Translational control mechanisms in metabolic regulation: critical role of RNA binding proteins, microRNAs, and cytoplasmic RNA granules
Bibliographic record
Abstract
Regulated cell metabolism involves acute and chronic regulation of gene expression by various nutritional and endocrine stimuli. To respond effectively to endogenous and exogenous signals, cells require rapid response mechanisms to modulate transcript expression and protein synthesis and cannot, in most cases, rely on control of transcriptional initiation that requires hours to take effect. Thus, co- and posttranslational mechanisms have been increasingly recognized as key modulators of metabolic function. This review highlights the critical role of mRNA translational control in modulation of global protein synthesis as well as specific protein factors that regulate metabolic function. First, the complex lifecycle of eukaryotic mRNAs will be reviewed, including our current understanding of translational control mechanisms, regulation by RNA binding proteins and microRNAs, and the role of RNA granules, including processing bodies and stress granules. Second, the current evidence linking regulation of mRNA translation with normal physiological and metabolic pathways and the associated disease states are reviewed. A growing body of evidence supports a key role of translational control in metabolic regulation and implicates translational mechanisms in the pathogenesis of metabolic disorders such as type 2 diabetes. The review also highlights translational control of apolipoprotein B (apoB) mRNA by insulin as a clear example of endocrine modulation of mRNA translation to bring about changes in specific metabolic pathways. Recent findings made on the role of 5'-untranslated regions (5'-UTR), 3'-UTR, RNA binding proteins, and RNA granules in mediating insulin regulation of apoB mRNA translation, apoB protein synthesis, and hepatic lipoprotein production are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".