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Record W4411224482 · doi:10.1093/brain/awaf212

Loss of DOT1L disrupts neuronal transcription and leads to a neurodevelopmental disorder

2025· article· en· W4411224482 on OpenAlexafffund
Marissa J. Maroni, M. Kathryn Barton, Katherine A. Lynch, Ashish R. Deshwar, Philip D. Campbell, Rachel Lee, Annastelle Cohen, Rili Ahmad, Alekh Paranjapye, Víctor Faùndes, Gabriela M. Repetto, Caoimhe McKenna, Chanika Phornphutkul, Hanne Hove, Grazia M.S. Mancini, Rachel Schot, Tahsin Stefan Barakat, Christopher M. Richmond, Julie Lauzon, Ahmed Aly Ibrahim, Caroline Nava, Delphine Héron, Minke M A van Aalst, Slavena Atemin, Mila Sleptsova, Iliyana Aleksandrova, Албена Тодорова, Debra Watkins, Mariya Kozenko, Daniel Natera‐de Benito, C. Ortez, Berta Estévez‐Arias, François Lecoquierre, Kévin Cassinari, Anne-Marie Guerrot, Jonathan Lévy, Xénia Latypova, Alain Verloes, A. Micheil Innes, Xiao‐Ru Yang, Siddharth Banka, Katharina Vill, Maureen Jacob, Michael C. Kruer, Peter T. Skidmore, Carolina I. Galaz-Montoya, Somayeh Bakhtiari, Jessica L. Mester, Michael Granato, Karim‐Jean Armache, Gregory Costain, Erica Korb

Bibliographic record

VenueBrain · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsMcMaster Children's HospitalUniversity of CalgaryAlberta Children's HospitalUniversity of TorontoSickKids FoundationUniversity of British ColumbiaHospital for Sick Children
FundersNational Institute of Child Health and Human DevelopmentMedical Research CouncilManchester Biomedical Research CentreIntellectual and Developmental Disabilities Research CenterNational Institutes of HealthIntellectual and Developmental Disabilities Research Center, Washington University School of Medicine in St. LouisGeneralitat de CatalunyaAgencia Nacional de Investigación y DesarrolloNational Institute of Mental HealthHospital for Sick ChildrenCitizens United for Research in EpilepsyNederlandse Organisatie voor Wetenschappelijk OnderzoekEuropean CommissionNational Institute of Neurological Disorders and StrokeNational Institute for Health and Care ResearchUniversity of PennsylvaniaEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentZonMwNational Alliance for Research on Schizophrenia and DepressionInstituto de Salud Carlos IIIEsther A. and Joseph Klingenstein FundSickkids Research InstituteSimons FoundationAlfred P. Sloan FoundationBrain and Behavior Research FoundationAutism Speaks
KeywordsBiologyZebrafishNeuroscienceLoss functionGeneticsFrameshift mutationNeurodevelopmental disorderGenePhenotype

Abstract

fetched live from OpenAlex

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 behavioural 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 downregulated genes. Lastly, using both zebrafish and mouse models, we found behavioural disruptions that include developmental deficits and sex-specific social behavioural changes. Overall, we define how DOT1L loss leads to neurological dysfunction by demonstrating that partial Dot1l loss impacts neuronal transcription, neuron morphology and behaviour across multiple models and systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.259
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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