Common Themes in RNA Subcellular Transport, Stress Granule Formation and Abnormal Protein Aggregation
Bibliographic record
Abstract
Control of protein synthesis and quality are critical steps to support eukaryotic cells maintenance and survival. Two very distinctive mechanisms emerge as key checkpoints of protein synthesis regulation. The first one is the delivery of mRNA molecules, packed into ribonucleoprotein (mRNP) granules, to specific subcellular regions in order to restrict protein synthesis to distinct cytoplasmic domains. In the presence of cellular stress or injury, translation is aborted by sequestering mRNA molecules into a sub-type of RNP particles called stress granules (SGs). The second mechanism deals with the folding state and further processing of synthesized proteins. Misbehavior of a particular protein, affecting its processing, functioning, and/or conformation can cause the formation of protein inclusions called aggresomes. Interestingly, self-aggregation of abnormal proteins is one of the leading causes of neurodegenerative disorders. Recently, intracellular transport directed by microtubule-motors, has emerged as an important step in the assembly and dynamic of SGs and aggresomes. This mechanism allows for a precise temporal and spatial trafficking of RNA and protein complexes. Furthermore, it facilitates the regulation of the RNA silencing domains and targets abnormal protein aggregates for degradation. In this review we will explore the specific and common features of mRNA transport and of SG and aggresome formation, and will provide details on the role of the microtubule network and motors in their movement and dynamics. Keywords: Abnormal protein aggregates, aggresome, cell stress, cytoskeleton, dynein, kinesin, RNP, stress granules, RNA Subcellular Transport, Drosophila S2 cells, bicoid mRNA, prion-like proteins, neurodegenerative disorders, Double knockdown (KD) experiments
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".