AKAGAWA HIROYUKI
   Department   Research Institutes and Facilities, Research Institutes and Facilities
   Position   Associate Professor
Article types Original article
Language English
Peer review Peer reviewed
Title Gene expression in a canine basilar artery vasospasm model: a genome-wide network-based analysis.
Journal Formal name:Neurosurgical review
Abbreviation:Neurosurg Rev
ISSN code:(0344-5607)0344-5607(Linking)
Volume, Issue, Page 31(3),pp.283-90
Author and coauthor Sasahara Atsushi, Kasuya Hidetoshi, Krischek Boris, Tajima Atsushi, Onda Hideaki, Sasaki Toshiyuki, Akagawa Hiroyuki, Hori Tomokatsu, Inoue Ituro
Publication date 2008/07
Summary To investigate the changes of gene expression on the cerebral vasospasm after subarachnoid hemorrhage, we used genome-wide microarray for a canine double-hemorrhage model and analyzed the data by using a network-based analysis. Six dogs were assigned to two groups of three animals: control and hemorrhage. The effects were assessed by the changes in gene expressions in the artery 7 days after the first blood injection. Among 23,914 genes, 447 and 66 genes were up-regulated more than two- and fivefold, respectively, and 332 and 25 genes were down-regulated more than two- and fivefold, respectively. According to gene ontology, genes related to cell communication (P = 5.28E-10), host-pathogen interaction (7.65E-8), and defense-immunity protein activity (0.000183) were significantly overrepresented. The top high-level function for the merged network derived from the network-based analysis was cell signaling, revealing that the subgroup that regulates the quantity of Ca(2+) to have the strongest association significance (P = 4.75E-16). Canine microarray analysis followed by gene ontology profiling and connectivity analysis identified several functional groups and individual genes responding to cerebral vasospasm. Ca(2+) regulation may play a key role in these gene expression changes and may be involved in the pathogenesis of cerebral vasospasm.
DOI 10.1007/s10143-008-0135-7
PMID 18463908