Generalized pustular psoriasis diagnostic marker based on metabonomics and application thereof

Abstract

The invention discloses a generalized pustular psoriasis diagnosis marker based on metabonomics and application of the generalized pustular psoriasis diagnosis marker. The diagnostic marker is prepared from one or more of the following 35 compounds: pyruvic acid, alpha-ketoisovaleric acid, 2-hydroxybutyric acid, 3-hydroxybutyric acid, methane thiophosphoric acid, proline, uracil, tranexamic acid, 4-aminobutyric acid, threonine, scopoletin, dodecanol, N-methyl-L-leucine, L-cysteine-glycine and L-kynurenine. The feed additive is prepared from the following raw materials: 3-hydroxybenzoic acid, allantoin, delta-tocopherol, xylofuranose, glucose-1-phosphoric acid, pyrophosphate, taurine, L-asparagine, phthalic acid, 4-(dimethylamino) azobenzene, 5-tert-butyl-1h-indole-2, 3-dione, quinic acid, glucose, histidine, lysine, palmitic acid, 7-methylguanine, oleic acid and whale acid. The marker can be used for accurately distinguishing patients with generalized pustular psoriasis from healthy people.

Description

[0001] This invention is a divisional application of application number 2020109716968, filed on September 16, 2020. Technical Field

[0002] This invention belongs to the field of clinical laboratory diagnostics and relates to a diagnostic biomarker for generalized pustular psoriasis based on metabolomics and its application. Background Technology

[0003] Generalized pustular psoriasis (GPP) is the most severe type of psoriasis, clinically characterized by millet-sized sterile pustules on an erythematous base. It is often accompanied by high fever, elevated white blood cell count, and hypoproteinemia, and can even be life-threatening. In addition to skin lesions, GPP can affect multiple organs and tissues, including the eyes, liver, lungs, gastrointestinal tract, cardiovascular system, kidneys, and bones. Due to the incomplete understanding of its pathogenesis, GPP is challenging to diagnose and treat. Although biological agents for psoriasis have made rapid progress in recent years and have shown good efficacy in treating pustular psoriasis, their high cost prevents large-scale deployment, and current treatments have not achieved satisfactory results in treating and controlling systemic symptoms. Therefore, prevention, early diagnosis, and early treatment remain a significant and challenging clinical task.

[0004] We anticipate the availability of one or a set of biomarkers for more precise etiological diagnosis of generalized pustular psoriasis. The role of biomarkers as quantitative indicators in the auxiliary quantitative diagnosis of acute exacerbations of generalized pustular psoriasis, severity assessment, and determination of the etiology and prognosis of acute exacerbations is currently a hot research area in generalized pustular psoriasis.

[0005] Given the heterogeneity of generalized pustular psoriasis, single biomarkers are insufficient to accurately reflect its characteristics. The establishment and development of metabolomics provides an effective means to address this issue. Metabolomics primarily obtains information on the dynamic changes of metabolites over time and in pathophysiological processes by detecting changes in small molecule metabolites (MWK < 1000), including sugars, lipids, amino acids, and vitamins. As the final products of cellular physiological activities, metabolites can accurately and sensitively reflect the functional state of cells. Metabolomics has changed the traditional approach of single-marker detection, using a group of metabolites as "pattern markers" to diagnose diseases, offering unique advantages. Although metabolomics started relatively late, it has already demonstrated significant advantages compared to traditional diagnostic methods and research approaches. Acute exacerbations of generalized pustular psoriasis inevitably cause characteristic changes in endogenous small molecule metabolites during their occurrence and development. Metabolomics, with its advanced separation, analysis, and computational techniques, has the ability and advantage to distinguish characteristic metabolites under different pathophysiological conditions as a whole. Studying the characteristic metabolites of generalized pustular psoriasis using metabolomics can help explore the pathogenesis of this complex clinical syndrome as a whole. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies and address the lack of reliable biomarkers for generalized pustular psoriasis, this invention utilizes high-performance liquid chromatography-mass spectrometry (HPLC-MS / MS) to perform metabolomics analysis on patient serum, identifying specific differentially expressed metabolites characteristic of generalized pustular psoriasis—that is, diagnostic molecules for the disease. This invention provides a diagnostic biomarker suitable for the diagnosis of generalized pustular psoriasis, and its application in the diagnosis of this disease.

[0007] This invention analyzed plasma samples from 24 patients with generalized pustular psoriasis and 12 healthy volunteers. Gas chromatography-mass spectrometry (GC-MS) was used to obtain fingerprint profiles of small molecule metabolites. Univariate and multivariate analyses and feature screening were performed on the fingerprint profiles of small molecule metabolites from generalized pustular psoriasis and healthy controls to obtain diagnostic biomarkers suitable for generalized pustular psoriasis, which have high clinical application and promotion value.

[0008] The specific technical solution for achieving the present invention is as follows: The present invention provides diagnostic biomarkers for generalized pustular psoriasis based on metabolomics, wherein the diagnostic biomarkers comprise one or more of the following 35 compounds: tyramine, pyruvic acid, alpha-ketoisovaleric acid, 2-hydroxybutyric acid, 3-hydroxybutyric acid, methanephosphonothioic acid, L-proline, uracil, tranexamic acid, and 4-aminobutyric acid. acid), threonine, scopoletin, dodecanol, N-methyl-L-leucine, L-cysteine-glycine, L-kynurenine, 3-hydroxybenzoic acid, allantoin, delta-tocopherol, xylofuranose, glucose-1-phosphate, pyrophosphate, taurine, L-asparagine, phthalic acid The following are listed: acid, 4-(dimethylamino)azobenzene, 5-tert-butyl-1h-indole-2,3-dione, quinic acid, glucose, L-histidine, L-lysine, palmitic acid, 7-methylguanine, oleic acid, and cis-gondoic acid.

[0009] Among them, one or more of the following five compounds are preferred: L-threonine, L-cysteine-glycine, pyrophosphate, glucose, and L-histidine. Any one of these five compounds is associated with the severity of generalized pustular psoriasis.

[0010] The present invention also provides the use of the aforementioned diagnostic markers in the preparation of diagnostic agents for generalized pustular psoriasis.

[0011] Preferably, the diagnostic agent for generalized pustular psoriasis is a preparation for diagnosing serum metabolites of generalized pustular psoriasis.

[0012] Preferably, the diagnostic marker is a plasma metabolic marker.

[0013] Preferably, the diagnostic biomarker is used as a standard for a gas chromatography-mass spectrometry (GC-MS) instrument.

[0014] This invention also provides a method for screening the various diagnostic markers suitable for the diagnosis of generalized pustular psoriasis, comprising the following steps:

[0015] (1) Collect serum samples from patients with generalized pustular psoriasis and healthy volunteers as analytical samples;

[0016] (2) Non-targeted metabolomics analysis was performed on each sample using GC-MS to obtain the original metabolic fingerprint of each serum sample;

[0017] (3) Data preprocessing and multivariate statistical analysis were performed on the obtained serum metabolomics fingerprint profiles to screen differential metabolites, and Pearson correlation analysis between differential metabolites and disease severity scores was used to further screen marker metabolites.

[0018] (4) Differential metabolites with AUC values ​​greater than 0.9 were obtained by ROC curve analysis, which are serum metabolic markers suitable for the diagnosis of generalized pustular psoriasis.

[0019] The advantage of this invention is that it uses serum metabolomics technology to analyze patients with generalized pustular psoriasis and healthy controls to obtain diagnostic biomarkers suitable for generalized pustular psoriasis. These biomarkers classify the metabolomics data of generalized pustular psoriasis patients and healthy individuals very well and can accurately distinguish between patients with generalized pustular psoriasis and healthy individuals. Attached Figure Description

[0020] Figure 1 The OPLS-DA model score plot for serum samples shows that there is a good distinction between healthy individuals (CON) and patients with generalized pustular psoriasis (GPP).

[0021] Figure 2 To evaluate the reliability of the OPLS-DA model for serum samples using the permutation test method;

[0022] Figure 3 The diagnostic efficacy of serum L-threonine, L-cysteine-glycine, pyrophosphate, glucose, and L-histidine was evaluated using the ROC curve method. The ordinate represents sensitivity, and the abscissa represents specificity. Detailed Implementation

[0023] The present invention will be further illustrated below with specific implementation examples. The embodiments of the present invention are only used to explain the present invention and do not mean to limit the scope of protection of the present invention.

[0024] Example 1: Screening and characterization of differential metabolites between healthy individuals and patients with generalized pustular psoriasis

[0025] I. Objects and Methods

[0026] 1. Specimen source

[0027] After obtaining patient consent, serum samples were collected from 12 healthy volunteers and 24 patients with generalized pustular psoriasis. All volunteers and patients were from Shanghai Skin Disease Hospital, and the age, sex, and body mass index (BMI) of the healthy volunteers were matched with those of the patients to exclude metabolic differences caused by dietary habits, sex, age, and BMI. Blood samples were collected in the morning on an empty stomach. All samples were stored at -80℃ until use.

[0028] 2. Main reagents

[0029] Acetonitrile, methanol, and formic acid (chromatographic grade) were purchased from Sigma-Aldrich; analytical grade chloroform, pyridine, and anhydrous sodium sulfate were purchased from China National Pharmaceutical Group Corporation; L-2-chlorophenylalanine, methoxyamine, N-methyl-N-(trimethylsilane)trifluoroacetamide (containing 1% trimethylchlorosilane), heptadecanoic acid, and leucine-enkephalin were purchased from Sigma-Aldrich; deionized water was prepared using the Milli-Q ultrapure water system from Millipore.

[0030] 3. Characterization of serum differential metabolites

[0031] 3.1. GC-MS screening characterization

[0032] 3.1.1. Sample Preparation

[0033] Sample preparation: Take 100 μL of serum into a 1.5 mL centrifuge tube, add 10 μL of L-2-chlorophenylalanine (0.3 mg / mL, dissolved in water) and 10 μL of heptadecanoic acid (1 mg / mL, dissolved in methanol) as internal controls, and vortex for 10 seconds to mix; then add 300 μL of methanol / chloroform mixture (volume ratio 3:1), vortex for 30 seconds to mix, incubate the sample at -20℃ for 10 minutes, and centrifuge at 12000g for 10 minutes (4℃); take 300 μL of supernatant into a glass sampling bottle, dry it with nitrogen evaporation at room temperature, add 80 μL of methoxyamine (15 mg / mL, dissolved in pyridine), incubate at 30℃ for 90 minutes, then add 80 μL of N-methyl-N-(trimethylsilane)trifluoroacetamide (containing 1% trimethylchlorosilane by volume), and incubate at 70℃ for 60 minutes to obtain the final product.

[0034] 3.1.2. Detection and Characterization

[0035] GC-MS conditions: Ultra-high performance gas chromatography (UHPLC) was used for chromatographic separation, and quadrupole-time-of-flight mass spectrometry (QTF) was used for mass spectrometry analysis. The chromatographic column was a DB-5ms capillary column (30m × 250μm id, 0.25μm); the carrier gas was high-purity helium, with a flow rate of 1.0mL / min; the injection volume was 1μL; the temperature program was: 80℃ for 2 minutes, 80℃-180℃ (10℃ / min), 180℃-240℃ (5℃ / min), 240℃-290℃ (25℃ / min), and 290℃ for 9 minutes; splitless injection at 260℃; interface temperature at 270℃; ion source temperature at 200℃; electron energy at 70eV; full scan mode was used, with a scan mass range of m / z 30-600 Daltons and a spectral acquisition rate of 20 spectra / s.

[0036] 4. Data processing and analysis

[0037] The JiaLib™ metabolic database was used to annotate serum metabolites detected untargeted by GC-MS, followed by multivariate and univariate analyses of the detection data. First, the data were imported into SIMCA software (version 14.0.1, Umetrics) for Orthogonal Partial Least Squares-Discrimination Analysis (OPLS-DA). Signals irrelevant to the model classification were filtered to obtain the OPLS-DA model. The model quality was tested using cross-validation, and the R²Y and Q² (the explanatory variables of Y and the predictive variables of the model) obtained after cross-validation were used to evaluate model effectiveness. A higher R²Y and Q² value indicates a better model quality, with Q² greater than 0.5 indicating a good model. Permutation tests were performed on the OPLS-DA model to obtain R² and Q² values ​​for further evaluation; a Q² less than 0 indicates a good model. The Variable Importance in the Projection (VIP) of the first principal component of the OPLS-DA model was calculated. Then, univariate analysis of the metabolic data was performed using the online analysis software MetaboAnalys (http: / / www.metaboanalyst.ca / ). The fold change in metabolites between patients with generalized pustular psoriasis and healthy volunteers was calculated, and a Student's t-test was performed to obtain the False Discovery Rate (FDR). Metabolites with an FDR value less than 0.05 were screened. Combining the results of multivariate and univariate analyses, metabolites with a VIP > 1.5 and an FDR < 0.05 were selected and defined as candidate metabolic biomarkers for generalized pustular psoriasis.

[0038] II. Results

[0039] 1. Screening for differentially expressed metabolites in serum.

[0040] GC-MS detected 123 annotable metabolites in serum samples. OPLS-DA analysis was performed using SIMCA software to obtain the OPLS-DA model. Figure 1 The cross-validation method was used to test the quality of the model, yielding R²Y = 0.984 and Q² = 0.893. Figure 2Based on the results of multivariate and univariate analyses, a total of 35 differentially expressed metabolites (VIP>1.5 and FDR<0.05) were screened from the serum samples. See Table 1. These markers are tyramine, pyruvic acid, alpha-ketoisovaleric acid, 2-hydroxybutyric acid, 3-hydroxybutyric acid, methanephosphonothioic acid, L-proline, uracil, tranexamic acid, 4-aminobutyric acid, L-threonine, scopoletin, dodecanol, N-methyl-L-leucine, L-cysteine-glycine, L-kynurenine, and 3-hydroxybenzoic acid. The following are listed: acid, allantoin, delta-tocopherol, xylofuranose, glucose-1-phosphate, pyrophosphate, taurine, L-asparagine, phthalic acid, 4-(dimethylamino)azobenzene, 5-tert-butyl-1h-indole-2,3-dione, quinic acid, glucose, L-histidine, L-lysine, palmitic acid, 7-methylguanine, oleic acid, and cis-gondoic acid. After reviewing published literature, it was found that these 35 plasma metabolic markers were discovered for the first time in the early diagnosis of generalized pustular psoriasis, which is of great significance for the diagnosis and treatment of generalized pustular psoriasis.

[0041] Table 1.

[0042]

[0043]

[0044] 2. Screening for metabolites related to disease severity.

[0045] Based on the 35 differentially expressed serum metabolites identified in the previous step, Pearson correlation analysis was performed between them and the generalized pustular psoriasis disease severity score (JDA score). Five metabolites associated with disease severity were selected according to P<0.05: L-threonine, L-cysteine-glycine, pyrophosphate, glucose, and L-histidine (Table 2).

[0046] Table 2.

[0047]

[0048] ROC curve analysis showed that the AUC values ​​of all five metabolites were greater than 0.9: L-threonine: AUC = 0.93, 95% CI: 0.85–1.00, P < 0.0001; L-cysteine-glycine: AUC = 0.94, 95% CI: 0.88–1.00, P < 0.0001; pyrophosphate: AUC = 0.98, 95% CI: 0.95–1.00, P < 0.0001; glucose: AUC = 1.00, 95% CI: 1.00–1.00, P < 0.0001; L-histidine: AUC = 0.96, 95% CI: 0.90–1.00, P < 0.0001. (See [link to ROC curve analysis]) Figure 3 This indicates that these five metabolites effectively classify the metabolomics data of patients with generalized pustular psoriasis and healthy individuals, accurately distinguishing between patients and healthy individuals.

[0049] Thirty-five metabolites can accurately distinguish between patients and healthy individuals. Five metabolites were selected from these, and the levels of these five metabolites were individually correlated with the severity of generalized pustular psoriasis. The ROC curves further indicate that these five metabolites can individually predict the disease status.

[0050] The diagnostic markers of this invention can effectively distinguish patients with generalized pustular psoriasis from healthy controls, which is beneficial for the clinical diagnosis of generalized pustular psoriasis. They are of great help in improving the clinical diagnosis, treatment and evaluation of generalized pustular psoriasis and have good clinical application and promotion value.

[0051] The above description of the embodiments is only for understanding the method and core ideas of the present invention. It should be noted that, for those skilled in the art, other embodiments based on the patent concept of the present invention will also fall within the protection scope of the claims of the present invention without departing from the principles of the present invention.

Claims

1. The application of a diagnostic marker in the preparation of a diagnostic agent for generalized pustular psoriasis, wherein the diagnostic marker is L-cysteine-glycine.

2. The application according to claim 1, characterized in that, The diagnostic markers mentioned are plasma metabolic markers.

3. The application according to claim 1, characterized in that, The diagnostic biomarkers were used as standards for the gas chromatography-mass spectrometry (GC-MS) instrument.

4. The method for screening diagnostic markers for generalized pustular psoriasis as described in claim 1, characterized in that, Includes the following steps: (1) Collect serum samples from patients with generalized pustular psoriasis and healthy volunteers as analytical samples; (2) Non-targeted metabolomics analysis was performed on each sample using GC-MS to obtain the original metabolic fingerprint of each serum sample; (3) Data preprocessing and multivariate statistical analysis were performed on the obtained serum metabolomics fingerprint profiles to screen differential metabolites, and Pearson correlation analysis between differential metabolites and disease severity scores was used to further screen marker metabolites. (4) Differential metabolites with AUC values ​​greater than 0.9 were obtained by ROC curve analysis, which are serum metabolic markers suitable for the diagnosis of generalized pustular psoriasis.

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