An integrated network analysis approach to identify potential key genes, transcription factors, and microRNAs regulating human hematopoietic stem cell aging

摘要

Hematopoietic stem cells (HSCs) undergo functional deterioration with increasing age that causes lossof their self-renewal and regenerative potential. Despite various efforts, significant success in identifyingmolecular regulators of HSC aging has not been achieved, one prime reason being the non-availabilityof appropriate human HSC samples. To demonstrate the scope of integrating and re-analyzing the HSCtranscriptomics data available, we used existing tools and databases to structure a sequential dataanalysis pipeline to predict potential candidate genes, transcription factors, and microRNAssimultaneously. This sequential approach comprises (i) collecting matched young and aged mice HSCsample datasets, (ii) identifying differentially expressed genes, (iii) identifying human homologs ofdifferentially expressed genes, (iv) inferring gene co-expression network modules, and (v) inferring themicroRNA–transcription factor–gene regulatory network. Systems-level analyses of HSC interactionnetworks provided various insights based on which several candidates were predicted. For example, 16HSC aging-related candidate genes were predicted (e.g., CD38, BRCA1, AGTR1, GSTM1, etc.) from GCNanalysis. Following this, the shortest path distance-based analyses of the regulatory network predictedseveral novel candidate miRNAs and TFs. Among these, miR-124-3p was a common regulator incandidate gene modules, while TFs MYC and SP1 were identified to regulate various candidate genes.Based on the regulatory interactions among candidate genes, TFs, and miRNAs, a potential regulationmodel of biological processes in each of the candidate modules was predicted, which providedsystems-level insights into the molecular complexity of each module to regulate HSC aging.