Abstract
Background
The liver X receptor (LXR), a transcription factor that forms a heterodimer with the retinoid X receptor, plays a key role in the transcriptional regulation of many important genes implicated in prevalent metabolic diseases. In spite of numerous studies, a complete list of LXR direct target genes remains elusive. To complement experimental approaches, computational prediction can be used to help build such a list because all LXR target genes are expected to carry the response elements (LXREs) in their promoter or enhancer regions. In practice, however, such a prediction has been hampered by the inaccuracies of currently available predictive models of LXREs. We report on a novel computational application for the highly accurate prediction of LXREs in DNA sequences.
Methods
We first conducted a comprehensive review of experimentally determined LXR target genes and collected all known LXREs. Subsequently, all such sites were classified using various computational methods based on sequence similarity to identify multiple subtypes. A library of Hidden Markov Models (LXRE.HMM) was developed to represent all subtypes and to enable the promoter scanning of LXR target genes.
Results and conclusion
Our model outperformed the widely used LXRE model in MatInspector in identifying the LXREs for all known LXR direct target genes at the experimentally verified positions. As a result, this new approach will make the genomewide prediction of LXR target genes feasible.
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Acknowledgments
The authors wish to thank Ernst Dow, Alex Varshavsky, Tao Wei, Robert Gadski, Thomas Burris, Keith Stayrook, Berket Khalifa, Eric Su, Vaibhav Narayan, Jude Onyia, and Arun Nayar for helpful comments and discussions.
No sources of funding were used to assist in the preparation of this study. The authors have no conflicts of interest that are directly relevant to the content of this study.
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Varga, G., Su, C. Classification and Predictive Modeling of Liver X Receptor Response Elements. BioDrugs 21, 117–124 (2007). https://doi.org/10.2165/00063030-200721020-00006
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DOI: https://doi.org/10.2165/00063030-200721020-00006