Journal Article

Transcriptional Dynamics of LTR Retrotransposons in Early Generation and Ancient Sunflower Hybrids

Mark C. Ungerer and Takeshi Kawakami

in Genome Biology and Evolution

Published on behalf of Society for Molecular Biology and Evolution

Volume 5, issue 2, pages 329-337
Published in print January 2013 |
Published online January 2013 | e-ISSN: 1759-6653 | DOI:

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  • Bioinformatics and Computational Biology
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Hybridization and abiotic stress are natural agents hypothesized to influence activation and proliferation of transposable elements in wild populations. In this report, we examine the effects of these agents on expression dynamics of both quiescent and transcriptionally active sublineages of long terminal repeat (LTR) retrotransposons in wild sunflower species with a notable history of transposable element proliferation. For annual sunflower species Helianthus annuus and H. petiolaris, neither early generation hybridization nor abiotic stress, alone or in combination, induced transcriptional activation of quiescent sublineages of LTR retrotransposons. These treatments also failed to further induce expression of sublineages that are transcriptionally active; instead, expression of active sublineages in F1 and backcross hybrids was nondistinguishable from, or intermediate relative to, parental lines, and abiotic stress generally decreased normalized expression relative to controls. In contrast to findings for early generation hybridization between H. annuus and H. petiolaris, ancient sunflower hybrid species derived from these same two species and which have undergone massive proliferation events of LTR retrotransposons display 2× to 6× higher expression levels of transcriptionally active sublineages relative to parental sunflower species H. annuus and H. petiolaris. Implications and possible explanations for these findings are discussed.

Keywords: LTR retrotransposon; TE activation; hybridization; abiotic stress; gypsy; copia

Journal Article.  5002 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology ; Evolutionary Biology ; Genetics and Genomics

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