Proteomic analysis of serum from patients with major depressive disorder to compare their depressive and remission statuses

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.

Discovery and validation of protein biomarkers for monitoring the effectiveness of drug treatment for major depressive disorder

Major depressive disorder (MDD) has a high prevalence worldwide. Although the economic burden of depression increases annually, the proportion of patients with MDD receiving treatment did not increase between 2010 and 2018, suggesting an unmet treatment need. The burden of long-term treatment for depression is borne by patients. In this context, biomarkers associated with drug-treatment responses can be used as reference indicators to reduce unnecessary treatment and costs. Changes in biomolecules in response to drug treatment for depression and drug-treatment response markers have been studied extensively. The Hamilton Depression Rating Scale (HAM-D) is mainly used as an indicator of response and remission; however, it is difficult to determine whether the medication contributes to recovery when evaluating the effect of drug treatment for depression based on this assessment. Therefore, it is necessary to monitor the effect of medication compared to normal health conditions. Here, serum protein levels were compared using liquid chromatography-tandem mass spectrometry among a group of patients with depression who did not receive medication, a group of patients receiving medication, and a control group. Eight selected biomarkers, including Apolipoproteins A-I, Complement factor H, Complement C5, Complement C1q subcomponent subunit B, Alpha-2-HS-glycoprotein, Complement C1q subcomponent subunit C, Vitamin D-binding protein and Corticosteroid-binding globulin were distinguished between disease states, and protein levels in the drug-treated group were similar to those in the control group. These markers can be used to monitor the effectiveness of drug treatment.

Discovery and validation of a protein biomarker for the diagnosis and classification of disease severity of major depressive disorder

BACKGROUND AND AIMS

Diagnosis and classification of disease severity of major depressive disorder (MDD) are determined through a doctor's consultation and questionnaire-based rating scale. This study aimed to identify and validate a serum protein biomarker for diagnosing and classifying the disease severity of MDD.

MATERIALS AND METHODS

Based on the Hamilton Depression Rating Scale (HAMD) score, participants were divided into control, mild, moderate, and severe groups. Samples prepared from collected sera were analyzed using non-targeted qualitative and targeted quantitative tools to identify potential biomarkers.

RESULTS

Four proteins were selected as biomarker candidates, which showed statistically significant consistent tendencies depending on MDD severity. Among them, tetranectin was the only validated protein in the quantitative analysis that showed the same decreasing tendency as that in the qualitative analysis. Furthermore, tetranectin showed fair discrimination performance between the control and MDD group.

CONCLUSIONS

Tetranectin may be a novel potential biomarker for diagnosing and classifying the severity of MDD, though further verification and validation studies of its efficacy are needed.

Comparison of serum copper levels between coronary artery disease patients and normal individuals: a case-control study

Cardiovascular diseases (CVD) are the leading causes of death in technologically developed and developing countries. Copper, an active redox element, is involved in energy production through various mechanisms. Copper and coronary artery disease can be associated directly, through its direct effect on the vascular endothelium, or indirectly through lipoprotein metabolism. Hence an evaluation of copper in the coronary artery disease individual is important. The aim is to compare the relationship of serum copper levels between coronary artery disease patients and control individuals based on age, sex, hypertension and diabetes mellitus. Materials and methods: The study design was a case-control study in which proven coronary artery disease patients attending cardiology OPD were selected as cases. Control individuals were mainly selected from the master health check-up. Serum copper levels, plasma glucose, cholesterol, serum triglycerides, and serum HDL & LDL cholesterol were done. Glycated haemoglobin (HbA1C) was also measured. The data were analyzed using IBM SPSS software version 24. Result: The correlation of serum copper level with other quantitative parameters is determined by calculating Pearson’s correlation coefficient among cases and controls. Conclusion: The serum copper level is significantly (p=0.001) higher in CAD patients than in age, sex, DM, and HT-matched controls. The serum copper level has a significant (p=0.001) effect on disease, and the adjusted odds ratio is 1.032 (CI 1.011–1.054). In addition, the serum copper level has a significant (0.01) negative correlation with LDL cholesterol and total cholesterol.

Systemic Proteomic Analysis Reveals Distinct Exosomal Protein Profiles in Rheumatoid Arthritis

Objective

Rheumatoid arthritis (RA) is a complex disease with unknown pathogenesis. In recent years, fewer have paid attention to the broad spectrum of systemic markers of RA. The aim of this study was to identify exosomal candidate proteins in the pathogenesis of RA.

Methods

Totally, 12 specimens of plasma from 6 RA patients and 6 age- and gender-matched controls from the Chinese population were obtained for nanoscale liquid chromatography coupled to tandem mass spectrometry (nano-LC-MS/MS) analysis to identify exosomal profiles.

Results

A total of 278 exosomal proteins were detected. Among them, 32 proteins were significantly upregulated (FC ≥ 2.0 and P < 0.05) and 5 proteins were downregulated (FC ≤ 0.5 and P < 0.05). Bioinformatics analysis revealed that transthyretin (TTR), angiotensinogen (AGT), lipopolysaccharide-binding protein (LBP), monocyte differentiation antigen CD14 (CD14), cartilage oligomeric matrix protein (COMP), serum amyloid P (SAP/APCS), and tenascin (TNC) can interact with each other. Subsequently, these cross-linked proteins may be mainly involved in the inflammatory-related pathways to mediate the onset of RA. Noteworthy, the LBP/CD14 complex can promote the expression of IL-8 and TNF-α, eventually leading to the development of RA.

Conclusions

Our findings suggest distinct plasmatic exosomal protein profiles in RA patients. These proteins not only take important parts in the vicious circle in the pathogenic process of RA but also serve as novel biomarkers in RA diagnosis and prognosis.

Serum biomarker discovery related to pathogenesis in acute coronary syndrome by proteomic approach

Acute coronary syndrome (ACS) results from inadequate supply of blood flow from the coronary arteries to the heart or ischemia. ACS has an extremely high morbidity and mortality. The levels of biomarkers currently used for detection of ACS also increase in response to myocardial necrosis and other diseases and are not elevated immediately after symptoms appear, thus limiting their diagnostic capacity. Therefore, we aimed to discover new ACS diagnostic biomarkers with high sensitivity and specificity that are specifically related to ACS pathogenesis. Sera from 50 patients with ACS and healthy controls (discovery cohort) each were analyzed using mass spectrometry (MS) to identify differentially expressed proteins, and protein candidates were evaluated as ACS biomarkers in 120 people in each group (validation cohort). α-1-acid glycoprotein 1 (AGP1), complement C5 (C5), leucine-rich α-2-glycoprotein (LRG), and vitronectin (VN) were identified as biomarkers whose levels increase and gelsolin (GSN) as a biomarker whose levels decrease in patients with ACS. We concluded that these biomarkers are associated with the pathogenesis of ACS and can predict the onset of ACS prior to the appearance of necrotic biomarkers.

Serum proteomic analysis of major depressive disorder patients and their remission status: Novel biomarker set of zinc-alpha-2-glycoprotein and keratin type II cytoskeletal 1

Major depressive disorder (MDD) is the most common mood disorder, and causes various mental, physical and cognitive symptoms. Clinicians diagnose MDD using multiple interviews and overall impression during the interviews, which makes MDD diagnosis highly subjective. To overcome this, we investigated novel protein biomarker for MDD. Serum from each subject were analyzed using nano liquid chromatography-triple time-of-flight mass spectrometry. We identified two proteins, zinc-alpha-2-glycoprotein (ZA2G) and keratin type II cytoskeletal 1 (K2C1), as final biomarkers. These biomarkers were downregulated during depression (p < 0.05, AUC of ROC >0.7). ZA2G is related to tryptophan metabolism, which is a main serotonin synthesis pathway. K2C1 is involved in the kinin-kallikrein system, which produces bradykinin, an anti-inflammatory mediator in the brain. Our results suggest that the two protein candidates are related to inflammation and that MDD is highly associated with inflammation. Finally, since all subjects in the two groups were taking antidepressants, our results suggest that the identified biomarkers could determine the presence or absence of illness and could be used to monitor therapeutic effects.

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.

Discovery of Screening Biomarkers for Major Depressive Disorder in Remission by Proteomic Approach

Major depressive disorder (MDD) is a common disorder involving depressive mood and decreased motivation. Due to its high heterogeneity, novel biomarkers are required to diagnose MDD. In this study, a proteomic method was used to identify a new MDD biomarker. Using sequential window acquisition of all theoretical mass spectra acquisitions and multiple reaction monitoring analysis via mass spectrometry, relative and absolute quantification of proteins in the sera was performed. The results of the relative quantitation by sequential window acquisition for all theoretical mass spectra data showed that seven proteins were significantly differently expressed between MDD patients and other patients with remission status. However, absolute quantification by multiple reaction monitoring analysis identified prothrombin as the only significantly upregulated protein in the depressive state compared to remission (p < 0.05) and was, thus, subsequently selected as an MDD biomarker. The area under the curve for prothrombin was 0.66. Additionally, increased prothrombin/thrombin induced hyper-activation of platelets via activating protease-activated receptors, a feature associated with MDD; specifically, activated platelets secrete various molecules related to MDD, including brain-derived neurotropic factors and serotonin. Therefore, prothrombin is a potential screening, prognostic, and diagnostic marker for MDD.

Serum biomarker panel for the diagnosis of rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is an autoimmune disease of inflammatory joint damage, wherein C-reactive protein and autoantibodies including rheumatoid factor (RF) and anti-cyclic citrullinated peptide (anti-CCP) are rapidly elevated. These serological factors are diagnostic markers of RA; however, their sensitivity and specificity for prediction warrant improvement for an early and accurate diagnosis.

Methods

We aimed to identify alternative biomarkers by serum protein profiling using LC-MS/MS. We performed statistical and functional analysis of differentially expressed proteins to identify biomarker candidates complementing conventional serological tests.

Results

Seven biomarker candidates were verified through multiple reaction monitoring-based quantitative analysis, of which angiotensinogen (AGT), serum amyloid A-4 protein (SAA4), vitamin D-binding protein (VDBP), and retinol-binding protein-4 (RBP4) had an area under the curve over 0.8, thus distinguishing RA patients, including seronegative (RF- and anti-CCP-negative) RA patients, from healthy controls.

Conclusions

Therefore, among seronegative RA patients, a four-biomarker panel (AGT, SAA4, VDBP, and RBP4) can prevent false negatives and help diagnose RA accurately.

Identifying Plasma Biomarkers with high specificity for major depressive disorder: A multi-level proteomics study

BACKGROUND

There are currently no objective diagnostic biomarkers for major depressive disorder (MDD) due to the biological complexity of the disorder. The existence of blood-based biomarkers with high specificity would be convenient for the clinical diagnosis of MDD.

METHODS

A comprehensive plasma proteomic analysis was conducted in a highly homogeneous cohort [7 drug-naïve MDD patients and 7 healthy controls (HCs)], with bioinformatics analysis combined with machine learning used to screen candidate proteins. Verification of reproducibility and specificity was conducted in independent cohorts [60 HCs and 74 MDD, 42 schizophrenia (SZ) and 39 bipolar I disorder (BD-I) drug-naïve patients]. Furthermore, verification of consistency was accomplished by proteomic analysis of postmortem brain tissue from 16 MDD patients and 16 HCs.

RESULTS

Levels of C-reactive protein (CRP), antithrombin III (ATIII), inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4) and vitamin D-binding protein (VDB) were significantly higher in MDD patients, both in the discovery cohort and independent replication cohort. In comparison with SZ or BD-I patients, two proteins (VDB and ITIH4) were significantly elevated only in MDD patients. In addition, increased VDB and ITIH4 were observed consistently in both plasma and postmortem dorsolateral prefrontal cortex tissues of MDD patients. Furthermore, a panel consisting of all four plasma proteins was able to distinguish MDD patients from HCs or SZ or BD-I patients with the highest accuracy.

CONCLUSION

Plasma ITIH4 and VDB may be potential plasma biomarkers of MDD with high specificity. The four-protein panel is more suitable as a potential clinical diagnostic marker for MDD.

Peripheral Markers of Depression

Major Depressive Disorder (MDD) is a leading cause of disability worldwide, creating a high medical and socioeconomic burden. There is a growing interest in the biological underpinnings of depression, which are reflected by altered levels of biological markers. Among others, enhanced inflammation has been reported in MDD, as reflected by increased concentrations of inflammatory markers-C-reactive protein, interleukin-6, tumor necrosis factor-α and soluble interleukin-2 receptor. Oxidative and nitrosative stress also plays a role in the pathophysiology of MDD. Notably, increased levels of lipid peroxidation markers are characteristic of MDD. Dysregulation of the stress axis, along with increased cortisol levels, have also been reported in MDD. Alterations in growth factors, with a significant decrease in brain-derived neurotrophic factor and an increase in fibroblast growth factor-2 and insulin-like growth factor-1 concentrations have also been found in MDD. Finally, kynurenine metabolites, increased glutamate and decreased total cholesterol also hold promise as reliable biomarkers for MDD. Research in the field of MDD biomarkers is hindered by insufficient understanding of MDD etiopathogenesis, substantial heterogeneity of the disorder, common co-morbidities and low specificity of biomarkers. The construction of biomarker panels and their evaluation with use of new technologies may have the potential to overcome the above mentioned obstacles.

Serum proteomic profiling of patients with amphetamine use disorder

BACKGROUND

Amphetamine use disorder has been recently classified as an epidemic condition. Amphetamine use/abuse has been associated with several neurological and inflammatory effects. However, the exact mechanism involved in these effects warrants further investigation. The aim of this study was to determine any alterations in the serum proteome of individuals classified as patients with amphetamine use disorder compared to that of control subjects.

METHODS

An untargeted proteomic approach employing two-dimensional difference in gel electrophoresis coupled with mass spectrometry was used to identify the patterns of differentially expressed proteins. Serum samples were collected from 20 individuals (males) including 10 subjects with amphetamine use disorder and 10 healthy controls for the present study.

RESULTS

The analysis revealed 78 proteins with a significant difference in protein abundance between the amphetamine-addicted subjects and controls. Among them, 71 proteins were upregulated while 7 proteins remained downregulated in the amphetamine-addicted group. These proteins were further analyzed by ingenuity pathway analysis (IPA) to investigate their correlation with other biomarkers. IPA revealed the correlation of altered proteins with mitogen-activated protein kinase (MAP2K1/K2), p38MAPK, protein kinase-B (PKB; Akt), extracellular signal-regulated kinase (ERK1/2), and nuclear factor-κB signaling pathways. Importantly, these pathways are highly involved in neurological diseases, inflammatory responses, and cellular compromise.

CONCLUSIONS

Our data suggest that the changes in the levels of serum proteins between amphetamine and control groups might affect cellular compromise, inflammatory response, and neurological diseases.

A discovery of screening markers for rheumatoid arthritis by liquid chromatography mass spectrometry: A metabolomic approach

AIM

This study aimed to discover serum metabolite biomarkers for potential use in screening for rheumatoid arthritis (RA).

METHODS

The sera from 43 healthy controls (HCs) and 49 RA patients were globally analyzed using high-performance liquid chromatography- tandem mass spectrometry. Molecular features (MFs) from samples were analyzed using volcano plots, partial least squares discriminant analysis, and variable importance in projection scores to select candidates. The spectra of candidate MFs were matched with the METLIN database. We confirmed the association between candidates and RA and analyzed the receiver-operating characteristic (ROC) curves.

RESULTS

We selected a total of 57 candidate MFs that had a fold change ≥1.5, P value ≤.05, and over 80% of frequency. Among them, 18 MFs were identified as metabolites with the METLIN database. Six metabolites (dehydroepiandrosterone sulfate, androsterone sulfate, γ-linolenic acid, 9[E],11[E]-conjugated linoleic acid, docosahexaenoic acid, and docosapentaenoic acid [22n-3]) out of the 18 were associated with mechanisms of RA and were selected as final candidates. ROC curve analysis revealed their area under the curve (AUC) values were all above 0.75 and the combined AUC of the six candidates was 0.89.

CONCLUSION

Using six candidates as a marker set showed potential in distinguishing RA patients from HCs, based on high AUC values. Therefore, we propose that a marker set of these six candidates has potential clinical application in RA screening.

Significant correlation between plasma proteome profile and pain intensity, sensitivity, and psychological distress in women with fibromyalgia

Fibromyalgia (FM) is a complex pain condition where the pathophysiological and molecular mechanisms are not fully elucidated. The primary aim of this study was to investigate the plasma proteome profile in women with FM compared to controls. The secondary aim was to investigate if plasma protein patterns correlate with the clinical variables pain intensity, sensitivity, and psychological distress. Clinical variables/background data were retrieved through questionnaires. Pressure pain thresholds (PPT) were assessed using an algometer. The plasma proteome profile of FM (n = 30) and controls (n = 32) was analyzed using two-dimensional gel electrophoresis and mass spectrometry. Quantified proteins were analyzed regarding group differences, and correlations to clinical parameters in FM, using multivariate statistics. Clear significant differences between FM and controls were found in proteins involved in inflammatory, metabolic, and immunity processes. Pain intensity, PPT, and psychological distress in FM had associations with specific plasma proteins involved in blood coagulation, metabolic, inflammation and immunity processes. This study further confirms that systemic differences in protein expression exist in women with FM compared to controls and that altered levels of specific plasma proteins are associated with different clinical parameters.

Computational chemoproteomics to understand the role of selected psychoactives in treating mental health indications

We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behaviour at a proteomic level by constructing and analysing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to the phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls. Also, we analysed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders.

The difference in serum proteomes in schizophrenia and bipolar disorder

Background

Purpose of study is revealing significant differences in serum proteomes in schizophrenia and bipolar disorder (BD).

Results

Quantitative mass-spectrometry based proteomic analysis was used to quantify proteins in the blood serum samples after the depletion of six major blood proteins. Comparison of proteome profiles of different groups revealed 27 proteins being specific for schizophrenia, and 18 – for BD. Protein set in schizophrenia was mostly associated with immune response, cell communication, cell growth and maintenance, protein metabolism and regulation of nucleic acid metabolism. Protein set in BD was mostly associated with immune response, regulating transport processes across cell membrane and cell communication, development of neurons and oligodendrocytes and cell growth. Concentrations of ankyrin repeat domain-containing protein 12 (ANKRD12) and cadherin 5 in serum samples were determined by ELISA. Significant difference between three groups was revealed in ANKRD12 concentration (p = 0.02), with maximum elevation of ANKRD12 concentration (median level) in schizophrenia followed by BD. Cadherin 5 concentration differed significantly (p = 0.035) between schizophrenic patients with prevailing positive symptoms (4.78 [2.71, 7.12] ng/ml) and those with prevailing negative symptoms (1.86 [0.001, 4.11] ng/ml).

Conclusions

Our results are presumably useful for discovering the new pathways involved in endogenous psychotic disorders.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5848-1) contains supplementary material, which is available to authorized users.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.

Plasma Protein Pattern Correlates With Pain Intensity and Psychological Distress in Women With Chronic Widespread Pain

Objectives: Although generalized muscle pain, tiredness, anxiety, and depression are commonly present among chronic widespread pain (CWP) patients, the molecular mechanisms behind CWP are not fully elucidated. Moreover, the lack of biomarkers often makes diagnosis and treatment problematic. In this study, we investigated the correlation between pain intensity, psychological distress, and plasma proteins among CWP patients and controls (CON). Methods: The plasma proteome of CWP (n = 15) and CON (n = 23) was analyzed using two-dimensional gel electrophoresis. Orthogonal Partial Least Square analysis (OPLS) was used to determine proteins associated with pain intensity (numeric rating scale) in CWP and psychological distress (Hospital and Depression Scale, HADS) in CWP and CON. Significant proteins were identified by MALDI-TOF and tandem MS. Results: In CWP, pain intensity was associated with plasma proteins mostly involved in metabolic and immunity processes (e.g., kininogen-1, fibrinogen gamma chain, and ceruloplasmin), and psychological distress was associated with plasma proteins related to immunity response, iron ion, and lipid metabolism (e.g., complement factor B, complement C1r subcomponent, hemopexin, and clusterin). Discussion: This study suggests that different plasma protein patterns are associated with different pain intensity and psychological distress in CWP. Proteins belonging to the coagulation cascade and immunity processes showed strong associations to each clinical outcome. Using the plasma proteome profile of CWP to study potential biomarker candidates provides a snapshot of ongoing systemic mechanisms in CWP.

Peripheral biomarkers of major depression and antidepressant treatment response: Current knowledge and future outlooks

BACKGROUND

In recent years, we have accomplished a deeper understanding about the pathophysiology of major depressive disorder (MDD). Nevertheless, this improved comprehension has not translated to improved treatment outcome, as identification of specific biologic markers of disease may still be crucial to facilitate a more rapid, successful treatment. Ongoing research explores the importance of screening biomarkers using neuroimaging, neurophysiology, genomics, proteomics, and metabolomics measures.

RESULTS

In the present review, we highlight the biomarkers that are differentially expressed in MDD and treatment response and place a particular emphasis on the most recent progress in advancing technology which will continue the search for blood-based biomarkers.

LIMITATIONS

Due to space constraints, we are unable to detail all biomarker platforms, such as neurophysiological and neuroimaging markers, although their contributions are certainly applicable to a biomarker review and valuable to the field.

CONCLUSIONS

Although the search for reliable biomarkers of depression and/or treatment outcome is ongoing, the rapidly-expanding field of research along with promising new technologies may provide the foundation for identifying key factors which will ultimately help direct patients toward a quicker and more effective treatment for MDD.

Proteomics analysis reveals differential pattern of widespread protein expression and novel role of histidine-rich glycoprotein and lipopolysaccharide-binding protein in rheumatoid arthritis

Rheumatoid factor (RF) is an auto-antibody against antigen-antibody immune complexes. RF is valuable as a biomarker for the screening of autoimmune and infectious diseases. However, it is suggested that RF would be a more powerful biomarker when used complementarily with RF-correlated proteins. In this study, we utilized a proteomic approach to analyze global protein expression in RF-low and RF-high subjects using high-performance liquid chromatography tandem mass spectrometry. Histidine-rich glycoprotein (HRG) and lipopolysaccharide-binding protein (LBP) were found to be differentially expressed between RF-low and RF-high subjects (cut-off > 2-fold, p < 0.05), which was validated by enzyme-linked immunosorbent assay. To evaluate whether both proteins allow discriminating rheumatoid arthritis patients from healthy controls, receiver-operating characteristic (ROC) curves were analyzed. Areas under the ROC curves of HRG and LBP were 0.861 and 0.888, respectively. The correlation between RF and HRG was statistically significant (p = 0.003), and LBP was also correlated with RF (p = 0.044), as indicated by correlation analysis. HRG and LBP are reportedly involved in RF-producing and RF-correlated diseases. Thus, we propose that HRG and LBP could be useful screening markers for RF-correlated diseases.

Systemic alterations in plasma proteins from women with chronic widespread pain compared to healthy controls: a proteomic study

Chronic widespread pain (CWP) is a complex pain condition that is difficult to treat. The prevalence of CWP approximates ~10% of the general population, with higher prevalence in women. Lack of understanding of molecular mechanisms has been a challenge for diagnosis and treatment of chronic pain. The aim of this study was to explore the systemic protein changes in CWP compared to those in healthy controls (CON). By applying 2-dimensional gel electrophoresis, we analyzed the protein pattern of plasma samples from women with CWP (n=16) and healthy women (n=23). The proteomic data were analyzed using multivariate statistical models, and altered proteins were identified using mass spectrometry. The proteome analysis was further validated by gel-free Western blot. Multivariate statistical data analysis of quantified proteins revealed 22 altered proteins in women with CWP, compared to CON group. Many of the identified proteins are previously known to be involved in different parts of the complement system and metabolic and inflammatory processes, e.g., complement factor B, vitamin D-binding protein, ceruloplasmin, transthyretin and alpha-2-HS-glycoprotein. These results indicate that important systemic protein differences exist between women with CWP and healthy women. Further, this study illustrates the potential use of proteomics to detect biomarkers that may provide new insights into the molecular mechanism(s) of chronic pain. However, further larger investigations are required in order to confirm these findings before it will be possible to identify proteins as potential pain biomarkers for clinical use.

LC-MSE, Multiplex MS/MS, Ion Mobility, and Label-Free Quantitation in Clinical Proteomics

Proteomic tools can only be implemented in clinical settings if high-throughput, automated, sensitive, and accurate methods are developed. This has driven researchers to the edge of mass spectrometry (MS)-based proteomics capacity. Here we provide an overview of recent achievements in mass spectrometric technologies and instruments. This includes development of high and ultra definition-MS^E (HDMS^E and UDMS^E) through implementation of ion mobility (IM) MS towards sensitive and accurate label-free proteomics using ultra performance liquid chromatography (UPLC). Label free UPLC-HDMS^E is less expensive than labeled-based quantitative proteomics and has no limits regarding the number of samples that can be analyzed and compared, which is an important requirement for supporting clinical applications.

Study of the Serum Copper Levels in Patients with Major Depressive Disorder

Copper may be involved in the pathophysiology of depression. Clinical data on this issue are very limited and not conclusive. The purpose of the study was to determine the copper concentration in the serum of patients with major depressive disorder and to discuss its potential clinical usefulness as a biomarker of the disease. A case-control clinical study included 69 patients with current depressive episode, 45 patients in remission and 50 healthy volunteers. Cu concentration was measured by electrothermal atomic absorption spectrometry (ETAAS). The mean serum copper level in depressed patients was slightly lower (by 11 %; not statistically significant) than in the control group. Furthermore, there was no significant difference in Cu2+ concentration between depressive episode and remission, nor between remission and control group. In the remission group were observed significant correlations between copper levels and the average number of relapses over the past years or time of remission. There was no correlation between serum copper and severity of depression, as measured by HDRS and MADRS. The obtained results showed no significant differences between the copper concentration in the blood serum of patients (both with current depressive episode and in remission) and healthy volunteers, as well as the lack of correlations between the copper level in the active stage of the disease and clinical features of the population. Our study is the first conducted on such a large population of patients, so the results may be particularly important and reliable source of knowledge about the potential role of copper in depression.

The significance of routine biochemical markers in patients with major depressive disorder

The aim of our study is to examine the levels of routine biochemical markers in patients with major depressive disorder (MDD), and combine multiple biochemical parameters to assess the discriminative power for patients with MDD. We used the Hamilton Depression (HAMD) score to evaluate the severity of depressive symptoms in 228 patients with MDD. The phase of depression severity was between moderate and severe in MDD patients. There were significant differences between MDD patients and healthy controls in alanine transaminase (ALT), urea nitrogen (UN), lactate dehydrogenase (LDH), uric acid (UA), total protein (TP), total bile acid (TBA), creatinine (Cr), total bilirubin (Tbil), direct bilirubin (Dbil) and indirect bilirubin (Ibil), high density lipoprotein-cholesterol (HDL-C), fasting blood-glucose (FBG) and fructosamine (SF). Multivariate analysis showed that UN, FBG, HDL-C, SF, TP, Cr and Tbil remained independently association with MDD. Further, a logit equation was established to identify patients with MDD. The composite markers exhibited an area under the curve of 0.810 with cut-off values of 0.410. Our results suggest the associations between UN, FBG, HDL-C, TP, Cr, Tbil, SF and MDD, use of these routine biochemical markers in combination may contribute to improve the complete management for patients with MDD.

Second Generation Antipsychotics in the Treatment of Major Depressive Disorder: An Update

Less than one third of patients who suffer from major depressive disorder (MDD) report remission following antidepressant treatments requiring more diverse treatment approaches. Augmentation of second generation antipsychotics (SGAs) has been increasingly recognized as an important treatment option. The authors have previously provided a comprehensive review of SGAs for the treatment of MDD in 2013. Since then, numerous additional clinical trials have been conducted to investigate diverse issues regarding the utility of SGAs in MDD. Moreover, a new SGA, brexpiprazole, was recently approved by the Food and Drug Administration in July 2015 for the treatment of MDD as an augmentation agent to antidepressants. Thus, the aim of this study was to provide a concise update of all the available SGAs for the treatment of MDD, in particular on the additional clinical trials which have been published since 2013.

Seeking an objective diagnosis of depression

Major depressive disorder (MDD: unipolar depression) is widely distributed in the USA and world-wide populations and it is one of the leading causes of disability in both adolescents and adults. Traditional diagnostic approaches for MDD are based on patient interviews, which provide a subjective assessment of clinical symptoms which are frequently shared with other maladies. Reliance upon clinical assessments and patient interviews for diagnosing MDD is frequently associated with misdiagnosis and suboptimal treatment outcomes. As such, there is increasing interest in the identification of objective methods for the diagnosis of depression. Newer technologies from genomics, transcriptomics, proteomics, metabolomics and imaging are technically sophisticated and objective but their application to diagnostic tests in psychiatry is still emerging. This brief overview evaluates the technical basis for these technologies and discusses how the extension of their clinical performance can lead to an objective diagnosis of MDD.

iTRAQ technology-based identification of human peripheral serum proteins associated with depression

Clinical depression is one of the most common and debilitating psychiatric disorders and contributes to increased risks of disability and suicide. Differentially expressed serum proteins may serve as biomarkers for diagnosing depression. In this study, samples from depressed patients are aggregated into a pool (22×100μL serum was used) and samples from healthy volunteers are aggregated into the other pool (20×100μL serum was used). Isobaric tag for relative and absolute quantitation (iTRAQ) technology and tandem mass spectrometry were employed to screen for differentially expressed serum protein in two separate pools. We identified 472 proteins in the serum samples, and 154 of these presented differences in abundance between the depression and control groups. Ingenuity pathway analysis (IPA) was employed to identify the highest scoring proteins in signaling pathway networks. Finally, four differentially expressed proteins were validated by enzyme-linked immuno sorbent assay (ELISA). Proteomic studies revealed that levels of c-reaction protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), serum amyloid A1 (SAA1) and angiopoietin-like 3 (ANGPTL3) were substantially increased in depressed patients compared with the healthy control group. Therefore, these differentially expressed proteins may represent potential markers for the clinical diagnosis of depression.

Innate and adaptive immunity in the development of depression: An update on current knowledge and technological advances

The inflammation theory of depression, proposed over 20years ago, was influenced by early studies on T cell responses and since then has been a stimulus for numerous research projects aimed at understanding the relationship between immune function and depression. Observational studies have shown that indicators of immunity, especially C reactive protein and proinflammatory cytokines, such as interleukin 6, are associated with an increased risk of depressive disorders, although the evidence from randomized trials remains limited and only few studies have assessed the interplay between innate and adaptive immunity in depression. In this paper, we review current knowledge on the interactions between central and peripheral innate and adaptive immune molecules and the potential role of immune-related activation of microglia, inflammasomes and indoleamine-2,3-dioxygenase in the development of depressive symptoms. We highlight how combining basic immune methods with more advanced 'omics' technologies would help us to make progress in unravelling the complex associations between altered immune function and depressive disorders, in the identification of depression-specific biomarkers and in developing immunotherapeutic treatment strategies that take individual variability into account.

Systems Genomics Support for Immune and Inflammation Hypothesis of Depression

BACKGROUND

Immune system plays an important role in brain development and function. With the discovery of increased circulating inflammatory cytokine levels in depression over two decades ago, evidence implicating immune system alterations in the disease has increasingly accumulated.

OBJECTIVE

To assess the underlying etiology and pathophysiology, a brief overview of the hypothesis free genomic, transcriptomic and proteomic studies in depression is presented here in order to specifically examine if the immune and inflammation hypothesis of depression is supported.

RESULTS

It is observed that genes identified in genome-wide association studies, and genes showing differential expression in transcriptomic studies in human depression do separately overrepresent processes related to both development as well as functioning of the immune system, and inflammatory response. These processes are also enriched in differentially expressed genes reported in animal models of antidepressant treatment. It is further noted that some of the genes identified in genome sequencing and proteomic analyses in human depression, and transcriptomic studies in chronic social defeat stress, an established animal model of depression, relate to immune and inflammatory pathways.

CONCLUSION

In conclusion, integrative genomics evidence supports the immune and inflammation hypothesis of depression.