Constructing protein-protein interaction network of hypertension with blood stasis syndrome via digital gene expression sequencing and database mining.

Yong-hong Lian; Mei-xia Fang; Li-guo Chen; E-mail: Tchenly@jnueducn

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Constructing protein-protein interaction network of hypertension with blood stasis syndrome via digital gene expression sequencing and database mining.

Author: Yong-hong LIAN ¹ ; Mei-xia FANG ¹ ; Li-guo CHEN ² ; E-mail: TCHENLY@JNU.EDU.CN.
Author Information

1. Institute of Integrated Chinese and Western Medicine, Medical School of Ji'nan University, Guangzhou 510632, Guangdong Province, China.
2. Institute of Integrated Chinese and Western Medicine, Medical School of Ji'nan University, Guangzhou 510632, Guangdong Province, China
Publication Type:Journal Article
MeSH: Aged; Data Mining; methods; Databases, Factual; Female; Gene Expression; Hemostatic Disorders; epidemiology; genetics; Humans; Hypertension; epidemiology; genetics; Male; Medicine, Chinese Traditional; methods; Middle Aged; Protein Interaction Maps
From: Journal of Integrative Medicine 2014;12(6):476-482
CountryChina
Language:English
Abstract:
OBJECTIVETo construct a protein-protein interaction (PPI) network in hypertension patients with blood-stasis syndrome (BSS) by using digital gene expression (DGE) sequencing and database mining techniques.
METHODSDGE analysis based on the Solexa Genome Analyzer platform was performed on vascular endothelial cells incubated with serum of hypertension patients with BSS. The differentially expressed genes were filtered by comparing the expression levels between the different experimental groups. Then functional categories and enriched pathways of the unique genes for BSS were analyzed using Database for Annotation, Visualization and Integrated Discovery (DAVID) to select those in the enrichment pathways. Interologous Interaction Database (I2D) was used to construct PPI networks with the selected genes for hypertension patients with BSS. The potential candidate genes related to BSS were identified by comparing the number of relationships among genes. Confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR), gene ontology (GO) analysis was used to infer the functional annotations of the potential candidate genes for BSS.
RESULTSWith gene enrichment analysis using DAVID, a list of 58 genes was chosen from the unique genes. The selected 58 genes were analyzed using I2D, and a PPI network was constructed. Based on the network analysis results, candidate genes for BSS were identified: DDIT3, JUN, HSPA8, NFIL3, HSPA5, HIST2H2BE, H3F3B, CEBPB, SAT1 and GADD45A. Verified through qRT-PCR and analyzed by GO, the functional annotations of the potential candidate genes were explored.
CONCLUSIONCompared with previous methodologies reported in the literature, the present DGE analysis and data mining method have shown a great improvement in analyzing BSS.