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 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.

Keywords: Correlation-interaction-network; DSCR9; Down syndrome; Neurological diseases; Protein–protein interaction; lncRNA.

Fig. 1

Schematic diagram of the co-expression-interaction network (COIN) Bioinformatics Analysis

Fig. 2

Prediction of lncRNAs HOTTIP and HOTAIR top co-expression genes and functional enrichment pathways by our COIN analysis pipeline

Fig. 3

Correlation analysis between DSCR9 probes and top 20 co-expressed genes in HG U133 Plus 2.0 microarray. x axis: DSCR9 expression level in microarray experiments; y-axis: expression level of gene (with probeset ID) in the corresponding microarray; red dash line represented correlation of 1.0; blue dot represented the expression level of DSCR9-gene pairs in the same microarray

Fig. 4

Pathway enrichment analysis of top 1000 co-expression genes. Blue bars represented the enrichment significance levels of each pathway. The orange empty circle points represented the number of co-expression genes in the corresponding pathway. The area colored in light orange represented the number of genes involved

Fig. 5

Protein–protein interaction (PPI) network of DSCR9 co-expression genes. a DSCR9 Network structure visualized with Cytoscape. Each node represented one gene. Nodes with red border represented co-expressed genes involved in neuroactive ligand-receptor interaction pathway. Nodes filled with light yellow color represented co-expressed genes functioned in calcium signaling pathway, while nodes shaped in hexagonal represent co-expressed genes related to neuronal system. Orange lines show PPI between those highly correlated co-expressed genes of DSCR9. Red lines represented potential relationships between DSCR9 and its targets. b The core DSCR targeted genes in the PPI network were listed with their gene symbols and weights. Numbers in the bars showed the interaction weight of the corresponding genes in DSCR9 network (Fig. 4a). Orange-colored bars indicated that the corresponding genes were members of the neuro-related pathways

Fig. 6

Expression and regulation of DSCR9 in human tissues and brain regions. a DSCR9 expression levels (shown by FPKM levels, fragment per kilometer) in a series of human tissues (data obtained from NHPRTR project). b DSCR9 expression levels in different brain regions (data obtained from molecularbrain.org). c DSCR9 with four transcription-factor-binding-sites (TFBS) in the third DSCR9 exon displayed with UCSC genome browser

Fig. 7

Bioinformatics predictions were validated by QPCR. a The DSCR9 stable overexpression A549 and U251 cell lines were constructed. b QPCR analysis showing the predicted co-expressed genes were upregulated in both A549 and U251 upon DSCR9 overexpression