Network based analysis of microarray gene expression profiles in response to electroacupuncture

Abstract

Electroacupuncture (EA) has been extensively considered as a tool for treating diseases and relieving various pains. However, understanding the molecular mechanisms underlying its effect is of high importance. In this study, we performed a weighted gene co-expression network analysis (WGCNA) on data collected from a microarray experiment to investigate the relationship underlying EA within three factors, time, frequency and tissue regions (periaqueductal grey (PAG) and spinal dorsal horn (DH)) as well as the biological implication of gene expression changes. Gene expression on rats in PAG-DH regions induced by EA with 2 Hz and 100 Hz at l h and 24 h were measured using microarray technology. The WGCNA was performed to identify distinct network modules related to EA effects. To find the biological function of genes and pathways, the gene ontology (GO) Consortium was applied and the gene-gene interaction network of top genes in important modules was visualized. We identified one network module (466 genes) which is significantly associated with time, another module (402 genes) significantly related to frequency, and three modules each consisting of 1144, 402 and 3148 genes that are significantly associated with tissue regions. Furthermore, meaningful biological pathways were enriched in association with each of the experimental factors during EA stimulation.Our analysis showed the robustness of WGCNA and revealed important genes within specific modules and pathways which might be activated in response to EA analgesia. The findings may help to clarify the underlying mechanisms of EA and provide references for future verification of this study.

Keywords

• WGCNA
• Electroacupuncture
• Gene expression profiling
• Hub genes
• Analgesia