Analysis of the Robustness of Network-Based Disease-Gene Prioritization Methods Reveals Redundancy in the Human Interactome and Functional Diversity of Disease-Genes

Abstract

Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO) analysis highlighted the role of functional diversity for such diseases.

Transcription Factor NRF2 as a Therapeutic Target for Chronic Diseases: A Systems Medicine Approach

“…We have also retrieved disease-gene associations for these pathologies using DisGeNET, OMIM, and GWAS databases (Menche et al, 2015) (Table 1). The interactome-based proximity (Guney and Oliva, 2014) of NRF2 to known disease genes for each of the NRF2-related disease phenotypes is shown in Fig. 4.…”

Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine

“…The robustness of the PPI network used is critical for higher accuracy of these approaches [79–82]. Guney and Oliva [83] tested several network-based methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man (OMIM) database. They found that disease proteins are connected via multiples pathways in a PPI network.…”

“…Even when these networks are significantly perturbed, network-based methods can reveal hidden disease association proteins, particularly in cases of breast cancer and diabetes. In general, the PPI network approaches can identify certain proteins associated with specific disease better than the rest [77,83]. …”

Advances in Human Biology: Combining Genetics and Molecular Biophysics to Pave the Way for Personalized Diagnostics and Medicine

“…Combined with dynamic nature of interactome [71,72], it is clear that significant work needs to be done to better understand how mutations affect the network and, in turn, how the changes in the interactome, local or global, are associated with the wild type function of the cell. In particular, it is important to take into account the redundancy in the human interactome to prioritize plausible genes involved in a disease [73].…”