Mimicry, deception and competition: The life of competing endogenous RNAs
Bibliographic record
Abstract
Since their discovery, small regulatory RNAs (sRNAs) were thought to be regulated exclusively at the transcriptional level. However, accumulating data from recent reports indicate that posttranscriptional signals can also modulate the function and stability of sRNAs. One of these posttranscriptional signals are competing endogenous RNAs (ceRNAs). Commonly called RNA sponges, ceRNAs can effectively sequester sRNAs and prevent them from binding their cognate target messenger RNAs (mRNAs). Subsequently, they prevent sRNA-dependent regulation of translation and stability of mRNA targets. While some ceRNAs seem to be expressed constitutively, others are intricately regulated according to environmental conditions. The outcome of ceRNA binding to a sRNA reaches beyond simple sequestration. Various effects observed on sRNA functions extend from reducing transcriptional noise to promote RNA turnover. Here, we present a historical perspective of the discovery of ceRNAs in eukaryotic organisms and mainly focus on the synthesis and function of select, well-described, ceRNAs in bacterial cells. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions Translation > Translation Regulation RNA Turnover and Surveillance > Regulation of RNA Stability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".