Cargando…
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...
Autores principales: | , , , , , , , , , , , |
---|---|
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 |
_version_ | 1783293944502681600 |
---|---|
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. |
format | Online Article Text |
id | pubmed-5775600 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT chenmin identifydownsyndrometranscriptomeassociationsusingintegrativeanalysisofmicroarraydatabaseandcorrelationinteractionnetwork AT wangjiayan identifydownsyndrometranscriptomeassociationsusingintegrativeanalysisofmicroarraydatabaseandcorrelationinteractionnetwork AT luoyingjun identifydownsyndrometranscriptomeassociationsusingintegrativeanalysisofmicroarraydatabaseandcorrelationinteractionnetwork AT huangkailing identifydownsyndrometranscriptomeassociationsusingintegrativeanalysisofmicroarraydatabaseandcorrelationinteractionnetwork AT shixiaoshun identifydownsyndrometranscriptomeassociationsusingintegrativeanalysisofmicroarraydatabaseandcorrelationinteractionnetwork AT liuyanhui identifydownsyndrometranscriptomeassociationsusingintegrativeanalysisofmicroarraydatabaseandcorrelationinteractionnetwork AT lijin identifydownsyndrometranscriptomeassociationsusingintegrativeanalysisofmicroarraydatabaseandcorrelationinteractionnetwork AT laizhengfei identifydownsyndrometranscriptomeassociationsusingintegrativeanalysisofmicroarraydatabaseandcorrelationinteractionnetwork AT xueshuya identifydownsyndrometranscriptomeassociationsusingintegrativeanalysisofmicroarraydatabaseandcorrelationinteractionnetwork AT gaohaimei identifydownsyndrometranscriptomeassociationsusingintegrativeanalysisofmicroarraydatabaseandcorrelationinteractionnetwork AT chenallen identifydownsyndrometranscriptomeassociationsusingintegrativeanalysisofmicroarraydatabaseandcorrelationinteractionnetwork AT chendunjin identifydownsyndrometranscriptomeassociationsusingintegrativeanalysisofmicroarraydatabaseandcorrelationinteractionnetwork |