Direct control of translational elongation by TAOK2β highlights altered protein synthesis as a fundamental underlying component of autism
Bibliographic record
Abstract
Abstract Microdeletions in the 16p11.2 region of the human genome are frequently associated with autism spectrum disorders (ASDs), but how these genomic rearrangements cause ASD remains unclear. Here, we reveal that TAOK2β, a protein isoform encoded by the human TAOK2 gene located in the 16p11.2 locus, regulates mRNA translation. To identify key functional interaction partners of TAOK2β, we performed proteomic screening from Neuro-2a (N2a) cells, mouse cortices, and cultured neurons. This revealed translation factors as a major class of enriched interacting proteins. Consistently, TAOK2β is present in mouse cortical polyribosomes and cortices from Taok2 knockout mice show increased ribosome density on mRNAs and enhanced protein synthesis. Several lines of evidence support an effect of TAOK2β on translation elongation via phosphorylation of eukaryotic elongation factor (eEF2). TAOK2 can directly phosphorylate eEF2 on Threonine 56 and this phosphorylation is reduced in cortices from Taok2 knockout mice. TAOK2β WT overexpression increased eEF2 phosphorylation levels and reduced protein synthesis, whereas a kinase-dead allele of TAOK2β showed opposite effects. Finally, we show that cortices from the mouse model of the human 16p11.2 microdeletion have increased polysome/monosome (P/M) ratios and protein synthesis, phenocopying Taok2 loss of function. Importantly, defective translation phenotypes observed in the mouse 16p11.2 microdeletion model of ASD could be normalized either by reintroducing Taok 2 in vivo or by delivering TAOK2β to cortical neurons derived from 16p11.2 microdeletion mice. Our results uncover a critical role of TAOK2β as a regulator of protein synthesis and support the idea that translational control is a common endpoint of ASD-associated signaling pathways.
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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".