The BLUEPRINT Data Analysis Portal

CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology

Cell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification of cell clusters by considering the rich hierarchical structure of known cell types. Furthermore, CellO comes pre-trained on a comprehensive data set of human, healthy, untreated primary samples in the Sequence Read Archive. CellO's comprehensive training set enables it to run out of the box on diverse cell types and achieves competitive or even superior performance when compared to existing state-of-the-art methods. Lastly, CellO's linear models are easily interpreted, thereby enabling exploration of cell-type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellO's models across the ontology.

Epigenomics and transcriptomics of systemic sclerosis CD4+ T cells reveal long-range dysregulation of key inflammatory pathways mediated by disease-associated susceptibility loci

BACKGROUND

Systemic sclerosis (SSc) is a genetically complex autoimmune disease mediated by the interplay between genetic and epigenetic factors in a multitude of immune cells, with CD4+ T lymphocytes as one of the principle drivers of pathogenesis.

METHODS

DNA samples exacted from CD4+ T cells of 48 SSc patients and 16 healthy controls were hybridized on MethylationEPIC BeadChip array. In parallel, gene expression was interrogated by hybridizing total RNA on Clariom™ S array. Downstream bioinformatics analyses were performed to identify correlating differentially methylated CpG positions (DMPs) and differentially expressed genes (DEGs), which were then confirmed utilizing previously published promoter capture Hi-C (PCHi-C) data.

RESULTS

We identified 9112 and 3929 DMPs and DEGs, respectively. These DMPs and DEGs are enriched in functional categories related to inflammation and T cell biology. Furthermore, correlation analysis identified 17,500 possible DMP-DEG interaction pairs within a window of 5 Mb, and utilizing PCHi-C data, we observed that 212 CD4+ T cell-specific pairs of DMP-DEG also formed part of three-dimensional promoter-enhancer networks, potentially involving CTCF. Finally, combining PCHi-C data with SSc GWAS data, we identified four important SSc-associated susceptibility loci, TNIP1 (rs3792783), GSDMB (rs9303277), IL12RB1 (rs2305743), and CSK (rs1378942), that could potentially interact with DMP-DEG pairs cg17239269-ANXA6, cg19458020-CCR7, cg10808810-JUND, and cg11062629-ULK3, respectively.

CONCLUSION

Our study unveils a potential link between genetic, epigenetic, and transcriptional deregulation in CD4+ T cells of SSc patients, providing a novel integrated view of molecular components driving SSc pathogenesis.

Genetic architecture of Alzheimer's disease

Advances in genetic and genomic technologies over the last thirty years have greatly enhanced our knowledge concerning the genetic architecture of Alzheimer's disease (AD). Several genes including APP, PSEN1, PSEN2, and APOE have been shown to exhibit large effects on disease susceptibility, with the remaining risk loci having much smaller effects on AD risk. Notably, common genetic variants impacting AD are not randomly distributed across the genome. Instead, these variants are enriched within regulatory elements active in human myeloid cells, and to a lesser extent liver cells, implicating these cell and tissue types as critical to disease etiology. Integrative approaches are emerging as highly effective for identifying the specific target genes through which AD risk variants act and will likely yield important insights related to potential therapeutic targets in the coming years. In the future, additional consideration of sex- and ethnicity-specific contributions to risk as well as the contribution of complex gene-gene and gene-environment interactions will likely be necessary to further improve our understanding of AD genetic architecture.

A pathogenic and clonally expanded B cell transcriptome in active multiple sclerosis

B cells serve as a key weapon against infectious diseases. They also contribute to multiple autoimmune diseases, including multiple sclerosis (MS) where depletion of B cells is a highly effective therapy. We describe a comprehensive profile of central nervous system (CNS)-specific transcriptional B cell phenotypes in MS at single-cell resolution with paired immune repertoires. We reveal a polyclonal immunoglobulin M (IgM) and IgG1 cerebrospinal fluid B cell expansion polarized toward an inflammatory, memory and plasmablast/plasma cell phenotype, with differential up-regulation of specific proinflammatory pathways. We did not find evidence that CNS B cells harbor a neurotropic virus. These data support the targeting of activated resident B cells in the CNS as a potentially effective strategy for control of treatment-resistant chronic disease.

Central nervous system B cells have several potential roles in multiple sclerosis (MS): secretors of proinflammatory cytokines and chemokines, presenters of autoantigens to T cells, producers of pathogenic antibodies, and reservoirs for viruses that trigger demyelination. To interrogate these roles, single-cell RNA sequencing (scRNA-Seq) was performed on paired cerebrospinal fluid (CSF) and blood from subjects with relapsing-remitting MS (RRMS; n = 12), other neurologic diseases (ONDs; n = 1), and healthy controls (HCs; n = 3). Single-cell immunoglobulin sequencing (scIg-Seq) was performed on a subset of these subjects and additional RRMS (n = 4), clinically isolated syndrome (n = 2), and OND (n = 2) subjects. Further, paired CSF and blood B cell subsets (RRMS; n = 7) were isolated using fluorescence activated cell sorting for bulk RNA sequencing (RNA-Seq). Independent analyses across technologies demonstrated that nuclear factor kappa B (NF-κB) and cholesterol biosynthesis pathways were activated, and specific cytokine and chemokine receptors were up-regulated in CSF memory B cells. Further, SMAD/TGF-β1 signaling was down-regulated in CSF plasmablasts/plasma cells. Clonally expanded, somatically hypermutated IgM+ and IgG1+ CSF B cells were associated with inflammation, blood–brain barrier breakdown, and intrathecal Ig synthesis. While we identified memory B cells and plasmablast/plasma cells with highly similar Ig heavy-chain sequences across MS subjects, similarities were also identified with ONDs and HCs. No viral transcripts, including from Epstein–Barr virus, were detected. Our findings support the hypothesis that in MS, CSF B cells are driven to an inflammatory and clonally expanded memory and plasmablast/plasma cell phenotype.

A practical guide for DNase-seq data analysis: from data management to common applications

Deoxyribonuclease I (DNase I)-hypersensitive site sequencing (DNase-seq) has been widely used to determine chromatin accessibility and its underlying regulatory lexicon. However, exploring DNase-seq data requires sophisticated downstream bioinformatics analyses. In this study, we first review computational methods for all of the major steps in DNase-seq data analysis, including experimental design, quality control, read alignment, peak calling, annotation of cis-regulatory elements, genomic footprinting and visualization. The challenges associated with each step are highlighted. Next, we provide a practical guideline and a computational pipeline for DNase-seq data analysis by integrating some of these tools. We also discuss the competing techniques and the potential applications of this pipeline for the analysis of analogous experimental data. Finally, we discuss the integration of DNase-seq with other functional genomics techniques.

scMatch: a single-cell gene expression profile annotation tool using reference datasets

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) measures gene expression at the resolution of individual cells. Massively multiplexed single-cell profiling has enabled large-scale transcriptional analyses of thousands of cells in complex tissues. In most cases, the true identity of individual cells is unknown and needs to be inferred from the transcriptomic data. Existing methods typically cluster (group) cells based on similarities of their gene expression profiles and assign the same identity to all cells within each cluster using the averaged expression levels. However, scRNA-seq experiments typically produce low-coverage sequencing data for each cell, which hinders the clustering process.

RESULTS

We introduce scMatch, which directly annotates single cells by identifying their closest match in large reference datasets. We used this strategy to annotate various single-cell datasets and evaluated the impacts of sequencing depth, similarity metric and reference datasets. We found that scMatch can rapidly and robustly annotate single cells with comparable accuracy to another recent cell annotation tool (SingleR), but that it is quicker and can handle larger reference datasets. We demonstrate how scMatch can handle large customized reference gene expression profiles that combine data from multiple sources, thus empowering researchers to identify cell populations in any complex tissue with the desired precision.

AVAILABILITY AND IMPLEMENTATION

scMatch (Python code) and the FANTOM5 reference dataset are freely available to the research community here https://github.com/forrest-lab/scMatch.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Inferring changes in histone modification during cell differentiation by ancestral state estimation based on phylogenetic trees of cell types: Human hematopoiesis as a model case

Revealing the landscape of epigenetic changes in cells during differentiation is important for understanding the development of organisms. In this study, to infer such epigenetic changes during human hematopoiesis, ancestral state estimation based on a phylogenetic tree was applied to map the epigenomic changes in six kinds of histone modifications onto the hierarchical cell differentiation process of hematopoiesis using epigenomes of eight types of differentiated hematopoietic cells. The histone modification changes inferred during hematopoiesis showed that changes that occurred on the branches separating different cell types reflected the characteristics of hematopoiesis in terms of genomic position and gene function. These results suggested that ancestral state estimation based on phylogenetic analysis of histone modifications in differentiated hematopoietic cells could reconstruct an appropriate landscape of histone modification changes during hematopoiesis. Since integration of the inferred changes of different histone modifications could reveal genes with specific histone marks such as active histone marks and bivalent histone marks on each internal branch of cell-type trees, this approach could provide valuable information for understanding the cell differentiation steps of each cell lineage.

The road towards data integration in human genomics: players, steps and interactions

Thousands of new experimental datasets are becoming available every day; in many cases, they are produced within the scope of large cooperative efforts, involving a variety of laboratories spread all over the world, and typically open for public use. Although the potential collective amount of available information is huge, the effective combination of such public sources is hindered by data heterogeneity, as the datasets exhibit a wide variety of notations and formats, concerning both experimental values and metadata. Thus, data integration is becoming a fundamental activity, to be performed prior to data analysis and biological knowledge discovery, consisting of subsequent steps of data extraction, normalization, matching and enrichment; once applied to heterogeneous data sources, it builds multiple perspectives over the genome, leading to the identification of meaningful relationships that could not be perceived by using incompatible data formats. In this paper, we first describe a technological pipeline from data production to data integration; we then propose a taxonomy of genomic data players (based on the distinction between contributors, repository hosts, consortia, integrators and consumers) and apply the taxonomy to describe about 30 important players in genomic data management. We specifically focus on the integrator players and analyse the issues in solving the genomic data integration challenges, as well as evaluate the computational environments that they provide to follow up data integration by means of visualization and analysis tools.

ENdb: a manually curated database of experimentally supported enhancers for human and mouse

Enhancers are a class of cis-regulatory elements that can increase gene transcription by forming loops in intergenic regions, introns and exons. Enhancers, as well as their associated target genes, and transcription factors (TFs) that bind to them, are highly associated with human disease and biological processes. Although some enhancer databases have been published, most only focus on enhancers identified by high-throughput experimental techniques. Therefore, it is highly desirable to construct a comprehensive resource of manually curated enhancers and their related information based on low-throughput experimental evidences. Here, we established a comprehensive manually-curated enhancer database for human and mouse, which provides a resource for experimentally supported enhancers, and to annotate the detailed information of enhancers. The current release of ENdb documents 737 experimentally validated enhancers and their related information, including 384 target genes, 263 TFs, 110 diseases and 153 functions in human and mouse. Moreover, the enhancer-related information was supported by experimental evidences, such as RNAi, in vitro knockdown, western blotting, qRT-PCR, luciferase reporter assay, chromatin conformation capture (3C) and chromosome conformation capture-on-chip (4C) assays. ENdb provides a user-friendly interface to query, browse and visualize the detailed information of enhancers. The database is available at http://www.licpathway.net/ENdb.

Convolutional neural network model to predict causal risk factors that share complex regulatory features

Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional features, we developed a convolutional neural network framework for combinatorial, nonlinear modeling of complex patterns shared by risk variants scattered among multiple associated loci. When applied for major psychiatric disorders and autoimmune diseases, neural and immune features, respectively, exhibited high explanatory power while reflecting the pathophysiology of the relevant disease. The predicted causal variants were concentrated in active regulatory regions of relevant cell types and tended to be in physical contact with transcription factors while residing in evolutionarily conserved regions and resulting in expression changes of genes related to the given disease. We demonstrate some examples of novel candidate causal variants and associated genes. Our method is expected to contribute to the identification and functional interpretation of potential causal noncoding variants in post-GWAS analyses.

Resolving mechanisms of immune-mediated disease in primary CD4 T cells

Deriving mechanisms of immune-mediated disease from GWAS data remains a formidable challenge, with attempts to identify causal variants being frequently hampered by strong linkage disequilibrium. To determine whether causal variants could be identified from their functional effects, we adapted a massively parallel reporter assay for use in primary CD4 T cells, the cell type whose regulatory DNA is most enriched for immune-mediated disease SNPs. This enabled the effects of candidate SNPs to be examined in a relevant cellular context and generated testable hypotheses into disease mechanisms. To illustrate the power of this approach, we investigated a locus that has been linked to six immune-mediated diseases but cannot be fine-mapped. By studying the lead expression-modulating SNP, we uncovered an NF-κB-driven regulatory circuit which constrains T-cell activation through the dynamic formation of a super-enhancer that upregulates TNFAIP3 (A20), a key NF-κB inhibitor. In activated T cells, this feedback circuit is disrupted-and super-enhancer formation prevented-by the risk variant at the lead SNP, leading to unrestrained T-cell activation via a molecular mechanism that appears to broadly predispose to human autoimmunity.

Haemopedia RNA-seq: a database of gene expression during haematopoiesis in mice and humans

During haematopoiesis, haematopoietic stem cells differentiate into restricted potential progenitors before maturing into the many lineages required for oxygen transport, wound healing and immune response. We have updated Haemopedia, a database of gene-expression profiles from a broad spectrum of haematopoietic cells, to include RNA-seq gene-expression data from both mice and humans. The Haemopedia RNA-seq data set covers a wide range of lineages and progenitors, with 57 mouse blood cell types (flow sorted populations from healthy mice) and 12 human blood cell types. This data set has been made accessible for exploration and analysis, to researchers and clinicians with limited bioinformatics experience, on our online portal Haemosphere: https://www.haemosphere.org. Haemosphere also includes nine other publicly available high-quality data sets relevant to haematopoiesis. We have added the ability to compare gene expression across data sets and species by curating data sets with shared lineage designations or to view expression gene vs gene, with all plots available for download by the user.

Epigenomics-Guided Drug Development: Recent Advances in Solving the Cancer Treatment "jigsaw puzzle"

The human epigenome plays a key role in determining cellular identity and eventually function. Drug discovery undertakings have focused mainly on the role of genomics in carcinogenesis, with the focus turning to the epigenome recently. Drugs targeting DNA and histone modifications are under development with some such as 5-azacytidine, decitabine, vorinostat, and panobinostat already approved by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This expert review offers a critical analysis of the epigenomics-guided drug discovery and development and the opportunities and challenges for the next decade. Importantly, the coupling of epigenetic editing techniques, such as clustered regularly interspersed short palindromic repeat (CRISPR)-CRISPR-associated protein-9 (Cas9) and APOBEC-coupled epigenetic sequencing (ACE-seq) with epigenetic drug screens, will allow the identification of small-molecule inhibitors or drugs able to reverse epigenetic changes responsible for many diseases. In addition, concrete and sustainable innovation in cancer treatment ought to integrate epigenome targeting drugs with classic therapies such as chemotherapy and immunotherapy.

The Promises and Challenges of Toxico-Epigenomics: Environmental Chemicals and Their Impacts on the Epigenome

BACKGROUND

It has been estimated that a substantial portion of chronic and noncommunicable diseases can be caused or exacerbated by exposure to environmental chemicals. Multiple lines of evidence indicate that early life exposure to environmental chemicals at relatively low concentrations could have lasting effects on individual and population health. Although the potential adverse effects of environmental chemicals are known to the scientific community, regulatory agencies, and the public, little is known about the mechanistic basis by which these chemicals can induce long-term or transgenerational effects. To address this question, epigenetic mechanisms have emerged as the potential link between genetic and environmental factors of health and disease.

OBJECTIVES

We present an overview of epigenetic regulation and a summary of reported evidence of environmental toxicants as epigenetic disruptors. We also discuss the advantages and challenges of using epigenetic biomarkers as an indicator of toxicant exposure, using measures that can be taken to improve risk assessment, and our perspectives on the future role of epigenetics in toxicology.

DISCUSSION

Until recently, efforts to apply epigenomic data in toxicology and risk assessment were restricted by an incomplete understanding of epigenomic variability across tissue types and populations. This is poised to change with the development of new tools and concerted efforts by researchers across disciplines that have led to a better understanding of epigenetic mechanisms and comprehensive maps of epigenomic variation. With the foundations now in place, we foresee that unprecedented advancements will take place in the field in the coming years. https://doi.org/10.1289/EHP6104.

A cell-free DNA metagenomic sequencing assay that integrates the host injury response to infection

High-throughput metagenomic sequencing offers an unbiased approach to identify pathogens in clinical samples. Conventional metagenomic sequencing, however, does not integrate information about the host, which is often critical to distinguish infection from infectious disease, and to assess the severity of disease. Here, we explore the utility of high-throughput sequencing of cell-free DNA (cfDNA) after bisulfite conversion to map the tissue and cell types of origin of host-derived cfDNA, and to profile the bacterial and viral metagenome. We applied this assay to 51 urinary cfDNA isolates collected from a cohort of kidney transplant recipients with and without bacterial and viral infection of the urinary tract. We find that the cell and tissue types of origin of urinary cfDNA can be derived from its genome-wide profile of methylation marks, and strongly depend on infection status. We find evidence of kidney and bladder tissue damage due to viral and bacterial infection, respectively, and of the recruitment of neutrophils to the urinary tract during infection. Through direct comparison to conventional metagenomic sequencing as well as clinical tests of infection, we find this assay accurately captures the bacterial and viral composition of the sample. The assay presented here is straightforward to implement, offers a systems view into bacterial and viral infections of the urinary tract, and can find future use as a tool for the differential diagnosis of infection.

DiseaseEnhancer: a resource of human disease-associated enhancer catalog

Large-scale sequencing studies discovered substantial genetic variants occurring in enhancers which regulate genes via long range chromatin interactions. Importantly, such variants could affect enhancer regulation by changing transcription factor bindings or enhancer hijacking, and in turn, make an essential contribution to disease progression. To facilitate better usage of published data and exploring enhancer deregulation in various human diseases, we created DiseaseEnhancer (http://biocc.hrbmu.edu.cn/DiseaseEnhancer/), a manually curated database for disease-associated enhancers. As of July 2017, DiseaseEnhancer includes 847 disease-associated enhancers in 143 human diseases. Database features include basic enhancer information (i.e. genomic location and target genes); disease types; associated variants on the enhancer and their mediated phenotypes (i.e. gain/loss of enhancer and the alterations of transcription factor bindings). We also include a feature on our website to export any query results into a file and download the full database. DiseaseEnhancer provides a promising avenue for researchers to facilitate the understanding of enhancer deregulation in disease pathogenesis, and identify new biomarkers for disease diagnosis and therapy.

Functionalization of CD36 cardiovascular disease and expression associated variants by interdisciplinary high throughput analysis

CD36 is a platelet membrane glycoprotein whose engagement with oxidized low-density lipoprotein (oxLDL) results in platelet activation. The CD36 gene has been associated with platelet count, platelet volume, as well as lipid levels and CVD risk by genome-wide association studies. Platelet CD36 expression levels have been shown to be associated with both the platelet oxLDL response and an elevated risk of thrombo-embolism. Several genomic variants have been identified as associated with platelet CD36 levels, however none have been conclusively demonstrated to be causative. We screened 81 expression quantitative trait loci (eQTL) single nucleotide polymorphisms (SNPs) associated with platelet CD36 expression by a Massively Parallel Reporter Assay (MPRA) and analyzed the results with a novel Bayesian statistical method. Ten eQTLs located 13kb to 55kb upstream of the CD36 transcriptional start site of transcript ENST00000309881 and 49kb to 92kb upstream of transcript ENST00000447544, demonstrated significant transcription shifts between their minor and major allele in the MPRA assay. Of these, rs2366739 and rs1194196, separated by only 20bp, were confirmed by luciferase assay to alter transcriptional regulation. In addition, electromobility shift assays demonstrated differential DNA:protein complex formation between the two alleles of this locus. Furthermore, deletion of the genomic locus by CRISPR/Cas9 in K562 and Meg-01 cells results in upregulation of CD36 transcription. These data indicate that we have identified a variant that regulates expression of CD36, which in turn affects platelet function. To assess the clinical relevance of our findings we used the PhenoScanner tool, which aggregates large scale GWAS findings; the results reinforce the clinical relevance of our variants and the utility of the MPRA assay. The study demonstrates a generalizable paradigm for functional testing of genetic variants to inform mechanistic studies, support patient management and develop precision therapies.

Platelets are anucleate cells that are best known as regulators of vascular hemostasis and thrombosis but also play important roles in cancer, angiogenesis, and inflammation. CD36 is a platelet surface marker that can activate platelet in response to oxidized low density lipoprotein (oxLDL). CD36 has been associated with numerous cardiovascular traits in human including blood lipid levels, platelet count, and cardiovascular disease prevalence in human genetic studies. Human variability in platelet CD36 levels are associated with the platelet response to oxLDL. However, the genetic mechanisms responsible for the variability of CD36 levels are unknown. We examined 81 genetic variants associated with CD36 levels for functionality using a high-throughput assay. Of the ten variants that were identified in that assay, one doublet, rs2366739 and rs1194196, were confirmed using additional molecular and cellular assays. Deletion of the genomic region containing rs2366739 and rs1194196 resulted in overexpression of CD36 in a cell culture system. This finding indicates a control locus which can serve as a potential target in modulating CD36 expression and altering platelet function in cardiovascular disease.

Identification of the Regulatory Elements and Target Genes of Megakaryopoietic Transcription Factor MEF2C

Megakaryopoiesis produces specialized haematopoietic stem cells in the bone marrow that give rise to megakaryocytes which ultimately produce platelets. Defects in megakaryopoiesis can result in altered platelet counts and physiology, leading to dysfunctional haemostasis and thrombosis. Additionally, dysregulated megakaryopoiesis is also associated with myeloid pathologies. Transcription factors play critical roles in cell differentiation by regulating the temporal and spatial patterns of gene expression which ultimately decide cell fate. Several transcription factors have been described as regulating megakaryopoiesis including myocyte enhancer factor 2C (MEF2C); however, the genes regulated by MEF2C that influence megakaryopoiesis have not been reported. Using chromatin immunoprecipitation-sequencing and Gene Ontology data we identified five candidate genes that are bound by MEF2C and regulate megakaryopoiesis: MOV10, AGO3, HDAC1, RBBP5 and WASF2. To study expression of these genes, we silenced MEF2C gene expression in the Meg01 megakaryocytic cell line and in induced pluripotent stem cells by CRISPR/Cas9 editing. We also knocked down MEF2C expression in cord blood-derived haematopoietic stem cells by siRNA. We found that absent or reduced MEF2C expression resulted in defects in megakaryocytic differentiation and reduced levels of the candidate target genes. Luciferase assays confirmed that genomic sequences within the target genes are regulated by MEF2C levels. Finally, we demonstrate that small deletions linked to a platelet count-associated single nucleotide polymorphism alter transcriptional activity, suggesting a mechanism by which genetic variation in MEF2C alters platelet production. These data help elucidate the mechanism behind MEF2C regulation of megakaryopoiesis and genetic variation driving platelet production.

Big data analytics for personalized medicine

Big Data are radically changing biomedical research. The unprecedented advances in automated collection of large-scale molecular and clinical data pose major challenges to data analysis and interpretation, calling for the development of new computational approaches. The creation of powerful systems for the effective use of biomedical Big Data in Personalized Medicine (a.k.a. Precision Medicine) will require significant scientific and technical developments, including infrastructure, engineering, project and financial management. We review here how the evolution of data-driven methods offers the possibility to address many of these problems, guiding the formulation of hypotheses on systems functioning and the generation of mechanistic models, and facilitating the design of clinical procedures in Personalized Medicine.

Circular synthesized CRISPR/Cas gRNAs for functional interrogations in the coding and noncoding genome

Current technologies used to generate CRISPR/Cas gene perturbation reagents are labor intense and require multiple ligation and cloning steps. Furthermore, increasing gRNA sequence diversity negatively affects gRNA distribution, leading to libraries of heterogeneous quality. Here, we present a rapid and cloning-free mutagenesis technology that can efficiently generate covalently-closed-circular-synthesized (3Cs) CRISPR/Cas gRNA reagents and that uncouples sequence diversity from sequence distribution. We demonstrate the fidelity and performance of 3Cs reagents by tailored targeting of all human deubiquitinating enzymes (DUBs) and identify their essentiality for cell fitness. To explore high-content screening, we aimed to generate the largest up-to-date gRNA library that can be used to interrogate the coding and noncoding human genome and simultaneously to identify genes, predicted promoter flanking regions, transcription factors and CTCF binding sites that are linked to doxorubicin resistance. Our 3Cs technology enables fast and robust generation of bias-free gene perturbation libraries with yet unmatched diversities and should be considered an alternative to established technologies.

A genetic variant associated with multiple sclerosis inversely affects the expression of CD58 and microRNA-548ac from the same gene

Genome-wide association studies have identified more than 200 genetic variants to be associated with an increased risk of developing multiple sclerosis (MS). Still, little is known about the causal molecular mechanisms that underlie the genetic contribution to disease susceptibility. In this study, we investigated the role of the single-nucleotide polymorphism (SNP) rs1414273, which is located within the microRNA-548ac stem-loop sequence in the first intron of the CD58 gene. We conducted an expression quantitative trait locus (eQTL) analysis based on public RNA-sequencing and microarray data of blood-derived cells of more than 1000 subjects. Additionally, CD58 transcripts and mature hsa-miR-548ac molecules were measured using real-time PCR in peripheral blood samples of 32 MS patients. Cell culture experiments were performed to evaluate the efficiency of Drosha-mediated stem-loop processing dependent on genotype and to determine the target genes of this underexplored microRNA. Across different global populations and data sets, carriers of the MS risk allele showed reduced CD58 mRNA levels but increased hsa-miR-548ac levels. We provide evidence that the SNP rs1414273 might alter Drosha cleavage activity, thereby provoking partial uncoupling of CD58 gene expression and microRNA-548ac production from the shared primary transcript in immune cells. Moreover, the microRNA was found to regulate genes, which participate in inflammatory processes and in controlling the balance of protein folding and degradation. We thus uncovered new regulatory implications of the MS-associated haplotype of the CD58 gene locus, and we remind that paradoxical findings can be encountered in the analysis of eQTLs upon data aggregation. Our study illustrates that a better understanding of RNA processing events might help to establish the functional nature of genetic variants, which predispose to inflammatory and neurological diseases.

More than 200 genetic loci have been associated with an increased risk of developing multiple sclerosis (MS). Here, we investigated the role of a single-nucleotide polymorphism (SNP), which is located within the microRNA-548ac stem-loop sequence in the first intron of the CD58 gene. We analyzed expression data of blood-derived cells of about 1000 subjects and observed that MS risk allele carriers have reduced CD58 mRNA levels but increased hsa-miR-548ac levels. Our findings suggest that Drosha cleavage activity is affected, perhaps attributable to the specific SNP. This may contribute to partial uncoupling of CD58 gene expression and hsa-miR-548ac production from the shared primary transcript in immune cells. We discovered that the mature microRNA downregulates genes involved in inflammatory processes and in controlling the balance of protein folding and degradation. Our study exemplifies that paradoxical findings can be encountered in the analysis of genetic variants regulating transcription and/or RNA processing.

Genetic correlation between multiple myeloma and chronic lymphocytic leukaemia provides evidence for shared aetiology

The clustering of different types of B-cell malignancies in families raises the possibility of shared aetiology. To examine this, we performed cross-trait linkage disequilibrium (LD)-score regression of multiple myeloma (MM) and chronic lymphocytic leukaemia (CLL) genome-wide association study (GWAS) data sets, totalling 11,734 cases and 29,468 controls. A significant genetic correlation between these two B-cell malignancies was shown (Rg = 0.4, P = 0.0046). Furthermore, four of the 45 known CLL risk loci were shown to associate with MM risk and five of the 23 known MM risk loci associate with CLL risk. By integrating eQTL, Hi-C and ChIP-seq data, we show that these pleiotropic risk loci are enriched for B-cell regulatory elements and implicate B-cell developmental genes. These data identify shared biological pathways influencing the development of CLL and, MM and further our understanding of the aetiological basis of these B-cell malignancies.

Automatic detection of genomic regions with informative epigenetic patterns

BACKGROUND

Epigenetic phenomena are crucial for explaining the phenotypic plasticity seen in the cells of different tissues, developmental stages and diseases, all holding the same DNA sequence. As technology is allowing to retrieve epigenetic information in a genome-wide fashion, massive epigenomic datasets are being accumulated in public repositories. New approaches are required to mine those data to extract useful knowledge. We present here an automatic approach for detecting genomic regions with epigenetic variation patterns across samples related to a grouping of these samples, as a way of detecting regions functionally associated to the phenomenon behind the classification.

RESULTS

We show that the regions automatically detected by the method in the whole human genome associated to three different classifications of a set of epigenomes (cancer vs. healthy, brain vs. other organs, and fetal vs. adult tissues) are enriched in genes associated to these processes.

CONCLUSIONS

The method is fully automatic and can exhaustively scan the whole human genome at any resolution using large collections of epigenomes as input, although it also produces good results with small datasets. Consequently, it will be valuable for obtaining functional information from the incoming epigenomic information as it continues to accumulate.

FAANG, establishing metadata standards, validation and best practices for the farmed and companion animal community

The Functional Annotation of ANimal Genomes (FAANG) project aims, through a coordinated international effort, to provide high quality functional annotation of animal genomes with an initial focus on farmed and companion animals. A key goal of the initiative is to ensure high quality and rich supporting metadata to describe the project's animals, specimens, cell cultures and experimental assays. By defining rich sample and experimental metadata standards and promoting best practices in data descriptions, deposition and openness, FAANG champions higher quality and reusability of published datasets. FAANG has established a Data Coordination Centre, which sits at the heart of the Metadata and Data Sharing Committee. It continues to evolve the metadata standards, support submissions and, crucially, create powerful and accessible tools to support deposition and validation of metadata. FAANG conforms to the findable, accessible, interoperable, and reusable (FAIR) data principles, with high quality, open access and functionally interlinked data. In addition to data generated by FAANG members and specific FAANG projects, existing datasets that meet the main—or more permissive legacy—standards are incorporated into a central, focused, functional data resource portal for the entire farmed and companion animal community. Through clear and effective metadata standards, validation and conversion software, combined with promotion of best practices in metadata implementation, FAANG aims to maximise effectiveness and inter‐comparability of assay data. This supports the community to create a rich genome‐to‐phenotype resource and promotes continuing improvements in animal data standards as a whole.

Deconstructing and targeting the genomic architecture of human neurodegeneration

The field of neurodegenerative disease research has seen tremendous advances over the last two decades as new technologies and analytic methods have enabled well-powered human genomic studies. Driven first by genetic studies and more recently by transcriptomic and epigenomic studies of proper size, we have uncovered a large repertoire of loci, genes, and molecular features that are implicated in discrete, syndromically defined neurodegenerative conditions, such as Alzheimer's disease, amyotrophic lateral sclerosis, frontotemporal dementia, multiple sclerosis, and Parkinson's disease. As we begin to understand the impact of these genomic features in each disease, we also appreciate that many aging individuals accumulate each of these pathologies without fulfilling criteria for syndromic diagnoses, that other pathologies are common in individuals with a given diagnosis, and that there may be shared protective factors against central nervous system injury. Thus, we now need to bring these disparate observations together into a person-centered approach that considers all neurodegenerative and protective processes simultaneously to modulate the trajectory of cognitive and functional decline that comes with brain aging.

Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma

Genome-wide association studies (GWAS) have transformed our understanding of susceptibility to multiple myeloma (MM), but much of the heritability remains unexplained. We report a new GWAS, a meta-analysis with previous GWAS and a replication series, totalling 9974 MM cases and 247,556 controls of European ancestry. Collectively, these data provide evidence for six new MM risk loci, bringing the total number to 23. Integration of information from gene expression, epigenetic profiling and in situ Hi-C data for the 23 risk loci implicate disruption of developmental transcriptional regulators as a basis of MM susceptibility, compatible with altered B-cell differentiation as a key mechanism. Dysregulation of autophagy/apoptosis and cell cycle signalling feature as recurrently perturbed pathways. Our findings provide further insight into the biological basis of MM.

Enhancer and super-enhancer: Positive regulators in gene transcription

Enhancer is a positive regulator for spatiotemporal development in eukaryotes. As a cluster, super-enhancer is closely related to cell identity- and fate-determined processes. Both of them function tightly depending on their targeted transcription factors, cofactors, and genes through distal genomic interactions. They have been recognized as critical components and played positive roles in transcriptional regulatory network or factory. Recent advances of next-generation sequencing have dramatically expanded our ability and knowledge to interrogate the molecular mechanism of enhancer and super-enhancer for transcription. Here, we review the history, importance, advances and challenges on enhancer and super-enhancer field. This will benefit our understanding of their function mechanism for transcription underlying precise gene expression.

Non-coding RNAs in cardiovascular diseases: diagnostic and therapeutic perspectives

Recent research has demonstrated that the non-coding genome plays a key role in genetic programming and gene regulation during development as well as in health and cardiovascular disease. About 99% of the human genome do not encode proteins, but are transcriptionally active representing a broad spectrum of non-coding RNAs (ncRNAs) with important regulatory and structural functions. Non-coding RNAs have been identified as critical novel regulators of cardiovascular risk factors and cell functions and are thus important candidates to improve diagnostics and prognosis assessment. Beyond this, ncRNAs are rapidly emgerging as fundamentally novel therapeutics. On a first level, ncRNAs provide novel therapeutic targets some of which are entering assessment in clinical trials. On a second level, new therapeutic tools were developed from endogenous ncRNAs serving as blueprints. Particularly advanced is the development of RNA interference (RNAi) drugs which use recently discovered pathways of endogenous short interfering RNAs and are becoming versatile tools for efficient silencing of protein expression. Pioneering clinical studies include RNAi drugs targeting liver synthesis of PCSK9 resulting in highly significant lowering of LDL cholesterol or targeting liver transthyretin (TTR) synthesis for treatment of cardiac TTR amyloidosis. Further novel drugs mimicking actions of endogenous ncRNAs may arise from exploitation of molecular interactions not accessible to conventional pharmacology. We provide an update on recent developments and perspectives for diagnostic and therapeutic use of ncRNAs in cardiovascular diseases, including atherosclerosis/coronary disease, post-myocardial infarction remodelling, and heart failure.

DeepBlueR: large-scale epigenomic analysis in R

Motivation

While large amounts of epigenomic data are publicly available, their retrieval in a form suitable for downstream analysis is a bottleneck in current research. The DeepBlue Epigenomic Data Server provides a powerful interface and API for filtering, transforming, aggregating and downloading data from several epigenomic consortia.

Results

To make public epigenomic data conveniently available for analysis in R, we developed an R/Bioconductor package that connects to the DeepBlue Epigenomic Data Server, enabling users to quickly gather and transform epigenomic data from selected experiments for analysis in the Bioconductor ecosystem.

Availability and Implementation

http://deepblue.mpi-inf.mpg.de/R.

Requirements

R 3.3, Bioconductor 3.4.

Supplementary information

Supplementary data are available at Bioinformatics online.

Quantifying tumor-infiltrating immune cells from transcriptomics data

By exerting pro- and anti-tumorigenic actions, tumor-infiltrating immune cells can profoundly influence tumor progression, as well as the success of anti-cancer therapies. Therefore, the quantification of tumor-infiltrating immune cells holds the promise to unveil the multi-faceted role of the immune system in human cancers and its involvement in tumor escape mechanisms and response to therapy. Tumor-infiltrating immune cells can be quantified from RNA sequencing data of human tumors using bioinformatics approaches. In this review, we describe state-of-the-art computational methods for the quantification of immune cells from transcriptomics data and discuss the open challenges that must be addressed to accurately quantify immune infiltrates from RNA sequencing data of human bulk tumors.

The Ensembl Genome Browser: Strategies for Accessing Eukaryotic Genome Data

The Ensembl Genome Browser provides a wealth of freely available genomic data that can be accessed for many purposes by genetics, genomics, and molecular biology researchers. Herein we present two protocols for exploring different aspects of these data: a phenotype and its associated variants and genes, and a promoter and the epigenetic marks and protein-binding activity associated with it. These workflows illustrate a subset of the data types available through the Ensembl Browser, and can be considered a springboard for further exploration.

Automatic identification of informative regions with epigenomic changes associated to hematopoiesis

Hematopoiesis is one of the best characterized biological systems but the connection between chromatin changes and lineage differentiation is not yet well understood. We have developed a bioinformatic workflow to generate a chromatin space that allows to classify 42 human healthy blood epigenomes from the BLUEPRINT, NIH ROADMAP and ENCODE consortia by their cell type. This approach let us to distinguish different cells types based on their epigenomic profiles, thus recapitulating important aspects of human hematopoiesis. The analysis of the orthogonal dimension of the chromatin space identify 32,662 chromatin determinant regions (CDRs), genomic regions with different epigenetic characteristics between the cell types. Functional analysis revealed that these regions are linked with cell identities. The inclusion of leukemia epigenomes in the healthy hematological chromatin sample space gives us insights on the healthy cell types that are more epigenetically similar to the disease samples. Further analysis of tumoral epigenetic alterations in hematopoietic CDRs points to sets of genes that are tightly regulated in leukemic transformations and commonly mutated in other tumors. Our method provides an analytical approach to study the relationship between epigenomic changes and cell lineage differentiation. Method availability: https://github.com/david-juan/ChromDet.

Translational Perspective on Epigenetics in Cardiovascular Disease

A plethora of environmental and behavioral factors interact, resulting in changes in gene expression and providing a basis for the development and progression of cardiovascular diseases. Heterogeneity in gene expression responses among cells and individuals involves epigenetic mechanisms. Advancing technology allowing genome-scale interrogation of epigenetic marks provides a rapidly expanding view of the complexity and diversity of the epigenome. In this review, the authors discuss the expanding landscape of epigenetic modifications and highlight their importance for future understanding of disease. The epigenome provides a mechanistic link between environmental exposures and gene expression profiles ultimately leading to disease. The authors discuss the current evidence for transgenerational epigenetic inheritance and summarize the data linking epigenetics to cardiovascular disease. Furthermore, the potential targets provided by the epigenome for the development of future diagnostics, preventive strategies, and therapy for cardiovascular disease are reviewed. Finally, the authors provide some suggestions for future directions.

Clinical implications of genome-wide DNA methylation studies in acute myeloid leukemia

Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults. AML is a heterogeneous malignancy characterized by distinct genetic and epigenetic abnormalities. Recent genome-wide DNA methylation studies have highlighted an important role of dysregulated methylation signature in AML from biological and clinical standpoint. In this review, we will outline the recent advances in the methylome study of AML and overview the impacts of DNA methylation on AML diagnosis, treatment, and prognosis.