Search

Despite being a major cause of disability worldwide, the pathophysiology of schizophrenia and molecular basis of treatment response heterogeneity continue to be unresolved. Recent evidence suggests that multiple aspects of pathophysiology, including genetic risk factors, converge on key cell signaling pathways and that exploration of peripheral blood cells might represent a practical window into cell signaling alterations in the disease state. We employed multiplexed phospho-specific flow cytometry to examine cell signaling epitope expression in periph eral blood mononuclear cell (PBMC) subtypes in drug-naïve schizophrenia patients (n = 49) relative to controls (n = 61) and relate these changes to serum immune response proteins, schizophrenia polygenic risk scores and clinical effects of treatment, including drug response and side effects, over the longitudinal course of antipsy chotic treatment. This revealed both previously characterized (Akt1) and novel cell signaling epitopes (IRF-7 (pS477/pS479), CrkL (pY207), Stat3 (pS727), Stat3 (pY705) and Stat5 (pY694)) across PBMC subtypes which were associated with schizophrenia at disease onset, and correlated with type I interferon-related serum mole cules CD40 and CXCL11. Alterations in Akt1 and IRF-7 (pS477/pS479) were additionally associated with polygenic risk of schizophrenia. Finally, changes in Akt1, IRF-7 (pS477/pS479) and Stat3 (pS727) predicted development of metabolic and cardiovascular side effects following antipsychotic treatment, while IRF-7 (pS477/pS479) and Stat3 (pS727) predicted early improvements in general psychopathology scores measured using the Brief Psychiatric Rating Scale (BPRS). These findings suggest that peripheral blood cells can provide an accessible surrogate model for intracellular signaling alterations in schizophrenia and have the potential to stratify subgroups of patients with different clinical outcomes or a greater risk of developing metabolic and cardiovascular side effects following antipsychotic therapy.

Funder: Stanley Medical Research Institute (SMRI); doi: https://doi.org/10.13039/100007123, Mental health disorders are a leading cause of disability worldwide. Challenges such as disease heterogeneity, incomplete characterization of the targets of existing drugs and a limited understanding of functional interactions of complex genetic risk loci and environmental factors have compromised the identification of novel drug candidates. There is a pressing clinical need for drugs with new mechanisms of action which address the lack of efficacy and debilitating side effects of current medications. Here we discuss a novel strategy for neuropsychiatric drug discovery which aims to address these limitations by identifying disease-related functional responses (‘functional cellular endophenotypes’) in a variety of patient-derived cells, such as induced pluripotent stem cell (iPSC)-derived neurons and organoids or peripheral blood mononuclear cells (PBMCs). Disease-specific alterations in cellular responses can subsequently yield novel drug screening targets and drug candidates. We discuss the potential of this approach in the context of recent advances in patient-derived cellular models, high-content single-cell screening of cellular networks and changes in the diagnostic framework of neuropsychiatric disorders.

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., This work was supported by grants from the Stanley Medical Research Institute (SMRI); the Engineering and Physical Sciences Research Council UK (EPSRC); the Dutch Government-funded Virgo consortium (ref. FES0908); the Netherlands Genomics Initiative (ref. 050-060-452); the European Union FP7 funding scheme: Marie Curie Actions Industry Academia Partnerships and Pathways (ref. 286334, PSYCH-AID project); SAF2016-76046-R and SAF2013-46292-R (MINECO) and PI16/00156 (ISCIII and FEDER).

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18–45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86–0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86–0.91) and 0.90 (0.87–0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57–0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

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.

With less than half of patients with major depressive disorder (MDD) correctly diagnosed within the primary care setting, there is a clinical need to develop an objective and readily accessible test to enable earlier and more accurate diagnosis. The aim of this study was to develop diagnostic prediction models to identify MDD patients among individuals presenting with subclinical low mood, based on data from dried blood spot (DBS) proteomics (194 peptides representing 115 proteins) and a novel digital mental health assessment (102 sociodemographic, clinical and personality characteristics). To this end, we investigated 130 low mood controls, 53 currently depressed individuals with an existing MDD diagnosis (established current MDD), 40 currently depressed individuals with a new MDD diagnosis (new current MDD), and 72 currently not depressed individuals with an existing MDD diagnosis (established non-current MDD). A repeated nested cross-validation approach was used to evaluate variation in model selection and ensure model reproducibility. Prediction models that were trained to differentiate between established current MDD patients and low mood controls (AUC = 0.94 ± 0.01) demonstrated a good predictive performance when extrapolated to differentiate between new current MDD patients and low mood controls (AUC = 0.80 ± 0.01), as well as between established non-current MDD patients and low mood controls (AUC = 0.79 ± 0.01). Importantly, we identified DBS proteins A1AG1, A2GL, AL1A1, APOE and CFAH as important predictors of MDD, indicative of immune system dysregulation; as well as poor self-rated mental health, BMI, reduced daily experiences of positive emotions, and tender-mindedness. Despite the need for further validation, our preliminary findings demonstrate the potential of such prediction models to be used as a diagnostic aid for detecting MDD in clinical practice.

There have been no new drugs for the treatment of schizophrenia in several decades and treatment resistance represents a major unmet clinical need. The drugs that exist are based on serendipitous clinical observations rather than an evidence-based understanding of disease pathophysiology. In the present review, we address these bottlenecks by integrating common, rare, and expression-related schizophrenia risk genes with knowledge of the druggability of the human genome as a whole. We highlight novel drug repurposing opportunities, clinical trial candidates which are supported by genetic evidence, and unexplored therapeutic opportunities in the lesser-known regions of the schizophrenia genome. By identifying translational gaps and opportunities across the schizophrenia disease space, we discuss a framework for translating increasingly well-powered genetic association studies into personalized treatments for schizophrenia and initiating the vital task of characterizing clinically relevant drug targets in underexplored regions of the human genome.

This study examines the association between insulin resistance, schizophrenia polygenic risk, and treatment outcomes in first-episode, antipsychotic-naive patients with schizophrenia. Funding/Support: This work was supported by grants from the Stanley Medical Research Institute (Dr Bahn).

High-content functional screening of primary patient blood cells reveals repurposed psychiatric drug candidates., There is a paucity of efficacious new compounds to treat neuropsychiatric disorders. We present a novel approach to neuropsychiatric drug discovery based on high-content characterization of druggable signaling network responses at the single-cell level in patient-derived lymphocytes ex vivo. Primary T lymphocytes showed functional responses encompassing neuropsychiatric medications and central nervous system ligands at established (e.g., GSK-3β) and emerging (e.g., CrkL) drug targets. Clinical application of the platform to schizophrenia patients over the course of antipsychotic treatment revealed therapeutic targets within the phospholipase Cγ1–calcium signaling pathway. Compound library screening against the target phenotype identified subsets of L-type calcium channel blockers and corticosteroids as novel therapeutically relevant drug classes with corresponding activity in neuronal cells. The screening results were validated by predicting in vivo efficacy in an independent schizophrenia cohort. The approach has the potential to discern new drug targets and accelerate drug discovery and personalized medicine for neuropsychiatric conditions.

Abnormal activation of brain microglial cells is widely implicated in the pathogenesis of schizophrenia. Previously the pathophysiology of microglial activation was considered to be intrinsic to the central nervous system. We hypothesised that due to their perivascular localization, microglia can also be activated by factors present in circulating blood. Through application of high-content functional screening, we show that peripheral blood serum from first-onset drug-naive schizophrenia patients is sufficient to provoke microglial cell signalling network responses in vitro which are indicative of proinflammatory activation. We further explore the composition of the serum for the presence of analytes, with the potential to activate microglia, and the utility of the resultant microglial cellular phenotype for novel drug discovery.

There is an increasing interest in the use of dried blood spot (DBS) sampling and multiple reaction monitoring in proteomics. Although several groups have explored the utility of DBS by focusing on protein detection, the reproducibility of the approach and whether it can be used for biomarker discovery in high throughput studies is yet to be determined. We assessed the reproducibility of multiplexed targeted protein measurements in DBS compared to serum. Eighty-two medium to high abundance proteins were monitored in a number of technical and biological replicates. Importantly, as part of the data analysis, several statistical quality control approaches were evaluated to detect inaccurate transitions. After implementing statistical quality control measures, the median CV on the original scale for all detected peptides in DBS was 13.2% and in Serum 8.8%. We also found a strong correlation (r = 0.72) between relative peptide abundance measured in DBS and serum. The combination of minimally invasive sample collection with a highly specific and sensitive mass spectrometry (MS) technique allows for targeted quantification of multiple proteins in a single MS run. This approach has the potential to fundamentally change clinical proteomics and personalized medicine by facilitating large-scale studies.

Proteomic studies have increased our understanding of the molecular pathways affected in psychiatric disorders. Mass spectrometry and two-dimensional gel electrophoresis analyses of post-mortem brain samples from psychiatric patients have revealed effects on synaptic, cytoskeletal, antioxidant and mitochondrial protein networks. Multiplex immunoassay profiling studies have found alterations in hormones, growth factors, transport and inflammation-related proteins in serum and plasma from living first-onset patients. Despite these advances, there are still difficulties in translating these findings into platforms for improved treatment of patients and for discovery of new drugs with better efficacy and side effect profiles. This review describes how the next phase of proteomic investigations in psychiatry should include stringent replication studies for validation of biomarker candidates and functional follow-up studies which can be used to test the impact on physiological function. All biomarker candidates should now be tested in series with traditional and emerging cell biological approaches. This should include investigations of the effects of post-translational modifications, protein dynamics and network analyses using targeted proteomic approaches. Most importantly, there is still an urgent need for development of disease-relevant cellular models for improved translation of proteomic findings into a means of developing novel drug treatments for patients with these life-altering disorders.