Metabolomic and proteomic profiling in bipolar disorder patients revealed potential molecular signatures related to hemostasis.

INTRODUCTION: Bipolar disorder (BD) is a mood disorder characterized by the occurrence of depressive episodes alternating with episodes of elevated mood (known as mania). There is also an increased risk of other medical comorbidities. OBJECTIVES: This work uses a systems biology approach to compare BD treated patients with healthy controls (HCs), integrating proteomics and metabolomics data using partial correlation analysis in order to observe the interactions between altered proteins and metabolites, as well as proposing a potential metabolic signature panel for the disease. METHODS: Data integration between proteomics and metabolomics was performed using GC-MS data and label-free proteomics from the same individuals (N = 13; 5 BD, 8 HC) using generalized canonical correlation analysis and partial correlation analysis, and then building a correlation network between metabolites and proteins. Ridge-logistic regression models were developed to stratify between BD and HC groups using an extended metabolomics dataset (N = 28; 14 BD, 14 HC), applying a recursive feature elimination for the optimal selection of the metabolites. RESULTS: Network analysis demonstrated links between proteins and metabolites, pointing to possible alterations in hemostasis of BD patients. Ridge-logistic regression model indicated a molecular signature comprising 9 metabolites, with an area under the receiver operating characteristic curve (AUROC) of 0.833 (95% CI 0.817-0.914). CONCLUSION: From our results, we conclude that several metabolic processes are related to BD, which can be considered as a multi-system disorder. We also demonstrate the feasibility of partial correlation analysis for integration of proteomics and metabolomics data in a case-control study setting.

A pilot study indicating the dysregulation of the complement and coagulation cascades in treated schizophrenia and bipolar disorder patients.

A better understanding of the proteome profile after bipolar disorder (BD) and schizophrenia (SCZ) treatment, besides monitoring disease progression, may assist on the development of novel therapeutic strategies with the ability to reduce or control possible side effects. In this pilot study, proteomics analysis employing nano liquid chromatography coupled to mass spectrometry (nLC-MS) and bioinformatic tools were applied to identify differentially abundant proteins in serum of treated BD and SCZ patients. In total, 10 BD patients, 10 SCZ patients, and 14 healthy controls (HC) were included in this study. 24 serum proteins were significantly altered (p < 0.05) in BD and SCZ treated patients and, considering log2FC > 0.58, 8 proteins presented lower abundance in the BD group, while 7 proteins presented higher abundance and 2 lower abundance in SCZ group when compared against HC. Bioinformatics analysis based on these 24 proteins indicated two main altered pathways previously described in the literature; furthermore, it revealed that opposite abundances of the complement and coagulation cascades were the most significant biological processes involved in these pathologies. Moreover, we describe disease-related proteins and pathways associations suggesting the necessity of clinical follow-up improvement besides treatment, as a precaution or safety measure, along with the disease progression. Further biological validation and investigations are required to define whether there is a correlation between complement and coagulation cascade expression for BD and SCZ and cardiovascular diseases.