AUTHOR=Chai Keping , Zhang Xiaolin , Tang Huitao , Gu Huaqian , Ye Weiping , Wang Gangqiang , Chen Shufang , Wan Feng , Liang Jiawei , Shen Daojiang 

TITLE=The Application of Consensus Weighted Gene Co-expression Network Analysis to Comparative Transcriptome Meta-Datasets of Multiple Sclerosis in Gray and White Matter

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.807349

DOI=10.3389/fneur.2022.807349

ISSN=1664-2295

ABSTRACT=Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system characterized by demyelination which leads to the formation of white matter lesions (WMLs) and gray matter lesions(GMLs). Recently, a large amount of transcriptomics or proteomics researches explored multiple sclerosis, but few studies focused on the differences and similarities between GMLs and WMLs in transcriptomics. Furthermore, there are surprising pathological differences between WMLs and GMLs, for example, the population abundance of infiltrating immune cell populations exist differences between WMLs and GMLs. Here, we used consensus weighted gene co-expression network analysis (WGCNA), Single sample gene set enrichment analysis (ssGSEA) and machine learning methods to identify the transcriptomic differences and similarities of the MS between GMLs and WMLs, and to find the co-expression modules with big differences and similarities between them. Through weighted co-expression network analysis and ssGSEA analysis, CD56 bright natural killer cell was identified as the key immune infiltration pathway in MS, whether it is GM or WM. We also found that the co-expression networks between the two groups are quite similar(Density=0.79), and 28 differentially expressed genes (DEGs) are distributed in midnightblue module, which is most related to CD56 bright natural killer cell in GM. At the same time, we also found that there are huge differences between the modules, such as darkred and lightyeellow, and these differences may be relevant to the functions of the genes in the modules.