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

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, bu...

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Autores principales: Chai, Keping, Zhang, Xiaolin, Tang, Huitao, Gu, Huaqian, Ye, Weiping, Wang, Gangqiang, Chen, Shufang, Wan, Feng, Liang, Jiawei, Shen, Daojiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907380/
https://www.ncbi.nlm.nih.gov/pubmed/35280300
http://dx.doi.org/10.3389/fneur.2022.807349
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author Chai, Keping
Zhang, Xiaolin
Tang, Huitao
Gu, Huaqian
Ye, Weiping
Wang, Gangqiang
Chen, Shufang
Wan, Feng
Liang, Jiawei
Shen, Daojiang
author_facet Chai, Keping
Zhang, Xiaolin
Tang, Huitao
Gu, Huaqian
Ye, Weiping
Wang, Gangqiang
Chen, Shufang
Wan, Feng
Liang, Jiawei
Shen, Daojiang
author_sort Chai, Keping
collection PubMed
description 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.
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spelling pubmed-89073802022-03-11 The Application of Consensus Weighted Gene Co-expression Network Analysis to Comparative Transcriptome Meta-Datasets of Multiple Sclerosis in Gray and White Matter Chai, Keping Zhang, Xiaolin Tang, Huitao Gu, Huaqian Ye, Weiping Wang, Gangqiang Chen, Shufang Wan, Feng Liang, Jiawei Shen, Daojiang Front Neurol Neurology 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. Frontiers Media S.A. 2022-02-24 /pmc/articles/PMC8907380/ /pubmed/35280300 http://dx.doi.org/10.3389/fneur.2022.807349 Text en Copyright © 2022 Chai, Zhang, Tang, Gu, Ye, Wang, Chen, Wan, Liang and Shen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Chai, Keping
Zhang, Xiaolin
Tang, Huitao
Gu, Huaqian
Ye, Weiping
Wang, Gangqiang
Chen, Shufang
Wan, Feng
Liang, Jiawei
Shen, Daojiang
The Application of Consensus Weighted Gene Co-expression Network Analysis to Comparative Transcriptome Meta-Datasets of Multiple Sclerosis in Gray and White Matter
title The Application of Consensus Weighted Gene Co-expression Network Analysis to Comparative Transcriptome Meta-Datasets of Multiple Sclerosis in Gray and White Matter
title_full The Application of Consensus Weighted Gene Co-expression Network Analysis to Comparative Transcriptome Meta-Datasets of Multiple Sclerosis in Gray and White Matter
title_fullStr The Application of Consensus Weighted Gene Co-expression Network Analysis to Comparative Transcriptome Meta-Datasets of Multiple Sclerosis in Gray and White Matter
title_full_unstemmed The Application of Consensus Weighted Gene Co-expression Network Analysis to Comparative Transcriptome Meta-Datasets of Multiple Sclerosis in Gray and White Matter
title_short The Application of Consensus Weighted Gene Co-expression Network Analysis to Comparative Transcriptome Meta-Datasets of Multiple Sclerosis in Gray and White Matter
title_sort application of consensus weighted gene co-expression network analysis to comparative transcriptome meta-datasets of multiple sclerosis in gray and white matter
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907380/
https://www.ncbi.nlm.nih.gov/pubmed/35280300
http://dx.doi.org/10.3389/fneur.2022.807349
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