Serum metabolism marker for detecting lymphatic tuberculosis and application thereof

Abstract

The embodiment of the invention discloses a serum metabolism marker for detecting lymphatic tuberculosis and application of the serum metabolism marker. The serum metabolism marker comprises acetyl alcohol, and / or leucine-alanine, and / or phenaquine, and / or ethylenediamine. The serum metabolism marker is used for preparing a reagent, and the reagent is used for detecting lymphatic tuberculosis.

Description

Technical Field

[0001] The present specification relates to the field of biological detection technology, and in particular to a serum metabolic marker for detecting lymph node tuberculosis and its application. Background Art

[0002] Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium tuberculosis (MTB). The life cycle of Mycobacterium tuberculosis mainly depends on the ability of the bacteria to survive continuously and avoid being cleared by the host immune system between aerosol transmission cycles. Mycobacterium tuberculosis infects macrophages, monocyte-derived macrophages, dendritic cells and neutrophils, thereby entering the lymphatic system or spreading to other organs through the bloodstream, forming a chronic pathogenic process. Lymph node tuberculosis (LNTB) ranks first in the incidence of extrapulmonary tuberculosis, accounting for about 30% to 40% of extrapulmonary tuberculosis. It is a chronic wasting disease caused by Mycobacterium tuberculosis invading the lymph nodes, causing chronic inflammation of the lymph nodes.

[0003] Both lymphadenopathy and non-lymphadenopathy present enlarged lymph nodes as the primary manifestation, and it is difficult to completely diagnose the two clinically and by imaging. With the continuous development of metabolite research, metabolites have become potential biomarkers of disease. Metabolite biosignatures can provide the connection between genotype, environment and phenotype, thus serving as important biomarkers for clinical diagnosis, prognosis assessment and disease classification. Metabolic signatures have been used in the study of many diseases, but have not yet been widely used in tuberculosis.

[0004] Based on this, the embodiments of this specification provide a serum metabolic marker for detecting lymph node tuberculosis and its application. Summary of the invention

[0005] The embodiments of this specification provide a serum metabolic marker for detecting lymph node tuberculosis and its application, which is used to solve the following technical problems: Both lymph node tuberculosis and non-lymph node tuberculosis are characterized by lymph node enlargement as the primary manifestation, and it is difficult to completely diagnose the two clinically and imagingly. Although metabolite characteristics can be used as important biomarkers for clinical diagnosis, prognosis assessment and disease classification, and have been used in the study of many diseases, they have not been widely used in tuberculosis.

[0006] To solve the above technical problems, the embodiments of this specification are implemented as follows:

[0007] The present specification provides a serum metabolic marker for detecting lymph node tuberculosis, and the serum metabolic marker includes:

[0008] Acetyl alcohol, and / or leucine-alanine, and / or phenaquine, and / or ethylenediamine.

[0009] Furthermore, the relative expression level of leucine-alanine among the serum metabolic markers is the highest.

[0010] The embodiments of this specification also provide an application of serum metabolic markers for detecting lymph node tuberculosis, wherein the serum metabolic markers are used to prepare a reagent, and the reagent detects lymph node tuberculosis by quantifying the serum metabolic markers.

[0011] Furthermore, the reagent is a kit, and the serum metabolic markers are quantified, and the quantification of the serum metabolic markers is determined by relative expression.

[0012] Furthermore, the relative expression level is obtained by ultra-high performance liquid chromatography-mass spectrometry.

[0013] Furthermore, when the serum metabolic marker in the reagent is leucine-alanine, the reagent has the best effect in detecting lymph node tuberculosis.

[0014] Furthermore, when the serum metabolic marker is leucine-alanine, the relative expression level is the highest.

[0015] The serum metabolic markers for detecting lymph node tuberculosis and their applications provided in the embodiments of this specification can realize the detection of lymph node tuberculosis, have the advantages of high detection accuracy, simple operation, good repeatability, etc., and can be used for the detection of lymph node tuberculosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1 The identification results of LNTB and non-LNTB patients based on ion features provided in the embodiments of this specification;

[0018] Figure 2 The difference diagram of plasma metabolites between LNTB and non-LNTB patients provided in the examples of this specification;

[0019] Figure 3 The enrichment analysis diagram of differential metabolite pathways in plasma provided in the examples of this specification;

[0020] Figure 4This is a schematic diagram of the results of the importance and diagnostic value of differential metabolites in plasma provided in the examples of this specification. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0022] Metabolites refer to small molecules that are stably expressed during the life activities of the body. They can reflect the pathophysiological changes of the corresponding disease as a whole and clarify the mechanism of disease progression. Due to their specificity, metabolites have become potential biomarkers of disease. Biomarkers refer to metabolites or characteristics that are indicators of changes in the life activities of the body. Metabolite biosignatures can provide a link between genotype, environment and phenotype, and are important biomarkers for clinical diagnosis, prognosis assessment and disease classification. Metabolic characteristics have been used in the study of many diseases, but have not yet been widely used in tuberculosis.

[0023] Metabolomics was first proposed by Professor Nicholson in 1999. Metabolomics is the quantitative measurement of the dynamic multi-parameter metabolic response of biological systems to pathophysiological stimuli or genetically modified ions. It helps to identify metabolites and metabolic pathways. Its quantification can reflect the pathophysiological phenotypes associated with various diseases, and thus be used for diagnosis, treatment and other purposes. Currently, metabolomics is mainly divided into non-targeted metabolomics and targeted metabolomics. The detection methods of non-targeted metabolomics mainly include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS).

[0024] Metabolomics analysis has been applied to the study of many different diseases. Through the use of corresponding detection methods and statistical analysis, the number of newly discovered tuberculosis biomarkers has increased in recent years. These biomarkers can also be used to improve the study of the biology of Mycobacterium tuberculosis and the evaluation of tuberculosis disease status, monitor the progression of tuberculosis, and predict the efficacy. However, the research and exploration of biomarkers for cervical lymph node tuberculosis has not been widely carried out.

[0025] Based on this, the embodiments of this specification use a non-targeted metabolomics method to obtain serum metabolic markers for detecting lymph node tuberculosis, and based on the serum metabolic markers, provide the application of serum metabolic markers for detecting lymph node tuberculosis.

[0026] The present specification provides a serum metabolic marker for detecting lymph node tuberculosis, and the serum metabolic marker includes:

[0027] Acetyl alcohol, and / or leucine-alanine, and / or phenaquine, and / or ethylenediamine.

[0028] In the examples of this specification, the relative expression level of leucine-alanine in the serum metabolic markers is the highest.

[0029] This will be described in detail below in conjunction with the screening of serum metabolic markers.

[0030] Example 1: Screening of serum metabolic markers

[0031] 1. Serum samples from patients with lymph node tuberculosis (LNTB) and non-lymph node tuberculosis (non-LNTB) confirmed by puncture examination were selected.

[0032] 2. Pre-treat serum samples.

[0033] After the serum samples were slowly thawed at 4°C, an appropriate amount of sample was added to precooled methanol / acetonitrile / water solution (2:2:1, v / v), vortexed, low-temperature ultrasonicated for 30 min, allowed to stand at -20°C for 10 min, centrifuged at 14000g for 20 min at 4°C, the supernatant was vacuum dried, and for mass spectrometry analysis, 100 μL of acetonitrile aqueous solution (acetonitrile: water = 1:1, v / v) was added for re-dissolution, vortexed, centrifuged at 14000g for 15 min at 4°C, and the supernatant was taken for sampling and analysis.

[0034] 3. Ultra-high performance liquid chromatography-mass spectrometry for separation

[0035] The supernatant sample obtained in the previous step was separated by a Vanquish LC ultra-high performance liquid chromatography system (UHPLC) HILIC column; column temperature 25°C; flow rate 0.3mL / min; injection volume 2μL; mobile phase composition A: water + 25mM ammonium acetate + 25mM ammonia, B: acetonitrile; gradient elution program is as follows: 0-1.5min, 98% B; 1.5-12min, B linearly changes from 98% to 2%; 12-14min, B is maintained at 2%; 14-14.1min, B linearly changes from 2% to 98%; 14.1-17min, B is maintained at 98%; the sample was placed in a 4°C autosampler during the entire analysis process. In order to avoid the influence of fluctuations in instrument detection signals, the samples were analyzed continuously in random order. QC samples were inserted into the sample queue to monitor and evaluate the stability of the system and the reliability of the experimental data.

[0036] The Q Exactive series mass spectrometer was used to collect the primary and secondary spectra of the samples. After separation by VanquishLC ultra-high performance liquid chromatography system (UHPLC), the samples were analyzed by mass spectrometry using Q Exactive series mass spectrometer (Thermo), and electrospray ionization (ESI) positive and negative ion modes were used for detection. The ESI source and mass spectrometry setting parameters are as follows: nebulizer gas auxiliary heating gas 1 (Gas1): 60, auxiliary heating gas 2 (Gas2): 60, curtain gas (CUR): 30psi, ion source temperature: 600℃, spray voltage (ISVF) ±5500V (positive and negative modes); primary mass-to-charge ratio detection range: 80-1200Da, resolution: 60000, scanning accumulation time: 100ms, secondary using segmented acquisition method, scanning range is 70-1200Da, secondary resolution: 30000, scanning accumulation time: 50ms, dynamic exclusion time: 4s.

[0037] 4. Metabolomics data processing and analysis

[0038] The raw data were converted into .mzXML format by ProteoWizard, and then peak alignment, retention time correction and peak area extraction were performed using XCMS software. The data extracted by XCMS were first subjected to metabolite structure identification and data preprocessing, then experimental data quality evaluation, and finally data analysis. The LNTB group and the CN group were distinguished by using unsupervised principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The variable importance (VIP) of each metabolite projection in the model was calculated based on OPLS-DA, and the p-value was determined using the Student's t-test. Metabolites with a VIP value greater than 1 and a corrected p-value (FDR) less than 0.05 were defined as differential metabolites. Metabolic pathway enrichment analysis was performed on the differential metabolites based on existing databases, specifically, metabolic pathway enrichment analysis was performed on the differential metabolites based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https: / / www.genome.jp / kegg / ). Random forest and support vector machine were used to analyze the importance of differential metabolites. The diagnostic performance of the differential metabolites was evaluated by receiver operating characteristic (ROC) curve analysis.

[0039] 5. Statistical analysis

[0040] For measurement data that conformed to normal distribution, mean ± standard deviation was used as approximate value, and independent sample t test was used for comparison; when measurement data did not conform to normal distribution, median (quartile) [M(Q1, Q3)] was used, inter-group comparison was done with Mann–Whitney U test, and categorical variables were compared with inter-group differences with χ2 test.

[0041] Example 2

[0042] 1. Differences in metabolites between LNTB and non-LNTB patients

[0043] In order to ensure the stability of the analysis system and the reliability of the experimental data, QC samples were introduced during the analysis process to ensure the stability of the analysis system and the reliability of the experimental data. Figure 1 The identification results of LNTB and non-LNTB patients based on ion features provided in the embodiments of this specification; wherein, Figure 1 A is the principal component analysis (PCA) score graph of the quality control (QC) samples. Figure 1 B is the PCA score graph, where each point represents a sample. The x-axis and y-axis represent the values ​​of the two factors with the best discriminant effect, and the ellipse represents the 95% confidence region of the sample point; Figure 1 C is the orthogonal partial least squares discriminant analysis (OPLS-DA) score graph; Figure 1 D is the permutation test (1000 iterations) to evaluate the effectiveness of the OPLS-DA model.

[0044] like Figure 1 As shown in A, the quality control samples are closely clustered together in the PCA score plot, indicating that the mass spectrometry system exhibits good stability, thus ensuring the accuracy and reproducibility of the test results. To elucidate the differences in plasma metabolites between LNTB and non-LNTB patients, unsupervised PCA was performed. The results showed that some samples in the two groups clustered together, resulting in suboptimal separation ( Figure 1 B). Therefore, in this example, OPLS-DA was used to identify the factors that can best distinguish the sample groups, and the permutation test was used to evaluate the predictive ability of the model. Figure 1 As shown in CD, the two groups of samples are clearly distributed in different regions, and the observed test statistics (Q2) are located to the right of the random distribution. The p value is less than 0.05, indicating that the model has significant predictive ability and there are significant differences in metabolites between the groups.

[0045] 2. Identification of differential metabolites

[0046] Through metabolomics analysis, 1294 metabolites were identified between LNTB and non-LNTB patients. Based on the criteria of VIP value > 1 and adjusted p value (FDR) < 0.05, 89 differential metabolites were screened (Table 1). Figure 2 This is a graph showing the difference in plasma metabolites between LNTB and non-LNTB patients provided in the examples of this specification; wherein, Figure 2 A is a volcano plot of differential metabolites, in which each point represents a metabolite, and the top 15 metabolites with the smallest FDR values ​​are marked in the figure; Figure 2B is a cluster heat map of the top 20 differential metabolites based on VIP values. The color gradient from blue to red indicates the abundance of metabolites from low to high, and the cluster heat map shows the changes in the levels of these differential metabolites between the two patient groups, with most metabolites showing higher levels in LNTB patients.

[0047] Table 1 List of differential metabolites

[0048]

[0049]

[0050]

[0051] 3. KEGG pathway analysis of differential metabolites

[0052] In order to further study which metabolic pathways are mainly affected by the differential metabolites, KEGG pathway enrichment analysis was performed in the examples of this specification. Figure 3 This is a plasma differential metabolite pathway enrichment analysis diagram provided in the embodiments of this specification; wherein, Figure 3 AORA enrichment analysis (over-representation analysis of metabolic pathways), the x-axis represents the enrichment ratio, that is, the ratio of observed metabolites to theoretical metabolites in the metabolic pathway, and the p-value is represented by color, the darker the color, the smaller the p-value; the results showed that the differential metabolites were mainly related to the arginine and proline metabolic pathways and the nitrogen metabolic pathway. Figure 3 B is a combination of ORA enrichment analysis and topological analysis. The x-axis represents the p-value of ORA analysis, and the blue area represents significance (p<0.05). The y-axis represents the impact value of topological analysis, reflecting the importance of metabolites in metabolic pathways. The importance of these two metabolic pathways in the disease metabolic process was further evaluated by topological analysis, indicating that the arginine and proline metabolic pathways may play a vital role.

[0053] 4. Screening and evaluation of biomarkers

[0054] To further identify plasma differential metabolites that may serve as potential diagnostic biomarkers, the diagnostic performance of the differential metabolites was further evaluated using machine learning. Figure 4 This is a schematic diagram of the results of the importance and diagnostic value of differential metabolites in plasma provided in the examples of this specification. Figure 4 A is the top 15 important metabolites in the random forest (RF) analysis. The x-axis of the left figure represents the "mean reduced precision", which measures the importance of each metabolite in the random forest model. The right figure is a heat map showing the abundance of these 15 metabolites in the two groups. Figure 4B is the top 15 important metabolites in the SVM analysis. The x-axis of the left figure represents the average importance in the SVM model, and the right figure is a heat map showing the abundance of these 15 metabolites in the two groups. Figure 4 C is the multivariate ROC curve of the SVM model. The x-axis represents specificity (false positive rate) and the y-axis represents sensitivity (true positive rate). The higher the specificity and sensitivity values, the better the predictive performance of the model. Figure 4 C shows that the AUC value of the SVM model constructed by the first five important metabolites is greater than 0.8, which has good diagnostic accuracy. Figure 4 D is the intersection of the top 15 important metabolites determined by RF and SVM analysis. (E) Univariate ROC curve analysis of the four core metabolites. AUC (area under the curve) represents the predictive performance of the model, and the closer the value is to 1, the better the performance. The AUC values ​​are classified as follows: AUC (0.5-0.7), low accuracy; AUC (0.7-0.9), medium accuracy; AUC (>0.9), high accuracy. Figure 4 In E, the AUC value of leucine-alanine was 0.829, the AUC value of ethoxydiamine was 0.756, the AUC value of phenaquine was 0.718, and the AUC value of acetyl alcohol was 0.712, indicating that leucine-alanine had the highest diagnostic accuracy. Figure 4 E can be used as a model for detecting lymph node tuberculosis and for detecting serum metabolic markers of lymph node tuberculosis.

[0055] The experimental results showed that four metabolites - acetyl alcohol, leucine-alanine, phenaquine and ethylvolumab - were important serum metabolic markers and could be used for the detection of lymph node tuberculosis.

[0056] The above method is used to construct a lymph node tuberculosis detection model.

[0057] The lymph node tuberculosis detection model generated above is used to detect lymph node tuberculosis. The specific method is as follows: after pre-processing the serum sample, it is separated by ultra-high performance liquid chromatography-mass spectrometry, and then metabolomics data processing and analysis are performed. The obtained data is input into the lymph node tuberculosis detection model, and whether the individual corresponding to the blood sample has lymph node tuberculosis can be obtained according to the model output result.

[0058] The embodiments of this specification also provide an application of serum metabolic markers for detecting lymph node tuberculosis, wherein the serum metabolic markers are used to prepare a reagent, and the reagent detects lymph node tuberculosis by quantifying the serum metabolic markers.

[0059] In the examples of this specification, the reagent is a kit, the serum metabolic markers are quantified, and the quantification of the serum metabolic markers is determined by relative expression.

[0060] Serum metabolic markers are used as an important component of the kit to quantify serum metabolic markers and detect lymph node tuberculosis. Specifically, a preset threshold value for the relative expression of serum metabolic markers can be set. When the relative expression of serum metabolic markers is greater than or equal to the preset threshold value, it is confirmed to be lymph node tuberculosis; when the relative expression of serum metabolic markers is less than the preset threshold value, it is confirmed not to be lymph node tuberculosis. Since the relative expression levels of different serum metabolic markers are different, different preset thresholds can be set for different serum metabolic markers. When one of the relative expression levels of serum metabolic markers meets the conditions of lymph node tuberculosis, the individual corresponding to the serum is considered to be a patient with lymph node tuberculosis.

[0061] In the examples of this specification, the relative expression amount is obtained by ultra-high performance liquid chromatography-mass spectrometry. Specifically, the Vanquish LC ultra-high performance liquid chromatography system (UHPLC) HILIC column is used for separation; column temperature 25°C; flow rate 0.3mL / min; injection volume 2μL; mobile phase composition A: water + 25mM ammonium acetate + 25mM ammonia, B: acetonitrile; gradient elution program is as follows: 0-1.5min, 98% B; 1.5-12min, B changes linearly from 98% to 2%; 12-14min, B is maintained at 2%; 14-14.1min, B changes linearly from 2% to 98%; 14.1-17min, B is maintained at 98%; the sample is placed in a 4°C autosampler during the entire analysis process. In order to avoid the influence caused by fluctuations in instrument detection signals, the continuous analysis of samples is carried out in random order. QC samples are inserted into the sample queue to monitor and evaluate the stability of the system and the reliability of experimental data.

[0062] The Q Exactive series mass spectrometer was used to collect the primary and secondary spectra of the samples. After separation by VanquishLC ultra-high performance liquid chromatography system (UHPLC), the samples were analyzed by mass spectrometry using Q Exactive series mass spectrometer (Thermo), and electrospray ionization (ESI) positive and negative ion modes were used for detection. The ESI source and mass spectrometry setting parameters are as follows: nebulizer gas auxiliary heating gas 1 (Gas1): 60, auxiliary heating gas 2 (Gas2): 60, curtain gas (CUR): 30psi, ion source temperature: 600℃, spray voltage (ISVF) ±5500V (positive and negative modes); primary mass-to-charge ratio detection range: 80-1200Da, resolution: 60000, scanning accumulation time: 100ms, secondary using segmented acquisition method, scanning range is 70-1200Da, secondary resolution: 30000, scanning accumulation time: 50ms, dynamic exclusion time: 4s.

[0063] In the examples of this specification, when the serum metabolic marker in the reagent is leucine-alanine, the reagent has the best effect in detecting lymph node tuberculosis.

[0064] In the examples of this specification, when the serum metabolic marker is leucine-alanine, the relative expression level is the highest.

[0065] The serum metabolic markers and kit for detecting lymph node tuberculosis provided in the embodiments of this specification can realize the detection of lymph node tuberculosis, have the advantages of high detection accuracy, simple operation, good repeatability, etc., and can be used for the detection of lymph node tuberculosis.

[0066] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0067] The above is only an embodiment of this specification and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A serum metabolic marker for detecting lymph node tuberculosis, characterized in that: The serum metabolic markers include: Acetyl alcohol, and / or leucine-alanine, and / or phenaquine, and / or ethylenediamine.

2. The serum metabolic marker according to claim 1, characterized in that Among the serum metabolic markers, the relative expression level of leucine-alanine is the highest.

3. An application of serum metabolic markers for detecting lymph node tuberculosis, characterized in that: The serum metabolic markers as claimed in claim 1 are used to prepare a reagent, and the reagent detects lymph node tuberculosis by quantifying the serum metabolic markers.

4. The use of the serum metabolic marker according to claim 3, characterized in that: The reagent is a kit, and the serum metabolic markers are quantified, and the quantification of the serum metabolic markers is determined by relative expression.

5. The use of the serum metabolic marker according to claim 4, characterized in that: The relative expression level is obtained by ultra-high performance liquid chromatography-mass spectrometry.

6. The use of the serum metabolic marker according to claim 3, characterized in that: When the serum metabolic marker in the reagent is leucine-alanine, the reagent has the best effect in detecting lymph node tuberculosis.

7. The use of serum metabolic markers according to claim 5, characterized in that: When the serum metabolic marker is leucine-alanine, the relative expression level is the highest.

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