bioRxivPlant Biology
Sensitive detection of pre-integration intermediates of LTR retrotransposons in crop plants
J. Cho, M. Benoit, M. Catoni, H.-G. Drost, A. Brestovitsky, M. Oosterbeek, J. Paszkowski
Abstract
Retrotransposons have played an important role in the evolution of host genomes1,2. Their impact on host chromosomes is mainly deduced from the composition of DNA sequences, which have been fixed over evolutionary time. These studies provide important \"snapshots\" reflecting historical activities of transposons but do not predict current transposition potential. We previously reported Sequence-Independent Retrotransposon Trapping (SIRT) as a methodology that, by identification of extrachromosomal linear DNA (eclDNA), revealed the presence of active LTR retrotransposons in Arabidopsis9. Unfortunately, SIRT cannot be applied to large and transposon-rich genomes of crop plants. We have since developed an alternative approach named ALE-seq (amplification of LTR of eclDNAs followed by sequencing). ALE-seq reveals sequences of 5 LTRs of eclDNAs after two-step amplification: in vitro transcription and subsequent reverse transcription. Using ALE-seq in rice, we detected eclDNAs for a novel Copia family LTR retrotransposon, Go-on, which is activated by heat stress. Sequencing of rice accessions revealed that Go-on has preferentially accumulated in indica rice grown at higher temperatures. Furthermore, ALE-seq applied to tomato fruits identified a developmentally regulated Gypsy family of retrotransposons. Importantly, a bioinformatic pipeline adapted for ALE-seq data analyses allows the direct and reference-free annotation of new active retroelements. This pipeline allows assessment of LTR retrotransposon activities in organisms for which genomic sequences and/or reference genomes are unavailable or are of low quality.
- organism: plant (other)
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: variant calling
- approach: experimental assay
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