New use of gpr18 in combination with other gene detection agents and depression detection reagent

Abstract

The application belongs to the field of biochemical detection, and specifically discloses a new use of a GPR18 and other gene detection agent combination and a depression detection reagent. The application of the GPR18 expression level detection agent in the preparation of a major depressive disorder detection product. The expression amount of GPR18, PDK4, NRG1 and EPHB2 in the application actually has high diagnostic efficiency, and can be used as an independent candidate diagnostic biomarker. The accuracy of each as a screening indicator reaches 0.6 to 0.8, reaching the standard of a medium-strength screening indicator, and the accuracy of the combined expression amount indicator as a screening indicator reaches 0.779.

Description

[0001] The application is a divisional application of the invention patent with the title of a severe depression detection reagent, system and application, application number 202110451314.3, and the application date of April 26, 2021. TECHNICAL FIELD

[0002] The application belongs to the technical field of biochemical detection, and specifically relates to a new use of a GPR18 and other gene detection reagent combination and a depression detection reagent. BACKGROUND

[0003] Severe depression is one of the most common neuropsychiatric diseases. With the development of social economy and the increase of people's work pressure, the incidence of depression in the world is increasing year by year, and the incidence of depression accounts for 1.4% of the world. The WHO predicts that by 2020, depression will become the second leading cause of disability and death after cancer. Therefore, depression will cause serious harm to individuals, families and society. At present, the diagnosis and treatment of severe depression is based on the emotional disorder characteristics of patients. The objective criteria for early diagnosis of patients with severe depression are still not clear enough, and the existing biomarkers are questioned because of poor specificity and sensitivity.

[0004] In addition, depression is a chronic disease caused by multiple factors, and is affected by epigenetics, endocrine, physiological changes and environmental factors. More and more evidence shows that environmental factors and genetic factors interact with each other, induce epigenetic changes through long-term changes in neural circuits, endocrine and synaptic structure, and thus lead to the risk of depression symptoms and other neuropsychiatric diseases. This evidence shows that genetic factors play a very complex role in the occurrence and development of severe depression.

[0005] Autophagy is a highly conserved biodegradation process in eukaryotes, which transports intracellular components (including proteins and organelles) to lysosomes. When autophagy genes are involved in metabolic regulation, there will be obvious changes in expression levels, for example, to provide amino acids for energy supply under starvation, and the expression level is significantly increased, especially in the liver. In neurons, autophagy maintains the balance and function of neurons by eliminating misfolded proteins and organelles, and plays a role as a housekeeping gene, maintaining a stable expression level.

[0006] More than 30 autophagy-related genes (ARGs) are considered to be closely related to diseases and metabolic disorders, and are considered to be involved in a variety of pathological processes, however, the role of autophagy-related genes in severe depression is not clear. We found through data mining that autophagy-related genes may be involved in the immune disorder accompanying severe depression, especially the depression metabolic pathway involving prostate cytokines. The data verified that autophagy-related genes can be used as biomarkers for severe depression as a biochemical detection indicator. SUMMARY

[0007] In order to solve the above defects or improvement needs of the prior art, the present application provides a new use of GPR18 combined with other gene detection agents and a depression detection reagent, mainly solving some research gaps of GPR18, PDK4, NRG1 and EPHB2, and the lack of depression detection.

[0008] In order to solve the above problems, the present application adopts the following technical solutions:

[0009] The combination of the GPR18 expression level detection agent and the expression level detection agent of at least one of PDK4, NRG1 and EPHB2 is used in the preparation of a severe depression detection product.

[0010] In some ways, the combination of the GPR18 expression level detection agent and the expression level detection agent of at least two of PDK4, NRG1 and EPHB2 is used in the preparation of a severe depression detection product.

[0011] In some ways, the combination of the GPR18 expression level detection agent and the expression level detection agent of PDK4, NRG1 and EPHB2 is used in the preparation of a severe depression detection product.

[0012] In some ways, the severe depression detection product is an early non-invasive quantitative detection reagent for severe depression.

[0013] In some ways, the expression level detection agent includes a GSE98793 chip.

[0014] The severe depression detection reagent includes a GPR18 expression level detection agent and an expression level detection agent of at least one of PDK4, NRG1 and EPHB2.

[0015] In some ways, the severe depression detection reagent is an early non-invasive quantitative detection reagent for severe depression.

[0016] The present application has the following beneficial effects:

[0017] The expression level detection of GPR18, PDK4, NRG1 and EPHB2 actually has high diagnostic efficiency and can be used as an independent candidate diagnostic biomarker. The blood sample can be detected by using a GSE98793 chip for non-invasive and high-throughput detection of its expression level. The accuracy of each as a screening indicator reaches 0.6 to 0.8, reaching the standard of a medium-strength screening indicator, and the accuracy of the combined expression level indicator as a screening indicator reaches 0.779. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1is Example 1 analysis of GSE98793 dataset (tissue source is whole blood) for expression difference of different genes between normal healthy control population and depression patients;

[0019] Figure 2 is Example 3 analysis of GSE53987 dataset for significant decrease of GPR18 expression in prefrontal cortex tissue of depression patients relative to normal controls, *P<0.05;

[0020] Figure 3 is Example 4 evaluation of diagnostic efficacy of GPR18, PDK4, NRG1 and EPHB2 genes;

[0021] Figure 4 is Example 5 chart of test results of depression animal model modeling;

[0022] Figure 5 is Example 5 test results of GPR18 expression in prefrontal cortex tissue of depression model mice; DETAILED DESCRIPTION

[0023] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0024] The severe depression detection reagent provided by the present application comprises one or more combinations of CAPNS2, WDR41, GPR18, PDK4, NRG1 and EPHB2 gene expression level detection reagents; preferably comprises one or more combinations of GPR18, PDK4, NRG1 and EPHB2 gene expression level detection reagents; more preferably comprises GPR18, PDK4, NRG1 and EPHB2 gene expression level detection reagents.

[0025] It is a gene expression level detection reagent, preferably a gene mRNA expression level detection reagent, such as GSE98793 chip.

[0026] The depression detection system provided by the present application comprises a gene expression level acquisition module and a judgment module.

[0027] The gene expression level acquisition module is configured to acquire the expression levels of one or more of the CAPNS2, WDR41, GPR18, PDK4, NRG1 and EPHB2 genes and provide the acquired expression levels to the judgment module; preferably, the expression levels of one or more of the GPR18, PDK4, NRG1 and EPHB2 genes are acquired; more preferably, the expression levels of the GPR18, PDK4, NRG1 and EPHB2 genes are acquired.

[0028] The judgment module takes the expression levels of one or more of the CAPNS2, WDR41, GPR18, PDK4, NRG1 and EPHB2 genes as inputs of a classifier to determine whether the sample is from a patient with major depressive disorder.

[0029] The expression levels of the GPR18, PDK4, NRG1 and EPHB2 genes are used as classification criteria, and the AUC reaches more than 0.7, which can be used as a reference for clinical screening of major depressive disorder.

[0030] The following is an example:

[0031] Example 1 Statistical analysis of expression difference between normal controls and patients with major depressive disorder

[0032] Data source: The gene differential expression data of the verified depressive patients and normal controls comes from the GSE98793 dataset of the GEO database (Gene Expression Omnibus), which is analyzed by using the GPL570 platform ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.) for blood sample analysis. Among them, there are 128 cases of patients with major depressive disorder and 64 cases of normal controls, including two batches of data, and the batch effect is eliminated by using the tool "removeBatchEffect" of the limma package.

[0033] Statistics: The limma toolkit in R language is used to analyze the expression difference of the CAPNS2, WDR41, GPR18, PDK4, NRG1 and EPHB2 genes. The results show that the expression levels of the above genes in patients with major depressive disorder are significantly different (P value < 0.05 and (|log FC|) ≥ 0.2), and part of the differentially expressed genes are as shown in Table 1. Figure 1

[0034]

[0035] Example 2 Logistic regression analysis of expression difference between normal controls and patients with major depressive disorder

[0036] ​Multivariate logistic regression analysis: multivariate logistic regression analysis was performed using the backward stepwise method, the results are shown in the following table, showing that the influence between GPR18, PDK4, NRG1 and EPHB2 genes is independent, suitable as a biochemical detection index of depression, and together provides a screening basis.

[0037]

[0038]

[0039] Example 3 GSE53987 postmortem brain tissue verification

[0040] In order to further verify the biological function of GPR18 and depression, we analyzed the expression changes of these diagnostic marker genes in the postmortem brain tissue of patients with depression. The GSE53987 test results show that in the prefrontal cortex of the postmortem brain tissue of patients with depression, the expression of GPR18 is significantly down-regulated, which is consistent with our peripheral results. Then we established a chronic social defeat stress mouse depression model (CSDS model), compared with normal mice, the mRNA level of GPR18 in the prefrontal cortex tissue of CSDS model was significantly decreased. The results of the experimental animal model are consistent with the foregoing peripheral blood and postmortem brain tissue results of human body.

[0041] Example 4 Depression detection system

[0042] Taking the expression of GPR18, PDK4, NRG1 and EPHB2 genes in blood as a detection index, the efficiency of the genes as diagnostic markers of major depressive disorder was calculated.

[0043] Verification data: the data of differential expression of genes between depression and normal people were from GSE98793 dataset of GEO database (Gene Expression Omnibus), using GPL570 platform ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.) for blood sample analysis. Among them, 128 cases of patients with severe depression and 64 cases of normal controls, including two batches of data, using the tool "removeBatchEffect" of limma package to eliminate batch effect.

[0044] The results show that the expression level of GPR18 gene reaches 0.702 (95% CI 0.628-0.777), the expression level of PDK4 gene reaches 0.620 (95% CI 0.536-0.705), the expression level of NRG1 gene reaches 0.660 (95% CI 0.581-0.741), and the expression level of EPHB2 gene reaches 0.631 (95% CI 0.550-0.710). All of them reach the accuracy requirement of the medium-intensity screening index (AUC is between 0.5 and 0.8).

[0045] It should be noted that the accuracy of the expression level of the remaining genes can also be calculated by the foregoing method. The calculation of the joint model is performed by using the commonly used AUC analysis model.

[0046] The best diagnostic indicator is shown by multivariate logistic regression: (-0.651*PDK4 expression) + (-1.958*GPR18 expression) + (0.638*NRG1 expression) + (0.899*EPHB2 expression). The accuracy AUC value of the joint gene index is as high as 0.779 (95% CI=0.709-0.848), which is higher than the detection accuracy of the expression level of a single gene. The following table shows:

[0047]

[0048] Although NRG1, PDK4, and EPHB2 can play a role in the pathophysiological process of depression, there is no evidence that the expression level in the pathological state of severe depression is different from that in the healthy state. For example, the polymorphism of the NRG1 gene may be associated with depression, but whether the expression level can be used as an early screening indicator for the diagnosis of depression depends on whether it is suitable for detection under different influencing factors (age, gender), such as expression level, and whether it has sufficient sample discrimination ability (reaching a medium accuracy level) as a depression screening marker. The present application first confirms the association between the previously unreported GPR18 gene and severe depression, and then provides data and experimental evidence that the expression levels of six genes (CAPNS2, WDR41, GPR18, PDK4, NRG1, and EPHB2) can be used as biochemical indicators for the detection of severe depression, including effectiveness and stability. The preferred scheme analyzes the independent influence between these genes and confirms that the best combination of severe depression gene expression detection markers is the expression levels of GPR18, PDK4, NRG1, and EPHB2 genes.

[0049] Example 5 Animal model verification

[0050] The animal model of depression was established, and the animal model of depression was evaluated by behavioral tests (sucrose preference, tail suspension, forced swimming), and the results are shown in Figure 4 The results show that the animal model of depression is successfully established, and the animal model of depression is significantly different from the normal control *P<0.05.

[0051] The GPR18 primer sequence was designed, and the prefrontal cortex tissues of the depression mice and normal animals were taken, and the relative expression of GPR-18 mRNA was detected by RT-PCR. The GPR18 primer sequence is as follows:

[0052] Forward sequence: 5'-GAAGCCCAAGGTCAAGGAGAAGTC-3'

[0053] Reverse sequence: 5'-GCGAACACTGCGAAGGTAATTGC-3'

[0054] The results are shown in Figure 5 Compared with the normal mice, the expression of GPR18 in the prefrontal cortex tissues of the depression model mice is significantly reduced *P<0.01.

[0055] Those skilled in the art can clearly make various modifications to the above embodiments without departing from the overall spirit and concept of the present application. All fall within the scope of the present application. The protection scheme of the present application is subject to the claims attached to the present application.

Claims

1. The use of a GPR18 expression level detection agent in combination with an expression level detection agent of at least one of PDK4, NRG1, and EPHB2 in the preparation of a major depressive disorder detection product.

2. Use according to claim 1, characterized in that, The use of a GPR18 expression level detection agent in combination with an expression level detection agent of at least two of PDK4, NRG1, and EPHB2 in the preparation of a major depressive disorder detection product.

3. Use according to claim 2, characterized in that, The use of a GPR18 expression level detection agent in combination with an expression level detection agent of PDK4, NRG1, and EPHB2 in the preparation of a major depressive disorder detection product.

4. Use according to any one of claims 2-3, characterized in that, The major depressive disorder detection product is an early non-invasive quantitative detection reagent for major depressive disorder.

5. Use according to any one of claims 2 to 3, characterized in that, The expression level detection agent comprises a GSE98793 chip.

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