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Pancreatic ductal adenocarcinoma (PDAC) is believed to arise from the accumulation of a series of somatic mutations and is also frequently associated with pancreatic intraepithelial neoplasia (PanIN) lesions. However, there is still debate as to whether the cell type-of-origin of PanINs and PDACs in humans is acinar or ductal. As cell type identity is maintained epigenetically, DNA methylation changes during pancreatic neoplasia can provide a compelling perspective to examine this question. Here, we performed laser-capture microdissection on surgically resected specimens from 18 patients to isolate, with high purity, DNA for whole-genome bisulfite sequencing from four relevant cell types: acini, nonneoplastic ducts, PanIN lesions, and PDAC lesions. Differentially methylated regions (DMR) were identified using two complementary analytical approaches: bsseq, which identifies any DMRs but is particularly useful for large block-like DMRs, and informME, which profiles the potential energy landscape across the genome and is particularly useful for identifying differential methylation entropy. Both global methylation profiles and block DMRs clearly implicated an acinar origin for PanINs. At the gene level, PanIN lesions exhibited an intermediate acinar-ductal phenotype resembling acinar-to-ductal metaplasia. In 97.6% of PanIN-specific DMRs, PanIN lesions had an intermediate methylation level between normal and PDAC, which suggests from an information theory perspective that PanIN lesions are epigenetically primed to progress to PDAC. Thus, epigenomic analysis complements histopathology to define molecular progression toward PDAC. The shared epigenetic lineage between PanIN and PDAC lesions could provide an opportunity for prevention by targeting aberrantly methylated progression-related genes. Significance: Analysis of DNA methylation landscapes provides insights into the cell-of-origin of PanIN lesions, clarifies the role of PanIN lesions as metaplastic precursors to human PDAC, and suggests potential targets for chemoprevention.

Summary In complex tissues containing cells that are difficult to dissociate, single-nucleus RNA-sequencing (snRNA-seq) has become the preferred experimental technology over single-cell RNA-sequencing (scRNA-seq) to measure gene expression. To accurately model these data in downstream analyses, previous work has shown that droplet-based scRNA-seq data are not zero-inflated, but whether droplet-based snRNA-seq data follow the same probability distributions has not been systematically evaluated. Using pseudonegative control data from nuclei in mouse cortex sequenced with the 10x Genomics Chromium system and mouse kidney sequenced with the DropSeq system, we found that droplet-based snRNA-seq data follow a negative binomial distribution, suggesting that parametric statistical models applied to scRNA-seq are transferable to snRNA-seq. Furthermore, we found that the quantification choices in adapting quantification mapping strategies from scRNA-seq to snRNA-seq can play a significant role in downstream analyses and biological interpretation. In particular, reference transcriptomes that do not include intronic regions result in significantly smaller library sizes and incongruous cell type classifications. We also confirmed the presence of a gene length bias in snRNA-seq data, which we show is present in both exonic and intronic reads, and investigate potential causes for the bias.

Pairwise comparisons of promoter jsDMRs

Pairwise mean-based differentially methylated regions

Pairwise jsDMRs

Motif analysis

Supplementary figures and captions for supplementary tables

Pancreatic ductal adenocarcinoma (PDAC) is believed to arise from the accumulation of a series of somatic mutations and is also frequently associated with pancreatic intraepithelial neoplasia (PanIN) lesions. However, there is still debate as to whether the cell type-of-origin of PanINs and PDACs in humans is acinar or ductal. As cell type identity is maintained epigenetically, DNA methylation changes during pancreatic neoplasia can provide a compelling perspective to examine this question. Here, we performed laser-capture microdissection on surgically resected specimens from 18 patients to isolate, with high purity, DNA for whole-genome bisulfite sequencing from four relevant cell types: acini, nonneoplastic ducts, PanIN lesions, and PDAC lesions. Differentially methylated regions (DMR) were identified using two complementary analytical approaches: bsseq, which identifies any DMRs but is particularly useful for large block-like DMRs, and informME, which profiles the potential energy landscape across the genome and is particularly useful for identifying differential methylation entropy. Both global methylation profiles and block DMRs clearly implicated an acinar origin for PanINs. At the gene level, PanIN lesions exhibited an intermediate acinar-ductal phenotype resembling acinar-to-ductal metaplasia. In 97.6% of PanIN-specific DMRs, PanIN lesions had an intermediate methylation level between normal and PDAC, which suggests from an information theory perspective that PanIN lesions are epigenetically primed to progress to PDAC. Thus, epigenomic analysis complements histopathology to define molecular progression toward PDAC. The shared epigenetic lineage between PanIN and PDAC lesions could provide an opportunity for prevention by targeting aberrantly methylated progression-related genes.Significance:Analysis of DNA methylation landscapes provides insights into the cell-of-origin of PanIN lesions, clarifies the role of PanIN lesions as metaplastic precursors to human PDAC, and suggests potential targets for chemoprevention.

Variant and mutation calling

Progression DMRs

Diet-related metabolic syndrome is the largest contributor to adverse health in the United States. However, the study of gene-environment interactions and their epigenomic and transcriptomic integration is complicated by the lack of environmental and genetic control in humans that is possible in mouse models. Here we exposed three mouse strains, C57BL/6J (BL6), A/J, and NOD/ShiLtJ (NOD), to a high-fat high-carbohydrate diet, leading to varying degrees of metabolic syndrome. We then performed transcriptomic and genomic DNA methylation analyses and found overlapping but also highly divergent changes in gene expression and methylation upstream of the discordant metabolic phenotypes. Strain-specific pathway analysis of dietary effects reveals a dysregulation of cholesterol biosynthesis common to all three strains but distinct regulatory networks driving this dysregulation. This suggests a strategy for strain-specific targeted pharmacologic intervention of these upstream regulators informed by transcriptional regulation. As a pilot study, we administered the drug GW4064 to target one of these genotype-dependent networks, the Farnesoid X receptor pathway, and found that GW4064 exerts genotype-specific protection against dietary effects in BL6, as predicted by our transcriptomic analysis, as well as increased inflammatory-related gene expression changes in NOD. This pilot study demonstrates the potential efficacy of precision therapeutics for genotype-informed dietary metabolic intervention, and a mouse platform for guiding this approach.

Supplementary Methods and Example Analyses. This file provides additional detail on the MultiAssayExperiment data class, methods of preparation of the TCGA objects, methods and additional results for the example analyses. It includes Supplemental Figures 1-2 (Mogsa enrichment scores for Hallmark gene sets), Supplemental Figure 3 (correlation between copy number, mRNA, and protein profiles for the NCI-60 cell lines), Supplemental Table 1 (comparison between the features of MultiAssayExperiment and MultiDataSet), Supplemental Table 2 (summary table of TCGA datasets), and Supplemental Table 3 (elements of the MultiAssayExperiment data class).

Multiomics experiments are increasingly commonplace in biomedical research and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multiomics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple ‘omics data for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze MultiAssayExperiment objects and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable, and reproducible statistical analysis of multiomics data and enhances data science applications of multiple omics datasets. Cancer Res; 77(21); e39–42. ©2017 AACR.

Uremic symptoms are common in patients with advanced chronic kidney disease, but the toxins that cause these symptoms are unknown. To evaluate this, we performed a cross-sectional study of the 12 month post-randomization follow-up visit of Modification of Diet in Renal Disease (MDRD) participants reporting uremic symptoms who also had available stored serum. We quantified 1,163 metabolites by liquid chromatography-tandem mass spectrometry. For each uremic symptom, we calculated a score as the severity multiplied by the number of days the symptom was experienced. We analyzed the associations of the individual symptom scores with metabolites using linear models with empirical Bayesian inference, adjusted for multiple comparisons. Among 695 participants, the mean measured glomerular filtration rate (mGFR) was 28 mL/min/1.73 m(2). Uremic symptoms were more common in the subgroup of 214 patients with an mGFR under 20 mL/min/1.73 m(2) (mGFR under 20 subgroup) than in the full group. For all metabolites with significant associations, the direction of the association was concordant in the full group and the subgroup. For gastrointestinal symptoms (bad taste, loss of appetite, nausea, and vomiting), eleven metabolites were associated with symptoms. For neurologic symptoms (decreased alertness, falling asleep during the day, forgetfulness, lack of pep and energy, tiring easily/weakness), seven metabolites were associated with symptoms. Associations were consistent across sensitivity analyses. Thus, our proof of principle study demonstrates the potential for metabolomics to understand metabolic pathways associated with uremic symptoms. Larger, prospective studies with external validation are needed.

Whether due to mutagens or replication errors, DNA mismatches arise spontaneouslyin vivo. Unrepaired mismatches are sources of genetic variation and point mutations which can alter cellular phenotype and cause dysfunction, diseases, and cancer. To understand how diverse mismatches in various sequence contexts are recognized and repaired, we developed a high-throughput sequencing-based approach to track single mismatch repair outcomesin vivoand determined the mismatch repair efficiencies of 5682 distinct singly mispaired sequences inE. coli. We found that CC mismatches are always poorly repaired, whereas local sequence context is a strong determinant of the hypervariable repair efficiency of TT, AG, and CT mismatches. Single molecule FRET analysis of MutS interactions with mismatched DNA showed that well-repaired mismatches have a higher effective rate of sliding clamp formation. The hypervariable repair of TT mismatches can cause selectively enhanced mutability if a failure to repair would result in synonymous codon change or a conservative amino acid change. Sequence-dependent repair efficiency inE. colican explain the patterns of substitution mutations in mismatch repair-deficient tumors, human cells, andC. elegans. Comparison to biophysical and biochemical analyses indicate that DNA physics is the primary determinant of repair efficiency by its impact on the mismatch recognition by MutS.

SummaryThousands of DNA methylation (DNAm) array samples from human blood are publicly available on the Gene Expression Omnibus (GEO), but they remain underutilized for experiment planning, replication and cross-study and cross-platform analyses. To facilitate these tasks, we augmented our recountmethylation R/Bioconductor package with 12 537 uniformly processed EPIC and HM450K blood samples on GEO as well as several new features. We subsequently used our updated package in several illustrative analyses, finding (i) study ID bias adjustment increased variation explained by biological and demographic variables, (ii) most variation in autosomal DNAm was explained by genetic ancestry and CD4+ T-cell fractions and (iii) the dependence of power to detect differential methylation on sample size was similar for each of peripheral blood mononuclear cells (PBMC), whole blood and umbilical cord blood. Finally, we used PBMC and whole blood to perform independent validations, and we recovered 38–46% of differentially methylated probes between sexes from two previously published epigenome-wide association studies.Availability and implementationSource code to reproduce the main results are available on GitHub (repo: recountmethylation_flexible-blood-analysis_manuscript; url: https://github.com/metamaden/recountmethylation_flexible-blood-analysis_manuscript). All data was publicly available and downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). Compilations of the analyzed public data can be accessed from the website recount.bio/data (preprocessed HM450K array data: https://recount.bio/data/remethdb_h5se-gm_epic_0-0-2_1589820348/; preprocessed EPIC array data: https://recount.bio/data/remethdb_h5se-gm_epic_0-0-2_1589820348/).Supplementary informationSupplementary data are available at Bioinformatics Advances online.

The advent of long-read sequencing methods provides new opportunities for profiling the epigenome - especially as the methylation signature comes for “free” when native DNA is sequenced on either Oxford Nanopore or Pacific Biosciences instruments. However, we lack tools to visualize and analyze data generated from these new sources. Recent efforts from the GA4GH consortium have standardized methods to encode modification location and probabilities in the BAM format. Leveraging this standard format, we developed a technology-agnostic tool, modbamtools to visualize, manipulate and compare base modification/methylation data in a fast and robust way. modbamtools can produce high quality, interactive, and publication-ready visualizations as well as provide modules for downstream analysis of base modifications. Modbamtools comprehensive manual and tutorial can be found at https://rrazaghi.github.io/modbamtools/.

RNA velocity analysis of single cells promises to predict temporal dynamics from gene expression. Indeed, in many systems, it has been observed that RNA velocity produces a vector field that qualitatively reflects known features of the system. Despite this observation, the limitations of RNA velocity estimates are poorly understood. Using real data and simulations, we dissect the impact of different steps in the RNA velocity workflow on the estimated vector field. We find that the process of mapping RNA velocity estimates into a low-dimensional representation, such as those produced by UMAP, has a large impact on the result. The RNA velocity vector field strongly depends on the k-NN graph of the data. This dependence leads to significant estimator errors when the k-NN graph is not a faithful representation of the true data structure, a feature that cannot be known for most real datasets. Finally, we establish that RNA velocity estimates expression speed neither at the gene nor cellular level. We propose that RNA velocity is best considered a smoothed interpolation of the observed k-NN structure, as opposed to an extrapolation of future cellular states, and that the use of RNA velocity as a validation of latent space embedding structures is circular.

Thousands of DNA methylation (DNAm) array samples from human blood are publicly available on the Gene Expression Omnibus (GEO), but they remain underutilized for experiment planning, replication, and cross-study and cross-platform analyses. To facilitate these tasks, we augmented our recountmethylation R/Bioconductor package with 12,537 uniformly processed EPIC and HM450K blood samples on GEO as well as several new features. We subsequently used our updated package in several illustrative analyses, finding (1) study ID bias adjustment increased variation explained by biological and demographic variables, (2) most variation in autosomal DNAm was explained by genetic ancestry and CD4+ T-cell fractions, and (3) the dependence of power to detect differential methylation on sample size was similar for each of peripheral blood mononuclear cells (PBMC), whole blood, and umbilical cord blood. Finally, we used PBMC and whole blood to perform independent validations, and we recovered 40-46% of differentially methylated probes (DMPs) between sexes from two previously published epigenome-wide association studies (EWAS).

In complex tissues containing cells that are difficult to dissociate, single-nucleus RNA-sequencing (snRNA-seq) has become the preferred experimental technology over single-cell RNA-sequencing (scRNA-seq) to measure gene expression. To accurately model these data in downstream analyses, previous work has shown that droplet-based scRNA-seq data are not zero-inflated, but whether droplet-based snRNA-seq data follow the same probability distributions has not been systematically evaluated. Using pseudo-negative control data from nuclei in mouse cortex sequenced with the 10x Genomics Chromium system, we found that snRNA-seq data follow a negative binomial distribution, suggesting that parametric statistical models applied to scRNA-seq are transferable to snRNA-seq. Furthermore, we found that the quantification choices in adapting quantification mapping strategies from scRNA-seq to snRNA-seq can play a significant role in downstream analyses and biological interpretation. In particular, reference transcriptomes that do not include intronic regions result in significantly smaller library sizes and incongruous cell type classifications. We also confirmed the presence of a gene length bias in snRNA-seq data, which we show is present in both exonic and intronic reads, and investigate potential causes for the bias.

Feature selection to identify spatially variable genes or other biologically informative genes is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose nnSVG, a scalable approach to identify spatially variable genes based on nearest-neighbor Gaussian processes. Our method (i) identifies genes that vary in expression continuously across the entire tissue or withina prioridefined spatial domains, (ii) uses gene-specific estimates of length scale parameters within the Gaussian process models, and (iii) scales linearly with the number of spatial locations. We demonstrate the performance of our method using experimental data from several technological platforms and simulations. A software implementation is available athttps://bioconductor.org/packages/nnSVG.

Probing epigenetic features on DNA has tremendous potential to advance our understanding of the phased epigenome. In this study, we use nanopore sequencing to evaluate CpG methylation and chromatin accessibility simultaneously on long strands of DNA by applying GpC methyltransferase to exogenously label open chromatin. We performed nanopore sequencing of Nucleosome Occupancy and Methylome (nanoNOMe) on four human cell lines (GM12878, MCF-10A, MCF-7, MDA-MB-231). The single-molecule resolution allows footprinting of protein and nucleosome binding and determining the combinatorial promoter epigenetic signature on individual molecules. Long-read sequencing makes it possible to robustly assign reads to haplotypes, allowing us to generate the first fully phased human epigenome, consisting of chromosome-level allele-specific profiles of CpG methylation and chromatin accessibility. We further apply this to a breast cancer model to evaluate differential methylation and accessibility between cancerous and non-cancerous cells.

Air pollution involving particulate matter smaller than 2.5 μm in size (PM(2.5)) is the world’s leading environmental risk factor contributing to mortality through cardiometabolic pathways. In this study, we modeled early life exposure using chow-fed C57BL/6J male mice that were exposed to real-world inhaled, concentrated PM(2.5) (~10 times ambient levels/~60–120 μg/m(3)) or filtered air over a 14-week period. We investigated the effects of PM(2.5) on phenotype, the transcriptome, and chromatin accessibility and compared these with the effects of a prototypical high-fat diet (HFD) as well as cessation of exposure on phenotype reversibility. Exposure to PM(2.5) impaired glucose and insulin tolerance and reduced energy expenditure and (18)FDG-PET uptake in brown adipose tissue. Multiple differentially expressed gene clusters in pathways involving metabolism and circadian rhythm were noted in insulin-responsive tissues. Although the magnitude of transcriptional change detected with PM(2.5) exposure was lower than that observed with a HFD, the degree of alteration in chromatin accessibility after PM(2.5) exposure was significant. The novel chromatin remodeler SMARCA5 (SWI/SNF complex) was regulated in response to PM(2.5) exposure, the cessation of which was associated with a reversal of insulin resistance and restoration of chromatin accessibility and nucleosome positioning near transcription start sites, as well as a reversal of exposure-induced changes in the transcriptome, including SMARCA5. These changes indicate pliable epigenetic control mechanisms following cessation of exposure.

The aggregation and joint analysis of large numbers of exome sequences has recently made it possible to de-rive estimates of intolerance to loss-of-function (LoF) variation for human genes. Here, we demonstrate strong and widespread coupling between genic LoF-intolerance and promoter CpG density across the human genome. Genes downstream of the most CpG-rich pro-moters (top 10% CpG density) have a 67.2% probability of being highly LoF-intolerant, using the LOEUF metric from gnomAD. This is in contrast to 7.4% of genes downstream of the most CpG-poor (bottom 10% CpG density) promoters. Combining promoter CpG density with exonic and promoter conservation explains 33.4% of the variation in LOEUF, and the contribution of CpG density exceeds the individual contributions of exonic and promoter conservation. We leverage this to train a simple and easily interpretable predictive model that out-performs other existing predictors and allows us to classify 1,760 genes – which currently lack reliable LOEUF estimates – as highly LoF-intolerant or not. These predictions have the potential to aid in the interpretation of novel patient variants. Moreover, our results reveal that high CpG density is not merely a generic feature of human promoters, but is preferentially encountered at the promoters of the most selectively constrained genes, calling into question the prevailing view that CpG islands are not subject to selection.

We present recount3, a resource consisting of over 750,000 publicly available human and mouse RNA sequencing (RNA-seq) samples uniformly processed by our new Monorail analysis pipeline. To facilitate access to the data, we provide the recount3 and snapcount R/Bioconductor packages as well as complementary web resources. Using these tools, data can be downloaded as study-level summaries or queried for specific exon-exon junctions, genes, samples, or other features. Monorail can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, our tools help biologists maximize the utility of publicly available RNA-seq data, especially to improve their understanding of newly collected data. recount3 is available from http://rna.recount.bio.

The Mendelian Disorders of the Epigenetic Machinery (MDEMs) have emerged as a class of Mendelian disorders caused by loss-of-function variants in epigenetic regulators. Although each MDEM has a different causative gene, they exhibit several overlapping disease manifestations. Here, we hypothesize that this phenotypic convergence is a consequence of common abnormalities at the epigenomic level, which directly or indirectly lead to downstream convergence at the transcriptomic level. Therefore, we seek to identify abnormalities shared across multiple MDEMs, in order to pinpoint locations where epigenetic variation is causally related to disease phenotypes. To this end, we perform a comprehensive interrogation of chromatin (ATAC-Seq) and expression (RNA-Seq) states in B cells from mouse models of three MDEMs (Kabuki types 1&2 and Rubinstein-Taybi syndromes). We build on recent work in covariate-powered multiple testing to develop a new approach for the overlap analysis, which enables us to find extensive overlap primarily localized in gene promoters. We show that disruption of chromatin accessibility at promoters often leads to disruption of downstream gene expression, and identify 463 loci and 249 genes with shared disruption across all three MDEMs. As an example of how widespread dysregulation leads to specific phenotypes, we show that subtle expression alterations of multiple, IgA-relevant genes, collectively contribute to IgA deficiency in KS1 and RT1. In contrast, we predict that KS2 does not have IgA deficiency, and confirm this observationin vivo. We propose that the joint study of MDEMs offers a principled approach for systematically mapping functional epigenetic variation in mammals.

Single-cell profiling of chromatin structure remains a challenge due to cost, throughput, and resolution. We introduce compartmap to reconstruct higher-order chromatin domains in individual cells from transcriptomic (RNAseq) and epigenomic (ATACseq) assays. In cell lines and primary human samples, compartmap infers higher-order chromatin structure comparable to specialized chromatin capture methods, and identifies clinically relevant structural alterations in single cells. This provides a common lens to integrate transcriptional and epigenomic results, linking higher-order chromatin architecture to gene regulation and to clinically relevant phenotypes in individual cells.

Estimates of correlation between pairs of genes in co-expression analysis are commonly used to construct networks among genes using gene expression data. As previously noted, the distribution of such correlations depends on the observed expression level of the involved genes, which we refer to this as amean-correlation relationshipin RNA-seq data, both bulk and single-cell. This dependence introduces an unwanted technical bias in co-expression analysis whereby highly expressed genes are more likely to be highly correlated. Such a relationship is not observed in protein-protein interaction data, suggesting that it is not reflecting biology. Ignoring this bias can lead to missing potentially biologically relevant pairs of genes that are lowly expressed, such as transcription factors. To address this problem, we introduce spatial quantile normalization (SpQN), a method for normalizing local distributions in a correlation matrix. We show that spatial quantile normalization removes the mean-correlation relationship and corrects the expression bias in network reconstruction.

Epigenetic modifications confer stable transcriptional patterns in the brain, and both normal and abnormal brain function involve specialized brain regions. We examined DNA methylation by whole-genome bisulfite sequencing in neuronal and non-neuronal populations from four brain regions (anterior cingulate gyrus, hippocampus, prefrontal cortex, and nucleus accumbens) as well as chromatin accessibility in the latter two. We find pronounced differences in both CpG and non-CpG methylation (CG-DMRs and CH-DMRs) only in neuronal cells across brain regions. Neuronal CH-DMRs were highly associated with differential gene expression, whereas CG-DMRs were consistent with chromatin accessibility and enriched for regulatory regions. These CG-DMRs comprise ~12 Mb of the genome that is highly enriched for genomic regions associated with heritability of neuropsychiatric traits including addictive behavior, schizophrenia, and neuroticism, thus suggesting a mechanistic link between pathology and differential neuron-specific epigenetic regulation in distinct brain regions. An extensive profile of DNA methylation in neuronal and non-neuronal cells across four brain regions is reported, showing that differential epigenetic marks are enriched for DNA variants associated with neuropsychiatric traits.