[show abstract][hide abstract] ABSTRACT: Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.
Nucleic Acids Research 12/2011; 40(6):2377-98. · 8.28 Impact Factor
[show abstract][hide abstract] ABSTRACT: Fenofibrate is a synthetic ligand for the nuclear receptor peroxisome proliferator-activated receptor (PPAR) alpha and has been widely used in the treatment of metabolic disorders, especially hyperlipemia, due to its lipid-lowering effect. The molecular mechanism of lipid-lowering is relatively well defined: an activated PPARalpha forms a PPAR-RXR heterodimer and this regulates the transcription of genes involved in energy metabolism by binding to PPAR response elements in their promoter regions, so-called "trans-activation". In addition, fenofibrate also has anti-inflammatory and anti-athrogenic effects in vascular endothelial and smooth muscle cells. We have limited information about the anti-inflammatory mechanism of fenofibrate; however, "trans-repression" which suppresses production of inflammatory cytokines and adhesion molecules probably contributes to this mechanism. Furthermore, there are reports that fenofibrate affects endothelial cells in a PPARalpha-independent manner. In order to identify PPARalpha-dependently and PPARalpha-independently regulated transcripts, we generated microarray data from human endothelial cells treated with fenofibrate, and with and without siRNA-mediated knock-down of PPARalpha. We also constructed dynamic Bayesian transcriptome networks to reveal PPARalpha-dependent and -independent pathways. Our transcriptome network analysis identified growth differentiation factor 15 (GDF15) as a hub gene having PPARalpha-independently regulated transcripts as its direct downstream children. This result suggests that GDF15 may be PPARalpha-independent master-regulator of fenofibrate action in human endothelial cells.
[show abstract][hide abstract] ABSTRACT: Some drugs affect secretion of secreted proteins (e.g. cytokines) released from target cells, but it remains unclear whether these proteins act in an autocrine manner and directly effect the cells on which the drugs act. In this study, we propose a computational method for testing a biological hypothesis: there exist autocrine signaling pathways that are dynamically regulated by drug response transcriptome networks and control them simultaneously. If such pathways are identified, they could be useful for revealing drug mode-of-action and identifying novel drug targets. By the node-set separation method proposed, dynamic structural changes can be embedded in transcriptome networks that enable us to find master-regulator genes or critical paths at each observed time. We then combine the protein-protein interaction network with the estimated dynamic transcriptome network to discover drug-affected autocrine pathways if they exist. The statistical significance (p-values) of the pathways are evaluated by the meta-analysis technique. The dynamics of the interactions between the transcriptome networks and the signaling pathways will be shown in this framework. We illustrate our strategy by an application using anti-hyperlipidemia drug, Fenofibrate. From over one million protein-protein interaction pathways, we extracted significant 23 autocrine-like pathways with the Bonferroni correction, including VEGF-NRP1-GIPC1-PRKCA-PPARalpha, that is one of the most significant ones and contains PPARalpha, a target of Fenofibrate.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 02/2009;