 μL per well was dispensed into a joint tube. Take out the 96-well plate, add 7.2 μL of the mixed solution to each well. After the extension amplification program is completed, add 2.8 μL of product to each well, vortex and mix. Prepare the diluted sample plate, primer plate and pretreated chip. Add 5 μL of primer to the left side of the chip and 5 μL of diluted product to the right side of the chip in turn to avoid air bubbles. After the sample is added, place it in the sample loading platform, set the running name, and click run.

[0054] 4.7 Data analysis and export

[0055] After the instrument is turned off, the running name file is exported, the Olink NPX Signature 1.5.3.0 software is opened, the corresponding file is imported, the reagent panel information, reagent version number and sample name are set, "OK" is clicked, and the NPX Excel document and QCAR PDF document are outputted after automatic analysis by the software.

[0056] 4.8 Quality control analysis

[0057] Quality control analysis includes QC_distribution and QC_IQR. In QC_distribution, the parallelism of quality control detection between samples is evaluated by statistical analysis of the internal quality control values of each sample. The distribution of quality control values is represented by five statistical quantities, i.e. maximum value, upper quartile, median value, lower quartile and minimum value. If there is deviation in the detection process of some samples, the quality control analysis will mark these samples and prompt a warning (Warning), but by default these samples are not excluded. In QC_IQR analysis, the deviation degree of quality control detection between samples is evaluated by calculating the interquartile range (IQR) of the internal quality control values of each sample. If the quality control values of the samples are uniformly distributed, the detection is stable; if the quality control values of the samples deviate greatly, the detection has certain variability, but if the protein concentration is within the linear range, these samples can still be retained in actual analysis. Quality control analysis will also mark samples with deviations and prompt a warning.

[0058] 5、Results

[0059] 5.1 Screening of differentially expressed proteins

[0060] By comparing the protein expression levels of healthy elderly people, LTBI elderly people and subclinical elderly TB patients, the significantly differentially expressed markers were screened. Figure 2The results of dimensionality reduction of proteomic data of three groups of people by principal component analysis (PCA) are shown, and the distribution of the three groups of people on the proteomic data is significantly different. Further analysis selects the most significant differentially expressed markers (EN-RAGE and MCP-3), and by using single factor analysis of variance (One-way ANOVA) and multiple comparison test (multiple comparisons) by graphpad software, the statistical significance between the three groups is determined (*P<0.05, **P<0.01, ***P<0.001, ****P<0.0001).

[0061] The expression levels of EN-RAGE and MCP-3 proteins in the subclinical elderly TB patient group are significantly different from those in the healthy elderly and LTBI elderly groups, while there is no significant difference between the healthy elderly and LTBI elderly groups Figure 3 A-B).

[0062] 5.2 Analysis of diagnostic efficiency of differentially expressed markers

[0063] Based on ROC comparison, the plasma protein expression levels of EN-RAGE and MCP-3 in the subclinical elderly TB group and the healthy elderly + LTBI elderly group are analyzed.

[0064] The results show that EN-RAGE has a significant ability to distinguish subclinical TB and healthy + LTBI samples, with an AUC value as high as 0.856 (95% confidence interval, 0.719-0.942; sensitivity, 80%; specificity, 86.67%) Figure 4 A). MCP-3 has a significant ability to distinguish subclinical TB and healthy + LTBI samples, with an AUC value as high as 0.863 (95% confidence interval, 0.728-0.947; sensitivity, 93.33%; specificity, 66.67%) Figure 4 B).

[0065] Further, the differentially expressed proteins EN-RAGE and MCP-3 are combined to evaluate their joint diagnostic efficiency. By multivariate logistic regression analysis, a combined diagnostic model is constructed, and the ROC curve of the combined diagnosis is drawn.

[0066] As shown in Figure 4 C, the AUC value of the combined marker is as high as 0.896 (95% confidence interval, 0.768-0.967; sensitivity, 86.7%; specificity, 86.7%), higher than that of a single marker, with higher specificity, indicating that the combined diagnostic efficiency is better.

[0067] The subjects include healthy elderly, LTBI elderly and subclinical elderly TB patients. Each group selects 30 subjects from Guangxi Zhuang Autonomous Region Chest Hospital, a total of 90 subjects as a validation cohort. Collect the plasma samples of each subject for subsequent experiments according to the method described in Example 1.

[0068] 2. ELISA kit source

[0069] The ELISA kits of EN-RAGE and MCP-3 are provided by Enzyme-Linked Immunoassay Company. ELISA kit (EN-RAGE): item number LD13698, 96T / box; ELISA kit (MCP-3): item number: LD10478, 96T / box. All kits are operated according to the manufacturer's instructions, and the kit performance verification is carried out before the experiment to ensure the reliability and consistency of the experimental results.

[0070] 3. ELISA verification

[0071] The specific antibodies of the markers EN-RAGE and MCP-3 screened in Example 1 are used for ELISA experiments.

[0072] According to the standard ELISA operation process, the expression levels of EN-RAGE and MCP-3 markers in each plasma sample are detected respectively. The specific steps include sample dilution, sample addition, incubation, washing, addition of enzyme-labeled secondary antibody, re-incubation, reaction termination and plate reading, etc. Each sample is detected at least three times for repeated experiments to ensure the accuracy and repeatability of the data. The expression of each marker in different groups is recorded.

[0073] 4. Data analysis

[0074] Through graphpad software, One-way ANOVA and multiple comparisons are used to determine the statistical significance between the three groups. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, verify the biomarkers for diagnosing subclinical elderly TB patients.

[0075] 5. Results

[0076] As shown in Figure 5 , the plasma expression levels of EN-RAGE and MCP-3 in the subclinical elderly TB patient group are significantly different from those in the healthy elderly and LTBI elderly groups, while there is no significant difference between the healthy elderly and LTBI elderly groups. Further based on ROC comparison, the diagnostic efficiency of EN-RAGE and MCP-3 in the subclinical elderly TB group and the healthy elderly + LTBI elderly group is verified. As shown in Figure 6AUC value of EN-RAGE was 0.638 (95% confidence interval, 0.530-0.737; sensitivity, 56.7%; specificity, 78.3%) (Fig. 1A). Figure 6 AUC value of MCP-3 was 0.701 (95% confidence interval, 0.595-0.793; sensitivity, 50%; specificity, 90%) (Fig. 1B). Figure 6 B). The diagnostic efficiency of the combined markers was evaluated. A combined diagnostic model was constructed by multivariate logistic regression analysis, and the ROC curve of the combined diagnosis was plotted. As shown in Fig. 1C, the AUC value of the combined markers was as high as 0.838 (95% confidence interval, 0.745-0.907; sensitivity, 56.7%; specificity, 100%). The results verified the diagnostic efficiency of the marker combination in the expanded clinical sample, and determined the feasibility of its application in actual clinical diagnosis. Figure 6 B). The diagnostic efficiency of the combined markers was evaluated. A combined diagnostic model was constructed by multivariate logistic regression analysis, and the ROC curve of the combined diagnosis was plotted. As shown in Fig. 1C, the AUC value of the combined markers was as high as 0.838 (95% confidence interval, 0.745-0.907; sensitivity, 56.7%; specificity, 100%). The results verified the diagnostic efficiency of the marker combination in the expanded clinical sample, and determined the feasibility of its application in actual clinical diagnosis.

Claims

1. Use of a combination of biomarkers in the manufacture of a reagent for diagnosing subclinical tuberculosis, said combination of biomarkers being EN-RAGE and MCP-3, said reagent comprising reagents for detecting the levels of EN-RAGE and MCP-3 in a biological sample from a patient, wherein elevated levels of EN-RAGE and MCP-3 are indicative of subclinical tuberculosis.

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