New uses of gpr18 and depression detection reagents

Abstract

The application belongs to the field of biochemical detection, and specifically discloses a new use of GPR18 and a depression detection reagent. The application of the expression level detection reagent of GPR18 in the preparation of a major depressive disorder detection product. The expression level detection of GPR18, CAPNS2 and WDR41 in the application actually has high diagnostic efficiency, and can be used as an independent candidate diagnostic biomarker. Blood samples can be detected for expression level by using a GSE98793 chip in a non-invasive and high-throughput manner. The accuracy of each as a screening indicator reaches 0.6 to 0.8, and the accuracy of the combined expression level indicator as a screening indicator is also improved to more than 0.7.

Description

[0001] This application is a divisional application of the invention patent entitled "A Reagent, System and Application for the Detection of Severe Depression", application number 202110451314.3, and application date April 26, 2021. Technical Field

[0002] This invention belongs to the field of biochemical detection technology, specifically relating to new uses of GPR18 and a reagent for detecting depression. Background Technology

[0003] Major depressive disorder is one of the most common neuropsychiatric disorders. With socioeconomic development and increasing work pressure, the global incidence of depression is rising year by year, accounting for 1.4% of the world's total. Therefore, depression can cause serious harm to individuals, families, and society. Currently, the clinical criteria for the diagnosis and treatment of major depressive disorder are based on the patient's mood disorder characteristics. The objective criteria for early diagnosis of major depressive disorder remain unclear, and existing biomarkers are questioned due to poor specificity and sensitivity.

[0004] Furthermore, depression is a chronic illness caused by multiple factors, influenced by epigenetics, endocrine factors, physiological changes, and environmental factors. Increasing evidence suggests that environmental and genetic factors interact, inducing epigenetic changes through long-term alterations in neural circuits, endocrine systems, and synaptic structures, thereby increasing the risk of depressive symptoms and other neuropsychiatric disorders. This evidence demonstrates that genetic factors play a highly complex role in the occurrence and development of severe depression.

[0005] Autophagy is a highly preserved biodegradation process in eukaryotes, transporting intracellular components (including proteins and organelles) to lysosomes. Autophagy genes involved in metabolic regulation exhibit significant changes in expression levels; for example, their expression increases dramatically under starvation to provide amino acids for energy supply, particularly in the liver. In neurons, autophagy maintains neuronal homeostasis and function by clearing misfolded proteins and organelles, acting as a housekeeping gene with stable expression levels.

[0006] More than 30 autophagy-associated genes (ARGs) are considered closely related to disease and metabolic disorders, and are thought to be involved in various pathological processes. However, the role of autophagy-associated genes in severe depression remains unclear. Through data mining, we discovered that autophagy-associated genes may be involved in the immune dysregulation associated with severe depression, particularly the metabolic pathways of depression involving prostate cytokines. Data validate that autophagy-associated genes can serve as biomarkers and biochemical indicators for severe depression. Summary of the Invention

[0007] In response to the above-mentioned deficiencies or improvement needs of existing technologies, this invention provides new uses for GPR18 and a reagent for detecting depression, mainly addressing some research gaps in GPR18, CAPNS2, and WDR41, as well as the lack of depression detection.

[0008] To solve the above problems, the present invention adopts the following technical solution:

[0009] Application of GPR18 expression level detection reagent in the preparation of severe depression detection products.

[0010] Application of the combination of GPR18 expression level detection reagent with at least one of CAPNS2 and WDR41 expression level detection reagents in the preparation of severe depression detection products.

[0011] In some approaches, the combination of GPR18 expression level detection reagents with CAPNS2 and WDR41 expression level detection reagents is used in the preparation of severe depression detection products.

[0012] In some embodiments, the severe depression detection product is a non-invasive quantitative detection reagent for the early stage of severe depression.

[0013] In some approaches, expression level detection agents include the GSE98793 chip.

[0014] A diagnostic kit for severe depression, comprising a GPR18 expression level assay and an expression level assay for at least one of CAPNS2 and WDR41.

[0015] In some embodiments, the severe depression detection product is a non-invasive quantitative detection reagent for the early stage of severe depression.

[0016] The beneficial effects of this invention are:

[0017] The expression levels of GPR18, CAPNS2, and WDR41 demonstrate high diagnostic efficiency and can function as independent candidate diagnostic biomarkers. Their expression levels in blood samples can be detected non-invasively and with high throughput using the GSE98793 chip. The accuracy of each individual expression level as a screening indicator reaches 0.6 to 0.8, meeting the standard for moderate-intensity screening indicators. The combined expression level of these three indicators also improves the accuracy to over 0.7. Attached Figure Description

[0018] Figure 1 Example 1 analyzes the GSE98793 dataset (tissue source: whole blood) to show the expression differences of different genes between normal healthy controls and patients with depression.

[0019] Figure 2Example 3 analyzed the GSE53987 dataset and found that GPR18 expression was significantly decreased in the prefrontal cortex tissue of patients with depression compared to normal controls (*P<0.05).

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

[0021] Figure 4 This is a diagram showing the modeling and testing results of the animal model of depression in Example 5;

[0022] Figure 5 This is the result of the GPR18 expression level test in the prefrontal cortex tissue of a mouse model of depression in Example 5; Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention 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 invention includes one or more of the following: CAPNS2, WDR41, GPR18, PDK4, NRG1 and EPHB2 gene expression level detection reagents; preferably, it includes one or more of the following: GPR18, PDK4, NRG1 and EPHB2 gene expression level detection reagents; more preferably, it includes 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 the GSE98793 chip.

[0026] The depression detection system provided by this invention includes a gene expression level acquisition module and a judgment module;

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

[0028] The judgment module uses the expression levels of one or more genes among CAPNS2, WDR41, GPR18, PDK4, NRG1, and EPHB2 as input to the classifier to determine whether the sample comes from a patient with severe depression.

[0029] The expression levels of GPR18, PDK4, NRG1, and EPHB2 genes were used as the classification criteria. An AUC of 0.7 or higher can be used as a reference for clinical screening of severe depression.

[0030] The following is an example:

[0031] Example 1: Statistical analysis of differences in expression levels between normal controls and patients with severe depression.

[0032] Data Source: Gene expression differentials between patients with depression and healthy controls were validated using the GEO (Gene Expression Omnibus) dataset GSE98793. Blood sample analysis was performed using the GPL570 platform ([HG-U133_Plus_2]Affymetrix Human Genome U133 Plus 2.0 Array). The dataset included 128 patients with severe depression and 64 healthy controls, comprising two batches of data. Batch effects were eliminated using the "removeBatchEffect" tool from the limma package.

[0033] Statistical analysis: The limma toolkit in R was used to analyze the differential expression of CAPNS2, WDR41, GPR18, PDK4, NRG1, and EPHB2 genes. The results showed that the expression levels of these genes differed significantly in patients with severe depression (P < 0.05 and (|log FC|) ≥ 0.2). Some differentially expressed genes, such as... Figure 1 As shown.

[0034] Gene name Expression differences p-value CAPNS2 0.45 1.33E-02 WDR41 -0.76 5.8E-04 GPR18 -0.65 7.9E-03 PDK4 0.51 2.21E-03 NRG1 0.47 2.17E-03 EPHB2 0.61 2.23E-03

[0035] Example 2: Logistic Regression Analysis - Differences in Expression Levels Between Normal Controls and Patients with Severe Depression

[0036] Data source: Same as Example 1

[0037] Multivariate logistic regression analysis: Multivariate logistic regression analysis was performed using the stepwise backward method. The results are shown in the table below. The results show that the effects of GPR18, PDK4, NRG1, and EPHB2 genes are independent of each other and are suitable as biochemical indicators for the detection of depression, providing a basis for screening.

[0038] Gene name Expression differences p-value GPR18 -1.45 0.027 PDK4 -0.59 0.034 NRG1 0.58 0.045 EPHB2 0.86 0.034

[0039] Example 3: Verification of GSE53987 autopsy brain tissue

[0040] To further validate the biological functions of GPR18 and depression, we analyzed the expression changes of these diagnostic biomarker genes in the brain tissue of cadavers with depression. GSE53987 assays showed that GPR18 expression was significantly downregulated in the prefrontal cortex of cadaveric brain tissue, consistent with our peripheral blood findings. Subsequently, we established a chronic social frustration stress (CSDS) mouse model of depression. Compared to normal mice, the CSDS model showed a significant decrease in GPR18 mRNA levels in the prefrontal cortex. The results from the animal model were consistent with the aforementioned results from human peripheral blood and cadaveric brain tissue.

[0041] Example 4: Depression Detection System

[0042] The expression levels of GPR18, PDK4, NRG1, and EPHB2 genes in blood were used as detection indicators to calculate their efficacy as diagnostic markers for major depressive disorder.

[0043] Validation data: Data on differential gene expression between individuals with depression and healthy controls were obtained from the GEO (Gene Expression Omnibus) dataset GSE98793. Blood sample analysis was performed using the GPL570 platform ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array). The dataset included 128 patients with severe depression and 64 healthy controls, comprising two batches of data. Batch effects were eliminated using the "removeBatchEffect" tool from the limma package.

[0044] The results showed that the accuracy of the expression level detection for GPR18 gene was 0.702 (95% CI 0.628–0.777), for PDK4 gene it was 0.620 (95% CI 0.536–0.705), for NRG1 gene it was 0.660 (95% CI 0.581–0.741), and for EPHB2 gene it was 0.631 (95% CI 0.550–0.710). All met the accuracy requirements for moderate-intensity screening indicators (AUC between 0.5 and 0.8).

[0045] It should be noted that the accuracy of the remaining gene expression level detection results can also be calculated using the aforementioned methods. The joint model is calculated using the commonly used AUC analysis model.

[0046] Multivariate logistic regression analysis showed that the optimal diagnostic indicator was: (-0.651 * PDK4 expression level) + (−1.958 * GPR18 expression level) + (0.638 * NRG1 expression level) + (0.899 * EPHB2 expression level). The accuracy AUC of the combined gene indicator was as high as 0.779 (95% CI = 0.709–0.848), which is higher than the accuracy of detecting the expression level of a single gene. See the table below:

[0047] index AUC GPR18 0.702 PDK4 0.620 NRG1 0.660 EPHB2 0.631 Gene joint model 0.779

[0048] Although NRG1, PDK4, and EPHB2 have been shown to play roles in the pathophysiology of depression, there is no evidence to suggest differences in their expression levels between severe depression and healthy individuals. For example, NRG1 gene polymorphism may be associated with depression, but whether its expression level can be used as an early screening indicator for depression diagnosis depends on whether it is a suitable indicator for detection under different influencing factors (age, gender), whether its expression level shows relatively stable differences, and whether it has sufficient discriminative power (reaching a moderate level of accuracy) as a screening biomarker for depression. This invention first confirms the association between the previously unreported gene GPR18 and severe depression, and then provides empirical 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. An analysis of the independence of influence among these genes was conducted, confirming that the optimal combination of gene expression level detection biomarkers for severe depression is the expression levels of GPR18, PDK4, NRG1, and EPHB2.

[0049] Example 5: Animal Model Validation

[0050] An animal model of depression was established, and behavioral tests (sucrose preference, tail suspension, forced swimming) were used to evaluate the animal model of depression. The results are as follows: Figure 4 As shown in the figure. The results showed that the depression model was successfully established, and the animal model of depression was significantly different from the normal control (P<0.05).

[0051] GPR18 primer sequences were designed and synthesized. Prefrontal cortex tissues from depressed mice and normal animals were collected, and the relative expression level of GPR-18 mRNA was detected by RT-PCR. The primer sequences for GPR18 are as follows:

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

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

[0054] The results show that... Figure 5 As shown, compared with normal mice, GPR18 expression was significantly reduced in the prefrontal cortex tissue of a mouse model of depression*P<0.01.

[0055] Those skilled in the art will appreciate that various modifications to the above embodiments can be made without departing from the overall spirit and concept of the present invention. All such modifications fall within the protection scope of the present invention. The protection scheme of the present invention is defined by the appended claims.

Claims

1. Application of GPR18 expression level detection reagent in the preparation of severe depression detection products.

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