Computational gene network analysis reveals TNF-induced angiogenesis

Kentaro Ogami, Rui Yamaguchi, Seiya Imoto, Yoshinori Tamada, Hiromitsu Araki, Cristin Print, Satoru Miyano

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Background: TNF (Tumor Necrosis Factor-α) induces HUVEC (Human Umbilical Vein Endothelial Cells) to proliferate and form new blood vessels. This TNF-induced angiogenesis plays a key role in cancer and rheumatic disease. However, the molecular system that underlies TNF-induced angiogenesis is largely unknown.Methods: We analyzed the gene expression changes stimulated by TNF in HUVEC over a time course using microarrays to reveal the molecular system underlying TNF-induced angiogenesis. Traditional k-means clustering analysis was performed to identify informative temporal gene expression patterns buried in the time course data. Functional enrichment analysis using DAVID was then performed for each cluster. The genes that belonged to informative clusters were then used as the input for gene network analysis using a Bayesian network and nonparametric regression method. Based on this TNF-induced gene network, we searched for sub-networks related to angiogenesis by integrating existing biological knowledge.Results: k-means clustering of the TNF stimulated time course microarray gene expression data, followed by functional enrichment analysis identified three biologically informative clusters related to apoptosis, cellular proliferation and angiogenesis. These three clusters included 648 genes in total, which were used to estimate dynamic Bayesian networks. Based on the estimated TNF-induced gene networks, we hypothesized that a sub-network including IL6 and IL8 inhibits apoptosis and promotes TNF-induced angiogenesis. More particularly, IL6 promotes TNF-induced angiogenesis by inducing NF-κB and IL8, which are strong cell growth factors.Conclusions: Computational gene network analysis revealed a novel molecular system that may play an important role in the TNF-induced angiogenesis seen in cancer and rheumatic disease. This analysis suggests that Bayesian network analysis linked to functional annotation may be a powerful tool to provide insight into disease.

Original languageEnglish
Article numberS12
JournalBMC systems biology
Volume6
Issue numberSUPPL.2
DOIs
Publication statusPublished - Dec 12 2012

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Tumor Necrosis Factor
Angiogenesis
Gene Networks
Gene Regulatory Networks
Network Analysis
Electric network analysis
Tumor Necrosis Factor-alpha
Genes
Bayesian networks
Gene expression
Endothelial Cells
Apoptosis
K-means Clustering
Veins
Endothelial cells
Human Umbilical Vein Endothelial Cells
Cell death
Microarrays
Rheumatic Diseases
Bayesian Networks

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Modelling and Simulation
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Ogami, K., Yamaguchi, R., Imoto, S., Tamada, Y., Araki, H., Print, C., & Miyano, S. (2012). Computational gene network analysis reveals TNF-induced angiogenesis. BMC systems biology, 6(SUPPL.2), [S12]. https://doi.org/10.1186/1752-0509-6-S2-S12

Computational gene network analysis reveals TNF-induced angiogenesis. / Ogami, Kentaro; Yamaguchi, Rui; Imoto, Seiya; Tamada, Yoshinori; Araki, Hiromitsu; Print, Cristin; Miyano, Satoru.

In: BMC systems biology, Vol. 6, No. SUPPL.2, S12, 12.12.2012.

Research output: Contribution to journalArticle

Ogami, K, Yamaguchi, R, Imoto, S, Tamada, Y, Araki, H, Print, C & Miyano, S 2012, 'Computational gene network analysis reveals TNF-induced angiogenesis', BMC systems biology, vol. 6, no. SUPPL.2, S12. https://doi.org/10.1186/1752-0509-6-S2-S12
Ogami, Kentaro ; Yamaguchi, Rui ; Imoto, Seiya ; Tamada, Yoshinori ; Araki, Hiromitsu ; Print, Cristin ; Miyano, Satoru. / Computational gene network analysis reveals TNF-induced angiogenesis. In: BMC systems biology. 2012 ; Vol. 6, No. SUPPL.2.
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