Genome-wide analysis of a recently active retrotransposon, Au SINE, in wheat: content, distribution within subgenomes and chromosomes, and gene associations
Bibliographic record
Abstract
KEY MESSAGE: Here, we show that Au SINE elements have strong associations with protein-coding genes in wheat. Most importantly Au SINE insertion within introns causes allelic variation and might induce intron retention. The impact of transposable elements (TEs) on genome structure and function is intensively studied in eukaryotes, especially in plants where TEs can reach up to 90% of the genome in some cases, such as in wheat. Here, we have performed a genome-wide in-silico analysis using the updated publicly available genome draft of bread wheat (T. aestivum), in addition to the updated genome drafts of the diploid donor species, T. urartu and Ae. tauschii, to retrieve and analyze a non-LTR retrotransposon family, termed Au SINE, which was found to be widespread in plant species. Then, we have performed site-specific PCR and realtime RT-PCR analyses to assess the possible impact of Au SINE on gene structure and function. To this end, we retrieved 133, 180 and 1886 intact Au SINE insertions from T. urartu, Ae. tauschii and T. aestivum genome drafts, respectively. The 1886 Au SINE insertions were distributed in the seven homoeologous chromosomes of T. aestivum, while ~ 67% of the insertions were associated with genes. Detailed analysis of 40 genes harboring Au SINE revealed allelic variation of those genes in the Triticum-Aegilops genus. In addition, expression analysis revealed that both regular transcripts and alternative Au SINE-containing transcripts were simultaneously amplified in the same tissue, indicating retention of Au SINE-containing introns. Analysis of the wheat transcriptome revealed that hundreds of protein-coding genes harbor Au SINE in at least one of their mature splice variants. Au SINE might play a prominent role in speciation by creating transcriptome variation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".