Protein biomarkers to distinguish between major depressive disorder and bipolar disorder

Protein biomarkers to distinguish between major depressive disorder and bipolar disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Protein biomarkers to distinguish between major depressive disorder and bipolar disorder Jiyeong Lee, Yeeun Yun, Seungyeon Lee, Sora Mun, Hee-Gyoo Kang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7640890/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract It is challenging to distinguish between major depressive disorder (MDD) and bipolar disorder (BD) owing to the clinical similarity of their depressive symptoms. The poor response of patients with BD to antidepressants emphasizes the need for disease-specific biomarkers. Therefore, we aimed to identify disease-specific biomarkers of MDD and BD. After obtaining sera from patients with MDD and BD, and healthy controls (HCs), a non-targeted qualitative analysis of the serum proteome was performed. Biomarkers whose expression was specifically altered in patients with MDD were selected and compared with those in HCs and patients with BD. Similarly, biomarkers that were specifically expressed in patients with BD were selected and compared with those in HCs and patients with MDD. The selected biomarker candidates were validated via multiple reaction monitoring (MRM). Vitamin D-binding proteins (DBP), complement factor H (CFH), and keratin, type I cytoskeletal 9 (K1C9) were identified as BD-specific biomarkers. They had the highest intensity levels in the BD group compared with those in the MDD and HC groups. Fibronectin, an MDD-specific biomarker, showed the lowest intensity in the MDD group. Our study provides molecular evidence to improve comprehension of the pathophysiological mechanisms of MDD and BD. Health sciences/Biomarkers/Prognostic markers Health sciences/Diseases/Psychiatric disorders/Bipolar disorder Health sciences/Diseases/Psychiatric disorders/Depression affective disorder biological psychiatry clinical psychopathology bipolar disorder major depressive disorder biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Mood disorders, marked by emotional disturbances, include bipolar disorder (BD), cyclothymic disorder, manic depression, major depressive disorder (MDD), disruptive mood dysregulation disorder, persistent depressive disorder, and premenstrual dysphoric disorder 1 . According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), mood disorders are broadly classified as bipolar and depressive 2 , 3 . MDD mainly manifests as decreased motivation, depression, and various cognitive and psychosomatic symptoms 4 . BD manifests as mania, hypomania, and depression 4 . MDD is a highly prevalent disease worldwide, while BD prevalence is 5% 5 . Both MDD and BD are associated with decreased quality of life and increased mortality and suicide risk 6 . The depressive symptoms of MDD and BD share clinical similarities; 50%–60% of patients with BD begin with a depressive episode, and therefore, BD is easily misdiagnosed as MDD 7 – 9 . In a retrospective study involving patients diagnosed with MDD, 20% of patients experienced diagnostic conversion to BD 10 . A proper diagnosis of BD can take more than a decade owing to issues such as overdiagnosis and misdiagnosis 11 , 12 . Moreover, an important issue in BD management is that antidepressants are ineffective, and proper treatment can be started only after an accurate diagnosis 13 . In this milieu, biomarkers that can help distinguish between MDD and BD are necessary. Reportedly, B2RAN2, B4E1B2, APOA1, ENG, SBSN, and QSOX2 expression is upregulated, whereas ORM1, MRC2, and SLPI expression is downregulated in patients with MDD and BD. Most of these proteins are related to the immune system 14 . Meanwhile, a study that categorized patients with MDD and BD with depressive symptoms into one group revealed that apolipoprotein A-IV expression decreases when clinical depressive symptoms are present 15 . However, despite attempts to distinguish MDD from BD, useful biomarkers have not been established. Therefore, here, we aimed to identify disease-specific biomarkers that distinguish BD from MDD when patients exhibit depressive symptoms. Methods and materials Sample preparation This study was approved by the Institutional Review Board of Eulji University (EU24-57; October 4, 2024) and conducted in accordance with the tenets of the Declaration of Helsinki. The workflow is shown in Supplementary Figure 1. This study involved the analysis of cross-sectional samples. MDD and BD were diagnosed by a psychiatrist. The discovery set comprised 28 controls, 34 patients with MDD, and 9 patients with BD. The validation set comprised 37 controls, 50 patients with MDD, and 17 patients with BD. The control, MDD discovery, and validation sets were independent, and the BD validation set included a discovery set (Table 1). All participants provided written informed consent prior to study commencement. Serum was separated from blood using centrifugation (4000 × g, 5 min) and stored at −80°C. Equal amounts of serum from all samples were pooled and used for protein library construction. To deplete the levels of proteins present at high concentrations in the serum, a multiple-affinity removal column (hu-6, 4.6 mm × 50 mm; Agilent Technologies, CA, USA) with Agilent Technologies 1200 was used. Thereafter, the less abundant proteins were concentrated through an Omega™ Membrane with a 3K pore (Pall Corporation, NY, USA). The protein concentration was measured using a bicinchoninic acid assay kit (Thermo Scientific, OH, USA). Each sample contained 100 μg of serum protein, and the pooled sample contained 1 mg of serum protein. Protein reduction was performed with 5 mM Tris(2-carboxyethyl) phosphine (Pierce, IL, USA) at 400 rpm for 30 min at 37°C to break the disulfide bond. Alkylation was performed with 15 mM iodoacetamide (Sigma-Aldrich, MO, USA) at 400 rpm for 1 h at 25°C in the dark to prevent the disulfide bond from reattaching. Digestion was performed using mass spectrometry (MS)-grade trypsin (Promega, WI, USA) at 800 rpm for 15 h at 37°C. Residual chemical reagents in the samples were removed using a C18 cartridge (Waters, MA, USA). Finally, the pooled sample was separated into 12 fractions using an OFFGEL Fractionator (Agilent Technologies) with Immobiline DryStrip (pH 3-10; GE Healthcare, WI, USA). Qualitative analysis using liquid chromatography with tandem mass spectrometry (LC-MS-MS) Samples were analyzed using the Nano-LC system Ekspert nLC415 (Eksigent Technologies, CA, USA) and 5600+ triple TOF MS (AB SCIEX, Canada). Data were analyzed in the positive ion mode at a flow rate of 300 nL/min. The LC gradient used for elution was as follows: 0 min, 5% B; 10.5 min, 40% B; 80 min, 90% B; and returning to 5% B; 95 min. The mobile phases consisted of (a) 0.1% formic acid (FA) in high-performance liquid chromatography (HPLC)-grade water and (b) 0.1% FA in HPLC-grade acetonitrile. Samples were injected into an Eksigent ChromXP nano-LC column (75 μm id × 15 cm) and ionized using a nanospray tip (PicoTip Emitter Silica Tip by New Objective, MA, USA). The pooled serum samples were analyzed in an information-dependent acquisition mode. Sequential window acquisition of all theoretical fragment-ion spectra modes was used to generate spectral data from individual serum samples. Absolute quantification of candidate biomarkers using multiple reaction monitoring (MRM) Q1 and Q3 of the candidates were selected using Skyline software v.4.1.0 (AB Sciex) (Supplementary Table 3). Parameters required for analysis, such as collision energy, declustering potential, and collision exit potential, were optimized to one. MRM was performed using a Sciex Exion LC and QTRAP 5500 mass spectrometer (AB Sciex). The sample injection volume was 5 μL, and the analysis was performed using an ACQUITY UPLC BEH C18 column (130 Å, 1.7 μm, 2.1 mm × 150 mm, Waters). The source parameters were as follows: curtain gas, 30 psi; low collision gas; ion spray, 5500 V; 400°C; ion source gas 1, 40 psi; and ion source 2, 60 psi. The flow rate was 250 μL/min and chromatography time was 30 min. Mobile phase A was 0.1% formic acid in HPLC-grade water and mobile phase B was 0.1% FA in acetonitrile. The mixture was allowed to flow at 5% B for 1 min, 40% B for 20 min, 90% B for 25 min, and 5% B for 30 min. Mobile phases A and B were the same as those used in the qualitative analysis. Peptides with >90% purity were obtained (Peptron, South Korea). Data analysis For protein identification, a protein library was generated using ProteinPilot v.5.0.2 (AB Sciex), and acquisition masses and retention times were matched. The parameters used were as follows: cys-alkylation (iodoacetamide), digestion (trypsin, allowing for two missed cleavages), instrument (TripleTOF 5600), and species ( Homo sapiens ). The generated library was used to quantify the peptides using PeakView v.2.2 (AB SCIEX). The false discovery rate of the peptides was <1% and modified peptides were excluded. Normalized data were obtained using total area sum normalization in MarkerView v.1.3.1 (AB SCIEX). Statistical analyses were performed using MarkerView v.1.3.1 (AB SCIEX), MetaboAnalyst (version 6.0), and GraphPad Prism (version 8.4.2). First, the outliers were removed using the ROUT method. Subsequently, normality and equal variance tests were performed using Kruskal–Wallis, Brown–Forsythe, and Welch ANOVA tests. Gene Ontology (GO) analysis was performed in STRING v.12.0. Results Protein candidate discovery Information-dependent acquisition (IDA) mode data of the pooled samples were used to generate an ion library. We identified 89 proteins by matching SWATH data of the samples to the ion library. Sparse partial least squares discriminant analysis (sPLS-DA) of 89 proteins using MetaboAnalyst (ver 6.0) showed that the variance of components of all groups was 9% and 10.2%, respectively, and the error rate was 15.7% (Figure 1A). For HC versus MDD, the component variance was 9.6% and 4.8%, respectively, and the error rate was 9.7% (Figure 1B). For HC versus BD, the component variance was 16.9% and 4.3%, respectively, and the error rate was 5.6% (Figure 1C). For BD versus MDD, the component variance was 15% and 8.1%, respectively, and the error rate was 5.6% (Figure 1D). The sPLS-DA error rate was below 10% in each pairwise comparison, indicating good group separation. The loading plots are presented in Supplementary Figure 2. To identify biomarkers whose expression is specifically altered in BD and biomarkers specifically expressed in MDD, differentially expressed proteins (DEPs) were identified in the comparison between groups using a volcano plot. Proteins satisfying the conditions of fold change (FC) > 1.2 and p -value < 0.05 were identified. In HC versus MDD, 9 proteins increased and 2 decreased in HC. In HC versus BD, the expression of 14 proteins increased and that of 12 proteins decreased in the HC group. In MDD versus BD comparison, the expression of 11 proteins increased and that of 13 proteins decreased in the MDD group. Of the 24 proteins identified in the MDD versus BD comparison, the expression of 21 proteins differed among the groups, including the control group. The expression of the remaining three proteins differed only between the MDD and BD groups. The expression of 15 of the 21 proteins was similar in the control and depression groups, but significantly different in the BD group. We found two proteins whose expression was similar in the control and BD groups but significantly different in the MDD group (Figure 1H). The 17 candidate proteins are listed in Supplementary Table 1. The scatter plots for 15 BD-specific biomarkers are presented in Supplementary Figure 3 and those for 2 MDD-specific biomarkers are presented in Supplementary Figure 4. The expression levels of the 17 proteins in each group were confirmed using a heatmap (Supplementary Figure 5A). sPLS–DA with the components of all groups presented a variance of 27.2% and 9%, respectively, accounting for a total variation of 36.2% and an error rate of 22.9% (Supplementary Figur 5B). Comparison between the HC and MDD groups revealed that the component variance was 10.8% and 6.4%, with a total variation of 17.2% and an error rate of 24.2% (Supplementary Figure 5C). In the comparison between the BD and MDD groups, the component variance was 35.1% and 6.1%, respectively, total variation was 41.2%, and error rate was 0% (Supplementary Figure 5D). For BD versus HC, the component variance was 35.8% and 7.3%, respectively, total variation was 43.1%, and error rate was 2.8% (Supplementary Figure 5E). The results in Figure 4D and E are relatively good. The area under the curve (AUC) values for the 17 biomarkers are shown in Figure 4F. The red bars indicate 15 BD-specific biomarkers. The AUC values of the comparison between HC and MDD for the 15 BD-specific biomarkers were low, with an average of 0.5728. In the HC versus BD and MDD versus BD group comparisons, high AUC values of 0.8153 and 0.8128 were recorded, respectively. The HC and MDD groups were not well distinguished, while the BD group had values higher or lower than the HC and MDD groups. Thus, it was confirmed that that the 15 biomarker are BD-specific biomarker. Similarly, the two MDD-specific biomarkers had a low AUC value of 0.5834 on average in the comparison between the HC and BD groups. The average AUC values of the HC versus MDD and MDD versus BD group comparison were relatively high at 0.7653 and 0.8039, respectively. These two biomarkers were confirmed as MDD-specific biomarkers because the values of the HC and BD groups were similar, whereas the values of the MDD group were different from those of the HC and BD groups. The specificity, recall, negative predictive value, precision, and accuracy of the receiver operating characteristic (ROC) curve of the 17 marker candidates are presented in Supplementary Table 2. Among the 15 biomarkers specific to BD, the expression of 6 was upregulated and that of 9 was downregulated. The expression levels of proteins in the HC and BD groups were similar, but both groups contained significantly DEPs compared with those in the MDD group. Pathway and GO analyses of selected DEPs The correlation between the 2 MDD- and 15 BD-specific biomarkers was confirmed (Figure 2A). Pink circles represent BD-specific biomarkers and blue circles represent MDD-specific biomarkers. All 17 biomarkers were found to be interconnected. Cluster 1 (with an orange background) represents complement and coagulation cascades, and positive regulation of opsonization. Cluster 2 (with a green background) represents high-density lipoproteins (HDLs) and HDL particles. Cluster 3 (with a gray background) represents GC and K1C9. The GO analysis revealed the top 10 pathways related to false discovery rate (FDR) (Figure 2B–2D). In terms of biological processes, the defense response, acute inflammatory response, and acute-phase response were highly correlated (Figure 2B). In terms of cellular components, blood microparticle, extracellular exosomes, and extracellular space were significant (Figure 2C). Finally, in the Reactome pathways, regulation of complement cascade, post-translational protein phosphorylation, and platelet degranulation were highly correlated (Figure 2D). In the GO analysis, all 17 proteins were included in the extracellular space within the cellular component. Additionally, the proteins were broadly classified according to associations with inflammatory responses, the extracellular skeleton, and coagulation factors (Figure 2E). Through this, it can be speculated that changes in the expression of proteins related to immune inflammatory response, the extracellular matrix, and coagulation affect the pathological mechanisms of MDD and BD. Candidate protein validation results Seventeen peptide standards were purchased and subjected to validation and MRM analyses. The transitions for the 17 peptides are presented in Supplementary Table 3. Figure 3 presents the scatter plot of the MRM results and density distribution data. The density distributions of the BD-specific biomarkers overlapped between the HC and MDD groups, while the confidence intervals of the BD group did not overlap. Thus, we confirmed that the BD group was well distinguished. Fibronectin can be used as an MDD-specific biomarker because the confidence intervals of the HC and BD groups overlapped, but those of the MDD group did not overlap (Figure 3D). The proteins differentially expressed in BD included vitamin D-binding protein (DBP), complement factor H (CFH), and keratin, type I cytoskeletal 9 (K1C9). DBP showed the same trend in both the discovery and validation sets; furthermore, the BD group had the highest level of DBP compared to the other groups. Additionally, it showed a commonality of not being significant in comparison to HCs and patients with MDD. Post-test validation confirmed a significant difference in DBP expression between the MDD and BD groups ( p = 0.0026; Figure 6A). CFH also showed the same tendency, with the BD group having the highest concentration (Figure 3B). The post-hoc test for validation confirmed a significant difference between the MDD and BD groups ( p < 0.0001). In the validation set, K1C9 showed consistent statistical significance and concentration change trends compared with those in the discovery set. The post-test validation confirmed a significant difference in K1C9 expression between the MDD and BD groups ( p = 0.0061; Figure 3C). Fibronectin was differentially expressed in patients with MDD. As the validation results, which were similar to the discovery results, showed the lowest concentration tendency of fibronectin in MDD, it was selected as an MDD-specific biomarker. The post-hoc analysis confirmed a significant difference in fibronectin expression between the MDD and BD groups ( p = 0.0063; Figure 3D). A scatter plot of MRM results of the disease-specific biomarkers is presented in Figure S4. The diagnostic performance of the MRM results for the final four biomarkers (VTDB, CFH, K1C9, and fibronectin) was confirmed through ROC curves (Figure 4A). The 17 candidate proteins are shown in the loading plot in Supplementary Figure 1. As fibronectin was the only MDD-specific biomarker, the discriminatory power of fibronectin was confirmed. The BD-specific biomarkers (DBP, CFH, and K1C9) showed low AUC values with an average of 0.5699 in the HC versus MDD group comparison, similar to the discovery set results, but relatively high diagnostic performance with an average of 0.7856 and 0.7901 in the HC versus BD and MDD versus BD group comparisons, respectively, confirming that they are BD-specific biomarkers. In addition, when analyzing the combined results of the three BD-specific biomarkers, we were able to confirm the diagnostic performance of the biomarker that best differentiates MDD from BD with an AUC of 0.8350. The MDD-specific biomarker fibronectin showed a low AUC value close to 0.5 in the HC versus BD group comparison and a high AUC value in other group comparisons, confirming its MDD-specific biomarker status (Figure 4B). The ability of the four final biomarkers to discriminate between MDD and BD was confirmed, demonstrating diagnostic performance with an AUC value of 0.8363 (Figure 4C). The results confirmed an accuracy of 89.39% for MDD vs. BD (Figure 4D). When BD-specific biomarkers were combined, we confirmed high discriminatory power with an AUC value of 0.8341 to distinguish BD from the remaining groups (HC and MDD) (Figure 4E). The accuracy of this result was 93.20% (Figure 4F). Similarly, for the MDD-specific biomarker fibronectin, we confirmed high discriminatory power with an AUC value of 0.7407 to distinguish MDD from the remaining groups (HC and BD) (Figure 7G). The ROC curve has an accuracy of 68.27%. We then analyzed the correlation between clinical indicators and biomarkers. Pearson correlation coefficients (Figure 5A) and p -values (Figure 5B) were calculated. The correlation results are briefly presented on Figure 5C. The BD-specific biomarkers are indicated with blue semicircles, MDD-specific biomarkers with pink semicircles, and clinical indicators with white semicircles. We confirmed that body mass index (BMI) and K1C9 showed a positive correlation, and beck depression inventory (BDI) and fibronectin showed a negative correlation. K1C9, which is a BD-specific biomarker, and fibronectin, which is an MDD-specific biomarker, showed a positive correlation. Discussion DBP, CFH, and K1C9 were identified as BD-specific biomarkers. Meanwhile, fibronectin was identified as a MDD-specific biomarker. Circulating vitamin D metabolites are primarily bound to DBP, with albumin as the major secondary carrier, particularly in patients with a low serum DBP level 16 . However, as only 1%–2% of the sterol-binding sites are utilized, DBP has several additional metabolic roles beyond vitamin D transport 17 . One metabolic role of DBP is to prevent the activation of the actin polymerization, aggregation, and coagulation cascade when actin binds glia maturation factor beta (GMFβ) in the brain to DBP in the cerebrospinal fluid and circulation 18 , 19 . In mental illnesses, both increased and decreased DBP levels have an effect on patients 19 , 20 . It is known that defects in actin polymerization are associated with BD pathology 21 – 23 . BD is associated with cytoskeletal abnormalities 21 , 22 . Therefore, it can be rationalized that an increase in DBP level in BD leads to increased actin binding, which leads to actin polymerization defects and cytoskeleton abnormalities 19 . Intermediate filaments (IFs) are among three major cytoskeletal filament assemblies found in higher eukaryotes. IFs form a stable filamentous network that extends throughout the cytoplasm to provide structural support to many cell types 24 . The interdependence of the cytoskeletal elements can be explained by the presence of linker proteins that bind IFs to microtubules or microfilaments 24 . Not only do keratins provide protection from apoptosis, but type I (not type II) keratins are also major caspase substrates that carry out cytolysis during apoptosis 25 . Among them, we selected K1C9 as a BD-specific biomarker, a type I intermediate filament protein specifically expressed in the basal layer of the primary epidermal ridge of the palmar and plantar epidermis in humans 26 – 28 . In a study comparing the metabolomic and proteomic characteristics of plasma from cognitively normal (CN) individuals and patients with dementia diagnosed with mild cognitive impairment or Alzheimer’s disease (AD), K1C9 expression showed an increasing pattern in patients with Alzheimer's compared with that in HCs. K1C9 also showed the ability to separate the Alzheimer's group with 100% accuracy 29 . In a study comparing the serum proteomes, keratin type II cytoskeletal 1 level decreased in patients with MDD compared with that in those in remission 30 . Thus, DBP interacts with K1C9 to form the cytoskeleton. A correlation among changes in these protein levels, the cytoskeleton, and the pathophysiological mechanism of BD has been suggested. This is the first study to suggest that K1C9 is a biomarker for BD. K1C9 was found to positively correlate with BMI. A study involving women under 38 years of age who underwent an 8-week weight loss program found that the keratin levels increased 31 . Additionally, a previous study reported a decrease in keratin level in a group of elderly women with obesity 32 . Thus, the keratin level varies depending on obesity status, warranting further research. In a study that quantified complement factors, the C3, C4, and CFH levels were higher in the BD group than in the MDD group, and this is similar to the results of our study showing that CFH levels were higher in BD 33 . A serological analysis revealed higher peripheral concentrations of C3, C4, and C6 during manic episodes in 45 patients with BD compared with those in HCs 34 . Additionally, a study analyzing the peripheral blood of 30 patients to observe the complement cascade reaction in patients with BD confirmed that immune system regulation disorder in patients with BD occurred owing to increased C3a and C5a levels 35 . Here, the STRING analysis confirmed that the acute inflammatory response and regulation of the complement cascade were highly related to MDD and BD. It is presumed that immune regulation disorders in patients with BD lead to an increase in the levels of components of the complement cascade, followed by an increase in CFH level for complement regulation. Here, the fibronectin level decreased in patients with MDD, and it could be a specific biomarker. Plasma fibronectin is produced in the liver, released into the bloodstream, and circulated in the blood 36 . Plasma fibronectin can be transformed into cellular fibronectin by homeostatic regulation during ECM remodeling 37 . Its expression can be triggered by exposure to traumatic life events or stressors 38 – 40 . Inflammatory reactions occur in response to stress, which disrupts homeostasis 41 . The ECM is highly dynamic because it is continuously deposited, remodeled, and degraded throughout development until maturity to maintain tissue homeostasis 42 . Stress can also cause hippocampal ECM regulation, which in turn causes perineural neural networks to be deposited in the CNS, thereby reducing the E/A balance of neurons and regulating brain oscillations 43 – 45 . That is, the finding that stress causes changes in the ECM suggests an association between MDD and the ECM. To enable ECM remodeling, the plasma fibronectin level appears to decrease as plasma fibronectin is converted to cellular fibronectin. This assumption is consistent with the analysis results showing that 22 proteins in the STRING results are associated with the ECM. Additionally, the correlation analysis results showed that fibronectin had a negative correlation with BDI, one of the indicators of depression, suggesting a significant association between depression severity and fibronectin. Furthermore, a positive correlation was observed between K1C9 and fibronectin. A study demonstrated that when the level of fibronectin decreases, cell migration becomes more active and the flow of keratin becomes faster 46 . It is possible that changes in fibronectin and keratin levels are interrelated. Currently, mood disorders lack specific mechanisms and biomarkers. We focused on the “clinical symptoms” of depression between BD and MDD and identified four disease-specific biomarkers for these two diseases. After discovering biomarkers using a non-targeted analysis method, we validated them using MRM. A brief discussion of each biomarker is summarized in Supplementary Fig. 7. Our results provide insights into the mechanisms shared by the “depressive symptoms” of these two diseases. DBP, K1C9, and CFH were identified as BD-specific biomarkers. Alterations in the levels of these proteins suggest that cytoskeletal dysfunction involving actin and IFs and the complement system may contribute to the pathological mechanisms of BD. Alterations in the level of fibronectin may be associated with stress-induced ECM remodeling in depression. Our findings offer a reference for situations in which patients with BD are misdiagnosed with MDD and are not receiving appropriate treatment. Using these four biomarkers, we expect to gain further insights into MDD and BD. Declarations Conflict of Interest The authors report there are no competing interests to declare. Supplementary Information Supplementary information is available at MP’s website. 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Regulation of keratin network dynamics by the mechanical properties of the environment in migrating cells. Sci Rep 2020; 10 (1) : 4574. Table Table 1. Demographics of participants Variables Discovery set Validation set HC a MDD b BD c p- value HC MDD BD p- value Number of participants 28 34 9 37 50 17 Age Mean ± SD d 60.39 ± 11.60 53.32 ± 19.01 36.78 ± 10.73 <0.0001 g 60.05 ± 8.84 46.74 ± 17.66 36.76 ± 10.74 <0.0001 g Minimum 41 20 21 41 19 21 Maximum 80 82 53 80 80 56 Sex, n (%) Male 7 (25.0%) 7 (25.6%) 2 (22.2%) 0.9177 h 5 (13.5%) 16 (32.0%) 5 (29.4%) 0.1296 h Female 21 (75.0%) 27 (79.4%) 7 (77.8%) 32 (86.5%) 34 (68.0%) 12 (70.6%) BMI e Mean ± SD 24.01 ± 3.45 25.35 ± 3.33 24.31 (3.92) 0.4216 i 22.53 ± 2.65 23.09 ± 3.15 25.34 ± 2.86 0.0157 i HAMD-17 f Mean ± SD - 19.71 ± 5.79 17.11 ± 6.81 0.2680 j - 19.84 ± 4.79 17.76 ± 5.28 0.1168 k Treatmen t , n (%) + - 17 (50.0%) 8 (88.9%) - 26 (52.0%) 16 (94.1%) - 28 (100.0%) 17 (50.0%) 1 (11.1%) 37 (100.0%) 24 (48.0%) 1 (5.9%) a HC : Healthy control, b MDD : Major Depressive Disease, c BD : biopolar disease, treatment, d SD : Standard Deviation, e BMI : body mass index, f HAMD-17 : Hamilton Depression Rating Scale, 17-item, g Welch's ANOVA test, h Chi-square, i Kruskal-Wallis test, j Unpaired t test, k Mann Whitney test Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files 20250616BD1SupplementaryTables.docx supplemetary tables Supplementaryfigures1.docx supplementary figures Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":1134608,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHealthy control (HC), major depressive disorder (MDD), and bipolar disorder (BD) grouping using\u003c/strong\u003e \u003cstrong\u003esparse partial least squares discriminant analysis (sPLS-DA). sPLS-DA of 89 proteins in total. Candiate selection using a volcan plot. \u003c/strong\u003e(A) HC vs. MDD vs. BD score plots. Error rate: 15.7%, (B) HC vs. MDD\u0026nbsp; score plots. Error rate: 9.7%, (C) HC vs. BD score plots. Error rate: 5.6%, (D) MDD vs. BD score plots. Error rate: 5.6%.\u003cstrong\u003e \u003c/strong\u003e(E)-(G) The size of the dot represents the \u003cem\u003ep\u003c/em\u003e-value and the color represents log\u003csub\u003e2\u003c/sub\u003e(fold change (FC)). The horizontal line represents log\u003csub\u003e2\u003c/sub\u003e(FC) ≥ 1.2 and the vertical line represents \u003cem\u003ep\u003c/em\u003e-value ≤ 0.05. (E) Comparison of healthy control (HC) and major depressive disorder (MDD). (F) Comparison of HC and bipolar disorder (BD). (G) Comparison of MDD and BD. (H) Overall flow of biomarker selection. Of the 89 identified proteins, 21 proteins were selected based on the criteria of \u003cem\u003ep\u003c/em\u003e-value ≤ 0.05 and log\u003csub\u003e2\u003c/sub\u003e(FC) ≥ 1.2. Subsequently, 15 BD-specific biomarkers and 2 MDD-specific biomarkers were selected.\u003c/p\u003e","description":"","filename":"figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-7640890/v1/e2beaedf31012ea53c04a1dc.png"},{"id":93941747,"identity":"9b0f5b17-e607-4abe-8fcd-306e3b910957","added_by":"auto","created_at":"2025-10-20 13:40:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1614000,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork connectivity between the disease-specific proteins and enriched biological pathways.\u003c/strong\u003e (A) STRING analysis results showing that the 17 proteins are connected. The bolder the results are expressed, the stronger is the correlation. Red circles are bipolar disorder (BD)-specific biomarkers and blue circles are major depressive disorder (MDD)-specific biomarkers. Proteins were divided according to k-means cluster analysis. Orange represents Cluster 1, green represents Cluster 2, and gray represents Cluster 3. (B)-(D) The top 10 enriched Gene Ontology terms. The X-axis is -log\u003csub\u003e10\u003c/sub\u003e(FDR). FDR means false discovery rate. The size of the circle is the gene count, and the larger the circle, the more associated are the proteins. (B) Biological process (Gene Ontology); (C) cellular component (Gene Ontology); (D) Reactome pathways (E). The correlation among inflammation, extracellular matrix (ECM), and coagulation is presented in each pathway.\u003c/p\u003e","description":"","filename":"20250820BD1figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7640890/v1/41f856214ecc89e6c2b26cd8.png"},{"id":93941756,"identity":"777a3aea-fc37-455e-95a2-0c8f597b3b74","added_by":"auto","created_at":"2025-10-20 13:40:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":663089,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of candidates verified via MRM\u003c/strong\u003e (A) Vitamin D-binding protein, (B) complement factor H, and (C) keratin, type I cytoskeletal 9 are differentially expressed specifically in bipolar disorder (BD). (D) Fibronectin is differentially expressed specifically in major depressive disorder (MDD). A scatter plot is provided on the left side. The graph values represent mean ± standard deviation. Green represents HC, blue represents MDD, and red represents BD. The density distribution of serum proteins across the diagnostic groups (BD, MDD, and HC) is presented on the right side. Vertical lines indicate the 95% confidence intervals (CIs) for the group means. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001, and ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"20250820BD1figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7640890/v1/ff8abac30227c965b664e925.png"},{"id":93942473,"identity":"b331b250-6029-48b0-a8ec-63caa7dbd78f","added_by":"auto","created_at":"2025-10-20 13:48:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":554705,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) curve analysis results of the verified candidates \u003c/strong\u003e(A) The area under the curve (AUC) values of the final four biomarkers. Green indicates higher values and yellow indicates lower values. The red bar indicates bipolar disorder (BD)-specific biomarkers and blue bar indicates major depressive disorder\u003c/p\u003e\n\u003cp\u003e(MDD)-specific biomarkers. (B) Description of the confusion matrix. (C) ROC curve analysis results of the four biomarkers distinguishing MDD and BD. (D) Confusion matrix of the final four biomarkers for MDD and BD. (E) Results of analysis confirming the ability of BD-specific biomarkers to distinguish BD. (F) Confusion matrix of BD-specific biomarkers to distinguish BD. (G) Results of analysis confirming the ability of fibronectin, an MDD-specific biomarker, to distinguish MDD. (H) Confusion matrix of the MDD-specific biomarker to distinguish MDD\u003c/p\u003e","description":"","filename":"20250820BD1figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7640890/v1/9101f70079ae8cf188078180.png"},{"id":93941751,"identity":"27216fc6-92d8-4bd5-bd67-15fbac2f6000","added_by":"auto","created_at":"2025-10-20 13:40:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":437723,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between verified candidates and clinical variables. \u003c/strong\u003e(A) A graph of Pearson correlation coefficients. Correlation coefficients are presented for each protein. (B) \u003cem\u003ep\u003c/em\u003e-values ​​for biomarkers and clinical information.\u003cem\u003e p\u003c/em\u003e-values ​​are presented for each protein. (C) White semicircles represent clinical information, blue semicircles represent BD-specific biomarkers, pink semicircles represent major depressive disorder (MDD)-specific biomarkers, blue lines represent negative correlations, and red lines represent positive correlations.\u003c/p\u003e","description":"","filename":"20250820BD1figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7640890/v1/1d73e545a8ba03feb73ec76a.png"},{"id":95654313,"identity":"a535ee95-7f42-47ab-b169-4495197a45e0","added_by":"auto","created_at":"2025-11-11 16:10:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5117490,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7640890/v1/04d20890-aa06-46fd-b5a9-ff88e0561527.pdf"},{"id":93942471,"identity":"2a1c20a0-810d-4ca1-943c-c97dc8e045b3","added_by":"auto","created_at":"2025-10-20 13:48:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":53234,"visible":true,"origin":"","legend":"supplemetary tables","description":"","filename":"20250616BD1SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7640890/v1/6c9a8ee964670950b03558d7.docx"},{"id":93942476,"identity":"6196d046-2140-48a2-8759-8c1c86aac5b2","added_by":"auto","created_at":"2025-10-20 13:48:59","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1707004,"visible":true,"origin":"","legend":"\u003cp\u003esupplementary figures\u003c/p\u003e","description":"","filename":"Supplementaryfigures1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7640890/v1/72ed367ef72aab1cb8bd9975.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Protein biomarkers to distinguish between major depressive disorder and bipolar disorder","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMood disorders, marked by emotional disturbances, include bipolar disorder (BD), cyclothymic disorder, manic depression, major depressive disorder (MDD), disruptive mood dysregulation disorder, persistent depressive disorder, and premenstrual dysphoric disorder \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), mood disorders are broadly classified as bipolar and depressive \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. MDD mainly manifests as decreased motivation, depression, and various cognitive and psychosomatic symptoms \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. BD manifests as mania, hypomania, and depression \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. MDD is a highly prevalent disease worldwide, while BD prevalence is 5% \u003csup\u003e5\u003c/sup\u003e. Both MDD and BD are associated with decreased quality of life and increased mortality and suicide risk \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe depressive symptoms of MDD and BD share clinical similarities; 50%\u0026ndash;60% of patients with BD begin with a depressive episode, and therefore, BD is easily misdiagnosed as MDD \u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In a retrospective study involving patients diagnosed with MDD, 20% of patients experienced diagnostic conversion to BD \u003csup\u003e10\u003c/sup\u003e. A proper diagnosis of BD can take more than a decade owing to issues such as overdiagnosis and misdiagnosis \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Moreover, an important issue in BD management is that antidepressants are ineffective, and proper treatment can be started only after an accurate diagnosis \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this milieu, biomarkers that can help distinguish between MDD and BD are necessary. Reportedly, B2RAN2, B4E1B2, APOA1, ENG, SBSN, and QSOX2 expression is upregulated, whereas ORM1, MRC2, and SLPI expression is downregulated in patients with MDD and BD. Most of these proteins are related to the immune system \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Meanwhile, a study that categorized patients with MDD and BD with depressive symptoms into one group revealed that apolipoprotein A-IV expression decreases when clinical depressive symptoms are present \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, despite attempts to distinguish MDD from BD, useful biomarkers have not been established. Therefore, here, we aimed to identify disease-specific biomarkers that distinguish BD from MDD when patients exhibit depressive symptoms.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003ch2\u003eSample preparation\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Eulji University (EU24-57; October 4, 2024) and conducted in accordance with the tenets of the Declaration of Helsinki.\u0026nbsp;The workflow is shown in\u0026nbsp;Supplementary Figure 1. This study involved the analysis of cross-sectional samples. MDD and BD were diagnosed by a psychiatrist. The discovery set comprised 28 controls, 34 patients with MDD, and 9 patients with BD. The validation set comprised 37 controls, 50 patients with MDD, and 17 patients with BD. The control, MDD discovery, and validation sets were independent, and the BD validation set included a discovery set (Table 1). All participants provided written informed consent prior to study commencement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSerum was separated from blood using centrifugation (4000 \u0026times; g, 5 min) and stored at \u0026minus;80\u0026deg;C.\u0026nbsp;Equal amounts of serum from all samples were pooled and used for protein library construction. To deplete the levels of proteins present at high concentrations in the serum, a multiple-affinity removal column (hu-6, 4.6 mm \u0026times; 50 mm; Agilent Technologies,\u0026nbsp;CA,\u0026nbsp;USA) with Agilent Technologies 1200 was used. Thereafter, the less abundant proteins were concentrated through an Omega\u0026trade; Membrane with a 3K pore (Pall Corporation, NY,\u0026nbsp;USA). The protein concentration was measured using a bicinchoninic acid assay kit (Thermo Scientific, OH,\u0026nbsp;USA). Each sample contained 100 \u0026mu;g of serum protein, and the pooled sample contained 1 mg of serum protein. Protein reduction was performed with 5 mM Tris(2-carboxyethyl) phosphine (Pierce, IL,\u0026nbsp;USA) at 400 rpm for 30 min at 37\u0026deg;C to break the disulfide bond. Alkylation was performed with 15 mM iodoacetamide (Sigma-Aldrich, MO,\u0026nbsp;USA) at 400 rpm for 1 h at 25\u0026deg;C in the dark to prevent the disulfide bond from reattaching. Digestion was performed using mass spectrometry (MS)-grade trypsin (Promega, WI,\u0026nbsp;USA) at 800 rpm for 15 h at 37\u0026deg;C. Residual chemical reagents in the samples were removed using a C18 cartridge (Waters, MA,\u0026nbsp;USA). Finally, the pooled sample was separated into 12 fractions using an OFFGEL Fractionator (Agilent Technologies) with Immobiline DryStrip (pH 3-10; GE Healthcare, WI,\u0026nbsp;USA).\u003c/p\u003e\n\u003ch2\u003eQualitative analysis using liquid chromatography with tandem mass spectrometry (LC-MS-MS)\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eSamples were analyzed using the Nano-LC system Ekspert nLC415 (Eksigent Technologies, CA,\u0026nbsp;USA) and 5600+ triple TOF\u0026nbsp;MS\u0026nbsp;(AB SCIEX, Canada). Data were analyzed in the positive ion mode at a flow rate of 300 nL/min. The LC gradient used for elution was as follows: 0 min, 5% B; 10.5 min, 40% B;\u0026nbsp;80\u0026nbsp;min, 90% B; and returning to 5%\u0026nbsp;B;\u0026nbsp;95 min.\u0026nbsp;The mobile phases consisted of (a) 0.1% formic acid\u0026nbsp;(FA)\u0026nbsp;in high-performance liquid chromatography (HPLC)-grade water and (b) 0.1%\u0026nbsp;FA\u0026nbsp;in HPLC-grade acetonitrile. Samples were injected into an Eksigent ChromXP nano-LC column (75 \u0026mu;m id \u0026times; 15 cm) and ionized using a nanospray tip (PicoTip Emitter Silica Tip by New Objective, MA, USA). The pooled serum samples were analyzed in an information-dependent acquisition mode. Sequential window acquisition of all theoretical fragment-ion spectra modes was used to generate spectral data from individual serum samples.\u003c/p\u003e\n\u003ch2\u003eAbsolute quantification of candidate biomarkers using multiple reaction monitoring (MRM)\u003c/h2\u003e\n\u003cp\u003eQ1 and Q3 of the candidates were selected using Skyline software v.4.1.0\u0026nbsp;(AB\u0026nbsp;Sciex) (Supplementary Table\u0026nbsp;3). Parameters required for analysis, such as collision energy, declustering potential, and collision exit potential, were optimized to one. MRM was performed using a Sciex Exion LC and QTRAP 5500 mass spectrometer (AB Sciex). The sample injection volume was 5 \u0026mu;L, and the analysis was performed using an ACQUITY UPLC BEH C18 column (130 \u0026Aring;, 1.7 \u0026mu;m, 2.1 mm \u0026times; 150 mm, Waters). The source parameters were as follows: curtain gas, 30 psi; low collision gas; ion spray, 5500 V; 400\u0026deg;C; ion source gas 1, 40 psi; and ion source 2, 60 psi. The flow rate was 250 \u0026mu;L/min and chromatography time was 30 min. Mobile phase A was 0.1% formic acid in HPLC-grade water and mobile phase B was 0.1%\u0026nbsp;FA\u0026nbsp;in acetonitrile. The mixture was allowed to flow at 5% B for 1 min, 40% B for 20 min, 90% B for 25 min, and 5% B for 30 min. Mobile phases A and B were the same as those used in the qualitative analysis. Peptides with \u0026gt;90% purity were obtained (Peptron, South Korea).\u003c/p\u003e\n\u003ch2\u003eData analysis\u003c/h2\u003e\n\u003cp\u003eFor protein identification, a protein library was generated using ProteinPilot v.5.0.2 (AB\u0026nbsp;Sciex), and acquisition masses and retention times were matched. The parameters used were as follows: cys-alkylation (iodoacetamide), digestion (trypsin, allowing for two missed cleavages), instrument (TripleTOF 5600), and species (\u003cem\u003eHomo sapiens\u003c/em\u003e). The generated library was used to quantify the peptides using PeakView v.2.2 (AB SCIEX). The false discovery rate of the peptides was \u0026lt;1% and modified peptides were excluded. Normalized data were obtained using total area sum normalization in MarkerView v.1.3.1 (AB SCIEX). Statistical analyses were performed using MarkerView v.1.3.1 (AB SCIEX), MetaboAnalyst (version 6.0), and GraphPad Prism (version 8.4.2). First, the outliers were removed using the ROUT method. Subsequently, normality and equal variance tests were performed using Kruskal\u0026ndash;Wallis, Brown\u0026ndash;Forsythe, and Welch ANOVA tests. Gene Ontology (GO) analysis was performed in STRING v.12.0.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eProtein candidate discovery\u003c/h2\u003e\n\u003cp\u003eInformation-dependent acquisition (IDA) mode data of the pooled samples were used to generate an ion library. We identified 89 proteins by matching SWATH data of the samples to the ion library. Sparse partial least squares discriminant analysis (sPLS-DA) of 89 proteins using MetaboAnalyst (ver 6.0) showed that the variance of components of all groups was 9% and 10.2%, respectively, and the error rate was 15.7% (Figure\u0026nbsp;1A).\u0026nbsp;For HC versus MDD, the component variance was 9.6% and 4.8%, respectively, and the error rate was 9.7% (Figure\u0026nbsp;1B). For HC versus BD, the component variance was 16.9% and 4.3%, respectively, and the error rate was 5.6% (Figure\u0026nbsp;1C). For BD versus MDD, the component variance was 15% and 8.1%, respectively, and the error rate was 5.6% (Figure\u0026nbsp;1D). The sPLS-DA error rate was below 10% in each pairwise comparison, indicating good group separation. The loading plots are presented in Supplementary Figure\u0026nbsp;2. To identify biomarkers whose expression is specifically altered in BD and biomarkers specifically expressed in MDD, differentially expressed proteins (DEPs) were identified in the comparison between groups using a volcano plot. Proteins satisfying the conditions of fold change (FC)\u0026nbsp;\u0026gt; 1.2 and \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 were identified. In HC versus MDD, 9 proteins increased and 2 decreased in HC.\u0026nbsp;In HC versus BD, the expression of 14 proteins increased and that of 12 proteins decreased in the HC group. In MDD versus BD comparison, the expression of 11 proteins increased and that of 13 proteins decreased in the MDD group.\u003c/p\u003e\n\u003cp\u003eOf the 24 proteins identified in the MDD versus BD comparison, the expression of 21 proteins differed among the groups, including the control group. The expression of the remaining three proteins differed only between the MDD and BD groups. The expression of 15 of the 21 proteins was similar in the control and depression groups, but significantly different in the BD group. We found two proteins whose expression was similar in the control and BD groups but significantly different in the MDD group (Figure\u0026nbsp;1H). The 17 candidate proteins are listed in Supplementary Table\u0026nbsp;1. The scatter plots for 15 BD-specific biomarkers are presented in Supplementary Figure\u0026nbsp;3\u0026nbsp;and those for 2 MDD-specific biomarkers are presented in Supplementary Figure\u0026nbsp;4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe expression levels of the 17 proteins in each group were confirmed using a heatmap (Supplementary Figure\u0026nbsp;5A). sPLS\u0026ndash;DA with the components of all groups presented a variance of 27.2% and 9%, respectively, accounting for a total variation of 36.2% and an error rate of 22.9% (Supplementary Figur\u0026nbsp;5B). Comparison between the HC and MDD groups revealed that the component variance was 10.8% and 6.4%, with a total variation of 17.2% and an error rate of 24.2% (Supplementary Figure\u0026nbsp;5C). \u0026nbsp;In the comparison between the BD and MDD groups, the component variance was 35.1% and 6.1%, respectively, total variation was 41.2%, and error rate was 0% (Supplementary Figure\u0026nbsp;5D). For BD versus HC, the component variance was 35.8% and 7.3%, respectively, total variation was 43.1%, and error rate was 2.8% (Supplementary Figure\u0026nbsp;5E). The results in Figure 4D and E are relatively good. The area under the curve (AUC) values for the 17 biomarkers are shown in Figure 4F. The red bars indicate 15 BD-specific biomarkers. The AUC values of the comparison between HC and MDD for the 15 BD-specific biomarkers were low, with an average of 0.5728. In the HC versus BD and MDD versus BD group comparisons, high AUC values of 0.8153 and 0.8128 were recorded, respectively. The HC and MDD groups were not well distinguished, while the BD group had values higher or lower than the HC and MDD groups. Thus, it was confirmed that that the 15 biomarker are BD-specific biomarker. Similarly, the two MDD-specific biomarkers had a low AUC value of 0.5834 on average in the comparison between the HC and BD groups. The average AUC values of the HC versus MDD and MDD versus BD group comparison were relatively high at 0.7653 and 0.8039, respectively. These two biomarkers were confirmed as MDD-specific biomarkers because the values of the HC and BD groups were similar, whereas the values of the MDD group were different from those of the HC and BD groups. The specificity, recall, negative predictive value, precision, and accuracy of the receiver operating characteristic (ROC) curve of the 17 marker candidates are presented in Supplementary Table\u0026nbsp;2.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Among the 15 biomarkers specific to BD, the expression of 6 was upregulated and that of 9 was downregulated. The expression levels of proteins in the HC and BD groups were similar, but both groups contained significantly DEPs compared with those in the MDD group.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003ePathway and GO analyses of selected DEPs\u003c/h2\u003e\n\u003cp\u003eThe correlation between the 2 MDD- and 15 BD-specific biomarkers was confirmed (Figure\u0026nbsp;2A). Pink circles represent BD-specific biomarkers and blue circles represent MDD-specific biomarkers. All 17 biomarkers were found to be interconnected. Cluster 1 (with an orange background) represents complement and coagulation cascades, and positive regulation of opsonization. Cluster 2 (with a green background) represents high-density lipoproteins (HDLs) and HDL particles. Cluster 3 (with a gray background) represents GC and K1C9. The GO analysis revealed the top 10 pathways related to false discovery rate\u0026nbsp;(FDR)\u0026nbsp;(Figure\u0026nbsp;2B\u0026ndash;2D). In terms of biological processes, the defense response, acute inflammatory response, and acute-phase response were highly correlated (Figure\u0026nbsp;2B). In terms of cellular components, blood microparticle, extracellular exosomes, and extracellular space were significant (Figure\u0026nbsp;2C). Finally, in the Reactome pathways, regulation of complement cascade, post-translational protein phosphorylation, and platelet degranulation were highly correlated (Figure\u0026nbsp;2D). In the GO analysis, all 17 proteins were included in the extracellular space within the cellular\u0026nbsp;component. Additionally, the proteins were broadly classified according to associations with inflammatory responses, the extracellular skeleton, and coagulation factors (Figure\u0026nbsp;2E). Through this, it can be speculated that changes in the expression of proteins related to immune inflammatory response, the extracellular matrix, and coagulation affect the pathological mechanisms of MDD and BD.\u003c/p\u003e\n\u003ch2\u003eCandidate protein validation results\u003c/h2\u003e\n\u003cp\u003eSeventeen peptide standards were purchased and subjected to validation and MRM analyses.\u0026nbsp;The transitions for the 17 peptides are presented in Supplementary Table\u0026nbsp;3. Figure\u0026nbsp;3\u0026nbsp;presents the scatter plot of the MRM results and density distribution data. The density distributions of the BD-specific biomarkers overlapped between the HC and MDD groups, while the confidence intervals of the BD group did not overlap. Thus, we confirmed that the BD group was well distinguished. Fibronectin can be used as an MDD-specific biomarker because the confidence intervals of the HC and BD groups overlapped, but those of the MDD group did not overlap (Figure\u0026nbsp;3D). The proteins differentially expressed in BD included vitamin D-binding protein (DBP), complement factor H (CFH), and keratin, type I cytoskeletal 9 (K1C9). DBP showed the same trend in both the discovery and validation sets; furthermore, the BD group had the highest level of DBP compared to the other groups. Additionally, it showed a commonality of not being significant in comparison to HCs and patients with MDD. Post-test validation confirmed a significant difference in DBP expression between the MDD and BD groups (\u003cem\u003ep\u003c/em\u003e = 0.0026; Figure 6A). CFH also showed the same tendency, with the BD group having the highest concentration (Figure\u0026nbsp;3B). The post-hoc test for validation confirmed a significant difference between the MDD and BD groups (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). In the validation set, K1C9 showed consistent statistical significance and concentration change trends compared with those in the discovery set. The post-test validation confirmed a significant difference in K1C9 expression between the MDD and BD groups (\u003cem\u003ep\u003c/em\u003e = 0.0061; Figure\u0026nbsp;3C). Fibronectin was differentially expressed in patients with MDD. As the validation results, which were similar to the discovery results, showed the lowest concentration tendency of fibronectin in MDD, it was selected as an MDD-specific biomarker. The post-hoc analysis confirmed a significant difference in fibronectin expression between the MDD and BD groups (\u003cem\u003ep\u003c/em\u003e = 0.0063; Figure\u0026nbsp;3D). A scatter plot of MRM results of the disease-specific biomarkers is presented in Figure S4. The diagnostic performance of the MRM results for the final four biomarkers (VTDB, CFH, K1C9, and fibronectin) was confirmed through ROC curves (Figure\u0026nbsp;4A). The 17 candidate proteins are shown in the loading plot in Supplementary Figure 1. As fibronectin was the only MDD-specific biomarker, the discriminatory power of fibronectin was confirmed. The BD-specific biomarkers (DBP, CFH, and K1C9) showed low AUC values with an average of 0.5699 in the HC versus MDD group comparison, similar to the discovery set results, but relatively high diagnostic performance with an average of 0.7856 and 0.7901 in the HC versus BD and MDD versus BD group comparisons, respectively, confirming that they are BD-specific biomarkers. In addition, when analyzing the combined results of the three BD-specific biomarkers, we were able to confirm the diagnostic performance of the biomarker that best differentiates MDD from BD with an AUC of 0.8350. The MDD-specific biomarker fibronectin showed a low AUC value close to 0.5 in the HC versus BD group comparison and a high AUC value in other group comparisons, confirming its MDD-specific biomarker status (Figure\u0026nbsp;4B). The ability of the four final biomarkers to discriminate between MDD and BD was confirmed, demonstrating diagnostic performance with an AUC value of 0.8363 (Figure\u0026nbsp;4C). The results confirmed an accuracy of 89.39% for MDD vs. BD (Figure\u0026nbsp;4D). When BD-specific biomarkers were combined, we confirmed high discriminatory power with an AUC value of 0.8341 to distinguish BD from the remaining groups (HC and MDD) (Figure\u0026nbsp;4E). The accuracy of this result was 93.20% (Figure\u0026nbsp;4F). Similarly, for the MDD-specific biomarker fibronectin, we confirmed high discriminatory power with an AUC value of 0.7407 to distinguish MDD from the remaining groups (HC and BD) (Figure 7G). The ROC curve has an accuracy of 68.27%. We then analyzed the correlation between clinical indicators and biomarkers. Pearson correlation coefficients (Figure\u0026nbsp;5A) and \u003cem\u003ep\u003c/em\u003e-values (Figure 5B) were calculated. The correlation results are briefly presented on Figure 5C. The BD-specific biomarkers are indicated with blue semicircles, MDD-specific biomarkers with pink semicircles, and clinical indicators with white semicircles. We confirmed that body mass index (BMI) and K1C9 showed a positive correlation, and beck depression inventory (BDI) and fibronectin showed a negative correlation. K1C9, which is a BD-specific biomarker, and fibronectin, which is an MDD-specific biomarker, showed a positive correlation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDBP, CFH, and K1C9 were identified as BD-specific biomarkers. Meanwhile, fibronectin was identified as a MDD-specific biomarker. Circulating vitamin D metabolites are primarily bound to DBP, with albumin as the major secondary carrier, particularly in patients with a low serum DBP level \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, as only 1%\u0026ndash;2% of the sterol-binding sites are utilized, DBP has several additional metabolic roles beyond vitamin D transport \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. One metabolic role of DBP is to prevent the activation of the actin polymerization, aggregation, and coagulation cascade when actin binds glia maturation factor beta (GMFβ) in the brain to DBP in the cerebrospinal fluid and circulation \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In mental illnesses, both increased and decreased DBP levels have an effect on patients \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. It is known that defects in actin polymerization are associated with BD pathology \u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. BD is associated with cytoskeletal abnormalities \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Therefore, it can be rationalized that an increase in DBP level in BD leads to increased actin binding, which leads to actin polymerization defects and cytoskeleton abnormalities \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Intermediate filaments (IFs) are among three major cytoskeletal filament assemblies found in higher eukaryotes. IFs form a stable filamentous network that extends throughout the cytoplasm to provide structural support to many cell types \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The interdependence of the cytoskeletal elements can be explained by the presence of linker proteins that bind IFs to microtubules or microfilaments \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNot only do keratins provide protection from apoptosis, but type I (not type II) keratins are also major caspase substrates that carry out cytolysis during apoptosis \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Among them, we selected K1C9 as a BD-specific biomarker, a type I intermediate filament protein specifically expressed in the basal layer of the primary epidermal ridge of the palmar and plantar epidermis in humans \u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn a study comparing the metabolomic and proteomic characteristics of plasma from cognitively normal (CN) individuals and patients with dementia diagnosed with mild cognitive impairment or Alzheimer\u0026rsquo;s disease (AD), K1C9 expression showed an increasing pattern in patients with Alzheimer's compared with that in HCs. K1C9 also showed the ability to separate the Alzheimer's group with 100% accuracy \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In a study comparing the serum proteomes, keratin type II cytoskeletal 1 level decreased in patients with MDD compared with that in those in remission \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Thus, DBP interacts with K1C9 to form the cytoskeleton. A correlation among changes in these protein levels, the cytoskeleton, and the pathophysiological mechanism of BD has been suggested. This is the first study to suggest that K1C9 is a biomarker for BD. K1C9 was found to positively correlate with BMI. A study involving women under 38 years of age who underwent an 8-week weight loss program found that the keratin levels increased \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Additionally, a previous study reported a decrease in keratin level in a group of elderly women with obesity \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Thus, the keratin level varies depending on obesity status, warranting further research.\u003c/p\u003e\u003cp\u003eIn a study that quantified complement factors, the C3, C4, and CFH levels were higher in the BD group than in the MDD group, and this is similar to the results of our study showing that CFH levels were higher in BD \u003csup\u003e33\u003c/sup\u003e. A serological analysis revealed higher peripheral concentrations of C3, C4, and C6 during manic episodes in 45 patients with BD compared with those in HCs \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Additionally, a study analyzing the peripheral blood of 30 patients to observe the complement cascade reaction in patients with BD confirmed that immune system regulation disorder in patients with BD occurred owing to increased C3a and C5a levels \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Here, the STRING analysis confirmed that the acute inflammatory response and regulation of the complement cascade were highly related to MDD and BD. It is presumed that immune regulation disorders in patients with BD lead to an increase in the levels of components of the complement cascade, followed by an increase in CFH level for complement regulation.\u003c/p\u003e\u003cp\u003eHere, the fibronectin level decreased in patients with MDD, and it could be a specific biomarker. Plasma fibronectin is produced in the liver, released into the bloodstream, and circulated in the blood \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Plasma fibronectin can be transformed into cellular fibronectin by homeostatic regulation during ECM remodeling \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Its expression can be triggered by exposure to traumatic life events or stressors \u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Inflammatory reactions occur in response to stress, which disrupts homeostasis \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The ECM is highly dynamic because it is continuously deposited, remodeled, and degraded throughout development until maturity to maintain tissue homeostasis \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Stress can also cause hippocampal ECM regulation, which in turn causes perineural neural networks to be deposited in the CNS, thereby reducing the E/A balance of neurons and regulating brain oscillations \u003csup\u003e\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. That is, the finding that stress causes changes in the ECM suggests an association between MDD and the ECM. To enable ECM remodeling, the plasma fibronectin level appears to decrease as plasma fibronectin is converted to cellular fibronectin. This assumption is consistent with the analysis results showing that 22 proteins in the STRING results are associated with the ECM. Additionally, the correlation analysis results showed that fibronectin had a negative correlation with BDI, one of the indicators of depression, suggesting a significant association between depression severity and fibronectin. Furthermore, a positive correlation was observed between K1C9 and fibronectin. A study demonstrated that when the level of fibronectin decreases, cell migration becomes more active and the flow of keratin becomes faster \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. It is possible that changes in fibronectin and keratin levels are interrelated. Currently, mood disorders lack specific mechanisms and biomarkers. We focused on the \u0026ldquo;clinical symptoms\u0026rdquo; of depression between BD and MDD and identified four disease-specific biomarkers for these two diseases. After discovering biomarkers using a non-targeted analysis method, we validated them using MRM. A brief discussion of each biomarker is summarized in Supplementary Fig.\u0026nbsp;7. Our results provide insights into the mechanisms shared by the \u0026ldquo;depressive symptoms\u0026rdquo; of these two diseases. DBP, K1C9, and CFH were identified as BD-specific biomarkers. Alterations in the levels of these proteins suggest that cytoskeletal dysfunction involving actin and IFs and the complement system may contribute to the pathological mechanisms of BD. Alterations in the level of fibronectin may be associated with stress-induced ECM remodeling in depression. Our findings offer a reference for situations in which patients with BD are misdiagnosed with MDD and are not receiving appropriate treatment. Using these four biomarkers, we expect to gain further insights into MDD and BD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest\u003c/h2\u003e\u003cp\u003eThe authors report there are no competing interests to declare.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eSupplementary Information\u003c/h2\u003e\u003cp\u003eSupplementary information is available at MP\u0026rsquo;s website.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThis work was supported by the National Research Foundation of Korea, which is supported by the Korean Government (MSIT), under Grant number 2020R1C1C1009196.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSekhon S, Gupta V. Mood disorder. 2020.\u003c/li\u003e\n\u003cli\u003eSpijker J, Claes S. 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Stress and loss of adult neurogenesis differentially reduce hippocampal volume. \u003cem\u003eBiol Psychiatry. \u003c/em\u003e2017; \u003cstrong\u003e82\u003c/strong\u003e(12)\u003cstrong\u003e: \u003c/strong\u003e914-923.\u003c/li\u003e\n\u003cli\u003eMedzhitov R. The spectrum of inflammatory responses. \u003cem\u003eScience\u003c/em\u003e 2021; \u003cstrong\u003e374\u003c/strong\u003e(6571)\u003cstrong\u003e: \u003c/strong\u003e1070-1075.\u003c/li\u003e\n\u003cli\u003eWalker C, Mojares E, del R\u0026iacute;o Hern\u0026aacute;ndez A. Role of extracellular matrix in development and cancer progression. \u003cem\u003eInt J Mol Sci \u003c/em\u003e2018; \u003cstrong\u003e19\u003c/strong\u003e(10)\u003cstrong\u003e: \u003c/strong\u003e3028.\u003c/li\u003e\n\u003cli\u003eCarulli D, Verhaagen J. An extracellular perspective on CNS maturation: perineuronal nets and the control of plasticity. \u003cem\u003eInt J Mol Sci \u003c/em\u003e2021; \u003cstrong\u003e22\u003c/strong\u003e(5)\u003cstrong\u003e: \u003c/strong\u003e2434.\u003c/li\u003e\n\u003cli\u003eBlanco I, Conant K. Extracellular matrix remodeling with stress and depression: Studies in human, rodent and zebrafish models. \u003cem\u003eEur J Neurosci \u003c/em\u003e2021; \u003cstrong\u003e53\u003c/strong\u003e(12)\u003cstrong\u003e: \u003c/strong\u003e3879-3888.\u003c/li\u003e\n\u003cli\u003eSpijker S, Koskinen M-K, Riga D. Incubation of depression: ECM assembly and parvalbumin interneurons after stress. \u003cem\u003eNeurosci Biobehav Rev \u003c/em\u003e2020; \u003cstrong\u003e118: \u003c/strong\u003e65-79.\u003c/li\u003e\n\u003cli\u003ePora A, Yoon S, Dreissen G, Hoffmann B, Merkel R, Windoffer R\u003cem\u003e et al.\u003c/em\u003e Regulation of keratin network dynamics by the mechanical properties of the environment in migrating cells. \u003cem\u003eSci Rep \u003c/em\u003e2020; \u003cstrong\u003e10\u003c/strong\u003e(1)\u003cstrong\u003e: \u003c/strong\u003e4574.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Demographics of participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"907\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 233px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 345px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscovery set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 329px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDD\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBD\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 233px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of participants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e60.39 \u0026plusmn; 11.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e53.32 \u0026plusmn; 19.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e36.78 \u0026plusmn; 10.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e60.05 \u0026plusmn; 8.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e46.74 \u0026plusmn; 17.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e36.76 \u0026plusmn; 10.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex,\u0026nbsp;\u003c/strong\u003e\u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.9177\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e5 (13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e16 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5 (29.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.1296\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e21 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e27 (79.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (77.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e32 (86.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e34 (68.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e12 (70.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e24.01 \u0026plusmn; 3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e25.35 \u0026plusmn; 3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e24.31 (3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.4216\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e22.53 \u0026plusmn; 2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e23.09 \u0026plusmn; 3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e25.34 \u0026plusmn; 2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.0157\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHAMD-17\u003csup\u003ef\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e19.71 \u0026plusmn; 5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e17.11 \u0026plusmn; 6.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.2680\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e19.84 \u0026plusmn; 4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e17.76 \u0026plusmn; 5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.1168\u003csup\u003ek\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatmen\u003c/strong\u003et\u003cem\u003e, n\u0026nbsp;\u003c/em\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e17 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e8 (88.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e26 (52.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e16 (94.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e28 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e17 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e37 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e24 (48.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eHC : Healthy control, \u003csup\u003eb\u003c/sup\u003eMDD : Major Depressive Disease, \u003csup\u003ec\u003c/sup\u003eBD : biopolar disease, treatment, \u003csup\u003ed\u003c/sup\u003eSD : Standard Deviation, \u003csup\u003ee\u003c/sup\u003eBMI : \u0026nbsp; body mass index, \u003csup\u003ef\u003c/sup\u003eHAMD-17 : Hamilton Depression Rating Scale, 17-item, \u003csup\u003eg\u003c/sup\u003eWelch\u0026apos;s ANOVA test, \u003csup\u003eh\u003c/sup\u003eChi-square, \u003csup\u003ei\u003c/sup\u003eKruskal-Wallis test, \u003csup\u003ej\u003c/sup\u003eUnpaired t test, \u003csup\u003ek\u003c/sup\u003eMann Whitney test\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"affective disorder, biological psychiatry, clinical psychopathology, bipolar disorder, major depressive disorder, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-7640890/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7640890/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIt is challenging to distinguish between major depressive disorder (MDD) and bipolar disorder (BD) owing to the clinical similarity of their depressive symptoms. The poor response of patients with BD to antidepressants emphasizes the need for disease-specific biomarkers. Therefore, we aimed to identify disease-specific biomarkers of MDD and BD. After obtaining sera from patients with MDD and BD, and healthy controls (HCs), a non-targeted qualitative analysis of the serum proteome was performed. Biomarkers whose expression was specifically altered in patients with MDD were selected and compared with those in HCs and patients with BD. Similarly, biomarkers that were specifically expressed in patients with BD were selected and compared with those in HCs and patients with MDD. The selected biomarker candidates were validated via multiple reaction monitoring (MRM). Vitamin D-binding proteins (DBP), complement factor H (CFH), and keratin, type I cytoskeletal 9 (K1C9) were identified as BD-specific biomarkers. They had the highest intensity levels in the BD group compared with those in the MDD and HC groups. Fibronectin, an MDD-specific biomarker, showed the lowest intensity in the MDD group. Our study provides molecular evidence to improve comprehension of the pathophysiological mechanisms of MDD and BD.\u003c/p\u003e","manuscriptTitle":"Protein biomarkers to distinguish between major depressive disorder and bipolar disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 13:40:54","doi":"10.21203/rs.3.rs-7640890/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5c8b24b1-3909-41f0-94c6-aa3a4ab508b9","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55899790,"name":"Health sciences/Biomarkers/Prognostic markers"},{"id":55899791,"name":"Health sciences/Diseases/Psychiatric disorders/Bipolar disorder"},{"id":55899792,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"}],"tags":[],"updatedAt":"2025-11-10T11:06:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 13:40:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7640890","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7640890","identity":"rs-7640890","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}