Identify Down syndrome transcriptome associations using integrative analysis of microarray database and correlation-interaction network
Abstract 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...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-01-01
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| Series: | Human Genomics |
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| Online Access: | http://link.springer.com/article/10.1186/s40246-018-0133-y |
| _version_ | 1811305540339367936 |
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| author | Min Chen Jiayan Wang Yingjun Luo Kailing Huang Xiaoshun Shi Yanhui Liu Jin Li Zhengfei Lai Shuya Xue Haimei Gao Allen Chen Dunjin Chen |
| author_facet | Min Chen Jiayan Wang Yingjun Luo Kailing Huang Xiaoshun Shi Yanhui Liu Jin Li Zhengfei Lai Shuya Xue Haimei Gao Allen Chen Dunjin Chen |
| author_sort | Min Chen |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-13T08:27:08Z |
| format | Article |
| id | doaj.art-c85b4b1810044535ab55fac7ad2dcc81 |
| institution | Directory Open Access Journal |
| issn | 1479-7364 |
| language | English |
| last_indexed | 2024-04-13T08:27:08Z |
| publishDate | 2018-01-01 |
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| series | Human Genomics |
| spelling | doaj.art-c85b4b1810044535ab55fac7ad2dcc812022-12-22T02:54:23ZengBMCHuman Genomics1479-73642018-01-0112111210.1186/s40246-018-0133-yIdentify Down syndrome transcriptome associations using integrative analysis of microarray database and correlation-interaction networkMin Chen0Jiayan Wang1Yingjun Luo2Kailing Huang3Xiaoshun Shi4Yanhui Liu5Jin Li6Zhengfei Lai7Shuya Xue8Haimei Gao9Allen Chen10Dunjin Chen11Department of Fetal Medicine and Prenatal Diagnosis, the Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Fetal Medicine and Prenatal Diagnosis, the Third Affiliated Hospital of Guangzhou Medical UniversityMendel Genes Inc, Manhattan Beach, CAMendel Genes Inc, Manhattan Beach, CADepartment of Thoracic Surgery, Nanfang Hospital, Southern Medical UniversityMendel Genes Inc, Manhattan Beach, CAState Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, National Clinical Research Center for Respiratory DiseaseDepartment of Fetal Medicine and Prenatal Diagnosis, the Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Fetal Medicine and Prenatal Diagnosis, the Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Fetal Medicine and Prenatal Diagnosis, the Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Mathematics, University of CaliforniaDepartment of Fetal Medicine and Prenatal Diagnosis, the Third Affiliated Hospital of Guangzhou Medical UniversityAbstract 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.http://link.springer.com/article/10.1186/s40246-018-0133-ylncRNADSCR9Down syndromeProtein–protein interactionCorrelation-interaction-networkNeurological diseases |
| spellingShingle | Min Chen Jiayan Wang Yingjun Luo Kailing Huang Xiaoshun Shi Yanhui Liu Jin Li Zhengfei Lai Shuya Xue Haimei Gao Allen Chen Dunjin Chen Identify Down syndrome transcriptome associations using integrative analysis of microarray database and correlation-interaction network Human Genomics lncRNA DSCR9 Down syndrome Protein–protein interaction Correlation-interaction-network Neurological diseases |
| 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 | lncRNA DSCR9 Down syndrome Protein–protein interaction Correlation-interaction-network Neurological diseases |
| url | http://link.springer.com/article/10.1186/s40246-018-0133-y |
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