Karagiannidis, I. | bioRxiv Science

bioRxiv2024

Mutant EZH2 alters the epigenetic network and increases epigenetic heterogeneity in B cell lymphoma

Diffuse large B cell lymphomas and follicular lymphomas show recurrent mutations in epigenetic regulators; among these are loss-of-function mutations in KMT2D and gain-of-function mutations in EZH2. To systematically explore the effects of these mutations on the wiring of the epigenetic network, we applied a single-cell approach to probe a wide array of histone modifications. We show that mutant-EZH2 elicits extensive effects on the epigenome of lymphomas, beyond alterations to H3K27 methylations, and is dominant over KMT2D mutations. Utilizing the single-cell data, we present computational methods to measure epigenetic heterogeneity. We identify an unexpected characteristic of mutant-EZH2, but not KMT2D, in increasing heterogeneity, shedding light on a novel oncogenic mechanism mediated by this mutation. Finally, we present tools to reconstruct known interactions within the epigenetic network, as well as reveal potential novel cross talk between various modifications, validated by functional perturbations. Our work highlights novel roles for mutant-EZH2 in lymphomagenesis and establishes new concepts for measuring epigenetic heterogeneity and intra-chromatin connectivity in cancer cells.

cancer 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↗