<i>MARK2</i> variants cause autism spectrum disorder <i>via</i> the downregulation of WNT/β-catenin signaling pathway
Bibliographic record
Abstract
Abstract MARK2 , a member of the evolutionarily conserved PAR1/MARK serine/threonine kinase family, has been identified as a novel risk gene for autism spectrum disorder (ASD) based on the enrichment of de novo loss-of-function (Lof) variants in large-scale sequencing studies of ASD individuals. However, the features shared by affected individuals and the molecular mechanism of MARK2 variants during early neural development remained unclear. Here, we report 31 individuals carrying heterozygous MARK2 variants and presenting with ASD, other neurodevelopmental disorders, and typical facial dysmorphisms. Lof variants predominate (81%) in affected individuals, while computational analysis and in vitro transfection assay also point to MARK2 loss resulting from missense variants. Using patient-derived and CRISPR-engineered isogenic induced pluripotent stem cells (iPSCs), and Mark 2 +/- (HET) mice, we show that MARK2 loss leads to systemic neurodevelopmental deficits, including anomalous polarity in neural rosettes, imbalanced proliferation and differentiation in neural progenitor cells (NPCs), abnormal cortical development and ASD-like behaviors in mice. Further using RNA-Seq and lithium treatment, we link MARK2 loss to the downregulated WNT/β-catenin signaling pathway and identify lithium as a potential drug for treating MARK2 -related ASD.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".