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The mechanistic link between the complex mutational landscape ofde novomethyltransferaseDNMT3Aand the pathology of acute myeloid leukemia (AML) has not been clearly elucidated so far. A recent discovery on the catalogue of DNMT3A-destabilizing mutations throughout theDNMT3Agene as well as the oligomerization-dependent catalytic property of DNMT3A prompted us to investigate the common effect of DNMT3A-destabilizing mutations (DNMT3AINS) on the genomewide methylation patterns of AML cells. In this study, we describe the characteristics ofDNMT3AINSAML methylomes through the comprehensive computational analyses on three independent AML cohorts. As a result, we show that methylomes ofDNMT3AINSAMLs are considerably different from those ofDNMT3AR882AMLs in that they exhibit both locally disordered DNA methylation states and increased across-cell DNA methylation heterogeneity in bivalent chromatin domains. This increased epigenetic heterogeneity was functionally associated with heterogeneous expression of membrane-associated factors shaping stem cell niche, implying the diversification of the modes of leukemic stem cell-niche interactions. We also present that the level of methylation disorder at bivalent domains predicts the response of AML cells to hypomethylating agents through cell line- and patient-level analyses, which supports that the survival of AML cells depends on stochastic DNA methylations at bivalent domains. Altogether, our work provides a novel mechanistic model suggesting the genomic origin of the aberrant epigenomic heterogeneity in disease conditions.

Phased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often ignored by computing average methylation levels at CpG sites, even though such information exists in bisulfite sequencing data in the form of phased methylation states, or methylation patterns. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. As the analysis of DNA methylation heterogeneity requires the examination of pairs or groups of CpGs throughout the genome, existing softwares suffer from high computational burden, which almost make a large-scale DNA methylation heterogeneity studies intractable for researchers with limited resources. In this study, we benchmark the performance of Metheor against existing code implementations for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing identical results with the original implementation, thereby facilitating a large-scale study of DNA methylation heterogeneity profiles. To demonstrate the utility of the low computational burden of Metheor, we show that the methylation heterogeneity profiles of 928 cancer cell lines can be computed with standard computing resources. With those profiles, we reveal the association between DNA methylation heterogeneity and various omics features. Source code for Metheor is at https://github.com/dohlee/metheor and is freely available under the GPL-3.0 license.

The quantitative characterization of the transcriptional control by histone modifications (HMs) has been challenged by many computational studies, but still most of them exploit only partial aspects of intricate mechanisms involved in gene regulation, leaving a room for improvement. We present Chromoformer, a new transformer-based deep learning architecture that achieves the state-of-the-art performance in the quantitative deciphering of the histone codes of gene regulation. The core essence of Chromoformer architecture lies in the three variants of attention operation, each specialized to model individual hierarchy of three-dimensional (3D) transcriptional regulation including (1) histone codes at core promoters, (2) pairwise interaction between a core promoter and a distal cis-regulatory element mediated by 3D chromatin interactions, and (3) the collective effect of the pairwise cis-regulations. In-depth interpretation of the trained model behavior based on attention scores suggests that Chromoformer adaptively exploits the distant dependencies between HMs associated with transcription initiation and elongation. We also demonstrate that the quantitative kinetics of transcription factories and polycomb group bodies, in which the coordinated gene regulation occurs through spatial sequestration of genes with regulatory elements, can be captured by Chromoformer. Together, our study shows the great power of attention-based deep learning as a versatile modeling approach for the complex epigenetic landscape of gene regulation and highlights its potential as an effective toolkit that facilitates scientific discoveries in computational epigenetics.

Identifying additional risk factors for COVID-19 severity in numerous previously healthy patients without canonical clinical risk factors remains challenging. In this study, we investigate whether clonal hematopoiesis of indeterminate potential (CHIP), a common aging-related process that predisposes various inflammatory responses, may exert COVID-19 severity. We examine the clinical impact of CHIP in 143 laboratory-confirmed COVID-19 patients. Both stratified analyses and logistic regression including the interaction between canonical risk factors and CHIP show that CHIP is an independent risk factor for severe COVID-19, especially in previously healthy patients. Analyses of 60,310 single-cell immune transcriptome profiles identify distinct immunological signatures for CHIP (+) severe COVID-19 patients, particularly in classical monocytes, with a marked increase in pro-inflammatory cytokine responses and potent IFN-γ mediated hyperinflammation signature. We further demonstrate that the enhanced expression of CHIP (+) specific IFN-γ response genes is attributed to the CHIP mutation-dependent epigenetic reprogramming of poised or bivalent cis-regulatory elements. Our results highlight a unique immunopathogenic mechanism of CHIP in the progression of severe COVID-19, which could be extended to elucidate how CHIP contributes to a variety of human infectious diseases.