A viral marker for depression diagnosis and screening method and application thereof

Abstract

The application discloses a virus marker for depression diagnosis and a screening method and application thereof, relates to the technical field of biological detection, and the virus marker for depression diagnosis comprises at least one of s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1 and g_Alexandravirus. The application firstly takes the enterovirus group characteristics as the biomarker of depression, breaks through the limitation that traditional mental illness diagnosis relies on subjective scale evaluation; compared with the prior art, the four virus markers can be used for the diagnosis of clinical depression alone or in combination, have the characteristics of non-invasiveness and high sensitivity, can effectively distinguish the depression patients from the healthy people, can early discover the high-risk groups, and provide a new objective diagnosis method for the precise typing of depression.

Description

Technical Field

[0001] This invention relates to the field of biological detection technology, and in particular to a viral biomarker for the diagnosis of depression, its screening method, and its application. Background Technology

[0002] Depression is characterized by a significant and persistent low mood and is a major type of mood disorder. Currently, the clinical diagnosis and classification of depression mainly rely on depression scales, based on the patient's subjective description and the psychiatric examination by a psychiatrist. This method is highly subjective, easily influenced by the doctor's experience, and lacks objective examination methods, biological indicators, and evidence, resulting in a high rate of misdiagnosis and missed diagnosis.

[0003] In recent years, many studies have shown that depression involves changes in multiple systems and indicators in the body. These structural and biological changes can be used for early prediction and diagnostic classification of depression, and are directly related to the efficacy and selection of antidepressants.

[0004] However, there is still a lack of an effective and objective diagnostic method for clinical depression. Summary of the Invention

[0005] The main objective of this invention is to propose a viral biomarker for the diagnosis of depression, its screening method, and its application, aiming to provide a novel and effective objective diagnostic method for clinical depression.

[0006] To achieve the above objectives, the present invention proposes a viral biomarker for the diagnosis of depression, wherein the viral biomarker for the diagnosis of depression includes at least one of s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1 and g_Alexandravirus.

[0007] The present invention provides a viral biomarker for the diagnosis of depression, including at least one of s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1, and g_Alexandravirus. This invention is the first to use enteroviral characteristics as biomarkers for depression, overcoming the limitations of traditional reliance on subjective scale assessments in the diagnosis of mental illness. Compared to existing technologies, the above four viral biomarkers can be used alone or in combination for the clinical diagnosis of depression, featuring non-invasiveness and high sensitivity. They can effectively distinguish between patients with depression and healthy individuals, enabling early detection of high-risk groups and providing a novel, objective diagnostic method for the precise subtyping of depression.

[0008] It should be noted that enteroviruses s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1, and g_Alexandravirus can be used individually as biomarkers for the diagnosis of depression, and the combined use of enteroviruses s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1, and g_Alexandravirus for the diagnosis of depression is also within the scope of protection of this invention.

[0009] The present invention also provides the application of a viral biomarker for the diagnosis of depression in the preparation of products for the diagnosis of depression.

[0010] Preferably, the product includes rapid detection devices such as test strips, reagent kits, microfluidic chips, or biosensors. The viral biomarker can serve as a biological target, enabling specific identification and quantitative detection of the viral biomarker in the blood, saliva, or other bodily fluid samples of the subject through methods such as immunoassay, nucleic acid amplification technology, or mass spectrometry.

[0011] In one embodiment, the product includes test strips, such as colloidal gold immunochromatographic test strips, fluorescent immunochromatographic test strips, or electrochemical sensing test strips. These test strips offer advantages such as ease of use, rapid detection, high sensitivity, and strong specificity, making them suitable for on-site screening and home self-testing.

[0012] In one embodiment, the product includes a kit, which comprises at least one of a real-time quantitative PCR kit, a multiplex PCR / nucleic acid microarray kit, a digital PCR kit, a high-throughput sequencing kit, a colloidal gold immunochromatographic kit, and an enzyme-linked immunosorbent assay (ELISA) kit. The multiplex PCR / nucleic acid microarray kit combines the advantages of multiplex PCR and nucleic acid microarray technologies, enabling the simultaneous detection of the presence and quantity of multiple target viral sequences, making it highly suitable for multi-target viral detection in complex samples.

[0013] In one embodiment, the product includes a kit comprising primers and probes, the primers and probes including at least one of primers and probes for s_Stenotrophomonas_virus_Pokken, primers and probes for g_Pokkenvirus, primers and probes for s_Dickeya_virus_AD1, and primers and probes for g_Alexandravirus, wherein the primers and probes are nucleic acids. It is understood that the above four primers and probes can rapidly detect the presence of their corresponding viral markers, thereby rapidly identifying depression.

[0014] In one embodiment, the product includes a kit containing antibodies, including at least one of the following: antibodies against the s_Stenotrophomonas_virus_Pokken antigen, antibodies against the g_Pokkenvirus antigen, antibodies against the s_Dickeya_virus_AD1 antigen, and antibodies against the g_Alexandravirus antigen. The kit may also contain reagents for detecting viral biomarkers, such as enzyme markers, and positive controls, wherein the positive controls are different concentrations of a single viral biomarker or a combination thereof, suitable for high-throughput screening and diagnostic analysis in a laboratory setting.

[0015] This invention also provides a method for screening viral biomarkers for the diagnosis of depression, comprising the following steps:

[0016] S1. Collect fecal samples from patients with depression and healthy individuals, and extract total DNA from the fecal samples;

[0017] S2. Perform high-throughput sequencing on the total DNA of each fecal sample in step S1, use Trimmomatic for quality control, remove the host sequence using BMTagger, and assemble the viral sequence using Metahit.

[0018] S3. Differentially expressed viruses were screened using Wilcoxon rank-sum test and LEfSe analysis.

[0019] S4. The performance of the differentially expressed virus in the diagnosis of depression was analyzed using a Logistic binary regression model and ROC analysis.

[0020] This method is highly efficient at screening differentially expressed viruses and can identify patients with depression using fewer differentially expressed viruses as biomarkers.

[0021] In one embodiment, a verification step is included after step S4:

[0022] A verification queue is provided, which includes patients with depression and healthy individuals;

[0023] Viral metagenomic analysis was performed on fecal samples from the patients with depression and the healthy individuals to obtain the expression abundance matrix of differentially expressed viruses;

[0024] The expression abundance matrix of the differentially expressed virus was substituted into the Logistic binary regression analysis model for verification to confirm the effectiveness of the viral biomarker for the diagnosis of depression.

[0025] The expression abundance matrix of the differentially expressed virus refers to the viral metagenomic detection results of the validation cohort. The expression abundance of the differentially expressed virus in the validation cohort is substituted into the diagnostic model (Logistic binary regression analysis model) of the training set in step S4 to verify the diagnostic performance of the features in the validation cohort and confirm the effectiveness of the viral biomarker for the diagnosis of depression. This verification method allows for a rapid determination of the general applicability of the differentially expressed viruses screened by this invention.

[0026] In one embodiment, the Logistic binary regression model is at least one of the following formulas:

[0027] Logit(P) = a1 + b1 × s_Stenotrophomonas_virus_Pokken, where a1 ∈ [0.25, 1.87], b1 ∈

[0028] [-4839.97, -1145.14];

[0029] Logit(P) = a² + b² × g_Pokkenvirus, where a² ∈ [0.25, 1.87], b² ∈ [-4839.97, -1145.14];

[0030] Logit(P) = a3 + b3 × s_Dickeya_virus_AD1, where a3 ∈ [0.61, 2.35], b3 ∈ [-21813, -5267];

[0031] Logit(P) = a4 + b4 × g_Alexandravirus, where a4 ∈ [0.39, 1.96], b4 ∈ [-11417, -2664];

[0032] Logit(P)=a5+b5×s_Stenotrophomonas_virus_Pokken, where a5∈[0.25,1.87], b5∈

[0033] [-4839.97, -1145.14];

[0034] Logit(P)=a6+b6×s_Stenotrophomonas_virus_Pokken+c6×s_Dickeya_virus_AD1, which

[0035] In the given information, a6∈[0.51,2.68], b6∈[-3510.5,2361.5], c6∈[-21866,-5034];

[0036] Logit(P)=a7+b7×s_Stenotrophomonas_virus_Pokken+c7×g_Alexandravirus, where, a7

[0037] b7∈[0.38,2.26], b7∈[-3358.64,1776.56], c7∈[-11250.90,-1911.82];

[0038] Logit(P) = a8 + b8 × g_Pokkenvirus + c8 × s_Dickeya_virus_AD1, where a8 ∈ [0.52, 2.68].

[0039] b8∈[-3510.58,2361.58], c8∈[-21866.24,-5033.76];

[0040] Logit(P)=a9+b9×g_Pokkenvirus+c9×g_Alexandravirus, where a9∈[0.38,2.26], b9∈

[0041] [-3358.64,1776.56], c9∈[-11250.90,-1911.82];

[0042] Logit(P) = a 10 +b 10 ×s_Dickeya_virus_AD1+c 10 ×g_Alexandravirus, where a 10 ∈[0.58,

[0043] 2.34], b 10 ∈[-26827.18,-2266.02], c 10 ∈[-7441.80,9341.04];

[0044] Logit(P) = a 11 +b 11 ×s_Stenotrophomonas_virus_Pokken+c 11 ×s_Dickeya_virus_AD1, its

[0045] In the middle, a 11 ∈[0.52,2.68], b 11 ∈[-3510.58,2361.58], c 11 ∈[-21866.24,-5033.76];

[0046] Logit(P) is 12 +b 12 ×s_Stenotrophomonas_virus_Pokken+c 12 ×g_Alexandravirus,people,

[0047] a 12 ∈[0.38,2.26],b 12 ∈[-3358.64,1776.56],c 12 ∈[-11250.90,-1911.82]

[0048] Logit(P) is 13 +b 13 ×s_Stenotrophomonas_virus_Pokken+c 13 ×s_Dickeya_virus_AD1+

[0049] d 13 ×g_Alexandravirus,personally,a 13 ∈[0.50,2.66],b 13 ∈[-3540.84.2315.64],c 13 ∈[-27057.96,-2162.04],d 13 ∈[-7362.32,9560.32]

[0050] Logit(P) is 14 +b 14 ×g_Pokkenvirus+c 14 ×s_Dickeya_virus_AD1+d 14 ×

[0051] g_Alexandravirus,individual,a 14 ∈[0.50,2.66],b 14 ∈[-3540.84.2315.64],c 14 ∈[-27057.96,-2162.04],d 14 ∈[-7362.32,9560.32]

[0052] Logit(P) is 15 +b 15 ×s_Stenotrophomonas_virus_Pokken+c 15×s_Dickeya_virus_AD1+

[0053] d 15 ×g_Alexandravirus, where a 15 ∈[0.50,2.66],b 15 ∈[-3540.84,2315.64], c 15 ∈[-27057.96,-2162.04], d 15 ∈[-7362.32,9560.32].

[0054] In the diagnostic model described above, the features s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1, and g_Alexandravirus represent their relative abundances.

[0055] In one embodiment, in step S3, the method for confirming whether the differentially expressed virus is the viral biomarker used for the diagnosis of depression is as follows:

[0056] When the differentially expressed virus is s_Stenotrophomonas_virus_Pokken, and the cutoff value of the ROC analysis is A1, where A1∈(0.158,0.737), then s_Stenotrophomonas_virus_Pokken is determined to be a viral biomarker for the diagnosis of depression; or,

[0057] When the differentially expressed virus is g_Pokkenvirus, and the cutoff value of the ROC analysis is A2, where A2∈(0.158,0.737), then g_Pokkenvirus is determined to be a viral biomarker for the diagnosis of depression; or,

[0058] When the differentially expressed virus is s_Dickeya_virus_AD1, and the cutoff value of the ROC analysis is A3, where A3 ∈ (0.387, 0.792), then s_Dickeya_virus_AD1 is determined to be a viral biomarker for the diagnosis of depression; or,

[0059] When the differentially expressed virus is g_Alexandravirus, and the cutoff value of the ROC analysis is A4, A4∈(0.537,0.751), then g_Alexandravirus is determined to be a viral biomarker for the diagnosis of depression; or,

[0060] When the differentially expressed viruses are s_Stenotrophomonas_virus_Pokken and g_Pokkenvirus, and the cutoff value of ROC analysis is A5, A5∈(0.185,0.737), then s_Stenotrophomonas_virus_Pokken and g_Pokkenvirus are determined to be viral biomarkers for the diagnosis of depression; or,

[0061] When the differentially expressed viruses are s_Stenotrophomonas_virus_Pokken and s_Dickeya_virus_AD1, and the cutoff value of ROC analysis is A6, A6∈(0.387,0.791), then s_Stenotrophomonas_virus_Pokken and s_Dickeya_virus_AD1 are determined to be viral biomarkers for the diagnosis of depression; or,

[0062] When the differentially expressed viruses are s_Stenotrophomonas_virus_Pokken and g_Alexandravirus, and the cutoff value of ROC analysis is A7, A7∈(0.527,0.692), then s_Stenotrophomonas_virus_Pokken and g_Alexandravirus are determined to be viral biomarkers for the diagnosis of depression; or,

[0063] When the differentially expressed viruses are g_Pokkenvirus and s_Dickeya_virus_AD1, and the cutoff value of ROC analysis is A8, where A8 ∈ (0.395, 0.799), then g_Pokkenvirus and s_Dickeya_virus_AD1 are determined to be viral biomarkers for the diagnosis of depression; or,

[0064] When the differentially expressed viruses are g_Pokkenvirus and g_Alexandravirus, and the cutoff value of ROC analysis is A9, where A9 ∈ (0.558, 0.698), then g_Pokkenvirus and g_Alexandravirus are determined to be viral biomarkers for the diagnosis of depression; or,

[0065] When the differentially expressed viruses are s_Dickeya_virus_AD1 and g_Alexandravirus, and the cutoff value of ROC analysis is A10, where A10∈(0.382,0.789), then s_Dickeya_virus_AD1 and g_Alexandravirus are determined to be viral biomarkers for the diagnosis of depression; or,

[0066] When the differentially expressed viruses are s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, and s_Dickeya_virus_AD1, and the cutoff value of ROC analysis is A11, where A11∈(0.387,0.785), then s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, and s_Dickeya_virus_AD1 are determined to be viral biomarkers for the diagnosis of depression; or,

[0067] When the differentially expressed viruses are s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, and g_Alexandravirus, and the cutoff value of ROC analysis is A12, A12∈(0.543,0.694), then s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, and g_Alexandravirus are determined to be viral biomarkers for the diagnosis of depression; or,

[0068] When the differentially expressed viruses are s_Stenotrophomonas_virus_Pokken, s_Dickeya_virus_AD1, and g_Alexandravirus, and the cutoff value of ROC analysis is A13, where A13 ∈ (0.389, 0.757), then s_Stenotrophomonas_virus_Pokken, s_Dickeya_virus_AD1, and g_Alexandravirus are determined to be viral biomarkers for the diagnosis of depression; or,

[0069] When the differentially expressed viruses are g_Pokkenvirus, s_Dickeya_virus_AD1, and g_Alexandravirus, and the cutoff value of ROC analysis is A14, where A14∈(0.393,0.787), then g_Pokkenvirus, s_Dickeya_virus_AD1, and g_Alexandravirus are determined to be viral biomarkers for the diagnosis of depression; or,

[0070] When the differentially expressed viruses are s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1, and g_Alexandravirus, and the cutoff value of ROC analysis is A15, where A15 ∈ (0.389-0.787), then s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1, and g_Alexandravirus are determined to be viral biomarkers for the diagnosis of depression.

[0071] The present invention provides a viral biomarker for the diagnosis of depression, including at least one of s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1, and g_Alexandravirus. This invention is the first to use enteroviral characteristics as biomarkers for depression, overcoming the limitations of traditional mental illness diagnosis which relies on subjective scale assessments. Compared to existing technologies, the above four viral biomarkers can be used alone or in combination for the clinical diagnosis of depression, featuring non-invasiveness and high sensitivity. They can effectively distinguish between patients with depression and healthy individuals, enabling early detection of high-risk groups and providing a novel, objective diagnostic method for the precise subtyping of depression. Attached Figure Description

[0072] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0073] Figure 1 Here is a scatter plot of four viral characteristics from the normal group and the depression group in Example 1 of this invention: Figure 1 (A) shows the results of the relative expression levels of s_Stenotrophomonas_virus_Pokken in the normal group and the depression group; Figure 1 (B) shows the results of the relative expression levels of g_Pokkenvirus in the normal group and the depression group; Figure 1 (C) shows the results of the relative expression levels of s_Dickeya_virus_AD1 in the normal group and the depression group; Figure 1 (D) shows the results of relative expression levels of g_Alexandravirus in the normal group and the depression group;

[0074] Figure 2 This is a bar chart of LDA scores from the metavinomic analysis of the normal group and the depression group in Example 1 of this invention.

[0075] Figure 3 The following is a graph showing the univariate ROC analysis results of the training set of four viral biomarkers in the normal group and the depression group in Example 1 of this invention: Figure 3 (A) represents the univariate ROC results of the training set of s_Stenotrophomonas_virus_Pokken;

[0076] Figure 3 (B) shows the univariate ROC analysis results of the training set of g_Pokkenvirus; Figure 3 (C) represents the univariate ROC analysis results of the training set of s_Dickeya_virus_AD1; Figure 3 (D) represents the univariate ROC analysis results of the training set for g_Alexandravirus;

[0077] Figure 4 The following is a graph showing the bivariate ROC analysis results of the training set of four viral biomarkers in the normal group and the depression group in Example 1 of this invention: Figure 4 (A) represents the bivariate ROC analysis results of the training set of s_Stenotrophomonas_virus_Pokken and g_Pokkenvirus; Figure 4 (B) represents the bivariate ROC analysis results of the training set of s_Stenotrophomonas_virus_Pokken and s_Dickeya_virus_AD1; Figure 4 (C) represents the bivariate ROC analysis results of the training set of s_Stenotrophomonas_virus_Pokken and g_Alexandravirus;

[0078] Figure 4 (D) represents the bivariate ROC analysis results of the training set of g_Pokkenvirus and s_Dickeya_virus_AD1; Figure 4 (E) represents the bivariate ROC analysis results of the training set of g_Pokkenvirus and g_Alexandravirus; Figure 4 (F) represents the bivariate ROC analysis results of the training set of s_Dickeya_virus_AD1 and g_Alexandravirus;

[0079] Figure 5The following is a graph showing the results of a three-variable ROC analysis of the training set of four viral biomarkers in the normal group and the depression group in Example 1 of this invention: Figure 5 (A) shows the results of the trivariate ROC analysis of the training set of s_Stenotrophomonas_virus_Pokken in conjunction with g_Pokkenvirus and s_Dickeya_virus_AD1; Figure 5 (B) shows the results of the trivariate ROC analysis of the training set of s_Stenotrophomonas_virus_Pokken in conjunction with g_Pokkenvirus and g_Alexandravirus; Figure 5 (C) represents the results of the trivariate ROC analysis of the training set of s_Stenotrophomonas_virus_Pokken in conjunction with s_Dickeya_virus_AD1 and g_Alexandravirus; Figure 5 (D) represents the results of the trivariate ROC analysis of the training set of g_Pokkenvirus in combination with s_Dickeya_virus_AD1 and g_Alexandravirus;

[0080] Figure 6 This is a graph showing the results of four-variable ROC analysis of the training set of four viral biomarkers s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1, and g_Alexandravirus in the normal group and the depression group in Embodiment 1 of the present invention.

[0081] Figure 7 The following is a graph showing the univariate ROC analysis results of the validation set of four viral biomarkers in the normal group and the depression group in Example 2 of this invention: Figure 7 (A) represents the univariate ROC results of the validation set of s_Stenotrophomonas_virus_Pokken;

[0082] Figure 7 (B) shows the univariate ROC analysis results of the validation set of g_Pokkenvirus; Figure 7 (C) represents the univariate ROC analysis results of the validation set of s_Dickeya_virus_AD1; Figure 7 (D) represents the univariate ROC analysis results of the validation set for g_Alexandravirus;

[0083] Figure 8 The following is a graph showing the bivariate ROC analysis results of the validation set of four viral biomarkers in the normal group and the depression group in Example 2 of this invention: Figure 8(A) represents the bivariate ROC analysis results of the validation set of s_Stenotrophomonas_virus_Pokken and g_Pokkenvirus; Figure 8 (B) represents the bivariate ROC analysis results of the validation set of s_Stenotrophomonas_virus_Pokken and s_Dickeya_virus_AD1; Figure 8 (C) represents the bivariate ROC analysis results of the validation set of s_Stenotrophomonas_virus_Pokken in conjunction with g_Alexandravirus;

[0084] Figure 8 (D) represents the bivariate ROC analysis results of the validation set of g_Pokkenvirus and s_Dickeya_virus_AD1; Figure 8 (E) represents the bivariate ROC analysis results of the validation set of g_Pokkenvirus and g_Alexandravirus; Figure 8 (F) represents the bivariate ROC analysis results of the validation set of s_Dickeya_virus_AD1 in combination with g_Alexandravirus;

[0085] Figure 9 The following is a graph showing the results of a three-variable ROC analysis of the validation set of four viral biomarkers in the normal group and the depression group in Example 2 of this invention: Figure 9 (A) shows the results of the trivariate ROC analysis of the validation set of s_Stenotrophomonas_virus_Pokken in combination with g_Pokkenvirus and s_Dickeya_virus_AD1; Figure 9 (B) shows the results of the trivariate ROC analysis of the validation set of s_Stenotrophomonas_virus_Pokken in conjunction with g_Pokkenvirus and g_Alexandravirus; Figure 9 (C) represents the results of the trivariate ROC analysis of the validation set of s_Stenotrophomonas_virus_Pokken in conjunction with s_Dickeya_virus_AD1 and g_Alexandravirus; Figure 9 (D) represents the results of the trivariate ROC analysis of the validation set of g_Pokkenvirus in combination with s_Dickeya_virus_AD1 and g_Alexandravirus;

[0086] Figure 10This is a graph showing the results of a four-variable ROC analysis of the validation set of four viral biomarkers, s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1, and g_Alexandravirus, in the normal group and the depression group in Embodiment 2 of the present invention.

[0087] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0088] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall apply. Where the manufacturers of reagents or instruments are not specified, they are all conventional products that can be purchased commercially. Furthermore, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, or solution B, or a solution where both A and B are satisfied simultaneously. In addition, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0089] The technical solution of the present invention will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that the following embodiments are only used to explain the present invention and are not intended to limit the present invention.

[0090] Example 1: Screening of viral markers for the diagnosis of depression

[0091] I. Sample Source

[0092] Thirty-three patients with depression admitted to Daizhuang Hospital of Shandong Province from January 2020 to December 2020 were selected as the depression group, and 29 healthy volunteers from Daizhuang Hospital of Shandong Province were selected as the healthy control group (i.e., the normal group) during the same period.

[0093] II. Inclusion and Exclusion Criteria for Patients with Depression

[0094] (1) Diagnostic criteria

[0095] Diagnosis was made using the Structured Clinical Interriew for DSM-IV Axis I Disorders Research Version (DSM-IV-TR). The severity of depression was assessed using the 24-item Hamilton Depression Rating Scale (HAMD).

[0096] (2) Inclusion criteria

[0097] Meets the DSM-IV diagnostic criteria for first onset and relapse of depressive disorder; has a 24-item score ≥20; is aged 15 to 60 years; and has not received antidepressant treatment or taken other psychotropic medications within the 12 weeks prior to enrollment.

[0098] (3) Exclusion criteria

[0099] Patients with a history of schizophrenia, alcohol and drug dependence; a history of organic brain disease and endocrine disorders; abnormal blood counts or liver and kidney function; pregnant or lactating women; a history of manic or hypomanic episodes; a family history of severe suicidal tendencies or mental disorders; or recent inflammatory diseases or antibiotic use were not included in the cohort of mental patients in this experiment.

[0100] III. Sample Collection and Processing

[0101] Fecal samples were collected from 33 patients with depression and 29 healthy individuals. Sample concentration, integrity, and purity were assessed. Metagenomic samples were extracted from the fecal samples, and viral DNA and RNA were extracted from the metagenomic samples. The obtained genomic DNA and RNA were subjected to ultrasonic fragmentation, end repair, A-tailing, adapter ligation, amplification, purification, and circularization to construct single-stranded circular libraries.

[0102] IV. Library Construction and Sequencing

[0103] After library construction, preliminary quantification was performed using Qubit 2.0, insert size was detected using an Agilent 2100, and the effective concentration of the library (>2 nM) was accurately quantified using qPCR. Qualified libraries were mixed according to the target data volume and sequenced using the HiSeq platform (PE150) to generate raw sequencing reads, which were stored in FASTQ format along with their quality information.

[0104] V. Data Processing and Bioinformatics Analysis Methods

[0105] (1) Data filtering and quantity control

[0106] The raw data was filtered using Trimmomatic (v0.38) with the following parameters: ILLUMINACLIP: / path / to / adapter.fa:2:30:10:8; SLIDINGWINDOW:5:20; LEADING:5; TRAILING:5; MINLEN:50. The filtered data was then processed using BMTagger to remove host sequence contamination. A host sequence database was constructed based on the sample environment to ensure that the remaining data contained the target sequences.

[0107] (2) Host sequence removal and viral sequence assembly

[0108] After quality control and host sequence removal, Megahit was used to independently assemble the samples (parameters: --k-min21; --k-max 149; --k-step 10; -m 0.3), filtering contigs below 200bp to finally obtain the effective assembled sequences.

[0109] VI. Statistical Analysis

[0110] Data from the Hamilton Psychiatric Rating Scale and the Short Form Psychiatric Rating Scale were compared using the Wilcoxon rank-sum test. The Hamilton Depression Rating Scale (HAMD) was used to assess all participants at enrollment. Species annotation and community structure analysis of the metagenomic data were performed using Kraken and Bracken, and the vegan package in R was used to analyze metaviromic α and β diversity. Wilcoxon rank-sum tests and LEfSe screening for differentially expressed viral features were performed using R (v4.2.1). Logistic binary regression analysis was conducted using the pROC and multipleROC packages in R, and receiver operating characteristic (ROC) curve analysis was performed using the ggplot2 package. The pROC package calculated ROC-related parameters, and the boot package calculated confidence intervals for these parameters using the Bootstrap resampling method.

[0111] The results are shown below:

[0112] 1. Demographic data

[0113] Wilcoxon rank-sum test was performed on the sex and age of the normal group (HC) and the depression group (DEP). The results showed no statistically significant differences in sex (P=0.938) and age (P=0.544) between the two groups (Table 1).

[0114] Table 1. Demographic data of the training set

[0115] Sample size Gender (female%) age HAMD BPRS normal group 29 66.7 35.67±12.88 - - Depression group 33 65.5 34.27±13.79 25.83±6.59 36.52±4.99 p-value - 0.93 0.54 - -

[0116] 2. Viral community composition analysis

[0117] (1) Compositional analysis of viruses at the phylum, class, and family levels

[0118] In the multi-level (phylum, class, family) composition analysis, the following high-density taxa were mainly detected in all samples: at the genus level, the five most important virus genera were: Toutatisvirus, Taranisvirus, Mushuvirus, Lughvirus, and Punavirus; at the species level, the most common species were: Faecalibacterium virus Toutatis, F. virus Lugh, F. virus Oengus, F. virus Mushu, and F. virus Taranis.

[0119] GraPhlAn analysis results showed that the relative abundance of the depression group was significantly increased in the phylum Uroviricota and family Myoviridae; the relative abundance of the healthy control group was even higher in the phylum Phixviricota and family Podoviridae.

[0120] (2) Viral community diversity analysis

[0121] The results of metaviridae alpha diversity analysis showed that there were no statistically significant differences in viral alpha diversity at the genus and species levels between the normal group and the depression group.

[0122] The results of metaviridae β diversity showed that there were significant differences in the viral community composition at the species level between the two groups; and the viral community structure at the genus level between the two groups was clearly separated.

[0123] 3. Differential analysis of viral communities

[0124] (1) Differential viral community analysis based on Wilcox

[0125] The virome composition was compared at the read level to gain a deeper understanding of the differential virome characteristics between the normal and depressed groups. Analysis of the top 100 viral reads with the highest relative abundance revealed that 82% (82 / 100) were enriched in the normal group. Wilcox analysis of representative differentially abundant viruses is shown in Table 2. Figure 1 (A) Figure 1 (B) Figure 1 (C) Figure 1 (D)

[0126] Table 2. Analysis of viral abundance differences between the normal group and the depression group.

[0127] variable mean of normal group Mean of depression group W statistic p-value Padjust value S.virus Pokken 7.94E-04 1.97E-04 776.5 2.36E-05 2.367E-05 g. Pokkenvirus 7.94E-04 1.97E-04 776.5 2.36E-05 2.367E-05 D.virus AD1 7.34E-04 2.00E-05 877 2.48E-09 9.92E-09 g. Alexandravirus 7.72E-04 4.52E-05 858 1.67E-08 3.33E-08

[0128] (2) Differential virus analysis based on linear discriminant analysis effect size (LEfSe)

[0129] LEfSe analysis was performed on differentially expressed viruses between the normal and depressed groups to identify species with significant differences between the two groups.

[0130] Figure 2 Bar graphs of LDA scores for the normal group and the depression group in Embodiment 1 of this invention are shown. The results indicate that:

[0131] The following viruses were enriched in the normal group: viruses at the Microviridae family level and Phixviricota phylum level; the following viruses were enriched in the depression group: viruses at the s_Stenotrophomonas_virus_Pokken level, g_Pokkenvirus level, s_Dickeya_virus_AD1 level, g_Alexandravirus level, and Baculoviridae family level.

[0132] 4. Biomarker Analysis

[0133] Based on the preceding differential analysis, ROC analysis was performed on the differentially expressed viral communities, revealing four viruses with an AUC greater than 0.8: s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1, and g_Alexandravirus.

[0134] Logistic binary regression modeling was performed on the four differentially expressed viral species selected as s_Stenotrophomonas_virus_Pokken (feature 1), g_Pokkenvirus (feature 2), s_Dickeya_virus_AD1 (feature 3), and g_Alexandravirus (feature 4) (Table 3) to evaluate the ability of these four viruses to distinguish between the two groups (normal group vs. depression group) when used alone. Univariate ROC analysis was then conducted based on this model to evaluate the diagnostic efficacy of these viruses as potential biomarkers. The ROC analysis results are shown in Table 4. Figure 3 As shown.

[0135] Table 3 Univariate logistic diagnostic models for four viral diagnostic biomarkers

[0136]

[0137] Note: 95% CI: 95% confidence interval.

[0138] Table 4. Univariate ROC analysis results of four viral diagnostic biomarkers

[0139]

[0140] Note: 95% CI: 95% confidence interval.

[0141] The first column of Table 3 is the metabolites, where “1” represents s_Stenotrophomonas_virus_Pokken, “2” represents g_Pokkenvirus, “3” represents s_Dickeya_virus_AD1, and “4” represents g_Alexandravirus.

[0142] From Table 4 and Figure 3 It can be seen that using any one of the differential viral markers, s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1, or g_Alexandravirus alone, has good diagnostic accuracy (AUC>0.8).

[0143] Similarly, a logistic binary regression model was constructed by combining the four viral biomarkers, and multivariate ROC analysis was performed. The results are shown in Tables 5 and 6. Figure 4 , Figure 5 and Figure 6 As shown. The first column of Tables 5 and 6 is the metabolite combination, where 1, 2, 3, and 4 represent s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1, and g_Alexandravirus, respectively.

[0144] Table 5. Multivariate logistic diagnostic models for four viral diagnostic biomarkers.

[0145]

[0146] Note: 95% CI: 95% confidence interval.

[0147] Table 6. Results of multivariate ROC analysis of four viral diagnostic biomarkers

[0148]

[0149]

[0150] Note: 95% CI: 95% confidence interval.

[0151] As shown in Table 6, the AUC of all combined models is higher than 0.8, indicating extremely high predictive accuracy; the accuracy, specificity, and sensitivity are all high. Therefore, all the combinations in the table can be used for the detection of depression.

[0152] Example 2 verifies the effectiveness of the four viral biomarkers in Example 1.

[0153] The diagnostic model constructed based on the data from the aforementioned 62 cohorts was further validated using another 55-case validation cohort. The statistical information of the validation set is shown in Table 7 below, and the diagnostic capability is shown in Table 8. Figure 7 , Figure 8 , Figure 9 , Figure 10 .

[0154] Table 7. Population data for the training set.

[0155] Sample size Gender (female%) age HAMD BPRS normal group 28 67.86％ 37.11±13.29 - - Depression group 27 62.96％ 35.29±13.86 26.37±8.63 36.70±6.42 p-value - 0.7964 0.394 - -

[0156] The model was validated using the holdout test, and the results are shown in Table 8.

[0157] Table 8. Diagnostic capability of the four viral markers in the validation set, both individually and in combination.

[0158]

[0159]

[0160] Note: 95% CI: 95% confidence interval.

[0161] The first column of Table 8 is the metabolite combination, where 1, 2, 3, and 4 represent s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1, and g_Alexandravirus, respectively.

[0162] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the patent protection scope of the present invention.

Claims

1. The application of an enterovirus marker detection reagent in the preparation of products for the diagnosis of depression, characterized in that, The viral markers include any of the following: A. s_Dickeya_virus_AD1; B, g_Alexandravirus; C. s_Stenotrophomonas_virus_Pokken and s_Dickeya_virus_AD1; D. s_Stenotrophomonas_virus_Pokken and g_Alexandravirus; E. s_Dickeya_virus_AD1 and g_Alexandravirus; F. s_Stenotrophomonas_virus_Pokken, s_Dickeya_virus_AD1 and g_Alexandravirus.

2. The application as described in claim 1, characterized in that, The products include test strips, reagent kits, microfluidic chips, or biosensors.

3. The application as described in claim 2, characterized in that, The product includes a kit, which includes at least one of the following: real-time quantitative PCR kit, multiplex PCR / nucleic acid microarray kit, digital PCR kit, high-throughput sequencing kit, colloidal gold immunochromatography kit, and enzyme-linked immunosorbent assay kit.

4. The application as described in claim 2, characterized in that, The product includes a kit, which includes primers and probes, wherein the primers and probes are nucleic acids; The primers and probes include primers and probes targeting s_Dickeya_virus_AD1; or, The primers and probes include primers and probes targeting g_Alexandravirus; or, The primers and probes include primers and probes for s_Stenotrophomonas_virus_Pokken and primers and probes for s_Dickeya_virus_AD1; or, The primers and probes include primers and probes for s_Stenotrophomonas_virus_Pokken and primers and probes for g_Alexandravirus; or, The primers and probes include primers and probes for s_Dickeya_virus_AD1 and primers and probes for g_Alexandravirus; or, The primers and probes include primers and probes for s_Stenotrophomonas_virus_Pokken, primers and probes for s_Dickeya_virus_AD1, and primers and probes for g_Alexandravirus.

5. The application as described in claim 2, characterized in that, The product includes a kit, and the kit includes antibodies; The antibodies include antibodies against the s_Dickeya_virus_AD1 antigen; or, The antibodies include antibodies against the g_Alexandravirus antigen; or, The antibodies include antibodies against the s_Stenotrophomonas_virus_Pokken antigen and antibodies against the s_Dickeya_virus_AD1 antigen; or, The antibodies include antibodies against the s_Stenotrophomonas_virus_Pokken antigen and antibodies against the g_Alexandravirus antigen; or, The antibodies include antibodies against the s_Dickeya_virus_AD1 antigen and antibodies against the g_Alexandravirus antigen; or, The antibodies include antibodies against the s_Stenotrophomonas_virus_Pokken antigen, antibodies against the s_Dickeya_virus_AD1 antigen, and antibodies against the g_Alexandravirus antigen.

6. The application as described in claim 1, characterized in that, The Logistic binary regression model for the viral biomarker is at least one of the following formulas: Logit(P) = a3 + b3 × s_Dickeya_virus_AD1, where a3 ∈ [0.61, 2.35], b3 ∈ [-21813, -5267]; Logit(P) = a4 + b4 × g_Alexandravirus, where a4 ∈ [0.39, 1.96] and b4 ∈ [-11417, -2664]. Logit(P) = a6 + b6 × s_Stenotrophomonas_virus_Pokken + c6 × s_Dickeya_virus_AD1, where a6 ∈ [0.51, 2.68], b6 ∈ [-3510.5, 2361.5], and c6 ∈ [-21866, -5034]. Logit(P)=a7 + b7 × s_Stenotrophomonas_virus_Pokken + c7 × g_Alexandravirus, where a7 ∈ [0.38, 2.26], b7 ∈ [-3358.64, 1776.56], c7 ∈ [-11250.90, -1911.82]; Logit(P)=a 10 + b 10 ×s_Dickeya_virus_AD1 + c 10 × g_Alexandravirus,individual,a 10 ∈ [0.58, 2.34],b 10 ∈ [-26827.18, -2266.02],c 10 ∈ [-7441.80, 9341.04] Logit(P)=a 13 + b 13 × s_Stenotrophomonas_Pokken_Virus + c 13 × s_Dickeya_virus_AD1 + d 13 × g_Alexandravirus,individual,a 13 ∈ [0.50, 2.66],b 13 ∈ [-3540.84.2315.64], c 13 ∈ [-27057.96, -2162.04],d 13 ∈ [-7362.32, 9560.32] 7. The application as described in claim 6, characterized in that, The viral biomarker is s_Dickeya_virus_AD1, and the Logistic binary regression model of the viral biomarker satisfies the following: the cutoff value for ROC analysis is A3, A3 ∈ (0.387, 0.792); or, The viral biomarker is g_Alexandravirus, and the Logistic binary regression model for the viral biomarker satisfies the following: the cutoff value for ROC analysis is A4, A4 ∈ (0.537, 0.751); or, The viral biomarkers are s_Stenotrophomonas_virus_Pokken and s_Dickeya_virus_AD1. The logistic binary regression model for these viral biomarkers satisfies the following: the cutoff value for ROC analysis is A6, A6 ∈ (0.387, 0.791); or, The viral biomarkers are s_Stenotrophomonas_virus_Pokken and g_Alexandravirus. The Logistic binary regression model for these viral biomarkers satisfies the following: the cutoff value for ROC analysis is A7, A7 ∈ (0.527, 0.692); or, The viral biomarkers are s_Dickeya_virus_AD1 and g_Alexandravirus, and the Logistic binary regression model for these viral biomarkers satisfies the following: the cutoff value for ROC analysis is A10, where A10 ∈ (0.382, 0.789); or, The viral biomarkers are s_Stenotrophomonas_virus_Pokken, s_Dickeya_virus_AD1, and g_Alexandravirus. The Logistic binary regression model of the viral biomarkers satisfies the following: the cutoff value of the ROC analysis is A13, where A13 ∈ (0.389, 0.757).

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