Latest Articles Include:
- Towards a medical grade human genome sequence
- Nat Genet 43(3):173 (2011)
Nature Genetics | Editorial Towards a medical grade human genome sequence Journal name:Nature GeneticsVolume: 43,Page:173Year published:(2011)DOI:doi:10.1038/ng0311-173Published online24 February 2011 The substantial $10 million purse of the Archon Genomics X PRIZE (AGXP) is being offered for the generation of rapid, accurate and complete human DNA sequences. Because so many genomics researchers have a stake, we offer to help with a process of community consultation to help evolve fair and efficient methods to validate contestant data for the competition. View full text Additional data - Judging the Archon Genomics X PRIZE for whole human genome sequencing
- Nat Genet 43(3):175 (2011)
Nature Genetics | Correspondence Judging the Archon Genomics X PRIZE for whole human genome sequencing * Larry Kedes1, 5, 6 * Edison Liu2, 5 * C Victor Jongeneel3 * Granger Sutton4, 5 * Affiliations * Corresponding authorJournal name:Nature GeneticsVolume: 43,Page:175Year published:(2011)DOI:doi:10.1038/ng0311-175Published online24 February 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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. To the Editor: The $10 million Archon Genomics X PRIZE (AGXP) will be awarded to organizations that can swiftly and inexpensively sequence 100 human genomes at high degrees of accuracy and completeness. The Prize was conceived as an incentive to speed the development of technologies that will make possible personalized genomic medicine. Although the daunting task of making sense out of the myriad differences among human genomes remains, and the unraveling of features linked to or causal for medical and behavioral differences among our species is just beginning, the ability to obtain an accurate and full whole human genome sequence easily, quickly and cheaply must remain a near-term goal to enable such decipherment. Since the AGXP was launched in 2006, there have been important advances in DNA sequencing technologies both in terms of speed and reduction in costs. However, no current human genome sequence is fully complete, fully accurate or certain to contain all rearrangements or information of chromosome phasing (haplotype). Highly repetitive and other regions remain difficult to sequence but are likely critical in defining heritable features. Hence the ideals of the Prize remain as critical for the future of human genetics and genetic medicine as ever. The AGXP and Nature Genetics take this opportunity to announce the creation of a forum open to the worldwide genetics community to help further define appropriate standards for measuring the quality of whole human genome sequencing. The AGXP and its advisors have conceived of a set of tests and standards (known collectively as the AGXP Validation Protocol (VP)) that will be used to evaluate the whole genome sequencing capability of any group wishing to be so tested. The current AGXP VP will be posted online in the citable preprint archive Nature Precedings on 24 February 2011 for comment together with an invitation to send comments to the corresponding author. The archive displays comments on the draft, and more substantial related articles can be independently uploaded within the archive's Human Genome Standards topic collection. Updated draft versions of the AGXP VP will be posted periodically to reflect substantial community input. The community consensus AGXP VP achieved by the refereeing deadline of May 1st will be published in Nature Genetics. The current AGXP VP proposes a sampling method that selects data from a small subset of the 100 human test genomes and uses that data as a measure of the accuracy, completeness and phasing capability of contestants. Sampling remains the method of choice, as the cost of sequencing the sample set itself remains a serious consideration and financial resources are limited. To ensure that the contest is fairly judged, the identity of the sample sets (that is, which of the test genomes are sampled and which segments) must remain a closed secret. Advice will be sought through the posting in Nature Precedings on ways to reduce costs. The Archon X PRIZE Foundation also seeks pro bono assistance from qualified interested parties for both physical characterization of the DNA sample sets and development of the required bioinformatic methodology. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * X PRIZE Foundation, Playa Vista, California, USA. * Larry Kedes * Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore. * Edison Liu * National Center for Supercomputing Applications and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA. * C Victor Jongeneel * J. Craig Venter Institute, Rockville, Maryland, USA. * Granger Sutton * Members of the Archon Genomics X PRIZE Scientific Advisory Board. * Larry Kedes, * Edison Liu & * Granger Sutton * Senior Advisor to the X PRIZE Foundation. * Larry Kedes Competing financial interests L.K. is a paid consultant of the X PRIZE Foundation. L.K., E.L. and G.S. are members of the Archon Genomics X PRIZE Scientific Advisory Board. Corresponding author Correspondence to: * Larry Kedes Author Details * Larry Kedes Contact Larry Kedes Search for this author in: * NPG journals * PubMed * Google Scholar * Edison Liu Search for this author in: * NPG journals * PubMed * Google Scholar * C Victor Jongeneel Search for this author in: * NPG journals * PubMed * Google Scholar * Granger Sutton Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - On the endosomal function and gene nomenclature of human SPE-39
- Nat Genet 43(3):176 (2011)
Nature Genetics | Correspondence On the endosomal function and gene nomenclature of human SPE-39 * Steven W L'Hernault1 * Victor Faundez2 * Affiliations * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Page:176Year published:(2011)DOI:doi:10.1038/ng0311-176Published online24 February 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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. To the Editor: It was a great satisfaction to see our findings on the endosomal function of SPE-39, a Vps33b-interacting protein, recapitulated in a recent study by Cullinane et al.1, as we previously identified a phylogenetically conserved function for SPE-39 in the endocytic route2, 3. However, we think that the journal missed an important opportunity to respect existing gene nomenclature and failed to record the availability of our published antibody resource. In a 2003 study2, we discovered Caenorhabditis elegans spe-39, described the phenotypic consequences of spe-39 loss of function and showed that it had human and Drosophila orthologs, C14ORF133 (FLJ12707) and CG18112. Further work in Drosophila suggested that there was an interaction between Vps33b and SPE-39 orthologs4, 5, but the functional importance of this interaction was not determined. Subsequently, we defined the function of the human C14ORF133 gene product3. First, we showed that human and C. elegans SPE-39 interact with Vps33b and form a complex with the hexameric HOPS complex3. Second, we showed that human SPE-39 (C14ORF133) is required for trafficking from early stages of the endocytic pathway (Rab11 and Rab5 compartments) toward lysosomes both in human and in several cell types in C. elegans3. The paper you published1 stated "As no anti-VIPAR antibody was available, we used transfections of epitope-tagged...". This statement was incorrect as published because we had already produced a monoclonal antibody against residues 406–493 of human SPE-39 (the conceptual translation product of C14ORF133) and it was used for experiments described in our paper3. We have made this antibody available to the scientific community according to US National Institutes of Health and editorial guidelines. The use of this antibody enables experiments that detect endogenous proteins without the need for transgenic constructs that may not faithfully reproduce physiological levels of expression. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Department of Biology, Emory University, Atlanta, Georgia, USA. * Steven W L'Hernault * Department of Cell Biology, Emory University, Atlanta, Georgia, USA. * Victor Faundez Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Steven W L'Hernault or * Victor Faundez Author Details * Steven W L'Hernault Contact Steven W L'Hernault Search for this author in: * NPG journals * PubMed * Google Scholar * Victor Faundez Contact Victor Faundez Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - New insights into oncogenic stress
- Nat Genet 43(3):177-178 (2011)
Nature Genetics | News and Views New insights into oncogenic stress * Kevin M Haigis1 * Alejandro Sweet-Cordero2 * Affiliations * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:177–178Year published:(2011)DOI:doi:10.1038/ng0311-177Published online24 February 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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. Expression of oncogenes in otherwise normal cells often leads to the activation of anti-oncogenic pathways through a poorly understood process described as 'oncogenic stress'. A new study implicates the Jnk pathway signaling in the activation of p53 in response to both K-Ras and Neu oncogene expression. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Kevin M. Haigis is in the Molecular Pathology Unit and Center for Cancer Research at the Massachusetts General Hospital and the Department of Pathology at Harvard Medical School, Charlestown, Massachusetts, USA * Alejandro Sweet-Cordero is in the Department of Pediatrics at Stanford University Medical School, Stanford, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Kevin M Haigis or * Alejandro Sweet-Cordero Author Details * Kevin M Haigis Contact Kevin M Haigis Search for this author in: * NPG journals * PubMed * Google Scholar * Alejandro Sweet-Cordero Contact Alejandro Sweet-Cordero Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - A twist on admixture mapping
- Nat Genet 43(3):178-179 (2011)
Nature Genetics | News and Views A twist on admixture mapping * Stephen J Chanock1Journal name:Nature GeneticsVolume: 43,Pages:178–179Year published:(2011)DOI:doi:10.1038/ng0311-178Published online24 February 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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. A new study uses genome-wide SNP genotypes to identify a subset of children undergoing therapy for acute lymphoblastic leukemia that are at increased risk for relapse. Borrowing from the classical approach of admixture mapping, the work shows how genome-wide assessment of genetic ancestry can be used as a biomarker for disease outcome. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Stephen J. Chanock is at the Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Stephen J Chanock Author Details * Stephen J Chanock Contact Stephen J Chanock Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - The long and winding road from correlation to causation
- Nat Genet 43(3):180-181 (2011)
Nature Genetics | News and Views The long and winding road from correlation to causation * Michel Georges1Journal name:Nature GeneticsVolume: 43,Pages:180–181Year published:(2011)DOI:doi:10.1038/ng0311-180Published online24 February 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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. Follow-up studies of a Crohn's disease risk locus encompassing IRGM have revealed unexpected complexity. A new study shows that a synonymous variant in the IRGM coding region alters a binding site for miR-196 and modulates IRGM-dependent autophagy, adding to the list of possible mechanisms by which this locus influences disease risk. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Michel Georges is at the Unit of Animal Genomics, GIGA-Research & Faculty of Veterinary Medicine, University of Liège (B34), Liège, Belgium. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Michel Georges Author Details * Michel Georges Contact Michel Georges Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Research highlights
- Nat Genet 43(3):183 (2011)
Nature Genetics | Research Highlights Research highlights Journal name:Nature GeneticsVolume: 43,Page:183Year published:(2011)DOI:doi:10.1038/ng0311-183Published online24 February 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * 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. Pancreatic cancer exomes Pancreatic neuroendocrine tumors (PanNETs) are a rare but poorly understood form of pancreatic cancer. Nickolas Papadopoulos and colleagues now report (Science published online, doi:10.1126/science.1200609, 20 January 2011) exome sequencing of 10 PanNETs and a further analysis of 58 PanNETs. They found mutations in 149 genes in the ten tumor exomes. The genes mutated in PanNETs are quite different from those mutated in the more common form of pancreatic cancer, pancreatic ductal adenocarcinoma. Four genes had mutations in at least two of the PanNETs: MEN1, DAXX, PTEN and TSC2. ATRX, which forms a heterodimer with DAXX, was mutated in one tumor. These five genes were then sequenced in 58 other PanNETs. MEN1, a histone methyltransferase component that was previously known to be inactivated in PanNETs, was mutated in 44.1% of tumors. Forty-two point six percent of tumors had mutations in DAXX and ATRX, which are also involved in chromatin remodeling. There was evidence to sugge! st that mutations in MEN1, DAXX and ATRX are associated with better prognosis, as patients with metastatic disease and mutations in these genes survived at least 10 years after diagnosis, whereas over 60% of patients without these mutations died within 5 years of diagnosis. PC Ant genomes The genomes of three ant species were reported recently in the Proceedings of the National Academy of Sciences of the United States of America. Yannick Wurm and colleagues now report the draft genome of the fire ant (Solenopsis invicta), a major pest that causes crop and livestock loss (Proc. Natl. Acad. Sci. USA published online, doi:10.1073/pnas.1009690108, 31 January 2011). Odorant receptors are important for chemical communication, which is a trait that likely contributes to complex social behavior in ants. The authors identified more than 400 putative olfactory receptors, which is the most reported so far in insects. Jurgen Gadau and colleagues report the draft genome of the red harvester ant (Pogonomyrmex barbatus), which has a unique system of genetically controlled queen-worker caste determination (Proc. Natl. Acad. Sci. USA published online, doi:10.1073/pnas.1007901108, 31 January 2011). The authors manually annotated candidate gene families that may be involved in ! this process, including insulin/TOR-signaling genes, yellow/major royal jelly genes, biogenic amine receptors and hexamerin storage proteins. Finally, Neil Tsutsui and colleagues report the draft genome of the Argentine ant (Linepithema humile), a widely distributed invasive species that outcompetes and eliminates native ants (Proc. Natl. Acad. Sci. USA published online, doi:10.1073/pnas.1008617108, 31 January 2011). The authors discovered 231,248 SNPs that should be useful for future analyses of migration patterns of this invasive ant. PC View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Common variants in ZNF365 are associated with both mammographic density and breast cancer risk
- Nat Genet 43(3):185-187 (2011)
Nature Genetics | Brief Communication Common variants in ZNF365 are associated with both mammographic density and breast cancer risk * Sara Lindström1, 2 * Celine M Vachon3 * Jingmei Li4, 5 * Jajini Varghese6 * Deborah Thompson6 * Ruth Warren7 * Judith Brown6 * Jean Leyland6 * Tina Audley6 * Nicholas J Wareham8 * Ruth J F Loos8 * Andrew D Paterson9, 10 * Johanna Rommens10 * Darryl Waggott11 * Lisa J Martin12 * Christopher G Scott3 * V Shane Pankratz3 * Susan E Hankinson2, 13 * Aditi Hazra1, 2, 13 * David J Hunter1, 2, 13 * John L Hopper14 * Melissa C Southey15 * Stephen J Chanock16 * Isabel dos Santos Silva17 * JianJun Liu5 * Louise Eriksson4 * Fergus J Couch18 * Jennifer Stone14 * Carmel Apicella14 * Kamila Czene4 * Peter Kraft1, 2, 19 * Per Hall4 * Douglas F Easton6 * Norman F Boyd12 * Rulla M Tamimi2, 13 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:185–187Year published:(2011)DOI:doi:10.1038/ng.760Received27 July 2010Accepted06 January 2011Published online30 January 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg High-percent mammographic density adjusted for age and body mass index is one of the strongest risk factors for breast cancer. We conducted a meta analysis of five genome-wide association studies of percent mammographic density and report an association with rs10995190 in ZNF365 (combined P = 9.6 × 10−10). Common variants in ZNF365 have also recently been associated with susceptibility to breast cancer. View full text Author information * Author information * Supplementary information Affiliations * Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. * Sara Lindström, * Aditi Hazra, * David J Hunter & * Peter Kraft * Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. * Sara Lindström, * Susan E Hankinson, * Aditi Hazra, * David J Hunter, * Peter Kraft & * Rulla M Tamimi * Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA. * Celine M Vachon, * Christopher G Scott & * V Shane Pankratz * Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. * Jingmei Li, * Louise Eriksson, * Kamila Czene & * Per Hall * Human Genetics, Genome Institute of Singapore, Singapore. * Jingmei Li & * JianJun Liu * Centre for Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. * Jajini Varghese, * Deborah Thompson, * Judith Brown, * Jean Leyland, * Tina Audley & * Douglas F Easton * Department of Radiology, Addenbrookes Hospital, Cambridge, UK. * Ruth Warren * Medical Research Council (MRC) Epidemiology Unit, Cambridge, UK. * Nicholas J Wareham & * Ruth J F Loos * Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. * Andrew D Paterson * Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada. * Andrew D Paterson & * Johanna Rommens * Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada. * Darryl Waggott * Campbell Family Institute for Breast Cancer Research, Ontario Cancer Institute, Toronto, Ontario, Canada. * Lisa J Martin & * Norman F Boyd * Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. * Susan E Hankinson, * Aditi Hazra, * David J Hunter & * Rulla M Tamimi * Centre for Molecular, Environmental, Genetic and Analytic Epidemiology School of Population Health, The University of Melbourne, Melbourne, Australia. * John L Hopper, * Jennifer Stone & * Carmel Apicella * Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Melbourne, Australia. * Melissa C Southey * Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. * Stephen J Chanock * Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK. * Isabel dos Santos Silva * Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA. * Fergus J Couch * Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. * Peter Kraft Contributions S.L., C.M.V., J.L.H., P.K., P.H., D.F.E., N.F.B. and R.M.T. designed and executed the study. S.L., J. Li, J.V., A.D.P., D.W., C.G.S., V.S.P., J.S., K.C. and P.K. led the statistical analysis. C.M.V., D.T., R.W., J.B., J. Leyland, T.A., N.J.W., R.J.F.L., A.D.P., L.J.M., S.E.H., A.H., D.J.H., J.L.H., M.C.S., S.J.C., I.d.S.S., J. Liu, L.E., F.J.C., C.A., K.C., P.H., D.F.E., N.F.B. and R.M.T. collected and provided data to the initial GWAS analysis and replication studies. S.L., C.M.V., J. Li, D.T., R.J.F.L., J.L.H., J.S., D.F.E. and R.M.T. wrote the manuscript with contributions from all the authors. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Rulla M Tamimi Author Details * Sara Lindström Search for this author in: * NPG journals * PubMed * Google Scholar * Celine M Vachon Search for this author in: * NPG journals * PubMed * Google Scholar * Jingmei Li Search for this author in: * NPG journals * PubMed * Google Scholar * Jajini Varghese Search for this author in: * NPG journals * PubMed * Google Scholar * Deborah Thompson Search for this author in: * NPG journals * PubMed * Google Scholar * Ruth Warren Search for this author in: * NPG journals * PubMed * Google Scholar * Judith Brown Search for this author in: * NPG journals * PubMed * Google Scholar * Jean Leyland Search for this author in: * NPG journals * PubMed * Google Scholar * Tina Audley Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas J Wareham Search for this author in: * NPG journals * PubMed * Google Scholar * Ruth J F Loos Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew D Paterson Search for this author in: * NPG journals * PubMed * Google Scholar * Johanna Rommens Search for this author in: * NPG journals * PubMed * Google Scholar * Darryl Waggott Search for this author in: * NPG journals * PubMed * Google Scholar * Lisa J Martin Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher G Scott Search for this author in: * NPG journals * PubMed * Google Scholar * V Shane Pankratz Search for this author in: * NPG journals * PubMed * Google Scholar * Susan E Hankinson Search for this author in: * NPG journals * PubMed * Google Scholar * Aditi Hazra Search for this author in: * NPG journals * PubMed * Google Scholar * David J Hunter Search for this author in: * NPG journals * PubMed * Google Scholar * John L Hopper Search for this author in: * NPG journals * PubMed * Google Scholar * Melissa C Southey Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen J Chanock Search for this author in: * NPG journals * PubMed * Google Scholar * Isabel dos Santos Silva Search for this author in: * NPG journals * PubMed * Google Scholar * JianJun Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Louise Eriksson Search for this author in: * NPG journals * PubMed * Google Scholar * Fergus J Couch Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer Stone Search for this author in: * NPG journals * PubMed * Google Scholar * Carmel Apicella Search for this author in: * NPG journals * PubMed * Google Scholar * Kamila Czene Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Kraft Search for this author in: * NPG journals * PubMed * Google Scholar * Per Hall Search for this author in: * NPG journals * PubMed * Google Scholar * Douglas F Easton Search for this author in: * NPG journals * PubMed * Google Scholar * Norman F Boyd Search for this author in: * NPG journals * PubMed * Google Scholar * Rulla M Tamimi Contact Rulla M Tamimi Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (372K) Supplementary Figures 1 and 2, Supplementary Tables 1–6 and Supplementary Methods Additional data - TTC21B contributes both causal and modifying alleles across the ciliopathy spectrum
- Nat Genet 43(3):189-196 (2011)
Nature Genetics | Article TTC21B contributes both causal and modifying alleles across the ciliopathy spectrum * Erica E Davis1, 2 * Qi Zhang3 * Qin Liu3 * Bill H Diplas1 * Lisa M Davey1 * Jane Hartley4 * Corinne Stoetzel5 * Katarzyna Szymanska6 * Gokul Ramaswami7 * Clare V Logan6 * Donna M Muzny8 * Alice C Young9 * David A Wheeler8 * Pedro Cruz9 * Margaret Morgan8 * Lora R Lewis8 * Praveen Cherukuri9 * Baishali Maskeri9 * Nancy F Hansen9 * James C Mullikin9 * Robert W Blakesley9 * Gerard G Bouffard9 * NISC Comparative Sequencing Program9 * Gabor Gyapay10 * Susanne Rieger11 * Burkhard Tönshoff11 * Ilse Kern12 * Neveen A Soliman13 * Thomas J Neuhaus14 * Kathryn J Swoboda15, 16 * Hulya Kayserili17 * Tomas E Gallagher18 * Richard A Lewis19, 20, 21, 22 * Carsten Bergmann23, 24 * Edgar A Otto7 * Sophie Saunier25 * Peter J Scambler26 * Philip L Beales26 * Joseph G Gleeson27 * Eamonn R Maher4 * Tania Attié-Bitach28 * Hélène Dollfus5 * Colin A Johnson6 * Eric D Green9 * Richard A Gibbs8 * Friedhelm Hildebrandt7, 29 * Eric A Pierce3 * Nicholas Katsanis1, 2, 30 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:189–196Year published:(2011)DOI:doi:10.1038/ng.756Received15 November 2010Accepted22 December 2010Published online23 January 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 Ciliary dysfunction leads to a broad range of overlapping phenotypes, collectively termed ciliopathies. This grouping is underscored by genetic overlap, where causal genes can also contribute modifier alleles to clinically distinct disorders. Here we show that mutations in TTC21B, which encodes the retrograde intraflagellar transport protein IFT139, cause both isolated nephronophthisis and syndromic Jeune asphyxiating thoracic dystrophy. Moreover, although resequencing of TTC21B in a large, clinically diverse ciliopathy cohort and matched controls showed a similar frequency of rare changes, in vivo and in vitro evaluations showed a significant enrichment of pathogenic alleles in cases (P < 0.003), suggesting that TTC21B contributes pathogenic alleles to ~5% of ciliopathy cases. Our data illustrate how genetic lesions can be both causally associated with diverse ciliopathies and interact in trans with other disease-causing genes and highlight how saturated resequencing follow! ed by functional analysis of all variants informs the genetic architecture of inherited disorders. View full text Figures at a glance * Figure 1: In vivo assay of TTC21B variants in mid-somitic zebrafish embryos. () Lateral and dorsal views of ttc21b morphants. Morpholino (MO)-based suppression of ttc21b results in gastrulation defects. Class I is defined as having a shortened anterior-posterior body axis with small anterior structures and mild somite defects. Class II is defined as having a severely shortened body axis, severely affected anterior structures, broadening and kinking of the notochord, thinning and widening of the somites and tail extension defects. () In vivo rescue assay of ttc21b MO with human mRNA. Co-injection of wildtype (WT) human TTC21B with ttc21b translation-blocking MO (tb-MO) results in significant rescue at the ten-somite stage, whereas mRNAs encoding missense alleles result in either partial rescue (p.Pro209Leu, p.Arg411Gly or p.Thr1103Arg) or results that are indistinguishable from MO alone (p.Ile1208Ser). χ2 values for rescue compared to WT are denoted as *P < 0.05 or ***P < 0.0001. () Whole embryo flat mounts hybridized in situ with krox20, pax2 and my! oD riboprobes. Measurements reflect phenotypes at two different axes: length of the notochord as indicated by adaxial cell labeling (l) and width spanning from the lateral ends of the fifth appreciable somites (w), expressed as a ratio (w/l). () Quantitative morphological data for age-matched embryos, measured as indicated in . The variants shown are significantly different from WT rescue. Student's t-test values for rescue compared to WT are denoted as *P < 0.05. Error bars show s.e.m. * Figure 2: In vitro rescue assay of cilia length defects in mIMCD3-Ttc21b shRNA cells. () Immunofluorescent staining of mIMCD3-EV or mIMCD3-Ttc21b shRNA cells transfected transiently with plasmids encoding wildtype (WT) or mutant versions of pCAG-V5-TTC21B-IRES-EGFP constructs demonstrate failure to rescue shortened cilia phenotypes. We detected cilia and centrosomes with anti-acetylated α-tubulin and anti-γ-tubulin (red), green signal indicates transfected cells (GFP) and nuclei are stained with Hoechst dye (blue). Asterisks indicate cilia on transiently transfected cells, dashed boxes depict inset. EV, empty vector. Horizontal scale bars, 10 μm; vertical scale bars, 4 μm (insets). () Quantification of cilia length measurements. Whereas wildtype TTC21B rescues the cilia length defects induced by Ttc21b shRNA, mutant proteins result in either partial rescue (p.Pro209Leu, p.Arg411Gly or p.Thr1103Arg) or are indistinguishable from shRNA cells alone (p.Ile1208Ser). Student's t-test values for rescue compared to WT are denoted as *P < 0.0001. Green bar, mIMCD3! -EV cells; blue bars, mIMCD3-Ttc21b shRNA cells; error bars, s.e.m. (see Supplementary Table 6 for measurement data). () Protein stability defects for some TTC21B missense variants. Compared to wildtype, p.Pro209Leu and p.Arg411Gly result in diminished levels of TTC21B; we did not detect the p.Ile1208Ser protein (see Supplementary Table 7 for densitometry data). Na+/K+-ATPase was used as a loading control. NT, not transfected. () Transiently transfected pCAG-V5-TTC21B-IRES-EGFP constructs express at similar levels in mIMCD3 cells. RT-PCR data are shown for human TTC21B amplified from complementary DNA (cDNA) generated from total RNA extracted from mIMCD3 cells 72 h post transfection. Murine β-actin was used as a loading control. * Figure 3: TTC21B mutant proteins mislocalize in photoreceptor sensory cilia. We transfected neonatal rat retinas with expression plasmids encoding V5-tagged wildtype (WT) and mutant TTC21B cDNAs and an IRES-EGFP cassette using in vivo electroporation. Four weeks post transfection, the transfected portions of the retinas (EGFP-positive) were stained with V5 antibody (red). The images shown are three dimensional reconstructions of confocal image stacks. Grids are included to show perspective; grid size is 8.2 mm in all images. Wildtype TTC21B localizes to the transition zones of photoreceptor cilia in transfected cells. The p.Arg411Gly mutant protein localized predominantly to the transition zone of transfected cells but more diffusely than the wildtype protein. In contrast, p.Pro209Leu and p.Thr1103Arg mutant proteins were present both in transition zones and inner segments as well as cell bodies of the transfected cells. We did not detect p.Ile1208Ser protein in transfected (GFP-positive) photoreceptor cells. The choroid (Ch) is visible in some image! s due to detection of mouse immunoglobulin in choroidal blood vessels by the mouse secondary antibody used. White arrows indicate examples. IS, inner segment; ONL, outer nuclear layer. * Figure 4: Summary of all TTC21B variants detected. () Pedigrees of six ciliopathy families in which two TTC21B mutations are sufficient to explain disease. JATD, Jeune asphyxiating thoracic dystrophy; NPHP, nephronophthisis. Filled circles or squares indicate individuals clinically diagnosed with a ciliopathy; unfilled circles or squares indicates phenotypically normal individuals. () Schematic of the human TTC21B genomic locus on chromosome 2; black boxes represent the 29 exons. () Human TTC21B protein is depicted as a black line, with tetratricopeptide (TPR) domains shown in blue. () All previously unreported variants detected by medical resequencing of TTC21B in our cohort of 753 ciliopathy cases and 398 controls are shown with respect to their genomic and protein locations (dashed lines from and ). Overall pathogenicity of each variant as determined by in vivo functional assay (Supplementary Table 2) is indicated with asterisks. Green, benign; orange, hypomorphic; red, null. Boxes around asterisks indicate that a variant! was detected in more than one individual. With the exception of p.Pro209Leu, all alleles were detected in heterozygosity. * Figure 5: In vivo modeling of TTC21B genetic interaction with other ciliopathy loci. Zebrafish ttc21b interacts genetically with all loci known to contribute to disease in our ciliopathy cohort, including bbs1, bbs2, bbs4, bbs6, bbs7, bbs10, bbs12, tmem216, nphp4, cc2d2a, rpgrip1l, mks1 and cep290 (Table 1). Co-injection of a subeffective dose of ttc21b morpholino with any other single ciliopathy morpholino markedly exacerbates either the overall penetrance of gastrulation phenotypes (in comparison to the same doses injected alone) or a specific severity class. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Entrez Nucleotide * NM_024753 * NM_001128258 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Center for Human Disease Modeling, Department of Cell Biology, Duke University Medical Center, Durham, North Carolina, USA. * Erica E Davis, * Bill H Diplas, * Lisa M Davey & * Nicholas Katsanis * Department of Pediatrics, Duke University Medical Center, Durham, North Carolina, USA. * Erica E Davis & * Nicholas Katsanis * F.M. Kirby Center for Molecular Ophthalmology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Qi Zhang, * Qin Liu & * Eric A Pierce * Department of Medical and Molecular Genetics, Institute of Biomedical Research, University of Birmingham, Birmingham, UK. * Jane Hartley & * Eamonn R Maher * Laboratoire de Génétique Médicale EA3949, Avenir INSERM, Université de Strasbourg, Strasbourg, France. * Corinne Stoetzel & * Hélène Dollfus * Section of Ophthalmology and Neurosciences, Leeds Institute of Molecular Medicine, St. James's University Hospital, Leeds, UK. * Katarzyna Szymanska, * Clare V Logan & * Colin A Johnson * Department of Pediatrics, University of Michigan, Ann Arbor, Michigan, USA. * Gokul Ramaswami, * Edgar A Otto & * Friedhelm Hildebrandt * Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA. * Donna M Muzny, * David A Wheeler, * Margaret Morgan, * Lora R Lewis & * Richard A Gibbs * National Institutes of Health Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA. * NISC Comparative Sequencing Program, * Alice C Young, * Pedro Cruz, * Praveen Cherukuri, * Baishali Maskeri, * Nancy F Hansen, * James C Mullikin, * Robert W Blakesley, * Gerard G Bouffard & * Eric D Green * Genoscope Centre National de Séquençage, Crémieux, Evry, France. * Gabor Gyapay * University Children's Hospital, Heidelberg, Germany. * Susanne Rieger & * Burkhard Tönshoff * Department of Pediatrics, University Hospital of Geneva, Switzerland. * Ilse Kern * Department of Pediatrics, Kasralainy School of Medicine, Cairo University, Cairo, Egypt. * Neveen A Soliman * Division of Nephrology, University Children's Hospital Zurich, Zurich, Switzerland. * Thomas J Neuhaus * Department of Neurology, University of Utah School of Medicine, Salt Lake City, Utah, USA. * Kathryn J Swoboda * Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, Utah, USA. * Kathryn J Swoboda * Istanbul University, Istanbul Medical Faculty, Medical Genetics, Millet Caddesi, Capa, Fatih, Istanbul, Turkey. * Hulya Kayserili * Developmental Pediatrics, University of Hawaii at Manoa, Honolulu, Hawaii, USA. * Tomas E Gallagher * Department of Ophthalmology, Baylor College of Medicine, Houston, Texas, USA. * Richard A Lewis * Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA. * Richard A Lewis * Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA. * Richard A Lewis * Department of Medicine, Baylor College of Medicine, Houston, Texas, USA. * Richard A Lewis * Center for Human Genetics, Bioscientia, Ingelheim, Germany. * Carsten Bergmann * Department of Human Genetics, Rheinisch-Westfälische Technische Hochschule (RWTH) University of Aachen, Aachen, Germany. * Carsten Bergmann * Inserm U-983, Hôpital Necker-Enfants Malades, Université Paris Descartes, Paris, France. * Sophie Saunier * Molecular Medicine Unit, Institute of Child Health, University College London, London, UK. * Peter J Scambler & * Philip L Beales * Department of Neurosciences, Howard Hughes Medical Institute, University of California, San Diego, La Jolla, California, USA. * Joseph G Gleeson * Département de Génétique et INSERM U-781, Hôpital Necker-Enfants Malades, Université Paris Descartes, Paris, France. * Tania Attié-Bitach * Howard Hughes Medical Institute and Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA. * Friedhelm Hildebrandt * Wilmer Eye Institute and Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Nicholas Katsanis Consortia * NISC Comparative Sequencing Program Contributions Experiments were designed by E.E.D., E.A.P. and N.K. Mutational screening, analysis and confirmation was conducted by E.E.D., J.H., C.S., K.S., G.R., C.V.L., D.M.M., A.C.Y., D.A.W., P. Cruz., M.M., L.R.L., P. Cherukuri., B.M., N.F.H., J.C.M., R.W.B., G.G.B., the NISC Comparative Sequencing Program, G.G., E.A.O., J.G.G., T.A.-B., C.A.J., E.D.G. and R.A.G. Ciliopathy case samples were provided by J.H., S.R., B.T., I.K., N.A.S., T.J.N., K.J.S., H.K., T.E.G., R.A.L., C.B., S.S., P.J.S., P.L.B., J.G.G., E.R.M., T.A.-B., H.D., C.A.J., F.H. and N.K. In vivo and in vitro functional studies were carried out by E.E.D., Q.Z., Q.L., B.H.D. and L.M.D. The manuscript was written by E.E.D., Q.Z., E.A.P. and N.K. with helpful comments from C.B., J.G.G., E.R.M., T.A.-B., C.A.J. and F.H. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Nicholas Katsanis Author Details * Erica E Davis Search for this author in: * NPG journals * PubMed * Google Scholar * Qi Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Qin Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Bill H Diplas Search for this author in: * NPG journals * PubMed * Google Scholar * Lisa M Davey Search for this author in: * NPG journals * PubMed * Google Scholar * Jane Hartley Search for this author in: * NPG journals * PubMed * Google Scholar * Corinne Stoetzel Search for this author in: * NPG journals * PubMed * Google Scholar * Katarzyna Szymanska Search for this author in: * NPG journals * PubMed * Google Scholar * Gokul Ramaswami Search for this author in: * NPG journals * PubMed * Google Scholar * Clare V Logan Search for this author in: * NPG journals * PubMed * Google Scholar * Donna M Muzny Search for this author in: * NPG journals * PubMed * Google Scholar * Alice C Young Search for this author in: * NPG journals * PubMed * Google Scholar * David A Wheeler Search for this author in: * NPG journals * PubMed * Google Scholar * Pedro Cruz Search for this author in: * NPG journals * PubMed * Google Scholar * Margaret Morgan Search for this author in: * NPG journals * PubMed * Google Scholar * Lora R Lewis Search for this author in: * NPG journals * PubMed * Google Scholar * Praveen Cherukuri Search for this author in: * NPG journals * PubMed * Google Scholar * Baishali Maskeri Search for this author in: * NPG journals * PubMed * Google Scholar * Nancy F Hansen Search for this author in: * NPG journals * PubMed * Google Scholar * James C Mullikin Search for this author in: * NPG journals * PubMed * Google Scholar * Robert W Blakesley Search for this author in: * NPG journals * PubMed * Google Scholar * Gerard G Bouffard Search for this author in: * NPG journals * PubMed * Google Scholar * Gabor Gyapay Search for this author in: * NPG journals * PubMed * Google Scholar * Susanne Rieger Search for this author in: * NPG journals * PubMed * Google Scholar * Burkhard Tönshoff Search for this author in: * NPG journals * PubMed * Google Scholar * Ilse Kern Search for this author in: * NPG journals * PubMed * Google Scholar * Neveen A Soliman Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas J Neuhaus Search for this author in: * NPG journals * PubMed * Google Scholar * Kathryn J Swoboda Search for this author in: * NPG journals * PubMed * Google Scholar * Hulya Kayserili Search for this author in: * NPG journals * PubMed * Google Scholar * Tomas E Gallagher Search for this author in: * NPG journals * PubMed * Google Scholar * Richard A Lewis Search for this author in: * NPG journals * PubMed * Google Scholar * Carsten Bergmann Search for this author in: * NPG journals * PubMed * Google Scholar * Edgar A Otto Search for this author in: * NPG journals * PubMed * Google Scholar * Sophie Saunier Search for this author in: * NPG journals * PubMed * Google Scholar * Peter J Scambler Search for this author in: * NPG journals * PubMed * Google Scholar * Philip L Beales Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph G Gleeson Search for this author in: * NPG journals * PubMed * Google Scholar * Eamonn R Maher Search for this author in: * NPG journals * PubMed * Google Scholar * Tania Attié-Bitach Search for this author in: * NPG journals * PubMed * Google Scholar * Hélène Dollfus Search for this author in: * NPG journals * PubMed * Google Scholar * Colin A Johnson Search for this author in: * NPG journals * PubMed * Google Scholar * Eric D Green Search for this author in: * NPG journals * PubMed * Google Scholar * Richard A Gibbs Search for this author in: * NPG journals * PubMed * Google Scholar * Friedhelm Hildebrandt Search for this author in: * NPG journals * PubMed * Google Scholar * Eric A Pierce Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas Katsanis Contact Nicholas Katsanis Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (7M) Supplementary Tables 1–9 and Supplementary Figures 1–4 Additional data - Mutations in lectin complement pathway genes COLEC11 and MASP1 cause 3MC syndrome
- Nat Genet 43(3):197-203 (2011)
Nature Genetics | Article Mutations in lectin complement pathway genes COLEC11 and MASP1 cause 3MC syndrome * Caroline Rooryck1, 13 * Anna Diaz-Font1, 13 * Daniel P S Osborn1, 13 * Elyes Chabchoub2 * Victor Hernandez-Hernandez1 * Hanan Shamseldin3 * Joanna Kenny4 * Aoife Waters1 * Dagan Jenkins1 * Ali Al Kaissi5 * Gabriela F Leal6 * Bruno Dallapiccola7 * Franco Carnevale8 * Maria Bitner-Glindzicz4 * Melissa Lees4 * Raoul Hennekam9 * Philip Stanier10 * Alan J Burns10 * Hilde Peeters2 * Fowzan S Alkuraya3, 11, 12 * Philip L Beales1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:197–203Year published:(2011)DOI:doi:10.1038/ng.757Received10 September 2010Accepted15 December 2010Published online23 January 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 3MC syndrome has been proposed as a unifying term encompassing the overlapping Carnevale, Mingarelli, Malpuech and Michels syndromes. These rare autosomal recessive disorders exhibit a spectrum of developmental features, including characteristic facial dysmorphism, cleft lip and/or palate, craniosynostosis, learning disability and genital, limb and vesicorenal anomalies. Here we studied 11 families with 3MC syndrome and identified two mutated genes, COLEC11 and MASP1, both of which encode proteins in the lectin complement pathway (collectin kidney 1 (CL-K1) and MASP-1 and MASP-3, respectively). CL-K1 is highly expressed in embryonic murine craniofacial cartilage, heart, bronchi, kidney and vertebral bodies. Zebrafish morphants for either gene develop pigmentary defects and severe craniofacial abnormalities. Finally, we show that CL-K1 serves as a guidance cue for neural crest cell migration. Together, these findings demonstrate a role for complement pathway factors in fundam! ental developmental processes and in the etiology of 3MC syndrome. View full text Figures at a glance * Figure 1: Summary of mutations identified in 3MC syndrome. () Locations of five different mutations identified in COLEC11 and corresponding protein CL-K1. () Locations of three different mutations identified in MASP1 and encoded protein. Ex, exon. * Figure 2: Immunolocalization of CL-K1 protein with polyclonal antibody. () In E13.5 mouse embryo whole sections (scale bars: main panel, 1 mm; inset panels, 500 μm), CL-K1 is expressed in the developing nasal septum (I), cartilage primordium of the basisphenoid bone (II, arrow), Meckel's cartilage (II, mc), myocardium (III), bronchioles and vertebrae (IV). () At E13.5, CL-K1 is highly expressed in the palatal mesenchyme (arrow) and epithelium (arrow head). At E15.5, CL-K1 is downregulated in the fused palatal shelf (asterisk), whereas expression is maintained in the palatal shelf epithelium (arrow head) (scale bar, 500 μm). () ATDC5 cells showing immunocolocalization of endogenous CL-K1 and Golgi marker 58K (scale bar, 10 μm). () colec11 in situ hybridization in zebrafish at ten-somite (10s), 24 h.p.f. and 48 h.p.f. stages. At the ten-somite stage, expression is localized to the cranial paraxial mesendoderm (arrow heads and transverse section, inset). Eyes, asterisks; relative position of the section, dotted line. Scale bar, 100 μm. At 24 h.! p.f., transcripts were detected in the glomeruli (arrow heads) and cranial ventral midline (arrow). Inset, dorsal flatmount at 24 h.p.f. highlighting expression in glomeruli and pronephric ducts (PNDs, black arrow). At 48 h.p.f., colec11 remained in the glomeruli (arrow head), weakly in the PNDs (black arrow) and strongly in the liver (white arrow). Scale bar, 200 μm. * Figure 3: Defects in zebrafish colec11 morphants. () colec11 zebrafish morphants at higher (4 ng) and lower (3 ng) doses of colec11 morpholino. Note dose-dependent loss of medial trunk pigmentation (insets, ×1.5 magnification). Scale bar, 500 μm. () Alcian blue cartilage staining in colec11 morphants (3 and 4 ng) at 5 d.p.f. (scale bar, 200 μm). () Palate measurements in colec11 morphants (3 ng); Meckel's cartilage (mc) and ceratohyal cartilage (ch). Scale bar, 200 μm. () Graph of ethmoid plate length and width measurements expressed as a ratio against cranial length and width, respectively, in presence or absence of colec11 morpholino. (Control length ratio, 3.164 ± 0.062, n = 8; colec11 MO length ratio, 3.702 ± 0.245, n = 8; control width ratio, 2.563 ± 0.0441, n = 8; colec11 MO width ratio, 2.819 ± 0.168, n = 8; values are mean ± s.e.m.). () Alcian blue cartilage staining of colec11 morphants rescued with human COLEC11 RNA (75 pg) compared with colec11 morphants (3 ng), embryos with control uninjected and COLEC1! 1 RNA injected alone (scale bar, 200 μm). pq, palatoquadrate; mc, Meckel's cartilage. () Graph of length ratio of both pq cartilages plus mc standardized by cranial length. Control (0.821 ± 0.004, n = 51), colec11 MO (0.896 ± 0.009, n = 50), COLEC11 RNA (0.844 ± 0.007, n = 21) and colec11 MO plus COLEC11 RNA (0.820 ± 0.014, n = 16). Values, mean ± s.e.m. One-way ANOVA with Tukey's multiple comparison test. Key angles and measures show cartilage defects. ep, ethmoid plate; t, trabeculae; n, notochord; bp, basal plate; pq, palatoquadrate; cb, ceratobranchials; NS, not significant. * Figure 4: Morphological defects in zebrafish masp1 morphants. () General morphology of masp1 zebrafish morphants with pigmentation defects (arrows). Scale bars: main panel, 500 μm; inset, 200 μm. () Alcian blue cartilage staining at 5 d.p.f. showing cartilage defects in masp1 morphants (6 ng) (second panel from left). Alcian blue staining in morphants double-injected with suboptimal doses of colec11 (2 ng) and masp1 (3 ng) MO (far right panel). We observed a cartilage defect only in double-injected morphants compared with single suboptimal injections of either colec11 or masp1 MO (panels third and fourth from left). * Figure 5: Effects of colec11 and masp1 knockdown on neural crest cell migration in zebrafish. () sox10-myod in situ experiments on zebrafish colec11 and masp1 morphants showing an abnormal distribution of cranial NCCs in the hindbrain of ten-somite-stage embryos (9–10s), with a massive expansion of cells across the midline compared with controls (left, white arrowheads). () Streaming of NCCs into the head and through the somites (arrow heads) was also disrupted in colec11 and masp1 morphants at 24 h.p.f., as indicated by aberrant sox10 expression. Scale bar: main panels, 200 μm; inset panels, 100 μm. () Expression of Sox10:eGFP (green) in the head at 24 h.p.f. highlights disorganized NCCs in colec11 and masp1 morphants. Scale bar, 100 μm. () At 48 h.p.f., clearly defined tracks of Sox10:eGFP (white arrow heads) were detected migrating through tail somites of uninjected control embryos, however both colec11 and masp1 morphants showed abnormal and ectopic migration of NCCs throughout the tail. Scale bar, 100 μm. * Figure 6: Cell migration assays. () Zebrafish in vivo NCC chemoattraction assay. We implanted embryos with either BSA- or CL-K1-coated microbeads (red) into the head of 18 somite-stage embryos that were subsequently grown to 24 h.p.f. In situ hybridization for sox10 (blue). Scale bars: main panels, 100 μm; inset panels, 50 μm. () Recombinant protein assays: representative images of control agarose spots containing either PBS, BSA, A1AT or CL-K1. White dashed lines, border of spot. No cells migrated under the control agarose spots. In contrast, HeLa cells were attracted to the CL-K1 containing spot, migrating through the agarose (black arrowheads). Scale bar, 200 μm. () Average number of cells moving across the agarose border in five independent experiments. () Quail neural tube assay: we placed CL-K1 and PBS control agarose spots on coverslips to which explanted neural tubes were carefully laid adjacent to (but not touching) the spot. Scale bar, 200 μm. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Entrez Nucleotide * NM_024027.3 * NM_139125.3 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Caroline Rooryck, * Anna Diaz-Font & * Daniel P S Osborn Affiliations * Molecular Medicine Unit, University College London Institute of Child Health, London, UK. * Caroline Rooryck, * Anna Diaz-Font, * Daniel P S Osborn, * Victor Hernandez-Hernandez, * Aoife Waters, * Dagan Jenkins & * Philip L Beales * Centre for Human Genetics, University Hospitals of Leuven, Leuven, Belgium. * Elyes Chabchoub & * Hilde Peeters * Developmental Genetics Unit, Department of Genetics, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia. * Hanan Shamseldin & * Fowzan S Alkuraya * Department of Clinical Genetics, Great Ormond Street Hospital, London, UK. * Joanna Kenny, * Maria Bitner-Glindzicz & * Melissa Lees * Ludwig Boltzmann Institute of Osteology, Hanusch Hospital of WGKK and AUVA Trauma Centre, Meidlin 4th Medical Department, Hanusch Hospital, Vienna, Austria. * Ali Al Kaissi * Instituto de Medicina Integral Professor Fernando Figueira, Recife, Brazil. * Gabriela F Leal * Bambino Gesù Children Hospital, Istituto Di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy. * Bruno Dallapiccola * Department of Pediatrics, University of Bari, Bari, Italy. * Franco Carnevale * Department of Pediatrics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. * Raoul Hennekam * Neural Development Unit, University College London (UCL) Institute of Child Health, London, UK. * Philip Stanier & * Alan J Burns * Department of Pediatrics, King Khalid University Hospital and College of Medicine, King Saud University, Riyadh, Saudi Arabia. * Fowzan S Alkuraya * Department of Anatomy and Cell Biology, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia. * Fowzan S Alkuraya Contributions C.R., A.D.-F. and D.P.S.O. planned and carried out experiments, analyzed data and co-wrote the manuscript. E.C., V.H.-H. and A.W. carried out experiments and analyzed data. H.S., J.K. and D.J. carried out experiments. A.A.K., G.F.L., B.D., F.C. and M.L. clinically ascertained patients and samples. M.B.-G. clinically ascertained patients and samples and planned the study. R.H. clinically ascertained patients and samples, planned the study, analyzed data and edited the manuscript. P.S. provided samples and analyzed data. A.J.B. and H.P. planned and carried out experiments and analyzed data. F.S.A. planned the study, ascertained samples, carried out experiments, analyzed data and edited the manuscript. P.L.B. planned and supervised the study, analyzed data, and co-wrote and edited the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Philip L Beales Author Details * Caroline Rooryck Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Diaz-Font Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel P S Osborn Search for this author in: * NPG journals * PubMed * Google Scholar * Elyes Chabchoub Search for this author in: * NPG journals * PubMed * Google Scholar * Victor Hernandez-Hernandez Search for this author in: * NPG journals * PubMed * Google Scholar * Hanan Shamseldin Search for this author in: * NPG journals * PubMed * Google Scholar * Joanna Kenny Search for this author in: * NPG journals * PubMed * Google Scholar * Aoife Waters Search for this author in: * NPG journals * PubMed * Google Scholar * Dagan Jenkins Search for this author in: * NPG journals * PubMed * Google Scholar * Ali Al Kaissi Search for this author in: * NPG journals * PubMed * Google Scholar * Gabriela F Leal Search for this author in: * NPG journals * PubMed * Google Scholar * Bruno Dallapiccola Search for this author in: * NPG journals * PubMed * Google Scholar * Franco Carnevale Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Bitner-Glindzicz Search for this author in: * NPG journals * PubMed * Google Scholar * Melissa Lees Search for this author in: * NPG journals * PubMed * Google Scholar * Raoul Hennekam Search for this author in: * NPG journals * PubMed * Google Scholar * Philip Stanier Search for this author in: * NPG journals * PubMed * Google Scholar * Alan J Burns Search for this author in: * NPG journals * PubMed * Google Scholar * Hilde Peeters Search for this author in: * NPG journals * PubMed * Google Scholar * Fowzan S Alkuraya Search for this author in: * NPG journals * PubMed * Google Scholar * Philip L Beales Contact Philip L Beales Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–11 and Supplementary Tables 1 and 2 Additional data - miRNA regulation of Sdf1 chemokine signaling provides genetic robustness to germ cell migration
- Nat Genet 43(3):204-211 (2011)
Nature Genetics | Article miRNA regulation of Sdf1 chemokine signaling provides genetic robustness to germ cell migration * Alison A Staton1 * Holger Knaut2 * Antonio J Giraldez1, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:204–211Year published:(2011)DOI:doi:10.1038/ng.758Received27 July 2010Accepted14 December 2010Published online23 January 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) function as genetic rheostats to control gene output. Based on their role as modulators, it has been postulated that miRNAs canalize development and provide genetic robustness. Here, we uncover a previously unidentified regulatory layer of chemokine signaling by miRNAs that confers genetic robustness on primordial germ cell (PGC) migration. In zebrafish, PGCs are guided to the gonad by the ligand Sdf1a, which is regulated by the sequestration receptor Cxcr7b. We find that miR-430 regulates sdf1a and cxcr7 mRNAs. Using target protectors, we demonstrate that miR-430–mediated regulation of endogenous sdf1a (also known as cxcl12a) and cxcr7b (i) facilitates dynamic expression of sdf1a by clearing its mRNA from previous expression domains, (ii) modulates the levels of the decoy receptor Cxcr7b to avoid excessive depletion of Sdf1a and (iii) buffers against variation in gene dosage of chemokine signaling components to ensure accurate PGC migration. Our results! indicate that losing miRNA-mediated regulation can expose otherwise buffered genetic lesions leading to developmental defects. View full text Figures at a glance * Figure 1: miR-430 target validation of chemokine signaling genes. () Schematic representation of the experimental setup. Expression of GFP reporters with the 3′ UTRs of putative targets is compared in wildtype and MZdicer embryos. DsRed mRNA lacking a target site was co-injected as a control. To test whether the miR-430 target site plays a role in the regulation, wildtype embryos were also injected with wildtype reporters (wt-3′UTR) or mutant reporters where three bases in the miR-430 target site were mutated (mut-3′UTR). All three miR-430 target sites in the sdf1a 3′ UTR were mutated to generate the mutant reporter. () Quantification of the GFP fluorescence in MZdicer compared to wildtype embryos injected with each GFP reporter mRNA and DsRed control. GFP fluorescence was normalized to the DsRed control. Data are shown as mean ± standard deviation (s.d.). (,) Fluorescence microscopy shows GFP (green) and DsRed (red) expression in 24–28 hpf embryos injected with GFP reporters with sdf1a or cxcr7b 3′ UTR. Endogenous miR-430 rep! resses the expression of each reporter in wildtype but not in MZdicer embryos. Similarly, wildtype embryos injected with the mut-3′UTR reporter fail to repress GFP. The sequences of the wildtype or the mutant target site and miR-430 are shown in Supplementary Figure 2. * Figure 2: Target protectors prevent miRNA-mediated repression of target GFP reporters. () The schematic shows repression of targets by an miRNA and loss of repression of mutated reporters. GFP reporters are protected by target protectors (purple), whereas binding of the control target protector (black) downstream of the miR-430 target site does not prevent miRNA-mediated repression. (,) GFP and DsRed fluorescence in 24–28 hpf embryos injected with GFP reporters. Expression of a wildtype GFP sdf1a 3′ UTR reporter is repressed. Injection of sdf1a target protector (TP1), but not a control target protector, blocks miR-430–mediated repression of the GFP reporter. Expression of the mut-3′UTR GFP reporter with the first (mut1-3′UTR) or all three mutated target sites (mut123-3′UTR) is shown for comparison. () Derepression of the GFP-cxcr7b wt-3′UTR reporter was also observed upon injection of the cxcr7b target protector (TP). Predicted Watson-Crick pairing of the 3′ UTR target sites with each target protector and miR-430 are shown in Supplementary Figu! re 3. * Figure 3: Blocking miR-430–mediated repression of sdf1a and cxcr7b causes PGC mislocalization and expanded sdf1a expression. () Schematic representation of the experimental setup. Injecting the target protector (purple) blocks miRNA-mediated repression, increasing mRNA expression and leading to mislocalization of cells. Co-injecting a morpholino to reduce translation of the target gene (red, AUG MO) rescues the mislocalization phenotype. The inset shows the region of the embryo depicted in and –. (,–) Whole mount in situ of nanos mRNA, labeling PGCs in 24 hpf embryos. Bracket shows correct localization of PGCs. Arrowheads identify mislocalized PGCs. () Quantification of the percentage of embryos with mislocalized PGCs in each experimental condition as indicated. A significantly increased number of target protector–injected embryos have mislocalized PGCs. *P = 1.185 × 10−7 for sdf1a-TP; **P = 2.52 × 10−7 for cxcr7b-TP; two-tailed Fisher's exact test. Error bars, ± s.d. (,) Representative images of PGC mislocalization are shown. (,) Co-injection of a low level of the corresponding AUG M! O rescues the target-protector phenotype (sdf1a AUG MO, 0.01 pM; cxcr7b AUG MO, 0.045 pM). () In situ hybridization to detect sdf1a mRNA. The trunk of embryos at 20 hpf, 22 hpf and 24 hpf are wildtype embryos, embryos injected with sdf1a-TP or MZdicer embryos. Brackets illustrate the extension of the sdf1a expression domain along the pronephric region. () qPCR for sdf1a in 24 hpf wildtype, sdf1a-TP–injected and MZdicer embryos. We observed an increase in expression in the absence of miR-430–mediated repression. () Schematic summary of Sdf1a tail expression and the resulting PGC mislocalization. * Figure 4: miR-430 and Cxcr7b act in a functionally redundant manner to refine Sdf1a expression. (,) Quantification of PGC mislocalization. We injected embryos at the one-cell stage with a morpholino targeting the start site (AUG MO) of cxcr7b () or sdf1a (). We injected these AUG MOs at low concentrations, which were insufficient to completely knock down the transcript and which caused a weak mislocalization phenotype. () Co-injecting sdf1a-TP and cxcr7b AUG MO caused significantly more mismigration than injection of sdf1a-TP or the same amount of the AUG MO alone (*P = 4.08 × 10−3 for sdf1a-TP + 45 fM compared to 45 fM alone; **P = 4.87 × 10−3 for sdf1a-TP + 90 fM compared to 90 fM alone; two-tailed Fisher's exact test), suggesting that miR-430 regulation of sdf1a mRNA can partially compensate for a reduction of cxcr7b. Similarly, co-injecting cxcr7b-TP and sdf1a AUG MO significantly enhances the mislocalization phenotype (*P = 8.93 × 10−4 for cxcr7b-TP + 10 fM compared to 10 fM alone; **P = 0.016 for cxcr7b-TP + 15 fM compared to 15 fM alone; two-tailed Fish! er's exact test), suggesting that regulation of cxcr7b by miR-430 prevents excessive clearance of the sdf1a. Data are shown as mean ± s.d. (,) nanos in situ at 24 hpf to visualize the location of germ cells. Brackets indicate correctly localized PGCs, and arrowheads show mislocalized cells. (,) Scheme representing the predicted effect of the experimental conditions on Sdf1a and Cxcr7b shown in and . The added effect of removing miR-430 targeting and modulation by Cxcr7b supports a functional redundancy of miR-430 and Cxcr7b. * Figure 5: miR-430 buffers against overexpression of the chemokine signaling components. () Schematic representation of the experiment. mRNA encoding the open reading frame for the target gene with either the wildtype 3′ UTR (wt-3′UTR) or a mutant 3′ UTR (mut-3′UTR) with the miR-430 target site mutated was injected at the one-cell stage. PGC localization was assayed at 24 hpf using an in situ for nanos. The rectangle illustrates the region of the embryos shown in , and . (,) Quantification of the percentage of embryos with PGCs outside of the gonad region upon injection of sdf1a mRNA () or cxcr7b mRNA (). We saw a significant difference between transcripts with the wt-3′UTR (blue) and those with the mut-3′UTR (red) for sdf1a (*P = 8.4 × 10−4, **P = 7 × 10−6; two-tailed Fisher's exact test) and cxcr7b (*P = 6.3 × 10−3; two-tailed Fisher's exact test). (,) Representative images of injected embryos are shown. The bracket illustrates the PGCs that are correctly localized. Arrowheads indicate mislocalized PGCs. Error bars, ± s.d. * Figure 6: Regulation by miR-430 guards against variation in gene dosage. () Representative examples of PGC mislocalization, shown by nanos in situ. Brackets show correctly localized PGCs, and arrowheads indicate mislocalized cells. () Quantification of the percentage of embryos with mislocalized PGCs in different experimental conditions as shown. Co-injection of sdf1a-TP, cxcr7b-TP and a low level of translation-blocking morpholino (MO) against cxcr7b (0.02 pM), sdf1a (0.005 pM) or cxcr4b (0.005 pM) caused a significant increase in PGC mismigration. Asterisks denote P < 0.05 (*P = 0.011 for cxcr4b MO; *P = 2.87 × 10−3 for sdf1a MO; and *P = 8.89 × 10−3 for cxcr7b MO; two-tailed Fisher's exact test). A similar effect was seen upon injection of sdf1a-TP and cxcr7b-TP in cxcr4b heterozygous mutants (*P = 7.50 × 10−6) and cxcr7b heterozygous mutants (*P = 1.64 × 10−6) Error bars, ± s.d. * Figure 7: Model of miR-430–mediated repression of chemokine signaling. () Our results are consistent with a model in which miR-430 regulates sdf1a at the RNA level, whereas previous results indicate that Cxcr7b, a decoy receptor, restricts the spatial expression pattern of the Sdf1a protein. () A model adapted from reference 17 to represent how miR-430 regulates the dynamic expression of Sdf1a (blue gradient). miR-430–mediated regulation of sdf1a facilitates the formation of a sharp Sdf1a gradient by accelerating the clearance of sdf1a mRNA, concentrating expression on the actively transcribing domains (gray box) (middle). miR-430 modulates the levels of cxcr7b to prevent excessive clearance of the Sdf1a protein (right). () Model for generating robustness by regulating translation of abundantly transcribed genes. High levels of transcription coupled with inefficient translation can lower intrinsic noise in protein output4, 5. () By dampening the expression of chemokine signaling genes, miR-430 buffers against changes in gene dosage (blue). We! postulate that injecting target protectors to block miR-430–mediated repression of sdf1a and cxcr7b increases the variability of gene expression (red). This reduces the ability of the system to compensate for minor perturbations in expression (Figs. 4 and 5 and Supplementary Fig. 9). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA. * Alison A Staton & * Antonio J Giraldez * Developmental Genetics Program, Skirball Institute of Biomolecular Medicine, New York University School of Medicine, New York, New York, USA. * Holger Knaut * Yale Stem Cell Center, Yale University School of Medicine, New Haven, Connecticut, USA. * Antonio J Giraldez Contributions A.A.S. and A.J.G. designed the experiments and interpreted the results. A.A.S. performed all experiments except the genetic interactions in the cxcr7b and cxcr4b mutant backgrounds, which were performed by H.K. A.A.S. wrote the manuscript with input from H.K. and A.J.G. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Antonio J Giraldez Author Details * Alison A Staton Search for this author in: * NPG journals * PubMed * Google Scholar * Holger Knaut Search for this author in: * NPG journals * PubMed * Google Scholar * Antonio J Giraldez Contact Antonio J Giraldez 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 1–10, Supplementary Table 1 and Supplementary Note Additional data - The stress kinase MKK7 couples oncogenic stress to p53 stability and tumor suppression
- Nat Genet 43(3):212-219 (2011)
Nature Genetics | Article The stress kinase MKK7 couples oncogenic stress to p53 stability and tumor suppression * Daniel Schramek1 * Athanassios Kotsinas2 * Arabella Meixner1 * Teiji Wada1 * Ulrich Elling1 * J Andrew Pospisilik1 * G Gregory Neely3 * Ralf-Harun Zwick4 * Verena Sigl1 * Guido Forni5 * Manuel Serrano6 * Vassilis G Gorgoulis2 * Josef M Penninger1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:212–219Year published:(2011)DOI:doi:10.1038/ng.767Received01 June 2010Accepted19 January 2011Published online13 February 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 Most preneoplastic lesions are quiescent and do not progress to form overt tumors. It has been proposed that oncogenic stress activates the DNA damage response and the key tumor suppressor p53, which prohibits tumor growth. However, the molecular pathways by which cells sense a premalignant state in vivo are largely unknown. Here we report that tissue-specific inactivation of the stress signaling kinase MKK7 in KRasG12D-driven lung carcinomas and NeuT-driven mammary tumors markedly accelerates tumor onset and reduces overall survival. Mechanistically, MKK7 acts through the kinases JNK1 and JNK2, and this signaling pathway directly couples oncogenic and genotoxic stress to the stability of p53, which is required for cell cycle arrest and suppression of epithelial cancers. These results show that MKK7 functions as a major tumor suppressor in lung and mammary cancer in mouse and identify MKK7 as a vital molecular sensor to set a cellular anti-cancer barrier. View full text Figures at a glance * Figure 1: MKK7 controls onset, incidence and progression of KRasG12D-driven lung tumorigenesis. Analysis of lung tumor progression in Map2k7+/+, Map2k7fl/+ and Map2k7fl/Δ mice harboring the inducible Lox-Stop-Lox-KRasG12D oncogene. We treated the mice with AdCre (2.5 × 107 PFU) and killed them at the indicated time points. () Survival of mice infected with AdCre depicted by Kaplan Meier blot. n = 16 per genotype; P < 0.0001 (log rank test for KRas; Map2k7+/+ versus KRas; Map2k7fl/Δ and KRas; Map2k7fl/+ versus KRas; Map2k7fl/Δ). (,) Representative sections (stained with hematoxylin and eosin) showing accelerated progression and increased tumor burden in KRas; Map2k7fl/Δ mice 4 weeks () and 6 weeks () after AdCre infection. Insets show the highest grade lesion in each lung. Arrow indicates the hyperplastic region. Arrowheads indicate adenomas. The asterisk marks an adenocarcinoma. Magnifications are ×40. () Average tumor-to-lung area at the indicated time points after AdCre infection. At least three planes from each lung were stained with hematoxylin and eosin and ! analyzed in a blinded fashion. Data are shown as means ± s.e.m. At least eight mice per genotype and time point were analyzed. () Distribution of hyperplasia, adenomas and adenocarcinomas in KRas; Map2k77fl/+ and KRas; Map2k7fl/Δ littermates 6 weeks after AdCre infection. For quantification, we combined hyperplastic lesions and atypical adenomatous hyperplasia (AAH). Data are shown as means ± s.e.m. (n = 12 per genotype). Magnifications are ×10. *P < 0.05; **P < 0.01; ***P < 0.001 (Student's t-test). Scale bars, 10 μm for insets and 2 mm for whole-lung pictures. * Figure 2: Mapk8+/−; Mapk9−/− compound mutant mice phenocopy the effect of MKK7 deletion. Genetic dissection of JNK isoform functions in KRasG12D-driven lung tumors. () Analysis of tumor burden in Mapk8−/− (JNK1−/−), Mapk9−/− (JNK2−/−) and Mapk8/9 compound mutant mice reveals increased tumor-to-lung area in Mapk8+/−; Mapk9−/− but not in Mapk8−/− single, Mapk9−/− single or Mapk8−/−; Mapk9+/− mice. We analyzed the lungs 9 weeks after AdCre infection. Changes are given as fold increases compared to control littermates (control). Data are shown as means ± s.e.m. **P < 0.01; ***P < 0.001 (Student's t-test). () Representative sections (stained with hematoxylin and eosin) of KRas; Mapk8+/+; Mapk9−/− and KRas; Mapk8+/−; Mapk9−/− littermates 9 weeks after AdCre infection. Insets show an adenoma in a KRas; Mapk8+/+; Mapk9−/− mouse (arrow) and an adenocarcinoma in a KRas; Mapk8+/−; Mapk9−/− mouse (arrowhead). Magnifications are ×40. () Expression of JNK and p53 in Mapk8+/−; Mapk9−/− compound mutant KRasG12D-ind! uced tumors. β-actin is shown as loading control. An overexposed blot for total JNK is presented to show residual total JNK expression in lung tumors of Mapk8+/−; Mapk9−/− compound mutant mice. Scale bars, 100 μm for insets and 2 mm for whole-lung pictures. * Figure 3: The MKK7-JNK pathway controls p53 expression in lung cancer. () Gene profiling of primary pneumocytes from KRas; Map2k7fl/+ and KRas; Map2k7fl/Δ mice treated with AdCre in vitro. We used gene set enrichment analysis to determine whether a defined set of genes or pathways showed statistically significant enrichment. All datasets will be published online at NCBI. () Immunohistochemical analysis of γH2AX, pCHK2 and p53 in premalignant lesions 4 weeks after AdCre infection. Magnifications are ×400. Staining for γH2AX and pCHK2 in hyperplastic lesions from KRas; Map2k7fl/+ and KRas; Map2k7fl/Δ mice was comparable. Whereas 80% of pneumocytes in adenomas of KRas; Map2k7fl/+ mice showed intense nuclear p53 immunostaining, only 20% of pneumocytes in hyperplastic regions of KRas; Map2k7fl/Δ mice showed nuclear p53 staining. Furthermore, p53 staining was more intense in hyperplastic lung lesions from control KRas; Map2k7fl/+ (arrowhead) than from KRas; Map2k7fl/Δ mice (thin arrow). The bar graphs denote percentage of cells positive for γ! H2AX, pCHK2 and p53. Data are shown as means ± s.e.m. ***P < 0.001 (Student's t-test). (,) Protein blot analysis of p53, MKK7 and total JNK in Map2k7-deficient () and Mapk8+/−; Mapk9−/− compound mutant () KRasG12D-driven lung tumors 6 weeks after AdCre infection. Data from individual tumors isolated from different mice are shown. Scale bars, 50 μm. * Figure 4: MKK7 regulates senescence and p53 stability. () Immunohistochemical analysis of PCNA, p16INK4a and HP1γ in adenomas 6 weeks after AdCre infection. The bar graphs show the percentage of cells positive for PCNA, p16INK4a and γHP1. Data are shown as means ± s.e.m. *P < 0.05; **P < 0.01 (Student's t-test). Magnifications are ×400. () p53 expression in the human lung tumor cell line A549 after short interfering RNA (siRNA)-mediated knockdown of Mkk7 and treatment with doxorubicin (Dox; 1 μM). MKK7 and β-actin levels are indicated. C indicates scrambled control cells treated with scrambled siRNA and 7 denotes Mkk7-specific siRNA. () Protein blot analysis of p53 and various phosphorylated (P) forms of p53 after knockdown of Mkk7 and treatment with doxorubicin (Dox; 1 μM). We used the proteasome inhibitor MG132 (30 μM) to ensure equal p53 levels. β-actin is shown as loading control. () Protein blot analysis of p53 in A549 cells after siRNA-mediated knockdown of Mkk7. Cells were exposed to 5 Gy of γ-irradiation. () Ef! fect of stable Mkk7 knockdown using shRNA in A549 lung tumor cells to form xenograft tumors. We injected 5 × 106 A549 cells transfected with pSIREN shScramble or shMkk7 into nude mice and monitored tumor formation over 26 days. Data are shown as means ± s.e.m. *P < 0.05 (Student's t-test). Scale bars, 50 μm. * Figure 5: Loss of MKK7 can be rescued by overexpression of p53. () Genetic inactivation of Map2k7 on a Tp53−/− background does not further increase lung tumor burden. Average tumor-to-lung area is shown 6 weeks after AdCre infection. At least three planes from each lung were stained with hematoxylin and eosin and analyzed in a blinded fashion. At least eight mice per genotype were analyzed. Data are shown as means ± s.e.m. (–) Rescue of MKK7 tumor suppressive function by overexpression of p53. () Representative histology of littermate control and KRas; Map2k7fl/Δ mice harboring an extra copy of p53 analyzed 9 weeks after AdCre infection. Insets show adenocarcinomas (arrowheads) in KRas; Map2k7fl/+ and KRas; Map2k77fl/Δ mice and adenomas (arrows) in p53T+; KRas; Map2k7fl/+ and p53T+; KRas; Map2k7fl/Δ mice. Magnification is ×40. () Tumor burden 9 weeks after AdCre infection is reduced to control levels in p53T+; KRas; Map2k7fl/Δ mice. We analyzed at least eight mice per genotype. Data are shown as means ± s.e.m. () Protein blo! t analysis shows increased p53 in tumors of p53T+; KRas; Map2k7fl/Δ mice. *P < 0.05 (Student's t-test). Scale bars, 100 μm for insets and 2 mm for whole-lung pictures. * Figure 6: MKK7 controls onset and incidence of NeuT-driven mammary cancer. () Kaplan Meier blot for tumor onset in Map2k7fl/fl (n = 6), Map2k7Δ/+mam (n = 21) and Map2k7Δmam (n = 14) mice harboring the MMTV-NeuT oncogene (NeuT; MKK7Δmam). P < 0.0001; log rank test for NeuT; Map2k7fl/fl versus NeuT; Map2k7Δmam and NeuT; Map2k7Δ/+mam versus NeuT; Map2k7Δmam mice. () Kaplan Meier blot for overall survival. P < 0.0004; log rank test. () Representative whole-mount analysis of mammary glands from 10-week-old nulliparous control NeuT; Map2k7fl/fl and NeuT; Map2k7Δmam littermate females showing hyperplastic regions only in NeuT; Map2k7Δmam mice. LN, lymph node. Arrowhead indicates a normal mammary epithelial duct. Arrow indicates neoplastic region. () Representative histology of mammary cancers that developed in 16-week-old NeuT; Map2k7Δ/+mam and NeuT; Map2k7Δmam littermate females (sections stained with hematoxylin and eosin). () Analysis of p53 activation in primary mammary epithelial cells purified from Map2k7fl/fl mice and treated in vitro wit! h AdGFP and AdCre to generate Map2k7fl/fl (fl) and Map2k7Δ/Δ (Δ) cells. Protein blot analysis of p53, phosphorylated (P) CHK1, CHK1, phosphorylated (P) CHK2 and CHK2 at different time points after doxorubicin treatment (Dox; 1 μM). β-actin is shown as loading control. () Protein blot analysis of p53 and MKK7 levels in NeuT-driven breast tumors. β-actin is shown as loading control. Scale bars, 1 mm () and 5 mm (). Author information * Abstract * Author information * Supplementary information Affiliations * Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna, Austria. * Daniel Schramek, * Arabella Meixner, * Teiji Wada, * Ulrich Elling, * J Andrew Pospisilik, * Verena Sigl & * Josef M Penninger * Department of Histology and Embryology, School of Medicine, University of Athens, Athens, Greece. * Athanassios Kotsinas & * Vassilis G Gorgoulis * Garvan Institute of Medical Research, Darlinghurst, Sydney, Australia. * G Gregory Neely * Department of Respiratory and Critical Care Medicine, Otto Wagner Hospital, Vienna, Austria. * Ralf-Harun Zwick * Molecular Biotechnology Center, Department of Clinical and Biological Sciences, University of Turin, Turin, Italy. * Guido Forni * Spanish National Cancer Research Centre (CNIO), Madrid, Spain. * Manuel Serrano Contributions D.S. designed and performed most experiments. A.K. and V.G.G. performed the DNA damage and p53 immunohistochemistry and analysis. A.M. performed all RT-PCR analyses. T.W. generated the MKK7floxed mice. U.E. and V.S. helped with immunohistochemistry. R.-H.Z. analyzed the tumor section as the expert pathologist. J.A.P. and G.G.N. helped in microarray and gene set enrichment analysis. G.F. and M.S. contributed to the characterization of the ErbB-2 and Super p53 transgenic mice, respectively. J.M.P. coordinated the project and wrote the manuscript with D.S. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Josef M Penninger Author Details * Daniel Schramek Search for this author in: * NPG journals * PubMed * Google Scholar * Athanassios Kotsinas Search for this author in: * NPG journals * PubMed * Google Scholar * Arabella Meixner Search for this author in: * NPG journals * PubMed * Google Scholar * Teiji Wada Search for this author in: * NPG journals * PubMed * Google Scholar * Ulrich Elling Search for this author in: * NPG journals * PubMed * Google Scholar * J Andrew Pospisilik Search for this author in: * NPG journals * PubMed * Google Scholar * G Gregory Neely Search for this author in: * NPG journals * PubMed * Google Scholar * Ralf-Harun Zwick Search for this author in: * NPG journals * PubMed * Google Scholar * Verena Sigl Search for this author in: * NPG journals * PubMed * Google Scholar * Guido Forni Search for this author in: * NPG journals * PubMed * Google Scholar * Manuel Serrano Search for this author in: * NPG journals * PubMed * Google Scholar * Vassilis G Gorgoulis Search for this author in: * NPG journals * PubMed * Google Scholar * Josef M Penninger Contact Josef M Penninger Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–14 and Supplementary Tables 1 and 2. Additional data - SUMOylation promotes de novo targeting of HP1α to pericentric heterochromatin
- Nat Genet 43(3):220-227 (2011)
Nature Genetics | Article SUMOylation promotes de novo targeting of HP1α to pericentric heterochromatin * Christèle Maison1, 2 * Delphine Bailly1, 2 * Danièle Roche1, 2 * Rocio Montes de Oca1, 2 * Aline V Probst1, 2, 4 * Isabelle Vassias1, 2 * Florent Dingli1, 3 * Bérengère Lombard1, 3 * Damarys Loew1, 3 * Jean-Pierre Quivy1, 2 * Geneviève Almouzni1, 2 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:220–227Year published:(2011)DOI:doi:10.1038/ng.765Received04 August 2010Accepted18 January 2011Published online13 February 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 HP1 enrichment at pericentric heterochromatin is considered important for centromere function. Although HP1 binding to H3K9me3 can explain its accumulation at pericentric heterochromatin, how it is initially targeted there remains unclear. Here, in mouse cells, we reveal the presence of long nuclear noncoding transcripts corresponding to major satellite repeats at the periphery of pericentric heterochromatin. Furthermore, we find that major transcripts in the forward orientation specifically associate with SUMO-modified HP1 proteins. We identified this modification as SUMO-1 and mapped it in the hinge domain of HP1α. Notably, the hinge domain and its SUMOylation proved critical to promote the initial targeting of HP1α to pericentric domains using de novo localization assays, whereas they are dispensable for maintenance of HP1 domains. We propose that SUMO-HP1, through a specific association with major forward transcript, is guided at the pericentric heterochromatin domain ! to seed further HP1 localization. View full text Figures at a glance * Figure 1: Strand-specific localization of centromeric RNAs. () Transcription from both strands of major and minor satellite repeats. Above is a schematic representation of mouse major and minor satellite repeats and strand specific primers (forward (For) and reverse (Rev)) used for analysis by RT-PCR. Below are the corresponding results for RNAs isolated from NIH 3T3 mouse cells. Shown are PCR reactions in the presence (+RT) or absence (−RT) of reverse transcriptase or primers as controls. () Nuclear localization of centromeric RNA compared to major and minor satellite DNA in NIH 3T3 cells. At left is DNA FISH. Scheme of an acrocentric mouse chromosome with telomeres (black), major (red) and minor (green) satellites. Major (red) and minor (green) satellites are shown, along with a merged image of major and minor satellites and DAPI-stained DNA. In the middle is RNA FISH. We localized major (forward in green and reverse in red) and minor (forward in red and reverse in green) RNAs with strand-specific LNA probes and show a merged ima! ge of DAPI and centromeric RNA staining. Insets show magnifications of chromocenters. At right is immuno-RNA FISH. Forward major RNAs (green) and HP1α antibody (red) staining are shown, along with a merged image of DAPI and major RNA staining. Scale bar, 10 μm. () HP1α accumulation at major satellite DNA domains. Immuno-DNA FISH with anti-HP1α antibodies (green) and major or minor satellite DNA (red) probes. Insets are as in . Scale bar, 10 μm. () RNAs associated with HP1α. Chromatin immunoprecipation (ChIP) experiments using pre-immune serum (Ctr) or anti-HP1α antibodies (HP1α) analyzed by strand-specific RT-PCR as indicated. We show PCR reactions with (+RT) or without (−RT) reverse transcriptase and without cDNA (mock) as controls. The input corresponds to the soluble chromatin before ChIP. * Figure 2: SUMO-1–modified HP1α interacts specifically with forward major RNAs. () Northwestern blot using recombinant GST-HP1γ, GST-HP1α or GST-HP1α domain fragments and in vitro transcribed radioactively labeled RNAs; forward (F) and reverse (R) major (Maj2) and minor (Min) RNAs and U1 was used as the negative control. () Experimental scheme. () RNA pull down using forward major (Maj2 F) or minor (Min F) RNAs or no RNA as the negative control, in the absence of NEM. We show protein blot analysis with vigilin, RHA, G3bp or HP1α antibodies. Input is 10% of nuclear extracts. () RNA pull down using forward (F) and reverse (R) major (Maj2 F, Maj1 F, Maj2 R) or minor (Min F, Min R) RNAs as baits or no RNA as the control in the presence of NEM. Protein blot analysis using HP1α, SUMO-1 and SUMO-2/3 antibodies revealed endogenous unmodified HP1α (HP1α), modified HP1α, SUMO-HP1α (S-HP1α) and free SUMO-2/3. Input is 10% of nuclear extracts. *SUMO-HP1α in the input. * Figure 3: SUMOylation of HP1α occurs at its hinge domain in vitro. () Experimental scheme. () HP1α SUMOylation in vitro. At left is the schematic representation of full-length HP1α and fragments thereof. At right, the protein blot analysis of the SUMOylation reaction mixture with GST antibodies revealed the positions of SUMO-1–modified full-length HP1α (S-HP1α) or fragment domains (S-CD+H and S-H) marked by an asterisk. W, wild type; M, mutant. () HP1α-Ubc9 SUMOylation in vitro. Protein blot analysis of the SUMOylation reaction mixture with GST antibodies revealed the positions of SUMO-1–modified HP1α-Ubc9 (S-HP1α-Ubc9) and unmodified HP1α-Ubc9. The arrow indicates a degradation product of GST-HP1α-Ubc9. () Mass spectrometry analysis of the in vitro SUMO-1–modified HP1α hinge fragment. Shown are the mass spectrometry (right) and tandem mass spectrometry (left) fragmentation spectra of the tryptic peptide corresponding to residues 79–97 of SUMO-1 (top right, black) and residues 83–89 of HP1α (EKSEGNK; red) where Lys84 is! sumoylated. The precursor ion mass was fragmented and acquired in QStar (m/z 981.8 (3+); left) and Orbitrap (m/z 981.77563, mass deviation 2 ppm; right, arrow) mass spectrometers. The majority of the fragment ions could be assigned to the y or b ion series, as annotated in the spectra and peptide sequence (top right). () Localization of e-HP1α and e-HP1α-Ubc9 in Triton-extracted NIH 3T3 cells. At left is the experimental scheme. At right is immunofluorescence using HA (red) and HP1α (green) antibodies. Scale bar, 10 μm. * Figure 4: SUMOylation of HP1α promotes its targeting and accumulation at pericentric heterochromatin. () Experimental scheme. () Endogenous HP1α (red) and H3K9me3 (green) localization in wild-type and in Suv39h double-null (dn) cells by immunofluorescence (IF). Transfection of Myc-SUV39H1 in Suv39h double-null cells restored HP1α and H3K9me3 localization in DAPI-dense domains. Scale bar, 10 μm. () De novo localization of e-HP1α or e-HP1α-Ubc9 in Suv39h double-null cells by immunofluorescence. At left, HA (red) and DAPI (green) staining with ×3 magnification of selected chromocenters (arrows). In the middle, HA (red) and H3K9me3 (green) staining. For each condition, we examined 300 transfected cells and calculated the percentage of cells with HA signal enriched (positive) or not (negative) at pericentric domains. Scale bar, 10 μm. At right, comparison of protein expression by protein blot, revealing HA and β actin. The arrow indicates degradation of e-HP1α-Ubc9. () Experimental scheme. () De novo localization of e-hnRNPC, e-HP1α, e-HP1α-Ubc9C93S and e-HP1α-Ubc9wt ! in Suv39h double-null cells co-transfected with Myc-SUV39H1 by immunofluorescence to reveal HA (red) and Myc (green). The percentage of positive cells was calculated as in . Scale bar, 10 μm. () Time-course analysis of the de novo localization of e-hnRNPC, e-HP1α and e-HP1α-Ubc9 in Suv39h double-null cells co-transfected with Myc-SUV39H1. At the top, the percentage of positive cells as a function of the time after transfection is represented. Symbols indicate the mean, and error bars indicate the s.d. of three independent experiments (300 co-transfected cells counted in each condition). At the bottom is a comparison of protein expression as in . The arrow indicates degradation of e-HP1α-Ubc9. * Figure 5: The hinge domain is required for de novo localization of HP1α at pericentric heterochromatin. () Experimental scheme. () Localization of wild-type (WT) or mutant (ΔH) e-HP1α and e-HP1α-Ubc9 in NIH 3T3 cells by immunofluorescence using HA antibodies (red) 24 h after transfection. Scale bar, 10 μm. () De novo localization of wild-type (WT) or mutant (ΔH) e-HP1α and e-HP1α-Ubc9 in Suv39h double-null cells co-transfected with Myc-SUV39H1 by immunofluorescence using HA (red) and Myc (green) antibodies 6 h after transfection. For each condition, we calculated the percentage of cells with the HA signal enriched at the pericentric domains (positive cells). Scale bar, 10 μm. () Comparison of protein expression corresponding to the experiment in by protein blot using HA and β actin antibodies. Arrows indicate degradation products of e-HP1α-Ubc9 and e-HP1αΔH-Ubc9. * Figure 6: Model for a de novo HP1α targeting to pericentric heterochromatin. A schematic representation of a nucleus with pericentric domains enriched in HP1 (red) is depicted showing the nuclear noncoding forward major RNA (green) at the periphery. HP1 (red), most likely as part of a complex, becomes SUMOylated. This SUMO-modified form of HP1α recognizes and binds to major RNAs (green) at pericentric heterochromatin, providing specificity to the initial targeting of HP1α to these domains (1). HP1α stabilization is then ensured by the recognition of H3K9me3 (blue) introduced by SUV39 (light brown) (2). Further HP1α accumulation involves a 'self-enforcing' loop in which new HP1α directly binds to chromatin by multimerizing with other HP1α molecules or by associating with other proteins and/or newly methylated H3K9 (3). Author information * Abstract * Author information * Supplementary information Affiliations * Institut Curie, Centre de recherche, Paris, France. * Christèle Maison, * Delphine Bailly, * Danièle Roche, * Rocio Montes de Oca, * Aline V Probst, * Isabelle Vassias, * Florent Dingli, * Bérengère Lombard, * Damarys Loew, * Jean-Pierre Quivy & * Geneviève Almouzni * Centre National de la Recherche Scientifique (CNRS), Unité Mixte de Recherche UMR218, Laboratory of Nuclear Dynamics and Genome Plasticity, Paris, France. * Christèle Maison, * Delphine Bailly, * Danièle Roche, * Rocio Montes de Oca, * Aline V Probst, * Isabelle Vassias, * Jean-Pierre Quivy & * Geneviève Almouzni * Laboratory of Proteomic Mass Spectrometry, Paris, France. * Florent Dingli, * Bérengère Lombard & * Damarys Loew * Present address: CNRS, UMR 6247 GreD, Clermont Université, INSERM U931, Aubière, France. * Aline V Probst Contributions C.M., J.-P.Q. and G.A. conceived and designed the experiments. C.M., D.B. and J.-P.Q. performed most of the experiments. D.R. and I.V. performed immuno-DNA FISH and immuno-RNA FISH. A.V.P. performed RNA FISH. F.D., B.L. and D.L. performed and analyzed mass spectrometry data using samples prepared by R.M.d.O. and they wrote together the corresponding parts. C.M. generated all figures. C.M., J.-P.Q. and G.A. analyzed the data. C.M. and G.A. wrote the paper. All authors contributed to final editing of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Jean-Pierre Quivy or * Geneviève Almouzni Author Details * Christèle Maison Search for this author in: * NPG journals * PubMed * Google Scholar * Delphine Bailly Search for this author in: * NPG journals * PubMed * Google Scholar * Danièle Roche Search for this author in: * NPG journals * PubMed * Google Scholar * Rocio Montes de Oca Search for this author in: * NPG journals * PubMed * Google Scholar * Aline V Probst Search for this author in: * NPG journals * PubMed * Google Scholar * Isabelle Vassias Search for this author in: * NPG journals * PubMed * Google Scholar * Florent Dingli Search for this author in: * NPG journals * PubMed * Google Scholar * Bérengère Lombard Search for this author in: * NPG journals * PubMed * Google Scholar * Damarys Loew Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-Pierre Quivy Contact Jean-Pierre Quivy Search for this author in: * NPG journals * PubMed * Google Scholar * Geneviève Almouzni Contact Geneviève Almouzni Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (8M) Supplementary Figures 1–9 and Supplementary Tables 1 and 2. Additional data - The draft genome of the parasitic nematode Trichinella spiralis
- Nat Genet 43(3):228-235 (2011)
Nature Genetics | Article Open The draft genome of the parasitic nematode Trichinella spiralis * Makedonka Mitreva1, 2 * Douglas P Jasmer3 * Dante S Zarlenga4 * Zhengyuan Wang1 * Sahar Abubucker1 * John Martin1 * Christina M Taylor1 * Yong Yin1, 7 * Lucinda Fulton1, 2 * Pat Minx1 * Shiaw-Pyng Yang1, 7 * Wesley C Warren1, 2 * Robert S Fulton1, 2 * Veena Bhonagiri1 * Xu Zhang1 * Kym Hallsworth-Pepin1 * Sandra W Clifton1, 2 * James P McCarter2, 5 * Judith Appleton6 * Elaine R Mardis1, 2 * Richard K Wilson1, 2 * Affiliations * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:228–235Year published:(2011)DOI:doi:10.1038/ng.769Received16 April 2010Accepted21 January 2011Published online20 February 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 Genome evolution studies for the phylum Nematoda have been limited by focusing on comparisons involving Caenorhabditis elegans. We report a draft genome sequence of Trichinella spiralis, a food-borne zoonotic parasite, which is the most common cause of human trichinellosis. This parasitic nematode is an extant member of a clade that diverged early in the evolution of the phylum, enabling identification of archetypical genes and molecular signatures exclusive to nematodes. We sequenced the 64-Mb nuclear genome, which is estimated to contain 15,808 protein-coding genes, at ~35-fold coverage using whole-genome shotgun and hierarchal map–assisted sequencing. Comparative genome analyses support intrachromosomal rearrangements across the phylum, disproportionate numbers of protein family deaths over births in parasitic compared to a non-parasitic nematode and a preponderance of gene-loss and -gain events in nematodes relative to Drosophila melanogaster. This genome sequence and ! the identified pan-phylum characteristics will contribute to genome evolution studies of Nematoda as well as strategies to combat global parasites of humans, food animals and crops. View full text Figures at a glance * Figure 1: Protein and gene family changes associated with the origin and evolution of Nematoda. () Protein family changes. At the branch of each lineage, the '+' number indicates family birth events, and the '−' number indicates family death events represented by all members indicated for that lineage. For example, there are 702 protein family births ancestral to the phylum Nematoda and 88 protein family deaths in common among the four nematodes in comparison to arthropods (represented by D. melanogaster). We reconstructed these events from 12,206 interspecific orthologous families (63,273 proteins). () Gene duplications and losses over the evolution of the common protein families. We reconstructed the gene duplication and loss events using 858 orthologous multi-member protein families (containing 8,260 proteins) conserved among all six species. At the branch of each lineage, the '+' number indicates the number of gene duplication events, and the '−' number indicates the number of gene loss events for that lineage. * Figure 2: Comparison of orthologous protein families among nematodes that span the phylum. Orthologous families comprised of each of the three parasites and D. melanogaster and C. elegans are plotted separately. The size of the dot represents the size of the orthologous family; the position represents the composition of the family based on the three represented species. With the assumption that evolutionarily close species have similar orthologous family size (fewer duplications and deletions), these plots illustrate that T. spiralis is equally distinct from both C. elegans and D. melanogaster, whereas the two other parasites share greater commonality with C. elegans. P values (derived using a χ2 test in pairwise plot comparison) indicate a greater number of families present in C. elegans compared to D. melanogaster and show that statistically significantly (P < 1 × 10−5) fewer families are biased to C. elegans when T. spiralis is present in the orthologous family. * Figure 3: Genes from T. spiralis show macrosyntenic relationships with predicted orthologs from other nematodes. () T. spiralis genes on the six largest ultracontigs with orthologs in C. elegans, colored to indicate the C. elegans chromosome on which the ortholog is located. The correlation was strong (R = 0.95, R = 0.76 and R = 0.99) and was even stronger when the X chromosome was excluded (R = 0.97, R = 0.97 and R = 0.99). For example, R = 0.95 indicates that genes from both T. spiralis ultracontigs 1 and 4 are strongly associated with one predominant C. elegans chromosome, chromosome 3, and this organization is not a result of random gene distribution. () Orthologous segments shared among nematode species shown on the C. elegans chromosomes. Red segments are considered to be ancestral orthologous segments among nematodes. The size of segments corresponds to the C. elegans orthologous segment that may be different than the orthologous segment in the other two species (Supplementary Table 7). * Figure 4: Distribution of orthologous families among the four nematode representatives spanning the phylum Nematoda. The lineages represented in the Nematoda are Rhabditida (C. elegans), Tylenchina (M. incognita), Spirurina (B. malayi) and Dorylaimia (T. spiralis). The trophic ecology of each of the four nematode species used in this study for the pan-phylum analysis is indicated next to the species name. The 2,517 orthologous groups are conserved in all four nematodes. Sixty-four orthologous groups are conserved among the parasitic species but not in the free-living C. elegans. The enrichment of functional categories related to certain orthologous groups compared to the complete functional repertoire for the four nematode species is presented in Supplementary Tables 8 and 9. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Entrez Nucleotide * ABIR02000001 * ABIR02009267 * GL622784 * GL629646 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * The Genome Center, Washington University School of Medicine, St. Louis, Missouri, USA. * Makedonka Mitreva, * Zhengyuan Wang, * Sahar Abubucker, * John Martin, * Christina M Taylor, * Yong Yin, * Lucinda Fulton, * Pat Minx, * Shiaw-Pyng Yang, * Wesley C Warren, * Robert S Fulton, * Veena Bhonagiri, * Xu Zhang, * Kym Hallsworth-Pepin, * Sandra W Clifton, * Elaine R Mardis & * Richard K Wilson * Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA. * Makedonka Mitreva, * Lucinda Fulton, * Wesley C Warren, * Robert S Fulton, * Sandra W Clifton, * James P McCarter, * Elaine R Mardis & * Richard K Wilson * Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, Washington, USA. * Douglas P Jasmer * US Department of Agriculture, Animal Parasitic Disease Laboratory, Beltsville, Maryland, USA. * Dante S Zarlenga * Divergence, Inc., St. Louis, Missouri, USA. * James P McCarter * James A. Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA. * Judith Appleton * Present address: Monsanto Company, St. Louis, Missouri, USA. * Yong Yin & * Shiaw-Pyng Yang Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Richard K Wilson or * Makedonka Mitreva Author Details * Makedonka Mitreva Contact Makedonka Mitreva Search for this author in: * NPG journals * PubMed * Google Scholar * Douglas P Jasmer Search for this author in: * NPG journals * PubMed * Google Scholar * Dante S Zarlenga Search for this author in: * NPG journals * PubMed * Google Scholar * Zhengyuan Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Sahar Abubucker Search for this author in: * NPG journals * PubMed * Google Scholar * John Martin Search for this author in: * NPG journals * PubMed * Google Scholar * Christina M Taylor Search for this author in: * NPG journals * PubMed * Google Scholar * Yong Yin Search for this author in: * NPG journals * PubMed * Google Scholar * Lucinda Fulton Search for this author in: * NPG journals * PubMed * Google Scholar * Pat Minx Search for this author in: * NPG journals * PubMed * Google Scholar * Shiaw-Pyng Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Wesley C Warren Search for this author in: * NPG journals * PubMed * Google Scholar * Robert S Fulton Search for this author in: * NPG journals * PubMed * Google Scholar * Veena Bhonagiri Search for this author in: * NPG journals * PubMed * Google Scholar * Xu Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Kym Hallsworth-Pepin Search for this author in: * NPG journals * PubMed * Google Scholar * Sandra W Clifton Search for this author in: * NPG journals * PubMed * Google Scholar * James P McCarter Search for this author in: * NPG journals * PubMed * Google Scholar * Judith Appleton Search for this author in: * NPG journals * PubMed * Google Scholar * Elaine R Mardis Search for this author in: * NPG journals * PubMed * Google Scholar * Richard K Wilson Contact Richard K Wilson Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–14, Supplementary Tables 1–13 and Supplementary Note. Creative Commons Attribution-Noncommercial-Share Alike license This article is distributed under the terms of the Creative Commons Attribution-Non- Commercial-ShareAlike license (http://creativecommons.org/licenses/by-nc-sa/3.0/), which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation, and derivative works must be licensed under the same or similar license. Additional data - Ancestry and pharmacogenomics of relapse in acute lymphoblastic leukemia
- Nat Genet 43(3):237-241 (2011)
Nature Genetics | Letter Ancestry and pharmacogenomics of relapse in acute lymphoblastic leukemia * Jun J Yang1 * Cheng Cheng2 * Meenakshi Devidas3 * Xueyuan Cao2 * Yiping Fan4 * Dario Campana5 * Wenjian Yang1 * Geoff Neale4 * Nancy J Cox6 * Paul Scheet7 * Michael J Borowitz8 * Naomi J Winick9 * Paul L Martin10 * Cheryl L Willman11 * W Paul Bowman12 * Bruce M Camitta13 * Andrew Carroll14 * Gregory H Reaman15 * William L Carroll16 * Mignon Loh17 * Stephen P Hunger18 * Ching-Hon Pui5 * William E Evans1 * Mary V Relling1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:237–241Year published:(2011)DOI:doi:10.1038/ng.763Received13 August 2010Accepted14 January 2011Published online06 February 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Although five-year survival rates for childhood acute lymphoblastic leukemia (ALL) are now over 80% in most industrialized countries1, not all children have benefited equally from this progress2. Ethnic differences in survival after childhood ALL have been reported in many clinical studies3, 4, 5, 6, 7, 8, 9, 10, 11, with poorer survival observed among African Americans or those with Hispanic ethnicity when compared with European Americans or Asians3, 4, 5. The causes of ethnic differences remain uncertain, although both genetic and non-genetic factors are likely important4, 12. Interrogating genome-wide germline SNP genotypes in an unselected large cohort of children with ALL, we observed that the component of genomic variation that co-segregated with Native American ancestry was associated with risk of relapse (P = 0.0029) even after adjusting for known prognostic factors (P = 0.017). Ancestry-related differences in relapse risk were abrogated by the addition of a single e! xtra phase of chemotherapy, indicating that modifications to therapy can mitigate the ancestry-related risk of relapse. View full text Figures at a glance * Figure 1: Principal component analysis of genome-wide germline SNP genotypes in children with ALL. (–) Comparison of the top three axes of variation (principal components) among children with ALL of different self-reported races or ethnicities and among different reference populations (HapMap samples from the CEU (n = 60), YRI (n = 60) and CHB and JPT populations (n = 90) and 105 Native American samples). Descriptions on x axes represent self-declared designations. Boxes include data between the twenty-fifth and the seventy-fifth percentiles. Note that the first (PC1, ), second (PC2, ) and third axes (PC3, ) reflect genetic variation characteristic of African, East Asian and Native American ancestry, respectively. Also note that self-reported Asians consisted of both South Asians and East Asians (top and bottom cluster of far right bar in , respectively), with varying levels of East Asian genetic ancestry. * Figure 2: Genetic ancestry and risk of relapse in childhood ALL. () Genetic ancestral composition of 2,534 children with ALL. Each patient's ancestry is shown as a column and the color represents the proportion of ancestry estimated for that patient (European, red; African, gray; Asian, green; Native American, blue). Genetic ancestry was estimated using STRUCTURE. Patients were clustered using the Ward clustering method based on dissimilarity in genetic ancestry measured by 1-minus pair-wise correlation (Supplementary Note). () Higher levels of Native American (NA) ancestry were linked to increased risk of relapse in all patients () and within the self-reported whites () and for those who did not receive delayed intensification () but not within those who did receive delayed intensification in the COG P9904/9905 trial (). Although cumulative incidence of relapse is plotted separately for patients with <10% (red) versus ≥10% (blue) Native American ancestry, we estimated all P values using a Fine and Gray's cumulative incidence hazard reg! ression model treating Native American ancestry as a continuous variable (see the Supplementary Note for details on the Native American ancestry dichotomization). Author information * Author information * Supplementary information Affiliations * Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA. * Jun J Yang, * Wenjian Yang, * William E Evans & * Mary V Relling * Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA. * Cheng Cheng & * Xueyuan Cao * Department of Epidemiology and Health Policy Research, University of Florida, Gainesville, Florida, USA. * Meenakshi Devidas * Hartwell Center for Bioinformatics and Biotechnology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA. * Yiping Fan & * Geoff Neale * Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA. * Dario Campana & * Ching-Hon Pui * Department of Medicine, University of Chicago, Chicago, Illinois, USA. * Nancy J Cox * Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. * Paul Scheet * Johns Hopkins Medical Institute, Baltimore, Maryland, USA. * Michael J Borowitz * Pediatric Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA. * Naomi J Winick * Department of Pediatrics, Duke University, Durham, North Carolina, USA. * Paul L Martin * University of New Mexico Cancer Center, Albuquerque, New Mexico, USA. * Cheryl L Willman * Cook Children's Medical Center, Ft. Worth, Texas, USA. * W Paul Bowman * Medical College of Wisconsin, Milwaukee, Wisconsin, USA. * Bruce M Camitta * Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA. * Andrew Carroll * George Washington University, Children's National Medical Center, Washington, D.C., USA. * Gregory H Reaman * New York University Cancer Institute, New York, New York, USA. * William L Carroll * Department of Pediatrics, University of California at San Francisco, San Francisco, California, USA. * Mignon Loh * University of Colorado Denver School of Medicine and The Children's Hospital, Aurora, Colorado, USA. * Stephen P Hunger Contributions J.J.Y., C.C. and M.V.R. D.C., C.-H.P., W.P.B., P.L.M., N.J.W., C.L.W., M.J.B., M.V.R., G.N., Y.F., M.D., B.M.C., W.E.E. and A.C. J.J.Y. and M.V.R. J.J.Y., C.C., W.Y., W.E.E., C.-H.P., B.M.C., M.J.B., W.L.C., S.P.H., G.H.R., M.L., Y.F. and M.V.R. C.C., W.Y., X.C., M.D., N.J.C. and P.S. G.H.R., D.C., W.E.E., M.J.B., W.L.C., S.P.H. and M.V.R. M.V.R. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Mary V Relling Author Details * Jun J Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Cheng Cheng Search for this author in: * NPG journals * PubMed * Google Scholar * Meenakshi Devidas Search for this author in: * NPG journals * PubMed * Google Scholar * Xueyuan Cao Search for this author in: * NPG journals * PubMed * Google Scholar * Yiping Fan Search for this author in: * NPG journals * PubMed * Google Scholar * Dario Campana Search for this author in: * NPG journals * PubMed * Google Scholar * Wenjian Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Geoff Neale Search for this author in: * NPG journals * PubMed * Google Scholar * Nancy J Cox Search for this author in: * NPG journals * PubMed * Google Scholar * Paul Scheet Search for this author in: * NPG journals * PubMed * Google Scholar * Michael J Borowitz Search for this author in: * NPG journals * PubMed * Google Scholar * Naomi J Winick Search for this author in: * NPG journals * PubMed * Google Scholar * Paul L Martin Search for this author in: * NPG journals * PubMed * Google Scholar * Cheryl L Willman Search for this author in: * NPG journals * PubMed * Google Scholar * W Paul Bowman Search for this author in: * NPG journals * PubMed * Google Scholar * Bruce M Camitta Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew Carroll Search for this author in: * NPG journals * PubMed * Google Scholar * Gregory H Reaman Search for this author in: * NPG journals * PubMed * Google Scholar * William L Carroll Search for this author in: * NPG journals * PubMed * Google Scholar * Mignon Loh Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen P Hunger Search for this author in: * NPG journals * PubMed * Google Scholar * Ching-Hon Pui Search for this author in: * NPG journals * PubMed * Google Scholar * William E Evans Search for this author in: * NPG journals * PubMed * Google Scholar * Mary V Relling Contact Mary V Relling Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (688K) Supplementary Note, Supplementary Tables 1–4 and Supplementary Figures 1–6. Additional data - A synonymous variant in IRGM alters a binding site for miR-196 and causes deregulation of IRGM-dependent xenophagy in Crohn's disease
- Nat Genet 43(3):242-245 (2011)
Nature Genetics | Letter A synonymous variant in IRGM alters a binding site for miR-196 and causes deregulation of IRGM-dependent xenophagy in Crohn's disease * Patrick Brest1, 2 * Pierre Lapaquette3, 4 * Mouloud Souidi5, 6 * Kevin Lebrigand2, 7 * Annabelle Cesaro1, 2 * Valérie Vouret-Craviari1, 2 * Bernard Mari2, 7 * Pascal Barbry2, 7 * Jean-François Mosnier8 * Xavier Hébuterne1, 2, 9 * Annick Harel-Bellan5, 6 * Baharia Mograbi1, 2 * Arlette Darfeuille-Michaud3, 4, 12 * Paul Hofman1, 2, 10, 11, 12 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:242–245Year published:(2011)DOI:doi:10.1038/ng.762Received10 October 2010Accepted10 January 2010Published online30 January 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Susceptibility to Crohn's disease, a complex inflammatory disease, is influenced by common variants at many loci. The common exonic synonymous SNP (c.313C>T) in IRGM, found in strong linkage disequilibrium with a deletion polymorphism, has been classified as non-causative because of the absence of an alteration in the IRGM protein sequence or splice sites. Here we show that a family of microRNAs (miRNAs), miR-196, is overexpressed in the inflammatory intestinal epithelia of individuals with Crohn's disease and downregulates the IRGM protective variant (c.313C) but not the risk-associated allele (c.313T). Subsequent loss of tight regulation of IRGM expression compromises control of intracellular replication of Crohn's disease–associated adherent invasive Escherichia coli by autophagy. These results suggest that the association of IRGM with Crohn's disease arises from a miRNA-based alteration in IRGM regulation that affects the efficacy of autophagy, thereby implicating a sy! nonymous polymorphism as a likely causal variant. View full text Figures at a glance * Figure 1: Allele-specific regulation of IRGM by miR-196. () In silico prediction of miR-196 and IRGM mRNA interactions showed differences in binding within the seed region. () IRGMC mRNA was significantly enriched in miR-196B complexes. Extracts of cells expressing FLAG-tagged-AGO1 and transfected with biotinylated miR-196B or control miR-20 as indicated and then with IRGMC or IRGMT plasmids were submitted to tandem affinity purification (immunoprecipitation with FLAG antibodies followed by affinity purification on streptavidin beads). IRGM mRNA variants were quantified using quantitative RT-PCR; results are presented as the ratio between miR-196B and miR-20 (non-relevant miRNA) pull-downs and the mean of three independent experiments ± standard deviation (s.d.). IP strep, immunoprecipitation straptavidine. () HEK293 cells (IRGMC/C) were transfected with either FLAG-tagged IRGMC or FLAG-tagged IRGMT plasmids and co-transfected with miR-196B. Immunoblotting with an IRGM antibody revealed the specificity of the downregulation effec! t mediated by the miRNA IRGM mRNA interaction. Quantification of the immunoblot signals are presented as IRGM expression relative to actin (mean of at least three independent experiments ± s.d.). * Figure 2: miR-196 overexpression in inflamed mucosa correlates with decreased expression of the IRGM c.313C variant ex vivo. () Representative in situ hybridization of frozen sections obtained from colon biopsies of genotyped healthy controls (n = 40) or individuals with Crohn's disease (CD) (n = 67) without or with active inflammation and labeled for miR-196A. (L, lumen; LP, lamina propria). Scale bars in the upper panel, 25 μm; scale bars in the lower panel, 10 μm. () Epithelial or laminal fractions were captured from sections of biopsies of healthy controls (n = 8) or individuals with Crohn's disease with no inflammation (n = 16), quiescent (defined as low-grade inflammation) (n = 8) or acute inflammation (n = 8) using laser capture microdissection. After RNA extraction, miR-196A (black bars) and miR-196B (white bars) relative expression was analyzed using RNU19, 44 and U6. To overcome possible inter-individual bias, the lamina propria fraction value was used for relative quantification. Due to high differences in expression between healthy and inflamed tissues, the results are presented as a! log2-fold ratio. Error bars indicate the s.d. of the ΔΔCt values. () Representative in situ staining for IRGM of TMAs from colon biopsies of healthy individuals or individuals with Crohn's disease with a defined genotype in a healthy non-inflamed or an acute inflamed phase. Scale bars, 32 μm. () Mean (black line), s.e.m. (white box) and 95% CI of the mean of the IRGM expression level for 40 healthy subjects (32 individuals with C/C and 8 individuals with C/T) and 67 individuals with Crohn's disease (45 with C/C and 22 with C/T) with quiescent or inflamed colon mucosa. We performed statistical analysis using ANOVA (P = 0.0015) and an unpaired Student's t-test (the one tail P value is indicated on the figure). * Figure 3: IRGM expression and miR-196 affect autophagic flux and AIEC-bacteria–mediated autophagy. () The basal flux of autophagy is affected by IRGM expression level. HEK293 cells transfected with an IRGM-expressing plasmid, miR-196 or siIRGM were treated with bafilomycin A1 for 2 h and processed for immunoblotting with anti-LC3B. () Quantification of LC3-II relative to actin (mean of three independent experiments ± s.d.). () Downregulation of IRGM expression by miR-196 abrogates AIEC-mediated autophagy in cells treated with autophagic inhibitors (Inh) or transfected with miR-196B and infected for 4 h with AIEC LF82 (mean of three independent experiments ± s.d.). () Confocal microscopic examination of LC3 revealed a significant decrease in the percentage of LC3-associated (red) LF82 bacteria (green) in miR-196 transfected cells compared to control cells (mean ± s.d.). () miR-196 transfection leads to increased intracellular LF82 replication. Results are expressed as a fold increase ± s.e.m. of intracellular bacteria. () IRGM overexpression did not inhibit autophagic ! flux and it increased LC3-II accumulation slightly in response to AIEC infection. HEK293 cells were transfected with IRGM-expressing plasmid, treated with autophagic inhibitors and infected with AIEC bacteria for 4 h (mean of three independent experiments ± s.d.). () Confocal microscopic examination showed an increased percentage of LC3-associated AIEC bacteria in IRGM cells compared to control cells (mean ± s.d.). () IRGM overexpression led to a high rate of intracellular replication of LF82 bacteria. () Most of the bacteria reside in non-acidic vacuoles, as shown with lysotracker at 8 h post infection (means of three independent experiments ± s.d.). Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Arlette Darfeuille-Michaud & * Paul Hofman Affiliations * INSERM ERI-21, EA4319, Faculty of Medicine, Nice, France. * Patrick Brest, * Annabelle Cesaro, * Valérie Vouret-Craviari, * Xavier Hébuterne, * Baharia Mograbi & * Paul Hofman * University of Nice Sophia Antipolis, Nice, France. * Patrick Brest, * Kevin Lebrigand, * Annabelle Cesaro, * Valérie Vouret-Craviari, * Bernard Mari, * Pascal Barbry, * Xavier Hébuterne, * Baharia Mograbi & * Paul Hofman * Clermont Université, Université d'Auvergne, Jeune Equipe JE 2526, Clermont-Ferrand, France. * Pierre Lapaquette & * Arlette Darfeuille-Michaud * INRA, Institut de Recherche Agronomique, Unité sous contrat USC-2018, Clermont-Ferrand, France. * Pierre Lapaquette & * Arlette Darfeuille-Michaud * Université Paris-Sud 11, Epigenetics and Cancer, FRE 3239, Villejuif, France. * Mouloud Souidi & * Annick Harel-Bellan * Centre National de la Recherche Scientifique (CNRS), Villejuif, France. * Mouloud Souidi & * Annick Harel-Bellan * CNRS UMR 6097, Institut de Pharmacologie Moléculaire et Cellulaire, Valbonne, France. * Kevin Lebrigand, * Bernard Mari & * Pascal Barbry * EA4273, University of Nantes, Nantes, France. * Jean-François Mosnier * Centre Hospitalier Universitaire de Nice, Hôpital de l'Archet, Service de Gastroentérologie, Nice, France. * Xavier Hébuterne * Centre Hospitalier Universitaire de Nice, Hôpital Pasteur, Tumorothèque, Centre de Ressource Biologique INSERM, Nice, France. * Paul Hofman * Centre Hospitalier Universitaire de Nice, Hôpital Pasteur, Laboratoire de Pathologie Clinique et Expérimentale, Nice, France. * Paul Hofman Contributions P. Brest, B. Mograbi, A.D.-M. and P.H. designed the study. P. Brest, P.L., M.S. and A.C. performed the experiments. P. Brest, P.L., M.S., B. Mograbi, A.D.-M. and P.H. collected and analyzed the data. P.H., J.-F.M. and X.H. participated in subject recruitment and in the Tissue Bank. P. Brest, P.L., A.H.-B., B. Mograbi, A.D.-M. and P.H. wrote the manuscript. K.L., V.V.-C., A.H.-B., B. Mari and P. Barbry gave technical support and conceptual advice. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Paul Hofman or * Arlette Darfeuille-Michaud Author Details * Patrick Brest Search for this author in: * NPG journals * PubMed * Google Scholar * Pierre Lapaquette Search for this author in: * NPG journals * PubMed * Google Scholar * Mouloud Souidi Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin Lebrigand Search for this author in: * NPG journals * PubMed * Google Scholar * Annabelle Cesaro Search for this author in: * NPG journals * PubMed * Google Scholar * Valérie Vouret-Craviari Search for this author in: * NPG journals * PubMed * Google Scholar * Bernard Mari Search for this author in: * NPG journals * PubMed * Google Scholar * Pascal Barbry Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-François Mosnier Search for this author in: * NPG journals * PubMed * Google Scholar * Xavier Hébuterne Search for this author in: * NPG journals * PubMed * Google Scholar * Annick Harel-Bellan Search for this author in: * NPG journals * PubMed * Google Scholar * Baharia Mograbi Search for this author in: * NPG journals * PubMed * Google Scholar * Arlette Darfeuille-Michaud Contact Arlette Darfeuille-Michaud Search for this author in: * NPG journals * PubMed * Google Scholar * Paul Hofman Contact Paul Hofman 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 1–16, Supplementary Table 1 and Supplementary Note Additional data - Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47
- Nat Genet 43(3):246-252 (2011)
Nature Genetics | Letter Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47 * Carl A Anderson1 * Gabrielle Boucher2, 3, 80 * Charlie W Lees4, 80 * Andre Franke5, 80 * Mauro D'Amato6, 80 * Kent D Taylor7 * James C Lee8 * Philippe Goyette2, 3 * Marcin Imielinski9 * Anna Latiano10 * Caroline Lagacé2, 3 * Regan Scott11 * Leila Amininejad12 * Suzannah Bumpstead1 * Leonard Baidoo11 * Robert N Baldassano13 * Murray Barclay14 * Theodore M Bayless15 * Stephan Brand16 * Carsten Büning17 * Jean-Frédéric Colombel18 * Lee A Denson19 * Martine De Vos20 * Marla Dubinsky21 * Cathryn Edwards22 * David Ellinghaus5 * Rudolf S N Fehrmann23 * James A B Floyd1 * Timothy Florin24 * Denis Franchimont25 * Lude Franke23 * Michel Georges26 * Jürgen Glas16 * Nicole L Glazer27 * Stephen L Guthery28 * Talin Haritunians29 * Nicholas K Hayward30 * Jean-Pierre Hugot31 * Gilles Jobin2, 32 * Debby Laukens20 * Ian Lawrance33 * Marc Lémann34 * Arie Levine35 * Cecile Libioulle36 * Edouard Louis36 * Dermot P McGovern7, 29 * Monica Milla37 * Grant W Montgomery38 * Katherine I Morley1 * Craig Mowat39 * Aylwin Ng40, 41 * William Newman42 * Roel A Ophoff43 * Laura Papi44 * Orazio Palmieri10 * Laurent Peyrin-Biroulet45 * Julián Panés46 * Anne Phillips39 * Natalie J Prescott47 * Deborah D Proctor48 * Rebecca Roberts14 * Richard Russell49 * Paul Rutgeerts50 * Jeremy Sanderson51 * Miquel Sans52 * Philip Schumm53 * Frank Seibold54 * Yashoda Sharma48 * Lisa A Simms55 * Mark Seielstad56, 57 * A Hillary Steinhart58 * Stephan R Targan7 * Leonard H van den Berg59 * Morten Vatn60 * Hein Verspaget61 * Thomas Walters62 * Cisca Wijmenga23 * David C Wilson49, 63 * Harm-Jan Westra23 * Ramnik J Xavier40, 41 * Zhen Z Zhao38 * Cyriel Y Ponsioen64 * Vibeke Andersen65 * Leif Torkvist66 * Maria Gazouli67 * Nicholas P Anagnou67 * Tom H Karlsen60 * Limas Kupcinskas68 * Jurgita Sventoraityte68 * John C Mansfield69 * Subra Kugathasan70 * Mark S Silverberg58 * Jonas Halfvarson71 * Jerome I Rotter29 * Christopher G Mathew47 * Anne M Griffiths62 * Richard Gearry14 * Tariq Ahmad72 * Steven R Brant15 * Mathias Chamaillard73 * Jack Satsangi4 * Judy H Cho48, 74 * Stefan Schreiber5, 75 * Mark J Daly76 * Jeffrey C Barrett1 * Miles Parkes8 * Vito Annese10, 37 * Hakon Hakonarson13, 77, 81 * Graham Radford-Smith55, 81 * Richard H Duerr11, 78, 81 * Séverine Vermeire50, 81 * Rinse K Weersma79, 81 * John D Rioux2, 3 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:246–252Year published:(2011)DOI:doi:10.1038/ng.764Received15 September 2010Accepted14 January 2011Published online06 February 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Genome-wide association studies and candidate gene studies in ulcerative colitis have identified 18 susceptibility loci. We conducted a meta-analysis of six ulcerative colitis genome-wide association study datasets, comprising 6,687 cases and 19,718 controls, and followed up the top association signals in 9,628 cases and 12,917 controls. We identified 29 additional risk loci (P < 5 × 10−8), increasing the number of ulcerative colitis–associated loci to 47. After annotating associated regions using GRAIL, expression quantitative trait loci data and correlations with non-synonymous SNPs, we identified many candidate genes that provide potentially important insights into disease pathogenesis, including IL1R2, IL8RA-IL8RB, IL7R, IL12B, DAP, PRDM1, JAK2, IRF5, GNA12 and LSP1. The total number of confirmed inflammatory bowel disease risk loci is now 99, including a minimum of 28 shared association signals between Crohn's disease and ulcerative colitis. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Gabrielle Boucher, * Charlie W Lees, * Andre Franke & * Mauro D'Amato Affiliations * Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. * Carl A Anderson, * Suzannah Bumpstead, * James A B Floyd, * Katherine I Morley & * Jeffrey C Barrett * Université de Montréal, Medicine, Montréal, Québec, Canada. * Gabrielle Boucher, * Philippe Goyette, * Caroline Lagacé, * Gilles Jobin & * John D Rioux * Montreal Heart Institute, Research Center, Montréal, Québec, Canada. * Gabrielle Boucher, * Philippe Goyette, * Caroline Lagacé & * John D Rioux * University of Edinburgh, Western General Hospital, Gastrointestinal Unit, Molecular Medicine Centre, Edinburgh, UK. * Charlie W Lees & * Jack Satsangi * Christian-Albrechts-University Kiel, Institute of Clinical Molecular Biology, Kiel, Germany. * Andre Franke, * David Ellinghaus & * Stefan Schreiber * Karolinska Institute, Department of Biosciences and Nutrition, Stockholm, Sweden. * Mauro D'Amato * Cedars-Sinai Medical Center, Inflammatory Bowel and Immunobiology Research Institute, Los Angeles, California, USA. * Kent D Taylor, * Dermot P McGovern & * Stephan R Targan * Addenbrooke's Hospital, University of Cambridge, Gastroenterology Research Unit, Cambridge, UK. * James C Lee & * Miles Parkes * The Children's Hospital of Philadelphia, Center for Applied Genomics, Philadelphia, Pennsylvania, USA. * Marcin Imielinski * Unit of Gastroenterology, Istituto di Ricovero e Cura a Carattere Scientifico-Casa Sollievo della Sofferenza (IRCCS-CSS) Hospital, San Giovanni Rotondo, Italy. * Anna Latiano, * Orazio Palmieri & * Vito Annese * University of Pittsburgh School of Medicine, Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, Pittsburgh, Pennsylvania, USA. * Regan Scott, * Leonard Baidoo & * Richard H Duerr * Erasmus Hospital, Free University of Brussels, Department of Gastroenterology, Brussels, Belgium. * Leila Amininejad * The Children's Hospital of Philadelphia, Department of Pediatrics, Center for Pediatric Inflammatory Bowel Disease, Philadelphia, Pennsylvania, USA. * Robert N Baldassano & * Hakon Hakonarson * University of Otago, Department of Medicine, Christchurch, New Zealand. * Murray Barclay, * Rebecca Roberts & * Richard Gearry * Johns Hopkins University School of Medicine, Meyerhoff Inflammatory Bowel Disease Center, Department of Medicine, Baltimore, Maryland, USA. * Theodore M Bayless & * Steven R Brant * University Hospital Munich, Department of Medicine II, Munich, Germany. * Stephan Brand & * Jürgen Glas * Universitätsmedizin Berlin, Department of Gastroenterology, Charité, Campus Mitte, Berlin, Germany. * Carsten Büning * Université de Lille Department of Hepato-Gastroenterology, Lille, France. * Jean-Frédéric Colombel * Cincinnati Children's Hospital Medical Center, Pediatric Gastroenterology, Cincinnati, Ohio, USA. * Lee A Denson * Ghent University Hospital, Department of Hepatology and Gastroenterology, Ghent, Belgium. * Martine De Vos & * Debby Laukens * Cedars-Sinai Medical Center, Department of Pediatrics, Los Angeles, California, USA. * Marla Dubinsky * Torbay Hospital, Department of Gastroenterology, Torbay, Devon, UK. * Cathryn Edwards * University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands. * Rudolf S N Fehrmann, * Lude Franke, * Cisca Wijmenga & * Harm-Jan Westra * Mater Health Services, Department of Gastroenterology, Brisbane, Australia. * Timothy Florin * Erasmus Hospital, Free University of Brussels, Department of Gastroenterology, Brussels, Belgium. * Denis Franchimont * University of Liège, Department of Genetics, Faculty of Veterinary Medicine, Liège, Belgium. * Michel Georges * University of Washington, Cardiovascular Health Research Unit, Department of Internal Medicine, Seattle, Washington, USA. * Nicole L Glazer * University of Utah School of Medicine, Department of Pediatrics, Salt Lake City, Utah, USA. * Stephen L Guthery * Cedars-Sinai Medical Center, Medical Genetics Institute, Los Angeles, California, USA. * Talin Haritunians, * Dermot P McGovern & * Jerome I Rotter * Queensland Institute of Medical Research, Oncogenomics Laboratory, Brisbane, Australia. * Nicholas K Hayward * Université Paris Diderot, Paris, France. * Jean-Pierre Hugot * Hôpital Maisonneuve-Rosemont, Department of Gastroenterology, Montréal, Québec, Canada. * Gilles Jobin * The University of Western Australia, School of Medicine and Pharmacology, Fremantle, Australia. * Ian Lawrance * Université Paris Diderot, GETAID group, Paris, France. * Marc Lémann * Tel Aviv University, Pediatric Gastroenterology Unit, Wolfson Medical Center and Sackler School of Medicine, Tel Aviv, Israel. * Arie Levine * Centre Hospitalier Universitaire Université de Liège, Division of Gastroenterology, Liège, Belgium. * Cecile Libioulle & * Edouard Louis * Azienda Ospedaliero Universitaria (AOU) Careggi, Unit of Gastroenterology SOD2, Florence, Italy. * Monica Milla & * Vito Annese * Queensland Institute of Medical Research, Molecular Epidemiology, Brisbane, Australia. * Grant W Montgomery & * Zhen Z Zhao * Ninewells Hospital and Medical School, Department of Medicine, Dundee, UK. * Craig Mowat & * Anne Phillips * Massachusetts General Hospital, Harvard Medical School, Gastroenterology Unit, Boston, Massachusetts, USA. * Aylwin Ng & * Ramnik J Xavier * Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts, USA. * Aylwin Ng & * Ramnik J Xavier * University of Manchester Department of Medical Genetics, Manchester, UK. * William Newman * University Medical Center Utrecht, Department of Medical Genetics, Utrecht, The Netherlands. * Roel A Ophoff * University of Florence, Institute of Human Genetics, Florence, Italy. * Laura Papi * University Hospital of Nancy, Department of Hepato-Gastroenterology, Vandoeuvre-lès-Nancy, France. * Laurent Peyrin-Biroulet * Hospital Clínic de Barcelona, IDIBAPS, CIBERehd, Department of Gastroenterology, Barcelona, Spain. * Julián Panés * King's College London School of Medicine, Guy's Hospital, Department of Medical and Molecular Genetics, London, UK. * Natalie J Prescott & * Christopher G Mathew * Yale University, Section of Digestive Diseases, Department of Medicine, New Haven, Connecticut, USA. * Deborah D Proctor, * Yashoda Sharma & * Judy H Cho * Royal Hospital for Sick Children, Paediatric Gastroenterology and Nutrition, Glasgow, UK. * Richard Russell & * David C Wilson * University Hospital Gasthuisberg, Division of Gastroenterology, Leuven, Belgium. * Paul Rutgeerts & * Séverine Vermeire * Guy's & St. Thomas' NHS Foundation Trust, St. Thomas' Hospital, Department of Gastroenterology, London, UK. * Jeremy Sanderson * Hospital Clínic de Barcelona, IDIBAPS, CIBERehd, Department of Gastroenterology, Barcelona, Spain. * Miquel Sans * University of Chicago, Department of Health Studies, Chicago, Illinois, USA. * Philip Schumm * University of Bern, Division of Gastroenterology, Inselspital, Bern, Switzerland. * Frank Seibold * Queensland Institute of Medical Research, Inflammatory Bowel Diseases, Brisbane, Australia. * Lisa A Simms & * Graham Radford-Smith * Genome Institute of Singapore, Human Genetics, Singapore. * Mark Seielstad * Institute for Human Genetics, University of California San Francisco, San Francisco, California, USA. * Mark Seielstad * University of Toronto, Mount Sinai Hospital Inflammatory Bowel Disease Centre, Toronto, Ontario, Canada. * A Hillary Steinhart & * Mark S Silverberg * Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Department of Neurology, Utrecht, The Netherlands. * Leonard H van den Berg * Rikshospitalet University Hospital, Medical Department, Oslo, Norway. * Morten Vatn & * Tom H Karlsen * Leiden University Medical Center, Experimental Gastroenterology, Leiden, The Netherlands. * Hein Verspaget * The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada. * Thomas Walters & * Anne M Griffiths * Child Life and Health, University of Edinburgh, Edinburgh, Scotland, UK. * David C Wilson * Academic Medical Center, Department of Gastroenterology, Amsterdam, The Netherlands. * Cyriel Y Ponsioen * Viborg Regional Hospital, Medical Department, Viborg, Denmark. * Vibeke Andersen * Karolinska Institutet, Department of Clinical Science Intervention and Technology, Stockholm, Sweden. * Leif Torkvist * University of Athens, Department of Biology, School of Medicine, Athens, Greece. * Maria Gazouli & * Nicholas P Anagnou * Kaunas University of Medicine, Department of Gastroenterology, Kaunas, Lithuania. * Limas Kupcinskas & * Jurgita Sventoraityte * Newcastle University, Institute of Human Genetics, Newcastle upon Tyne, UK. * John C Mansfield * Emory School of Medicine, Department of Genetics and Department of Pediatrics, Atlanta, Georgia, USA. * Subra Kugathasan * Örebro University Hospital, Department of Medicine, Örebro, Sweden. * Jonas Halfvarson * Peninsula College of Medicine and Dentistry, Barrack Road, Exeter, UK. * Tariq Ahmad * INSERM, U1019, Lille, France. * Mathias Chamaillard * Yale University, Department of Genetics, Yale School of Medicine, New Haven, Connecticut, USA. * Judy H Cho * Department for General Internal Medicine, Christian-Albrechts-University, Kiel, Germany. * Stefan Schreiber * Massachusetts General Hospital, Harvard Medical School, Center for Human Genetic Research, Boston, Massachusetts, USA. * Mark J Daly * Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Hakon Hakonarson * University of Pittsburgh Graduate School of Public Health, Department of Human Genetics, Pittsburgh, Pennsylvania, USA. * Richard H Duerr * University Medical Center Groningen, Department of Gastroenterology, Groningen, The Netherlands. * Rinse K Weersma * These authors jointly supervised this work. * Hakon Hakonarson, * Graham Radford-Smith, * Richard H Duerr, * Séverine Vermeire & * Rinse K Weersma Contributions C.W.L., A.F., K.D.T., J.C.L., M.I., A. Latiano, L.A., L.B., R.N.B., M.B., T.M.B., S. Brand, C.B., J.-F.C., L.A.D., M.D.V., M.D., C.E., R.S.N.F., T.F., D.F., M. Georges, J.G., N.L.G., S.L.G., T.H., N.K.H., J.-P.H., G.J., D.L., I.L., M.L., A. Levine, C. Libioulle, E.L., D.P.M., M.M., C.M., A.N., W.N., R.A.O., L.P., O.P., L.P.-B., J.P., A.P., N.J.P., D.D.P., R. Roberts, R. Russell, P.R., J. Sanderson, M. Sans, P.S., F.S., Y.S., M. Seielstad, A.H.S., S.R.T., L.H.v.d.B., M.V., H.V., T.W., C.W., D.C.W., H.-J.W., C.Y.P., V. Andersen, L.T., M. Gazouli, N.P.A., T.H.K., L.K., J. Sventoraityte, J.C.M., S.K., M.S.S., J.H., J.I.R., C.G.M., A.M.G., R.G., T.A., S.R.B., M.C., J. Satsangi, J.H.C., S.S., M.P., V. Annese, H.H., G.R.-S., R.H.D., S.V., R.K.W. and J.D.R. established DNA collections, recruited patients or assembled phenotypic data. A.F., M.D., P.G., C. Lagacé, R.S., S. Bumpstead, C. Libioulle, D.P.M., G.W.M., L.A.S., Z.Z.Z., M.C., R.H.D. and J.D.R. conducted or supervised laborat! ory work. C.A.A., G.B., D.E., J.A.B.F., L.F., K.I.M., A.N., R.A.O., R.J.X., M.J.D., J.C.B., R.K.W. and J.D.R. performed or supervised statistical analyses. C.A.A., G.B., C.W.L., G.R.-S., R.H.D., S.V., R.K.W. and J.D.R. drafted the manuscript. All authors read and approved the final manuscript before submission. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Carl A Anderson or * John D Rioux Author Details * Carl A Anderson Contact Carl A Anderson Search for this author in: * NPG journals * PubMed * Google Scholar * Gabrielle Boucher Search for this author in: * NPG journals * PubMed * Google Scholar * Charlie W Lees Search for this author in: * NPG journals * PubMed * Google Scholar * Andre Franke Search for this author in: * NPG journals * PubMed * Google Scholar * Mauro D'Amato Search for this author in: * NPG journals * PubMed * Google Scholar * Kent D Taylor Search for this author in: * NPG journals * PubMed * Google Scholar * James C Lee Search for this author in: * NPG journals * PubMed * Google Scholar * Philippe Goyette Search for this author in: * NPG journals * PubMed * Google Scholar * Marcin Imielinski Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Latiano Search for this author in: * NPG journals * PubMed * Google Scholar * Caroline Lagacé Search for this 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1 and Table 2 * Supplementary Table 3b (40K) Cohort specific results for all SNPs included in follow-up phase, but failed our thresholds for follow-up * Supplementary Table 7 (16K) Summary of candidate genes mapping from in silico analysis on the 47 UC loci * Supplementary Table 9 (40K) Results from the International IBD Genetics Consortium CD and UC meta-analyses for the 99 loci reported for association in CD and/or UC PDF files * Supplementary Text and Figures (3M) Supplementary Tables 1, 2, 4–6 and 8 and Supplementary Figures 1–3. Additional data - Association of a functional variant downstream of TNFAIP3 with systemic lupus erythematosus
- Nat Genet 43(3):253-258 (2011)
Nature Genetics | Letter Association of a functional variant downstream of TNFAIP3 with systemic lupus erythematosus * Indra Adrianto1 * Feng Wen1 * Amanda Templeton2 * Graham Wiley1 * Jarrod B King2 * Christopher J Lessard1, 2 * Jared S Bates1 * Yanqing Hu2 * Jennifer A Kelly1 * Kenneth M Kaufman1, 2, 3 * Joel M Guthridge1 * Marta E Alarcón-Riquelme1, 4 * Juan-Manuel Anaya5 * Sang-Cheol Bae6 * So-Young Bang6 * Susan A Boackle7 * Elizabeth E Brown8 * Michelle A Petri9 * Caroline Gallant10 * Rosalind Ramsey-Goldman11 * John D Reveille12 * Luis M Vila13 * Lindsey A Criswell14 * Jeffrey C Edberg15 * Barry I Freedman16 * Peter K Gregersen17 * Gary S Gilkeson18 * Chaim O Jacob19 * Judith A James1, 2 * Diane L Kamen18 * Robert P Kimberly15 * Javier Martin20 * Joan T Merrill21 * Timothy B Niewold22 * So-Yeon Park6 * Bernardo A Pons-Estel23 * R Hal Scofield1, 2 * Anne M Stevens24, 25 * Betty P Tsao26 * Timothy J Vyse27, 28 * Carl D Langefeld29 * John B Harley3, 30 * Kathy L Moser1, 2 * Carol F Webb31 * Mary Beth Humphrey2, 32 * Courtney Gray Montgomery1, 32 * Patrick M Gaffney1, 32 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:253–258Year published:(2011)DOI:doi:10.1038/ng.766Received29 September 2010Accepted19 January 2010Published online20 February 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Systemic lupus erythematosus (SLE, MIM152700) is an autoimmune disease characterized by self-reactive antibodies resulting in systemic inflammation and organ failure. TNFAIP3, encoding the ubiquitin-modifying enzyme A20, is an established susceptibility locus for SLE. By fine mapping and genomic re-sequencing in ethnically diverse populations, we fully characterized the TNFAIP3 risk haplotype and identified a TT>A polymorphic dinucleotide (deletion T followed by a T to A transversion) associated with SLE in subjects of European (P = 1.58 × 10−8, odds ratio = 1.70) and Korean (P = 8.33 × 10−10, odds ratio = 2.54) ancestry. This variant, located in a region of high conservation and regulatory potential, bound a nuclear protein complex composed of NF-κB subunits with reduced avidity. Further, compared with the non-risk haplotype, the haplotype carrying this variant resulted in reduced TNFAIP3 mRNA and A20 protein expression. These results establish this TT>A variant as t! he most likely functional polymorphism responsible for the association between TNFAIP3 and SLE. View full text Figures at a glance * Figure 1: Variants in the TNFAIP3 region associated with SLE. Genotyped SNPs are depicted with blue diamonds. Imputed SNPs are shown with red circles. An orange solid line represents recombination rates across the region. The dashed line represents a Bonferroni corrected P < 1 × 10−4. Arrows identify SNPs demonstrating the most significant association results in each population. () Individuals of European ancestry. () Asians. () Koreans. Recomb., recombination. * Figure 2: TNFAIP3 haplotype and conditional association analyses. () Haplotypes present at a frequency >1% were compared in the European-ancestry and Korean populations. Alleles in white boxes represent the major allele and those in gray boxes represent the minor allele for each haplotype. Black bold rectangles identify minor alleles that differentiate the SLE risk haplotype from the non-risk haplotype. (,) We performed conditional association analysis in the European-ancestry () and Korean () populations for each of the SNPs within the 48.5-kb segment bounded by rs5029937 and rs61117627. We assessed three models: first, conditioning on the p.Phe127Cys coding variant rs2230926 (white bars), then conditioning on the TT>A variant (gray bars) and, finally, conditioning on rs7749323 (black bars). * Figure 3: Functional characterization of the TT>A polymorphic dinucleotide and TNFAIP3 risk haplotype. () Shown is a representative EMSA from six independent experiments for THP1 and three for U937. The first two lanes show a free probe for wild type (WT) and the TT>A variant followed by increasing amounts of nuclear protein and labeled probes as indicated. A nonspecific band is labeled N.S. () We performed supershift using antibodies specific for NF-κB subunits. Complexes formed in the presence or absence of antibodies are identified by arrows on the left of the figure. (,) We performed densitometric quantification of nuclear protein binding in independent experiments for THP1 cells () and U937 cells () using optimal concentrations of nuclear extract. (,) Expression of TNFAIP3 transcripts were evaluated from CEU, CHB and JPT populations (AA, n = 2; AG, n = 24; GG, n = 115) () and compared to the YRI population (AG, n = 6; GG, n = 54) () using a one-way ANOVA and unpaired t-test, respectively. (,) We compared A20 protein expression from cell lines of European-ancestry subjec! ts (AA, n = 2; AG, n = 5; GG, n = 5) () to African-American subjects (AG, n = 10, GG, n = 9) () using one-way ANOVA and unpaired t-test, respectively. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions GenBank * NP_006281.1 Author information * Accession codes * Author information * Supplementary information Affiliations * Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA. * Indra Adrianto, * Feng Wen, * Graham Wiley, * Christopher J Lessard, * Jared S Bates, * Jennifer A Kelly, * Kenneth M Kaufman, * Joel M Guthridge, * Marta E Alarcón-Riquelme, * Judith A James, * R Hal Scofield, * Kathy L Moser, * Courtney Gray Montgomery & * Patrick M Gaffney * College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA. * Amanda Templeton, * Jarrod B King, * Christopher J Lessard, * Yanqing Hu, * Kenneth M Kaufman, * Judith A James, * R Hal Scofield, * Kathy L Moser & * Mary Beth Humphrey * US Department of Veterans Affairs Medical Center, Oklahoma City, Oklahoma, USA. * Kenneth M Kaufman & * John B Harley * Center for Genomics and Oncological Research (GENyO), Granada, Spain. * Marta E Alarcón-Riquelme * Center for Autoimmune Diseases Research (CREA), Universidad del Rosario, Bogotá, Colombia. * Juan-Manuel Anaya * Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea. * Sang-Cheol Bae, * So-Young Bang & * So-Yeon Park * Division of Rheumatology, University of Colorado Denver, Aurora, Colorado, USA. * Susan A Boackle * Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, USA. * Elizabeth E Brown * Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Michelle A Petri * Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden. * Caroline Gallant * Division of Rheumatology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. * Rosalind Ramsey-Goldman * Rheumatology and Clinical Immunogenetics, University of Texas Health Science Center at Houston, Houston, Texas, USA. * John D Reveille * Department of Medicine, Division of Rheumatology, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico. * Luis M Vila * Rosalind Russell Medical Research Center for Arthritis, University of California San Francisco, San Francisco, California, USA. * Lindsey A Criswell * Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA. * Jeffrey C Edberg & * Robert P Kimberly * Department of Internal Medicine, Wake Forest University Health Sciences, Winston-Salem, North Carolina, USA. * Barry I Freedman * The Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, North Shore Long Island Jewish (LIJ) Health System, Manhasset, New York, USA. * Peter K Gregersen * Division of Rheumatology, Medical University of South Carolina, Charleston, South Carolina, USA. * Gary S Gilkeson & * Diane L Kamen * Department of Medicine, University of Southern California, Los Angeles, California, USA. * Chaim O Jacob * Instituto de Parasitología y Biomedicina 'López-Neyra', Consejo Superior de Investigaciones Científicas (CSIC), Granada, Spain. * Javier Martin * Clinical Pharmacology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA. * Joan T Merrill * Section of Rheumatology and Gwen Knapp Center for Lupus and Immunology Research, University of Chicago, Chicago, Illinois, USA. * Timothy B Niewold * Sanatorio Parque, Rosario, Argentina. * Bernardo A Pons-Estel * Division of Rheumatology, Department of Pediatrics, University of Washington, Seattle, Washington, USA. * Anne M Stevens * Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, Washington, USA. * Anne M Stevens * Division of Rheumatology, Department of Medicine, University of California Los Angeles, Los Angeles, California, USA. * Betty P Tsao * Division of Genetics and Molecular Medicine, King's College London, London, UK. * Timothy J Vyse * Division of Immunology, Infection and Inflammatory Diseases, Kings College London, London, UK. * Timothy J Vyse * Department of Biostatistical Sciences, Wake Forest University Health Sciences, Winston-Salem, North Carolina, USA. * Carl D Langefeld * Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. * John B Harley * Immunobiology and Cancer Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA. * Carol F Webb * These authors jointly directed this work. * Mary Beth Humphrey, * Courtney Gray Montgomery & * Patrick M Gaffney Contributions P.M.G., C.G.M., K.L.M., C.J.L., J.A.K., K.M.K., C.D.L. and J.B.H. selected SNPs and were responsible for the study design. J.M.G., M.E.A.-R., J.-M.A., S.-C.B., S.-Y.B., S.A.B., E.E.B., M.A.P., C.G., R.R.-G., J.D.R., L.M.V., L.A.C., J.C.E., B.I.F., P.K.G., G.S.G., C.O.J., J.A.J., D.L.K., R.P.K., J.M., J.T.M., T.B.N., S.-Y.P., B.A.P.-E., R.H.S., A.M.S., B.P.T., L.M.V., T.J.V., J.B.H., K.L.M. and P.M.G. assisted in the collection and characterization of the SLE cases and controls. K.M.K. and P.M.G. performed the genotyping. K.M.K. and C.D.L. performed quality control analyses. I.A. and J.S.B. performed association analyses and imputation under the guidance of C.G.M. and P.M.G. F.W., G.W. and P.M.G. performed the sequencing. F.W., A.T., J.B.K., Y.H., C.F.W., M.B.H. and P.M.G. performed functional studies. I.A., C.G.M. and P.M.G. prepared the manuscript. All authors approved the final draft. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Patrick M Gaffney Author Details * Indra Adrianto Search for this author in: * NPG journals * PubMed * Google Scholar * Feng Wen Search for this author in: * NPG journals * PubMed * Google Scholar * Amanda Templeton Search for this author in: * NPG journals * PubMed * Google Scholar * Graham Wiley Search for this author in: * NPG journals * PubMed * Google Scholar * Jarrod B King Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher J Lessard Search for this author in: * NPG journals * PubMed * Google Scholar * Jared S Bates Search for this author in: * NPG journals * PubMed * Google Scholar * Yanqing Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer A Kelly Search for this author in: * NPG journals * PubMed * Google Scholar * Kenneth M Kaufman Search for this author in: * NPG journals * PubMed * Google Scholar * Joel M Guthridge Search for this author in: * NPG journals * PubMed * Google Scholar * Marta E Alarcón-Riquelme Search for this author in: * NPG journals * PubMed * Google Scholar * Juan-Manuel Anaya Search for this author in: * NPG journals * PubMed * Google Scholar * Sang-Cheol Bae Search for this author in: * NPG journals * PubMed * Google Scholar * So-Young Bang Search for this author in: * NPG journals * PubMed * Google Scholar * Susan A Boackle Search for this author in: * NPG journals * PubMed * Google Scholar * Elizabeth E Brown Search for this author in: * NPG journals * PubMed * Google Scholar * Michelle A Petri Search for this author in: * NPG journals * PubMed * Google Scholar * Caroline Gallant Search for this author in: * NPG journals * PubMed * Google Scholar * Rosalind Ramsey-Goldman Search for this author in: * NPG journals * PubMed * Google Scholar * John D Reveille Search for this author in: * NPG journals * PubMed * Google Scholar * Luis M Vila Search for this author in: * NPG journals * PubMed * Google Scholar * Lindsey A Criswell Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey C Edberg Search for this author in: * NPG journals * PubMed * Google Scholar * Barry I Freedman Search for this author in: * NPG journals * PubMed * Google Scholar * Peter K Gregersen Search for this author in: * NPG journals * PubMed * Google Scholar * Gary S Gilkeson Search for this author in: * NPG journals * PubMed * Google Scholar * Chaim O Jacob Search for this author in: * NPG journals * PubMed * Google Scholar * Judith A James Search for this author in: * NPG journals * PubMed * Google Scholar * Diane L Kamen Search for this author in: * NPG journals * PubMed * Google Scholar * Robert P Kimberly Search for this author in: * NPG journals * PubMed * Google Scholar * Javier Martin Search for this author in: * NPG journals * PubMed * Google Scholar * Joan T Merrill Search for this author in: * NPG journals * PubMed * Google Scholar * Timothy B Niewold Search for this author in: * NPG journals * PubMed * Google Scholar * So-Yeon Park Search for this author in: * NPG journals * PubMed * Google Scholar * Bernardo A Pons-Estel Search for this author in: * NPG journals * PubMed * Google Scholar * R Hal Scofield Search for this author in: * NPG journals * PubMed * Google Scholar * Anne M Stevens Search for this author in: * NPG journals * PubMed * Google Scholar * Betty P Tsao Search for this author in: * NPG journals * PubMed * Google Scholar * Timothy J Vyse Search for this author in: * NPG journals * PubMed * Google Scholar * Carl D Langefeld Search for this author in: * NPG journals * PubMed * Google Scholar * John B Harley Search for this author in: * NPG journals * PubMed * Google Scholar * Kathy L Moser Search for this author in: * NPG journals * PubMed * Google Scholar * Carol F Webb Search for this author in: * NPG journals * PubMed * Google Scholar * Mary Beth Humphrey Search for this author in: * NPG journals * PubMed * Google Scholar * Courtney Gray Montgomery Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick M Gaffney Contact Patrick M Gaffney Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 4 (664K) Genotyping results of all observed and imputed markers PDF files * Supplementary Text and Figures (7M) Supplementary Figures 1–7 and Supplementary Tables 1–3 and 5–8 Additional data - Mutations in TTC19 cause mitochondrial complex III deficiency and neurological impairment in humans and flies
- Nat Genet 43(3):259-263 (2011)
Nature Genetics | Letter Mutations in TTC19 cause mitochondrial complex III deficiency and neurological impairment in humans and flies * Daniele Ghezzi1, 7 * Paola Arzuffi1, 7 * Mauro Zordan2 * Caterina Da Re2 * Costanza Lamperti1 * Clara Benna2 * Pio D'Adamo3 * Daria Diodato1 * Rodolfo Costa2 * Caterina Mariotti4 * Graziella Uziel5 * Cristina Smiderle6 * Massimo Zeviani1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:259–263Year published:(2011)DOI:doi:10.1038/ng.761Received28 June 2010Accepted07 January 2011Published online30 January 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Although mutations in CYTB (cytochrome b) or BCS1L have been reported in isolated defects of mitochondrial respiratory chain complex III (cIII), most cIII-defective individuals remain genetically undefined. We identified a homozygous nonsense mutation in the gene encoding tetratricopeptide 19 (TTC19) in individuals from two families affected by progressive encephalopathy associated with profound cIII deficiency and accumulation of cIII-specific assembly intermediates. We later found a second homozygous nonsense mutation in a fourth affected individual. We demonstrated that TTC19 is embedded in the inner mitochondrial membrane as part of two high–molecular‐weight complexes, one of which coincides with cIII. We then showed a physical interaction between TTC19 and cIII by coimmunoprecipitation. We also investigated a Drosophila melanogaster knockout model for TTC19 that showed low fertility, adult-onset locomotor impairment and bang sensitivity, associated with cIII deficie! ncy. TTC19 is a putative cIII assembly factor whose disruption is associated with severe neurological abnormalities in humans and flies. View full text Figures at a glance * Figure 1: Clinical and molecular genetic features of individuals with mutations in TTC19. () Brain magnetic resonance image of subject 1. I indicates a transverse T2-weighted image of the cerebellum and medulla oblongata. The arrows indicate bilateral hyperintense signals in the inferior olives. II indicates a transverse T2-weighted image of the supratentorial brain. The arrow shows a hyperintense signal in the right putamen. III indicates a median sagittal T1-weighted image showing atrophy of the cerebral cortex and cerebellar vermis (arrow). IV indicates a coronal T1-weighted image showing brain atrophy with dilation of the lateral ventricles, atrophy of the caudate nuclei and bilateral hyperintensities of the substantia nigra (arrows). () Oxygen consumption rate (OCR) of patients fibroblasts (Fb, left panel) and myoblasts (Mb, right panel) before and after (+TTC19) re-expression of wild-type TTC19. Bars indicate the standard deviation for 8–16 technical replicates. *P < 0.05, **P < 0.01. (P values for unpaired, two-tailed Student's t-test). () Quantitative r! eal-time PCR of TTC19 mRNA relative to GAPDH mRNA in affected and control (Ct) fibroblasts (Fb), myoblasts (Mb) and muscle biopsies (Ms). P1, P2, P3 and P4 correspond to subjects 1, 2, 3 and 4, respectively. Each value refers to the mean of three independent experiments performed in duplicate. Bars indicate the standard deviation. () Immunoblot analysis of total lysates from myoblasts and fibroblasts using α-TTC19, α-Core1 and α-SDHA antibodies. P1, P2 and P3 correspond to subjects 1, 2 and 3. Fb BCS1L indicate fibroblasts of a subject with cIII deficiency due to mutations in BCS1L. * Figure 2: TTC19 subcellular localization. () Confocal immunofluorescence of COS7 cells transfected with TTC19HA. The pattern of the green signal, corresponding to TTCHA, coincides with that of the red signal, corresponding to MitoTracker, a mitochondrial marker, in transfected cells, producing a confocalized image in yellow. Scale bars, 25 μm. () Import assay on isolated HeLa cell mitochondria. The in vitro translated TTC protein (lane 1) is partially imported and cleaved in the presence of freshly prepared, fully coupled, energized mitochondria (lane 2). The mature protein species is internalized within the mitochondria, whereas the precursor protein species that remains outside mitochondria is digested by proteinase K (PK) (lane 3). Both mature and precursor TTC19 proteins are digested when the mitochondria are solubilized in Triton-X100 (lane 4). The import is dependent on the integrity of ΔΨ, as no mature protein is formed when ΔΨ is dissipated by valinomycin (lane 5) and is completely digested by PK (lane ! 6). () Immunoblot analysis of HeLa cell fractions. ivT, in vitro translated TTC19; Mt, mitochondria; L, cell lysate; PMF, post-mitochondrial fraction; GAPDH (a cytosolic protein) and Core1 (a mitochondrial protein) were used as controls. () Immunoblot analysis of mouse liver mitochondria after treatment with detergent deoxycholate (DOC) or Na2CO3. Mt, mitochondrial fraction; MM, mitochondrial matrix, Mb, mitochondrial membrane fraction. HSP60 (mitochondrial matrix protein) and Core1 (a mitochondrial inner-membrane protein) were used as controls. * Figure 3: Protein characterization in individuals with mutations in TTC19. () Immunofluorescence images of muscle from a control (Ctrl), a disease control (Ctrl-tRNA) and subject 1 (P1) using α-Core1 antibody. Scale bars, 25 μm. () One dimensional BNGE of muscle homogenates from subject 2 (P2) and control (Ctrl). We used an antibody against the Core1 subunit to detect complex III, an antibody against SDH 70 kDa for complex II and an antibody against subunit COX-IV for complex IV. () Two dimensional BNGE of subject 2 (P2) and control (Ctrl) muscle homogenates. We used antibodies against Core1, Core2 and RISP to detect complex III and an antibody against COX-IV for complex IV. * Figure 4: Structural analysis of TTC19 interactions. () Two dimensional BNGE on mouse liver mitochondria using antibodies against TTC19, Core1 and Core2. Dotted vertical lines indicate the dimeric form of cIII (cIII2) and the supercomplex composed of cIII and cIV (cIII2 + cIV). () Coimmunoprecipitation assays on mouse liver mitochondria. The antibody used for immunoprecipitation (IP) is indicated on top and the antibodies used for immunodetection are indicated on the right. Sn, supernatant; 2, 5, 10, materials released from beads after treatment with 2%, 5% and 10% Triton X-100, respectively. () Two-dimensional BNGE on 143B, 143 rho° and HeLa cells. * Figure 5: Characterization of TTC19-null flies. () Spontaneous locomotor activity in D. melanogaster CantonS control (CTRL) and TTC19-null (TTC19) flies. We measured the number of passages of individual flies across an infrared light beam during 30 min. The flies were divided into males (-M) and females (-F). Bars indicate the standard deviation. () Locomotor activity after the bang test. Percentage of flies able to climb to four selected 'end-points', corresponding to 2.8, 5.6, 8.4 and 11.2 cm after vigorous shaking in a test tube by vortexing for 10 s at maximum speed. CS, CantonS control flies; TTC19, TTC19-null flies. () Number of correct responses (taken in 10 trials) to the Optomotor test in CantonS control (CTRL) and TTC19 knockout (TTC19) flies at different days of age. The stimulus consisted of a black and white striped drum rotating either clockwise or counterclockwise until a response was obtained from the individual flies. Bars indicate the standard deviation. () Specific activities of MRC complexes in control! s (CTRL1, CantonS; CTRL2, WTALA) and TTC19-null (TTC19) flies normalized to that of Citrate Synthase (CS). For each genotype, we performed three replicate mitochondrial extractions and for each extraction, we determined enzymatic activities from at least ten replicate reactions. The assays have been previously described22. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions GenBank * FBgn0032744 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Daniele Ghezzi & * Paola Arzuffi Affiliations * Unit of Molecular Neurogenetics, The Foundation 'Carlo Besta' Institute of Neurology, Milan, Italy. * Daniele Ghezzi, * Paola Arzuffi, * Costanza Lamperti, * Daria Diodato & * Massimo Zeviani * Neurogenetics and Behaviour of Drosophila Lab, Department of Biology, University of Padova, Padova, Italy. * Mauro Zordan, * Caterina Da Re, * Clara Benna & * Rodolfo Costa * Medical Genetics, Istituto di Ricovero e Cura a Carattere Scientifico, Burlo Garofolo, University of Trieste, Trieste, Italy. * Pio D'Adamo * Unit of Laboratory Medicine, The Foundation 'Carlo Besta' Institute of Neurology, Milan, Italy. * Caterina Mariotti * Unit of Child Neurology, The Foundation 'Carlo Besta' Institute of Neurology, Milan, Italy. * Graziella Uziel * Division of Physical Medicine and Rehabilitation, Public Health Hospital, Bassano del Grappa, Italy. * Cristina Smiderle Contributions D.G. and P.A. found TTC19 and characterized the mutations in human cells. C.L. performed the histological analysis of muscle biopsies. M. Zordan, C.D.R., C.B. and R.C. carried out the experiments in flies. C.M., G.U. and C.S. identified the subjects and carried out the clinical workout. P.D'A. performed linkage analysis. D.D. carried out the mutational screening on subjects 3 and 4 and the controls. M. Zeviani conceived the experimental planning and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Massimo Zeviani Author Details * Daniele Ghezzi Search for this author in: * NPG journals * PubMed * Google Scholar * Paola Arzuffi Search for this author in: * NPG journals * PubMed * Google Scholar * Mauro Zordan Search for this author in: * NPG journals * PubMed * Google Scholar * Caterina Da Re Search for this author in: * NPG journals * PubMed * Google Scholar * Costanza Lamperti Search for this author in: * NPG journals * PubMed * Google Scholar * Clara Benna Search for this author in: * NPG journals * PubMed * Google Scholar * Pio D'Adamo Search for this author in: * NPG journals * PubMed * Google Scholar * Daria Diodato Search for this author in: * NPG journals * PubMed * Google Scholar * Rodolfo Costa Search for this author in: * NPG journals * PubMed * Google Scholar * Caterina Mariotti Search for this author in: * NPG journals * PubMed * Google Scholar * Graziella Uziel Search for this author in: * NPG journals * PubMed * Google Scholar * Cristina Smiderle Search for this author in: * NPG journals * PubMed * Google Scholar * Massimo Zeviani Contact Massimo Zeviani Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (7M) Supplementary Note, Supplementary Figures 1–12 and Supplementary Tables 1–5 Additional data - Chromatin accessibility pre-determines glucocorticoid receptor binding patterns
- Nat Genet 43(3):264-268 (2011)
Nature Genetics | Letter Chromatin accessibility pre-determines glucocorticoid receptor binding patterns * Sam John1 * Peter J Sabo2 * Robert E Thurman2 * Myong-Hee Sung1 * Simon C Biddie1 * Thomas A Johnson1 * Gordon L Hager1 * John A Stamatoyannopoulos2, 3 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:264–268Year published:(2011)DOI:doi:10.1038/ng.759Received30 August 2010Accepted29 December 2010Published online23 January 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Development, differentiation and response to environmental stimuli are characterized by sequential changes in cellular state initiated by the de novo binding of regulated transcriptional factors to their cognate genomic sites1, 2, 3. The mechanism whereby a given regulatory factor selects a limited number of in vivo targets from a myriad of potential genomic binding sites is undetermined. Here we show that up to 95% of de novo genomic binding by the glucocorticoid receptor4, a paradigmatic ligand-activated transcription factor, is targeted to preexisting foci of accessible chromatin. Factor binding invariably potentiates chromatin accessibility. Cell-selective glucocorticoid receptor occupancy patterns appear to be comprehensively predetermined by cell-specific differences in baseline chromatin accessibility patterns, with secondary contributions from local sequence features. The results define a framework for understanding regulatory factor–genome interactions and provide! a molecular basis for the tissue selectivity of steroid pharmaceuticals and other agents that intersect the living genome. View full text Figures at a glance * Figure 1: Dominant effect of chromatin accessibility on glucocorticoid receptor occupancy patterns. (,) Examples of DNase I sensitivity and glucocorticoid receptor occupancy patterns in relation to dexamethasone exposure (see Supplementary Fig. 3a–c for additional examples). Each data track shows tag density (150-bp sliding window) from either DNase I–seq or glucocorticoid receptor ChIP-seq, normalized to allow comparison across different samples (Online Methods). Green arrows mark sites of post-hormone glucocorticoid receptor occupancy in pre-existing DNase I–sensitive chromatin ('preprogrammed' sites). Red arrows mark glucocorticoid receptor occupancy sites in pre-hormone inaccessible chromatin that result in post-hormone chromatin remodeling ('reprogrammed' sites). Blue arrows mark hormone-induced DHSs not directly associated with glucocorticoid receptor occupancy (Supplementary Fig. 4c). () Venn diagram summarizing global glucocorticoid receptor occupancy compared to the chromatin accessibility landscape (~25 million read depth) in mammary cells (for legibility, ! the glucocorticoid receptor circle is shown at ×5 scale). Most glucocorticoid receptor occupancy occurs within pre-hormone accessible chromatin. A small fraction of generally weak glucocorticoid receptor peaks (5.2% of total peaks) are not associated with reprogrammed or preprogrammed chromatin. () DNase I sensitivity (tag density) pre-hormone (horizontal axis) compared to post-hormone (vertical axis) treatment. Colors match those used in . Black, pre-hormone accessible regions with no post-hormone glucocorticoid receptor occupancy; blue, DNase I–sensitive regions induced post-hormone without glucocorticoid receptor occupancy (Supplementary Fig. 4c); green; pre-hormone DNase I–sensitive regions occupied by glucocorticoid receptor post-hormone ('preprogrammed' sites); red, pre-hormone inaccessible chromatin remodeled by glucocorticoid receptor occupancy ('reprogrammed' sites), resulting in marked alteration in DNase I sensitivity (Supplementary Fig. 4a,b). GR, glucocort! icoid receptor. * Figure 2: The quantitative effect of chromatin context on glucocorticoid receptor occupancy of GRBEs. () The top scoring motif recovered from de novo motif discovery performed on the top 500 glucocorticoid receptor occupancy sites by ChIP-seq tag density (MEME E value = 8.6 × 10−753) closely matches the consensus glucocorticoid receptor binding element (GRBE). () A 50-kb genomic region comparing pre- and post-hormone chromatin accessibility and glucocorticoid receptor occupancy in relation to GRBE genomic sequence matches (P < 10−3). Only a small fraction of the ~2.3 × 106 GRBE consensus sites are occupied in vivo, and occupied sites differ in their underlying combinations of consensus GRBE motif nucleotides. () GRBE sequence classes ranked by chromatin context coefficient (CCC). Genomic GRBE motif matches could be partitioned into discrete sequence classes, each comprising an identical, and distinct, combination of consensus nucleotides. Within each class of identical sequence elements, occurrence of member genomic sequences in a range of pre-hormone DNase I–sensiti! vity environments (from inaccessible to hyperaccessible) enabled quantification of the effect of chromatin context on the probability of post-hormone glucocorticoid receptor occupancy. Ranking specific GRBE sequence classes by CCC revealed graded sensitivity to chromatin context, from highly context-dependent elements that engender glucocorticoid receptor occupancy only when situated in accessible chromatin, to relatively context-independent elements associated with sites where glucocorticoid receptor occupancy induces chromatin remodeling. () Model illustrating the contribution of chromatin accessibility to transcription factor binding. CCC encodes the occupancy potential of different GRBE sequence classes relative to accessibility. GR, glucocorticoid receptor. * Figure 3: Cell-specific chromatin landscapes determine cell-selective glucocorticoid receptor occupancy. (,) Pituitary-specific glucocorticoid receptor occupancy dictated by pituitary-specific DNase I–sensitivity transitions. Shown are examples of DNase I sensitivity and glucocorticoid receptor occupancy patterns in relation to hormone exposure comparing mouse mammary (3134) and pituitary (AtT-20) cells (see Fig. 1 legend and Supplementary Fig. 8a–c for additional examples). () Global glucocorticoid receptor occupancy compared to the chromatin accessibility landscape in pituitary cells. In pituitary cells, virtually all sites of glucocorticoid receptor occupancy (94.9%, or 3,079 out of 3,242 sites) occurred within pre-hormone accessible chromatin. The small fraction of reprogrammed glucocorticoid receptor sites (138 glucocorticoid receptor ChIP peaks, 4.2% of total) is shown in red. As in mammary cells, only a small fraction of pre-hormone accessible chromatin was occupied (for legibility, the glucocorticoid receptor circle is shown at ×5 scale). () Significant differences! in the genomic distribution of pre-hormone DNase I sensitivity in mammary (gray) compared to pituitary (green) cells; only 0.78% of the genome (20.5 Mb) was accessible in both cell types. () Glucocorticoid receptor occupancy is highly cell selective. Only 371 glucocorticoid receptor occupancy sites are shared between mammary and pituitary cells (4.5% of 3134 cell sites and 11.4% of AtT-20 cell sites). * Figure 4: Regulatory motifs in glucocorticoid receptor–occupied regions differ substantially between cell types. (,) Results of de novo motif discovery (Supplementary Note) performed on the top 500 glucocorticoid receptor occupancy sites identified in 3134 () and AtT-20 () cells. The glucocorticoid receptor sites were further separated into preprogrammed (glucocorticoid receptor occupancy within pre-hormone accessible chromatin) and reprogrammed (glucocorticoid receptor occupancy within pre-hormone inaccessible chromatin) sites. Shown are motifs with highly significant enrichment (P < 10−5). In all cases, the GRBE was the most highly enriched single motif (E = 8.6 × 10−753). Notably, AP1 and AML1 motifs were enriched in 3134 cells (), whereas HNF3 and NF1 were correspondingly enriched in AtT-20 cells (). () Motif occurrence patterns across all glucocorticoid receptor occupancy sites. Bar plots show percentage of all glucocorticoid receptor occupancy sites (8,236 sites in 3134 cells compared to 3,242 sites in AtT-20 cells) that harbor significant matches to the de novo–identified! motifs from and . Note that canonical GRBEs are highly enriched in reprogrammed sites compared to preprogrammed sites (>80% of reprogrammed sites compared to <30% of preprogrammed sites; P < 10−4). Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE26189 * GSM642864 * GSM642865 * GSM642866 * GSM642867 * GSM642868 * GSM642869 * GSM642870 * GSM642871 * GSM642872 * GSM642873 * GSM642874 * GSM642875 * GSM642876 * GSM642877 * GSM642878 Sequence Read Archive * SRP004871 * SRX034804 * SRX034802 * SRX034811 * SRX034818 * SRX034860 * SRX034861 * SRX034862 * SRX034863 * SRX034864 * SRX034865 * SRX034837 * SRX034838 * SRX034867 * SRX034868 * SRX034869 * SRX034870 * SRX034871 * SRX034872 Author information * Accession codes * Author information * Supplementary information Affiliations * Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, National Institutes of Health (NIH), Bethesda, Maryland, USA. * Sam John, * Myong-Hee Sung, * Simon C Biddie, * Thomas A Johnson & * Gordon L Hager * Department of Genome Sciences, University of Washington, Seattle, Washington, USA. * Peter J Sabo, * Robert E Thurman & * John A Stamatoyannopoulos * Department of Medicine, Division of Oncology, University of Washington, Seattle Cancer Care Alliance, Seattle, Washington, USA. * John A Stamatoyannopoulos Contributions S.J., P.J.S., G.L.H. and J.A.S. designed the experiments. S.J., P.J.S., S.C.B. and T.A.J. conducted the DNase-seq, ChIP-seq and expression array experiments. S.J., P.J.S., R.E.T., M.-H.S. and J.A.S. analyzed the data. S.J., P.J.S., R.E.T., M.-H.S., G.L.H. and J.A.S. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * John A Stamatoyannopoulos or * Gordon L Hager Author Details * Sam John Search for this author in: * NPG journals * PubMed * Google Scholar * Peter J Sabo Search for this author in: * NPG journals * PubMed * Google Scholar * Robert E Thurman Search for this author in: * NPG journals * PubMed * Google Scholar * Myong-Hee Sung Search for this author in: * NPG journals * PubMed * Google Scholar * Simon C Biddie Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas A Johnson Search for this author in: * NPG journals * PubMed * Google Scholar * Gordon L Hager Contact Gordon L Hager Search for this author in: * NPG journals * PubMed * Google Scholar * John A Stamatoyannopoulos Contact John A Stamatoyannopoulos Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 1 (4M) DNaseI sensitive regions in the baseline (pre-hormone) state in the murine mammary adenocarcinoma cell line, 3134; DNase I sensitive regions in post dexamethasone-treated 3134 cells * Supplementary Table 2 (4M) DNaseI hypersensitive sites (DHSs) in the baseline (pre-hormone) state in the murine mammary adenocarcinoma cell line, 3134 * Supplementary Table 3 (4M) DNaseI hypersensitive sites (DHSs) in post dexamethasone-treated 3134 cells * Supplementary Table 4 (504K) GR occupancy sites in the murine mammary adenocarcinoma cell line, 3134 (FDR 0%) * Supplementary Table 5 (139K) Expression analysis of mammary (3134) and pituitary (AtT-20) cells * Supplementary Table 6 (311K) GRBE sequence classes with greater than 50 instances in the genome. Chromatin Context Coefficient (CCC) classes in the murine genome * Supplementary Table 7 (4M) DNaseI sensitive regions in the baseline (pre-hormone) state in the murine pituitary cell line, AtT-20; DNase I sensitive regions in the post-hormone state in the murine pituitary cell line, AtT-20 * Supplementary Table 8 (4M) DNaseI hypersensitive sites (DHSs) in the baseline (pre-hormone) state in the murine pituitary cell line, AtT-20 * Supplementary Table 9 (4M) DNaseI hypersensitive sites (DHSs) post-hormone in AtT-20 cells * Supplementary Table 10 (209K) GR occupancy sites in the murine pituitary cell line, AtT-20 (FDR 0%) PDF files * Supplementary Text and Figures (147K) Supplementary Note, Supplementary Figures 1–10 Additional data - Discovery and genotyping of genome structural polymorphism by sequencing on a population scale
- Nat Genet 43(3):269-276 (2011)
Nature Genetics | Technical Report Discovery and genotyping of genome structural polymorphism by sequencing on a population scale * Robert E Handsaker1, 2 * Joshua M Korn1, 2 * James Nemesh1, 2 * Steven A McCarroll1, 2, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:269–276Year published:(2011)DOI:doi:10.1038/ng.768Received23 August 2010Accepted20 January 2011Published online13 February 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 Accurate and complete analysis of genome variation in large populations will be required to understand the role of genome variation in complex disease. We present an analytical framework for characterizing genome deletion polymorphism in populations using sequence data that are distributed across hundreds or thousands of genomes. Our approach uses population-level concepts to reinterpret the technical features of sequence data that often reflect structural variation. In the 1000 Genomes Project pilot, this approach identified deletion polymorphism across 168 genomes (sequenced at 4× average coverage) with sensitivity and specificity unmatched by other algorithms. We also describe a way to determine the allelic state or genotype of each deletion polymorphism in each genome; the 1000 Genomes Project used this approach to type 13,826 deletion polymorphisms (48–995,664 bp) at high accuracy in populations. These methods offer a way to relate genome structural polymorphism to c! omplex disease in populations. View full text Figures at a glance * Figure 1: A population-aware analytical framework for analyzing Genome STRucture in Populations (Genome STRiP). () Population-scale sequence data contain two classes of information: technical features of the sequence data within a genome and population-scale patterns that span all the genomes analyzed. Technical features include breakpoint-spanning reads2, 3, paired-end sequences4, 5, 6 and local variation in read depth of coverage7, 8, 9. Genome STRiP combines these with population-scale patterns that span many genomes, including: the sharing of structural alleles by multiple genomes; the pattern of sequence heterogeneity within a population; the substitution of alternative structural alleles for each other; and the haplotype structure of human genome polymorphism. () Goals of structural variation (SV) analysis in Genome STRiP. 'Variation discovery' involves identifying the structural alleles that are segregating in a population. The power to observe a variant in any one genome is only partial, but the evidence defining a segregating site can be derived from many genomes at once. 'Po! pulation genotyping' requires accurately determining the allelic state of each variant in every diploid genome in a population. * Figure 2: Identifying coherent sets of aberrantly mapping reads from a population of genomes. () Millions of end-sequence pairs from sequencing libraries show aberrant alignment locations, appearing to span vast genomic distances. Almost all of these observations derive not from true structural variants but from chimeric inserts in molecular sequencing libraries. Data shown are paired-end alignments on chromosome 5 from 41 initial genome sequencing libraries from the 1000 Genomes Project. () A set of 'coherently aberrant' end-sequence pairs from many genomes. At this genomic locus, paired-end sequences (sequences of the two ends of the inserts in a molecular library) fall into two classes: (i) end-sequence pairs that show the genomic spacing expected given the insert size distribution of each sequencing library, such as the three-read–pair alignments for genome NA07037; and (ii) end-sequence pairs that align to genomic locations unexpectedly far apart but which relate to their expected insert size distributions by a shared correction factor (red arrows). A unifying! model in which these eight read pairs from five genomes arise from a shared deletion allele (size of red arrows) converts all of these aberrant read pairs to likely observations. In the right panel, the black tick marks indicate genomic distance between left and right end sequences; the black curves indicate insert size distributions of the molecular library from which each sequence-pair was drawn. * Figure 3: Evaluating the population-heterogeneity and allele-substitution properties of population-scale sequence data. () At a candidate deletion locus, the distribution across genomes of 'evidentiary reads' (read pairs suggesting the presence of a deletion allele at a locus) (blue bars) is compared to a null model under which genomes are equally likely, per molecule sequenced, to give rise to such evidentiary reads (green curve). For the locus shown, the distribution of evidentiary reads across genomes differs from the null distribution (P = 1 × 10−4), confirming that evidentiary sequence data appears differentially within the population at this locus. () At another genomic locus, putative structural variation–supporting read pairs arise from many genomes but in a pattern that does not significantly differ from a null distribution based on equal probability per molecule sequenced. Subsequent assays confirmed that this is not a true deletion. () Distribution of a population-heterogeneity statistic (from ,) for read-pair data at 1,420 sites of known deletion polymorphism. () Distribution! of the same population-heterogeneity statistic from read-pair data at 45,000 candidate deletion loci nominated by read-pair analysis. (,) If a putative deletion is real, then genomes with molecular evidence for the deletion allele would be expected to have less evidence for the reference allele ('allelic substitution'). A simple test of allelic substitution is to compare average read depth (across a putative deletion segment) between two subpopulations—the genomes with read-pair evidence for the deletion (blue curve) and the genomes lacking such evidence (black trace). The locus in was subsequently validated as containing a real deletion; the locus in was not. () Distribution of this 'subpopulation depth ratio' statistic (,) for sequence data at 1,420 sites of known deletion polymorphism. () Distribution of the same statistic for sequence data at 45,000 candidate deletion loci. * Figure 4: Deletion polymorphisms identified by Genome STRiP in low-coverage sequence data from 168 genomes. () Size distribution. Sensitivity for large deletions (>10 kb) is similar to that of the array-based approaches applied in large, population-scale studies (red); sensitivity for deletions smaller than 10 kb is much greater. A strong peak near 300 bp arises from ALU insertion polymorphisms; a smaller peak near 6 kb arises from L1 insertion polymorphisms. Number of evidentiary sequence reads () and genomes () contributing to each deletion discovery in population-scale sequence data. We identified 1,033 of these deletions (14.7%) with evidentiary pairs from single genomes. () Specificity: false discovery rates of ten deletion discovery methods evaluated by the 1000 Genomes Project in the Project's population-scale low-coverage sequence data. () Sensitivity: power of the same ten discovery methods in identifying known deletions as a function of the allele frequency of the deletion. () Localization of the breakpoints of a common deletion allele using read-pair data from many geno! mes. The difference between (i) the genomic separation of each read-pair sequence and (ii) the insert-size distribution of the molecular library from which is it drawn (Fig. 2b) allows a likelihood-based estimate of deletion length from each read pair (blue curves). Combining this likelihood information across many genomes (black curve) allows fine-scale localization of the breakpoint. () Resolution of breakpoint estimates from Genome STRiP, as estimated using Genome STRiP confidence intervals (red) and comparison to molecularly established breakpoint sequences (blue). () Fine-scale localization of a structural variation breakpoint facilitates directed local assembly of the deletion allele from sequence data derived from many genomes. * Figure 5: Determining the allelic state (genotype) of 13,826 deletions in 156 genomes. () Four of the 13,826 deletion polymorphisms analyzed, representing diverse properties in terms of size and alignability of the affected sequence. Gray vertical rectangles indicate a sequence that is repeat masked or otherwise non-alignable. The locus in the bottom row is an ALU insertion polymorphism. () Population-scale distribution of read depth across genomes at each of the deletion loci in . For each locus, normalized measurements of read depth (across the deleted segment) from 156 genomes were fitted to a Gaussian mixture model. Colored squares represent genomes for which genotype could be called at 95% confidence based on read depth. () Genotype likelihood from read depth. Each horizontal stripe (corresponding to 1 of the 156 genomes) is divided into three sections with length proportional to the estimated relative likelihood of the sequence data given each genotype model (blue, copy-number 2; green, copy-number 1; orange, copy-number 0). () Genotype likelihood based ! on evidence from read pairs (RP) and breakpoint-spanning reads (BR). At the third locus from top, the absence of an established breakpoint sequence limits inference to read pairs. () Genotype likelihood based on integrating evidence from read depth (RD), read pairs (RP) and breakpoint-spanning reads (BR). () Genotype likelihood based on integrating evidence from with flanking SNP data in a population haplotype model. () Population-scale sequence data at each locus as resolved into genotype classes. Traces indicate average read depth for genomes of each inferred genotype. Orange and green rectangles indicate evidentiary read pairs and breakpoint-spanning reads, colored by the genotype determination for the genome from which they arise. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA. * Robert E Handsaker, * Joshua M Korn, * James Nemesh & * Steven A McCarroll * Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. * Robert E Handsaker, * Joshua M Korn, * James Nemesh & * Steven A McCarroll * Stanley Center for Psychiatric Disease Research, Cambridge, Massachusetts, USA. * Steven A McCarroll Contributions R.E.H., J.M.K., J.N. and S.A.M. conceived the analytical approaches. R.E.H. implemented the algorithms and performed the data analysis. R.E.H. and S.A.M. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Steven A McCarroll Author Details * Robert E Handsaker Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua M Korn Search for this author in: * NPG journals * PubMed * Google Scholar * James Nemesh Search for this author in: * NPG journals * PubMed * Google Scholar * Steven A McCarroll Contact Steven A McCarroll Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Other * Supplementary Table 2 (56K) Evaluation of genotype likelihood calibration * Supplementary Table 3 (4M) tagSNPs identified by Genome STRiP for deletions from the 1000 Genomes Project * Supplementary Table 4 (96K) Phenotype associated SNPs in linkage disequilibrium with 1000 Genomes pilot deletions PDF files * Supplementary Text and Figures (808K) Supplementary Figures 1–6, Supplementary Table 1 and Supplementary Note. Additional data - Erratum: Barton Childs 1916–2010
- Nat Genet 43(3):277 (2011)
Nature Genetics | Erratum Erratum: Barton Childs 1916–2010 * Barbara R MigeonJournal name:Nature GeneticsVolume: 43,Page:277Year published:(2011)DOI:doi:10.1038/ng0311-277c Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Genet.43, 7 (2011); published online 28 December 2010; corrected after print 4 February 2011 In the version of this obituary initially published, the date of death reported was incorrect. The correct date is 18 February 2010. Also, a quote was incorrectly attributed to Kurt Hirschhorn rather than David Valle, who introduced Childs at the 1999 meeting of the American College of Medical Genetics and not the American Society of Human Genetics. These errors have been corrected in the HTML and PDF versions of the article. Additional data Author Details * Barbara R Migeon Search for this author in: * NPG journals * PubMed * Google Scholar - Corrigendum: Mutations in VIPAR cause an arthrogryposis, renal dysfunction and cholestasis syndrome phenotype with defects in epithelial polarization
- Nat Genet 43(3):277 (2011)
Nature Genetics | Corrigendum Corrigendum: Mutations in VIPAR cause an arthrogryposis, renal dysfunction and cholestasis syndrome phenotype with defects in epithelial polarization * Andrew R Cullinane * Anna Straatman-Iwanowska * Andreas Zaucker * Yoshiyuki Wakabayashi * Christopher K Bruce * Guanmei Luo * Fatimah Rahman * Figen Gürakan * Eda Utine * Tanju B Özkan * Jonas Denecke * Jurica Vukovic * Maja Di Rocco * Hanna Mandel * Hakan Cangul * Randolph P Matthews * Steve G Thomas * Joshua Z Rappoport * Irwin M Arias * Hartwig Wolburg * A S Knisely * Deirdre A Kelly * Ferenc Müller * Eamonn R Maher * Paul GissenJournal name:Nature GeneticsVolume: 43,Page:277Year published:(2011)DOI:doi:10.1038/ng0311-277a Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Genet.42, 303–312 (2010); published online 28 February 2010; corrected after print 24 February 2011 In the version of this article initially published, the first and second paragraphs of the Results incorrectly stated that the C14ORF133 gene product was a previously unidentified protein and that no antibody against the C14ORF133 gene product (VIPAR) was available. In fact, an earlier study (ref. 20 in the original manuscript) reported functional analyses of the C14ORF133 gene product (also called SPE-39), described the generation of a polyclonal antibody against human SPE-39 and reported an interaction between SPE-39 and VPS33B, similar to the interaction shown in Figure 1b. These errors have been corrected in the HTML and PDF versions of the article. Additional data Author Details * Andrew R Cullinane Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Straatman-Iwanowska Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Zaucker Search for this author in: * NPG journals * PubMed * Google Scholar * Yoshiyuki Wakabayashi Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher K Bruce Search for this author in: * NPG journals * PubMed * Google Scholar * Guanmei Luo Search for this author in: * NPG journals * PubMed * Google Scholar * Fatimah Rahman Search for this author in: * NPG journals * PubMed * Google Scholar * Figen Gürakan Search for this author in: * NPG journals * PubMed * Google Scholar * Eda Utine Search for this author in: * NPG journals * PubMed * Google Scholar * Tanju B Özkan Search for this author in: * NPG journals * PubMed * Google Scholar * Jonas Denecke Search for this author in: * NPG journals * PubMed * Google Scholar * Jurica Vukovic Search for this author in: * NPG journals * PubMed * Google Scholar * Maja Di Rocco Search for this author in: * NPG journals * PubMed * Google Scholar * Hanna Mandel Search for this author in: * NPG journals * PubMed * Google Scholar * Hakan Cangul Search for this author in: * NPG journals * PubMed * Google Scholar * Randolph P Matthews Search for this author in: * NPG journals * PubMed * Google Scholar * Steve G Thomas Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua Z Rappoport Search for this author in: * NPG journals * PubMed * Google Scholar * Irwin M Arias Search for this author in: * NPG journals * PubMed * Google Scholar * Hartwig Wolburg Search for this author in: * NPG journals * PubMed * Google Scholar * A S Knisely Search for this author in: * NPG journals * PubMed * Google Scholar * Deirdre A Kelly Search for this author in: * NPG journals * PubMed * Google Scholar * Ferenc Müller Search for this author in: * NPG journals * PubMed * Google Scholar * Eamonn R Maher Search for this author in: * NPG journals * PubMed * Google Scholar * Paul Gissen Search for this author in: * NPG journals * PubMed * Google Scholar - Corrigendum: Common variants in DGKK are strongly associated with risk of hypospadias
- Nat Genet 43(3):277 (2011)
Nature Genetics | Corrigendum Corrigendum: Common variants in DGKK are strongly associated with risk of hypospadias * Loes F M van der Zanden * Iris A L M van Rooij * Wout F J Feitz * Jo Knight * A Rogier T Donders * Kirsten Y Renkema * Ernie M H F Bongers * Sita H H M Vermeulen * Lambertus A L M Kiemeney * Joris A Veltman * Alejandro Arias-Vásquez * Xufeng Zhang * Ellen Markljung * Liang Qiao * Laurence S Baskin * Agneta Nordenskjöld * Nel Roeleveld * Barbara Franke * Nine V A M KnoersJournal name:Nature GeneticsVolume: 43,Page:277Year published:(2011)DOI:doi:10.1038/ng0311-277b Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Genet.43, 48–50 (2011); published online 28 November 2010; corrected after print 24 February 2011 In the version of this article initially published, the third and fourth column headings in Table 2 were mislabeled and the abbreviations in the footnotes of Table 2 were inadvertently duplicated. The correct heading for the third column is "T" and the correct heading for the fourth column is "NT". These errors have been corrected in the HTML and PDF versions of the article. Additional data Author Details * Loes F M van der Zanden Search for this author in: * NPG journals * PubMed * Google Scholar * Iris A L M van Rooij Search for this author in: * NPG journals * PubMed * Google Scholar * Wout F J Feitz Search for this author in: * NPG journals * PubMed * Google Scholar * Jo Knight Search for this author in: * NPG journals * PubMed * Google Scholar * A Rogier T Donders Search for this author in: * NPG journals * PubMed * Google Scholar * Kirsten Y Renkema Search for this author in: * NPG journals * PubMed * Google Scholar * Ernie M H F Bongers Search for this author in: * NPG journals * PubMed * Google Scholar * Sita H H M Vermeulen Search for this author in: * NPG journals * PubMed * Google Scholar * Lambertus A L M Kiemeney Search for this author in: * NPG journals * PubMed * Google Scholar * Joris A Veltman Search for this author in: * NPG journals * PubMed * Google Scholar * Alejandro Arias-Vásquez Search for this author in: * NPG journals * PubMed * Google Scholar * Xufeng Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Ellen Markljung Search for this author in: * NPG journals * PubMed * Google Scholar * Liang Qiao Search for this author in: * NPG journals * PubMed * Google Scholar * Laurence S Baskin Search for this author in: * NPG journals * PubMed * Google Scholar * Agneta Nordenskjöld Search for this author in: * NPG journals * PubMed * Google Scholar * Nel Roeleveld Search for this author in: * NPG journals * PubMed * Google Scholar * Barbara Franke Search for this author in: * NPG journals * PubMed * Google Scholar * Nine V A M Knoers Search for this author in: * NPG journals * PubMed * Google Scholar
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