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Identify Down syndrome transcriptome associations using integrative analysis of microarray database and correlation-interaction network

BACKGROUND: Long non-coding RNAs (lncRNAs) have previously been emerged as key players in a series of biological processes. Dysregulation of lncRNA is correlated to human diseases including neurological disorders. Here, we developed a multi-step bioinformatics analysis to study the functions of a pa...

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Autores principales: Chen, Min, Wang, Jiayan, Luo, Yingjun, Huang, Kailing, Shi, Xiaoshun, Liu, Yanhui, Li, Jin, Lai, Zhengfei, Xue, Shuya, Gao, Haimei, Chen, Allen, Chen, Dunjin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5775600/
https://www.ncbi.nlm.nih.gov/pubmed/29351810
http://dx.doi.org/10.1186/s40246-018-0133-y
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author Chen, Min
Wang, Jiayan
Luo, Yingjun
Huang, Kailing
Shi, Xiaoshun
Liu, Yanhui
Li, Jin
Lai, Zhengfei
Xue, Shuya
Gao, Haimei
Chen, Allen
Chen, Dunjin
author_facet Chen, Min
Wang, Jiayan
Luo, Yingjun
Huang, Kailing
Shi, Xiaoshun
Liu, Yanhui
Li, Jin
Lai, Zhengfei
Xue, Shuya
Gao, Haimei
Chen, Allen
Chen, Dunjin
author_sort Chen, Min
collection PubMed
description BACKGROUND: Long non-coding RNAs (lncRNAs) have previously been emerged as key players in a series of biological processes. Dysregulation of lncRNA is correlated to human diseases including neurological disorders. Here, we developed a multi-step bioinformatics analysis to study the functions of a particular Down syndrome-associated gene DSCR9 including the lncRNAs. The method is named correlation-interaction-network (COIN), based on which a pipeline is implemented. Co-expression gene network analysis and biological network analysis results are presented. METHODS: We identified the regulation function of DSCR9, a lncRNA transcribed from the Down syndrome critical region (DSCR) of chromosome 21, by analyzing its co-expression genes from over 1700 sets and nearly 60,000 public Affymetrix human U133-Plus 2 transcriptional profiling microarrays. After proper evaluations, a threshold is chosen to filter the data and get satisfactory results. Microarray data resource is from EBI database and protein–protein interaction (PPI) network information is incorporated from the most complete network databases. PPI integration strategy guarantees complete information regarding DSCR9. Enrichment analysis is performed to identify significantly correlated pathways. RESULTS: We found that the most significant pathways associated with the top DSCR9 co-expressed genes were shown to be involved in neuro-active ligand-receptor interaction (GLP1R, HTR4, P2RX2, UCN3, and UTS2R), calcium signaling pathway (CACNA1F, CACNG4, HTR4, P2RX2, and SLC8A3), neuronal system (KCNJ5 and SYN1) by the KEGG, and GO analysis. The A549 and U251 cell lines with stable DSCR9 overexpression were constructed. We validated 10 DSCR9 co-expression genes by qPCR in both cell lines with over 70% accuracy. CONCLUSIONS: DSCR9 was highly correlated with genes that were known as important factors in the developments and functions of nervous system, indicating that DSCR9 may regulate neurological proteins regarding Down syndrome and other neurological-related diseases. The pipeline can be properly adjusted to other applications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40246-018-0133-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-57756002018-01-31 Identify Down syndrome transcriptome associations using integrative analysis of microarray database and correlation-interaction network Chen, Min Wang, Jiayan Luo, Yingjun Huang, Kailing Shi, Xiaoshun Liu, Yanhui Li, Jin Lai, Zhengfei Xue, Shuya Gao, Haimei Chen, Allen Chen, Dunjin Hum Genomics Genome Database BACKGROUND: Long non-coding RNAs (lncRNAs) have previously been emerged as key players in a series of biological processes. Dysregulation of lncRNA is correlated to human diseases including neurological disorders. Here, we developed a multi-step bioinformatics analysis to study the functions of a particular Down syndrome-associated gene DSCR9 including the lncRNAs. The method is named correlation-interaction-network (COIN), based on which a pipeline is implemented. Co-expression gene network analysis and biological network analysis results are presented. METHODS: We identified the regulation function of DSCR9, a lncRNA transcribed from the Down syndrome critical region (DSCR) of chromosome 21, by analyzing its co-expression genes from over 1700 sets and nearly 60,000 public Affymetrix human U133-Plus 2 transcriptional profiling microarrays. After proper evaluations, a threshold is chosen to filter the data and get satisfactory results. Microarray data resource is from EBI database and protein–protein interaction (PPI) network information is incorporated from the most complete network databases. PPI integration strategy guarantees complete information regarding DSCR9. Enrichment analysis is performed to identify significantly correlated pathways. RESULTS: We found that the most significant pathways associated with the top DSCR9 co-expressed genes were shown to be involved in neuro-active ligand-receptor interaction (GLP1R, HTR4, P2RX2, UCN3, and UTS2R), calcium signaling pathway (CACNA1F, CACNG4, HTR4, P2RX2, and SLC8A3), neuronal system (KCNJ5 and SYN1) by the KEGG, and GO analysis. The A549 and U251 cell lines with stable DSCR9 overexpression were constructed. We validated 10 DSCR9 co-expression genes by qPCR in both cell lines with over 70% accuracy. CONCLUSIONS: DSCR9 was highly correlated with genes that were known as important factors in the developments and functions of nervous system, indicating that DSCR9 may regulate neurological proteins regarding Down syndrome and other neurological-related diseases. The pipeline can be properly adjusted to other applications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40246-018-0133-y) contains supplementary material, which is available to authorized users. BioMed Central 2018-01-19 /pmc/articles/PMC5775600/ /pubmed/29351810 http://dx.doi.org/10.1186/s40246-018-0133-y 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 Genome Database
Chen, Min
Wang, Jiayan
Luo, Yingjun
Huang, Kailing
Shi, Xiaoshun
Liu, Yanhui
Li, Jin
Lai, Zhengfei
Xue, Shuya
Gao, Haimei
Chen, Allen
Chen, Dunjin
Identify Down syndrome transcriptome associations using integrative analysis of microarray database and correlation-interaction network
title Identify Down syndrome transcriptome associations using integrative analysis of microarray database and correlation-interaction network
title_full Identify Down syndrome transcriptome associations using integrative analysis of microarray database and correlation-interaction network
title_fullStr Identify Down syndrome transcriptome associations using integrative analysis of microarray database and correlation-interaction network
title_full_unstemmed Identify Down syndrome transcriptome associations using integrative analysis of microarray database and correlation-interaction network
title_short Identify Down syndrome transcriptome associations using integrative analysis of microarray database and correlation-interaction network
title_sort identify down syndrome transcriptome associations using integrative analysis of microarray database and correlation-interaction network
topic Genome Database
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5775600/
https://www.ncbi.nlm.nih.gov/pubmed/29351810
http://dx.doi.org/10.1186/s40246-018-0133-y
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