Comparison of serum protein profiles between major depressive disorder and bipolar disorder

Identifying clinical and proteomic markers for early diagnosis and prognosis prediction of major psychiatric disorders

BACKGROUND

To clarify if blood proteins can predict disease progression among individuals at clinical high-risk of severe mental illness (CHR-SMI), we developed a statistical model incorporating clinical and blood protein markers to distinguish the transition group (who developed severe mental illness) (CHR-SMI-T) and from non-transition group (CHR-SMI-NT) at baseline.

METHODS

Ninety individuals (74 at CHR-SMI: 16 patients) were monitored for ≤4 years and were the focus of predictive models. Three predictive models (1 [100 clinical variables], 2 [158 peptides], and 3 [100 clinical variables +158 peptides]) were evaluated using area under the receiver operating characteristic (AUROC) values. Clinical and protein feature patterns were evaluated by linear mixed-effect analysis within the model at 12 and 24 months among patients who did (CHR-SMI-T) and did not transition (CHR-SMI-NT) and the entire group.

RESULT

Eighteen CHR-SMI individuals with major psychiatric disorders (first episode psychosis: 2; bipolar II disorder: 13; major depressive disorder; 3) developed disorders over an average of 17.7 months. The combined model showed the highest discriminatory performance (AUROC = 0.73). Cytosolic malate dehydrogenase and transgelin-2 levels were lower in the CHR-SMI-T than the CHR-SMI-NT group. Complement component C9, inter-alpha-trypsin inhibitor heavy chain H4, von Willebrand factor, and C-reactive protein were lower in the patient than the CHR-SMI-NT group. These differences were non-significant after FDR adjustment.

LIMITATIONS

Small sample, no control for medication use.

CONCLUSION

This exploratory study identified clinical and proteomic markers that might predict severe mental illness early onset, which could aid in early detection and intervention. Future studies with larger samples and controlled variables are needed to validate these findings.

Proteomic profiling of cerebrospinal fluid reveals TKT as a potential biomarker for medulloblastoma

Cerebrospinal fluid (CSF) plays an important role in brain tumors, including medulloblastoma (MBL). Recent advancements in mass spectrometry systems and 'Omics' data analysis methods enable unbiased, high proteome depth research. We conducted proteomic profiling of the total CSF in MBL patients with the purpose of finding a potential diagnostic biomarker for MBL. We quantified 1112 proteins per CSF sample. Feature selection identified four elevated soluble proteins (SPTBN1, HSP90AA1, TKT, and NME1-NME2) in MBL CSF. Validation with ELISA confirmed that TKT was significantly elevated in MBL. Additionally, TKT-positive extracellular vesicles were significantly enriched in MBL CSF and correlated with the burden of leptomeningeal seeding. Our results provide insights into the proteomics data of the total CSF of MBL patients. Furthermore, we identified the significance of TKT within the total CSF and its presence within circulating EVs in the CSF. We suggest that TKT may serve as a biomarker for MBL.

Nilotinib treatment outcomes in autosomal dominant spinocerebellar ataxia over one year

We evaluated the efficacy and safety of 1-year treatment with nilotinib (Tasigna®) in patients with autosomal dominant spinocerebellar ataxia (ADSCA) and the factors associated with responsiveness. From an institutional cohort, patients with ADSCA who completed a 1-year treatment with nilotinib (150-300 mg/day) were included. Ataxia severity was assessed using the Scale for the Rating and Assessment of Ataxia (SARA), scores at baseline and 1, 3, 6, and 12 months. A subject was categorized 'responsive' when the SARA score reduction at 12 M was > 0. Pretreatment serum proteomic analysis included subjects with the highest (n = 5) and lowest (n = 5) SARA score change at 12 months and five non-ataxia controls. Thirty-two subjects (18 [56.2%] females, median age 42 [30-49.5] years) were included. Although SARA score at 12 M did not significantly improve in overall population, 20 (62.5%) subjects were categorized as responsive. Serum proteomic analysis identified 4 differentially expressed proteins, leucine-rich alpha-2-glycoprotein (LRG1), vitamin-D binding protein (DBP), and C4b-binding protein (C4BP) beta and alpha chain, which are involved in the autophagy process. This preliminary data suggests that nilotinib might improve ataxia severity in some patients with ADSCA. Serum protein markers might be a clue to predict the response to nilotinib.Trial Registration Information: Effect of Nilotinib in Cerebellar Ataxia Patients (NCT03932669, date of submission 01/05/2019).

Integrated proteomic and genomic analysis to identify predictive biomarkers for valproate response in bipolar disorder: a 6-month follow-up study

BACKGROUND

Several genetic studies have been undertaken to elucidate the intricate interplay between genetics and drug responses in bipolar disorder (BD). However, there has been notably limited research on biomarkers specifically linked to valproate, with only a few studies investigating integrated proteomic and genomic factors in response to valproate treatment. Therefore, this study aimed to identify biological markers for the therapeutic response to valproate treatment in BD. Patients with BD in remission were assessed only at baseline, whereas those experiencing acute mood episodes were evaluated at three points (baseline, 8 ± 2 weeks, and 6 ± 1 months). The response to valproate treatment was measured using the Alda scale, with individuals scoring an Alda A score ≥ 5 categorized into the acute-valproate responder (acute-VPAR) group. We analyzed 158 peptides (92 proteins) from peripheral blood samples using multiple reaction monitoring mass spectrometry, and proteomic result-guided candidate gene association analyses, with 1,627 single nucleotide variants (SNVs), were performed using the Korean chip.

RESULTS

The markers of 37 peptides (27 protein) showed temporal upregulation, indicating possible association with response to valproate treatment. A total of 58 SNVs in 22 genes and 37 SNVs in 16 genes showed nominally significant associations with the Alda A continuous score and the acute-VPAR group, respectively. No SNVs reached the genome-wide significance threshold; however, three SNVs (rs115788299, rs11563197, and rs117669164) in the secreted phosphoprotein 2 gene reached a gene-based false discovery rate-corrected significance threshold with response to valproate treatment. Significant markers were associated with the pathophysiological processes of bipolar disorders, including the immune response, acute phase reaction, and coagulation cascade. These results suggest that valproate effectively suppresses mechanisms associated with disease progression.

CONCLUSIONS

The markers identified in this study could be valuable indicators of the underlying mechanisms associated with response to valproate treatment.

Evaluation of a Nondepleted Plasma Multiprotein-Based Model for Discriminating Psychiatric Disorders Using Multiple Reaction Monitoring-Mass Spectrometry: Proof-of-Concept Study

Psychiatric evaluation relies on subjective symptoms and behavioral observation, which sometimes leads to misdiagnosis. Despite previous efforts to utilize plasma proteins as objective markers, the depletion method is time-consuming. Therefore, this study aimed to enhance previous quantification methods and construct objective discriminative models for major psychiatric disorders using nondepleted plasma. Multiple reaction monitoring-mass spectrometry (MRM-MS) assays for quantifying 453 peptides in nondepleted plasma from 132 individuals [35 major depressive disorder (MDD), 47 bipolar disorder (BD), 23 schizophrenia (SCZ) patients, and 27 healthy controls (HC)] were developed. Pairwise discriminative models for MDD, BD, and SCZ, and a discriminative model between patients and HC were constructed by machine learning approaches. In addition, the proteins from nondepleted plasma-based discriminative models were compared with previously developed depleted plasma-based discriminative models. Discriminative models for MDD versus BD, BD versus SCZ, MDD versus SCZ, and patients versus HC were constructed with 11 to 13 proteins and showed reasonable performances (AUROC = 0.890-0.955). Most of the shared proteins between nondepleted and depleted plasma models had consistent directions of expression levels and were associated with neural signaling, inflammatory, and lipid metabolism pathways. These results suggest that multiprotein markers from nondepleted plasma have a potential role in psychiatric evaluation.

Proteomic profiling in the progression of psychosis: Analysis of clinical high-risk, first episode psychosis, and healthy controls

BACKGROUND AND HYPOTHESIS

Recent evidence has highlighted the benefits of early detection and treatment for better clinical outcomes in patients with psychosis. Biological markers of the disease have become a focal point of research. This study aimed to identify protein markers detectable in the early stages of psychosis and indicators of progression by comparing them with those of healthy controls (HC) and first episode psychosis (FEP).

STUDY DESIGN

The participants comprised 28 patients in the clinical high-risk (CHR) group, 49 patients with FEP, and 61 HCs aged 15-35 years. Blood samples were collected and analyzed using multiple reaction monitoring-mass spectrometry to measure the expression of 158 peptide targets. Data were adjusted for age, sex, and use of psychotropic drugs.

STUDY RESULTS

A total of 18 peptides (17 proteins) differed significantly among the groups. The protein PRDX2 was higher in the FEP group than in the CHR and HC groups and showed increased expression according to disease progression. The levels of six proteins were significantly higher in the FEP group than in the CHR group. Nine proteins differed significantly in the CHR group compared to the other groups. Sixteen proteins were significantly correlated with symptom severity. These proteins are primarily related to the coagulation cascade, inflammatory response, brain structure, and synaptic plasticity.

CONCLUSIONS

Our findings suggested that peripheral protein markers reflect disease progression in patients with psychosis. Further longitudinal research is needed to confirm these findings and to identify the specific roles of these markers in the pathogenesis of schizophrenia.

A preliminary composite of blood-based biomarkers to distinguish major depressive disorder and bipolar disorder in adolescents and adults

BACKGROUND

Since diagnosis of mood disorder heavily depends on signs and symptoms, emerging researches have been studying biomarkers with the attempt to improve diagnostic accuracy, but none of the findings have been broadly accepted. The purpose of the present study was to construct a preliminary diagnostic model to distinguish major depressive disorder (MDD) and bipolar disorder (BD) using potential commonly tested blood biomarkers.

METHODS

Information of 721 inpatients with an ICD-10 diagnosis of MDD or BD were collected from the electronic medical record system. Variables in the nomogram were selected by best subset selection method after a prior univariable screening, and then constructed using logistic regression with inclusion of the psychotropic medication use. The discrimination, calibration and internal validation of the nomogram were evaluated by the receiver operating characteristic curve (ROC), the calibration curve, cross validation and subset validation method.

RESULTS

The nomogram consisted of five variables, including age, eosinophil count, plasma concentrations of prolactin, total cholesterol, and low-density lipoprotein cholesterol. The model could discriminate between MDD and BD with an area under the ROC curve (AUC) of 0.858, with a sensitivity of 0.716 and a specificity of 0.890.

CONCLUSION

The comprehensive nomogram constructed by the present study can be convenient to distinguish MDD and BD since the incorporating variables were common indicators in clinical practice. It could help avoid misdiagnoses and improve prognosis of the patients.

Endocan: A novel biomarker of endothelial dysfunction in depression?

Endocan is a proteoglycan secreted from endothelium upon endothelial damage. Since depression is associated with higher inflammation and oxidative stress to the vascular endothelium, endothelial dysfunction is prevalent and it is one of the responsible mechanisms for increased cardiovascular morbidity and mortality in depressive disorders. This study aimed to investigate endocan levels in patients with depression (either bipolar or unipolar) and healthy controls to evaluate the projected endothelial injury. We included nonsmoker patients without comorbid inflammatory conditions: 31 with Bipolar Disorder Depression (BDD), 30 with Major Depressive Disorder (MDD) and 25 healthy controls (HC). The severity of depression was assessed with the Hamilton Depression Rating Scale (HDRS). Ultimately, serum endocan levels were significantly higher in patients with BDD than in patients with MDD (p < .000) and HCs (p < .000). Also, patients with MDD had significantly higher endocan levels than HCs (p < .000). The AUC value for the endocan to differentiate patients with depression from controls was 0.990 (95% CI: 0.971-1.000; p < .001) with sensitivity and specificity of 98.4 and 100%, respectively, and an optimal cut-off value of 316.92 ng/L. Serum endocan levels showed a mild positive correlation with HDRS scores (r = 0.372, p = .039) in the BDD group but not in the MDD group (r = -0.242, p = .20). Patients with BDD had higher endocan levels than MDD; this finding, while preliminary, could be an implication of higher endothelial dysfunction in BDD.

An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value

INTRODUCTION

Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics.

AREAS COVERED

In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value.

EXPERT OPINION

A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.

The causal relationship between circulating biomarkersand the risk of bipolar disorder: A two-sample Mendelian randomization study

OBJECTIVE

To identify susceptible biomarkers for the development of bipolar disorder (BD), we conducted a Mendelian Randomization (MR) design to screen circulating proteins for the potential risk of bipolar disorder systematically.

METHODS

We performed a two-sample Mendelian randomization (MR) analysis to estimate the causality of 4782 human circulating proteins on the risk of bipolar disorder. 376 circulating biomarkers were selected in MR estimation (4406 circulating proteins with less than 3 SNPs were excluded) with 5368 European descents. GWAS meta-analysis of the potential role of all-cause bipolar disorder arose from the Psychiatric Genomics Consortium (41,917 cases, 371,549 controls).

RESULTS

After IVW and sensitivity analysis, 4 circulating proteins having causal effects on bipolar disorder were identified. ISG15, as a key player in the innate immune response, decreased the risk of bipolar disorder causally (OR = 0.92, 95% CI = 0.89-0.94, P = 1.46e-09). Furthermore, MLN decreased the risk of bipolar disorder causally (OR = 0.94, 95% CI = 0.91-0.97, P = 1.04e-04). In addition, SFTPC (OR = 0.91, 95% CI = 0.86-0.96, P = 4.47e-04) and VCY (OR = 0.86, 95% CI = 0.77-0.96, P = 8.55e-03) presented a suggestive association with bipolar disorder.

CONCLUSIONS

Our findings indicated that ISG15 and MLN showed evidence of causality in bipolar disorder and provided a promising target for the diagnosis and treatment of diseases.

Network analysis of plasma proteomes in affective disorders

The conventional differentiation of affective disorders into major depressive disorder (MDD) and bipolar disorder (BD) has insufficient biological evidence. Utilizing multiple proteins quantified in plasma may provide critical insight into these limitations. In this study, the plasma proteomes of 299 patients with MDD or BD (aged 19-65 years old) were quantified using multiple reaction monitoring. Based on 420 protein expression levels, a weighted correlation network analysis was performed. Significant clinical traits with protein modules were determined using correlation analysis. Top hub proteins were determined using intermodular connectivity, and significant functional pathways were identified. Weighted correlation network analysis revealed six protein modules. The eigenprotein of a protein module with 68 proteins, including complement components as hub proteins, was associated with the total Childhood Trauma Questionnaire score (r = -0.15, p = 0.009). Another eigenprotein of a protein module of 100 proteins, including apolipoproteins as hub proteins, was associated with the overeating item of the Symptom Checklist-90-Revised (r = 0.16, p = 0.006). Functional analysis revealed immune responses and lipid metabolism as significant pathways for each module, respectively. No significant protein module was associated with the differentiation between MDD and BD. In conclusion, childhood trauma and overeating symptoms were significantly associated with plasma protein networks and should be considered important endophenotypes in affective disorders.

Discovery of Novel Digital Biomarkers for Type 2 Diabetic Nephropathy Classification via Integration of Urinary Proteomics and Pathology

Background

The heterogeneous phenotype of diabetic nephropathy (DN) from type 2 diabetes complicates appropriate treatment approaches and outcome prediction. Kidney histology helps diagnose DN and predict its outcomes, and an artificial intelligence (AI)-based approach will maximize clinical utility of histopathological evaluation. Herein, we addressed whether AI-based integration of urine proteomics and image features improves DN classification and its outcome prediction, altogether augmenting and advancing pathology practice.

Methods

We studied whole slide images (WSIs) of periodic acid-Schiff-stained kidney biopsies from 56 DN patients with associated urinary proteomics data. We identified urinary proteins differentially expressed in patients who developed end-stage kidney disease (ESKD) within two years of biopsy. Extending our previously published human-AI-loop pipeline, six renal sub-compartments were computationally segmented from each WSI. Hand-engineered image features for glomeruli and tubules, and urinary protein measurements, were used as inputs to deep-learning frameworks to predict ESKD outcome. Differential expression was correlated with digital image features using the Spearman rank sum coefficient.

Results

A total of 45 urinary proteins were differentially detected in progressors, which was most predictive of ESKD (AUC=0.95), while tubular and glomerular features were less predictive (AUC=0.71 and AUC=0.63, respectively). Accordingly, a correlation map between canonical cell-type proteins, such as epidermal growth factor and secreted phosphoprotein 1, and AI-based image features was obtained, which supports previous pathobiological results.

Conclusions

Computational method-based integration of urinary and image biomarkers may improve the pathophysiological understanding of DN progression as well as carry clinical implications in histopathological evaluation.

Children with Early-Onset Psychosis Have Increased Burden of Rare GRIN2A Variants

BACKGROUND

Children and adolescents with early-onset psychosis (EOP) have more rare genetic variants than individuals with adult-onset forms of the illness, implying that fewer EOP participants are needed for genetic discovery. The Schizophrenia Exome Sequencing Meta-analysis (SCHEMA) study predicted that 10 genes with ultra-rare variation were linked to adult-onset schizophrenia. We hypothesized that rare variants predicted "High" and "Moderate" by the Variant Effect Predictor Algorithm (abbreviated as VEPHMI) in these 10 genes would be enriched in our EOP cohort.

METHODS

We compared rare VEPHMI variants in individuals with EOP (N = 34) with race- and sex-matched controls (N = 34) using the sequence kernel association test (SKAT).

RESULTS

GRIN2A variants were significantly increased in the EOP cohort (p = 0.004), with seven individuals (20% of the EOP cohort) carrying a rare VEPHMI variant. The EOP cohort was then compared to three additional control cohorts. GRIN2A variants were significantly increased in the EOP cohort for two of the additional control sets (p = 0.02 and p = 0.02), and trending towards significance for the third (p = 0.06).

CONCLUSION

Despite a small sample size, GRIN2A VEPHMI variant burden was increased in a cohort of individuals with EOP in comparison to controls. GRIN2A variants have been associated with a range of neuropsychiatric disorders including adult-onset psychotic spectrum disorder and childhood-onset schizophrenia. This study supports the role of GRIN2A in EOP and emphasizes its role in neuropsychiatric disorders.

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry-based proteomics methods. We conducted label-free liquid chromatography-tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073-2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.

Clinical Utility of Fluid Biomarker in Depressive Disorder

Major depressive disorders are ranked as the single largest contributor to non-fatal health loss and biomarkers could largely improve our routine clinical activity by predicting disease course and guiding treatment. However there is still a dearth of valid biomarkers in the field of psychiatry. The initial assumption that a single biomarker can capture the myriad of complex processes proved to be naive. The purpose of this paper is to critically review the field and to illustrate the possible practical application for routine clinical care. Biomarkers derived from DNA analysis are the ones that have received the most attention. Other potential candidates include circulating transcription products, proteins, and inflammatory markers. DNA polygenic risk scores proved to be useful in other fields of medicine and preliminary results suggest that they could be useful both as risk and diagnostic biomarkers also in depression and for the choice of treatment. A number of other possible fluid biomarkers are currently under investigation for diagnosis, outcome prediction, staging, and stratification of interventions, however research is still needed before they can be used for routine clinical care. When available, clinicians may be able to receive a lab report with detailed information about disease risk, outcome prediction, and specific indications about preferred treatments.

Peripheral biomarkers to predict the diagnosis of bipolar disorder from major depressive disorder in adolescents

The onset of bipolar disorder (BD) occurs in childhood or adolescence in half of the patients. Early stages of BD usually present depressive episodes, which makes it difficult to be distinguished from major depressive disorder (MDD). Objective biomarkers for discriminating BD from MDD in adolescent patients are limited. We collected basic demographic data and the information of the first blood examination performed after the admission to psychiatry unit of BD and MDD inpatients during 2009-2018. We recruited 261 adolescents (aged from 10 to 18), including 160 MDD and 101 BD. Forward-Stepwise Selection of binary logistic regression was used to construct predictive models for the total sample and subgroups by gender. Independent external validation was made by 255 matched patients from another hospital in China. Regression models of total adolescents, male and female subgroups showed accuracy of 73.3%, 70.6% and 75.2%, with area under curves (AUC) as 0.785, 0.816 and 0.793, respectively. Age, direct bilirubin (DBIL), lactic dehydrogenase (LDH), free triiodothyronine (FT3) and C-reactive protein (CRP) were final factors included into the models. The discrimination was well at external validation (AUC = 0.714). This study offers the evidence that accessible information of common clinical laboratory examination might be valuable in distinguishing BD form MDD in adolescents. With good diagnostic accuracies and external validation, the total regression equation might potentially be applied to individualized clinical inferences on adolescent BD patients.

Personality mediates the association between juvenile conduct problems and adulthood mood disorders

This study aimed to examine the association between conduct problems and mood disorders, and to evaluate the mediating roles of personality traits in it. Adult participants (N = 309), for which patients with major depressive disorder (MDD) or bipolar disorder (BD), and controls without major psychiatric history were recruited. Juvenile conduct problem was defined by the items in Composite International Diagnosis Interview. We assessed personality traits of extraversion and neuroticism. Multiple mediation model was performed to investigate the intervening effect of personality traits between juvenile conduct problems and adulthood mood disorders. Participants had on average 2.7 symptoms of conduct problems, and 43.4% had conduct problems. Having more symptoms of conduct problems was associated with a higher likelihood of BD (OR = 1.20). Higher neuroticism was associated with elevated risks of both MDD and BD. There was no direct effect of binary conduct problems on the risk of BD, and showed significant total indirect effect mediated by neuroticism for BD (OR = 1.49; bias-corrected and accelerated 95% CI = 1.10–2.05), but not through extraversion. Conduct problems defined as a continuous variable had a direct effect on the risk of adult MDD (OR = 1.36; bias-corrected and accelerated 95% CI = 1.05–1.76), while had an indirect effect on the risk of BD via the mediation of neuroticism (OR = 1.08; bias-corrected and accelerated 95% CI = 1.02–1.14). Neuroticism mediates between the association of juvenile conduct problems and adult BD. This finding raises our attention to assess personality traits in individuals with juvenile conduct problems for timely intervention strategies of reducing the vulnerability for developing mood disorders.

Systematic Review and Meta-Analysis on MS-Based Proteomics Applied to Human Peripheral Fluids to Assess Potential Biomarkers of Bipolar Disorder

Bipolar disorder (BD) is a clinically heterogeneous condition, presenting a complex underlying etiopathogenesis that is not sufficiently characterized. Without molecular biomarkers being used in the clinical environment, several large screen proteomics studies have been conducted to provide valuable molecular information. Mass spectrometry (MS)-based techniques can be a powerful tool for the identification of disease biomarkers, improving prediction and diagnosis ability. Here, we evaluate the efficacy of MS proteomics applied to human peripheral fluids to assess BD biomarkers and identify relevant networks of biological pathways. Following PRISMA guidelines, we searched for studies using MS proteomics to identify proteomic differences between BD patients and healthy controls (PROSPERO database: CRD42021264955). Fourteen articles fulfilled the inclusion criteria, allowing the identification of 266 differentially expressed proteins. Gene ontology analysis identified complement and coagulation cascades, lipid and cholesterol metabolism, and focal adhesion as the main enriched biological pathways. A meta-analysis was performed for apolipoproteins (A-I, C-III, and E); however, no significant differences were found. Although the proven ability of MS proteomics to characterize BD, there are several confounding factors contributing to the heterogeneity of the findings. In the future, we encourage the scientific community to use broader samples and validation cohorts, integrating omics with bioinformatics tools towards providing a comprehensive understanding of proteome alterations, seeking biomarkers of BD, and contributing to individualized prognosis and stratification strategies, besides aiding in the differential diagnosis.

Alterations in blood proteins in the prodromal stage of bipolar II disorders

Although early intervention may help prevent the progression of bipolar disorder, there are some controversies over early pharmacological intervention. In this study, we recruited 40 subjects in the prodromal stage of BD-II (BP), according to bipolar at-risk state criteria. We compared the expression of their plasma proteins with that of 48 BD-II and 75 healthy control (HC) to identify markers that could be detected in a high-risk state. The multiple reaction monitoring method was used to measure target peptide levels with high accuracy. A total of 26 significant peptides were identified through analysis of variance with multiple comparisons, of which 19 were differentially expressed in the BP group when compared to the BD-II and HC groups. Two proteins were overexpressed in the BP group; and were related to pro-inflammation and impaired neurotransmission. The other under-expressed peptides in the BP group were related to blood coagulation, immune reactions, lipid metabolism, and the synaptic plasticity. In this study, significant markers observed in the BP group have been reported in patients with psychiatric disorders. Overall, the results suggest that the pathophysiological changes included in BD-II had already occurred with BP, thus justifying early pharmacological treatment to prevent disease progression.

A serum proteomic study of two case-control cohorts identifies novel biomarkers for bipolar disorder

We set out to identify novel protein associations with potential as clinically viable biomarkers for bipolar disorder. To this end, we used proximity extension assay to analyze 201 unique proteins in blood serum from two independent cohorts comprising patients with bipolar disorder and healthy controls (total n = 493). We identified 32 proteins significantly associated with bipolar disorder in both case-control cohorts after adjusting for relevant covariates. Twenty-two findings are novel to bipolar disorder, but 10 proteins have previously been associated with bipolar disorder: chitinase-3-like protein 1, C-C motif chemokine 3 (CCL3), CCL4, CCL20, CCL25, interleukin 10, growth/differentiation factor-15, matrilysin (MMP-7), pro-adrenomedullin, and TNF-R1. Next, we estimated the variance in serum protein concentrations explained by psychiatric drugs and found that some case-control associations may have been driven by psychiatric drugs. The highest variance explained was observed between lithium use and MMP-7, and in post-hoc analyses and found that the serum concentration of MMP-7 was positively associated with serum lithium concentration, duration of lithium therapy, and inversely associated with estimated glomerular filtration rate in an interaction with lithium. This is noteworthy given that MMP-7 has been suggested as a mediator of renal tubulointerstitial fibrosis, which is characteristic of lithium-induced nephropathy. Finally, we used machine learning to evaluate the classification performance of the studied biomarkers but the average performance in unseen data was fair to moderate (area under the receiver operating curve = 0.72). Taken together, our serum biomarker findings provide novel insight to the etiopathology of bipolar disorder, and we present a suggestive biomarker for lithium-induced nephropathy.

Computational Integration of Renal Histology and Urinary Proteomics using Neural Networks

Histological image data and molecular profiles provide context into renal condition. Often, a biopsy is drawn to diagnose or monitor a suspected kidney problem. However, molecular profiles can go beyond a pathologist's ability to see and diagnose. Using AI, we computationally incorporated urinary proteomic profiles with microstructural morphology from renal biopsy to investigate new and existing molecular links to image phenotypes. We studied whole slide images of periodic acid-Schiff stained renal biopsies from 56 DN patients matched with 2,038 proteins measured from each patient's urine. Using Seurat, we identified differentially expressed proteins in patients that developed end-stage renal disease within 2 years of biopsy. Glomeruli, globally sclerotic glomeruli, and tubules were segmented from WSI using our previously published HAIL pipeline. For each glomerulus, 315 handcrafted digital image features were measured, and for tubules, 207 features. We trained fully connected networks to predict urinary protein measurements that were differentially expressed between patients who did/ did not progress to ESRD within 2 years of biopsy. The input to this network was either glomerular or tubular histomorphological features in biopsy. Trained network weights were used as a proxy to rank which morphological features correlated most highly with specific urinary proteins. We identified significant image feature-protein pairs by ranking network weights by magnitude. We also looked at which features on average were most significant in predicting proteins. For both glomeruli and tubules, RGB color values and variance in PAS+ areas (specifically basement membrane for tubules) were, on average, more predictive of molecular profiles than other features. There is a strong connection between molecular profile and image phenotype, which can be elucidated through computational methods. These discovered links can provide insight to disease pathways, and discover new factors contributing to incidence and progression.

Plasma proteomic data in bipolar II disorders and major depressive disorders

The proteomics data included in this article supplement the research article titled "Predictive protein markers for the severity of depression in mood disorders: A preliminary trans-diagnostic approach study (manuscript ID: JPSYCHIATRES-D-20-00437)." Plasma protein was analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). This data article included 370 plasma protein profiles expressed in patients with bipolar II disorder (BD-II) and major depressive disorder (MDD). The tables present the comparison of protein expressions between BD-II and MDD, and the relationship between the severity of the depressive symptoms and protein expression. In addition, details of results adjusting the use of each psychotropic medication (antipsychotics, mood stabilizers, and antidepressants) for 20 proteins that showed a significant relationship with the severity of the depressive symptom were presented in the table. Results of the bioinformatics analysis of proteins, which were significantly related to the severity of depressive symptom, are presented. The blood protein profiles and the results of the analyses presented in this data article provide detailed information on the proteins associated with mood disorders, and could be used as the basis for further mass spectrometry studies in psychiatric disorders.

Predictive protein markers for depression severity in mood disorders: A preliminary trans-diagnostic approach study

Depression is a common symptom of many mental disorders, especially major depressive disorder (MDD) and bipolar disorder (BD). Previous studies have reported that these diseases share common pathophysiological pathways; therefore, this study elucidated whether the plasma levels of protein markers related to common depressive symptoms differed between patients with BD and those with MDD. Plasma samples of 71 patients with mood disorders and clinical manifestations were analyzed in this study. After depleting the abundant proteins, liquid chromatography-tandem mass spectrometry and label-free quantification were performed. Five proteins, viz., cholesteryl ester transfer protein (CETP), apolipoprotein D (APOD), mannan-binding lectin serine protease 2 (MASP2), Ig lambda chain V-II region BO (IGLV2-8) and Ig kappa chain V-III region NG9 (IGKV3-20) were negatively associated with the total scores of the Hamilton depression rating scale (HAM-D), after adjusting for the covariates. CETP and APOD also showed significant negative correlations with the anhedonia/retardation and guilt/agitation scores of the HAM-D. Four proteins, namely, Ig kappa chain V-II region TEW (IGKC; IGKV2D-28), Ig lambda variable 5-45 (IGLV5-45), complement factor H (CFH) and attractin (ATRN), showed significant associations with anhedonia/retardation after adjusting for covariates. Proteins that significantly correlated with the symptoms could predict the remission state of depression (area under the curve [AUC], 0.83) and anhedonia/retardation (AUC, 0.80). Bioinformatics analysis revealed that complement activation, immune response, and lipid metabolism were significantly enriched pathways. Although our study design was cross-sectional and no controls were included, protein markers identified in this preliminary study will be further investigated in our subsequent longitudinal study.

Longitudinal proteomic profiling provides insights into host response and proteome dynamics in COVID-19 progression

In managing patients with coronavirus disease 2019 (COVID‐19), early identification of those at high risk and real‐time monitoring of disease progression to severe COVID‐19 is a major challenge. We aimed to identify potential early prognostic protein markers and to expand understanding of proteome dynamics during clinical progression of the disease. We performed in‐depth proteome profiling on 137 sera, longitudinally collected from 25 patients with COVID‐19 (non‐severe patients, n = 13; patients who progressed to severe COVID‐19, n = 12). We identified 11 potential biomarkers, including the novel markers IGLV3‐19 and BNC2, as early potential prognostic indicators of severe COVID‐19. These potential biomarkers are mainly involved in biological processes associated with humoral immune response, interferon signalling, acute phase response, lipid metabolism, and platelet degranulation. We further revealed that the longitudinal changes of 40 proteins persistently increased or decreased as the disease progressed to severe COVID‐19. These 40 potential biomarkers could effectively reflect the clinical progression of the disease. Our findings provide some new insights into host response to SARS‐CoV‐2 infection, which are valuable for understanding of COVID‐19 disease progression. This study also identified potential biomarkers that could be further validated, which may support better predicting and monitoring progression to severe COVID‐19.

Quantitative Proteomic Approach for Discriminating Major Depressive Disorder and Bipolar Disorder by Multiple Reaction Monitoring-Mass Spectrometry

Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.

Identification of altered protein expression in major depressive disorder and bipolar disorder patients using liquid chromatography-tandem mass spectrometry

Emerging high-throughput proteomic technologies have recently been considered as a powerful means of identifying substrates involved in mood disorders. We performed proteomic profiling using liquid chromatography-tandem mass spectrometry to identify dysregulated proteins in plasma samples of 42 and 45 patients with major depressive disorder (MDD) and bipolar disorder (BD), respectively, in comparison to 51 healthy controls (HCs). Fourteen and six proteins in MDD and BD patients, respectively, were differentially expressed compared to HCs, among which coagulation factor XIII A chain (F13A1), platelet basic protein (PPBP), platelet facor 4 (PF4), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and thymosin beta-4 (TMSB4X) were altered in both disorders. For proteins dysregulated in both, except F13A1, higher fold changes were observed in MDD than in BD patients. These findings may help identify candidate biomarkers of mood disorders and elucidate their underlying pathophysiology and biochemical abnormalities.

In-depth blood proteome profiling analysis revealed distinct functional characteristics of plasma proteins between severe and non-severe COVID-19 patients

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over forty million patients worldwide. Although most coronavirus disease 2019 (COVID-19) patients have a good prognosis, some develop severe illness. Markers that define disease severity or predict clinical outcome need to be urgently developed as the mortality rate in critical cases is approximately 61.5%. In the present study, we performed in-depth proteome profiling of undepleted plasma from eight COVID-19 patients. Quantitative proteomic analysis using the BoxCar method revealed that 91 out of 1222 quantified proteins were differentially expressed depending on the severity of COVID-19. Importantly, we found 76 proteins, previously not reported, which could be novel prognostic biomarker candidates. Our plasma proteome signatures captured the host response to SARS-CoV-2 infection, thereby highlighting the role of neutrophil activation, complement activation, platelet function, and T cell suppression as well as proinflammatory factors upstream and downstream of interleukin-6, interleukin-1B, and tumor necrosis factor. Consequently, this study supports the development of blood biomarkers and potential therapeutic targets to aid clinical decision-making and subsequently improve prognosis of COVID-19.