An integrative transcriptomic analysis for identifying novel target genes corresponding to severity spectrum in spinal muscular atrophy

Abstract

Spinal muscular atrophy (SMA) is an inherited neuromuscular disease resulting from a recessive mutation in the SMN1 gene. This disease affects multiple organ systems with varying degrees of severity. Exploration of the molecular pathological changes occurring in different cell types in SMA is crucial for developing new therapies. This study collected 39 human microarray datasets from ArrayExpress and GEO databases to build an integrative transcriptomic analysis for recognizing novel SMA targets. The transcriptomic analysis was conducted through combining weighted correlation network analysis (WGCNA) for gene module detection, gene set enrichment analysis (GSEA) for functional categorization and filtration, and Cytoscape (visual interaction gene network analysis) for target gene identification. Seven novel target genes (Bmp4, Serpine1, Gata6, Ptgs2, Bcl2, IL6 and Cntn1) of SMA were revealed, and are all known in the regulation of TNFα for controlling neural, cardiac and bone development. Sequentially, the differentially expressed patterns of these 7 target genes in mouse tissues (e.g., spinal cord, heart, muscles and bone) were validated in SMA mice of different severities (pre-symptomatic, mildly symptomatic, and severely symptomatic). In severely symptomatic SMA mice, TNFα was up-regulated with attenuation of Bmp4 and increase of Serpine1 and Gata6 (a pathway in neural and cardiac development), but not in pre-symptomatic and mildly symptomatic SMA mice. The severely symptomatic SMA mice also had the elevated levels of Ptgs2 and Bcl2 (a pathway in skeletal development) as well as IL6 and Cntn1 (a pathway in nervous system development). Thus, the 7 genes identified in this study might serve as potential target genes for future investigations of disease pathogenesis and SMA therapy. ? 2016 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.