An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value

ABSTRACTIntroduction Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics.Areas covered In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value.Expert opinion A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomics and metabolomics findings into clinical routine assays.KEYWORDS: Bipolar disordermetabolomicsproteomicsbiomarkersdiagnosisDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Article highlightsBipolar disorder (BD) pathophysiology and molecular mechanisms are not completely elucidated.Metabolomic and proteomic profiling are powerful strategies that have the ability to describe a biological system in terms of metabolite and protein content.Molecular signatures (biomarkers) can be determined through these strategies, with the potential to improve BD diagnosis, treatment, and outcome.Further studies should focus on biomarker validation and development of suitable analytical protocols for their determination in the clinical routine.Declaration of interestsThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.Reviewer disclosuresPeer reviewers on this manuscript have no relevant financial or other relationships to disclose.Author contributionsHC Ribeiro and FS Zandonadi: original draft – writing and revising. A Sussulini: conception, original draft – writing and revising, critical review. All authors have given approval to the final version of the manuscript.Figure 1. Potential protein biomarkers pathway enrichment analysis. Cluster 1 represents differential proteins in bipolar disorder vs. control comparison (A); cluster 2 represents differential proteins in bipolar disorder vs. other psychiatric diseases comparison (B). Protein attribution to corresponding genes with functional annotations in all ontologies was recognized by ClueGO.Display full sizeFigure 2. Specific terms/pathways from clusterized functional genes. The percentage of genes associated with the term is based on the calculation of the number of genes correlated to the total functional genes of the pathway enriched by the dataset in cluster 1 (A) and cluster 2 (B).Display full sizeAdditional informationFundingThis manuscript was funded by Fundação de Amparo à Pesquisa do Estado de São Paulo – FAPESP (grant number 2023/01800-2), Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq (grant number 306662/2022-1), and INCT of Bioanalytics (FAPESP 2014/50867-3 and CNPq 465389/2014-7 grant numbers).