Topology Driven Analysis of Protein - Protein Interactome for Prioritizing Key Comorbid Genes via Sub Graph Based Average Path Length Centrality

• Published in: IEEE/ACM transactions on computational biology and bioinformatics 2023-01, Vol.20 (1), p.742-751
• Main Authors: Suresh, Nikhila T., E. R., Vimina, Krishnakumar, U.
• Format: Article
• Language: English
• Subjects: Alzheimer's disease; Amyotrophic lateral sclerosis; Atrophy; Biology; Biomarkers; Case studies; Comorbid genes; Comorbidity; Comparative analysis; Connectivity analysis; Diabetes; Diabetes mellitus; Disease; Diseases; Drugs; Feedback loops; Focusing; Gene therapy; Genes; local centrality measure; Modules; Neural networks; Neurodegenerative diseases; p53 Protein; Perturbation; Protein interaction; protein-protein interaction network; Proteins; Spinal muscular atrophy; topological analysis; Topology
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2022.3140388

In gene-based therapies, local perturbations associated with one disease can lead to comorbidity as it influences the pathways involved with the other diseases. The key genes orchestrating the common biological mechanisms are need to be prioritized for addressing the challenges introduced by the cross talks between disease modules. Here, a local centrality measure named S ub graph based A verage P ath length D ouble S pecific B etweenness centrality (SAPDSB) for prioritizing the comorbid genes via Protein-Protein Interaction Network (PPIN) analysis is presented. This approach can be used to identify putative biomarkers which can be repurposed for the management of comorbidity. Proposed network based topological measure is designed specifically to prioritize the comorbid genes that are most likely to be present in the overlap of disease modules. In order to attain this, the estimated average path length of the seed network which holds Protein-Protein Interactions (PPIs) of the disease genes is exploited. Prioritized comorbid genes are further pruned using centrality-based cut-off values and specificity scores. The biological significance of the resultant genes is corroborated with connectivity analysis using leave-one-out method, pathway enrichment analysis and a comparative analysis using single disease-based gene prioritization tools. For performance analysis, proposed approach is tested using case studies involving common diseases and rare neurodegenerative diseases. For case study1, diseases such as Diabetes, Carcinoma and Alzheimer's are considered in a pairwise manner while for case study2, Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) are considered. As outcome, prioritized candidate genes and biological pathways associated with respective disease pairs have been found. The associations from top 10 candidate genes in different disease pair combinations of Diabetes-Carcinoma-Alzheimer's revealed common genes like CREBBP, TP53, HSP90AA1 and the common pathway namely p53 pathway feedback loops 2. Out of the pathways retrieved from the top 10 genes associated with ALS-SMA disease pair, 60% of unique pathways are found to be leading to both diseases and its comorbidities. Comparative analysis of the proposed method with recent similar approach also reported a clear degree of benefits in performance.