Dataset Information

Mapping Dmef2-binding regulatory modules by using a ChIP-enriched in silico targets approach.

ABSTRACT: Mapping the regulatory modules to which transcription factors bind in vivo is a key step toward understanding of global gene expression programs. We have developed a chromatin immunoprecipitation (ChIP)-chip strategy for identifying factor-specific regulatory regions acting in vivo. This method, called the ChIP-enriched in silico targets (ChEST) approach, combines immunoprecipitation of cross-linked protein-DNA complexes (X-ChIP) with in silico prediction of targets and generation of computed DNA microarrays. We report the use of ChEST in Drosophila to identify several previously unknown targets of myocyte enhancer factor 2 (MEF2), a key regulator of myogenic differentiation. Our approach was validated by demonstrating that the identified sequences act as enhancers in vivo and are able to drive reporter gene expression specifically in MEF2-positive muscle cells. Presented here, the ChEST strategy was originally designed to identify regulatory modules in Drosophila, but it can be adapted for any sequenced and annotated genome.

SUBMITTER: Junion G

PROVIDER: S-EPMC1317932 | biostudies-literature | 2005 Dec

REPOSITORIES: biostudies-literature

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Mapping Dmef2-binding regulatory modules by using a ChIP-enriched in silico targets approach.

Junion Guillaume G Jagla Teresa T Duplant Sebastien S Tapin Romain R Da Ponte Jean-Philippe JP Jagla Krzysztof K

Proceedings of the National Academy of Sciences of the United States of America 20051209 51

Mapping the regulatory modules to which transcription factors bind in vivo is a key step toward understanding of global gene expression programs. We have developed a chromatin immunoprecipitation (ChIP)-chip strategy for identifying factor-specific regulatory regions acting in vivo. This method, called the ChIP-enriched in silico targets (ChEST) approach, combines immunoprecipitation of cross-linked protein-DNA complexes (X-ChIP) with in silico prediction of targets and generation of computed DN ...[more]

PMID: 16339902

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Project description:BackgroundThe increased sequencing of pathogen genomes and the subsequent availability of genome-scale functional datasets are expected to guide the experimental work necessary for target-based drug discovery. However, a major bottleneck in this has been the difficulty of capturing and integrating relevant information in an easily accessible format for identifying and prioritizing potential targets. The open-access resource TDRtargets.org facilitates drug target prioritization for major tropical disease pathogens such as the mycobacteria Mycobacterium leprae and Mycobacterium tuberculosis; the kinetoplastid protozoans Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi; the apicomplexan protozoans Plasmodium falciparum, Plasmodium vivax, and Toxoplasma gondii; and the helminths Brugia malayi and Schistosoma mansoni.Methodology/principal findingsHere we present strategies to prioritize pathogen proteins based on whether their properties meet criteria considered desirable in a drug target. These criteria are based upon both sequence-derived information (e.g., molecular mass) and functional data on expression, essentiality, phenotypes, metabolic pathways, assayability, and druggability. This approach also highlights the fact that data for many relevant criteria are lacking in less-studied pathogens (e.g., helminths), and we demonstrate how this can be partially overcome by mapping data from homologous genes in well-studied organisms. We also show how individual users can easily upload external datasets and integrate them with existing data in TDRtargets.org to generate highly customized ranked lists of potential targets.Conclusions/significanceUsing the datasets and the tools available in TDRtargets.org, we have generated illustrative lists of potential drug targets in seven tropical disease pathogens. While these lists are broadly consistent with the research community's current interest in certain specific proteins, and suggest novel target candidates that may merit further study, the lists can easily be modified in a user-specific manner, either by adjusting the weights for chosen criteria or by changing the criteria that are included.

Dataset Information

Mapping Dmef2-binding regulatory modules by using a ChIP-enriched in silico targets approach.

Publications

Mapping Dmef2-binding regulatory modules by using a ChIP-enriched in silico targets approach.

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