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Cystic fibrosis (CF) is a lethal autosomal recessive disorder that afflicts more than 70,000 people. People with CF experience multi-organ dysfunction resulting from aberrant electrolyte transport across polarized epithelia due to mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. CF-related lung disease is by far the most important determinant of morbidity and mortality. Here we report results from a multi-institute consortium in which single-cell transcriptomics were applied to define disease-related changes by comparing the proximal airway of CF donors (n = 19) undergoing transplantation for end-stage lung disease with that of previously healthy lung donors (n = 19). Disease-dependent differences observed include an overabundance of epithelial cells transitioning to specialized ciliated and secretory cell subsets coupled with an unexpected decrease in cycling basal cells. Our study yields a molecular atlas of the proximal airway epithelium that will provide insights for the development of new targeted therapies for CF airway disease. Single-cell RNA profiling of human cystic fibrosis proximal airway tissue reveals an overabundance of epithelial cells transitioning to specialized ciliated and secretory cells coupled with a decrease in cycling basal cells.

Given the global impact and severity of COVID-19, there is a pressing need for a better understanding of the SARS-CoV-2 genome and mutations. Multi-strain sequence alignments of coronaviruses (CoV) provide important information for interpreting the genome and its variation. We apply a comparative genomics method, ConsHMM, to the multi-strain alignments of CoV to annotate every base of the SARS-CoV-2 genome with conservation states based on sequence alignment patterns among CoV. The learned conservation states show distinct enrichment patterns for genes, protein domains, and other regions of interest. Certain states are strongly enriched or depleted of SARS-CoV-2 mutations, which can be used to predict potentially consequential mutations. We expect the conservation states to be a resource for interpreting the SARS-CoV-2 genome and mutations., Kwon and Ernst applied the comparative genomics method, ConsHMM, to the multi-strain alignments of different coronaviruses in order to annotate every base of the SARS-CoV-2 genome with conservation states. The conservation states reflect sequence alignment patterns among different coronaviruses, which would assist with understanding the functional consequences of SARS-CoV-2 mutations.

Motivation Chromatin interactions play an important role in genome architecture and gene regulation. The Hi-C assay generates such interactions maps genome-wide, but at relatively low resolutions (e.g. 5-25 kb), which is substantially coarser than the resolution of transcription factor binding sites or open chromatin sites that are potential sources of such interactions. Results To predict the sources of Hi-C-identified interactions at a high resolution (e.g. 100 bp), we developed a computational method that integrates data from DNase-seq and ChIP-seq of TFs and histone marks. Our method, χ-CNN, uses this data to first train a convolutional neural network (CNN) to discriminate between called Hi-C interactions and non-interactions. χ-CNN then predicts the high-resolution source of each Hi-C interaction using a feature attribution method. We show these predictions recover original Hi-C peaks after extending them to be coarser. We also show χ-CNN predictions enrich for evolutionarily conserved bases, eQTLs and CTCF motifs, supporting their biological significance. χ-CNN provides an approach for analyzing important aspects of genome architecture and gene regulation at a higher resolution than previously possible. Availability and implementation χ-CNN software is available on GitHub (https://github.com/ernstlab/X-CNN). Supplementary information Supplementary data are available at Bioinformatics online.

The National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting's objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and 'omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration—particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs., This report discusses gaps in knowledge and possibilities for the next phase of functional discovery for addiction.

Annotations of evolutionary sequence constraint based on multi-species genome alignments and genome-wide maps of epigenomic marks and transcription factor binding provide important complementary information for understanding the human genome and genetic variation. Here we developed the Constrained Non-Exonic Predictor (CNEP) to quantify the evidence of each base in the genome being in an evolutionarily constrained non-exonic element from an input of over 60,000 epigenomic and transcription factor binding features. We find that the CNEP score outperforms baseline and related existing scores at predicting evolutionarily constrained non-exonic bases from such data. However, a subset of them are still not well predicted by CNEP. We developed a complementary Conservation Signature Score by CNEP (CSS-CNEP) that is predictive of those bases. We further characterize the nature of constrained non-exonic bases with low CNEP scores using additional types of information. CNEP and CSS-CNEP are resources for analyzing constrained non-exonic bases in the genome., Genome-wide maps of evolutionary constraint and large-scale compendia of epigenomic and transcription factor data provide complementary information for genome annotation. Here, the authors develop the Constrained Non-Exonic Predictor (CNEP) that enables better understanding of their relationship.

ConsHMM is a method recently introduced to annotate genomes into conservation states, which are defined based on the combinatorial and spatial patterns of which species align to and match a reference genome in a multi-species DNA sequence alignment. Previously, ConsHMM was only applied to a single genome for one multi-species sequence alignment. Here, we apply ConsHMM to produce 22 additional genome annotations covering human and seven other organisms for a variety of multi-species alignments. Additionally, we extend ConsHMM to generate allele-specific annotations, which we use to produce conservation state annotations for every possible single-nucleotide mutation in the human genome. Finally, we provide a web interface to interactively visualize parameters and annotation enrichments for ConsHMM models. These annotations and visualizations comprise the ConsHMM Atlas, which we expect will be a valuable resource for analyzing a variety of major genomes and genetic variation.

Lipoprotein(a), Lp(a), is a modified low-density lipoprotein particle that contains apolipoprotein(a), encoded by LPA, and is a highly heritable, causal risk factor for cardiovascular diseases that varies in concentrations across ancestries. Here, we use deep-coverage whole genome sequencing in 8392 individuals of European and African ancestry to discover and interpret both single-nucleotide variants and copy number (CN) variation associated with Lp(a). We observe that genetic determinants between Europeans and Africans have several unique determinants. The common variant rs12740374 associated with Lp(a) cholesterol is an eQTL for SORT1 and independent of LDL cholesterol. Observed associations of aggregates of rare non-coding variants are largely explained by LPA structural variation, namely the LPA kringle IV 2 (KIV2)-CN. Finally, we find that LPA risk genotypes confer greater relative risk for incident atherosclerotic cardiovascular diseases compared to directly measured Lp(a), and are significantly associated with measures of subclinical atherosclerosis in African Americans.

Many disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. Previous studies have found enrichment of expression quantitative trait loci (eQTLs) in disease risk loci, indicating that identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as linkage disequilibrium (LD) and the presence of multiple causal variants within a locus. Although several fine-mapping approaches have been developed to overcome these challenges, they may produce large sets of putative causal variants when true causal variants are in high LD with many non-causal variants. In eQTL studies, there is an additional source of information that can be used to improve fine-mapping called allelic imbalance (AIM) that measures imbalance in gene expression on two chromosomes of a diploid organism. In this work, we develop a novel statistical method that leverages both AIM and total expression data to detect causal variants that regulate gene expression. We illustrate through simulations and application to 10 tissues of the Genotype-Tissue Expression (GTEx) dataset that our method identifies the true causal variants with higher specificity than an approach that uses only eQTL information. Across all tissues and genes, our method achieves a median reduction rate of 11% in the number of putative causal variants. We use chromatin state data from the Roadmap Epigenomics Consortium to show that the putative causal variants identified by our method are enriched for active regions of the genome, providing orthogonal support that our method identifies causal variants with increased specificity., Author summary In recent years, many studies have identified genetic variants that are associated with the expression of genes (eQTLs). While thousands of eQTLs have been identified, not all associated variants cause changes in gene expression. This is in part due to the complex patterns of genetic correlation in the human genome. If a region of the genome contains many genetic variants that are highly correlated with each other, non-causal genetic variants close to a causal variant are also correlated with gene expression. Statistical fine-mapping is the process of identifying true causal variants from a set of candidate variants. In regions with high genetic correlation, previous fine-mapping methods may not be able to differentiate causal variants from nearby variants. We propose a method that utilizes a complementary source of information called allelic imbalance (AIM). We show that by combining eQTL and AIM data, we can identify the true causal variants more efficiently and substantially decrease the number of putative causal variants for downstream analysis.

Large-scale deep-coverage whole-genome sequencing (WGS) is now feasible and offers potential advantages for locus discovery. We perform WGS in 16,324 participants from four ancestries at mean depth >29X and analyze genotypes with four quantitative traits—plasma total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides. Common variant association yields known loci except for few variants previously poorly imputed. Rare coding variant association yields known Mendelian dyslipidemia genes but rare non-coding variant association detects no signals. A high 2M-SNP LDL-C polygenic score (top 5th percentile) confers similar effect size to a monogenic mutation (~30 mg/dl higher for each); however, among those with severe hypercholesterolemia, 23% have a high polygenic score and only 2% carry a monogenic mutation. At these sample sizes and for these phenotypes, the incremental value of WGS for discovery is limited but WGS permits simultaneous assessment of monogenic and polygenic models to severe hypercholesterolemia., Common genetic variants associated with plasma lipids have been extensively studied for a better understanding of common diseases. Here, the authors use whole-genome sequencing of 16,324 individuals to analyze rare variant associations and to determine their monogenic and polygenic contribution to lipid traits.

To model spatial changes of chromatin mark peaks over time we develop and apply ChromTime, a computational method that predicts peaks to be either expanding, contracting, or holding steady between time points. Predicted expanding and contracting peaks can mark regulatory regions associated with transcription factor binding and gene expression changes. Spatial dynamics of peaks provide information about gene expression changes beyond localized signal density changes. ChromTime detects asymmetric expansions and contractions, which for some marks associate with the direction of transcription. ChromTime facilitates the analysis of time course chromatin data in a range of biological systems. Electronic supplementary material The online version of this article (10.1186/s13059-018-1485-2) contains supplementary material, which is available to authorized users.

Lipoprotein(a), Lp(a), is a modified low-density lipoprotein particle that contains apolipoprotein(a), encoded by LPA, and is a highly heritable, causal risk factor for cardiovascular diseases that varies in concentrations across ancestries. Here, we use deep-coverage whole genome sequencing in 8392 individuals of European and African ancestry to discover and interpret both single-nucleotide variants and copy number (CN) variation associated with Lp(a). We observe that genetic determinants between Europeans and Africans have several unique determinants. The common variant rs12740374 associated with Lp(a) cholesterol is an eQTL for SORT1 and independent of LDL cholesterol. Observed associations of aggregates of rare non-coding variants are largely explained by LPA structural variation, namely the LPA kringle IV 2 (KIV2)-CN. Finally, we find that LPA risk genotypes confer greater relative risk for incident atherosclerotic cardiovascular diseases compared to directly measured Lp(a), and are significantly associated with measures of subclinical atherosclerosis in African Americans., Circulating lipoprotein(a) is an important risk factor for cardiovascular disease and shows variability between different ethnic groups. Here, Zekavat et al. perform whole-genome sequencing in individuals of European and African ancestries and find ancestry-specific genetic determinants for lipoprotein(a) levels.

The cellular mechanisms driving cardiac tissue formation remain poorly understood, largely due to the structural and functional complexity of the heart. It is unclear whether newly generated myocytes originate from cardiac stem/progenitor cells or from pre-existing cardiomyocytes that re-enter the cell cycle. Here, we identify the source of new cardiomyocytes during mouse development and after injury. Our findings suggest that cardiac progenitors maintain proliferative potential and are the main source of cardiomyocytes during development; however, the onset of αMHC expression leads to reduced cycling capacity. Single-cell RNA sequencing reveals a proliferative, “progenitor-like” population abundant in early embryonic stages that decreases to minimal levels postnatally. Furthermore, cardiac injury by ligation of the left anterior descending artery was found to activate cardiomyocyte proliferation in neonatal but not adult mice. Our data suggest that clonal dominance of differentiating progenitors mediates cardiac development, while a distinct subpopulation of cardiomyocytes may have the potential for limited proliferation during late embryonic development and shortly after birth., During cardiac tissue formation it is unclear whether newly generated myocytes originate from cardiac progenitor cells or from pre-existing cardiomyocytes. Here, the authors use a stochastic four-colour reporter system (Rainbow) to identify the source of new cardiomyocytes during mouse development.

The extent and nature of epigenomic changes associated with melanoma progression is poorly understood. Through systematic epigenomic profiling of 35 epigenetic modifications and transcriptomic analysis, we define chromatin state changes associated with melanomagenesis by using a cell phenotypic model of non-tumorigenic and tumorigenic states. Computation of specific chromatin state transitions showed loss of histone acetylations and H3K4me2/3 on regulatory regions proximal to specific cancer-regulatory genes in important melanoma-driving cell signaling pathways. Importantly, such acetylation changes were also observed between benign nevi and malignant melanoma human tissues. Intriguingly, only a small fraction of chromatin state transitions correlated with expected changes in gene expression patterns. Restoration of acetylation levels on deacetylated loci by histone deacetylase (HDAC) inhibitors selectively blocked excessive proliferation in tumorigenic cells and human melanoma cells, suggesting functional roles of observed chromatin state transitions in driving hyperproliferative phenotype. Through these results, we define functionally relevant chromatin states associated with melanoma progression.

Comparative genomics sequence data is an important source of information for interpreting genomes. Genome-wide annotations based on this data have largely focused on univariate scores or binary elements of evolutionary constraint. Here we present a complementary whole genome annotation approach, ConsHMM, which applies a multivariate hidden Markov model to learn de novo ‘conservation states’ based on the combinatorial and spatial patterns of which species align to and match a reference genome in a multiple species DNA sequence alignment. We applied ConsHMM to a 100-way vertebrate sequence alignment to annotate the human genome at single nucleotide resolution into 100 conservation states. These states have distinct enrichments for other genomic information including gene annotations, chromatin states, repeat families, and bases prioritized by various variant prioritization scores. Constrained elements have distinct heritability partitioning enrichments depending on their conservation state assignment. ConsHMM conservation states are a resource for analyzing genomes and genetic variants., Adriana Arneson and Jason Ernst present a computational model, ConsHMM, for learning the conservation states of DNA sequences based on a multiple-species alignment. They apply ConsHMM to a 100-way vertebrate sequence alignment to provide single nucleotide annotations of conservation states of the human genome.

Pancreatic islet failure is a characteristic feature of impaired glucose control in diabetes mellitus. Circadian control of islet function is essential for maintaining proper glucose homeostasis. Circadian variations in transcriptional pathways have been described in diverse cell types and shown to be critical for optimization of cellular function in vivo. In the current study, we utilized Short Time Series Expression Miner (STEM) analysis to identify diurnally expressed transcripts and biological pathways from mouse islets isolated at 4 h intervals throughout the 24 h light-dark cycle. STEM analysis identified 19 distinct chronological model profiles, and genes belonging to each profile were subsequently annotated to significantly enriched Kyoto Encyclopedia of Genes and Genomes biological pathways. Several transcriptional pathways essential for proper islet function (e.g., insulin secretion, oxidative phosphorylation), cell survival (e.g., insulin signaling, apoptosis) and cell proliferation (DNA replication, homologous recombination) demonstrated significant time-dependent variations. Notably, KEGG pathway analysis revealed “protein processing in endoplasmic reticulum - mmu04141” as one of the most enriched time-dependent pathways in islets. This study provides unique data set on time-dependent diurnal profiles of islet gene expression and biological pathways, and suggests that diurnal variation of the islet transcriptome is an important feature of islet homeostasis, which should be taken into consideration for optimal experimental design and interpretation of future islet studies.

Tissue-specific gene expression defines cellular identity and function, but knowledge of early human development is limited, hampering application of cell-based therapies. Here we profiled 5 distinct cell types at a single fetal stage, as well as chondrocytes at 4 stages in vivo and 2 stages during in vitro differentiation. Network analysis delineated five tissue-specific gene modules; these modules and chromatin state analysis defined broad similarities in gene expression during cartilage specification and maturation in vitro and in vivo, including early expression and progressive silencing of muscle- and bone-specific genes. Finally, ontogenetic analysis of freshly isolated and pluripotent stem cell-derived articular chondrocytes identified that integrin alpha 4 defines 2 subsets of functionally and molecularly distinct chondrocytes characterized by their gene expression, osteochondral potential in vitro and proliferative signature in vivo. These analyses provide new insight into human musculoskeletal development and provide an essential comparative resource for disease modeling and regenerative medicine., Human development provides a roadmap for advancing pluripotent stem cell-based regenerative therapies. Here the authors mapped human skeletogenesis using RNA sequencing on 5 cell types from a single foetal stage as well as chondrocytes at 4 stages in vivo and 2 stages during in vitro differentiation.

A recent study in Genome Biology has characterized the evolution of candidate hominoid-specific liver enhancers by using massively parallel reporter assays (MPRAs).

Comparative genomics sequence data is an important source of information for interpreting genomes. Genome-wide annotations based on this data have largely focused on univariate scores or binary calls of evolutionary constraint. Here we present a complementary whole genome annotation approach, ConsHMM, which applies a multivariate hidden Markov model to learn de novo different ‘conservation states’ based on the combinatorial and spatial patterns of which species align to and match a reference genome in a multiple species DNA sequence alignment. We applied ConsHMM to a 100-way vertebrate sequence alignment to annotate the human genome at single nucleotide resolution into 100 different conservation states. These states have distinct enrichments for other genomic information including gene annotations, chromatin states, and repeat families, which were used to characterize their biological significance. Conservation states have greater or complementary predictive information than standard constraint based measures for a variety of genome annotations. Bases in constrained elements have distinct heritability enrichments depending on the conservation state assignment, demonstrating their relevance to analyzing phenotypic associated variation. The conservation states also highlight differences in the conservation patterns of bases prioritized by a number of scores used for variant prioritization. The ConsHMM method and conservation state annotations provide a valuable resource for interpreting genomes and genetic variation.

Many disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. It is hypothesized that these variants affect complex traits by acting as expression quantitative trait loci (eQTLs) that influence expression of nearby genes. This indicates that many causal variants for complex traits are likely to be causal variants for gene expression. Hence, identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as linkage disequilibrium (LD) and the presence of multiple causal variants within a locus. Although several fine-mapping approaches have been developed to overcome these challenges, they may produce large sets of putative causal variants when true causal variants are in high LD with many non-causal variants. In eQTL studies, there is an additional source of information that can be used to improve fine-mapping called allele-specific expression (ASE) that measures imbalance in gene expression due to different alleles. In this work, we develop a novel statistical method that leverages both ASE and eQTL information to detect causal variants that regulate gene expression. We illustrate through simulations and application to the Genotype-Tissue Expression (GTEx) dataset that our method identifies the true causal variants with higher specificity than an approach that uses only eQTL information. In the GTEx dataset, our method achieves the median reduction rate of 11% in the number of putative causal variants.ContactJaeHoonSul@mednet.ucla.edu, eeskin@cs.ucla.edu

To model spatial changes of chromatin mark peaks over time we developed and applied ChromTime, a computational method that predicts regions for which peaks either expand or contract significantly or hold steady between time points. Predicted expanding and contracting peaks can mark regulatory regions associated with transcription factor binding and gene expression changes. Spatial dynamics of peaks provided information about gene expression changes beyond localized signal density changes. ChromTime detected asymmetric expansions and contractions, which for some marks associated with the direction of transcription. ChromTime facilitates the analysis of time course chromatin data in a range of biological systems.

Noncoding DNA regions have central roles in human biology, evolution, and disease. ChromHMM helps to annotate the noncoding genome using epigenomic information across one or multiple cell types. It combines multiple genome-wide epigenomic maps, and uses combinatorial and spatial mark patterns to infer a complete annotation for each cell type. ChromHMM learns chromatin-state signatures using a multivariate hidden Markov model (HMM) that explicitly models the combinatorial presence or absence of each mark. ChromHMM uses these signatures to generate a genome-wide annotation for each cell type by calculating the most probable state for each genomic segment. ChromHMM provides an automated enrichment analysis of the resulting annotations to facilitate the functional interpretations of each chromatin state. ChromHMM is distinguished by its modeling emphasis on combinations of marks, its tight integration with downstream functional enrichment analyses, its speed, and its ease of use. Chromatin states are learned, annotations are produced, and enrichments are computed within 1 d.

Lipoprotein(a), Lp(a), is a modified low-density lipoprotein particle where apolipoprotein(a) (protein product of theLPAgene) is covalently attached to apolipoprotein B. Lp(a) is a highly heritable, causal risk factor for cardiovascular diseases and varies in concentrations across ancestries. To comprehensively delineate the inherited basis for plasma Lp(a), we performed deep-coverage whole genome sequencing in 8,392 individuals of European and African American ancestries. Through whole genome variant discovery and direct genotyping of all structural variants overlappingLPA, we quantified the 5.5kb kringle IV-2 copy number (KIV2-CN), a knownLPAstructural polymorphism, and developed a model for its imputation. Through common variant analysis, we discovered a novel locus (SORT1) associated with Lp(a)-cholesterol, and also genetic modifiers of KIV2-CN. Furthermore, in contrast to previous GWAS studies, we explain most of the heritability of Lp(a), observing Lp(a) to be 85% heritable among African Americans and 75% among Europeans, yet with notable inter-ethnic heterogeneity. Through analyses of aggregates of rare coding and non-coding variants with Lp(a)-cholesterol, we found the only genome-wide significant signal to be at a non-codingSLC22A3intronic window also previously described to be associated with Lp(a); however, this association was mitigated by adjustment with KIV2-CN. Finally, using an additional imputation dataset (N=27,344), we performed Mendelian randomization ofLPAvariant classes, finding that genetically regulated Lp(a) is more strongly associated with incident cardiovascular diseases than directly measured Lp(a), and is significantly associated with measures of subclinical atherosclerosis in African Americans.

In eukaryotic cells, histone lysines are frequently acetylated. However, unlike modifications such as methylations, histone acetylation modifications are often considered redundant. As such, the functional roles of distinct histone acetylations are largely unexplored. We previously developed an algorithm RFECS to discover the most informative modifications associated with the classification or prediction of mammalian enhancers. Here, we used this tool to identify the modifications most predictive of promoters, enhancers, and gene bodies. Unexpectedly, we found that histone acetylation alone performs well in distinguishing these unique genomic regions. Further, we found the association of characteristic acetylation patterns with genic regions and association of chromatin state with splicing. Taken together, our work underscores the diverse functional roles of histone acetylation in gene regulation and provides several testable hypotheses to dissect these roles., National Institutes of Health (U.S.) (Roadmap Epigenomics Program U01 ES017166-01)

SummaryThe relationship between 3D organization of the genome and gene-regulatory networks is poorly understood. Here, we examined long-range chromatin interactions genome-wide in mouse embryonic stem cells (ESCs), iPSCs, and fibroblasts and uncovered a pluripotency-specific genome organization that is gradually reestablished during reprogramming. Our data confirm that long-range chromatin interactions are primarily associated with the spatial segregation of open and closed chromatin, defining overall chromosome conformation. Additionally, we identified two further levels of genome organization in ESCs characterized by colocalization of regions with high pluripotency factor occupancy and strong enrichment for Polycomb proteins/H3K27me3, respectively. Underlining the independence of these networks and their functional relevance for genome organization, loss of the Polycomb protein Eed diminishes interactions between Polycomb-regulated regions without altering overarching chromosome conformation. Together, our data highlight a pluripotency-specific genome organization in which pluripotency factors such as Nanog and H3K27me3 occupy distinct nuclear spaces and reveal a role for cell-type-specific gene-regulatory networks in genome organization.

Three-dimensional physical interactions within chromosomes dynamically regulate gene expression in a tissue-specific manner1–3. However, the 3D organization of chromosomes during human brain development and its role in regulating gene networks dysregulated in neurodevelopmental disorders, such as autism or schizophrenia4–6, are unknown. Here we generate high-resolution 3D maps of chromatin contacts during human corticogenesis, permitting large-scale annotation of previously uncharacterized regulatory relationships relevant to the evolution of human cognition and disease. Our analyses identify hundreds of genes that physically interact with enhancers gained on the human, many of which are under purifying selection and associated with human cognitive function. We integrate chromatin contacts with non-coding variants identified in schizophrenia genome-wide association studies (GWAS), highlighting multiple new candidate schizophrenia risk genes and pathways, including transcription factors involved in neurogenesis, as well as cholinergic signalling, several of which are supported by independent expression quantitative trait loci and gene expression analyses. Genome editing in human neural progenitors suggests that one of these distal schizophrenia GWAS loci regulates FOXG1 expression, supporting its potential role as a novel schizophrenia risk gene. This work provides a framework for understanding the impact of non-coding regulatory elements on human brain development and the evolution of cognition, and highlights novel mechanisms underlying neuropsychiatric disorders.

Oct4, Sox2, Klf4, and cMyc (OSKM) reprogram somatic cells to pluripotency. To gain a mechanistic understanding of their function, we mapped OSKM-binding, stage-specific transcription factors (TFs), and chromatin states in discrete reprogramming stages and performed loss- and gain-of-function experiments. We found that OSK predominantly bind active somatic enhancers early in reprogramming and immediately initiate their inactivation genome-wide by inducing the redistribution of somatic TFs away from somatic enhancers to sites elsewhere engaged by OSK, recruiting Hdac1, and repressing the somatic TF Fra1. Pluripotency enhancer selection is a stepwise process that also begins early in reprogramming through collaborative binding of OSK at sites with high OSK-motif density. Most pluripotency enhancers are selected later in the process and require OS and other pluripotency TFs. Somatic and pluripotency TFs modulate reprogramming efficiency when overexpressed by altering OSK targeting, somatic-enhancer inactivation, and pluripotency enhancer selection. Together, our data indicate that collaborative interactions among OSK andwith stage-specific TFs direct both somatic-enhancer inactivation and pluripotency-enhancer selection to drive reprogramming.

Motivation: Expression quantitative trait loci (eQTLs) are genetic variants that affect gene expression. In eQTL studies, one important task is to find eGenes or genes whose expressions are associated with at least one eQTL. The standard statistical method to determine whether a gene is an eGene requires association testing at all nearby variants and the permutation test to correct for multiple testing. The standard method however does not consider genomic annotation of the variants. In practice, variants near gene transcription start sites (TSSs) or certain histone modifications are likely to regulate gene expression. In this article, we introduce a novel eGene detection method that considers this empirical evidence and thereby increases the statistical power. Results: We applied our method to the liver Genotype-Tissue Expression (GTEx) data using distance from TSSs, DNase hypersensitivity sites, and six histone modifications as the genomic annotations for the variants. Each of these annotations helped us detected more candidate eGenes. Distance from TSS appears to be the most important annotation; specifically, using this annotation, our method discovered 50% more candidate eGenes than the standard permutation method. Contact: buhm.han@amc.seoul.kr or eeskin@cs.ucla.edu

The comparison of related genomes has emerged as a powerful lens for genome interpretation. Here we report the sequencing and comparative analysis of 29 eutherian genomes. We confirm that at least 5.5% of the human genome has undergone purifying selection, and locate constrained elements covering ~4.2% of the genome. We use evolutionary signatures and comparisons with experimental data sets to suggest candidate functions for ~60% of constrained bases. These elements reveal a small number of new coding exons, candidate stop codon readthrough events and over 10,000 regions of overlapping synonymous constraint within protein-coding exons. We find 220 candidate RNA structural families, and nearly a million elements overlapping potential promoter, enhancer and insulator regions. We report specific amino acid residues that have undergone positive selection, 280,000 non-coding elements exapted from mobile elements and more than 1,000 primate- and human-accelerated elements. Overlap with disease-associated variants indicates that our findings will be relevant for studies of human biology, health and disease., National Human Genome Research Institute (U.S.), National Institute of General Medical Sciences (U.S.) (Grant number GM82901), National Science Foundation (U.S.). Postdoctural Fellowship (Award 0905968), National Science Foundation (U.S.). Career (0644282), National Institutes of Health (U.S.) (R01-HG004037), Alfred P. Sloan Foundation., Austrian Science Fund. Erwin Schrodinger Fellowship

Chromatin profiling has emerged as a powerful means of genome annotation and detection of regulatory activity. The approach is especially well suited to the characterization of non-coding portions of the genome, which critically contribute to cellular phenotypes yet remain largely uncharted. Here we map nine chromatin marks across nine cell types to systematically characterize regulatory elements, their cell-type specificities and their functional interactions. Focusing on cell-type-specific patterns of promoters and enhancers, we define multicell activity profiles for chromatin state, gene expression, regulatory motif enrichment and regulator expression. We use correlations between these profiles to link enhancers to putative target genes, and predict the cell-type-specific activators and repressors that modulate them. The resulting annotations and regulatory predictions have implications for the interpretation of genome-wide association studies. Top-scoring disease single nucleotide polymorphisms are frequently positioned within enhancer elements specifically active in relevant cell types, and in some cases affect a motif instance for a predicted regulator, thus suggesting a mechanism for the association. Our study presents a general framework for deciphering cis-regulatory connections and their roles in disease., National Human Genome Research Institute (U.S.) (R01HG004037), National Human Genome Research Institute (U.S.) (RC1HG005334), National Science Foundation (U.S.). (Award 0644282), National Science Foundation (U.S.). (Award 0905968), Alfred P. Sloan Foundation

Chromatin is composed of DNA and a variety of modified histones and non-histone proteins, which have an impact on cell differentiation, gene regulation and other key cellular processes. Here we present a genome-wide chromatin landscape for Drosophila melanogaster based on eighteen histone modifications, summarized by nine prevalent combinatorial patterns. Integrative analysis with other data (non-histone chromatin proteins, DNase I hypersensitivity, GRO-Seq reads produced by engaged polymerase, short/long RNA products) reveals discrete characteristics of chromosomes, genes, regulatory elements and other functional domains. We find that active genes display distinct chromatin signatures that are correlated with disparate gene lengths, exon patterns, regulatory functions and genomic contexts. We also demonstrate a diversity of signatures among Polycomb targets that include a subset with paused polymerase. This systematic profiling and integrative analysis of chromatin signatures provides insights into how genomic elements are regulated, and will serve as a resource for future experimental investigations of genome structure and function., United States. Dept. of Energy (Contract DE-AC02-05CH11231), RC2 HG005639, U01 HG004279, R01 GM082798, R37 GM45744, R01 GM071923, U54 HG004592, National Science Foundation (U.S.) (NSF 0905968)

Transcriptional enhancers play critical roles in regulation of gene expression, but their identification in the eukaryotic genome has been challenging. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of cell types or chromatin marks have previously been investigated for this purpose, leaving the question unanswered whether there exists an optimal set of histone modifications for enhancer prediction in different cell types. Here, we address this issue by exploring genome-wide profiles of 24 histone modifications in two distinct human cell types, embryonic stem cells and lung fibroblasts. We developed a Random-Forest based algorithm, RFECS (Random Forest based Enhancer identification from Chromatin States) to integrate histone modification profiles for identification of enhancers, and used it to identify enhancers in a number of cell-types. We show that RFECS not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify the most informative and robust set of three chromatin marks for enhancer prediction., Author Summary Enhancers are regions in the genome that can activate the expression of a gene irrespective of their location with respect to the gene. Identifying these elements is critical in understanding regulatory differences between different cell-types. Since enhancers lack characteristic sequence features and can be far away from the gene they regulate, their identification is not trivial. Experimentally determining the genome-wide binding sites of transcriptional co-activator p300 is one way of finding enhancers but it can only identify a subset of enhancers. A few years ago, it was observed that the binding sites of p300 are marked by distinctive, post-translational histone modifications. Several groups have exploited this discovery to predict genome-wide enhancers based on their similarity to the histone modification profiles of p300 binding sites. We here report a novel algorithm for this purpose and show that it has much greater accuracy than existing methods. Another unique feature of our algorithm is the ability to automatically deduce the most informative set of histone modifications required for enhancer prediction. We expect that this method will become increasingly useful with the expanding number of known histone modifications and rapid accumulation of epigenomic datasets for various cell types and species.

The ENCODE Project has generated a wealth of experimental information mapping diverse chromatin properties in several human cell lines. Although each such data track is independently informative toward the annotation of regulatory elements, their interrelations contain much richer information for the systematic annotation of regulatory elements. To uncover these interrelations and to generate an interpretable summary of the massive datasets of the ENCODE Project, we apply unsupervised learning methodologies, converting dozens of chromatin datasets into discrete annotation maps of regulatory regions and other chromatin elements across the human genome. These methods rediscover and summarize diverse aspects of chromatin architecture, elucidate the interplay between chromatin activity and RNA transcription, and reveal that a large proportion of the genome lies in a quiescent state, even across multiple cell types. The resulting annotation of non-coding regulatory elements correlate strongly with mammalian evolutionary constraint, and provide an unbiased approach for evaluating metrics of evolutionary constraint in human. Lastly, we use the regulatory annotations to revisit previously uncharacterized disease-associated loci, resulting in focused, testable hypotheses through the lens of the chromatin landscape., National Institutes of Health (U.S.) (HG005334), National Institutes of Health (U.S.) (HG004570), National Science Foundation (U.S.) (0905968)

The regions bound by sequence-specific transcription factors can be highly variable across different cell types despite the static nature of the underlying genome sequence. This has been partly attributed to changes in chromatin accessibility, but a systematic picture has been hindered by the lack of large-scale data sets. Here, we use 456 binding experiments for 119 regulators and 84 chromatin maps generated by the ENCODE in six human cell types, and relate those to a global map of regulatory motif instances for these factors. We find specific and robust chromatin state preferences for each regulator beyond the previously reported open-chromatin association, suggesting a much richer chromatin landscape beyond simple accessibility. The preferentially bound chromatin states of regulators were enriched for sequence motifs of regulators relative to all states, suggesting that these preferences are at least partly encoded by the genomic sequence. Relative to all regions bound by a regulator, however, regulatory motifs were surprisingly depleted in the regulator's preferentially bound states, suggesting additional non-sequence-specific binding beyond the level predicted by the regulatory motifs. Such permissive binding was largely restricted to open-chromatin regions showing histone modification marks characteristic of active enhancer and promoter regions, whereas open-chromatin regions lacking such marks did not show permissive binding. Lastly, the vast majority of cobinding of regulator pairs is predicted by the chromatin state preferences of individual regulators. Overall, our results suggest a joint role of sequence motifs and specific chromatin states beyond mere accessibility in mediating regulator binding dynamics across different cell types., National Institutes of Health (U.S.) (Grant R01HG004037), National Institutes of Health (U.S.) (Grant RC1HG005334)

DNA methylation, mediated by the combined action of three DNA methyltransferases (DNMT1, DNMT3A, and DNMT3B), is essential for mammalian development and is a major contributor to cellular transformation. To elucidate how DNA methylation is targeted, we mapped the genome-wide localization of all DNMTs and methylation, and examined the relationships among these markers, histone modifications, and nucleosome structure in a pluripotent human tumor cell line in its undifferentiated and differentiated states. Our findings reveal a strong link between DNMTs and transcribed loci, and that DNA methylation is not a simple sum of DNMT localization patterns. A comparison of the epigenomes of normal and cancerous stem cells, and pluripotent and differentiated states shows that the presence of at least two DNMTs is strongly associated with loci targeted for DNA hypermethylation. Taken together, these results shed important light on the determinants of DNA methylation and how it may become disrupted in cancer cells., National Institutes of Health (U.S.) (Grant RC1HG005334), National Science Foundation (U.S.) (Postdoctoral Fellowship 0905968)

Optic nerve degeneration caused by glaucoma is a leading cause of blindness worldwide. Patients affected by the normal-pressure form of glaucoma are more likely to harbor risk alleles for glaucoma-related optic nerve disease. We have performed a meta-analysis of two independent genome-wide association studies for primary open angle glaucoma (POAG) followed by a normal-pressure glaucoma (NPG, defined by intraocular pressure (IOP) less than 22 mmHg) subgroup analysis. The single-nucleotide polymorphisms that showed the most significant associations were tested for association with a second form of glaucoma, exfoliation-syndrome glaucoma. The overall meta-analysis of the GLAUGEN and NEIGHBOR dataset results (3,146 cases and 3,487 controls) identified significant associations between two loci and POAG: the CDKN2BAS region on 9p21 (rs2157719 [G], OR = 0.69 [95%CI 0.63–0.75], p = 1.86×10−18), and the SIX1/SIX6 region on chromosome 14q23 (rs10483727 [A], OR = 1.32 [95%CI 1.21–1.43], p = 3.87×10−11). In sub-group analysis two loci were significantly associated with NPG: 9p21 containing the CDKN2BAS gene (rs2157719 [G], OR = 0.58 [95% CI 0.50–0.67], p = 1.17×10−12) and a probable regulatory region on 8q22 (rs284489 [G], OR = 0.62 [95% CI 0.53–0.72], p = 8.88×10−10). Both NPG loci were also nominally associated with a second type of glaucoma, exfoliation syndrome glaucoma (rs2157719 [G], OR = 0.59 [95% CI 0.41–0.87], p = 0.004 and rs284489 [G], OR = 0.76 [95% CI 0.54–1.06], p = 0.021), suggesting that these loci might contribute more generally to optic nerve degeneration in glaucoma. Because both loci influence transforming growth factor beta (TGF-beta) signaling, we performed a genomic pathway analysis that showed an association between the TGF-beta pathway and NPG (permuted p = 0.009). These results suggest that neuro-protective therapies targeting TGF-beta signaling could be effective for multiple forms of glaucoma., Author Summary Loss of vision from glaucoma, a common cause of blindness worldwide, is due to irreversible damage to the optic nerve. Current therapies cannot prevent glaucoma-related optic nerve disease and very little is known about the underlying responsible molecular events. Glaucoma patients affected by the “normal-pressure” subtype of glaucoma (NPG) have increased susceptibility to optic nerve degeneration. Although NPG has a high heritability, common predisposing genetic variants have not been identified. Therefore, we performed a meta-analysis of two independent genome-wide association studies for a common form of glaucoma, primary open angle glaucoma (POAG), followed by NPG subgroup analysis. We found that SNPs in the CDKN2BAS gene region on 9p21 and a highly conserved region with a probable regulatory function on 8q22 were associated with NPG and with optic nerve disease in a second type of glaucoma, exfoliation glaucoma. Both genomic regions are predicted to influence TGF-beta activity, and using whole-genome data we showed that the TGF-beta signaling pathway overall is associated with NPG. These results reveal new insights into the molecular pathogenesis of optic nerve disease in glaucoma and are an important step toward the development of preventative and protective therapies.

The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome., National Human Genome Research Institute (U.S.), National Institutes of Health (U.S.)

Viral and bacterial infections of the lower respiratory tract are major causes of morbidity and mortality worldwide. Alveolar macrophages line the alveolar spaces and are the first cells of the immune system to respond to invading pathogens. To determine the similarities and differences between the responses of mice and macaques to invading pathogens we profiled alveolar macrophages from these species following infection with two viral (PR8 and Fuj/02 influenza A) and two bacterial (Mycobacterium tuberculosis and Francisella tularensis Schu S4) pathogens. Cells were collected at 6 time points following each infection and expression profiles were compared across and between species. Our analyses identified a core set of genes, activated in both species and across all pathogens that were predominantly part of the interferon response pathway. In addition, we identified similarities across species in the way innate immune cells respond to lethal versus non-lethal pathogens. On the other hand we also found several species and pathogen specific response patterns. These results provide new insights into mechanisms by which the innate immune system responds to, and interacts with, invading pathogens.

A plethora of epigenetic modifications have been described in the human genome and shown to play diverse roles in gene regulation, cellular differentiation and the onset of disease. Although individual modifications have been linked to the activity levels of various genetic functional elements, their combinatorial patterns are still unresolved and their potential for systematic de novo genome annotation remains untapped. Here, we use a multivariate Hidden Markov Model to reveal chromatin states in human T cells, based on recurrent and spatially coherent combinations of chromatin marks.We define 51 distinct chromatin states, including promoter-associated, transcription-associated, active intergenic, largescale repressed and repeat-associated states. Each chromatin state shows specific enrichments in functional annotations, sequence motifs and specific experimentally observed characteristics, suggesting distinct biological roles. This approach provides a complementary functional annotation of the human genome that reveals the genome-wide locations of diverse classes of epigenetic function.

A plethora of epigenetic modifications have been described in the human genome and shown to play diverse roles in gene regulation, cellular differentiation and the onset of disease. Although individual modifications have been linked to the activity levels of various genetic functional elements, their combinatorial patterns are still unresolved and their potential for systematic de novo genome annotation remains untapped. Here, we use a multivariate Hidden Markov Model to reveal 'chromatin states' in human T cells, based on recurrent and spatially coherent combinations of chromatin marks. We define 51 distinct chromatin states, including promoter-associated, transcription-associated, active intergenic, large-scale repressed and repeat-associated states. Each chromatin state shows specific enrichments in functional annotations, sequence motifs and specific experimentally observed characteristics, suggesting distinct biological roles. This approach provides a complementary functional annotation of the human genome that reveals the genome-wide locations of diverse classes of epigenetic function., National Science Foundation (U.S.). (Award 0905968), National Human Genome Research Institute (U.S.) (Award U54-HG004570), National Human Genome Research Institute (U.S.) (Award RC1-HG005334)

While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi-supervised REgulatory Network Discoverer), a semi-supervised learning method that uses a curated database of verified transcriptional factor–gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor–gene interactions, including whether the transcription factor activates or represses the gene. Using genome-wide binding datasets for several transcription factors, we demonstrate that our semi-supervised classification strategy improves the prediction of targets for a given transcription factor. To further demonstrate the utility of our inferred interactions, we generated a new microarray gene expression dataset for the aerobic to anaerobic shift response in E. coli. We used our inferred interactions with the verified interactions to reconstruct a dynamic regulatory network for this response. The network reconstructed when using our inferred interactions was better able to correctly identify known regulators and suggested additional activators and repressors as having important roles during the aerobic–anaerobic shift interface., Author Summary The proper functioning of transcriptional gene regulation is essential for all living organisms. Several diseases are associated with loss of appropriate transcriptional regulation. Even in relatively simple organisms, such as the bacterium E. coli, response to environmental stress is a complex and highly regulated process. This process is controlled by a set of transcription factors that causes an increase or decrease in the expression levels of their target's gene. However, identifying the set of targets regulated by each of these factors remains a challenge. Even after decades of experimental research on E. coli, only a quarter of all gene products have a known regulator. Here, we develop a method that extends the known set of regulator–target relationships with additional predictions. Our method utilizes the DNA sequence control code and expression levels of known targets in a variety of conditions, as well as genes for which it is not known if they are targets of a specific regulator. We show that our method more accurately identifies true targets of known regulators than previous methods suggested for this task. We then applied our predictions to identify active regulators involved in the dynamic response that occurs in E. coli when it is deprived of oxygen.

Background Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data. Results We introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology. Conclusion The unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at http://www.cs.cmu.edu/~jernst/stem.

Even simple organisms have the ability to respond to internal and external stimuli. This response is carried out by a dynamic network of protein-DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an input-output hidden Markov model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified temporal map. Applying our method to study yeast response to stress, we derive dynamic models that are able to recover many of the known aspects of these responses. Predictions made by our method have been experimentally validated leading to new roles for Ino4 and Gcn4 in controlling yeast response to stress. The temporal cascade of factors reveals common pathways and highlights differences between master and secondary factors in the utilization of network motifs and in condition-specific regulation.