Identification of mouse heart transcriptomic network sensitive to various heart diseases.
Exploring biological systems from highly complex datasets is an important task for systems biology. The present study examined co-expression dynamics of mouse heart transcriptome by spectral graph clustering (SGC) to identify a heart transcriptomic network. SGC of microarray data produced 17 classified biological conditions (called condition spectrum, CS) and co-expression patterns by generating bi-clusters. The results showed dynamic co-expression patterns with a modular structure enriched in heart-related CS (CS-1 and -13) containing abundant heart-related microarray data. Consequently, a mouse heart transcriptomic network was constructed by clique analysis from the gene clusters exclusively present in the heart-related CS; 31 cliques were used for constructing the network. The participating genes in the network were closely associated with important cardiac functions (e. g., development, lipid and glycogen metabolisms). Online Mendelian Inheritance in Man (OMIM) database indicates that mutations of the genes in the network induced serious heart diseases. Many of the tested genes in the network showed significantly altered gene expression in an animal model of hypertrophy. The results suggest that the present approach is critical for constructing a heart-related transcriptomic network and for deducing important genes involved in the pathogenesis of various heart diseases.
Hong
Seong-Eui
SE
Department of Life Science, Gwangju Institute of Science and Technology, Gwangju, Korea.
Park
Inju
I
Cha
Hyeseon
H
Rho
Seong-Hwan
SH
Park
Woo Jin
WJ
Cho
Chunghee
C
Kim
Do Han
DH
D000818:Animals; D003198:Computer Simulation; D006331:Heart Diseases / Q000378:metabolism*; D051379:Mice; D008955:Models, Cardiovascular; D005810:Multigene Family; D009206:Myocardium / Q000378:metabolism*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction; D014157:Transcription Factors / Q000378:metabolism*
D020543:Proteome; D014157:Transcription Factors