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Exercise Modifies the Transcriptional Regulatory Features of Monocytes in Alzheimer’s Patients: A Multi-Omics Integration Analysis Based on Single Cell Technology

Monocytes have been reported to be important mediators of the protective effect of exercise against the development of Alzheimer’s disease (AD). This study aims explored the mechanism by which monocytes achieve this. Using single cell transcriptome analysis, results showed that CD14 + and CD16 + mon...

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Autores principales: Chen, Yisheng, Sun, Yaying, Luo, Zhiwen, Chen, Xiangjun, Wang, Yi, Qi, Beijie, Lin, Jinrong, Lin, Wei-Wei, Sun, Chenyu, Zhou, Yifan, Huang, Jiebin, Xu, Yuzhen, Chen, Jiwu, Chen, Shiyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9110789/
https://www.ncbi.nlm.nih.gov/pubmed/35592698
http://dx.doi.org/10.3389/fnagi.2022.881488
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author Chen, Yisheng
Sun, Yaying
Luo, Zhiwen
Chen, Xiangjun
Wang, Yi
Qi, Beijie
Lin, Jinrong
Lin, Wei-Wei
Sun, Chenyu
Zhou, Yifan
Huang, Jiebin
Xu, Yuzhen
Chen, Jiwu
Chen, Shiyi
author_facet Chen, Yisheng
Sun, Yaying
Luo, Zhiwen
Chen, Xiangjun
Wang, Yi
Qi, Beijie
Lin, Jinrong
Lin, Wei-Wei
Sun, Chenyu
Zhou, Yifan
Huang, Jiebin
Xu, Yuzhen
Chen, Jiwu
Chen, Shiyi
author_sort Chen, Yisheng
collection PubMed
description Monocytes have been reported to be important mediators of the protective effect of exercise against the development of Alzheimer’s disease (AD). This study aims explored the mechanism by which monocytes achieve this. Using single cell transcriptome analysis, results showed that CD14 + and CD16 + monocytes interacted with other cells in the circulating blood. TNF, CCR1, APP, and AREG, the key ligand-receptor-related genes, were found to be differentially expressed between exercise-treated and AD patients. The SCENIC analysis was performed to identify individual clusters of the key transcription factors (TFs). Nine clusters (M1-M9) were obtained from the co-expression network. Among the identified TFs, MAFB, HES4, and FOSL1 were found to be differentially expressed in AD. Moreover, the M4 cluster to which MAFB, HES4, and FOSL1 belonged was defined as the signature cluster for AD phenotype. Differential analysis by bulkRNA-seq revealed that the expression of TNF, CCR1, and APP were all upregulated after exercise (p < 0.05). And ATF3, MAFB, HES4, and KLF4 that were identified in M4 clusters may be the TFs that regulate TNF, CCR1, and APP in exercise prescription. After that, APP, CCR1, TNF, ATF3, KLF4, HES4, and MAFB formed a regulatory network in the ERADMT gene set, and all of them were mechanistically linked. The ERADMT gene set has been found to be a potential risk marker for the development of AD and can be used as an indicator of compliance to exercise therapy in AD patients. Using single-cell integration analysis, a network of exercise-regulating TFs in monocytes was constructed for AD disease. The constructed network reveals the mechanism by which exercise regulated monocytes to confer therapeutic benefits against AD and its complications. However, this study, as a bioinformatic research, requires further experimental validation.
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spelling pubmed-91107892022-05-18 Exercise Modifies the Transcriptional Regulatory Features of Monocytes in Alzheimer’s Patients: A Multi-Omics Integration Analysis Based on Single Cell Technology Chen, Yisheng Sun, Yaying Luo, Zhiwen Chen, Xiangjun Wang, Yi Qi, Beijie Lin, Jinrong Lin, Wei-Wei Sun, Chenyu Zhou, Yifan Huang, Jiebin Xu, Yuzhen Chen, Jiwu Chen, Shiyi Front Aging Neurosci Neuroscience Monocytes have been reported to be important mediators of the protective effect of exercise against the development of Alzheimer’s disease (AD). This study aims explored the mechanism by which monocytes achieve this. Using single cell transcriptome analysis, results showed that CD14 + and CD16 + monocytes interacted with other cells in the circulating blood. TNF, CCR1, APP, and AREG, the key ligand-receptor-related genes, were found to be differentially expressed between exercise-treated and AD patients. The SCENIC analysis was performed to identify individual clusters of the key transcription factors (TFs). Nine clusters (M1-M9) were obtained from the co-expression network. Among the identified TFs, MAFB, HES4, and FOSL1 were found to be differentially expressed in AD. Moreover, the M4 cluster to which MAFB, HES4, and FOSL1 belonged was defined as the signature cluster for AD phenotype. Differential analysis by bulkRNA-seq revealed that the expression of TNF, CCR1, and APP were all upregulated after exercise (p < 0.05). And ATF3, MAFB, HES4, and KLF4 that were identified in M4 clusters may be the TFs that regulate TNF, CCR1, and APP in exercise prescription. After that, APP, CCR1, TNF, ATF3, KLF4, HES4, and MAFB formed a regulatory network in the ERADMT gene set, and all of them were mechanistically linked. The ERADMT gene set has been found to be a potential risk marker for the development of AD and can be used as an indicator of compliance to exercise therapy in AD patients. Using single-cell integration analysis, a network of exercise-regulating TFs in monocytes was constructed for AD disease. The constructed network reveals the mechanism by which exercise regulated monocytes to confer therapeutic benefits against AD and its complications. However, this study, as a bioinformatic research, requires further experimental validation. Frontiers Media S.A. 2022-05-03 /pmc/articles/PMC9110789/ /pubmed/35592698 http://dx.doi.org/10.3389/fnagi.2022.881488 Text en Copyright © 2022 Chen, Sun, Luo, Chen, Wang, Qi, Lin, Lin, Sun, Zhou, Huang, Xu, Chen and Chen. 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 Neuroscience
Chen, Yisheng
Sun, Yaying
Luo, Zhiwen
Chen, Xiangjun
Wang, Yi
Qi, Beijie
Lin, Jinrong
Lin, Wei-Wei
Sun, Chenyu
Zhou, Yifan
Huang, Jiebin
Xu, Yuzhen
Chen, Jiwu
Chen, Shiyi
Exercise Modifies the Transcriptional Regulatory Features of Monocytes in Alzheimer’s Patients: A Multi-Omics Integration Analysis Based on Single Cell Technology
title Exercise Modifies the Transcriptional Regulatory Features of Monocytes in Alzheimer’s Patients: A Multi-Omics Integration Analysis Based on Single Cell Technology
title_full Exercise Modifies the Transcriptional Regulatory Features of Monocytes in Alzheimer’s Patients: A Multi-Omics Integration Analysis Based on Single Cell Technology
title_fullStr Exercise Modifies the Transcriptional Regulatory Features of Monocytes in Alzheimer’s Patients: A Multi-Omics Integration Analysis Based on Single Cell Technology
title_full_unstemmed Exercise Modifies the Transcriptional Regulatory Features of Monocytes in Alzheimer’s Patients: A Multi-Omics Integration Analysis Based on Single Cell Technology
title_short Exercise Modifies the Transcriptional Regulatory Features of Monocytes in Alzheimer’s Patients: A Multi-Omics Integration Analysis Based on Single Cell Technology
title_sort exercise modifies the transcriptional regulatory features of monocytes in alzheimer’s patients: a multi-omics integration analysis based on single cell technology
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9110789/
https://www.ncbi.nlm.nih.gov/pubmed/35592698
http://dx.doi.org/10.3389/fnagi.2022.881488
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