Melis, D. | bioRxiv Science

bioRxiv2026

Epigenetic signature at FOXP3 distal enhancer affects regulatory T cell development in Kabuki syndrome

Kabuki syndrome (KS) is a congenital developmental disorder caused by germinal pathogenic variants in the lysine methyltransferase 2D (KMT2D, KS1) or lysine demethylase 6A (KDM6A, KS2) genes. Kabuki patients display mental retardation, multiorgan malformations and immune dysregulation - ranging from immunodeficiency to autoimmunity - which strongly compromises their life expectancy. We explored whether the complex immunological scenario of Kabuki syndrome 1 subjects (Ks) could be ascribed to an altered generation of CD4+FOXP3+ regulatory T cells (Tregs). We report that pediatric Ks carrying KMT2D pathogenic variants show a significant reduction of Tregs. DNA methylation analysis reveals a specific methylation pattern at the FOXP3 distal enhancer that correlates with decreased FOXP3 transcription early during Treg cell induction and promotes T helper (Th)-2 lineage differentiation. Finally, in vitro T cell demethylation rescues FOXP3 expression and Treg induction in Ks, offering a novel potential therapeutic perspective. Our findings connect KMT2D loss-of-function to the inhibition of human FOXP3 gene transcription and provide novel molecular insights to explain the immunological phenotype in Ks, thus pinpointing this syndrome as a novel Tregopathy.

immunology↗

bioRxiv2025

Prediction of metabolic dynamics through deep learning and high-throughput multiomics data

Synthetic biologys remarkable potential to tackle important societal problems is held back by our inability to predictably engineer biological systems. Here, we collected one of the largest public multiomics synthetic biology datasets generated to date, and used it to train a novel deep learning algorithm able to predict product and metabolic dynamics with great accuracy, starting to approach the predictive capabilities found in physics and chemistry. We were able to predict production time series with 90-99% accuracy, and final production with 96% accuracy. Further, we were able to produce good predictions for a majority of extracellular metabolites, and twenty different intracellular metabolites. These predictions were provided for a target of industrial relevance: a non-model yeast (Pichia kudriavzevii) engineered to produce large amounts of malonic acid, a desirable biomanufacturing target. This approach is generally applicable to any host, pathway, and product because all required knowledge is inferred from experimental data.

bioengineering↗