Functional annotation represents a key step toward the understanding and interpretation of germline and somatic variation as revealed by genome-wide association studies (GWAS) and The Cancer Genome ...Atlas (TCGA), respectively. GWAS have revealed numerous genetic risk variants residing in non-coding DNA associated with complex diseases. For sequences that lie within enhancers or promoters of transcription, it is not straightforward to assess the effects of variants on likely transcription factor binding sites. Consequently we introduce motifbreakR, which allows the biologist to judge whether the sequence surrounding a polymorphism or mutation is a good match, and how much information is gained or lost in one allele of the polymorphism or mutation relative to the other. MotifbreakR is flexible, giving a choice of algorithms for interrogation of genomes with motifs from many public sources that users can choose from. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within bioconductor.
https://github.com/Simon-Coetzee/MotifBreakR, www.bioconductor.org.
dennis.hazelett@cshs.org.
Genome-wide association studies (GWAS) have revolutionized the field of cancer genetics, but the causal links between increased genetic risk and onset/progression of disease processes remain to be ...identified. Here we report the first step in such an endeavor for prostate cancer. We provide a comprehensive annotation of the 77 known risk loci, based upon highly correlated variants in biologically relevant chromatin annotations--we identified 727 such potentially functional SNPs. We also provide a detailed account of possible protein disruption, microRNA target sequence disruption and regulatory response element disruption of all correlated SNPs at r(2) ≥ 0.88%. 88% of the 727 SNPs fall within putative enhancers, and many alter critical residues in the response elements of transcription factors known to be involved in prostate biology. We define as risk enhancers those regions with enhancer chromatin biofeatures in prostate-derived cell lines with prostate-cancer correlated SNPs. To aid the identification of these enhancers, we performed genomewide ChIP-seq for H3K27-acetylation, a mark of actively engaged enhancers, as well as the transcription factor TCF7L2. We analyzed in depth three variants in risk enhancers, two of which show significantly altered androgen sensitivity in LNCaP cells. This includes rs4907792, that is in linkage disequilibrium (r(2) = 0.91) with an eQTL for NUDT11 (on the X chromosome) in prostate tissue, and rs10486567, the index SNP in intron 3 of the JAZF1 gene on chromosome 7. Rs4907792 is within a critical residue of a strong consensus androgen response element that is interrupted in the protective allele, resulting in a 56% decrease in its androgen sensitivity, whereas rs10486567 affects both NKX3-1 and FOXA-AR motifs where the risk allele results in a 39% increase in basal activity and a 28% fold-increase in androgen stimulated enhancer activity. Identification of such enhancer variants and their potential target genes represents a preliminary step in connecting risk to disease process.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
DNA methylation has been used to identify functional changes at transcriptional enhancers and other cis-regulatory modules (CRMs) in tumors and other disease tissues. Our R/Bioconductor package ELMER ...(Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs altered gene regulatory networks (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set.
We present a completely revised version 2 of ELMER that provides numerous new features including an optional web-based interface and a new Supervised Analysis mode to use pre-defined sample groupings. We show that Supervised mode significantly increases statistical power and identifies additional GRNs and associated Master Regulators, such as SOX11 and KLF5 in Basal-like breast cancer.
ELMER v.2 is available as an R/Bioconductor package at http://bioconductor.org/packages/ELMER/.
Supplementary data are available at Bioinformatics online.
The precise nature of how cell type specific chromatin structures at enhancer sites affect gene expression is largely unknown. Here we identified cell type specific enhancers coupled with gene ...expression in two different types of breast epithelial cells, HMEC (normal breast epithelial cells) and MDAMB231 (triple negative breast cancer cell line).
Enhancers were defined by modified neighboring histones using chromatin immunoprecipitation followed by sequencing (ChIP-seq) and nucleosome depletion using formaldehyde-assisted isolation of regulatory elements followed by sequencing (FAIRE-seq). Histone modifications at enhancers were related to the expression levels of nearby genes up to 750 kb away. These expression levels were correlated with enhancer status (poised or active), defined by surrounding histone marks. Furthermore, about fifty percent of poised and active enhancers contained nucleosome-depleted regions. We also identified response element motifs enriched at these enhancer sites that revealed key transcription factors (e.g. TP63) likely involved in regulating breast epithelial enhancer-mediated gene expression. By utilizing expression data, potential target genes of more than 600 active enhancers were identified. These genes were involved in proteolysis, epidermis development, cell adhesion, mitosis, cell cycle, and DNA replication.
These findings facilitate the understanding of epigenetic regulation specifically, such as the relationships between regulatory elements and gene expression and generally, how breast epithelial cellular phenotypes are determined by cell type specific enhancers.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Granulocyte-monocyte progenitors (GMPs) and monocyte-dendritic cell progenitors (MDPs) produce monocytes during homeostasis and in response to increased demand during infection. Both progenitor ...populations are thought to derive from common myeloid progenitors (CMPs), and a hierarchical relationship (CMP-GMP-MDP-monocyte) is presumed to underlie monocyte differentiation. Here, however, we demonstrate that mouse MDPs arose from CMPs independently of GMPs, and that GMPs and MDPs produced monocytes via similar but distinct monocyte-committed progenitors. GMPs and MDPs yielded classical (Ly6Chi) monocytes with gene expression signatures that were defined by their origins and impacted their function. GMPs produced a subset of “neutrophil-like” monocytes, whereas MDPs gave rise to a subset of monocytes that yielded monocyte-derived dendritic cells. GMPs and MDPs were also independently mobilized to produce specific combinations of myeloid cell types following the injection of microbial components. Thus, the balance of GMP and MDP differentiation shapes the myeloid cell repertoire during homeostasis and following infection.
Display omitted
•GMPs and MDPs independently produce functionally distinct inflammatory monocytes•GMPs produce “neutrophil-like” inflammatory monocytes•MDPs produce monocyte-derived dendritic cells (moDCs)•Microbial stimuli differentially regulate monocyte production by GMPs and MDPs
Yáñez et al. show that functionally distinct inflammatory monocytes, including those producing monocyte-derived DCs and a “neutrophil-like” subset, arise from bone marrow progenitors via two independent pathways. Their analyses reveal that the repertoire of myeloid cells produced under steady-state and stress conditions depends on which progenitors are mobilized to differentiate.
Breast Cancer (BCa) genome-wide association studies revealed allelic frequency differences between cases and controls at index single nucleotide polymorphisms (SNPs). To date, 71 loci have thus been ...identified and replicated. More than 320,000 SNPs at these loci define BCa risk due to linkage disequilibrium (LD). We propose that BCa risk resides in a subgroup of SNPs that functionally affects breast biology. Such a shortlist will aid in framing hypotheses to prioritize a manageable number of likely disease-causing SNPs. We extracted all the SNPs, residing in 1 Mb windows around breast cancer risk index SNP from the 1000 genomes project to find correlated SNPs. We used FunciSNP, an R/Bioconductor package developed in-house, to identify potentially functional SNPs at 71 risk loci by coinciding them with chromatin biofeatures. We identified 1,005 SNPs in LD with the index SNPs (r(2)≥0.5) in three categories; 21 in exons of 18 genes, 76 in transcription start site (TSS) regions of 25 genes, and 921 in enhancers. Thirteen SNPs were found in more than one category. We found two correlated and predicted non-benign coding variants (rs8100241 in exon 2 and rs8108174 in exon 3) of the gene, ANKLE1. Most putative functional LD SNPs, however, were found in either epigenetically defined enhancers or in gene TSS regions. Fifty-five percent of these non-coding SNPs are likely functional, since they affect response element (RE) sequences of transcription factors. Functionality of these SNPs was assessed by expression quantitative trait loci (eQTL) analysis and allele-specific enhancer assays. Unbiased analyses of SNPs at BCa risk loci revealed new and overlooked mechanisms that may affect risk of the disease, thereby providing a valuable resource for follow-up studies.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Treatment of prostate cancer (PC) by androgen suppression promotes the emergence of aggressive variants that are androgen receptor (AR) independent. Here we identify the transcription factor ONECUT2 ...(OC2) as a master regulator of AR networks in metastatic castration-resistant prostate cancer (mCRPC). OC2 acts as a survival factor in mCRPC models, suppresses the AR transcriptional program by direct regulation of AR target genes and the AR licensing factor FOXA1, and activates genes associated with neural differentiation and progression to lethal disease. OC2 appears active in a substantial subset of human prostate adenocarcinoma and neuroendocrine tumors. Inhibition of OC2 by a newly identified small molecule suppresses metastasis in mice. These findings suggest that OC2 displaces AR-dependent growth and survival mechanisms in many cases where AR remains expressed, but where its activity is bypassed. OC2 is also a potential drug target in the metastatic phase of aggressive PC.
Single nucleotide polymorphisms (SNPs) are increasingly used to tag genetic loci associated with phenotypes such as risk of complex diseases. Technically, this is done genome-wide without prior ...restriction or knowledge of biological feasibility in scans referred to as genome-wide association studies (GWAS). Depending on the linkage disequilibrium (LD) structure at a particular locus, such tagSNPs may be surrogates for many thousands of other SNPs, and it is difficult to distinguish those that may play a functional role in the phenotype from those simply genetically linked. Because a large proportion of tagSNPs have been identified within non-coding regions of the genome, distinguishing functional from non-functional SNPs has been an even greater challenge. A strategy was recently proposed that prioritizes surrogate SNPs based on non-coding chromatin and epigenomic mapping techniques that have become feasible with the advent of massively parallel sequencing. Here, we introduce an R/Bioconductor software package that enables the identification of candidate functional SNPs by integrating information from tagSNP locations, lists of linked SNPs from the 1000 genomes project and locations of chromatin features which may have functional significance.
FunciSNP is available from Bioconductor (bioconductor.org).
Recent genome-wide association studies (GWAS) of Parkinson's disease (PD) revealed at least 26 risk loci, with associated single nucleotide polymorphisms (SNPs) located in non-coding DNA having ...unknown functions in risk. In order to explore in which cell types these SNPs (and their correlated surrogates at r(2) ≥ 0.8) could alter cellular function, we assessed their location overlap with histone modification regions that indicate transcription regulation in 77 diverse cell types. We found statistically significant enrichment of risk SNPs at 12 loci in active enhancers or promoters. We investigated 4 risk loci in depth that were most significantly enriched (-logeP > 14) and contained 8 putative enhancers in the different cell types. These enriched loci, along with eQTL associations, were unexpectedly present in non-neuronal cell types. These included lymphocytes, mesendoderm, liver- and fat-cells, indicating that cell types outside the brain are involved in the genetic predisposition to PD. Annotating regulatory risk regions within specific cell types may unravel new putative risk mechanisms and molecular pathways that contribute to PD development.
The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these ...datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge.
We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis.
The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
PDF
