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Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients
BACKGROUND: Gene co-expression network (GCN) mining is a systematic approach to efficiently identify novel disease pathways, predict novel gene functions and search for potential disease biomarkers. However, few studies have systematically identified GCNs in multiple brain transcriptomic data of Alz...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311927/ https://www.ncbi.nlm.nih.gov/pubmed/30598117 http://dx.doi.org/10.1186/s12920-018-0431-1 |
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author | Xiang, Shunian Huang, Zhi Wang, Tianfu Han, Zhi Yu, Christina Y. Ni, Dong Huang, Kun Zhang, Jie |
author_facet | Xiang, Shunian Huang, Zhi Wang, Tianfu Han, Zhi Yu, Christina Y. Ni, Dong Huang, Kun Zhang, Jie |
author_sort | Xiang, Shunian |
collection | PubMed |
description | BACKGROUND: Gene co-expression network (GCN) mining is a systematic approach to efficiently identify novel disease pathways, predict novel gene functions and search for potential disease biomarkers. However, few studies have systematically identified GCNs in multiple brain transcriptomic data of Alzheimer’s disease (AD) patients and looked for their specific functions. METHODS: In this study, we first mined GCN modules from AD and normal brain samples in multiple datasets respectively; then identified gene modules that are specific to AD or normal samples; lastly, condition-specific modules with similar functional enrichments were merged and enriched differentially expressed upstream transcription factors were further examined for the AD/normal-specific modules. RESULTS: We obtained 30 AD-specific modules which showed gain of correlation in AD samples and 31 normal-specific modules with loss of correlation in AD samples compared to normal ones, using the network mining tool lmQCM. Functional and pathway enrichment analysis not only confirmed known gene functional categories related to AD, but also identified novel regulatory factors and pathways. Remarkably, pathway analysis suggested that a variety of viral, bacteria, and parasitic infection pathways are activated in AD samples. Furthermore, upstream transcription factor analysis identified differentially expressed upstream regulators such as ZFHX3 for several modules, which can be potential driver genes for AD etiology and pathology. CONCLUSIONS: Through our state-of-the-art network-based approach, AD/normal-specific GCN modules were identified using multiple transcriptomic datasets from multiple regions of the brain. Bacterial and viral infectious disease related pathways are the most frequently enriched in modules across datasets. Transcription factor ZFHX3 was identified as a potential driver regulator targeting the infectious diseases pathways in AD-specific modules. Our results provided new direction to the mechanism of AD as well as new candidates for drug targets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-018-0431-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6311927 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63119272019-01-07 Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients Xiang, Shunian Huang, Zhi Wang, Tianfu Han, Zhi Yu, Christina Y. Ni, Dong Huang, Kun Zhang, Jie BMC Med Genomics Research BACKGROUND: Gene co-expression network (GCN) mining is a systematic approach to efficiently identify novel disease pathways, predict novel gene functions and search for potential disease biomarkers. However, few studies have systematically identified GCNs in multiple brain transcriptomic data of Alzheimer’s disease (AD) patients and looked for their specific functions. METHODS: In this study, we first mined GCN modules from AD and normal brain samples in multiple datasets respectively; then identified gene modules that are specific to AD or normal samples; lastly, condition-specific modules with similar functional enrichments were merged and enriched differentially expressed upstream transcription factors were further examined for the AD/normal-specific modules. RESULTS: We obtained 30 AD-specific modules which showed gain of correlation in AD samples and 31 normal-specific modules with loss of correlation in AD samples compared to normal ones, using the network mining tool lmQCM. Functional and pathway enrichment analysis not only confirmed known gene functional categories related to AD, but also identified novel regulatory factors and pathways. Remarkably, pathway analysis suggested that a variety of viral, bacteria, and parasitic infection pathways are activated in AD samples. Furthermore, upstream transcription factor analysis identified differentially expressed upstream regulators such as ZFHX3 for several modules, which can be potential driver genes for AD etiology and pathology. CONCLUSIONS: Through our state-of-the-art network-based approach, AD/normal-specific GCN modules were identified using multiple transcriptomic datasets from multiple regions of the brain. Bacterial and viral infectious disease related pathways are the most frequently enriched in modules across datasets. Transcription factor ZFHX3 was identified as a potential driver regulator targeting the infectious diseases pathways in AD-specific modules. Our results provided new direction to the mechanism of AD as well as new candidates for drug targets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-018-0431-1) contains supplementary material, which is available to authorized users. BioMed Central 2018-12-31 /pmc/articles/PMC6311927/ /pubmed/30598117 http://dx.doi.org/10.1186/s12920-018-0431-1 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Xiang, Shunian Huang, Zhi Wang, Tianfu Han, Zhi Yu, Christina Y. Ni, Dong Huang, Kun Zhang, Jie Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients |
title | Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients |
title_full | Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients |
title_fullStr | Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients |
title_full_unstemmed | Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients |
title_short | Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer’s disease patients |
title_sort | condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of alzheimer’s disease patients |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311927/ https://www.ncbi.nlm.nih.gov/pubmed/30598117 http://dx.doi.org/10.1186/s12920-018-0431-1 |
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