Loss of DOT1L disrupts neuronal transcription, behavior, and leads to a neurodevelopmental disorder
Bibliographic record
Abstract
Abstract Individuals with monoallelic gain-of-function variants in the histone lysine methyltransferase DOT1L display global developmental delay and varying congenital anomalies. However, the impact of monoallelic loss of DOT1L remains unclear. Here, we sought to define the effects of partial DOT1L loss by applying bulk and single-nucleus RNA-sequencing, ChIP-sequencing, imaging, multielectrode array recordings, and behavioral analysis of zebrafish and multiple mouse models. We present a cohort of 16 individuals (12 females, 4 males) with neurodevelopmental disorders and monoallelic DOT1L variants, including a frameshift deletion, an in-frame deletion, a nonsense, and missense variants clustered in the catalytic domain. We demonstrate that specific variants cause loss of methyltransferase activity. In primary cortical neurons, Dot1l knockdown disrupts transcription of synaptic genes, neuron branching, expression of a synaptic protein, and neuronal activity. Further in the cortex of heterozygous Dot1l mice, Dot1l loss causes sex-specific transcriptional responses and H3K79me2 depletion, including within down-regulated genes. Lastly using both zebrafish and mouse models, we found behavioral disruptions that include developmental deficits and sex-specific social behavioral changes. Overall, we define how DOT1L loss leads to neurological dysfunction by demonstrating that partial Dot1l loss impacts neuronal transcription, neuron morphology, and behavior across multiple models and systems.
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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".