Conifers have a unique small RNA silencing signature
Bibliographic record
Abstract
Plants produce small RNAs to negatively regulate genes, viral nucleic acids, and repetitive elements at either the transcriptional or post-transcriptional level in a process that is referred to as RNA silencing. While RNA silencing has been extensively studied across the different phyla of the animal kingdom (e.g., mouse, fly, worm), similar studies in the plant kingdom have focused primarily on angiosperms, thus limiting evolutionary studies of RNA silencing in plants. Here we report on an unexpected phylogenetic difference in the size distribution of small RNAs among the vascular plants. By extracting total RNA from freshly growing shoot tissue, we conducted a survey of small RNAs in 24 vascular plant species. We find that conifers, which radiated from the other seed-bearing plants approximately 260 million years ago, fail to produce significant amounts of 24-nucleotide (nt) RNAs that are known to guide DNA methylation and heterochromatin formation in angiosperms. Instead, they synthesize a diverse population of small RNAs that are exactly 21-nt long. This finding was confirmed by high-throughput sequencing of the small RNA sequences from a conifer, Pinus contorta. A conifer EST search revealed the presence of a novel Dicer-like (DCL) family, which may be responsible for the observed change in small RNA expression. No evidence for DCL3, an enzyme that matures 24-nt RNAs in angiosperms, was found. We hypothesize that the diverse class of 21-nt RNAs found in conifers may help to maintain organization of their unusually large genomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".