medRxivGenetic & Genomic MedicinePreprint
EHMT2 LOSS-OF-FUNCTION ALTERATIONS CAUSE A KLEEFSTRA-LIKE SYNDROME
Beatriz Martínez–DelgadoiD, Estrella López MartíniD, Jennifer KerkhofiD, Beatriz BaladróniD, Lidia Mirela MieluiD, Diana Sánchez-PonceiD, Ariadna Bada-NavarroiD, Marina Herrero-MatesanziD, Lidia LópeziD, Jessica RzasaiD, Dmitrijs RotsiD, Marta Fernandez, Esther Hernández‐SanMigueliD, Gema Gómez‐MarianoiD, Purificación Marín ReinaiD, Rosario Cazorla-Calleja, Javier AlonsoiD, Tjitske KleefstraiD, Manuel Posada de la PaziD, Eva BermejoiD, Bekim SadikovićiD, María J. BarreroiD
Abstract
Dysregulation of the epigenetic machinery is associated with neurodevelopmental defects in humans. Kleefstra syndrome (KS) is a neurodevelopmental syndrome caused by heterozygous alterations in the gene EHMT1 that cause loss-of-function. EHTM1 and EHMT2 are highly similar histone methyltransferases that play relevant roles in development. Despite their similarity, individuals with alterations in EHMT2 have never been described. Here, we describe a pediatric patient with a KS-overlapping phenotype and a single base de novo substitution in EHMT2 that causes the amino acid change p.Ala1077Ser in the catalytic SET domain. This change causes a reduction in the affinity of the catalytic domain for the H3 tail and in the activity of the enzyme by three- to five-fold. DNA methylation, histone methylation and gene expression profiles suggest a significant overlap between the EHMT2 p.Ala1077Ser variant and KS. Based on this evidence we suggest that EHMT2 haploinsufficiency causes a Kleefstra-like syndrome. Although we cannot rule out dominant negative effects caused by the EHMT2 p.Ala1077Ser variant, our data and previously published data suggest that loss of EHMT2 function is probably more detrimental to cells than loss of EHMT1, explaining why individuals with alterations in EHMT2 are very rare.
- organism: human
- assay: DNA methylation
- assay: ChIP-seq
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 3
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