Abstract
BackgroundThe 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.ResultsUsing 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.ConclusionWe 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.
Highlights
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
We found that our integrative modeling strategy, namely the transcription factors (TFs) binding sites (TFBS)-integrating Least Angle Regression (LARS) (TILAR), is able to reconstruct gene regulatory network (GRN) more reliably than other established methods
Signal intensities were calculated by applying a custom chip definition file by Ferrari et al that is composed of custom-probesets including only probes matching a single gene [15]
Summary
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. One aims to formulate the complex interactions of biological processes by mathematical models. Bayesian networks model gene expression by random variables and quantify interactions by conditional probabilities. Microarray gene expression data are typically used to derive rather phenomenological GRN models of how the expression level of a gene is influenced by the expression level of other genes, i.e. the model includes indirect regulatory mechanisms. The incorporation of other types of data in addition to gene expression data (e.g. gene functional annotations, genome sequence data, protein-protein and protein-DNA interaction data) as well as the integration of prior biological knowledge (e.g. from scientific literature) supports the inference process. Bayesian networks and systems of linear equations have been most studied for such combined analyses [3,4,5]
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