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

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

doi:10.3389/fneur.2022.807349

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 research works explored MS, but few studies focused on the differences and similarities between GMLs and WMLs in transcriptomics. Furthermore, there are astonishing pathological differences between WMLs and GMLs, for example, there are differences in the type and abundance of infiltrating immune cells 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 significant differences or similarities between them. Through weighted co-expression network analysis and ssGSEA analysis, CD56 bright natural killer cell was identified as the key immune infiltration factor in MS, whether in 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 the midnightblue module, which is most related to CD56 bright natural killer cell in GM. Simultaneously, we also found that there are huge disparities between the modules, such as divergences between darkred module and lightyellow module, and these divergences may be relevant to the functions of the genes in the modules.

Highlights

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) [1, 2]
We found 181 differentially expressed genes (DEGs) in the WM group (p < 0.001, Figures 1A,B), 123 DEGs in the GM group (p < 0.001, Supplementary Figures 1B,C), and 122 overlapped DEGs were identified through overlapping analysis (Figure 1C)
The single-sample gene set enrichment analysis (ssGSEA) analysis was performed on the gene expression matrix of 20 GM (10 GMLs vs. 10 normal) and 20 WM (10 WMLs vs. 10 normal)

Summary

Introduction

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) [1, 2]. Weighted gene co-expression network analysis (WGCNA) is a classic gene clustering biological method, which relates genes with phenotypes or pathways It is mainly based on two theories: [1] genes with similar expression patterns may be co-regulated, functionally related, or in the same pathway, and [2] the distribution of gene networks conforms to scale-free. In WGCNA, the weighted value of the correlation coefficient is used, that is, the gene correlation coefficient is taken to the power of N so that the connections among the genes in the network conform to the scale-free network distribution, and this algorithm is more biologically meaningful It has been widely applied in Alzheimer’s disease, Parkinson’s disease, cancers, and green halophytic microalgae Dunaliella salina [8,9,10,11,12,13]. It has been performed to measure the similarity of gene expression patterns in the livers of patients with different gender, or in neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, etc. [14, 15]

Methods

Results

Discussion

Conclusion