Reference-free deconvolution, visualization and interpretation of complex DNA methylation data using DecompPipeline, MeDeCom and FactorViz.

DNA methylation profiling offers unique insights into human development and diseases. Often the analysis of complex tissues and cell mixtures is the only feasible option to study methylation changes across large patient cohorts. Since DNA methylomes are highly cell type specific, deconvolution methods can be used to recover cell type-specific information in the form of latent methylation components (LMCs) from such 'bulk' samples. Reference-free deconvolution methods retrieve these components without the need for DNA methylation profiles of purified cell types. Currently no integrated and guided procedure is available for data preparation and subsequent interpretation of deconvolution results. Here, we describe a three-stage protocol for reference-free deconvolution of DNA methylation data comprising: (i) data preprocessing, confounder adjustment using independent component analysis (ICA) and feature selection using DecompPipeline, (ii) deconvolution with multiple parameters using MeDeCom, RefFreeCellMix or EDec and (iii) guided biological inference and validation of deconvolution results with the R/Shiny graphical user interface FactorViz. Our protocol simplifies the analysis and guides the initial interpretation of DNA methylation data derived from complex samples. The harmonized approach is particularly useful to dissect and evaluate cell heterogeneity in complex systems such as tumors. We apply the protocol to lung cancer methylomes from The Cancer Genome Atlas (TCGA) and show that our approach identifies the proportions of stromal cells and tumor-infiltrating immune cells, as well as associations of the detected components with clinical parameters. The protocol takes slightly >3 d to complete and requires basic R skills.

Quantitative comparison of within-sample heterogeneity scores for DNA methylation data.

DNA methylation is an epigenetic mark with important regulatory roles in cellular identity and can be quantified at base resolution using bisulfite sequencing. Most studies are limited to the average DNA methylation levels of individual CpGs and thus neglect heterogeneity within the profiled cell populations. To assess this within-sample heterogeneity (WSH) several window-based scores that quantify variability in DNA methylation in sequencing reads have been proposed. We performed the first systematic comparison of four published WSH scores based on simulated and publicly available datasets. Moreover, we propose two new scores and provide guidelines for selecting appropriate scores to address cell-type heterogeneity, cellular contamination and allele-specific methylation. Most of the measures were sensitive in detecting DNA methylation heterogeneity in these scenarios, while we detected differences in susceptibility to technical bias. Using recently published DNA methylation profiles of Ewing sarcoma samples, we show that DNA methylation heterogeneity provides information complementary to the DNA methylation level. WSH scores are powerful tools for estimating variance in DNA methylation patterns and have the potential for detecting novel disease-associated genomic loci not captured by established statistics. We provide an R-package implementing the WSH scores for integration into analysis workflows.

Unique and assay specific features of NOMe-, ATAC- and DNase I-seq data.

Chromatin accessibility maps are important for the functional interpretation of the genome. Here, we systematically analysed assay specific differences between DNase I-seq, ATAC-seq and NOMe-seq in a side by side experimental and bioinformatic setup. We observe that most prominent nucleosome depleted regions (NDRs, e.g. in promoters) are roboustly called by all three or at least two assays. However, we also find a high proportion of assay specific NDRs that are often 'called' by only one of the assays. We show evidence that these assay specific NDRs are indeed genuine open chromatin sites and contribute important information for accurate gene expression prediction. While technically ATAC-seq and DNase I-seq provide a superb high NDR calling rate for relatively low sequencing costs in comparison to NOMe-seq, NOMe-seq singles out for its genome-wide coverage allowing to not only detect NDRs but also endogenous DNA methylation and as we show here genome wide segmentation into heterochromatic B domains and local phasing of nucleosomes outside of NDRs. In summary, our comparisons strongly suggest to consider assay specific differences for the experimental design and for generalized and comparative functional interpretations.

RnBeads 2.0: comprehensive analysis of DNA methylation data.

DNA methylation is a widely investigated epigenetic mark with important roles in development and disease. High-throughput assays enable genome-scale DNA methylation analysis in large numbers of samples. Here, we describe a new version of our RnBeads software - an R/Bioconductor package that implements start-to-finish analysis workflows for Infinium microarrays and various types of bisulfite sequencing. RnBeads 2.0 ( https://rnbeads.org/ ) provides additional data types and analysis methods, new functionality for interpreting DNA methylation differences, improved usability with a novel graphical user interface, and better use of computational resources. We demonstrate RnBeads 2.0 in four re-runnable use cases focusing on cell differentiation and cancer.

Integrative analysis of single-cell expression data reveals distinct regulatory states in bidirectional promoters.

BACKGROUND: Bidirectional promoters (BPs) are prevalent in eukaryotic genomes. However, it is poorly understood how the cell integrates different epigenomic information, such as transcription factor (TF) binding and chromatin marks, to drive gene expression at BPs. Single-cell sequencing technologies are revolutionizing the field of genome biology. Therefore, this study focuses on the integration of single-cell RNA-seq data with bulk ChIP-seq and other epigenetics data, for which single-cell technologies are not yet established, in the context of BPs. RESULTS: We performed integrative analyses of novel human single-cell RNA-seq (scRNA-seq) data with bulk ChIP-seq and other epigenetics data. scRNA-seq data revealed distinct transcription states of BPs that were previously not recognized. We find associations between these transcription states to distinct patterns in structural gene features, DNA accessibility, histone modification, DNA methylation and TF binding profiles. CONCLUSIONS: Our results suggest that a complex interplay of all of these elements is required to achieve BP-specific transcriptional output in this specialized promoter configuration. Further, our study implies that novel statistical methods can be developed to deconvolute masked subpopulations of cells measured with different bulk epigenomic assays using scRNA-seq data.

A comprehensive analysis of 195 DNA methylomes reveals shared and cell-specific features of partially methylated domains.

BACKGROUND: Partially methylated domains are extended regions in the genome exhibiting a reduced average DNA methylation level. They cover gene-poor and transcriptionally inactive regions and tend to be heterochromatic. We present a comprehensive comparative analysis of partially methylated domains in human and mouse cells, to identify structural and functional features associated with them. RESULTS: Partially methylated domains are present in up to 75% of the genome in human and mouse cells irrespective of their tissue or cell origin. Each cell type has a distinct set of partially methylated domains, and genes expressed in such domains show a strong cell type effect. The methylation level varies between cell types with a more pronounced effect in differentiating and replicating cells. The lowest level of methylation is observed in highly proliferating and immortal cancer cell lines. A decrease of DNA methylation within partially methylated domains tends to be linked to an increase in heterochromatic histone marks and a decrease of gene expression. Characteristic combinations of heterochromatic signatures in partially methylated domains are linked to domains of early and middle S-phase and late S-G2 phases of DNA replication. CONCLUSIONS: Partially methylated domains are prominent signatures of long-range epigenomic organization. Integrative analysis identifies them as important general, lineage- and cell type-specific topological features. Changes in partially methylated domains are hallmarks of cell differentiation, with decreased methylation levels and increased heterochromatic marks being linked to enhanced cell proliferation. In combination with broad histone marks, partially methylated domains demarcate distinct domains of late DNA replication.

Exposure to the gut microbiota drives distinct methylome and transcriptome changes in intestinal epithelial cells during postnatal development.

BACKGROUND: The interplay of epigenetic processes and the intestinal microbiota may play an important role in intestinal development and homeostasis. Previous studies have established that the microbiota regulates a large proportion of the intestinal epithelial transcriptome in the adult host, but microbial effects on DNA methylation and gene expression during early postnatal development are still poorly understood. Here, we sought to investigate the microbial effects on DNA methylation and the transcriptome of intestinal epithelial cells (IECs) during postnatal development. METHODS: We collected IECs from the small intestine of each of five 1-, 4- and 12 to 16-week-old mice representing the infant, juvenile, and adult states, raised either in the presence or absence of a microbiota. The DNA methylation profile was determined using reduced representation bisulfite sequencing (RRBS) and the epithelial transcriptome by RNA sequencing using paired samples from each individual mouse to analyze the link between microbiota, gene expression, and DNA methylation. RESULTS: We found that microbiota-dependent and -independent processes act together to shape the postnatal development of the transcriptome and DNA methylation signatures of IECs. The bacterial effect on the transcriptome increased over time, whereas most microbiota-dependent DNA methylation differences were detected already early after birth. Microbiota-responsive transcripts could be attributed to stage-specific cellular programs during postnatal development and regulated gene sets involved primarily immune pathways and metabolic processes. Integrated analysis of the methylome and transcriptome data identified 126 genomic loci at which coupled differential DNA methylation and RNA transcription were associated with the presence of intestinal microbiota. We validated a subset of differentially expressed and methylated genes in an independent mouse cohort, indicating the existence of microbiota-dependent "functional" methylation sites which may impact on long-term gene expression signatures in IECs. CONCLUSIONS: Our study represents the first genome-wide analysis of microbiota-mediated effects on maturation of DNA methylation signatures and the transcriptional program of IECs after birth. It indicates that the gut microbiota dynamically modulates large portions of the epithelial transcriptome during postnatal development, but targets only a subset of microbially responsive genes through their DNA methylation status.

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

DNA Methylation Dynamics of Human Hematopoietic Stem Cell Differentiation.

Hematopoietic stem cells give rise to all blood cells in a differentiation process that involves widespread epigenome remodeling. Here we present genome-wide reference maps of the associated DNA methylation dynamics. We used a meta-epigenomic approach that combines DNA methylation profiles across many small pools of cells and performed single-cell methylome sequencing to assess cell-to-cell heterogeneity. The resulting dataset identified characteristic differences between HSCs derived from fetal liver, cord blood, bone marrow, and peripheral blood. We also observed lineage-specific DNA methylation between myeloid and lymphoid progenitors, characterized immature multi-lymphoid progenitors, and detected progressive DNA methylation differences in maturing megakaryocytes. We linked these patterns to gene expression, histone modifications, and chromatin accessibility, and we used machine learning to derive a model of human hematopoietic differentiation directly from DNA methylation data. Our results contribute to a better understanding of human hematopoietic stem cell differentiation and provide a framework for studying blood-linked diseases.

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.

Sensitive detection of viral transcripts in human tumor transcriptomes.

In excess of 12% of human cancer incidents have a viral cofactor. Epidemiological studies of idiopathic human cancers indicate that additional tumor viruses remain to be discovered. Recent advances in sequencing technology have enabled systematic screenings of human tumor transcriptomes for viral transcripts. However, technical problems such as low abundances of viral transcripts in large volumes of sequencing data, viral sequence divergence, and homology between viral and human factors significantly confound identification of tumor viruses. We have developed a novel computational approach for detecting viral transcripts in human cancers that takes the aforementioned confounding factors into account and is applicable to a wide variety of viruses and tumors. We apply the approach to conducting the first systematic search for viruses in neuroblastoma, the most common cancer in infancy. The diverse clinical progression of this disease as well as related epidemiological and virological findings are highly suggestive of a pathogenic cofactor. However, a viral etiology of neuroblastoma is currently contested. We mapped 14 transcriptomes of neuroblastoma as well as positive and negative controls to the human and all known viral genomes in order to detect both known and unknown viruses. Analysis of controls, comparisons with related methods, and statistical estimates demonstrate the high sensitivity of our approach. Detailed investigation of putative viral transcripts within neuroblastoma samples did not provide evidence for the existence of any known human viruses. Likewise, de-novo assembly and analysis of chimeric transcripts did not result in expression signatures associated with novel human pathogens. While confounding factors such as sample dilution or viral clearance in progressed tumors may mask viral cofactors in the data, in principle, this is rendered less likely by the high sensitivity of our approach and the number of biological replicates analyzed. Therefore, our results suggest that frequent viral cofactors of metastatic neuroblastoma are unlikely.

A method for finding consensus breakpoints in the cancer genome from copy number data.

MOTIVATION: Recurrent DNA breakpoints in cancer genomes indicate the presence of critical functional elements for tumor development. Identifying them can help determine new therapeutic targets. High-dimensional DNA microarray experiments like arrayCGH afford the identification of DNA copy number breakpoints with high precision, offering a solid basis for computational estimation of recurrent breakpoint locations. RESULTS: We introduce a method for identification of recurrent breakpoints (consensus breakpoints) from copy number aberration datasets. The method is based on weighted kernel counting of breakpoints around genomic locations. Counts larger than expected by chance are considered significant. We show that the consensus breakpoints facilitate consensus segmentation of the samples. We apply our method to three arrayCGH datasets and show that by using consensus segmentation we achieve significant dimension reduction, which is useful for the task of prediction of tumor phenotype based on copy number data. We use our approach for classification of neuroblastoma tumors from different age groups and confirm the recent recommendation for the choice of age cut-off for differential treatment of 18 months. We also investigate the (epi)genetic properties at consensus breakpoint locations for seven datasets and show enrichment in overlap with important functional genomic regions. AVAILABILITY: Implementation in R of our approach can be found at http://www.mpi-inf.mpg.de/ ∼laura/FeatureGrouping.html. CONTACT: laura@mpi-inf.mpg.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparative genome-wide DNA methylation analysis of colorectal tumor and matched normal tissues.

Aberrant DNA methylation often occurs in colorectal cancer (CRC). In our study we applied a genome-wide DNA methylation analysis approach, MethylCap-seq, to map the differentially methylated regions (DMRs) in 24 tumors and matched normal colon samples. In total, 2687 frequently hypermethylated and 468 frequently hypomethylated regions were identified, which include potential biomarkers for CRC diagnosis. Hypermethylation in the tumor samples was enriched at CpG islands and gene promoters, while hypomethylation was distributed throughout the genome. Using epigenetic data from human embryonic stem cells, we show that frequently hypermethylated regions coincide with bivalent loci in human embryonic stem cells. DNA methylation is commonly thought to lead to gene silencing; however, integration of publically available gene expression data indicates that 75% of the frequently hypermethylated genes were most likely already lowly or not expressed in normal tissue. Collectively, our study provides genome-wide DNA methylation maps of CRC, comprehensive lists of DMRs, and gives insights into the role of aberrant DNA methylation in CRC formation.

Managing drug resistance in cancer: lessons from HIV therapy.

Drug resistance is a common cause of treatment failure for HIV infection and cancer. The high mutation rate of HIV leads to genetic heterogeneity among viral populations and provides the seed from which drug-resistant clones emerge in response to therapy. Similarly, most cancers are characterized by extensive genetic, epigenetic, transcriptional and cellular diversity, and drug-resistant cancer cells outgrow their non-resistant peers in a process of somatic evolution. Patient-specific combination of antiviral drugs has emerged as a powerful approach for treating drug-resistant HIV infection, using genotype-based predictions to identify the best matched combination therapy among several hundred possible combinations of HIV drugs. In this Opinion article, we argue that HIV therapy provides a 'blueprint' for designing and validating patient-specific combination therapies in cancer.

Analyzing epigenome data in context of genome evolution and human diseases.

This chapter describes bioinformatic tools for analyzing epigenome differences between species and in diseased versus normal cells. We illustrate the interplay of several Web-based tools in a case study of CpG island evolution between human and mouse. Starting from a list of orthologous genes, we use the Galaxy Web service to obtain gene coordinates for both species. These data are further analyzed in EpiGRAPH, a Web-based tool that identifies statistically significant epigenetic differences between genome region sets. Finally, we outline how the use of the statistical programming language R enables deeper insights into the epigenetics of human diseases, which are difficult to obtain without writing custom scripts. In summary, our tutorial describes how Web-based tools provide an easy entry into epigenome data analysis while also highlighting the benefits of learning a scripting language in order to unlock the vast potential of public epigenome datasets.

APOBEC3G/F as one possible driving force for co-receptor switch of the human immunodeficiency virus-1.

Human immunodeficiency virus-1 tropism highly correlates with the amino acid (aa) composition of the third hypervariable region (V3) of gp120. A shift towards more positively charged aa is seen when binding to CXCR4 compared with CCR5 (X4 vs. R5 strains), especially positions 11 and 25 (11/25-rule) predicting X4 viruses in the presence of positively charged residues. At nucleotide levels, negatively or uncharged aa, e.g., aspartic and glutamic acid and glycine, which are encoded by the triplets GAN (guanine-adenosine-any nucleotide) or GGN are found more often in R5 strains. Positively charged aa such as arginine and lysine encoded by AAR or AGR (CGN) (R means A or G) are seen more frequently in X4 strains suggesting our hypothesis that a switch from R5 to X4 strains occurs via a G-to-A mutation. 1527 V3 sequences from three independent data sets of X4 and R5 strains were analysed with respect to their triplet composition. A higher number of G-containing triplets was found in R5 viruses, whereas X4 strains displayed a higher content of A-comprising triplets. These findings also support our hypothesis that G-to-A mutations are leading to the co-receptor switch from R5 to X4 strains. Causative agents for G-to-A mutations are the deaminases APOBEC3F and APOBEC3G. We therefore hypothesize that these proteins are one driving force facilitating the appearance of X4 variants. G-to-A mutations can lead to a switch from negatively to positively charged aa and a respective alteration of the net charge of gp120 resulting in a change of co-receptor usage.

A DNA methylation fingerprint of 1628 human samples.

Most of the studies characterizing DNA methylation patterns have been restricted to particular genomic loci in a limited number of human samples and pathological conditions. Herein, we present a compromise between an extremely comprehensive study of a human sample population with an intermediate level of resolution of CpGs at the genomic level. We obtained a DNA methylation fingerprint of 1628 human samples in which we interrogated 1505 CpG sites. The DNA methylation patterns revealed show this epigenetic mark to be critical in tissue-type definition and stemness, particularly around transcription start sites that are not within a CpG island. For disease, the generated DNA methylation fingerprints show that, during tumorigenesis, human cancer cells underwent a progressive gain of promoter CpG-island hypermethylation and a loss of CpG methylation in non-CpG-island promoters. Although transformed cells are those in which DNA methylation disruption is more obvious, we observed that other common human diseases, such as neurological and autoimmune disorders, had their own distinct DNA methylation profiles. Most importantly, we provide proof of principle that the DNA methylation fingerprints obtained might be useful for translational purposes by showing that we are able to identify the tumor type origin of cancers of unknown primary origin (CUPs). Thus, the DNA methylation patterns identified across the largest spectrum of samples, tissues, and diseases reported to date constitute a baseline for developing higher-resolution DNA methylation maps and provide important clues concerning the contribution of CpG methylation to tissue identity and its changes in the most prevalent human diseases.

Peptidomimetic escape mechanisms arise via genetic diversity in the ligand-binding site of the hepatitis C virus NS3/4A serine protease.

BACKGROUND & AIMS: It is a challenge to develop direct-acting antiviral agents that target the nonstructural protein 3/4A protease of hepatitis C virus because resistant variants develop. Ketoamide compounds, designed to mimic the natural protease substrate, have been developed as inhibitors. However, clinical trials have revealed rapid selection of resistant mutants, most of which are considered to be pre-existing variants. METHODS: We identified residues near the ketoamide-binding site in x-ray structures of the genotype 1a protease, co-crystallized with boceprevir or a telaprevir-like ligand, and then identified variants at these positions in 219 genotype-1 sequences from a public database. We used side-chain modeling to assess the potential effects of these variants on the interaction between ketoamide and the protease, and compared these results with the phenotypic effects on ketoamide resistance, RNA replication capacity, and infectious virus yields in a cell culture model of infection. RESULTS: Thirteen natural binding-site variants with potential for ketoamide resistance were identified at 10 residues in the protease, near the ketoamide binding site. Rotamer analysis of amino acid side-chain conformations indicated that 2 variants (R155K and D168G) could affect binding of telaprevir more than boceprevir. Measurements of antiviral susceptibility in cell-culture studies were consistent with this observation. Four variants (ie, Q41H, I132V, R155K, and D168G) caused low-to-moderate levels of ketoamide resistance; 3 of these were highly fit (Q41H, I132V, and R155K). CONCLUSIONS: Using a comprehensive sequence and structure-based analysis, we showed how natural variation in the hepatitis C virus protease nonstructural protein 3/4A sequences might affect susceptibility to first-generation direct-acting antiviral agents. These findings increase our understanding of the molecular basis of ketoamide resistance among naturally existing viral variants.

Preserving charge and oxidation state of Au(III) ions in an agent-functionalized nanocrystal model system.

Supporting functional molecules on crystal facets is an established technique in nanotechnology. To preserve the original activity of ionic metallorganic agents on a supporting template, conservation of the charge and oxidation state of the active center is indispensable. We present a model system of a metallorganic agent that, indeed, fulfills this design criterion on a technologically relevant metal support with potential impact on Au(III)-porphyrin-functionalized nanoparticles for an improved anticancer-drug delivery. Employing scanning tunneling microscopy and -spectroscopy in combination with photoemission spectroscopy, we clarify at the single-molecule level the underlying mechanisms of this exceptional adsorption mode. It is based on the balance between a high-energy oxidation state and an electrostatic screening-response of the surface (image charge). Modeling with first principles methods reveals submolecular details of the metal-ligand bonding interaction and completes the study by providing an illustrative electrostatic model relevant for ionic metalorganic agent molecules, in general.

Classification with correlated features: unreliability of feature ranking and solutions.

MOTIVATION: Classification and feature selection of genomics or transcriptomics data is often hampered by the large number of features as compared with the small number of samples available. Moreover, features represented by probes that either have similar molecular functions (gene expression analysis) or genomic locations (DNA copy number analysis) are highly correlated. Classical model selection methods such as penalized logistic regression or random forest become unstable in the presence of high feature correlations. Sophisticated penalties such as group Lasso or fused Lasso can force the models to assign similar weights to correlated features and thus improve model stability and interpretability. In this article, we show that the measures of feature relevance corresponding to the above-mentioned methods are biased such that the weights of the features belonging to groups of correlated features decrease as the sizes of the groups increase, which leads to incorrect model interpretation and misleading feature ranking. RESULTS: With simulation experiments, we demonstrate that Lasso logistic regression, fused support vector machine, group Lasso and random forest models suffer from correlation bias. Using simulations, we show that two related methods for group selection based on feature clustering can be used for correcting the correlation bias. These techniques also improve the stability and the accuracy of the baseline models. We apply all methods investigated to a breast cancer and a bladder cancer arrayCGH dataset and in order to identify copy number aberrations predictive of tumor phenotype. AVAILABILITY: R code can be found at: http://www.mpi-inf.mpg.de/~laura/Clustering.r.