Dataset Information

Genome-based identification of cancer genes by proviral tagging in mouse retrovirus-induced T-cell lymphomas.

ABSTRACT: The identification of tumor-inducing genes is a driving force for elucidating the molecular mechanisms underlying cancer. Many retroviruses induce tumors by insertion of viral DNA adjacent to cellular oncogenes, resulting in altered expression and/or structure of the encoded proteins. The availability of the mouse genome sequence now allows analysis of retroviral common integration sites in murine tumors to be used as a genetic screen for identification of large numbers of candidate cancer genes. By positioning the sequences of inverse PCR-amplified, virus-host junction fragments within the mouse genome, 19 target genes were identified in T-cell lymphomas induced by the retrovirus SL3-3. The candidate cancer genes included transcription factors (Fos, Gfi1, Lef1, Myb, Myc, Runx3, and Sox3), all three D cyclins, Ras signaling pathway components (Rras2/TC21 and Rasgrp1), and Cmkbr7/CCR7. The most frequent target was Rras2. Insertions as far as 57 kb away from the transcribed portion were associated with substantially increased transcription of Rras2, and no coding sequence mutations, including those typically involved in Ras activation, were detected. These studies demonstrate the power of genome-based analysis of retroviral insertion sites for cancer gene discovery, identify several new genes worth examining for a role in human cancer, and implicate the pathways in which those genes act in lymphomagenesis. They also provide strong genetic evidence that overexpression of unmutated Rras2 contributes to tumorigenesis, thus suggesting that it may also do so if it is inappropriately expressed in human tumors.

SUBMITTER: Kim R

PROVIDER: S-EPMC140962 | biostudies-literature | 2003 Feb

REPOSITORIES: biostudies-literature

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Genome-based identification of cancer genes by proviral tagging in mouse retrovirus-induced T-cell lymphomas.

Kim Rachel R Trubetskoy Alla A Suzuki Takeshi T Jenkins Nancy A NA Copeland Neal G NG Lenz Jack J

Journal of virology 20030201 3

The identification of tumor-inducing genes is a driving force for elucidating the molecular mechanisms underlying cancer. Many retroviruses induce tumors by insertion of viral DNA adjacent to cellular oncogenes, resulting in altered expression and/or structure of the encoded proteins. The availability of the mouse genome sequence now allows analysis of retroviral common integration sites in murine tumors to be used as a genetic screen for identification of large numbers of candidate cancer genes ...[more]

PMID: 12525640

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Project description:Identifying Mycobacterium tuberculosis persistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predict M. tuberculosis genes required for long-term survival in mouse lungs. As the input, we used high-throughput M. tuberculosis mutant library screen data, mycobacterial global transcriptional profiles in mice and macrophages, and functional interaction networks. We selected 57 unique, genetically defined mutants (18 previously tested and 39 untested) to assess the predictive power of this approach in the murine model of TB infection. We observed a 6-fold enrichment in the predicted set of M. tuberculosis genes required for persistence in mouse lungs relative to randomly selected mutant pools. Our results also allowed us to reclassify several genes as required for M. tuberculosis persistence in vivo. Finally, the new results implicated additional high-priority candidate genes for testing. Experimental validation of computational predictions demonstrates the power of this systems biology approach for elucidating M. tuberculosis persistence genes.Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), has a genetic repertoire that permits it to persist in the face of host immune responses. Identification of such persistence genes could reveal novel drug targets and elucidate mechanisms by which the organism eludes the immune system and resists drugs. Genetic screens have identified a total of 31 persistence genes, but to date only 15% of the ~4,000 M. tuberculosis genes have been tested experimentally. In this paper, as an alternative to brute force experimental screens, we describe computational methods that predict new persistence genes by combining known examples with growing databases of biological networks. Experimental testing demonstrated that these predictions are highly accurate, validating the computational approach and providing new information about M. tuberculosis persistence in host tissues. Using the new experimental results as additional input highlights additional genes for testing. Our approach can be extended to other data types and target organisms to characterize host-pathogen interactions relevant to this and other infectious diseases.

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Dataset Information

Genome-based identification of cancer genes by proviral tagging in mouse retrovirus-induced T-cell lymphomas.

Publications

Genome-based identification of cancer genes by proviral tagging in mouse retrovirus-induced T-cell lymphomas.

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