Nathan C. Sheffield

The accessible chromatin landscape of the human genome.

DNase I hypersensitive sites (DHSs) are markers of regulatory DNA and have underpinned the discovery of all classes of cis-regulatory elements including enhancers, promoters, insulators, silencers and locus control regions. Here we present the first extensive map of human DHSs identified through genome-wide profiling in 125 diverse cell and tissue types. We identify ∼2.9 million DHSs that encompass virtually all known experimentally validated cis-regulatory sequences and expose a vast trove of novel elements, most with highly cell-selective regulation. Annotating these elements using ENCODE data reveals novel relationships between chromatin accessibility, transcription, DNA methylation and regulatory factor occupancy patterns. We connect ∼580,000 distal DHSs with their target promoters, revealing systematic pairing of different classes of distal DHSs and specific promoter types. Patterning of chromatin accessibility at many regulatory regions is organized with dozens to hundreds of co-activated elements, and the transcellular DNase I sensitivity pattern at a given region can predict cell-type-specific functional behaviours. The DHS landscape shows signatures of recent functional evolutionary constraint. However, the DHS compartment in pluripotent and immortalized cells exhibits higher mutation rates than that in highly differentiated cells, exposing an unexpected link between chromatin accessibility, proliferative potential and patterns of human variation.

Single-Cell DNA Methylome Sequencing and Bioinformatic Inference of Epigenomic Cell-State Dynamics

Summary Methods for single-cell genome and transcriptome sequencing have contributed to our understanding of cellular heterogeneity, whereas methods for single-cell epigenomics are much less established. Here, we describe a whole-genome bisulfite sequencing (WGBS) assay that enables DNA methylation mapping in very small cell populations (μWGBS) and single cells (scWGBS). Our assay is optimized for profiling many samples at low coverage, and we describe a bioinformatic method that analyzes collections of single-cell methylomes to infer cell-state dynamics. Using these technological advances, we studied epigenomic cell-state dynamics in three in vitro models of cellular differentiation and pluripotency, where we observed characteristic patterns of epigenome remodeling and cell-to-cell heterogeneity. The described method enables single-cell analysis of DNA methylation in a broad range of biological systems, including embryonic development, stem cell differentiation, and cancer. It can also be used to establish composite methylomes that account for cell-to-cell heterogeneity in complex tissue samples.

A Comparative Analysis of Mitochondrial Genomes in Coleoptera (Arthropoda: Insecta) and Genome Descriptions of Six New Beetles

Coleoptera is the most diverse group of insects with over 360,000 described species divided into four suborders: Adephaga, Archostemata, Myxophaga, and Polyphaga. In this study, we present six new complete mitochondrial genome (mtgenome) descriptions, including a representative of each suborder, and analyze the evolution of mtgenomes from a comparative framework using all available coleopteran mtgenomes. We propose a modification of atypical cox1 start codons based on sequence alignment to better reflect the conservation observed across species as well as findings of TTG start codons in other genes. We also analyze tRNA-Ser(AGN) anticodons, usually GCU in arthropods, and report a conserved UCU anticodon as a possible synapomorphy across Polyphaga. We further analyze the secondary structure of tRNA-Ser(AGN) and present a consensus structure and an updated covariance model that allows tRNAscan-SE (via the COVE software package) to locate and fold these atypical tRNAs with much greater consistency. We also report secondary structure predictions for both rRNA genes based on conserved stems. All six species of beetle have the same gene order as the ancestral insect. We report noncoding DNA regions, including a small gap region of about 20 bp between tRNA-Ser(UCN) and nad1 that is present in all six genomes, and present results of a base composition analysis.

Predicting cell-type–specific gene expression from regions of open chromatin

Complex patterns of cell-type-specific gene expression are thought to be achieved by combinatorial binding of transcription factors (TFs) to sequence elements in regulatory regions. Predicting cell-type-specific expression in mammals has been hindered by the oftentimes unknown location of distal regulatory regions. To alleviate this bottleneck, we used DNase-seq data from 19 diverse human cell types to identify proximal and distal regulatory elements at genome-wide scale. Matched expression data allowed us to separate genes into classes of cell-type-specific up-regulated, down-regulated, and constitutively expressed genes. CG dinucleotide content and DNA accessibility in the promoters of these three classes of genes displayed substantial differences, highlighting the importance of including these aspects in modeling gene expression. We associated DNase I hypersensitive sites (DHSs) with genes, and trained classifiers for different expression patterns. TF sequence motif matches in DHSs provided a strong performance improvement in predicting gene expression over the typical baseline approach of using proximal promoter sequences. In particular, we achieved competitive performance when discriminating up-regulated genes from different cell types or genes up- and down-regulated under the same conditions. We identified previously known and new candidate cell-type-specific regulators. The models generated testable predictions of activating or repressive functions of regulators. DNase I footprints for these regulators were indicative of their direct binding to DNA. In summary, we successfully used information of open chromatin obtained by a single assay, DNase-seq, to address the problem of predicting cell-type-specific gene expression in mammalian organisms directly from regulatory sequence.

Nonstationary evolution and compositional heterogeneity in beetle mitochondrial phylogenomics.

Many published phylogenies are based on methods that assume equal nucleotide composition among taxa. Studies have shown, however, that this assumption is often not accurate, particularly in divergent lineages. Nonstationary sequence evolution, when taxa in different lineages evolve in different ways, can lead to unequal nucleotide composition. This can cause inference methods to fail and phylogenies to be inaccurate. Recent advancements in phylogenetic theory have proposed new models of nonstationary sequence evolution; these models often outperform equivalent stationary models. A variety of new phylogenetic software implementing such models has been developed, but the studies employing the new methodology are still few. We discovered convergence of nucleotide composition within mitochondrial genomes of the insect order Coleoptera (beetles). We found variation in base content both among species and among genes in the genome. To this data set, we have applied a broad range of phylogenetic methods, including some traditional stationary models of evolution and all the more recent nonstationary models. We compare 8 inference methods applied to the same data set. Although the more commonly used methods universally fail to recover established clades, we find that some of the newer software packages are more appropriate for data of this nature. The software packages p4, PHASE, and nhPhyML were able to overcome the systematic bias in our data set, but parsimony, MrBayes, NJ, LogDet, and PhyloBayes were not.

Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions

Regulatory elements recruit transcription factors that modulate gene expression distinctly across cell types, but the relationships among these remains elusive. To address this, we analyzed matched DNase-seq and gene expression data for 112 human samples representing 72 cell types. We first defined more than 1800 clusters of DNase I hypersensitive sites (DHSs) with similar tissue specificity of DNase-seq signal patterns. We then used these to uncover distinct associations between DHSs and promoters, CpG islands, conserved elements, and transcription factor motif enrichment. Motif analysis within clusters identified known and novel motifs in cell-type-specific and ubiquitous regulatory elements and supports a role for AP-1 regulating open chromatin. We developed a classifier that accurately predicts cell-type lineage based on only 43 DHSs and evaluated the tissue of origin for cancer cell types. A similar classifier identified three sex-specific loci on the X chromosome, including the XIST lincRNA locus. By correlating DNase I signal and gene expression, we predicted regulated genes for more than 500K DHSs. Finally, we introduce a web resource to enable researchers to use these results to explore these regulatory patterns and better understand how expression is modulated within and across human cell types.

When phylogenetic assumptions are violated: base compositional heterogeneity and among-site rate variation in beetle mitochondrial phylogenomics

The ability to generate large molecular datasets for phylogenetic studies benefits biologists, but such data expansion introduces numerous analytical problems. A typical molecular phylogenetic study implicitly assumes that sequences evolve under stationary, reversible and homogeneous conditions, but this assumption is often violated in real datasets. When an analysis of large molecular datasets results in unexpected relationships, it often reflects violation of phylogenetic assumptions, rather than a correct phylogeny. Molecular evolutionary phenomena such as base compositional heterogeneity and among‐site rate variation are known to affect phylogenetic inference, resulting in incorrect phylogenetic relationships. The ability of methods to overcome such bias has not been measured on real and complex datasets. We investigated how base compositional heterogeneity and among‐site rate variation affect phylogenetic inference in the context of a mitochondrial genome phylogeny of the insect order Coleoptera. We show statistically that our dataset is affected by base compositional heterogeneity regardless of how the data are partitioned or recoded. Among‐site rate variation is shown by comparing topologies generated using models of evolution with and without a rate variation parameter in a Bayesian framework. When compared for their effectiveness in dealing with systematic bias, standard phylogenetic methods tend to perform poorly, and parsimony without any data transformation performs worst. Two methods designed specifically to overcome systematic bias, LogDet and a Bayesian method implementing variable composition vectors, can overcome some level of base compositional heterogeneity, but are still affected by among‐site rate variation. A large degree of variation in both noise and phylogenetic signal among all three codon positions is observed. We caution and argue that more data exploration is imperative, especially when many genes are included in an analysis.

Extensive evolutionary changes in regulatory element activity during human origins are associated with altered gene expression and positive selection.

Understanding the molecular basis for phenotypic differences between humans and other primates remains an outstanding challenge. Mutations in non-coding regulatory DNA that alter gene expression have been hypothesized as a key driver of these phenotypic differences. This has been supported by differential gene expression analyses in general, but not by the identification of specific regulatory elements responsible for changes in transcription and phenotype. To identify the genetic source of regulatory differences, we mapped DNaseI hypersensitive (DHS) sites, which mark all types of active gene regulatory elements, genome-wide in the same cell type isolated from human, chimpanzee, and macaque. Most DHS sites were conserved among all three species, as expected based on their central role in regulating transcription. However, we found evidence that several hundred DHS sites were gained or lost on the lineages leading to modern human and chimpanzee. Species-specific DHS site gains are enriched near differentially expressed genes, are positively correlated with increased transcription, show evidence of branch-specific positive selection, and overlap with active chromatin marks. Species-specific sequence differences in transcription factor motifs found within these DHS sites are linked with species-specific changes in chromatin accessibility. Together, these indicate that the regulatory elements identified here are genetic contributors to transcriptional and phenotypic differences among primate species.

Epigenome mapping reveals distinct modes of gene regulation and widespread enhancer reprogramming by the oncogenic fusion protein EWS-FLI1.

Summary Transcription factor fusion proteins can transform cells by inducing global changes of the transcriptome, often creating a state of oncogene addiction. Here, we investigate the role of epigenetic mechanisms in this process, focusing on Ewing sarcoma cells that are dependent on the EWS-FLI1 fusion protein. We established reference epigenome maps comprising DNA methylation, seven histone marks, open chromatin states, and RNA levels, and we analyzed the epigenome dynamics upon downregulation of the driving oncogene. Reduced EWS-FLI1 expression led to widespread epigenetic changes in promoters, enhancers, and super-enhancers, and we identified histone H3K27 acetylation as the most strongly affected mark. Clustering of epigenetic promoter signatures defined classes of EWS-FLI1-regulated genes that responded differently to low-dose treatment with histone deacetylase inhibitors. Furthermore, we observed strong and opposing enrichment patterns for E2F and AP-1 among EWS-FLI1-correlated and anticorrelated genes. Our data describe extensive genome-wide rewiring of epigenetic cell states driven by an oncogenic fusion protein.

Chromatin accessibility reveals insights into androgen receptor activation and transcriptional specificity

BackgroundEpigenetic mechanisms such as chromatin accessibility impact transcription factor binding to DNA and transcriptional specificity. The androgen receptor (AR), a master regulator of the male phenotype and prostate cancer pathogenesis, acts primarily through ligand-activated transcription of target genes. Although several determinants of AR transcriptional specificity have been elucidated, our understanding of the interplay between chromatin accessibility and AR function remains incomplete.ResultsWe used deep sequencing to assess chromatin structure via DNase I hypersensitivity and mRNA abundance, and paired these datasets with three independent AR ChIP-seq datasets. Our analysis revealed qualitative and quantitative differences in chromatin accessibility that corresponded to both AR binding and an enrichment of motifs for potential collaborating factors, one of which was identified as SP1. These quantitative differences were significantly associated with AR-regulated mRNA transcription across the genome. Base-pair resolution of the DNase I cleavage profile revealed three distinct footprinting patterns associated with the AR-DNA interaction, suggesting multiple modes of AR interaction with the genome.ConclusionsIn contrast with other DNA-binding factors, AR binding to the genome does not only target regions that are accessible to DNase I cleavage prior to hormone induction. AR binding is invariably associated with an increase in chromatin accessibility and, consequently, changes in gene expression. Furthermore, we present the first in vivo evidence that a significant fraction of AR binds only to half of the full AR DNA motif. These findings indicate a dynamic quantitative relationship between chromatin structure and AR-DNA binding that impacts AR transcriptional specificity.