Cavity pulmonary tuberculosis marker based on serum DSG4 protein and amyl salicylate and detection kit

Abstract

The invention belongs to the field of detection, and particularly relates to a cavity pulmonary tuberculosis marker based on serum DSG4 protein and amyl salicylate and a detection kit. The marker is DSG4 protein and / or amyl salicylate. The invention provides a biomarker for distinguishing a subject with cavernous tuberculosis cPTB or non-cavernous tuberculosis PTB by systematically analyzing proteomics and metabonomics characteristics of PTB and cPTB patients. According to the technical scheme, new insights and clues are provided for early diagnosis and personalized treatment of cPTB. And new scientific evidences are provided for early diagnosis and personalized treatment strategies.

Description

Technical Field

[0001] The present invention belongs to the field of detection, and in particular relates to a cavitary pulmonary tuberculosis marker and a detection kit based on serum DSG4 protein and amyl salicylate. Background Art

[0002] Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (Mtb), remains one of the world's most significant public health challenges. According to the World Health Organization's latest "Global Tuberculosis Report 2024," an estimated 10.8 million new TB infections and 1.25 million deaths will occur worldwide in 2023. Despite global efforts to combat TB and a gradual decline in its incidence, the complexity and length of treatment remain a significant burden on global healthcare systems.

[0003] Pulmonary tuberculosis (TB) involves severe lung inflammation, immune system activation, and extensive tissue damage, further exacerbating the disease burden and transmission risk. Cavitary PTB (cPTB) is the most destructive and contagious form of PTB and is often considered a hallmark of disease severity and treatment complexity. In recent years, significant progress has been made in the study of cPTB. Dheda et al. performed RNA sequencing on different regions of cavities in patients with multidrug-resistant PTB and found that proinflammatory signals were significantly upregulated in the cavitary walls, while immune pathways were significantly suppressed in the cavitary center [1]. Maseko et al. showed that elevated IL-6 levels were closely associated with a high risk of cavitation in patients with drug-resistant PTB [2]. Alisjahbana et al. found that the levels of neutrophils and MMP-8 / MMP-9 in the serum of cPTB patients were positively correlated, indicating that neutrophil-driven tissue destruction is a key factor in cavitation formation, while lymphocyte depletion is associated with more severe lung tissue damage [3]. In addition, Fan et al. showed that the IFN-γ response was significantly reduced in patients with severe cPTB, indicating significant immune dysfunction [4]. Overall, research on the mechanism of cPTB remains limited, with most studies focusing on the immune and inflammatory signals in the cavity wall and central local tissues, while comprehensive and systematic studies on the systemic effects caused by cPTB are still lacking.

[0004] [References]

[0005] [1], Dheda K, Lenders L, Srivastava S, et al. Spatial Network Mapping ofPulmonary Multidrug-Resistant Tuberculosis Cavities Using RNA Sequencing[J]. Am J Respir Crit Care Med, 2019, 200(3): 370-380.

[0006] [2].Maseko TG, Ngubane S, Letsoalo M, et al. Higher plasma interleukin-6levels are associated with lung cavitation in drug-resistant tuberculosis[J]. BMC Immunol, 2023, 24(1):26.

[0007] [3]. Alisjahbana B, Sulastri N, Livia R, et al. Neutrophils and lymphocytes in relation to MMP-8 and MMP-9 levels in pulmonary tuberculosis and HIV co-infection [J]. J Clin Tuberc Other Mycobact Dis, 2022, 27: 100308.

[0008] [4].Fan L, Xiao H, Mai G, et al.Impaired M.tuberculosis Antigen-SpecificIFN-gamma Response without IL-17Enhancement in Patients with Severe CavitaryPulmonary Tuberculosis[J].PLoS One,2015,10(5):e0127087. Summary of the Invention

[0009] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a cavitary tuberculosis marker and a detection kit based on serum DSG4 protein and amyl salicylate.

[0010] To achieve the above object, the technical solution adopted by the present invention is:

[0011] A marker for cavitary pulmonary tuberculosis based on serum DSG4 protein and amyl salicylate, wherein the marker is DSG4 protein and / or amyl salicylate.

[0012] The biomarkers are used to distinguish whether a subject has cavitary pulmonary tuberculosis cPTB or non-cavitary pulmonary tuberculosis PTB.

[0013] The present invention also includes a method for detecting the biomarker for non-diagnostic and non-therapeutic purposes, comprising the following steps: collecting a peripheral blood sample, separating the serum by centrifugation, and performing proteomic and / or metabolomic analysis.

[0014] The present invention also includes a use of the biomarker in preparing a detection or diagnosis kit, wherein the kit is used to distinguish whether a subject has cavitary pulmonary tuberculosis cPTB or non-cavitary pulmonary tuberculosis PTB.

[0015] The present invention also includes a use of a reagent for determining the level of the biomarker in preparing a kit for distinguishing whether a subject has cavitary pulmonary tuberculosis cPTB or non-cavitary pulmonary tuberculosis PTB.

[0016] The reagents are used to measure the level of a biomarker in a biological sample by chromatography and / or mass spectrometry, fluorescence, electrophoresis, immunoaffinity, hybridization, immunochemistry, ultraviolet spectroscopy (UV), fluorescence analysis, radiochemical analysis, near infrared spectroscopy (near IR), nuclear magnetic resonance spectroscopy (NMR), light scattering analysis (LS), and turbidimetry.

[0017] The present invention also includes a kit.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] The present invention provides a biomarker for distinguishing subjects with cavitary tuberculosis cPTB or non-cavitary tuberculosis PTB by systematically analyzing the proteomic and metabolomic characteristics of PTB and cPTB patients. In view of the complex pathological characteristics of cPTB and the limitations of existing research, the present invention conducted an in-depth analysis of serum samples, using the Olink protein detection platform combined with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) to reveal systemic metabolic reprogramming and protein regulation characteristics. Through bioinformatics analysis of differential proteins and metabolites, the systemic impact of Mtb on PTB patients was explored, and the protein and metabolomic changes caused by cPTB in multiple body systems were further revealed. The technical solution of this application will provide new insights and clues for the early diagnosis and personalized treatment of cPTB. Provide new scientific evidence for early diagnosis and personalized treatment strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Serum proteomic differences between PTB patients and controls, (A) Schematic diagram of the study design; (B) principal component analysis (PCA) diagram; (C) heat map; (D) scatter plot with marginal histograms;

[0021] Figure 2Protein functional enrichment analysis diagrams of PTB patients and control groups; (A) Gene Ontology (GO) analysis of downregulated proteins; (B) Gene Ontology (GO) analysis of upregulated proteins;

[0022] Figure 3 The differences in metabolomics characteristics between PTB and the control group; (A) PCA plot; (B) heat map of significantly changed metabolites; (C) volcano plot; (D) pie chart of differential metabolite classification;

[0023] Figure 4 KEGG pathway enrichment analysis of differential metabolites between PTB and control groups; (A) up-regulated metabolite pathway enrichment analysis; (B) down-regulated metabolite pathway enrichment analysis;

[0024] Figure 5 Comparative analysis of cPTB and PTB protein expression; (A) PCA diagram; (B) log10 protein average intensity scatter plot; C) differential protein expression heat map, (D) EggNOG functional classification map.

[0025] Figure 6 GO analysis of differentially expressed proteins between cPTB and PTB; (A) GO analysis of down-regulated proteins; (B) GO analysis of up-regulated proteins;

[0026] Figure 7 Comparison of the metabolomics of cPTB and PTB, (A) PCA plot; (B) heat map of differential metabolite expression; (C) volcano plot; (D) classification of the 10 major categories of differential metabolites;

[0027] Figure 8 KEGG pathway enrichment analysis of cPTB and PTB metabolites; (A) KEGG analysis of upregulated metabolites; (B) KEGG analysis of downregulated metabolites;

[0028] Figure 9 Integrated analysis of differentially expressed molecules; (A, B) Venn diagrams showing the unique and overlapping distributions of differentially expressed proteins / metabolites between cPTB, PTB, and control groups; (C, D) Box plots showing the differential expression of DSG4 and amyl salicylate; (E) Correlation between DSG4 and amyl salicylate; (F) ROC curve of DSG4 for distinguishing PTB from controls; (G) ROC curve of amyl salicylate for distinguishing PTB from controls; (H) ROC curve of combined markers (DSG4+amyl salicylate); (IK) ROC curve analysis corresponding to the comparison between cPTB and PTB. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and the best embodiments.

[0030] The present invention will be further described below with reference to the embodiments.

[0031] Example

[0032] The samples used in this study were obtained from the biobank of Tianjin Haihe Hospital. All patients were diagnosed with active PTB by sputum Mtb culture, Xpert MTB / RIF, or Mtb drug resistance gene testing. Inclusion and exclusion criteria were as follows: 1) patients with diabetes, malignancy, or pregnancy were excluded; 2) patients with viral hepatitis or severe liver and kidney dysfunction were excluded; 3) patients with immune system or psychiatric disorders were excluded; and 4) patients infected with nontuberculous mycobacteria (NTM) were excluded. Participants were divided into three groups: the cPTB group (n=30), consisting of 22 men and 8 women, whose cavity diameters were measured by a tuberculosis specialist and a radiologist; the PTB group (n=30), consisting of 21 men and 9 women; and the healthy control group (n=30), consisting of 21 men and 9 women, all of whom met the same exclusion criteria. There were no significant differences in age or gender among the three groups. Peripheral blood samples were collected from each participant after an overnight fast. Serum was separated by centrifugation and stored at −80°C for proteomic and metabolomic analyses.

[0033] 1. Proteomic analysis

[0034] Multiplex proximity extension assay (PEA) technology (Olink Proteomics TM Serum protein abundance was measured using the Olink Multiplex Target 96 Metabolism Kit (V.3045) (http: / / www.olink.com) and the Fluidigm BioMark TM The HD system performs real-time quantitative PCR. PEA technology utilizes matched antibody pairs, each linked to a unique DNA oligonucleotide strand to form highly specific probes. When these probes bind to the target protein, the DNA strands come into close proximity, hybridizing and forming new DNA templates. These templates are then amplified and quantified by real-time qPCR.

[0035] Serum samples from different groups are randomly and evenly distributed across a single assay plate. Each assay plate can simultaneously measure 92 proteins from 88 samples, requiring only 1 μL of each sample for each omics analysis. PEA technology incorporates a rigorous internal quality control system, with four internal controls added to each sample to monitor the assay process. Additionally, the assay plate includes three inter-plate positive controls and three negative controls for data normalization and determination of detection limits.

[0036] Specifically, 1 μL of serum sample or technical control sample was incubated with 3 μL of incubation solution containing antibody probe at 4°C overnight. After antigen-antibody binding, an extension mixture was added to initiate 17 cycles of PCR amplification and pre-PCR amplification. In this step, the sample was diluted 100-fold to reduce matrix effects and reduce the possibility of nonspecific extension of unpaired oligonucleotides. Subsequently, 2.8 μL of the first-round PCR product was mixed with 7.2 μL of detection reagent, and 5 μL of this mixture was loaded into the sample detection well. At the same time, complementary PCR primers were loaded into the primer wells of the microfluidic chip. The chip was processed in the Fluidigm IFC controller and then loaded into the Fluidigm BioMark TM Real-time qPCR was performed in a thermal cycler to determine Ct values. Finally, Ct values were converted to normalized protein expression (NPX, normalized log2 protein expression) using extension and negative controls added to each sample.

[0037] Differentially expressed proteins (DEPs) were analyzed to reveal their primary biological functions and associated signaling pathways. Gene Ontology (GO) analysis was performed using Blast2Go (https: / / www.blast2go.com / ) to identify enriched terms for biological processes, molecular functions, and cellular components, and statistical significance was determined using Fisher's exact test. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was further performed to explore robust DEPs, highlighting their roles in genomic, chemical, and systemic functions.

[0038] Figure 1 Serum proteomic differences between PTB patients and controls; (A) Schematic diagram of the study design, including three cohorts: control group (n=30), PTB patients (n=30), and cPTB patients (n=30). The design encompassed clinical parameter assessment, serum collection, LC-MS metabolomics and Olink platform analysis, and subsequent data analysis. (B) Principal component analysis (PCA) plots showed clear separation between the PTB and control groups, indicating significant differences in protein expression patterns. (C) Heat map displays the expression levels of differentially regulated proteins, with red indicating upregulation and blue indicating downregulation. (D) Scatter plot with marginal histograms showing the log10 mean protein intensity distribution between the two groups. Green dots represent downregulated proteins in the PTB group, and red dots represent upregulated proteins. Dot size corresponds to statistical significance (-Log10 p-value), p < 0.05.

[0039] Figure 2 Protein functional enrichment analysis between PTB patients and controls; (A) Gene Ontology (GO) analysis of downregulated proteins (B) Gene Ontology (GO) analysis of upregulated proteins;

[0040] Figure 5Comparative analysis of protein expression between cPTB and PTB; (A) PCA plot showed significant separation of protein expression profiles between cPTB and PTB patients (PC1: 22.45%, PC3: 7.63%); (B) Log10 protein mean intensity scatter plot showed 13 upregulated and 2 downregulated proteins in the cPTB group, with dot size representing -Log10 (p-value); (C) Heat map of differential protein expression (upregulated in red, downregulated in blue); (D) EggNOG functional classification (levels 1 and 2) showing enriched categories.

[0041] Figure 6 GO analysis of differentially expressed proteins between cPTB and PTB; (A) GO analysis of down-regulated proteins; (B) GO analysis of up-regulated proteins;

[0042] 2. Metabolomics analysis

[0043] Thaw the serum samples in a 4°C refrigerator. After thawing, transfer 100 μL of serum to a 1.5 mL EP tube and add 500 μL of acetonitrile to mix thoroughly. Let the mixture stand at -20°C for 2 hours, then centrifuge at 20,000 g for 10 minutes. Carefully remove the supernatant and dry the residue using a vacuum freeze dryer. Reconstitute the residue with 100 μL of acetonitrile. For quality control (QC), prepare a QC sample by mixing 10 μL of each sample.

[0044] Data acquisition for all samples was performed according to the LC-MS system's specifications. Chromatographic separation was performed using a Thermo Vanquish ultra-high-performance liquid chromatography system (Thermo, Vanquish, USA) and an ACQUITY UPLC T3 column (100 mm × 2.1 mm, 1.8 μm, Waters, UK). The column temperature was maintained at 40°C, and the flow rate was 0.35 mL / min. The mobile phase consisted of solvent A (5 mmol / L ammonium acetate and 5 mmol / L acetic acid in water) and solvent B (LC-MS-grade acetonitrile). A 4 μL injection volume was used for each sample. The liquid phase gradient was set as follows: hold 2% B for 0.5 min; increase B from 2% to 70% over the next 2 min; increase B from 70% to 90% over the next 4 min; increase B from 90% to 99% over the next 5.5 min; hold 99% B for the next 7 min; decrease B from 99% to 2% from 7.5 to 7.6 min; hold 2% B from 7.6 to 10 min.

[0045] Metabolite detection was performed using a high-resolution tandem mass spectrometer Orbitrap Exploris 120 (Thermo Fisher Scientific, Bremen, Germany) in positive and negative ion modes. The curtain gas was set to 1, the ion source gas 1 was set to 50, the ion source gas 2 was set to 15, and the ion source temperature was maintained at 350°C. The voltage was set to 3800 V in positive ion mode and -3400 V in negative ion mode. Data acquisition was performed in data-dependent acquisition (DDA) mode. In each acquisition cycle, the full MS scan range was 70–1050 Da, the resolution was 60 K (at m / z 200), the AGC target was set to “standard”, and the maximum IT was set to “auto”. The first four ions with an accumulated intensity greater than 5000 from the full MS scan were selected for MS / MS fragmentation at a resolution of 15 K (at m / z 200), the maximum IT was set to “auto”, and the dynamic exclusion was set to “custom”. Instrument maintenance and mass axis calibration were performed weekly, and QC sample scans were performed every ten samples. Systematic errors across the entire batch were corrected using mass deviations between QC samples. Acquired MS data were preprocessed using XCMS software, including peak extraction, peak grouping, retention time correction, secondary peak grouping, and annotation of isotopes and adducts. LC–MS raw data files were converted to mzXML format and processed using the XCMS, CAMERA, and metaX toolboxes implemented in R software. Each ion was identified by combining retention time (RT) and m / z data. The intensity of each peak was recorded, and a three-dimensional matrix containing arbitrarily assigned peak indices (retention time–m / z pairs), sample names (observations), and ion intensity information (variables) was generated.

[0046] MetaX performed further preprocessing on the intensity data. Features detected at less than 50% in the QC samples or less than 80% in the biological samples were removed. Remaining missing values were imputed using the k-nearest neighbor algorithm to improve data quality. Metabolomics data were normalized using probability quotient normalization. A quality control-based robust LOESS signal correction was applied to the QC data to minimize signal intensity drift over time. Furthermore, the relative standard deviation of metabolic features across all QC samples was calculated, and features with an RSD greater than 50% were removed.

[0047] Differences in metabolite concentrations between phenotypes were detected using the Student's t-test. P values were adjusted for multiple testing using the Benjamini–Hochberg method. Supervised partial least squares discriminant analysis (PLS-DA) was performed using metaX to discriminate variables between groups and calculate VIP values. A VIP cutoff of 1.0 was used to select significant features.

[0048] Metabolites were annotated using the online KEGG and HMDB databases by matching the sample's precise molecular mass data (m / z) with the database data. Metabolites were annotated if the mass difference between the observed value and the database value was less than 10 ppm. Isotope distribution measurements were further used to identify and verify the molecular formulas of metabolites. Fragmentation databases (Lipidblast version 37, MassBank, HMDB version 4.0, and an in-house fragmentation spectral library) were also incorporated to verify metabolite identification.

[0049] Figure 3 Figure 3. Metabolomic profile differences between PTB and controls. (A) PCA plot showing significant separation of metabolic profiles between PTB patients and controls (PC1: 13.13%, PC2: 8.58%). (B) Heat map of significantly altered metabolites, with blue indicating downregulation and red indicating upregulation. (C) Volcano plot showing 113 downregulated (green) and 64 upregulated (red) metabolites in PTB patients compared with controls (based on log2 fold change and -Log10 (p value), p < 0.05). (D) Pie chart of differential metabolite classification (by major chemical group).

[0050] Figure 4 KEGG pathway enrichment analysis of differential metabolites between PTB and the control group; (A) pathway enrichment analysis of upregulated metabolites; (B) pathway enrichment analysis of downregulated metabolites.

[0051] Figure 7 Comparison of the metabolomics of cPTB and PTB; (A) PCA plot showing significant separation of the metabolic profiles of the two groups; (B) Heat map of differential metabolite expression; (C) Volcano plot showing 19 significantly upregulated and 40 downregulated metabolites in the cPTB group; (D) Classification of differential metabolites into 10 categories.

[0052] Figure 8 KEGG pathway enrichment analysis of cPTB and PTB metabolites; (A) KEGG analysis of upregulated metabolites; (B) KEGG analysis of downregulated metabolites

[0053] 3. Statistical analysis

[0054] All statistical analyses were performed using GraphPad Prism 9.0 and R software. Data are presented as mean ± standard deviation for continuous variables and percentages for categorical variables. Categorical variables of baseline characteristics between groups were compared using the chi-square test, and continuous variables were compared using one-way analysis of variance or the Kruskal-Wallis test. For proteomics, cluster analysis was performed using PCA, differential expression analysis was performed using one-way analysis of variance, and significant proteins were visualized using volcano plots. Functional enrichment was assessed using GO and KEGG analyses. For metabolomics, data were preprocessed and normalized, and PCA and partial least squares discriminant analysis (PLS-DA) were used for visualization and variable importance scoring. Student's t-test (Bonferroni correction) was used to identify differential metabolites, and significant pathways were analyzed using the KEGG and HMDB databases. Figure 9 Integrated analysis of differentially expressed molecules; (A, B) Venn diagrams showing the unique and overlapping distributions of differentially expressed proteins / metabolites between cPTB, PTB, and control groups; (C, D) Box plots showing the differential expression of DSG4 and amyl salicylate; (E) Correlation between DSG4 and amyl salicylate; (F) ROC curve of DSG4 for distinguishing PTB from controls; (G) ROC curve of amyl salicylate for distinguishing PTB from controls; (H) ROC curve of combined markers (DSG4+amyl salicylate); (IK) ROC curve analysis corresponding to the comparison between cPTB and PTB.

[0055] In summary, the present invention provides a biomarker for distinguishing subjects with cavitary tuberculosis cPTB or non-cavitary tuberculosis PTB by systematically analyzing the proteomic and metabolomic characteristics of PTB and cPTB patients. In view of the complex pathological characteristics of cPTB and the limitations of existing research, the present invention conducted an in-depth analysis of serum samples, using the Olink protein detection platform combined with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) to reveal systemic metabolic reprogramming and protein regulation characteristics. Through bioinformatics analysis of differential proteins and metabolites, the systemic impact of Mtb on PTB patients was explored, and the protein and metabolomic changes caused by cPTB in multiple body systems were further revealed. The technical solution of this application will provide new insights and clues for the early diagnosis and personalized treatment of cPTB. Provide new scientific evidence for early diagnosis and personalized treatment strategies.

[0056] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A marker for cavitary pulmonary tuberculosis based on serum DSG4 protein and amyl salicylate, characterized in that: The marker is DSG4 protein and / or amyl salicylate.

2. The marker according to claim 1, characterized in that The markers are used to distinguish whether a subject has cavitary pulmonary tuberculosis cPTB or non-cavitary pulmonary tuberculosis PTB.

3. A method for detecting the marker according to claim 1 or 2 for non-diagnostic and non-therapeutic purposes, characterized in that: The method comprises the following steps: collecting peripheral blood samples, separating serum by centrifugation, and performing proteomic and / or metabolomic analysis.

4. Use of the marker according to claim 1 or 2 in preparing a detection or diagnosis kit, characterized in that: The kit is used to distinguish whether a subject has cavitary pulmonary tuberculosis cPTB or non-cavitary pulmonary tuberculosis PTB.

5. Use of a reagent for determining the marker level according to claim 1 or 2 in preparing a kit, characterized in that: The kit is used to distinguish whether a subject has cavitary pulmonary tuberculosis cPTB or non-cavitary pulmonary tuberculosis PTB.

6. Use of the reagent for determining marker levels according to claim 5 in preparing a kit, characterized in that: The reagents measure the level of a marker in a biological sample by the following methods: chromatography and / or mass spectrometry, fluorescence, electrophoresis, immunoaffinity, hybridization, immunochemistry, ultraviolet spectroscopy, fluorescence analysis, radiochemical analysis, near infrared spectroscopy, nuclear magnetic resonance spectroscopy, light scattering analysis, and turbidimetry.

7. A kit according to any one of claims 4 to 6.

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