Methodology article

Integrative modeling of transcriptional regulation in response to antirheumatic therapy

Michael Hecker¹ , Robert Hermann Goertsches² , Robby Engelmann² , Hans-Juergen Thiesen² and Reinhard Guthke¹

¹Leibniz Institute for Natural Product Research and Infection Biology – Hans-Knoell-Institute, Beutenbergstr. 11a, D-07745 Jena, Germany

²University of Rostock, Institute of Immunology, Schillingallee 70, D-18055 Rostock, Germany

author email corresponding author email

BMC Bioinformatics 2009, 10:262doi:10.1186/1471-2105-10-262

Published:	24 August 2009

Abstract

Background

The investigation of gene regulatory networks is an important issue in molecular systems biology and significant progress has been made by combining different types of biological data. The purpose of this study was to characterize the transcriptional program induced by etanercept therapy in patients with rheumatoid arthritis (RA). Etanercept is known to reduce disease symptoms and progression in RA, but the underlying molecular mechanisms have not been fully elucidated.

Results

Using a DNA microarray dataset providing genome-wide expression profiles of 19 RA patients within the first week of therapy we identified significant transcriptional changes in 83 genes. Most of these genes are known to control the human body's immune response. A novel algorithm called TILAR was then applied to construct a linear network model of the genes' regulatory interactions. The inference method derives a model from the data based on the Least Angle Regression while incorporating DNA-binding site information. As a result we obtained a scale-free network that exhibits a self-regulating and highly parallel architecture, and reflects the pleiotropic immunological role of the therapeutic target TNF-alpha. Moreover, we could show that our integrative modeling strategy performs much better than algorithms using gene expression data alone.

Conclusion

We present TILAR, a method to deduce gene regulatory interactions from gene expression data by integrating information on transcription factor binding sites. The inferred network uncovers gene regulatory effects in response to etanercept and thus provides useful hypotheses about the drug's mechanisms of action.