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Computation-based discovery of related transcriptional regulatory modules and motifs using an experimentally validated combinatorial model.

ABSTRACT: Gene expression is regulated by transcription factors that interact with cis-regulatory elements. Predicting these elements from sequence data has proven difficult. We describe here a successful computational search for elements that direct expression in a particular temporal-spatial pattern in the Drosophila embryo, based on a single well characterized enhancer model. The fly genome was searched to identify sequence elements containing the same combination of transcription factors as those found in the model. Experimental evaluation of the search results demonstrates that our method can correctly predict regulatory elements and highlights the importance of functional testing as a means of identifying false-positive results. We also show that the search results enable the identification of additional relevant sequence motifs whose functions can be empirically validated. This approach, combined with gene expression and phylogenetic sequence data, allows for genome-wide identification of related regulatory elements, an important step toward understanding the genetic regulatory networks involved in development.

SUBMITTER: Halfon MS

PROVIDER: S-EPMC186630 | biostudies-literature | 2002 Jul

REPOSITORIES: biostudies-literature

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Computation-based discovery of related transcriptional regulatory modules and motifs using an experimentally validated combinatorial model.

Halfon Marc S MS Grad Yonatan Y Church George M GM Michelson Alan M AM

Genome research 20020701 7

Gene expression is regulated by transcription factors that interact with cis-regulatory elements. Predicting these elements from sequence data has proven difficult. We describe here a successful computational search for elements that direct expression in a particular temporal-spatial pattern in the Drosophila embryo, based on a single well characterized enhancer model. The fly genome was searched to identify sequence elements containing the same combination of transcription factors as those foun ...[more]

PMID: 12097338

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Project description:BackgroundAnticancer therapies that target single signal transduction pathways often fail to prevent proliferation of cancer cells because of overlapping functions and cross-talk between different signaling pathways. Recent research has identified that balanced multi-component therapies might be more efficacious than highly specific single component therapies in certain cases. Ideally, synergistic combinations can provide 1) increased efficacy of the therapeutic effect 2) reduced toxicity as a result of decreased dosage providing equivalent or increased efficacy 3) the avoidance or delayed onset of drug resistance. Therefore, the interest in combinatorial drug discovery based on systems-oriented approaches has been increasing steadily in recent years.MethodologyHere we describe the development of Combinatorial Drug Assembler (CDA), a genomics and bioinformatics system, whereby using gene expression profiling, multiple signaling pathways are targeted for combinatorial drug discovery. CDA performs expression pattern matching of signaling pathway components to compare genes expressed in an input cell line (or patient sample data), with expression patterns in cell lines treated with different small molecules. Then it detects best pattern matching combinatorial drug pairs across the input gene set-related signaling pathways to detect where gene expression patterns overlap and those predicted drug pairs could likely be applied as combination therapy. We carried out in vitro validations on non-small cell lung cancer cells and triple-negative breast cancer (TNBC) cells. We found two combinatorial drug pairs that showed synergistic effect on lung cancer cells. Furthermore, we also observed that halofantrine and vinblastine were synergistic on TNBC cells.ConclusionsCDA provides a new way for rational drug combination. Together with phExplorer, CDA also provides functional insights into combinatorial drugs. CDA is freely available at http://cda.i-pharm.org.

Dataset Information

Computation-based discovery of related transcriptional regulatory modules and motifs using an experimentally validated combinatorial model.

Publications

Computation-based discovery of related transcriptional regulatory modules and motifs using an experimentally validated combinatorial model.

Similar Datasets

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