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Old drug, new target: ellipticines selectively inhibit RNA polymerase I transcription.

ABSTRACT: Transcription by RNA polymerase I (Pol-I) is the main driving force behind ribosome biogenesis, a fundamental cellular process that requires the coordinated transcription of all three nuclear polymerases. Increased Pol-I transcription and the concurrent increase in ribosome biogenesis has been linked to the high rates of proliferation in cancers. The ellipticine family contains a number of potent anticancer therapeutic agents, some having progressed to stage I and II clinical trials; however, the mechanism by which many of the compounds work remains unclear. It has long been thought that inhibition of Top2 is the main reason behind the drugs antiproliferative effects. Here we report that a number of the ellipticines, including 9-hydroxyellipticine, are potent and specific inhibitors of Pol-I transcription, with IC(50) in vitro and in cells in the nanomolar range. Essentially, the drugs did not affect Pol-II and Pol-III transcription, demonstrating a high selectivity. We have shown that Pol-I inhibition occurs by a p53-, ATM/ATR-, and Top2-independent mechanism. We discovered that the drug influences the assembly and stability of preinitiation complexes by targeting the interaction between promoter recognition factor SL1 and the rRNA promoter. Our findings will have an impact on the design and development of novel therapeutic agents specifically targeting ribosome biogenesis.

SUBMITTER: Andrews WJ

PROVIDER: S-EPMC3576063 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Old drug, new target: ellipticines selectively inhibit RNA polymerase I transcription.

Andrews William J WJ Panova Tatiana T Normand Christophe C Gadal Olivier O Tikhonova Irina G IG Panov Konstantin I KI

The Journal of biological chemistry 20130104 7

Transcription by RNA polymerase I (Pol-I) is the main driving force behind ribosome biogenesis, a fundamental cellular process that requires the coordinated transcription of all three nuclear polymerases. Increased Pol-I transcription and the concurrent increase in ribosome biogenesis has been linked to the high rates of proliferation in cancers. The ellipticine family contains a number of potent anticancer therapeutic agents, some having progressed to stage I and II clinical trials; however, th ...[more]

PMID: 23293027

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Project description:BACKGROUND:Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interactions (i.e., positive samples) for the predictions. Their performance is severely impeded by the lack of reliable negative samples. RESULTS:We propose a method to construct highly-reliable negative samples for drug target prediction by a pairwise drug-target similarity measurement and OCSVM with a high-recall constraint. On one hand, we measure the pairwise similarity between every two drug-target interactions by combining the chemical similarity between their drugs and the Gene Ontology-based similarity between their targets. Then we calculate the accumulative similarity with all known drug-target interactions for each unobserved drug-target interaction. On the other hand, we obtain the signed distance from OCSVM learned from the known interactions with high recall (?0.95) for each unobserved drug-target interaction. After normalizing all accumulative similarities and signed distances to the range [0,1], we compute the score for each unobserved drug-target interaction via averaging its accumulative similarity and signed distance. Unobserved interactions with lower scores are preferentially served as reliable negative samples for the classification algorithms. The performance of the proposed method is evaluated on the interaction data between 1094 drugs and 1556 target proteins. Extensive comparison experiments using four classical classifiers and one domain predictive method demonstrate the superior performance of the proposed method. A better decision boundary has been learned from the constructed reliable negative samples. CONCLUSIONS:Proper construction of highly-reliable negative samples can help the classification models learn a clear decision boundary which contributes to the performance improvement.

Dataset Information

Old drug, new target: ellipticines selectively inhibit RNA polymerase I transcription.

Publications

Old drug, new target: ellipticines selectively inhibit RNA polymerase I transcription.

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