An integrative network analysis framework for identifying molecular functions in complex disorders examining major depressive disorder as a test case

Abstract

In addition to the psychological depressive phenotype, major depressive disorder (MDD) patients are also associated with underlying immune dysregulation that correlates with metabolic syndrome prevalent in depressive patients. A robust integrative analysis of biological pathways underlying the dysregulated neural connectivity and systemic inflammatory response will provide implications in the development of effective strategies for the diagnosis, management and the alleviation of associated comorbidities. In the current study, focusing on MDD, we explored an integrative network analysis methodology to analyze transcriptomic data combined with the meta-analysis of biomarker data available throughout public databases and published scientific peer-reviewed articles. Detailed gene set enrichment analysis and complex protein–protein, gene regulatory and biochemical pathway analysis has been undertaken to identify the functional significance and potential biomarker utility of differentially regulated genes, proteins and metabolite markers. This integrative analysis method provides insights into the molecular mechanisms along with key glycosylation dysregulation underlying altered neutrophil-platelet activation and dysregulated neuronal survival maintenance and synaptic functioning. Highlighting the significant gap that exists in the current literature, the network analysis framework proposed reduces the impact of data gaps and permits the identification of key molecular signatures underlying complex disorders with multiple etiologies such as within MDD and presents multiple treatment options to address their molecular dysfunction.

Bioinformatics Applications in Chronic Diseases: A Comprehensive Review of Genomic, Transcriptomics, Proteomic, Metabolomics, and Machine Learning Approaches

“…In order to comprehend the molecular causes of these complicated disorders, biomedical research targeted at identifying dysregulated genes in chronic diseases is essential. Identification of dysregulated genes, whose expression levels depart from the typical or healthy state, is essential to understanding the (Oommen et al, 2021). Transcriptomics profiling, particularly using technologies like RNA sequencing (RNA-Seq) and microarrays (Figure 3), is one of the main tools used to find dysregulated genes.…”

A rationale for considering heart/brain axis control in neuropsychiatric disease

“…cloud/ Enric hr/ ◂ et al 2019). Ust encodes the protein uronyl 2-sulfotransferase (UST) (Nikolovska et al 2015), which is downregulated in MDD (Oommen et al 2021). Acap2 encodes ArfGAP with coiled-coil, ankyrin repeat and PH domains 2 (ACAP2) protein with no prior NPD annotations.…”

“…Adnp2 encodes the activity-dependent neuroprotective protein (ADNP2), expressed during development and associated with ASD and SCZ (Dresner et al 2011 ; Helsmoortel et al 2014 ; Van Dijck et al 2019 ). Ust encodes the protein uronyl 2-sulfotransferase (UST) (Nikolovska et al 2015 ), which is downregulated in MDD (Oommen et al 2021 ). Acap2 encodes ArfGAP with coiled-coil, ankyrin repeat and PH domains 2 (ACAP2) protein with no prior NPD annotations.…”