epigenetics Search Results


  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
  • 83
    Qiagen rt2 profiler pcr array mouse epigenetic chromatin modification enzymes
    Blocking NKG2D signaling during in vivo killing altered the expression of epigenetic modifier enzymes. Effector pMel CD8 T cells were generated as described in Fig. 1 a. One day after injecting the anti-NKG2D blocking antibody ( a ) or one day after the in vivo killing assay ( b ), effector CD8 pMel T cells were isolated from the spleen of 5 pooled mice using CD90.1 + congenic marker. After mRNA isolation and conversion into cDNA, 84 different epigenetic modifier enzymes were selectively quantified using <t>RT2</t> Profiler Epigenetic modifier enzymes <t>PCR</t> array. The fold of change was calculated using the online software provided by Qiagen. The enzymes with > 2.5-fold change in expression are numbered on the plot and summarized in the tables on the right side, together with their fold of change
    Rt2 Profiler Pcr Array Mouse Epigenetic Chromatin Modification Enzymes, supplied by Qiagen, used in various techniques. Bioz Stars score: 83/100, based on 7 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/rt2 profiler pcr array mouse epigenetic chromatin modification enzymes/product/Qiagen
    Average 83 stars, based on 7 article reviews
    Price from $9.99 to $1999.99
    rt2 profiler pcr array mouse epigenetic chromatin modification enzymes - by Bioz Stars, 2019-05
    83/100 stars
      Buy from Supplier

    96
    Epigenomics roadmap epigenomics project
    AD GWAS loci are preferentially enriched in increasing enhancer orthologs with immune function a, Enrichment (y-axis) of changing mouse AD enhancer orthologs, with a focus on consistently increasing (red) category of enhancers, in 127 cell and tissue types profiled by the <t>Roadmap</t> <t>Epigenomics</t> Consortium 10 (columns). Roadmap samples are grouped into fetal brain (purple), adult brain (green), immune/blood cell types (orange), and all other (grey). b, Cell-type-specific fold luciferase reporter expression change relative to control (ctrl) for selected increasing enhancer regions in BV-2 microglia (orange) vs N2A neurons (purple) (n=3, * P
    Roadmap Epigenomics Project, supplied by Epigenomics, used in various techniques. Bioz Stars score: 96/100, based on 256 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/roadmap epigenomics project/product/Epigenomics
    Average 96 stars, based on 256 article reviews
    Price from $9.99 to $1999.99
    roadmap epigenomics project - by Bioz Stars, 2019-05
    96/100 stars
      Buy from Supplier

    86
    SABiosciences mouse epigenetic chromatin modification enzymes rt2 profiler pcr array
    Gene expression of DNA methyltransferases, histone deacetylases, histone acetyltransferases, histone methyltransferases and SET domain proteins, histone phosphorylation, and histone ubiquitination enzymes at 1, 4, and 24 h in CSE-treated H292 cells. Human bronchial epithelial cells were treated with and without CSE for 1 h (2% CSE) and 4 and 24 h (1% CSE). RNA extracted from control and CSE-treated cells was analyzed using <t>RT2</t> Profiler <t>PCR</t> array for human epigenetic chromatin modification enzymes. A : the transcription levels of genes encoding specific DNMTs ( Dnmt1 , Dnmt3a , and Dnmt3b ) and HDACs ( Hdac2 , Hdac3 , and Hdac4) were examined by qPCR using the 2−ΔΔCt method. B : the transcription levels of genes encoding specific HATs ( Cdyl , Csrp2bp , Hat1 , Myst3 , Myst4 , and Ncoa3 ) were examined by qPCR using the 2−ΔΔCt method. C : the transcription levels of genes encoding specific HMTs ( Prmt1 , Prmt5 , and Nsd1 ) and SET domain proteins ( Setdb2 , Setd4 , and Setd5 ) were examined by qPCR using the 2−ΔΔCt method. D : the transcription levels of genes encoding specific histone phosphorylation ( Aurkc and Nek6 ) and histone ubiquitination ( Ube2b , Usp16 , and Usp22 ) were examined by qPCR using the 2−ΔΔCt method. Bar graphs represent the mean normalized expression of samples in control vs. CSE-treated H292 cells. Data were normalized using the endogenous housekeeping gene ribosomal protein L13a ( Rpl13a ) as reference and controls as calibrators. Statistical significance ( P < 0.05) was analyzed by two-way ANOVA (Tukey's multiple-comparison test) using GraphPad Prism 6. Values are means ± SE ( n = 4/group). * P < 0.05, ** P < 0.01, and *** P < 0.001 vs. control. # P < 0.05, ## P < 0.01, and ### P < 0.001 vs. CSE (1 or 4 h).
    Mouse Epigenetic Chromatin Modification Enzymes Rt2 Profiler Pcr Array, supplied by SABiosciences, used in various techniques. Bioz Stars score: 86/100, based on 10 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/mouse epigenetic chromatin modification enzymes rt2 profiler pcr array/product/SABiosciences
    Average 86 stars, based on 10 article reviews
    Price from $9.99 to $1999.99
    mouse epigenetic chromatin modification enzymes rt2 profiler pcr array - by Bioz Stars, 2019-05
    86/100 stars
      Buy from Supplier

    86
    New England Biolabs fog1 epigenetic effector construct
    dCpf1-epigenetic fusions do not repress HER2 gene expression. ( A ) Schematic of dCpf1-fusions with ED. Catalytically inactive As Cpf1 contains nuclease-inactivating mutation D908A (dCpf1). ( B ) UCSC genome browser graphic showing HER2 target regions of sgRNAs containing the 5′-NGG-3′ PAM required by dCas9, and crRNA target sites flanked by the 5′-NTTT-3′ PAM required by dCpf1. HCT116 ENCODE tracks for DNase Hypersensitivity (DNase HS) and H3K27ac binding are shown. ( C ) Abundance of HER2 mRNA was measured after co-transfection of HCT116 cells with a pool of three crRNAs with the indicated dCpf1-ED fusions. No significant repression was observed compared to a dCpf1 with no ED. Negative control cells (‘−’) were transfected with mCherry reporter plasmid instead of dCpf1. As a positive control, repression was assessed after co-transfection of dCas9 with no ED or <t>FOG1[1–45]-dCas9-FOG1[1–45]</t> and three sgRNAs (Tukey-test, P
    Fog1 Epigenetic Effector Construct, supplied by New England Biolabs, used in various techniques. Bioz Stars score: 86/100, based on 11 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/fog1 epigenetic effector construct/product/New England Biolabs
    Average 86 stars, based on 11 article reviews
    Price from $9.99 to $1999.99
    fog1 epigenetic effector construct - by Bioz Stars, 2019-05
    86/100 stars
      Buy from Supplier

    85
    SLIT2 LTD ezh2 epigenetic target
    <t>EZH2</t> Enhances Androgen Signaling in Both ADPC and CRPC Cells (A and B) Androgen-induced genes (A) are enriched for downregulation upon EZH2 knockdown (false discovery rate [FDR] q
    Ezh2 Epigenetic Target, supplied by SLIT2 LTD, used in various techniques. Bioz Stars score: 85/100, based on 9 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/ezh2 epigenetic target/product/SLIT2 LTD
    Average 85 stars, based on 9 article reviews
    Price from $9.99 to $1999.99
    ezh2 epigenetic target - by Bioz Stars, 2019-05
    85/100 stars
      Buy from Supplier

    80
    Epigenomics crispr cas9 based epigenomic regulatory element screening
    <t>CRISPR–Cas9-based</t> <t>Epigenetic</t> Regulatory Element Screening (CERES) identifies regulatory elements of the β-globin locus in a loss-of-function screen. ( a ) CERES involves the design and synthesis of libraries of gRNAs targeted to all candidate gene regulatory elements in a genomic region, in this case as defined by DNase I hypersensitive sites (DHS) identified by DNase-seq. Lentiviral vectors encoding the gRNA library are delivered to cell lines expressing the dCas9 KRAB repressor, for loss-of-function screens, or the dCas9 p300 activator, for gain-of-function screens. The cells can then be selected for changes in phenotype, such as gain or loss of expression of a target gene. Sequencing the gRNAs in the selected cell subpopulations and mapping them back to the genome reveals regulatory elements involved in controlling the selected phenotype. In the example shown here, a gRNA library was designed to all DHSs in a 4.5 Mb region surrounding the β-globin locus, and introduced into human K562 cells expressing dCas9 KRAB and containing an mCherry reporter at the HBE1 gene. ( b ) Representative flow cytometry data of the HBE1 reporter cells containing the gRNA library, and expression levels of cells sorted for gRNA enrichment. ( c ) Manhattan plot of a high-throughput screen for regulatory elements in the 4.5 Mb surrounding the globin locus using the dCas9 KRAB repressor. ( d ) Enriched DHSs following selection for decreased HBE1 expression were found only in the HBE1 promoter and enhancers (HS1-4), while the promoters of HBG1 / 2 were enriched in cells with increased HBE1 expression. Diamonds indicate adjusted p-value
    Crispr Cas9 Based Epigenomic Regulatory Element Screening, supplied by Epigenomics, used in various techniques. Bioz Stars score: 80/100, based on 5 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/crispr cas9 based epigenomic regulatory element screening/product/Epigenomics
    Average 80 stars, based on 5 article reviews
    Price from $9.99 to $1999.99
    crispr cas9 based epigenomic regulatory element screening - by Bioz Stars, 2019-05
    80/100 stars
      Buy from Supplier

    80
    Epigenomics diverse epigenomic assays
    <t>Epigenomic</t> information across tissues and marks a. Chromatin state annotations across 127 reference epigenomes (rows, Fig. 2 ) in a ~3.5Mb region on chromosome 9. Promoters are primarily constitutive (red vertical lines), while enhancers are highly dynamic (dispersed yellow regions). b. Signal tracks for IMR90 showing RNA-seq, a total of 28 histone modification marks, whole-genome bisulfite DNA methylation, DNA accessibility, Digital Genomic Footprints (DGF), input DNA, and chromatin conformation information 71 . c. Individual epigenomic marks across all epigenomes in which they are available. d. Relationship of figure panels highlights dataset dimensions.
    Diverse Epigenomic Assays, supplied by Epigenomics, used in various techniques. Bioz Stars score: 80/100, based on 9 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/diverse epigenomic assays/product/Epigenomics
    Average 80 stars, based on 9 article reviews
    Price from $9.99 to $1999.99
    diverse epigenomic assays - by Bioz Stars, 2019-05
    80/100 stars
      Buy from Supplier

    80
    Epigenomics cell type specific epigenomic landscapes
    <t>Epigenomic</t> model of rod and cone photoreceptor development. Enhancers that are active only in progenitor cells (termed 'fetal-only', as the fetal brain was used as a rich source of generic neural progenitors) have low levels of DNA methylation and are enriched for H3K27ac and H3K4me1 histone modifications. In mature cones, histones near fetal-only enhancers lose H3K27ac and H3K4me1 and there is a gain of DNA methylcytosines. In contrast, in mature rods, fetal-only enhancers lose H3K27ac and H3K4me1 but the DNA remains unmethylated, potentially due to the barrier to cytosine methyltransferases posed by their high level of chromatin condensation. In both rods and cones, expressed genes, including rod- and cone-specific photoreceptor genes, have promoters marked by low DNA methylation, high chromatin accessibility, and enrichment for H3K27ac and H3K4me3. Active enhancers are marked by low DNA methylation, high chromatin accessibility, and enrichment for H3K27ac and H3K4me1 (not shown). DOI: http://dx.doi.org/10.7554/eLife.11613.027
    Cell Type Specific Epigenomic Landscapes, supplied by Epigenomics, used in various techniques. Bioz Stars score: 80/100, based on 9 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/cell type specific epigenomic landscapes/product/Epigenomics
    Average 80 stars, based on 9 article reviews
    Price from $9.99 to $1999.99
    cell type specific epigenomic landscapes - by Bioz Stars, 2019-05
    80/100 stars
      Buy from Supplier

    79
    Epigenomics roadmap epigenomics blood cell types
    Evaluation by FANTOM5 enhancers. ( A ) Correlation between states- and tissue-specific enhancers in 808 FANTOM5 cap-analysis gene expression (CAGE) libraries. Dark lines show the mean adjusted r 2 over 127 cell types and the shaded areas show the 95% confidence intervals of mean. The insert shows the paired t -test statistics for the mean difference of adjusted r 2 between IDEAS and ChromHMM in 808 CAGE libraries, where the dashed line marks the Bonferroni adjusted significance level of 0.05. ( B ) Z -scores of adjusted r 2 for predicting enhancers in 808 CAGE libraries (rows) by each <t>Roadmap</t> <t>Epigenomics</t> cell types (columns), calculated by removing row and column means and dividing an overall standard deviation. Library-specific predictions (similar cell types between Roadmap and FANTOM5) are highlighted in boxes, such as blood cell types (the two boxes on the left), brain tissues (the box in the middle) and epithelial cells (the box on the right). Color keys of the Roadmap Epigenomics cell types are the same as those defined in Figure 2A . Color keys for FANTOM5 libraries are manually assigned to match with those used by the Roadmap Epigenomics project. ( C ) State composition and enrichment within significant FANTOM5 enhancer peaks, averaged over 127 cell types and 808 CAGE libraries. The fold enrichment measures the frequency with which the specified segmentation state is found in the FANTOM5 enhancer peaks relative to the genome-wide state distribution. Color keys of states are the same as those given in Figure 2B . ( D ) Distribution of enhancer and TSS-related states in the FANTOM5 significant enhancer–TSS interacting regions.
    Roadmap Epigenomics Blood Cell Types, supplied by Epigenomics, used in various techniques. Bioz Stars score: 79/100, based on 4 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/roadmap epigenomics blood cell types/product/Epigenomics
    Average 79 stars, based on 4 article reviews
    Price from $9.99 to $1999.99
    roadmap epigenomics blood cell types - by Bioz Stars, 2019-05
    79/100 stars
      Buy from Supplier

    78
    Illumina Inc epigenomic research
    <t>EPIGENETICS</t> AND TRANSCRIPTIONAL (dys) REGULATION IN DISEASED HUMAN BRAIN. A ‘SUBJECT-SPECIFIC' MATTER?
    Epigenomic Research, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 78/100, based on 9 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/epigenomic research/product/Illumina Inc
    Average 78 stars, based on 9 article reviews
    Price from $9.99 to $1999.99
    epigenomic research - by Bioz Stars, 2019-05
    78/100 stars
      Buy from Supplier

    77
    Epigenomics roadmap epigenomics project website
    Evaluation by FANTOM5 enhancers. ( A ) Correlation between states- and tissue-specific enhancers in 808 FANTOM5 cap-analysis gene expression (CAGE) libraries. Dark lines show the mean adjusted r 2 over 127 cell types and the shaded areas show the 95% confidence intervals of mean. The insert shows the paired t -test statistics for the mean difference of adjusted r 2 between IDEAS and ChromHMM in 808 CAGE libraries, where the dashed line marks the Bonferroni adjusted significance level of 0.05. ( B ) Z -scores of adjusted r 2 for predicting enhancers in 808 CAGE libraries (rows) by each <t>Roadmap</t> <t>Epigenomics</t> cell types (columns), calculated by removing row and column means and dividing an overall standard deviation. Library-specific predictions (similar cell types between Roadmap and FANTOM5) are highlighted in boxes, such as blood cell types (the two boxes on the left), brain tissues (the box in the middle) and epithelial cells (the box on the right). Color keys of the Roadmap Epigenomics cell types are the same as those defined in Figure 2A . Color keys for FANTOM5 libraries are manually assigned to match with those used by the Roadmap Epigenomics project. ( C ) State composition and enrichment within significant FANTOM5 enhancer peaks, averaged over 127 cell types and 808 CAGE libraries. The fold enrichment measures the frequency with which the specified segmentation state is found in the FANTOM5 enhancer peaks relative to the genome-wide state distribution. Color keys of states are the same as those given in Figure 2B . ( D ) Distribution of enhancer and TSS-related states in the FANTOM5 significant enhancer–TSS interacting regions.
    Roadmap Epigenomics Project Website, supplied by Epigenomics, used in various techniques. Bioz Stars score: 77/100, based on 2 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/roadmap epigenomics project website/product/Epigenomics
    Average 77 stars, based on 2 article reviews
    Price from $9.99 to $1999.99
    roadmap epigenomics project website - by Bioz Stars, 2019-05
    77/100 stars
      Buy from Supplier

    Image Search Results


    Blocking NKG2D signaling during in vivo killing altered the expression of epigenetic modifier enzymes. Effector pMel CD8 T cells were generated as described in Fig. 1 a. One day after injecting the anti-NKG2D blocking antibody ( a ) or one day after the in vivo killing assay ( b ), effector CD8 pMel T cells were isolated from the spleen of 5 pooled mice using CD90.1 + congenic marker. After mRNA isolation and conversion into cDNA, 84 different epigenetic modifier enzymes were selectively quantified using RT2 Profiler Epigenetic modifier enzymes PCR array. The fold of change was calculated using the online software provided by Qiagen. The enzymes with > 2.5-fold change in expression are numbered on the plot and summarized in the tables on the right side, together with their fold of change

    Journal: Journal for Immunotherapy of Cancer

    Article Title: NKG2D signaling certifies effector CD8 T cells for memory formation

    doi: 10.1186/s40425-019-0531-2

    Figure Lengend Snippet: Blocking NKG2D signaling during in vivo killing altered the expression of epigenetic modifier enzymes. Effector pMel CD8 T cells were generated as described in Fig. 1 a. One day after injecting the anti-NKG2D blocking antibody ( a ) or one day after the in vivo killing assay ( b ), effector CD8 pMel T cells were isolated from the spleen of 5 pooled mice using CD90.1 + congenic marker. After mRNA isolation and conversion into cDNA, 84 different epigenetic modifier enzymes were selectively quantified using RT2 Profiler Epigenetic modifier enzymes PCR array. The fold of change was calculated using the online software provided by Qiagen. The enzymes with > 2.5-fold change in expression are numbered on the plot and summarized in the tables on the right side, together with their fold of change

    Article Snippet: After purification with RNeasy Protect Kit (Qiagen), 10 ng of mRNA was used as a template for cDNA using RT2 PreAMP cDNA Synthesis Kit (Qiagen). cDNA was subsequently pre-amplified by PCR using 84 different sets of primers, corresponding to RT2 Profiler™ PCR Array Mouse Epigenetic Chromatin Modification Enzymes (Qiagen).

    Techniques: Blocking Assay, In Vivo, Expressing, Generated, Isolation, Mouse Assay, Marker, Polymerase Chain Reaction, Software

    AD GWAS loci are preferentially enriched in increasing enhancer orthologs with immune function a, Enrichment (y-axis) of changing mouse AD enhancer orthologs, with a focus on consistently increasing (red) category of enhancers, in 127 cell and tissue types profiled by the Roadmap Epigenomics Consortium 10 (columns). Roadmap samples are grouped into fetal brain (purple), adult brain (green), immune/blood cell types (orange), and all other (grey). b, Cell-type-specific fold luciferase reporter expression change relative to control (ctrl) for selected increasing enhancer regions in BV-2 microglia (orange) vs N2A neurons (purple) (n=3, * P

    Journal: Nature

    Article Title: Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease

    doi: 10.1038/nature14252

    Figure Lengend Snippet: AD GWAS loci are preferentially enriched in increasing enhancer orthologs with immune function a, Enrichment (y-axis) of changing mouse AD enhancer orthologs, with a focus on consistently increasing (red) category of enhancers, in 127 cell and tissue types profiled by the Roadmap Epigenomics Consortium 10 (columns). Roadmap samples are grouped into fetal brain (purple), adult brain (green), immune/blood cell types (orange), and all other (grey). b, Cell-type-specific fold luciferase reporter expression change relative to control (ctrl) for selected increasing enhancer regions in BV-2 microglia (orange) vs N2A neurons (purple) (n=3, * P

    Article Snippet: The information from human tissues was collected according to protocols described in more detail in the companion publication as a part of the Roadmap Epigenomics project ( http://www.roadmapepigenomics.org/ ).

    Techniques: GWAS, Luciferase, Expressing

    Chromatin state conservation a, Combinatorial patterns of seven histone modifications profiled were used to define promoter (1-3; A, active; D, downstream; U, upstream), gene body (4-6; tx, transcribed, 3P, 3 prime), enhancer (7-9; G, genic, 1 = strong, 2 = weak), bivalent (10), repressed Polycomb (11), heterochromatin (12), and low signal (13-14) chromatin states. Darker blue indicates a higher enrichment of the measured histone mark (x axis) to be found in a particular state (y-axis). b, Promoter, enhancer, and repressed chromatin states in mouse hippocampus (rows), as profiled in this study, align to matching chromatin states in human (columns), as profiled by the Roadmap Epigenomics Consortium 10 . Shading indicates enrichment relative to human chromatin state abundance (columns). The number of regions overlapping is shown in each cell of the heatmap.

    Journal: Nature

    Article Title: Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease

    doi: 10.1038/nature14252

    Figure Lengend Snippet: Chromatin state conservation a, Combinatorial patterns of seven histone modifications profiled were used to define promoter (1-3; A, active; D, downstream; U, upstream), gene body (4-6; tx, transcribed, 3P, 3 prime), enhancer (7-9; G, genic, 1 = strong, 2 = weak), bivalent (10), repressed Polycomb (11), heterochromatin (12), and low signal (13-14) chromatin states. Darker blue indicates a higher enrichment of the measured histone mark (x axis) to be found in a particular state (y-axis). b, Promoter, enhancer, and repressed chromatin states in mouse hippocampus (rows), as profiled in this study, align to matching chromatin states in human (columns), as profiled by the Roadmap Epigenomics Consortium 10 . Shading indicates enrichment relative to human chromatin state abundance (columns). The number of regions overlapping is shown in each cell of the heatmap.

    Article Snippet: The information from human tissues was collected according to protocols described in more detail in the companion publication as a part of the Roadmap Epigenomics project ( http://www.roadmapepigenomics.org/ ).

    Techniques:

    Enrichment of tissue-specific enhancer annotations from the Roadmap Epigenomics Consortium for AD-associated SNPs and mouse enhancers Enrichment of AD-associated SNPs (y-axis, permutation P value) in tissue-specific enhancer annotations from the Roadmap Epigenomics Consortium (points), relative to their enrichment for a, increased-level and b, decreased-level (colors of different classes along y-axis) of orthologous enhancer regions in the mouse AD model (x-axis, hypergeometric P value). Linear regression trend line and R 2 , based on Pearson correlation is shown.

    Journal: Nature

    Article Title: Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease

    doi: 10.1038/nature14252

    Figure Lengend Snippet: Enrichment of tissue-specific enhancer annotations from the Roadmap Epigenomics Consortium for AD-associated SNPs and mouse enhancers Enrichment of AD-associated SNPs (y-axis, permutation P value) in tissue-specific enhancer annotations from the Roadmap Epigenomics Consortium (points), relative to their enrichment for a, increased-level and b, decreased-level (colors of different classes along y-axis) of orthologous enhancer regions in the mouse AD model (x-axis, hypergeometric P value). Linear regression trend line and R 2 , based on Pearson correlation is shown.

    Article Snippet: The information from human tissues was collected according to protocols described in more detail in the companion publication as a part of the Roadmap Epigenomics project ( http://www.roadmapepigenomics.org/ ).

    Techniques:

    Evaluation by FANTOM5 enhancers. ( A ) Correlation between states- and tissue-specific enhancers in 808 FANTOM5 cap-analysis gene expression (CAGE) libraries. Dark lines show the mean adjusted r 2 over 127 cell types and the shaded areas show the 95% confidence intervals of mean. The insert shows the paired t -test statistics for the mean difference of adjusted r 2 between IDEAS and ChromHMM in 808 CAGE libraries, where the dashed line marks the Bonferroni adjusted significance level of 0.05. ( B ) Z -scores of adjusted r 2 for predicting enhancers in 808 CAGE libraries (rows) by each Roadmap Epigenomics cell types (columns), calculated by removing row and column means and dividing an overall standard deviation. Library-specific predictions (similar cell types between Roadmap and FANTOM5) are highlighted in boxes, such as blood cell types (the two boxes on the left), brain tissues (the box in the middle) and epithelial cells (the box on the right). Color keys of the Roadmap Epigenomics cell types are the same as those defined in Figure 2A . Color keys for FANTOM5 libraries are manually assigned to match with those used by the Roadmap Epigenomics project. ( C ) State composition and enrichment within significant FANTOM5 enhancer peaks, averaged over 127 cell types and 808 CAGE libraries. The fold enrichment measures the frequency with which the specified segmentation state is found in the FANTOM5 enhancer peaks relative to the genome-wide state distribution. Color keys of states are the same as those given in Figure 2B . ( D ) Distribution of enhancer and TSS-related states in the FANTOM5 significant enhancer–TSS interacting regions.

    Journal: Nucleic Acids Research

    Article Title: Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation

    doi: 10.1093/nar/gkx659

    Figure Lengend Snippet: Evaluation by FANTOM5 enhancers. ( A ) Correlation between states- and tissue-specific enhancers in 808 FANTOM5 cap-analysis gene expression (CAGE) libraries. Dark lines show the mean adjusted r 2 over 127 cell types and the shaded areas show the 95% confidence intervals of mean. The insert shows the paired t -test statistics for the mean difference of adjusted r 2 between IDEAS and ChromHMM in 808 CAGE libraries, where the dashed line marks the Bonferroni adjusted significance level of 0.05. ( B ) Z -scores of adjusted r 2 for predicting enhancers in 808 CAGE libraries (rows) by each Roadmap Epigenomics cell types (columns), calculated by removing row and column means and dividing an overall standard deviation. Library-specific predictions (similar cell types between Roadmap and FANTOM5) are highlighted in boxes, such as blood cell types (the two boxes on the left), brain tissues (the box in the middle) and epithelial cells (the box on the right). Color keys of the Roadmap Epigenomics cell types are the same as those defined in Figure 2A . Color keys for FANTOM5 libraries are manually assigned to match with those used by the Roadmap Epigenomics project. ( C ) State composition and enrichment within significant FANTOM5 enhancer peaks, averaged over 127 cell types and 808 CAGE libraries. The fold enrichment measures the frequency with which the specified segmentation state is found in the FANTOM5 enhancer peaks relative to the genome-wide state distribution. Color keys of states are the same as those given in Figure 2B . ( D ) Distribution of enhancer and TSS-related states in the FANTOM5 significant enhancer–TSS interacting regions.

    Article Snippet: We downloaded RNA-seq reads per kilobase per million mapped reads (RPKM) data in 56 cell types (excluding E000) from the Roadmap Epigenomics Project ( http://www.roadmapproject.org/ ).

    Techniques: Expressing, Standard Deviation, Genome Wide

    Evaluation by gene expression. ( A ) Within-cell type prediction of gene expression in 56 cell types. Each point shows one cell type, where color keys for cell type lineages are adopted from the Roadmap Epigenomics consortium ( Supplementary Data ). ( B ) State contribution to gene expression as a function of distance to genes. The panel on the top shows the overall predictive power of states on expression. The two barplots in the middle show the individual state contribution to expression. Color keys of states are shown at the bottom. ( C ) Prediction of differential gene expression across 56 cell types. Genes are stratified by their expression standard deviation across cell types. Each panel shows the adjusted r 2 for predicting differential expression by states as a function of distance to gene ( x -axis, in the same scale as in (B) and the two vertical dashed lines in each panel show the transcription start site (TSS) and transcription termination site (TTS) locations, respectively). Red: IDEAS; green: ChromHMM.

    Journal: Nucleic Acids Research

    Article Title: Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation

    doi: 10.1093/nar/gkx659

    Figure Lengend Snippet: Evaluation by gene expression. ( A ) Within-cell type prediction of gene expression in 56 cell types. Each point shows one cell type, where color keys for cell type lineages are adopted from the Roadmap Epigenomics consortium ( Supplementary Data ). ( B ) State contribution to gene expression as a function of distance to genes. The panel on the top shows the overall predictive power of states on expression. The two barplots in the middle show the individual state contribution to expression. Color keys of states are shown at the bottom. ( C ) Prediction of differential gene expression across 56 cell types. Genes are stratified by their expression standard deviation across cell types. Each panel shows the adjusted r 2 for predicting differential expression by states as a function of distance to gene ( x -axis, in the same scale as in (B) and the two vertical dashed lines in each panel show the transcription start site (TSS) and transcription termination site (TTS) locations, respectively). Red: IDEAS; green: ChromHMM.

    Article Snippet: We downloaded RNA-seq reads per kilobase per million mapped reads (RPKM) data in 56 cell types (excluding E000) from the Roadmap Epigenomics Project ( http://www.roadmapproject.org/ ).

    Techniques: Expressing, Standard Deviation

    Evaluation by chromatin interaction. ( A ) Correlation between CHiCAGO interaction scores in 17 IHEC blood cell types and the inferred chromatin states in each of the 127 cell types. Adjusted r 2 is calculated from a regression model including interaction terms of states between bait and target regions. Hollow bars show the mean adjusted r 2 by ChromHMM states, averaged over 127 cell types and solid bars show the improvement in mean adjusted r 2 by IDEAS states. Color keys of cell types are the same as those given in Figure 2A , where green and dark green indicates Blood and T cells and HSC and B cell types, respectively. ( B ) Detailed comparison in each IHEC blood cell type using the states of Blood and T cells and HSC and B cell types. Bonferroni adjusted significance by paired t -test is indicated under each IHEC cell type. Red: IDEAS; green: ChromHMM. ( C ) Prediction of bait gene expression by the states in bait and target regions as a function of expression specificity for blood cell types relative to other cell types in Roadmap Epigenomics ( Z -scores, x -axis). Dashed lines: mean adjusted r 2 of bait gene expression explained by the states in individual target regions. Solid lines: mean adjusted r 2 of bait gene expression explained by the sum of states in all target regions captured by the same bait. Dotted lines: mean adjusted r 2 of bait gene expression explained by the states in the same bait regions. Shaded area shows the 95% confidence intervals of means.

    Journal: Nucleic Acids Research

    Article Title: Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation

    doi: 10.1093/nar/gkx659

    Figure Lengend Snippet: Evaluation by chromatin interaction. ( A ) Correlation between CHiCAGO interaction scores in 17 IHEC blood cell types and the inferred chromatin states in each of the 127 cell types. Adjusted r 2 is calculated from a regression model including interaction terms of states between bait and target regions. Hollow bars show the mean adjusted r 2 by ChromHMM states, averaged over 127 cell types and solid bars show the improvement in mean adjusted r 2 by IDEAS states. Color keys of cell types are the same as those given in Figure 2A , where green and dark green indicates Blood and T cells and HSC and B cell types, respectively. ( B ) Detailed comparison in each IHEC blood cell type using the states of Blood and T cells and HSC and B cell types. Bonferroni adjusted significance by paired t -test is indicated under each IHEC cell type. Red: IDEAS; green: ChromHMM. ( C ) Prediction of bait gene expression by the states in bait and target regions as a function of expression specificity for blood cell types relative to other cell types in Roadmap Epigenomics ( Z -scores, x -axis). Dashed lines: mean adjusted r 2 of bait gene expression explained by the states in individual target regions. Solid lines: mean adjusted r 2 of bait gene expression explained by the sum of states in all target regions captured by the same bait. Dotted lines: mean adjusted r 2 of bait gene expression explained by the states in the same bait regions. Shaded area shows the 95% confidence intervals of means.

    Article Snippet: We downloaded RNA-seq reads per kilobase per million mapped reads (RPKM) data in 56 cell types (excluding E000) from the Roadmap Epigenomics Project ( http://www.roadmapproject.org/ ).

    Techniques: Expressing

    Alignment of TPM1 variants with regulatory markers. Shown is a 100 kb segment of DNA surrounding the variants that associate with age-at-onset of PD in the TPM1 region (rs116860970 ± 50kb; chr15: 63,301,500–63,401,500, genome build 37). The box on top was generated using LocusZoom and shows the SNPs with their associated P- values (left Y-axis) and their positions on the chromosome (X-axis). rs116860970 is shown in purple. LD ( r 2 ) was calculated in relation to rs116860970. The colors denote the strength of LD. The top SNPs shown in purple and red span from Intron 3 to 3’ of TPM1 . The next section is from the Roadmap Epigenomics Project and shows regulatory marks (orange=enhancers, red=transcription start sites, and green=transcribed regions) predicted by ChromHMM, with each line representing a different brain tissue that was analyzed (BAG=brain angular gyrus; BAC=brain anterior caudate; BCG=brain cingulate gyrus; BGM=brain germinal matrix; BHM=brain hippocampus middle; BITL=brain inferior temporal lobe; BMFL=brain mid frontal lobe; BSN=brain substantia nigra). The bottom panel is from ENCODE and shows histone acetylation and methylation marks (black) in brain cells (NH-A cell line).

    Journal: Human Molecular Genetics

    Article Title: Identification of genetic modifiers of age-at-onset for familial Parkinson’s disease

    doi: 10.1093/hmg/ddw206

    Figure Lengend Snippet: Alignment of TPM1 variants with regulatory markers. Shown is a 100 kb segment of DNA surrounding the variants that associate with age-at-onset of PD in the TPM1 region (rs116860970 ± 50kb; chr15: 63,301,500–63,401,500, genome build 37). The box on top was generated using LocusZoom and shows the SNPs with their associated P- values (left Y-axis) and their positions on the chromosome (X-axis). rs116860970 is shown in purple. LD ( r 2 ) was calculated in relation to rs116860970. The colors denote the strength of LD. The top SNPs shown in purple and red span from Intron 3 to 3’ of TPM1 . The next section is from the Roadmap Epigenomics Project and shows regulatory marks (orange=enhancers, red=transcription start sites, and green=transcribed regions) predicted by ChromHMM, with each line representing a different brain tissue that was analyzed (BAG=brain angular gyrus; BAC=brain anterior caudate; BCG=brain cingulate gyrus; BGM=brain germinal matrix; BHM=brain hippocampus middle; BITL=brain inferior temporal lobe; BMFL=brain mid frontal lobe; BSN=brain substantia nigra). The bottom panel is from ENCODE and shows histone acetylation and methylation marks (black) in brain cells (NH-A cell line).

    Article Snippet: Many of the markers that associated with onset of familial PD map to sequences that are identified by the Roadmap Epigenomics Project ( http://genomebrowser.wustl.edu ) and ENCODE ( ) as being active regulatory elements in the brain ( ).

    Techniques: Generated, BAC Assay, Methylation

    Alignment of LHFPL2 variants with regulatory markers. Shown is a 400 kb segment of DNA surrounding the variants that associate with age-at-onset of PD in the LHFPL2 region (rs344650 ± 200kb; chr5: 77,660,608–78,060,608, genome build 37). The box on top was generated using LocusZoom and shows the SNPs with their associated P- values (left Y-axis) and their positions on the chromosome (X-axis). rs344650 is shown in purple. LD ( r 2 ) was calculated in relation to rs344650. The colors denote the strength of LD. The top four SNPs shown in purple, red, and orange are all in the same intron. The next section is from the Roadmap Epigenomics Project and shows regulatory marks (orange=enhancers and red=transcription start sites) predicted by ChromHMM, with each line representing a different brain tissue that was analyzed (BAG=brain angular gyrus; BAC=brain anterior caudate; BCG=brain cingulate gyrus; BGM=brain germinal matrix; BHM=brain hippocampus middle; BITL=brain inferior temporal lobe; BMFL=brain mid frontal lobe; BSN=brain substantia nigra). The bottom panel is from ENCODE and shows histone acetylation and methylation marks (black) in brain cells (NH-A cell line).

    Journal: Human Molecular Genetics

    Article Title: Identification of genetic modifiers of age-at-onset for familial Parkinson’s disease

    doi: 10.1093/hmg/ddw206

    Figure Lengend Snippet: Alignment of LHFPL2 variants with regulatory markers. Shown is a 400 kb segment of DNA surrounding the variants that associate with age-at-onset of PD in the LHFPL2 region (rs344650 ± 200kb; chr5: 77,660,608–78,060,608, genome build 37). The box on top was generated using LocusZoom and shows the SNPs with their associated P- values (left Y-axis) and their positions on the chromosome (X-axis). rs344650 is shown in purple. LD ( r 2 ) was calculated in relation to rs344650. The colors denote the strength of LD. The top four SNPs shown in purple, red, and orange are all in the same intron. The next section is from the Roadmap Epigenomics Project and shows regulatory marks (orange=enhancers and red=transcription start sites) predicted by ChromHMM, with each line representing a different brain tissue that was analyzed (BAG=brain angular gyrus; BAC=brain anterior caudate; BCG=brain cingulate gyrus; BGM=brain germinal matrix; BHM=brain hippocampus middle; BITL=brain inferior temporal lobe; BMFL=brain mid frontal lobe; BSN=brain substantia nigra). The bottom panel is from ENCODE and shows histone acetylation and methylation marks (black) in brain cells (NH-A cell line).

    Article Snippet: Many of the markers that associated with onset of familial PD map to sequences that are identified by the Roadmap Epigenomics Project ( http://genomebrowser.wustl.edu ) and ENCODE ( ) as being active regulatory elements in the brain ( ).

    Techniques: Generated, BAC Assay, Methylation

    Hypermethylation caused by TET2 loss is enriched at active enhancer regions. a , b Regions with different chromatin marks in 24 different subtypes of primary human blood cells (each subtype represented by a dot) were available from the Roadmap Epigenomics project. These were compared to the hyper- and hypomethylated sites detected in blood whole-genome bisulfite sequencing (WGBS) of three cancer-free TET2delA carriers relative to three age-matched non-carriers. a The hypermethylated sites in TET2delA carriers were characterized by frequent presence of normally H3K4me1-marked chromatin in primary blood cells. In addition, hypermethylation was enriched strongly at DNaseI hypersensitive and H3K27ac-marked chromatin, suggesting that methylation was increased in enhancer regions that are normally active. b The hypomethylated sites in TET2delA carriers were enriched at normally H3K9me3-marked regions that typically represent transcriptionally repressed heterochromatin. Odds ratios and p -values from the Fisher’s exact test implemented in LOLA R package. c Chromatin immunoprecipitation with an anti-H3K27ac antibody in lymphoblastoid cells from TET2delA carriers (Ly9, Ly11, and Ly14) was compared to that from wild-type individuals (Ly8 and Ly10). The difference in the enrichment of active H3K27ac-marked chromatin at CpG islands is displayed as a function of increasing methylation at CpG islands in whole blood of the TET2delA carriers as measured by WGBS. Negative correlation is stronger at CpG islands outside transcription start sites (TSS). Boxplots show the median, and the first and third quartiles

    Journal: Nature Communications

    Article Title: Impact of constitutional TET2 haploinsufficiency on molecular and clinical phenotype in humans

    doi: 10.1038/s41467-019-09198-7

    Figure Lengend Snippet: Hypermethylation caused by TET2 loss is enriched at active enhancer regions. a , b Regions with different chromatin marks in 24 different subtypes of primary human blood cells (each subtype represented by a dot) were available from the Roadmap Epigenomics project. These were compared to the hyper- and hypomethylated sites detected in blood whole-genome bisulfite sequencing (WGBS) of three cancer-free TET2delA carriers relative to three age-matched non-carriers. a The hypermethylated sites in TET2delA carriers were characterized by frequent presence of normally H3K4me1-marked chromatin in primary blood cells. In addition, hypermethylation was enriched strongly at DNaseI hypersensitive and H3K27ac-marked chromatin, suggesting that methylation was increased in enhancer regions that are normally active. b The hypomethylated sites in TET2delA carriers were enriched at normally H3K9me3-marked regions that typically represent transcriptionally repressed heterochromatin. Odds ratios and p -values from the Fisher’s exact test implemented in LOLA R package. c Chromatin immunoprecipitation with an anti-H3K27ac antibody in lymphoblastoid cells from TET2delA carriers (Ly9, Ly11, and Ly14) was compared to that from wild-type individuals (Ly8 and Ly10). The difference in the enrichment of active H3K27ac-marked chromatin at CpG islands is displayed as a function of increasing methylation at CpG islands in whole blood of the TET2delA carriers as measured by WGBS. Negative correlation is stronger at CpG islands outside transcription start sites (TSS). Boxplots show the median, and the first and third quartiles

    Article Snippet: Hypomethylation, on the other hand, showed strongest enrichment at normally H3K9me3-marked, transcriptionally repressed chromatin in all primary human blood cells characterized in the Roadmap Epigenomics Project (Fig. ).

    Techniques: Methylation Sequencing, Methylation, Chromatin Immunoprecipitation

    In vitro enhancer screening for cis -regulatory elements harbouring Top 10 NS CL/P-associated SNPs. ( a ) Summary of all the elements cloned and tested in 1p22 NS CL/P-associated locus in this study. (Vertical stack view of epilogos was generated from a chromatin state model based on imputed data provided by NIH Roadmap Epigenomics Consortium; http://egg2.wustl.edu/roadmap/web_portal ). Shown are the locations of ten SNPs with strongest association to CL/P in the Asian population (data from ref. 22 ). Red font , functional SNPs validated in this study, Blue font , non-functional SNPs, based on the results of this study. ( b ) Schematic illustration of the in vitro enhancer screening strategy using the cloned elements. ( c ) Relative luciferase activities of all the cloned candidate regulatory elements and control elements in GMSM-K (human oral keratinocyte cell line) and HEPM (human embryonic palatal mesenchymal cells). Black dash line indicates the average relative luciferase activity of control elements in GMSM-K, and grey dash line indicates the average relative luciferase activity of control elements in HEPM. n =3 for each group, data represent means±s.d., t -test, * P

    Journal: Nature Communications

    Article Title: Identification of common non-coding variants at 1p22 that are functional for non-syndromic orofacial clefting

    doi: 10.1038/ncomms14759

    Figure Lengend Snippet: In vitro enhancer screening for cis -regulatory elements harbouring Top 10 NS CL/P-associated SNPs. ( a ) Summary of all the elements cloned and tested in 1p22 NS CL/P-associated locus in this study. (Vertical stack view of epilogos was generated from a chromatin state model based on imputed data provided by NIH Roadmap Epigenomics Consortium; http://egg2.wustl.edu/roadmap/web_portal ). Shown are the locations of ten SNPs with strongest association to CL/P in the Asian population (data from ref. 22 ). Red font , functional SNPs validated in this study, Blue font , non-functional SNPs, based on the results of this study. ( b ) Schematic illustration of the in vitro enhancer screening strategy using the cloned elements. ( c ) Relative luciferase activities of all the cloned candidate regulatory elements and control elements in GMSM-K (human oral keratinocyte cell line) and HEPM (human embryonic palatal mesenchymal cells). Black dash line indicates the average relative luciferase activity of control elements in GMSM-K, and grey dash line indicates the average relative luciferase activity of control elements in HEPM. n =3 for each group, data represent means±s.d., t -test, * P

    Article Snippet: Eight of the ten SNPs lie within chromatin regions expressing marks indicative of enhancer activity in one or more of the 127 cell lines evaluated in the Roadmap Epigenomics project ( , middle track).

    Techniques: In Vitro, Clone Assay, Generated, Functional Assay, Luciferase, Activity Assay

    Gene expression of DNA methyltransferases, histone deacetylases, histone acetyltransferases, histone methyltransferases and SET domain proteins, histone phosphorylation, and histone ubiquitination enzymes at 1, 4, and 24 h in CSE-treated H292 cells. Human bronchial epithelial cells were treated with and without CSE for 1 h (2% CSE) and 4 and 24 h (1% CSE). RNA extracted from control and CSE-treated cells was analyzed using RT2 Profiler PCR array for human epigenetic chromatin modification enzymes. A : the transcription levels of genes encoding specific DNMTs ( Dnmt1 , Dnmt3a , and Dnmt3b ) and HDACs ( Hdac2 , Hdac3 , and Hdac4) were examined by qPCR using the 2−ΔΔCt method. B : the transcription levels of genes encoding specific HATs ( Cdyl , Csrp2bp , Hat1 , Myst3 , Myst4 , and Ncoa3 ) were examined by qPCR using the 2−ΔΔCt method. C : the transcription levels of genes encoding specific HMTs ( Prmt1 , Prmt5 , and Nsd1 ) and SET domain proteins ( Setdb2 , Setd4 , and Setd5 ) were examined by qPCR using the 2−ΔΔCt method. D : the transcription levels of genes encoding specific histone phosphorylation ( Aurkc and Nek6 ) and histone ubiquitination ( Ube2b , Usp16 , and Usp22 ) were examined by qPCR using the 2−ΔΔCt method. Bar graphs represent the mean normalized expression of samples in control vs. CSE-treated H292 cells. Data were normalized using the endogenous housekeeping gene ribosomal protein L13a ( Rpl13a ) as reference and controls as calibrators. Statistical significance ( P < 0.05) was analyzed by two-way ANOVA (Tukey's multiple-comparison test) using GraphPad Prism 6. Values are means ± SE ( n = 4/group). * P < 0.05, ** P < 0.01, and *** P < 0.001 vs. control. # P < 0.05, ## P < 0.01, and ### P < 0.001 vs. CSE (1 or 4 h).

    Journal:

    Article Title: Gene expression profiling of epigenetic chromatin modification enzymes and histone marks by cigarette smoke: implications for COPD and lung cancer

    doi: 10.1152/ajplung.00253.2016

    Figure Lengend Snippet: Gene expression of DNA methyltransferases, histone deacetylases, histone acetyltransferases, histone methyltransferases and SET domain proteins, histone phosphorylation, and histone ubiquitination enzymes at 1, 4, and 24 h in CSE-treated H292 cells. Human bronchial epithelial cells were treated with and without CSE for 1 h (2% CSE) and 4 and 24 h (1% CSE). RNA extracted from control and CSE-treated cells was analyzed using RT2 Profiler PCR array for human epigenetic chromatin modification enzymes. A : the transcription levels of genes encoding specific DNMTs ( Dnmt1 , Dnmt3a , and Dnmt3b ) and HDACs ( Hdac2 , Hdac3 , and Hdac4) were examined by qPCR using the 2−ΔΔCt method. B : the transcription levels of genes encoding specific HATs ( Cdyl , Csrp2bp , Hat1 , Myst3 , Myst4 , and Ncoa3 ) were examined by qPCR using the 2−ΔΔCt method. C : the transcription levels of genes encoding specific HMTs ( Prmt1 , Prmt5 , and Nsd1 ) and SET domain proteins ( Setdb2 , Setd4 , and Setd5 ) were examined by qPCR using the 2−ΔΔCt method. D : the transcription levels of genes encoding specific histone phosphorylation ( Aurkc and Nek6 ) and histone ubiquitination ( Ube2b , Usp16 , and Usp22 ) were examined by qPCR using the 2−ΔΔCt method. Bar graphs represent the mean normalized expression of samples in control vs. CSE-treated H292 cells. Data were normalized using the endogenous housekeeping gene ribosomal protein L13a ( Rpl13a ) as reference and controls as calibrators. Statistical significance ( P < 0.05) was analyzed by two-way ANOVA (Tukey's multiple-comparison test) using GraphPad Prism 6. Values are means ± SE ( n = 4/group). * P < 0.05, ** P < 0.01, and *** P < 0.001 vs. control. # P < 0.05, ## P < 0.01, and ### P < 0.001 vs. CSE (1 or 4 h).

    Article Snippet: Human and mouse epigenetic chromatin modification enzymes RT2 Profiler PCR array was obtained from SABiosciences (Frederick, MD).

    Techniques: Expressing, Polymerase Chain Reaction, Modification, Real-time Polymerase Chain Reaction

    Gene expression profiles of chromatin modification enzymes that were increased above or below 1.5-fold cutoff in 24-h control vs. CSE-treated H292 cells. Human bronchial epithelial cells were treated for 24 h with or without CSE. RNA extracted from control and CSE-treated (1%) cells was analyzed using RT2 Profiler PCR array for human epigenetic chromatin modification enzymes. A : scatterplot analysis of human chromatin modification enzymes show marked upregulation or downregulation of genes by ≥ or ≤1.5-fold in 24-h control vs. CSE-treated (1%) H292 cells. Red denotes high expression (upregulated), and green denotes low expression (downregulated). Values are means ± SE ( n = 4/group). B : table shows gene symbol, fold change, P value (Student's t -test; P < 0.01), and Benjamini-Hochberg adjusted P value (multiple-target analysis) for genes altered by CSE compared with control.

    Journal:

    Article Title: Gene expression profiling of epigenetic chromatin modification enzymes and histone marks by cigarette smoke: implications for COPD and lung cancer

    doi: 10.1152/ajplung.00253.2016

    Figure Lengend Snippet: Gene expression profiles of chromatin modification enzymes that were increased above or below 1.5-fold cutoff in 24-h control vs. CSE-treated H292 cells. Human bronchial epithelial cells were treated for 24 h with or without CSE. RNA extracted from control and CSE-treated (1%) cells was analyzed using RT2 Profiler PCR array for human epigenetic chromatin modification enzymes. A : scatterplot analysis of human chromatin modification enzymes show marked upregulation or downregulation of genes by ≥ or ≤1.5-fold in 24-h control vs. CSE-treated (1%) H292 cells. Red denotes high expression (upregulated), and green denotes low expression (downregulated). Values are means ± SE ( n = 4/group). B : table shows gene symbol, fold change, P value (Student's t -test; P < 0.01), and Benjamini-Hochberg adjusted P value (multiple-target analysis) for genes altered by CSE compared with control.

    Article Snippet: Human and mouse epigenetic chromatin modification enzymes RT2 Profiler PCR array was obtained from SABiosciences (Frederick, MD).

    Techniques: Expressing, Modification, Polymerase Chain Reaction

    dCpf1-epigenetic fusions do not repress HER2 gene expression. ( A ) Schematic of dCpf1-fusions with ED. Catalytically inactive As Cpf1 contains nuclease-inactivating mutation D908A (dCpf1). ( B ) UCSC genome browser graphic showing HER2 target regions of sgRNAs containing the 5′-NGG-3′ PAM required by dCas9, and crRNA target sites flanked by the 5′-NTTT-3′ PAM required by dCpf1. HCT116 ENCODE tracks for DNase Hypersensitivity (DNase HS) and H3K27ac binding are shown. ( C ) Abundance of HER2 mRNA was measured after co-transfection of HCT116 cells with a pool of three crRNAs with the indicated dCpf1-ED fusions. No significant repression was observed compared to a dCpf1 with no ED. Negative control cells (‘−’) were transfected with mCherry reporter plasmid instead of dCpf1. As a positive control, repression was assessed after co-transfection of dCas9 with no ED or FOG1[1–45]-dCas9-FOG1[1–45] and three sgRNAs (Tukey-test, P

    Journal: Nucleic Acids Research

    Article Title: dCas9-based epigenome editing suggests acquisition of histone methylation is not sufficient for target gene repression

    doi: 10.1093/nar/gkx578

    Figure Lengend Snippet: dCpf1-epigenetic fusions do not repress HER2 gene expression. ( A ) Schematic of dCpf1-fusions with ED. Catalytically inactive As Cpf1 contains nuclease-inactivating mutation D908A (dCpf1). ( B ) UCSC genome browser graphic showing HER2 target regions of sgRNAs containing the 5′-NGG-3′ PAM required by dCas9, and crRNA target sites flanked by the 5′-NTTT-3′ PAM required by dCpf1. HCT116 ENCODE tracks for DNase Hypersensitivity (DNase HS) and H3K27ac binding are shown. ( C ) Abundance of HER2 mRNA was measured after co-transfection of HCT116 cells with a pool of three crRNAs with the indicated dCpf1-ED fusions. No significant repression was observed compared to a dCpf1 with no ED. Negative control cells (‘−’) were transfected with mCherry reporter plasmid instead of dCpf1. As a positive control, repression was assessed after co-transfection of dCas9 with no ED or FOG1[1–45]-dCas9-FOG1[1–45] and three sgRNAs (Tukey-test, P

    Article Snippet: Finally, the FOG1 epigenetic effector construct was Gibson assembled (New England Biolabs).

    Techniques: Expressing, Mutagenesis, Binding Assay, Cotransfection, Negative Control, Transfection, Plasmid Preparation, Positive Control

    The novel transcriptional repressor FOG1[1–45]-dCas9-FOG1[1–45] tri-methylates H3K27 at the target promoter. ( A ) Models for two approaches of targeted H3K27 methylation mediated by dCas9-fusion proteins. Top: fusion of dCas9 to the enzyme Ezh2 directly tri-methylates H3K27 at the genomic target region. Bottom: fusion of dCas9 to subunits or interaction domains of endogenous co-repressor complexes, such as FOG1[1–45]-dCas9 that interacts with the nucleosome remodeling and deacetylase (NuRD) complex, recruits the NuRD complex to the target sites causing HDAC1/2-mediated H3K27 deacetylation, as well as facilitation of H3K27 tri-methylation through recruitment of the PRC2 complex. ( B ) Schematic of dCas9-FOG1[1–45] fusion proteins. Fusions to the N- and/or C-termini of dCas9 are labeled with [N] and/or [C], respectively. Arrays of two, three and four FOG1[1–45] repeats are fused to dCas9. NLSs, 3XFlag epitope tag and the 15-aa linkers [(GGS) 5 ] are indicated. ( C ) Relative HER2 mRNA was assessed in HCT116 cells co-transfected with a pool of three sgRNAs targeted to the HER2 promoter and the indicated dCas9-FOG1[1–45] fusions. Repressive activity was measured relative to Cas9 with no ED (Tukey-test, * P

    Journal: Nucleic Acids Research

    Article Title: dCas9-based epigenome editing suggests acquisition of histone methylation is not sufficient for target gene repression

    doi: 10.1093/nar/gkx578

    Figure Lengend Snippet: The novel transcriptional repressor FOG1[1–45]-dCas9-FOG1[1–45] tri-methylates H3K27 at the target promoter. ( A ) Models for two approaches of targeted H3K27 methylation mediated by dCas9-fusion proteins. Top: fusion of dCas9 to the enzyme Ezh2 directly tri-methylates H3K27 at the genomic target region. Bottom: fusion of dCas9 to subunits or interaction domains of endogenous co-repressor complexes, such as FOG1[1–45]-dCas9 that interacts with the nucleosome remodeling and deacetylase (NuRD) complex, recruits the NuRD complex to the target sites causing HDAC1/2-mediated H3K27 deacetylation, as well as facilitation of H3K27 tri-methylation through recruitment of the PRC2 complex. ( B ) Schematic of dCas9-FOG1[1–45] fusion proteins. Fusions to the N- and/or C-termini of dCas9 are labeled with [N] and/or [C], respectively. Arrays of two, three and four FOG1[1–45] repeats are fused to dCas9. NLSs, 3XFlag epitope tag and the 15-aa linkers [(GGS) 5 ] are indicated. ( C ) Relative HER2 mRNA was assessed in HCT116 cells co-transfected with a pool of three sgRNAs targeted to the HER2 promoter and the indicated dCas9-FOG1[1–45] fusions. Repressive activity was measured relative to Cas9 with no ED (Tukey-test, * P

    Article Snippet: Finally, the FOG1 epigenetic effector construct was Gibson assembled (New England Biolabs).

    Techniques: Methylation, Histone Deacetylase Assay, Labeling, Transfection, Activity Assay

    Combinations of epigenetic modifiers can achieve long-term gene repression. ( A ) Schematic of experimental design for transient transfection assays with partial puromycin enrichment. ( B ) Relative HER2 mRNA production in HCT116 cells co-transfected with a pool of three sgRNAs targeted to the HER2 gene promoter and combinations of N- or C-terminal DNMT3A-dCas9 fusions, KRAB-dCas9 and DNMT3L. ( C ) Relative HER2 mRNA production using combinations of N-terminal DNMT3A-dCas9, KRAB-dCas9, DNMT3L, FOG1[1–45]-dCas9-FOG1[1–45] and Ezh2[FL]-dCas9. Expression data are shown in comparison to cells transfected by dCas9 with no ED. Statistical significance was analyzed for the transient effect by comparing dCas9 fusions to dCas9 without an effector domain after 4 days, while significance of persistent repression was calculated by comparing dCas9 fusions to dCas9 without ED after 14 days (Tukey-test, * P

    Journal: Nucleic Acids Research

    Article Title: dCas9-based epigenome editing suggests acquisition of histone methylation is not sufficient for target gene repression

    doi: 10.1093/nar/gkx578

    Figure Lengend Snippet: Combinations of epigenetic modifiers can achieve long-term gene repression. ( A ) Schematic of experimental design for transient transfection assays with partial puromycin enrichment. ( B ) Relative HER2 mRNA production in HCT116 cells co-transfected with a pool of three sgRNAs targeted to the HER2 gene promoter and combinations of N- or C-terminal DNMT3A-dCas9 fusions, KRAB-dCas9 and DNMT3L. ( C ) Relative HER2 mRNA production using combinations of N-terminal DNMT3A-dCas9, KRAB-dCas9, DNMT3L, FOG1[1–45]-dCas9-FOG1[1–45] and Ezh2[FL]-dCas9. Expression data are shown in comparison to cells transfected by dCas9 with no ED. Statistical significance was analyzed for the transient effect by comparing dCas9 fusions to dCas9 without an effector domain after 4 days, while significance of persistent repression was calculated by comparing dCas9 fusions to dCas9 without ED after 14 days (Tukey-test, * P

    Article Snippet: Finally, the FOG1 epigenetic effector construct was Gibson assembled (New England Biolabs).

    Techniques: Transfection, Expressing

    EZH2 Enhances Androgen Signaling in Both ADPC and CRPC Cells (A and B) Androgen-induced genes (A) are enriched for downregulation upon EZH2 knockdown (false discovery rate [FDR] q

    Journal: Cell reports

    Article Title: Polycomb- and Methylation-Independent Roles of EZH2 as a Transcription Activator

    doi: 10.1016/j.celrep.2018.11.035

    Figure Lengend Snippet: EZH2 Enhances Androgen Signaling in Both ADPC and CRPC Cells (A and B) Androgen-induced genes (A) are enriched for downregulation upon EZH2 knockdown (false discovery rate [FDR] q

    Article Snippet: By contrast, strong EZH2 and H3K27me3 occupancy and lack of the active histone mark H3K27ac were found at the promoter of the EZH2 epigenetic target SLIT2.

    Techniques:

    EZH2 Activates the AR Independently of Its Histone Methyltransferase Activity (A) The AR promoter is occupied by EZH2 and H3K27ac but not H3K27me3, whereas the promoter of SLIT2, an epigenetic target of EZH2, is occupied with EZH2 and H3K27me3 but not H3K27ac. HA-EZH2 ChIP-seq was performed using anti-HA in LNCaP cells with HA-EZH2 overexpression. H3K27me3 and H3K27ac ChIP-seq was performed in LNCaP cells. (B) EZH2, but not SUZ12, decreased AR expression levels. LNCaP or C4–2B cells were infected with pLKO.1V, shEZH2, shSUZ12, or shAR for 48 hr, and cell lysates were subjected to western blot analysis. (C–F) EZH2 methyltransferase inhibitors failed to abolish AR expression. LNCaP cells were treated with EZH2 inhibitors GSK126 (C and D) or EPZ (E) for 72 hr, and the cell lysates were subjected to western blot (C and D) and qRT-PCR (E and F) analyses. The data shown in (E) and (F) are mean (±SEM) of technical replicates from one representative experiment of three. (G and H) Both WT and the catalytically dead mutant H689A of EZH2 rescued AR expression. LNCaP cells were subjected to EZH2 knockdown (siEZH2), followed by re-introduction of WT or mutant (H689A) EZH2 for 72 hr. Cell lysates were then collected and analyzed by qRT-PCR (G) or western blotting (H). (I) Both WT and H689A EZH2 are able to bind to the AR promoter. LNCaP cells were infected with pLVX control, HA-EZH2 WT, or HA-EZH2 H689A for 48 hr and then subjected to HA ChIP-qPCR. SLIT2 was used as a positive control and KIAA0066 as a negative control. Data shown are mean (±SEM) of technical replicates from one representative experiment of three. Overexpression of the HA-tagged WT and H689A EZH2 were validated by western blot (inset).

    Journal: Cell reports

    Article Title: Polycomb- and Methylation-Independent Roles of EZH2 as a Transcription Activator

    doi: 10.1016/j.celrep.2018.11.035

    Figure Lengend Snippet: EZH2 Activates the AR Independently of Its Histone Methyltransferase Activity (A) The AR promoter is occupied by EZH2 and H3K27ac but not H3K27me3, whereas the promoter of SLIT2, an epigenetic target of EZH2, is occupied with EZH2 and H3K27me3 but not H3K27ac. HA-EZH2 ChIP-seq was performed using anti-HA in LNCaP cells with HA-EZH2 overexpression. H3K27me3 and H3K27ac ChIP-seq was performed in LNCaP cells. (B) EZH2, but not SUZ12, decreased AR expression levels. LNCaP or C4–2B cells were infected with pLKO.1V, shEZH2, shSUZ12, or shAR for 48 hr, and cell lysates were subjected to western blot analysis. (C–F) EZH2 methyltransferase inhibitors failed to abolish AR expression. LNCaP cells were treated with EZH2 inhibitors GSK126 (C and D) or EPZ (E) for 72 hr, and the cell lysates were subjected to western blot (C and D) and qRT-PCR (E and F) analyses. The data shown in (E) and (F) are mean (±SEM) of technical replicates from one representative experiment of three. (G and H) Both WT and the catalytically dead mutant H689A of EZH2 rescued AR expression. LNCaP cells were subjected to EZH2 knockdown (siEZH2), followed by re-introduction of WT or mutant (H689A) EZH2 for 72 hr. Cell lysates were then collected and analyzed by qRT-PCR (G) or western blotting (H). (I) Both WT and H689A EZH2 are able to bind to the AR promoter. LNCaP cells were infected with pLVX control, HA-EZH2 WT, or HA-EZH2 H689A for 48 hr and then subjected to HA ChIP-qPCR. SLIT2 was used as a positive control and KIAA0066 as a negative control. Data shown are mean (±SEM) of technical replicates from one representative experiment of three. Overexpression of the HA-tagged WT and H689A EZH2 were validated by western blot (inset).

    Article Snippet: By contrast, strong EZH2 and H3K27me3 occupancy and lack of the active histone mark H3K27ac were found at the promoter of the EZH2 epigenetic target SLIT2.

    Techniques: Activity Assay, Chromatin Immunoprecipitation, Over Expression, Expressing, Infection, Western Blot, Quantitative RT-PCR, Mutagenesis, Real-time Polymerase Chain Reaction, Positive Control, Negative Control

    Methylation-Dependent and -Independent Transcriptional Programs of EZH2 in Prostate Cancer (A) Dual EZH2 transcriptional programs in prostate cancer (PCa). LNCaP cells were treated with either EPZ versus vehicle control or siEZH2 versus siCtrl and then profiled in triplicate microarray experiments. Genes that were significantly up- or downregulated by siEZH2 compared with the control were clustered across all samples and are shown as heatmaps. Each row represents one gene and each column one sample. The siEZH2-induce genes that were also induced by EPZ were termed class I genes and those unchanged by EPZ class II genes. Genes that were activated by EZH2 were defined as class III genes. (B) EZH2-regulated genes that contain at least one EZH2 ChIP-seq binding site at their promoter regions (±5 kb) were defined as direct targets of EZH2. H3K27ac and H3K27me3 ChIP-seq was performed in LNCaP cells with siCtrl or siEZH2, and their intensities around the three classes of direct EZH2-target genes were analyzed by boxplots. The p values evaluate the differences of ChIP-seq signals in siEZH2 versus siCtrl cells. (C) All EZH2 binding sites identified in control LNCaP cells were rank-ordered based on EZH2 ChIP-seq intensities. Shown at the top are average intensities, and at the bottom are heatmaps of EZH2, H3K27ac, and H3K27me3 ChIP-seq around all EZH2 binding sites. (D) Venn diagram showing overlap among EZH2, H3K27ac, and HEK27me3 binding sites. ChIP-seq was performed in control LNCaP cells. (E) EZH2 target genes marked with H3K27ac are abundantly expressed, whereas those marked by H3K27me3 are repressed. Genes whose promoters (±1 kb to the TSS) contain at least one EZH2 binding site with a peak score greater than 12 were selected. The subset (1,415) marked by H3K27ac, but not H3K27me3, was defined as EZH2-ac genes, whereas the subset (1,294) marked by H3K27me3, but not H3K27ac, was defined as EZH2-me genes. The expression levels (FPKM) of these genes in publicly available RNA-seq data (GSM3018523 and GSM3018524) that were performed in LNCaP cells are shown as boxplots. (F) EZH2-me genes are enriched for upregulation by EZH2 knockdown or EPZ treatment, whereas EZH2-ac genes are enriched for downregulation by EZH2 knockdown independently of EPZ. About 800 of 1,415 (57%) EZH2-ac genes, but only 60 of 1,294 (4.6%) EZH2-me genes, were detected in microarray experiments. The percentages of the genes that were significantly up- or downregulated by siEZH2 compared with siCtrl or by EPZ treatment compared with DMSO were calculated and plotted.

    Journal: Cell reports

    Article Title: Polycomb- and Methylation-Independent Roles of EZH2 as a Transcription Activator

    doi: 10.1016/j.celrep.2018.11.035

    Figure Lengend Snippet: Methylation-Dependent and -Independent Transcriptional Programs of EZH2 in Prostate Cancer (A) Dual EZH2 transcriptional programs in prostate cancer (PCa). LNCaP cells were treated with either EPZ versus vehicle control or siEZH2 versus siCtrl and then profiled in triplicate microarray experiments. Genes that were significantly up- or downregulated by siEZH2 compared with the control were clustered across all samples and are shown as heatmaps. Each row represents one gene and each column one sample. The siEZH2-induce genes that were also induced by EPZ were termed class I genes and those unchanged by EPZ class II genes. Genes that were activated by EZH2 were defined as class III genes. (B) EZH2-regulated genes that contain at least one EZH2 ChIP-seq binding site at their promoter regions (±5 kb) were defined as direct targets of EZH2. H3K27ac and H3K27me3 ChIP-seq was performed in LNCaP cells with siCtrl or siEZH2, and their intensities around the three classes of direct EZH2-target genes were analyzed by boxplots. The p values evaluate the differences of ChIP-seq signals in siEZH2 versus siCtrl cells. (C) All EZH2 binding sites identified in control LNCaP cells were rank-ordered based on EZH2 ChIP-seq intensities. Shown at the top are average intensities, and at the bottom are heatmaps of EZH2, H3K27ac, and H3K27me3 ChIP-seq around all EZH2 binding sites. (D) Venn diagram showing overlap among EZH2, H3K27ac, and HEK27me3 binding sites. ChIP-seq was performed in control LNCaP cells. (E) EZH2 target genes marked with H3K27ac are abundantly expressed, whereas those marked by H3K27me3 are repressed. Genes whose promoters (±1 kb to the TSS) contain at least one EZH2 binding site with a peak score greater than 12 were selected. The subset (1,415) marked by H3K27ac, but not H3K27me3, was defined as EZH2-ac genes, whereas the subset (1,294) marked by H3K27me3, but not H3K27ac, was defined as EZH2-me genes. The expression levels (FPKM) of these genes in publicly available RNA-seq data (GSM3018523 and GSM3018524) that were performed in LNCaP cells are shown as boxplots. (F) EZH2-me genes are enriched for upregulation by EZH2 knockdown or EPZ treatment, whereas EZH2-ac genes are enriched for downregulation by EZH2 knockdown independently of EPZ. About 800 of 1,415 (57%) EZH2-ac genes, but only 60 of 1,294 (4.6%) EZH2-me genes, were detected in microarray experiments. The percentages of the genes that were significantly up- or downregulated by siEZH2 compared with siCtrl or by EPZ treatment compared with DMSO were calculated and plotted.

    Article Snippet: By contrast, strong EZH2 and H3K27me3 occupancy and lack of the active histone mark H3K27ac were found at the promoter of the EZH2 epigenetic target SLIT2.

    Techniques: Methylation, Microarray, Chromatin Immunoprecipitation, Binding Assay, Expressing, RNA Sequencing Assay

    EZH2 Directly Activates AR Gene Transcription (A) EZH2 protein occupies the AR gene promoter. EZH2 ChIP-seq was performed in LNCaP cells with an antibody targeting endogenous EZH2 (top). HA ChIP-seq was performed using an anti-HA antibody in LNCaP cells with ectopic HA-EZH2 overexpression. Two biological replicates are shown (center and bottom). (B) ChIP-qPCR showing EZH2 binding along the AR gene promoter. ChIP was performed in LNCaP cells using anti-EZH2 and IgG antibodies and then subjected to qPCR using primer pairs targeting ~60-bp sliding windows within −1 kb to +3 kb of the AR gene. The x axis indicates the central location of the PCR products relative to the AR TSS. Data shown are mean (±SEM) of technical replicates from one representative experiment of three. (C) Different regions (of 400 bp) of the AR promoter (from 0 to +3 kb) were cloned into the pRetroX-Tight-Pur-Luc vector and transfected into 293T cells, which were then subjected to ChIP by anti-EZH2 or IgG. EZH2 occupancy at the ectopically expressed AR promoter was determined by qPCR using a common forward primer targeting the vector sequence and a reverse primer specific to each fragment. Data shown are mean (±SEM) of technical replicates from one representative experiment of two. (D) Various AR promoter regions were cloned into the pGL4.10 vector and transfected into 293T cells with either control pLVX or HA-EZH2 overexpression. Cells were then subjected to luciferase reporter assays. Results were normalized to the Renilla internal control. Data shown are mean (±SEM) of technical replicates from one representative experiment of three. (E) Schematic view of the AR promoter sequence starting from the transcription start site (TSS). The sgRNAs were labeled sgAR1 to 4, their sequences are shown in green font, and their distances to the AR TSS are marked as numbers. The primers (F2 and R2) for PCR validation are shown in purple. (F and G) The distal AR promoter region is required for EZH2 activation of AR transcription. LNCaP cells were infected with lentiCRISPR-Cas9 containing the pLENTI.V2 control, sgAR1+2, sgAR3+4, or sgAR1+4 for 48 hr. CRISPR-Cas9-mediated genome editing was confirmed by Sanger sequencing (F) and genomic DNA PCR (G) using primers F2 and R2 (indicated in A and E). (H) CRISPR-Cas9-edited LNCaP cells were transfected with control or EZH2-targeting siRNA for 48 hr. Total RNA was harvested and subjected to RT-PCR analysis using F2 and R2, which are expected to yield a wild-type (AR WT, top band with black asterisk) and a CRISPR-Cas9-deleted (AR del, bottom bands with red asterisk) AR mRNA.

    Journal: Cell reports

    Article Title: Polycomb- and Methylation-Independent Roles of EZH2 as a Transcription Activator

    doi: 10.1016/j.celrep.2018.11.035

    Figure Lengend Snippet: EZH2 Directly Activates AR Gene Transcription (A) EZH2 protein occupies the AR gene promoter. EZH2 ChIP-seq was performed in LNCaP cells with an antibody targeting endogenous EZH2 (top). HA ChIP-seq was performed using an anti-HA antibody in LNCaP cells with ectopic HA-EZH2 overexpression. Two biological replicates are shown (center and bottom). (B) ChIP-qPCR showing EZH2 binding along the AR gene promoter. ChIP was performed in LNCaP cells using anti-EZH2 and IgG antibodies and then subjected to qPCR using primer pairs targeting ~60-bp sliding windows within −1 kb to +3 kb of the AR gene. The x axis indicates the central location of the PCR products relative to the AR TSS. Data shown are mean (±SEM) of technical replicates from one representative experiment of three. (C) Different regions (of 400 bp) of the AR promoter (from 0 to +3 kb) were cloned into the pRetroX-Tight-Pur-Luc vector and transfected into 293T cells, which were then subjected to ChIP by anti-EZH2 or IgG. EZH2 occupancy at the ectopically expressed AR promoter was determined by qPCR using a common forward primer targeting the vector sequence and a reverse primer specific to each fragment. Data shown are mean (±SEM) of technical replicates from one representative experiment of two. (D) Various AR promoter regions were cloned into the pGL4.10 vector and transfected into 293T cells with either control pLVX or HA-EZH2 overexpression. Cells were then subjected to luciferase reporter assays. Results were normalized to the Renilla internal control. Data shown are mean (±SEM) of technical replicates from one representative experiment of three. (E) Schematic view of the AR promoter sequence starting from the transcription start site (TSS). The sgRNAs were labeled sgAR1 to 4, their sequences are shown in green font, and their distances to the AR TSS are marked as numbers. The primers (F2 and R2) for PCR validation are shown in purple. (F and G) The distal AR promoter region is required for EZH2 activation of AR transcription. LNCaP cells were infected with lentiCRISPR-Cas9 containing the pLENTI.V2 control, sgAR1+2, sgAR3+4, or sgAR1+4 for 48 hr. CRISPR-Cas9-mediated genome editing was confirmed by Sanger sequencing (F) and genomic DNA PCR (G) using primers F2 and R2 (indicated in A and E). (H) CRISPR-Cas9-edited LNCaP cells were transfected with control or EZH2-targeting siRNA for 48 hr. Total RNA was harvested and subjected to RT-PCR analysis using F2 and R2, which are expected to yield a wild-type (AR WT, top band with black asterisk) and a CRISPR-Cas9-deleted (AR del, bottom bands with red asterisk) AR mRNA.

    Article Snippet: By contrast, strong EZH2 and H3K27me3 occupancy and lack of the active histone mark H3K27ac were found at the promoter of the EZH2 epigenetic target SLIT2.

    Techniques: Chromatin Immunoprecipitation, Over Expression, Real-time Polymerase Chain Reaction, Binding Assay, Polymerase Chain Reaction, Clone Assay, Plasmid Preparation, Transfection, Sequencing, Luciferase, Labeling, Activation Assay, Infection, CRISPR, Reverse Transcription Polymerase Chain Reaction

    EZH2 Increases AR mRNA and Protein Levels (A–D) EZH2 knockdown decreases AR mRNA and protein levels. LNCaP (A), LAPC4 (B), C4–2B (C), and 22RV1 (D) cells were transfected with control or siEZH2s or infected with control shRNA or shEZH2, followed by qRT-PCR (left) and western blot analysis (right). Data shown are mean (±SEM) of technical replicates from one representative experiment of three. (E and F) EZH2 overexpression increases AR mRNA and protein levels. LNCaP (E) and LAPC4 (F) cells were infected with CMV or an EZH2-expressing adenovirus for 48 hr, followed by qRT-PCR (left) and western blot analysis (right). Data shown are mean (±SEM) of technical replicates from one representative experiment of three.

    Journal: Cell reports

    Article Title: Polycomb- and Methylation-Independent Roles of EZH2 as a Transcription Activator

    doi: 10.1016/j.celrep.2018.11.035

    Figure Lengend Snippet: EZH2 Increases AR mRNA and Protein Levels (A–D) EZH2 knockdown decreases AR mRNA and protein levels. LNCaP (A), LAPC4 (B), C4–2B (C), and 22RV1 (D) cells were transfected with control or siEZH2s or infected with control shRNA or shEZH2, followed by qRT-PCR (left) and western blot analysis (right). Data shown are mean (±SEM) of technical replicates from one representative experiment of three. (E and F) EZH2 overexpression increases AR mRNA and protein levels. LNCaP (E) and LAPC4 (F) cells were infected with CMV or an EZH2-expressing adenovirus for 48 hr, followed by qRT-PCR (left) and western blot analysis (right). Data shown are mean (±SEM) of technical replicates from one representative experiment of three.

    Article Snippet: By contrast, strong EZH2 and H3K27me3 occupancy and lack of the active histone mark H3K27ac were found at the promoter of the EZH2 epigenetic target SLIT2.

    Techniques: Transfection, Infection, shRNA, Quantitative RT-PCR, Western Blot, Over Expression, Expressing

    Combination of the Enzymatic EZH2 Inhibitor with Enz Markedly Reduced Xenograft Tumor Growth (A) EZH2-mediated transcription activities were blocked by combinatorial EPZ and Enz treatment. C4–2B cells were treated with DMSO, EPZ (1 μM), Enz (10 μM), or both for 7 days and then subjected to RNA-seq. FPKM values of EZH2-induced and -repressed gene sets across all samples were clustered and visualized as heatmaps. (B and C) Enz and EPZ combination greatly reduced C4–2B xenograft tumor growth in vivo . C4–2B cells were implanted subcutaneously in surgically castrated NOD.SCID mice. Upon palpable tumor formation, the mice (n = 7/group) were randomized to receive vehicle (1% carboxymethylcellulose sodium [CMC-Na + ] and 1% Tween 30), 10 mg/kg Enz (once a day), 250 mg/kg EPZ (twice a day), or both by oral gavage for 3 weeks. Tumor volume (B) and weight at the endpoint (C) were measured by a second person in a blinded fashion. Statistical differences in tumor volume and tumor weight among groups were determined using two-way repeated-measures ANOVA (p

    Journal: Cell reports

    Article Title: Polycomb- and Methylation-Independent Roles of EZH2 as a Transcription Activator

    doi: 10.1016/j.celrep.2018.11.035

    Figure Lengend Snippet: Combination of the Enzymatic EZH2 Inhibitor with Enz Markedly Reduced Xenograft Tumor Growth (A) EZH2-mediated transcription activities were blocked by combinatorial EPZ and Enz treatment. C4–2B cells were treated with DMSO, EPZ (1 μM), Enz (10 μM), or both for 7 days and then subjected to RNA-seq. FPKM values of EZH2-induced and -repressed gene sets across all samples were clustered and visualized as heatmaps. (B and C) Enz and EPZ combination greatly reduced C4–2B xenograft tumor growth in vivo . C4–2B cells were implanted subcutaneously in surgically castrated NOD.SCID mice. Upon palpable tumor formation, the mice (n = 7/group) were randomized to receive vehicle (1% carboxymethylcellulose sodium [CMC-Na + ] and 1% Tween 30), 10 mg/kg Enz (once a day), 250 mg/kg EPZ (twice a day), or both by oral gavage for 3 weeks. Tumor volume (B) and weight at the endpoint (C) were measured by a second person in a blinded fashion. Statistical differences in tumor volume and tumor weight among groups were determined using two-way repeated-measures ANOVA (p

    Article Snippet: By contrast, strong EZH2 and H3K27me3 occupancy and lack of the active histone mark H3K27ac were found at the promoter of the EZH2 epigenetic target SLIT2.

    Techniques: RNA Sequencing Assay, In Vivo, Mouse Assay

    Simultaneous EZH2 and AR Targeting Remarkably Inhibited PCa Cell Growth (A) Combinatorial GSK126 and enzalutamide (Enz) treatment significantly inhibited LNCaP cell growth and drug resistance. LNCaP cells were maintained in DMSO, GSK126 (0.5uM), Enz (0.5uM), or both for 55 days. Cells were counted and re-plated whenever needed, and accumulated cell numbers were determined. Data shown are for one representative experiment of two. (B and C) LNCaP (B) or C4–2B (C) cells were treated with DMSO, Enz (1 μM for LNCaP and10 μM for C4–2B), EPZ (1 μM), or both. Cell growth was measured with WST-1 reagent every 2 days. Data shown are mean ± SEM of technical replicates from one representative experiment of three. (D and E) LNCaP (D) or C4–2B (E) cells were treated with DMSO, Enz (1 μM for LNCaP and 10 μM for C4–2B), EPZ (1 μM), or both for 2 weeks, followed by 0.002% crystal violet staining to assay colony formation. Data shown are technical replicates from one representative experiment of three. (F and G) Combinatorial Enz and EPZ treatment induced cell cycle arrest. LNCaP (F) or C4–2B (G) cells were treated with DMSO, Enz (1 μM for LNCaP and 10 μM for C4–2B), EPZ (1 μM), or both for 3 days, followed by cell cycle analysis via flow cytometry with propidium iodide staining.

    Journal: Cell reports

    Article Title: Polycomb- and Methylation-Independent Roles of EZH2 as a Transcription Activator

    doi: 10.1016/j.celrep.2018.11.035

    Figure Lengend Snippet: Simultaneous EZH2 and AR Targeting Remarkably Inhibited PCa Cell Growth (A) Combinatorial GSK126 and enzalutamide (Enz) treatment significantly inhibited LNCaP cell growth and drug resistance. LNCaP cells were maintained in DMSO, GSK126 (0.5uM), Enz (0.5uM), or both for 55 days. Cells were counted and re-plated whenever needed, and accumulated cell numbers were determined. Data shown are for one representative experiment of two. (B and C) LNCaP (B) or C4–2B (C) cells were treated with DMSO, Enz (1 μM for LNCaP and10 μM for C4–2B), EPZ (1 μM), or both. Cell growth was measured with WST-1 reagent every 2 days. Data shown are mean ± SEM of technical replicates from one representative experiment of three. (D and E) LNCaP (D) or C4–2B (E) cells were treated with DMSO, Enz (1 μM for LNCaP and 10 μM for C4–2B), EPZ (1 μM), or both for 2 weeks, followed by 0.002% crystal violet staining to assay colony formation. Data shown are technical replicates from one representative experiment of three. (F and G) Combinatorial Enz and EPZ treatment induced cell cycle arrest. LNCaP (F) or C4–2B (G) cells were treated with DMSO, Enz (1 μM for LNCaP and 10 μM for C4–2B), EPZ (1 μM), or both for 3 days, followed by cell cycle analysis via flow cytometry with propidium iodide staining.

    Article Snippet: By contrast, strong EZH2 and H3K27me3 occupancy and lack of the active histone mark H3K27ac were found at the promoter of the EZH2 epigenetic target SLIT2.

    Techniques: Staining, Cell Cycle Assay, Flow Cytometry, Cytometry

    CRISPR–Cas9-based Epigenetic Regulatory Element Screening (CERES) identifies regulatory elements of the β-globin locus in a loss-of-function screen. ( a ) CERES involves the design and synthesis of libraries of gRNAs targeted to all candidate gene regulatory elements in a genomic region, in this case as defined by DNase I hypersensitive sites (DHS) identified by DNase-seq. Lentiviral vectors encoding the gRNA library are delivered to cell lines expressing the dCas9 KRAB repressor, for loss-of-function screens, or the dCas9 p300 activator, for gain-of-function screens. The cells can then be selected for changes in phenotype, such as gain or loss of expression of a target gene. Sequencing the gRNAs in the selected cell subpopulations and mapping them back to the genome reveals regulatory elements involved in controlling the selected phenotype. In the example shown here, a gRNA library was designed to all DHSs in a 4.5 Mb region surrounding the β-globin locus, and introduced into human K562 cells expressing dCas9 KRAB and containing an mCherry reporter at the HBE1 gene. ( b ) Representative flow cytometry data of the HBE1 reporter cells containing the gRNA library, and expression levels of cells sorted for gRNA enrichment. ( c ) Manhattan plot of a high-throughput screen for regulatory elements in the 4.5 Mb surrounding the globin locus using the dCas9 KRAB repressor. ( d ) Enriched DHSs following selection for decreased HBE1 expression were found only in the HBE1 promoter and enhancers (HS1-4), while the promoters of HBG1 / 2 were enriched in cells with increased HBE1 expression. Diamonds indicate adjusted p-value

    Journal: Nature biotechnology

    Article Title: CRISPR–Cas9 epigenome editing enables high-throughput screening for functional regulatory elements in the human genome

    doi: 10.1038/nbt.3853

    Figure Lengend Snippet: CRISPR–Cas9-based Epigenetic Regulatory Element Screening (CERES) identifies regulatory elements of the β-globin locus in a loss-of-function screen. ( a ) CERES involves the design and synthesis of libraries of gRNAs targeted to all candidate gene regulatory elements in a genomic region, in this case as defined by DNase I hypersensitive sites (DHS) identified by DNase-seq. Lentiviral vectors encoding the gRNA library are delivered to cell lines expressing the dCas9 KRAB repressor, for loss-of-function screens, or the dCas9 p300 activator, for gain-of-function screens. The cells can then be selected for changes in phenotype, such as gain or loss of expression of a target gene. Sequencing the gRNAs in the selected cell subpopulations and mapping them back to the genome reveals regulatory elements involved in controlling the selected phenotype. In the example shown here, a gRNA library was designed to all DHSs in a 4.5 Mb region surrounding the β-globin locus, and introduced into human K562 cells expressing dCas9 KRAB and containing an mCherry reporter at the HBE1 gene. ( b ) Representative flow cytometry data of the HBE1 reporter cells containing the gRNA library, and expression levels of cells sorted for gRNA enrichment. ( c ) Manhattan plot of a high-throughput screen for regulatory elements in the 4.5 Mb surrounding the globin locus using the dCas9 KRAB repressor. ( d ) Enriched DHSs following selection for decreased HBE1 expression were found only in the HBE1 promoter and enhancers (HS1-4), while the promoters of HBG1 / 2 were enriched in cells with increased HBE1 expression. Diamonds indicate adjusted p-value

    Article Snippet: Here we demonstrate the utility of CRISPR–Cas9-based Epigenomic Regulatory Element Screening (CERES) by targeting DHSs surrounding genes of interest to identify endogenous regulatory elements through loss- and gain-of-function epigenome editing.

    Techniques: CRISPR, Expressing, Sequencing, Flow Cytometry, Cytometry, High Throughput Screening Assay, Selection

    Epigenomic information across tissues and marks a. Chromatin state annotations across 127 reference epigenomes (rows, Fig. 2 ) in a ~3.5Mb region on chromosome 9. Promoters are primarily constitutive (red vertical lines), while enhancers are highly dynamic (dispersed yellow regions). b. Signal tracks for IMR90 showing RNA-seq, a total of 28 histone modification marks, whole-genome bisulfite DNA methylation, DNA accessibility, Digital Genomic Footprints (DGF), input DNA, and chromatin conformation information 71 . c. Individual epigenomic marks across all epigenomes in which they are available. d. Relationship of figure panels highlights dataset dimensions.

    Journal: Nature

    Article Title: Integrative analysis of 111 reference human epigenomes

    doi: 10.1038/nature14248

    Figure Lengend Snippet: Epigenomic information across tissues and marks a. Chromatin state annotations across 127 reference epigenomes (rows, Fig. 2 ) in a ~3.5Mb region on chromosome 9. Promoters are primarily constitutive (red vertical lines), while enhancers are highly dynamic (dispersed yellow regions). b. Signal tracks for IMR90 showing RNA-seq, a total of 28 histone modification marks, whole-genome bisulfite DNA methylation, DNA accessibility, Digital Genomic Footprints (DGF), input DNA, and chromatin conformation information 71 . c. Individual epigenomic marks across all epigenomes in which they are available. d. Relationship of figure panels highlights dataset dimensions.

    Article Snippet: The resulting datasets provide global views of the epigenomic landscape in a wide range of human cell and tissue types , including: the largest and most diverse collection to date of chromatin state annotations ( ); some of the deepest surveys of individual cell types using diverse epigenomic assays (with 21-31 distinct epigenomic marks for seven deeply-profiled epigenomes, ); and some of the broadest surveys of individual epigenomic marks across multiple cell types ( ).

    Techniques: RNA Sequencing Assay, Modification, DNA Methylation Assay

    Epigenomic enrichments of genetic variants associated with diverse traits Tissue-specific H3K4me1 peak enrichment for genetic variants associated with diverse traits. Circles denote reference epigenome (column) of highest enrichment for SNPs reported by a given study (row), defined by trait and publication (PubMed identifier, PMID). Tissue (Abbrev) and p-value (-log 10 ) of highest enrichment are shown. Only rows and columns containing a value meeting a FDR of 2% are shown (Full matrix for all studies showing at least 2% FDR in Extended Data 11 - 12 ).

    Journal: Nature

    Article Title: Integrative analysis of 111 reference human epigenomes

    doi: 10.1038/nature14248

    Figure Lengend Snippet: Epigenomic enrichments of genetic variants associated with diverse traits Tissue-specific H3K4me1 peak enrichment for genetic variants associated with diverse traits. Circles denote reference epigenome (column) of highest enrichment for SNPs reported by a given study (row), defined by trait and publication (PubMed identifier, PMID). Tissue (Abbrev) and p-value (-log 10 ) of highest enrichment are shown. Only rows and columns containing a value meeting a FDR of 2% are shown (Full matrix for all studies showing at least 2% FDR in Extended Data 11 - 12 ).

    Article Snippet: The resulting datasets provide global views of the epigenomic landscape in a wide range of human cell and tissue types , including: the largest and most diverse collection to date of chromatin state annotations ( ); some of the deepest surveys of individual cell types using diverse epigenomic assays (with 21-31 distinct epigenomic marks for seven deeply-profiled epigenomes, ); and some of the broadest surveys of individual epigenomic marks across multiple cell types ( ).

    Techniques:

    Epigenomic enrichments of genetic variants associated with diverse traits Tissue-specific enrichments for peaks of diverse epigenomic marks for genetic variants associated with complex disease, expanding Fig. 9 . Enrichments are shown for: a. H3K4me1 peaks (enhancers). This panel includes all the data shown in Fig. 9 , but expands the enrichments shown to all reference epigenomes (columns), and additional traits (rows) that did not meet the FDR=0.02 threshold. b. H3K27ac peaks (active enhancers). a-b. Studies were defined by a set of SNPs annotated in the GWAS catalog with the same combination of a trait (far left column) and publication shown by the Pubmed ID (far right column), uncorrected p-value (in -log 10 ), and estimated FDR.

    Journal: Nature

    Article Title: Integrative analysis of 111 reference human epigenomes

    doi: 10.1038/nature14248

    Figure Lengend Snippet: Epigenomic enrichments of genetic variants associated with diverse traits Tissue-specific enrichments for peaks of diverse epigenomic marks for genetic variants associated with complex disease, expanding Fig. 9 . Enrichments are shown for: a. H3K4me1 peaks (enhancers). This panel includes all the data shown in Fig. 9 , but expands the enrichments shown to all reference epigenomes (columns), and additional traits (rows) that did not meet the FDR=0.02 threshold. b. H3K27ac peaks (active enhancers). a-b. Studies were defined by a set of SNPs annotated in the GWAS catalog with the same combination of a trait (far left column) and publication shown by the Pubmed ID (far right column), uncorrected p-value (in -log 10 ), and estimated FDR.

    Article Snippet: The resulting datasets provide global views of the epigenomic landscape in a wide range of human cell and tissue types , including: the largest and most diverse collection to date of chromatin state annotations ( ); some of the deepest surveys of individual cell types using diverse epigenomic assays (with 21-31 distinct epigenomic marks for seven deeply-profiled epigenomes, ); and some of the broadest surveys of individual epigenomic marks across multiple cell types ( ).

    Techniques: GWAS

    a-i. Multidimensional scaling (MDS) plots showing tissue/cell type similarity using different epigenomic marks. Multi-Dimensional Scaling (MDS) analysis results, showing reference epigenomes using their group coloring defined in Fig. 2 . Thin lines connect same-group reference epigenomes. The first 4 axes of variation are shown in pairs. Marks are assessed in regions with relevant chromatin states (see Methods). j. Variance explained by each MDS dimension. The first 5 dimensions shown in Fig. S10 ( Fig. 6b,c ) explain between 45% and 80% of the total epigenometo-epigenome variance for all histone modification mark correlations, and additional dimensions explain less than 10%. Only a few components of H3K4me3 in TssA chromatin states explains a much larger fraction of the variance than other marks, possibly due to its stability across cell types.

    Journal: Nature

    Article Title: Integrative analysis of 111 reference human epigenomes

    doi: 10.1038/nature14248

    Figure Lengend Snippet: a-i. Multidimensional scaling (MDS) plots showing tissue/cell type similarity using different epigenomic marks. Multi-Dimensional Scaling (MDS) analysis results, showing reference epigenomes using their group coloring defined in Fig. 2 . Thin lines connect same-group reference epigenomes. The first 4 axes of variation are shown in pairs. Marks are assessed in regions with relevant chromatin states (see Methods). j. Variance explained by each MDS dimension. The first 5 dimensions shown in Fig. S10 ( Fig. 6b,c ) explain between 45% and 80% of the total epigenometo-epigenome variance for all histone modification mark correlations, and additional dimensions explain less than 10%. Only a few components of H3K4me3 in TssA chromatin states explains a much larger fraction of the variance than other marks, possibly due to its stability across cell types.

    Article Snippet: The resulting datasets provide global views of the epigenomic landscape in a wide range of human cell and tissue types , including: the largest and most diverse collection to date of chromatin state annotations ( ); some of the deepest surveys of individual cell types using diverse epigenomic assays (with 21-31 distinct epigenomic marks for seven deeply-profiled epigenomes, ); and some of the broadest surveys of individual epigenomic marks across multiple cell types ( ).

    Techniques: Modification

    Chromatin state model robustness and enrichments a. Chromatin state model robustness. Clustering of 15-state ‘core’ chromatin state model learned jointly across reference epigenomes ( Fig. 4a ) with chromatin state models learned independently in 111 reference epigenomes. We applied ChromHMM to learn a 15-state ChromHMM model using the five core marks in each of the 111 reference epigenomes generated by the Roadmap Epigenomics program, and clustered the resulting 1680 state emission probability vectors (leaves of the tree) with the 15 states from the joint model (indicated by arrows). We found that the vast majority of states learned across cell types clustered into 15 clusters, corresponding to the joint model states, validating the robustness of chromatin states across cell types. This analysis revealed two new clusters (red crosses) which are not represented in the 15 states of the jointly-learned model: ‘HetWk’, a cluster showing weak enrichment for H3K9me3; and ‘Rpts’, a cluster showing H3K9me3 along with a diversity of other marks, and enriched in specific types of repetitive elements (satellite repeats) in each cell type, which may be due to mapping artifacts. This joint clustering also revealed subtle variations in the relative intensity of H3K4me1 in states TxFlnk, Enh, and TssBiv, and H3K27me3 in state TssBiv. Overall, this analysis confirms that the 15-state chromatin state model based on the core set of five marks provides a robust framework for interpreting epigenomic complexity across tissues and cell types. b. Enrichments for 15-state model based on five histone modification marks. Top Left: TF binding site overlap enrichments of 15 states in H1-ESC from the ‘core’ model for transcription factor binding sites (TFBS) based on ChIP-seq data in H1-ESC. TF binding coverage for other cell-types based on matched TF ChIP-seq data is shown in Fig. S2 . Top Right: Enrichments for expressed and non-expressed genes in H1-ESC and GM12878. Bottom: Positional enrichments at the transcription start site (TSS) and transcription end site (TES) of expressed (expr.) and repressed (repr.) genes in H1-ESC. Transition probabilities show frequency of co-occurrence of each pair of chromatin states in neighboring 200-bp bins. d. Definition and enrichments for 18-state ‘expanded’ model that also includes H3K27ac associated with active enhancer and active promoter regions, but which was only available for 98 of the 127 reference epigenomes. Inclusion of H3K27ac distinguishes active enhancers and active promoters. Top: TFBS enrichments in H1-ESC (E003) chromatin states using ENCODE TF ChIP-seq data in H1-ESC . Bottom: Positional enrichments in H1-ESC for genomic annotations, expressed and repressed genes, TSS and TES, and state transitions as in Extended Data 2b and Fig. 4a-c . Right: Average fold-enrichment (colors bars) and standard deviation (black line) across 98 reference epigenomes ( Fig. S3d ) for the fold enrichment for non-coding of genomic segments (GERP) in each chromatin state (rows) in the 18-state model. Even after excluding protein-coding exons (see Fig. S3b vs. Fig. S3d ), the TSS-proximal states show the highest levels of conservation, followed by EnhBiv and the three non-transcribed enhancer states. In contrast, Tx and TxWk elements are weakly depleted for conserved regions, and Znf/Rpts, and Het are strongly depleted for conserved elements.

    Journal: Nature

    Article Title: Integrative analysis of 111 reference human epigenomes

    doi: 10.1038/nature14248

    Figure Lengend Snippet: Chromatin state model robustness and enrichments a. Chromatin state model robustness. Clustering of 15-state ‘core’ chromatin state model learned jointly across reference epigenomes ( Fig. 4a ) with chromatin state models learned independently in 111 reference epigenomes. We applied ChromHMM to learn a 15-state ChromHMM model using the five core marks in each of the 111 reference epigenomes generated by the Roadmap Epigenomics program, and clustered the resulting 1680 state emission probability vectors (leaves of the tree) with the 15 states from the joint model (indicated by arrows). We found that the vast majority of states learned across cell types clustered into 15 clusters, corresponding to the joint model states, validating the robustness of chromatin states across cell types. This analysis revealed two new clusters (red crosses) which are not represented in the 15 states of the jointly-learned model: ‘HetWk’, a cluster showing weak enrichment for H3K9me3; and ‘Rpts’, a cluster showing H3K9me3 along with a diversity of other marks, and enriched in specific types of repetitive elements (satellite repeats) in each cell type, which may be due to mapping artifacts. This joint clustering also revealed subtle variations in the relative intensity of H3K4me1 in states TxFlnk, Enh, and TssBiv, and H3K27me3 in state TssBiv. Overall, this analysis confirms that the 15-state chromatin state model based on the core set of five marks provides a robust framework for interpreting epigenomic complexity across tissues and cell types. b. Enrichments for 15-state model based on five histone modification marks. Top Left: TF binding site overlap enrichments of 15 states in H1-ESC from the ‘core’ model for transcription factor binding sites (TFBS) based on ChIP-seq data in H1-ESC. TF binding coverage for other cell-types based on matched TF ChIP-seq data is shown in Fig. S2 . Top Right: Enrichments for expressed and non-expressed genes in H1-ESC and GM12878. Bottom: Positional enrichments at the transcription start site (TSS) and transcription end site (TES) of expressed (expr.) and repressed (repr.) genes in H1-ESC. Transition probabilities show frequency of co-occurrence of each pair of chromatin states in neighboring 200-bp bins. d. Definition and enrichments for 18-state ‘expanded’ model that also includes H3K27ac associated with active enhancer and active promoter regions, but which was only available for 98 of the 127 reference epigenomes. Inclusion of H3K27ac distinguishes active enhancers and active promoters. Top: TFBS enrichments in H1-ESC (E003) chromatin states using ENCODE TF ChIP-seq data in H1-ESC . Bottom: Positional enrichments in H1-ESC for genomic annotations, expressed and repressed genes, TSS and TES, and state transitions as in Extended Data 2b and Fig. 4a-c . Right: Average fold-enrichment (colors bars) and standard deviation (black line) across 98 reference epigenomes ( Fig. S3d ) for the fold enrichment for non-coding of genomic segments (GERP) in each chromatin state (rows) in the 18-state model. Even after excluding protein-coding exons (see Fig. S3b vs. Fig. S3d ), the TSS-proximal states show the highest levels of conservation, followed by EnhBiv and the three non-transcribed enhancer states. In contrast, Tx and TxWk elements are weakly depleted for conserved regions, and Znf/Rpts, and Het are strongly depleted for conserved elements.

    Article Snippet: The resulting datasets provide global views of the epigenomic landscape in a wide range of human cell and tissue types , including: the largest and most diverse collection to date of chromatin state annotations ( ); some of the deepest surveys of individual cell types using diverse epigenomic assays (with 21-31 distinct epigenomic marks for seven deeply-profiled epigenomes, ); and some of the broadest surveys of individual epigenomic marks across multiple cell types ( ).

    Techniques: Generated, Modification, Binding Assay, Chromatin Immunoprecipitation, Standard Deviation

    Datasets available for each reference epigenome List of 127 epigenomes including 111 by the Roadmap Epigenomics program (E001-E113) and 16 by ENCODE (E114-E129). Full list of names and quality scores in Table S1 . a-d: Tissue and cell types grouped by type of biological material (a), anatomical location (b), showing reference epigenome identifier (EID, c), and abbreviated name (d). PB=Peripheral Blood. ENCODE 2012 reference epigenomes shown separately. e-g. Normalized strand cross-correlation quality scores (NSC) 37 for the core set of five histone marks (e), additional acetylation marks (f) and DNase-seq (g). h. Methylation data by WGBS (red), RRBS (blue), and mCRF (green). 104 methylation datasets available in 95 distinct reference epigenomes. i. Gene expression data using RNA-seq (Brown) and microarray expression (Yellow). j. 26 epigenomes contain a total of 184 additional histone modification marks. k. 60 highest-quality epigenomes (purple) were used for training the core chromatin state model, which was then applied to the full set of epigenomes (purple and orange).

    Journal: Nature

    Article Title: Integrative analysis of 111 reference human epigenomes

    doi: 10.1038/nature14248

    Figure Lengend Snippet: Datasets available for each reference epigenome List of 127 epigenomes including 111 by the Roadmap Epigenomics program (E001-E113) and 16 by ENCODE (E114-E129). Full list of names and quality scores in Table S1 . a-d: Tissue and cell types grouped by type of biological material (a), anatomical location (b), showing reference epigenome identifier (EID, c), and abbreviated name (d). PB=Peripheral Blood. ENCODE 2012 reference epigenomes shown separately. e-g. Normalized strand cross-correlation quality scores (NSC) 37 for the core set of five histone marks (e), additional acetylation marks (f) and DNase-seq (g). h. Methylation data by WGBS (red), RRBS (blue), and mCRF (green). 104 methylation datasets available in 95 distinct reference epigenomes. i. Gene expression data using RNA-seq (Brown) and microarray expression (Yellow). j. 26 epigenomes contain a total of 184 additional histone modification marks. k. 60 highest-quality epigenomes (purple) were used for training the core chromatin state model, which was then applied to the full set of epigenomes (purple and orange).

    Article Snippet: The resulting datasets provide global views of the epigenomic landscape in a wide range of human cell and tissue types , including: the largest and most diverse collection to date of chromatin state annotations ( ); some of the deepest surveys of individual cell types using diverse epigenomic assays (with 21-31 distinct epigenomic marks for seven deeply-profiled epigenomes, ); and some of the broadest surveys of individual epigenomic marks across multiple cell types ( ).

    Techniques: Methylation, Expressing, RNA Sequencing Assay, Microarray, Modification

    Epigenomic enrichments of genetic variants associated with diverse traits Tissue-specific enrichments for peaks of diverse epigenomic marks for genetic variants associated with complex disease, expanding Fig. 9 . Enrichments are shown for: a. H3K4me3 peaks (promoters). b. H3K9ac peaks (active promoters and active enhancers). c. DNase peaks (accessible regions). d. H3K36me3 peaks (transcribed regions). e. H3K27me3 peaks (Polycomb-repressed regions). f. H3K9me3 peaks (heterochromatin regions). a-f. Studies were defined by a set of SNPs annotated in the GWAS catalog with the same combination of a trait (far left column) and publication shown by the Pubmed ID (far right column), uncorrected p-value (in -log 10 ), and estimated FDR.

    Journal: Nature

    Article Title: Integrative analysis of 111 reference human epigenomes

    doi: 10.1038/nature14248

    Figure Lengend Snippet: Epigenomic enrichments of genetic variants associated with diverse traits Tissue-specific enrichments for peaks of diverse epigenomic marks for genetic variants associated with complex disease, expanding Fig. 9 . Enrichments are shown for: a. H3K4me3 peaks (promoters). b. H3K9ac peaks (active promoters and active enhancers). c. DNase peaks (accessible regions). d. H3K36me3 peaks (transcribed regions). e. H3K27me3 peaks (Polycomb-repressed regions). f. H3K9me3 peaks (heterochromatin regions). a-f. Studies were defined by a set of SNPs annotated in the GWAS catalog with the same combination of a trait (far left column) and publication shown by the Pubmed ID (far right column), uncorrected p-value (in -log 10 ), and estimated FDR.

    Article Snippet: The resulting datasets provide global views of the epigenomic landscape in a wide range of human cell and tissue types , including: the largest and most diverse collection to date of chromatin state annotations ( ); some of the deepest surveys of individual cell types using diverse epigenomic assays (with 21-31 distinct epigenomic marks for seven deeply-profiled epigenomes, ); and some of the broadest surveys of individual epigenomic marks across multiple cell types ( ).

    Techniques: GWAS

    Tissues and cell types profiled in the Roadmap Epigenomics Consortium Primary tissues and cell types representative of all major lineages in the human body were profiled, including multiple brain, heart, muscle, GI-tract, adipose, skin, and reproductive samples, as well as immune lineages, ESCs and induced Pluripotent Stem (iPS) cells, and differentiated lineages derived from ESCs. Box colors match groups shown in Fig. 2b . Epigenome identifiers (EIDs, Fig. 2c ) for each sample shown in Extended Data 1 .

    Journal: Nature

    Article Title: Integrative analysis of 111 reference human epigenomes

    doi: 10.1038/nature14248

    Figure Lengend Snippet: Tissues and cell types profiled in the Roadmap Epigenomics Consortium Primary tissues and cell types representative of all major lineages in the human body were profiled, including multiple brain, heart, muscle, GI-tract, adipose, skin, and reproductive samples, as well as immune lineages, ESCs and induced Pluripotent Stem (iPS) cells, and differentiated lineages derived from ESCs. Box colors match groups shown in Fig. 2b . Epigenome identifiers (EIDs, Fig. 2c ) for each sample shown in Extended Data 1 .

    Article Snippet: The resulting datasets provide global views of the epigenomic landscape in a wide range of human cell and tissue types , including: the largest and most diverse collection to date of chromatin state annotations ( ); some of the deepest surveys of individual cell types using diverse epigenomic assays (with 21-31 distinct epigenomic marks for seven deeply-profiled epigenomes, ); and some of the broadest surveys of individual epigenomic marks across multiple cell types ( ).

    Techniques: Derivative Assay

    Chromatin state variability, switching, and genomic coverage a. Variability level for 18-state model. Chromatin state variability (similar to Fig. 5a ), quantified based on the fraction of the genomic coverage (y-axis) of each state (color) that is consistently labeled with that state in at most N (ranging from 1 to 98) reference epigenomes, using the 18-state model learned based on 6 chromatin marks, including H3K27ac. b. Chromatin state over- and under-representation for 18-state expanded model. c. Log-ratio (log 10 ) of chromatin state switching probabilities for the 18-state expanded model across 34 high-quality, non-redundant epigenomes that have H3K27ac data, relative to intra-tissue switching probabilities across replicates or samples from multiple individuals. d. Chromatin state coverage grouped by epigenomic domains. Top: Chromosome ‘painting’ of 11 clusters shown in Fig. 5d and discovered based on chromatin state co-occurrence at the 2Mb scale across reference epigenomes. Bottom: Enrichment of CpG islands in each cluster clearly showing higher CpG density ‘active’ clusters 3 and 6 comparing to passive clusters 9-11. Each box plot shows a distribution of CpG total occupancy in 2Mb bins in each cluster (with box boundaries indicate 25th and 75th percentiles the whiskers extend to the most extreme datapoints the algorithm considers to not be outliers. Points are drawn as outliers if they are larger than Q3+W*(Q3-Q1) or smaller than Q1-W*(Q3-Q1), where Q1 and Q3 are the 25th and 75th percentiles, respectively.).

    Journal: Nature

    Article Title: Integrative analysis of 111 reference human epigenomes

    doi: 10.1038/nature14248

    Figure Lengend Snippet: Chromatin state variability, switching, and genomic coverage a. Variability level for 18-state model. Chromatin state variability (similar to Fig. 5a ), quantified based on the fraction of the genomic coverage (y-axis) of each state (color) that is consistently labeled with that state in at most N (ranging from 1 to 98) reference epigenomes, using the 18-state model learned based on 6 chromatin marks, including H3K27ac. b. Chromatin state over- and under-representation for 18-state expanded model. c. Log-ratio (log 10 ) of chromatin state switching probabilities for the 18-state expanded model across 34 high-quality, non-redundant epigenomes that have H3K27ac data, relative to intra-tissue switching probabilities across replicates or samples from multiple individuals. d. Chromatin state coverage grouped by epigenomic domains. Top: Chromosome ‘painting’ of 11 clusters shown in Fig. 5d and discovered based on chromatin state co-occurrence at the 2Mb scale across reference epigenomes. Bottom: Enrichment of CpG islands in each cluster clearly showing higher CpG density ‘active’ clusters 3 and 6 comparing to passive clusters 9-11. Each box plot shows a distribution of CpG total occupancy in 2Mb bins in each cluster (with box boundaries indicate 25th and 75th percentiles the whiskers extend to the most extreme datapoints the algorithm considers to not be outliers. Points are drawn as outliers if they are larger than Q3+W*(Q3-Q1) or smaller than Q1-W*(Q3-Q1), where Q1 and Q3 are the 25th and 75th percentiles, respectively.).

    Article Snippet: The resulting datasets provide global views of the epigenomic landscape in a wide range of human cell and tissue types , including: the largest and most diverse collection to date of chromatin state annotations ( ); some of the deepest surveys of individual cell types using diverse epigenomic assays (with 21-31 distinct epigenomic marks for seven deeply-profiled epigenomes, ); and some of the broadest surveys of individual epigenomic marks across multiple cell types ( ).

    Techniques: Labeling

    Epigenetic landscape of CHRNA4 in human. a Epigenetic landscape and transcription signal around CHRNA4 in human liver, hippocampus, and CD34 hematopoietic stem cells. The RefSeq promoter of CHRNA4 (P1, pink-shaded ) shows enrichment of H3K4me3 and H3K27ac histone modifications, especially in human hippocampus. CAGE-seq signal of fetal brain indicates that CHRNA4 was transcribed from RefSeq canonical promoter P1. Liver-specific promoter P2 ( blue-shade ), which is located ~3.9 KB upstream of the RefSeq promoter, is enriched for strong, active histone modifications. The CAGE-seq signal of adult liver indicates CHRNA4 is transcribed from the liver-specific promoter P2. b Averaged DNA methylation levels of promoters P1 and P2 in human liver, hippocampus, and CD34 hematopoietic stem cells. Mann-Whitney U test was performed to detect statistical significance. c Relative expression level of CHRNA4 measured by qRT-PCR in human GM12878 cell line, brain, and liver. Student t -test was performed to detect statistical significance

    Journal: BMC Genomics

    Article Title: Uncovering the transcriptomic and epigenomic landscape of nicotinic receptor genes in non-neuronal tissues

    doi: 10.1186/s12864-017-3813-4

    Figure Lengend Snippet: Epigenetic landscape of CHRNA4 in human. a Epigenetic landscape and transcription signal around CHRNA4 in human liver, hippocampus, and CD34 hematopoietic stem cells. The RefSeq promoter of CHRNA4 (P1, pink-shaded ) shows enrichment of H3K4me3 and H3K27ac histone modifications, especially in human hippocampus. CAGE-seq signal of fetal brain indicates that CHRNA4 was transcribed from RefSeq canonical promoter P1. Liver-specific promoter P2 ( blue-shade ), which is located ~3.9 KB upstream of the RefSeq promoter, is enriched for strong, active histone modifications. The CAGE-seq signal of adult liver indicates CHRNA4 is transcribed from the liver-specific promoter P2. b Averaged DNA methylation levels of promoters P1 and P2 in human liver, hippocampus, and CD34 hematopoietic stem cells. Mann-Whitney U test was performed to detect statistical significance. c Relative expression level of CHRNA4 measured by qRT-PCR in human GM12878 cell line, brain, and liver. Student t -test was performed to detect statistical significance

    Article Snippet: Processed mRNA-seq datasets (aligned to human reference genome hg19) from 56 reference epigenomics were obtained from Roadmap epigenomics project through data portal ( http://egg2.wustl.edu/roadmap/web_portal/ ).

    Techniques: DNA Methylation Assay, MANN-WHITNEY, Expressing, Quantitative RT-PCR

    Regulation of CHRNA4 by HNF4 and RXRA . a HNF4A ( top ) and RXRA ( bottom ) are highly expressed in human liver as compared to other human tissues. The Y-axis indicates expression level (RPKM) measured by RNA-seq data from the GTEx project. The tissues are ranked by median expression of HNF4A and RXRA . b Evolutionarily conserved epigenetic landscape and transcriptional pattern of Chrna4 in mouse and human liver. The liver-specific promoter ( blue-shade ), which is located ~4.8 KB upstream of the RefSeq promoter, is enriched for strong, active histone modifications and RNA polymerase II ChIP-seq signal (Pol2). The CAGE-seq signal of liver indicates Chrna4 is predominantly transcribed in hepatocyte from the liver-specific promoter P2. Available ChIP-seq data indicate that HNF4A/Hnf4a and RXRA/Rxra directly bind to the liver-specific promoter of CHRNA4 /Chrna4 in human and mouse liver, respectively

    Journal: BMC Genomics

    Article Title: Uncovering the transcriptomic and epigenomic landscape of nicotinic receptor genes in non-neuronal tissues

    doi: 10.1186/s12864-017-3813-4

    Figure Lengend Snippet: Regulation of CHRNA4 by HNF4 and RXRA . a HNF4A ( top ) and RXRA ( bottom ) are highly expressed in human liver as compared to other human tissues. The Y-axis indicates expression level (RPKM) measured by RNA-seq data from the GTEx project. The tissues are ranked by median expression of HNF4A and RXRA . b Evolutionarily conserved epigenetic landscape and transcriptional pattern of Chrna4 in mouse and human liver. The liver-specific promoter ( blue-shade ), which is located ~4.8 KB upstream of the RefSeq promoter, is enriched for strong, active histone modifications and RNA polymerase II ChIP-seq signal (Pol2). The CAGE-seq signal of liver indicates Chrna4 is predominantly transcribed in hepatocyte from the liver-specific promoter P2. Available ChIP-seq data indicate that HNF4A/Hnf4a and RXRA/Rxra directly bind to the liver-specific promoter of CHRNA4 /Chrna4 in human and mouse liver, respectively

    Article Snippet: Processed mRNA-seq datasets (aligned to human reference genome hg19) from 56 reference epigenomics were obtained from Roadmap epigenomics project through data portal ( http://egg2.wustl.edu/roadmap/web_portal/ ).

    Techniques: Expressing, RNA Sequencing Assay, Chromatin Immunoprecipitation

    Epigenomic model of rod and cone photoreceptor development. Enhancers that are active only in progenitor cells (termed 'fetal-only', as the fetal brain was used as a rich source of generic neural progenitors) have low levels of DNA methylation and are enriched for H3K27ac and H3K4me1 histone modifications. In mature cones, histones near fetal-only enhancers lose H3K27ac and H3K4me1 and there is a gain of DNA methylcytosines. In contrast, in mature rods, fetal-only enhancers lose H3K27ac and H3K4me1 but the DNA remains unmethylated, potentially due to the barrier to cytosine methyltransferases posed by their high level of chromatin condensation. In both rods and cones, expressed genes, including rod- and cone-specific photoreceptor genes, have promoters marked by low DNA methylation, high chromatin accessibility, and enrichment for H3K27ac and H3K4me3. Active enhancers are marked by low DNA methylation, high chromatin accessibility, and enrichment for H3K27ac and H3K4me1 (not shown). DOI: http://dx.doi.org/10.7554/eLife.11613.027

    Journal: eLife

    Article Title: Epigenomic landscapes of retinal rods and cones

    doi: 10.7554/eLife.11613

    Figure Lengend Snippet: Epigenomic model of rod and cone photoreceptor development. Enhancers that are active only in progenitor cells (termed 'fetal-only', as the fetal brain was used as a rich source of generic neural progenitors) have low levels of DNA methylation and are enriched for H3K27ac and H3K4me1 histone modifications. In mature cones, histones near fetal-only enhancers lose H3K27ac and H3K4me1 and there is a gain of DNA methylcytosines. In contrast, in mature rods, fetal-only enhancers lose H3K27ac and H3K4me1 but the DNA remains unmethylated, potentially due to the barrier to cytosine methyltransferases posed by their high level of chromatin condensation. In both rods and cones, expressed genes, including rod- and cone-specific photoreceptor genes, have promoters marked by low DNA methylation, high chromatin accessibility, and enrichment for H3K27ac and H3K4me3. Active enhancers are marked by low DNA methylation, high chromatin accessibility, and enrichment for H3K27ac and H3K4me1 (not shown). DOI: http://dx.doi.org/10.7554/eLife.11613.027

    Article Snippet: This research article by Mo et al. continues exploration of cell type-specific epigenomic landscapes, building on their own recently published work in Neuron (NeuroResource: Epigenomic Signatures of Neuronal Diversity in the Mammalian Brain; http://dx.doi.org/10.1016/j.neuron.2015.05.018 ).

    Techniques: DNA Methylation Assay

    Epigenomic patterns of WT rods, rd7 rods, and cones near photoreceptor genes. Browser images showing ATAC-seq signals (top), CG DNA methylation (middle), and TF ChIP-seq signals (bottom) at examples of rod-specific ( Nrl, Nr2e3, Pde6b ) and cone-specific ( Pde6c, Cnga3 ) genes. DOI: http://dx.doi.org/10.7554/eLife.11613.019

    Journal: eLife

    Article Title: Epigenomic landscapes of retinal rods and cones

    doi: 10.7554/eLife.11613

    Figure Lengend Snippet: Epigenomic patterns of WT rods, rd7 rods, and cones near photoreceptor genes. Browser images showing ATAC-seq signals (top), CG DNA methylation (middle), and TF ChIP-seq signals (bottom) at examples of rod-specific ( Nrl, Nr2e3, Pde6b ) and cone-specific ( Pde6c, Cnga3 ) genes. DOI: http://dx.doi.org/10.7554/eLife.11613.019

    Article Snippet: This research article by Mo et al. continues exploration of cell type-specific epigenomic landscapes, building on their own recently published work in Neuron (NeuroResource: Epigenomic Signatures of Neuronal Diversity in the Mammalian Brain; http://dx.doi.org/10.1016/j.neuron.2015.05.018 ).

    Techniques: DNA Methylation Assay, Chromatin Immunoprecipitation

    The epigenomic and regulatory landscape of Pouf51/Oct4 in ES cells, endoderm cells, and adult liver

    Journal:

    Article Title: Browsing (Epi)genomes: A guide to data resources and epigenome browsers for stem cell researchers

    doi: 10.1016/j.stem.2013.06.006

    Figure Lengend Snippet: The epigenomic and regulatory landscape of Pouf51/Oct4 in ES cells, endoderm cells, and adult liver

    Article Snippet: One of the earliest organized large-scale efforts to gather epigenomic data was the Human Epigenomic Project (HEP).

    Techniques:

    Epigenomic datasets

    Journal:

    Article Title: Browsing (Epi)genomes: A guide to data resources and epigenome browsers for stem cell researchers

    doi: 10.1016/j.stem.2013.06.006

    Figure Lengend Snippet: Epigenomic datasets

    Article Snippet: One of the earliest organized large-scale efforts to gather epigenomic data was the Human Epigenomic Project (HEP).

    Techniques:

    Evaluation by FANTOM5 enhancers. ( A ) Correlation between states- and tissue-specific enhancers in 808 FANTOM5 cap-analysis gene expression (CAGE) libraries. Dark lines show the mean adjusted r 2 over 127 cell types and the shaded areas show the 95% confidence intervals of mean. The insert shows the paired t -test statistics for the mean difference of adjusted r 2 between IDEAS and ChromHMM in 808 CAGE libraries, where the dashed line marks the Bonferroni adjusted significance level of 0.05. ( B ) Z -scores of adjusted r 2 for predicting enhancers in 808 CAGE libraries (rows) by each Roadmap Epigenomics cell types (columns), calculated by removing row and column means and dividing an overall standard deviation. Library-specific predictions (similar cell types between Roadmap and FANTOM5) are highlighted in boxes, such as blood cell types (the two boxes on the left), brain tissues (the box in the middle) and epithelial cells (the box on the right). Color keys of the Roadmap Epigenomics cell types are the same as those defined in Figure 2A . Color keys for FANTOM5 libraries are manually assigned to match with those used by the Roadmap Epigenomics project. ( C ) State composition and enrichment within significant FANTOM5 enhancer peaks, averaged over 127 cell types and 808 CAGE libraries. The fold enrichment measures the frequency with which the specified segmentation state is found in the FANTOM5 enhancer peaks relative to the genome-wide state distribution. Color keys of states are the same as those given in Figure 2B . ( D ) Distribution of enhancer and TSS-related states in the FANTOM5 significant enhancer–TSS interacting regions.

    Journal: Nucleic Acids Research

    Article Title: Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation

    doi: 10.1093/nar/gkx659

    Figure Lengend Snippet: Evaluation by FANTOM5 enhancers. ( A ) Correlation between states- and tissue-specific enhancers in 808 FANTOM5 cap-analysis gene expression (CAGE) libraries. Dark lines show the mean adjusted r 2 over 127 cell types and the shaded areas show the 95% confidence intervals of mean. The insert shows the paired t -test statistics for the mean difference of adjusted r 2 between IDEAS and ChromHMM in 808 CAGE libraries, where the dashed line marks the Bonferroni adjusted significance level of 0.05. ( B ) Z -scores of adjusted r 2 for predicting enhancers in 808 CAGE libraries (rows) by each Roadmap Epigenomics cell types (columns), calculated by removing row and column means and dividing an overall standard deviation. Library-specific predictions (similar cell types between Roadmap and FANTOM5) are highlighted in boxes, such as blood cell types (the two boxes on the left), brain tissues (the box in the middle) and epithelial cells (the box on the right). Color keys of the Roadmap Epigenomics cell types are the same as those defined in Figure 2A . Color keys for FANTOM5 libraries are manually assigned to match with those used by the Roadmap Epigenomics project. ( C ) State composition and enrichment within significant FANTOM5 enhancer peaks, averaged over 127 cell types and 808 CAGE libraries. The fold enrichment measures the frequency with which the specified segmentation state is found in the FANTOM5 enhancer peaks relative to the genome-wide state distribution. Color keys of states are the same as those given in Figure 2B . ( D ) Distribution of enhancer and TSS-related states in the FANTOM5 significant enhancer–TSS interacting regions.

    Article Snippet: Further investigation of the relative predictive power of all pairs of CAGE libraries and Roadmap Epigenomics cell types revealed cell type-specific predictions (Figure ), including blood-, brain- and epithelial-specific enhancers.

    Techniques: Expressing, Standard Deviation, Genome Wide

    Evaluation by gene expression. ( A ) Within-cell type prediction of gene expression in 56 cell types. Each point shows one cell type, where color keys for cell type lineages are adopted from the Roadmap Epigenomics consortium ( Supplementary Data ). ( B ) State contribution to gene expression as a function of distance to genes. The panel on the top shows the overall predictive power of states on expression. The two barplots in the middle show the individual state contribution to expression. Color keys of states are shown at the bottom. ( C ) Prediction of differential gene expression across 56 cell types. Genes are stratified by their expression standard deviation across cell types. Each panel shows the adjusted r 2 for predicting differential expression by states as a function of distance to gene ( x -axis, in the same scale as in (B) and the two vertical dashed lines in each panel show the transcription start site (TSS) and transcription termination site (TTS) locations, respectively). Red: IDEAS; green: ChromHMM.

    Journal: Nucleic Acids Research

    Article Title: Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation

    doi: 10.1093/nar/gkx659

    Figure Lengend Snippet: Evaluation by gene expression. ( A ) Within-cell type prediction of gene expression in 56 cell types. Each point shows one cell type, where color keys for cell type lineages are adopted from the Roadmap Epigenomics consortium ( Supplementary Data ). ( B ) State contribution to gene expression as a function of distance to genes. The panel on the top shows the overall predictive power of states on expression. The two barplots in the middle show the individual state contribution to expression. Color keys of states are shown at the bottom. ( C ) Prediction of differential gene expression across 56 cell types. Genes are stratified by their expression standard deviation across cell types. Each panel shows the adjusted r 2 for predicting differential expression by states as a function of distance to gene ( x -axis, in the same scale as in (B) and the two vertical dashed lines in each panel show the transcription start site (TSS) and transcription termination site (TTS) locations, respectively). Red: IDEAS; green: ChromHMM.

    Article Snippet: Further investigation of the relative predictive power of all pairs of CAGE libraries and Roadmap Epigenomics cell types revealed cell type-specific predictions (Figure ), including blood-, brain- and epithelial-specific enhancers.

    Techniques: Expressing, Standard Deviation

    Evaluation by chromatin interaction. ( A ) Correlation between CHiCAGO interaction scores in 17 IHEC blood cell types and the inferred chromatin states in each of the 127 cell types. Adjusted r 2 is calculated from a regression model including interaction terms of states between bait and target regions. Hollow bars show the mean adjusted r 2 by ChromHMM states, averaged over 127 cell types and solid bars show the improvement in mean adjusted r 2 by IDEAS states. Color keys of cell types are the same as those given in Figure 2A , where green and dark green indicates Blood and T cells and HSC and B cell types, respectively. ( B ) Detailed comparison in each IHEC blood cell type using the states of Blood and T cells and HSC and B cell types. Bonferroni adjusted significance by paired t -test is indicated under each IHEC cell type. Red: IDEAS; green: ChromHMM. ( C ) Prediction of bait gene expression by the states in bait and target regions as a function of expression specificity for blood cell types relative to other cell types in Roadmap Epigenomics ( Z -scores, x -axis). Dashed lines: mean adjusted r 2 of bait gene expression explained by the states in individual target regions. Solid lines: mean adjusted r 2 of bait gene expression explained by the sum of states in all target regions captured by the same bait. Dotted lines: mean adjusted r 2 of bait gene expression explained by the states in the same bait regions. Shaded area shows the 95% confidence intervals of means.

    Journal: Nucleic Acids Research

    Article Title: Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation

    doi: 10.1093/nar/gkx659

    Figure Lengend Snippet: Evaluation by chromatin interaction. ( A ) Correlation between CHiCAGO interaction scores in 17 IHEC blood cell types and the inferred chromatin states in each of the 127 cell types. Adjusted r 2 is calculated from a regression model including interaction terms of states between bait and target regions. Hollow bars show the mean adjusted r 2 by ChromHMM states, averaged over 127 cell types and solid bars show the improvement in mean adjusted r 2 by IDEAS states. Color keys of cell types are the same as those given in Figure 2A , where green and dark green indicates Blood and T cells and HSC and B cell types, respectively. ( B ) Detailed comparison in each IHEC blood cell type using the states of Blood and T cells and HSC and B cell types. Bonferroni adjusted significance by paired t -test is indicated under each IHEC cell type. Red: IDEAS; green: ChromHMM. ( C ) Prediction of bait gene expression by the states in bait and target regions as a function of expression specificity for blood cell types relative to other cell types in Roadmap Epigenomics ( Z -scores, x -axis). Dashed lines: mean adjusted r 2 of bait gene expression explained by the states in individual target regions. Solid lines: mean adjusted r 2 of bait gene expression explained by the sum of states in all target regions captured by the same bait. Dotted lines: mean adjusted r 2 of bait gene expression explained by the states in the same bait regions. Shaded area shows the 95% confidence intervals of means.

    Article Snippet: Further investigation of the relative predictive power of all pairs of CAGE libraries and Roadmap Epigenomics cell types revealed cell type-specific predictions (Figure ), including blood-, brain- and epithelial-specific enhancers.

    Techniques: Expressing

    Evaluation by FANTOM5 enhancers. ( A ) Correlation between states- and tissue-specific enhancers in 808 FANTOM5 cap-analysis gene expression (CAGE) libraries. Dark lines show the mean adjusted r 2 over 127 cell types and the shaded areas show the 95% confidence intervals of mean. The insert shows the paired t -test statistics for the mean difference of adjusted r 2 between IDEAS and ChromHMM in 808 CAGE libraries, where the dashed line marks the Bonferroni adjusted significance level of 0.05. ( B ) Z -scores of adjusted r 2 for predicting enhancers in 808 CAGE libraries (rows) by each Roadmap Epigenomics cell types (columns), calculated by removing row and column means and dividing an overall standard deviation. Library-specific predictions (similar cell types between Roadmap and FANTOM5) are highlighted in boxes, such as blood cell types (the two boxes on the left), brain tissues (the box in the middle) and epithelial cells (the box on the right). Color keys of the Roadmap Epigenomics cell types are the same as those defined in Figure 2A . Color keys for FANTOM5 libraries are manually assigned to match with those used by the Roadmap Epigenomics project. ( C ) State composition and enrichment within significant FANTOM5 enhancer peaks, averaged over 127 cell types and 808 CAGE libraries. The fold enrichment measures the frequency with which the specified segmentation state is found in the FANTOM5 enhancer peaks relative to the genome-wide state distribution. Color keys of states are the same as those given in Figure 2B . ( D ) Distribution of enhancer and TSS-related states in the FANTOM5 significant enhancer–TSS interacting regions.

    Journal: Nucleic Acids Research

    Article Title: Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation

    doi: 10.1093/nar/gkx659

    Figure Lengend Snippet: Evaluation by FANTOM5 enhancers. ( A ) Correlation between states- and tissue-specific enhancers in 808 FANTOM5 cap-analysis gene expression (CAGE) libraries. Dark lines show the mean adjusted r 2 over 127 cell types and the shaded areas show the 95% confidence intervals of mean. The insert shows the paired t -test statistics for the mean difference of adjusted r 2 between IDEAS and ChromHMM in 808 CAGE libraries, where the dashed line marks the Bonferroni adjusted significance level of 0.05. ( B ) Z -scores of adjusted r 2 for predicting enhancers in 808 CAGE libraries (rows) by each Roadmap Epigenomics cell types (columns), calculated by removing row and column means and dividing an overall standard deviation. Library-specific predictions (similar cell types between Roadmap and FANTOM5) are highlighted in boxes, such as blood cell types (the two boxes on the left), brain tissues (the box in the middle) and epithelial cells (the box on the right). Color keys of the Roadmap Epigenomics cell types are the same as those defined in Figure 2A . Color keys for FANTOM5 libraries are manually assigned to match with those used by the Roadmap Epigenomics project. ( C ) State composition and enrichment within significant FANTOM5 enhancer peaks, averaged over 127 cell types and 808 CAGE libraries. The fold enrichment measures the frequency with which the specified segmentation state is found in the FANTOM5 enhancer peaks relative to the genome-wide state distribution. Color keys of states are the same as those given in Figure 2B . ( D ) Distribution of enhancer and TSS-related states in the FANTOM5 significant enhancer–TSS interacting regions.

    Article Snippet: As shown in Figure , IDEAS uniformly better predicted the CHiCAGO scores and as expected, the Roadmap Epigenomics blood cell types (e.g.

    Techniques: Expressing, Standard Deviation, Genome Wide

    Evaluation by gene expression. ( A ) Within-cell type prediction of gene expression in 56 cell types. Each point shows one cell type, where color keys for cell type lineages are adopted from the Roadmap Epigenomics consortium ( Supplementary Data ). ( B ) State contribution to gene expression as a function of distance to genes. The panel on the top shows the overall predictive power of states on expression. The two barplots in the middle show the individual state contribution to expression. Color keys of states are shown at the bottom. ( C ) Prediction of differential gene expression across 56 cell types. Genes are stratified by their expression standard deviation across cell types. Each panel shows the adjusted r 2 for predicting differential expression by states as a function of distance to gene ( x -axis, in the same scale as in (B) and the two vertical dashed lines in each panel show the transcription start site (TSS) and transcription termination site (TTS) locations, respectively). Red: IDEAS; green: ChromHMM.

    Journal: Nucleic Acids Research

    Article Title: Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation

    doi: 10.1093/nar/gkx659

    Figure Lengend Snippet: Evaluation by gene expression. ( A ) Within-cell type prediction of gene expression in 56 cell types. Each point shows one cell type, where color keys for cell type lineages are adopted from the Roadmap Epigenomics consortium ( Supplementary Data ). ( B ) State contribution to gene expression as a function of distance to genes. The panel on the top shows the overall predictive power of states on expression. The two barplots in the middle show the individual state contribution to expression. Color keys of states are shown at the bottom. ( C ) Prediction of differential gene expression across 56 cell types. Genes are stratified by their expression standard deviation across cell types. Each panel shows the adjusted r 2 for predicting differential expression by states as a function of distance to gene ( x -axis, in the same scale as in (B) and the two vertical dashed lines in each panel show the transcription start site (TSS) and transcription termination site (TTS) locations, respectively). Red: IDEAS; green: ChromHMM.

    Article Snippet: As shown in Figure , IDEAS uniformly better predicted the CHiCAGO scores and as expected, the Roadmap Epigenomics blood cell types (e.g.

    Techniques: Expressing, Standard Deviation

    Evaluation by chromatin interaction. ( A ) Correlation between CHiCAGO interaction scores in 17 IHEC blood cell types and the inferred chromatin states in each of the 127 cell types. Adjusted r 2 is calculated from a regression model including interaction terms of states between bait and target regions. Hollow bars show the mean adjusted r 2 by ChromHMM states, averaged over 127 cell types and solid bars show the improvement in mean adjusted r 2 by IDEAS states. Color keys of cell types are the same as those given in Figure 2A , where green and dark green indicates Blood and T cells and HSC and B cell types, respectively. ( B ) Detailed comparison in each IHEC blood cell type using the states of Blood and T cells and HSC and B cell types. Bonferroni adjusted significance by paired t -test is indicated under each IHEC cell type. Red: IDEAS; green: ChromHMM. ( C ) Prediction of bait gene expression by the states in bait and target regions as a function of expression specificity for blood cell types relative to other cell types in Roadmap Epigenomics ( Z -scores, x -axis). Dashed lines: mean adjusted r 2 of bait gene expression explained by the states in individual target regions. Solid lines: mean adjusted r 2 of bait gene expression explained by the sum of states in all target regions captured by the same bait. Dotted lines: mean adjusted r 2 of bait gene expression explained by the states in the same bait regions. Shaded area shows the 95% confidence intervals of means.

    Journal: Nucleic Acids Research

    Article Title: Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation

    doi: 10.1093/nar/gkx659

    Figure Lengend Snippet: Evaluation by chromatin interaction. ( A ) Correlation between CHiCAGO interaction scores in 17 IHEC blood cell types and the inferred chromatin states in each of the 127 cell types. Adjusted r 2 is calculated from a regression model including interaction terms of states between bait and target regions. Hollow bars show the mean adjusted r 2 by ChromHMM states, averaged over 127 cell types and solid bars show the improvement in mean adjusted r 2 by IDEAS states. Color keys of cell types are the same as those given in Figure 2A , where green and dark green indicates Blood and T cells and HSC and B cell types, respectively. ( B ) Detailed comparison in each IHEC blood cell type using the states of Blood and T cells and HSC and B cell types. Bonferroni adjusted significance by paired t -test is indicated under each IHEC cell type. Red: IDEAS; green: ChromHMM. ( C ) Prediction of bait gene expression by the states in bait and target regions as a function of expression specificity for blood cell types relative to other cell types in Roadmap Epigenomics ( Z -scores, x -axis). Dashed lines: mean adjusted r 2 of bait gene expression explained by the states in individual target regions. Solid lines: mean adjusted r 2 of bait gene expression explained by the sum of states in all target regions captured by the same bait. Dotted lines: mean adjusted r 2 of bait gene expression explained by the states in the same bait regions. Shaded area shows the 95% confidence intervals of means.

    Article Snippet: As shown in Figure , IDEAS uniformly better predicted the CHiCAGO scores and as expected, the Roadmap Epigenomics blood cell types (e.g.

    Techniques: Expressing

    EPIGENETICS AND TRANSCRIPTIONAL (dys) REGULATION IN DISEASED HUMAN BRAIN. A ‘SUBJECT-SPECIFIC' MATTER?

    Journal:

    Article Title: Epigenetics in the Human Brain

    doi: 10.1038/npp.2012.78

    Figure Lengend Snippet: EPIGENETICS AND TRANSCRIPTIONAL (dys) REGULATION IN DISEASED HUMAN BRAIN. A ‘SUBJECT-SPECIFIC' MATTER?

    Article Snippet: Several NGS sequencing platforms are in use today for epigenomic research, including Illumina Genome Analyzer, ABI SOLID, Roche 454, Helicos.

    Techniques:

    EPIGENETICS AND TRANSCRIPTIONAL (dys) REGULATION IN DISEASED HUMAN BRAIN. A ‘SUBJECT-SPECIFIC' MATTER?

    Journal:

    Article Title: Epigenetics in the Human Brain

    doi: 10.1038/npp.2012.78

    Figure Lengend Snippet: EPIGENETICS AND TRANSCRIPTIONAL (dys) REGULATION IN DISEASED HUMAN BRAIN. A ‘SUBJECT-SPECIFIC' MATTER?

    Article Snippet: Several NGS sequencing platforms are in use today for epigenomic research, including Illumina Genome Analyzer, ABI SOLID, Roche 454, Helicos.

    Techniques:

    Flow chart aimed at defining subject-specific genetic and epigenetic risk architectures of psychiatric disease. See text for additional details.

    Journal:

    Article Title: Epigenetics in the Human Brain

    doi: 10.1038/npp.2012.78

    Figure Lengend Snippet: Flow chart aimed at defining subject-specific genetic and epigenetic risk architectures of psychiatric disease. See text for additional details.

    Article Snippet: Several NGS sequencing platforms are in use today for epigenomic research, including Illumina Genome Analyzer, ABI SOLID, Roche 454, Helicos.

    Techniques: Flow Cytometry

    Evaluation by FANTOM5 enhancers. ( A ) Correlation between states- and tissue-specific enhancers in 808 FANTOM5 cap-analysis gene expression (CAGE) libraries. Dark lines show the mean adjusted r 2 over 127 cell types and the shaded areas show the 95% confidence intervals of mean. The insert shows the paired t -test statistics for the mean difference of adjusted r 2 between IDEAS and ChromHMM in 808 CAGE libraries, where the dashed line marks the Bonferroni adjusted significance level of 0.05. ( B ) Z -scores of adjusted r 2 for predicting enhancers in 808 CAGE libraries (rows) by each Roadmap Epigenomics cell types (columns), calculated by removing row and column means and dividing an overall standard deviation. Library-specific predictions (similar cell types between Roadmap and FANTOM5) are highlighted in boxes, such as blood cell types (the two boxes on the left), brain tissues (the box in the middle) and epithelial cells (the box on the right). Color keys of the Roadmap Epigenomics cell types are the same as those defined in Figure 2A . Color keys for FANTOM5 libraries are manually assigned to match with those used by the Roadmap Epigenomics project. ( C ) State composition and enrichment within significant FANTOM5 enhancer peaks, averaged over 127 cell types and 808 CAGE libraries. The fold enrichment measures the frequency with which the specified segmentation state is found in the FANTOM5 enhancer peaks relative to the genome-wide state distribution. Color keys of states are the same as those given in Figure 2B . ( D ) Distribution of enhancer and TSS-related states in the FANTOM5 significant enhancer–TSS interacting regions.

    Journal: Nucleic Acids Research

    Article Title: Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation

    doi: 10.1093/nar/gkx659

    Figure Lengend Snippet: Evaluation by FANTOM5 enhancers. ( A ) Correlation between states- and tissue-specific enhancers in 808 FANTOM5 cap-analysis gene expression (CAGE) libraries. Dark lines show the mean adjusted r 2 over 127 cell types and the shaded areas show the 95% confidence intervals of mean. The insert shows the paired t -test statistics for the mean difference of adjusted r 2 between IDEAS and ChromHMM in 808 CAGE libraries, where the dashed line marks the Bonferroni adjusted significance level of 0.05. ( B ) Z -scores of adjusted r 2 for predicting enhancers in 808 CAGE libraries (rows) by each Roadmap Epigenomics cell types (columns), calculated by removing row and column means and dividing an overall standard deviation. Library-specific predictions (similar cell types between Roadmap and FANTOM5) are highlighted in boxes, such as blood cell types (the two boxes on the left), brain tissues (the box in the middle) and epithelial cells (the box on the right). Color keys of the Roadmap Epigenomics cell types are the same as those defined in Figure 2A . Color keys for FANTOM5 libraries are manually assigned to match with those used by the Roadmap Epigenomics project. ( C ) State composition and enrichment within significant FANTOM5 enhancer peaks, averaged over 127 cell types and 808 CAGE libraries. The fold enrichment measures the frequency with which the specified segmentation state is found in the FANTOM5 enhancer peaks relative to the genome-wide state distribution. Color keys of states are the same as those given in Figure 2B . ( D ) Distribution of enhancer and TSS-related states in the FANTOM5 significant enhancer–TSS interacting regions.

    Article Snippet: We downloaded the 15-state model by ChromHMM from the Roadmap Epigenomics project website ( http://egg2.wustl.edu/roadmap/web_portal/chr_state_learning.html#core_15state ).

    Techniques: Expressing, Standard Deviation, Genome Wide

    Evaluation by gene expression. ( A ) Within-cell type prediction of gene expression in 56 cell types. Each point shows one cell type, where color keys for cell type lineages are adopted from the Roadmap Epigenomics consortium ( Supplementary Data ). ( B ) State contribution to gene expression as a function of distance to genes. The panel on the top shows the overall predictive power of states on expression. The two barplots in the middle show the individual state contribution to expression. Color keys of states are shown at the bottom. ( C ) Prediction of differential gene expression across 56 cell types. Genes are stratified by their expression standard deviation across cell types. Each panel shows the adjusted r 2 for predicting differential expression by states as a function of distance to gene ( x -axis, in the same scale as in (B) and the two vertical dashed lines in each panel show the transcription start site (TSS) and transcription termination site (TTS) locations, respectively). Red: IDEAS; green: ChromHMM.

    Journal: Nucleic Acids Research

    Article Title: Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation

    doi: 10.1093/nar/gkx659

    Figure Lengend Snippet: Evaluation by gene expression. ( A ) Within-cell type prediction of gene expression in 56 cell types. Each point shows one cell type, where color keys for cell type lineages are adopted from the Roadmap Epigenomics consortium ( Supplementary Data ). ( B ) State contribution to gene expression as a function of distance to genes. The panel on the top shows the overall predictive power of states on expression. The two barplots in the middle show the individual state contribution to expression. Color keys of states are shown at the bottom. ( C ) Prediction of differential gene expression across 56 cell types. Genes are stratified by their expression standard deviation across cell types. Each panel shows the adjusted r 2 for predicting differential expression by states as a function of distance to gene ( x -axis, in the same scale as in (B) and the two vertical dashed lines in each panel show the transcription start site (TSS) and transcription termination site (TTS) locations, respectively). Red: IDEAS; green: ChromHMM.

    Article Snippet: We downloaded the 15-state model by ChromHMM from the Roadmap Epigenomics project website ( http://egg2.wustl.edu/roadmap/web_portal/chr_state_learning.html#core_15state ).

    Techniques: Expressing, Standard Deviation

    Evaluation by chromatin interaction. ( A ) Correlation between CHiCAGO interaction scores in 17 IHEC blood cell types and the inferred chromatin states in each of the 127 cell types. Adjusted r 2 is calculated from a regression model including interaction terms of states between bait and target regions. Hollow bars show the mean adjusted r 2 by ChromHMM states, averaged over 127 cell types and solid bars show the improvement in mean adjusted r 2 by IDEAS states. Color keys of cell types are the same as those given in Figure 2A , where green and dark green indicates Blood and T cells and HSC and B cell types, respectively. ( B ) Detailed comparison in each IHEC blood cell type using the states of Blood and T cells and HSC and B cell types. Bonferroni adjusted significance by paired t -test is indicated under each IHEC cell type. Red: IDEAS; green: ChromHMM. ( C ) Prediction of bait gene expression by the states in bait and target regions as a function of expression specificity for blood cell types relative to other cell types in Roadmap Epigenomics ( Z -scores, x -axis). Dashed lines: mean adjusted r 2 of bait gene expression explained by the states in individual target regions. Solid lines: mean adjusted r 2 of bait gene expression explained by the sum of states in all target regions captured by the same bait. Dotted lines: mean adjusted r 2 of bait gene expression explained by the states in the same bait regions. Shaded area shows the 95% confidence intervals of means.

    Journal: Nucleic Acids Research

    Article Title: Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation

    doi: 10.1093/nar/gkx659

    Figure Lengend Snippet: Evaluation by chromatin interaction. ( A ) Correlation between CHiCAGO interaction scores in 17 IHEC blood cell types and the inferred chromatin states in each of the 127 cell types. Adjusted r 2 is calculated from a regression model including interaction terms of states between bait and target regions. Hollow bars show the mean adjusted r 2 by ChromHMM states, averaged over 127 cell types and solid bars show the improvement in mean adjusted r 2 by IDEAS states. Color keys of cell types are the same as those given in Figure 2A , where green and dark green indicates Blood and T cells and HSC and B cell types, respectively. ( B ) Detailed comparison in each IHEC blood cell type using the states of Blood and T cells and HSC and B cell types. Bonferroni adjusted significance by paired t -test is indicated under each IHEC cell type. Red: IDEAS; green: ChromHMM. ( C ) Prediction of bait gene expression by the states in bait and target regions as a function of expression specificity for blood cell types relative to other cell types in Roadmap Epigenomics ( Z -scores, x -axis). Dashed lines: mean adjusted r 2 of bait gene expression explained by the states in individual target regions. Solid lines: mean adjusted r 2 of bait gene expression explained by the sum of states in all target regions captured by the same bait. Dotted lines: mean adjusted r 2 of bait gene expression explained by the states in the same bait regions. Shaded area shows the 95% confidence intervals of means.

    Article Snippet: We downloaded the 15-state model by ChromHMM from the Roadmap Epigenomics project website ( http://egg2.wustl.edu/roadmap/web_portal/chr_state_learning.html#core_15state ).

    Techniques: Expressing