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News Article | February 3, 2016
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No statistical methods were used to predetermine sample size. We sought to identify candidate Group 3 and Group 4 super-enhancer constituents for validation by reporter assays. We identified candidate Group 3 and Group 4 super-enhancer constituents by first locating nucleosome free “valleys” in the H3K27ac data using an algorithm adapted from ref. 35. Valleys that showed strong evidence of TF ChIP-seq binding for respective Group 3 (HLX and LHX2) and Group 4 (LHX2 and LMX1A) TFs were selected and manually curated for validation in reporter assays. Based on restrictions for DNA synthesis and cloning, candidate reporter regions of roughly ±1 kb flanking the valley centre were used (Fig. 4 and Extended Data Fig. 5). All experiments involving zebrafish (Danio rerio, AB strain) were approved by the Vanderbilt Institutional Animal Care and Use Committee. For in vivo zebrafish reporter assays, a minimum, ~150–200 embryos (male and female) were injected per reporter construct and assays were repeated 2–3 times per construct to confirm reproducibility. No randomization of enhancer assays was performed. The scientist who performed the injections had no prior knowledge related to the enhancer constructs and was therefore blinded to the experiment. Microinjection was done as described previously36. In brief, a mixture of individual enhancer-containing vector DNA (25 μg ml−1) and transposase RNA (25 μg ml−1) was injected into zebrafish zygotes (1 nl per zygote). The injected embryos were cultured in 0.3× Danieau’s solution at 28.5 °C. After 24 hours, the embryos were examined for eGFP expression under a fluorescent dissecting microscope (Zeiss Discovery V12) to determine the stereotypic expression pattern conferred by the enhancer. The total number of embryos injected with the construct and the number of embryos with the stereotypical eGFP pattern were determined to calculate the frequency of the pattern. Embryos were dechorionated and imaged using a Zeiss AxioCam HRc digital camera. Spatial protein expression of medulloblastoma -specific transcription factors in e13.5 cerebella was determined by IHC. PFA-fixed frozen tissues were sectioned (12 μm thickness) and processed without antigen retrieval steps. The antibodies used here are Tbr2 (1:100, Abcam, ab23345), Lmx1a (1:100, Novus Biologicals, NBP1-81303), Atoh1 (1:500, Abcam, ab105497) and appropriate secondary antibodies conjugated with Alexa fluorophores (1:400, Invitrogen). The images were captured by an epifluorescence microscopy. Endogenous expression of candidate TFs was determined by querying the Allen Brain Atlas Data Portal (http://developingmouse.brain-map.org) at various developmental time points. The molecular subgroup of 49 medulloblastoma samples on tissue microarrays were determined as previously described37. Immunohistochemistry was performed using clone ALK01 (#790-2918, Ventana) with appropriate secondary reagents. Individual tumours were scored positive in the presence of cytoplasmic immunoreactivity for ALK1, whereas the tumour was considered negative in the absence of immunoreactivity. All mouse (Mus musculus, B6C3HFe background) experiments were done in accordance with the guidelines laid down by the Institutional Animal Care and Use Committee (IACUC), of Seattle Children’s Research Institute. No randomization or experimental blinding related to mouse experiments was performed. Lmx1a+/− mice were crossed and the day of plug was taken as e0.5. WT and Lmx1a−/− embryos (male and female) were dissected out between e12.5 and e17.5 and subsequently fixed in 4% paraformaldehyde (PFA) for 2–6 hours. The fixed embryos were washed in PBS and incubated in 30% sucrose overnight. The following day, embryos were frozen in optimum cutting temperature (OCT) compound. Mid-sagittal cryo-sections of the cerebellum at 11 μm were taken. Haematoxylin and eosin staining and immunohistochemistry were performed as described previously38. Briefly, cryosections were incubated at room temperature for 1 hour after which they were subjected to heat-mediated antigen retrieval. All sections were blocked using 5% serum containing 0.35% Triton X, and then incubated with the primary antibody (Eomes (Tbr2); #14-4875, ebioscience, mouse, 1:200), overnight. The following day fluorescent-dye-labelled secondary antibodies (Alexa Fluor 488, 1:1000, Molecular Probes) were used. Sections were counter stained using DAPI (4′,6-diamidino-2-phenylindole) (Vector Laboratories). All images were captured at room temperature. Haematoxylin and eosin-stained sections were imaged a using Hamamatsu Nanozoomer whole slide scanner. All confocal images were captured using Zeiss LSM Meta and Zen 2009 software. An Institutional Review Board ethical vote (Ethics Committee of the Medical Faculty of Heidelberg) was obtained according to ICGC guidelines (http://www.icgc.org), along with informed consent for all participants. No patient underwent chemotherapy or radiotherapy before surgical removal of the primary tumour. Tumour tissues were subjected to neuropathological review for confirmation of histology and for tumour cell content >80%. The ChIP-seq cohort was established based on tissue availability and availability of orthogonal data types (for example, WGS, RNA-seq) and patient metadata (for example, molecular subgroup). Subgroup assignments were made using the Illumina 450K DNA methylation array as described39. Medulloblastoma cell lines were cultured at 37 °C with 5% CO . D425_Med (D425; a gift from D. D. Bigner) and MED8A cells (from the authors’ own stocks; T. Pietsch) were cultured in DMEM with 10% FCS (Life Technologies). HD-MB03 cells20 were grown in RPMI-1640 with 10% FCS (Life Technologies). All cells were regularly authenticated and tested for mycoplasma (Multiplexion, Heidelberg, Germany). H3K27ac, BRD4, H3K27me3, H3K4me1, LMX1A, LHX2, and HLX ChIP was performed at ActiveMotif (Carlsbad, CA) using antibodies against H3K27ac (AM#39133, Active Motif), BRD4 (#A301-985A, Bethyl Laboratories), H3K27me3 (#07-449, Millipore), H3K4me1 (AM#39298, ActiveMotif), LMX1A (#AB10533, Millipore), LHX2 (#sc-19344, Santa Cruz), and HLX (#HPA005968, Sigma). Fresh-frozen medulloblastoma tissues (or cell lines) were submersed in PBS + 1% formaldehyde, cut into small pieces and incubated at room temperature for 15 min. Fixation was stopped by the addition of 0.125 M glycine (final concentration). The tissue pieces were then treated with a TissueTearer and finally spun down and washed 2× in PBS. Chromatin was isolated by the addition of lysis buffer, followed by disruption with a Dounce homogenizer. Lysates were sonicated and the DNA sheared to an average length of ~300–500 bp. Genomic DNA (input) was prepared by treating aliquots of chromatin with RNase, proteinase K and heat for de-crosslinking, followed by ethanol precipitation. Pellets were resuspended and the resulting DNA was quantified on a NanoDrop spectrophotometer. Extrapolation to the original chromatin volume allowed quantitation of the total chromatin yield. An aliquot of chromatin (30 μg) was precleared with protein A (G – for goat pc or monoclonal antibodies) agarose beads (Invitrogen). Genomic DNA regions of interest were isolated using 4 μg of antibody. ChIP complexes were washed, eluted from the beads with SDS buffer, and subjected to RNase and proteinase K treatment. Crosslinks were reversed by incubation overnight at 65 °C, and ChIP DNA was purified by phenol-chloroform extraction and ethanol precipitation. Quantitative PCR (qPCR) reactions were carried out in triplicate on specific genomic regions using SYBR Green Supermix (Bio-Rad). The resulting signals were normalized for primer efficiency by carrying out qPCR for each primer pair using Input DNA. Illumina sequencing libraries were prepared from the ChIP and Input DNAs by the standard consecutive enzymatic steps of end-polishing, dA-addition, and adaptor ligation. After a final PCR amplification step, the resulting DNA libraries were quantified and sequenced on the Illumina HiSeq 2000 platform using 2 × 101 cycles according to the manufacturer’s instructions. Alignment, and downstream processing of ChIP-seq data was performed as described6. RNA was extracted from fresh frozen tissue samples using the AllPrep DNA/RNA/Protein Mini kit (Qiagen) including DNase I treatment on column. All samples were subjected to quality control on a Bioanalyzer instrument. RNA sequencing libraries were prepared from 10 μg of total RNA. Strand-specific RNA sequencing was performed following a protocol described previously40, 41. Sequencing was carried out with 2×51 cycles on a HiSeq 2000 instrument (Illumina). All reads were aligned to the human reference genome (1000 genomes version of human reference genome hg19/GRCh37) using BWA (v 0.5.9-r16). Aligned reads were converted to the SAM/BAM format using SAMtools. Gene annotation was based on Ensembl v70 (Homo sapiens). 4C samples were prepared from Group 3 medulloblastoma cell line HD-MB03 using the method as described19, 42. DpnII was used as the primary restriction enzyme and Csp6I as the secondary restriction enzyme in template generation. Sample libraries for SMAD9 and TGFBR1 were amplified using the primers, SMAD9_F: TTATCCAGGCAAGGAAGATC, SMAD9_R: ATTACCTCATCTGCAAAACC, TGFBR1_F: CATTCTTTCTCCCCATGATC, and TGFBR1_R: ACACAATCTTGGGTGTTTTT, respectively. Amplified libraries were multiplexed, spiked with 40% PhiX viral genome and sequenced on Hiseq 2000. Reads were mapped to human genome (hg19) using Bowtie (v 1.0.0)43. Forward and reverse RNA transcription based on directional RNA sequencing data was quantified in 3 kb windows upstream and downstream of enhancer peaks that were based on H3K27ac ChIP-seq data, resulting in four RNA expression values for each enhancer region: (L_fwd) forward transcription left of enhancer peak, (R_fwd) forward transcription right of enhancer peak, (L_rev) reverse transcription left of enhancer peak, and (R_rev) reverse transcription right of enhancer peak. We calculated the “directionality index” D, a measure of the directionality of transcription inside an enhancer region, with D ranging from 0 to 1, by D = |R_fwd – L_rev|/(R_fwd + L_rev) as described before14, with low D values representing bidirectional eRNA transcription. For correlation of eRNA transcription values with corresponding gene expression values, we calculated eRNA transcription values in 3 kb windows upstream and downstream of enhancer peaks by eRNA_transcription = (R_fwd + L_rev)/2. All coordinates in this study were based on human reference genome assembly hg19, GRCh37 (http://www.ncbi.nlm.nih.gov/assembly/2758/). Gene annotations were based on gencode annotation release 19 (http://www.gencodegenes.org/releases/19.html). We calculated the normalized read density of a ChIP-seq data set in any genomic region using the Bamliquidator (version 1.0) read density calculator (https://github.com/BradnerLab/pipeline/wiki/bamliquidator). Briefly, ChIP-seq reads aligning to the region were extended by 200 bp and the density of reads per base pair (bp) was calculated. The density of reads in each region was normalized to the total number of million mapped reads producing read density in units of reads per million mapped reads per bp (rpm per bp). To compactly display medulloblastoma H3K27ac ChIP-seq signal at individual genomic loci and across subgroups, we developed a simple meta representation (Fig. 1d and others). For all samples within a group, ChIP-seq signal is smoothed using a simple spline function and plotted as a translucent shape in units of rpm per bp. Darker regions indicate regions with signal in more samples. An opaque line is plotted and gives the average signal across all samples in a group. H3K27ac peak finding was performed using MACS12 with a P-value threshold of 1 × 10−9, and with other settings as default parameters. Peak finding for each medulloblastoma was performed separately and as a control background for each H3K27ac ChIP-seq sample, its matched genomic DNA was used. The SPOT statistic44, a measure of read fraction found in enriched regions developed by the ENCODE consortium, was used to quantify H3K27ac enrichment quality. Primary medulloblastoma data sets had a median SPOT score of 0.62 which was equivalent to cell line data and on par with primary human data generated in the Epigenome ROADMAP. Afterwards, H3K27ac peaks were merged into a single coordinate file. Peaks which can not be identified in at least two primary medulloblastomas and contained completely within the region surrounding ±1 kb TSS were excluded from any further analysis. This resulted in final combined and filtered peak set (n = 78516). H3K27ac enrichments were calculated on the final peak set using the following formula: log2(((Cnt /LSize *min(LSize , LSize ))+pscnt)/ ((Cnt /LSize *min(LSize , LSize ))+pscnt)), where Cnt denotes the total number of reads mapping to the enhancer coordinate in ChIP sample, LSize is the total library size for the ChIP sample, Cnt is the total number of reads mapping to the enhancer coordinate in the control genomic DNA, LSize is the total library size for the control sample, and pscnt is a constant number (pscnt = 8), which was used to stabilize enrichments based on low read counts. (Peaks showing statistically significant differential H3K27ac enrichment across medulloblastoma subgroups were determined using ANOVA and the ones with FDR < 0.01 were preserved after multiple testing correction. From the resulting peak-set, peaks having 1.5 (log ) fold change difference across any medulloblastoma subgroup comparison were called as “subgroup specific” enhancers (n = 20,406). Peaks that do not fulfil these criteria were referred as “common” enhancers (n = 58,110). Subgroup-specific enhancers were further clustered using k means, with k = 6 into 6 groups as “SHH”, “WNT”, “Group4”, “WNT-SHH”, “Group3-Group4”, and “Group3” (Fig. 2). Genome was classified into regions as exon, intron, intergenic and promoter (region surrounding ± 1 kb transcriptional start sites) by following the hierarchy promoter > exon > intron > intergenic. Then, medulloblastoma enhancers were intersected with these defined elements and fraction covered by each element was calculated. To better understand whether our enhancer profiling adequately captured the primary medulloblastoma enhancer landscape, we performed a saturation analysis. We measured the total number of discreet regions and the fraction of novel regions gained by increasing sample number. This was performed across 1,000 permutations of the 28 medulloblastoma samples to establish 95% confidence intervals (Extended Data Fig. 1d). Enrichment values for H3K27ac at enhancers were calculated as the ratio between library size normalized read counts for H3K27ac ChIP and its sample matched genomic DNA control. The formula used for the enrichment calculation is as follows: log2(((Cnt /LSize *min(LSize , LSize ))+pscnt)/ ((Cnt /LSize *min(LSize , LSize ))+pscnt)), where Cnt denotes the total number of reads mapping to the enhancer coordinate in ChIP sample, LSize is the total library size for the ChIP sample, Cnt is the total number of reads mapping to the enhancer coordinate in the control genomic DNA, LSize is the total library size for the control sample, and pscnt is a constant number (pscnt = 8), which was used to stabilize enrichments based on low read counts. To compare BRD4 enrichment with H3K27ac enrichment at the enhancers, BRD4 enrichments were calculated in the same way as H3K27ac enrichments. DNA methylation values at enhancers were determined by calculating the average DNA methylation of all medulloblastoma samples where DNA methylation data are available6. We generated ChIP-seq data for H3K4me1 and H3K27me3 for only three Group 3 medulloblastomas (MB-1M21,MB-4M23, and MB-4M26).Therefore, comparison of H3K27ac occupancy with H3K4me1, H3K27me3 and BRD4 (Extended Data Fig. 1f) was done using the data from only these three Group 3 samples. To analyse the occupancy of the marks at H3K27me3 enriched regions, we called H3K27me3 peaks using MACS. ChIP-seq reads covering each base pair either in the region ± 5 kb around Group 3-specific enhancer midpoints (Extended Data Fig. 1f top panel) or in the region ± 5 kb around H3K27me3 peak midpoints (Extended Data Fig. 1f bottom panel) were quantified. Read coverage was averaged in 100-bp windows along the regions and the values were scaled to arrange between 0–1. Resulting values were represented as heat maps. We repeated H3K27ac peak finding (running MACS with a P-value threshold of 1 × 10−9, and with other settings as default parameters) for the two medulloblastomas (MB12 and MB200) using their input chromatin as the backgrounds instead of using their matched whole genome sequencing. Resulting set of peaks identified using whole chromatin extract were compared to the ones identified using whole genome sequencing in scatter plots in Extended Data Fig. 1c. ENCODE8 H3K27ac peaks were downloaded from http://ftp.ebi.ac.uk/pub/databases/ensembl/encode/integration_data_jan2011/byDataType/peaks/jan2011/histone_macs/optimal/hub/ and all peaks were merged into a single coordinate file. Regarding ROADMAP data45, 46, all available H3K27ac alignment files were downloaded and peak finding on individual samples was performed using MACS12. All ROADMAP H3K27ac peaks were as well merged into a single coordinate file. Resulting peaks from both ENCODE and ROADMAP were intersected with medulloblastoma H3K27ac peaks (with a minimum 50% overlap criteria; Fig. 1e, f). To determine the overlap of enhancer loci with CNVs, medulloblastoma enhancer loci were intersected with focal amplifications and deletions obtained from4. To determine the statistical significance of the overlap, we performed 10,000 random simulations whereby CNV locations were randomly permuted across the genome without overlap using the bedtools shuffle utility (http://bedtools.readthedocs.org) and excluding regions found in the ENCODE8 blacklist (https://sites.google.com/site/anshulkundaje/projects/blacklists). This distribution of random overlaps was used to calculate an empirical P-value of the observed overlap significance (Extended Data Fig. 1g). Expression values in RPKM were calculated using “qCount” function of Bioconductor package “quasR” (http://www.bioconductor.org/packages/release/bioc/html/QuasR.html). Genes showing differential gene expression across four medulloblastoma subgroups were determined using ANOVA (FDR less than 1%). Then, subgroup specific assignment of gene expression was done by performing a post-hoc test (using “glht” function of R package “multicomp”56. Target gene identification of enhancers was performed as described47. For each enhancer, topology-associated domain (TAD)18 which it belongs to was identified. Then, genes with transcriptional start sites falling into the same TAD were determined. We filtered nearby genes for protein coding status, as eRNAs and other enhancer associated ncRNAs are likely to emanate from enhancers and obfuscate distal target genes. Correlation tests (Spearman’s rank correlation coefficient) for H3K27ac enrichment of the enhancer and expression level of genes which are in the same TAD were performed. After repeating this procedure for each enhancer, all P-values obtained via correlation tests were combined and corrected for multiple testing globally using Bioconductor package “qvalue” (http://www.bioconductor.org/packages/release/bioc/html/qvalue.html). Correlations with a FDR less than 5% were preserved. For each enhancer, gene whose expression best correlates with the H3K27ac enrichment of the enhancer was selected as the potential target gene. For the cases where the difference between spearman correlation coefficients for the best and second best correlating genes were less than 0.1, the second best correlating gene was also selected as another potential target gene. Identification of enhancer target genes was performed for subgroup specific and common enhancers separately. After getting final gene lists for targets of subgroup specific and common enhancers, genes which are identified as targets both for subgroup specific and common enhancers were removed from common enhancer target gene list. Genes regulated by differential enhancers were classified into categories depending on the number of differential enhancers they are targeted by (Fig. 2d). As mentioned in “identification of enhancer targets” part, to assign the enhancers to their targets with highest probability, in the final list of enhancer target genes, number of genes per enhancer was restricted to 2 genes having the highest correlation coefficient. However, to evaluate the number of genes targeted by each enhancer overall, enhancers were classified into categories depending on the number of genes they target by including all the genes targeted by enhancers (satisfying FDR < 0.05 criteria) (Fig. 2e). Medulloblastoma signature genes were defined to be the genes regulated differentially in 4 medulloblastoma subgroups16. To be conservative on the signature genes, for each medulloblastoma subgroup, top 100 genes differentially regulated in the respective subgroups were included in the analysis. Resulting gene list were compared to the genes regulated by medulloblastoma subgroup specific enhancers and super-enhancers. Comparison to cancer genes was performed using the gene list provide in cancer gene census (http://cancer.sanger.ac.uk/cancergenome/projects/census). Target genes were overlapped with consensus TFs provided48. Inference whether the target genes we identified was druggable was done by intersecting target genes with the genes provided in the drug gene interaction database (http://dgidb.genome.wustl.edu/) by using “Expert curated” option in the source trust level category of the interactions. All information showing the overlap of target genes with gene lists from literature can be found in Supplementary Table 3. Functional characterization of enhancer/gene assignments was conducted using the ClueGO plugin for cytoscape49. Subgroup-specific enhancer gene targets or SE-regulated TFs were queried against a compendium of gene sets from GO (Biological Process), KEGG, and REACTOME to identify processes/pathways that were significantly enriched in tested gene lists from our data set. Analyses were performed using the GO Term Fusion option in ClueGO and only processes/pathways with a P-value < 0.05 (right-sided hypergeometric test) following P-value correction (Bonferroni step down) were visualized. Manual trimming of ClueGO output was performed to remove processes/pathways affiliated with only a single gene set. To identify subgroup specific enhancers and their associated functional pathways, we performed a differential enhancer analysis50 on Group 3 and Group 4 enhancers. We first took the union of the top 1,000 enhancer in Group 3 and Group 4 as defined by total H3K27ac signal (area under the curve). We next ranked all enhancer regions by the log fold change in H3K27ac (Extended Data Fig. 3b). Differential enhancer target genes as previously defined were depicted under associated enhancers. Visual inspection revealed a number of TGF-β pathway components associated with Group 3 specific enhancers. We visualized this by identifying all enhancer regulated TGF-β pathway components (obtained from KEGG, REACTOME, and GO Biological Process databases) and depicting their specific regulation by Group 3, Group 4, or Group 3-4 differential enhancers (Extended Data Fig. 3c). We identified a focal amplification of the TGF-β pathway receptor gene ACVR2A in the Group 3 medulloblastoma sample MB-4M23. Whole genome sequencing log read depth ratio is plotted in Extended Data Fig. 3d. We hypothesized that in MB-4M23, amplification of ACVR2A leads to increased TGF-β pathway activity, including the increased H3K27ac at enhancers regulating TGF-β pathway components. We identified all Group 3 enhancers regulating TGF-β pathway components and compared the median enhancer normalized H3K27ac signal in MB-4M23 vs all other Group 3 medulloblastomas. Extended Data Fig. 3e shows all enhancers ranked by their log fold change in H3K27ac for MB-4M23 vs other Group 3 samples. The standard error of the mean was calculated for the fold change and is displayed as error bars in Extended Data Fig. 3e. H3K27ac data for the samples within the same subgroup was combined. Nucleosome free regions per subgroup were identified by feeding these combine data sets to HOMER software (http://homer.salk.edu/homer/ngs/index.html) using “findPeaks” function with the option “-nfr”. TF binding sites obtained from TRANSFAC51 and detected at NFRs using FIMO52 were overlapped with NFRs located within each class of differentially regulated enhancers. For each TF, contingency tables showing the number of NFRs overlapping and non-overlapping with the respective TF were constructed. Significance of enrichment of TFs in NFRs of differentially regulated enhancers was determined using Chi-squared test. Resulting P-values were corrected for multiple testing (FDR < 0.01). TF enrichments were calculated as the ratio between observed counts over expected counts. To represent TF enrichments as a heat map (Extended Data Fig. 6b), for each class of enhancers, 4–5 TFs showing the highest enrichments were selected. For each of differentially regulated enhancers in the classes of WNT, SHH, Group 3 and Group 4, NFRs belonging to each subgroup were overlapped with the respective subgroup-specific enhancers targeting at least one gene. Overlapping NFRs were intersected with TF binding sites having top 20th percentile enrichment scores in the respective subgroup-specific enhancers and differentially expressed in the same subgroup. For each TF, NFRs having the top 10th percentile number of binding sites were identified as sites occupied by the respective TF. Then, resulting NFRs were linked back to enhancers they are located, which enabled the linking of TFs having binding sites in the respective enhancers with the target genes of the enhancers. TF regulatory networks for each subgroup (Extended Data Fig. 7), where TFs represented as “sources” and enhancer target genes represented as “targets” were constructed using visualization platform Gephi (http://gephi.github.io/). To connect LMX1A, LHX2 and EOMES with their targets (Extended Data Fig. 9b), same strategy was applied by restricting the initial set of TFs to only those three. Aligned 4C data was further processed, filtered and visualized using Bioconductor package “Basic4Cseq”53. H3K27ac super-enhancers (SEs) and typical enhancers (TEs) in individual medulloblastoma samples were mapped using the ROSE2 software package described13, 23 and available at https://github.com/BradnerLab/pipeline. A 12.5 kb stitching window was used to connect proximal clusters of H3K27ac peaks into contiguous enhancer regions. These mappings identified on average ~600 SEs per sample. Relationships between SE landscapes between samples were determined as in ref. 11. First, we defined the union of all regions considered to be an SE in any individual primary sample and in three Group 3 cell lines. Next H3K27ac signal was calculated at each region and median normalized for each sample. Samples were hierarchically clustered based on similarity of patterns of median normalized H3K27ac enhancer signal as determined using pairwise Pearson correlations. In order to map and quantify enhancer regions for each medulloblastoma subgroup, we first mapped all enhancers in each individual sample within the group. Across a group, we used the union of all enhancer regions within group samples as the landscape of enhancers. Within this landscape, enhancers were ranked by average H3K27ac signal (area under curve) and classified as SEs or TEs as previously described. This produced SE and TE meta enhancer landscapes for WNT, SHH, Group 3, and Group 4 medulloblastoma with between 558 and 1,110 SEs called per group (Fig. 3a). Locations for all SEs and TEs in each subgroup are provided in Supplementary Table 4. To compare the dynamic range of SEs and TEs defined in each medulloblastoma subgroup, we quantified H3K27ac signal variance across samples. For SE and TE enhancer constituents (individual peaks of H3K27ac enrichment within broader enhancer domains) defined in each group, H3K27ac signal variance across samples as a fraction of the mean sample was calculated. The average H3K27ac signal variance across all SEs or TEs within a group is plotted in Extended Data Fig. 4f. We sought to examine trends in H3K27ac signal across medulloblastoma samples at regions defined as SEs or TEs in each group. First we mapped H3K27ac across all samples to enhancer constituents defined in each group. For each medulloblastoma sample, the average median normalized H3K27ac signal was plotted for SE and TE constituents respectively. For SEs and TEs defined in each group, the average sample H3K27ac signal is plotted with the mean and standard deviation shown as lines. This visualization enables a rapid assessment of H3K27ac variance within a group and of trends in H3K27ac signal for SEs and TEs defined in each group (Extended Data Fig. 4h). For instance, enhancer constituents in Group 3 SEs tend to have high signal in Group 4. SEs have been shown to have higher H3K27ac and BRD4 signal density at constituents when compared to typical enhancers13, 23. To determine if these trends were observed at medulloblastoma enhancers, we calculated H3K27ac and BRD4 ChIP-seq signal density across all samples at all regions defined as enhancers across groups (meta enhancers). In order to properly compare ChIP-seq signal density between SEs and TEs, for each enhancer constituent, we first determined if it was considered part of an SE in one or more groups, and if so, these groups defined the “active group context” for that particular enhancer constituent. Groups in which the enhancer constituent showed no evidence of enhancer activity (SE or TE) were considered the inactive group context. For enhancer constituents considered only part of a TE in one or more groups, groups in which the enhancer constituent was classified as a TE were considered the active group context and all other groups were considered the inactive group context. For each SE or TE constituent, average H3K27ac or BRD4 signal density was calculated at all samples in the active group context or in the inactive group context. The distributions of H3K27ac or BRD4 signal for enhancer constituents classified by SE or TE status were plotted and the statistical significance of the difference in the mean was tested in the active or inactive group context using a Welch’s two-tailed t-test (Extended Data Fig. 4g). We developed a method to identify SEs that were conserved across all medulloblastoma subgroups as well as SEs that showed highly group specific patterns of enhancer activity. We first took as the SE landscape all regions identified as SEs in the meta subgroup enhancer mapping. To account for sample-to-sample variability in H3K27ac ChIP-seq dynamic range, H3K27ac signal at enhancers in each medulloblastoma sample was rank transformed (Fig. 3b). As each medulloblastoma sample contained on average ~600 SEs, enhancer regions with an average rank of 600 or better in each subgroup were considered conserved. To identify enhancers with group specific patterns of activity, we calculated a “group rank Z-score” that compared average signal in one group to average signal in other groups. Here we considered whether enhancers might show group specific patterns for WNT, SHH, Group 3, Group 4, and as well for groupings of WNT/SHH, and Group 3/4. For each enhancer, this group rank Z-score was calculated for each group vs other combination. Enhancers with a group rank Z-score >1 (that is, those whose mean rank within a group was >1 standard deviation above the mean rank of all other samples) were considered group specific. To account for variability in enhancer ranks, only enhancers with a statistically significant difference in ranks (within group vs all other samples, Welch’s two-tailed t test, P-value <0.01) were considered. Supplementary Table 4 contains all SE regions identified in medulloblastoma subgroups and their corresponding max group rank Z-score, P-value, and classification. To provide a developmental context for medulloblastoma MYC SEs, we mapped H3K27ac enrichment at the MYC locus. H3K27ac data was obtained from the Epigenome ROADMAP as in Fig. 1e. The 500 kb region flanking the MYC SE No. 2 was divided into 5 kb bins and each bin was tested for overlap with a H3K27ac peak in each ROADMAP sample. ROADMAP samples were hierarchically clustered by similarity of H3K27ac peak pattern at the MYC locus (Extended Data Fig. 5m). Overlap with MYC SE No. 2 was found in 4/77 ROADMAP samples. Medulloblastoma core regulatory circuitry analysis was performed using the COLTRON (https://pypi.python.org/pypi/coltron) that calculated inward and outward degree regulation of SE-regulated TFs. To quantify the interaction network of TF regulation, we calculated the IN and OUT degree of all SE associated TFs. The 92 SE associated TFs were those defined as either proximal to an SE (within 50 kb) or the target of a differential SE enhancer element. For any given TF (TF ), the IN degree was defined as the number of TFs with an enriched binding motif at the proximal SE of TF (Fig. 5a). The OUT degree was defined as the number of TF associated SEs containing an enriched binding site for TF . Within any given SE, enriched TF binding sites were determined at putative nucleosome free regions (valleys) flanked by high levels of H3K27ac. Valleys were calculated using an algorithm adapted from ref. 35. In these regions, we searched for enriched TF binding sites using the FIMO52 algorithm with TF position weight matrices defined in the TRANSFAC database51. An FDR cutoff of 0.01 was used to identify enriched TF binding sites. Using this approach, we calculated IN and OUT degree for all SE associated TFs within the meta H3K27ac landscape (average of all samples) of each medulloblastoma subgroup. This approach resulted in an IN and OUT degree estimate for each SE associated TF in each medulloblastoma subgroup (Extended Data Fig. 8a–d). We sought to identify TF binding motifs for each TF in each subgroup. For each TF, we defined binding regions as the ±1,000 bp flanking the enriched region summit (as defined using MACS 1.4.2 with a P-value cutoff of 1 × 10−9). We took the union of all regions bound in a given subgroup (for example, HLX bound regions in Group 3 samples) that overlapped an enhancer in that subgroup and did not overlap any ENCODE8 blacklist regions. We next took the top 10,000 discreet regions as ranked by average TF ChIP-seq signal and used the ±100 bp region flanking the region centre as the input for de novo motif finding. De novo motif finding was performed using the MEME54 suite using a 1st order background model and searching for motifs between 6 and 30 bp in length. The top motif for each TF is displayed as a position weight matrix in Extended Data Fig. 8i–l. To visualize SE associated TF interactions in each subgroup, we ranked all SE associated TF by TOTAL degree (IN + OUT). We visualized the top 50% of SE associated TFs in each subgroup as a network diagram with each node representing a SE associated TF, and with nodes coloured and ordered by increasing TOTAL degree (Extended Data Fig. 8e–h). Interactions between SE associated TF nodes were defined as a TF motif identified in the SE of a TF and are depicted as edges. For Group 3 and Group 4, edges validated by the presence of a TF ChIP-seq peak are coloured. To identify SE associated TFs with similar regulatory patterns likely to influence subgroup identity, we first normalized the TOTAL degree for each SE associated TF in each subgroup from 0 to 1. We then calculated the normalized TOTAL degree for each SE associated TF in each subgroup. We filtered out all TFs with a max TOTAL degree across medulloblastomas of less than 0.7. We next clustered all remaining TFs by their TOTAL degree pattern. Hierarchical clustering was performed using a Euclidian distance metric and the resulting clustergram tree was cut at a distance of 0.5 to produce 26 individual clusters. Of these 26 clusters, 12 showed a median TOTAL degree >0.7 in 1, 2, or all 4 subgroups. Clusters with >0.7 TOTAL degree in 3 subgroups were omitted for simplicity. TOTAL degree patterns of TFs in these 12 clusters are shown in Extended Data Fig. 9a. This filtering produced a list of 102 SE associated TFs, of which 71 had predicted interactions with one another. These 71 TFs fall into either conserved, subgroup specific, or dual subgroup clusters and together they comprise the inferred core regulatory circuitry of medulloblastoma subgroups. As in Extended Data Fig. 8e–h, regulatory interactions between these core regulatory circuitry TFs are depicted in Extended Data Fig. 9a with Group 3 and Group 4 validated edges coloured. A subset of this larger network containing the TFs HLX, LHX2, EOMES, and LMX1A is depicted in Fig. 5c with ChIP-seq validated edges drawn as solid lines and motif prediction edges drawn in dotted lines. We used the STRING interaction database55 to quantify protein–protein interaction frequencies of SE associated TFs with similar regulatory patterns. TF pairs were considered co-regulatory if they shared 50% of the same OUT degree edges. Interaction frequencies for co-regulatory pairs were compared to those from 10,000 randomly assigned pairs of TFs expressed in that subgroup (Extended Data Fig. 8o). To determine the fraction of motif predicted edges with evidence of actual TF ChIP-seq binding, we first took all predicted edges for HLX, LHX2, and LMX1A interacting SE associated with other TFs in Group 3 and Group 4. We validated all edges that contained a ChIP-seq peak within the same enhancer as the predicted TF motif. The fraction of validated edges for each TF in each subgroup is shown in Extended Data Fig. 8g, h, m. To determine how Group 3 and Group 4 TF ChIP-seq levels varied at Group 3 and Group 4 specific enhancers, we quantified TF ChIP-seq signal at Group 3 and Group 4 enhancers. We first took the union of the top 1,000 enhancer regions as defined by H3K27ac signal in Group 3 and Group 4 (as in Extended Data Fig. 3b). We identified as Group 3 and Group 4 specific enhancer regions with a >1.0 log absolute fold change between Group 3 and Group 4. We identified as conserved enhancer regions with a <0.05 log absolute fold change between Group 3 and Group 4. We next identified all enhancer regions bound by LHX2 and HLX in Group 3 (G3 HLX and LHX2) or by LHX2 and LMX1A in Group 4 (G4 LMX1A and LHX2). TF ChIP-seq occupancy in units of average area under the curve (AUC) were quantified at TF bound regions overlapping Group 3 specific, Group 4 specific, and conserved enhancer region (Extended Data Fig. 8n). Statistical differences in the means of the distributions of TF ChIP-seq signal at different enhancer populations was determined using a Welch’s two tailed t-test (Extended Data Fig. 8n). To identify genes transcriptionally regulated by Lmx1a in the developing cerebellum, we isolated cerebellar uRL from WT and Lmx1a−/− embryos by laser capture microdissection. uRL was isolated from WT (n = 3) and Lmx1a−/− (n = 3) embryos (~3,000 cells per embryo) at e13.5, just before abnormal RL regression in Lmx1a−/− embryos. RNA was extracted using PicoPure RNA Isolation Kit (Arcturus) and hybridized to Illumina MouseRef8 v2 Expression BeadChips at the Johns Hopkins Array Core Facility. Next we identified all human TF genes with unambiguous mouse homologues that were detectably expressed in the WT mouse cerebellum (cut off of 100 arbitrary units). We subsequently quantified median normalized expression in WT or Lmx1a−/− samples and calculated the log fold-change for all TFs. We ranked the expression fold-change of all SE-associated TFs in medulloblastoma and plotted their log fold change in Lmx1a−/− vs WT (Fig. 6d). SE-associated TFs present in the Group 4 TF network (Extended Data Fig. 8h) were coloured in green.


News Article | November 10, 2016
Site: www.eurekalert.org

Diabetes mellitus is a chronic disease that has become increasingly prevalent in the population: More than six million people are affected by the disease alone in Germany. It is characterized by a disruption of the glucose metabolism and (except for type 1 diabetes) an impaired response of the body to the hormone insulin. Scientists are currently seeking to find the cause and possible regulators of the disease in order to intervene therapeutically. A team led by the metabolism expert Professor Stephan Herzig, director of the Institute for Diabetes and Cancer at Helmholtz Zentrum München (IDC), has discovered a new mechanism that is responsible for the regulation of the glucose metabolism. The transforming growth factor beta 1-stimulated clone 22 D4, abbreviated TSC22D4, acts as a molecular switch in the liver and from there regulates genes that can influence the metabolism throughout the body. "The current study is a successful continuation of our research activities with colleagues from the Internal Medicine at Heidelberg University Hospital," said study leader Herzig, who left Heidelberg in 2015 to become director of the Institute for Diabetes and Cancer at Helmholtz Zentrum München. Already in 2013 the researchers showed that increased production of TSC22D4 in the liver of mice with cancer leads to severe weight loss (cachexia) . In the present study, they investigated the role of this gene regulator in connection with diabetes. "The strong influence of TSC22D4 on the metabolism in tumor diseases suggested that it could also play a role in metabolic diseases," said first author Dr. Bilgen Ekim Üstünel of the IDC. In the current study, the researchers showed in diabetic mice that inactivation of TSC22D4 led to an improvement of the insulin action and glucose metabolism. Further analyses revealed that TSC22D4 in particular inhibits the production of the lipocalin13 protein, which is released as a messenger substance from the liver and can regulate the glucose metabolism in other organs. To check the relevance of the new mechanism in the clinic, the researchers examined liver tissue specimens of 66 patients with and without type 2 diabetes. They found that in the liver of the diabetes patients compared to people with normal glucose metabolism, the TSC22D4 gene was expressed significantly more often and lipocalin13 was produced correspondingly less often. "For the treatment of diabetes there is only a very limited number of therapeutic targets," said Herzig. "Next, we want to investigate whether our findings can lead to the development of a new therapeutic approach to treat diabetes and insulin resistance." Original Publication: Üstünel, BE. et al. (2016): Control of diabetic hyperglycemia and insulin resistance through TSC22D4. Nature Communications, doi: 10.1038/ncomms13267 The Helmholtz Zentrum München, the German Research Center for Environmental Health, pursues the goal of developing personalized medical approaches for the prevention and therapy of major common diseases such as diabetes and lung diseases. To achieve this, it investigates the interaction of genetics, environmental factors and lifestyle. The Helmholtz Zentrum München is headquartered in Neuherberg in the north of Munich and has about 2,300 staff members. It is a member of the Helmholtz Association, a community of 18 scientific-technical and medical-biological research centers with a total of about 37,000 staff members. http://www. The Institute for Diabetes and Cancer (IDC) is a member of the Helmholtz Diabetes Center (HDC) at the Helmholtz Zentrum München and a partner in the joint Heidelberg-IDC Translational Diabetes Program. The Institute for Diabetes and Cancer is tightly integrated into the German Center for Diabetes Research (DZD) and into the special research area "Reactive Metabolites and Diabetic Complications" at the Heidelberg University Medical School. The IDC conducts research on the molecular basis of severe metabolic disorders, including metabolic syndrome and type 2 diabetes, as well as their roles in tumor initiation and progression. http://www. Technical University of Munich (TUM) is one of Europe's leading research universities, with more than 500 professors, around 10,000 academic and non-academic staff, and 39,000 students. Its focus areas are the engineering sciences, natural sciences, life sciences and medicine, reinforced by schools of management and education. TUM acts as an entrepreneurial university that promotes talents and creates value for society. In that it profits from having strong partners in science and industry. It is represented worldwide with a campus in Singapore as well as offices in Beijing, Brussels, Cairo, Mumbai, San Francisco, and São Paulo. Nobel Prize winners and inventors such as Rudolf Diesel, Carl von Linde, and Rudolf Mößbauer have done research at TUM. In 2006 and 2012 it won recognition as a German "Excellence University." In international rankings, TUM regularly places among the best universities in Germany. http://www. Heidelberg University Hospital is one of the largest and most prestigious medical centers in Germany. The Medical Faculty of Heidelberg University belongs to the internationally most renowned biomedical research institutions in Europe. Both institutions have the common goal of developing new therapies and implementing them rapidly for patients. With about 12,600 employees, training and qualification is an important issue. Every year, around 66,000 patients are treated on an inpatient basis and around 1.000.000 cases on an outpatient basis in more than 50 clinics and departments with 1,900 beds. Currently, about 3,500 future physicians are studying in Heidelberg; the reform Heidelberg Curriculum Medicinale (HeiCuMed) is one of the top medical training programs in Germany. http://www. Universität Leipzig has the second-oldest uninterrupted history of any university in Germany. The Faculty of Medicine is a training centre for some 3,200 students of human and dental medicine. Made up of about 50 institutes, units and clinics it's one of the largest medical faculties anywhere in Germany. It works closely together with both related faculties and other areas of research, providing superb conditions for students, lecturers and researchers alike. http://www. Contact for the media: Department of Communication, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg - Tel. +49 89 3187 2238 - Fax: +49 89 3187 3324 - E-mail: presse@helmholtz-muenchen.de Scientific Contact at Helmholtz Zentrum München: Prof. Dr. Stephan Herzig, Helmholtz Zentrum München - German Research Center for Environmental Health, Institute for Diabetes and Cancer, Ingolstädter Landstr. 1, 85764 Neuherberg - Tel. +49 89 3187 1045, E-mail: stephan.herzig@helmholtz-muenchen.de


News Article | November 19, 2016
Site: www.sciencedaily.com

Infectious diseases are a leading cause of mortality worldwide. The development of novel therapies or vaccines requires improved understanding of how viruses, pathogenic fungi or bacteria cause illnesses. Some bacterial pathogens such as Salmonella invade and replicate within human cells. Science is steadily shifting its focus towards studying infected cells and how differences between individual host cells affect the cellular response to pathogens. A research team headed by Professor Jörg Vogel from the University of Würzburg has made significant progress in this area. They have developed a novel technique that allows them to investigate the interplay of individual host cells with infecting bacteria. This study is based on close collaboration between Jörg Vogel's team at the Institute of Molecular Infection Biology, the core unit Systems Medicine of the Medical Faculty and researchers at Imperial College in London. Their results have now been published in the scientific journal Nature Microbiology. The team used a technique called "single-cell RNA-seq" to study the infection of macrophages by Salmonella. Macrophages are immune cells that belong to the group of white blood cells. Salmonella on the other hand are pathogenic bacteria that may be taken up by the ingestion of contaminated water or food to cause local gastroenteritis and diarrhea. In immunocompromised patients however, Salmonella may disseminate throughout the entire body and cause life-threatening diseases. Upon the invasion of macrophages, Salmonella pursue two strategies: The bacteria either replicate to high numbers inside the host cell or adopt a non-growing state that allows them to persist for years within the body of their host. "This disparate growth behavior impacts disease progression and plays a major role in the success of antibiotic treatment" says lead researcher Jörg Vogel. To date, very little is known if and how macrophages respond to these disparate lifestyles of intracellular Salmonella. To answer this question the Würzburg scientists cultured macrophages in the laboratory and infected them with Salmonella. The RNA from the infected cells was subsequently extracted and analyzed using deep-sequencing, leading to the detection of more than 5,000 different transcripts per macrophage. These data on the host's gene expression were combined with information about the growth behavior of the intracellular pathogens. The results: Macrophages containing non-growing bacteria adopt the hallmark signature associated with inflammation. They express signaling molecules to attract further immune cells to the site of infection. In this respect, they respond similarly to macrophages that have encountered Salmonella, but have not been infected. "These macrophages cannot detect their intracellular bacteria -- they are below their radar" explains Emmanuel Saliba, first author of the study. In contrast, macrophages with fast-growing bacteria develop an anti-inflammatory response. These interesting results open many questions, Do Salmonella induce this different response? Do they manipulate the macrophages so they do not raise the alarm to facilitate the bacteria to evade an immune response? Are there situations where Salmonella are unable to perform this trick? In these cases, will there still be an immune response forcing the bacteria to switch to their resting growth state? Of interest for many biomedical areas "Currently we have just looked at a single time point after infection and thus cannot differentiate between cause and consequence" explains Alexander Westermann, another member of the team. Follow-up studies are required. Nevertheless, the current findings already provide a new perspective on the host response to pathogenic microbes. And using the new technology, bacterial infections can be studied in unprecedented resolution -- namely on the single-cell level. The method established at the Würzburg core unit Systems Medicine should be of great interest for many further biomedical projects. "Among others, heterogeneity amongst tumor cells or the effect of drugs on single cells may be analyzed in unknown accuracy" says Professor Vogel.


News Article | November 18, 2016
Site: www.eurekalert.org

Infectious diseases are a leading cause of mortality worldwide. The development of novel therapies or vaccines requires improved understanding of how viruses, pathogenic fungi or bacteria cause illnesses. Some bacterial pathogens such as Salmonella invade and replicate within human cells. Science is steadily shifting its focus towards studying infected cells and how differences between individual host cells affect the cellular response to pathogens. A research team headed by Professor Jörg Vogel from the University of Würzburg has made significant progress in this area. They have developed a novel technique that allows them to investigate the interplay of individual host cells with infecting bacteria. This study is based on close collaboration between Jörg Vogel's team at the Institute of Molecular Infection Biology, the core unit Systems Medicine of the Medical Faculty and researchers at Imperial College in London. Their results have now been published in the scientific journal Nature Microbiology. The team used a technique called "single-cell RNA-seq" to study the infection of macrophages by Salmonella. Macrophages are immune cells that belong to the group of white blood cells. Salmonella on the other hand are pathogenic bacteria that may be taken up by the ingestion of contaminated water or food to cause local gastroenteritis and diarrhea. In immunocompromised patients however, Salmonella may disseminate throughout the entire body and cause life-threatening diseases. Upon the invasion of macrophages, Salmonella pursue two strategies: The bacteria either replicate to high numbers inside the host cell or adopt a non-growing state that allows them to persist for years within the body of their host. "This disparate growth behavior impacts disease progression and plays a major role in the success of antibiotic treatment" says lead researcher Jörg Vogel. To date, very little is known if and how macrophages respond to these disparate lifestyles of intracellular Salmonella. To answer this question the Würzburg scientists cultured macrophages in the laboratory and infected them with Salmonella. The RNA from the infected cells was subsequently extracted and analyzed using deep-sequencing, leading to the detection of more than 5,000 different transcripts per macrophage. These data on the host's gene expression were combined with information about the growth behavior of the intracellular pathogens. The results: Macrophages containing non-growing bacteria adopt the hallmark signature associated with inflammation. They express signaling molecules to attract further immune cells to the site of infection. In this respect, they respond similarly to macrophages that have encountered Salmonella, but have not been infected. "These macrophages cannot detect their intracellular bacteria - they are below their radar" explains Emmanuel Saliba, first author of the study. In contrast, macrophages with fast-growing bacteria develop an anti-inflammatory response. These interesting results open many questions, Do Salmonella induce this different response? Do they manipulate the macrophages so they do not raise the alarm to facilitate the bacteria to evade an immune response? Are there situations where Salmonella are unable to perform this trick? In these cases, will there still be an immune response forcing the bacteria to switch to their resting growth state? "Currently we have just looked at a single time point after infection and thus cannot differentiate between cause and consequence" explains Alexander Westermann, another member of the team. Follow-up studies are required. Nevertheless, the current findings already provide a new perspective on the host response to pathogenic microbes. And using the new technology, bacterial infections can be studied in unprecedented resolution - namely on the single-cell level. The method established at the Würzburg core unit Systems Medicine should be of great interest for many further biomedical projects. "Among others, heterogeneity amongst tumor cells or the effect of drugs on single cells may be analyzed in unknown accuracy" says Professor Vogel.


News Article | November 18, 2016
Site: www.sciencedaily.com

Technological advances are making the analysis of single bacterial infected human cells feasible, Würzburg researchers have used this technology to provide new insight into the Salmonella infection process. The study has just been published in Nature Microbiology. Infectious diseases are a leading cause of mortality worldwide. The development of novel therapies or vaccines requires improved understanding of how viruses, pathogenic fungi or bacteria cause illnesses. Some bacterial pathogens such as Salmonella invade and replicate within human cells. Science is steadily shifting its focus towards studying infected cells and how differences between individual host cells affect the cellular response to pathogens. A research team headed by Professor Jörg Vogel from the University of Würzburg has made significant progress in this area. They have developed a novel technique that allows them to investigate the interplay of individual host cells with infecting bacteria. This study is based on close collaboration between Jörg Vogel's team at the Institute of Molecular Infection Biology, the core unit Systems Medicine of the Medical Faculty and researchers at Imperial College in London. Their results have now been published in the scientific journal "Nature Microbiology." The team used a technique called "single-cell RNA-seq" to study the infection of macrophages by Salmonella. Macrophages are immune cells that belong to the group of white blood cells. Salmonella on the other hand are pathogenic bacteria that may be taken up by the ingestion of contaminated water or food to cause local gastroenteritis and diarrhea. In immunocompromised patients however, Salmonella may disseminate throughout the entire body and cause life-threatening diseases. Upon the invasion of macrophages, Salmonella pursue two strategies: The bacteria either replicate to high numbers inside the host cell or adopt a non-growing state that allows them to persist for years within the body of their host. "This disparate growth behavior impacts disease progression and plays a major role in the success of antibiotic treatment" says lead researcher Jörg Vogel. To date, very little is known if and how macrophages respond to these disparate lifestyles of intracellular Salmonella. To answer this question the Würzburg scientists cultured macrophages in the laboratory and infected them with Salmonella. The RNA from the infected cells was subsequently extracted and analyzed using deep-sequencing, leading to the detection of more than 5,000 different transcripts per macrophage. These data on the host's gene expression were combined with information about the growth behavior of the intracellular pathogens. The results: Macrophages containing non-growing bacteria adopt the hallmark signature associated with inflammation. They express signaling molecules to attract further immune cells to the site of infection. In this respect, they respond similarly to macrophages that have encountered Salmonella, but have not been infected. "These macrophages cannot detect their intracellular bacteria -- they are below their radar" explains Emmanuel Saliba, first author of the study. In contrast, macrophages with fast-growing bacteria develop an anti-inflammatory response. These interesting results open many questions, Do Salmonella induce this different response? Do they manipulate the macrophages so they do not raise the alarm to facilitate the bacteria to evade an immune response? Are there situations where Salmonella are unable to perform this trick? In these cases, will there still be an immune response forcing the bacteria to switch to their resting growth state? Of interest for many biomedical areas "Currently we have just looked at a single time point after infection and thus cannot differentiate between cause and consequence" explains Alexander Westermann, another member of the team. Follow-up studies are required. Nevertheless, the current findings already provide a new perspective on the host response to pathogenic microbes. And using the new technology, bacterial infections can be studied in unprecedented resolution -- namely on the single-cell level. The method established at the Würzburg core unit Systems Medicine should be of great interest for many further biomedical projects. "Among others, heterogeneity amongst tumor cells or the effect of drugs on single cells may be analyzed in unknown accuracy" says Professor Vogel.


News Article | November 18, 2016
Site: phys.org

Infectious diseases are a leading cause of mortality worldwide. The development of novel therapies or vaccines requires improved understanding of how viruses, pathogenic fungi or bacteria cause illnesses. Some bacterial pathogens such as Salmonella invade and replicate within human cells. Science is steadily shifting its focus towards studying infected cells and how differences between individual host cells affect the cellular response to pathogens. A research team headed by Professor Jörg Vogel from the University of Würzburg has made significant progress in this area. They have developed a novel technique that allows them to investigate the interplay of individual host cells with infecting bacteria. This study is based on close collaboration between Jörg Vogel's team at the Institute of Molecular Infection Biology, the core unit Systems Medicine of the Medical Faculty and researchers at Imperial College in London. Their results have now been published in the scientific journal Nature Microbiology. The team used a technique called "single-cell RNA-seq" to study the infection of macrophages by Salmonella. Macrophages are immune cells that belong to the group of white blood cells. Salmonella on the other hand are pathogenic bacteria that may be taken up by the ingestion of contaminated water or food to cause local gastroenteritis and diarrhea. In immunocompromised patients however, Salmonella may disseminate throughout the entire body and cause life-threatening diseases. Upon the invasion of macrophages, Salmonella pursue two strategies: The bacteria either replicate to high numbers inside the host cell or adopt a non-growing state that allows them to persist for years within the body of their host. "This disparate growth behavior impacts disease progression and plays a major role in the success of antibiotic treatment" says lead researcher Jörg Vogel. To date, very little is known if and how macrophages respond to these disparate lifestyles of intracellular Salmonella. To answer this question the Würzburg scientists cultured macrophages in the laboratory and infected them with Salmonella. The RNA from the infected cells was subsequently extracted and analyzed using deep-sequencing, leading to the detection of more than 5,000 different transcripts per macrophage. These data on the host's gene expression were combined with information about the growth behavior of the intracellular pathogens. The results: Macrophages containing non-growing bacteria adopt the hallmark signature associated with inflammation. They express signaling molecules to attract further immune cells to the site of infection. In this respect, they respond similarly to macrophages that have encountered Salmonella, but have not been infected. "These macrophages cannot detect their intracellular bacteria – they are below their radar," explains Emmanuel Saliba, first author of the study. In contrast, macrophages with fast-growing bacteria develop an anti-inflammatory response. These interesting results open many questions, Do Salmonella induce this different response? Do they manipulate the macrophages so they do not raise the alarm to facilitate the bacteria to evade an immune response? Are there situations where Salmonella are unable to perform this trick? In these cases, will there still be an immune response forcing the bacteria to switch to their resting growth state? Of interest for many biomedical areas "Currently we have just looked at a single time point after infection and thus cannot differentiate between cause and consequence" explains Alexander Westermann, another member of the team. Follow-up studies are required. Nevertheless, the current findings already provide a new perspective on the host response to pathogenic microbes. And using the new technology, bacterial infections can be studied in unprecedented resolution – namely on the single-cell level. The method established at the Würzburg core unit Systems Medicine should be of great interest for many further biomedical projects. "Among others, heterogeneity amongst tumor cells or the effect of drugs on single cells may be analyzed in unknown accuracy," says Professor Vogel. More information: Antoine-Emmanuel Saliba et al. Single-cell RNA-seq ties macrophage polarization to growth rate ofintracellular Salmonella, Nature Microbiology (2016). DOI: 10.1038/NMICROBIOL.2016.206


News Article | November 10, 2016
Site: www.sciencedaily.com

A key genetic switch in the liver regulates glucose metabolism and insulin action in other organs of the body. Researchers of Helmholtz Zentrum München, in collaboration with colleagues of the Heidelberg University Hospital, Technische Universität München and the Medical Faculty of the University of Leipzig, have now reported these findings in the journal Nature Communications. Diabetes mellitus is a chronic disease that has become increasingly prevalent in the population: More than six million people are affected by the disease alone in Germany. It is characterized by a disruption of the glucose metabolism and (except for type 1 diabetes) an impaired response of the body to the hormone insulin. Scientists are currently seeking to find the cause and possible regulators of the disease in order to intervene therapeutically. A team led by the metabolism expert Professor Stephan Herzig, director of the Institute for Diabetes and Cancer at Helmholtz Zentrum München (IDC), has discovered a new mechanism that is responsible for the regulation of the glucose metabolism. The transforming growth factor beta 1-stimulated clone 22 D4, abbreviated TSC22D4, acts as a molecular switch in the liver and from there regulates genes that can influence the metabolism throughout the body. "The current study is a successful continuation of our research activities with colleagues from the Internal Medicine at Heidelberg University Hospital," said study leader Herzig, who left Heidelberg in 2015 to become director of the Institute for Diabetes and Cancer at Helmholtz Zentrum München. Already in 2013 the researchers showed that increased production of TSC22D4 in the liver of mice with cancer leads to severe weight loss (cachexia) . In the present study, they investigated the role of this gene regulator in connection with diabetes. "The strong influence of TSC22D4 on the metabolism in tumor diseases suggested that it could also play a role in metabolic diseases," said first author Dr. Bilgen Ekim Üstünel of the IDC. In the current study, the researchers showed in diabetic mice that inactivation of TSC22D4 led to an improvement of the insulin action and glucose metabolism. Further analyses revealed that TSC22D4 in particular inhibits the production of the lipocalin13 protein, which is released as a messenger substance from the liver and can regulate the glucose metabolism in other organs. To check the relevance of the new mechanism in the clinic, the researchers examined liver tissue specimens of 66 patients with and without type 2 diabetes. They found that in the liver of the diabetes patients compared to people with normal glucose metabolism, the TSC22D4 gene was expressed significantly more often and lipocalin13 was produced correspondingly less often. "For the treatment of diabetes there is only a very limited number of therapeutic targets," said Herzig. "Next, we want to investigate whether our findings can lead to the development of a new therapeutic approach to treat diabetes and insulin resistance."


News Article | November 10, 2016
Site: www.rdmag.com

Researchers have discovered that a key genetic switch in the liver regulates glucose metabolism and insulin action in other organs of the body and continue to investigate how this impacts diabetes. Researchers from Helmholtz Zentrum München, in collaboration with colleagues of the Heidelberg University Hospital, Technische Universität München and the Medical Faculty of the University of Leipzig, have discovered a new mechanism that is responsible for the regulation of glucose metabolism. A team led by the metabolism expert Prof. Stephan Herzig, director of the Institute for Diabetes and Cancer at Helmholtz Zentrum München (IDC), has discovered that the transforming growth factor beta 1-stimulated clone 22 D4, abbreviated TSC22D4, acts as a molecular switch in the liner and from there regulates genes that can influence the metabolism throughout the body. “The current study is a successful continuation of our research activities with colleagues from the Internal Medicine at Heidelberg University Hospital,” Herzig said in a statement. Researchers were able to observe that increased production of TSC22D4 in the liver of mice with cancer lead to severe weight loss in 2013 and in the current study they investigated the role of this gene regulator in connection with diabetes. Diabetes mellitus is a chronic disease characterized by a disruption of the glucose metabolism and impaired response of the body to the hormone insulin that has become increasingly prevalent in the population with more than 6 million people affected in Germany alone. The researchers showed in diabetic mice that inactivation of TSC22D4 led to an improvement of the insulin action and glucose metabolism and after further analysis TSC22D4 showed to inhibit the production of the lipcalin13 protein, which is released as a messenger substance from the liver and can regulate the glucose metabolism in other organs. They also examined liver tissue specimens of 66 patients with and without type 2 diabetes and found that in the liver of the diabetes patients compared to people with normal glucose metabolism, the TSC22D4 gene was expressed significantly more often and lipcalin13 was produced correspondingly less often. “The strong influence of TSC22D4 on the metabolism in tumor diseases suggested that it could also play a role in metabolic diseases,” first author Bilgen Ekim Üstünel, Ph.D., of the IDC, said in a statement. Herzig explained what will happen next regarding the study. “For the treatment of diabetes there is only a very limited number of therapeutic targets,” he said. “Next, we want to investigate whether our findings can lead to the development of a new therapeutic approach to treat diabetes and insulin resistance.”


News Article | November 9, 2016
Site: www.chromatographytechniques.com

A key genetic switch in the liver regulates glucose metabolism and insulin action in other organs of the body. Researchers of Helmholtz Zentrum München, in collaboration with colleagues of the Heidelberg University Hospital, Technische Universität München and the Medical Faculty of the University of Leipzig, have now reported these findings in the journal Nature Communications. Diabetes mellitus is a chronic disease that has become increasingly prevalent in the population: More than 6 million people are affected by the disease alone in Germany. It is characterized by a disruption of the glucose metabolism and (except for type 1 diabetes) an impaired response of the body to the hormone insulin. Scientists are currently seeking to find the cause and possible regulators of the disease in order to intervene therapeutically. A team led by metabolism expert Stephan Herzig, director of the Institute for Diabetes and Cancer at Helmholtz Zentrum München (IDC), has discovered a new mechanism that is responsible for the regulation of the glucose metabolism. The transforming growth factor beta 1-stimulated clone 22 D4, abbreviated TSC22D4, acts as a molecular switch in the liver and from there regulates genes that can influence the metabolism throughout the body. "The current study is a successful continuation of our research activities with colleagues from the Internal Medicine at Heidelberg University Hospital," said Herzig, who left Heidelberg in 2015 to become director of the Institute for Diabetes and Cancer at Helmholtz Zentrum München. In 2013 the researchers showed that increased production of TSC22D4 in the liver of mice with cancer leads to severe weight loss (cachexia). In the present study, they investigated the role of this gene regulator in connection with diabetes. "The strong influence of TSC22D4 on the metabolism in tumor diseases suggested that it could also play a role in metabolic diseases," said first author Bilgen Ekim Üstünel of the IDC. In the current study, the researchers showed in diabetic mice that inactivation of TSC22D4 led to an improvement of the insulin action and glucose metabolism. Further analyses revealed that TSC22D4 in particular inhibits the production of the lipocalin13 protein, which is released as a messenger substance from the liver and can regulate the glucose metabolism in other organs. To check the relevance of the new mechanism in the clinic, the researchers examined liver tissue specimens of 66 patients with and without type 2 diabetes. They found that in the liver of the diabetes patients compared to people with normal glucose metabolism, the TSC22D4 gene was expressed significantly more often and lipocalin13 was produced correspondingly less often. "For the treatment of diabetes there is only a very limited number of therapeutic targets," said Herzig. "Next, we want to investigate whether our findings can lead to the development of a new therapeutic approach to treat diabetes and insulin resistance."

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