Biomarker for depressive disorder and application thereof

Abstract

The invention provides a biological marker for depressive disorder and application of the biological marker, and the biological marker is selected from any one or a combination of more than two of phosphatidylcholine (PC 36: 3), 2, 3-dihydroxyvalerate (2, 3-Dihydroxyvalerate) and / or anisic acid (Avenolic acid). The biomarker has the advantages of high sensitivity and high precision, and development and utilization of the key biomarker have important value in precise diagnosis, subsequent treatment and drug screening of depressive disorder.

Description

Technical Field

[0001] The present invention belongs to the field of medical technology, and in particular to a biomarker for depression and use thereof. Background Art

[0002] Depression is a very common chronic psychiatric disorder, the main clinical features of which are persistent low mood, loss of interest, and even self-harm and suicidal behavior, which pose a serious threat to patients and society. However, there is still a lack of very effective biomarkers for the early diagnosis of depression. Therefore, the discovery of new biomarkers is of great significance for the early diagnosis and treatment of depression in clinical practice.

[0003] Currently, metabolomics research has been widely used in various medical disease areas, and metabolite analysis can identify changes that occur in the early stages of a disease. More and more studies have found that metabolomics has achieved phased results in disease diagnosis, differential diagnosis, model evaluation, and drug efficacy evaluation. Summary of the invention

[0004] In response to the above problems, the present invention proposes a biomarker for depressive disorder based on metabolomics and its use. The present invention uses non-targeted metabolomics technology to analyze plasma samples of people with depressive disorder and healthy people, screens data to obtain potential biomarkers and analyzes their metabolic pathways, provides a reference for the pathogenesis of depressive disorder, and provides targeted assistance for the diagnosis and treatment of depressive disorder.

[0005] In a first aspect of the present invention, a biomarker for depressive disorder is provided, wherein the biomarker is selected from any one or a combination of two or more of phosphatidylcholine (PC), 2,3-dihydroxyvaleric acid and / or anisic acid.

[0006] Preferably, the phosphatidylcholine is PC 36:3, and more preferably PC 36:3; PC (16:0 / 20:3).

[0007] Each biomarker described in the present invention can be used alone or in combination as a basis for diagnosing depressive disorders.

[0008] Preferably, compared with healthy people, phosphatidylcholine (PC 36:3) is down-regulated in patients with depressive disorders, 2,3-dihydroxyvaleric acid is up-regulated in patients with depressive disorders, and anisic acid is up-regulated in patients with depressive disorders.

[0009] Preferably, the sample of the biomarker is derived from blood, urine, saliva, tears or tissue, and more preferably, the sample of the biomarker is derived from blood. More preferably, the sample is derived from plasma.

[0010] The second aspect of the present invention is the use of a biomarker in the preparation of a diagnostic product related to depressive disorders or in the screening of drugs related to the treatment and / or prevention of depressive disorders, wherein the biomarker is selected from any one or a combination of two or more of phosphatidylcholine (PC 36:3), 2,3-dihydroxyvaleric acid and / or anisic acid, and the biomarker serves as a target for the diagnostic product and / or drug.

[0011] Preferably, the phosphatidylcholine is PC 36:3, and more preferably PC 36:3; PC (16:0 / 20:3).

[0012] Preferably, compared with healthy people, phosphatidylcholine (PC 36:3) is down-regulated in patients with depressive disorders, 2,3-dihydroxyvaleric acid is up-regulated in patients with depressive disorders, and anisic acid is up-regulated in patients with depressive disorders.

[0013] Preferably, the sample of the biomarker is derived from blood, urine, saliva, tears or tissue, further preferably, the sample is derived from blood, and more preferably, the sample is derived from plasma.

[0014] Furthermore, the diagnosis includes: detecting biomarkers in the sample to be tested; and comparing the expression levels of biomarkers in the sample to be tested and those in healthy people.

[0015] Preferably, compared with healthy people, in the test sample, phosphatidylcholine (PC) expression is downregulated, 2,3-dihydroxyvaleric acid expression is upregulated, and / or anisic acid expression is upregulated, which can be diagnosed as a depressive disorder-related disease.

[0016] Preferably, the biomarker can be used as a biomarker for depressive disorder, and more preferably, the biomarker for depressive disorder includes anisic acid.

[0017] Furthermore, the drug screening includes detecting biomarkers in the sample to be tested; and comparing the expression levels of the biomarkers in the sample to be tested before and after the candidate is administered.

[0018] Preferably, the detection of biomarkers is to detect the expression amount of the biomarkers in the sample.

[0019] Candidates that upregulate phosphatidylcholine, downregulate 2,3-dihydroxyvaleric acid and / or downregulate anisic acid are target candidates and can be used as drugs for treating and / or preventing depressive disorders.

[0020] The third aspect of the present invention provides the use of a biomarker detection reagent in the preparation of a diagnostic product for depressive disorders, wherein the biomarker is selected from any one or a combination of two or more of phosphatidylcholine (PC 36:3), 2,3-dihydroxyvaleric acid and / or anisic acid.

[0021] Preferably, the phosphatidylcholine is PC 36:3, and more preferably PC 36:3; PC (16:0 / 20:3).

[0022] Preferably, the sample of the biomarker is derived from blood, urine, saliva, tears or tissue, further preferably, the sample is derived from blood, and more preferably, the sample is derived from plasma.

[0023] Preferably, the detection of biomarkers is to detect the expression amount of biomarkers in the sample.

[0024] Furthermore, the diagnosis includes: detecting biomarkers in the sample to be tested; and comparing the expression levels of biomarkers in the sample to be tested and those in healthy people.

[0025] Preferably, compared with healthy people, in the test sample, phosphatidylcholine (PC) expression is downregulated, 2,3-dihydroxyvaleric acid expression is upregulated, and / or anisic acid expression is upregulated, which can be diagnosed as a depressive disorder-related disease.

[0026] Preferably, the biomarker can be used as a biomarker for depressive disorder, and more preferably, the biomarker for depressive disorder includes anisic acid.

[0027] A fourth aspect of the present invention provides a diagnostic product, which includes a reagent for detecting biomarkers associated with depressive disorders, and the biomarkers are as described in the first aspect.

[0028] Preferably, the detection of biomarkers is to detect the expression amount of biomarkers in the sample.

[0029] Furthermore, the diagnosis includes: detecting biomarkers in the sample to be tested; and comparing the expression levels of biomarkers in the sample to be tested and those in healthy people.

[0030] Preferably, compared with healthy people, in the test sample, phosphatidylcholine (PC 36:3) expression is downregulated, 2,3-dihydroxyvaleric acid expression is upregulated, and / or anisic acid expression is upregulated, which can be diagnosed as a depressive disorder-related disease.

[0031] Preferably, the biomarker can be used as a biomarker for depressive disorder, and more preferably, the biomarker for depressive disorder includes anisic acid.

[0032] Preferably, the diagnostic product comprises a kit or a diagnostic chip.

[0033] In a specific embodiment, the kit is an ELISA kit.

[0034] Furthermore, the kit also includes instructions for assessing whether the subject suffers from a depressive disorder and / or the risk of developing a depressive disorder (ie, the assessment is performed based on the comparison of the biomarker concentration in the subject's plasma with the Cut-Off value).

[0035] Furthermore, the subject refers to any animal, including humans and non-human animals, wherein non-human animals include all vertebrates, such as mammals and non-mammals, the mammals include non-human primates, mice, rats, guinea pigs, goats, sheep, dogs, rabbits, pigs, cats, cows, and the non-mammals include chickens, amphibians or reptiles;

[0036] Preferably, the subject is a human.

[0037] Furthermore, the kit also includes a container, a positive control, a negative control, a buffer, an adjuvant, a solvent, and an instruction manual for use of the kit, which records how to use the kit for testing and how to use the test results to judge the results.

[0038] A fifth aspect of the present invention provides a diagnostic system for depressive disorders, the diagnostic system comprising:

[0039] (1) Detection unit: The detection unit is used to detect the expression level of the biomarker described in the first aspect of the present invention;

[0040] (2) Result judgment unit: The result judgment unit is used to analyze the results of the biomarker expression levels detected by the detection unit and output the results of whether the patient suffers from depression and / or the risk of depression.

[0041] Preferably, the detection unit is a kit.

[0042] Preferably, the result judgment unit includes an input unit, an analysis unit and an output unit;

[0043] More preferably, the input unit is used to input the result of the expression level of the biomarker;

[0044] More preferably, the analysis unit is used to analyze whether the patient suffers from a depressive disorder and / or the risk of a depressive disorder according to the result of the expression level of the biomarker;

[0045] More preferably, the output unit is used to output the analysis result of the analysis unit.

[0046] Preferably, the diagnostic system comprises the use of the diagnostic product described in the fourth aspect.

[0047] A sixth aspect of the present invention provides a method for detecting biomarkers of depressive disorders, wherein the method comprises using the reagent for detecting biomarkers as described in the fourth aspect.

[0048] A seventh aspect of the present invention provides a method for diagnosing a depressive disorder-related disease, wherein the method comprises detecting the above-mentioned biomarker, using the above-mentioned diagnostic product or the above-mentioned diagnostic system.

[0049] The products described in the present invention include all products related to the diagnosis of depressive disorders or drug screening. Preferably, the products include but are not limited to kits, diagnostic chips, drugs, etc.

[0050] In an eighth aspect, the present invention provides a method for screening drugs for treating and / or preventing depressive disorders, the screening method comprising:

[0051] (1) Establish disease model;

[0052] (2) administering the candidate to a disease model;

[0053] (3) Detecting the effect of the candidate on a biomarker in a disease model sample, wherein the biomarker is selected from any one or a combination of two or more of phosphatidylcholine (PC 36:3), 2,3-dihydroxyvaleric acid and / or anisic acid.

[0054] Preferably, the phosphatidylcholine is PC 36:3, and more preferably PC 36:3; PC (16:0 / 20:3).

[0055] Preferably, the sample is derived from blood, urine, saliva, tears or tissue, further preferably, the sample is derived from blood, and more preferably, the sample is derived from plasma.

[0056] Preferably, the detection of biomarkers is to detect the expression amount of the biomarkers in the sample.

[0057] More preferably, after administration of the candidate, the expression of phosphatidylcholine (PC 36:3) is upregulated, the expression of 2,3-dihydroxyvaleric acid is downregulated, and the expression of anisic acid is downregulated, then the candidate is considered to be a target candidate and can be used as a drug for treating and / or preventing depressive disorders.

[0058] Further preferably, the expression of phosphatidylcholine (PC 36:3) is upregulated, the expression of 2,3-dihydroxyvaleric acid is downregulated, and the expression of anisic acid is downregulated, then the candidate can treat and / or prevent all types of depressive disorders.

[0059] Preferably, the biomarker can be used as a biomarker for depressive disorder, and more preferably, the biomarker for depressive disorder includes anisic acid.

[0060] In a ninth aspect, the present invention provides a drug target for treating and / or preventing depressive disorders, wherein the target is a biomarker for depressive disorders, wherein the biomarker is selected from any one or a combination of two or more of phosphatidylcholine (PC), 2,3-dihydroxyvaleric acid and / or anisic acid, and wherein the drug upregulates phosphatidylcholine, downregulates 2,3-dihydroxyvaleric acid and / or downregulates anisic acid.

[0061] Preferably, the drug target includes any one or a combination of two or more of acylcholine (PC), 2,3-dihydroxyvaleric acid and / or anisic acid.

[0062] Preferably, the phosphatidylcholine is PC 36:3, and more preferably PC 36:3; PC (16:0 / 20:3).

[0063] The depressive disorders described in the present invention include but are not limited to double depression, bipolar disorder or simple depression.

[0064] The term "include" or "comprising" in the present invention is an open description containing the specified components or steps described, and other specified components or steps that will not be substantially affected.

[0065] The present invention discloses biomarkers related to depressive disorders and their common and unique diagnostic products, screens out common and unique key biomarkers that change significantly in patients with depressive disorders, the common key biomarkers include phosphatidylcholine, 2,3-dihydroxyvaleric acid and anisic acid, and verifies the differential expression and diagnostic ability of related markers in patient samples. The key biomarkers can be used for the diagnosis of depressive disorders, have the advantages of high sensitivity and strong specificity, and can effectively distinguish between depressive disorders and healthy people. The development and utilization of key biomarkers will provide new ideas and methods for the accurate diagnosis of depressive disorders and subsequent treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 Total ion current graphs of different types of depression, normal control group and quality control group in positive ion mode;

[0067] Figure 2 Total ion current diagrams of different types of depression, normal control group and quality control group under negative ion mode;

[0068] Figure 3 It is the multivariate statistical analysis of the depression attack group and the normal control group, where: A is the PLS-DA scatter plot of the depression attack group VS the normal control group, and B is the PLS-DA permutation test diagram;

[0069] Figure 4Cluster heat map of the constructed depression episode group and normal control group;

[0070] Figure 5 This is the bubble diagram of metabolic pathway enrichment of biomarkers in the serum of the depression episode group and the normal control group;

[0071] Figure 6 The expression of 2,3-dihydroxyvaleric acid as a biomarker in plasma samples;

[0072] Figure 7 The expression of phosphatidylcholine (PC) as a biomarker in plasma samples;

[0073] Figure 8 The expression of anisic acid as a biomarker in plasma samples;

[0074] Fig. 9 ROC curves for phosphatidylcholine (PC), 2,3-dihydroxyvaleric acid, and anisic acid;

[0075] Fig.10 is the ROC curve of 2,3-dihydroxyvaleric acid;

[0076] Fig.11 is the ROC curve of anisic acid;

[0077] Fig.12 This is the sample situation in the enzyme-linked immunosorbent assay. DETAILED DESCRIPTION

[0078] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0079] Unless otherwise specified, the materials, reagents, instruments, etc. used in the following examples can be obtained from commercial sources. Example 1 Screening for candidate biomarkers (differentially expressed molecules) in depressive disorders

[0080] The specific implementation process of the present invention for screening and verifying key biomarkers specific to depressive disorders is as follows:

[0081] Firstly, 6 patients with simple depression (Depression, DEP) and 6 healthy subjects (NP) were selected as samples for this experiment. These patients with depressive disorder were first-time patients, had not taken any psychotropic drugs or had stopped taking them for 2 weeks or more.

[0082] 1. Experimental instruments and reagents

[0083] (1) Instrument

[0084] BSA124S-CW balance (Sartorius); Mingche D24 UV water purifier (Merck Millipore); JXFSTPRP-24 tissue disruptor (Jingxin); T3 column (100mm*2.1mm, 1.8μm) chromatographic column (Waters); Vanquish FlexUHPLC ultra-high performance liquid phase (Thermo); Q-Exactive high-resolution mass spectrometer (Thermo).

[0085] (2) Reagents

[0086] Methanol, formic acid and acetonitrile were purchased from Fisher, USA.

[0087] 2. Sample Collection and Preparation

[0088] 1.4 ml of fasting venous blood was collected from the participants and the upper clear plasma was separated by centrifugation with a centrifugal force and time of 3000 g*15 min. The separated plasma was divided into 200 μl EP tubes and quickly frozen in a -80 degree Celsius refrigerator for use.

[0089] 3. Use LC-MS to test serum samples and obtain raw data

[0090] (1) Sample preparation

[0091] Resuscitate the frozen blood at room temperature and take 20ul of sample (adjust according to the actual situation of the sample and take equal volume). Add 120ul of 50% methanol to the sample, shake and mix thoroughly, extract the metabolites in the sample, and let it stand at room temperature for 10 minutes. The extract is placed at -20 degrees Celsius overnight to precipitate the protein in the sample. Centrifuge at 4000g for 20 minutes, and transfer the supernatant metabolite extract to a 96-well plate. Take out 10ul of dilution from each sample and mix it into a quality control (QUALITY CONTROL, QC) sample. QC samples are used to evaluate the stability and repeatability of the data.

[0092] (2) Liquid chromatography conditions

[0093] All samples were collected by LC-MS system according to machine commands. First, all chromatographic separations were performed using Vanquish Flex UHPLC system (Thermo Fisher Scientific, Bremen, Germany). Reverse phase separation was performed using ACQUITYUPLC T3 column (100 mm*2.1 mm, 1.8 μm, Waters, Milford, USA). The column oven was maintained at 35 degrees °C. The flow rate was 0.4 ml / min, and the mobile phase was solvent A (water, 0.1% formic acid) and solvent B (acetonitrile, 0.1% formic acid). The gradient elution conditions were set as follows: 0-0.5 min, 5% B; 0.5-7 min, 5% to 100% B; 7-8 min, 100% B; 8-8.1 min, 100% to 5% B; 8.1-10 min, 5% B.

[0094] (3) Mass spectrometry conditions

[0095] Metabolites eluted from the column were detected using a high-resolution tandem mass spectrometer Q-Exactive (Thermo Scientific). The Q-Exactive was operated in both positive and negative ion modes. Precursor spectra (70–1050 m / z) were collected at 70,000 resolution to achieve an AGC target of 3e6. The maximum injection time was set to 100 ms. The first 3 configurations used to acquire the data were set in Data-Dependent Acquisition (DDA) mode. Fragment spectra were collected at 17,500 resolution to achieve an AGC target of 1e5 with a maximum injection time of 80 ms. To assess the robustness of the LC-MS throughout the acquisition, a quality control sample (all sample pools) was collected every 10 samples.

[0096] 4. Data Processing

[0097] (1) Analysis of differential metabolites

[0098] Untargeted metabolomics was used to identify and analyze differential metabolites; the raw data from the mass spectrometer were converted to the ".mz XML" format using Proteowizard's MSConvert software and then processed by the XCMS, CAMERA, and metaX toolboxes implemented using R software. Each ion was identified by combining retention time (RT) and mass-to-charge ratio (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 (observed values), and ion intensity information (variables) was generated. The online KEGG, HMDB database annotated metabolites by matching the precise molecular weight data (m / z) of the sample with the molecular weight data in the database. Metabolites were annotated if the observed mass difference was less than 10 ppm from the database value, and the molecular formula of the metabolite was further identified and verified by isotope distribution measurements. We also used an internal metabolite fragment library to verify metabolite identification. The intensity of the peak data was further preprocessed by metaX. Features detected in less than 50% of QC samples or 80% of biological samples were removed, and the remaining peaks with missing values ​​were interpolated using the k-nearest neighbor algorithm to further improve data quality. Principal component analysis (PCA) was performed using the preprocessed dataset for outlier detection and batch effect assessment. In addition, the relative standard deviation of the metabolic features of all QC samples was calculated, and then the metabolic features with >30% were removed. Student's t-test was performed to detect the differences in metabolite concentrations between the two phenotypes. The P value was adjusted for multiple tests using FDR (Benjamini-Hochberg). Supervised PLS-DA was performed by metaX to distinguish different variables between groups. At the same time, the standard of variable importance for the projection (VIP) of the first principal component was screened to find differential metabolites. T-test and difference fold analysis were performed on the data, and variables with P values ​​less than 0.05 and difference folds greater than or equal to 2 or less than 0.05 were screened as potential biomarkers. The corresponding differential metabolites were determined using the KEGG database with reference to relevant literature and combined with primary and secondary mass spectrometry information.

[0099] (2) Metabolic pathway analysis

[0100] The differential metabolites screened from serum metabolomics data were used to create heat maps and conduct metabolic pathway analysis.

[0101] 5. Data Analysis

[0102] (1) Serum metabolomics profile analysis

[0103] Total ion currents were obtained to show the metabolomics characteristics between different types of depression and healthy controls; the total ion currents of each group in positive and negative modes are shown in Figure 2. Figure 1-Figure 2 As shown, there are differences between the two groups.

[0104] (2) Serum metabolomics analysis

[0105] The present invention is based on serum samples collected by ultra-high performance liquid chromatography; metabolomics research adopts multivariate statistical analysis methods such as PCA, PLS-DA, and permutation test; and the number of hypothesis tests is set to 200.

[0106] The experimental data were subjected to partial least squares discriminant analysis (PLS-DA), and the results were as follows: Figure 3 As shown in the figure, the depression disorder group and the healthy control group can be well separated, and the samples in the group are well clustered within a certain range, indicating that there are obvious differential metabolites between the depression disorder group and the healthy control group. The data model was permuted 200 times and it was found that the R2 and Q2 values ​​were both smaller than the right end, the former was above the latter, and the intercept of the regression line and the vertical axis of the latter was negative, indicating that the model is effective and reliable, and there is no overfitting phenomenon.

[0107] ① Identification of differential metabolites

[0108] According to the pre-VIP screening variables of the PLS-DA model, with VIP>1, P value less than 0.05, and difference multiple greater than or equal to 2 or less than 0.05 as the standard, a total of 191 differential molecules were screened between the depressive episode (i.e., depressive disorder) and the healthy control group. After the screened data were sorted in descending order according to the size of the difference multiple, 8 types of differential metabolites were screened as potential biomarkers for each group based on the annotations of the compounds in the KEGG and HMDB databases. The specific candidate biomarkers are shown in Table 1.

[0109] Table 1 Differential metabolites of NP-DEP

[0110]

[0111]

[0112] ② Heat map analysis

[0113] Construct a cluster heat map for the data of depression disorder and normal control group ( Figure 4 ), which can characterize the relative content of each component to analyze the identified differential metabolites. Dark red indicates a higher relative content. Dark blue indicates a lower relative content.

[0114] ③ Metabolic pathway analysis

[0115] Metabolic pathway analysis was performed on the potential biomarkers screened for depression and normal control groups, and a bubble diagram of metabolic pathway enrichment of biomarkers in serum was drawn. The analysis results are as follows Figure 5 shown.

[0116] Example 2 Validation of key biomarkers in large samples

[0117] To identify key candidate biomarkers specific to depressive disorder in patients with first-ever depressive disorder who are not taking any psychotropic medication or have been off medication for ≥2 weeks.

[0118] 1. Sample Collection and Preparation

[0119] 1.4 ml of fasting venous blood was collected from the participants and the upper clear plasma was separated by centrifugation with a centrifugal force and time of 3000 g*15 min. The separated plasma was divided into 200 μl EP tubes and quickly frozen in a -80°C refrigerator for use.

[0120] 2. Identification of common and unique key candidate biomarkers

[0121] According to the pre-VIP screening variables of the PLS-DA model, VIP>1, P value less than 0.05, and difference multiple greater than or equal to 2 or less than 0.05 were used as the criteria, and a total of 191 differential molecules were screened between the depressive episode and the healthy control group. After the screened data were sorted in descending order according to the size of the difference multiple, a total of 8 differential metabolites were screened as potential biomarkers for each group based on the annotations of the compounds in the KEGG and HMDB databases. The specific candidate biomarkers are shown in Table 1.

[0122] After arranging the difference folds in descending order, the top-ranked differential molecules were selected as key candidate biomarkers specific for depressive disorders. The key biomarkers included: phosphatidylcholine (PC 36:3; PC (16:0 / 20:3)), 2,3-dihydroxyvaleric acid and anisic acid.

[0123] 3. Enzyme-linked immunosorbent assay (ELISA)

[0124] (I) 2,3-Dihydroxyvaleric acid

[0125] The sample size included in the validation of 2,3-dihydroxyvaleric acid was healthy controls: patients with depressive disorders = 21:21, see Fig.12 , wherein the samples are plasma samples from patients with depressive disorders and healthy controls, respectively.

[0126] Experimental methods:

[0127] (1) Sample addition: Set up standard wells, blank wells (blank wells do not contain samples and enzyme-labeled reagents, and the rest of the steps are the same), and wells for the samples to be tested. Add 50 μL to the standard wells on the enzyme-labeled plate, add 40 μL of sample diluent to the wells for the samples to be tested, and then add 10 μL of the sample to be tested. Note: The solution should be added to the bottom of the micro-ELISA plate, and try to avoid contact with the inner wall to generate foam.

[0128] (2) Add enzyme and incubate: Add 50 μL of enzyme-labeled reagent to each well, except for the blank well. Seal the plate with a sealing film and incubate at 37°C for 60 minutes.

[0129] (3) Washing: Discard the liquid, spin dry and add 300 μL of washing buffer to each well. Let stand for 30 seconds and then discard. Pat dry with clean absorbent paper. Repeat this washing step 5 times.

[0130] (4) Color development: Add 50μL of color developer A to each well, then add 50μL of developer B, mix gently with shaking, and color at 37℃ in the dark for 15 minutes. Protect the plate from light. Note: The reaction time can be shortened or extended according to the actual color change, but not more than 30 minutes. Among them, color developer A (containing hydrogen peroxide) is a hydrogen donor (DH2), and color developer B contains TMB.

[0131] (5) Stop: Add 50 μL of stop solution to each well. Note: The stop solution should be added in the same order as the matrix solution.

[0132] (6) Measure the optical density (OD value) of each well once using a microplate reader set at 450 nm. Note: The measurement should be performed within 15 minutes after adding the stop solution.

[0133] Experimental results: The expression of 2,3-dihydroxyvaleric acid as a biomarker in plasma samples is shown in Table 2. Figure 6 shown.

[0134] Table 2 Statistical analysis of 2,3-Dihydroxyvaleric expression in DEP

[0135]

[0136] in Figure 6 All data are expressed as mean standard deviation. The difference of 2,3-dihydroxyvaleric acid between NP group and DEP group was analyzed by single sample t test and P<0.05, so the difference between the two groups was statistically significant.

[0137] (ii) Phosphatidylcholine

[0138] The sample size included in the phosphatidylcholine validation was healthy controls: patients with depressive disorders = 21:21, see Fig.12, wherein the samples are plasma samples from patients with depressive disorders and healthy controls, respectively.

[0139] Experimental methods:

[0140] (1) Sample addition: Set up standard wells, blank wells (blank wells do not contain samples and enzyme-labeled reagents, and the rest of the steps are the same), and test sample wells. Add 50 μL to the standard wells on the enzyme-labeled plate, add 40 μL of sample diluent to the test sample wells, and then add 10 μL of the test sample. Note: The solution should be added to the bottom of the micro-ELISA plate, avoiding contact with the inner wall to generate foam, and gently shake to mix well.

[0141] (2) Incubation: Seal the plate with a sealing film and incubate at 37°C for 30 minutes.

[0142] (3) Washing: Discard the liquid, spin dry and add 300 μL of washing buffer to each well. Let stand for 30 seconds and then discard. Pat dry with clean absorbent paper. Repeat this washing step 5 times.

[0143] (4) Add enzyme and incubate: Add 50 μL of enzyme-labeled reagent to each well, except for the blank well; seal the plate with a sealing film, incubate at 37°C for 30 minutes, and then wash (the operation is the same as (3) washing).

[0144] (5) Color development: Add 50 μL of color developer A to each well, then add 50 μL of developer B, mix gently, and color at 37°C in the dark for 10 minutes. Protect the plate from light. Note: The reaction time can be shortened or extended according to the actual color change, but it should not exceed 30 minutes.

[0145] (6) Stop: Add 50 μL of stop solution to each well. Note: The stop solution should be added in the same order as the matrix solution.

[0146] (7) Measure the optical density (OD value) of each well once using a microplate reader set at 450 nm. Note: The measurement should be performed within 15 minutes after adding the stop solution.

[0147] Experimental results: The expression of phosphatidylcholine as a biomarker in plasma samples is shown in Table 3. Figure 7 shown.

[0148] Table 3 Statistical analysis of PC 36:3; PC (16:0 / 20:3) expression in DEP

[0149]

[0150] Figure 7All data are expressed as mean standard deviation; the difference of phosphatidylcholine between NP group and DEP group was analyzed by single sample t test and P<0.05, so the difference between the two groups was statistically significant. The results of comprehensive analysis showed that phosphatidylcholine showed significant differential expression in the validation data set, and the difference was statistically significant (P<0.05). Phosphatidylcholine should be downregulated in the blood of patients with simple depressive episodes.

[0151] (III) Anisic acid

[0152] The sample size included in the validation of anisic acid was healthy controls: patients with depressive disorders (healthy population: simple depression) = 21:21, see Fig.12 , wherein the samples are plasma samples from patients with depressive disorders and healthy controls, respectively.

[0153] Experimental methods:

[0154] (1) Sample addition: Set up standard wells, blank wells (blank wells do not contain samples and enzyme-labeled reagents, and the rest of the steps are the same), and test sample wells. Add 50 μL to the standard wells on the enzyme-labeled plate, add 40 μL of sample diluent to the test sample wells, and then add 10 μL of the test sample. Note: The solution should be added to the bottom of the micro-ELISA plate, avoiding contact with the inner wall to generate foam, and gently shake to mix well.

[0155] (2) Add enzyme and incubate: Add 50 μL of enzyme-labeled reagent to each well, except for the blank well. Seal the plate with a sealing film and incubate at 37°C for 60 minutes.

[0156] (3) Washing: Discard the liquid, spin dry and add 300 μL of washing buffer to each well. Let stand for 30 seconds and then discard. Pat dry with clean absorbent paper. Repeat this washing step 5 times.

[0157] (4) Color development: Add 50 μL of color developer A to each well, then add 50 μL of developer B, mix gently, and color at 37°C in the dark for 15 minutes. Protect the plate from light. Note: The reaction time can be shortened or extended according to the actual color change, but it should not exceed 30 minutes.

[0158] (5) Stop: Add 50 μL of stop solution to each well. Note: The stop solution should be added in the same order as the matrix solution.

[0159] (6) Measure the optical density (OD value) of each well once using a microplate reader set at 450 nm. Note: The measurement should be performed within 15 minutes after adding the stop solution.

[0160] Experimental results: The expression of anisic acid as a biomarker in plasma samples is shown in Table 4. Figure 8 shown.

[0161] Table 4 Statistical analysis of the expression of avenoleic acid in DEP

[0162]

[0163] Figure 8 All data are expressed as mean standard deviation; the difference of anisic acid between the NP group and the DEP group was analyzed by single sample t test and P<0.05, so the difference between the two groups was statistically significant. The results of comprehensive analysis showed that anisic acid showed significant differential expression in the validation data set, and the difference was statistically significant (P<0.05). Anisic acid should be upregulated in the blood of patients with all types of depressive disorders.

[0164] Example 3 ROC diagnostic prediction analysis

[0165] 1. Experimental Methods

[0166] For the key biomarkers screened in Example 2: phosphatidylcholine (PC), 2,3-dihydroxyvaleric acid and anisic acid, the receiver operating characteristic curve (ROC curve) was drawn using the Jizhi data analysis platform to analyze the sensitivity, specificity and AUC value of the differentially expressed proteins as detection variables to determine the diagnostic efficacy of the indicators;

[0167] When judging the diagnostic efficacy of the indicator, the expression level of the biomarker is directly used for analysis, and the level corresponding to the point with the largest Youden index is selected as its cutoff value.

[0168] 2. Experimental results

[0169] The results are shown in Table 5 and Figure 9-11 The results showed that phosphatidylcholine, 2,3-dihydroxyvaleric acid and anisic acid have high accuracy, sensitivity and specificity in the diagnosis of depressive disorders, especially anisic acid, which can accurately diagnose depressive episodes and normal control groups. Anisic acid is a biomarker for depressive episodes, and its concentration can be measured clinically to confirm whether it is a depressive episode.

[0170] Table 5 Analysis of diagnostic efficacy of each biomarker

[0171]

[0172] According to the above results, the present application shows that the serum metabolic profile of patients with depression is significantly different from that of healthy subjects; any one or a combination of two or more of phosphatidylcholine (PC), 2,3-dihydroxyvaleric acid and / or anisic acid is a potential biomarker with good diagnostic effect. The present invention helps to provide reference for in-depth research on the diagnosis, differential diagnosis and treatment of different types of patients with depression.

[0173] Although the present invention has been described in detail by way of preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and such modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily conceive of changes or substitutions within the technical scope disclosed by the present invention, and such changes or substitutions shall be within the scope of protection of the present invention.

Claims

1. A biomarker for depressive disorder, characterized in that: The biomarker is selected from any one or a combination of two or more of phosphatidylcholine (PC36:3), 2,3-dihydroxyvaleric acid and / or fenic acid.

2. Use of a biomarker in preparing a diagnostic product related to depressive disorders or screening a drug for treating and / or preventing depressive disorders, characterized in that: The biomarker is selected from any one or a combination of two or more of phosphatidylcholine (PC 36:3), 2,3-dihydroxyvaleric acid and / or anisic acid, and the biomarker is used as a target for diagnostic products and / or drugs.

3. Use of a biomarker detection reagent in the preparation of a diagnostic product for depressive disorders, characterized in that: The biomarker is selected from any one or a combination of two or more of phosphatidylcholine (PC 36:3), 2,3-dihydroxyvaleric acid and / or anisic acid.

4. The use according to claim 3, characterized in that: The application includes detecting biomarkers in a sample. Preferably, the sample is derived from blood, urine, saliva, tears or tissue. More preferably, the sample is derived from blood.

5. The use according to any one of claims 3-4, characterized in that: The detected biomarker is the expression amount of the biomarker in the detection sample.

6. A diagnostic product, characterized in that: The diagnostic product includes a reagent for detecting biomarkers related to depressive disorders, wherein the biomarkers are selected from any one or a combination of two or more of phosphatidylcholine (PC 36:3), 2,3-dihydroxyvaleric acid and / or anisic acid. Preferably, the diagnostic product includes a kit or a diagnostic chip.

7. A diagnostic system for depressive disorders, characterized in that: The diagnostic system comprises: (1) Detection unit: The detection unit is used to detect the expression level of any biomarker in claim 1; (2) Result judgment unit: The result judgment unit is used to analyze the expression level results detected by the detection unit and output the results of whether the patient suffers from depression and / or the risk of depression.

8. The diagnostic system according to claim 7, characterized in that The result judgment unit includes an input unit, an analysis unit and an output unit; Preferably, the input unit is used to input the result of the expression level of the biomarker; Preferably, the analysis unit is used to analyze whether the patient suffers from a depressive disorder and / or the risk of a depressive disorder based on the result of the expression level of the biomarker; Preferably, the output unit is used to output the analysis result of the analysis unit.

9. A method for screening drugs for treating and / or preventing depressive disorders, characterized in that: The screening method comprises: (1) Establish disease model; (2) administering the candidate to a disease model; (3) detecting the effect of the candidate on a biomarker in a disease model sample, wherein the biomarker is selected from any one or a combination of two or more of phosphatidylcholine (PC 36:3), 2,3-dihydroxyvaleric acid and / or anisic acid; Preferably, the sample is derived from blood, urine, saliva, tears or tissue, and more preferably, the sample is derived from blood.

10. A drug target for treating and / or preventing depressive disorders, characterized in that: The drug targets biomarkers of depressive disorders, and the biomarkers are selected from any one or a combination of two or more of phosphatidylcholine, 2,3-dihydroxyvaleric acid and / or anisic acid. The drug upregulates phosphatidylcholine and downregulates 2,3-dihydroxyvaleric acid and / or anisic acid.

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