A system biology approach based on metabolic biomarkers and protein–protein interactions for identifying pathways underlying schizophrenia and bipolar disorder

Abstract

Mental disorders (MDs), including schizophrenia (SCZ) and bipolar disorder (BD), have attracted special attention from scientists due to their high prevalence and significantly debilitating clinical features. The diagnosis of MDs is still essentially based on clinical interviews, and intensive efforts to introduce biochemical based diagnostic methods have faced several difficulties for implementation in clinics, due to the complexity and still limited knowledge in MDs. In this context, aiming for improving the knowledge in etiology and pathophysiology, many authors have reported several alterations in metabolites in MDs and other brain diseases. After potentially fishing all metabolite biomarkers reported up to now for SCZ and BD, we investigated here the proteins related to these metabolites in order to construct a protein–protein interaction (PPI) network associated with these diseases. We determined the statistically significant clusters in this PPI network and, based on these clusters, we identified 28 significant pathways for SCZ and BDs that essentially compose three groups representing three major systems, namely stress response, energy and neuron systems. By characterizing new pathways with potential to innovate the diagnosis and treatment of psychiatric diseases, the present data may also contribute to the proposal of new intervention for the treatment of still unmet aspects in MDs.

Angiotensin‐converting enzymes as druggable features of psychiatric and neurodegenerative disorders

“…Bipolar disorder is a chronic multifactorial psychiatric disorder that affects mood, cognition, and executive functioning. BD shares several genetic determinants, molecular pathways, and clinical symptoms with SCZ (Altaf‐Ul‐Amin et al, 2021). Studies evaluating the RAS roles in BD are still scarce and heterogeneous, although BD patients show higher plasma renin activity than healthy control individuals (Barbosa et al, 2020).…”

Factors associated with psychiatric and physical comorbidities in bipolar disorder: a nationwide multicenter cross-sectional observational study

“…Analyses at not only the genetic level but also the pathway and network level (gene co-expression network analysis of transcriptomic data, contextualization of signals from omics data analyses) help to elucidate the neurobiological underpinnings of BD and its comorbidity. Several dysregulated pathways related to the immune inflammatory system (e.g., TNF, IL-17, and NF-kappa B signaling pathways), nervous system (e.g., dopaminergic and GABAergic synapses) ( 14 ), stress response, energy systems, and neuron systems ( 15 ) and other risk factors (childhood adversity, diet, physical activity, iatrogenic effects due to medications) might mediate the link between BD and its comorbidity phenotypes. Despite the integrative, data-and knowledge-driven approach to better understand the comorbid associations of BD, the clinical basis and predictors of the BD comorbidity remain only partially explained.…”

Chemoinformatics-driven classification of Angiosperms using sulfur-containing compounds and machine learning algorithm

“…In this study, the graph clustering algorithm (DPClusO) was applied for the identification of overlapping clusters with similar structural SCCs. The DPClusO algorithm generates high density clusters and has been adopted in big data analyses such as protein–protein interactions [ 24 ], identification of functional gene relations from gene expression datasets [ 25 ], pathway prediction [ 26 ] and many others [ 27 ]. Chemical information offers important insights into biochemical systematics, however, the scope of SCC chemical structure information to draw organizational concepts in flowering plants is underexplored.…”