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- Learning to think continentally
- Nat Genet 43(9):817 (2011)
Nature Genetics | Editorial Learning to think continentally Journal name:Nature GeneticsVolume: 43,Page:817Year published:(2011)DOI:doi:10.1038/ng.933Published online29 August 2011 The US Department of Health and Human Services (DHHS) is proposing to enhance federal regulation intended to protect human research subjects, in particular to increase measures aimed at security of personal data. Since the ethical review process is partially based on respect for people and their autonomy, harmonization of these rules will be a process of convincing individuals and their states to accept uniform standards that give enough privacy but do not lock away personal data from either research participants or researchers. View full text Additional data - Admixture provides new insights into recombination
- Nat Genet 43(9):819-820 (2011)
Article preview View full access options Nature Genetics | News and Views Admixture provides new insights into recombination * Paul F O'Reilly1 * David J Balding2 * Affiliations * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:819–820Year published:(2011)DOI:doi:10.1038/ng.918Published online29 August 2011 Advances in both pedigree-based and population-based genetic maps in recent years have helped unravel some of the mysteries of human meiotic recombination. The publication of the first admixture-derived human genetic maps offers a new approach for inferring recombination events and provides insight into variation in recombination rate patterns across populations. 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 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Paul F. O'Reilly is at the Department of Epidemiology and Biostatistics, Imperial College, London, UK. * David J. Balding is at the UCL Genetics Institute, London, UK. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * David J Balding Author Details * Paul F O'Reilly Search for this author in: * NPG journals * PubMed * Google Scholar * David J Balding Contact David J Balding Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Lyp breakdown and autoimmunity
- Nat Genet 43(9):821-822 (2011)
Article preview View full access options Nature Genetics | News and Views Lyp breakdown and autoimmunity * Timothy W Behrens1Journal name:Nature GeneticsVolume: 43,Pages:821–822Year published:(2011)DOI:doi:10.1038/ng.914Published online29 August 2011 A new study shows that the PTPN22 coding variant associated with autoimmunity is a loss-of-function allele that causes the protein tyrosine phosphatase encoded by PTPN22 to undergo accelerated degradation, resulting in enhanced signaling in several immune cell types. 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 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Timothy W. Behrens is in the Division of Immunology, Tissue Growth & Repair, Biomarker Discovery and Human Genetics, Genentech, South San Francisco, California, USA. Competing financial interests T.W.B. is a full-time employee of Genentech. Corresponding author Correspondence to: * Timothy W Behrens Author Details * Timothy W Behrens Contact Timothy W Behrens Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - A20 edits ubiquitin and autoimmune paradigms
- Nat Genet 43(9):822-823 (2011)
Article preview View full access options Nature Genetics | News and Views A20 edits ubiquitin and autoimmune paradigms * Flavius Martin1 * Vishva M Dixit2 * Affiliations * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:822–823Year published:(2011)DOI:doi:10.1038/ng.916Published online29 August 2011 Complex autoimmune diseases such as rheumatoid arthritis, systemic lupus erythematosus, type 1 diabetes, multiple sclerosis, psoriasis and inflammatory bowel disease have different pathological presentations but have overlapping genetic susceptibility variants. A new study using mice lacking Tnfaip3, whose ortholog is linked to autoimmune disease in humans, leads to insights in the role of one molecular driver of varied clinical symptomatology in disparate autoimmune disorders. 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 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Flavius Martin is in the Department of Immunology, Genentech, South San Francisco, California, USA. * Vishva M. Dixit is in the Department of Physiological Chemistry, Genentech, South San Francisco, California, USA. Competing financial interests F.M. and V.M.D. are employees of Genentech, a member of the Roche group. Corresponding authors Correspondence to: * Flavius Martin or * Vishva M Dixit Author Details * Flavius Martin Contact Flavius Martin Search for this author in: * NPG journals * PubMed * Google Scholar * Vishva M Dixit Contact Vishva M Dixit Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Common variation at 10p12.31 near MLLT10 influences meningioma risk
- Nat Genet 43(9):825-827 (2011)
Nature Genetics | Brief Communication Common variation at 10p12.31 near MLLT10 influences meningioma risk * Sara E Dobbins1 * Peter Broderick1 * Beatrice Melin2 * Maria Feychting3 * Christoffer Johansen4 * Ulrika Andersson2 * Thomas Brännström5 * Johannes Schramm6 * Bianca Olver1 * Amy Lloyd1 * Yussanne P Ma1 * Fay J Hosking1 * Stefan Lönn7 * Anders Ahlbom3 * Roger Henriksson2, 8 * Minouk J Schoemaker9 * Sarah J Hepworth10 * Per Hoffmann11 * Thomas W Mühleisen11 * Markus M Nöthen11, 12 * Susanne Moebus13 * Lewin Eisele13 * Michael Kosteljanetz14 * Kenneth Muir15 * Anthony Swerdlow9 * Matthias Simon6, 16 * Richard S Houlston1, 16 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:825–827Year published:(2011)DOI:doi:10.1038/ng.879Received28 February 2011Accepted10 June 2011Published online31 July 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg To identify susceptibility loci for meningioma, we conducted a genome-wide association study of 859 affected individuals (cases) and 704 controls with validation in two independent sample sets totaling 774 cases and 1,764 controls. We identified a new susceptibility locus for meningioma at 10p12.31 (MLLT10, rs11012732, odds ratio = 1.46, Pcombined= 1.88 × 10−14). This finding advances our understanding of the genetic basis of meningioma development. View full text Author information * Author information * Supplementary information Affiliations * Section of Cancer Genetics, Institute of Cancer Research, Sutton, UK. * Sara E Dobbins, * Peter Broderick, * Bianca Olver, * Amy Lloyd, * Yussanne P Ma, * Fay J Hosking & * Richard S Houlston * Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden. * Beatrice Melin, * Ulrika Andersson & * Roger Henriksson * Division of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. * Maria Feychting & * Anders Ahlbom * Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen, Denmark. * Christoffer Johansen * Department of Medical Biosciences, Umeå University, Umeå, Sweden. * Thomas Brännström * Neurochirurgische Klinik, Universitätskliniken Bonn, Bonn, Germany. * Johannes Schramm & * Matthias Simon * Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. * Stefan Lönn * Department of Oncology, Karolinska University Hospital, Stockholm, Sweden. * Roger Henriksson * Section of Epidemiology, Institute of Cancer Research, Sutton, UK. * Minouk J Schoemaker & * Anthony Swerdlow * Centre for Epidemiology and Biostatistics, University of Leeds, Leeds, UK. * Sarah J Hepworth * Institute of Human Genetics, University of Bonn, Bonn, Germany. * Per Hoffmann, * Thomas W Mühleisen & * Markus M Nöthen * German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany. * Markus M Nöthen * Institute for Medical Informatics, Biometry and Epidemiology, University Hospital Essen, University Duisburg-Essen, Essen, Germany. * Susanne Moebus & * Lewin Eisele * Neurosurgical Department, Rigshospitalet, Copenhagen, Denmark. * Michael Kosteljanetz * Health Services Research Institute, Warwick Medical School, University of Warwick, Coventry, UK. * Kenneth Muir * These authors jointly directed this work. * Matthias Simon & * Richard S Houlston Contributions R.S.H. and M.S. conceived the study. R.S.H. designed the study and obtained financial support. R.S.H., M.S. and S.E.D. drafted the manuscript. S.E.D., P.B., F.J.H. and Y.P.M. performed statistical and bioinformatic analyses. P.B. managed sample coordination and laboratory analyses. B.O. and A.L. performed genotyping. M.S. and J.S. collected the German (Bonn) cases and obtained funding. M.S. oversaw sample preparation in Bonn. In the UK, A.S., M.J.S., K.M., S.J.H. and R.S.H. developed subject recruitment, sample acquisition and performed sample collection of cases. S.M. and L.E. managed collection of HNR controls. P.H. and T.W.M. performed genotyping of the HNR controls. M.M.N. managed sample coordination and laboratory analyses. For the Swedish INTERPHONE study, M.F., S.L. and A.A. developed the study design and conducted subject recruitment and control selection. M.F., S.L., A.A., B.M. and R.H. organized sample acquisition and performed sample collection of case and control! s. U.A. coordinated sample collection and complied information into data files of cases and controls for statistical analyses, and B.M. and R.H. performed laboratory management and oversaw DNA extraction. The NSHDS samples were collected by Umeå University (principal investigator G. Hallmans), and the additional samples were collected at the neurosurgery department in Umeå from 2005 and onwards (T.B. and R.H.) and through the national GLIOGENE study (principal investigator B.M.). In Denmark, C.J. and M.K. conducted subject recruitment and sample collection. All authors contributed to the final paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Richard S Houlston Author Details * Sara E Dobbins Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Broderick Search for this author in: * NPG journals * PubMed * Google Scholar * Beatrice Melin Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Feychting Search for this author in: * NPG journals * PubMed * Google Scholar * Christoffer Johansen Search for this author in: * NPG journals * PubMed * Google Scholar * Ulrika Andersson Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Brännström Search for this author in: * NPG journals * PubMed * Google Scholar * Johannes Schramm Search for this author in: * NPG journals * PubMed * Google Scholar * Bianca Olver Search for this author in: * NPG journals * PubMed * Google Scholar * Amy Lloyd Search for this author in: * NPG journals * PubMed * Google Scholar * Yussanne P Ma Search for this author in: * NPG journals * PubMed * Google Scholar * Fay J Hosking Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan Lönn Search for this author in: * NPG journals * PubMed * Google Scholar * Anders Ahlbom Search for this author in: * NPG journals * PubMed * Google Scholar * Roger Henriksson Search for this author in: * NPG journals * PubMed * Google Scholar * Minouk J Schoemaker Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah J Hepworth Search for this author in: * NPG journals * PubMed * Google Scholar * Per Hoffmann Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas W Mühleisen Search for this author in: * NPG journals * PubMed * Google Scholar * Markus M Nöthen Search for this author in: * NPG journals * PubMed * Google Scholar * Susanne Moebus Search for this author in: * NPG journals * PubMed * Google Scholar * Lewin Eisele Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Kosteljanetz Search for this author in: * NPG journals * PubMed * Google Scholar * Kenneth Muir Search for this author in: * NPG journals * PubMed * Google Scholar * Anthony Swerdlow Search for this author in: * NPG journals * PubMed * Google Scholar * Matthias Simon Search for this author in: * NPG journals * PubMed * Google Scholar * Richard S Houlston Contact Richard S Houlston Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Methods, Supplementary Tables 1–3 and Supplementary Figures 1–4. Additional data - Inactivating mutations of the chromatin remodeling gene ARID2 in hepatocellular carcinoma
- Nat Genet 43(9):828-829 (2011)
Nature Genetics | Brief Communication Inactivating mutations of the chromatin remodeling gene ARID2 in hepatocellular carcinoma * Meng Li1, 8 * Hong Zhao2, 8 * Xiaosong Zhang3 * Laura D Wood4 * Robert A Anders4 * Michael A Choti5 * Timothy M Pawlik5 * Hubert D Daniel4 * Rajesh Kannangai4 * G Johan A Offerhaus6 * Victor E Velculescu1 * Linfang Wang7 * Shibin Zhou1 * Bert Vogelstein1 * Ralph H Hruban4 * Nick Papadopoulos1 * Jianqiang Cai2 * Michael S Torbenson4 * Kenneth W Kinzler1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:828–829Year published:(2011)DOI:doi:10.1038/ng.903Received26 April 2011Accepted14 July 2011Published online07 August 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Through exomic sequencing of ten hepatitis C virus (HCV)-associated hepatocellular carcinomas (HCC) and subsequent evaluation of additional affected individuals, we discovered novel inactivating mutations of ARID2 in four major subtypes of HCC (HCV-associated HCC, hepatitis B virus (HBV)-associated HCC, alcohol-associated HCC and HCC with no known etiology). Notably, 18.2% of individuals with HCV-associated HCC in the United States and Europe harbored ARID2 inactivation mutations, suggesting that ARID2 is a tumor suppressor gene that is relatively commonly mutated in this tumor subtype. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Meng Li & * Hong Zhao Affiliations * Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Meng Li, * Victor E Velculescu, * Shibin Zhou, * Bert Vogelstein, * Nick Papadopoulos & * Kenneth W Kinzler * Department of Abdominal Surgical Oncology, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. * Hong Zhao & * Jianqiang Cai * Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA. * Xiaosong Zhang * Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Laura D Wood, * Robert A Anders, * Hubert D Daniel, * Rajesh Kannangai, * Ralph H Hruban & * Michael S Torbenson * Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Michael A Choti & * Timothy M Pawlik * Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands. * G Johan A Offerhaus * National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. * Linfang Wang Contributions M.L., B.V., K.W.K., S.Z., M.S.T. and R.H.H. designed the study. M.S.T., J.C., H.Z., S.Z., M.L., L.W., X.Z., L.D.W., R.A.A., M.A.C., T.M.P., H.D.D., R.K., G.J.A.O., R.H.H., V.E.V. and B.V. collected and analyzed the HCC samples. M.L., N.P. and K.W.K. performed genomic sequencing. M.L., K.W.K., B.V. and N.P. analyzed the genetic data. M.L., B.V. and K.W.K. wrote draft manuscripts. All authors contributed to the final version of the paper. Competing financial interests Under agreements between the Johns Hopkins University, Genzyme, Exact Sciences, Inostics, QIAGEN, Invitrogen and Personal Genome Diagnostics, N.P., B.V., K.W.K. and V.E.V. are entitled to a share of the royalties received by the University on sales of products related to genes described in this manuscript. N.P., B.V., K.W.K. and V.E.V. are cofounders of Inostics and Personal Genome Diagnostics, are members of their Scientific Advisory Boards and own Inostics and Personal Genome Diagnostics stock, which is subject to certain restrictions under Johns Hopkins University policy. Corresponding authors Correspondence to: * Kenneth W Kinzler or * Michael S Torbenson or * Jianqiang Cai Author Details * Meng Li Search for this author in: * NPG journals * PubMed * Google Scholar * Hong Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaosong Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Laura D Wood Search for this author in: * NPG journals * PubMed * Google Scholar * Robert A Anders Search for this author in: * NPG journals * PubMed * Google Scholar * Michael A Choti Search for this author in: * NPG journals * PubMed * Google Scholar * Timothy M Pawlik Search for this author in: * NPG journals * PubMed * Google Scholar * Hubert D Daniel Search for this author in: * NPG journals * PubMed * Google Scholar * Rajesh Kannangai Search for this author in: * NPG journals * PubMed * Google Scholar * G Johan A Offerhaus Search for this author in: * NPG journals * PubMed * Google Scholar * Victor E Velculescu Search for this author in: * NPG journals * PubMed * Google Scholar * Linfang Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Shibin Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Bert Vogelstein Search for this author in: * NPG journals * PubMed * Google Scholar * Ralph H Hruban Search for this author in: * NPG journals * PubMed * Google Scholar * Nick Papadopoulos Search for this author in: * NPG journals * PubMed * Google Scholar * Jianqiang Cai Contact Jianqiang Cai Search for this author in: * NPG journals * PubMed * Google Scholar * Michael S Torbenson Contact Michael S Torbenson Search for this author in: * NPG journals * PubMed * Google Scholar * Kenneth W Kinzler Contact Kenneth W Kinzler Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (770 K) Supplementary Methods, Supplementary Figures 1 and 2 and Supplementary Tables 1–8. Additional data - Analysis of the coding genome of diffuse large B-cell lymphoma
- Nat Genet 43(9):830-837 (2011)
Nature Genetics | Article Analysis of the coding genome of diffuse large B-cell lymphoma * Laura Pasqualucci1, 2, 3 * Vladimir Trifonov4 * Giulia Fabbri1 * Jing Ma5 * Davide Rossi6 * Annalisa Chiarenza1 * Victoria A Wells1 * Adina Grunn1 * Monica Messina1 * Oliver Elliot4 * Joseph Chan4 * Govind Bhagat2, 3 * Amy Chadburn7 * Gianluca Gaidano6 * Charles G Mullighan5 * Raul Rabadan4 * Riccardo Dalla-Favera1, 2, 3, 8 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:830–837Year published:(2011)DOI:doi:10.1038/ng.892Received20 April 2011Accepted30 June 2011Published online31 July 2011Corrected online19 August 2011 Abstract * Abstract * Change history * 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 Diffuse large B-cell lymphoma (DLBCL) is the most common form of human lymphoma. Although a number of structural alterations have been associated with the pathogenesis of this malignancy, the full spectrum of genetic lesions that are present in the DLBCL genome, and therefore the identity of dysregulated cellular pathways, remains unknown. By combining next-generation sequencing and copy number analysis, we show that the DLBCL coding genome contains, on average, more than 30 clonally represented gene alterations per case. This analysis also revealed mutations in genes not previously implicated in DLBCL pathogenesis, including those regulating chromatin methylation (MLL2; 24% of samples) and immune recognition by T cells. These results provide initial data on the complexity of the DLBCL coding genome and identify novel dysregulated pathways underlying its pathogenesis. View full text Figures at a glance * Figure 1: DLBCL non-silent mutation load. Number () and type () of Sanger-validated, non-silent mutations identified in the six DLBCL discovery cases. () Nucleotide targeting of the DLBCL-associated point mutations (nucleotide composition of the target exome: G, 26.2%; C, 25.7%; T, 22.0%; and A, 25.9%). () Observed mutation frequencies at specific dinucleotides (red bars). The expected frequencies (gray bars) correpond to the dinucleotide sequence composition of the target exome. Asterisks denote statistically significant differences in overrepresented changes as assessed by a Poisson distribution after correction for multiple hypotheses. * Figure 2: Copy number analysis of the six DLBCL discovery cases. () dChipSNP heatmap showing the median-smoothed log2 copy number ratio of all chromosomes in the six index DLBCL biopsies and their paired normal DNAs. In the red-blue scale, white corresponds to a normal (diploid) copy number log ratio, blue is a deletion and red is a gain. () Curated segmentation data for the six DLBCL samples shown in , obtained as described in the Online Methods and visualized using the Integrative Genomics Viewer (IGV) software (see URLs). () Overall number and frequency of copy number alterations. Chrom, chromosome. * Figure 3: DLBCL harbors a heterogeneous load of numerical and structural genomic aberrations. Shown are the combined loads of genetic lesions that were present in the major tumor clone of the six DLBCL discovery subjects (including point mutations, copy number alterations and chromosomal translocations at three common target loci). * Figure 4: Recurrent mutations in DLBCL. Percentage of DLBCL primary cases harboring mutations in 56 candidate genes after targeted resequencing of an expanded screening dataset. The final number of mutated samples over total samples analyzed (including the six DLBCL discovery cases) is given for each gene; this number includes validated somatic mutations as well as variants that are not reported in any available SNP databases (Online Methods). The somatic origin of the mutations was confirmed by analysis of paired normal DNA in at least one case per gene. Red asterisks denote putative tumor suppressor genes as suggested by the presence of predominantly inactivating mutations (stop codons, frameshift mutations and experimentally validated missense mutations). Green asterisks denote genes where the oncogenic activity of the mutation was functionally proven. Black asterisks indicate genes harboring mutations of unclear pathogenic role but which are predicted to truncate the encoded protein in at least one case. * Figure 5: MLL2 is mutated in a large fraction of DLBCLs. () Schematic diagram of the MLL2 gene (top) and protein (bottom) with its conserved functional domains (PHD, FYRN, FYRC and SET). Black represents untranslated exons. Arrows, color coded as in , indicate the position of the mutations found (with cell lines at the top and samples from primary cases at the bottom). () Overall percentage of cases with DLBCL carrying MLL2 mutations according to mutation type (cell lines and biopsies). () Prevalence of cases with MLL2 mutations in major DLBCL phenotypic subtypes; numbers on the top indicate the actual number of mutated samples over the total samples analyzed. () Allelic distribution of the mutations in 26 of the 28 affected samples for which this data could be obtained. Mutations are color coded as in . * Figure 6: Disruption of histone and/or chromatin modification genes is a major feature of DLBCL. () Mutation frequency of five genes encoding for histone-acetyltransferases and histone-methylation modifiers in GCB and ABC DLBCL (primary cases only). () dChipSNP heatmap showing median-smoothed log2 copy number ratio for DLBCL biopsies harboring KDM2B, MLL3 and MLL5 deletions or rearrangements; in the red-blue scale, white corresponds to a normal (diploid) copy number, blue is a deletion and red is a gain. Note that, because of the presence of non-tumor cells infiltrating the biopsies, the inferred copy number, and the corresponding color intensity, may vary across samples (Supplementary Table 11). The boxed area corresponds to the commonly deleted region, which is expanded below the heatmap to show the included genes. () Relationship between MLL2 mutations and mutations of genes encoding HATs. In the heatmap, rows correspond to the indicated genes and columns represent individual samples color coded according to the gene status (gray, unmutated; red, mutated or deleted).! Of the three cases with MLL2 mutations showing simultaneous lesions at CREBBP, one harbored an amino acid substitution whose functional relevance is currently unclear. Thus, it is possible that this change represents a passenger event or a private germline variant not annotated in currently available databases; alternatively, these mutations may have milder effects, requiring additional cooperating lesions. () Overall fraction of DLBCL biopsies carrying mutations at one or more of the four histone-modification genes shown in . Change history * Abstract * Change history * Author information * Supplementary informationCorrected online 19 August 2011In the version of this supplementary file originally posted online, Supplementary Figures 2, 4 and 6 and Supplementary Tables 12, 14, 18 and 19 contained errors. The errors have been corrected in this file as of 19 August 2011. Author information * Abstract * Change history * Author information * Supplementary information Affiliations * Institute for Cancer Genetics, Columbia University, New York, New York, USA. * Laura Pasqualucci, * Giulia Fabbri, * Annalisa Chiarenza, * Victoria A Wells, * Adina Grunn, * Monica Messina & * Riccardo Dalla-Favera * Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York, USA. * Laura Pasqualucci, * Govind Bhagat & * Riccardo Dalla-Favera * Department of Pathology & Cell Biology, Columbia University, New York, New York, USA. * Laura Pasqualucci, * Govind Bhagat & * Riccardo Dalla-Favera * Department of Biomedical Informatics and Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, USA. * Vladimir Trifonov, * Oliver Elliot, * Joseph Chan & * Raul Rabadan * Department of Pathology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA. * Jing Ma & * Charles G Mullighan * Division of Hematology, Department of Clinical and Experimental Medicine and Interdisciplinary Research Center of Autoimmune Diseases, Amedeo Avogadro University of Eastern Piedmont, Novara, Italy. * Davide Rossi & * Gianluca Gaidano * Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. * Amy Chadburn * Department of Genetics & Development, Columbia University, New York, New York, USA. * Riccardo Dalla-Favera Contributions L.P. and R.D.-F. designed the study and wrote the manuscript. L.P. conducted experiments, analyzed data and supervised the study. G.F., A. Chiarenza, A.G., V.A.W. and M.M. performed PCR amplification and sequencing analysis. C.G.M. and J.M. developed methods for analysis of high-density SNP array data, which was conducted by C.G.M., J.M., L.P. and G.F. Pathologically characterized subject samples were provided by D.R., G.G., G.B. and A. Chadburn. V.T. and R.R. analyzed high-throughput sequencing data and developed the ComFocal algorithm for analysis of copy number data with the help of O.E. and J.C. All authors read and approved the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Laura Pasqualucci or * Riccardo Dalla-Favera Author Details * Laura Pasqualucci Contact Laura Pasqualucci Search for this author in: * NPG journals * PubMed * Google Scholar * Vladimir Trifonov Search for this author in: * NPG journals * PubMed * Google Scholar * Giulia Fabbri Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Ma Search for this author in: * NPG journals * PubMed * Google Scholar * Davide Rossi Search for this author in: * NPG journals * PubMed * Google Scholar * Annalisa Chiarenza Search for this author in: * NPG journals * PubMed * Google Scholar * Victoria A Wells Search for this author in: * NPG journals * PubMed * Google Scholar * Adina Grunn Search for this author in: * NPG journals * PubMed * Google Scholar * Monica Messina Search for this author in: * NPG journals * PubMed * Google Scholar * Oliver Elliot Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph Chan Search for this author in: * NPG journals * PubMed * Google Scholar * Govind Bhagat Search for this author in: * NPG journals * PubMed * Google Scholar * Amy Chadburn Search for this author in: * NPG journals * PubMed * Google Scholar * Gianluca Gaidano Search for this author in: * NPG journals * PubMed * Google Scholar * Charles G Mullighan Search for this author in: * NPG journals * PubMed * Google Scholar * Raul Rabadan Search for this author in: * NPG journals * PubMed * Google Scholar * Riccardo Dalla-Favera Contact Riccardo Dalla-Favera Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Change history * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Note, Supplementary Figures 1–6 and Supplementary Tables 1–19. Additional data - A copy number variation morbidity map of developmental delay
- Nat Genet 43(9):838-846 (2011)
Nature Genetics | Article A copy number variation morbidity map of developmental delay * Gregory M Cooper1, 16, 17 * Bradley P Coe1, 17 * Santhosh Girirajan1, 17 * Jill A Rosenfeld2 * Tiffany H Vu1 * Carl Baker1 * Charles Williams3 * Heather Stalker3 * Rizwan Hamid4 * Vickie Hannig4 * Hoda Abdel-Hamid5 * Patricia Bader6 * Elizabeth McCracken7 * Dmitriy Niyazov8 * Kathleen Leppig9 * Heidi Thiese9 * Marybeth Hummel10 * Nora Alexander10 * Jerome Gorski11 * Jennifer Kussmann11 * Vandana Shashi12 * Krys Johnson13 * Catherine Rehder14 * Blake C Ballif2 * Lisa G Shaffer2 * Evan E Eichler1, 15 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:838–846Year published:(2011)DOI:doi:10.1038/ng.909Received11 April 2011Accepted22 July 2011Published online14 August 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 To understand the genetic heterogeneity underlying developmental delay, we compared copy number variants (CNVs) in 15,767 children with intellectual disability and various congenital defects (cases) to CNVs in 8,329 unaffected adult controls. We estimate that ~14.2% of disease in these children is caused by CNVs >400 kb. We observed a greater enrichment of CNVs in individuals with craniofacial anomalies and cardiovascular defects compared to those with epilepsy or autism. We identified 59 pathogenic CNVs, including 14 new or previously weakly supported candidates, refined the critical interval for several genomic disorders, such as the 17q21.31 microdeletion syndrome, and identified 940 candidate dosage-sensitive genes. We also developed methods to opportunistically discover small, disruptive CNVs within the large and growing diagnostic array datasets. This evolving CNV morbidity map, combined with exome and genome sequencing, will be critical for deciphering the genetic bas! is of developmental delay, intellectual disability and autism spectrum disorders. View full text Figures at a glance * Figure 1: CNV size distributions in affected and unaffected individuals. The population frequency of the largest CNV in a sample is displayed as a survivor function with the proportion of samples carrying a CNV of a given size displayed as a curve and the 95% confidence intervals indicated by dotted lines. () The distribution of large CNVs in the Signature set (filtered to only contain events detectable by the Illumina 550K array) compared to our control population (downsampled to only events detectable by the Signature 97K array) is indicated for the overall population. After corrections for different array densities, we observed a >13.5% increase in CNV burden beyond 500 kb in cases, with a proportion of the burden representing potentially new loci. () We also performed a similar analysis on subphenotypes; in this analysis, we included all Signature CNVs in conjunction with downsampled control CNVs, as we are highlighting interphenotype differences rather than case versus control frequencies. The plot depicts autism, cardiovascular and craniofa! cial phenotypes, which represent fairly distinct sample sets, and shows an increased burden for the cardiovascular and craniofacial phenotypes, even after excluding karyotypically visible (>10 Mb) events. * Figure 2: Maps of CNV locations for chromosomes 15 and 17. CNVs (>400 kb) in affected individuals are shown in the upper portion for each chromosome with control CNVs shown in the lower portion. Disease enrichment P values are plotted just below the control CNV maps, computed in 200-kb windows along each chromosome (with a step size of 50 kb). Deletions and duplications are shown in red and blue, respectively, with the P value wiggle plots colored accordingly and plotted on a negative log scale. In the middle of each plot, chromosomal features are colored as depicted. Significantly enriched regions are numbered and named on the right-hand side. * Figure 3: Discovery of new microdeletions associated with genomic disorders. () A newly discovered microdeletion on chromosome 15q25.2q25.3. Array CGH analysis for three individuals with a 660-kb (chr15:82,889,423–83,552,890) deletion is shown. This microdeletion maps within a genomic hotspot flanked by high-identity segmental duplication blocks. Intrachromosomal segmental duplications of high similarity relevant to this hotspot region are depicted as red (69.8 kb, 98.6% identity) and green (17.6 kb, 98.6% identity) block arrows. Note that the directly orientated segmental duplications (red block arrows) likely mediate the underlying 15q25 rearrangements by non-allelic homologous recombination. This region also contains a 60-kb (chr15:82,775,465–82,835,495) gap in the current builds (build 36 and build 37) of the reference genome assembly. () Atypical 17q21.31 microdeletions refine critical interval genes. High-density array CGH for the 17q21.31 microdeletion region is shown for three individuals. Probes with log2 ratios below a threshold of 1.5 ! standard deviation from the normalized mean log2 ratio denote deletions (red). We identified the typical deletions (top panel) in 23 individuals, whereas we identified atypical deletions in three individuals. Note that the smallest deletion (blue dashed box) refines the phenotype-associated critical region (chr17:41,356,798–41,631,306) to encompass only five RefSeq genes. () Photographs of two individuals (9888884 and 648) with atypical deletions. Subject 9888884 is a 5-year-old female child with clinical features typical of 17q21.31 microdeletion syndrome, including distinctive dysmorphic features with a bulbous nasal tip, upslanting and almond-shaped palpebral fissures, long face, strabismus, epicanthal folds and prominent ears; developmental delay with limited speech; hypotonia in infancy; and a friendly disposition. Additional features are low birth weight, short stature, microcephaly, long fingers and heart defects. This subject also presented with postaxial polysynd! actyly, neonatal cholestasis, resolved leucopenia, dry skin wi! th some hyperpigmented lesions and an anteriorly split tongue. Subject 648 is 9-year-old male child with a clinical history of generalized hypotonia, seizures, autism, intellectual disability, motor developmental delay and dysmorphic features consistent with the 17q21.31 microdeletion syndrome (epicanthal folds; ptosis; long, pear-shaped nose; and long, tapering fingers). We obtained informed consent to publish the photographs. * Figure 4: Discovery of new, exon-altering CNVs using the Signature CGH data. () For each coding exon (red bar), we used the three probes (black rectangles) nearest the exon for any given individual to define a cassette score. () Distribution of cassette intensities for exon 6 of PARK2 are sorted from lowest to highest (measured in standard deviation; y axis) across all samples (x axis). Red open circles correspond to known large deletion events that span the exon. () Validation results for the most strongly negative samples from not previously known to carry deletions. Log2 ratio values (y axis for each individual row) for PARK2 (coordinates on the x axis) in each of six tested samples are shown. Probes with very low intensities (<−0.5) are colored red and those with moderately low values (<−0.3) are shown in gray. The locations of PARK2 exons and probes on two of the most commonly used original oligonucleotide arrays are shown at the top. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions GenBank * nstd54 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Gregory M Cooper, * Bradley P Coe & * Santhosh Girirajan Affiliations * Department of Genome Sciences, University of Washington, Seattle, Washington, USA. * Gregory M Cooper, * Bradley P Coe, * Santhosh Girirajan, * Tiffany H Vu, * Carl Baker & * Evan E Eichler * Signature Genomic Laboratories, LLC, Spokane, Washington, USA. * Jill A Rosenfeld, * Blake C Ballif & * Lisa G Shaffer * Department of Pediatrics, Division of Genetics, University of Florida, Gainesville, Florida, USA. * Charles Williams & * Heather Stalker * Vanderbilt University Medical Center, Nashville, Tennessee, USA. * Rizwan Hamid & * Vickie Hannig * Department of Pediatrics, Division of Child Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. * Hoda Abdel-Hamid * Northeast Indiana Genetic Counseling Center, Ft. Wayne, Indiana, USA. * Patricia Bader * Children's Hospital Pittsburgh, Pittsburgh, Pennsylvania, USA. * Elizabeth McCracken * Ochsner Clinic, New Orleans, Louisiana, USA. * Dmitriy Niyazov * Group Health Cooperative, Seattle, Washington, USA. * Kathleen Leppig & * Heidi Thiese * West Virginia University, Morgantown, West Virginia, USA. * Marybeth Hummel & * Nora Alexander * University of Missouri, Columbia, Missouri, USA. * Jerome Gorski & * Jennifer Kussmann * Department of Pediatrics, Duke University Medical Center, Durham, North Carolina, USA. * Vandana Shashi * Department of Pathology, Duke University Medical Center, Durham, North Carolina, USA. * Krys Johnson * Clinical Molecular Diagnostic Laboratory, Duke University Health System, Durham, North Carolina, USA. * Catherine Rehder * Howard Hughes Medical Institute, University of Washington, Seattle, Washington, USA. * Evan E Eichler * Present address: HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA. * Gregory M Cooper Contributions G.M.C., B.P.C., S.G., E.E.E., J.A.R., B.C.B. and L.G.S. designed the study. L.G.S. supervised array-CGH experiments at Signature Genomics. J.A.R. and B.C.B. coordinated clinical data collection. G.M.C. and B.P.C. performed data analysis and curated control CNV data. S.G. curated genomic disorders data. S.G., T.H.V. and C.B. performed array CGH and PCR validations. C.W., H.S., R.H., V.H., H.A.-H., P.B., E.M., D.N., K.L., H.T., M.H., N.A., J.G., J.K., V.S., K.J. and C.R. provided clinical information. G.M.C., B.P.C., S.G. and E.E.E. wrote the manuscript. All authors have read and approved the final version of the manuscript. Competing financial interests E.E.E. is a member of the Scientific Advisory Board of Pacific Biosciences. J.A.R., B.C.B. and L.G.S. are employees of PerkinElmer. Corresponding author Correspondence to: * Evan E Eichler Author Details * Gregory M Cooper Search for this author in: * NPG journals * PubMed * Google Scholar * Bradley P Coe Search for this author in: * NPG journals * PubMed * Google Scholar * Santhosh Girirajan Search for this author in: * NPG journals * PubMed * Google Scholar * Jill A Rosenfeld Search for this author in: * NPG journals * PubMed * Google Scholar * Tiffany H Vu Search for this author in: * NPG journals * PubMed * Google Scholar * Carl Baker Search for this author in: * NPG journals * PubMed * Google Scholar * Charles Williams Search for this author in: * NPG journals * PubMed * Google Scholar * Heather Stalker Search for this author in: * NPG journals * PubMed * Google Scholar * Rizwan Hamid Search for this author in: * NPG journals * PubMed * Google Scholar * Vickie Hannig Search for this author in: * NPG journals * PubMed * Google Scholar * Hoda Abdel-Hamid Search for this author in: * NPG journals * PubMed * Google Scholar * Patricia Bader Search for this author in: * NPG journals * PubMed * Google Scholar * Elizabeth McCracken Search for this author in: * NPG journals * PubMed * Google Scholar * Dmitriy Niyazov Search for this author in: * NPG journals * PubMed * Google Scholar * Kathleen Leppig Search for this author in: * NPG journals * PubMed * Google Scholar * Heidi Thiese Search for this author in: * NPG journals * PubMed * Google Scholar * Marybeth Hummel Search for this author in: * NPG journals * PubMed * Google Scholar * Nora Alexander Search for this author in: * NPG journals * PubMed * Google Scholar * Jerome Gorski Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer Kussmann Search for this author in: * NPG journals * PubMed * Google Scholar * Vandana Shashi Search for this author in: * NPG journals * PubMed * Google Scholar * Krys Johnson Search for this author in: * NPG journals * PubMed * Google Scholar * Catherine Rehder Search for this author in: * NPG journals * PubMed * Google Scholar * Blake C Ballif Search for this author in: * NPG journals * PubMed * Google Scholar * Lisa G Shaffer Search for this author in: * NPG journals * PubMed * Google Scholar * Evan E Eichler Contact Evan E Eichler Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 1 (700K) Phenotype by sample * Supplementary Table 12 (5M) Gene level statistics * Supplementary Table 13 (3M) Control CNV burden by gene PDF files * Supplementary Text and Figures (3M) Supplementary Tables 2–11, Supplementary Figures 1–13 and Supplementary Note. Additional data - Recombination rates in admixed individuals identified by ancestry-based inference
- Nat Genet 43(9):847-853 (2011)
Nature Genetics | Article Recombination rates in admixed individuals identified by ancestry-based inference * Daniel Wegmann1 * Darren E Kessner2 * Krishna R Veeramah1 * Rasika A Mathias3, 4, 5 * Dan L Nicolae6, 7, 8, 9 * Lisa R Yanek3, 4 * Yan V Sun10, 11, 12 * Dara G Torgerson8, 9, 13 * Nicholas Rafaels5, 14 * Thomas Mosley11, 15 * Lewis C Becker3, 4 * Ingo Ruczinski5, 14 * Terri H Beaty5, 16 * Sharon L R Kardia10, 11 * Deborah A Meyers13, 17 * Kathleen C Barnes3, 5 * Diane M Becker3, 4 * Nelson B Freimer18 * John Novembre1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:847–853Year published:(2011)DOI:doi:10.1038/ng.894Received04 February 2011Accepted01 July 2011Published online20 July 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 Studies of recombination and how it varies depend crucially on accurate recombination maps. We propose a new approach for constructing high-resolution maps of relative recombination rates based on the observation of ancestry switch points among admixed individuals. We show the utility of this approach using simulations and by applying it to SNP genotype data from a sample of 2,565 African Americans and 299 African Caribbeans and detecting several hundred thousand recombination events. Comparison of the inferred map with high-resolution maps from non-admixed populations provides evidence of fine-scale differentiation in recombination rates between populations. Overall, the admixed map is well predicted by the average proportion of admixture and the recombination rate estimates from the source populations. The exceptions to this are in areas surrounding known large chromosomal structural variants, specifically inversions. These results suggest that outside of structurally vari! able regions, admixture does not substantially disrupt the factors controlling recombination rates in humans. View full text Figures at a glance * Figure 1: Sketch of the haplotype-copying Hidden Markov model used to detect ancestry switch points. () Yellow and blue represent the chromosomal segments of different ancestry and the shades of each color represent different haplotypes from each ancestry. Recombination creates a mosaic of haplotypes regardless of origin but recombination events between haplotypes of different ancestries leave signatures that can be detected in descendant, admixed individuals. () The genotypes observed for such an individual form observed states of a Hidden Markov model in which underlying states are based on which haplotypes from a reference population each allele of the genotype is copied. * Figure 2: Sensitivity and specificity of inference. () Estimated number of switches (cjk−(i)) between neighboring SNPs obtained for a simulated individual with two ancestry switches (vertical dashed lines). Below, the comparison at the 50-kb scale of the estimated rates (rjk) and the underlying recombination map used to perform the simulations for this segment. Both maps are normalized to the same total rate. () The inferred number of switch points (cjk−(i)) as function of the size of the interval between locations j and k. The black line represents the median for symmetric intervals around a single, isolated switch point. The red line represents the median for intervals with zero simulated switch points and which are located at least 1 Mb away from the closest switch point. Dashed lines mark the 2.5% and 97.5% quantiles. () Comparison of the inferred rates (rjk) with the true rates across all segments at 10-kb (blue), 50-kb (orange) and 1-Mb (red) scales. The 2.5% and 97.5% quantiles are shown with dashed lines. All maps! have been normalized to the same total rate for comparison. * Figure 3: Comparison of the African admixture-based map to existing maps. () Example of 1-Mb–scale map from 50 Mb of chromosome 1. () Example of 50-kb–scale map from the 2.5-Mb section of chromosome 1 indicated by the gray box in . () Proportion of the total recombination in various proportions of sequence intervals at the 50-kb scale. * Figure 4: Population differences in recombination patterns. (–) Independent of scale, the AfAdm map correlates better () and shares more hotspots () with the HapMapYRI than the HapMapCEU map. In contrast, the deCODE map correlates better () and shares more hotspots () with the HapMapCEU than the HapMapYRI map. Hotspots are defined as the 50-kb intervals with the top 1% largest rates. * Figure 5: Recombination rates in notable genomic locations. () The region with the largest deficit of the AfAdm map just outside the known inversion on chromosome 8p23.1–8p22 (gray). () The region with a large deficit of the AfAdm map on chromosome 9 near the boundary of multiple known polymorphic inversions. () The inversion on chromosome 17q21.31 (gray). () A region on chromosome 14 with an elevated average European-ancestry proportion (gray) framed by local peaks of recombination. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, USA. * Daniel Wegmann, * Krishna R Veeramah & * John Novembre * Interdepartmental Program in Bioinformatics, University of California, Los Angeles, California, USA. * Darren E Kessner & * John Novembre * Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA. * Rasika A Mathias, * Lisa R Yanek, * Lewis C Becker, * Kathleen C Barnes & * Diane M Becker * For the Genetic Study of Atherosclerosis Risk (GeneSTAR) consortium. * Rasika A Mathias, * Lisa R Yanek, * Lewis C Becker & * Diane M Becker * For the Genetic Research on Asthma in the African Diaspora (GRAAD) consortium. * Rasika A Mathias, * Nicholas Rafaels, * Ingo Ruczinski, * Terri H Beaty & * Kathleen C Barnes * Department of Medicine, University of Chicago, Chicago, Illinois, USA. * Dan L Nicolae * Department of Statistics, University of Chicago, Chicago, Illinois, USA. * Dan L Nicolae * Department of Human Genetics, University of Chicago, Chicago, Illinois, USA. * Dan L Nicolae & * Dara G Torgerson * For the Chicago Asthma Genetics (CAG) and Collaborative Study on the Genetics of Asthma (CSGA) consortium. * Dan L Nicolae & * Dara G Torgerson * Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA. * Yan V Sun & * Sharon L R Kardia * For the Genetic Epidemiology Network of Arteriopathy (GENOA) consortium. * Yan V Sun, * Thomas Mosley & * Sharon L R Kardia * Department of Epidemiology, Emory University, Atlanta, Georgia, USA. * Yan V Sun * For the Severe Asthma Research Program (SARP) consortium. * Dara G Torgerson & * Deborah A Meyers * Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA. * Nicholas Rafaels & * Ingo Ruczinski * Department of Medicine, University of Mississippi, Jackson, Mississippi, USA. * Thomas Mosley * Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA. * Terri H Beaty * Center for Human Genomics, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA. * Deborah A Meyers * Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, California, USA. * Nelson B Freimer Contributions J.N. and N.B.F. conceived of the project, and D.W., J.N., N.B.F. and D.L.N. designed the analyses. D.G.T. and D.L.N. worked as part of the Chicago Asthma Genetics (CAG) and Collaborative Study on the Genetics of Asthma (CGSA) consortium to gather and prepare primary data for subsequent analysis. R.A.M., L.R.Y., L.C.B. and D.M.B. worked as part of the Genetic Study of Atherosclerosis Risk (GeneSTAR) Consortium to gather and prepare primary data for subsequent analysis. I.R., N.R., R.A.M., T.H.B. and K.C.B. worked as part of the Genetic Research on Asthma in the African Diaspora (GRAAD) consortium to gather and prepare primary data for subsequent analysis. Y.V.S., T.M. and S.L.R.K. worked as part of the Genetic Epidemiology Network of Arteriopathy (GENOA) consortium to gather and prepare primary data for subsequent analysis. D.G.T. and D.A.M. worked as part of the Severe Asthma Research Program (SARP) to gather and prepare primary data for subsequent analysis. D.W., D.E.K., K.! R.V. and J.N. developed tools for the analysis and performed the analysis. D.W., N.B.F. and J.N. drafted the manuscript and revised it with D.E.K., K.R.V., R.A.M., D.L.N., L.R.Y., Y.V.S., L.C.B., N.R., I.R., T.H.B., S.L.R.K., D.A.M., K.C.B. and D.M.B. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * John Novembre Author Details * Daniel Wegmann Search for this author in: * NPG journals * PubMed * Google Scholar * Darren E Kessner Search for this author in: * NPG journals * PubMed * Google Scholar * Krishna R Veeramah Search for this author in: * NPG journals * PubMed * Google Scholar * Rasika A Mathias Search for this author in: * NPG journals * PubMed * Google Scholar * Dan L Nicolae Search for this author in: * NPG journals * PubMed * Google Scholar * Lisa R Yanek Search for this author in: * NPG journals * PubMed * Google Scholar * Yan V Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Dara G Torgerson Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas Rafaels Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Mosley Search for this author in: * NPG journals * PubMed * Google Scholar * Lewis C Becker Search for this author in: * NPG journals * PubMed * Google Scholar * Ingo Ruczinski Search for this author in: * NPG journals * PubMed * Google Scholar * Terri H Beaty Search for this author in: * NPG journals * PubMed * Google Scholar * Sharon L R Kardia Search for this author in: * NPG journals * PubMed * Google Scholar * Deborah A Meyers Search for this author in: * NPG journals * PubMed * Google Scholar * Kathleen C Barnes Search for this author in: * NPG journals * PubMed * Google Scholar * Diane M Becker Search for this author in: * NPG journals * PubMed * Google Scholar * Nelson B Freimer Search for this author in: * NPG journals * PubMed * Google Scholar * John Novembre Contact John Novembre Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Note, Supplementary Tables 1–4 and Supplementary Figures 1–14. Additional data - MicroRNAs can generate thresholds in target gene expression
- Nat Genet 43(9):854-859 (2011)
Nature Genetics | Article MicroRNAs can generate thresholds in target gene expression * Shankar Mukherji1, 7 * Margaret S Ebert2, 3, 7 * Grace X Y Zheng2, 4 * John S Tsang5, 8 * Phillip A Sharp2, 3 * Alexander van Oudenaarden2, 3, 6 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:854–859Year published:(2011)DOI:doi:10.1038/ng.905Received24 November 2010Accepted14 July 2011Published online21 August 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 MicroRNAs (miRNAs) are short, highly conserved noncoding RNA molecules that repress gene expression in a sequence-dependent manner. We performed single-cell measurements using quantitative fluorescence microscopy and flow cytometry to monitor a target gene's protein expression in the presence and absence of regulation by miRNA. We find that although the average level of repression is modest, in agreement with previous population-based measurements, the repression among individual cells varies dramatically. In particular, we show that regulation by miRNAs establishes a threshold level of target mRNA below which protein production is highly repressed. Near this threshold, protein expression responds sensitively to target mRNA input, consistent with a mathematical model of molecular titration. These results show that miRNAs can act both as a switch and as a fine-tuner of gene expression. View full text Figures at a glance * Figure 1: Quantitative fluorescence microscopy reveals the miRNA-mediated gene expression threshold. () The two-color fluorescent reporter construct consists of a bidirectional Tet promoter that co-regulates enhanced yellow fluorescent protein (eYFP) and mCherry. Each fluorescent protein is tagged with a nuclear localization sequence (NLS) to aid in image analysis. The 3′ UTR of the mCherry gene is engineered to contain N binding sites for the miRNA miR-20. () Sample fluorescence microscopy data from representative single cells stably expressing eYFP and mCherry both in the presence and absence of regulation of mCherry by miR-20. The cells are arranged according to eYFP intensity. Scale bars, 5 μm. () Transfer function relating eYFP to mCherry generated by binning according to eYFP intensity and plotting the mean mCherry in each bin (a.u: arbitrary units). Supplementary Figure 1 depicts a schematic of how the binning was performed on similarly structured flow cytometry data. * Figure 2: Biochemical model of miRNA-mediated gene regulation. () The model describes the steady-state level of mRNA free to be translated (r), which we experimentally observed as the mCherry signal, subject to regulation by miRNA (m) as a function of bare transcriptional activity in the absence of regulation by miRNA (r0), which we experimentally observed as eYFP. The target mRNA is transcribed (rate constant kr) and intrinsically decays (rate constant γr). miRNA and mRNA bind (rate constant kon) to form a complex (r*). The bound miRNA can reenter the pool of active miRNA either by unbinding the target mRNA (rate constant koff) or destroying the mRNA (rate constant γr*). The steady-state solution for r allows us to combine these microscopic parameters into two lumped parameters that govern the shape of the transfer function: λ, the effective dissociation constant characterizing the strength of the miRNA-mRNA interaction, and θ, which is proportional to the concentration of miRNA that acts on the target mRNA. () Steady-state solutio! ns for r as a function of r0 for various values of kon; increasing kon decreases λ. () Steady-state solutions for r as a function of r0 for various values of miRNAtotal; increasing miRNAtotal increases θ. (,) The same solutions as in and except depicted on log-log axes. The slope of the log-log curve is known as the logarithmic gain. Notably, thresholds in the linear representation appear as segments with logarithmic gain greater than 1 in the log-log representation. Increasing kon increases the maximum logarithmic gain but does not change its position along the r0 axis, whereas increasing miRNAtotal increases the maximum logarithmic gain and shifts it to higher levels of r0. Blue dots in – are guides to the eye to facilitate comparison between linear and logarithmic plots. * Figure 3: Modulating the threshold. () Log-log transfer functions for N = 0, 1, 4 and 7. We can abolish the threshold by using an miR-20 binding site that is perfectly complementary to miR-20. () Ratio of N = 0 transfer function to N = 1, 4 and 7 transfer functions, depicting the fold repression as a function of eYFP expression. Inset depicts the average fold repression as a function of N. Using the flow cytometry data from , we computed the ratio of the mean eYFP level to the mean mCherry level for N = 1, 4, and 7. We then normalized this ratio by the mean eYFP to mean mCherry ratio for N = 0; we refer to this normalized ratio as the fold repression. We estimated the error bars by bootstrap sampling of the flow cytometry data. (,) Effects of titrating defined amounts of miR-20 mimic siRNA on the transfer function for N = 4 () and N = 7 (). In , and the angle symbol followed by a number denotes the value of the logarithmic gain, either minimum (when gain = 1) or maximum (when gain >1). * Figure 4: Comparison to model. (,) Following simultaneous fitting of all transfer function data to the quantitative model, the fitting parameter θ, which is proportional to the total amount of active miR-20 in the cell, is plotted against the amount of miR-20 mimic transfected (), and 1/λ, which is proportional to the rate constant of mCherry–miR-20 association, is plotted against N (). * Figure 5: Thresholding in endogenous 3′ UTRs. () We fused to mCherry the 3′ UTR of HMGA2 or a version with the seven let-7 seed matches mutated. The reporters were cotransfected with varying concentrations of let-7b mimic. Cells were assayed by flow cytometry 48 h after transfection. () We fused the 3′ UTR of SLC6A1, which contains three seed matches for miR-218, to mCherry. The reporter was transfected with or without miR-218 mimic. Cells were assayed by flow cytometry 48 h after transfection. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Shankar Mukherji & * Margaret S Ebert Affiliations * Harvard–Massachusetts Institute of Technology Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Shankar Mukherji * Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Margaret S Ebert, * Grace X Y Zheng, * Phillip A Sharp & * Alexander van Oudenaarden * Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Margaret S Ebert, * Phillip A Sharp & * Alexander van Oudenaarden * Computational and Systems Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Grace X Y Zheng * Graduate Program in Biophysics, Harvard University, Cambridge, Massachusetts, USA. * John S Tsang * Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Alexander van Oudenaarden * Present addresses: Howard Hughes Medical Institute and FAS Center for Systems Biology, Harvard University, Cambridge, Massachusetts, USA (S.M.); Howard Hughes Medical Institute and Laboratory of Neural Circuits and Behavior, The Rockefeller University, New York, New York, USA (M.S.E.); Howard Hughes Medical Institute and Program in Epithelial Biology, Stanford School of Medicine, Stanford, California, USA (G.X.Y.Z.); and Systems Genomics and Bioinformatics Unit, Laboratory of Systems Biology, National Institute of Allergy and Infectious Diseases, US National Institutes of Health, Bethesda, Maryland, USA (J.S.T.). * John S Tsang Contributions M.S.E., J.S.T., P.A.S. and A.v.O. conceived the project. M.S.E., S.M. and G.X.Y.Z. performed the experiments. S.M. and M.S.E. processed the data and constructed the model, and S.M. quantitatively analyzed the model. S.M., M.S.E., A.v.O. and P.A.S. interpreted the results and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Alexander van Oudenaarden Author Details * Shankar Mukherji Search for this author in: * NPG journals * PubMed * Google Scholar * Margaret S Ebert Search for this author in: * NPG journals * PubMed * Google Scholar * Grace X Y Zheng Search for this author in: * NPG journals * PubMed * Google Scholar * John S Tsang Search for this author in: * NPG journals * PubMed * Google Scholar * Phillip A Sharp Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander van Oudenaarden Contact Alexander van Oudenaarden 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 Figures 1–6, Supplementary Table 1 and Supplementary Note. Additional data - Increased exonic de novo mutation rate in individuals with schizophrenia
- Nat Genet 43(9):860-863 (2011)
Nature Genetics | Letter Increased exonic de novo mutation rate in individuals with schizophrenia * Simon L Girard1 * Julie Gauthier1 * Anne Noreau1 * Lan Xiong1 * Sirui Zhou1 * Loubna Jouan1 * Alexandre Dionne-Laporte1 * Dan Spiegelman1 * Edouard Henrion1 * Ousmane Diallo1 * Pascale Thibodeau1 * Isabelle Bachand2 * Jessie Y J Bao3 * Amy Hin Yan Tong3 * Chi-Ho Lin3 * Bruno Millet4, 5 * Nematollah Jaafari4, 6 * Ridha Joober7 * Patrick A Dion1, 8 * Si Lok3 * Marie-Odile Krebs4, 9, 11 * Guy A Rouleau1, 2, 10, 11 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:860–863Year published:(2011)DOI:doi:10.1038/ng.886Received25 February 2011Accepted15 June 2011Published online10 July 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Schizophrenia is a severe psychiatric disorder that profoundly affects cognitive, behavioral and emotional processes. The wide spectrum of symptoms and clinical variability in schizophrenia suggest a complex genetic etiology, which is consistent with the numerous loci thus far identified by linkage, copy number variation and association studies1, 2, 3, 4. Although schizophrenia heritability may be as high as ~80%, the genes responsible for much of this heritability remain to be identified5. Here we sequenced the exomes of 14 schizophrenia probands and their parents. We identified 15 de novo mutations (DNMs) in eight probands, which is significantly more than expected considering the previously reported DNM rate6, 7, 8. In addition, 4 of the 15 identified DNMs are nonsense mutations, which is more than what is expected by chance9. Our study supports the notion that DNMs may account for some of the heritability reported for schizophrenia while providing a list of genes possibl! y involved in disease pathogenesis. View full text Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Entrez Nucleotide * NM_152477 * NM_003489 * NM_002332 * NM_199287 * NM_002264 * NM_144684 * NM_183004 * NM_001190707 * NM_001273 * NM_032590 * NM_005559 * NM_033306 * NM_016176 * NM_006219 * NM_001167856 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Marie-Odile Krebs & * Guy A Rouleau Affiliations * Centre of Excellence in Neuromics of Université de Montréal, Centre Hospitalier de l'Université de Montréal Research Center, Montréal, Québec, Canada. * Simon L Girard, * Julie Gauthier, * Anne Noreau, * Lan Xiong, * Sirui Zhou, * Loubna Jouan, * Alexandre Dionne-Laporte, * Dan Spiegelman, * Edouard Henrion, * Ousmane Diallo, * Pascale Thibodeau, * Patrick A Dion & * Guy A Rouleau * Centre of Excellence in Neuromics of Université de Montréal, Centre Hospitalier Universitaire Sainte-Justine Research Center, Montréal, Québec, Canada. * Isabelle Bachand & * Guy A Rouleau * Genome Research Centre, The Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China. * Jessie Y J Bao, * Amy Hin Yan Tong, * Chi-Ho Lin & * Si Lok * INSERM, Université Paris Descartes, Laboratoire de Physiopathologie des Maladies Psychiatriques, Centre de Psychiatrie et Neurosciences (UMR 894), Paris, France. * Bruno Millet, * Nematollah Jaafari & * Marie-Odile Krebs * Université Rennes 1, CH Guillaume Régnier Service Hospitalo-Universitaire de Psychiatrie, Rennes, France. * Bruno Millet * Université de Poitiers, CHU, Centre Hospitalier Henri Laborit, Unité de recherche clinique intersectorielle en psychiatrie, INSERM CIC-P 0802, Poitiers, France. * Nematollah Jaafari * Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, Québec, Canada. * Ridha Joober * Department of Pathology and Cellular Biology, Université de Montréal, Montréal, Québec, Canada. * Patrick A Dion * Université Paris Descartes, Faculté de Médecine Paris Descartes, Service Hospitalo Universitaire, Centre Hospitalier Sainte-Anne, Paris, France. * Marie-Odile Krebs * Faculty of Medicine, Department of Medicine, Université de Montréal, Montréal, Québec, Canada. * Guy A Rouleau Contributions J.G., L.X., S.L.G. and G.A.R. designed the study. M.-O.K., L.X., B.M., R.J. and N.J. recruited cases and collected clinical information. I.B. and A.H.Y.T. performed exome capture and sequencing. S.L.G., D.S., J.Y.J.B. and C.-H.L. performed alignments and variant detection. A.N., S.Z., L.J., S.L.G. and P.T. performed variant validation. S.L.G., A.D.-L., D.S., E.H., O.D., A.H.Y.T., J.Y.J.B., C.-H.L. and S.L. performed bioinformatic analyses. S.L.G., P.A.D., M.-O.K., S.L. and G.A.R. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Guy A Rouleau Author Details * Simon L Girard Search for this author in: * NPG journals * PubMed * Google Scholar * Julie Gauthier Search for this author in: * NPG journals * PubMed * Google Scholar * Anne Noreau Search for this author in: * NPG journals * PubMed * Google Scholar * Lan Xiong Search for this author in: * NPG journals * PubMed * Google Scholar * Sirui Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Loubna Jouan Search for this author in: * NPG journals * PubMed * Google Scholar * Alexandre Dionne-Laporte Search for this author in: * NPG journals * PubMed * Google Scholar * Dan Spiegelman Search for this author in: * NPG journals * PubMed * Google Scholar * Edouard Henrion Search for this author in: * NPG journals * PubMed * Google Scholar * Ousmane Diallo Search for this author in: * NPG journals * PubMed * Google Scholar * Pascale Thibodeau Search for this author in: * NPG journals * PubMed * Google Scholar * Isabelle Bachand Search for this author in: * NPG journals * PubMed * Google Scholar * Jessie Y J Bao Search for this author in: * NPG journals * PubMed * Google Scholar * Amy Hin Yan Tong Search for this author in: * NPG journals * PubMed * Google Scholar * Chi-Ho Lin Search for this author in: * NPG journals * PubMed * Google Scholar * Bruno Millet Search for this author in: * NPG journals * PubMed * Google Scholar * Nematollah Jaafari Search for this author in: * NPG journals * PubMed * Google Scholar * Ridha Joober Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick A Dion Search for this author in: * NPG journals * PubMed * Google Scholar * Si Lok Search for this author in: * NPG journals * PubMed * Google Scholar * Marie-Odile Krebs Search for this author in: * NPG journals * PubMed * Google Scholar * Guy A Rouleau Contact Guy A Rouleau Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (768K) Supplementary Tables 1–4 and Supplementary Figure 1 Additional data - Exome sequencing supports a de novo mutational paradigm for schizophrenia
- Nat Genet 43(9):864-868 (2011)
Nature Genetics | Letter Exome sequencing supports a de novo mutational paradigm for schizophrenia * Bin Xu1, 2 * J Louw Roos3 * Phillip Dexheimer4 * Braden Boone4 * Brooks Plummer4 * Shawn Levy4 * Joseph A Gogos2, 5 * Maria Karayiorgou1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:864–868Year published:(2011)DOI:doi:10.1038/ng.902Received26 May 2011Accepted12 July 2011Published online07 August 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Despite its high heritability, a large fraction of individuals with schizophrenia do not have a family history of the disease (sporadic cases). Here we examined the possibility that rare de novo protein-altering mutations contribute to the genetic component of schizophrenia by sequencing the exomes of 53 sporadic cases, 22 unaffected controls and their parents. We identified 40 de novo mutations in 27 cases affecting 40 genes, including a potentially disruptive mutation in DGCR2, a gene located in the schizophrenia-predisposing 22q11.2 microdeletion region. A comparison to rare inherited variants indicated that the identified de novo mutations show a large excess of non-synonymous changes in schizophrenia cases, as well as a greater potential to affect protein structure and function. Our analyses suggest a major role for de novo mutations in schizophrenia as well as a large mutational target, which together provide a plausible explanation for the high global incidence and pe! rsistence of the disease. View full text Accession codes * Accession codes * Author information * Supplementary information Referenced accessions GenBank * NM_001144382 * NM_018117 * NM_000110 * NM_001004703 * NM_019093 * NM_138805 * NM_007249 * NM_001114 * NM_207370 * NM_002675 * NM_052961 * NM_152389 * NM_001042646 * NM_021826 * NM_005137 * NM_001037162 * NM_001130848 * NM_006545 * NM_005462 * NM_003496 * NM_000090 * NM_005142 * NM_144674 * NM_003246 * NM_018440 * NM_002926 * NM_013260 * NM_020880 * NM_004958 * NM_005539 * NM_001145025 * NM_001083591 * NM_134266 * NM_018206 * NM_014243 * NM_153838 * NM_145207 * NM_014781 * NM_000426 * NM_138961 Author information * Accession codes * Author information * Supplementary information Affiliations * Department of Psychiatry, Columbia University, New York, New York, USA. * Bin Xu & * Maria Karayiorgou * Department of Physiology & Cellular Biophysics, Columbia University, New York, New York, USA. * Bin Xu & * Joseph A Gogos * Weskoppies Hospital & Department of Psychiatry, University of Pretoria, Pretoria, South Africa. * J Louw Roos * HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA. * Phillip Dexheimer, * Braden Boone, * Brooks Plummer & * Shawn Levy * Department of Neuroscience, Columbia University, New York, New York, USA. * Joseph A Gogos Contributions B.X., J.A.G. and M.K. designed the study, interpreted the data and prepared the manuscript. B.X. developed the analysis pipeline and had the primary role in analysis and validation of sequence data. J.L.R. collected the samples and was the primary clinician on the project. S.L. and B.P. performed exome library construction, capture and sequencing. P.D. contributed to the analysis of the data. B.B. contributed to the primary sequence data analysis. S.L. supervised the sequencing project at HudsonAlpha Institute and contributed to the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Maria Karayiorgou or * Joseph A Gogos Author Details * Bin Xu Search for this author in: * NPG journals * PubMed * Google Scholar * J Louw Roos Search for this author in: * NPG journals * PubMed * Google Scholar * Phillip Dexheimer Search for this author in: * NPG journals * PubMed * Google Scholar * Braden Boone Search for this author in: * NPG journals * PubMed * Google Scholar * Brooks Plummer Search for this author in: * NPG journals * PubMed * Google Scholar * Shawn Levy Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph A Gogos Contact Joseph A Gogos Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Karayiorgou Contact Maria Karayiorgou Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Note, Supplementary Figures 1–4 and Supplementary Tables 1 and 2. Additional data - Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis
- Nat Genet 43(9):869-874 (2011)
Nature Genetics | Letter Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis * Jason W Locasale1, 2 * Alexandra R Grassian3 * Tamar Melman1, 2 * Costas A Lyssiotis1, 2 * Katherine R Mattaini4 * Adam J Bass5, 6 * Gregory Heffron7 * Christian M Metallo8 * Taru Muranen3 * Hadar Sharfi1, 2 * Atsuo T Sasaki1, 2 * Dimitrios Anastasiou1, 2 * Edouard Mullarky1, 2 * Natalie I Vokes4 * Mika Sasaki1, 2 * Rameen Beroukhim5, 6, 9 * Gregory Stephanopoulos8 * Azra H Ligon5, 10 * Matthew Meyerson5, 6, 11 * Andrea L Richardson5, 10 * Lynda Chin5, 12 * Gerhard Wagner7 * John M Asara2 * Joan S Brugge3 * Lewis C Cantley1, 2 * Matthew G Vander Heiden4, 5 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:869–874Year published:(2011)DOI:doi:10.1038/ng.890Received19 July 2010Accepted27 June 2011Published online31 July 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Most tumors exhibit increased glucose metabolism to lactate, however, the extent to which glucose-derived metabolic fluxes are used for alternative processes is poorly understood1, 2. Using a metabolomics approach with isotope labeling, we found that in some cancer cells a relatively large amount of glycolytic carbon is diverted into serine and glycine metabolism through phosphoglycerate dehydrogenase (PHGDH). An analysis of human cancers showed that PHGDH is recurrently amplified in a genomic region of focal copy number gain most commonly found in melanoma. Decreasing PHGDH expression impaired proliferation in amplified cell lines. Increased expression was also associated with breast cancer subtypes, and ectopic expression of PHGDH in mammary epithelial cells disrupted acinar morphogenesis and induced other phenotypic alterations that may predispose cells to transformation. Our findings show that the diversion of glycolytic flux into a specific alternate pathway can be sele! cted during tumor development and may contribute to the pathogenesis of human cancer. View full text Figures at a glance * Figure 1: Observation of glycolytic metabolism being diverted into serine and glycine metabolism. () Spectral bins of [1H, 13C] HSQC NMR of [U-13C] glucose-labeled cell extracts sorted by intensity in standard units (z-score). The four highest intensity peaks, from left to right, correspond to metabolites lactate, lactate, alanine and glycine, respectively. () Relative intensity 13C glycine peak normalized to an internal 50 mM 4,4′-dimethyl-4-silapentane-1-sulfonic acid (DSS) standard in HEK293T, H1299 and MCF-10A cells. Error bars denote variation in peak integration. () Schematic of diversion of glucose metabolism into serine and glycine metabolism at the 3-phosphoglycerate (3PG) step through PHGDH. () Time (0, 5, 10, 15 and 30 min) courses of U-13C labeling intensities of 12 metabolites from [U-13C] glucose labeling experiments measured with targeted LC/MS/MS relative to baseline level at time 0. () Comparison of 3-phosphoserine (pSER) and phosphoenolpyruvate (PEP) labeling kinetics of [U-13C] glucose relative to baseline level at time 0 with targeted LC/MS/MS. Erro! r bars represent the s.d. of three biological replicates. () Relative glucose flux into serine biosynthesis measured by steady-state labeling of [U-13C] glucose into serine with targeted LC/MS/MS. The fraction of labeled to unlabeled glucose-derived metabolites, 13C/(12C + 13C) ion intensities (glucose incorporation), is plotted for 12 metabolites. Serine is compared with respect to the glucose-labeled fraction of downstream nucleotides and other nucleotide precursors. Error bars represent the s.d. of three biological replicates. () Relative protein levels (as determined by protein blot analysis) of PHGDH in HEK293T, H1299 and MCF-10A cells with a β-actin (actin) loading control shown below the PHGDH band. Quantitation relative to the levels in MCF-10a cells of the total intensity of the PHGDH band relative to the actin band is shown above. * Figure 2: PHGDH amplification in human cancers and requirement for proliferation. () PHGDH copy number intensity across 3,131 cancer samples. The left plot shows the significance of the amplifications (false discovery rate (FDR) q value) along chromosome 1p (from telomere to centromere). Candidate oncogenes (TP73, MYCL1 and JUN) in three peak regions and corresponding FDR q values are shown. The FDR q value of PHGDH is shown in the fourth peak region. The middle plot shows the copy number intensity along chromosome 1p of 150 cancers containing the highest intensity of PHGDH amplification, illustrating the localized intensity near the region of PHGDH. The right plot shows magnification of the 4-Mb region containing PHGDH. The solid line indicates the chromosome position of the PHGDH coding region. () Relative cell numbers of T.T. cells upon knockdown with shGFP, shPHGDH, shPSAT and shPSPH relative to an shRNA directed against GFP. Error bars represent the s.d. of three independent measurements. Shown below is an interphase FISH analysis showing PHGDH copy ! number gain in T.T. cells. The green probe maps to 1p12 and includes the PHGDH coding sequence. The red probe maps to the pericentromeric region of chromosome 1 (1p11.2–q11.1). FISH images were taken at 100× power. Also shown below are relative protein levels of PHGDH, PSAT and PSPH (as determined by protein blot analysis). () PHGDH protein expression and copy number gain in three representative human melanoma tissue samples. In the upper panels, we assessed PHGDH expression in tumor samples using immunohistochemistry (IHC). Nuclei are shown in blue (hematoxylin), and PHGDH antibody staining is shown in brown (3-3′-diaminobenzidine (DAB)). Scale bars, 50 μm. The lower panels show interphase FISH analysis carried out as in in matched samples to assess copy number. FISH images were taken at 100× power. * Figure 3: Growth dependence of PHGDH expression and altered serine metabolism in PHGDH-amplified human melanoma cells. () Growth assay of stable cell lines containing shGFP or shPHGDH in five human melanoma cell lines. Three cell lines (WM266-3, Malme-3M (Malme) and Sk-Mel28 (SK28)) contain 1p12 copy number gain; two other melanoma cell lines (GAK and Carney) were also considered. The left panel shows a protein blot analysis of protein levels of PHGDH, and the corresponding protein levels of actin are shown as a loading control. On the right, the cell numbers for shGFP and shPHGDH normalized to shGFP are plotted for each cell line. Error bars represent the s.d. of three independent measurements. () Relative amount of glucose flux into serine biosynthesis measured by steady-state labeling of [U-13C] glucose into serine with targeted LC/MS/MS. The fraction of labeled to unlabeled glucose-derived serine to total serine, 13C/(12C + 13C) (serine incorporation), is measured in each of the five cell lines. Error bars represent the s.d. of three independent measurements. () Relative ion intensities ! of 3-phosphoserine (pSer) in control (shGFP) and knockdown (shPHGDH) cells normalized to intensity in knockdown shGFP cells (pSer/shGFP). Error bars represent the s.d. of three independent measurements. () Scatter plot of the ratio of intensities (fold change) versus P value (Student's t-test) of shPHGDH relative to shGFP in Sk-Mel28 cells. () Ratio of intensities (fold change) of glycolytic intermediates upon PHGDH knockdown (shPHGDH) relative to (shGFP) in Sk-Mel28 cells. Error bars represent the propagation of error of the s.d. from three independent measurements. * Figure 4: Ectopic expression of PHGDH in breast ductal morphogenesis. () Protein expression of PHGDH by protein blot analysis, with actin as loading control for three concentrations of doxycycline (dox) (0 μg/ml, 1 μg/ml and 2 μg/ml). () pSER integrated intensities in –dox (0 μg/ml) and +dox (1 μg/ml) (a.u., arbitrary units). Error bars represent the s.d. of three independent measurements. () Confocal images of DAPI (blue) and laminin-5 (green). Representative images from four acini from MCF-10A cells expressing doxycyline-inducible PHGDH without doxycycline (−dox) or 1 μg/ml doxycyline (+dox). Scale bars, 50 μm. () Enhanced proliferation in the interior of PHGDH-expressing acini. Representative images from acini from MCF-10A cells expressing doxycyline-inducible PHGDH without doxycycline (–dox) or 1 μg/ml doxycyline (+dox). Confocal images of MCF-10A cells under the same conditions as in with DAPI (blue) and the proliferation marker Ki67 (red). Scale bar, 50 μm. () Quantification of acinar filling for 0 μg/ml, 1 μg/ml and 2 �! �g/ml dox. Each acinus was scored as filled, mostly filled, mostly clear or clear. Data are representative of multiple independent measurements. () Loss of apical polarity in PHGDH-expressing cells. Confocal images of MCF-10A cells under the same conditions as in with DAPI (blue) and Golgi apparatus (green). Solid white arrows indicate cells with an oriented Golgi apparatus. Dashed yellow arrows indicate cells with loss of polarity. Acini with ectopic expression of wild-type (WT) PHGDH, but not mutant p.Val490Met PHGDH, commonly display a mislocalized Golgi apparatus, indicative of a lack of cell polarity. Scale bars, 50 μm; –dox, 0 μg/ml; +dox, 1 μg/ml. Author information * Author information * Supplementary information Affiliations * Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA. * Jason W Locasale, * Tamar Melman, * Costas A Lyssiotis, * Hadar Sharfi, * Atsuo T Sasaki, * Dimitrios Anastasiou, * Edouard Mullarky, * Mika Sasaki & * Lewis C Cantley * Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA. * Jason W Locasale, * Tamar Melman, * Costas A Lyssiotis, * Hadar Sharfi, * Atsuo T Sasaki, * Dimitrios Anastasiou, * Edouard Mullarky, * Mika Sasaki, * John M Asara & * Lewis C Cantley * Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA. * Alexandra R Grassian, * Taru Muranen & * Joan S Brugge * Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Katherine R Mattaini, * Natalie I Vokes & * Matthew G Vander Heiden * Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA. * Adam J Bass, * Rameen Beroukhim, * Azra H Ligon, * Matthew Meyerson, * Andrea L Richardson, * Lynda Chin & * Matthew G Vander Heiden * Cancer Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. * Adam J Bass, * Rameen Beroukhim & * Matthew Meyerson * Department of Biochemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA. * Gregory Heffron & * Gerhard Wagner * Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Christian M Metallo & * Gregory Stephanopoulos * Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA. * Rameen Beroukhim * Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA. * Azra H Ligon & * Andrea L Richardson * Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA. * Matthew Meyerson * Department of Dermatology, Harvard Medical School, Boston, Massachusetts, USA. * Lynda Chin Contributions J.W.L., M.G.V.H. and L.C.C. designed the study and wrote the paper. J.W.L., C.A.L., E.M., K.R.M., D.A., H.S., M.G.V.H. and T. Melman carried out experiments. J.W.L. and T. Melman carried out computational analyses. A.J.B., R.B. and M.M. provided help with copy number data. L.C. and A.L.R. provided human cancer samples. N.I.V. and A.H.L. carried out the FISH analysis. J.W.L. and J.M.A. carried out the LC/MS/MS experiments. J.W.L., G.H. and G.W. carried out the NMR experiments. J.W.L., N.I.V., C.M.M. and G.S. carried out the GC/MS experiments. M.S. and A.T.S. generated reagents. J.S.B., T. Muranen and A.R.G. carried out experiments involving acinar morphogenesis and imaging analysis. Competing financial interests J.W.L., M.V.H. and L.C.C. are consultants, scientific advisors and part owners of Agios Pharmaceuticals and hold patents pertaining to targeting cellular metabolism for cancer treatment. Agios Pharmaceuticals is interested in developing therapeutics that target altered metabolism in cancer. Corresponding authors Correspondence to: * Jason W Locasale or * Matthew G Vander Heiden or * Lewis C Cantley Author Details * Jason W Locasale Contact Jason W Locasale Search for this author in: * NPG journals * PubMed * Google Scholar * Alexandra R Grassian Search for this author in: * NPG journals * PubMed * Google Scholar * Tamar Melman Search for this author in: * NPG journals * PubMed * Google Scholar * Costas A Lyssiotis Search for this author in: * NPG journals * PubMed * Google Scholar * Katherine R Mattaini Search for this author in: * NPG journals * PubMed * Google Scholar * Adam J Bass Search for this author in: * NPG journals * PubMed * Google Scholar * Gregory Heffron Search for this author in: * NPG journals * PubMed * Google Scholar * Christian M Metallo Search for this author in: * NPG journals * PubMed * Google Scholar * Taru Muranen Search for this author in: * NPG journals * PubMed * Google Scholar * Hadar Sharfi Search for this author in: * NPG journals * PubMed * Google Scholar * Atsuo T Sasaki Search for this author in: * NPG journals * PubMed * Google Scholar * Dimitrios Anastasiou Search for this author in: * NPG journals * PubMed * Google Scholar * Edouard Mullarky Search for this author in: * NPG journals * PubMed * Google Scholar * Natalie I Vokes Search for this author in: * NPG journals * PubMed * Google Scholar * Mika Sasaki Search for this author in: * NPG journals * PubMed * Google Scholar * Rameen Beroukhim Search for this author in: * NPG journals * PubMed * Google Scholar * Gregory Stephanopoulos Search for this author in: * NPG journals * PubMed * Google Scholar * Azra H Ligon Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew Meyerson Search for this author in: * NPG journals * PubMed * Google Scholar * Andrea L Richardson Search for this author in: * NPG journals * PubMed * Google Scholar * Lynda Chin Search for this author in: * NPG journals * PubMed * Google Scholar * Gerhard Wagner Search for this author in: * NPG journals * PubMed * Google Scholar * John M Asara Search for this author in: * NPG journals * PubMed * Google Scholar * Joan S Brugge Search for this author in: * NPG journals * PubMed * Google Scholar * Lewis C Cantley Contact Lewis C Cantley Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew G Vander Heiden Contact Matthew G Vander Heiden 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 1–7 and Supplementary Tables 1–4. Additional data - Frequent mutations of chromatin remodeling genes in transitional cell carcinoma of the bladder
- Nat Genet 43(9):875-878 (2011)
Nature Genetics | Letter Frequent mutations of chromatin remodeling genes in transitional cell carcinoma of the bladder * Yaoting Gui1, 12 * Guangwu Guo2, 12 * Yi Huang1, 12 * Xueda Hu2, 12 * Aifa Tang1, 3, 12 * Shengjie Gao2 * Renhua Wu2 * Chao Chen2 * Xianxin Li1 * Liang Zhou1 * Minghui He2 * Zesong Li1, 3 * Xiaojuan Sun3 * Wenlong Jia2 * Jinnong Chen2 * Shangming Yang2 * Fangjian Zhou4 * Xiaokun Zhao5 * Shengqing Wan2 * Rui Ye2 * Chaozhao Liang6 * Zhisheng Liu2 * Peide Huang2 * Chunxiao Liu7 * Hui Jiang2 * Yong Wang1 * Hancheng Zheng2 * Liang Sun1 * Xingwang Liu2 * Zhimao Jiang1 * Dafei Feng2 * Jing Chen1 * Song Wu1 * Jing Zou2 * Zhongfu Zhang1 * Ruilin Yang1 * Jun Zhao1 * Congjie Xu1 * Weihua Yin1 * Zhichen Guan1 * Jiongxian Ye1 * Hong Zhang1 * Jingxiang Li2 * Karsten Kristiansen2, 8 * Michael L Nickerson9 * Dan Theodorescu10, 11 * Yingrui Li2 * Xiuqing Zhang2 * Songgang Li2 * Jian Wang2 * Huanming Yang2 * Jun Wang2, 8 * Zhiming Cai1, 3 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:875–878Year published:(2011)DOI:doi:10.1038/ng.907Received13 May 2011Accepted15 July 2011Published online07 August 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Transitional cell carcinoma (TCC) is the most common type of bladder cancer. Here we sequenced the exomes of nine individuals with TCC and screened all the somatically mutated genes in a prevalence set of 88 additional individuals with TCC with different tumor stages and grades. In our study, we discovered a variety of genes previously unknown to be mutated in TCC. Notably, we identified genetic aberrations of the chromatin remodeling genes (UTX, MLL-MLL3, CREBBP-EP300, NCOR1, ARID1A and CHD6) in 59% of our 97 subjects with TCC. Of these genes, we showed UTX to be altered substantially more frequently in tumors of low stages and grades, highlighting its potential role in the classification and diagnosis of bladder cancer. Our results provide an overview of the genetic basis of TCC and suggest that aberration of chromatin regulation might be a hallmark of bladder cancer. View full text Figures at a glance * Figure 1: Somatic mutations in UTX, ARID1A and CREBBP-EP300. The types and relative positions of confirmed somatic mutations are shown in the transcripts of UTX (), ARID1A () and CREBBPEP300 () using the following symbols: red stars, nonsense mutations; bullets, missense mutations; red triangles, frame-shift indels; green triangles, in-frame indels; and diamond, mutations at splice sites. Domains and motifs in each encoded protein product, as well as the key region responsible for the histone acetyltransferase activity of CREBBP, are also indicated. TPR, tetratricopeptide repeat; JmjC, transcription factor jumonji/aspartyl beta-hydroxylase; ARID, AT-rich interactive domain; LXXLL, C-terminal leucine-rich LXXLL motif; CH1, CH2 and CH3, cysteine–histidine-rich domains; KIX, CREB-binding domain; Bromo, bromodomain. * Figure 2: Concurrent and mutually exclusive mutations observed in the frequently mutated genes. For each gene (row) indicated, tumors (columns) with or without mutations are labeled in red or blue, respectively. P values for the occurrence of concurrent and mutual exclusive mutations in two genes across tumors are provided in Supplementary Table 8. We selected only genes harboring non-silent mutations in at least five subjects for this analysis. * Figure 3: Frequencies of mutations in highlighted genes across different tumor stages and grades. T, the stage of tumors under the TNM (tumor, lymph node and distant metastasis) classification system; G, grade of tumors. For each tumor stage or grade, we calculated the frequency of mutations in a given gene as the proportion of tumors harboring no silent mutations in the gene among all tumors of the indicated stage or grade. We determined the significance of the correlation between mutations in each gene (altered in at least 10% of TCCs) and tumor grade or stage using χ2 tests. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Yaoting Gui, * Guangwu Guo, * Yi Huang, * Xueda Hu & * Aifa Tang Affiliations * Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Institute of Urology, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen, China. * Yaoting Gui, * Yi Huang, * Aifa Tang, * Xianxin Li, * Liang Zhou, * Zesong Li, * Yong Wang, * Liang Sun, * Zhimao Jiang, * Jing Chen, * Song Wu, * Zhongfu Zhang, * Ruilin Yang, * Jun Zhao, * Congjie Xu, * Weihua Yin, * Zhichen Guan, * Jiongxian Ye, * Hong Zhang & * Zhiming Cai * BGI-Shenzhen, Shenzhen, China. * Guangwu Guo, * Xueda Hu, * Shengjie Gao, * Renhua Wu, * Chao Chen, * Minghui He, * Wenlong Jia, * Jinnong Chen, * Shangming Yang, * Shengqing Wan, * Rui Ye, * Zhisheng Liu, * Peide Huang, * Hui Jiang, * Hancheng Zheng, * Xingwang Liu, * Dafei Feng, * Jing Zou, * Jingxiang Li, * Karsten Kristiansen, * Yingrui Li, * Xiuqing Zhang, * Songgang Li, * Jian Wang, * Huanming Yang & * Jun Wang * Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China. * Aifa Tang, * Zesong Li, * Xiaojuan Sun & * Zhiming Cai * Department of Urology, Sun Yat-Sen University Cancer Center, Guangzhou, China. * Fangjian Zhou * Department of Urology, the Second Xiangya Hospital of Central-Southern University, Changsha, China. * Xiaokun Zhao * Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China. * Chaozhao Liang * Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China. * Chunxiao Liu * Department of Biology, University of Copenhagen, Copenhagen, Denmark. * Karsten Kristiansen & * Jun Wang * Cancer and Inflammation Program, National Cancer Institute, US National Institutes of Health (NIH), Frederick, Maryland, USA. * Michael L Nickerson * Department of Surgery, University of Colorado Comprehensive Cancer Center, Aurora, Colorado, USA. * Dan Theodorescu * Department of Pharmacology, University of Colorado Comprehensive Cancer Center, Aurora, Colorado, USA. * Dan Theodorescu Contributions Jun Wang, Z.C., Jian Wang, H.Y., S.L. and Y.G. managed the project. A.T., X. Li, L.Z., Z. Li, F.Z., X. Zhao, C. Liang, C. Liu, Y.W., L.S., Z.J., Jing Chen, S. Wu, Z.Z., R. Yang, J. Zhao, C.X., Z.G., J.Y., H. Zhang and W.Y. prepared the samples. X.H., R.W., P.H., H.J., J.L. and X. Zhang performed the sequencing. Y.G., G.G., Y.H., S.G., C.C., M.H., W.J., R. Ye, Z. Liu, S. Wan, H. Zheng, K.K., M.L.N. and Y.L. performed the bioinformatic analysis. Y.H., X.H., Jinnong Chen, S.Y., X. Liu, D.F. and J. Zou performed the validation of somatic mutations. G.G. and Y.H. wrote the paper. Y.G., Jun Wang, Z.C., X.H., Y.L., D.T. and X.S. revised the paper. Competing financial interests The authors declare no competing financial interests. 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Additional data - Germline mutations in RAD51D confer susceptibility to ovarian cancer
- Nat Genet 43(9):879-882 (2011)
Nature Genetics | Letter Germline mutations in RAD51D confer susceptibility to ovarian cancer * Chey Loveday1, 30 * Clare Turnbull1, 30 * Emma Ramsay1 * Deborah Hughes1 * Elise Ruark1 * Jessica R Frankum2 * Georgina Bowden1 * Bolot Kalmyrzaev1 * Margaret Warren-Perry1 * Katie Snape1 * Julian W Adlard3 * Julian Barwell4 * Jonathan Berg5 * Angela F Brady6 * Carole Brewer7 * Glen Brice8 * Cyril Chapman9 * Jackie Cook10 * Rosemarie Davidson11 * Alan Donaldson12 * Fiona Douglas13 * Lynn Greenhalgh14 * Alex Henderson15 * Louise Izatt16 * Ajith Kumar17 * Fiona Lalloo18 * Zosia Miedzybrodzka19 * Patrick J Morrison20 * Joan Paterson21 * Mary Porteous22 * Mark T Rogers23 * Susan Shanley24 * Lisa Walker25 * Breast Cancer Susceptibility Collaboration (UK) * Diana Eccles27 * D Gareth Evans28 * Anthony Renwick1 * Sheila Seal1 * Christopher J Lord2 * Alan Ashworth2 * Jorge S Reis-Filho2 * Antonis C Antoniou29 * Nazneen Rahman1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:879–882Year published:(2011)DOI:doi:10.1038/ng.893Received03 March 2011Accepted01 July 2011Published online07 August 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Recently, RAD51C mutations were identified in families with breast and ovarian cancer1. This observation prompted us to investigate the role of RAD51D in cancer susceptibility. We identified eight inactivating RAD51D mutations in unrelated individuals from 911 breast-ovarian cancer families compared with one inactivating mutation identified in 1,060 controls (P = 0.01). The association found here was principally with ovarian cancer, with three mutations identified in the 59 pedigrees with three or more individuals with ovarian cancer (P = 0.0005). The relative risk of ovarian cancer for RAD51D mutation carriers was estimated to be 6.30 (95% CI 2.86–13.85, P = 4.8 × 10−6). By contrast, we estimated the relative risk of breast cancer to be 1.32 (95% CI 0.59–2.96, P = 0.50). These data indicate that RAD51D mutation testing may have clinical utility in individuals with ovarian cancer and their families. Moreover, we show that cells deficient in RAD51D are sensitive to tre! atment with a PARP inhibitor, suggesting a possible therapeutic approach for cancers arising in RAD51D mutation carriers. View full text Figures at a glance * Figure 1: Abridged pedigrees of eight families with RAD51D mutations. Individuals with ovarian cancer are shown as red circles; individuals with breast cancer are shown as black circles. Other cancers are shown as unfilled circles or squares. Where known, the age of cancer diagnosis is listed under the individual, with two ages given for metachronous bilateral breast cancers. The relevant RAD51D mutation is listed under the affected individuals analyzed, but not the unaffected individuals, to preserve confidentiality. BC, breast cancer; BC bilat., bilateral breast cancer; OC, ovarian cancer; CRC, colorectal cancer; LC, lung cancer; NHL, non-Hodgkin lymphoma; PaC, pancreatic cancer; PrC, prostate cancer. * Figure 2: Average age-related cumulative risk of ovarian cancer in RAD51D mutation carriers, BRCA1 and BRCA2 mutation carriers22 and the general population23. * Figure 3: Effect of RAD51D silencing on olaparib sensitivity. (,) We transfected CAL51 () or MCF7 () cells with siCONTROL, siRNA directed against RAD51D or siRNA directed against BRCA2. For siRNA targeting RAD51D, cells were transfected with one of two individual siRNAs or a pool of both siRNAs combined. We then treated transfected cells with olaparib for 7 days before assaying for cell viability. () We treated wild-type (WT) CHO cells or CHO cells mutated (MUT) in RAD51D with olaparib for 7 days before assaying for cell viability. M, molar concentration. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Chey Loveday & * Clare Turnbull Affiliations * Section of Cancer Genetics, The Institute of Cancer Research, Sutton, UK. * Chey Loveday, * Clare Turnbull, * Emma Ramsay, * Deborah Hughes, * Elise Ruark, * Georgina Bowden, * Bolot Kalmyrzaev, * Margaret Warren-Perry, * Katie Snape, * Anthony Renwick, * Sheila Seal & * Nazneen Rahman * The Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, UK. * Jessica R Frankum, * Christopher J Lord, * Alan Ashworth & * Jorge S Reis-Filho * Yorkshire Regional Centre for Cancer Treatment, Cookridge Hospital, Leeds, UK. * Julian W Adlard * Leicestershire Genetics Centre, University Hospitals of Leicester National Health Service (NHS) Trust, Leicester, UK. * Julian Barwell * Human Genetics, Division of Medical Sciences, University of Dundee, Dundee, UK. * Jonathan Berg * North West Thames Regional Genetics Service, Kennedy Galton Centre, London, UK. * Angela F Brady * Peninsula Regional Genetics Service, Royal Devon & Exeter Hospital, Exeter, UK. * Carole Brewer * South West Thames Regional Genetics Service, St. George's Hospital, London, UK. * Glen Brice * West Midlands Regional Genetics Service, Birmingham Women's Hospital, Birmingham, UK. * Cyril Chapman * Sheffield Regional Genetics Service, Sheffield Children's NHS Foundation Trust, Sheffield, UK. * Jackie Cook * West of Scotland Regional Genetics Service, FergusonSmith Centre for Clinical Genetics, Glasgow, UK. * Rosemarie Davidson * South Western Regional Genetics Service, University Hospitals of Bristol NHS Foundation Trust, Bristol, UK. * Alan Donaldson * Northern Genetics Service, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK. * Fiona Douglas * Cheshire and Merseyside Clinical Genetics Service, Alder Hey Children's NHS Foundation Trust, Liverpool, UK. * Lynn Greenhalgh * Northern Genetics Service (Cumbria), Newcastle upon Tyne Hospitals NHS Trust, Newcastle upon Tyne, UK. * Alex Henderson * South East Thames Regional Genetics Service, Guy′s and St. Thomas NHS Foundation Trust, London, UK. * Louise Izatt * North East Thames Regional Genetics Service, Great Ormond St. Hospital, London, UK. * Ajith Kumar * University Department of Medical Genetics & Regional Genetics Service, St. Mary's Hospital, Manchester, UK. * Fiona Lalloo * University of Aberdeen and North of Scotland Clinical Genetics Service, Aberdeen Royal Infirmary, Aberdeen, UK. * Zosia Miedzybrodzka * Northern Ireland Regional Genetics Service, Belfast Health and Social Care (HSC) Trust & Department of Medical Genetics, Queen's University Belfast, Belfast, UK. * Patrick J Morrison * East Anglian Regional Genetics Service, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK. * Joan Paterson * South East of Scotland Clinical Genetics Service, Western General Hospital, Edinburgh, UK. * Mary Porteous * All Wales Medical Genetics Service, University Hospital of Wales, Cardiff, UK. * Mark T Rogers * Royal Marsden NHS Foundation Trust, London, UK. * Susan Shanley * Oxford Regional Genetics Service, Oxford Radcliffe Hospitals NHS Trust, Oxford, UK. * Lisa Walker * Faculty of Medicine, University of Southampton, Southampton University Hospitals NHS Trust, Southampton, UK. * Diana Eccles * University Department of Medical Genetics & Regional Genetics Service, St. Mary's Hospital, Manchester, UK. * D Gareth Evans * Center for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. * Antonis C Antoniou Consortia * Breast Cancer Susceptibility Collaboration (UK) Contributions N.R., C.L. and C.T. designed the experiment. M.W.-P., C.T. and N.R. coordinated recruitment to the FBCS. J.W.A., J. Barwell, J. Berg, A.F.B., C.B., G. Brice, C.C., J.C., R.D., A.D., F.D., D.G.E., D.E., L.G., A.H., L.I., A.K., F.L., Z.M., P.J.M., J.P., M.P., M.T.R., S. Shanley and L.W. coordinated the FBCS sample recruitment from their respective Genetics centers. C.L., E. Ramsay, D.H., G. Bowden, B.K., K.S., A.R. and S. Seal performed sequencing of RAD51D. J.R.F., C.J.L. and A.A. designed and conducted drug sensitivity experiments. J.S.R.-F. undertook examination and dissection of pathological specimens. C.T., E. Ruark and A.C.A. performed statistical analyses. C.L., C.T. and N.R. drafted the manuscript with substantial input from D.G.E., D.E., A.C.A., A.A. and J.S.R.-F. C.T. and N.R. oversaw and managed all aspects of the study. A full list of members appears in the Supplementary Note. Breast Cancer Susceptibility Collaboration (UK) Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Nazneen Rahman Author Details * Chey Loveday Search for this author in: * NPG journals * PubMed * Google Scholar * Clare Turnbull Search for this author in: * NPG journals * PubMed * Google Scholar * Emma Ramsay Search for this author in: * NPG journals * PubMed * Google Scholar * Deborah Hughes Search for this author in: * NPG journals * PubMed * Google Scholar * Elise Ruark Search for this author in: * NPG journals * PubMed * Google Scholar * Jessica R Frankum Search for this author in: * NPG journals * PubMed * Google Scholar * Georgina Bowden Search for this author in: * NPG journals * PubMed * Google Scholar * Bolot Kalmyrzaev Search for this author in: * NPG journals * PubMed * Google Scholar * Margaret Warren-Perry Search for this author in: * NPG journals * PubMed * Google Scholar * Katie Snape Search for this author in: * NPG journals * PubMed * Google Scholar * Julian W Adlard Search for this author in: * NPG journals * PubMed * Google Scholar * Julian Barwell Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan Berg Search for this author in: * NPG journals * PubMed * Google Scholar * Angela F Brady Search for this author in: * NPG journals * PubMed * Google Scholar * Carole Brewer Search for this author in: * NPG journals * PubMed * Google Scholar * Glen Brice Search for this author in: * NPG journals * PubMed * Google Scholar * Cyril Chapman Search for this author in: * NPG journals * PubMed * Google Scholar * Jackie Cook Search for this author in: * NPG journals * PubMed * Google Scholar * Rosemarie Davidson Search for this author in: * NPG journals * PubMed * Google Scholar * Alan Donaldson Search for this author in: * NPG journals * PubMed * Google Scholar * Fiona Douglas Search for this author in: * NPG journals * PubMed * Google Scholar * Lynn Greenhalgh Search for this author in: * NPG journals * PubMed * Google Scholar * Alex Henderson Search for this author in: * NPG journals * PubMed * Google Scholar * Louise Izatt Search for this author in: * NPG journals * PubMed * Google Scholar * Ajith Kumar Search for this author in: * NPG journals * PubMed * Google Scholar * Fiona Lalloo Search for this author in: * NPG journals * PubMed * Google Scholar * Zosia Miedzybrodzka Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick J Morrison Search for this author in: * NPG journals * PubMed * Google Scholar * Joan Paterson Search for this author in: * NPG journals * PubMed * Google Scholar * Mary Porteous Search for this author in: * NPG journals * PubMed * Google Scholar * Mark T Rogers Search for this author in: * NPG journals * PubMed * Google Scholar * Susan Shanley Search for this author in: * NPG journals * PubMed * Google Scholar * Lisa Walker Search for this author in: * NPG journals * PubMed * Google Scholar * Breast Cancer Susceptibility Collaboration (UK) * Diana Eccles Search for this author in: * NPG journals * PubMed * Google Scholar * D Gareth Evans Search for this author in: * NPG journals * PubMed * Google Scholar * Anthony Renwick Search for this author in: * NPG journals * PubMed * Google Scholar * Sheila Seal Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher J Lord Search for this author in: * NPG journals * PubMed * Google Scholar * Alan Ashworth Search for this author in: * NPG journals * PubMed * Google Scholar * Jorge S Reis-Filho Search for this author in: * NPG journals * PubMed * Google Scholar * Antonis C Antoniou Search for this author in: * NPG journals * PubMed * Google Scholar * Nazneen Rahman Contact Nazneen Rahman Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (324K) Supplementary Figures 1 and 2, Supplementary Tables 1–4 and Supplementary Note. Additional data - Exome sequencing identifies ACSF3 as a cause of combined malonic and methylmalonic aciduria
- Nat Genet 43(9):883-886 (2011)
Nature Genetics | Letter Exome sequencing identifies ACSF3 as a cause of combined malonic and methylmalonic aciduria * Jennifer L Sloan1, 13 * Jennifer J Johnston2, 13 * Irini Manoli1 * Randy J Chandler1, 3 * Caitlin Krause2 * Nuria Carrillo-Carrasco1 * Suma D Chandrasekaran1 * Justin R Sysol1 * Kevin O'Brien4 * Natalie S Hauser1 * Julie C Sapp2 * Heidi M Dorward4 * Marjan Huizing4 * NIH Intramural Sequencing Center Group * Bruce A Barshop6 * Susan A Berry7 * Philip M James8 * Neena L Champaigne9 * Pascale de Lonlay10 * Vassilli Valayannopoulos10 * Michael D Geschwind11 * Dimitar K Gavrilov12 * William L Nyhan6 * Leslie G Biesecker2 * Charles P Venditti1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:883–886Year published:(2011)DOI:doi:10.1038/ng.908Received28 February 2011Accepted15 July 2011Published online14 August 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We used exome sequencing to identify the genetic basis of combined malonic and methylmalonic aciduria (CMAMMA). We sequenced the exome of an individual with CMAMMA and followed up with sequencing of eight additional affected individuals (cases). This included one individual who was identified and diagnosed by searching an exome database. We identify mutations in ACSF3, encoding a putative methylmalonyl-CoA and malonyl-CoA synthetase as a cause of CMAMMA. We also examined a canine model of CMAMMA, which showed pathogenic mutations in a predicted ACSF3 ortholog. ACSF3 mutant alleles occur with a minor allele frequency of 0.0058 in ~1,000 control individuals, predicting a CMAMMA population incidence of ~1:30,000. ACSF3 deficiency is the first human disorder identified as caused by mutations in a gene encoding a member of the acyl-CoA synthetase family, a diverse group of evolutionarily conserved proteins, and may emerge as one of the more common human metabolic disorders. View full text Figures at a glance * Figure 1: Alignment of the motif regions in ACSF3 orthologs and the malonyl-CoA synthetase enzymes in bacteria. We aligned the sequences with MegAlign using the Clustal W method (Online Methods). An additional three amino acids N-terminal to motif I are shown. We aligned motif II independent of the full-length protein to improve the alignment of the ACSF3 and MCS proteins. The ACSF3 alterations identified in the eight subjects and affected dog are indicated. The asterisk indicates the dog variant p.Gly430Ser, which is orthologous to position p.Gly480 in human ACSF3. * Figure 2: MMA production by CMAMMA fibroblasts and lentiviral complementation with ACSF3. () We incubated control fibroblasts and fibroblasts from subjects 1–4 in medium containing 5 mM sodium propionate (propionic acid) at 37 °C for 72 h and then removed the media for gas chromatography-mass spectrometry analysis of MMA. The cells from subjects with CMAMMA showed increased accumulation of MMA in the media, which were six fold, 2.4-fold, 5.3-fold and 2.4-fold elevated compared to the control cell lines, respectively. Error bars, ± 1 standard deviation (s.d.) (n = 3 measurements per cell line). () We transduced fibroblasts from subjects 1, 3 and 4 with lentivirus designed to express ACSF3 or GFP and then incubated them in medium containing 5 mM sodium propionate. Fibroblasts transduced with ACSF3 but not GFP showed MMA production similar to control fibroblasts treated in the same fashion. Error bars, ± 1 s.d. (n = 3 replicates per cell line). PA, propionic acid. * Figure 3: ACSF3 mitochondrial localization. () We co-stained control fibroblasts transfected with a plasmid expressing C-terminal GFP-tagged ACSF3 with anti-ACSF3 (red) and a mitochondrial antibody (white). Scale bar, 10 μm. () We co-stained fibroblasts from subject 4 that expressed ACSF3 after lentiviral transduction with anti-ACSF3 (red) and a mitochondrial antibody (green). At the bottom right is an enlargement of the area surrounding the arrow. Scale bars, 20 μm. We collected all images using a confocal microscope using a 63× objective with 0.7 zoom. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Entrez Nucleotide * NM_174917.2 GenBank * JF907588.1 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Jennifer L Sloan & * Jennifer J Johnston Affiliations * Genetics and Molecular Biology Branch, National Human Genome Research Institute (NHGRI), US National Institutes of Health (NIH), Bethesda, Maryland, USA. * Jennifer L Sloan, * Irini Manoli, * Randy J Chandler, * Nuria Carrillo-Carrasco, * Suma D Chandrasekaran, * Justin R Sysol, * Natalie S Hauser & * Charles P Venditti * Genetic Disease Research Branch, NHGRI, NIH, Bethesda, Maryland, USA. * Jennifer J Johnston, * Caitlin Krause, * Julie C Sapp & * Leslie G Biesecker * Department of Pharmacology, Institute for Biomedical Sciences, George Washington University, Washington, DC, USA. * Randy J Chandler * Medical Genetics Branch, NHGRI, NIH, Bethesda, Maryland, USA. * Kevin O'Brien, * Heidi M Dorward & * Marjan Huizing * Department of Pediatrics, Biochemical Genetics Laboratory, University of California, San Diego, La Jolla, California, USA. * Bruce A Barshop & * William L Nyhan * Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA. * Susan A Berry * Department of Pediatrics, Harvard Medical School, Children's Hospital Boston, Boston, Massachusetts, USA. * Philip M James * Greenwood Genetics Center, Greenwood, South Carolina, USA. * Neena L Champaigne * Reference Center for Inherited Metabolic Disorders, Necker-Enfants Malades Hospital, Paris, France. * Pascale de Lonlay & * Vassilli Valayannopoulos * Department of Neurology, University of California, San Francisco, San Francisco, California, USA. * Michael D Geschwind * Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA. * Dimitar K Gavrilov Consortia * NIH Intramural Sequencing Center Group Contributions J.C.S. and L.G.B. developed the exome sequencing protocol. J.L.S., I.M., J.C.S., L.G.B. and C.P.V. designed the clinical research studies. The NIH Intramural Sequencing Center Group (N.I.S.C.G.) performed exome sequencing. J.J.J., J.L.S., I.M., R.J.C., L.G.B. and C.P.V. performed bioinformatic analyses. J.J.J. and C.K. performed mutational analysis and genotyping. R.J.C., N.C.-C., S.D.C. and J.R.S. performed cell culture studies and protein blot analyses. R.J.C. created viral vectors and performed cellular complementation studies. J.L.S., M.H. and H.M.D. performed immunofluorescence experiments. J.L.S., J.R.S. and I.M. performed enzymatic assays. J.L.S., I.M., N.S.H., K.O., J.C.S., B.A.B., S.A.B., P.M.J., N.L.C., P.d.L., V.V., M.D.G., W.L.N., L.G.B. and C.P.V. contributed to clinical evaluations and the delineation of the subject phenotypes. D.K.G. performed organic acid measurements. J.L.S., J.J.J., L.G.B. and C.P.V. prepared the manuscript. L.G.B. and C.P.V. conceived of a! nd supervised the study. NIH Intramural Sequencing Center Group NIH Intramural Sequencing Center Group Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Leslie G Biesecker Author Details * Jennifer L Sloan Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer J Johnston Search for this author in: * NPG journals * PubMed * Google Scholar * Irini Manoli Search for this author in: * NPG journals * PubMed * Google Scholar * Randy J Chandler Search for this author in: * NPG journals * PubMed * Google Scholar * Caitlin Krause Search for this author in: * NPG journals * PubMed * Google Scholar * Nuria Carrillo-Carrasco Search for this author in: * NPG journals * PubMed * Google Scholar * Suma D Chandrasekaran Search for this author in: * NPG journals * PubMed * Google Scholar * Justin R Sysol Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin O'Brien Search for this author in: * NPG journals * PubMed * Google Scholar * Natalie S Hauser Search for this author in: * NPG journals * PubMed * Google Scholar * Julie C Sapp Search for this author in: * NPG journals * PubMed * Google Scholar * Heidi M Dorward Search for this author in: * NPG journals * PubMed * Google Scholar * Marjan Huizing Search for this author in: * NPG journals * PubMed * Google Scholar * NIH Intramural Sequencing Center Group * Bruce A Barshop Search for this author in: * NPG journals * PubMed * Google Scholar * Susan A Berry Search for this author in: * NPG journals * PubMed * Google Scholar * Philip M James Search for this author in: * NPG journals * PubMed * Google Scholar * Neena L Champaigne Search for this author in: * NPG journals * PubMed * Google Scholar * Pascale de Lonlay Search for this author in: * NPG journals * PubMed * Google Scholar * Vassilli Valayannopoulos Search for this author in: * NPG journals * PubMed * Google Scholar * Michael D Geschwind Search for this author in: * NPG journals * PubMed * Google Scholar * Dimitar K Gavrilov Search for this author in: * NPG journals * PubMed * Google Scholar * William L Nyhan Search for this author in: * NPG journals * PubMed * Google Scholar * Leslie G Biesecker Contact Leslie G Biesecker Search for this author in: * NPG journals * PubMed * Google Scholar * Charles P Venditti Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–4 and Supplementary Tables 1–3. Additional data - Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations
- Nat Genet 43(9):887-892 (2011)
Nature Genetics | Letter Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations * Dara G Torgerson1, 38 * Elizabeth J Ampleford2 * Grace Y Chiu3 * W James Gauderman4 * Christopher R Gignoux5 * Penelope E Graves6 * Blanca E Himes7 * Albert M Levin8 * Rasika A Mathias9 * Dana B Hancock10, 38 * James W Baurley4 * Celeste Eng5 * Debra A Stern6 * Juan C Celedón11 * Nicholas Rafaels9 * Daniel Capurso1 * David V Conti4 * Lindsey A Roth5 * Manuel Soto-Quiros12 * Alkis Togias9 * Xingnan Li2 * Rachel A Myers1 * Isabelle Romieu13 * David J Van Den Berg4 * Donglei Hu5 * Nadia N Hansel9, 14 * Ryan D Hernandez15 * Elliott Israel7 * Muhammad T Salam4 * Joshua Galanter5 * Pedro C Avila16 * Lydiana Avila12 * Jose R Rodriquez-Santana17 * Rocio Chapela18 * William Rodriguez-Cintron19 * Gregory B Diette9, 14 * N Franklin Adkinson9 * Rebekah A Abel1 * Kevin D Ross1 * Min Shi10 * Mezbah U Faruque20 * Georgia M Dunston21 * Harold R Watson22 * Vito J Mantese9 * Serpil C Ezurum23 * Liming Liang24, 25 * Ingo Ruczinski26 * Jean G Ford14 * Scott Huntsman5 * Kian Fan Chung27 * Hita Vora4 * Xia Li4 * William J Calhoun28 * Mario Castro29 * Juan J Sienra-Monge30 * Blanca del Rio-Navarro30 * Klaus A Deichmann31 * Andrea Heinzmann31 * Sally E Wenzel32 * William W Busse33 * James E Gern34 * Robert F Lemanske Jr34 * Terri H Beaty14 * Eugene R Bleecker2 * Benjamin A Raby7 * Deborah A Meyers2 * Stephanie J London10 * for Mexico City Childhood Asthma Study (MCAAS) * Frank D Gilliland4 * for Children's Health Study (CHS) and HARBORS study * Esteban G Burchard5, 15 * for Genetics of Asthma in Latino Americans (GALA) Study, the Study of Genes-Environment and Admixture in Latino Americans (GALA2) and the Study of African Americans, Asthma, Genes & Environments (SAGE) * Fernando D Martinez6 * for Childhood Asthma Research and Education (CARE) Network * Scott T Weiss7 * for Childhood Asthma Management Program (CAMP) * L Keoki Williams35 * for Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-Ethnicity (SAPPHIRE) * Kathleen C Barnes9 * for Genetic Research on Asthma in the African Diaspora (GRAAD) Study * Carole Ober1, 39 * Dan L Nicolae1, 36, 37, 39 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:887–892Year published:(2011)DOI:doi:10.1038/ng.888Received14 December 2010Accepted16 June 2011Published online31 July 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Asthma is a common disease with a complex risk architecture including both genetic and environmental factors. We performed a meta-analysis of North American genome-wide association studies of asthma in 5,416 individuals with asthma (cases) including individuals of European American, African American or African Caribbean, and Latino ancestry, with replication in an additional 12,649 individuals from the same ethnic groups. We identified five susceptibility loci. Four were at previously reported loci on 17q21, near IL1RL1, TSLP and IL33, but we report for the first time, to our knowledge, that these loci are associated with asthma risk in three ethnic groups. In addition, we identified a new asthma susceptibility locus at PYHIN1, with the association being specific to individuals of African descent (P = 3.9 × 10−9). These results suggest that some asthma susceptibility loci are robust to differences in ancestry when sufficiently large samples sizes are investigated, and tha! t ancestry-specific associations also contribute to the complex genetic architecture of asthma. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Carole Ober & * Dan L Nicolae Affiliations * Department of Human Genetics, University of Chicago, Chicago, Illinois, USA. * Dara G Torgerson, * Daniel Capurso, * Rachel A Myers, * Rebekah A Abel, * Kevin D Ross, * Carole Ober & * Dan L Nicolae * Center for Genomics, Wake Forest University School of Medicine, Winston Salem, North Carolina, USA. * Elizabeth J Ampleford, * Xingnan Li, * Eugene R Bleecker & * Deborah A Meyers * Westat Inc., Research Triangle Park, North Carolina, USA. * Grace Y Chiu * Department of Preventive Medicine, University of Southern California, Los Angeles, California, USA. * W James Gauderman, * James W Baurley, * David V Conti, * David J Van Den Berg, * Muhammad T Salam, * Hita Vora, * Xia Li & * Frank D Gilliland * Department of Medicine, University of California San Francisco, San Francisco, California, USA. * Christopher R Gignoux, * Celeste Eng, * Lindsey A Roth, * Donglei Hu, * Joshua Galanter, * Scott Huntsman & * Esteban G Burchard * Arizona Respiratory Center and BIO5 Institute, University of Arizona, Tucson, Arizona, USA. * Penelope E Graves, * Debra A Stern & * Fernando D Martinez * Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. * Blanca E Himes, * Elliott Israel, * Benjamin A Raby & * Scott T Weiss * Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan, USA. * Albert M Levin * Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA. * Rasika A Mathias, * Nicholas Rafaels, * Alkis Togias, * Nadia N Hansel, * Gregory B Diette, * N Franklin Adkinson, * Vito J Mantese & * Kathleen C Barnes * Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA. * Dana B Hancock, * Min Shi & * Stephanie J London * Division of Pediatric Pulmonology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. * Juan C Celedón * Hospital Nacional de Niños, San José, Costa Rica. * Manuel Soto-Quiros & * Lydiana Avila * Instituto Nacional de Salud Publica, Mexico City, Mexico. * Isabelle Romieu * Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA. * Nadia N Hansel, * Gregory B Diette, * Jean G Ford & * Terri H Beaty * Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA. * Ryan D Hernandez & * Esteban G Burchard * Department of Medicine, Northwestern University, Chicago, Illinois, USA. * Pedro C Avila * Centro de Neumologia Pediatrica, San Juan, Puerto Rico. * Jose R Rodriquez-Santana * Instituto Nacional de Enfermedades Respiratorias, Mexico City, Mexico. * Rocio Chapela * Veterans Affairs Medical Center, San Juan, Puerto Rico. * William Rodriguez-Cintron * Department of Community and Family Medicine, Howard University College of Medicine, Washington DC, USA. * Mezbah U Faruque * Department of Microbiology, Howard University, Washington DC, USA. * Georgia M Dunston * Faculty of Medical Sciences, University of the West Indies, Cave Hill Campus and Queen Elizabeth Hospital, St. Michael, Barbados. * Harold R Watson * The Lerner Research Institute and Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, USA. * Serpil C Ezurum * Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. * Liming Liang * Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. * Liming Liang * Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA. * Ingo Ruczinski * Respiratory Medicine, Imperial College School of Medicine, London, UK. * Kian Fan Chung * Department of Internal Medicine, University of Texas Medical Branch, Galveston, Texas, USA. * William J Calhoun * Department of Medicine, Washington University, St. Louis, Missouri, USA. * Mario Castro * Hospital Infantil de Mexico Federico Gomez, Mexico City, Mexico. * Juan J Sienra-Monge & * Blanca del Rio-Navarro * Department of Pediatrics, University of Freiburg, Freiburg, Germany. * Klaus A Deichmann & * Andrea Heinzmann * Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. * Sally E Wenzel * Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA. * William W Busse * Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA. * James E Gern & * Robert F Lemanske Jr * Center for Health Services Research, Department of Internal Medicine, Henry Ford Health System, Detroit, Michigan, USA. * L Keoki Williams * Department of Medicine, University of Chicago, Chicago, Illinois, USA. * Dan L Nicolae * Department of Statistics, University of Chicago, Chicago, Illinois, USA. * Dan L Nicolae * Present addresses: Department of Medicine, University of California San Francisco, San Francisco, California, USA (D.G.T.), Research Triangle Institute International, Research Triangle Park, North Carolina, USA (D.B.H.) and International Agency for Research on Cancer, Lyon, France (I. Romieu). * Dara G Torgerson & * Dana B Hancock Consortia * Mexico City Childhood Asthma Study (MCAAS) * Dara G Torgerson, * Elizabeth J Ampleford, * Grace Y Chiu, * W James Gauderman, * Christopher R Gignoux, * Penelope E Graves, * Blanca E Himes, * Albert M Levin, * Rasika A Mathias, * Dana B Hancock, * James W Baurley, * Celeste Eng, * Debra A Stern, * Juan C Celedón, * Nicholas Rafaels, * Daniel Capurso, * David V Conti, * Lindsey A Roth, * Manuel Soto-Quiros, * Alkis Togias, * Xingnan Li, * Rachel A Myers, * Isabelle Romieu, * David J Van Den Berg, * Donglei Hu, * Nadia N Hansel, * Ryan D Hernandez, * Elliott Israel, * Muhammad T Salam, * Joshua Galanter, * Pedro C Avila, * Lydiana Avila, * Jose R Rodriquez-Santana, * Rocio Chapela, * William Rodriguez-Cintron, * Gregory B Diette, * N Franklin Adkinson, * Rebekah A Abel, * Kevin D Ross, * Min Shi, * Mezbah U Faruque, * Georgia M Dunston, * Harold R Watson, * Vito J Mantese, * Serpil C Ezurum, * Liming Liang, * Ingo Ruczinski, * Jean G Ford, * Scott Huntsman, * Kian Fan Chung, * Hita Vora, * Xia Li, * William J Calhoun, * Mario Castro, * Juan J Sienra-Monge, * Blanca del Rio-Navarro, * Klaus A Deichmann, * Andrea Heinzmann, * Sally E Wenzel, * William W Busse, * James E Gern, * Robert F Lemanske Jr, * Terri H Beaty, * Eugene R Bleecker, * Benjamin A Raby, * Deborah A Meyers & * Stephanie J London * Children's Health Study (CHS) and HARBORS study * Frank D Gilliland * Genetics of Asthma in Latino Americans (GALA) Study, the Study of Genes-Environment and Admixture in Latino Americans (GALA2) and the Study of African Americans, Asthma, Genes & Environments (SAGE) * Esteban G Burchard * Childhood Asthma Research and Education (CARE) Network * Fernando D Martinez * Childhood Asthma Management Program (CAMP) * Scott T Weiss * Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-Ethnicity (SAPPHIRE) * L Keoki Williams * Genetic Research on Asthma in the African Diaspora (GRAAD) Study * Kathleen C Barnes Contributions E.R.B., B.A.R., D.A.M., S.J.L., F.D.G., W.J.G., E.G.B., F.D.M., S.T.W., L.K.W., K.C.B., C.O. and D.L.N. conceived and designed the experiments. W.J.G., A.M.L., C.E., D.J.V.D.B., M.T.S., K.D.R., E.R.B., D.A.M., S.T.W., E.G.B. and L.K.W. performed the experiments. D.G.T., E.J.A., G.Y.C., W.J.G., C.R.G., B.E.H., A.M.L., R.A. Mathias, D.B.H., J.W.B., D.A.S., N.R., D.C., D.V.C., L.A.R., Xingnan L., D.H., R.D.H., J.G., M.S., V.J.M., L.L., I. Ruczinski, S.H., H.V., Xia L., T.H.B., E.R.B., D.A.M. and D.L.N. performed statistical analysis. D.G.T., E.J.A., G.Y.C., W.J.G., C.R.G., B.E.H., A.M.L., R.A. Mathias, D.B.H., J.W.B., C.E., D.A.S., N.R., D.C., D.V.C., L.A.R., Xingnan L., R.A. Myers, D.H., M.T.S., J.G., V.J.M., L.L., I. Romieu, S.H., H.V., Xia L., T.H.B., E.R.B., D.A.M., F.D.G., L.K.W., K.C.B. and D.L.N. analyzed the data. P.E.G., A.M.L., J.C.C., D.V.C., M.S.-Q., A.T., I. Romieu, N.N.H., E.I., M.T.S., J.G., P.C.A., L.A., J.R.R.-S., R.C., W.R.-C., G.B.D., N.F.A., M.S., M.U.F., G.! M.D., H.R.W., S.C.E., J.G.F., K.F.C., W.J.C., M.C., J.-J.S.-M., B.d.R.-N., K.A.D., A.H., S.E.W., W.W.B., J.E.G., R.F.L., E.R.B., D.A.M., S.J.L., F.D.G., F.D.M., S.T.W., L.K.W., E.G.B., K.C.B. and C.O. contributed reagents, materials and analysis tools. D.G.T., G.Y.C., W.J.G., B.E.H., A.M.L., R.A. Mathias, D.B.H., E.R.B., B.A.R., D.A.M., S.J.L., F.D.G., E.G.B., F.D.M., S.T.W., L.K.W., K.C.B., C.O. and D.L.N. wrote the paper. R.A.A. coordinated the study. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Dan L Nicolae Author Details * Dara G Torgerson Search for this author in: * NPG journals * PubMed * Google Scholar * Elizabeth J Ampleford Search for this author in: * NPG journals * PubMed * Google Scholar * Grace Y Chiu Search for this author in: * NPG journals * PubMed * Google Scholar * W James Gauderman Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher R Gignoux Search for this author in: * NPG journals * PubMed * Google Scholar * Penelope E Graves Search for this author in: * NPG journals * PubMed * Google Scholar * Blanca E Himes Search for this author in: * NPG journals * PubMed * Google Scholar * Albert M Levin Search for this author in: * NPG journals * PubMed * Google Scholar * Rasika A Mathias Search for this author in: * NPG journals * PubMed * Google Scholar * Dana B Hancock Search for this author in: * NPG journals * PubMed * Google Scholar * James W Baurley Search for this author in: * NPG 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Additional data - Genome-wide association study identifies three new susceptibility loci for adult asthma in the Japanese population
- Nat Genet 43(9):893-896 (2011)
Nature Genetics | Letter Genome-wide association study identifies three new susceptibility loci for adult asthma in the Japanese population * Tomomitsu Hirota1 * Atsushi Takahashi2 * Michiaki Kubo3 * Tatsuhiko Tsunoda4 * Kaori Tomita1 * Satoru Doi5 * Kimie Fujita6 * Akihiko Miyatake6 * Tadao Enomoto7 * Takehiko Miyagawa8 * Mitsuru Adachi9 * Hiroshi Tanaka10 * Akio Niimi11 * Hisako Matsumoto11 * Isao Ito11 * Hironori Masuko12 * Tohru Sakamoto12 * Nobuyuki Hizawa12 * Masami Taniguchi13 * John J Lima14 * Charles G Irvin15 * Stephen P Peters16 * Blanca E Himes17 * Augusto A Litonjua17 * Kelan G Tantisira17 * Scott T Weiss17 * Naoyuki Kamatani18 * Yusuke Nakamura19 * Mayumi Tamari1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:893–896Year published:(2011)DOI:doi:10.1038/ng.887Received05 April 2011Accepted16 June 2011Published online31 July 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Bronchial asthma is a common inflammatory disease caused by the interaction of genetic and environmental factors1, 2. Through a genome-wide association study and a replication study consisting of a total of 7,171 individuals with adult asthma (cases) and 27,912 controls in the Japanese population, we identified five loci associated with susceptibility to adult asthma. In addition to the major histocompatibility complex and TSLP-WDR36 loci previously reported, we identified three additional loci: a USP38-GAB1 locus on chromosome 4q31 (combined P = 1.87 × 10−12), a locus on chromosome 10p14 (P = 1.79 × 10−15) and a gene-rich region on chromosome 12q13 (P = 2.33 × 10−13). We observed the most significant association with adult asthma at rs404860 in the major histocompatiblity complex region (P = 4.07 × 10−23), which is close to rs2070600, a SNP previously reported for association with FEV1/FVC in genome-wide association studies for lung function. Our findings offer ! a better understanding of the genetic contribution to asthma susceptibility. View full text Figures at a glance * Figure 1: Case-control association results and LD map of the MHC region. The blue plots show the –log10 of the Cochrane-Armitage trend P values for the association results of the GWAS. We drew the LD maps based on the D′ values using the genotype data of all cases and controls in the GWAS. Red dotted line, rs404860; blue dotted line, rs8192565; black dotted lines, locations of the SNPs giving significant signals for lung function (rs2070600), FEV1/FVC, in the GWAS and for bronchial asthma in a large scale, consortium-based GWAS (rs9273349 and rs1063355). rs1063355 is absolute LD with rs9273349 (r2 = 1) in the HapMap JPT and CEU populations. Blue arrows indicate the locations of genes. The LD values among the SNPs are shown in Supplementary Table 4. * Figure 2: Case-control association results and LD maps of the four candidate regions. (–) P value plot, genomic structures and LD maps of chromosomes 5q22 (), 10p14 (), 12q13 () and 4q31 (). The blue plots show the −log10 of Cochrane-Armitage trend P values for the association results of the GWAS. We drew the LD maps based on the D′ values using the genotype data of all cases and controls in the GWAS. Black and red dotted lines indicate the ranges of the susceptible regions and positions of marker SNPs, respectively. Blue arrows indicate the locations of genes. Author information * Author information * Supplementary information Affiliations * Laboratory for Respiratory Diseases, Center for Genomic Medicine, RIKEN, Yokohama, Kanagawa, Japan. * Tomomitsu Hirota, * Kaori Tomita & * Mayumi Tamari * Laboratory for Statistical Analysis, Center for Genomic Medicine, RIKEN, Tokyo, Japan. * Atsushi Takahashi * Laboratory for Genotyping Development, Center for Genomic Medicine, RIKEN, Yokohama, Kanagawa, Japan. * Michiaki Kubo * Laboratory for Medical Informatics, Center for Genomic Medicine, RIKEN, Yokohama, Kanagawa, Japan. * Tatsuhiko Tsunoda * Osaka Prefectural Medical Center for Respiratory and Allergic Diseases, Habikino, Osaka, Japan. * Satoru Doi * Miyatake Asthma Clinic, Osaka, Japan. * Kimie Fujita & * Akihiko Miyatake * Nonprofit Organization (NPO) Japan Health Promotion Supporting Network, Wakayama, Japan. * Tadao Enomoto * Miyagawa Clinic, Gifu, Japan. * Takehiko Miyagawa * First Department of Internal Medicine, Showa University School of Medicine, Shinagawa, Tokyo, Japan. * Mitsuru Adachi * Third Department of Internal Medicine, Sapporo Medical University School of Medicine, Sapporo, Hokkaido, Japan. * Hiroshi Tanaka * Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan. * Akio Niimi, * Hisako Matsumoto & * Isao Ito * Division of Respiratory Medicine, Institute of Clinical Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan. * Hironori Masuko, * Tohru Sakamoto & * Nobuyuki Hizawa * National Clinical Research Center, National Hospital Organization, Sagamihara National Hospital, Kanagawa, Japan. * Masami Taniguchi * Nemours Children's Clinic, Centers for Clinical Pediatric Pharmacology and Pharmacogenetics, Jacksonville, Florida, USA. * John J Lima * Vermont Lung Center, Department of Medicine and Physiology, University of Vermont, Burlington, Vermont, USA. * Charles G Irvin * Center for Human Genomics, Section of Pulmonary, Critical Care, Allergy and Immunologic Diseases, Wake Forest University School of Medicine, Winston Salem, North Carolina, USA. * Stephen P Peters * Channing Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. * Blanca E Himes, * Augusto A Litonjua, * Kelan G Tantisira & * Scott T Weiss * Laboratory for International Alliance, Center for Genomic Medicine, RIKEN, Yokohama, Kanagawa, Japan. * Naoyuki Kamatani * Laboratory of Molecular Medicine, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan. * Yusuke Nakamura Contributions T.H. and M. Tamari designed the study and drafted the manuscript. A.T. and T.T. analyzed the GWAS data. T.H., K.T. and M.K. performed the genotyping for the GWAS. S.D., K.F., A.M., T.E., T.M., M.A., H.T., A.N., H. Matsumoto, I.I., H. Masuko, T.S., N.H. and M. Taniguchi collected subjects and participated in the diagnostic evaluations. B.E.H., A.A.L., K.G.T., J.J.L., C.G.I., S.P.P. and S.T.W. conducted an association study in a non-Hispanic population of European ancestry. M. Tamari and S.T.W. wrote the manuscript. M.K., N.K. and Y.N. contributed to the overall GWAS study design. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Mayumi Tamari Author Details * Tomomitsu Hirota Search for this author in: * NPG journals * PubMed * Google Scholar * Atsushi Takahashi Search for this author in: * NPG journals * PubMed * Google Scholar * Michiaki Kubo Search for this author in: * NPG journals * PubMed * Google Scholar * Tatsuhiko Tsunoda Search for this author in: * NPG journals * PubMed * Google Scholar * Kaori Tomita Search for this author in: * NPG journals * PubMed * Google Scholar * Satoru Doi Search for this author in: * NPG journals * PubMed * Google Scholar * Kimie Fujita Search for this author in: * NPG journals * PubMed * Google Scholar * Akihiko Miyatake Search for this author in: * NPG journals * PubMed * Google Scholar * Tadao Enomoto Search for this author in: * NPG journals * PubMed * Google Scholar * Takehiko Miyagawa Search for this author in: * NPG journals * PubMed * Google Scholar * Mitsuru Adachi Search for this author in: * NPG journals * PubMed * Google Scholar * Hiroshi Tanaka Search for this author in: * NPG journals * PubMed * Google Scholar * Akio Niimi Search for this author in: * NPG journals * PubMed * Google Scholar * Hisako Matsumoto Search for this author in: * NPG journals * PubMed * Google Scholar * Isao Ito Search for this author in: * NPG journals * PubMed * Google Scholar * Hironori Masuko Search for this author in: * NPG journals * PubMed * Google Scholar * Tohru Sakamoto Search for this author in: * NPG journals * PubMed * Google Scholar * Nobuyuki Hizawa Search for this author in: * NPG journals * PubMed * Google Scholar * Masami Taniguchi Search for this author in: * NPG journals * PubMed * Google Scholar * John J Lima Search for this author in: * NPG journals * PubMed * Google Scholar * Charles G Irvin Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen P Peters Search for this author in: * NPG journals * PubMed * Google Scholar * Blanca E Himes Search for this author in: * NPG journals * PubMed * Google Scholar * Augusto A Litonjua Search for this author in: * NPG journals * PubMed * Google Scholar * Kelan G Tantisira Search for this author in: * NPG journals * PubMed * Google Scholar * Scott T Weiss Search for this author in: * NPG journals * PubMed * Google Scholar * Naoyuki Kamatani Search for this author in: * NPG journals * PubMed * Google Scholar * Yusuke Nakamura Search for this author in: * NPG journals * PubMed * Google Scholar * Mayumi Tamari Contact Mayumi Tamari Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–3 and Supplementary Tables 1–5. Additional data - A genome-wide association study identifies two new risk loci for Graves' disease
- Nat Genet 43(9):897-901 (2011)
Nature Genetics | Letter A genome-wide association study identifies two new risk loci for Graves' disease * Xun Chu1, 2, 15 * Chun-Ming Pan1, 3, 15 * Shuang-Xia Zhao1, 3, 15 * Jun Liang4, 15 * Guan-Qi Gao5, 15 * Xiao-Mei Zhang6, 15 * Guo-Yue Yuan7 * Chang-Gui Li8 * Li-Qiong Xue1 * Min Shen2 * Wei Liu1 * Fang Xie1, 2 * Shao-Ying Yang1 * Hai-Feng Wang2 * Jing-Yi Shi1 * Wei-Wei Sun2 * Wen-Hua Du5 * Chun-Lin Zuo1 * Jin-Xiu Shi1, 2 * Bing-Li Liu1 * Cui-Cui Guo1 * Ming Zhan1 * Zhao-Hui Gu1 * Xiao-Na Zhang1 * Fei Sun1 * Zhi-Quan Wang1 * Zhi-Yi Song1 * Cai-Yan Zou4 * Wei-Hua Sun6 * Ting Guo1, 3 * Huang-Ming Cao1 * Jun-Hua Ma1 * Bing Han1 * Ping Li1, 3 * He Jiang1 * Qiu-Hua Huang1 * Liming Liang9 * Li-Bin Liu10 * Gang Chen11 * Qing Su12 * Yong-De Peng13 * Jia-Jun Zhao14 * Guang Ning3, 16 * Zhu Chen1, 16 * Jia-Lun Chen3, 16 * Sai-Juan Chen1, 16 * Wei Huang1, 2, 16 * Huai-Dong Song1, 3, 16 * for The China Consortium for the Genetics of Autoimmune Thyroid Disease * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:897–901Year published:(2011)DOI:doi:10.1038/ng.898Received16 February 2011Accepted06 July 2011Published online14 August 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Graves' disease is a common autoimmune disorder characterized by thyroid stimulating hormone receptor autoantibodies (TRAb) and hyperthyroidism. To investigate the genetic architecture of Graves' disease, we conducted a genome-wide association study in 1,536 individuals with Graves' disease (cases) and 1,516 controls. We further evaluated a group of associated SNPs in a second set of 3,994 cases and 3,510 controls. We confirmed four previously reported loci (in the major histocompatibility complex, TSHR, CTLA4 and FCRL3) and identified two new susceptibility loci (the RNASET2-FGFR1OP-CCR6 region at 6q27 (Pcombined = 6.85 × 10−10 for rs9355610) and an intergenic region at 4p14 (Pcombined = 1.08 × 10−13 for rs6832151)). These newly associated SNPs were correlated with the expression levels of RNASET2 at 6q27, of CHRNA9 and of a previously uncharacterized gene at 4p14, respectively. Moreover, we identified strong associations of TSHR and major histocompatibility complex c! lass II variants with persistently TRAb-positive Graves' disease. View full text Figures at a glance * Figure 1: Genome-wide association results for SNPs in the MHC region at 6p21–22. The region plotted contains the 7.5-Mb extended MHC region from 6p22.2 (26.03 Mb) to 6p21.32 (33.59 Mb). Values of –log10 (Ptrend) are plotted against chromosome position, which is based on NCBI Build 36 coordinates. Green, red and blue dots represent the P values of 1,468 Graves' disease cases, 997 pTRAb+ Graves' disease cases and pTRAb– Graves' disease cases, respectively, compared with 1,490 control samples. Arrowtips in the lower panel specify the classical MHC genes, their approximate genomic size and direction of transcription. * Figure 2: Regional plots of association results and expression analysis of candidate genes. (,) Association results of both genotyped and imputed SNPs in the GWAS samples at 6q27 and 4p14. The color of each genotyped SNP spot reflects its r2 with the top SNP (large red diamond) within each association locus, changing from red to white. Genetic recombination rates, estimated using the HapMap CHB and JPT samples, are shown in cyan. Physical positions are based on NCBI build 36 of the human genome. (,) We measured relative expression levels of candidate genes at 6q27 for the different genotypes of rs9355610 in PBMCs from 242 individuals (AA, n = 77; AG, n = 115; GG, n = 50) and in subsets of PBMCs from 106 individuals (AA, n = 33; AG, n = 48; GG, n = 25). (,) We measured relative expression levels of candidate genes at 4p14 for the different genotypes of rs6832151 in PBMCs (TT, n = 113; TG, n = 100; GG, n = 29) and in subsets of PBMCs (TT, n = 45; TG, n = 43; GG, n = 18). *P < 0.05; (–) **P < 0.01; ***P < 0.001. Error bars, ± s.e.m. () Schematic structure of the ne! wly characterized gene GDCG4p14 at 4p14 based on the human reference sequence (GRCh37). The UCSC Genome Browser revealed five hypothetical genes and several ESTs within the 110-kb interval between CHRNA9 and RHOH at 4p14. The exon organization of the two isoforms of GDCG4p14 (GDCG4p14.1 and GDCG4p14.2) is shown in blue; the putative open reading frames are shown in red. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions GenBank * JN120857 * JN120858 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Xun Chu, * Chun-Ming Pan, * Shuang-Xia Zhao, * Jun Liang, * Guan-Qi Gao & * Xiao-Mei Zhang Affiliations * State Key Laboratory of Medical Genomics, Ruijin Hospital Affiliated to Shanghai Jiaotong University (SJTU) School of Medicine, Shanghai, China. * Xun Chu, * Chun-Ming Pan, * Shuang-Xia Zhao, * Li-Qiong Xue, * Wei Liu, * Fang Xie, * Shao-Ying Yang, * Jing-Yi Shi, * Chun-Lin Zuo, * Jin-Xiu Shi, * Bing-Li Liu, * Cui-Cui Guo, * Ming Zhan, * Zhao-Hui Gu, * Xiao-Na Zhang, * Fei Sun, * Zhi-Quan Wang, * Zhi-Yi Song, * Ting Guo, * Huang-Ming Cao, * Jun-Hua Ma, * Bing Han, * Ping Li, * He Jiang, * Qiu-Hua Huang, * Zhu Chen, * Sai-Juan Chen, * Wei Huang & * Huai-Dong Song * Department of Genetics, Shanghai-Ministry of Science and Technology (MOST) Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center, Shanghai, China. * Xun Chu, * Min Shen, * Fang Xie, * Hai-Feng Wang, * Wei-Wei Sun, * Jin-Xiu Shi & * Wei Huang * Shanghai Institute of Endocrinology and Metabolism, Department of Endocrinology, Ruijin Hospital Affiliated to SJTU School of Medicine, Shanghai, China. * Chun-Ming Pan, * Shuang-Xia Zhao, * Ting Guo, * Ping Li, * Guang Ning, * Jia-Lun Chen & * Huai-Dong Song * Department of Endocrinology, The Central Hospital of Xuzhou Affiliated to Xuzhou Medical College, Xuzhou, Jiangsu Province, China. * Jun Liang & * Cai-Yan Zou * Department of Endocrinology, The People's Hospital of Linyi, Linyi, Shandong Province, China. * Guan-Qi Gao & * Wen-Hua Du * Department of Endocrinology, The First Hospital Affiliated to Bengbu Medical College, Bengbu, Anhui Province, China. * Xiao-Mei Zhang & * Wei-Hua Sun * Department of Endocrinology, The Hospital Affiliated to Jiangsu University, Zhenjiang, Jiangsu Province, China. * Guo-Yue Yuan * Department of Endocrinology and Gout Laboratory, Medical School Hospital of Qingdao University, Qingdao, Shandong Province China. * Chang-Gui Li * Department of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. * Liming Liang * Department of Endocrinology, Xiehe Hospital Affiliated to Fujian Medical University, Fuzhou, Fujian Province, China. * Li-Bin Liu * Department of Endocrinology, Fujian Province Hospital, Fuzhou, Fujian Province, China. * Gang Chen * Department of Endocrinology, Xin-Hua Hospital Affiliated to SJTU School of Medicine, Shanghai, China. * Qing Su * Department of Endocrinology, The First People's Hospital Affiliated to SJTU, Shanghai, China. * Yong-De Peng * Department of Endocrinology, Shandong Province Hospital, Shandong University, Jinan, Shandong Province, China. * Jia-Jun Zhao * These authors jointly directed this work. * Guang Ning, * Zhu Chen, * Jia-Lun Chen, * Sai-Juan Chen, * Wei Huang & * Huai-Dong Song Consortia * The China Consortium for the Genetics of Autoimmune Thyroid Disease * Xun Chu, * Chun-Ming Pan, * Shuang-Xia Zhao, * Jun Liang, * Guan-Qi Gao, * Xiao-Mei Zhang, * Guo-Yue Yuan, * Chang-Gui Li, * Li-Qiong Xue, * Min Shen, * Wei Liu, * Fang Xie, * Shao-Ying Yang, * Hai-Feng Wang, * Jing-Yi Shi, * Wei-Wei Sun, * Wen-Hua Du, * Chun-Lin Zuo, * Jin-Xiu Shi, * Bing-Li Liu, * Cui-Cui Guo, * Ming Zhan, * Zhao-Hui Gu, * Xiao-Na Zhang, * Fei Sun, * Zhi-Quan Wang, * Zhi-Yi Song, * Cai-Yan Zou, * Wei-Hua Sun, * Ting Guo, * Huang-Ming Cao, * Jun-Hua Ma, * Bing Han, * Ping Li, * He Jiang, * Qiu-Hua Huang, * Liming Liang, * Li-Bin Liu, * Gang Chen, * Qing Su, * Yong-De Peng, * Jia-Jun Zhao, * Guang Ning, * Zhu Chen, * Jia-Lun Chen, * Sai-Juan Chen, * Wei Huang & * Huai-Dong Song Contributions Z.C. was responsible for the coordination of the project. The writing team consisted of H.-D.S., X.C., W.H., S.-X.Z., J.-L.C., S.-J.C. and Z.C. Z.C., H.-D.S., W.H., G.N., J.-L.C. and S.-J.C. contributed to the study design. H.-D.S., W.H., C.-M.P. and X.C. contributed to the project management. H.-D.S., J.L., G.-Q.G., X.-M.Z., C.-M.P., G.-Y.Y., C.-G.L., L.-Q.X., W.L., S.-Y.Y., S.-X.Z., W.-H.D., C.-L.Z., B.-L.L., X.-N.Z., F.S., Z.-Q.W., Z.-Y.S., C.-Y.Z., W.-H.S., H.-M.C., J.-H.M., B.H., P.L., H.J., C.-C.G., M.Z., L.-B.L., G.C., Q.S., Y.-D.P. and J.-J.Z. took part in the collection of clinical samples and DNA and sample quality control. W.H., H.-F.W., M.S., X.C., F.X., W.-W.S. and J.-X.S. contributed to whole-genome scan genotyping. C.-M.P., J.-Y.S., L.-Q.X., W.L., S.-Y.Y., S.-X.Z., T.G., X.C., M.S., F.X. and W.-W.S. took part in replication genotyping. S.-X.Z., C.-M.P., M.Z., B.-L.L., C.-C.G., Z.-H.G., W.L., S.-Y.Y., L.-Q.X. and Q.-H.H. contributed to cloning of GDCG4p14 and r! eal-time RT-PCR. X.C., W.H., J.-X.S., H.-D.S., S.-X.Z., Z.-H.G., J.-L.C., S.-J.C. and Z.C. took part in the statistical analysis. S.-X.Z. and L.L. performed imputation and cis-eQTL analysis. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Huai-Dong Song or * Wei Huang or * Sai-Juan Chen or * Jia-Lun Chen or * Zhu Chen or * Guang Ning Author Details * Xun Chu Search for this author in: * NPG journals * PubMed * Google Scholar * Chun-Ming Pan Search for this author in: * NPG journals * PubMed * Google Scholar * Shuang-Xia Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Liang Search for this author in: * NPG journals * PubMed * Google Scholar * Guan-Qi Gao Search for this author in: * NPG journals * PubMed * Google Scholar * Xiao-Mei Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Guo-Yue Yuan Search for this author in: * NPG journals * PubMed * Google Scholar * Chang-Gui Li Search for this author in: * NPG journals * PubMed * Google Scholar * Li-Qiong Xue Search for this author in: * NPG journals * PubMed * Google Scholar * Min Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Fang Xie Search for this author in: * NPG journals * PubMed * Google Scholar * Shao-Ying Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Hai-Feng Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Jing-Yi Shi Search for this author in: * NPG journals * PubMed * Google Scholar * Wei-Wei Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Wen-Hua Du Search for this author in: * NPG journals * PubMed * Google Scholar * Chun-Lin Zuo Search for this author in: * NPG journals * PubMed * Google Scholar * Jin-Xiu Shi Search for this author in: * NPG journals * PubMed * Google Scholar * Bing-Li Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Cui-Cui Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Ming Zhan Search for this author in: * NPG journals * PubMed * Google Scholar * Zhao-Hui Gu Search for this author in: * NPG journals * PubMed * Google Scholar * Xiao-Na Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Fei Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Zhi-Quan Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Zhi-Yi Song Search for this author in: * NPG journals * PubMed * Google Scholar * Cai-Yan Zou Search for this author in: * NPG journals * PubMed * Google Scholar * Wei-Hua Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Ting Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Huang-Ming Cao Search for this author in: * NPG journals * PubMed * Google Scholar * Jun-Hua Ma Search for this author in: * NPG journals * PubMed * Google Scholar * Bing Han Search for this author in: * NPG journals * PubMed * Google Scholar * Ping Li Search for this author in: * NPG journals * PubMed * Google Scholar * He Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Qiu-Hua Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Liming Liang Search for this author in: * NPG journals * PubMed * Google Scholar * Li-Bin Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Gang Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Qing Su Search for this author in: * NPG journals * PubMed * Google Scholar * Yong-De Peng Search for this author in: * NPG journals * PubMed * Google Scholar * Jia-Jun Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Guang Ning Search for this author in: * NPG journals * PubMed * Google Scholar * Zhu Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Jia-Lun Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Sai-Juan Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Huai-Dong Song Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Note, Supplementary Figures 1–9 and Supplementary Tables 1–11. Additional data - The autoimmune disease–associated PTPN22 variant promotes calpain-mediated Lyp/Pep degradation associated with lymphocyte and dendritic cell hyperresponsiveness
- Nat Genet 43(9):902-907 (2011)
Nature Genetics | Letter The autoimmune disease–associated PTPN22 variant promotes calpain-mediated Lyp/Pep degradation associated with lymphocyte and dendritic cell hyperresponsiveness * Jinyi Zhang1, 2, 3, 4, 5 * Naima Zahir1, 2, 3, 4, 5 * Qiuhong Jiang1, 2, 3, 4, 5 * Helen Miliotis1, 2, 3, 4, 5 * Stephanie Heyraud1, 2, 3, 4, 5 * Xianwang Meng1, 2, 3, 4, 5 * Baoxia Dong1, 2, 3, 4, 5 * Gang Xie1, 2, 3, 4, 5 * Frank Qiu1, 2, 3, 4, 5 * Zhenyue Hao6 * Christopher A McCulloch7 * Edward C Keystone1 * Alan C Peterson8 * Katherine A Siminovitch1, 2, 3, 4, 5 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:902–907Year published:(2011)DOI:doi:10.1038/ng.904Received02 May 2011Accepted14 July 2011Published online14 August 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg A variant of the PTPN22-encoded Lyp phosphatase (Lyp620W) confers risk for autoimmune disease, but the mechanisms underlying this association remain unclear. We show here that mice expressing the Lyp variant homolog Pep619W manifest thymic and splenic enlargement accompanied by increases in T-cell number, activation and positive selection and in dendritic- and B-cell activation. Although Ptpn22 (Pep) transcript levels were comparable in Pep619W and wild-type Pep619R mice, Pep protein levels were dramatically reduced in the mutant mice, with Pep619W protein being more rapidly degraded and showing greater association with and in vitro cleavage by calpain 1 than Pep619R. Similarly, levels of the Lyp620W variant were decreased in human T and B cells, and its calpain binding and cleavage were increased relative to wild-type Lyp620R. Thus, calpain-mediated degradation with consequently reduced Lyp/Pep expression and lymphocyte and dendritic cell hyperresponsiveness represents a me! chanism whereby Lyp620W may increase risk for autoimmune disease. View full text Figures at a glance * Figure 1: Enhanced T-cell activation in Pep619W mice. () Representative spleens and thymi from 3-month-old wild-type Pep619R (WT) and mutant Pep619W knock-in (619W) mice. () Total thymocyte, spleen and mesenteric, axillary and inguinal lymph node cell numbers at 2–3 months of age in wild-type and Pep619W mutant mice. Values represent means of eight independent experiments. () Flow cytometric analysis of splenocytes and lymph node cells from wild-type and Pep619W mice stained for CD4-FITC, CD44-PE and CD62L-PerCp. Percentages of naïve (CD44lowCD62Lhi) and effector/memory (CD44hiCD62Llow) cell populations in the gated fractions are shown. Quantification of naïve and memory splenic T cells is shown on the right. Values are the means ± s.e.m. of seven independent experiments. () We stained splenocytes for CD4-FITC, CD44-PE and CD69-APC or CD25-APC, and the percentages of CD44hi cells expressing the CD69 or CD25 activation markers are shown. Quantification of CD69hi and CD25hi CD44hi CD4+ T cells is shown on the right. Values r! epresent means ± s.e.m. of seven independent experiments. () We stimulated thymocytes from 2-month-old mice for 2 days with the indicated concentrations (μg/ml) of CD3, CD3/CD28 antibodies, ConA or PMA/ionomycin (P+l, 10 ng/ml; 100 ng/ml) and then determined proliferative responses after a 16-h [3H]thymidine pulse. Values represent the means ± s.e.m. of triplicate cultures and are representative of eight independent experiments. () We stimulated thymocytes from 2-month-old mice with CD3 and CD28 antibodies for the indicated times, then lysed the cells and subjected them to immunoblotting analysis with phospho-Lck (Y394) and phospho-ZAP70 (Y319) antibodies followed by reprobing with Lck or ZAP70 antibodies. Data are representative of seven independent experiments. * Figure 2: Enhanced activation and function of B and dendritic cells from Pep619W mice. () Histologic analysis showing splenic germinal centers (GC) of 3-month-old wild-type and Pep619W mice (top). Numbers of germinal centers per mm2 and the area of each germinal center (as averaged across ten low-power fields) are shown. Scale bar, 250 μm. () We cultured splenic CD19+ B cells from 2–3-month-old wild-type and Pep619W littermates for 48 h with IgM antibody or LPS and proliferation evaluated them after a 16-h 3[H]thymidine pulse. Values represent the means ± s.e.m. of quadruplicate cultures. () We assayed immunoglobulin levels by Luminex in sera from 8-month-old mice. Values represent individual wild-type mice or Pep619W littermates, with mean value shown by the dark bar. () Nitrophenyl (NP) (T dependent) and TNP (T independent) antibody titers from age matched 2–3-month-old wild-type and Pep619W mice (four per group) immunized with NP-CG or TNP-Ficoll (with alum). () Graph showing mean fluorescence intensity (MFI) for CD40 and CD86 staining of unstimulated! or LPS-stimulated bone-marrow–derived dendritic cells from 3-month-old mice. () Histograms showing percentages and MFI of CD40-stained cells within the splenic and lymph node CD11chigh population. () Quantitation of CD11chigh dendritic cell numbers in individual 3–8-month-old wild-type and Pep619W littermates. Values represent means ± s.e.m. () Flow cytometric analysis of Pep619W and wild-type bone-marrow–derived dendritic cells pulsed with ovalbumin or OVA323–339 peptide and co-cultured for 4 days with carboxy-fluorescein diacetate succinimidyl ester–labeled CD4+ OT-II T cells. The percentages of dividing cells are shown. () We pulsed wild-type and Pep619W CD11c+ dendritic cells for 2 h with OVA peptide, co-cultured them for 4 days with CD4+ OT-II T cells and assayed them for [3H]thymidine incorporation. Values represent the mean ± s.e.m. of three replicate cultures. () We stimulated wild-type and Pep619W bone-marrow–derived dendritic cells with LPS for 24 h! and assayed the supernatants for IL-12 levels. All data are r! epresentative of at least six independent experiments. Values in , and represent means ± s.e.m. * Figure 3: Levels and stability of Pep are reduced in Pep619W mice. () We immunoprecipitated lysates prepared from thymocytes of wild-type (WT) and Pep619W (619W) mice with Pep or Csk antibodies and then subjected them to sequential immunoblotting analysis with Pep and Csk antibodies. () We subjected lysates prepared from the thymus of wild-type, Pep619W homozygous and Pep619W heterozygous mice and from bone marrow-derived dendritic cells from wild-type and Pep619W mice to sequential immunoblotting analysis with Pep and actin antibodies. () We cultured thymocytes from 6-week-old wild-type and Pep619W mice for 24 h with CD3 and CD28 antibodies and IL-2, pulse labeled them for 1 h with 35S-L-methionine and chased them for the indicated times in culture medium. We then lysed the cells and subjected them to immunoprecipitation with Pep antibody followed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and autoradiography. Actual band intensities as quantitated using US National Institutes of Health Image J Software are sho! wn below each lane, and the relative band intensities expressed as a percent of band intensity at time 0 are shown in the graph. () We cultured thymocytes harvested from wild-type and Pep619W mice for 24 h with CD3/CD28 antibodies and IL-2 and for an additional 16 h with (+) or without (–) 0.5 μM MG132 or 1 μM ALLN. We then lysed the cells and subjected the lysate proteins to SDS-PAGE and sequential immunoblotting with Pep and actin antibodies. The numbers indicate the relative band intensities expressed as the ratio of Pep band intensity to that of β-actin. * Figure 4: Pep binding to and cleavage by calpain is increased in Pep619W mice. () We immunoprecipitated lysates prepared from BI-141 T cells with Pep antibody followed by SDS-PAGE and sequential immunoblotting with Pep and calpain antibodies. () We immunoprecipitated lysates prepared from Cos7 transfectants expressing Myc (empty vector; M-EV), Myc-tagged Pep619R (M-WT) or Myc-tagged Pep619W (M-619W) with Myc antibody and incubated the immunoprecipitates for 1 h at 4 °C with 10 μg calpain 1 in an in vitro binding assay. We then resolved the complexes by SDS-PAGE and subjected them to immunoblotting analysis with Pep and calpain 1 antibodies. () We immunoprecipitated lysates from Cos7-transfected cells expressing Myc-tagged Pep619R or Pep619W with Myc antibody and incubated the immunoprecipitates for 0–30 min at 37 °C with 0.1 U calpain 1 in CaCl2-containing (activated) or non-activated (NA; no CaCl2) buffer, resolved them over SDS-PAGE and immunoblotted them sequentially with Pep and actin antibodies. () We cultured lymph node T cells from wild-typ! e and Pep619W mice with CD3 (1 μg/ml) and CD28 (0.5 μg/ml) antibodies for 48 h and with 10 μM MG132 for 2 h before cell lysis. We adjusted the lysates to equalize the amounts of Pep619R and Pep619W per sample and then subjected them to Pep antibody immunoprecipitation, followed by sequential immunoblotting with ubiquitin and Pep antibodies. All data are representative of at least five independent experiments. * Figure 5: Reduced stability and level of Lyp620W in human cells. () We cultured GFP-Lyp620R– (wild-type) and GFP-Lyp620W–expressing Jurkat cells for 30 min in methionine-free media, pulse labeled them with 35S-L-methionine and chased them for the indicated times (0.5–2 h). Chasing was performed in the presence (+) or absence (–) of 10 μM MG132 or 50 μM ALLN. We then lysed the cells and immunoprecipitated the lysates with GFP antibody followed by SDS-PAGE and autoradiography. The numbers below show the band intensities. () We immunoprecipitated GFP (empty vector) and GFP-Lyp–expressing Jurkat cells with GFP antibody and subjected them to SDS-PAGE and immunoblotting with Lyp, calpain and GFP antibodies. The bottom panel is cut to show the GFP band in empty-vector–transduced cells. () We immunoprecipitated lysates from GFP-Lyp620R– and GFP-Lyp620W–expressing Cos7 cells with GFP antibody and incubated the immunoprecipitates with calpain 1 in an in vitro binding assay, resolved them over SDS-PAGE and subjected them to immunob! lotting analysis with Lyp and calpain antibodies. () We immunoprecipitated lysates from GFP-Lyp620R– and GFP-Lyp620W–expressing Jurkat cells with GFP antibody and incubated the immunocomplexes for 0–30 min at 37 °C with 0.05 U calpain 1 in CaCl2-containing (activated) or non-activated (NA; no CaCl2) buffer and immunoblotted them sequentially with Lyp and actin antibodies. () We cultured GFP-Lyp620R– (WT) and GFP-Lyp620W–expressing Jurkat cells for 2 h with 10 μM MG132, stimulated them for 5 min with CD3/CD28 antibodies and then lysed them. We then immunoprecipitated the lysates with GFP antibody and subjected them to immunoblotting with Lyp and ubiquitin antibodies. All data shown are representative of at least five independent experiments. The circles denote individual subjects with the CC (non-risk) genotype, and the triangles denote individual subjects with the TT (risk) genotype. * Figure 6: Lyp levels are reduced and activation is increased in peripheral blood mononuclear cells (PBMCs) from individuals homozygous for the PTPN22 risk allele. () We cultured PBMCs from healthy controls and individuals with rheumatoid arthritis (RA)homozygous for the non-risk (CC) or risk (TT) PTPN22 genotypes for 48 h with (STIM) or without (UNSTIM) ConA and then fixed, permeabilized and stained them with Lyp antibody. Histograms representative of stimulated T cell Lyp staining patterns for each genotype and subject group are shown on the left with the percentage of positive (Lyphi) cells indicated (the shaded area represents the isotype control). The graph shows the percentages of Lyp-staining PBMCs for each subject. Subjects included 11 controls (7 CC and 4 TT genotypes) and nine individuals with rheumatoid arthritis (5 CC and 4 TT genotypes). () We stimulated CD4+ T cells and CD19+ B cells from ten individuals with CC and seven individuals with TT PTPN22 genotypes for 2 days with CD3/CD28 or IgM antibodies, respectively, and determined the proliferative responses after a 16-h 3[H]thymidine pulse. Values represent the means ± s! .e.m. of quadruplicate cultures. () We stimulated CD4+ T cells purified from seven PTPN22 CC and four TT age-matched healthy controls with CD3 antibody and fixed, permeabilized and stained them with phospho-Erk antibody. The graph shows the post-stimulatory fold increase in phospho-Erk MFI relative to the baseline level for each subject. () We cultured PBMCs from PTPN22 CC or TT homozygous controls for 48 h with or without 0.5 μM MG132 and 1 μM ALLN and stained them with Lyp antibody. Staining patterns for each genotype are shown for treated (light line) or untreated (dark line) cells. The shaded area represents the isotype control, and the numbers are percentages of Lyphi cells. Author information * Author information * Supplementary information Affiliations * Department of Medicine, University of Toronto, Toronto, Ontario, Canada. * Jinyi Zhang, * Naima Zahir, * Qiuhong Jiang, * Helen Miliotis, * Stephanie Heyraud, * Xianwang Meng, * Baoxia Dong, * Gang Xie, * Frank Qiu, * Edward C Keystone & * Katherine A Siminovitch * Department of Immunology, University of Toronto, Toronto, Ontario, Canada. * Jinyi Zhang, * Naima Zahir, * Qiuhong Jiang, * Helen Miliotis, * Stephanie Heyraud, * Xianwang Meng, * Baoxia Dong, * Gang Xie, * Frank Qiu & * Katherine A Siminovitch * Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. * Jinyi Zhang, * Naima Zahir, * Qiuhong Jiang, * Helen Miliotis, * Stephanie Heyraud, * Xianwang Meng, * Baoxia Dong, * Gang Xie, * Frank Qiu & * Katherine A Siminovitch * Mount Sinai Hospital Samuel Lunenfeld Research Institute, Toronto, Ontario, Canada. * Jinyi Zhang, * Naima Zahir, * Qiuhong Jiang, * Helen Miliotis, * Stephanie Heyraud, * Xianwang Meng, * Baoxia Dong, * Gang Xie, * Frank Qiu & * Katherine A Siminovitch * Toronto General Research Institute, Toronto, Ontario, Canada. * Jinyi Zhang, * Naima Zahir, * Qiuhong Jiang, * Helen Miliotis, * Stephanie Heyraud, * Xianwang Meng, * Baoxia Dong, * Gang Xie, * Frank Qiu & * Katherine A Siminovitch * Ontario Cancer Institute, Princess Margaret Hospital, Toronto, Ontario, Canada. * Zhenyue Hao * Canadian Institutes of Health Research Group in Matrix Dynamics, University of Toronto, Toronto, Ontario, Canada. * Christopher A McCulloch * Laboratory of Developmental Biology, Royal Victoria Hospital, McGill University Health Center, McGill University, Montreal, Quebec, Canada. * Alan C Peterson Contributions J.Z. and K.A.S. designed the study. N.Z. and B.D. performed most of the flow cytometry analyses. Q.J. and A.C.P. derived the knock-in mice. H.M. and S.H. carried out the calpain and ubiquitination analyses. X.M., G.X., F.Q. and Z.H. carried out and/or derived reagents for other protein and RNA analyses. E.C.K., J.Z. and K.A.S. obtained and/or analyzed the human samples. J.Z., C.A.M., E.C.K., A.C.P. and K.A.S. played key roles in data analysis and manuscript preparation. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Katherine A Siminovitch Author Details * Jinyi Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Naima Zahir Search for this author in: * NPG journals * PubMed * Google Scholar * Qiuhong Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Helen Miliotis Search for this author in: * NPG journals * PubMed * Google Scholar * Stephanie Heyraud Search for this author in: * NPG journals * PubMed * Google Scholar * Xianwang Meng Search for this author in: * NPG journals * PubMed * Google Scholar * Baoxia Dong Search for this author in: * NPG journals * PubMed * Google Scholar * Gang Xie Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Qiu Search for this author in: * NPG journals * PubMed * Google Scholar * Zhenyue Hao Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher A McCulloch Search for this author in: * NPG journals * PubMed * Google Scholar * Edward C Keystone Search for this author in: * NPG journals * PubMed * Google Scholar * Alan C Peterson Search for this author in: * NPG journals * PubMed * Google Scholar * Katherine A Siminovitch Contact Katherine A Siminovitch Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (651K) Supplementary Figures 1–4 and Supplementary Table 1. Additional data - A20 (TNFAIP3) deficiency in myeloid cells triggers erosive polyarthritis resembling rheumatoid arthritis
- Nat Genet 43(9):908-912 (2011)
Nature Genetics | Letter A20 (TNFAIP3) deficiency in myeloid cells triggers erosive polyarthritis resembling rheumatoid arthritis * Mourad Matmati1, 2, 11 * Peggy Jacques3, 11 * Jonathan Maelfait1, 2 * Eveline Verheugen3 * Mirjam Kool1, 4 * Mozes Sze1, 2 * Lies Geboes5 * Els Louagie3 * Conor Mc Guire1, 2 * Lars Vereecke1, 2 * Yuanyuan Chu6 * Louis Boon7 * Steven Staelens8, 9 * Patrick Matthys5 * Bart N Lambrecht4 * Marc Schmidt-Supprian6 * Manolis Pasparakis10 * Dirk Elewaut3, 12 * Rudi Beyaert1, 2, 12 * Geert van Loo1, 2, 12 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:908–912Year published:(2011)DOI:doi:10.1038/ng.874Received22 April 2011Accepted29 July 2011Published online14 August 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg A20 (TNFAIP3) is a protein that is involved in the negative feedback regulation of NF-κB signaling in response to specific proinflammatory stimuli in different cell types and has been suggested as a susceptibility gene for rheumatoid arthritis. To define the contribution of A20 to rheumatoid arthritis pathology, we generated myeloid-specific A20-deficient mice and show that specific ablation of Tnfaip3 in myeloid cells results in spontaneous development of a severe destructive polyarthritis with many features of rheumatoid arthritis. Myeloid-A20–deficient mice have high levels of inflammatory cytokines in their serum, consistent with a sustained NF-κB activation and higher TNF production by macrophages. Destructive polyarthritis in myeloid A20 knockout mice was TLR4-MyD88 and IL-6 dependent but was TNF independent. Myeloid A20 deficiency also promoted osteoclastogenesis in mice. Together, these observations indicate a critical and cell-specific function for A20 in the et! iology of rheumatoid arthritis, supporting the idea of developing A20 modulatory drugs as cell-targeted therapies. View full text Figures at a glance * Figure 1: A20myel-KO mice develop spontaneous destructive arthritis. () Pictures of ankles of A20myel-KO and control littermate (WT) mice at the age of 13 weeks (top). Histological section of ankle joints stained with haematoxylin and eosin; magnification, 40× (middle). The detail of the ankle joint region illustrates the infiltration of mononuclear cells, cartilage destruction and bone erosion in A20myel-KO mice; magnification, 100× (bottom). () We clinically scored A20myel-KO mice (n = 6) and control littermates (WT, n = 6) twice a week for development of peripheral arthritis. () The cumulative incidence of arthritis in A20myel-KO (n = 6) and control (WT, n = 6) littermate mice. () Illustration of fluorodeoxyglucose (FDG) positron emission tomography (PET) activation in an A20myel-KO mouse and a control (WT) littermate. () The mean PET activity in hind paws from A20myel-KO mice (n = 5) and control (WT, n = 5) littermates normalized for injected activity. Results are shown as mean ± s.d. *P = 0.004. * Figure 2: A20myel-KO mice have high serum titers of inflammatory and rheumatoid arthritis–associated cytokines. () Levels of IL-6, IL-1β, MCP-1 and TNF in serum of A20myel-KO (n = 8) and control littermate (WT, n = 9) mice at the age of 12 weeks. Error bars, s.e.m. () Relative levels of Il-6, Il-1β, Il23a and Tnf mRNA in cells isolated from joints from A20myel-KO (n = 6) and control littermate (WT, n = 6) mice at the age of 12 weeks. () TNF secretion by resident peritoneal macrophages after 24 h of rest (n = 4). () TNF and IL-6 secretion by thioglycollate-induced peritoneal macrophages after 24 h of rest (n = 4). () Immunoblot analysis for IκBα in extracts of peritoneal macrophages incubated with 100 ng/ml LPS for the indicated durations; we used anti-actin as a loading control. () Kinetics of TNF secretion by A20myel-KO (n = 4) and control (WT, n = 4) peritoneal macrophages after stimulation with LPS for the indicated durations. ND, not detectable. Error bars, s.e.m. *P < 0.05. * Figure 3: The development of arthritis in A20myel-KO mice crucially depends on a TLR4-dependent signaling pathway. () Histological section of an ankle joint of a 25-week-old A20myel-KO (TNFR1+/+) mouse and a double homozygous A20myel-KO TNFR1−/− littermate mouse stained with haematoxylin and eosin; magnification, 40× (top). Detail of the ankle joint region illustrates the infiltration of mononuclear cells, cartilage destruction and bone erosion in double homozygous A20myel-KO TNFR1−/− mice; magnification, 100× (bottom). () Histological section of an ankle joint of a 25-week-old A20myel-KO mouse after systemic administration of LPS-Rs or phosphate-buffered saline (PBS) control; magnification, 40× (top); detail magnification, 100× (bottom). () Histological section of an ankle joint of a 25-week-old A20myel-KO (MyD88+/+) mouse and a double homozygous A20myel-KO MyD88−/− littermate mouse; magnification, 40× (top); detail magnification, 100× (bottom). () Histological section of an ankle joint of a 20-week-old A20myel-KO (RAG2+/−) mouse and a double homozygous A20myel-KO RA! G2−/− littermate mouse; magnification, 40× (top); detail magnification, 100× (bottom). Each picture is representative of at least four mice. * Figure 4: Increased osteoclastogenesis from blood leukocytes of A20myel-KO mice. () Percentage of CD11b+Gr1+ splenocytes of A20myel-KO mice (n = 5) and control littermates (WT, n = 5) as assessed by flow cytometry and gated on living cells. () Absolute numbers of CD115+CD117+ splenocytes within the CD3−CD45R−CD11b+ cell population of A20myel-KO mice (n = 5) and control littermates (WT, n = 4) at the ages of 8 and 42 weeks. (,) We cultured blood leukocytes of A20myel-KO (n = 6) and control mice (WT, n = 6) for 6 days in chamber slides () or on quartz substrates coated with a calcium phosphate film () in the presence of M-CSF (20 ng/ml) and RANKL (100 ng/ml). () After incubation, cultures were fixed and stained for TRAP. We counted TRAP+ multinucleated cells (three or more nuclei) in each cup. Error bars represent the mean ± s.e.m. Representative pictures of TRAP-stained blood leukocyte cultures of both groups are shown. () We removed the cells and assayed resorption of the film by light microscopy. Error bars represent the mean of 10 cups ± s.e.m. R! epresentative pictures of resorption pits are shown. *P < 0.05. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Mourad Matmati & * Peggy Jacques Affiliations * Department for Molecular Biomedical Research, Unit of Molecular Signal Transduction in Inflammation, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium. * Mourad Matmati, * Jonathan Maelfait, * Mirjam Kool, * Mozes Sze, * Conor Mc Guire, * Lars Vereecke, * Rudi Beyaert & * Geert van Loo * Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium. * Mourad Matmati, * Jonathan Maelfait, * Mozes Sze, * Conor Mc Guire, * Lars Vereecke, * Rudi Beyaert & * Geert van Loo * Department of Rheumatology, Laboratory for Molecular Immunology and Inflammation, Ghent University Hospital, Ghent, Belgium. * Peggy Jacques, * Eveline Verheugen, * Els Louagie & * Dirk Elewaut * Department of Respiratory Diseases, Laboratory of Immunoregulation and Mucosal Immunology, Ghent University Hospital, Ghent, Belgium. * Mirjam Kool & * Bart N Lambrecht * Rega Institute, Leuven University, Leuven, Belgium. * Lies Geboes & * Patrick Matthys * Max Planck Institute of Biochemistry, Martinsried, Germany. * Yuanyuan Chu & * Marc Schmidt-Supprian * Bioceros, Utrecht, The Netherlands. * Louis Boon * Medical Image and Signal Processing, Ghent University and Institute for Broadband Technology, Ghent, Belgium. * Steven Staelens * Molecular Imaging Center Antwerp, Antwerp University, Antwerp, Belgium. * Steven Staelens * Institute for Genetics, Centre for Molecular Medicine (CMMC) and Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany. * Manolis Pasparakis * These authors jointly directed this work. * Dirk Elewaut, * Rudi Beyaert & * Geert van Loo Contributions M.M., P.J., J.M., E.V., M.K., M.S., L.G., E.L., C.M.G., L.V., S.S. and G.v.L. performed the experiments. M.M., P.J., C.M.G., S.S., P.M., B.N.L., M.P., D.E., R.B. and G.v.L. analyzed the data. Y.C., L.B., P.M., M.S.-S. and M.P. provided materials. D.E., R.B. and G.v.L. provided ideas and coordinated the project. R.B. and G.v.L. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Geert van Loo or * Rudi Beyaert Author Details * Mourad Matmati Search for this author in: * NPG journals * PubMed * Google Scholar * Peggy Jacques Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan Maelfait Search for this author in: * NPG journals * PubMed * Google Scholar * Eveline Verheugen Search for this author in: * NPG journals * PubMed * Google Scholar * Mirjam Kool Search for this author in: * NPG journals * PubMed * Google Scholar * Mozes Sze Search for this author in: * NPG journals * PubMed * Google Scholar * Lies Geboes Search for this author in: * NPG journals * PubMed * Google Scholar * Els Louagie Search for this author in: * NPG journals * PubMed * Google Scholar * Conor Mc Guire Search for this author in: * NPG journals * PubMed * Google Scholar * Lars Vereecke Search for this author in: * NPG journals * PubMed * Google Scholar * Yuanyuan Chu Search for this author in: * NPG journals * PubMed * Google Scholar * Louis Boon Search for this author in: * NPG journals * PubMed * Google Scholar * Steven Staelens Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick Matthys Search for this author in: * NPG journals * PubMed * Google Scholar * Bart N Lambrecht Search for this author in: * NPG journals * PubMed * Google Scholar * Marc Schmidt-Supprian Search for this author in: * NPG journals * PubMed * Google Scholar * Manolis Pasparakis Search for this author in: * NPG journals * PubMed * Google Scholar * Dirk Elewaut Search for this author in: * NPG journals * PubMed * Google Scholar * Rudi Beyaert Contact Rudi Beyaert Search for this author in: * NPG journals * PubMed * Google Scholar * Geert van Loo Contact Geert van Loo Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Table 1 and Supplementary Figures 1–8. Additional data - The genome of the extremophile crucifer Thellungiella parvula
- Nat Genet 43(9):913-918 (2011)
Nature Genetics | Letter The genome of the extremophile crucifer Thellungiella parvula * Maheshi Dassanayake1, 9 * Dong-Ha Oh1, 9 * Jeffrey S Haas1, 2 * Alvaro Hernandez3 * Hyewon Hong1, 4 * Shahjahan Ali5 * Dae-Jin Yun4 * Ray A Bressan4, 6, 7 * Jian-Kang Zhu6, 7 * Hans J Bohnert1, 4, 7, 8 * John M Cheeseman1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:913–918Year published:(2011)DOI:doi:10.1038/ng.889Received21 March 2011Accepted24 June 2011Published online07 August 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Thellungiella parvula1 is related to Arabidopsis thaliana and is endemic to saline, resource-poor habitats2, making it a model for the evolution of plant adaptation to extreme environments. Here we present the draft genome for this extremophile species. Exclusively by next generation sequencing, we obtained the de novo assembled genome in 1,496 gap-free contigs, closely approximating the estimated genome size of 140 Mb. We anchored these contigs to seven pseudo chromosomes without the use of maps. We show that short reads can be assembled to a near-complete chromosome level for a eukaryotic species lacking prior genetic information. The sequence identifies a number of tandem duplications that, by the nature of the duplicated genes, suggest a possible basis for T. parvula's extremophile lifestyle. Our results provide essential background for developing genomically influenced testable hypotheses for the evolution of environmental stress tolerance. View full text Figures at a glance * Figure 1: Macro synteny between T. parvula contigs and A. thaliana chromosomes. Comparison of the 20 largest T. parvula contigs, c1–c20 () and the 40 next largest contigs, c21–c60 () with A. thaliana chromosomes. A. thaliana chromosomes 1–5 are depicted as red, green, yellow, purple and blue, respectively, with the centromeric regions indicated by black bands. T. parvula contigs are represented by gray blocks. Regions containing more than 75% similarity over a minimum of 2,000 bp with maximum gap allowance of 1,000 bp are connected with lines of colors matching those used for coloring the A. thaliana chromosomes. Ticks in each chromosome or contig block indicate lengths in 1 Mb. The distributions of protein coding regions and repetitive sequences are shown in the outer circles, with the percentage of protein coding genes, DNA transposons and retrotransposons shown in blue, yellow and orange, respectively, with a window size of 0.1 Mb. In the T. parvula contigs, predicted protein coding genes without BLASTn hits (e value < 0.0001) against the A. th! aliana cDNA database are shown in green. * Figure 2: Prediction and annotation of ORFs in the T. parvula draft genome. () Length distribution of predicted T. parvula ORFs. () Comparison of T. parvula predicted ORFs with A. thaliana cDNAs showing the highest BLASTn hit score. The ratio of T. parvula ORF length to A. thaliana cDNA length is given as a percentage. In both and , the vertical axes and numbers above the bars are counts. Comparison of GO 'biological processes' () and GO 'molecular function' categories () between A. thaliana cDNAs (At) and T. parvula predicted ORFs (Tp). The GO categories are as defined in TAIR GOslim (see URLs). Categories with significant differences calculated using a χ2 test, as described in the Online Methods, are indicated as *P < 0.05 or **P < 0.01. In , the GOslim categories 'other metabolic processes' (GO:0008152), 'other physiological processes' (GO:0007582) and 'other biological processes' (GO:0008150) are not shown. The complete list of cDNA and ORF numbers in each of the GO categories and their associated P values are listed in Supplementary Table 8. * Figure 3: Comparison of local tandem duplication (T.D.) events in the A. thaliana genome and the T. parvula draft genome. () Examples of tandem duplications. Examples shown are for the chromosome and contig regions containing HKT1, CBL10 and MYB47. () A Venn diagram showing shared and specific tandem duplication events in T. parvula and A. thaliana. We defined a tandem duplication event as the presence of more than one gene with the same annotation in one location or more than one gene in one location separated by not more than one other gene with a different annotation. The numbers of genes involved in the duplication events are given in parentheses. Tandem duplications of genes with the same annotations in both species are counted as shared events. Comparison of the GO 'biological processes' () and 'molecular function' categories () between T. parvula ORFs and A. thaliana cDNAs for genes showing tandem duplications. The radial axes are the percentages of cDNA or ORFs in each GO category compared to the number of total tandem duplicated cDNA or ORFs. Categories showing significant differences ! are marked as *P < 0.05 or **P < 0.01. The number of tandem duplicated cDNAs or ORFs in each GO category and P values are listed in Supplementary Table 8. The complete list of tandem duplicated cDNAs and ORFs is presented as Supplementary Table 9. * Figure 4: Assembly of the seven chromosomes of T. parvula. () Outline of the ancestral karyotype segments determined by comparative chromosome painting techniques26, 27 in A. thaliana chromosomes. The ancestral karyotype segments, denoted A to X, are drawn to scale based on the A. thaliana genome sequence. () T. parvula contigs aligned to the Eutremeae (n = 7) karyotype schema26, 28 and the ORFs defining the borders of the ancestral karyotype segments. A. thaliana locus IDs showing the highest homology with each ORF are given in parentheses. Shown are T. parvula contigs covering the ancestral karyotype segments. Complete chromosome assignment of the 40 largest contigs, including the contigs covering the centromeric regions, are presented in Supplementary Table 10. () Circos plot presenting the assembly of seven chromosomes. The 40 largest T. parvula contigs are shown. The links and histograms in the outer circles showing the distribution of protein coding genes and repetitive sequences were generated as in Figure 1. The ancestral ka! ryotype segments in the A. thaliana chromosomes and T. parvula contigs and the links connecting them are depicted with colors as in and . Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Entrez Nucleotide * AFAN00000000.1 GenBank * 63843 Sequence Read Archive * SRX047632 * SRX032604 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Maheshi Dassanayake & * Dong-Ha Oh Affiliations * Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA. * Maheshi Dassanayake, * Dong-Ha Oh, * Jeffrey S Haas, * Hyewon Hong, * Hans J Bohnert & * John M Cheeseman * Office of Networked Information Technology, School of Integrative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA. * Jeffrey S Haas * Center for Comparative & Functional Genomics, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA. * Alvaro Hernandez * Division of Applied Life Science (BK21 program), Gyeongsang National University, Jinju, Korea. * Hyewon Hong, * Dae-Jin Yun, * Ray A Bressan & * Hans J Bohnert * Bioscience Core Laboratory-Genomics, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. * Shahjahan Ali * Department of Horticulture & Landscape Architecture, Purdue University, West Lafayette, Indiana, USA. * Ray A Bressan & * Jian-Kang Zhu * Center for Plant Stress Genomics and Biotechnology, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. * Ray A Bressan, * Jian-Kang Zhu & * Hans J Bohnert * Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA. * Hans J Bohnert Contributions M.D., D.-H.O., H.J.B. and J.M.C. designed, performed, analyzed experiments and wrote the paper; J.S.H. compiled programs and wrote custom scripts; A.H. and S.A. performed sequencing; H.H. prepared materials; D.-J.Y., R.A.B. and J.-K.Z. provided materials and intellectual feedback. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Maheshi Dassanayake or * Dong-Ha Oh or * Dae-Jin Yun Author Details * Maheshi Dassanayake Contact Maheshi Dassanayake Search for this author in: * NPG journals * PubMed * Google Scholar * Dong-Ha Oh Contact Dong-Ha Oh Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey S Haas Search for this author in: * NPG journals * PubMed * Google Scholar * Alvaro Hernandez Search for this author in: * NPG journals * PubMed * Google Scholar * Hyewon Hong Search for this author in: * NPG journals * PubMed * Google Scholar * Shahjahan Ali Search for this author in: * NPG journals * PubMed * Google Scholar * Dae-Jin Yun Contact Dae-Jin Yun Search for this author in: * NPG journals * PubMed * Google Scholar * Ray A Bressan Search for this author in: * NPG journals * PubMed * Google Scholar * Jian-Kang Zhu Search for this author in: * NPG journals * PubMed * Google Scholar * Hans J Bohnert Search for this author in: * NPG journals * PubMed * Google Scholar * John M Cheeseman Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 3 (4M) Repetitive sequences in T. parvula draft genome * Supplementary Table 4 (11M) List of T. parvula predicted ORFs and their annotations * Supplementary Table 7 (244K) List of non-coding RNAs in T. parvula draft genome * Supplementary Table 8 (6M) List and comparison of GO annotations of the T. parvula predicted ORFs and A. thaliana cDNAs * Supplementary Table 9 (1004K) Tandem local duplications in the T. parvula draft genome and the A. thaliana genome * Supplementary Table 10 (56K) Assignments of the largest 40 T. parvula contigs in seven chromosomes PDF files * Supplementary Text and Figures (952K) Supplementary Tables 1, 2, 5 and 6 and Supplementary Figures 1–4. Additional data - Corrigendum: Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47
- Nat Genet 43(9):919 (2011)
Nature Genetics | Corrigendum Corrigendum: Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47 * Carl A Anderson * Gabrielle Boucher * Charlie W Lees * Andre Franke * Mauro D'Amato * Kent D Taylor * James C Lee * Philippe Goyette * Marcin Imielinski * Anna Latiano * Caroline Lagacé * Regan Scott * Leila Amininejad * Suzannah Bumpstead * Leonard Baidoo * Robert N Baldassano * Murray Barclay * Theodore M Bayless * Stephan Brand * Carsten Büning * Jean-Frédéric Colombel * Lee A Denson * Martine De Vos * Marla Dubinsky * Cathryn Edwards * David Ellinghaus * Rudolf S N Fehrmann * James A B Floyd * Timothy Florin * Denis Franchimont * Lude Franke * Michel Georges * Jürgen Glas * Nicole L Glazer * Stephen L Guthery * Talin Haritunians * Nicholas K Hayward * Jean-Pierre Hugot * Gilles Jobin * Debby Laukens * Ian Lawrance * Marc Lémann * Arie Levine * Cecile Libioulle * Edouard Louis * Dermot P McGovern * Monica Milla * Grant W Montgomery * Katherine I Morley * Craig Mowat * Aylwin Ng * William Newman * Roel A Ophoff * Laura Papi * Orazio Palmieri * Laurent Peyrin-Biroulet * Julián Panés * Anne Phillips * Natalie J Prescott * Deborah D Proctor * Rebecca Roberts * Richard Russell * Paul Rutgeerts * Jeremy Sanderson * Miquel Sans * Philip Schumm * Frank Seibold * Yashoda Sharma * Lisa A Simms * Mark Seielstad * A Hillary Steinhart * Stephan R Targan * Leonard H van den Berg * Morten Vatn * Hein Verspaget * Thomas Walters * Cisca Wijmenga * David C Wilson * Harm-Jan Westra * Ramnik J Xavier * Zhen Z Zhao * Cyriel Y Ponsioen * Vibeke Andersen * Leif Torkvist * Maria Gazouli * Nicholas P Anagnou * Tom H Karlsen * Limas Kupcinskas * Jurgita Sventoraityte * John C Mansfield * Subra Kugathasan * Mark S Silverberg * Jonas Halfvarson * Jerome I Rotter * Christopher G Mathew * Anne M Griffiths * Richard Gearry * Tariq Ahmad * Steven R Brant * Mathias Chamaillard * Jack Satsangi * Judy H Cho * Stefan Schreiber * Mark J Daly * Jeffrey C Barrett * Miles Parkes * Vito Annese * Hakon Hakonarson * Graham Radford-Smith * Richard H Duerr * Séverine Vermeire * Rinse K Weersma * John D RiouxJournal name:Nature GeneticsVolume: 43,Page:919Year published:(2011)DOI:doi:10.1038/ng0911-919bPublished online29 August 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Genet.43, 246–252 (2011); published online 6 February 2011; corrected after print 11 August 2011 In the version of this article initially published, an affiliation was missing for two authors, Maria Gazouli and Nicholas P. Anagnou. They are also affiliated with the Foundation for Biomedical Research of the Academy of Athens in Athens, Greece. The error has been corrected in the HTML and PDF versions of the article. 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- Nat Genet 43(9):919 (2011)
Nature Genetics | Corrigendum Corrigendum: Interaction between ERAP1 and HLA-B27 in ankylosing spondylitis implicates peptide handling in the mechanism for HLA-B27 in disease susceptibility Journal name:Nature GeneticsVolume: 43,Page:919Year published:(2011)DOI:doi:10.1038/ng0911-919aPublished online29 August 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The Australo-Anglo-American Spondyloarthritis Consortium (TASC) & the Wellcome Trust Case Control Consortium 2 (WTCCC2)Nat. Genet.43, 761–767 (2011); (published online 10 July 2011; corrected after print 11 August 2011 In the version of this article initially published, the name of author Udo Oppermann was incorrectly spelled as Udo Opperman, and the name of author Loukas Moutsianas was incorrectly spelled as Loukas Moutsianis. The errors have been corrected in the HTML and PDF versions of the article. Additional data
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