Screening and application of urine metabolic markers of pulmonary tuberculosis

Abstract

The application belongs to the technical field of biology and particularly relates to screening and application of a urine metabolic marker of tuberculosis. The urine metabolic marker comprises 19 kinds of metabolites. After verification, it is proved that the level change of the 19 kinds of metabolites is indeed related to pulmonary tuberculosis. When the AUC value of one or more of the markers is greater than 0.8, it indicates that the patient is a pulmonary tuberculosis patient. The application further provides a screening method of a pulmonary tuberculosis metabolic marker, a detection kit for diagnosing pulmonary tuberculosis and a model for diagnosing pulmonary tuberculosis. The 19 kinds of urine metabolic markers screened by the application have high accuracy in pulmonary tuberculosis detection and are of great significance for the diagnosis of pulmonary tuberculosis.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of biotechnology, and particularly relates to screening and application of a urine metabolic marker of tuberculosis. BACKGROUND

[0002] Tuberculosis (TB) is a chronic and consumptive zoonosis caused by Mycobacterium tuberculosis, and is also one of the global fatal diseases. Pulmonary tuberculosis is a chronic infectious disease caused in the lungs of human body, and is the most common one among tuberculosis. With the environmental pollution, prevalence of drug-resistant strains of tuberculosis and frequent occurrence of AIDS combined infection, the incidence of tuberculosis is more and more serious, which seriously threatens the global human health, and the prevention and control situation of tuberculosis is still severe. At present, the clinical diagnosis method of pulmonary tuberculosis is still mainly based on isolation and culture of Mycobacterium tuberculosis and sputum smear microscopy, but the detection period is long and the detection rate is low. Imaging diagnosis is a common means for preliminary diagnosis of pulmonary tuberculosis at present, but it has high price, has ray injury, and the tuberculosis nodule in the early pulmonary tuberculosis patient is far smaller than the minimum threshold value that can be measured by imaging technology; the antigen and antibody detection method depending on the host immune response is concentrated on the blood-based diagnosis, such as gamma interferon release method and traditional tuberculin test, which still has many defects, limiting its application in the diagnosis of tuberculosis. The traditional detection method is complicated in operation, has long detection period or low sensitivity; the molecular biology detection method has high cost, and has problems of false positive and false negative; the imaging performance of tuberculosis is atypical and is not easy to be identified with other diseases, and the interventional diagnosis has many problems such as trauma, which still cannot meet the needs of clinical diagnosis. At present, the biggest difficulty in tuberculosis prevention and treatment is still the lack of specific early diagnosis markers, and it is urgent to find sensitive, specific, convenient and effective early detection method, and the non-invasive sampling detection procedure is crucial for controlling pulmonary tuberculosis infection, blocking transmission and inhibiting prevalence.

[0003] Metabolomics can provide key information on downstream products in cells and metabolic processes, and health / disease conditions of specific tissues or organs. At present, this technology has been researched and applied in drug research and development, disease diagnosis, drug metabolism, adverse drug reactions and monitoring of treatment effect. Many biological fluid samples such as urine, blood and tissue homogenate can be used for metabolomics analysis. Metabolomics uses modern detection technology combined with bioinformatics analysis method to qualitatively or quantitatively detect the dynamic rules of changes in types and quantities of endogenous small molecule metabolites in tissues, cells or body fluids in vivo, and obtain differential metabolic markers under the influence of different pathological and physiological stimuli or environmental factors.

[0004] Compared with blood, urine can truly reflect the body condition without the influence of balance mechanism, and has the advantages of simple collection and non-invasive sampling, and is widely used in infants and patients who cannot obtain sputum specimens. The detection of related biomarkers in urine to determine the occurrence, development and prognosis of diseases has attracted more and more attention of researchers. Urine is easy to collect in large quantities and continuously and non-invasively, which is helpful for metabolite identification, quantification and subsequent data analysis; at the same time, the protein content in urine is low, the processing process is relatively simple, the metabolites in urine are relatively small, thermodynamically stable, and the intermolecular interaction of metabolites in urine samples is less. Therefore, through the study of urine of patients with tuberculosis, specific and effective urine metabolic markers are screened out, which is crucial for the diagnosis and differential diagnosis of pulmonary tuberculosis.

[0005] In summary, it is necessary to provide more potential urine metabolic markers as a rapid, efficient, sensitive, specific and economical diagnostic method, and to improve the detection efficiency is a problem to be solved by those skilled in the art. SUMMARY

[0006] In a first aspect, the present application provides a metabolic marker for diagnosing a patient with pulmonary tuberculosis, wherein the marker is one or more of 5-O-Methylvisammioside, Glutamate Conjugated Chenodeoxycholic Acid, Kaurenic Acid, Xylan, FA13:3+1O, Methyltestosterone, Eicosanoids, Cholesterol, Retinoic Acid, Vitamine A Acetate, 14:0 Lyso-PE (1-Myristoyl-2-Hydroxy-Sn-Glycero-3-Phosphoethanolamine), N-Methyllysine, Medroxyprogesterone acetate, Trimethylamine N-Oxide, Decanoyl-L-Carnitine, Monolinolein, Caffeoylcholine, Dehydroabietic Acid, and Homoarginine.

[0007] Further, when the AUC value of one or more of the markers is greater than 0.8, the patient is a tuberculosis patient.

[0008] Further, the tuberculosis is an infection caused by the presence of the pathogen Mycobacterium tuberculosis.

[0009] Further, the Mycobacterium tuberculosis infection includes primary infection, secondary infection, and extrapulmonary infection.

[0010] Further, the tuberculosis includes primary tuberculosis, secondary tuberculosis, hematogenous disseminated tuberculosis, tracheobronchial tuberculosis, tuberculous pleurisy, and bacteriologically negative pulmonary tuberculosis.

[0011] Further, the metabolic markers are derived from urine samples.

[0012] In a second aspect, the present application provides a use of the metabolic markers of the tuberculosis patient in the first aspect in the preparation of a tuberculosis disease diagnostic preparation.

[0013] In a third aspect, the present application provides a diagnostic kit for diagnosing tuberculosis disease, which contains reagents for detecting metabolic markers, instructions, etc., wherein the metabolic markers are the metabolic markers of the tuberculosis patient in the first aspect.

[0014] In a fourth aspect, the present application provides a screening method for urine metabolic markers for diagnosing tuberculosis disease, which comprises the following steps:

[0015] S1. Urine collection and preservation: collect urine samples from the middle section of the morning urine of the subject;

[0016] S2. Extraction of metabolites in urine: freeze, concentrate, dry, precipitate, and centrifuge the sample, take the supernatant, vacuum dry, and then perform chromatography and mass spectrometry analysis to obtain peak alignment, retention time correction, and extraction peak area;

[0017] S3. Compare the data obtained by the above chromatography and mass spectrometry with public databases such as HMDB, MassBank, and standard library baseDeepBP, and normalize the data;

[0018] S4. Multivariate statistical analysis: perform statistical analysis on the normalized data and the data between the healthy control group using PCA, PLS-DA, and OPLS-DA methods to screen the metabolic markers of tuberculosis patients.

[0019] S5. Identification of differential metabolic markers: evaluate by area under the curve (AUC) and confusion matrix of metabolic markers.

[0020] Further, the stability evaluation of the mass spectrometry method is evaluated by a quality control sample (QC).

[0021] Further, the quality control sample is a sample in which signal peaks with a relative standard deviation (RSD) of ≥30% are removed.

[0022] Further, the relative deviation refers to a relative standard deviation (RSD) after spectrum superposition comparison of total ion current chromatograms in positive ion mode and negative ion mode.

[0023] Further, the data normalization refers to obtaining information including compound retention time, mass-to-charge ratio and peak intensity by using accurate mass number matching (mass tolerance < 20 ppm) and secondary spectrum matching (mass tolerance < 0.02 Da) for metabolite structure identification and database comparison.

[0024] Further, the data normalization processing further includes missing value processing.

[0025] Further, the missing value processing refers to deleting ion peaks with missing values in a sample in a group of more than 50%.

[0026] Further, the screening standard of the metabolic marker for screening a tuberculosis patient is a standard of VIP value > 1.0,

[0027] |log2FoldChange (FC)| ≥ 1, and P < 0.05.

[0028] Further, the identification of the differential metabolic marker is a standard of an area under the curve (AUC) value of the metabolic marker AUC > 0.8.

[0029] In a fifth aspect, the present application provides a detection and diagnosis system for diagnosing a tuberculosis disease, which comprises a data input, data processing and result output module, wherein the data input is a metabolic marker obtained by processing a chromatogram and a mass spectrum of a urine metabolic extract of a subject; the data processing is to obtain an AUC value corresponding to the input data after data processing; the data output is the AUC value corresponding to the processed data, and when one or more data in the output data correspond to an AUC value > 0.8, it indicates that the patient is a tuberculosis patient.

[0030] Further, the urine metabolite is the urine metabolic marker of the first aspect of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 OPLS-DA permutation plot of the pulmonary tuberculosis patient group and the healthy control group in the cation mode;

[0032] Figure 2 OPLS-DA model score plot of the pulmonary tuberculosis patient group and the healthy control group in the cation mode;

[0033] Figure 3 PCA model score plot of the pulmonary tuberculosis patient group and the healthy control group in the cation mode;

[0034] Figure 4 PLS-DA permutation plot of the pulmonary tuberculosis patient group and the healthy control group in the cation mode;

[0035] Figure 5 PLS-DA model score plot of the pulmonary tuberculosis patient group and the healthy control group in the cation mode;

[0036] Figure 6 Volcano plot of the pulmonary tuberculosis patient group and the healthy control group in the cation mode;

[0037] Figure 7 OPLS-DA permutation plot of the pulmonary tuberculosis patient group and the healthy control group in the anion mode;

[0038] Figure 8 OPLS-DA model score plot of the pulmonary tuberculosis patient group and the healthy control group in the anion mode;

[0039] Figure 9 PCA model score plot of the pulmonary tuberculosis patient group and the healthy control group in the anion mode;

[0040] Figure 10 PLS-DA permutation plot of the pulmonary tuberculosis patient group and the healthy control group in the anion mode;

[0041] Figure 11 PLS-DA model score plot of the pulmonary tuberculosis patient group and the healthy control group in the anion mode;

[0042] Figure 12 Volcano plot of the pulmonary tuberculosis patient group and the healthy control group in the anion mode;

[0043] Figure 13 ROC curve plot DETAILED DESCRIPTION

[0044] The specific embodiments of the present application are further described below. It should be noted that the description of these embodiments is intended to assist in understanding the present application, but does not constitute a limitation on the present application. In addition, the technical features involved in the following described embodiments can be combined with each other as long as they do not conflict with each other.

[0045] 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.

[0046] Example 1 Screening and identification of urine metabolites

[0047] 1. Study subjects and grouping

[0048] A total of 79 people aged 18-65 were included, including pulmonary tuberculosis patients and healthy controls from Beijing Chest Hospital, Capital Medical University. All subjects signed informed consent and their clinical case information was recorded in detail. The specific grouping is as follows:

[0049] (1) Pulmonary tuberculosis patient group (40 people): The diagnosis of pulmonary tuberculosis refers to the health industry standard of the People's Republic of China (WS288-2017). All enrolled pulmonary tuberculosis patients meet the diagnostic criteria. Anyone who meets the following items is diagnosed with tuberculosis: ① Two sputum smear acid-fast bacillus tests are positive; ② 1 sputum smear acid-fast bacillus test is positive, and chest imaging shows typical active tuberculosis lesions; ③ 1 sputum smear acid-fast bacillus test is positive, and 1 sputum smear mycobacterium culture is positive, and the strain is identified as Mycobacterium tuberculosis complex; ④ Chest imaging shows typical active tuberculosis lesions, sputum smear is negative, but mycobacterium culture is positive and the strain is identified as Mycobacterium tuberculosis complex; ⑤ Chest imaging shows typical active tuberculosis lesions, and Mycobacterium tuberculosis nucleic acid test is positive; ⑥ Pulmonary histopathology and etiology are positive.

[0050] (2) Healthy control group (39 people): People with no history of active tuberculosis, no clinical symptoms, and normal chest X-ray examination.

[0051] 2. Urine collection and preservation

[0052] 50mL of midstream urine sample was collected from the morning urine of the subject, placed in a 50mL centrifuge tube, centrifuged at 1500g for 10min at 4℃, the supernatant was transferred to a new centrifuge tube, the precipitate was discarded, and stored at -80℃ until use. All samples were renamed in the form of groups and serial numbers to protect the privacy of the subjects.

[0053] 3. Screening of differential metabolites by metabolomics technology

[0054] 3.1 Metabolite extraction

[0055] The samples were thawed at 4°C, 1 mL of each sample was taken for freeze-concentration drying, 1 mL of pre-cooled methanol / acetonitrile / water (2:2:1, v / v / v) was added, and the sample was ultrasonicated in an ice bath for 60 min. The sample was incubated at -20°C for 1 h to precipitate the protein, and then centrifuged at 14,000 g at 4°C for 20 min. The supernatant was vacuum-dried. For mass spectrometry detection, 100 μL of acetonitrile-water solution (1:1, v / v) was added to reconstitute the sample, which was then centrifuged at 14,000 g at 4°C for 15 min. The supernatant was injected for analysis. 5 μL of each of the processed samples was mixed to prepare a quality control (QC) sample, which was used to evaluate the stability of the experimental method.

[0056] 3.2 Chromatographic separation

[0057] The samples were separated using a SHIMADZU Nexera X2 LC-30AD ultra-high pressure liquid chromatograph (Shimadzu, Japan), and an ACQUITY UPLC HSS T3 (2.1 x 150 mm, 1.8 μm) column (Waters, Milford, MA, USA) was used. The mobile phase was: A liquid was water containing 25 mM ammonium acetate and 25 mM ammonia water. B liquid was 100% acetonitrile. The sample was placed in a 4°C autosampler, the column temperature was 25°C, the flow rate was 0.3 mL / min, and the injection volume was 5 μL. The liquid chromatography gradient was as follows: 0-0.5 min, B liquid maintained at 95%; 0.5-7 min, B liquid linearly changed from 95% to 65%; 7-9 min, B liquid linearly changed from 65% to 40%; 9-10 min, B liquid maintained at 40%; 10-11.1 min, B liquid linearly changed from 40% to 95%; 11.1-16 min, B liquid maintained at 95%. In the sample queue, every 8 experimental samples were set with 1 QC sample, which was used to detect and evaluate the stability and repeatability of the system.

[0058] 3.3 Mass spectrometry

[0059] Mass spectrometry was performed using an ultra-performance liquid chromatography-quadrupole orbitrap mass spectrometer (UPLC-Q-Exactive-Orbitrap-MS / MS) in positive and negative ion modes, respectively. The ESI source parameters were as follows: Source Temperature 600 °C, Ion Source Gas1 (GAS1): 60, Ion Source Gas2 (GAS2): 60, Curtain Gas (CUR): 30, Ion Spray Voltage Floating (ISVF) ± 5500 V. TOF MS scan m / z range: 60-1200 Da, production scan m / z range: 25-1200 Da, TOF MS scan accumulation time 0.15 s / spectra, product ion scan accumulation time 0.03 s / spectra; the secondary mass spectrum was obtained by Information Dependent Acquisition (IDA), and the high sensitivity mode was used, Declustering potential (DP): ± 60 V, Collision Energy: 30 eV, IDA settings were as follows: Exclude isotopes within 4 Da, Candidate ions to monitor per cycle: 6.

[0060] 3.4 Quality control analysis

[0061] The mass spectra of the QC samples in positive ion mode and negative ion mode were compared by superimposing the spectra, and the relative standard deviation (RSD) of each signal peak of the QC sample was calculated. The signal peaks with RSD exceeding 30% were removed.

[0062] 3.5 Data preprocessing

[0063] Raw data were processed by MSDIAL software for peak alignment, retention time correction and peak area extraction. Metabolite identification was performed by searching public databases (HMDB, MassBank, etc.) and self-built baseDeepBP library using accurate mass matching (mass tolerance < 20 ppm) and MS / MS matching (mass tolerance < 0.02 Da) to obtain information including retention time, mass-to-charge ratio and peak intensity. Missing value processing was performed by deleting ion peaks with more than 50% missing values in each sample. The positive and negative ion data were normalized by total peak area, integrated, and subjected to pattern recognition using R software. The data were preprocessed by Unit variance scaling (UV) for subsequent data analysis.

[0064] 4. Multivariate statistical analysis

[0065] PCA, PLS-DA and OPLS-DA were performed on the data. The Variable Importance for the Projection (VIP) of OPLS-DA was calculated to measure the influence strength and explanatory power of the expression pattern of each metabolite on the classification and discrimination of each group of samples, thereby assisting in the screening of marker metabolites. OPLS-DA permutation test was used to avoid overfitting of the test model by randomly changing the arrangement order of the classification variable Y, establishing multiple corresponding OPLS-DA models to obtain the R2 and Q2 values of the random models, so as to evaluate the statistical significance of the model.

[0066] Statistical analysis was performed on the data between the pulmonary tuberculosis patient group and the healthy control group. Based on the OPLS-DA results, the VIP values of the multivariate statistical analysis OPLS-DA model were obtained (usually with VIP score > 1.0 as the screening standard). Combined with univariate analysis, including Fold Change Analysis (FC Analysis), T-test, and Volcano Plot, etc., with |log2FoldChange|≥0.26 and P<0.05 as the screening conditions, the potential differential metabolic markers were screened. The analysis results were represented in the form of scores plot (see Figures 1-12 ).

[0067] 5. Identification of differential metabolic markers

[0068] Metabolites satisfying VIP value > 1.0, |log2FoldChange| > 0.26, and P < 0.05 were obtained. The receiver operating curve (ROC) based on the logistic model was calculated using SPSS software, and finally 19 differential metabolites were identified. The area under the curve (AUC) of the 19 urine metabolic markers was obtained, and in the 95% confidence interval, the AUC of the 19 urine metabolic markers was between 0.838-0.998. As shown in Table 1.

[0069] Table 1 Differential metabolite data

[0070]

[0071]

[0072] Example 2 Construction of a tuberculosis diagnosis model

[0073] Through sample collection and data sorting, a tuberculosis diagnosis model was constructed for the 19 urine differential metabolic markers. The number of observation values of the misclassification and correct classification of the model was constructed by statistics, the confusion matrix was sorted, and the accuracy of the model was measured, as shown in Table 2. In the whole model, a total of 79 samples (40 cases of tuberculosis patients and 39 cases of healthy controls) were predicted, the true positive (TP) value was 40, the false positive (FP) value was 0, the true negative (TN) value was 39, and the false negative (FN) value was 0.

[0074] The accuracy (Accuracy) of the model was 100%; the precision (Precision) was 100%; the sensitivity (Sensitivity) was 100%; and the specificity (Specificity) was 100%. The F1-Score value of the model was 1, and the model output was good.

[0075] Table 2 Confusion matrix

[0076] N＝79 Predicted negative Predicted positive True negative 39 0 True positive 0 40

[0077] The partial least squares algorithm was used to model and draw the ROC curve according to the 19 urine differential metabolic markers obtained by screening, and the classification performance of the tuberculosis detection model was obtained (see Table 3). Figure 13). The AUC values of the top 2, 3, 5, 7, 10, 19 urine differential metabolic markers combination detection were 0.995, 1, 0.998, 0.998, 0.998, 0.997, respectively. It can be seen that the 19 urine metabolic markers screened for the detection of pulmonary tuberculosis have high accuracy, which is of great significance for the diagnosis of pulmonary tuberculosis.

Claims

1. A combination of metabolites for diagnosing a patient with pulmonary tuberculosis, said combination of metabolites being 5-O-Methylvisammioside, Glutamate Conjugated Chenodeoxycholic Acid, Kaurenic Acid, Xylan, FA 13:3+1 O, Methyltestosterone, Eicosanoids, Cholesterol, Retinoic Acid, Vitamine A Acetate, 14:0 Lyso-PE (1-Myristoyl-2-Hydroxy-Sn-Glycero-3-Phosphoethanolamine), N-Methyllysine, Medroxyprogesterone acetate, Trimethylamine N-Oxide, Decanoyl-L-Carnitine, Monolinolein, Caffeoylcholine, Dehydroabietic Acid and Homoarginine.

2. The combination of metabolic markers for diagnosing a patient with tuberculosis according to claim 1, wherein Said pulmonary tuberculosis is an infection caused by the presence of the pathogen Mycobacterium tuberculosis.

3. Use of a combination of metabolites for diagnosing a patient with pulmonary tuberculosis according to claim 1 for the preparation of a diagnostic formulation for pulmonary tuberculosis.

Images