<i>TSC2</i> loss in neural progenitor cells suppresses translation of ASD/NDD-associated transcripts in an mTORC1- and MNK1/2-reversible fashion
Bibliographic record
Abstract
SUMMARY Tuberous sclerosis complex (TSC) is an inherited neurodevelopmental disorder (NDD) with frequent manifestations of epilepsy and autism spectrum disorder (ASD). TSC is caused by inactivating mutations in TSC1 or TSC2 tumor suppressor genes, with encoded proteins hamartin (TSC1) and tuberin (TSC2) forming a functional complex inhibiting mechanistic target of rapamycin complex 1 (mTORC1) signaling. This has led to treatment with allosteric mTORC1 inhibitor rapamycin analogs (“rapalogs”) for TSC tumors; however, rapalogs are ineffective for treating neurodevelopmental manifestations. mTORC1 signaling controls protein synthesis by regulating formation of the eIF4F complex, with further modulation by MNK1/2 kinases via phosphorylation of the eIF4F subunit eIF4E. While both these pathways modulate translation, comparing their impact on transcriptome-wide mRNA translation, as well as effects of inhibiting these pathways in TSC has not been explored. Here, employing CRISPR-modified, isogenic TSC2 patient-derived neural progenitor cells (NPCs), we have examined transcriptome-wide changes in mRNA translation upon TSC2 loss. Our results reveal dysregulated translation in TSC2 -Null NPCs, which significantly overlaps with the translatome from TSC1 -Null NPCs. Interestingly, numerous non-monogenic ASD-, NDD-and epilepsy-associated genes identified in patients harboring putative loss-of-function mutations, were translationally suppressed in TSC2 -Null NPCs. Importantly, translation of these ASD- and NDD-associated genes was reversed upon inhibition of either mTORC1 or MNK1/2 signaling using RMC-6272 or eFT-508, respectively. This study establishes the importance of mTORC1-eIF4F- and MNK-eIF4E-sensitive mRNA translation in TSC, ASD and other neurodevelopmental disorders laying the groundwork for evaluating drugs in clinical development that target these pathways as a treatment strategy for these disorders.
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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 | 0.001 |
| 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".