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Identification of key genes implicated in the suppressive function of human FOXP3(+)CD25(+)CD4(+) regulatory T cells through the analysis of time-series data
Human forkhead box P3 (FOXP3)(+) cluster of differentiation (CD)25(+)CD4(+) regulatory T cells (Tregs) are a type of T cell that express CD4, CD25 and FOXP3, which are critical for maintaining immune homeostasis. The present study aimed to determine the mechanisms underlying Treg function. The GSE11...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5802170/ https://www.ncbi.nlm.nih.gov/pubmed/29286140 http://dx.doi.org/10.3892/mmr.2017.8366 |
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author | Bai, Xiaofeng Shi, Hua Yang, Mingxi Wang, Yuanlin Sun, Zhaolin Xu, Shuxiong |
author_facet | Bai, Xiaofeng Shi, Hua Yang, Mingxi Wang, Yuanlin Sun, Zhaolin Xu, Shuxiong |
author_sort | Bai, Xiaofeng |
collection | PubMed |
description | Human forkhead box P3 (FOXP3)(+) cluster of differentiation (CD)25(+)CD4(+) regulatory T cells (Tregs) are a type of T cell that express CD4, CD25 and FOXP3, which are critical for maintaining immune homeostasis. The present study aimed to determine the mechanisms underlying Treg function. The GSE11292 dataset was downloaded from the Gene Expression Omnibus, which included data from Treg cells at 19 time points (0–360 min) with an equal interval of 20 min, and corresponding repeated samples. However, data for Treg cells at time point 120 min were missing. Using the Mfuzz package, the key genes were identified by clustering analysis. Subsequently, regulatory networks and protein-protein interaction (PPI) networks were constructed and merged into integrated networks using Cytoscape software. Using Database for Annotation, Visualization and Integrated Discover software, enrichment analyses were performed for the genes involved in the PPI networks. Cluster 1 (including 292 genes), cluster 2 (including 111 genes), cluster 3 (including 194 genes) and cluster 4 (including 103 genes) were obtained from the clustering analysis. GAPDH (degree, 40) in cluster 1, Janus kinase 2 (JAK2) (degree, 10) and signal transducer and activator of transcription 5A (STAT5A) (degree, 9) in cluster 3, and tumor necrosis factor (TNF) (degree, 26) and interleukin 2 (IL2) (degree, 22) in cluster 4 had higher degrees in the PPI networks. In addition, it was indicated that several genes may have a role in Treg function by targeting other genes [e.g. microRNA (miR)-146b-3p→TNF, miR-146b-5p→TNF, miR-142-5p→TNF and tripartite motif containing 28 (TRIM28)→GAPDH]. Enrichment analyses indicated that IL2 and TNF were enriched in the immune response and T cell receptor signaling pathway. In conclusion, GAPDH targeted by TRIM28, TNF targeted by miR-146b-3p, miR-146b-5p and miR-142-5p, in addition to JAK2, IL2, and STAT5A may serve important roles in Treg function. |
format | Online Article Text |
id | pubmed-5802170 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-58021702018-02-26 Identification of key genes implicated in the suppressive function of human FOXP3(+)CD25(+)CD4(+) regulatory T cells through the analysis of time-series data Bai, Xiaofeng Shi, Hua Yang, Mingxi Wang, Yuanlin Sun, Zhaolin Xu, Shuxiong Mol Med Rep Articles Human forkhead box P3 (FOXP3)(+) cluster of differentiation (CD)25(+)CD4(+) regulatory T cells (Tregs) are a type of T cell that express CD4, CD25 and FOXP3, which are critical for maintaining immune homeostasis. The present study aimed to determine the mechanisms underlying Treg function. The GSE11292 dataset was downloaded from the Gene Expression Omnibus, which included data from Treg cells at 19 time points (0–360 min) with an equal interval of 20 min, and corresponding repeated samples. However, data for Treg cells at time point 120 min were missing. Using the Mfuzz package, the key genes were identified by clustering analysis. Subsequently, regulatory networks and protein-protein interaction (PPI) networks were constructed and merged into integrated networks using Cytoscape software. Using Database for Annotation, Visualization and Integrated Discover software, enrichment analyses were performed for the genes involved in the PPI networks. Cluster 1 (including 292 genes), cluster 2 (including 111 genes), cluster 3 (including 194 genes) and cluster 4 (including 103 genes) were obtained from the clustering analysis. GAPDH (degree, 40) in cluster 1, Janus kinase 2 (JAK2) (degree, 10) and signal transducer and activator of transcription 5A (STAT5A) (degree, 9) in cluster 3, and tumor necrosis factor (TNF) (degree, 26) and interleukin 2 (IL2) (degree, 22) in cluster 4 had higher degrees in the PPI networks. In addition, it was indicated that several genes may have a role in Treg function by targeting other genes [e.g. microRNA (miR)-146b-3p→TNF, miR-146b-5p→TNF, miR-142-5p→TNF and tripartite motif containing 28 (TRIM28)→GAPDH]. Enrichment analyses indicated that IL2 and TNF were enriched in the immune response and T cell receptor signaling pathway. In conclusion, GAPDH targeted by TRIM28, TNF targeted by miR-146b-3p, miR-146b-5p and miR-142-5p, in addition to JAK2, IL2, and STAT5A may serve important roles in Treg function. D.A. Spandidos 2018-03 2017-12-29 /pmc/articles/PMC5802170/ /pubmed/29286140 http://dx.doi.org/10.3892/mmr.2017.8366 Text en Copyright: © Bai et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
spellingShingle | Articles Bai, Xiaofeng Shi, Hua Yang, Mingxi Wang, Yuanlin Sun, Zhaolin Xu, Shuxiong Identification of key genes implicated in the suppressive function of human FOXP3(+)CD25(+)CD4(+) regulatory T cells through the analysis of time-series data |
title | Identification of key genes implicated in the suppressive function of human FOXP3(+)CD25(+)CD4(+) regulatory T cells through the analysis of time-series data |
title_full | Identification of key genes implicated in the suppressive function of human FOXP3(+)CD25(+)CD4(+) regulatory T cells through the analysis of time-series data |
title_fullStr | Identification of key genes implicated in the suppressive function of human FOXP3(+)CD25(+)CD4(+) regulatory T cells through the analysis of time-series data |
title_full_unstemmed | Identification of key genes implicated in the suppressive function of human FOXP3(+)CD25(+)CD4(+) regulatory T cells through the analysis of time-series data |
title_short | Identification of key genes implicated in the suppressive function of human FOXP3(+)CD25(+)CD4(+) regulatory T cells through the analysis of time-series data |
title_sort | identification of key genes implicated in the suppressive function of human foxp3(+)cd25(+)cd4(+) regulatory t cells through the analysis of time-series data |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5802170/ https://www.ncbi.nlm.nih.gov/pubmed/29286140 http://dx.doi.org/10.3892/mmr.2017.8366 |
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