Ant1 mutant mice bridge the mitochondrial and serotonergic dysfunctions in bipolar disorder

Multi-omics analysis of a drug-induced model of bipolar disorder in zebrafish

Emerging studies demonstrate that inflammation plays a crucial role in the pathogenesis of bipolar disorder (BD), but the underlying mechanism remains largely unclear. Given the complexity of BD pathogenesis, we performed high-throughput multi-omic profiling (metabolomics, lipidomics, and transcriptomics) of the BD zebrafish brain to comprehensively unravel the molecular mechanism. Our research proved that in BD zebrafish, JNK-mediated neuroinflammation altered metabolic pathways involved in neurotransmission. On one hand, disturbed metabolism of tryptophan and tyrosine limited the participation of the monoamine neurotransmitters serotonin and dopamine in synaptic vesicle recycling. On the other hand, dysregulated metabolism of the membrane lipids sphingomyelin and glycerophospholipids altered the synaptic membrane structure and neurotransmitter receptors (chrnα7, htr1b, drd5b, and gabra1) activity. Our findings revealed that disturbance of serotonergic and dopaminergic synaptic transmission mediated by the JNK inflammatory cascade was the key pathogenic mechanism in a zebrafish model of BD, provides critical biological insights into the pathogenesis of BD.

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.