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Metabolomic and transcriptomic signature in Kabuki syndrome

Jung, Y. L.; Hung, C.; Choi, J.; Lee, E. A.; Bodamer, O.

2025-04-30 genetic and genomic medicine
10.1101/2025.04.30.25326738 medRxiv
Show abstract

Kabuki Syndrome (KS) is a rare multisystem disorder with a variable clinical phenotype. The majority of KS cases are caused by dominant loss-of-function mutations in two genes, KMT2D (lysine methyltransferase 2D, KS1) and KDM6A (lysine demethylase 6A, KS2). Both KMT2D and KDM6A play a critical role in chromatin accessibility, which is essential for developmental processes and differentiation. In a previous study, we reported that KMT2D mutations could lead to increased enhancer activity in genes related to metabolomic pathways in KS1. Early detection of KS is crucial in order to offer improved treatment options. To uncover new biomarkers that could facilitate early detection and to inform clinical trial readiness, we conducted a study in which we collected and analyzed plasma and urine metabolites from 40 KS patients with pathogenic mutations in either KMT2D or KDM6A and 12 healthy controls. We employed an untargeted approach using Liquid Chromatography with tandem Mass Spectrometry (LC-MS/MS). Additionally, we profiled gene expression in the most KS patients and controls. Our analysis revealed > 100 significantly altered metabolites between KS patients and controls, with these metabolites being clustered based on genotypes. Importantly, we identified N2, N2-dimethylguanosine emerging as one of the top candidates in both KS1 and KS2 patients. We utilized machine learning classifiers and identified the most crucial metabolites. Using this trained model, we achieved a high level of discrimination between the KS data and controls. Furthermore, pathway analysis revealed several disrupted pathways, including the pyrimidine metabolism pathway, which are associated with the significantly altered in both metabolome and transcriptome in KS. Distinctive metabolites identified in KS can effectively serve as discriminative biomarkers. Our findings provide valuable insights into the metabolic dysregulation underlying KS and highlight potential targets for further investigation and therapeutic interventions.

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