Abstract
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.
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Data availabilty
The metabolomics dataset and clinical information analyzed in this study were deposited to MetabolomicsWorkbench (Sud et al., 75), under accession number ST001946. Proteomics data were deposited to the ProteomeXchange Consortium via the MassIVE partner repository (Vizcaíno et al., 84) with the dataset identifier PXD022417.
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Acknowledgements
The authors would like to thank Prof. Dr. Cláudio E.M. Banzato, Luiz F.A. Lima e Silva and all the medical staff from Hospital das Clínicas (HC-Unicamp) for the availability and patient information and Gustavo H.B. Duarte for the analytical support.
Funding
This project received funding from Sao Paulo Research Foundation [FAPESP, grant number 2018/01525-3] and INCT of Bioanalytics [FAPESP 2014/50867-3 and CNPq 465389/2014-7 grant numbers]. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. H.C.R also thanks the Finnish National Agency for Education (EDUFI Fellowship) [grant number TM-19-11164] and University of Turku for the scholarship.
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HCR: Investigation, Visualization, Formal analysis, Writing—original draft; PS: Software, Data curation, writing—review and editing; AD: Data curation, Supervision, Writing—review and editing; ECSC: Investigation; MO: Supervision, Resources, Writing—review and editing; AS: Conceptualization, Resources, Funding acquisition, Project administration, Supervision, Writing—review and editing.
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This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of University of Campinas, Brazil (protocol 775/2010).
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Ribeiro, H.C., Sen, P., Dickens, A. et al. Metabolomic and proteomic profiling in bipolar disorder patients revealed potential molecular signatures related to hemostasis. Metabolomics 18, 65 (2022). https://doi.org/10.1007/s11306-022-01924-5
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DOI: https://doi.org/10.1007/s11306-022-01924-5