Brief homogeneous TCR signals instruct common iNKT progenitors whose effector diversification is characterized by subsequent cytokine signaling.

Innate-like T cell populations expressing conserved TCRs play critical roles in immunity through diverse developmentally acquired effector functions. Focusing on the prototypical lineage of invariant natural killer T (iNKT) cells, we sought to dissect the mechanisms and timing of fate decisions and functional effector differentiation. Utilizing induced expression of the semi-invariant NKT cell TCR on double positive thymocytes, an initially highly synchronous wave of iNKT cell development was triggered by brief homogeneous TCR signaling. After reaching a uniform progenitor state characterized by IL-4 production potential and proliferation, effector subsets emerged simultaneously, but then diverged toward different fates. While NKT17 specification was quickly completed, NKT1 cells slowly differentiated and expanded. NKT2 cells resembled maturing progenitors, which gradually diminished in numbers. Thus, iNKT subset diversification occurs in dividing progenitor cells without acute TCR input but utilizes multiple active cytokine signaling pathways. These data imply a two-step model of iNKT effector differentiation.

CIARA: a cluster-independent algorithm for identifying markers of rare cell types from single-cell sequencing data.

A powerful feature of single-cell genomics is the possibility of identifying cell types from their molecular profiles. In particular, identifying novel rare cell types and their marker genes is a key potential of single-cell RNA sequencing. Standard clustering approaches perform well in identifying relatively abundant cell types, but tend to miss rarer cell types. Here, we have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a cluster-independent computational tool designed to select genes that are likely to be markers of rare cell types. Genes selected by CIARA are subsequently integrated with common clustering algorithms to single out groups of rare cell types. CIARA outperforms existing methods for rare cell type detection, and we use it to find previously uncharacterized rare populations of cells in a human gastrula and among mouse embryonic stem cells treated with retinoic acid. Moreover, CIARA can be applied more generally to any type of single-cell omic data, thus allowing the identification of rare cells across multiple data modalities. We provide implementations of CIARA in user-friendly packages available in R and Python.

Genetic sources of population epigenomic variation.

The field of epigenomics has rapidly progressed from the study of individual reference epigenomes to surveying epigenomic variation in populations. Recent studies in a number of species, from yeast to humans, have begun to dissect the cis- and trans-regulatory genetic mechanisms that shape patterns of population epigenomic variation at the level of single epigenetic marks, as well as at the level of integrated chromatin state maps. We show that this information is paving the way towards a more complete understanding of the heritable basis underlying population epigenomic variation. We also highlight important conceptual challenges when interpreting results from these genetic studies, particularly in plants, in which epigenomic variation can be determined both by genetic and epigenetic inheritance.

breakpointR: an R/Bioconductor package to localize strand state changes in Strand-seq data.

MOTIVATION: Strand-seq is a specialized single-cell DNA sequencing technique centered around the directionality of single-stranded DNA. Computational tools for Strand-seq analyses must capture the strand-specific information embedded in these data. RESULTS: Here we introduce breakpointR, an R/Bioconductor package specifically tailored to process and interpret single-cell strand-specific sequencing data obtained from Strand-seq. We developed breakpointR to detect local changes in strand directionality of aligned Strand-seq data, to enable fine-mapping of sister chromatid exchanges, germline inversion and to support global haplotype assembly. Given the broad spectrum of Strand-seq applications we expect breakpointR to be an important addition to currently available tools and extend the accessibility of this novel sequencing technique. AVAILABILITY AND IMPLEMENTATION: R/Bioconductor package https://bioconductor.org/packages/breakpointR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Region-level epimutation rates in Arabidopsis thaliana.

Failure to maintain DNA methylation patterns during plant development can occasionally give rise to so-called "spontaneous epimutations". These stochastic methylation changes are sometimes heritable across generations and thus accumulate in plant genomes over time. Recent evidence indicates that spontaneous epimutations have a major role in shaping patterns of methylation diversity in plant populations. Using single CG dinucleotides as units of analysis, previous work has shown that the epimutation rate is several orders of magnitude higher than the genetic mutation rate. While these large rate differences have obvious implications for understanding genome-methylome co-evolution, the functional relevance of single CG methylation changes remains questionable. In contrast to single CG, solid experimental evidence has linked methylation gains and losses in larger genomic regions with transcriptional variation and heritable phenotypic effects. Here we show that such region-level changes arise stochastically at about the same rate as those at individual CG sites, are only marginal dependent on region size and cytosine density, but strongly dependent on chromosomal location. We also find consistent evidence that region-level epimutations are not restricted to CG contexts but also frequently occur in non-CG regions at the genome-wide scale. Taken together, our results support the view that many differentially methylated regions (DMRs) in natural populations originate from epimutation events and may not be effectively tagged by proximal SNPs. This possibility reinforces the need for epigenome-wide association studies (EWAS) in plants as a way to identify the epigenetic basis of complex traits.

METHimpute: imputation-guided construction of complete methylomes from WGBS data.

BACKGROUND: Whole-genome bisulfite sequencing (WGBS) has become the standard method for interrogating plant methylomes at base resolution. However, deep WGBS measurements remain cost prohibitive for large, complex genomes and for population-level studies. As a result, most published plant methylomes are sequenced far below saturation, with a large proportion of cytosines having either missing data or insufficient coverage. RESULTS: Here we present METHimpute, a Hidden Markov Model (HMM) based imputation algorithm for the analysis of WGBS data. Unlike existing methods, METHimpute enables the construction of complete methylomes by inferring the methylation status and level of all cytosines in the genome regardless of coverage. Application of METHimpute to maize, rice and Arabidopsis shows that the algorithm infers cytosine-resolution methylomes with high accuracy from data as low as 6X, compared to data with 60X, thus making it a cost-effective solution for large-scale studies. CONCLUSIONS: METHimpute provides methylation status calls and levels for all cytosines in the genome regardless of coverage, thus yielding complete methylomes even with low-coverage WGBS datasets. The method has been extensively tested in plants, but should also be applicable to other species. An implementation is available on Bioconductor.

Rate, spectrum, and evolutionary dynamics of spontaneous epimutations.

Stochastic changes in cytosine methylation are a source of heritable epigenetic and phenotypic diversity in plants. Using the model plant Arabidopsis thaliana, we derive robust estimates of the rate at which methylation is spontaneously gained (forward epimutation) or lost (backward epimutation) at individual cytosines and construct a comprehensive picture of the epimutation landscape in this species. We demonstrate that the dynamic interplay between forward and backward epimutations is modulated by genomic context and show that subtle contextual differences have profoundly shaped patterns of methylation diversity in A. thaliana natural populations over evolutionary timescales. Theoretical arguments indicate that the epimutation rates reported here are high enough to rapidly uncouple genetic from epigenetic variation, but low enough for new epialleles to sustain long-term selection responses. Our results provide new insights into methylome evolution and its population-level consequences.

Ecological plant epigenetics: Evidence from model and non-model species, and the way forward.

Growing evidence shows that epigenetic mechanisms contribute to complex traits, with implications across many fields of biology. In plant ecology, recent studies have attempted to merge ecological experiments with epigenetic analyses to elucidate the contribution of epigenetics to plant phenotypes, stress responses, adaptation to habitat, and range distributions. While there has been some progress in revealing the role of epigenetics in ecological processes, studies with non-model species have so far been limited to describing broad patterns based on anonymous markers of DNA methylation. In contrast, studies with model species have benefited from powerful genomic resources, which contribute to a more mechanistic understanding but have limited ecological realism. Understanding the significance of epigenetics for plant ecology requires increased transfer of knowledge and methods from model species research to genomes of evolutionarily divergent species, and examination of responses to complex natural environments at a more mechanistic level. This requires transforming genomics tools specifically for studying non-model species, which is challenging given the large and often polyploid genomes of plants. Collaboration among molecular geneticists, ecologists and bioinformaticians promises to enhance our understanding of the mutual links between genome function and ecological processes.

Natural variation of histone modification and its impact on gene expression in the rat genome.

Histone modifications are epigenetic marks that play fundamental roles in many biological processes including the control of chromatin-mediated regulation of gene expression. Little is known about interindividual variability of histone modification levels across the genome and to what extent they are influenced by genetic variation. We annotated the rat genome with histone modification maps, identified differences in histone trimethyl-lysine levels among strains, and described their underlying genetic basis at the genome-wide scale using ChIP-seq in heart and liver tissues in a panel of rat recombinant inbred and their progenitor strains. We identified extensive variation of histone methylation levels among individuals and mapped hundreds of underlying cis- and trans-acting loci throughout the genome that regulate histone methylation levels in an allele-specific manner. Interestingly, most histone methylation level variation was trans-linked and the most prominent QTL identified influenced H3K4me3 levels at 899 putative promoters throughout the genome in the heart. Cis- acting variation was enriched in binding sites of distinct transcription factors in heart and liver. The integrated analysis of DNA variation together with histone methylation and gene expression levels showed that histoneQTLs are an important predictor of gene expression and that a joint analysis significantly enhanced the prediction of gene expression traits (eQTLs). Our data suggest that genetic variation has a widespread impact on histone trimethylation marks that may help to uncover novel genotype-phenotype relationships.

histoneHMM: Differential analysis of histone modifications with broad genomic footprints.

BACKGROUND: ChIP-seq has become a routine method for interrogating the genome-wide distribution of various histone modifications. An important experimental goal is to compare the ChIP-seq profiles between an experimental sample and a reference sample, and to identify regions that show differential enrichment. However, comparative analysis of samples remains challenging for histone modifications with broad domains, such as heterochromatin-associated H3K27me3, as most ChIP-seq algorithms are designed to detect well defined peak-like features. RESULTS: To address this limitation we introduce histoneHMM, a powerful bivariate Hidden Markov Model for the differential analysis of histone modifications with broad genomic footprints. histoneHMM aggregates short-reads over larger regions and takes the resulting bivariate read counts as inputs for an unsupervised classification procedure, requiring no further tuning parameters. histoneHMM outputs probabilistic classifications of genomic regions as being either modified in both samples, unmodified in both samples or differentially modified between samples. We extensively tested histoneHMM in the context of two broad repressive marks, H3K27me3 and H3K9me3, and evaluated region calls with follow up qPCR as well as RNA-seq data. Our results show that histoneHMM outperforms competing methods in detecting functionally relevant differentially modified regions. CONCLUSION: histoneHMM is a fast algorithm written in C++ and compiled as an R package. It runs in the popular R computing environment and thus seamlessly integrates with the extensive bioinformatic tool sets available through Bioconductor. This makeshistoneHMM an attractive choice for the differential analysis of ChIP-seq data. Software is available from http://histonehmm.molgen.mpg.de .

Features of the Arabidopsis recombination landscape resulting from the combined loss of sequence variation and DNA methylation.

The rate of meiotic crossing over (CO) varies considerably along chromosomes, leading to marked distortions between physical and genetic distances. The causes underlying this variation are being unraveled, and DNA sequence and chromatin states have emerged as key factors. However, the extent to which the suppression of COs within the repeat-rich pericentromeric regions of plant and mammalian chromosomes results from their high level of DNA polymorphisms and from their heterochromatic state, notably their dense DNA methylation, remains unknown. Here, we test the combined effect of removing sequence polymorphisms and repeat-associated DNA methylation on the meiotic recombination landscape of an Arabidopsis mapping population. To do so, we use genome-wide DNA methylation data from a large panel of isogenic epigenetic recombinant inbred lines (epiRILs) to derive a recombination map based on 126 meiotically stable, differentially methylated regions covering 81.9% of the genome. We demonstrate that the suppression of COs within pericentromeric regions of chromosomes persists in this experimental setting. Moreover, suppression is reinforced within 3-Mb regions flanking pericentromeric boundaries, and this effect appears to be compensated by increased recombination activity in chromosome arms. A direct comparison with 17 classical Arabidopsis crosses shows that these recombination changes place the epiRILs at the boundary of the range of natural variation but are not severe enough to transgress that boundary significantly. This level of robustness is remarkable, considering that this population represents an extreme with key recombination barriers having been forced to a minimum.

Path-based reasoning for biomedical knowledge graphs with BioPathNet.

Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) methods are limited in capturing this complexity. Representation-based learning techniques improve prediction accuracy by mapping nodes to low-dimensional embeddings, yet they often struggle with interpretability and scalability. We present BioPathNet, a novel graph neural network framework based on the Neural Bellman-Ford Network (NBFNet), addressing these limitations through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability. This allows visualization of influential paths and facilitates biological validation. BioPathNet leverages a background regulatory graph (BRG) for enhanced message passing and uses stringent negative sampling to improve precision. In evaluations across various LP tasks, such as gene function annotation, drug-disease indication, synthetic lethality, and lncRNA-mRNA interaction prediction, BioPathNet consistently outperformed shallow node embedding methods, relational graph neural networks and task-specific state-of-the-art methods, demonstrating robust performance and versatility. Our study predicts novel drug indications for diseases like acute lymphoblastic leukemia (ALL) and Alzheimer's, validated by medical experts and clinical trials. We also identified new synthetic lethality gene pairs and regulatory interactions involving lncRNAs and target genes, confirmed through literature reviews. BioPathNet's interpretability will enable researchers to trace prediction paths and gain molecular insights, making it a valuable tool for drug discovery, personalized medicine and biology in general.

Direct neuronal reprogramming of mouse astrocytes is associated with multiscale epigenome remodeling and requires Yy1.

Direct neuronal reprogramming is a promising approach to regenerate neurons from local glial cells. However, mechanisms of epigenome remodeling and co-factors facilitating this process are unclear. In this study, we combined single-cell multiomics with genome-wide profiling of three-dimensional nuclear architecture and DNA methylation in mouse astrocyte-to-neuron reprogramming mediated by Neurogenin2 (Ngn2) and its phosphorylation-resistant form (PmutNgn2), respectively. We show that Ngn2 drives multilayered chromatin remodeling at dynamic enhancer-gene interaction sites. PmutNgn2 leads to higher reprogramming efficiency and enhances epigenetic remodeling associated with neuronal maturation. However, the differences in binding sites or downstream gene activation cannot fully explain this effect. Instead, we identified Yy1, a transcriptional co-factor recruited by direct interaction with Ngn2 to its target sites. Upon deletion of Yy1, activation of neuronal enhancers, genes and ultimately reprogramming are impaired without affecting Ngn2 binding. Thus, our work highlights the key role of interactors of proneural factors in direct neuronal reprogramming.

MeDIP-HMM: genome-wide identification of distinct DNA methylation states from high-density tiling arrays.

MOTIVATION: Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatics methods only enable a binary classification into unmethylated and methylated genomic regions, which limit biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number and degree of methylated cytosines. Therefore, a method for the identification of more than two methylation states is highly desirable. RESULTS: Here, we present a three-state hidden Markov model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM uses a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum-Welch algorithm, integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study with existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of methylome data, enabling the identification of distinct DNA methylation levels. Finally, we provide evidence for the general applicability of MeDIP-HMM by analyzing promoter DNA methylation data obtained for chicken. AVAILABILITY: MeDIP-HMM is available as part of the open-source Java library Jstacs (www.jstacs.de/index.php/MeDIP-HMM). Data files are available from the Jstacs website. CONTACT: seifert@ipk-gatersleben.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Single-cell metabolic profiling reveals subgroups of primary human hepatocytes with heterogeneous responses to drug challenge.

BACKGROUND: Xenobiotics are primarily metabolized by hepatocytes in the liver, and primary human hepatocytes are the gold standard model for the assessment of drug efficacy, safety, and toxicity in the early phases of drug development. Recent advances in single-cell genomics demonstrate liver zonation and ploidy as main drivers of cellular heterogeneity. However, little is known about the impact of hepatocyte specialization on liver function upon metabolic challenge, including hepatic metabolism, detoxification, and protein synthesis. RESULTS: Here, we investigate the metabolic capacity of individual human hepatocytes in vitro. We assess how chronic accumulation of lipids enhances cellular heterogeneity and impairs the metabolisms of drugs. Using a phenotyping five-probe cocktail, we identify four functional subgroups of hepatocytes responding differently to drug challenge and fatty acid accumulation. These four subgroups display differential gene expression profiles upon cocktail treatment and xenobiotic metabolism-related specialization. Notably, intracellular fat accumulation leads to increased transcriptional variability and diminishes the drug-related metabolic capacity of hepatocytes. CONCLUSIONS: Our results demonstrate that, upon a metabolic challenge such as exposure to drugs or intracellular fat accumulation, hepatocyte subgroups display different and heterogeneous transcriptional responses.

epiAneufinder identifies copy number alterations from single-cell ATAC-seq data.

Single-cell open chromatin profiling via scATAC-seq has become a mainstream measurement of open chromatin in single-cells. Here we present epiAneufinder, an algorithm that exploits the read count information from scATAC-seq data to extract genome-wide copy number alterations (CNAs) for individual cells, allowing the study of CNA heterogeneity present in a sample at the single-cell level. Using different cancer scATAC-seq datasets, we show that epiAneufinder can identify intratumor clonal heterogeneity in populations of single cells based on their CNA profiles. We demonstrate that these profiles are concordant with the ones inferred from single-cell whole genome sequencing data for the same samples. EpiAneufinder allows the inference of single-cell CNA information from scATAC-seq data, without the need of additional experiments, unlocking a layer of genomic variation which is otherwise unexplored.

CSF3R T618I Collaborates With RUNX1-RUNX1T1 to Expand Hematopoietic Progenitors and Sensitizes to GLI Inhibition.

Activating colony-stimulating factor-3 receptor gene (CSF3R) mutations are recurrent in acute myeloid leukemia (AML) with t(8;21) translocation. However, the nature of oncogenic collaboration between alterations of CSF3R and the t(8;21) associated RUNX1-RUNX1T1 fusion remains unclear. In CD34+ hematopoietic stem and progenitor cells from healthy donors, double oncogene expression led to a clonal advantage, increased self-renewal potential, and blast-like morphology and distinct immunophenotype. Gene expression profiling revealed hedgehog signaling as a potential mechanism, with upregulation of GLI2 constituting a putative pharmacological target. Both primary hematopoietic cells and the t(8;21) positive AML cell line SKNO-1 showed increased sensitivity to the GLI inhibitor GANT61 when expressing CSF3R T618I. Our findings suggest that during leukemogenesis, the RUNX1-RUNXT1 fusion and CSF3R mutation act in a synergistic manner to alter hedgehog signaling, which can be exploited therapeutically.

Comparing genome-wide chromatin profiles using ChIP-chip or ChIP-seq.

MOTIVATION: ChIP-chip and ChIP-seq technologies provide genome-wide measurements of various types of chromatin marks at an unprecedented resolution. With ChIP samples collected from different tissue types and/or individuals, we can now begin to characterize stochastic or systematic changes in epigenetic patterns during development (intra-individual) or at the population level (inter-individual). This requires statistical methods that permit a simultaneous comparison of multiple ChIP samples on a global as well as locus-specific scale. Current analytical approaches are mainly geared toward single sample investigations, and therefore have limited applicability in this comparative setting. This shortcoming presents a bottleneck in biological interpretations of multiple sample data. RESULTS: To address this limitation, we introduce a parametric classification approach for the simultaneous analysis of two (or more) ChIP samples. We consider several competing models that reflect alternative biological assumptions about the global distribution of the data. Inferences about locus-specific and genome-wide chromatin differences are reached through the estimation of multivariate mixtures. Parameter estimates are obtained using an incremental version of the Expectation-Maximization algorithm (IEM). We demonstrate efficient scalability and application to three very diverse ChIP-chip and ChIP-seq experiments. The proposed approach is evaluated against several published ChIP-chip and ChIP-seq software packages. We recommend its use as a first-pass algorithm to identify candidate regions in the epigenome, possibly followed by some type of second-pass algorithm to fine-tune detected peaks in accordance with biological or technological criteria. AVAILABILITY: R source code is available at http://gbic.biol.rug.nl/supplementary/2009/ChromatinProfiles/. Access to Chip-seq data: GEO repository GSE17937.

EpiScanpy: integrated single-cell epigenomic analysis.

EpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data. To address the modality specific challenges from epigenomics data, epiScanpy quantifies the epigenome using multiple feature space constructions and builds a nearest neighbour graph using epigenomic distance between cells. EpiScanpy makes the many existing scRNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities, including methods for common clustering, dimension reduction, cell type identification and trajectory learning techniques, as well as an atlas integration tool for scATAC-seq datasets. The toolkit also features numerous useful downstream functions, such as differential methylation and differential openness calling, mapping epigenomic features of interest to their nearest gene, or constructing gene activity matrices using chromatin openness. We successfully benchmark epiScanpy against other scATAC-seq analysis tools and show its outperformance at discriminating cell types.