Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes.

in Biology (2021), 10(2),

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic ... [more ▼]

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities. [less ▲]

Systems biology integration of proteomic data in rodent models of depression reveals involvement of the immune response and glutamatergic signalling

in Proteomics. Clinical Applications (2016)

Purpose The pathophysiological basis of major depression is incompletely understood. Recently, numerous proteomic studies have been performed in rodent models of depression to investigate the molecular ... [more ▼]

Purpose The pathophysiological basis of major depression is incompletely understood. Recently, numerous proteomic studies have been performed in rodent models of depression to investigate the molecular underpinnings of depressive-like behaviours with an unbiased approach. The objective of the study was to integrate the results of these proteomic studies in depression models to shed light on the most relevant molecular pathways involved in the disease. Experimental design Network analysis was performed integrating pre-existing proteomic data from rodent models of depression. The IntAct mouse and the HRPD were used as reference protein-protein interaction databases. The functionality analyses of the networks were then performed by testing over-represented GO biological process terms and pathways. Results Functional enrichment analyses of the networks revealed an association with molecular processes related to depression in humans, such as those involved in the immune response. Pathways impacted by clinically effective antidepressants were modulated, including glutamatergic signalling and neurotrophic responses. Moreover, dysregulations of proteins regulating energy metabolism and circadian rhythms were implicated. The comparison with protein pathways modulated in depressive patients revealed significant overlapping. Conclusions and clinical relevance This systems biology study supports the notion that animal models could contribute to the research into the biology and therapeutics of depression. [less ▲]

Novel drug target identification for the treatment of dementia using multi-relational association mining

in Scientific Reports (2015), 5

Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance. Despite the fact that the number of people with dementia ... [more ▼]

Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance. Despite the fact that the number of people with dementia worldwide is steadily increasing and regardless of the advances in the molecular characterization of the disease, current medical treatments for dementia are purely symptomatic and hardly effective. We present a novel multi-relational association mining method that integrates the huge amount of scientific data accumulated in recent years to predict potential novel targets for innovative therapeutic treatment of dementia. Owing to the ability of processing large volumes of heterogeneous data, our method achieves a high performance and predicts numerous drug targets including several serine threonine kinase and a G-protein coupled receptor. The predicted drug targets are mainly functionally related to metabolism, cell surface receptor signaling pathways, immune response, apoptosis, and long-term memory. Among the highly represented kinase family and among the G-protein coupled receptors, DLG4 (PSD-95), and the bradikynin receptor 2 are highlighted also for their proposed role in memory and cognition, as described in previous studies. These novel putative targets hold promises for the development of novel therapeutic approaches for the treatment of dementia. [less ▲]

Inference of Autism-Related Genes by Integrating Protein-Protein Interactions and miRNA-Target Interactions

in Knowledge and Systems Engineering (2014)

The central role of AMP-kinase and energy homeostasis impairment in Alzheimer’s disease: a multifactor network analysis

in PLoS ONE (2013)