Urine protein markers for depression and bipolar disorder and their use in early diagnosis

Abstract

The present invention relates to urinary protein markers for depression and bipolar disorder and their use in early diagnosis. In particular, the present invention relates to the use of a reagent for detecting the content of proteins in the urine of a subject, wherein the proteins in the urine are selected from the group consisting of Uniprot ID: P11766, H7BY57, O95967, O95433, K7ELM9, P15328 and P68371, in the manufacture of a reagent for the diagnosis of depression and bipolar disorder.

Description

Technical Field

[0001] This invention relates to the technical field of diagnosis of depression and bipolar disorder. Specifically, this invention relates to urinary proteomics associated with depression and bipolar disorder and its use in the diagnosis of depression and bipolar disorder. Background Technology

[0002] Biomarkers are indicators that objectively reflect normal pathological and physiological processes. See Kyle Strimbu, Jorge A. Tavel. What are biomarkers? Current Opinion in HIV and AIDS. 2010, 5(6). Clinically, biomarkers can predict, monitor, and diagnose multifactorial diseases at different stages. See Gerszten Robert E, Wang Thomas J. The search for new cardiovascular biomarkers. Nature. 2008, 451(7181). Compared to the more widely used blood biomarkers, the potential of urine biomarkers has not been fully explored, especially in the early diagnosis and state prediction of diseases. Due to the regulation of blood homeostasis mechanisms, changes in the blood proteome caused by disease are metabolized and excreted, and cannot show obvious changes in the early stages of disease. However, urine is produced by the glomeruli filtering plasma and is not regulated by homeostasis mechanisms. It is sensitive to changes, and even small changes in the early stages of disease can be observed in urine. Furthermore, the research results show that the observed changes occur much earlier than in blood samples, pathological sections, and even before the onset of disease symptoms, and can be applied to the early diagnosis of diseases.

[0003] Depression is a mood disorder characterized by persistent sadness and an inability to experience pleasure, accompanied by impairment of daily functioning. Globally, depression is a leading cause of disability and loss of productive lifespan (see Gore FM, Bloem PJ, Patton GC, et al. Global burden of disease in young people aged 10–24 years: a systematic analysis. Lancet. 2011, 377:2093-102). In the United States, the prevalence of depression is 5%–10%, but in some primary care or specialist settings, it can be as high as 40%–50% (see Wang J, Wu X, Lai W, et al. Prevalence of depression and depressive symptoms among outpatients: a systematic review and meta-analysis. BMJ Open. 2017, 7:e017173). Despite the existence of high-quality evidence-based therapies, only about half of patients with depression receive appropriate treatment. See González HM, Vega WA, Williams DR, et al. Depressioncare in the United States: too little for too few. Arch Gen Psychiatry. 2010, 67: 37-46. Depression has a significant impact on the incidence, cost, and treatment outcomes of many common complications, such as diabetes (see Ali S, Stone MA, Peters JL, et al. The prevalence of comorbid depression in adults with type 2 diabetes: a systematic review and meta-analysis. Diabet Med. 2006, 23: 1165-73). It is also a major risk factor for suicide; between 1999 and 2018, the suicide rate in the United States increased by approximately 35% (see Hedegaard H, Curtin SC, Warner M. Increase in suicide mortality in the United States, 1999–2018. NCHS Data Brief. 2020, 1-8).The pathophysiological causes of depression are unclear, and there are currently no clinically useful bio-diagnostic markers or bio-screening tests. See Robert M. McCarron, Bryan Shapiro, Jody Rawles, et al. Depression. Ann Intern Med. 2021, 174: ITC65-ITC80.

[0004] Bipolar disorder (BD), also known as manic-depressive illness, is a chronic disease with severe debilitating symptoms that can have a profound impact on patients and their caregivers. See Miller, K. Bipolar disorder: Etiology, diagnosis, and management. Journal of the American Association of Nurse Practitioners. 2006, 8:368–373. Bipolar disorder usually begins in adolescence or early adulthood and can have lifelong adverse effects on patients’ physical and mental health, education and occupational functioning, and interpersonal relationships. See

[45] Valente, SM, & Kennedy, BL End the bipolar tug-of-war. Nurse Practitioner. 2010, 35:36–45. Although BD is not as common as major depressive disorder (MDD), the lifetime prevalence of BD in the United States is high (estimated at about 4%), with similar prevalence across races, ethnicities and sexes. See Ketter, TADiagnostic features, prevalence, and impact of bipolar disorder. Journal of Clinical Psychiatry. 2010, 71:e14; and Merikangas, KR, Akishal, HS, Kessler, RC, et al. Lifetime and 12-month prevalence of bipolar spectrum disorder in the National Comorbidity Survey replication. Archives of General Psychiatry. 2007, 64:543–552. Long-term outcomes have been unsatisfactory. See

[48] Geddes, JR, & Miklowitz, DJ Treatment of bipolar disorder. Lancet. 2013, 381:1672–1682. Because the manic or depressive symptoms of BD are often severe and recur throughout a patient's life, the disease places a huge burden on patients, caregivers, and society.Episodes in patients with BD involve repeated shifts between pathological emotional states characterized by manic or depressive symptoms, interspersed with relatively normal emotional periods. See Vieta, E., & Goikolea, J.M. Atypical antipsychotics: Newer options for mania and maintenance therapy. Bipolar Disorders. 2005, 7(4):21–33. The formal definitions of manic and depressive symptoms are included in the most recently updated Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). See American Psychiatric Association. Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association. 2013. It is worth noting that in DSM-5, the criteria for defining a depressive episode in BD are the same as those for MDD. Therefore, the distinction between BD and MDD usually depends on whether there is a history of mania or manic symptoms. See McCormick U, Murray B, McNew B. Diagnosis and treatment of patients with bipolar disorder: A review for advanced practice nurses. J Am Assoc Nurse Pract. 2015, 27(9): 530-42.

[0005] Since early-stage bipolar disorder (BD) often presents with depressive symptoms, diagnosing a patient with depression and administering medication at this stage may worsen their condition. If clinicians are aware that a patient may have BD, they can increase the likelihood of successful identification and appropriate treatment, thus having a beneficial impact on short-term efficacy and long-term course of the disease. See Geddes, JR, & Miklowitz, DJ Treatment of bipolar disorder. Lancet. 2013, 381:1672–1682; McCormick U, Murray B, McNew B. Diagnosis and treatment of patients with bipolar disorder: A review for advanced practice nurses. J Am Assoc Nurse Pract. 2015, 27(9):530-42; and Manning, JS Tools to improve differential diagnosis of bipolar disorder in primary care. Primary Care Companion to the Journal of Clinical Psychiatry. 2010, 12(1):17–22.Currently, the main screening tools for bipolar disorder are questionnaires and diagnostic clinical interviews. The Mood Disorder Questionnaire (MDQ) and the Composite International Diagnostic Interview (CIDI) version 3.0 are commonly used screening tools. Scores exceeding certain thresholds raise suspicion of mania. See Hirschfeld, RM, Williams, JB, Zajecka, J, et al. Development and validation of a screening instrument forbipolar spectrum disorder: The Mood Disorder Questionnaire. American Journal of Psychiatry. 2000, 157: 1873–1875; and Kessler, RC, & Ustun, TB. The World Mental Health (WMH) Survey Initiative Version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). International Journal of Methods in Psychiatric Research. 2004, 13: 93–121. However, there are no objective physiological indicators for the differential diagnosis of bipolar disorder. Summary of the Invention

[0006] In order to solve the above-mentioned technical problems in the prior art, the inventors collected urine samples from healthy people, patients with depression and patients with bipolar disorder, and explored whether biomarkers that can differentiate between depression and bipolar disorder could be found in urine based on urine proteomics.

[0007] On one hand, the present invention provides the use of a reagent for detecting protein content in the urine of a subject in the preparation of a reagent for diagnosing mental disorders, wherein the proteins in the urine are selected from a combination of the following: Uniprot ID: P11766, H7BY57, O95967, O95433, K7ELM9, P15328, and P68371; and the mental disorders are selected from depression and bipolar disorder.

[0008] In particular, the diagnostic reagents provided by this invention can better distinguish between patients with depression and bipolar disorder, providing an effective diagnostic method for patients with bipolar disorder who only present with depression in the early stages.

[0009] Therefore, the present invention provides the use of a reagent for detecting protein content in the urine of a subject in the preparation of a reagent for the differential diagnosis of depression and early bipolar disorder, wherein the protein in the urine is selected from a combination of the following: Uniprot ID: P11766, H7BY57, O95967, O95433, K7ELM9, P15328 and P68371.

[0010] In the detection application of the urinary protein markers provided by this invention:

[0011] When the levels of the following proteins in the urine of the subject were increased or decreased compared with those in the healthy control group, the subject was diagnosed with bipolar disorder.

[0012] Specifically, when the levels of proteins Uniprot ID: O95967, P15328, and O95433 in the urine of the subject are greater than or equal to 1.5 or less than or equal to 0.67 compared with the healthy control group, the subject is diagnosed with bipolar disorder.

[0013] Compared with the healthy control group, the subjects had increased or decreased levels of the following proteins in their urine: UniprotID: P15328, P11766, P68371, K7ELM9 and H7BY57, and were diagnosed with depression.

[0014] Specifically, when the levels of proteins Uniprot ID: P15328, P11766, P68371, K7ELM9, and H7BY57 in the urine of the subject are greater than or equal to 2 or less than or equal to 0.5 compared with the healthy control group, the subject is diagnosed with depression.

[0015] Compared with subjects diagnosed with depression, subjects diagnosed with bipolar disorder had increased or decreased levels of the following proteins in their urine: Uniprot ID: P11766, K7ELM9, and H7BY57.

[0016] Specifically, compared with subjects diagnosed with depression, subjects diagnosed with bipolar disorder showed a fold greater than or equal to 2 or less than or equal to 0.5 in the levels of the proteins Uniprot ID: P11766, K7ELM9, and H7BY57 in their urine.

[0017] According to the method or use of the present invention described above, the reagent for detecting the protein content in the urine of the subject is a mass spectrometry identification reagent, an antibody or its antigen-binding fragment, or an aptamer; preferably, the reagent for detecting the protein content in the urine of the subject is a monoclonal antibody.

[0018] On the other hand, the present invention provides the use of a reagent for detecting protein content in the urine of a subject in the preparation of a reagent for the differential diagnosis of depression and early bipolar disorder, wherein the protein in the urine is selected from a combination of the following: Uniprot ID: P11766, K7ELM9 and H7BY57;

[0019] Preferably, the proteins in the urine are selected from the following combinations: Uniprot ID: P11766, H7BY57, O95967, O95433, K7ELM9, P15328 and P68371.

[0020] On the other hand, the present invention provides the use of a reagent for detecting the protein content in the urine of a subject in the preparation of a reagent for diagnosing bipolar disorder, wherein the protein in the urine is selected from a combination of the following: Uniprot ID: O95967, P15328 and O95433.

[0021] On the other hand, the present invention provides the use of a reagent for detecting the protein content in the urine of a subject in the preparation of a reagent for diagnosing depression, wherein the protein in the urine is selected from a combination of the following: Uniprot ID: P15328, P11766, P68371, K7ELM9 and H7BY57.

[0022] In an embodiment of the present invention, the subject is a human subject.

[0023] In an embodiment of the present invention, the reagent for detecting the protein content in the urine of the subject is a mass spectrometry identification reagent, an antibody, or an antigen-binding fragment thereof.

[0024] In an embodiment of the present invention, the reagent used to detect the protein content in the urine of a subject is a monoclonal antibody.

[0025] On the other hand, the present invention provides a kit or chip for diagnosing mental disorders, comprising reagents for detecting the protein content in the urine of a subject, wherein the proteins in the urine are a combination of the following proteins: Uniprot ID: P11766, H7BY57, O95967, O95433, K7ELM9, P15328, and P68371; the mental disorder is selected from depression and bipolar disorder; preferably, the bipolar disorder is early-stage bipolar disorder. Attached Figure Description

[0026] Figure 1 A schematic diagram showing the technical approach to exploring the differences between depression and bipolar disorder through urinary proteomics.

[0027] Figure 2 The ROC curves for diagnosing bipolar disorder using a combination of biomarkers, P11766, H7BY57, O95967, O95433, K7ELM9, P15328, and P68371, are shown.

[0028] Figure 3 The ROC curves for the diagnosis of depression using a combination of biomarkers, including P11766, H7BY57, O95967, O95433, K7ELM9, P15328, and P68371, are shown.

[0029] Figure 4 The ROC curves of P11766, H7BY57, O95967, O95433, K7ELM9, P15328, and P68371 are shown for the differential diagnosis of bipolar disorder and depression. Detailed Implementation

[0030] As used in this article, the term "proteomics" is a rapidly developing "omics" technology following "genomics." It, along with metabolomics and genomics, constitutes the main body of systems biology, thereby helping humans to understand diseases comprehensively and multidimensionally from the perspectives of genes, proteins, and metabolites.

[0031] Proteomics studies and describes the expression of all protein components in an organism, organ, tissue, cell, or organelle at a specific time. Comparative proteomics studies the dynamic changes in protein expression levels under different organismal states, aiming to discover key regulatory molecules and disease-related protein biomarkers, ultimately providing a theoretical basis for disease prevention, diagnosis, and treatment. Therefore, quantitative protein analysis is necessary, leading to the development of quantitative proteomics techniques. Currently, the most commonly used techniques include LC-MS / MS, CE-MS, and 2DE MALDI-TOF.

[0032] LC-MS / MS is a high-resolution technique. Pre-separation before MS analysis allows for more refined proteomic characterization. While this technique can identify thousands of proteins, it is time-consuming, involves complex data analysis, and carries the risk of false alarms. Label-free quantification and stable isotope-labeled quantification are two commonly used techniques, with the latter being more widely used. Because a single MS run can analyze multiple samples simultaneously, isotope-labeled quantification significantly reduces measurement errors; however, the number of samples that can be analyzed at one time is limited by the number of labels. Conversely, with label-free quantification, each sample is analyzed sequentially, meaning variations generated during each MS run can affect the results. However, this method has no limitation on the number of samples.

[0033] CE-MS, as a complementary high-resolution technique, is used to identify pre-existing peptides and small proteins in bodily fluids (primarily urine). Compared to LC-MS / MS, it offers higher reproducibility and shorter run times (approximately 60 minutes). Furthermore, CE-MS possesses favorable technical characteristics for biomarker discovery, validation, and clinical applications. However, subsequent peptide sequencing requires more sensitive mass spectrometry detectors. Advances in MS-based technologies will help overcome some limitations of classic gel-based proteomics techniques (such as 2DE / DIGE), namely low throughput and low resolution. For validation, techniques capable of targeting selected proteins are typically used. While MS-based targeting techniques have made progress, immunoassays (such as ELISA and WB) remain the most commonly used methods. Immunoassays offer rapid monitoring and are generally compatible with clinical laboratory equipment, but their separation depends on antibody quality, and simultaneous analysis of multiple proteins is challenging. When developing a ensemble of multiple biomarkers, the inability to simultaneously analyze multiple proteins is a major obstacle to the application of this technology. Therefore, some MS-based platforms have been applied to biomarker validation, including targeted (SRM / MRM / PRM) and non-targeted mass spectrometry (CE-MS). These methods do not require antibodies and can be used for more specific and sensitive assays.

[0034] In recent years, quantitative proteomics has become a hot topic in proteomics research and is an important approach and tool for discovering disease-related biomarkers. It is of great significance for exploring disease-causing factors, pathogenesis, and screening disease biomarkers. Combinations of multiple molecular biomarkers can better reflect the overall characteristics of disease changes, and their sensitivity and specificity for disease diagnosis are superior to traditional methods.

[0035] Cerebrospinal fluid (CSF), blood, and urine are important body fluids commonly used for proteomics analysis. Obtaining CSF for testing requires lumbar puncture, usually performed routinely 1-2 weeks post-surgery. However, for children with medulloblastoma, the vast majority have preoperative obstructive hydrocephalus and high intracranial pressure, posing a risk of brain herniation during lumbar puncture. Therefore, obtaining CSF through this invasive method is challenging and may limit its clinical application. Furthermore, the heterogeneity of proteins in blood, large dynamic fluctuations in concentration, and rapid changes in proteomic conformation make blood a difficult subject for proteomics analysis. In contrast, urine can be obtained non-invasively, repeatedly, and in large quantities, making it highly advantageous for analyzing disease-specific biomarkers using mass spectrometry. Studies have shown that urine contains tumor-related biomarkers and can be used for long-term monitoring of treatment efficacy. Moreover, the large fluctuations in urine composition may better reflect changes in the body. Therefore, some scholars believe that urine is a better and more sensitive body fluid sample for detecting biomarkers than plasma.

[0036] Experimental methods in the following examples that do not specify specific conditions are generally performed under conventional conditions or as recommended by the raw material or product manufacturer; reagents and materials whose specific sources are not specified are commercially available.

[0037] Example 1. Collection and treatment of urinary protein

[0038] This study was approved by the Human Research and Ethics Committee of Beijing Anding Hospital, Capital Medical University [(2022) Research No. (14)-202221FS-2], and all participants provided written informed consent. Participants were from Beijing Anding Hospital and Tianjin Anding Hospital, both affiliated with Capital Medical University, and samples were collected from December 2022 to April 2023. Cases of schizophrenia, other psychotic disorders, personality disorders, or intellectual disability were excluded, and medication-free samples were selected from participants aged 18-23. The participant samples included 11 healthy individuals, 11 patients with bipolar disorder, and 13 patients with depression. All collected urine samples were stored at -80°C.

[0039] Table 1. Sample Information and Statistical Results

[0040]

[0041] Urine protein extraction and quantification: Collected urine samples were centrifuged at 12000×g for 30 min at 4℃, and the supernatant was transferred to a 50 mL centrifuge tube. Dithiothreitol solution (DTT, Sigma) was added to a final concentration of 20 mM, and the mixture was shaken and heated in a water bath at 37℃ for 1 h, then cooled to room temperature. Iodoacetamide (IAA, Sigma) was added to a final concentration of 50 mM, and the mixture was stirred and reacted at room temperature in the dark for 40 min. Six volumes of pre-cooled anhydrous ethanol were added, and the mixture was thoroughly mixed and precipitated at -20℃ for 24 h. The next day, the mixture was centrifuged at 12000×g for 30 min at 4℃, and the supernatant was discarded. The protein precipitate was resuspended in lysis buffer (containing 8 mol / L urea, 2 mol / L thiourea, 25 mmol / L dithiothreitol, and 50 mmol / L Tris). Centrifuge at 12000×g for 30 min at 4℃, and transfer the supernatant to a new EP tube. Measure protein concentration using the Bradford method.

[0042] Urine protein digestion: Take 100 μg of urine protein sample and add it to the filter membrane of a 10 kDa ultrafiltration tube (Pall, Port Washington, NY, USA), place it in an EP tube, and add 25 mmol / L NH4HCO3 solution to make the total volume 200 μL. Then, perform the membrane washing procedure: ① Add 200 μL of UA solution (8 mol / L urea, 0.1 mol / L Tris-HCl, pH 8.5), and centrifuge twice at 14000×g for 5 min at 18℃; ② Load the sample: Add the treated sample and centrifuge at 14000×g for 40 min at 18℃; ③ Add 200 μL of UA solution, centrifuge at 14000×g for 40 min at 18℃, and repeat twice; ④ Add 25 mmol / L NH4HCO3 solution, centrifuge at 14000×g for 40 min at 18℃, and repeat 3-4 times; ⑤ Add trypsin (Trypsin Gold, Promega, Fitchburg, WI, USA) at a trypsin:protein ratio of 1:50 for digestion, and incubate in a 37℃ water bath for 12-16 h. The peptides were collected by centrifugation at 13000×g for 30 min at 4℃ on the second day. They were desalted by passing them through an HLB column (Waters, Milford, MA), dried using a vacuum desiccator, and stored at -80℃.

[0043] Example 2. LC-MS / MS tandem mass spectrometry analysis

[0044] The enzymatically digested sample was dissolved in 0.1% formic acid, and the peptides were quantified using a BCA kit, diluting the peptide concentration to 0.5 μg / μL. A 4 μL sample of each peptide was used to prepare a mixed peptide sample, which was then separated using a high-pH reversed-phase peptide separation kit (Thermo Fisher Scientific) according to the manufacturer's instructions. Ten fractions were collected by centrifugation, dried under vacuum, and reconstituted with 0.1% formic acid. iRT reagent (Biognosys, Switzerland) was added at a sample:iRT volume ratio of 10:1 to calibrate the retention time of the extracted peptide peaks. For analysis, 1 μg of peptide from each sample was used for mass spectrometry analysis and data acquisition using an EASY-nLC1200 chromatography system (Thermo Fisher Scientific, USA) and an Orbitrap FusionLumos Tribrid mass spectrometer (Thermo Fisher Scientific, USA).

[0045] To generate the spectral library, the 10 isolated fractions were analyzed by mass spectrometry in Data Dependent Acquisition (DDA) mode. Mass spectrometry data were acquired using a high-sensitivity mode. A complete mass spectrum scan was obtained in the range of 350–1500 m / z at a resolution of 60,000. Individual samples were analyzed using Data Independent Acquisition (DIA) mode. DIA acquisition was performed using a 36-window method. A single DIA analysis of the pooled peptides was performed after every 8 samples as a quality control.

[0046] Database search and label-free DIA quantification

[0047] Raw data (RAW files) acquired from liquid chromatography-mass spectrometry (LC-MS) was imported into Proteome Discoverer (version 2.1, Thermo Scientific) using the SwissProt database (classification: Homo; containing 20,346 sequences). Alignment was performed, and iRT sequences were added to the database. The search results were then imported into Spectronaut Pulsar (Biognosys AG, Switzerland) for processing and analysis. Peptide abundance was calculated by summing the peak areas of individual fragment ions in MS2. Protein abundance was calculated by summing the abundances of individual peptides.

[0048] Data Analysis

[0049] Each sample was tested in triplicate, and the average value was used for statistical analysis. Identified proteins were compared to screen for differentially expressed proteins. The lenient screening criteria for differentially expressed proteins were: fold change (FC) ≥ 1.5 or ≤ 0.67, with a p-value < 0.05 for two-tailed unpaired t-tests. The strict screening criteria were: fold change (FC) ≥ 2 or ≤ 0.5, with a p-value < 0.01 for two-tailed unpaired t-tests. Functional enrichment analysis was performed on the screened differentially expressed proteins using the Omic Solution platform (https: / / www.omicsolution.org / wkomic / main / ), the Uniprot website (https: / / www.uniprot.org / ), and the DAVID database (https: / / david.ncifcrf.gov / ). Furthermore, a literature search was conducted in the PubMed database (https: / / pubmed.ncbi.nlm.nih.gov) to perform functional analysis on the differentially expressed proteins.

[0050] Example 3. Identification of urinary proteome

[0051] Peptides derived from enzymatic digestion of urine samples from 11 healthy individuals, 11 individuals with bipolar disorder, and 13 individuals with depression were analyzed by LC-MS / MS tandem mass spectrometry. A total of 2612 proteins were identified (≥2 specific peptides, protein level FDR <1%).

[0052] We compared a sample of 11 healthy individuals with a sample of 11 patients with bipolar disorder. Under lenient conditions, the screening criteria for differentially expressed proteins were: FC ≥ 1.5 or ≤ 0.67, and a two-tailed unpaired t-test showed P < 0.05, identifying 67 differentially expressed proteins. Under strict conditions, the screening criteria were: FC ≥ 2 or ≤ 0.5, and a two-tailed unpaired t-test showed P < 0.01, identifying 7 differentially expressed proteins. We selected 3 differentially expressed proteins identified under the lenient conditions for further investigation.

[0053] Table 2. Differentially expressed proteins between healthy individuals and bipolar patients (FC ≥ 1.5 or ≤ 0.67, P < 0.05)

[0054]

[0055] The trends in Table 2 represent the increase (↑) or decrease (↓) in urinary protein levels in biphasic patients relative to healthy individuals; the fold change represents the ratio of urinary protein levels in biphasic patients to those in healthy individuals.

[0056] We compared samples from 11 healthy individuals with samples from 13 patients with depression. Under lenient conditions, the screening criteria for differentially expressed proteins were: FC ≥ 1.5 or ≤ 0.67, and a two-tailed unpaired t-test showed P < 0.05, identifying 276 differentially expressed proteins. Under stringent conditions, the screening criteria were: FC ≥ 2 or ≤ 0.5, and a two-tailed unpaired t-test showed P < 0.01, identifying 61 differentially expressed proteins. We selected 5 differentially expressed proteins under the stringent conditions for further investigation.

[0057] Table 3. Differentially expressed proteins between healthy individuals and patients with depression (FC≥2 or ≤0.5, P<0.01)

[0058]

[0059] The trends in Table 3 represent the increase (↑) or decrease (↓) in urinary protein levels in depressed patients relative to healthy individuals; the fold change represents the ratio of urinary protein levels in depressed patients to those in healthy individuals.

[0060] A sample of 11 patients with bipolar disorder was compared with a sample of 13 patients with depression. Under lenient conditions, the screening criteria for differentially expressed proteins were: FC ≥ 1.5 or ≤ 0.67, and a two-tailed unpaired t-test showed P < 0.05, identifying 500 differentially expressed proteins. Under strict conditions, the screening criteria were: FC ≥ 2 or ≤ 0.5, and a two-tailed unpaired t-test showed P < 0.01, identifying 108 differentially expressed proteins. We selected three differentially expressed proteins under the lenient conditions for further investigation.

[0061] Table 4. Differentially expressed proteins between depressive and bipolar patients (FC ≥ 2 or ≤ 0.5, P < 0.01)

[0062]

[0063] The trends in Table 4 represent the increase (↑) or decrease (↓) in urinary protein levels in bipolar patients compared to depressive patients; the fold change represents the ratio of urinary protein levels in bipolar patients to those in depressive patients.

[0064] Table 5. Trends and fold changes in protein levels compared to healthy individuals

[0065]

[0066] Example 4. Randomized Group Validation

[0067] To determine the likelihood that the identified differentially expressed proteins were randomly generated, the total proteins identified from 10 randomly selected healthy individuals and 10 randomly selected biphasic patients were randomly assigned to groups for validation (FC ≥ 1.5 or ≤ 0.67, P < 0.05). The average number of differentially expressed proteins was 41.0, indicating that at least 32.9% of the differentially expressed proteins were not randomly generated (Table 6). Under more stringent conditions (FC ≥ 2 or ≤ 0.5, P < 0.01), the average number of differentially expressed proteins was 2.7, indicating that at least 60.8% of the differentially expressed proteins were not randomly generated (Table 6). Randomized validation of total proteins identified from 10 randomly selected healthy individuals and 10 randomly selected depressed patients (FC≥1.5 or ≤0.67, P<0.05) yielded an average of 54.7 differentially expressed proteins, indicating that at least 80.2% of the differentially expressed proteins were not randomly generated (Table 6). Randomized validation under more stringent conditions (FC≥2 or ≤0.5, P<0.01) yielded an average of 4.4 differentially expressed proteins, indicating that at least 92.7% of the differentially expressed proteins were not randomly generated (Table 6). Randomized validation of total proteins identified from 10 bipolar patients and 10 depressive patients (FC≥1.5 or ≤0.67, P<0.05) yielded an average of 53.4 differentially expressed proteins, indicating that at least 89.3% of the differentially expressed proteins were not randomly generated (Table 6). Randomized validation under more stringent conditions (FC≥2 or ≤0.5, P<0.01) yielded an average of 3.7 differentially expressed proteins, indicating that at least 96.6% of the differentially expressed proteins were not randomly generated (Table 6).

[0068] By comparing the proportions of randomly generated differentially expressed proteins in different groups, it can be seen that the differences between healthy samples and bipolar patient samples are relatively small, the differences between healthy samples and depressed patient samples are relatively large, and the differences between bipolar patient samples and depressed patient samples are relatively large, which has the highest reliability.

[0069] Table 6 Random Grouping Results

[0070]

[0071] Example 5. Use of the biomarker combination of the present invention in the diagnosis of depression and bipolar disorder.

[0072] In this embodiment, the inventors used seven selected biomarkers: P11766, H7BY57, O95967, O95433, K7ELM9, P15328, and P68371, to verify the ROC curves of these biomarkers in diagnosing healthy individuals and patients with bipolar disorder and depression.

[0073] Among them, when using three biomarkers O95967, P15328, and O95433 to diagnose bipolar disorder, the area under the curve (AUC) was 0.996, and the 95% confidence interval (CI) was 0.938–1 (results not shown); while when using a combination of biomarkers P11766, H7BY57, O95967, O95433, K7ELM9, P15328, and P68371 to diagnose bipolar disorder, the AUC was 0.971, and the 95% confidence interval (CI) was 0.75–1. See results below. Figure 2 .

[0074] On the other hand, when using P15328, P11766, P68371, K7ELM9, and H7BY57 to diagnose depression, the area under the curve (95% confidence interval) was 0.981 (0.895-1) (results not shown); while when using a combination of P11766, H7BY57, O95967, O95433, K7ELM9, P15328, and P68371 markers to diagnose depression, the area under the curve (95% confidence interval) was 0.99 (0.917-1). See the results below. Figure 3 .

[0075] Furthermore, we used a combination of biomarkers P11766, H7BY57, O95967, O95433, K7ELM9, P15328, and P68371 to differentiate between bipolar disorder and depression. We found that the area under the ROC curve (95% confidence interval) for distinguishing between bipolar disorder and depression using this group of biomarkers was 0.94 (0.9792-1). The results are shown in [see attached table]. Figure 4 .

[0076] Because our biomarker composition contains not only biomarkers that can effectively distinguish healthy individuals from patients with bipolar disorder or depression, but also biomarkers that can effectively distinguish between patients with bipolar disorder and depression, this combination significantly improves diagnostic accuracy for both bipolar disorder and depression, and has significant clinical implications for diagnosing early-stage bipolar disorder patients exhibiting depressive symptoms.

Claims

1. Use of a reagent for detecting protein content in the urine of a subject in the preparation of a reagent for diagnosing mental disorders, wherein the urine protein is selected from a combination of the following: Uniprot ID: P11766, H7BY57, O95967, O95433, K7ELM9, P15328, and P68371; and the mental disorder is selected from depression and bipolar disorder.

2. The use according to claim 1, wherein the bipolar disorder is early-stage bipolar disorder.

3. The use according to claim 1, wherein, compared with a healthy control group, the subject's urine showed an increase or decrease in the levels of the following proteins: Uniprot ID: O95967, P15328, and O95433, diagnosing the subject with bipolar disorder.

4. The use according to claim 3, wherein the increase or decrease in protein content means that the change in protein content compared with the healthy control group is greater than or equal to 1.5 or less than or equal to 0.

67.

5. The use according to claim 1, wherein, compared with a healthy control group, the subject's urine showed an increase or decrease in the levels of the following proteins: Uniprot ID: P15328, P11766, P68371, K7ELM9, and H7BY57, diagnosing the subject with depression.

6. The use according to claim 5, wherein, The increase or decrease in protein content refers to a change in protein content of 2 or more, or 0.5 or less, compared with the healthy control group.

7. The use according to any one of claims 1-6, wherein the urine of a subject diagnosed with bipolar disorder has increased or decreased levels of the following proteins compared to a subject diagnosed with depression: Uniprot ID: P11766, K7ELM9, and H7BY57.

8. The use according to claim 7, wherein, The increase or decrease in protein content refers to a change in protein content of 2 or more, or 0.5 or less, compared to subjects with depression.

9. The use according to any one of claims 1-6, wherein the subject is a human subject.

10. The use according to any one of claims 1-6, wherein the reagent for detecting protein content in the urine of a subject is a mass spectrometry identification reagent, an antibody or its antigen-binding fragment, or an aptamer.

11. The use according to claim 10, wherein, The mass spectrometry identification reagent is used in LC-MS / MS tandem mass spectrometry analysis.

12. The use according to claim 11, wherein, The reagent used to detect the protein content in the subject's urine is a monoclonal antibody.

13. A kit or chip for diagnosing mental disorders, comprising a reagent for detecting the protein content in the urine of a subject, wherein the proteins in the urine are a combination of the following proteins: Uniprot ID: P11766, H7BY57, O95967, O95433, K7ELM9, P15328, and P68371; wherein the mental disorder is selected from depression and bipolar disorder.

14. The reagent kit or chip for diagnosing mental disorders according to claim 13, wherein, The bipolar disorder mentioned refers to the early stage of bipolar disorder.

15. The reagent kit or chip for diagnosing mental disorders according to claim 13, wherein, The kit or chip is used in LC-MS / MS tandem mass spectrometry analysis.

16. Use of reagents for detecting protein content in the urine of a subject in the preparation of reagents for the differential diagnosis of depression and early bipolar disorder, wherein the proteins in the urine are selected from a combination of the following: Uniprot ID: P11766, H7BY57, O95967, O95433, K7ELM9, P15328 and P68371.

17. Use of a reagent for detecting protein content in the urine of a subject in the preparation of a reagent for diagnosing bipolar disorder, wherein the protein in the urine is selected from a combination of the following: Uniprot ID: O95967, P15328 and O95433.

18. Use of a reagent for detecting protein content in the urine of a subject in the preparation of a reagent for diagnosing depression, wherein the protein in the urine is selected from a combination of the following: Uniprot ID: P15328, P11766, P68371, K7ELM9 and H7BY57.

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