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Abstract Various computational approaches have been developed to annotate epigenomes on a per-position basis by modeling combinatorial and spatial patterns within epigenomic data. However, such annotations are less suitable for gene-based analyses. We present ChromGene, a method based on a mixture of learned hidden Markov models, to annotate genes based on multiple epigenomic maps across the gene body and flanks. We provide ChromGene assignments for over 100 cell and tissue types. We characterize the mixture components in terms of gene expression, constraint, and other gene annotations. The ChromGene method and annotations will provide a useful resource for gene-based epigenomic analyses.

Abstract A large-scale application of the “stacked modeling” approach for chromatin state discovery previously provides a single “universal” chromatin state annotation of the human genome based jointly on data from many cell and tissue types. Here, we produce an analogous chromatin state annotation for mouse based on 901 datasets assaying 14 chromatin marks in 26 cell or tissue types. To characterize each chromatin state, we relate the states to external annotations and compare them to analogously defined human states. We expect the universal chromatin state annotation for mouse to be a useful resource for studying this key model organism’s genome.

One of the essential processes in Mobile Ad hoc Networks (MANETs) is blind flooding to discover routes between source and destination mobile nodes. As the density of nodes in the network increases, the number of broadcast packets increases exponentially. This can lead to broadcast storms, a drain on the device’s battery, and reduced network efficiency. We propose a Cross-layer Adaptive Fuzzy-based Ad hoc On-Demand Distance Vector routing protocol (CLAF-AODV) to minimize the routing broadcast traffic by considering the quality of service (QoS) (e.g. delay, throughput, packet loss), stability, and adaptability of the network. The suggested method employs two-level fuzzy logic and a cross-layer design approach to select the appropriate nodes with a higher probability of participating in broadcasting by considering parameters from the three first layers of the Open Systems Interconnection (OSI) model to achieve a quality of service, stability, and adaptability. It not only investigates the quality of the node and the network density around the node to make a decision but also investigates the path that the broadcast packet traveled to reach this node. Simulation results reveal that our proposed protocol reduces the number of broadcast packets and significantly improves network performance with respect to throughput, packet loss, normalized routing load, collision rate, and average energy consumption compared to the standard AODV and the Fixed Probability AODV (FP-AODV) algorithms.

Methods to probe DNA methylation in the majority of non-human mammals are lacking. Here the authors developed a Mammalian Methylation Array that includes 36k well-conserved CpGs in mammals which will facilitate cross-species comparisons. They annotate the conserved CpGs in > 200 species. The array allows one to measure methylation in all mammalian species including unsequenced ones.

Abstract Background Genome-wide maps of chromatin marks such as histone modifications and open chromatin sites provide valuable information for annotating the non-coding genome, including identifying regulatory elements. Computational approaches such as ChromHMM have been applied to discover and annotate chromatin states defined by combinatorial and spatial patterns of chromatin marks within the same cell type. An alternative “stacked modeling” approach was previously suggested, where chromatin states are defined jointly from datasets of multiple cell types to produce a single universal genome annotation based on all datasets. Despite its potential benefits for applications that are not specific to one cell type, such an approach was previously applied only for small-scale specialized purposes. Large-scale applications of stacked modeling have previously posed scalability challenges. Results Using a version of ChromHMM enhanced for large-scale applications, we apply the stacked modeling approach to produce a universal chromatin state annotation of the human genome using over 1000 datasets from more than 100 cell types, with the learned model denoted as the full-stack model. The full-stack model states show distinct enrichments for external genomic annotations, which we use in characterizing each state. Compared to per-cell-type annotations, the full-stack annotations directly differentiate constitutive from cell type-specific activity and is more predictive of locations of external genomic annotations. Conclusions The full-stack ChromHMM model provides a universal chromatin state annotation of the genome and a unified global view of over 1000 datasets. We expect this to be a useful resource that complements existing per-cell-type annotations for studying the non-coding human genome.

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.

Understanding conserved functional genomic properties between human and mouse provides important context for mouse model studies. Here, the authors present a genome-wide conservation score integrating epigenomic, transcription factor binding, and transcriptomic data from mouse and human genomes.

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.

Background: Human embryonic stem cell-derived cardiomyocytes (hESC-CMs) can be used as a source for cell delivery to remuscularize the heart after myocardial infarction. Despite their therapeutic potential, the emergence of ventricular arrhythmias has limited their application. We previously developed a double reporter hESC line to isolate first heart field (FHF: TBX5+NKX2-5+) and second heart field (SHF: TBX5-NKX2-5+) CMs. Herein, we explore the role of TBX5 and its effects on underlying gene regulatory networks driving phenotypical and functional differences between these two populations.Methods: We used a combination of tools and techniques for rapid and unsupervised profiling of FHF and SHF populations at the transcriptional, translational, and functional level including single cell RNA (scRNA) and bulk RNA sequencing, atomic force and quantitative phase microscopy, respirometry, and electrophysiology.Results: Gene ontology analysis revealed three biological processes attributed to TBX5 expression: sarcomeric structure, oxidative phosphorylation, and calcium ion handling. Interestingly, migratory pathways were enriched in SHF population. SHF-like CMs display less sarcomeric organization compared to FHF-like CMs, despite prolonged in vitro culture. Atomic force and quantitative phase microscopy showed increased cellular stiffness and decreased mass distribution over time in FHF compared to SHF populations, respectively. Electrophysiological studies showed longer plateau in action potentials recorded from FHF-like CMs, consistent with their increased expression of calcium handling genes. Interestingly, both populations showed nearly identical respiratory profiles with the only significant functional difference being higher ATP generation-linked oxygen consumption rate in FHF-like CMs. Our findings suggest that FHF-like CMs display more mature features given their enhanced sarcomeric alignment, calcium handling, and decreased migratory characteristics. Finally, pseudotime analyses revealed a closer association of the FHF population to human fetal CMs along the developmental trajectory.Conclusion: Our studies reveal that distinguishing FHF and SHF populations based on TBX5 expression leads to a significant impact on their downstream functional properties. FHF CMs display more mature characteristics such as enhanced sarcomeric organization and improved calcium handling, with closer positioning along the differentiation trajectory to human fetal hearts. These data suggest that the FHF CMs may be a more suitable candidate for cardiac regeneration.

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.

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.

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.

Abstract 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.

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.

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.

Abstract Background Recombination rate is non-uniformly distributed across the human genome. The variation of recombination rate at both fine and large scales cannot be fully explained by DNA sequences alone. Epigenetic factors, particularly DNA methylation, have recently been proposed to influence the variation in recombination rate. Results We study the relationship between recombination rate and gene regulatory domains, defined by a gene and its linked control elements. We define these links using expression quantitative trait loci (eQTLs), methylation quantitative trait loci (meQTLs), chromatin conformation from publicly available datasets (Hi-C and ChIA-PET), and correlated activity links that we infer across cell types. Each link type shows a “recombination rate valley” of significantly reduced recombination rate compared to matched control regions. This recombination rate valley is most pronounced for gene regulatory domains of early embryonic development genes, housekeeping genes, and constitutive regulatory elements, which are known to show increased evolutionary constraint across species. Recombination rate valleys show increased DNA methylation, reduced doublestranded break initiation, and increased repair efficiency, specifically in the lineage leading to the germ line. Moreover, by using only the overlap of functional links and DNA methylation in germ cells, we are able to predict the recombination rate with high accuracy. Conclusions Our results suggest the existence of a recombination rate valley at regulatory domains and provide a potential molecular mechanism to interpret the interplay between genetic and epigenetic variations.

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.

The original version of this article contained an error in the name of the author Ramachandran S. Vasan, which was incorrectly given as Vasan S. Ramachandran. This has now been corrected in both the PDF and HTML versions of the article.

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

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.

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.

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.

The growth factor interleukin-3 (IL-3) promotes the survival and growth of multipotent hematopoietic progenitors and stimulates myelopoiesis. It has also been reported to oppose terminal granulopoiesis and to support leukemic cell growth through autocrine or paracrine mechanisms. The degree to which IL-3 acts at the posttranscriptional level is largely unknown. We have conducted global mRNA decay profiling and bioinformatic analyses in 32Dcl3 myeloblasts indicating that IL-3 caused immediate early stabilization of hundreds of transcripts in pathways relevant to myeloblast function. Stabilized transcripts were enriched for AU-Response elements (AREs), and an ARE-containing domain from the interleukin-6 (IL-6) 3'-UTR rendered a heterologous gene responsive to IL-3-mediated transcript stabilization. Many IL-3-stabilized transcripts had been associated with leukemic transformation. Deregulated Abl kinase shared with IL-3 the ability to delay turnover of transcripts involved in proliferation or differentiation blockade, relying, in part, on signaling through the Mek/Erk pathway. These findings support a model of IL-3 action through mRNA stability control and suggest that aberrant stabilization of an mRNA network linked to IL-3 contributes to leukemic cell growth.

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.