Interactome of FMRP-N-tat therapeutic unveils key interactions for cellular function in Fragile X neurons
Bibliographic record
Abstract
Therapeutic protein replacement has demonstrated preclinical and clinical efficacy in neurological disorders but has not been used clinically for Fragile X syndrome (FXS), a genetic neurodevelopmental disorder caused by loss of Fragile X messenger ribonucleoprotein (FMRP). FXS results from a triplet repeat expansion of more than 200 CGG repeats in the 5'-UTR of the FMR1 gene leading to epigenetic silencing of FMRP. Currently, no clinically approved disease-modifying treatments for FXS exist. Recently, a tat-conjugated FMRP fragment encompassing residues 1 to 297 (FMRP N-tat) was shown to restore aspects of neuronal function in a mouse model of FXS. Promising in vivo data hinted to the therapeutic potential of FMRP N-tat. Herein, affinity purification mass spectrometry was used to identify the FMRP N-tat interactome in tsA-201 FMR1 knockout cells and FXS patient iPSC-derived neurons. The FMRP N-tat interactome included RNA binding proteins and constituents of the ribosome, which aligned closely with the known functions of FMRP. Further, the FMRP N-tat associated proteins included FXR2, STAU1, TRIM28, C1QBP, VDAC2, and several ribosomal proteins to regulate mRNA stability, cellular stress responses, mitochondrial function, and translation. The results highlight the potential of FMRP N-tat to orchestrate assembly of factors to correct lost function in FMRP deficient cells.
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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 | 0.001 |
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".