Stable, intronic RNAs explain preservation of introns in <i>Cyanidioschyzon merolae</i>
Bibliographic record
Abstract
ABSTRACT Despite recent work identifying functional roles for introns, we lack a broad understanding of why some introns are preserved while others are removed. Here, we use the thermophilic red alga, Cyanidioschyzon merolae , as a model to investigate why only 39 of the approximately 2000 ancestral introns were preserved in this lineage. We observe that 23 of the 39 introns encode stable RNAs, 11 of which represent a novel class of non-coding RNA (ncRNA), which we call stable intronically-encoded RNAs (sieRNAs). These novel ncRNAs are expressed constitutively under normal growth conditions and are conserved in other extremophilic algae. One sieRNA, Q270 , is predicted to stabilize a chloroplast polycistronic transcript encoding 17 ribosomal protein genes through direct antisense base pairing. Strikingly, all sieRNAs are polyadenylated under heat stress with long tails ranging from 50-200 nucleotides long, linking them to a potential heat stress response that may be critical for heat adaptation. Furthermore, DMS-MaP chemical probing revealed that some sieRNAs contain three-way junctions, a common RNA regulatory element, while others undergo accessibility shifts between in vivo and in vitro conditions, indicative of cellular interactions. Our findings suggest that introns in C. merolae are preserved to encode ncRNAs and suggest that introns may serve as hosts for regulatory RNAs across eukaryotes.
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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 | 0.000 | 0.000 |
| 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".