Biology subjects

Barisic, D.

Publications and source records attributed to Barisic, D..

3 recordsLinked to original sources

bioRxiv2025

Deep dynamical models of single-cell multiomic velocities predict loss-of-function and rescue perturbations in B cells

We present DynaVelo, a generative neural ordinary differential equation model that learns the joint dynamics of gene expression and transcription factor (TF) motif activities in evolving cell systems using single-cell multiome with joint gene expression and chromatin accesibility readout. DynaVelo leverages partial RNA velocity information together with single-cell TF motif accessibility data to improve the modeling of cell state dynamics and identification of TF drivers. We show that DynaVelo recovers the complex and bifurcating in vivo dynamics of wildtype murine germinal center (GC) B cells and reveals how these cell dynamics change under loss-of-function mutations in epigenetic regulators Arid1a and Ctcf. DynaVelo resolves how TF motif activities evolve along latent time trajectories using analysis of training cells or through generated trajectories from the model. In silico perturbation analysis further enables DynaVelo to infer dynamic and cell-state-specific gene regulatory networks (GRNs), recovering many known TF-to-gene edges in the wildtype GC GRN and predicting those that are disrupted in mutants. Finally, in silico gene and TF perturbations allow both the prediction of cell dynamics under loss-of-function genetic mutations and the identification of TF perturbations to rescue loss-of-function dynamic and immunological phenotypes. This analysis predicted that Ctcf knockout would rescue Arid1a loss-of-function phenotype in the GC reaction and nominated Bcl6 and Stat3 as additional TFs whose knockout would rescue Arid1a loss. We validated these predictions in vivo using double heterozygous mutant mice, confirming rescue of the Arid1a dark zone phenotype in all cases and quantitatively assessing model predictions using multiome in Arid1aHet;CtcfHet double heterozygous mice. DynaVelo therefore provides a powerful new deep learning framework for modeling and perturbing dynamic cell systems by harnessing single-cell multiome data sets.

systems biology↗

bioRxiv2023

ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

The identification of cell-type-specific 3D chromatin interactions between regulatory elements can help to decipher gene regulation and to interpret the function of disease-associated non-coding variants. However, current chromosome conformation capture (3C) technologies are unable to resolve interactions at this resolution when only small numbers of cells are available as input. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps and regulatory interactions from single-cell ATAC sequencing (scATAC-seq) data alone. ChromaFold uses pseudobulk chromatin accessibility, co-accessibility profiles across metacells, and predicted CTCF motif tracks as input features and employs a lightweight architecture to enable training on standard GPUs. Once trained on paired scATAC-seq and Hi-C data in human cell lines and tissues, ChromaFold can accurately predict both the 3D contact map and peak-level interactions across diverse human and mouse test cell types. In benchmarking against a recent deep learning method that uses bulk ATAC-seq, DNA sequence, and CTCF ChIP-seq to make cell-type-specific predictions, ChromaFold yields superior prediction performance when including CTCF ChIP-seq data as an input and comparable performance without. Finally, fine-tuning ChromaFold on paired scATAC-seq and Hi-C in a complex tissue enables deconvolution of chromatin interactions across cell subpopulations. ChromaFold thus achieves state-of-the-art prediction of 3D contact maps and regulatory interactions using scATAC-seq alone as input data, enabling accurate inference of cell-type-specific interactions in settings where 3C-based assays are infeasible.

bioinformatics↗

bioRxiv2020

KDM5 inhibition offers a novel therapeutic strategy for the treatment of KMT2D mutant lymphomas

Loss-of-function mutations in KMT2D are a striking feature of the germinal centre (GC) lymphomas, resulting in decreased H3K4 methylation and altered gene expression. We hypothesised that inhibition of the KDM5 family, which demethylates H3K4me3/me2, would re-establish H3K4 methylation and restore the expression of genes repressed upon loss of KMT2D. KDM5-inhibition increased H3K4me3 levels and caused an anti-proliferative response in vitro, which was markedly greater in both endogenous and CRISPR-edited KMT2D mutant DLBCL cell lines, whilst tumour growth was inhibited in KMT2D mutant xenografts in vivo. KDM5-inhibition reactivated both KMT2D-dependent and -independent genes, resulting in diminished B-cell receptor signalling and altered expression of BCL2 family members, including BCL2 itself, allowing it to synergise with agents targeting these pathways. KDM5-inhibition may offer an effective therapeutic strategy for ameliorating KMT2D loss-of-function mutations in GC-lymphomas.Statement of significance We detail a novel way of reverting the effects of loss-of-function mutations in the histone methyltransferase KMT2D by inhibiting the KDM5 demethylase family, increasing levels of H3K4me3 and restoring expression of KMT2D regulated genes.Competing Interest StatementThe authors have declared no competing interest.View Full Text

cancer biology↗