TY - GEN
T1 - Weighted gene co-expression network analysis of microarray mRNA expression profiling in response to electroacupuncture
AU - Mohammadnejad, Afsaneh
AU - Li, Shuxia
AU - Duan, Hongmei
AU - Lund, Jesper
AU - Li, Weilong
AU - Baumbach, Jan
AU - Tan, Qihua
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/21
Y1 - 2019/1/21
N2 - 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 gray (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 1h and 24h 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.
AB - 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 gray (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 1h and 24h 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.
KW - Analgesia
KW - electroacupuncture
KW - gene Expression profiling
KW - hub genes
KW - wGCNA
UR - http://www.scopus.com/inward/record.url?scp=85062550173&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2018.8621258
DO - 10.1109/BIBM.2018.8621258
M3 - Conference contribution
AN - SCOPUS:85062550173
T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
SP - 1876
EP - 1883
BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
A2 - Schmidt, Harald
A2 - Griol, David
A2 - Wang, Haiying
A2 - Baumbach, Jan
A2 - Zheng, Huiru
A2 - Callejas, Zoraida
A2 - Hu, Xiaohua
A2 - Dickerson, Julie
A2 - Zhang, Le
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Y2 - 3 December 2018 through 6 December 2018
ER -