EHMT1 pathogenic variants and 9q34.3 microdeletions share altered DNA methylation patterns in patients with Kleefstra syndrome
Bibliographic record
Abstract
Aim: Kleefstra syndrome (KS) is a rare neurodevelopmental disorder caused by haploinsufficiency of the euchromatic histone lysine methyltransferase 1 gene, EHMT1 , due to either a submicroscopic 9q34.3 deletion or a pathogenic EHMT1 variant.KS is characterized by intellectual disability, autistic-like features, heart defects, hypotonia and distinctive facial features.Here, we aimed to (1) identify a unique DNA methylation signature in patients with KS, and (2) demonstrate the efficacy of DNA methylation in predicting the pathogenicity of copy number and sequence variants.Methods: We assayed genome-wide DNA methylation at > 850,000 CpG sites in the blood of KS patients (n = 10) carrying pathogenic variants in EHMT1 or 9q34.3 deletions, as compared to neurotypical controls (n = 42).Differentially methylated sites were validated using additional KS patients (n = 10) and controls (n = 29) to assess specificity and sensitivity of these patterns.Results: The DNA methylation signature of KS demonstrated high sensitivity and specificity; controls and KS patients with a confirmed molecular diagnosis were classified correctly.In additional individuals with EHMT1 alterations, including frameshift or missense variants and partial gene duplications, DNA methylation classifications were consistent with clinical presentation.Furthermore, genes containing differentially methylated CpG sites were enriched for functions related to KS features, including heart formation and synaptic activity. Conclusion:The KS DNA methylation signature did not differ in patients with deletions and variants, supporting haploinsufficiency of EHMT1 as the likely causative mechanism.Beyond this finding, it provides new insights into epigenetic dysregulation associated with KS and can be used to classify individuals with uncertain genomic findings or ambiguous clinical presentations.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".