Molecular dynamics of FMRP and other RNA-binding proteins in MEG-01 differentiation: the role of mRNP complexes in non-neuronal development
Bibliographic record
Abstract
Asymmetrically differentiating cells are formed with the aid of RNA-binding proteins (RBPs), which can bind, stabilize, regulate, and transport target mRNAs. The loss of RBPs in neurons may lead to severe neurodevelopmental diseases such as the Fragile X Syndrome with the absence of the Fragile X Mental Retardation Protein (FMRP). Because the latter is ubiquitous and shares many similarities with other RBPs involved in the development of peripheral cells, we suggest that FMRP would have a role in the differentiation of all tissues where it is expressed. A MEG-01 differentiation model was, therefore, established to study the global developmental functions of FMRP. PMA induction of MEG-01 cells causes important morphological changes driven by cytoskeletal dynamics. Cytoskeleton change and colocalization analyses were performed by confocal microscopy and sucrose gradient fractionation. Total cellular protein content and de novo synthesis were also analyzed. Microtubular transport mediates the displacement of FMRP and other RBP-containing mRNP complexes towards regions of the cell in development. De novo protein synthesis decreases significantly upon differentiation and total protein content composition is altered. Because those results are comparable with those obtained in neurons, the absence of FMRP would have significant consequences in cells everywhere in the body. The latter should be further investigated to give a better understanding of the systemic implications of imbalances of FMRP and other functionally similar RBPs.
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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".