Deletion of RBMX RGG/RG motif in Shashi-XLID syndrome leads to aberrant p53 activation and neuronal differentiation defects
Bibliographic record
Abstract
RNA-binding proteins play important roles in X-linked intellectual disability (XLID). In this study, we investigate the contribution of the XLID-associated RBMX in neuronal differentiation. We show that RBMX-depleted cells exhibit aberrant activation of the p53 pathway. Moreover, we identify that the RBMX RGG/RG motif is methylated by protein arginine methyltransferase 5 (PRMT5), and this regulates assembly with the SRSF1 splicing factor into higher-order complexes. Depletion of RBMX or disruption of the RBMX/SRSF1 complex in PRMT5-depleted cells reduces SRSF1 binding to the MDM4 precursor (pre-)mRNA, leading to exon 6 exclusion and lower MDM4 protein levels. Transcriptomic analysis of isogenic Shashi-XLID human-induced pluripotent stem cells (hiPSCs) generated using CRISPR-Cas9 reveals a dysregulation of MDM4 splicing and aberrant p53 upregulation. Shashi-XLID neural progenitor cells (NPCs) display differentiation and morphological abnormalities accompanied with excessive apoptosis. Our findings identify RBMX as a regulator of SRSF1 and the p53 pathway, suggesting that the loss of function of the RBMX RGG/RG motif is the cause of Shashi-XLID syndrome.
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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) | 0.002 | 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".