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Article

P-Body Components Are Required for Ty1 Retrotransposition during Assembly of Retrotransposition-Competent Virus-Like Particles

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Pages 382-398 | Received 25 Feb 2009, Accepted 29 Oct 2009, Published online: 20 Mar 2023
 

Abstract

Ty1 is a retrovirus-like retrotransposon whose replication is influenced by diverse cellular processes in Saccharomyces cerevisiae. We have identified cytoplasmic P-body components encoded by DHH1, KEM1, LSM1, and PAT1 as cofactors that posttranscriptionally enhance Ty1 retrotransposition. Using fluorescent in situ hybridization and immunofluorescence microscopy, we found that Ty1 mRNA and Gag colocalize to discrete cytoplasmic foci in wild-type cells. These foci, which are distinct from P-bodies, do not form in P-body component mutants or under conditions suboptimal for retrotransposition. Our immunoelectron microscopy (IEM) data suggest that mRNA/Gag foci are sites where virus-like particles (VLPs) cluster. Overexpression of Ty1 leads to a large increase in retrotransposition in wild-type cells, which allows VLPs to be detected by IEM. However, retrotransposition is still reduced in P-body component mutants under these conditions. Moreover, the percentage of Ty1 mRNA/Gag foci and VLP clusters and levels of integrase and reverse transcriptase are reduced in these mutants. Ty1 antisense RNAs, which have been reported to inhibit Ty1 transposition, are more abundant in the kem1Δ mutant and colocalize with Ty1 mRNA in the cytoplasm. Therefore, Kem1p may prevent the aggregation of Ty1 antisense and mRNAs. Overall, our results suggest that P-body components enhance the formation of retrotransposition-competent Ty1 VLPs.

We thank Benjamin Luttge, Alison Rattray, and Dwight Nissley for critical review of the manuscript. We also thank Sharon Moore, Jeffrey Strathern, Hendrik Nielsen, and Brenda Youngren for helpful comments or technical suggestions during the course of this study. Anne Kamata provided technical assistance with EM. We thank Prabhakar Gudla, Kaustav Nandy, and Mathew Philip for building the image segmentation software.

This work was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research. This project was also funded in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract HHSN261200800001E.

The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

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