Competition and collaboration between <scp>RNA</scp>‐binding proteins and <scp>microRNAs</scp>
Bibliographic record
Abstract
Posttranscriptional regulation of mRNA species represents a major regulatory checkpoint in the control of gene expression. Historically, RNA-binding proteins (RBPs) have been regarded as the primary regulators of mRNA stability and translation. More recently, however, microRNAs have emerged as a class of potent and pervasive posttranscriptional rheostats that similarly affect mRNA stability and translation. The observation that both microRNAs and RBPs regulate mRNA stability and translation has initiated a newer area of research that involves the examination of dynamic interactions between these two important classes of posttranscriptional regulators, the myriad of factors that influence these biological interactions, and ultimately, their effects on target mRNAs. Specifically, microRNAs and RBPs can act synergistically to effect mRNA destabilization and translational inhibition. They can also engage in competition with each other and exert opposing effects on target mRNAs. To date, several key studies have provided critical details regarding the mechanisms and principles of interaction between these molecules. Additionally, these findings raise important questions regarding the regulation of these interactions, including the roles of posttranslational modification, subcellular localization, target inhibition versus activation, and changes in expression levels of these regulatory factors, especially under stimulus- and cell-specific conditions. Indeed, further experimentation is warranted to address these key issues that pertain to the collaboration and competition between microRNAs and RBPs. Significantly, the elucidation of these important details bears critical implications for disease management, especially for those diseases in which these cellular factors are dysregulated.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".