ARID1A regulates DNA repair through chromatin organization and its deficiency triggers DNA damage-mediated anti-tumor immune response.

AT-rich interaction domain protein 1A (ARID1A), a SWI/SNF chromatin remodeling complex subunit, is frequently mutated across various cancer entities. Loss of ARID1A leads to DNA repair defects. Here, we show that ARID1A plays epigenetic roles to promote both DNA double-strand breaks (DSBs) repair pathways, non-homologous end-joining (NHEJ) and homologous recombination (HR). ARID1A is accumulated at DSBs after DNA damage and regulates chromatin loops formation by recruiting RAD21 and CTCF to DSBs. Simultaneously, ARID1A facilitates transcription silencing at DSBs in transcriptionally active chromatin by recruiting HDAC1 and RSF1 to control the distribution of activating histone marks, chromatin accessibility, and eviction of RNAPII. ARID1A depletion resulted in enhanced accumulation of micronuclei, activation of cGAS-STING pathway, and an increased expression of immunomodulatory cytokines upon ionizing radiation. Furthermore, low ARID1A expression in cancer patients receiving radiotherapy was associated with higher infiltration of several immune cells. The high mutation rate of ARID1A in various cancer types highlights its clinical relevance as a promising biomarker that correlates with the level of immune regulatory cytokines and estimates the levels of tumor-infiltrating immune cells, which can predict the response to the combination of radio- and immunotherapy.

Figeno: multi-region genomic figures with long-read support.

SUMMARY: The vast amount of publicly available genomic data requires analysis and visualization tools. Here, we present figeno, an application for generating publication-quality FIgures for GENOmics. Figeno particularly focuses on multi-region views across genomic breakpoints and on long reads with base modifications. In addition, we support epigenomic data including ATAC-seq, ChIP-seq or HiC, as well as whole genome sequencing data with copy numbers and structural variants. AVAILABILITY AND IMPLEMENTATION: Figeno is available as a python package with both a command line and graphical user interface. It can be installed via PyPI and the source code is available at https://github.com/CompEpigen/figeno.

scMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data.

The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.

Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes.

DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair and single-molecule resolution. It has tremendous potential for cell-type heterogeneity analysis using intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, systematic evaluation has not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection, DXM, PRISM, csmFinder+coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman, as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation, thus each was individually assessed. With this elaborate evaluation, we aimed to establish which method achieves the highest performance in different scenarios of synthetic bulk samples. We found that cell-type deconvolution performance is influenced by different factors depending on the number of cell types within the mixture. Finally, we propose a best-practice deconvolution strategy for sequencing data and point out limitations that need to be handled. Array-based methods-both reference-based and reference-free-generally outperformed sequencing-based methods, despite the absence of read-level information. This implies that the current sequencing-based methods still struggle with correctly identifying cell-type-specific signals and eliminating confounding methylation patterns, which needs to be handled in future studies.

Aberrant MNX1 expression associated with t(7;12)(q36;p13) pediatric acute myeloid leukemia induces the disease through altering histone methylation.

Certain subtypes of acute myeloid leukemia (AML) in children have inferior outcome, such as AML with translocation t(7;12)(q36;p13) leading to an MNX1::ETV6 fusion along with high expression of MNX1. We have identified the transforming event in this AML and possible ways of treatment. Retroviral expression of MNX1 was able to induce AML in mice, with similar gene expression and pathway enrichment to t(7;12) AML patient data. Importantly, this leukemia was only induced in immune incompetent mice using fetal but not adult hematopoietic stem and progenitor cells. The restriction in transforming capacity to cells from fetal liver is in alignment with t(7;12)(q36;p13) AML being mostly seen in infants. Expression of MNX1 led to increased histone 3 lysine 4 mono-, di- and trimethylation, reduction in H3K27me3, accompanied with changes in genome-wide chromatin accessibility and genome expression, likely mediated through MNX1 interaction with the methionine cycle and methyltransferases. MNX1 expression increased DNA damage, depletion of the Lin-/Sca1+/c-Kit+ population and skewing toward the myeloid lineage. These effects, together with leukemia development, were prevented by pre-treatment with the S-adenosylmethionine analog Sinefungin. In conclusion, we have shown the importance of MNX1 in development of AML with t(7;12), supporting a rationale for targeting MNX1 and downstream pathways.

Epigenomic technologies for precision oncology.

Epigenetic patterns in a cell control the expression of genes and consequently determine the phenotype of a cell. Cancer cells possess altered epigenomes which include aberrant patterns of DNA methylation, histone tail modifications, nucleosome positioning and of the three-dimensional chromatin organization within a nucleus. These altered epigenetic patterns are potential useful biomarkers to detect cancer cells and to classify tumor types. In addition, the cancer epigenome dictates the response of a cancer cell to therapeutic intervention and, therefore its knowledge, will allow to predict response to different therapeutic approaches. Here we review the current state-of-the-art technologies that have been developed to decipher epigenetic patterns on the genomic level and discuss how these methods are potentially useful for precision oncology.

TAL1 activation in T-cell acute lymphoblastic leukemia: a novel oncogenic 3' neo-enhancer.

T-cell acute lymphocytic leukemia protein 1 (TAL1) is one of the most frequently deregulated oncogenes in T-cell acute lymphoblastic leukemia (T-ALL). Its deregulation can occur through diverse cis-alterations, including SIL-TAL1 microdeletions, translocations with T-cell Receptor loci, and more recently described upstream intergenic non-coding mutations. These mutations consist of recurrent focal microinsertions that create an oncogenic neo-enhancer accompanied by activating epigenetic marks. This observation laid the groundwork for an innovative paradigm concerning the activation of proto-oncogenes via genomic alterations of non-coding intergenic regions. However, for the majority of T-ALL expressing TAL1 (TAL1+), the deregulation mechanism remains 'unresolved'. We took advantage of H3K27ac and H3K4me3 chromatin immunoprecipitation sequencing data of eight cases of T-ALL, including five TAL1+ cases. We identified a putative novel oncogenic neo-enhancer downstream of TAL1 in an unresolved monoallelic TAL1+ case. A rare but recurrent somatic heterozygous microinsertion within this region creates a de novo binding site for MYB transcription factor. Here we demonstrate that this mutation leads to increased enhancer activity, gain of active epigenetic marks, and TAL1 activation via recruitment of MYB. These results highlight the diversity of non-coding mutations that can drive oncogene activation.

ID3 promotes homologous recombination via non-transcriptional and transcriptional mechanisms and its loss confers sensitivity to PARP inhibition.

The inhibitor of DNA-binding 3 (ID3) is a transcriptional regulator that limits interaction of basic helix-loop-helix transcription factors with their target DNA sequences. We previously reported that ID3 loss is associated with mutational signatures linked to DNA repair defects. Here we demonstrate that ID3 exhibits a dual role to promote DNA double-strand break (DSB) repair, particularly homologous recombination (HR). ID3 interacts with the MRN complex and RECQL helicase to activate DSB repair and it facilitates RAD51 loading and downstream steps of HR. In addition, ID3 promotes the expression of HR genes in response to ionizing radiation by regulating both chromatin accessibility and activity of the transcription factor E2F1. Consistently, analyses of TCGA cancer patient data demonstrate that low ID3 expression is associated with impaired HR. The loss of ID3 leads to sensitivity of tumor cells to PARP inhibition, offering new therapeutic opportunities in ID3-deficient tumors.

Activation-induced deaminase is critical for the establishment of DNA methylation patterns prior to the germinal center reaction.

Activation-induced deaminase (AID) initiates antibody diversification in germinal center B cells by deaminating cytosines, leading to somatic hypermutation and class-switch recombination. Loss-of-function mutations in AID lead to hyper-IgM syndrome type 2 (HIGM2), a rare human primary antibody deficiency. AID-mediated deamination has been proposed as leading to active demethylation of 5-methycytosines in the DNA, although evidence both supports and casts doubt on such a role. In this study, using whole-genome bisulfite sequencing of HIGM2 B cells, we investigated direct AID involvement in active DNA demethylation. HIGM2 naïve and memory B cells both display widespread DNA methylation alterations, of which ∼25% are attributable to active DNA demethylation. For genes that undergo active demethylation that is impaired in HIGM2 individuals, our analysis indicates that AID is not directly involved. We demonstrate that the widespread alterations in the DNA methylation and expression profiles of HIGM2 naïve B cells result from premature overstimulation of the B-cell receptor prior to the germinal center reaction. Our data support a role for AID in B cell central tolerance in preventing the expansion of autoreactive cell clones, affecting the correct establishment of DNA methylation patterns.

Histone H3K27 demethylase KDM6A is an epigenetic gatekeeper of mTORC1 signalling in cancer.

OBJECTIVE: Large-scale genome sequencing efforts of human tumours identified epigenetic modifiers as one of the most frequently mutated gene class in human cancer. However, how these mutations drive tumour development and tumour progression are largely unknown. Here, we investigated the function of the histone demethylase KDM6A in gastrointestinal cancers, such as liver cancer and pancreatic cancer. DESIGN: Genetic alterations as well as expression analyses of KDM6A were performed in patients with liver cancer. Genetic mouse models of liver and pancreatic cancer coupled with Kdm6a-deficiency were investigated, transcriptomic and epigenetic profiling was performed, and in vivo and in vitro drug treatments were conducted. RESULTS: KDM6A expression was lost in 30% of patients with liver cancer. Kdm6a deletion significantly accelerated tumour development in murine liver and pancreatic cancer models. Kdm6a-deficient tumours showed hyperactivation of mTORC1 signalling, whereas endogenous Kdm6a re-expression by inducible RNA-interference in established Kdm6a-deficient tumours diminished mTORC1 activity resulting in attenuated tumour progression. Genome-wide transcriptional and epigenetic profiling revealed direct binding of Kdm6a to crucial negative regulators of mTORC1, such as Deptor, and subsequent transcriptional activation by epigenetic remodelling. Moreover, in vitro and in vivo genetic epistasis experiments illustrated a crucial function of Deptor and mTORC1 in Kdm6a-dependent tumour suppression. Importantly, KDM6A expression in human tumours correlates with mTORC1 activity and KDM6A-deficient tumours exhibit increased sensitivity to mTORC1 inhibition. CONCLUSION: KDM6A is an important tumour suppressor in gastrointestinal cancers and acts as an epigenetic toggle for mTORC1 signalling. Patients with KDM6A-deficient tumours could benefit of targeted therapy focusing on mTORC1 inhibition.

Genetic and methylation profiles distinguish benign, malignant and spitzoid melanocytic tumors.

Accurate classification of melanocytic tumors is important for prognostic evaluation, treatment and follow-up protocols of patients. The majority of melanocytic proliferations can be classified solely based on clinical and pathological criteria, however in select cases a definitive diagnostic assessment remains challenging and additional diagnostic biomarkers would be advantageous. We analyzed melanomas, nevi, Spitz nevi and atypical spitzoid tumors using parallel sequencing (exons of 611 genes and 507 gene translocation analysis) and methylation arrays (850k Illumina EPIC). By combining detailed genetic and epigenetic analysis with reference-based and reference-free DNA methylome deconvolution we compared Spitz nevi to nevi and melanoma and assessed the potential for these methods in classifying challenging spitzoid tumors. Results were correlated with clinical and histologic features. Spitz nevi were found to cluster independently of nevi and melanoma and demonstrated a different mutation profile. Multiple copy number alterations and TERT promoter mutations were identified only in melanomas. Genome-wide methylation in Spitz nevi was comparable to benign nevi while the Leukocytes UnMethylation for Purity (LUMP) algorithm in Spitz nevi was comparable to melanoma. Histologically difficult to classify Spitz tumor cases were assessed which, based on methylation arrays, clustered between Spitz nevi and melanoma and in terms of genetic profile or copy number variations demonstrated worrisome features suggesting a malignant neoplasm. Comprehensive sequencing and methylation analysis verify Spitz nevi as an independent melanocytic entity distinct from both nevi and melanoma. Combined genetic and methylation assays can offer additional insights in diagnosing difficult to classify Spitzoid tumors.

Methrix: an R/Bioconductor package for systematic aggregation and analysis of bisulfite sequencing data.

MOTIVATION: Whole-genome bisulfite sequencing (WGBS) measures DNA methylation at base pair resolution resulting in large bedGraph like coverage files. Current options for processing such files are hindered by discrepancies in file format specification, speed, and memory requirements. RESULTS: We developed methrix, an R package, which provides a toolset for systematic analysis of large datasets. Core functionality of the package includes a comprehensive bedGraph or similar tab-separated text file reader-which summarizes methylation calls based on annotated reference indices, infers and collapses strands and handles uncovered reference CpG sites while facilitating a flexible input file format specification. Additional optimized functions for quality control filtering, subsetting and visualization allow user-friendly and effective processing of WGBS results. Easy integration with tools for differentially methylated region (DMR) calling and annotation further eases the analysis of genome-wide methylation data. Overall, methrix enriches established WGBS workflows by bringing together computational efficiency and versatile functionality. AVAILABILITY AND IMPLEMENTATION: Methrix is implemented as an R package, made available under MIT license at https://github.com/CompEpigen/methrix and can be installed from the Bioconductor repository. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software.

BACKGROUND: Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking. RESULTS: Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30-35%, and that selection of cell-type informative probes has similar effect. We show that Cattell's rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms' performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. CONCLUSION: Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir.

Integrative Analysis Defines Distinct Prognostic Subgroups of Intrahepatic Cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver cancer. It is defined by cholangiocytic differentiation and has poor prognosis. Recently, epigenetic processes have been shown to play an important role in cholangiocarcinogenesis. We performed an integrative analysis on 52 iCCAs using both genetic and epigenetic data with a specific focus on DNA methylation components. We found recurrent isocitrate dehydrogenase 1 (IDH1) and IDH2 (28%) gene mutations, recurrent arm-length copy number alterations (CNAs), and focal alterations such as deletion of 3p21 or amplification of 12q15, which affect BRCA1 Associated Protein 1, polybromo 1, and mouse double minute 2 homolog. DNA methylome analysis revealed excessive hypermethylation of iCCA, affecting primarily the bivalent genomic regions marked with both active and repressive histone modifications. Integrative clustering of genetic and epigenetic data identified four iCCA subgroups with prognostic relevance further designated as IDH, high (H), medium (M), and low (L) alteration groups. The IDH group consisted of all samples with IDH1 or IDH2 mutations and showed, together with the H group, a highly disrupted genome, characterized by frequent deletions of chromosome arms 3p and 6q. Both groups showed excessive hypermethylation with distinct patterns. The M group showed intermediate characteristics regarding both genetic and epigenetic marks, whereas the L group exhibited few methylation changes and mutations and a lack of CNAs. Methylation-based latent component analysis of cell-type composition identified differences among these four groups. Prognosis of the H and M groups was significantly worse than that of the L group. Conclusion: Using an integrative genomic and epigenomic analysis approach, we identified four major iCCA subgroups with widespread genomic and epigenomic differences and prognostic implications. Furthermore, our data suggest differences in the cell-of-origin of the iCCA subtypes.

Comprehensive analysis of DNA methylation data with RnBeads.

RnBeads is a software tool for large-scale analysis and interpretation of DNA methylation data, providing a user-friendly analysis workflow that yields detailed hypertext reports (http://rnbeads.mpi-inf.mpg.de/). Supported assays include whole-genome bisulfite sequencing, reduced representation bisulfite sequencing, Infinium microarrays and any other protocol that produces high-resolution DNA methylation data. Notable applications of RnBeads include the analysis of epigenome-wide association studies and epigenetic biomarker discovery in cancer cohorts.

Mitochondrial DNA Variation and Heteroplasmy in Monozygotic Twins Clinically Discordant for Multiple Sclerosis.

We examined the debated link between mitochondrial DNA (mtDNA) variation and multiple sclerosis (MS) using 49 monozygotic (MZ) twin pairs clinically discordant for MS, which enables to associate de novo mtDNA variants, skewed heteroplasmy, and mtDNA copy number with MS manifestation. Ultra-deep sequencing of blood-derived mtDNA revealed 25 heteroplasmic variants with potentially pathogenic features in 18 pairs. All variants were pair-specific and had low and/or similar heteroplasmy levels in both cotwins. In one pair, a confirmed pathogenic variant (m.11778G>A, heteroplasmy ∼50%) associated with Leber hereditary optic neuropathy was detected. Detailed diagnostic investigation revealed subclinical MS signs in the prior nondiseased cotwin. Moreover, neither mtDNA deletions nor copy-number variations were involved. Furthermore, the majority of heteroplasmic variants were shared among MZ twins and exhibited more similar heteroplasmy levels in the same tissue of MZ twins as compared with different tissues of the same individual. Heteroplasmy levels were also more similar within MZ twins compared with nonidentical siblings. Our analysis excludes mtDNA variation as a major driver of the discordant clinical manifestation of MS in MZ twins, and provides valuable insights into the occurrence and distribution of heteroplasmic variants within MZ twins and nonidentical siblings, and across different tissues.

BiQ Analyzer HiMod: an interactive software tool for high-throughput locus-specific analysis of 5-methylcytosine and its oxidized derivatives.

Recent data suggest important biological roles for oxidative modifications of methylated cytosines, specifically hydroxymethylation, formylation and carboxylation. Several assays are now available for profiling these DNA modifications genome-wide as well as in targeted, locus-specific settings. Here we present BiQ Analyzer HiMod, a user-friendly software tool for sequence alignment, quality control and initial analysis of locus-specific DNA modification data. The software supports four different assay types, and it leads the user from raw sequence reads to DNA modification statistics and publication-quality plots. BiQ Analyzer HiMod combines well-established graphical user interface of its predecessor tool, BiQ Analyzer HT, with new and extended analysis modes. BiQ Analyzer HiMod also includes updates of the analysis workspace, an intuitive interface, a custom vector graphics engine and support of additional input and output data formats. The tool is freely available as a stand-alone installation package from http://biq-analyzer-himod.bioinf.mpi-inf.mpg.de/.

Dnmt3a1 regulates hippocampus-dependent memory via the downstream target Nrp1.

Epigenetic factors are well-established players in memory formation. Specifically, DNA methylation is necessary for the formation of long-term memory in multiple brain regions including the hippocampus. Despite the demonstrated role of DNA methyltransferases (Dnmts) in memory formation, it is unclear whether individual Dnmts have unique or redundant functions in long-term memory formation. Furthermore, the downstream processes controlled by Dnmts during memory consolidation have not been investigated. In this study, we demonstrated that Dnmt3a1, the predominant Dnmt in the adult brain, is required for long-term spatial object recognition and contextual fear memory. Using RNA sequencing, we identified an activity-regulated Dnmt3a1-dependent genomic program in which several genes were associated with functional and structural plasticity. Furthermore, we found that some of the identified genes are selectively dependent on Dnmt3a1, but not its isoform Dnmt3a2. Specifically, we identified Neuropilin 1 (Nrp1) as a downstream target of Dnmt3a1 and further demonstrated the involvement of Nrp1 in hippocampus-dependent memory formation. Importantly, we found that Dnmt3a1 regulates hippocampus-dependent memory via Nrp1. In contrast, Nrp1 overexpression did not rescue memory impairments triggered by reduced Dnmt3a2 levels. Taken together, our study uncovered a Dnmt3a-isoform-specific mechanism in memory formation, identified a novel regulator of memory, and further highlighted the complex and highly regulated functions of distinct epigenetic regulators in brain function.

SRSF2 safeguards efficient transcription of DNA damage and repair genes.

The serine-/arginine-rich splicing factor 2 (SRSF2) plays pivotal roles in pre-mRNA processing and gene transcription. Recurrent mutations, particularly a proline-to-histidine substitution at position 95 (P95H), are common in neoplastic diseases. Here, we assess SRSF2's diverse functions in squamous cell carcinoma. We show that SRSF2 deletion or homozygous P95H mutation both cause extensive DNA damage leading to cell-cycle arrest. Mechanistically, SRSF2 regulates efficient bi-directional transcription of DNA replication and repair genes, independent from its function in splicing. Further, SRSF2 haploinsufficiency induces DNA damage without halting the cell cycle. Exposing mouse skin to tumor-promoting carcinogens enhances the clonal expansion of heterozygous Srsf2 P95H epidermal cells but unexpectedly inhibits tumor formation. To survive carcinogen treatment, Srsf2 P95H+/- cells undergo substantial transcriptional rewiring and restore bi-directional gene expression. Thus, our study underscores SRSF2's importance in regulating transcription to orchestrate the cell cycle and the DNA damage response.

Discovery of non-genomic drivers of YAP signaling modulating the cell plasticity in CRC tumor lines.

In normal intestines, a fetal/regenerative/revival cell state can be induced upon inflammation. This plasticity in cell fate is also one of the current topics in human colorectal cancer (CRC). To dissect the underlying mechanisms, we generated human CRC organoids with naturally selected genetic mutation profiles and exposed them to two different conditions by modulating the extracellular matrix (ECM). Among tested mutation profiles, a fetal/regenerative/revival state was induced following YAP activation via a collagen type I-enriched microenvironment. Mechanistically, YAP transcription was promoted by activating AP-1 and TEAD-dependent transcription and suppressing intestinal lineage-determining transcription via mechanotransduction. The phenotypic conversion was also involved in chemoresistance, which could be potentially resolved by targeting the underlying YAP regulatory elements, a potential target of CRC treatment.