Application of Proteomic Techniques for Improved Stratification and Treatment of Schizophrenia Patients

BST-1 as a serum protein biomarker involved in neutrophil infiltration in schizophrenia

OBJECTIVES

Schizophrenia is a serious mental illness. The serum protein biomarkers of schizophrenia were explored using isobaric tags for relative and absolute quantitation (iTRAQ) technology. The underlying function of the identified protein biomarker was also investigated.

METHODS

We first collected serum samples from 12 schizophrenia patients and 12 healthy control (HC) subjects, followed by global screening with iTRAQ and tandem mass spectrometry (LC-MS/MS). In total, 691 serum proteins were detected and eight proteins, including ZYX, OSCAR, TPM4, SDPR, BST1, ARGHDB, ITIH5 and SH3BGRL3, were selected for further specific validation with enzyme-linked immunosorbent assay (ELISA) on the serum samples from 52 schizophrenia patients and 50 HC subjects.

RESULTS

Schizophrenia patients had significantly lower serum level of BST1 and higher ITIH5 level than the HC subjects did. Using the levels of BST1, ITIH5 and OSCAR combined with machine learning algorithm, we developed a prediction model of schizophrenia with an auROC value 0.78. Moreover, in vitro cell assay confirmed that BST1 significantly repressed neutrophil infiltration through endothelial layer, highlighted the anti-inflammation nature of BST1.

CONCLUSIONS

Four novel protein markers (BST1, ITIH5, SDPR, and OSCAR) of schizophrenia were identified, and BST-1 could serve as a serum protein biomarker involved in neutrophil infiltration in schizophrenia.

Proteomic Analysis of Plasma Markers in Patients Maintained on Antipsychotics: Comparison to Patients Off Antipsychotics and Normal Controls

BACKGROUND

Schizophrenia (SZ) and bipolar disorder (BD) share many features: overlap in mood and psychotic symptoms, common genetic predisposition, treatment with antipsychotics (APs), and similar metabolic comorbidities. The pathophysiology of both is still not well defined, and no biomarkers can be used clinically for diagnosis and management. This study aimed to assess the plasma proteomics profile of patients with SZ and BD maintained on APs compared to those who had been off APs for 6 months and to healthy controls (HCs).

METHODS

We analyzed the data using functional enrichment, random forest modeling to identify potential biomarkers, and multivariate regression for the associations with metabolic abnormalities.

RESULTS

We identified several proteins known to play roles in the differentiation of the nervous system like NTRK2, CNTN1, ROBO2, and PLXNC1, which were downregulated in AP-free SZ and BD patients but were "normalized" in those on APs. Other proteins (like NCAM1 and TNFRSF17) were "normal" in AP-free patients but downregulated in patients on APs, suggesting that these changes are related to medication's effects. We found significant enrichment of proteins involved in neuronal plasticity, mainly in SZ patients on APs. Most of the proteins associated with metabolic abnormalities were more related to APs use than having SZ or BD. The biomarkers identification showed specific and sensitive results for schizophrenia, where two proteins (PRL and MRC2) produced adequate results.

CONCLUSIONS

Our results confirmed the utility of blood samples to identify protein signatures and mechanisms involved in the pathophysiology and treatment of SZ and BD.

Metabolomic Identification of Exosome-Derived Biomarkers for Schizophrenia: A Large Multicenter Study

Exosomes have been suggested as promising targets for the diagnosis and treatment of neurological diseases, including schizophrenia (SCZ), but the potential role of exosome-derived metabolites in these diseases was rarely studied. Using ultra-performance liquid chromatography-tandem mass spectrometry, we performed the first metabolomic study of serum-derived exosomes from patients with SCZ. Our sample comprised 385 patients and 332 healthy controls recruited from 3 clinical centers and 4 independent cohorts. We identified 25 perturbed metabolites in patients that can be used to classify samples from patients and control participants with 95.7% accuracy (95% CI: 92.6%-98.9%) in the training samples (78 patients and 66 controls). These metabolites also showed good to excellent performance in differentiating between patients and controls in the 3 test sets of participants, with accuracies 91.0% (95% CI: 85.7%-96.3%; 107 patients and 62 controls), 82.7% (95% CI: 77.6%-87.9%; 104 patients and 142 controls), and 99.0% (95% CI: 97.7%-100%; 96 patients and 62 controls), respectively. Bioinformatic analysis suggested that these metabolites were enriched in pathways implicated in SCZ, such as glycerophospholipid metabolism. Taken together, our findings support a role for exosomal metabolite dysregulation in the pathophysiology of SCZ and indicate a strong potential for exosome-derived metabolites to inform the diagnosis of SCZ.

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.

Constructing the Immune Signature of Schizophrenia for Clinical Use and Research; An Integrative Review Translating Descriptives Into Diagnostics

Schizophrenia is considered a syndrome comprised by several disease phenotypes, covering a range of underlying pathologies. One of these disease mechanisms seems to involve immune dysregulation and neuroinflammation. While the current dopamine receptor-blocking antipsychotic drugs decrease psychotic symptoms and prevent relapse in the majority of patients with schizophrenia, there is a huge need to explore new treatment options that target other pathophysiological pathways. Such studies should aim at identifying robust biomarkers in order to diagnose and monitor the immune biophenotype in schizophrenia and develop better selection procedures for clinical trials with anti-inflammatory and immune-modulating drugs. In this focused review, we describe available methods to assess inflammatory status and immune disturbances in vivo. We also outline findings of immune disturbances and signs of inflammation at cellular, protein, and brain imaging levels in patients with schizophrenia. Furthermore, we summarize the results from studies with anti-inflammatory or other immune-modulating drugs, highlighting how such studies have dealt with participant selection. Finally, we propose a strategy to construct an immune signature that may be helpful in selecting and monitoring participants in studies with immune modulating drugs and also applicable in regular clinical work.