Evolutionary acquisition of promoter-associated non-coding RNA (pancRNA) repertoires diversifies species-dependent gene activation mechanisms in mammals
Bibliographic record
Abstract
BACKGROUND: Recent transcriptome analyses have shown that long non-coding RNAs (ncRNAs) play extensive roles in transcriptional regulation. In particular, we have reported that promoter-associated ncRNAs (pancRNAs) activate the partner gene expression via local epigenetic changes. RESULTS: Here, we identify thousands of genes under pancRNA-mediated transcriptional activation in five mammalian species in common. In the mouse, 1) pancRNA-partnered genes confined their expression pattern to certain tissues compared to pancRNA-lacking genes, 2) expression of pancRNAs was significantly correlated with the enrichment of active chromatin marks, H3K4 trimethylation and H3K27 acetylation, at the promoter regions of the partner genes, 3) H3K4me1 marked the pancRNA-partnered genes regardless of their expression level, and 4) C- or G-skewed motifs were exclusively overrepresented between-200 and-1 bp relative to the transcription start sites of the pancRNA-partnered genes. More importantly, the comparative transcriptome analysis among five different mammalian species using a total of 25 counterpart tissues showed that the overall pancRNA expression profile exhibited extremely high species-specificity compared to that of total mRNA, suggesting that interspecies difference in pancRNA repertoires might lead to the diversification of mRNA expression profiles. CONCLUSIONS: The present study raises the interesting possibility that the gain and/or loss of gene-activation-associated pancRNA repertoires, caused by formation or disruption of the genomic GC-skewed structure in the course of evolution, finely shape the tissue-specific pattern of gene expression according to a given species.
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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".