Latest Articles Include:
- Milestone in Anhui
- Nat Genet 43(7):613 (2011)
Nature Genetics | Editorial Milestone in Anhui Journal name:Nature GeneticsVolume: 43,Page:613Year published:(2011)DOI:doi:10.1038/ng.881Published online28 June 2011 Our first Nature Conference in China emphasized the value of extending genome-wide association studies (GWAS) to populations worldwide as a way to promote cooperation and high standards in research while gaining a wealth of biological insights into common and complex diseases and traits. View full text Additional data - Mapping higher order structure of chromatin domains
- Nat Genet 43(7):615-616 (2011)
Article preview View full access options Nature Genetics | News and Views Mapping higher order structure of chromatin domains * Celso A Espinoza1 * Bing Ren1 * Affiliations * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:615–616Year published:(2011)DOI:doi:10.1038/ng.869Published online28 June 2011 Large-scale mapping of chromatin state and transcription factor binding have uncovered many broad chromatin domains along linear genomic DNA, but it is unclear how these functional domains are organized in three-dimensional nuclear space. A new study now shows that many domains exist as loops connected by CCTC-binding factor (CTCF), providing new insights into the higher-order structure of chromatin organization in the nucleus. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Genetics for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Celso A. Espinoza and Bing Ren are at the Ludwig Institute for Cancer Research at the University of California, San Diego, La Jolla, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Bing Ren Author Details * Celso A Espinoza Search for this author in: * NPG journals * PubMed * Google Scholar * Bing Ren Contact Bing Ren Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - The long reach of noncoding RNAs
- Nat Genet 43(7):616-617 (2011)
Article preview View full access options Nature Genetics | News and Views The long reach of noncoding RNAs * Elena Sotillo1 * Andrei Thomas-Tikhonenko1 * Affiliations * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:616–617Year published:(2011)DOI:doi:10.1038/ng.870Published online28 June 2011 Transcription of genomic loci containing protein-coding genes often yields not only cognate mRNAs but also assorted noncoding RNAs (ncRNAs), which typically map in the vicinity of transcription start sites. A new study shows that far from being random byproducts of gene expression, many long ncRNAs (lncRNAs) are synthesized in a coordinate fashion and control important cellular processes, such as survival in the face of DNA damage. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Genetics for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Elena Sotillo and Andrei Thomas-Tikhonenko are in the Division of Cancer Pathobiology, Department of Pathology & Laboratory Medicine, The Children's Hospital of Philadelphia and University of Pennsylvania, Philadelphia, Pennsylvania, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Andrei Thomas-Tikhonenko Author Details * Elena Sotillo Search for this author in: * NPG journals * PubMed * Google Scholar * Andrei Thomas-Tikhonenko Contact Andrei Thomas-Tikhonenko Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Regulating mRNA complexity in the mammalian brain
- Nat Genet 43(7):618-619 (2011)
Article preview View full access options Nature Genetics | News and Views Regulating mRNA complexity in the mammalian brain * Thomas A Cooper1Journal name:Nature GeneticsVolume: 43,Pages:618–619Year published:(2011)DOI:doi:10.1038/ng.815Published online28 June 2011 Although there are only 22,000 human genes, most express multiple mRNA isoforms through alternative splicing and selection of alternative 5′ and 3′ ends. A new study identifies the role of alternative splicing in maintaining neuronal excitability in the adult mouse brain. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Genetics for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Thomas A. Cooper is at the Baylor College of Medicine, Houston, Texas, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Thomas A Cooper Author Details * Thomas A Cooper Contact Thomas A Cooper Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Research highlights
- Nat Genet 43(7):620 (2011)
Article preview View full access options Nature Genetics | Research Highlights Research highlights Journal name:Nature GeneticsVolume: 43,Page:620Year published:(2011)DOI:doi:10.1038/ng.878Published online28 June 2011 Shank3 mouse model of autism Heterozygous mutations in SHANK3 that disrupt binding to HOMER have been previously reported in autism spectrum disorder (Nature Genetics39, 25–27, 2006). Now, Paul Worley and colleagues have generated mice with heterozygous mutations in Shank3 that delete the C terminus and remove the Homer binding site (Cell145, 758–772, 2011). Shank3 (+/ΔC) mice have a 90% decrease of Shank3 protein in synapses despite equivalent mRNA levels compared to wild-type littermates. They observed a twofold increase in polyubiquitinated Shank3 in Shank3 (+/ΔC) mice as well as increased co-localization with Rpt6, a proteosomal marker. These data suggest that increased polyubiquitination leads to increased proteosomal degradation of Shank3. Behavioral tests of long-term spatial memory and fear memory showed that Shank3 (+/ΔC) mice retain learning and memory functions. However, Shank3 (+/ΔC) mice showed lower levels of social investigation compared to wild-type mice in an assay of reciprocal! social interaction. Shank3 (+/ΔC) mice also showed significant increases in approach latency when presented with sexually receptive females, although no differences were seen with male stimulus mice. Nevertheless, the data suggest Shank3 (+/ΔC) mice have altered social interaction and social approach behaviors, which parallel social deficits seen in autism spectrum disorder in humans. PC Industrial melanism in peppered moths The emergence of a darkly pigmented form of the peppered moth in nineteenth century Britain in response to industrialization is a classic example of evolutionary adaptation in response to environmental change. Ilik Saccheri and colleagues (Science332, 958–960, 2011) have now used genetic mapping to trace the origins of this adaptive change. The authors constructed linkage and physical maps for the peppered moth Biston betularia and localized the gene responsible for the carbonaria (darkly pigmented) morph to a 200-kb region. Next, they examined SNP markers across this region in peppered moth samples collected throughout the UK between 1925 and 2009. They found that all 97 carbonaria morphs shared a common allele for one marker in this region, suggesting that all were derived from a single ancestral haplotype that spread rapidly in the population under strong selective pressure. The authors have not determined which gene or variant in the region mediates the pigmentary effe! cts that distinguish the carbonaria morph from the wild-type form. However, the region is coincident with the position of pigmentation loci that have been mapped in other lepidopteran species, suggesting that the underlying pigmentary system might be ancestrally derived. KV Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Genetics for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Extensive and coordinated transcription of noncoding RNAs within cell-cycle promoters
- Nat Genet 43(7):621-629 (2011)
Nature Genetics | Article Extensive and coordinated transcription of noncoding RNAs within cell-cycle promoters * Tiffany Hung1, 2 * Yulei Wang3 * Michael F Lin4, 5 * Ashley K Koegel1, 2 * Yojiro Kotake6, 7, 8 * Gavin D Grant9 * Hugo M Horlings10 * Nilay Shah11 * Christopher Umbricht12 * Pei Wang13 * Yu Wang3 * Benjamin Kong3 * Anita Langerød14 * Anne-Lise Børresen-Dale14, 15 * Seung K Kim2, 13 * Marc van de Vijver10 * Saraswati Sukumar11 * Michael L Whitfield9 * Manolis Kellis4, 5 * Yue Xiong6 * David J Wong1, 16 * Howard Y Chang1, 2, 16 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:621–629Year published:(2011)DOI:doi:10.1038/ng.848Received19 August 2010Accepted06 May 2011Published online05 June 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Transcription of long noncoding RNAs (lncRNAs) within gene regulatory elements can modulate gene activity in response to external stimuli, but the scope and functions of such activity are not known. Here we use an ultrahigh-density array that tiles the promoters of 56 cell-cycle genes to interrogate 108 samples representing diverse perturbations. We identify 216 transcribed regions that encode putative lncRNAs, many with RT-PCR–validated periodic expression during the cell cycle, show altered expression in human cancers and are regulated in expression by specific oncogenic stimuli, stem cell differentiation or DNA damage. DNA damage induces five lncRNAs from the CDKN1A promoter, and one such lncRNA, named PANDA, is induced in a p53-dependent manner. PANDA interacts with the transcription factor NF-YA to limit expression of pro-apoptotic genes; PANDA depletion markedly sensitized human fibroblasts to apoptosis by doxorubicin. These findings suggest potentially widespread ro! les for promoter lncRNAs in cell-growth control. View full text Figures at a glance * Figure 1: Identification of ncRNAs near and within cell-cycle genes. () Flow chart of the strategy for systematic discovery of cell-cycle ncRNAs. () Representative tiling array data. RNA hybridization intensity and H3K36me3 and H3K4me3 ChIP-chip signals relative to the input at the CCNE1 locus in human fetal lung fibroblasts. The predicted transcripts are shown in red boxes. Known mRNA exons are shown in black boxes. Each bar represents a significant peak from one of the 108 array channels. () Chromatin state at the transcribed regions. The average ChIP-chip signal relative to the input calculated across transcriptional peaks expressed in human fetal lung fibroblasts with or without doxorubicin treatment. () Codon substitution frequency (CSF) analysis. Graph of the average evolutionary CSF of the exons of coding genes and their predicted transcripts. CSF < 10 indicates no protein coding potential. () Transcriptional landscape of cell-cycle promoters. We aligned all cell-cycle promoters at the TSS and calculated the average RNA hybridization s! ignal across the 12-kb window. The output represents a 150-bp running window of average transcription signals across all 54 arrays. See also Supplementary Table 1 and Supplementary Figure 1. * Figure 2: ncRNA expression across diverse cell cycle perturbations. () Hierarchical clustering of 216 predicted ncRNAs across 54 arrays, representing 108 conditions. Red indicates that the cell cycle perturbation induced transcription of the ncRNA. Green indicates that the cell cycle perturbation repressed transcription of the ncRNA. Black indicates no significant expression change. () Close up view of the ncRNAs in cluster 1. See also Supplementary Tables 2,3. * Figure 3: Functional associations of ncRNAs. () lncRNA expression patterns do not correlate with those of the mRNAs in cis. Histogram of Pearson correlations between each of the 216 ncRNAs and the cis mRNA across 108 samples. () lncRNA expression patterns have a positive correlation with neighboring lncRNA transcripts. Histogram of Pearson correlations between each of the 216 ncRNAs and nearby transcripts on the same locus across 108 samples. () Genes co-expressed with lncRNAs are enriched for functional groups in the cell cycle and in DNA damage response. Module map of lncRNA gene sets (columns) versus Gene Ontology Biological Processes gene sets (rows) across 17 samples (P < 0.05, false discovery rate <0.05). A yellow entry indicates that the Gene Ontology gene set is positively associated with the lncRNA gene set. A blue entry indicates that the Gene Ontology gene set is negatively associated with the lncRNA gene set. A black entry indicates no significant association. Representative enriched Gene Ontology gene sets! are listed. * Figure 4: Validated expression of ncRNAs in cell cycle progression, ESC differentiation and human cancers. We generated custom TaqMan probes and used them to interrogate independent biological samples for lncRNA expression. (,) Periodic expression of lncRNAs (blue) during synchronized cell cycle progression in HeLa cells () and foreskin fibroblasts (). Cell cycle phases were confirmed by fluorescence-activated cell sorting and expression of genes with known periodic expression in the cell cycle (orange). () Regulated expression of lncRNAs in human ESCs compared to fetal pancreas. D, day. () Differential expression of lncRNAs in normal breast epithelium compared to breast cancer samples. * Figure 5: ncRNAs at the CDKN1A locus are induced by DNA damage. () At the top is a map of all detected transcripts at the CDKN1A promoter. In the middle two tracks are examples of RNA hybridization intensity in the control or in 24 h doxorubicin (dox) treated (200 ng/ml) human fetal lung fibroblasts. Note that we did not observe all DNA-damage–inducible transcripts in one single time point. At the bottom, the p53 ChIP-chip signal relative to input confirmed the p53 binding site immediately upstream of the CDKN1A TSS after DNA damage. The RACE clone of upst:CDKN1A:−4,845 closely matches the predicted transcript on the tiling array. See also Supplementary Figure 7. () Quantitative RT-PCR of lncRNAs shows coordinate induction or repression across a 24 h time course of doxorubicin treatment. A cluster of lncRNAs transcribed from the CDKN1A locus are induced. () Expression of transcripts from the CDKN1A locus over a 24 h time course after doxorubicin treatment of normal human fibroblasts (FL3). See also Supplementary Figure 6. () RNA blot! of PANDA confirms a transcript size of 1.5 kb. () Doxorubicin induction of PANDA requires p53 but not CDKN1A. Mean ± s.d. are shown; *P < 0.05 relative to siCTRL (control siRNA) determined by student's t-test. () Expression of wild-type p53 in p53-null H1299 cells restores DNA damage induction of CDKN1A and PANDA. The p53 (p.Val272Cys) loss-of-function mutant fails to restore induction, whereas a gain-of-function Li-Fraumeni allele, p53 (p.Arg273His), selectively retains the ability to induce PANDA. * Figure 6: PANDA lncRNA regulates the apoptotic response to DNA damage. () siRNA knockdown of PANDA in the presence of DNA damage with doxorubicin in human fibroblasts (FL3). Custom siRNAs specifically target PANDA with no discernable effect on the LAP3 mRNA. Mean ± s.d. are shown in all bar graphs. *P < 0.05 compared to siCTRL for all panels determined by Student's t-test. () Heat map of gene expression changes with siPANDA relative to control siRNA after 24 h of doxorubicin treatment in FL3 cells. () Quantitative RT-PCR of canonical apoptosis pathway genes revealed induction with siPANDA relative to control siRNA after 28 h of doxorubicin treatment (in FL3 cells). () Quantitative RT-PCR of CDKN1A and TP53 in FL3 cells revealed no reduction in expression with siPANDA relative to control siRNA. () TUNEL immunofluorescence of control and siPANDA FL3 fibroblasts after 28 h of doxorubicin treatment. Scale bar, 20 μm. () Quantification of three independent TUNEL assays. P < 0.05 for each siPANDA sample compared to siCTRL determined by student's t-! test. () Protein blot of PARP cleavage in control and PANDA siRNA FL3 fibroblasts after 24 h of doxorubicin treatment. * Figure 7: PANDA regulates transcription factor NF-YA. () RNA chromatography of PANDA from doxorubicin-treated FL3 cell lysates. We visualized the retrieved proteins by immunoblot analysis. () Immunoprecipitation of NF-YA from doxorubicin-treated FL3 lysates specifically retrieves PANDA, as measured by qRT-PCR. Immunoblot confirms immunoprecipitation of NF-YA, as shown at the bottom. () ChIP of NF-YA in FL3 fibroblasts nucleofected with siCTRL or siPANDA. ChIP-qPCR at known NF-YA target sites on promoters of CCNB1, FAS, NOXA, BBC3 (PUMA) or a control downstream region in the FAS promoter lacking the NF-YA motif. Mean ± s.d. are shown in all bar graphs. *P < 0.05 determined by Student's t-test. () Concomitant knockdown of NF-YA attenuates induction of apoptotic genes by PANDA depletion, as measured by qRT-PCR. For knockdown efficiency see Supplementary Figure 11. () Concomitant knockdown of NF-YA rescues apoptosis induced by PANDA depletion. Quantification of TUNEL staining is shown. The legend for this panel is as in . * Figure 8: Model of coding and noncoding transcripts at the CDKN1A locus coordinating the DNA damage response. After DNA damage, p53 binding at the CDKN1A locus coordinately activates transcription of CDKN1A as well as noncoding transcripts PANDA and linc-p21. CDKN1A mediates cell cycle arrest, PANDA blocks apoptosis through NF-YA, and linc-p21 mediates gene silencing through recruitment of hnRPK. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE28631 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * David J Wong & * Howard Y Chang Affiliations * Program in Epithelial Biology, Stanford University School of Medicine, Stanford, California, USA. * Tiffany Hung, * Ashley K Koegel, * David J Wong & * Howard Y Chang * Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, USA. * Tiffany Hung, * Ashley K Koegel, * Seung K Kim & * Howard Y Chang * Life Technologies, Foster City, California, USA. * Yulei Wang, * Yu Wang & * Benjamin Kong * The Broad Institute, Cambridge, Massachusetts, USA. * Michael F Lin & * Manolis Kellis * Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Michael F Lin & * Manolis Kellis * Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. * Yojiro Kotake & * Yue Xiong * Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. * Yojiro Kotake * Department of Biochemistry 1, Hamamatsu University School of Medicine, Higashi-ku, Hamamatsu, Japan. * Yojiro Kotake * Department of Genetics, Dartmouth Medical School, Hanover, New Hampshire, USA. * Gavin D Grant & * Michael L Whitfield * Department of Pathology, Academic Medical Center, Amsterdam, The Netherlands. * Hugo M Horlings & * Marc van de Vijver * Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Nilay Shah & * Saraswati Sukumar * Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Christopher Umbricht * Department of Developmental Biology, Stanford University School of Medicine, Stanford, California, USA. * Pei Wang & * Seung K Kim * Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Montebello, Oslo, Norway. * Anita Langerød & * Anne-Lise Børresen-Dale * Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. * Anne-Lise Børresen-Dale Contributions H.Y.C. and D.J.W. initiated the project. H.Y.C., D.J.W. and T.H. designed the experiments. T.H. performed the experiments and the computational analysis. Yulei Wang, Yu Wang and B.K. conducted high-throughput TaqMan RT-PCRs. M.F.L. and M.K. contributed CSF analysis. The following authors contributed samples or reagents: A.K.K., Y.K., G.D.G., H.M.H., N.S., C.U., P.W., A.L., S.K.K., M.v.d.V., A.-L.B.-D., S.S., M.L.W. and Y.X. The manuscript was prepared by H.Y.C., T.H. and D.J.W. with input from all co-authors. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * David J Wong or * Howard Y Chang Author Details * Tiffany Hung Search for this author in: * NPG journals * PubMed * Google Scholar * Yulei Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Michael F Lin Search for this author in: * NPG journals * PubMed * Google Scholar * Ashley K Koegel Search for this author in: * NPG journals * PubMed * Google Scholar * Yojiro Kotake Search for this author in: * NPG journals * PubMed * Google Scholar * Gavin D Grant Search for this author in: * NPG journals * PubMed * Google Scholar * Hugo M Horlings Search for this author in: * NPG journals * PubMed * Google Scholar * Nilay Shah Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher Umbricht Search for this author in: * NPG journals * PubMed * Google Scholar * Pei Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Yu Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamin Kong Search for this author in: * NPG journals * PubMed * Google Scholar * Anita Langerød Search for this author in: * NPG journals * PubMed * Google Scholar * Anne-Lise Børresen-Dale Search for this author in: * NPG journals * PubMed * Google Scholar * Seung K Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Marc van de Vijver Search for this author in: * NPG journals * PubMed * Google Scholar * Saraswati Sukumar Search for this author in: * NPG journals * PubMed * Google Scholar * Michael L Whitfield Search for this author in: * NPG journals * PubMed * Google Scholar * Manolis Kellis Search for this author in: * NPG journals * PubMed * Google Scholar * Yue Xiong Search for this author in: * NPG journals * PubMed * Google Scholar * David J Wong Contact David J Wong Search for this author in: * NPG journals * PubMed * Google Scholar * Howard Y Chang Contact Howard Y Chang Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information Other * Supplementary Table 3 (29K) List of cell cycle promoter transcripts * Supplementary Table 4 (111K) Combined expression of all transcripts across all tiling arrays PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–11 and Supplementary Tables 1, 2 and 5. Additional data - CTCF-mediated functional chromatin interactome in pluripotent cells
- Nat Genet 43(7):630-638 (2011)
Nature Genetics | Article CTCF-mediated functional chromatin interactome in pluripotent cells * Lusy Handoko1, 5 * Han Xu1, 5 * Guoliang Li1, 5 * Chew Yee Ngan1 * Elaine Chew1 * Marie Schnapp1 * Charlie Wah Heng Lee1 * Chaopeng Ye1 * Joanne Lim Hui Ping1 * Fabianus Mulawadi1 * Eleanor Wong1, 2 * Jianpeng Sheng3 * Yubo Zhang1 * Thompson Poh1 * Chee Seng Chan1 * Galih Kunarso4 * Atif Shahab1 * Guillaume Bourque1 * Valere Cacheux-Rataboul1 * Wing-Kin Sung1, 2 * Yijun Ruan1 * Chia-Lin Wei1, 2, 6 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:630–638Year published:(2011)DOI:doi:10.1038/ng.857Received08 March 2011Accepted16 May 2011Published online19 June 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Mammalian genomes are viewed as functional organizations that orchestrate spatial and temporal gene regulation. CTCF, the most characterized insulator-binding protein, has been implicated as a key genome organizer. However, little is known about CTCF-associated higher-order chromatin structures at a global scale. Here we applied chromatin interaction analysis by paired-end tag (ChIA-PET) sequencing to elucidate the CTCF-chromatin interactome in pluripotent cells. From this analysis, we identified 1,480 cis- and 336 trans-interacting loci with high reproducibility and precision. Associating these chromatin interaction loci with their underlying epigenetic states, promoter activities, enhancer binding and nuclear lamina occupancy, we uncovered five distinct chromatin domains that suggest potential new models of CTCF function in chromatin organization and transcriptional control. Specifically, CTCF interactions demarcate chromatin-nuclear membrane attachments and influence prop! er gene expression through extensive cross-talk between promoters and regulatory elements. This highly complex nuclear organization offers insights toward the unifying principles that govern genome plasticity and function. View full text Figures at a glance * Figure 1: Genome-wide CTCF-mediated chromatin interactome. () Circos map of the whole-genome CTCF chromatin interactome, associated genes, p300 and lamin B occupancies from chromosome 1 to chromosome X, generated using the Circos software package (see URLs). Interchromosomal interactions are drawn in the innermost ring, followed by the gene density track (dark green). The CTCF track (black) shows the peak signals of CTCF, followed by the intrachromosomal interactions. The lamin B track (dark gray) represents the peak signals of lamin B. The p300 track (red) shows the fold change between the sample and the control. () An expanded view of the interactions found from chromosomes 13, 14 and 15. Profiles shown in different tracks from inner to outer rings are listed accordingly. The intensity of the color is proportional to the PET counts in the cluster as shown. () Chromosome-wide view of all the intrachromosomal interactions detected on chromosome 10. The content shown in each track is labeled on the side. * Figure 2: Validation of CTCF-mediated chromatin interactions. () A region on chr7:25,586,953–26,569,774 harboring a cis-interaction cluster with the Cyp2 gene family is shown as purple lines connecting CTCF binding sites (red peaks). Numbers on the panel below show the frequencies of the interactions detected by independent 4C sequence reads. A triangle indicates the anchored primer location. Confirmed interactions are circled. () FISH analysis confirms the trans interaction between chr.13:13,658,687 and chr.15:74,912,106 (red arrow). The co-localization is shown as staining of red and green fusion spots. In the control experiment (dotted arrow), the co-localization percentage of the negative control region (chr.16:52,100,818) is significantly lower (7.5% versus 14.6%). () FISH on CTCF knockdown cells. Mouse ES cells were transfected with CTCF siRNA (CTCF KD cells) or control siRNA. The western blot shows that CTCF protein in the CTCF KD cells was less than 10% of that in the control cells. Co-localization ratios were tested for inte! raction loci (x-axis). The y-axis is the fold change of the co-localization frequency between the interaction and the negative control loci. () Above, chromosome-wide view of cis interactions detected on chromosome 10. Middle, detailed view of a 70-kb loop harboring Efna2 and Mim1 between 79,564,519 and 79,700,518. Below, 3C validation between the anchor (green triangle) and the distal site (red star) in the control cells (white triangles) and the CTCF KD cells (black squares). The y axis shows the relative interaction frequency and the x axis displays the genomic coordinates. HindIII sites are marked in blue. * Figure 3: Cumulative histone modification patterns of CTCF loops. () The loop model. The 1,295 loops with a span of less than 1 Mb and their neighboring regions were first aligned; the same distance L of the loop span is extended upstream and downstream outside the loop boundaries. For each histone modification, normalized histone modification signals are determined from the total distance 3L and then normalized by their sequencing depth. We constructed a set of 1,295 negative-control loops randomly selected from 2M+ pairs of CTCF binding sites with similar distribution of spans and CTCF binding affinities as a control. (–) The cumulative normalized signals of H3K4me1 (), H3K36me3 () and H3K27me3 () were plotted for comparison between CTCF loops and control loops. The intensity plots show the significantly different patterns between the CTCF loops and simulated control loops. This random process was repeated ten times to calculate the P value as shown. Error bars, standard deviation of the normalized histone signals from ten negative con! trols. * Figure 4: Distinct types of chromatin domains defined by CTCF-tethered interactions. () Five chromatin domains are demarcated by CTCF loops through clustering of seven histone modification signatures. For each loop region, the same distance L is extended upstream and downstream of the loop boundaries. Normalized signals of seven histone modifications (listed on the top) from the total distance 3L (left, within and right of the loops) are determined for each loop and shown as one row. Loops showing similar combinatory signatures are clustered, and symmetrical clusters are grouped. The percentage of loops found in each category is listed on the right. () Correlation of unique histone modification signatures with loop span. CTCF loops are sorted in ascending order of span, and the histone patterns associated with different spans are shown. Each column corresponds to an aligned bin, and each row corresponds to CTCF-associated loops. A window containing 100 CTCF loops is moved vertically to average the signal. A clear transition of the histone patterns that diffe! rentiate active signals from inactive signals is observed at ~200 kb. () The proposed models of category I to IV based on the histone and RNAPII intensity profiles. * Figure 5: Promoter-p300 communications facilitated by CTCF-associated chromatin interactions. () A cis-interaction cluster (chr10:76,552,662–76,718,442; shown in connecting purple lines) between CTCF binding sites (red peaks) connects the promoter of Pofut2 (blue gene track) with its p300 enhancer binding site (green) located 124 kb 5′. Above, RNA-seq signal (dark red) detected for Pofut2 expression is shown as the track. () Percentage of genes upregulated in ES cells compared to NS cells from genes whose promoters and nearest p300 binding distances are less than 10 kb (1), genes whose promoters and nearest p300 are brought in close proximity by CTCF interactions to less than 10 kb (2) and genes whose promoters with distal p300 binding are more than 10 kb apart (3). Expression data for the ES cells and NS cells were derived from microarray experiments46. () Expression of genes whose promoters are connected to a p300 enhancer by CTCF-associated loops. Gene expression was assessed by real-time PCR from the CTCF knockdown (CTCF KD) and control cells. The western blo! t shows the CTCF protein level in CTCF KD cells compared to control cells. Expression levels were normalized against that of the housekeeping gene Gapdh and calculated from two independent qPCR reactions of two independent siRNA experiments (total 2 × 2 qPCR). Expression of genes in CTCF knockdown cells was then normalized against that of genes in the control cells (= 1). P value calculated by t-test is indicated. ***P ≤ 9.54 × 10−5; **P ≤ 5.01 × 10−3; *P ≤ 1.26 × 10−2. * Figure 6: Lamin-associated domains (LADs) in embryonic stem cells. () Multiple LADs found in a 47.4-Mb interval (chr3:97,107,505–144,477,019) represented by the profile of fold change between ChIP-Seq signal and input background (light blue track). As a comparison, LADs determined by DamID from mouse ES cells are shown as black bars, and CTCF loops are shown above the LAD tracks. Gene density and histone modification profiles are shown below. () CTCF loop occupancy profile in LADs and up to 1 Mb outside LAD boundaries. () Genes associated with LADs are transcriptionally repressed. The distributions of gene expression levels with different locations relative to LADs are plotted. The x axis shows the log value of gene expression based on Ivanova's microarray data set47 and the y axis shows the percentage of genes. () Percentage of genes upregulated in ES (blue bars) or NS cells (red bars) relative to their locations to LADs are shown. Genes located in or in close proximity to LADs are preferentially upregulated in NS cells. In NS cells, 64%! of the LAD-associated genes are upregulated. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE12241 * GSE11172 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Lusy Handoko, * Han Xu & * Guoliang Li Affiliations * Genome Institute of Singapore, Singapore. * Lusy Handoko, * Han Xu, * Guoliang Li, * Chew Yee Ngan, * Elaine Chew, * Marie Schnapp, * Charlie Wah Heng Lee, * Chaopeng Ye, * Joanne Lim Hui Ping, * Fabianus Mulawadi, * Eleanor Wong, * Yubo Zhang, * Thompson Poh, * Chee Seng Chan, * Atif Shahab, * Guillaume Bourque, * Valere Cacheux-Rataboul, * Wing-Kin Sung, * Yijun Ruan & * Chia-Lin Wei * National University of Singapore, Singapore. * Eleanor Wong, * Wing-Kin Sung & * Chia-Lin Wei * Nanyang Technological University, Singapore. * Jianpeng Sheng * Duke-NUS Graduate Medical School Singapore, Singapore. * Galih Kunarso * Present address: Joint Genome Institute, Walnut Creek, California, USA. * Chia-Lin Wei Contributions Y.R. and C.-L.W. designed the study. L.H., H.X. and G.L. conducted experiments and data analyses. C.Y.N., C.Y. and E.W. performed the ChIA-PET and ChIP-Seq experiments. L.H., E.C., M.S., C.Y., J.L.H.P., J.S. and V.C.-R. coordinated all the validation experiments. C.S.C. and A.S. provided sequencing data processing and management. F.M. and W.-K.S. provided ChIA-PET data processing and bioinformatics support. C.W.H.L., Y.Z., G.K. and G.B. carried out additional global bioinformatic analyses. T.P. offered high-throughput sequencing support. L.H., H.X., G.L. and C.L.W. analyzed the data and wrote the manuscript. Y.R. provided critical review of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Chia-Lin Wei or * Yijun Ruan Author Details * Lusy Handoko Search for this author in: * NPG journals * PubMed * Google Scholar * Han Xu Search for this author in: * NPG journals * PubMed * Google Scholar * Guoliang Li Search for this author in: * NPG journals * PubMed * Google Scholar * Chew Yee Ngan Search for this author in: * NPG journals * PubMed * Google Scholar * Elaine Chew Search for this author in: * NPG journals * PubMed * Google Scholar * Marie Schnapp Search for this author in: * NPG journals * PubMed * Google Scholar * Charlie Wah Heng Lee Search for this author in: * NPG journals * PubMed * Google Scholar * Chaopeng Ye Search for this author in: * NPG journals * PubMed * Google Scholar * Joanne Lim Hui Ping Search for this author in: * NPG journals * PubMed * Google Scholar * Fabianus Mulawadi Search for this author in: * NPG journals * PubMed * Google Scholar * Eleanor Wong Search for this author in: * NPG journals * PubMed * Google Scholar * Jianpeng Sheng Search for this author in: * NPG journals * PubMed * Google Scholar * Yubo Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Thompson Poh Search for this author in: * NPG journals * PubMed * Google Scholar * Chee Seng Chan Search for this author in: * NPG journals * PubMed * Google Scholar * Galih Kunarso Search for this author in: * NPG journals * PubMed * Google Scholar * Atif Shahab Search for this author in: * NPG journals * PubMed * Google Scholar * Guillaume Bourque Search for this author in: * NPG journals * PubMed * Google Scholar * Valere Cacheux-Rataboul Search for this author in: * NPG journals * PubMed * Google Scholar * Wing-Kin Sung Search for this author in: * NPG journals * PubMed * Google Scholar * Yijun Ruan Contact Yijun Ruan Search for this author in: * NPG journals * PubMed * Google Scholar * Chia-Lin Wei Contact Chia-Lin Wei Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 2 (4M) CTCF binding sites * Supplementary Table 3 (2M) Intra-and inter-chromosomal interactions detected by CTCF ChIA-PET * Supplementary Table 5 (750K) List of 5 categories assigned to intra-chromosomal interactions * Supplementary Table 6 (4M) RNA Pol II, p300 and LADs sites defined by ChIP-Seq * Supplementary Table 8 (184K) RNAP II interactions defined by ChIA-PET * Supplementary Table 9 (184K) SALL4 interactions defined by ChIA-PET PDF files * Supplementary Text and Figures (4M) Supplementary Note, Supplementary Figures 1–11 and Supplementary Tables 1, 4, 7 and 10. Additional data - A genetic interaction network of five genes for human polycystic kidney and liver diseases defines polycystin-1 as the central determinant of cyst formation
- Nat Genet 43(7):639-647 (2011)
Nature Genetics | Article A genetic interaction network of five genes for human polycystic kidney and liver diseases defines polycystin-1 as the central determinant of cyst formation * Sorin V Fedeles1, 2 * Xin Tian1, 6 * Anna-Rachel Gallagher1, 6 * Michihiro Mitobe1 * Saori Nishio1 * Seung Hun Lee1 * Yiqiang Cai1 * Lin Geng1 * Craig M Crews3, 4, 5 * Stefan Somlo1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:639–647Year published:(2011)DOI:doi:10.1038/ng.860Received13 July 2010Accepted18 May 2011Published online19 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Autosomal dominant polycystic liver disease results from mutations in PRKCSH or SEC63. The respective gene products, glucosidase IIβ and SEC63p, function in protein translocation and quality control pathways in the endoplasmic reticulum. Here we show that glucosidase IIβ and Sec63p are required in mice for adequate expression of a functional complex of the polycystic kidney disease gene products, polycystin-1 and polycystin-2. We find that polycystin-1 is the rate-limiting component of this complex and that there is a dose-response relationship between cystic dilation and levels of functional polycystin-1 following mutation of Prkcsh or Sec63. Reduced expression of polycystin-1 also serves to sensitize the kidney to cyst formation resulting from mutations in Pkhd1, the recessive polycystic kidney disease gene. Finally, we show that proteasome inhibition increases steady-state levels of polycystin-1 in cells lacking glucosidase IIβ and that treatment with a proteasome inhi! bitor reduces cystic disease in orthologous gene models of human autosomal dominant polycystic liver disease. View full text Figures at a glance * Figure 1: Pkd1 dosage is the main genetic determinant of Prkcsh-dependent cyst formation. () Prkcshflox (top) and Sec63flox (bottom) produce null alleles upon deletion. In the left panels, GIIβ and Sec63p disappear from kidney cell lines (flox/flox) following Cre expression (null). In the right panels, GIIβ and Sec63p are markedly reduced in mosaic Prkcshflox/flox; Ksp-Cre and Sec63flox/flox; Ksp-Cre kidney tissue (flox/flox) compared to heterozygous controls. () Prkcshflox/flox; pCX-CreER mice, with inducible Cre expression in bile duct epithelia31, develop liver cysts 8 weeks after treatment with tamoxifen from P28-P32 (black arrows). Expression of the Pkd1F/H-BAC transgene, but not Pkd2-BAC, abrogates bile duct cyst formation (white arrows). v, venule; c, cyst. Scale bars, 500 μm in the upper panels and 100 μm in the lower panels. () The severity of cystic disease is markedly increased on the Pkd1+/− background and moderately increased on the Pkd2+/− background. The Pkd1F/H-BAC transgene rescues the Prkcshflox/flox; Ksp-Cre;Pkd2+/− cystic phenotype; ! Pkd2-BAC has no effect. Ages, P42; scale bar, 2 mm. () Quantitative assessment of cyst severity by kidney weight to body weight ratio, cystic index and BUN. The colors of histogram bars correspond to the genotypes in . n = 7 for each group except Prkcshflox/flox; Ksp-Cre; Pkd1+/−, for which n = 8. Results are mean ± s.e.m. (ANOVA; ***P < 0.001, *P < 0.05). () Immunoblots with anti-HA (left) and anti-FLAG (middle) showing PC1 expression in membrane-enriched kidney tissue lysates from two representative Pkd1F/H-BAC transgenic lines (Tg248 and Tg276). The majority of PC1 is cleaved in vivo. Tg248 was used in the current study. Transgenic PC2 overexpression in the liver of littermate mice (right). WT, non-transgenic. () Absence of phenotypic effect in Pkd2flox/flox; Pkhd1-Cre mice with or without the Pkd1F/H-BAC transgene (P21). Scale bar, 1 mm. * Figure 2: Pkd1 and Pkd2 dosage in Sec63-dependent cyst formation. (,) Histological kidney sections () and aggregate data () from mice with the indicated genotypes at P21. The Pkd1+/− and Pkd2+/− backgrounds exacerbate cyst formation following loss of Sec63; the increase in severity is greater with Pkd1+/− than with Pkd2+/−. The Pkd1F/H-BAC transgene rescues the PKD in Sec63flox/flox; Ksp-Cre mice. The Pkd2-BAC transgene has no effect. Genotypes in the histograms () are indicated by the colors in ; n (from left to right) = 5, 8, 8, 9, 7, 5. Results are mean ± s.e.m. (ANOVA; ***P < 0.001, **P < 0.01). () Sec63flox/flox; Prkcshflox/flox; Ksp-Cre doubly mutant mice show genetic interaction between Prkcsh and Sec63, with marked exacerbation of the cystic phenotype compared to either Prkcshflox/flox; Ksp-Cre or Sec63flox/flox; Ksp-Cre mice at P21. Scale bar, 2 mm. * Figure 3: Impaired biogenesis and trafficking of PC1 in ADPLD. () Immunoblots showing steady state expression of PC1 from the Pkd1F/H-BAC transgene in Prkcshflox/flox; Pkd1F/H-BAC (flox/flox) control and Prkcsh−/−; Pkd1F/H-BAC null cells lines. Left panel, immunoblot of total cell lysates with anti-HA; right panel, immunoblot following immunoprecipitation by anti-HA showing reduction in both the intramembranous PC1-CTF and uncleaved full-length PC1 (PC1-FL). Densitometric quantitation of PC1-CTF normalized to tubulin shows PC1 levels in null cells are ~48% of their respective controls (n = 4; **P = 0.0060; results shown as mean ± s.e.m. (Student's t-test)). () Representative immunoblots showing PC2 expression in cell lines and kidney tissues. Densitometric quantitation normalized to tubulin shows that PC2 levels in null cells and cystic kidneys are reduced to ~66% of their respective controls (n = 4 for cell lines; n = 3 each for kidneys; *P = 0.0152, **P = 0.0095; mean ± s.e.m. (Student's t-test)). () Endo H resistant fraction (R! , upper band) of PC1-CTF is markedly reduced and the Endo H sensitive fraction (S, lower band) is increased in Prkcsh−/−; Pkd1F/H-BAC cells (null) compared to Prkcshflox/flox; Pkd1F/H-BAC controls (flox/flox), indicating reduced trafficking of PC1-CTF past the middle Golgi in null cells (,). Expression of PC2 () and PC1 () in cilia of Prkcshflox/flox; Pkd1F/H-BAC control and Prkcsh−/−; Pkd1F/H-BAC null cells. PC2 trafficking to cilia is not altered by loss of GIIβ (), whereas PC1 trafficking is markedly reduced in cilia (the arrow marks the weak PC1-HA signal) (). Red, acetylated α-tubulin; green, anti-PC2 in and anti-HA in . Scale bar, 5 μm. * Figure 4: Late stage tubule dilation in Prkcsh and Sec63 mutant kidneys overexpressing PC1 (a,b). Histological kidney sections () and aggregate data () from mice with the indicated genotypes at 3 months of age. Rescue by the Pkd1F/H-BAC remains complete through 3 months; Pkd2-BAC has no effect. n (from left to right) = 6, 7, 7, 5. Results are mean ± s.e.m. (ANOVA; ***P < 0.001). () Histological examination of Prkcsh mutant kidneys with the indicated genotypes at 3 and 6 months. The occurrence of microscopic tubule dilation at 6 months in Prkcshflox/flox; Ksp-Cre; Pkd1F/H-BAC kidneys shows that PC1-dependent cyst growth is a function of both gene dosage and time. () Mild onset cystic dilation in Sec63flox/flox; Ksp-Cre; Pkd1F/H-BAC at P45 (bottom panel) indicate similar temporal effects in Sec63 mutants. Scale bars, 500 μm. * Figure 5: Nephron segment-specific sensitivity to Pkd1 dosage-dependent proliferation and cyst growth. (–) Immunocytochemical analysis of Prkcsh mutant kidneys at P42 (–) and Sec63 mutant kidneys at P21 (–) showing the increased size of collecting duct cysts (green) resulting from the Pkd1+/− background (,,,). Thick ascending limb cysts (red) are unchanged by reduced dosage of Pkd1 (,,,). DBA, dolichos biflorus agglutinin; THP, Tamm Horsfall protein. Scale bars, 50 μm. (–) Representative images showing nuclear BrdU incorporation following five daily injections ending at P42 (,) or a single injection 3 h before being killed on P21 (,) to determine the impact on cyst proliferation of two copies of Pkd1 (,) compared to a single copy (,). We determined the comparative proliferation rates by counting >1,000 DBA-positive cystic collecting duct cells per kidney from six kidneys for each genotype. BrdU-positive nuclei are shown in red, DBA is green and DAPI is blue. Scale bar, 20 μm. For both ADPLD models, the increased growth of collecting duct cysts with reduced PC1 dos! age on the Pkd1+/− background was associated with increased proliferation () (***P < 0.001; mean ± s.e.m.; Student's t-test). (–) Representative images comparing apoptotic rates by TUNEL staining (red) for both ADPLD models as a function of Pkd1 dosage. () Apoptotic rates measured as above showed more modest increases on the Pkd1+/− background () (**P < 0.01; mean ± s.e.m.; Student's t-test), suggesting that the proliferative effects predominate. DBA, green; scale bar, 20 μm. * Figure 6: Genetic interaction of Pkhd1, Pkd1 and Sec63. (–) Histological kidney sections (,) and aggregate structural and functional data () from mice with the indicated genotypes at P21. The Pkhd1del4/del4 background results in increased severity of polycystic disease in Sec63 mutant kidneys. PC1 overexpression rescues the worsened Sec63flox/flox; Ksp-Cre; Pkhd1del4/del4 phenotype to a level that is milder than Sec63flox/flox; Ksp-Cre alone (–), although microscopic cysts persist (). Genotypes in the histograms () are indicated by the colors in ; n (from left to right) = 5, 6, 8, 6, 6. Results are mean ± s.e.m.; ANOVA; ***P < 0.001. () Immunocytochemical analysis of Sec63 mutant kidneys at P21 showing the increased size of collecting duct cysts (green) resulting from the Pkhddel4/del4 background. Thick ascending limb cysts (red) are unchanged. Scale bars, 2 mm (); 500 μm (); 50 μm (). * Figure 7: Proteasome inhibitor therapy ameliorates cyst formation following loss of Prkcsh. () Prkcsh−/− cells have increased sensitivity to proteasome inhibitors (n = 3 independent wells for each genotype and treatment). Results are shown as mean ± s.e.m. (Student's t-test). () Prkcsh−/−; Pkd1F/H-BAC cells treated with MG132 (10 μM for 16 h) showed increased levels of PC1. () Representative images of kidneys from Prkcshflox/flox; Ksp-Cre; Pkd1+/− mice treated twice weekly with carfilzomib (5 mg/kg) for 3 weeks beginning at P21. Effective inhibition of proteasome function is indicated by increased Hif1α expression in carfilzomib-treated kidneys (C) compared to the vehicle-treated controls (V). Scale bar, 1 mm. () Representative images of kidneys from Prkcshflox/flox; Ksp-Cre mice treated with carfilzomib (5 mg/kg) for 6 weeks beginning at P42. () Aggregate data for Prkcshflox/flox; Ksp-Cre; Pkd1+/− mice showing improvement following carfilzomib treatment (n = 6, carfilzomib group; n = 5, vehicle group; results are mean ± s.e.m.; Student's t-test; **! P = 0.0011 (weight ratios), P = 0.0082 (cystic index), **P = 0.0084 (BUN)). () Aggregate data for Prkcshflox/flox; Ksp-Cre mice showing improvement in the carfilzomib-treated group (n = 6, carfilzomib group; n = 3, vehicle group; results are mean ± s.e.m.; Student's t-test; ***P = 0.0007 (weight ratios), P < 0.0001 (cystic index), P = 0.0032 (BUN)). () The rate of apoptosis is increased (*P = 0.034), and the rate of proliferation decreased (*P = 0.025) in cyst-lining epithelia following treatment with carfilzomib in Prkcshflox/flox; Ksp-Cre; Pkd1+/− mice at P42. Results are mean ± s.e.m. (Student's t-test). Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Xin Tian & * Anna-Rachel Gallagher Affiliations * Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA. * Sorin V Fedeles, * Xin Tian, * Anna-Rachel Gallagher, * Michihiro Mitobe, * Saori Nishio, * Seung Hun Lee, * Yiqiang Cai, * Lin Geng & * Stefan Somlo * Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA. * Sorin V Fedeles & * Stefan Somlo * Department of Molecular, Cellular and Developmental Biology, Yale University School of Medicine, New Haven, Connecticut, USA. * Craig M Crews * Department of Pharmacology, Yale University School of Medicine, New Haven, Connecticut, USA. * Craig M Crews * Department of Chemistry, Yale University School of Medicine, New Haven, Connecticut, USA. * Craig M Crews Contributions S.V.F. co-designed the study, performed the experiments and co-wrote the manuscript. X.T. and M.M. generated the mouse models. A.-R.G. performed experiments, participated in the experimental design and assisted in manuscript preparation. S.N., S.H.L., Y.C. and L.G. carried out experiments. C.M.C. participated in the proteasome inhibitor studies. S.S. came up with the study design and co-wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Stefan Somlo Author Details * Sorin V Fedeles Search for this author in: * NPG journals * PubMed * Google Scholar * Xin Tian Search for this author in: * NPG journals * PubMed * Google Scholar * Anna-Rachel Gallagher Search for this author in: * NPG journals * PubMed * Google Scholar * Michihiro Mitobe Search for this author in: * NPG journals * PubMed * Google Scholar * Saori Nishio Search for this author in: * NPG journals * PubMed * Google Scholar * Seung Hun Lee Search for this author in: * NPG journals * PubMed * Google Scholar * Yiqiang Cai Search for this author in: * NPG journals * PubMed * Google Scholar * Lin Geng Search for this author in: * NPG journals * PubMed * Google Scholar * Craig M Crews Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan Somlo Contact Stefan Somlo Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Note and Supplementary Figures 1–9. Additional data - Subspecific origin and haplotype diversity in the laboratory mouse
- Nat Genet 43(7):648-655 (2011)
Nature Genetics | Article Subspecific origin and haplotype diversity in the laboratory mouse * Hyuna Yang1 * Jeremy R Wang2 * John P Didion3, 4, 5 * Ryan J Buus3, 4, 5 * Timothy A Bell3, 4, 5 * Catherine E Welsh2 * François Bonhomme6 * Alex Hon-Tsen Yu7, 8 * Michael W Nachman9 * Jaroslav Pialek10 * Priscilla Tucker11 * Pierre Boursot6 * Leonard McMillan2 * Gary A Churchill1 * Fernando Pardo-Manuel de Villena3, 4, 5 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:648–655Year published:(2011)DOI:doi:10.1038/ng.847Received13 July 2010Accepted05 May 2011Published online29 May 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Here we provide a genome-wide, high-resolution map of the phylogenetic origin of the genome of most extant laboratory mouse inbred strains. Our analysis is based on the genotypes of wild-caught mice from three subspecies of Mus musculus. We show that classical laboratory strains are derived from a few fancy mice with limited haplotype diversity. Their genomes are overwhelmingly Mus musculus domesticus in origin, and the remainder is mostly of Japanese origin. We generated genome-wide haplotype maps based on identity by descent from fancy mice and show that classical inbred strains have limited and non-randomly distributed genetic diversity. In contrast, wild-derived laboratory strains represent a broad sampling of diversity within M. musculus. Intersubspecific introgression is pervasive in these strains, and contamination by laboratory stocks has played a role in this process. The subspecific origin, haplotype diversity and identity by descent maps can be visualized using th! e Mouse Phylogeny Viewer (see URLs). View full text Figures at a glance * Figure 1: Overall contribution of each subspecies to the genome of wild and laboratory mice. For each sample, the figure depicts the cumulative contribution of M. m. domesticus (D, blue), M. m. musculus (M, red) and M. m. castaneus (C, green) subspecies for the autosomes. H, hybrid strains. * Figure 2: Subspecific origin and haplotype diversity of chromosomes 6 and X. () Subspecific origin of chromosome 6 (left) and X (right). Colors follow the same conventions as in Figure 1. (–) Phylogenetic trees for classical and wild-derived strains for two compatible intervals, one spanning positions 143,009,892–143,140,072 on chromosome 6 (,) and the other spanning positions 37,770,186–42,329,981 on chromosome X (,). * Figure 3: Intersubspecific introgression and contamination by classical strains in the wild-derived inbred strains. For each 1-Mb interval, we identified the classical inbred strain with maximum genotype similarity to a given wild-derived strain. (–) Frequency distribution of similarity for eight strains. Colors follow the same conventions as in the previous figures. * Figure 4: Identification of donor strains. (–) Examples of the approach used to identify the donor classical strain that contaminated a wild-derived strain. Red circles represent 1-Mb intervals in which a wild-derived strain is IBD to a haplotype present in classical inbred strains; black circles represent 1-Mb intervals that are not IBD. Author information * Abstract * Author information * Supplementary information Affiliations * The Jackson Laboratory, Bar Harbor, Maine, USA. * Hyuna Yang & * Gary A Churchill * Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. * Jeremy R Wang, * Catherine E Welsh & * Leonard McMillan * Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. * John P Didion, * Ryan J Buus, * Timothy A Bell & * Fernando Pardo-Manuel de Villena * Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. * John P Didion, * Ryan J Buus, * Timothy A Bell & * Fernando Pardo-Manuel de Villena * Carolina Center for Genome Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. * John P Didion, * Ryan J Buus, * Timothy A Bell & * Fernando Pardo-Manuel de Villena * Université Montpellier 2, CNRS UMR5554, Institut des Sciences de l'Evolution, Montpellier, France. * François Bonhomme & * Pierre Boursot * Institute of Zoology, National Taiwan University, Taipei, Taiwan. * Alex Hon-Tsen Yu * Department of Life Science, National Taiwan University, Taipei, Taiwan. * Alex Hon-Tsen Yu * Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA. * Michael W Nachman * Department of Population Biology, Institute of Vertebrate Biology, Academy of Sciences of the Czech Republic, Brno and Studenec, Czech Republic. * Jaroslav Pialek * Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, USA. * Priscilla Tucker Contributions F.P.-M.d.V., G.A.C. and H.Y. conceived the study design and wrote the paper. H.Y., J.R.W., J.P.D., L.M. and C.E.W. carried out the bioinformatics analyses. J.P.D., T.A.B. and R.J.B. prepared the samples and conducted the targeted PCR amplification and sequencing. F.B., P.B., A.H.-T.Y., M.W.N., J.P. and P.T. provided biological samples. All authors contributed to the interpretation of the results and the writing of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Fernando Pardo-Manuel de Villena or * Gary A Churchill Author Details * Hyuna Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Jeremy R Wang Search for this author in: * NPG journals * PubMed * Google Scholar * John P Didion Search for this author in: * NPG journals * PubMed * Google Scholar * Ryan J Buus Search for this author in: * NPG journals * PubMed * Google Scholar * Timothy A Bell Search for this author in: * NPG journals * PubMed * Google Scholar * Catherine E Welsh Search for this author in: * NPG journals * PubMed * Google Scholar * François Bonhomme Search for this author in: * NPG journals * PubMed * Google Scholar * Alex Hon-Tsen Yu Search for this author in: * NPG journals * PubMed * Google Scholar * Michael W Nachman Search for this author in: * NPG journals * PubMed * Google Scholar * Jaroslav Pialek Search for this author in: * NPG journals * PubMed * Google Scholar * Priscilla Tucker Search for this author in: * NPG journals * PubMed * Google Scholar * Pierre Boursot Search for this author in: * NPG journals * PubMed * Google Scholar * Leonard McMillan Search for this author in: * NPG journals * PubMed * Google Scholar * Gary A Churchill Contact Gary A Churchill Search for this author in: * NPG journals * PubMed * Google Scholar * Fernando Pardo-Manuel de Villena Contact Fernando Pardo-Manuel de Villena Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Other * Supplementary Table 1 (92K) Sample summary PDF files * Supplementary Text and Figures (7M) Supplementary Figures 1–10 and Supplementary Tables 2–6. Additional data - An integrated approach to characterize genetic interaction networks in yeast metabolism
- Nat Genet 43(7):656-662 (2011)
Nature Genetics | Article An integrated approach to characterize genetic interaction networks in yeast metabolism * Balázs Szappanos1, 10 * Károly Kovács1, 10 * Béla Szamecz1 * Frantisek Honti1, 2 * Michael Costanzo3, 4 * Anastasia Baryshnikova3, 4 * Gabriel Gelius-Dietrich5 * Martin J Lercher5 * Márk Jelasity6 * Chad L Myers7 * Brenda J Andrews3, 4 * Charles Boone3, 4 * Stephen G Oliver8 * Csaba Pál1 * Balázs Papp1, 9, 10 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:656–662Year published:(2011)DOI:doi:10.1038/ng.846Received30 December 2010Accepted05 May 2011Published online29 May 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Although experimental and theoretical efforts have been applied to globally map genetic interactions, we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we i, quantitatively measured genetic interactions between ~185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii, superposed the data on a detailed systems biology model of metabolism and iii, introduced a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigated the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy and gene dispensability. Last, we show the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosy! nthesis and confirming them by in vivo experiments. View full text Figures at a glance * Figure 1: Distribution and monochromaticity of genetic interactions between functional groups. The radii of the circles represent the fraction of screened gene pairs that show genetic interaction within and between functional annotation groups (for example, sterol metabolism has the highest prevalence of interactions with a value of 0.225). Enrichment of genetic interactions within functional groups is visually apparent and corresponds to the larger circles on the diagonal. The colors of the circles reflect the monochromatic score defined as the normalized ratio of positive to all interacting pairs (Online Methods). Functional groups displaying only positive genetic interactions between each other have a monochromatic score of +1 (green), whereas those interacting purely negatively have a score of −1 (red). The background ratio of positive to all interactions (0.348) corresponds to a score of 0 (gray). Only the top 20 functional groups with the largest number of screened gene pairs and those genes assigned to only one functional group are included in the plot. * Figure 2: Degree distribution of genetic interaction networks and gene dispensability. () Both negative and positive genetic interaction degrees predicted by FBA showed negative correlations with predicted single-mutant fitness. Only genes with nonzero in silico fitness defects are shown, and variables are rank transformed. See the Online Methods for details on selecting independent data points (genes) for the statistical analysis. To improve the visual representation of coincident data points, we added a small amount of noise over the x axis for plotting. () The FBA-predicted single-gene deletion effect is strongly associated with predicted system-level pleiotropy degree (that is, the number of biosynthetic processes to which a gene contributes). See the Online Methods for details on the gene selection procedure. () Comparison of the empirically determined positive-to-negative genetic interaction ratio between null mutants of non-essential genes and hypomorphic alleles of essential genes revealed no significant difference. Horizontal lines of the boxplots cor! respond to the medians, and the bottoms and the tops of the boxes show the twenty-fifth and seventy-fifth percentiles, respectively. Whiskers show either the maximum (minimum) value or 1.5 times the interquartile range of the data, whichever is smaller (larger). Points more than 1.5 times the interquartile range above the third quartile or below the first quartile are plotted individually as outliers. * Figure 3: Comparison of computationally predicted and empirically determined genetic interactions. We evaluated prediction accuracy by visualizing the trade-off between precision (fraction of predicted interactions that are supported by empirical data) and recall (fraction of empirical interactions that are successfully identified by the model), and true-positive and false-positive rates (partial receiver operating characteristic (ROC) curves, inset) at different in silico genetic interaction score cutoffs. Dashed lines represent the levels of discrimination expected by chance. Note the different scale of the y axes for the negative and positive interactions. * Figure 4: Automated model refinement procedure. () Workflow of the two-stage model refinement method. In the first stage, a coarse-grained search is executed in which candidate models are evaluated only for those gene pairs that show interaction either in vivo or in silico according to the original model. In the second stage, the best models are refined in a restricted search space that is based on the results of the first stage but using all available data to evaluate the models. This two-stage approach made it feasible to explore a large space of candidate hypotheses while also making use of all available phenotypic data. () Results of eight independent runs of the model refinement algorithm. Fits of the modified (blue to green) and unmodified original (red) models to our empirical genetic interaction data are visualized by both precision recall and partial ROC curves (inset). Dashed lines represent the levels of discrimination expected by chance. Note that the same empirical dataset was used for both model refinement a! nd model evaluation, meaning no unseen test data was used to generate these plots. For a cross-validation estimate of model improvement, see the main text and the Supplementary Note. * Figure 5: Automated model refinement suggests modifications in NAD biosynthesis. () Biosynthetic routes to nicotinate mononucleotide in the yeast metabolic network reconstruction. Genes involved in the de novo pathway from tryptophan show negative genetic interactions with the nicotinic acid transporter gene in vivo but not in silico because of the presence of a two-step biosynthetic route from aspartate to quinolinate in the reconstruction (ASPOcm, aspartate oxidase; QULNS, quinolinate synthase). () Experimental verification of suggested model modifications. Deletion of genes for kynurenine pathway enzymes causes nicotinic acid auxotrophy. We spotted strains deleted for the genes of the kynurenine pathway (bna1Δ, bna2Δ, bna4Δ and bna5Δ) along with wild type (WT) in four serial dilutions on solid SC medium lacking histidine, arginine and lysine and incubated at 30 °C for 48 h in the presence and absence of nicotinic acid as indicated. To prevent diffusion of any substances that would complement nicotinic acid auxotrophy, the strains were grown separ! ately from each other in a 24-well plate. Repeating the experiment using liquid media confirmed the nicotinic acid auxotrophy of the mutants (data not shown). Yeast strains used in the auxotrophy study are derivatives of the BY4741 yeast deletion collection47, 48. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Balázs Szappanos, * Károly Kovács & * Balázs Papp Affiliations * Institute of Biochemistry, Biological Research Centre, Szeged, Hungary. * Balázs Szappanos, * Károly Kovács, * Béla Szamecz, * Frantisek Honti, * Csaba Pál & * Balázs Papp * Department of Biology and Biochemistry, University of Bath, Bath, UK. * Frantisek Honti * Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada. * Michael Costanzo, * Anastasia Baryshnikova, * Brenda J Andrews & * Charles Boone * Department of Molecular Genetics, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada. * Michael Costanzo, * Anastasia Baryshnikova, * Brenda J Andrews & * Charles Boone * Department of Computer Science, Heinrich-Heine-University, Düsseldorf, Germany. * Gabriel Gelius-Dietrich & * Martin J Lercher * Research Group on Artificial Intelligence, University of Szeged and HAS, Szeged, Hungary. * Márk Jelasity * Department of Computer Science & Engineering, University of Minnesota, Minneapolis, Minnesota, USA. * Chad L Myers * Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, UK. * Stephen G Oliver * Cambridge Systems Biology Centre and Department of Genetics, University of Cambridge, Cambridge, UK. * Balázs Papp Contributions M.C., C.L.M., B.J.A. and C.B. designed genetic interaction screens; A.B., M.C. and C.L.M. collected and analyzed raw data; B.P., C.P., M.J. and S.G.O. designed the computational study; B. Szappanos, K.K., F.H. and B.P. performed computational and statistical analyses; B. Szamecz performed auxotrophy experiments; G.G.-D. and M.J.L. developed software tools; and B.P., C.P., B. Szappanos, K.K., M.J. and S.G.O. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Balázs Papp Author Details * Balázs Szappanos Search for this author in: * NPG journals * PubMed * Google Scholar * Károly Kovács Search for this author in: * NPG journals * PubMed * Google Scholar * Béla Szamecz Search for this author in: * NPG journals * PubMed * Google Scholar * Frantisek Honti Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Costanzo Search for this author in: * NPG journals * PubMed * Google Scholar * Anastasia Baryshnikova Search for this author in: * NPG journals * PubMed * Google Scholar * Gabriel Gelius-Dietrich Search for this author in: * NPG journals * PubMed * Google Scholar * Martin J Lercher Search for this author in: * NPG journals * PubMed * Google Scholar * Márk Jelasity Search for this author in: * NPG journals * PubMed * Google Scholar * Chad L Myers Search for this author in: * NPG journals * PubMed * Google Scholar * Brenda J Andrews Search for this author in: * NPG journals * PubMed * Google Scholar * Charles Boone Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen G Oliver Search for this author in: * NPG journals * PubMed * Google Scholar * Csaba Pál Search for this author in: * NPG journals * PubMed * Google Scholar * Balázs Papp Contact Balázs Papp Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–4, Supplementary Tables 1–3 and Supplementary Note. Additional data - Exome sequencing identifies MAX mutations as a cause of hereditary pheochromocytoma
- Nat Genet 43(7):663-667 (2011)
Nature Genetics | Letter Exome sequencing identifies MAX mutations as a cause of hereditary pheochromocytoma * Iñaki Comino-Méndez1, 2, 15 * Francisco J Gracia-Aznárez2, 3, 15 * Francesca Schiavi4, 15 * Iñigo Landa1 * Luis J Leandro-García1 * Rocío Letón1 * Emiliano Honrado5 * Rocío Ramos-Medina6 * Daniela Caronia7 * Guillermo Pita7 * Álvaro Gómez-Graña1 * Aguirre A de Cubas1 * Lucía Inglada-Pérez1, 2 * Agnieszka Maliszewska1 * Elisa Taschin4 * Sara Bobisse4 * Giuseppe Pica8 * Paola Loli9 * Rafael Hernández-Lavado10 * José A Díaz11 * Mercedes Gómez-Morales12 * Anna González-Neira7 * Giovanna Roncador6 * Cristina Rodríguez-Antona1, 2 * Javier Benítez2, 3 * Massimo Mannelli13 * Giuseppe Opocher4, 14 * Mercedes Robledo1, 2 * Alberto Cascón1, 2 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:663–667Year published:(2011)DOI:doi:10.1038/ng.861Received16 March 2011Accepted18 May 2011Published online19 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Hereditary pheochromocytoma (PCC) is often caused by germline mutations in one of nine susceptibility genes described to date1, 2, 3, 4, but there are familial cases without mutations in these known genes. We sequenced the exomes of three unrelated individuals with hereditary PCC (cases) and identified mutations in MAX, the MYC associated factor X gene. Absence of MAX protein in the tumors and loss of heterozygosity caused by uniparental disomy supported the involvement of MAX alterations in the disease. A follow-up study of a selected series of 59 cases with PCC identified five additional MAX mutations and suggested an association with malignant outcome and preferential paternal transmission of MAX mutations. The involvement of the MYC-MAX-MXD1 network in the development and progression of neural crest cell tumors is further supported by the lack of functional MAX in rat PCC (PC12) cells5 and by the amplification of MYCN in neuroblastoma6 and suggests that loss of MAX funct! ion is correlated with metastatic potential. View full text Figures at a glance * Figure 1: Detection of MAX by immunohistochemistry with a MAX C-terminus–specific antibody. () Negative staining of tumor cells in a MAX-mutation–positive PCC compared to positive stromal cells (indicated with arrows). () Positive staining of a RET-mutated PCC. () Normal adrenal tissue showing MAX-positive staining. * Figure 2: Loss of heterozygosity (LOH) analysis of three tumors with MAX mutations. () Tumor DNA sequence chromatograms of the three MAX mutations showing, in each case, loss of the wild-type allele. () SNP array analysis of chromosome 14 performed with tumor DNA from the individual carrying the c.1A>G mutation shows uniparental disomy (UPD). The lower panel shows the genomic plots of the log R ratio (log2 Rcase/Rreference) indicating the presence of two alleles, and the upper panel shows the allele frequency parameters along chromosome 14, indicating LOH. Chromosomal locations of MAX, MEG3, C-14q control and microsatellites are indicated. () Multiplex PCR microsatellite analysis performed in blood (red) and tumor (blue) DNA from the individual carrying the c.223C>T mutation shows biallelic chromosome 14 amplification and LOH of the three informative microsatellites caused by UPD. C-14q, control amplicon from chromosome 14q; C-1q, control amplicon from chromosome 1q; C-MAX, control amplicon from the MAX locus. * Figure 3: Schematic representation of MAX mutations found in individuals with PCC. MAX transcript 2 (ENST00000358664) contains five exons that encode 160 amino acids, including the bHLHZip domain (denoted with a light gray bar; amino acids 22 to 102) and the casein kinase II phosphorylation sites (denoted with dark gray bars; numbers indicate the first and the last amino acid of each site). Truncating and missense mutations are marked with vertical arrows above the gene schematic and below the protein schematic, respectively. The bottom panel shows the conservation of the three amino acids altered by missense substitutions in individuals with PCC (marked with a vertical gray bar). UTR, untranslated region. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE19422 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Iñaki Comino-Méndez, * Francisco J Gracia-Aznárez & * Francesca Schiavi Affiliations * Hereditary Endocrine Cancer Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. * Iñaki Comino-Méndez, * Iñigo Landa, * Luis J Leandro-García, * Rocío Letón, * Álvaro Gómez-Graña, * Aguirre A de Cubas, * Lucía Inglada-Pérez, * Agnieszka Maliszewska, * Cristina Rodríguez-Antona, * Mercedes Robledo & * Alberto Cascón * Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain. * Iñaki Comino-Méndez, * Francisco J Gracia-Aznárez, * Lucía Inglada-Pérez, * Cristina Rodríguez-Antona, * Javier Benítez, * Mercedes Robledo & * Alberto Cascón * Human Genetics Group, Spanish National Cancer Research Centre, Madrid, Spain. * Francisco J Gracia-Aznárez & * Javier Benítez * Familial Cancer Clinic, Veneto Institute of Oncology, Padova, Italy. * Francesca Schiavi, * Elisa Taschin, * Sara Bobisse & * Giuseppe Opocher * Anatomical Pathology Service, Hospital de León, León, Spain. * Emiliano Honrado * Monoclonal Antibodies Unit, Biotechnology Programme, Spanish National Cancer Research Centre, Madrid, Spain. * Rocío Ramos-Medina & * Giovanna Roncador * Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Centre, Madrid, Spain. * Daniela Caronia, * Guillermo Pita & * Anna González-Neira * Endocrinology and Metabolic Diseases, University of Foggia, Foggia, Italy. * Giuseppe Pica * Department of Endocrinology, Ospedale Niguarda Ca' Granda, Milan, Italy. * Paola Loli * Endocrinology Section, Hospital Infanta Cristina, Badajoz, Spain. * Rafael Hernández-Lavado * Department of Endocrinology, Hospital Universitario Clínico San Carlos, Madrid, Spain. * José A Díaz * Department of Pathology, University Hospital, University of Granada, Granada, Spain. * Mercedes Gómez-Morales * Department of Clinical Pathophysiology, University of Florence and Istituto Toscano Tumori, Florence, Italy. * Massimo Mannelli * Department of Medical and Surgical Sciences, University of Padova, Padova, Italy. * Giuseppe Opocher Contributions A.C., M.R., F.S. and G.O. conceived the project. G. Pica, P.L., R.H.-L., J.A.D., M.G.-M. and M.M. collected tumor samples. F.J.G.-A., I.C.-M. and A.C. performed next-generation sequencing analysis and filtering. I.C.-M., F.J.G.-A., F.S., I.L., L.J.L.-G., R.L., R.R.-M., D.C., A.G.-G., A.A.d.C., L.I.-P., E.T., S.B., A.M., A.G.-N. and G.R. performed additional experiments. A.C., I.C.-M., E.H. and G. Pita performed additional data analysis. A.C., I.C.-M., F.J.G.-A., M.R., C.R.-A. and J.B. wrote the manuscript. All authors approved the final draft. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Alberto Cascón or * Mercedes Robledo Author Details * Iñaki Comino-Méndez Search for this author in: * NPG journals * PubMed * Google Scholar * Francisco J Gracia-Aznárez Search for this author in: * NPG journals * PubMed * Google Scholar * Francesca Schiavi Search for this author in: * NPG journals * PubMed * Google Scholar * Iñigo Landa Search for this author in: * NPG journals * PubMed * Google Scholar * Luis J Leandro-García Search for this author in: * NPG journals * PubMed * Google Scholar * Rocío Letón Search for this author in: * NPG journals * PubMed * Google Scholar * Emiliano Honrado Search for this author in: * NPG journals * PubMed * Google Scholar * Rocío Ramos-Medina Search for this author in: * NPG journals * PubMed * Google Scholar * Daniela Caronia Search for this author in: * NPG journals * PubMed * Google Scholar * Guillermo Pita Search for this author in: * NPG journals * PubMed * Google Scholar * Álvaro Gómez-Graña Search for this author in: * NPG journals * PubMed * Google Scholar * Aguirre A de Cubas Search for this author in: * NPG journals * PubMed * Google Scholar * Lucía Inglada-Pérez Search for this author in: * NPG journals * PubMed * Google Scholar * Agnieszka Maliszewska Search for this author in: * NPG journals * PubMed * Google Scholar * Elisa Taschin Search for this author in: * NPG journals * PubMed * Google Scholar * Sara Bobisse Search for this author in: * NPG journals * PubMed * Google Scholar * Giuseppe Pica Search for this author in: * NPG journals * PubMed * Google Scholar * Paola Loli Search for this author in: * NPG journals * PubMed * Google Scholar * Rafael Hernández-Lavado Search for this author in: * NPG journals * PubMed * Google Scholar * José A Díaz Search for this author in: * NPG journals * PubMed * Google Scholar * Mercedes Gómez-Morales Search for this author in: * NPG journals * PubMed * Google Scholar * Anna González-Neira Search for this author in: * NPG journals * PubMed * Google Scholar * Giovanna Roncador Search for this author in: * NPG journals * PubMed * Google Scholar * Cristina Rodríguez-Antona Search for this author in: * NPG journals * PubMed * Google Scholar * Javier Benítez Search for this author in: * NPG journals * PubMed * Google Scholar * Massimo Mannelli Search for this author in: * NPG journals * PubMed * Google Scholar * Giuseppe Opocher Search for this author in: * NPG journals * PubMed * Google Scholar * Mercedes Robledo Contact Mercedes Robledo Search for this author in: * NPG journals * PubMed * Google Scholar * Alberto Cascón Contact Alberto Cascón Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (324K) Supplementary Figures 1–4 and Supplementary Tables 1–3. Additional data - The nuclear deubiquitinase BAP1 is commonly inactivated by somatic mutations and 3p21.1 losses in malignant pleural mesothelioma
- Nat Genet 43(7):668-672 (2011)
Nature Genetics | Letter The nuclear deubiquitinase BAP1 is commonly inactivated by somatic mutations and 3p21.1 losses in malignant pleural mesothelioma * Matthew Bott1, 2 * Marie Brevet1 * Barry S Taylor3 * Shigeki Shimizu1 * Tatsuo Ito1 * Lu Wang1 * Jenette Creaney4 * Richard A Lake4 * Maureen F Zakowski1 * Boris Reva3 * Chris Sander3 * Robert Delsite5 * Simon Powell5 * Qin Zhou6 * Ronglai Shen6 * Adam Olshen6 * Valerie Rusch2 * Marc Ladanyi1, 7 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:668–672Year published:(2011)DOI:doi:10.1038/ng.855Received03 November 2010Accepted16 May 2011Published online05 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Malignant pleural mesotheliomas (MPMs) often show CDKN2A and NF2 inactivation, but other highly recurrent mutations have not been described. To identify additional driver genes, we used an integrated genomic analysis of 53 MPM tumor samples to guide a focused sequencing effort that uncovered somatic inactivating mutations in BAP1 in 23% of MPMs. The BAP1 nuclear deubiquitinase is known to target histones (together with ASXL1 as a Polycomb repressor subunit) and the HCF1 transcriptional co-factor, and we show that BAP1 knockdown in MPM cell lines affects E2F and Polycomb target genes. These findings implicate transcriptional deregulation in the pathogenesis of MPM. View full text Figures at a glance * Figure 1: RAE analysis of genomic gains and losses in 53 MPM tumors. The effective frequency of gain (red) or loss (blue) and corresponding statistical significance (false discovery rate (FDR), q values) of aberrations are indicated (y axis, right and left, respectively), as is their chromosomal position (center, gray; centromeres in red). Green lines adjacent to the axis indicate the cutoff for statistical significance (q value = 0.1, FDR = 10%). Genes selected for sequencing are identified near their relevant peaks (CDKN2A, the target of the 9p21 deletion, was not sequenced in the current study). * Figure 2: Heat map of chromosome 3p in MPM tumors. Genomic loss is indicated in blue. A subset of cases harbor more focal alterations centered on BAP1. Overall, 16 out of the 53 tumors (30%) have at least single copy genomic loss of the BAP1 locus. In the insets (scale bars, 50 μm), BAP1 immunohistochemistry shows loss of nuclear staining for BAP1 protein in tumors with a BAP1 mutation (for example, tumor A102 with a 1-bp insertion in exon 15) compared to those with wild-type BAP1 (for example, tumor A36). Also shown is the BAP1 FISH analysis (red signals, BAP1 probe; green signals, chromosome 3 centromere) confirming single copy loss in tumor A17 and normal diploid BAP1 status in tumor A97, validating array CGH results. * Figure 3: Distribution of BAP1 mutations relative to functional domains. Shown are the N-terminal ubiquitin hydrolase domain (blue), the HCF1-binding domain (HBM) and the C-terminal protein interaction domain (green) containing two nuclear localization signals (black boxes). * Figure 4: Integrated gene map showing BAP1 losses and mutations in relation to other identified genomic events in all 53 samples. Three samples had no alterations in the genes listed. * Figure 5: Relationship between the BAP1 knockdown expression signature in MPM and three published Polycomb target gene sets. The Bracken signature was derived by siRNA depletion of four PRC complex proteins (EZH2, EED, SUZ12 and BMI1) in embryonic fibroblasts. The Hassan signature reflects PRC targets identified by previous ChIP-on-chip experiments using SUZ12 in human embryonic stem cells. We generated the Douglas list using siRNA targeting BMI1 in A4573 Ewing Sarcoma cells. The significance of enrichment (overrepresentation) was assessed using the hypergeometric distribution. We noted significant overlap between genes in the BAP1 list and genes in all three PRC sets, with each pairwise overlap with the BAP1 signature showing P < 10−19. The 77 genes that appear in the BAP1 profile and at least two other Polycomb signatures are listed in Supplementary Table 8. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE29211 Author information * Accession codes * Author information * Supplementary information Affiliations * Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. * Matthew Bott, * Marie Brevet, * Shigeki Shimizu, * Tatsuo Ito, * Lu Wang, * Maureen F Zakowski & * Marc Ladanyi * Department of Surgery, Memorial Sloan-Kettering Cancer Center (MSKCC), New York, New York, USA. * Matthew Bott & * Valerie Rusch * Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. * Barry S Taylor, * Boris Reva & * Chris Sander * National Centre for Asbestos Disease Research, School of Medicine and Pharmacology, University of Western Australia, Sir Charles Gairdner Hospital, Nedlands, Australia. * Jenette Creaney & * Richard A Lake * Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. * Robert Delsite & * Simon Powell * Epidemiology-Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. * Qin Zhou, * Ronglai Shen & * Adam Olshen * Human Oncology & Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. * Marc Ladanyi Contributions M.L. designed and oversaw the study. V.R. and M.F.Z. oversaw the tumor sample procurement and histopathologic review, respectively. S.S. performed tumor sample selection and analyte processing for tumor samples and cell lines. J.C. and R.A.L. contributed microarray data and analytes from additional cell lines. M. Bott and M.L. reviewed microarray data and selected genes for sequencing. M. Bott obtained and analyzed additional sequencing and genotyping data. M. Bott and T.I. performed functional validation experiments. M. Brevet analyzed immunohistochemistry data. L.W. performed and analyzed FISH studies. R.D. performed functional assays for DNA repair foci, and R.D. and S.P. interpreted the results. B.S.T., B.R., C.S., Q.Z., R.S. and A.O. performed statistical and bioinformatics analyses. M. Bott and M.L. drafted the manuscript. All authors contributed to critical review of the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Marc Ladanyi Author Details * Matthew Bott Search for this author in: * NPG journals * PubMed * Google Scholar * Marie Brevet Search for this author in: * NPG journals * PubMed * Google Scholar * Barry S Taylor Search for this author in: * NPG journals * PubMed * Google Scholar * Shigeki Shimizu Search for this author in: * NPG journals * PubMed * Google Scholar * Tatsuo Ito Search for this author in: * NPG journals * PubMed * Google Scholar * Lu Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Jenette Creaney Search for this author in: * NPG journals * PubMed * Google Scholar * Richard A Lake Search for this author in: * NPG journals * PubMed * Google Scholar * Maureen F Zakowski Search for this author in: * NPG journals * PubMed * Google Scholar * Boris Reva Search for this author in: * NPG journals * PubMed * Google Scholar * Chris Sander Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Delsite Search for this author in: * NPG journals * PubMed * Google Scholar * Simon Powell Search for this author in: * NPG journals * PubMed * Google Scholar * Qin Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Ronglai Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Adam Olshen Search for this author in: * NPG journals * PubMed * Google Scholar * Valerie Rusch Search for this author in: * NPG journals * PubMed * Google Scholar * Marc Ladanyi Contact Marc Ladanyi Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information Other * Supplementary Data 1 (16K) Basic clinical and pathologic data on the set of 53 subjects with MPMs PDF files * Supplementary Text and Figures (1M) Supplementary Tables 1–11 and Supplementary Figures 1–9 Additional data - A cooperative microRNA-tumor suppressor gene network in acute T-cell lymphoblastic leukemia (T-ALL)
- Nat Genet 43(7):673-678 (2011)
Nature Genetics | Letter A cooperative microRNA-tumor suppressor gene network in acute T-cell lymphoblastic leukemia (T-ALL) * Konstantinos J Mavrakis1, 11 * Joni Van Der Meulen2, 11 * Andrew L Wolfe1, 3 * Xiaoping Liu1 * Evelien Mets2 * Tom Taghon4 * Aly A Khan3, 5 * Manu Setti3, 5 * Pieter Rondou2 * Peter Vandenberghe6 * Eric Delabesse7 * Yves Benoit8 * Nicholas B Socci9 * Christina S Leslie5 * Pieter Van Vlierberghe2, 10 * Frank Speleman2, 11 * Hans-Guido Wendel1, 11 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:673–678Year published:(2011)DOI:doi:10.1038/ng.858Received12 November 2010Accepted16 May 2011Published online05 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The importance of individual microRNAs (miRNAs) has been established in specific cancers. However, a comprehensive analysis of the contribution of miRNAs to the pathogenesis of any specific cancer is lacking. Here we show that in T-cell acute lymphoblastic leukemia (T-ALL), a small set of miRNAs is responsible for the cooperative suppression of several tumor suppressor genes. Cross-comparison of miRNA expression profiles in human T-ALL with the results of an unbiased miRNA library screen allowed us to identify five miRNAs (miR-19b, miR-20a, miR-26a, miR-92 and miR-223) that are capable of promoting T-ALL development in a mouse model and which account for the majority of miRNA expression in human T-ALL. Moreover, these miRNAs produce overlapping and cooperative effects on tumor suppressor genes implicated in the pathogenesis of T-ALL, including IKAROS (also known as IKZF1), PTEN, BIM, PHF6, NF1 and FBXW7. Thus, a comprehensive and unbiased analysis of miRNA action in T-ALL re! veals a striking pattern of miRNA-tumor suppressor gene interactions in this cancer. View full text Figures at a glance * Figure 1: Comprehensive study of oncogenic miRNAs in T-ALL. () Schematic of the experimental strategy. () Average miRNA expression across 50 T-ALL samples by quantitative RT-PCR and normalized to the mean expression value of all expressed miRNAs in a given sample (mean and s.d.); miRNAs are ordered by expression levels, and the 'top-ten' most abundantly expressed miRNAs are indicated. () miRNA expression in different cytogenetic subgroups of T-ALL (mean and s.d.; ordered numerically, and the most abundant miRNAs are indicated). * Figure 2: Pooled library screen for oncogenic miRNAs. () Schematic of the screening protocol: the primary screen selected for bypass of oncogene-induced apoptosis and the secondary screen selected for lymphocyte proliferation in the absence of IL-3. () Primary screen: micrograph illustrating c-MYC–induced apoptosis in MEFs (inset). Shown below is the percentage of miRNA sequences retrieved from surviving and adherent cells. Briefly, we isolated DNA and amplified the integrated miRNA(s) by PCR, sub-cloned it, picked ~100 clones picked and sequenced the integrated miRNA. () Secondary screen: enrichment of FL5-12 cells expressing the secondary miRNA library and GFP upon IL-3 depletion (inset). Shown below is the percentage of miRNA sequences retrieved from FL5-12 cells after IL-3 depletion. () Screen validation: shown is the mean fold change and the s.d. of miRNA- or GFP-expressing FL5-12 cells before and after IL-3 depletion (results of three independent experiments). () Summary of interim results: the ten most highly expressed! miRNAs in human T-ALL (red circle), the validated 'hits' in the miRNA screen (blue circle) and miRNAs that bind tumor suppressor genes implicated in T-ALL (green circle). * Figure 3: Candidate miRNAs act as oncogenes in a mouse T-ALL model. () Schematic of the adoptive transfer model of NOTCH1-driven T-ALL. () Kaplan-Meier analysis of leukemia-free survival after transplantation of HPCs expressing NOTCH1-ICN and either vector (black, n = 13) or miR-19b (red, n = 7), miR-20a (orange, n = 4), miR-26a (magenta, n = 5), miR-30 (gray, n = 5), miR-92 (green, n = 5) or miR-223 (blue, n = 7). () Representative microphotographs of NOTCH1-induced T-ALL (all 10×; for lung, 40× and 100× shown; scale bars indicated). The pathologic appearance of leukemias expressing different miRNAs is identical (not shown). * Figure 4: miRNAs regulate the expression of tumor suppressor genes in mouse T-ALL. () Luciferase reporter assays testing the effect of miRNAs on 3′ UTRs of the indicated genes (shown are mean and s.d. of triplicate experiments; V, vector; numbers indicate the miRNA name; *P < 0.05 compared to vector). () Quantitative RT-PCR (qRT-PCR) measurement of gene expression in mouse T-ALLs expressing NOTCH1 and the indicated miRNA (shown is the range and mean of the measurement as a fold change compared to T-ALLs expressing the control vector; *P < 0.05). (–) Immunoblots on lysates from mouse T-ALLs expressing NOTCH1 and vector or the indicated miRNAs and probed with the indicated antibodies. * Figure 5: Individual and cooperative miRNA effects on T-ALL suppressor genes. () Kaplan-Meier analysis of leukemia-free survival after HPC transplantation. All HPCs express NOTCH1-ICN and vector (black, n = 13), or shNf1 (red, n = 6), shBim (orange, n = 6), shPten (magenta, n = 3), shFbxw7 (green, n = 4), DN-Ikzf1 (blue, n = 10) or shPhf6 (violet, n = 3). () Cell number during in vitro culture of TALL-1 cells expressing the indicated antagomirs (shown are mean and s.d. for each time point, significant differences (P < 0.05) at day 6 (*) and over the 6 day period (**)). The inset shows qRT-PCR measurement of miRNA expression in TALL-1 cells. () Viability of TALL-1 cells transduced with the indicated antagomirs (mean and s.d.; *P < 0.05). (,) qRT-PCR of BIM () and PTEN () mRNA levels in TALL-1 cells expressing the indicated antagomirs (mean and s.d.; *P < 0.05 compared to vector). (,) 3′ UTR luciferase reporter assays of BIM () and PTEN () in cells transduced with the indicated miRNAs (mean and s.d.; *P < 0.05 in triplicate measurements). () Diagramma! tic summary of the overlapping regulation of six tumor suppressor genes (circles) by miRNAs (sticks) identified in this study; each miRNA is represented by a stick, and the width of each stick is proportional to the calculated strength and conservation of the miRNA-mRNA interaction (Pct value); bold letters indicate highly expressed miRNAs in human T-ALL. Rel. relative. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Konstantinos J Mavrakis, * Joni Van Der Meulen, * Frank Speleman & * Hans-Guido Wendel Affiliations * Cancer Biology & Genetics Program, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. * Konstantinos J Mavrakis, * Andrew L Wolfe, * Xiaoping Liu & * Hans-Guido Wendel * Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium. * Joni Van Der Meulen, * Evelien Mets, * Pieter Rondou, * Pieter Van Vlierberghe & * Frank Speleman * Weill Cornell Graduate School of Medical Sciences, New York, New York, USA. * Andrew L Wolfe, * Aly A Khan & * Manu Setti * Department of Clinical Chemistry, Microbiology and Immunology, Ghent University Hospital, Ghent, Belgium. * Tom Taghon * Computational Biology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. * Aly A Khan, * Manu Setti & * Christina S Leslie * Centre for Human Genetics, University Hospital Leuven, Leuven, Belgium. * Peter Vandenberghe * INSERM U563, Toulouse, France. * Eric Delabesse * Department of Pediatric Hematology-Oncology, Ghent University Hospital, Ghent, Belgium. * Yves Benoit * Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. * Nicholas B Socci * Institute for Cancer Genetics, Columbia University, New York, New York, USA. * Pieter Van Vlierberghe Contributions K.J.M., A.L.W. and X.L. performed the screen, mouse model and data analysis. J.V.d.M. and P.V.V. performed miRNA profiling on T-ALL samples. E.M. and P.R. performed studies on miR-223 and FBXW7. T.T. performed cell sorting and miRNA profiling. P.V. and E.D. performed genetic analyses on T-ALL samples. Y.B. was the co-supervisor of the miRNA profiling project on childhood ALLs and integrated clinical data management. A.A.K., M.S., C.S.L. and N.D.S. performed computational analyses. F.S. supervised the miRNA expression analyses. H.G.W. designed the study and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Hans-Guido Wendel Author Details * Konstantinos J Mavrakis Search for this author in: * NPG journals * PubMed * Google Scholar * Joni Van Der Meulen Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew L Wolfe Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaoping Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Evelien Mets Search for this author in: * NPG journals * PubMed * Google Scholar * Tom Taghon Search for this author in: * NPG journals * PubMed * Google Scholar * Aly A Khan Search for this author in: * NPG journals * PubMed * Google Scholar * Manu Setti Search for this author in: * NPG journals * PubMed * Google Scholar * Pieter Rondou Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Vandenberghe Search for this author in: * NPG journals * PubMed * Google Scholar * Eric Delabesse Search for this author in: * NPG journals * PubMed * Google Scholar * Yves Benoit Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas B Socci Search for this author in: * NPG journals * PubMed * Google Scholar * Christina S Leslie Search for this author in: * NPG journals * PubMed * Google Scholar * Pieter Van Vlierberghe Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Speleman Search for this author in: * NPG journals * PubMed * Google Scholar * Hans-Guido Wendel Contact Hans-Guido Wendel Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information Other * Supplementary Table 1 (197K) miRNA expression in primary T-ALL samples * Supplementary Table 5 (233K) miRNA expression in T-ALL cell lines * Supplementary Table 7 (152K) miRNA expression in normal T-cells and progenitor populations * Supplementary Table 8 (82K) Comparative analysis of miRNA expression between normal T-cells and progenitor populations and human T-ALL samples * Supplementary Table 9 (25K) Computational target prediction (a: by total context score; b: by number of 7- and 8-mer sites) PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–10 and Supplementary Tables 2–16. Additional data - Genome-wide association study identifies three new susceptibility loci for esophageal squamous-cell carcinoma in Chinese populations
- Nat Genet 43(7):679-684 (2011)
Nature Genetics | Letter Genome-wide association study identifies three new susceptibility loci for esophageal squamous-cell carcinoma in Chinese populations * Chen Wu1 * Zhibin Hu2, 8 * Zhonghu He3, 8 * Weihua Jia4, 8 * Feng Wang5, 8 * Yifeng Zhou6 * Zhihua Liu1 * Qimin Zhan1 * Yu Liu1 * Dianke Yu1 * Kan Zhai1 * Jiang Chang1 * Yan Qiao1 * Guangfu Jin2 * Zhe Liu7 * Yuanyuan Shen7 * Chuanhai Guo3 * Jianhua Fu4 * Xiaoping Miao5 * Wen Tan1 * Hongbing Shen2, 8 * Yang Ke3, 8 * Yixin Zeng4, 8 * Tangchun Wu5, 8 * Dongxin Lin1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:679–684Year published:(2011)DOI:doi:10.1038/ng.849Received12 July 2010Accepted06 May 2011Published online05 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Esophageal squamous-cell carcinoma (ESCC) is one of the most prevalent cancers worldwide and occurs at a relatively high frequency in China. To identify genetic susceptibility loci for ESCC, we conducted a genome-wide association study on 2,031 individuals with ESCC (cases) and 2,044 controls of Chinese descent using 666,141 autosomal SNPs. We evaluated promising associations in an additional 6,276 cases and 6,165 controls of Chinese descent from different areas of China. We identified seven susceptibility loci on chromosomes 5q11, 6p21, 10q23, 12q24 and 21q22 (ranging from P = 7.48 × 10−12 to P = 2.44 × 10−31); among these loci, 5q11, 6p21 and 21q22 were newly identified. Three variants in high linkage disequilibrium on 12q24 confer their risks to ESCC in a gene-lifestyle interaction manner, with more pronounced risk enhancement seen in tobacco and alcohol users. Furthermore, the identified variants had a cumulative association with ESCC risk (Ptrend = 7.92 × 10−56! ). These findings highlight the involvement of multiple genetic loci and gene-environment interaction in the development of esophageal cancer. View full text Figures at a glance * Figure 1: Manhattan plot of the genome-wide P values of association. We assessed association using a Cochran-Armitage trend test in a logistic regression analysis with adjustment for age, sex, smoking, drinking and the top three principal components of population stratification. The −log10P values (y axis) of 666,141 SNPs in 2,031 cases with ESCC and 2,044 controls are presented on the basis of their chromosomal positions (x axis). The 29 points with P < 10−7 are shown above the blue horizontal line, and the smallest P value was 3.10 × 10−16. The arrows localize the seven loci significantly associated with susceptibility to ESCC. * Figure 2: Regional plots of association results and recombination rates within the five significant susceptibility loci. (–) 12q24 (), 5q11 (), 21q22 (), 6p21 () and 10q23 (). For each plot, the −log10P values (y axis) of the SNPs are presented according to their chromosomal positions (x axis). The genetic recombination rates (cM/Mb) estimated using the 1000 Genomes June 2010 CHB+JPT samples are shown with a blue line; we annotated the genes within the interested region and these genes are shown as arrows. The top genotyped SNP is labeled by rs ID and the r2 values of the rest of the SNPs with the top genotyped SNP are indicated by different colors. * Figure 3: Stratification analysis of the association between risk of esophageal squamous-cell carcinoma and the seven SNPs. (–) rs10052657 (), rs11066015 (), rs2014300 (), rs10484761 (), rs11066280 (), rs2074356 () and rs2274223 (). Each box and horizontal line represent the OR point estimate and 95% CI derived from the additive model. The area of each box is proportional to the statistical weight of the study. Diamonds represent the summary ORs obtained from the combined analysis with 95% confidence intervals indicated by their widths. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Zhibin Hu, * Zhonghu He, * Weihua Jia, * Feng Wang, * Hongbing Shen, * Yang Ke, * Yixin Zeng & * Tangchun Wu Affiliations * State Key Laboratory of Molecular Oncology, Cancer Institute & Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. * Chen Wu, * Zhihua Liu, * Qimin Zhan, * Yu Liu, * Dianke Yu, * Kan Zhai, * Jiang Chang, * Yan Qiao, * Wen Tan & * Dongxin Lin * Department of Epidemiology and Biostatistics, Cancer Center, Nanjing Medical University, Nanjing, China. * Zhibin Hu, * Guangfu Jin & * Hongbing Shen * Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University School of Oncology, Beijing Cancer Hospital & Institute, Beijing, China. * Zhonghu He, * Chuanhai Guo & * Yang Ke * State Key Laboratory of Oncology in Southern China and Department of Experimental Research, Sun Yat-Sen University Cancer Center, Guangzhou, China. * Weihua Jia, * Jianhua Fu & * Yixin Zeng * Key Laboratory for Environment and Health (Ministry of Education), School of Public Health, Huazhong University of Sciences and Technology, Wuhan, China. * Feng Wang, * Xiaoping Miao & * Tangchun Wu * Laboratory of Cancer Molecular Genetics, Medical College of Soochow University, Suzhou, China. * Yifeng Zhou * Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China. * Zhe Liu & * Yuanyuan Shen Contributions D.L. was the overall study principal investigator who conceived the study and obtained financial support and was responsible for study design, oversaw the entire study, interpreted the results and wrote parts of and synthesized the paper. C.W. performed overall project management, oversaw laboratory analyses and statistical analyses and drafted the initial manuscript. Z. Hu, G.J. and H.S. were responsible for subject recruitment and sample preparation of Nanjing samples. Z. He, C.G. and Y.K. were responsible for subject recruitment and sample preparation of the Anyang samples. W.J., J.F. and Y. Zeng were responsible for subject recruitment and sample preparation of Guangzhou samples. F.W. and T.W. provided some of the control samples. Y. Zhou was responsible for subject recruitment and sample preparation of the additional independent validation cohort. D.Y., K.Z., J.C., Y.Q. and W.T. performed subject recruitment and sample preparation of the Beijing samples. Y.L., X.M., Zhe! Liu and Y.S. performed statistical analyses. Q.Z. and Zhihua Liu provided financial support and reviewed the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Dongxin Lin Author Details * Chen Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Zhibin Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Zhonghu He Search for this author in: * NPG journals * PubMed * Google Scholar * Weihua Jia Search for this author in: * NPG journals * PubMed * Google Scholar * Feng Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Yifeng Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Zhihua Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Qimin Zhan Search for this author in: * NPG journals * PubMed * Google Scholar * Yu Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Dianke Yu Search for this author in: * NPG journals * PubMed * Google Scholar * Kan Zhai Search for this author in: * NPG journals * PubMed * Google Scholar * Jiang Chang Search for this author in: * NPG journals * PubMed * Google Scholar * Yan Qiao Search for this author in: * NPG journals * PubMed * Google Scholar * Guangfu Jin Search for this author in: * NPG journals * PubMed * Google Scholar * Zhe Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Yuanyuan Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Chuanhai Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Jianhua Fu Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaoping Miao Search for this author in: * NPG journals * PubMed * Google Scholar * Wen Tan Search for this author in: * NPG journals * PubMed * Google Scholar * Hongbing Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Yang Ke Search for this author in: * NPG journals * PubMed * Google Scholar * Yixin Zeng Search for this author in: * NPG journals * PubMed * Google Scholar * Tangchun Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Dongxin Lin Contact Dongxin Lin Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (848K) Supplementary Tables 1–7 and Supplementary Figures 1–4. Additional data - Genome-wide association identifies three new susceptibility loci for Paget's disease of bone
- Nat Genet 43(7):685-689 (2011)
Nature Genetics | Letter Genome-wide association identifies three new susceptibility loci for Paget's disease of bone * Omar M E Albagha1 * Sachin E Wani1 * Micaela R Visconti1 * Nerea Alonso1 * Kirsteen Goodman2 * Maria Luisa Brandi3 * Tim Cundy4 * Pui Yan Jenny Chung5 * Rosemary Dargie6 * Jean-Pierre Devogelaer7 * Alberto Falchetti3 * William D Fraser8 * Luigi Gennari9 * Fernando Gianfrancesco10 * Michael J Hooper11 * Wim Van Hul5 * Gianluca Isaia12 * Geoff C Nicholson13 * Ranuccio Nuti9 * Socrates Papapoulos14 * Javier del Pino Montes15 * Thomas Ratajczak16, 17 * Sarah L Rea16, 17 * Domenico Rendina18 * Rogelio Gonzalez-Sarmiento19 * Marco Di Stefano12 * Lynley C Ward16 * John P Walsh16, 20 * Stuart H Ralston1, 2 * for the Genetic Determinants of Paget's Disease (GDPD) Consortium * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:685–689Year published:(2011)DOI:doi:10.1038/ng.845Received19 November 2010Accepted04 May 2011Published online29 May 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Paget's disease of bone (PDB) is a common disorder characterized by focal abnormalities of bone remodeling. We previously identified variants at the CSF1, OPTN and TNFRSF11A loci as risk factors for PDB by genome-wide association study1. Here we extended this study, identified three new loci and confirmed their association with PDB in 2,215 affected individuals (cases) and 4,370 controls from seven independent populations. The new associations were with rs5742915 within PML on 15q24 (odds ratio (OR) = 1.34, P = 1.6 × 10−14), rs10498635 within RIN3 on 14q32 (OR = 1.44, P = 2.55 × 10−11) and rs4294134 within NUP205 on 7q33 (OR = 1.45, P = 8.45 × 10−10). Our data also confirmed the association of TM7SF4 (rs2458413, OR = 1.40, P = 7.38 × 10−17) with PDB. These seven loci explained ~13% of the familial risk of PDB. These studies provide new insights into the genetic architecture and pathophysiology of PDB. View full text Figures at a glance * Figure 1: Loci for susceptibility to PDB detected by GWAS. Manhattan plot of association test results of GWAS stage data showing the chromosomal position of 2,487,078 genotyped or imputed SNPs plotted against genomic-control–adjusted −log10P. The red horizontal line represents the threshold for genome-wide significance (P < 5 × 10−8). * Figure 2: Regional association plots of loci showing genome-wide significant association with PDB. (–) Details of loci on chromosome 7q33 (), 15q24.1 (), 8q22.3 () and 14q32.12 () showing the chromosomal position (based on NCBI human genome build 36) of SNPs in each region plotted against −log10P values. Genotyped (squares) and imputed (circles) SNPs are color coded according to the extent of LD with the SNP showing the highest association signal (represented as purple diamonds) from each region in the combined analysis. The estimated recombination rates (cM/Mb) from HapMap CEU release 22 are shown as light blue lines, and the blue arrows represent known genes in each region. We defined the associated regions based on LD with the highest association signal (r2 > 0.2) within a window of 500 kb. * Figure 3: Forest plots showing association in the different datasets for SNPs at 7q33, 8q22.3, 14q32.12 and 15q24.1. (–) Forest plots of overall effect size for SNPs associated with PDB risk from the identified loci on 7q33 (rs4294134) (), 8q22.3 (rs2458413) (), 14q32.12 (rs10498635) () and 15q24.1 (rs5742915) (). We estimated the overall effect size using meta-analysis of the GWAS sample and the six replication samples. The black squares represent the effect estimates for the individual cohorts, and the horizontal lines represent the 95% CIs of the estimates. The sizes of the squares are proportionate to the weights of the estimates. The diamonds and triangles represent the overall estimate under fixed-effect and random-effect models, respectively. The dotted vertical lines represent the overall fixed effect estimates. * Figure 4: Cumulative contribution of genome-wide significant loci to the risk of PDB. Risk allele scores defined by the seven loci associated with PDB risk are plotted against the OR for PDB. We weighted risk alleles according to their estimated effect size and divided weighted risk allele scores into ten equal parts (deciles) using data from the replication cohorts. We calculated the OR for PDB risk for each decile in reference to the fifth decile (D5). Vertical bars represent 95% CIs. Author information * Author information * Supplementary information Affiliations * Rheumatic Diseases Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK. * Omar M E Albagha, * Sachin E Wani, * Micaela R Visconti, * Nerea Alonso & * Stuart H Ralston * Edinburgh Clinical Trials Unit, University of Edinburgh, Western General Hospital, Edinburgh, UK. * Kirsteen Goodman & * Stuart H Ralston * Department of Internal Medicine, University of Florence, Florence, Italy. * Maria Luisa Brandi & * Alberto Falchetti * Department of Medicine, University of Auckland, Auckland, New Zealand. * Tim Cundy * Department of Medical Genetics, University of Antwerp, Antwerp, Belgium. * Pui Yan Jenny Chung & * Wim Van Hul * University Department of Medicine, Glasgow Royal Infirmary, Glasgow, UK. * Rosemary Dargie * Department of Rheumatology, Saint-Luc University Hospital, Université Catholique de Louvain, Brussels, Belgium. * Jean-Pierre Devogelaer * Department of Clinical Chemistry, Royal Liverpool University Hospital, Liverpool, UK. * William D Fraser * Department of Internal Medicine, Endocrine Metabolic Sciences and Biochemistry, University of Siena, Siena, Italy. * Luigi Gennari & * Ranuccio Nuti * Institute of Genetics and Biophysics 'Adriano Buzzati-Traverso', Italian National Research Council, Naples, Italy. * Fernando Gianfrancesco * Department of Medicine, University of Sydney, Sydney, Australia. * Michael J Hooper * Medical and Surgical Department, Geriatric Section, University of Torino, Torino, Italy. * Gianluca Isaia & * Marco Di Stefano * Department of Clinical and Biomedical Sciences, Barwon Health, Geelong Hospital, University of Melbourne, Melbourne, Australia. * Geoff C Nicholson * Department of Endocrinology & Metabolic Diseases, Leiden University Medical Centre, Leiden, The Netherlands. * Socrates Papapoulos * Departamento de Medicina, Universidad de Salamanca, Servicio de Reumatología, Hospital Universitario de Salamanca, Salamanca, Spain. * Javier del Pino Montes * Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia. * Thomas Ratajczak, * Sarah L Rea, * Lynley C Ward & * John P Walsh * Western Australian Institute for Medical Research, Centre for Medical Research, University of Western Australia, Crawley, Western Australia, Australia. * Thomas Ratajczak & * Sarah L Rea * Department of Clinical and Experimental Medicine Federico II University of Naples, Naples, Italy. * Domenico Rendina * Unidad de Medicina Molecular, Departamento de Medicina, Instituto de Biología Molecular y Celular del Cáncer, Universidad de Salamanca-CSIC, Salamanca, Spain. * Rogelio Gonzalez-Sarmiento * School of Medicine and Pharmacology, University of Western Australia, Crawley, Western Australia, Australia. * John P Walsh Consortia * the Genetic Determinants of Paget's Disease (GDPD) Consortium * Omar M E Albagha, * Sachin E Wani, * Micaela R Visconti, * Nerea Alonso, * Kirsteen Goodman, * Maria Luisa Brandi, * Tim Cundy, * Pui Yan Jenny Chung, * Rosemary Dargie, * Jean-Pierre Devogelaer, * Alberto Falchetti, * William D Fraser, * Luigi Gennari, * Fernando Gianfrancesco, * Michael J Hooper, * Wim Van Hul, * Gianluca Isaia, * Geoff C Nicholson, * Ranuccio Nuti, * Socrates Papapoulos, * Javier del Pino Montes, * Thomas Ratajczak, * Sarah L Rea, * Domenico Rendina, * Rogelio Gonzalez-Sarmiento, * Marco Di Stefano, * Lynley C Ward, * John P Walsh & * Stuart H Ralston Contributions O.M.E.A. contributed to the study design and funding, oversaw the genotyping, performed data management, quality control, statistical and bioinformatics analyses, and wrote the first draft of the manuscript. S.H.R. designed the study, obtained funding, coordinated the sample collection and phenotyping, and revised the manuscript. K.G., M.L.B., T.C., P.Y.J.C., R.D., J.-P. D., A.F., W.D.F., L.G., F.G., M.J.H., W.V.H, G.I., G.C.N., R.N., S.P., J.d.P.M., T.R., S.L.R, D.R., R.G.-S., M.d.S., L.C.W. and J.P.W. contributed toward clinical sample collection and phenotyping. M.R.V., N.A., S.E.W., R.G.-S., P.Y.J.C. and F.G. contributed to sample preparation and carried out DNA sequencing to identify samples with SQSTM1 mutations. All authors critically reviewed the article for important intellectual content and approved the final manuscript. Competing financial interests O.M.E.A. and S.H.R. have applied for an International PCT (Patent Co-operation Treaty) patent filing (European Office) on the use of genetic profiling to identify patients at risk of Paget's disease, which contains subject matter drawn from the work also published here. Corresponding authors Correspondence to: * Omar M E Albagha or * Stuart H Ralston Author Details * Omar M E Albagha Search for this author in: * NPG journals * PubMed * Google Scholar * Sachin E Wani Search for this author in: * NPG journals * PubMed * Google Scholar * Micaela R Visconti Search for this author in: * NPG journals * PubMed * Google Scholar * Nerea Alonso Search for this author in: * NPG journals * PubMed * Google Scholar * Kirsteen Goodman Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Luisa Brandi Search for this author in: * NPG journals * PubMed * Google Scholar * Tim Cundy Search for this author in: * NPG journals * PubMed * Google Scholar * Pui Yan Jenny Chung Search for this author in: * NPG journals * PubMed * Google Scholar * Rosemary Dargie Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-Pierre Devogelaer Search for this author in: * NPG journals * PubMed * Google Scholar * Alberto Falchetti Search for this author in: * NPG journals * PubMed * Google Scholar * William D Fraser Search for this author in: * NPG journals * PubMed * Google Scholar * Luigi Gennari Search for this author in: * NPG journals * PubMed * Google Scholar * Fernando Gianfrancesco Search for this author in: * NPG journals * PubMed * Google Scholar * Michael J Hooper Search for this author in: * NPG journals * PubMed * Google Scholar * Wim Van Hul Search for this author in: * NPG journals * PubMed * Google Scholar * Gianluca Isaia Search for this author in: * NPG journals * PubMed * Google Scholar * Geoff C Nicholson Search for this author in: * NPG journals * PubMed * Google Scholar * Ranuccio Nuti Search for this author in: * NPG journals * PubMed * Google Scholar * Socrates Papapoulos Search for this author in: * NPG journals * PubMed * Google Scholar * Javier del Pino Montes Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Ratajczak Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah L Rea Search for this author in: * NPG journals * PubMed * Google Scholar * Domenico Rendina Search for this author in: * NPG journals * PubMed * Google Scholar * Rogelio Gonzalez-Sarmiento Search for this author in: * NPG journals * PubMed * Google Scholar * Marco Di Stefano Search for this author in: * NPG journals * PubMed * Google Scholar * Lynley C Ward Search for this author in: * NPG journals * PubMed * Google Scholar * John P Walsh Search for this author in: * NPG journals * PubMed * Google Scholar * Stuart H Ralston Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–4 and Supplementary Tables 1–5. Additional data - Genome-wide association study identifies two new susceptibility loci for atopic dermatitis in the Chinese Han population
- Nat Genet 43(7):690-694 (2011)
Nature Genetics | Letter Genome-wide association study identifies two new susceptibility loci for atopic dermatitis in the Chinese Han population * Liang-Dan Sun1, 2, 18 * Feng-Li Xiao1, 2, 18 * Yang Li1, 2, 18 * Wen-Ming Zhou1, 2, 18 * Hua-Yang Tang1, 2 * Xian-Fa Tang1, 2 * Hui Zhang3 * Heidi Schaarschmidt4 * Xian-Bo Zuo2 * Regina Foelster-Holst5 * Su-Min He1, 2 * Mei Shi3 * Qiang Liu6 * Yong-Mei Lv1, 2 * Xi-Lan Chen3 * Kun-Ju Zhu1, 2 * Yi-Feng Guo3 * Da-Yan Hu1, 2 * Ming Li3 * Min Li1, 2 * Yan-Hong Zhang6 * Xin Zhang1, 2 * Jian-Ping Tang7 * Bi-Rong Guo1, 2 * Hua Wang8 * Yuan Liu1, 2 * Xiao-Yan Zou9 * Fu-Sheng Zhou2 * Xiao-Yan Liu10 * Gang Chen2 * Lin Ma11 * Shu-Mei Zhang1, 2 * Ai-Ping Jiang12 * Xiao-Dong Zheng2 * Xing-Hua Gao13 * Pan Li1, 2 * Cai-Xia Tu14 * Xian-Yong Yin1, 2 * Xiu-Ping Han15 * Yun-Qing Ren1, 2 * Shun-Peng Song16 * Zhi-Yong Lu3 * Xing-Lian Zhang6 * Yong Cui1, 2 * Jing Chang7 * Min Gao1, 2 * Xiao-Yan Luo8 * Pei-Guang Wang1, 2 * Xing Dai9 * Wei Su10 * Hui Li1, 2 * Chun-Pin Shen11 * Sheng-Xiu Liu1, 2 * Xiao-Bo Feng3 * Chun-Jun Yang1, 2 * Guo-Shu Lin1, 2 * Zai-Xing Wang1, 2 * Jian-Qing Huang12 * Xing Fan1, 2 * Yan Wang15 * Yi-Xiao Bao3 * Sen Yang1, 2 * Jian-Jun Liu2 * Andre Franke4 * Stephan Weidinger5, 17 * Zhi-Rong Yao3 * Xue-Jun Zhang1, 2 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:690–694Year published:(2011)DOI:doi:10.1038/ng.851Received24 August 2010Accepted10 May 2011Published online12 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Atopic dermatitis is a chronic, relapsing form of inflammatory skin disorder that is affected by genetic and environmental factors. We performed a genome-wide association study of atopic dermatitis in a Chinese Han population using 1,012 affected individuals (cases) and 1,362 controls followed by a replication study in an additional 3,624 cases and 12,197 controls of Chinese Han ethnicity, as well as 1,806 cases and 3,256 controls from Germany. We identified previously undescribed susceptibility loci at 5q22.1 (TMEM232 and SLC25A46, rs7701890, Pcombined = 3.15 × 10−9, odds ratio (OR) = 1.24) and 20q13.33 (TNFRSF6B and ZGPAT, rs6010620, Pcombined = 3.0 × 10−8, OR = 1.17) and replicated another previously reported locus at 1q21.3 (FLG, rs3126085, Pcombined = 5.90 × 10−12, OR = 0.82) in the Chinese sample. The 20q13.33 locus also showed evidence for association in the German sample (rs6010620, P = 2.87 × 10−5, OR = 1.25). Our study identifies new genetic susceptibil! ity factors and suggests previously unidentified biological pathways in atopic dermatitis. View full text Figures at a glance * Figure 1: Genome-wide association results from the initial GWAS analysis. The genome-wide P values of the Cochran-Armitage trend test from 491,905 polymorphic SNPs in 1,012 atopic dermatitis cases and 1,362 controls of Chinese Han ancestry are presented. The chromosomal distribution of all the P values (−log10P) is shown. * Figure 2: Association scatter plots for four atopic dermatitis susceptibility loci. We plotted the P values of SNPs (shown as −log10 values in y axis from the genome-wide single-marker association analysis using the Cochran-Armitage trend test) against their map positions (x axis). The color of each SNP spot reflects its r2 with the top SNP (large red diamond) within each association locus, changing from red to white. Estimated recombination rates (based on the combined CHB and JPT samples from the HapMap project) are plotted in light blue. Gene annotations were adapted from the UCSC Genome Browser (see URLs). () 1q21.3; () 5q22.1; () 10q21.2; () 20q13.33. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Liang-Dan Sun, * Feng-Li Xiao, * Yang Li & * Wen-Ming Zhou Affiliations * Institute of Dermatology and Department of Dermatology, No.1 Hospital, Anhui Medical University, Hefei, Anhui, China. * Liang-Dan Sun, * Feng-Li Xiao, * Yang Li, * Wen-Ming Zhou, * Hua-Yang Tang, * Xian-Fa Tang, * Su-Min He, * Yong-Mei Lv, * Kun-Ju Zhu, * Da-Yan Hu, * Min Li, * Xin Zhang, * Bi-Rong Guo, * Yuan Liu, * Shu-Mei Zhang, * Pan Li, * Xian-Yong Yin, * Yun-Qing Ren, * Yong Cui, * Min Gao, * Pei-Guang Wang, * Hui Li, * Sheng-Xiu Liu, * Chun-Jun Yang, * Guo-Shu Lin, * Zai-Xing Wang, * Xing Fan, * Sen Yang & * Xue-Jun Zhang * State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology, Hefei, Anhui, China. * Liang-Dan Sun, * Feng-Li Xiao, * Yang Li, * Wen-Ming Zhou, * Hua-Yang Tang, * Xian-Fa Tang, * Xian-Bo Zuo, * Su-Min He, * Yong-Mei Lv, * Kun-Ju Zhu, * Da-Yan Hu, * Min Li, * Xin Zhang, * Bi-Rong Guo, * Yuan Liu, * Fu-Sheng Zhou, * Gang Chen, * Shu-Mei Zhang, * Xiao-Dong Zheng, * Pan Li, * Xian-Yong Yin, * Yun-Qing Ren, * Yong Cui, * Min Gao, * Pei-Guang Wang, * Hui Li, * Sheng-Xiu Liu, * Chun-Jun Yang, * Guo-Shu Lin, * Zai-Xing Wang, * Xing Fan, * Sen Yang, * Jian-Jun Liu & * Xue-Jun Zhang * Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. * Hui Zhang, * Mei Shi, * Xi-Lan Chen, * Yi-Feng Guo, * Ming Li, * Zhi-Yong Lu, * Xiao-Bo Feng, * Yi-Xiao Bao & * Zhi-Rong Yao * Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, Kiel, Germany. * Heidi Schaarschmidt & * Andre Franke * Department of Dermatology, University Clinic Schleswig-Holstein, Campus Kiel, Kiel, Germany. * Regina Foelster-Holst & * Stephan Weidinger * Department of Dermatology, Shanxi Provincial Children's Hospital, Taiyuan, Shanxi, China. * Qiang Liu, * Yan-Hong Zhang & * Xing-Lian Zhang * Department of Dermatology, Hunan Children's Hospital, Changsha, Hunan, China. * Jian-Ping Tang & * Jing Chang * Department of Pediatric Dermatology, Children's Hospital of Chongqing Medical University, Chongqing, China. * Hua Wang & * Xiao-Yan Luo * Department of Dermatovenereology, Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China. * Xiao-Yan Zou & * Xing Dai * Department of Dermatology, Capital Institute of Pediatrics, Beijing, China. * Xiao-Yan Liu & * Wei Su * Department of Dermatology, Beijing Children's Hospital, Capital Medical University, Beijing, China. * Lin Ma & * Chun-Pin Shen * Dermatosis Preventing & Curing Hospital of Fuzhou, Fuzhou, Fujian, China. * Ai-Ping Jiang & * Jian-Qing Huang * Department of Dermatology, No.1 Hospital of China Medical University, Shenyang, Liaoning, China. * Xing-Hua Gao * Department of Dermatology, The 2nd Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China. * Cai-Xia Tu * Department of Dermatology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China. * Xiu-Ping Han & * Yan Wang * Dalian Dermatosis Hospital, Dalian, Liaoning, China. * Shun-Peng Song * Department of Dermatology and Allergy, Technical University Munich, Munich, Germany. * Stephan Weidinger Contributions X.-J.Z. conceived of this study and obtained financial support. X.-J.Z., Z.-R.Y., S.Y., L.-D.S., F.-L.X., Y. Li and W.-M.Z. participated in the design and were responsible for sample selection, genotyping and project management. H.Z., A.F., H.S., R.F.-H., S.W., M.S., Q.L., X.-L.C., Y.-F.G., Ming L., Y.-H.Z., J.-P.T., H.W., X.-Y.Z., X.-Y. Liu, L.M., A.-P.J., X.-H.G., C.-X.T., S.-P.S., Z.-Y.L., X.-L.Z., J.C., X.-Y. Luo, X.D., W.S., C.-P.S., X.-B.F., X.-P.H., J.-Q.H., Y.W., Y.-X.B., S.-M.H., Y.-M.L., K.-J.Z., D.-Y.H., Min L., X.Z., B.-R.G., Y. Liu, S.-M.Z., X.-Y.Y., Y.-Q.R., Y.C., M.G., P.-G.W., H.L., S.-X.L., C.-J.Y., G.-S.L., Z.-X.W., H.-Y.T. and X.F. conducted sample selection and data management, undertook recruitment, collected phenotype data, undertook related data handling and calculation, managed recruitment and obtained biological samples. F.-S.Z., G.C., X.-D.Z. and P.L. performed genotyping analysis. J.-J.L., H.-Y.T., X.-F.T. and X.-B.Z. undertook data processing, sta! tistical analysis and bioinformatics investigations. All the authors contributed to the final paper, with X.-J.Z., Z.-R.Y., S.Y., L.-D.S., F.-L.X., Y. Li and W.-M.Z. having key roles. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Xue-Jun Zhang or * Zhi-Rong Yao Author Details * Liang-Dan Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Feng-Li Xiao Search for this author in: * NPG journals * PubMed * Google Scholar * Yang Li Search for this author in: * NPG journals * PubMed * Google Scholar * Wen-Ming Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Hua-Yang Tang Search for this author in: * NPG journals * PubMed * Google Scholar * Xian-Fa Tang Search for this author in: * NPG journals * PubMed * Google Scholar * Hui Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Heidi Schaarschmidt Search for this author in: * NPG journals * PubMed * Google Scholar * Xian-Bo Zuo Search for this author in: * NPG journals * PubMed * Google Scholar * Regina Foelster-Holst Search for this author in: * NPG journals * PubMed * Google Scholar * Su-Min He Search for this author in: * NPG journals * PubMed * Google Scholar * Mei Shi Search for this author in: * NPG journals * PubMed * Google Scholar * Qiang Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Yong-Mei Lv Search for this author in: * NPG journals * PubMed * Google Scholar * Xi-Lan Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Kun-Ju Zhu Search for this author in: * NPG journals * PubMed * Google Scholar * Yi-Feng Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Da-Yan Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Ming Li Search for this author in: * NPG journals * PubMed * Google Scholar * Min Li Search for this author in: * NPG journals * PubMed * Google Scholar * Yan-Hong Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Xin Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Jian-Ping Tang Search for this author in: * NPG journals * PubMed * Google Scholar * Bi-Rong Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Hua Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Yuan Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Xiao-Yan Zou Search for this author in: * NPG journals * PubMed * Google Scholar * Fu-Sheng Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Xiao-Yan Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Gang Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Lin Ma Search for this author in: * NPG journals * PubMed * Google Scholar * Shu-Mei Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Ai-Ping Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Xiao-Dong Zheng Search for this author in: * NPG journals * PubMed * Google Scholar * Xing-Hua Gao Search for this author in: * NPG journals * PubMed * Google Scholar * Pan Li Search for this author in: * NPG journals * PubMed * Google Scholar * Cai-Xia Tu Search for this author in: * NPG journals * PubMed * Google Scholar * Xian-Yong Yin Search for this author in: * NPG journals * PubMed * Google Scholar * Xiu-Ping Han Search for this author in: * NPG journals * PubMed * Google Scholar * Yun-Qing Ren Search for this author in: * NPG journals * PubMed * Google Scholar * Shun-Peng Song Search for this author in: * NPG journals * PubMed * Google Scholar * Zhi-Yong Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Xing-Lian Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Yong Cui Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Chang Search for this author in: * NPG journals * PubMed * Google Scholar * Min Gao Search for this author in: * NPG journals * PubMed * Google Scholar * Xiao-Yan Luo Search for this author in: * NPG journals * PubMed * Google Scholar * Pei-Guang Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Xing Dai Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Su Search for this author in: * NPG journals * PubMed * Google Scholar * Hui Li Search for this author in: * NPG journals * PubMed * Google Scholar * Chun-Pin Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Sheng-Xiu Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Xiao-Bo Feng Search for this author in: * NPG journals * PubMed * Google Scholar * Chun-Jun Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Guo-Shu Lin Search for this author in: * NPG journals * PubMed * Google Scholar * Zai-Xing Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Jian-Qing Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Xing Fan Search for this author in: * NPG journals * PubMed * Google Scholar * Yan Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Yi-Xiao Bao Search for this author in: * NPG journals * PubMed * Google Scholar * Sen Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Jian-Jun Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Andre Franke Search for this author in: * NPG journals * PubMed * Google Scholar * Stephan Weidinger Search for this author in: * NPG journals * PubMed * Google Scholar * Zhi-Rong Yao Contact Zhi-Rong Yao Search for this author in: * NPG journals * PubMed * Google Scholar * Xue-Jun Zhang Contact Xue-Jun Zhang Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (897K) Supplementary Figures 1 and 2 and Supplementary Tables 1–6. Additional data - Genome-wide association study reveals three susceptibility loci for common migraine in the general population
- Nat Genet 43(7):695-698 (2011)
Nature Genetics | Letter Genome-wide association study reveals three susceptibility loci for common migraine in the general population * Daniel I Chasman1, 2, 23 * Markus Schürks1, 3, 23 * Verneri Anttila4, 5, 22 * Boukje de Vries6 * Ulf Schminke7 * Lenore J Launer8 * Gisela M Terwindt9 * Arn M J M van den Maagdenberg6, 9 * Konstanze Fendrich10 * Henry Völzke11 * Florian Ernst12 * Lyn R Griffiths13 * Julie E Buring1 * Mikko Kallela14, 22 * Tobias Freilinger15, 22 * Christian Kubisch16, 22 * Paul M Ridker1, 2 * Aarno Palotie4, 5, 17, 18, 19, 22 * Michel D Ferrari9 * Wolfgang Hoffmann10 * Robert Y L Zee1, 24 * Tobias Kurth1, 20, 21, 24 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:695–698Year published:(2011)DOI:doi:10.1038/ng.856Received07 March 2011Accepted16 May 2011Published online12 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Migraine is a common, heterogeneous and heritable neurological disorder. Its pathophysiology is incompletely understood, and its genetic influences at the population level are unknown. In a population-based genome-wide analysis including 5,122 migraineurs and 18,108 non-migraineurs, rs2651899 (1p36.32, PRDM16), rs10166942 (2q37.1, TRPM8) and rs11172113 (12q13.3, LRP1) were among the top seven associations (P < 5 × 10−6) with migraine. These SNPs were significant in a meta-analysis among three replication cohorts and met genome-wide significance in a meta-analysis combining the discovery and replication cohorts (rs2651899, odds ratio (OR) = 1.11, P = 3.8 × 10−9; rs10166942, OR = 0.85, P = 5.5 × 10−12; and rs11172113, OR = 0.90, P = 4.3 × 10−9). The associations at rs2651899 and rs10166942 were specific for migraine compared with non-migraine headache. None of the three SNP associations was preferential for migraine with aura or without aura, nor were any associati! ons specific for migraine features. TRPM8 has been the focus of neuropathic pain models, whereas LRP1 modulates neuronal glutamate signaling, plausibly linking both genes to migraine pathophysiology. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Daniel I Chasman & * Markus Schürks Affiliations * Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Daniel I Chasman, * Markus Schürks, * Julie E Buring, * Paul M Ridker, * Robert Y L Zee & * Tobias Kurth * Donald W. Reynolds Center for Cardiovascular Disease Prevention, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Daniel I Chasman & * Paul M Ridker * Department of Neurology, University Hospital Essen, Essen, Germany. * Markus Schürks * Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. * Verneri Anttila & * Aarno Palotie * Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. * Verneri Anttila & * Aarno Palotie * Department of Human Genetics, Leiden University Medical Centre, Leiden, The Netherlands. * Boukje de Vries & * Arn M J M van den Maagdenberg * Department of Neurology, Ernst-Moritz-Arndt University, Greifswald, Germany. * Ulf Schminke * National Institute of Aging, Laboratory for Epidemiology, Demography, and Biometry, Bethesda, Maryland, USA. * Lenore J Launer * Department of Neurology, Leiden University Medical Centre, Leiden, The Netherlands. * Gisela M Terwindt, * Arn M J M van den Maagdenberg & * Michel D Ferrari * Institute for Community Medicine, Section Epidemiology of Health Care and Community Health, Ernst-Moritz-Arndt University, Greifswald, Germany. * Konstanze Fendrich & * Wolfgang Hoffmann * Institute for Community Medicine, Section Clinical Epidemiological Research, Ernst-Moritz-Arndt University, Greifswald, Germany. * Henry Völzke * Interfaculty Institute for Genetics and Functional Genomics, Ernst-Moritz-Arndt University, Greifswald, Germany. * Florian Ernst * Genomics Research Centre, Griffith Health Institute, Griffith University, Gold Coast, Queensland, Australia. * Lyn R Griffiths * Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland. * Mikko Kallela * Department of Neurology, Klinikum Großhadern, Ludwig-Maximilians-Universität and Institute for Stroke and Dementia Research, Klinikum der Universität München, Munich, Germany. * Tobias Freilinger * Institute of Human Genetics, University of Ulm, Ulm, Germany. * Christian Kubisch * Department of Medical Genetics, University of Helsinki, Helsinki, Finland. * Aarno Palotie * Department of Medical Genetics, Helsinki University Central Hospital, Helsinki, Finland. * Aarno Palotie * The Broad Institute of MIT and Harvard, Boston, Massachusetts, USA. * Aarno Palotie * INSERM Unit 708—Neuroepidemiology, Paris, France. * Tobias Kurth * UPMC Univ Paris 06, F-75005, Paris, France. * Tobias Kurth * On behalf of the International Headache Genetics Consortium (IHGC) (full list of consortium members appears in the Supplementary Note). * Verneri Anttila, * Mikko Kallela, * Tobias Freilinger, * Christian Kubisch & * Aarno Palotie * These authors jointly directed this work. * Robert Y L Zee & * Tobias Kurth Contributions J.E.B., M.D.F., W.H., T.K., A.M.J.M.v.d.M., A.P., P.M.R., U.S., H.V., R.Y.L.Z. D.I.C., T.K., M.S. : V.A., J.E.B., K.F., M.D.F., T.F., W.H., M.K., C.K., T.K., L.J.L., A.P., P.M.R., U.S., M.S., G.M.T., H.V. V.A., D.I.C., K.F., L.R.G., W.H., A.M.J.M.v.d.M., P.M.R., U.S., M.S., F.E., B.d.V., R.Y.L.Z. D.I.C., M.S. All authors participated in critical review of the manuscript for intellectual content. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Markus Schürks or * Tobias Kurth Author Details * Daniel I Chasman Search for this author in: * NPG journals * PubMed * Google Scholar * Markus Schürks Contact Markus Schürks Search for this author in: * NPG journals * PubMed * Google Scholar * Verneri Anttila Search for this author in: * NPG journals * PubMed * Google Scholar * Boukje de Vries Search for this author in: * NPG journals * PubMed * Google Scholar * Ulf Schminke Search for this author in: * NPG journals * PubMed * Google Scholar * Lenore J Launer Search for this author in: * NPG journals * PubMed * Google Scholar * Gisela M Terwindt Search for this author in: * NPG journals * PubMed * Google Scholar * Arn M J M van den Maagdenberg Search for this author in: * NPG journals * PubMed * Google Scholar * Konstanze Fendrich Search for this author in: * NPG journals * PubMed * Google Scholar * Henry Völzke Search for this author in: * NPG journals * PubMed * Google Scholar * Florian Ernst Search for this author in: * NPG journals * PubMed * Google Scholar * Lyn R Griffiths Search for this author in: * NPG journals * PubMed * Google Scholar * Julie E Buring Search for this author in: * NPG journals * PubMed * Google Scholar * Mikko Kallela Search for this author in: * NPG journals * PubMed * Google Scholar * Tobias Freilinger Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Kubisch Search for this author in: * NPG journals * PubMed * Google Scholar * Paul M Ridker Search for this author in: * NPG journals * PubMed * Google Scholar * Aarno Palotie Search for this author in: * NPG journals * PubMed * Google Scholar * Michel D Ferrari Search for this author in: * NPG journals * PubMed * Google Scholar * Wolfgang Hoffmann Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Y L Zee Search for this author in: * NPG journals * PubMed * Google Scholar * Tobias Kurth Contact Tobias Kurth Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (201K) Supplementary Note, Supplementary Tables 1–9 and Supplementary Figures 1 and 2. Additional data - Identification of common variants influencing risk of the tauopathy progressive supranuclear palsy
- Nat Genet 43(7):699-705 (2011)
Nature Genetics | Letter Identification of common variants influencing risk of the tauopathy progressive supranuclear palsy * Günter U Höglinger1, 30 * Nadine M Melhem2, 30 * Dennis W Dickson3, 30 * Patrick M A Sleiman4, 30 * Li-San Wang5 * Lambertus Klei2 * Rosa Rademakers3 * Rohan de Silva6 * Irene Litvan7 * David E Riley8 * John C van Swieten9 * Peter Heutink10 * Zbigniew K Wszolek11 * Ryan J Uitti11 * Jana Vandrovcova6 * Howard I Hurtig12 * Rachel G Gross12 * Walter Maetzler13, 14 * Stefano Goldwurm15 * Eduardo Tolosa16 * Barbara Borroni17 * Pau Pastor18, 19, 20 * PSP Genetics Study Group29 * Laura B Cantwell5 * Mi Ryung Han5 * Allissa Dillman21 * Marcel P van der Brug22 * J Raphael Gibbs6, 21 * Mark R Cookson21 * Dena G Hernandez6, 21 * Andrew B Singleton21 * Matthew J Farrer23 * Chang-En Yu24, 25 * Lawrence I Golbe26 * Tamas Revesz27 * John Hardy6 * Andrew J Lees6, 27 * Bernie Devlin2 * Hakon Hakonarson4 * Ulrich Müller28, 30 * Gerard D Schellenberg5, 30 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:699–705Year published:(2011)DOI:doi:10.1038/ng.859Received29 November 2010Accepted16 May 2011Published online19 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Progressive supranuclear palsy (PSP) is a movement disorder with prominent tau neuropathology. Brain diseases with abnormal tau deposits are called tauopathies, the most common of which is Alzheimer's disease. Environmental causes of tauopathies include repetitive head trauma associated with some sports. To identify common genetic variation contributing to risk for tauopathies, we carried out a genome-wide association study of 1,114 individuals with PSP (cases) and 3,247 controls (stage 1) followed by a second stage in which we genotyped 1,051 cases and 3,560 controls for the stage 1 SNPs that yielded P ≤ 10−3. We found significant previously unidentified signals (P < 5 × 10−8) associated with PSP risk at STX6, EIF2AK3 and MOBP. We confirmed two independent variants in MAPT affecting risk for PSP, one of which influences MAPT brain expression. The genes implicated encode proteins for vesicle-membrane fusion at the Golgi-endosomal interface, for the endoplasmic reticul! um unfolded protein response and for a myelin structural component. View full text Figures at a glance * Figure 1: Regional association plots. () Association results for 1q25.3 STX6. () Association results for 2p11.2 EIF2AK3. () Association results for 3p22.1 MOBP regions. −log10P values are shown for stages 1 and 2 and for the joint analyses. The recombination rate, calculated from the linkage disequilibrium (LD) structure of the region, was derived from HapMap3 data. LD, encoded by the intensity of the colors, is the pairwise LD of the most highly associated SNP in stage 1 with each of the SNPs in the region. Transcript positions are shown below each graph. * Figure 2: Regional association results for the MAPT region of chromosome 17. () Association results for the 17q21.31 H1/H2 inversion polymorphism (40,974,015–41,926,692 kb) and flanking segments. () Association results for 17q21.31 controlling for H1/H2. Results are shown for stages 1 and 2 and the joint analyses. The recombination rate, calculated from the linkage disequilibrium (LD) structure of the region, was derived from HapMap3 data. LD, encoded by intensity of the colors, is the pairwise LD of the most highly associated SNP in stage 1 with each of the SNPs in the region. * Figure 3: Effects of genotypes on gene expression for the MOBP region of chromosome 3 and for the MAPT region of chromosome 17. () Association results for the relationship between SNP genotypes and mRNA transcripts from the cerebellum and frontal cortex for the SLC25A38-MOBP region. () Association results for the relationship between SNP genotypes and mRNA transcripts from the cerebellum and frontal cortex for the H1/H2 inversion polymorphism region. () Association results for the relationship between SNP genotypes and mRNA transcripts from the cerebellum and frontal cortex for the H1/H2 inversion polymorphism region controlling for H1/H2. The color of the circle corresponds to the color assigned to each gene, and we tested each SNP against multiple cis transcripts. The data presented here are independent samples from those used previously12. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Günter U Höglinger, * Nadine M Melhem, * Dennis W Dickson, * Patrick M A Sleiman, * Ulrich Müller & * Gerard D Schellenberg Affiliations * Department of Neurology, Philipps-Universität, Marburg, Germany. * Günter U Höglinger * Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA. * Nadine M Melhem, * Lambertus Klei & * Bernie Devlin * Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA. * Dennis W Dickson & * Rosa Rademakers * Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Patrick M A Sleiman & * Hakon Hakonarson * Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Li-San Wang, * Laura B Cantwell, * Mi Ryung Han & * Gerard D Schellenberg * Reta Lila Weston Institute, University College London (UCL) Institute of Neurology, London, UK. * Rohan de Silva, * Jana Vandrovcova, * J Raphael Gibbs, * Dena G Hernandez, * John Hardy & * Andrew J Lees * Department of Neurology, Division of Movement Disorders, University of Louisville, Louisville, Kentucky, USA. * Irene Litvan * Department of Neurology, University Hospitals, Case Western Reserve University, Cleveland, Ohio, USA. * David E Riley * Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands. * John C van Swieten * Department of Clinical Genetics, Vrije Universiteit (VU) Medical Center, Section Medical Genomics, Amsterdam, The Netherlands. * Peter Heutink * Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA. * Zbigniew K Wszolek & * Ryan J Uitti * Department of Neurology, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA. * Howard I Hurtig & * Rachel G Gross * Center of Neurology, Department of Neurodegeneration, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany. * Walter Maetzler * German Center for Neurodegenerative Diseases, University of Tübingen, Tübingen, Germany. * Walter Maetzler * Parkinson Institute, Istituti Clinici di Perfezionamento, Milano, Italy. * Stefano Goldwurm * Neurology Service, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain. * Eduardo Tolosa * Department of Medical and Surgical Sciences, Institute of Neurology, University of Brescia, Brescia, Italy. * Barbara Borroni * CIBERNED, Instituto de Salud Carlos III, Madrid, Spain. * Pau Pastor * Neurogenetics laboratory, Division of Neurosciences, University of Navarra Center for Applied Medical Research, Pamplona, Spain. * Pau Pastor * Department of Neurology, University of Navarra, Clínica Universidad de Navarra, Pamplona, Spain. * Pau Pastor * Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA. * Allissa Dillman, * J Raphael Gibbs, * Mark R Cookson, * Dena G Hernandez & * Andrew B Singleton * Department of Neuroscience, The Scripps Research Institute, Jupiter, Florida, USA. * Marcel P van der Brug * Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada. * Matthew J Farrer * Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA. * Chang-En Yu * Geriatric Research, Education, and Clinical Center (GRECC), Veterans Affairs Puget Sound Health Care System, Seattle, Washington, USA. * Chang-En Yu * Department of Neurology, University of Medicine and Dentistry of New Jersey, Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA. * Lawrence I Golbe * Department of Molecular Neuroscience, Queen Square Brain Bank for Neurological Disorders, UCL Institute of Neurology, University College London, London, UK. * Tamas Revesz & * Andrew J Lees * Institut for Humangenetik, Justus-Liebig-Universität, Giessen, Germany. * Ulrich Müller * A list of members appears at the end of the paper. * PSP Genetics Study Group * Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA. * Roger L Albin * Geriatrics Research, Education, and Clinical Center, Veterans Affairs (VA) Ann Arbor Health System, Ann Arbor, Michigan, USA. * Roger L Albin * CIBERNED, Instituto de Salud Carlos III, Madrid, Spain. * Elena Alonso * Neurogenetics Laboratory, Division of Neurosciences, University of Navarra Center for Applied Medical Research, Pamplona, Spain. * Elena Alonso * Parkinson Institute, Istituti Clinici di Perfezionamento, Milan, Italy. * Angelo Antonini, * Margherita Canesi, * Roberto Cilia, * Claudio Mariani, * Nicoletta Meucci, * Gianni Pezzoli, * Giorgio Sacilotto, * Silvana Tesei & * Anna L Zecchinelli * Department for Parkinson's Disease, Istituto Di Ricovero e Cura a Carattere Scientifico (IRCCS) San Camillo, Venice, Italy. * Angelo Antonini * Institute of Legal Medicine, University of Würzburg, Würzburg, Germany. * Manuela Apfelbacher * Department of Psychiatry, Center for Neurobiology and Behavior, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Steven E Arnold * Centro de Biologia Molecular Severo Ochoa (CSIC-UAM), Campus Cantoblanco, Universidad Autonoma de Madrid, Madrid, Spain. * Jesus Avila * Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, Arizona, USA. * Thomas G Beach * Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Sherry Beecher, * Evan T Geller, * Virginia M Lee, * John Q Trojanowski & * Vivianna M Van Deerlin * Center of Neurology, Department of Neurodegeneration, Hertie Institute for Clinical Brain Research, University of Tübingen and German Center for Neurodegenerative diseases (DZNE), Tübingen, Germany. * Daniela Berg, * Thomas Gasser & * Karin Srulijes * Geriatrics Research Education and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA. * Thomas D Bird * Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Hudding University Hospital, Stockholm, Sweden. * Nenad Bogdanovic * Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands. * Agnita J W Boon, * Wang Zheng Chiu & * Laura Donker Kaat * Department of Neurology, University of California Los Angeles, Los Angeles, California, USA. * Yvette Bordelon * Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière, Université Pierre et Marie Curie, Paris, France. * Alexis Brice, * Alexandra Durr & * Isabelle Leber * Institut National de la Santé et de la Recherche Médicale, Paris, France. * Alexis Brice, * Alexandra Durr & * Isabelle Leber * Centre National de la Recherche Scientifique, Paris, France. * Alexis Brice, * Alexandra Durr & * Isabelle Leber * Institute of Neurology, Medical University Vienna, Vienna, Austria. * Herbert Budka * Dipartimento di Scienze Neurologiche e Psichiatriche, Sapienza Università di Roma, Rome, Italy. * Carlo Colosimo, * Giovanni Fabbrini & * Donatella Ottaviani * Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium. * Peter P De Deyn & * Sebastiaan Engelborghs * Department of Neurology, Hospital Ramón y Cajal, Madrid, Spain. * Justo García de Yebenes * Wien Center for Alzheimer's Disease and Memory Disorders, Mt. Sinai Medical Center, Miami Beach, Florida, USA. * Ranjan Duara * Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA. * NiCole A Finch & * Owen A Ross * Centre for Neuroscience, Flinders University and Australian Brain Bank Network, Victoria, Australia. * Robyn Flook * C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA. * Matthew P Frosch * Neurology Service, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic, IDIBAPS, Universitat de Barcelona, Barcelona, Spain. * Carles Gaig * Department of Neurosciences, University of California San Diego, La Jolla, California, USA. * Douglas R Galasko & * Eliezer Masliah * Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, Georgia, USA. * Marla Gearing * Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA. * Bernardino Ghetti & * Salvatore Spina * Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA. * Neill R Graff-Radford * Department of Neurology, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA. * Murray Grossman * Department of Neurological Sciences, Rush University, Chicago, Illinois, USA. * Deborah A Hall * Tanz Centre for Research in Neurodegenerative Disease, University of Toronto, Toronto, Ontario, Canada. * Lili-Naz Hazrati & * Peter St. George-Hyslop * Department of Neurology, Philipps University, Marburg, Germany. * Matthias Höllerhage, * Jens C Möller, * Wolfgang H Oertel, * Gesine Respondek & * Maria Stamelou * Department of Neurology, Baylor College of Medicine, Houston, Texas, USA. * Joseph Jankovic * Department of Neurology, Emory University, Atlanta, Georgia, USA. * Jorge L Juncos * Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, California, USA. * Anna Karydas, * Bruce L Miller & * William W Seeley * Institut für Neuropathologie, Ludwig-Maximilians-Universität and Brain Net Germany, Munich, Germany. * Hans A Kretzschmar & * Sigrun Roeber * Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, USA. * Andrew P Lieberman * Department of Neurology, University of Kansas Medical Center, Kansas City, Kansas, USA. * Kelly E Lyons & * Rajesh Pahwa * Department of Pathology, University of California San Diego, La Jolla, California, USA. * Eliezer Masliah * Reta Lila Weston Institute, UCL Institute of Neurology, University College London, London, UK. * Luke A Massey, * Sean S O'Sullivan & * Laura Silveira-Moriyama * Victorian Brain Bank Network, Mental Health Research Institute, Victoria, Australia. * Catriona A McLean * Department of Neurology, Georg-August University, Goettingen, Germany. * Brit Mollenhauer * Paracelsus-Elena-Klinik, University of Goettingen, Kassel, Germany. * Brit Mollenhauer & * Claudia Trenkwalder * Medical Research Council (MRC) Centre for Neuropsychiatric Genetics and Department of Neurology, School of Medicine, Cardiff University, Cardiff, UK. * Huw R Morris * Newcastle Brain Tissue Resource, Newcastle University, Institute for Ageing and Health, Newcastle upon Tyne, UK. * Chris Morris * Department of Medical and Surgical Sciences, Institute of Neurology, University of Brescia, Brescia, Italy. * Alessandro Padovani * Neurodegeneration and Mental Health Research Group, Faculty of Human and Medical Sciences, University of Manchester, Manchester, UK. * Stuart Pickering-Brown * Department of Neurology, Innsbruck Medical University, Innsbruck, Austria. * Werner Poewe, * Klaus Seppi & * Gregor K Wenning * Department of Neuropathology and Tissue Bank, Fundación Centro Investigación Enfermedades Neurológicas (CIEN), Instituto de Salud Carlos III, Madrid, Spain. * Alberto Rabano * Division of Neurology, Royal University Hospital, University of Saskatchewan, Saskatchewan, Canada. * Alex Rajput * Department of Neurology, University of Maryland School of Medicine, Baltimore, Maryland, USA. * Stephen G Reich * Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, UCL, London, UK. * Jonathan D Rohrer & * Martin N Rossor * Cambridge Institute for Medical Research and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK. * Peter St. George-Hyslop * Department of Neurology, Center for Neurodegeneration and Experimental Therapeutics, University of Alabama at Birmingham, Birmingham, Alabama, USA. * David G Standaert * Human Brain and Spinal Fluid Resource Center, Veterans Affairs West Los Angeles Healthcare Center, Los Angeles, California, USA. * Wallace W Tourtellotte * Department of Clinical Neuroscience, MRC Centre for Neurodegeneration Research, King's College London, London, UK. * Claire Troakes * Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Juan C Troncoso * Department of Pathology and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, New York, USA. * Jean Paul G Vonsattel * Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA. * Charles L White * Institute of Human Genetics, Justus-Liebig University, Giessen, Germany. * Pia Winter * Rancho Los Amigos National Rehabilitation Center, University of Southern California, Downey, California, USA. * Chris Zarow Consortia * PSP Genetics Study Group * Roger L Albin, * Elena Alonso, * Angelo Antonini, * Manuela Apfelbacher, * Steven E Arnold, * Jesus Avila, * Thomas G Beach, * Sherry Beecher, * Daniela Berg, * Thomas D Bird, * Nenad Bogdanovic, * Agnita J W Boon, * Yvette Bordelon, * Alexis Brice, * Herbert Budka, * Margherita Canesi, * Wang Zheng Chiu, * Roberto Cilia, * Carlo Colosimo, * Peter P De Deyn, * Justo García de Yebenes, * Laura Donker Kaat, * Ranjan Duara, * Alexandra Durr, * Sebastiaan Engelborghs, * Giovanni Fabbrini, * NiCole A Finch, * Robyn Flook, * Matthew P Frosch, * Carles Gaig, * Douglas R Galasko, * Thomas Gasser, * Marla Gearing, * Evan T Geller, * Bernardino Ghetti, * Neill R Graff-Radford, * Murray Grossman, * Deborah A Hall, * Lili-Naz Hazrati, * Matthias Höllerhage, * Joseph Jankovic, * Jorge L Juncos, * Anna Karydas, * Hans A Kretzschmar, * Isabelle Leber, * Virginia M Lee, * Andrew P Lieberman, * Kelly E Lyons, * Claudio Mariani, * Eliezer Masliah, * Luke A Massey, * Catriona A McLean, * Nicoletta Meucci, * Bruce L Miller, * Brit Mollenhauer, * Jens C Möller, * Huw R Morris, * Chris Morris, * Sean S O'Sullivan, * Wolfgang H Oertel, * Donatella Ottaviani, * Alessandro Padovani, * Rajesh Pahwa, * Gianni Pezzoli, * Stuart Pickering-Brown, * Werner Poewe, * Alberto Rabano, * Alex Rajput, * Stephen G Reich, * Gesine Respondek, * Sigrun Roeber, * Jonathan D Rohrer, * Owen A Ross, * Martin N Rossor, * Giorgio Sacilotto, * William W Seeley, * Klaus Seppi, * Laura Silveira-Moriyama, * Salvatore Spina, * Karin Srulijes, * Peter St. George-Hyslop, * Maria Stamelou, * David G Standaert, * Silvana Tesei, * Wallace W Tourtellotte, * Claudia Trenkwalder, * Claire Troakes, * John Q Trojanowski, * Juan C Troncoso, * Vivianna M Van Deerlin, * Jean Paul G Vonsattel, * Gregor K Wenning, * Charles L White, * Pia Winter, * Chris Zarow & * Anna L Zecchinelli Contributions Co-first authors G.U.H., N.M.M., D.W.D. and P.M.A.S. and senior authors U.M. and G.D.S. contributed equally to this project. G.U.H. and U.M. initiated this study and consortium, drafted the first grant and protocol, coordinated the European sample acquisition and preparation, contributed to data interpretation and contributed to the preparation of the manuscript. N.M.M. conducted the analyses and contributed to the preparation of the manuscript. D.W.D. contributed to study design, data interpretation and preparation of the manuscript. P.M.A.S. contributed in the selection of controls for both phases of the experiment, data quality control, data analysis and content curation for the replication phase custom array. L.-S.W. participated in the initial association analysis, eSNP and pathway analysis and functional annotation of SNPs in the top genes. L.K. participated in genotype quality control and analysis. R.R. and R.d.S. participated in study design, sample preparation and r! evising the manuscript for content. I. Litvan, D.E.R., J.C.V.S., P.H., Z.K.W., R.J.U., J.V., H.I.H., R.G.G., W.M., S.G., E.T., B.B., P.P. and the PSP Genetics Study Group (R.L.A., E.A., A.A., M.A., S.E.A., J.A., T.B., S.B., D.B., T.D.B., N.B., A.J.W.B., Y.B., A.B., H.B., M.C., W.Z.C., R.C., C.C., P.P.D., J.G.D., L.D.K., R.D., A. Durr, S.E., G.F., N.A.F., R.F., M.P.F., C.G., D.R.G., T.G., M. Gearing, E.T.G., B.G., N.R.G.R., M. Grossman, D.A.H., L.H., M.H., J.J., J.L.J., A.K., H.A.K., I. Leber, V.M.L., A.P.L., K.L., C. Mariani, E.M., L.A.M., C.A.M., N.M., B.L.M., B.M., J.C.M., H.R.M., C. Morris, S.S.O., W.H.O., D.O., A.P., R.P., G.P., S.P.B., W.P., A. Rabano, A. Rajput, S.G.R., G.R., S.R., J.D.R., O.A.R., M.N.R., G.S., W.W.S., K. Seppi, L.S.M., S.S., K. Srulijes, P.S.G., M.S., D.G.S., S.T., W.W.T., C. Trenkwalder, C. Troakes, J.Q.T., J.C.T., V.M.V., J.P.G.V., G.K.W., C.L.W., P.W., C.Z. and A.L.Z.) participated in characterization, preparation and contribution of samples from ! individuals with PSP. L.B.C. coordinated the project, sample a! cquisition and selection and managed phenotypes. M.R.H. conducted eSNP and pathway analysis. A. Dillman performed mRNA expression experiments in human brain. M.P.v.d.B. and D.G.H. performed mRNA expression experiments in human brain and contributed to the design of eQTL experiments. J.R.G. performed computational and statistical analysis of the eQTL data and contributed to the design of eQTL experiments. M.R.C. and A.B.S. were responsible for overall supervision, design and analysis of eQTL experiments. J.C.V.S., M.J.F., L.I.G., J.H. and A.J.L. participated in study design and data analysis discussions. C.-E.Y. and T.R. participated in the initial design of experiments. B.D. supervised analyses and contributed to the writing of the manuscript. H.H. supervised genotyping and platform and sample selection, participated in analyses and reviewed the manuscript. G.D.S. led the consortium, supervised study design, coordinated the US sample acquisition and preparation, contributed! to data interpretation and wrote and coordinated assembly of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Gerard D Schellenberg or * Ulrich Müller Author Details * Günter U Höglinger Search for this author in: * NPG journals * PubMed * Google Scholar * Nadine M Melhem Search for this author in: * NPG journals * PubMed * Google Scholar * Dennis W Dickson Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick M A Sleiman Search for this author in: * NPG journals * PubMed * Google Scholar * Li-San Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Lambertus Klei Search for this author in: * NPG journals * PubMed * Google Scholar * Rosa Rademakers Search for this author in: * NPG journals * PubMed * Google Scholar * Rohan de Silva Search for this author in: * NPG journals * PubMed * Google Scholar * Irene Litvan Search for this author in: * NPG journals * PubMed * Google Scholar * David E Riley Search for this author in: * NPG journals * PubMed * Google Scholar * John C van Swieten Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Heutink Search for this author in: * NPG journals * PubMed * Google Scholar * Zbigniew K Wszolek Search for this author in: * NPG journals * PubMed * Google Scholar * Ryan J Uitti Search for this author in: * NPG journals * PubMed * Google Scholar * Jana Vandrovcova Search for this author in: * NPG journals * PubMed * Google Scholar * Howard I Hurtig Search for this author in: * NPG journals * PubMed * Google Scholar * Rachel G Gross Search for this author in: * NPG journals * PubMed * Google Scholar * Walter Maetzler Search for this author in: * NPG journals * PubMed * Google Scholar * Stefano Goldwurm Search for this author in: * NPG journals * PubMed * Google Scholar * Eduardo Tolosa Search for this author in: * NPG journals * PubMed * Google Scholar * Barbara Borroni Search for this author in: * NPG journals * PubMed * Google Scholar * Pau Pastor Search for this author in: * NPG journals * PubMed * Google Scholar * Laura B Cantwell Search for this author in: * NPG journals * PubMed * Google Scholar * Mi Ryung Han Search for this author in: * NPG journals * PubMed * Google Scholar * Allissa Dillman Search for this author in: * NPG journals * PubMed * Google Scholar * Marcel P van der Brug Search for this author in: * NPG journals * PubMed * Google Scholar * J Raphael Gibbs Search for this author in: * NPG journals * PubMed * Google Scholar * Mark R Cookson Search for this author in: * NPG journals * PubMed * Google Scholar * Dena G Hernandez Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew B Singleton Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew J Farrer Search for this author in: * NPG journals * PubMed * Google Scholar * Chang-En Yu Search for this author in: * NPG journals * PubMed * Google Scholar * Lawrence I Golbe Search for this author in: * NPG journals * PubMed * Google Scholar * Tamas Revesz Search for this author in: * NPG journals * PubMed * Google Scholar * John Hardy Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew J Lees Search for this author in: * NPG journals * PubMed * Google Scholar * Bernie Devlin Search for this author in: * NPG journals * PubMed * Google Scholar * Hakon Hakonarson Search for this author in: * NPG journals * PubMed * Google Scholar * Ulrich Müller Contact Ulrich Müller Search for this author in: * NPG journals * PubMed * Google Scholar * Gerard D Schellenberg Contact Gerard D Schellenberg Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information Excel files * Supplementary Table 7 (57K) Location and functional consequence of SNPs in genes from the PSP GWAS signals PDF files * Supplementary Text and Figures (721K) Supplementary Tables 1–6 and 8–10 and Supplementary Figures 1–5. Additional data - The splicing regulator Rbfox1 (A2BP1) controls neuronal excitation in the mammalian brain
- Nat Genet 43(7):706-711 (2011)
Nature Genetics | Letter The splicing regulator Rbfox1 (A2BP1) controls neuronal excitation in the mammalian brain * Lauren T Gehman1 * Peter Stoilov2 * Jamie Maguire3 * Andrey Damianov4 * Chia-Ho Lin4 * Lily Shiue5 * Manuel Ares Jr5 * Istvan Mody6, 7 * Douglas L Black1, 4, 8 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:706–711Year published:(2011)DOI:doi:10.1038/ng.841Received22 March 2011Accepted02 May 2011Published online29 May 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The Rbfox family of RNA binding proteins regulates alternative splicing of many important neuronal transcripts, but its role in neuronal physiology is not clear1. We show here that central nervous system–specific deletion of the gene encoding Rbfox1 results in heightened susceptibility to spontaneous and kainic acid–induced seizures. Electrophysiological recording revealed a corresponding increase in neuronal excitability in the dentate gyrus of the knockout mice. Whole-transcriptome analyses identified multiple splicing changes in the Rbfox1−/− brain with few changes in overall transcript abundance. These splicing changes alter proteins that mediate synaptic transmission and membrane excitation. Thus, Rbfox1 directs a genetic program required in the prevention of neuronal hyperexcitation and seizures. The Rbfox1 knockout mice provide a new model to study the post-transcriptional regulation of synaptic function. View full text Figures at a glance * Figure 1: Rbfox1−/− brains lack Rbfox1 protein expression but possess normal morphology. () Confocal immunofluorescence microscopy on coronal sections of wild-type (WT) dentate gyrus probed for Rbfox1 (green) and Rbfox2 (red); overlayed images are shown in the far right panels. Arrows point to cells expressing only Rbfox1; arrowheads point to cells expressing only Rbfox2. Scale bar, 100 μm. () Immunoblot analysis of Rbfox1 and Rbfox2 in nuclear lysates isolated from wild-type, Rbfox1+/− and Rbfox1−/− brains. We used U1-70K as a loading control for total nuclear protein. Below each gel is the amount of Rbfox1 or Rbfox2 protein in each sample as a percentage of wild type, normalized by U1-70K expression. () Confocal immunofluorescence microscopy on coronal sections of wild-type and Rbfox1−/− dentate gyrus probed for Rbfox1 (green) and Rbfox2 (red) expression. Overlayed images are shown in the far right panels. Scale bar, 100 μm. () Representative Nissl stain of wild-type and Rbfox1−/− hippocampus at 1 month of age showing normal gross morphology. S! cale bar, 0.5 mm. CA1/CA3, pyramidal layers of the hippocampus; DG, dentate gyrus. * Figure 2: Rbfox1−/− brains are epileptic and hyperexcitable. () c-Fos immunostaining on wild-type and Rbfox1−/− coronal sections 1 h after a spontaneous seizure in the Rbfox1loxP/loxP; Nestin-Cre+/− mouse. Relevant brain areas are indicated. Amyg, amygdala; CA1/CA3, pyramidal cell layers of the hippocampus; DG, dentate gyrus; ent, entorhinal cortex; II–III indicate layers of the cerebral cortex. Scale bar, 2 mm. () Progression of behavioral changes after systemic kainic acid administration (20 mg/kg, intraperitoneally) in wild-type, Rbfox1loxP/loxP; Nestin-Cre+/− and Rbfox2loxP/loxP; Nestin-Cre+/− mice over a 2 h observation period. We scored seizures on the Racine Scale as described31, and data shown are the mean scores. Error bars, s.e.m. () Representative fEPSP traces from individual electrophysiological recordings in wild-type and Rbfox1−/− dentate gyrus. () Average synaptic input/output (I/O) curves in wild-type and Rbfox1−/− dentate fit with a Boltzmann function (solid lines). Circles are grand-averaged score! s; error bars, s.e.m. W50, stimulus width that elicits 50% of the maximum response; k, slope factor. n = 3 mice, 16–20 slices per experimental group. * Figure 3: Rbfox1−/− brain exhibits splicing changes in transcripts affecting synaptic function and neuronal excitation. () Denaturing gel electrophoresis of RT-PCR products for three Rbfox1-dependent exons. Above each gel is a schematic depicting the alternative exon (horizontal black box) and the relative location of (U)GCAUG binding sites (yellow boxes) in the flanking introns (thin horizontal lines). Shown below each gel is a graph quantifying the mean percentage of alternative exon inclusion (% ex in, PSI) in wild-type (black bars), Rbfox1 knockout (KO; red bars) and Rbfox2 knockout (blue bars) brain. () A schematic showing the Snap25 mutually exclusive exon pair, 5a (horizontal black box) and 5b (horizontal gray box), plus the intervening intron and the promixal 500 nucleotides of the adjacent introns. Yellow boxes represent (U)GCAUG motifs. The distribution of Rbfox1 iCLIP reads (horizontal green bars) is shown above. A histogram displaying the conservation of this region among 30 vertebrate species as determined by phastCons (see URLs) is shown below. A score of 1 indicates 100% identi! ty among all species at that nucleotide position. Chromosomal location in nucleotides is shown above the iCLIP data. At the bottom, the RT-PCR assay and quantification of exons 5a and 5b splicing is shown, with the inclusion of the downstream exon plotted. For and , error bars represent s.e.m.; n = 3. *P < 0.05 and n.s. means not significant by paired, one-tailed Student's t test. Exact P values are shown in Table 1. Author information * Author information * Supplementary information Affiliations * Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California, USA. * Lauren T Gehman & * Douglas L Black * Department of Biochemistry, School of Medicine, West Virginia University, Morgantown, West Virginia, USA. * Peter Stoilov * Department of Neuroscience, Tufts University School of Medicine, Boston, Massachusetts, USA. * Jamie Maguire * Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, California, USA. * Andrey Damianov, * Chia-Ho Lin & * Douglas L Black * Department of Molecular, Cell and Developmental Biology, Sinsheimer Labs, University of California, Santa Cruz, Santa Cruz, California, USA. * Lily Shiue & * Manuel Ares Jr * Department of Neurology, The David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA. * Istvan Mody * Department of Physiology, The David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA. * Istvan Mody * Howard Hughes Medical Institute, University of California, Los Angeles, Los Angeles, California, USA. * Douglas L Black Contributions D.L.B., P.S. and L.T.G. P.S. L.T.G. J.M., L.T.G. and I.M. J.M. and I.M. A.D. and C.-H.L. L.S., L.T.G. and M.A. L.T.G. and D.L.B. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Douglas L Black Author Details * Lauren T Gehman Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Stoilov Search for this author in: * NPG journals * PubMed * Google Scholar * Jamie Maguire Search for this author in: * NPG journals * PubMed * Google Scholar * Andrey Damianov Search for this author in: * NPG journals * PubMed * Google Scholar * Chia-Ho Lin Search for this author in: * NPG journals * PubMed * Google Scholar * Lily Shiue Search for this author in: * NPG journals * PubMed * Google Scholar * Manuel Ares Jr Search for this author in: * NPG journals * PubMed * Google Scholar * Istvan Mody Search for this author in: * NPG journals * PubMed * Google Scholar * Douglas L Black Contact Douglas L Black Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–4 and Supplementary Tables 1–3. Additional data - Variation in genome-wide mutation rates within and between human families
- Nat Genet 43(7):712-714 (2011)
Nature Genetics | Letter Variation in genome-wide mutation rates within and between human families * Donald F Conrad1, 2 * Jonathan E M Keebler3, 4 * Mark A DePristo5 * Sarah J Lindsay1 * Yujun Zhang1 * Ferran Casals3 * Youssef Idaghdour3 * Chris L Hartl5 * Carlos Torroja1 * Kiran V Garimella5 * Martine Zilversmit3 * Reed Cartwright6 * Guy A Rouleau7 * Mark Daly5 * Eric A Stone4, 6 * Matthew E Hurles1 * Philip Awadalla3 * for the 1000 Genomes Project8 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:712–714Year published:(2011)DOI:doi:10.1038/ng.862Received14 October 2010Accepted19 May 2011Published online12 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg J.B.S. Haldane proposed in 1947 that the male germline may be more mutagenic than the female germline1. Diverse studies have supported Haldane's contention of a higher average mutation rate in the male germline in a variety of mammals, including humans2, 3. Here we present, to our knowledge, the first direct comparative analysis of male and female germline mutation rates from the complete genome sequences of two parent-offspring trios. Through extensive validation, we identified 49 and 35 germline de novo mutations (DNMs) in two trio offspring, as well as 1,586 non-germline DNMs arising either somatically or in the cell lines from which the DNA was derived. Most strikingly, in one family, we observed that 92% of germline DNMs were from the paternal germline, whereas, in contrast, in the other family, 64% of DNMs were from the maternal germline. These observations suggest considerable variation in mutation rates within and between families. View full text Figures at a glance * Figure 1: Overview of the study design. The two phases of the project, discovery and validation, are shown schematically, including the samples from each family that were used in each phase. LCL, lymphoblastoid cell line; WG, whole genome. * Figure 2: Comparison of mutation rate estimates. Mutation rates estimated from previous studies are shown above the dashed green line. Solid lines encompassing point estimates represent 95% confidence intervals. Dashed lines encompassing point estimates represent reported plausible ranges. The disease-gene sex-averaged rate comes from reference 13, with 95% confidence intervals calculated as 1.96 times the standard error. The species-divergence sex-averaged rate comes from reference 14, which specifies the plausible range shown here. The species-divergence sex-specific rates come from scaling the sex-averaged point estimate and the upper and lower bounds by the ratio of male to female mutation rate of 6.11 estimated in reference 3. The family genome-wide sex-average comes from reference 7 and the families sex-average comes from reference 4. Author information * Author information * Supplementary information Affiliations * Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. * Donald F Conrad, * Sarah J Lindsay, * Yujun Zhang, * Carlos Torroja & * Matthew E Hurles * Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA. * Donald F Conrad * Ste Justine Hospital Research Centre, Department of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada. * Jonathan E M Keebler, * Ferran Casals, * Youssef Idaghdour, * Martine Zilversmit & * Philip Awadalla * Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA. * Jonathan E M Keebler & * Eric A Stone * Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. * Mark A DePristo, * Chris L Hartl, * Kiran V Garimella & * Mark Daly * Department of Genetics, North Carolina State University, Raleigh, North Carolina, USA. * Reed Cartwright & * Eric A Stone * Ste Justine Hospital Research Centre, Department of Medicine, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada. * Guy A Rouleau * A full list of members is provided in the Supplementary Note. * the 1000 Genomes Project Consortia * the 1000 Genomes Project * Donald F Conrad, * Jonathan E M Keebler, * Mark A DePristo, * Sarah J Lindsay, * Yujun Zhang, * Ferran Casals, * Youssef Idaghdour, * Chris L Hartl, * Carlos Torroja, * Kiran V Garimella, * Martine Zilversmit, * Reed Cartwright, * Guy A Rouleau, * Mark Daly, * Eric A Stone, * Matthew E Hurles & * Philip Awadalla Contributions M.E.H. and P.A. conceived the study. D.F.C., J.E.M.K., M.A.D., M.D., R.C., E.A.S. and P.A. developed statistical methodologies. D.F.C., J.E.M.K., M.A.D., C.L.H., K.V.G., E.A.S., M.E.H. and P.A. analyzed the data. F.C., Y.I., G.A.R., C.T., M.Z., S.J.L. and Y.Z. generated validation data. D.F.C., P.A. and M.E.H. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Philip Awadalla or * Matthew E Hurles Author Details * Donald F Conrad Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan E M Keebler Search for this author in: * NPG journals * PubMed * Google Scholar * Mark A DePristo Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah J Lindsay Search for this author in: * NPG journals * PubMed * Google Scholar * Yujun Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Ferran Casals Search for this author in: * NPG journals * PubMed * Google Scholar * Youssef Idaghdour Search for this author in: * NPG journals * PubMed * Google Scholar * Chris L Hartl Search for this author in: * NPG journals * PubMed * Google Scholar * Carlos Torroja Search for this author in: * NPG journals * PubMed * Google Scholar * Kiran V Garimella Search for this author in: * NPG journals * PubMed * Google Scholar * Martine Zilversmit Search for this author in: * NPG journals * PubMed * Google Scholar * Reed Cartwright Search for this author in: * NPG journals * PubMed * Google Scholar * Guy A Rouleau Search for this author in: * NPG journals * PubMed * Google Scholar * Mark Daly Search for this author in: * NPG journals * PubMed * Google Scholar * Eric A Stone Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew E Hurles Search for this author in: * NPG journals * PubMed * Google Scholar * Philip Awadalla Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information Excel files * Supplementary Table 1 (4M) Validation and haplotyping results PDF files * Supplementary Text and Figures (104M) Supplementary Figures 1–4, Supplementary Tables 2–3 and Supplementary Note. Additional data - Arabidopsis REF6 is a histone H3 lysine 27 demethylase
- Nat Genet 43(7):715-719 (2011)
Nature Genetics | Letter Arabidopsis REF6 is a histone H3 lysine 27 demethylase * Falong Lu1, 2, 4 * Xia Cui1, 4 * Shuaibin Zhang1, 2 * Thomas Jenuwein3 * Xiaofeng Cao1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:715–719Year published:(2011)DOI:doi:10.1038/ng.854Received07 December 2010Accepted12 May 2011Published online05 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Polycomb group (PcG)-mediated histone H3 lysine 27 trimethylation (H3K27me3) has a key role in gene repression and developmental regulation1, 2, 3, 4. There is evidence that H3K27me3 is actively removed in plants5, 6, 7, 8, but it is not known how this occurs. Here we show that RELATIVE OF EARLY FLOWERING 6 (REF6), also known as Jumonji domain–containing protein 12 (JMJ12), specifically demethylates H3K27me3 and H3K27me2, whereas its metazoan counterparts, the KDM4 proteins, are H3K9 and H3K36 demethylases9, 10. Plants overexpressing REF6 resembled mutants defective in H3K27me3-mediated gene silencing. Genetic interaction tests indicated that REF6 acts downstream of H3K27me3 methyltransferases. Mutations in REF6 caused ectopic and increased H3K27me3 level and decreased mRNA expression of hundreds of genes involved in regulating developmental patterning and responses to various stimuli. Our work shows that plants and metazoans use conserved mechanisms to regulate H3K27me3 d! ynamics but use distinct subfamilies of enzymes. View full text Figures at a glance * Figure 1: REF6 is an H3K27me3/2 demethylase. () Diagrams of tagged REF6 and REF6H246A. () Overexpression of REF6-YFP-HA reduced H3K27me3 and H3K27me2 but not H3K27me1. () Statistical analysis of . () Overexpression of REF6H246A-YFP-HA has no effect on H3K27 methylation. () Statistical analysis of . In and , histone methylation is shown in red (right). We visualized nuclei transfected with REF6-YFP-HA or REF6H246A-YFP-HA by YFP signal (green; middle). Nuclei were stained with DAPI (blue; left). Arrows indicate transfected nuclei. Scale bars, 2 μm. We observed more than fifty pairs of transfected nuclei versus non-transfected nuclei in the same field of view and the results were consistent with those shown in and . For each of the quantifications shown in and , we analyzed 25 regions by comparing the integrated histone modification staining density of transfected nuclei to that of non-transfected nuclei. Error bars, s.d. () REF6-YFP-HA demethylates H3K27me3 and H3K27me2 in vitro. The in vitro demethylation mixture was s! eparated by SDS-PAGE and immunoblotted using the antibodies specified on the right. We re-probed the membrane blotted with H3K27me3 with anti-H3 to confirm equal loading. * Figure 2: REF6ox plants show similar phenotypes to H3K27me3 silencing–deficient mutants. () Phenotype of Col, lhp1 and two REF6ox plants grown under long-day conditions. (–) A weak REF6ox plant shows reduced apical dominance (), and a terminal flower (–), with carpelloid sepal () and no petals (). (–) Stronger REF6ox plants that are dwarfed with short stems and unelongated pedicels (flower stems) are shown from weak to strong. (–) The strongest mutants show embryonic flowering () or embryonic flowering-like structures (,). The inside structures of and are shown enlarged in and . (–) Scanning electronic microscopy shows leaf epidermal cells of Col (), lhp1 () and two REF6ox plants (,). White scale bars, 5 mm; yellow scale bars, 0.2 mm. () Mean size of the epidermal cells shown above. Error bars, s.d. () Expression of H3K27me3 target genes in REF6ox plants determined by RT-qPCR. Expression levels were normalized to Tubulin2. Error bars, s.d. () H3 lysine methylation status in two strong REF6ox plants and a ref6 loss-of-function mutant as determined by im! munoblot with the antibodies specified on the right. Immunoblotting with H3 antibody showed equal H3 loading. * Figure 3: Genetic interaction between H3K27me3 methyltransferases and REF6. (,) The shoot parts of clf swn double () and ref6 clf swn triple mutants () show a callus-like structure. Scale bars, 1 mm. () Phenotypes of Col, ref6, clf and ref6 clf plants. Scale bar, 2 cm. () Assessment of flowering time by counting leaf numbers in bolting plants. All the above plants were grown under long-day conditions. () Expression of H3K27me3 target genes in the plants shown in – determined by RT-qPCR. Expression levels were normalized to Tubulin2. Error bars, s.d. * Figure 4: REF6 mutation causes H3K27me3 hypermethylation of several hundred endogenous genes. () Diagram of the significant overlap between genes downregulated more than 20.6-fold and H3K27me3 hypermethylated genes in ref6. Chromatin and RNA were from 10-day-old seedlings planted on Murashige-Skoog (MS) plates. () Plot of all expressed genes in Col seedlings and H3K27me3 hypermethylated genes in ref6. Total transcript expression levels are quantified as reads per kb of exon model per million mapped reads (RPKM) derived from 10-day-old Col seedlings from a previous study33. REF6 activates TCH4 expression by removing the H3K27me3 mark. () H3K27me3 ChIP-Seq data for the TCH4 locus (left). Two subregions (1 and 2) were validated by qPCR using ChIP samples of the other biological replicate (right). We used two intergenic regions without H3K27me3 modification as background controls. Plus and minus signs indicate the two DNA strands; TCH4 is on the minus strand. () TCH4 expression was validated by RT-qPCR using RNA samples of the other biological replicate. Expression was n! ormalized to Tubulin2. Error bars, s.d. * Figure 5: Diagram comparing the biochemical roles of HP1 and KDM4 in metazoans (left) with LHP1 and REF6 in Arabidopsis (right). Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE25447 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Falong Lu & * Xia Cui Affiliations * State Key Laboratory of Plant Genomics and National Center for Plant Gene Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China. * Falong Lu, * Xia Cui, * Shuaibin Zhang & * Xiaofeng Cao * Graduate School of the Chinese Academy of Sciences, Beijing, China. * Falong Lu & * Shuaibin Zhang * Max-Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany. * Thomas Jenuwein Contributions F.L., X. Cui and X. Cao conceived and designed the study. F.L., X. Cui and S.Z. performed the experiments. T.J. contributed essential reagents and edited the manuscript. F.L., X. Cui and X. Cao analyzed data and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Xiaofeng Cao Author Details * Falong Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Xia Cui Search for this author in: * NPG journals * PubMed * Google Scholar * Shuaibin Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Jenuwein Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaofeng Cao Contact Xiaofeng Cao Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 1 (3M) The list of genes marked by H3K27me3 in Col. * Supplementary Table 2 (428K) Chromosomal regions in which H3K27me3 changed more than 3-fold in ref6-3. * Supplementary Table 3 (112K) Plain text format of enriched Gene Ontology (GO) terms. * Supplementary Table 4 (72K) List of genes for which transcription levels changed more than 20.6-fold with a q-value < 0.05 in ref6-3. * Supplementary Table 5 (52K) Sequences of primers used in this study. PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–11 Additional data - Corrigendum: Mutations in FRMD7, a newly identified member of the FERM family, cause X-linked idiopathic congenital nystagmus
- Nat Genet 43(7):720 (2011)
Nature Genetics | Corrigendum Corrigendum: Mutations in FRMD7, a newly identified member of the FERM family, cause X-linked idiopathic congenital nystagmus * Patrick Tarpey * Shery Thomas * Nagini Sarvananthan * Uma Mallya * Steven Lisgo * Chris J Talbot * Eryl O Roberts * Musarat Awan * Mylvaganam Surendran * Rebecca J McLean * Robert D Reinecke * Andrea Langmann * Susanne Lindner * Martina Koch * Sunila Jain * Geoffrey Woodruff * Richard P Gale * Chris Degg * Konstantinos Droutsas * Ioannis Asproudis * Alina A Zubcov * Christina Pieh * Colin D Veal * Rajiv D Machado * Oliver C Backhouse * Laura Baumber * Cris S Constantinescu * Michael C Brodsky * David G Hunter * Richard W Hertle * Randy J Read * Sarah Edkins * Sarah O'Meara * Adrian Parker * Claire Stevens * Jon Teague * Richard Wooster * P Andrew Futreal * Richard C Trembath * Michael R Stratton * F Lucy Raymond * Irene GottlobJournal name:Nature GeneticsVolume: 43,Page:720Year published:(2011)DOI:doi:10.1038/ng0711-720Published online28 June 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Genet.38, 1242–1244 (2006); published online 1 October 2006; corrected after print 6 June 2011 In the version of this article initially published, the author Andrew Bastawrous was omitted from the author list. The error has been corrected in the HTML and PDF versions of the article. Additional data Author Details * Patrick Tarpey Search for this author in: * NPG journals * PubMed * Google Scholar * Shery Thomas Search for this author in: * NPG journals * PubMed * Google Scholar * Nagini Sarvananthan Search for this author in: * NPG journals * PubMed * Google Scholar * Uma Mallya Search for this author in: * NPG journals * PubMed * Google Scholar * Steven Lisgo Search for this author in: * NPG journals * PubMed * Google Scholar * Chris J Talbot Search for this author in: * NPG journals * PubMed * Google Scholar * Eryl O Roberts Search for this author in: * NPG journals * PubMed * Google Scholar * Musarat Awan Search for this author in: * NPG journals * PubMed * Google Scholar * Mylvaganam Surendran Search for this author in: * NPG journals * PubMed * Google Scholar * Rebecca J McLean Search for this author in: * NPG journals * PubMed * Google Scholar * Robert D Reinecke Search for this author in: * NPG journals * PubMed * Google Scholar * Andrea Langmann Search for this author in: * NPG journals * PubMed * Google Scholar * Susanne Lindner Search for this author in: * NPG journals * PubMed * Google Scholar * Martina Koch Search for this author in: * NPG journals * PubMed * Google Scholar * Sunila Jain Search for this author in: * NPG journals * PubMed * Google Scholar * Geoffrey Woodruff Search for this author in: * NPG journals * PubMed * Google Scholar * Richard P Gale Search for this author in: * NPG journals * PubMed * Google Scholar * Chris Degg Search for this author in: * NPG journals * PubMed * Google Scholar * Konstantinos Droutsas Search for this author in: * NPG journals * PubMed * Google Scholar * Ioannis Asproudis Search for this author in: * NPG journals * PubMed * Google Scholar * Alina A Zubcov Search for this author in: * NPG journals * PubMed * Google Scholar * Christina Pieh Search for this author in: * NPG journals * PubMed * Google Scholar * Colin D Veal Search for this author in: * NPG journals * PubMed * Google Scholar * Rajiv D Machado Search for this author in: * NPG journals * PubMed * Google Scholar * Oliver C Backhouse Search for this author in: * NPG journals * PubMed * Google Scholar * Laura Baumber Search for this author in: * NPG journals * PubMed * Google Scholar * Cris S Constantinescu Search for this author in: * NPG journals * PubMed * Google Scholar * Michael C Brodsky Search for this author in: * NPG journals * PubMed * Google Scholar * David G Hunter Search for this author in: * NPG journals * PubMed * Google Scholar * Richard W Hertle Search for this author in: * NPG journals * PubMed * Google Scholar * Randy J Read Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah Edkins Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah O'Meara Search for this author in: * NPG journals * PubMed * Google Scholar * Adrian Parker Search for this author in: * NPG journals * PubMed * Google Scholar * Claire Stevens Search for this author in: * NPG journals * PubMed * Google Scholar * Jon Teague Search for this author in: * NPG journals * PubMed * Google Scholar * Richard Wooster Search for this author in: * NPG journals * PubMed * Google Scholar * P Andrew Futreal Search for this author in: * NPG journals * PubMed * Google Scholar * Richard C Trembath Search for this author in: * NPG journals * PubMed * Google Scholar * Michael R Stratton Search for this author in: * NPG journals * PubMed * Google Scholar * F Lucy Raymond Search for this author in: * NPG journals * PubMed * Google Scholar * Irene Gottlob Search for this author in: * NPG journals * PubMed * Google Scholar
No comments:
Post a Comment