Proteomic Insights into Biology of Bipolar Disorder: Implications for Health Complexity and Mortality.

OBJECTIVE: The present study has the following objectives: 1) identify differentially expressed proteins and pathways in blood samples of BD compared to healthy controls by employing high-throughput proteomics and bioinformatics and 2) characterize disease-related molecular signatures through in-depth analysis of the differentially expressed proteins and pathways. METHODS: Blood samples from BD patients (n=10) classified into high (BD+) or poor functioning (BD-), based on functional and cognitive status, and healthy controls (n=5) were analyzed using mass spectrometry-based proteomic analysis. Bioinformatics was performed to detect biological processes, pathways, and diseases related to BD. RESULTS: Eight proteins exclusively characterized the molecular profile of patients with BD+ compared to HC, while 26 altered proteins were observed in the BD- group. These altered proteins were mainly enriched in biological processes related to lipid metabolism, complement system and coagulation cascade, and cardiovascular diseases; all these changes were more prominent in the BD- group. CONCLUSION: These findings may represent systemic alterations that occur with the progression of the illness and a possible link between BD and medical comorbidities. Such comprehensive understanding provides valuable insights for targeted interventions, addressing mental and physical health aspects in subjects with BD. Despite these promising findings, further research is warranted, encompassing larger sample cohorts and incorporating biological validation through molecular biology methods.

Drug Repurposing and Personalized Treatment Strategies for Bipolar Disorder Using Transcriptomic.

OBJECTIVE: The present study combined transcriptomic data and computational techniques based on gene expression signatures to identify novel bioactive compounds or FDA-approved drugs for the management of Bipolar Disorder (BD). METHODS: Five transcriptomic datasets, comprising a total of 165 blood samples from BD case-control, were selected from the Gene Expression Omnibus repository (GEO). The number of subjects varied from 6 to 60, with a mean age ranging from 35 to 48, with a gender variation between them. Most of the patients were on pharmacological treatment. Master Regulator Analysis (MRA) and Gene Set Enrichment Analysis (GSEA) were performed to identify statistically significant genes between BD and HC and their association with the mood states of BD. Additionally, existing molecules with the potential to reverse the transcriptomic profiles of disease-altered regulons in BD were identified using the LINCS and cMap databases. RESULTS: MRA identified 59 potential MRs candidates modulating the regulatory units enriched with genes altered in BD, while the GSEA identified 134 enriched genes, and a total of 982 regulons had their activation state determined. Both analyses showed genes exclusively associated with mania, depression, or euthymia, and some genes were common between the three mood states. We identified bioactive compounds and licensed drug candidates, including antihypertensives and antineoplastics, as promising candidates for treating BD. Nevertheless, experimental validation is essential to authenticate these findings in subsequent studies. CONCLUSION: Although preliminary, our data provides some insights regarding the biological patterns of BD into distinct mood states and potential therapeutic targets. The combined transcriptomic and bioinformatics strategy offers a route to advance drug discovery and personalized medicine by tapping into gene expression information.

Potential Candidates for Biomarkers in Bipolar Disorder: A Proteomic Approach through Systems Biology.

Bipolar disorder (BD) is one of the most disabling diseases characterized by severe humor fluctuation. It is accompanied by cognitive and functional impairment in addiction to high suicide rates. BD is often underdiagnosed and treated incorrectly because many of the reported symptoms are not exclusive to the disorder. Once the diagnosis is exclusively clinical, it is not possible to state precisely. From that, proteomic approaches were used to identify, in a large scale, all proteins involved in cellular or tissue processes. This review aggregate data from blood proteomes, by using protein association network, of subjects with BD and healthy controls to suggest dysfunctional molecular pathways involved in disease. Original articles containing proteomic analysis were searched in PubMed. Seven studies were selected and data were extracted for posterior analysis. A protein-protein interaction network was created by STRING database. A final set of proteins in this network were employed as input in ClueGO and, the main biological process was visualized using R package pathview. The analysis revealed proteins associated with many biological processes, including growth and endocrine regulation, iron transportation, protease inhibition, protection against pathogens and cholesterol transport. Moreover, pathway analysis indicated the association of uncovered proteins with two main metabolic pathways: complement system and coagulation cascade. Thus, a better understanding on the pathophysiology of psychiatric disorders and the identification of potential biomarker candidates are essential to improve diagnostic, prognostic and design pharmacological strategies.