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

Abstract

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.

Expanding the Disease Network of Glioblastoma Multiforme via Topological Analysis

“…In this study, disease-specific networks were constructed using seed DAPs and a protein–protein interaction (PPI) network. The datasets used are described below, while the computational pipeline used was as previously described [ 19 ]. Briefly, we compared, for each protein, its BC score in the disease network with the distribution of its BC scores in 10,000 degree-stratified random networks.…”

“…The centrality analysis and background correction were as described previously [ 19 ], depicted in Figure 1 B. The pipeline was built in Python 3, using NetworkX for centrality computations.…”

“…Hence, the objectives of this study were two-fold—1. Identification of disease-specific central proteins that are likely to be involved in GBM, based on background-corrected BC [ 19 ], incorporating variability in the definition of DAPs. 2.…”