Exploring cellular markers of metabolic syndrome in peripheral blood mononuclear cells across the neuropsychiatric spectrum.

Recent evidence suggests that comorbidities between neuropsychiatric conditions and metabolic syndrome may precede and even exacerbate long-term side-effects of psychiatric medication, such as a higher risk of type 2 diabetes and cardiovascular disease, which result in increased mortality. In the present study we compare the expression of key metabolic proteins, including the insulin receptor (CD220), glucose transporter 1 (GLUT1) and fatty acid translocase (CD36), on peripheral blood mononuclear cell subtypes from patients across the neuropsychiatric spectrum, including schizophrenia, bipolar disorder, major depression and autism spectrum conditions (n = 25/condition), relative to typical controls (n = 100). This revealed alterations in the expression of these proteins that were specific to schizophrenia. Further characterization of metabolic alterations in an extended cohort of first-onset antipsychotic drug-naïve schizophrenia patients (n = 58) and controls (n = 63) revealed that the relationship between insulin receptor expression in monocytes and physiological insulin sensitivity was disrupted in schizophrenia and that altered expression of the insulin receptor was associated with whole genome polygenic risk scores for schizophrenia. Finally, longitudinal follow-up of the schizophrenia patients over the course of antipsychotic drug treatment revealed that peripheral metabolic markers predicted changes in psychopathology and the principal side effect of weight gain at clinically relevant time points. These findings suggest that peripheral blood cells can provide an accessible surrogate model for metabolic alterations in schizophrenia and have the potential to stratify subgroups of patients with different clinical outcomes or a greater risk of developing metabolic complications following antipsychotic therapy.

Exploring the neuropsychiatric spectrum using high-content functional analysis of single-cell signaling networks.

Neuropsychiatric disorders overlap in symptoms and share genetic risk factors, challenging their current classification into distinct diagnostic categories. Novel cross-disorder approaches are needed to improve our understanding of the heterogeneous nature of neuropsychiatric diseases and overcome existing bottlenecks in their diagnosis and treatment. Here we employ high-content multi-parameter phospho-specific flow cytometry, fluorescent cell barcoding and automated sample preparation to characterize ex vivo signaling network responses (n = 1764) measured at the single-cell level in B and T lymphocytes across patients diagnosed with four major neuropsychiatric disorders: autism spectrum condition (ASC), bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ; n = 25 each), alongside matched healthy controls (n = 100). We identified 25 nodes (individual cell subtype-epitope-ligand combinations) significantly altered relative to the control group, with variable overlap between different neuropsychiatric diseases and heterogeneously expressed at the level of each individual patient. Reconstruction of the diagnostic categories from the altered nodes revealed an overlapping neuropsychiatric spectrum extending from MDD on one end, through BD and SCZ, to ASC on the other end. Network analysis showed that although the pathway structure of the epitopes was broadly preserved across the clinical groups, there were multiple discrete alterations in network connectivity, such as disconnections within the antigen/integrin receptor pathway and increased negative regulation within the Akt1 pathway in CD4+ T cells from ASC and SCZ patients, in addition to increased correlation of Stat1 (pY701) and Stat5 (pY694) responses in B cells from BD and MDD patients. Our results support the "dimensional" approach to neuropsychiatric disease classification and suggest potential novel drug targets along the neuropsychiatric spectrum.

Towards a blood-based diagnostic panel for bipolar disorder.

BACKGROUND: Bipolar disorder (BD) is a costly, devastating and life shortening mental disorder that is often misdiagnosed, especially on initial presentation. Misdiagnosis frequently results in ineffective treatment. We investigated the utility of a biomarker panel as a diagnostic test for BD. METHODS AND FINDINGS: We performed a meta-analysis of eight case-control studies to define a diagnostic biomarker panel for BD. After validating the panel on established BD patients, we applied it to undiagnosed BD patients. We analysed 249 BD, 122 pre-diagnostic BD, 75 pre-diagnostic schizophrenia and 90 first onset major depression disorder (MDD) patients and 371 controls. The biomarker panel was identified using ten-fold cross-validation with lasso regression applied to the 87 analytes available across the meta-analysis studies. We identified 20 protein analytes with excellent predictive performance [area under the curve (AUC)⩾0.90]. Importantly, the panel had a good predictive performance (AUC 0.84) to differentiate 12 misdiagnosed BD patients from 90 first onset MDD patients, and a fair to good predictive performance (AUC 0.79) to differentiate between 110 pre-diagnostic BD patients and 184 controls. We also demonstrated the disease specificity of the panel. CONCLUSIONS: An early and accurate diagnosis has the potential to delay or even prevent the onset of BD. This study demonstrates the potential utility of a biomarker panel as a diagnostic test for BD.

Proteomic changes in serum of first onset, antidepressant drug-naïve major depression patients.

Major depressive disorder (MDD) is a complex and multi-factorial disorder. Although genetic factors and other molecular aspects of MDD have been widely studied, the underlying pathological mechanisms are still mostly unknown. We sought to investigate the pathophysiology of MDD by identifying and characterising serum molecular differences and their correlation to symptom severity in first onset, antidepressant drug-naïve MDD patients. We performed an exploratory molecular profiling study on serum samples of MDD patients and controls using multiplex immunoassay and label-free liquid chromatography mass spectrometry in data independent mode (LC-MSE). We included two independent cohorts of first onset, antidepressant drug-naïve MDD patients (n = 23 and 15) and matched controls (n = 42 and 21) in our study in order to validate the results. The main outcome included the following list of circulatory molecules changing and/or correlating to symptom severity: angiotensin-converting enzyme, acute phase proteins (e.g. ferritin and serotransferrin), brain-derived neurotrophic factor, complement component C4-B, cortisol, cytokines (e.g. macrophage migration inhibitory factor and interleukin-16), extracellular newly identified receptor for advanced glycosylation end products-binding protein, growth hormone and superoxide dismutase-1. This study provides evidence of an increased pro-inflammatory and oxidative stress response, followed by a hyperactivation of the HPA-axis in the acute stages of first onset MDD, as well as a dysregulation in growth factor pathways. These findings help to elucidate MDD related pathways in more detail and further studies may lead to identification of novel drug targets, including components of the inflammatory and oxidative stress response.

Application of meta-analysis methods for identifying proteomic expression level differences.

We present new statistical approaches for identification of proteins with expression levels that are significantly changed when applying meta-analysis to two or more independent experiments. We showed that the Euclidean distance measure has reduced risk of false positives compared to the rank product method. Our Ψ-ranking method has advantages over the traditional fold-change approach by incorporating both the fold-change direction as well as the p-value. In addition, the second novel method, Π-ranking, considers the ratio of the fold-change and thus integrates all three parameters. We further improved the latter by introducing our third technique, Σ-ranking, which combines all three parameters in a balanced nonparametric approach.

Diagnostic model development for schizophrenia based on peripheral blood mononuclear cell subtype-specific expression of metabolic markers.

A significant proportion of the personal and economic burden of schizophrenia can be attributed to the late diagnosis or misdiagnosis of the disorder. A novel, objective diagnostic approaches could facilitate the early detection and treatment of schizophrenia and improve patient outcomes. In the present study, we aimed to identify robust schizophrenia-specific blood biomarkers, with the goal of developing an accurate diagnostic model. The levels of selected serum and peripheral blood mononuclear cell (PBMC) markers relevant to metabolic and immune function were measured in healthy controls (n = 26) and recent-onset schizophrenia patients (n = 36) using multiplexed immunoassays and flow cytometry. Analysis of covariance revealed significant upregulation of insulin receptor (IR) and fatty acid translocase (CD36) levels in T helper cells (F = 10.75, P = 0.002, Q = 0.024 and F = 21.58, P = 2.8 × 10-5, Q = 0.0004, respectively), as well as downregulation of glucose transporter 1 (GLUT1) expression in monocytes (F = 21.46, P = 2.9 × 10-5, Q = 0.0004). The most robust predictors, monocyte GLUT1 and T helper cell CD36, were used to develop a diagnostic model, which showed a leave-one-out cross-validated area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI: 0.66-0.92). The diagnostic model was validated in two independent datasets. The model was able to distinguish first-onset, drug-naïve schizophrenia patients (n = 34) from healthy controls (n = 39) with an AUC of 0.75 (95% CI: 0.64-0.86), and also differentiated schizophrenia patients (n = 22) from patients with other neuropsychiatric conditions, including bipolar disorder, major depressive disorder and autism spectrum disorder (n = 68), with an AUC of 0.83 (95% CI: 0.75-0.92). These findings indicate that PBMC-derived biomarkers have the potential to support an accurate and objective differential diagnosis of schizophrenia.

Proteomic Profiling as a Diagnostic Biomarker for Discriminating Between Bipolar and Unipolar Depression.

INTRODUCTION: Affective disorders are a major global burden, with approximately 15% of people worldwide suffering from some form of affective disorder. In patients experiencing their first depressive episode, in most cases it cannot be distinguished whether this is due to bipolar disorder (BD) or major depressive disorder (MDD). Valid fluid biomarkers able to discriminate between the two disorders in a clinical setting are not yet available. MATERIAL AND METHODS: Seventy depressed patients suffering from BD (bipolar I and II subtypes) and 42 patients with major MDD were recruited and blood samples were taken for proteomic analyses after 8 h fasting. Proteomic profiles were analyzed using the Multiplex Immunoassay platform from Myriad Rules Based Medicine (Myriad RBM; Austin, Texas, USA). Human DiscoveryMAPTM was used to measure the concentration of various proteins, peptides, and small molecules. A multivariate predictive model was consequently constructed to differentiate between BD and MDD. RESULTS: Based on the various proteomic profiles, the algorithm could discriminate depressed BD patients from MDD patients with an accuracy of 67%. DISCUSSION: The results of this preliminary study suggest that future discrimination between bipolar and unipolar depression in a single case could be possible, using predictive biomarker models based on blood proteomic profiling.

Multiplex immunoassay analysis of plasma shows differences in biomarkers related to manic or mixed mood states in bipolar disorder patients.

BACKGROUND: The molecular understanding of bipolar disorder (BD) aetiology has advanced over the last years through the identification of peripheral disease biomarkers. Here, we have attempted to identify plasma biomarkers associated with distinct BD mood states. METHODS: Plasma from BD patients with either a current manic (n=29) or mixed (n=17) mood state and healthy controls (n=53) were analysed using a multiplex immunoassay platform. A total of 145 hormones, growth factors, transport proteins and inflammatory factors were measured. RESULTS: Plasma levels of the hormones C-peptide, progesterone and insulin, and the inflammatory protein cancer antigen 125 were altered in both mood states. The hormone peptide YY and the growth factor trafficking protein sortilin were changed only in mania patients. Finally, the inflammatory factors haptoglobin, chemokine CC4 and matrix metalloproteinase 7 were altered specifically in mixed mood patients. LIMITATIONS: This study was limited by a small sample size, potential confounding effects of multiple drug treatments in the patient groups, and lack of dietary restrictions at sampling. CONCLUSIONS: Plasma from mania and mixed mood BD patients revealed similar changes in proteins related to insulin signalling, suggesting that these could be trait biomarkers. However, mania patients showed specific changes in hormonal and growth factor functions and mixed mood patients had a higher number of changes in inflammation-related molecules. Further studies of these and other biomarker candidates will increase our understanding of the systemic biological pathways affected in different BD mood states. This could lead to the identification of differential surrogate readouts and potential new drug targets for improved treatment outcomes.

Multiplex immunoassay analysis of plasma shows prominent upregulation of growth factor activity pathways linked to GSK3ß signaling in bipolar patients.

BACKGROUND: Our understanding of bipolar disorder (BD) aetiology has advanced in recent years but our ability to translate this to improve patient care in the clinic is still limited. METHODS: In this study, we have measured the concentrations of 190 different molecules using sensitive multiplex immunoassays in plasma of 17 BD patients compared to 46 matched control subjects. RESULTS: The analyses led to the identification of 26 dysregulated proteins in BD patients compared to controls. These molecules were comprised mostly of growth factors, hormones, lipid transport and inflammatory proteins. Decreased apolipoprotein A1 has previously been associated with BD patients and this was confirmed in our study. LIMITATIONS: The present pilot study was limited by its small sample size, use of multiple drug treatments and the lack of dietary restrictions at the time of sampling. CONCLUSIONS: Future studies may increase our understanding of BD which will help to pave the way for much-needed patient stratification for better treatment outcomes.

Applications of blood-based protein biomarker strategies in the study of psychiatric disorders.

Major psychiatric disorders such as schizophrenia, major depressive and bipolar disorders are severe, chronic and debilitating, and are associated with high disease burden and healthcare costs. Currently, diagnoses of these disorders rely on interview-based assessments of subjective self-reported symptoms. Early diagnosis is difficult, misdiagnosis is a frequent occurrence and there are no objective tests that aid in the prediction of individual responses to treatment. Consequently, validated biomarkers are urgently needed to help address these unmet clinical needs. Historically, psychiatric disorders are viewed as brain disorders and consequently only a few researchers have as yet evaluated systemic changes in psychiatric patients. However, promising research has begun to challenge this concept and there is an increasing awareness that disease-related changes can be traced in the peripheral system which may even be involved in the precipitation of disease onset and course. Converging evidence from molecular profiling analysis of blood serum/plasma have revealed robust molecular changes in psychiatric patients, suggesting that these disorders may be detectable in other systems of the body such as the circulating blood. In this review, we discuss the current clinical needs in psychiatry, highlight the importance of biomarkers in the field, and review a representative selection of biomarker studies to highlight opportunities for the implementation of personalized medicine approaches in the field of psychiatry. It is anticipated that the implementation of validated biomarker tests will not only improve the diagnosis and more effective treatment of psychiatric patients, but also improve prognosis and disease outcome.

Challenges in drug target discovery in bipolar disorder.

INTRODUCTION: Misdiagnosis and subsequent inappropriate treatment of patients with bipolar disorder (BD) can worsen their clinical condition and outcome. AREAS COVERED: This review focuses on the therapeutic targets which have been implicated in BD, including the glycogen synthase kinase 3 (GSK-3) and phosphoinositide signaling pathways. In addition, evidence is presented for potential new molecular strategies which involve targeting neuropeptide-converting endopeptidases, glutamatergic excitotoxicity, insulin signaling and dysfunctions in mitochondrial metabolism. Current limitations in study design, molecular platforms, preclinical and cellular models in the context of BD drug target discovery, suggest that there are many areas for improvement. EXPERT OPINION: For the future outlook, this review outlines the importance of developments such as the use of BD patient-derived cellular models for providing better understanding of the BD etiology and robust translational drug screening tools in combination with developments in the fields of bioinformatics and systems biology.