tag:blogger.com,1999:blog-59691815900501024572012-01-26T21:39:13.918-08:00news and updates about the fastest way to search science <a href="http://pubget.com/">http://pubget.com</a>ian connorhttp://www.blogger.com/profile/17012291553690617903noreply@blogger.comBlogger39041100tag:blogger.com,1999:blog-5969181590050102457.post-50373981533942910442012-01-26T21:39:00.001-08:002012-01-26T21:39:14.084-08:00The Jan 01 issue of the <a href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol[latest]" ><i>Nat Struct Mol Biol</i></a> is now up on <a href="http://pubget.com/">Pubget</a> (<a href="http://pubget.com/profile/journal/Nat%20Struct%20Mol%20Biol"><i>About Nat Struct Mol Biol</i></a>): if you're at a subscribing institution, just click the link in the latest link at the home page. (Note you'll only be able to get all the PDFs in the issue if your institution <a href="http://pubget.com/site/contact/contact_box">subscribes to Pubget</a>.) <p>Latest Articles Include:</p> <ul> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_9f608776e7e5954cc9a5bd90206fcbbd"> Finding the missing links in EGFR</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_9f608776e7e5954cc9a5bd90206fcbbd">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_9f608776e7e5954cc9a5bd90206fcbbd"><strong>19</strong>(1):1-3</a> (2012)<br /> ARTICLE NAVIGATION - ISSUE Previous January 2012, Volume 19 No 1 pp1-127 * News and Views * Research Highlights * Review * Articles * Brief Communication * Technical ReportsAbout the cover News and Views Finding the missing links in EGFR - pp1 - 3 Nicholas J Bessman & Mark A Lemmon doi:10.1038/nsmb.2221 Structural studies of the epidermal growth factor receptor (EGFR) have advanced greatly in recent years, but they have used a 'divide-and-conquer' approach for independent study of the intracellular and extracellular regions. Several recent papers provide important new perspectives on 'undivided' EGFR and describe the initial steps in reconstructing signaling behavior of the intact receptor. Full Text - Finding the missing links in EGFR | PDF (638 KB) - Finding the missing links in EGFR Claims and counterclaims of X-chromosome compensation - pp3 - 5 James A Birchler doi:10.1038/nsmb.2218 Is there upregulation of the single active X chromosome in mammals or not? Recent studies take different points of view. Full Text - Claims and counterclaims of X-chromosome compensation | PDF (414 KB) - Claims and counterclaims of X-chromosome compensation See also:Article by Yildirim et al. Thresholds of replication stress signaling in cancer development and treatment - pp5 - 7 Jiri Bartek, Martin Mistrik & Jirina Bartkova doi:10.1038/nsmb.2220 Oncogene-induced replication stress and DNA damage are among the hallmarks of cancer. A recent study explores how different levels of replication stress affect animal development and tumorigenesis, and how targeting of the replication stress–signaling pathway of ATR and Chk1 kinases can be exploited for selective killing of cancer cells. Full Text - Thresholds of replication stress signaling in cancer development and treatment | PDF (8,036 KB) - Thresholds of replication stress signaling in cancer development and treatment Research Highlights * Methylating fingers * Structural basis of silencing * Stalled out * Rli1 does the splits Review New approaches for dissecting protease functions to improve probe development and drug discovery - pp9 - 16 Edgar Deu, Martijn Verdoes & Matthew Bogyo doi:10.1038/nsmb.2203 Abstract - New approaches for dissecting protease functions to improve probe development and drug discovery | Full Text - New approaches for dissecting protease functions to improve probe development and drug discovery | PDF (2,959 KB) - New approaches for dissecting protease functions to improve probe development and drug discovery | Supplementary information Articles RAD51- and MRE11-dependent reassembly of uncoupled CMG helicase complex at collapsed replication forks - pp17 - 24 Yoshitami Hashimoto, Fabio Puddu & Vincenzo Costanzo doi:10.1038/nsmb.2177 A system to reconstitute a collapsed replication fork using Xenopus laevis egg extracts is developed. The study shows that upon fork collapse, DNA Pol epsilon and the GINS complex are uncoupled from the replisome, and their reloading onto DNA requires repair proteins Rad51 and Mre11. Abstract - RAD51- and MRE11-dependent reassembly of uncoupled CMG helicase complex at collapsed replication forks | Full Text - RAD51- and MRE11-dependent reassembly of uncoupled CMG helicase complex at collapsed replication forks | PDF (915 KB) - RAD51- and MRE11-dependent reassembly of uncoupled CMG helicase complex at collapsed replication forks | Supplementary information A unique H2A histone variant occupies the transcriptional start site of active genes - pp25 - 30 Tatiana A Soboleva, Maxim Nekrasov, Anuj Pahwa, Rohan Williams, Gavin A Huttley & David J Tremethick doi:10.1038/nsmb.2161 The histone variant H2A.Bbd inhibits folding of nucleosomal arrays and reverses chromatin-mediated transcriptional repression in vitro. New studies have uncovered the related mouse H2A variant H2A.Lap1 as a novel component of the transcription start site of active genes during specific stages of spermatogenesis, which enables transcriptional activation by unfolding the chromatin locally. Abstract - A unique H2A histone variant occupies the transcriptional start site of active genes | Full Text - A unique H2A histone variant occupies the transcriptional start site of active genes | PDF (1,514 KB) - A unique H2A histone variant occupies the transcriptional start site of active genes | Supplementary information Signal-dependent dynamics of transcription factor translocation controls gene expression - pp31 - 39 Nan Hao & Erin K O'Shea doi:10.1038/nsmb.2192 The Msn2 transcription factor is translocated to the nucleus to activate transcription of hundreds of genes in response to various environmental stimuli. Experimental and computational single-molecule analyses reveal how different stimuli elicit different dynamical patterns of Msn2 translocation, which are interpreted by promoters with distinct properties to produce specific patterns of target gene expression. Abstract - Signal-dependent dynamics of transcription factor translocation controls gene expression | Full Text - Signal-dependent dynamics of transcription factor translocation controls gene expression | PDF (1,086 KB) - Signal-dependent dynamics of transcription factor translocation controls gene expression | Supplementary information Intrinsic tethering activity of endosomal Rab proteins - pp40 - 47 Sheng-Ying Lo, Christopher L Brett, Rachael L Plemel, Marissa Vignali, Stanley Fields, Tamir Gonen & Alexey J Merz doi:10.1038/nsmb.2162 Rab small G proteins regulate membrane trafficking events by recruiting effectors that mediate vesicle tethering. In vitro studies now suggest that Vps21 and other endosomal Rabs in budding yeast can undergo GTP-regulated Rab-Rab interactions that drive tethering in the absence of effectors, implying that they have an intrinsic tethering activity that may function in concert with conventional effectors. Abstract - Intrinsic tethering activity of endosomal Rab proteins | Full Text - Intrinsic tethering activity of endosomal Rab proteins | PDF (1,333 KB) - Intrinsic tethering activity of endosomal Rab proteins | Supplementary information Ndc10 is a platform for inner kinetochore assembly in budding yeast - pp48 - 55 Uhn-Soo Cho & Stephen C Harrison doi:10.1038/nsmb.2178 * PDB code * 3SQI * 3T79 * 3D view * 3SQI * 3T79 Kinetochores assemble on centromeric DNA and link centromeres to spindle microtubules, thus allowing proper segregation during mitosis. The kinetochore subunit Ndc10 makes contacts with centromeric DNA elements, which are now directly observed in a crystal structure. Along with biochemical analyses, the work indicates that Ndc10 functions as a central organizing hub to assemble the inner kinetochore. Abstract - Ndc10 is a platform for inner kinetochore assembly in budding yeast | Full Text - Ndc10 is a platform for inner kinetochore assembly in budding yeast | PDF (1,395 KB) - Ndc10 is a platform for inner kinetochore assembly in budding yeast | Supplementary information X-chromosome hyperactivation in mammals via nonlinear relationships between chromatin states and transcription - pp56 - 61 Eda Yildirim, Ruslan I Sadreyev, Stefan F Pinter & Jeannie T Lee doi:10.1038/nsmb.2195 In addition to balancing X-chromosome dosage between males and females via X inactivation, mammals also balance dosage of X chromosomes and autosomes. Allele-specific chromatin immunoprecipitation with deep sequencing (ChIP-seq) analyses now show that the active X chromosome is upregulated at the level of both transcription initiation and elongation. Abstract - X-chromosome hyperactivation in mammals via nonlinear relationships between chromatin states and transcription | Full Text - X-chromosome hyperactivation in mammals via nonlinear relationships between chromatin states and transcription | PDF (1,029 KB) - X-chromosome hyperactivation in mammals via nonlinear relationships between chromatin states and transcription | Supplementary information See also:News and Views by Birchler An ankyrin-repeat ubiquitin-binding domain determines TRABID's specificity for atypical ubiquitin chains - pp62 - 71 Julien D F Licchesi, Juliusz Mieszczanek, Tycho E T Mevissen, Trevor J Rutherford, Masato Akutsu, Satpal Virdee, Farid El Oualid, Jason W Chin, Huib Ovaa, Mariann Bienz & David Komander doi:10.1038/nsmb.2169 * PDB code * 3ZRH * 3D view * 3ZRH The OTU domain deubiquitinase TRABID specifically hydrolyzes atypical Lys29- and Lys33-linked diubiquitin chains. Structural analysis of TRABID reveals an unpredicted ankyrin-repeat domain that binds ubiquitin and is crucial for TRABID efficiency and linkage specificity in vitro and in vivo. Abstract - An ankyrin-repeat ubiquitin-binding domain determines TRABID's specificity for atypical ubiquitin chains | Full Text - An ankyrin-repeat ubiquitin-binding domain determines TRABID's specificity for atypical ubiquitin chains | PDF (2,198 KB) - An ankyrin-repeat ubiquitin-binding domain determines TRABID's specificity for atypical ubiquitin chains | Supplementary information Mechanism of mismatch recognition revealed by human MutSβ bound to unpaired DNA loops - pp72 - 78 Shikha Gupta, Martin Gellert & Wei Yang doi:10.1038/nsmb.2175 * PDB code * 3THY * 3THX * 3THW * 3THZ * 3D view * 3THY * 3THX * 3THW * 3THZ Eukaryotic MutSβ is a heterodimer composed of Msh2 and Msh3 that recognizes insertion-deletion loops (IDLs) and 3′ overhangs during mismatch repair. Now crystal structures of MutSβ in complex with DNA, containing IDLs of varying lengths, reveal that this complex interacts with its substrate differently than MutSα and bacterial MutS do. Abstract - Mechanism of mismatch recognition revealed by human MutSβ bound to unpaired DNA loops | Full Text - Mechanism of mismatch recognition revealed by human MutSβ bound to unpaired DNA loops | PDF (1,980 KB) - Mechanism of mismatch recognition revealed by human MutSβ bound to unpaired DNA loops | Supplementary information The extracellular chaperone clusterin sequesters oligomeric forms of the amyloid-β1−40 peptide - pp79 - 83 Priyanka Narayan, Angel Orte, Richard W Clarke, Benedetta Bolognesi, Sharon Hook, Kristina A Ganzinger, Sarah Meehan, Mark R Wilson, Christopher M Dobson & David Klenerman doi:10.1038/nsmb.2191 Genome-wide association studies have established a link between the extracellular chaperone clusterin and susceptibility to Alzheimer's disease. A fluorescence approach is now used to reveal that clusterin sequesters Aβ1–40 oligomers and prevents them from undergoing further aggregation. Abstract - The extracellular chaperone clusterin sequesters oligomeric forms of the amyloid-β1−40 peptide | Full Text - The extracellular chaperone clusterin sequesters oligomeric forms of the amyloid-β1−40 peptide | PDF (625 KB) - The extracellular chaperone clusterin sequesters oligomeric forms of the amyloid-β1−40 peptide | Supplementary information Structural basis of pre-let-7 miRNA recognition by the zinc knuckles of pluripotency factor Lin28 - pp84 - 89 Fionna E Loughlin, Luca F R Gebert, Harry Towbin, Andreas Brunschweiger, Jonathan Hall & Frdric H-T Allain doi:10.1038/nsmb.2202 * PDB code * 2LI8 * 3D view * 2LI8 Lin28 prevents the processing of pre-let-7 RNAs, but it is not clear where the Lin28 RNA binding domains interact with the RNA. The NMR structure of the Lin28 zinc knuckles with a short RNA motif reveals that each knuckle interacts with an AG dinucleotide, allowing the determination of a consensus motif for pre-let-7 recognition. Abstract - Structural basis of pre-let-7 miRNA recognition by the zinc knuckles of pluripotency factor Lin28 | Full Text - Structural basis of pre-let-7 miRNA recognition by the zinc knuckles of pluripotency factor Lin28 | PDF (1,138 KB) - Structural basis of pre-let-7 miRNA recognition by the zinc knuckles of pluripotency factor Lin28 | Supplementary information Tudor domain ERI-5 tethers an RNA-dependent RNA polymerase to DCR-1 to potentiate endo-RNAi - pp90 - 97 Caroline Thivierge, Neetha Makil, Mathieu Flamand, Jessica J Vasale, Craig C Mello, James Wohlschlegel, Darryl Conte Jr & Thomas F Duchaine doi:10.1038/nsmb.2186 The type III ribonuclease DCR-1 is essential for ERI endogenous RNAi and exogenous RNAi in Caenorhabditis elegans. A new study shows that DCR-1 forms exclusive complexes in each pathway, and characterization of the ERI complex implicates a tudor domain protein in tethering an RNA-dependent RNA polymerase to DCR-1 to potentiate endo-RNAi. Abstract - Tudor domain ERI-5 tethers an RNA-dependent RNA polymerase to DCR-1 to potentiate endo-RNAi | Full Text - Tudor domain ERI-5 tethers an RNA-dependent RNA polymerase to DCR-1 to potentiate endo-RNAi | PDF (727 KB) - Tudor domain ERI-5 tethers an RNA-dependent RNA polymerase to DCR-1 to potentiate endo-RNAi | Supplementary information Mispaired rNMPs in DNA are mutagenic and are targets of mismatch repair and RNases H - pp98 - 104 Ying Shen, Kyung Duk Koh, Bernard Weiss & Francesca Storici doi:10.1038/nsmb.2176 Ribonucleoside monophosphates (rNMPs) are often incorporated into genomic DNA. Misincorporated rNMPs are now shown to be repaired by mismatch repair and RNases H. If not repaired, they can serve as a template for DNA synthesis and can cause mutagenesis in Escherichia coli and yeast. Abstract - Mispaired rNMPs in DNA are mutagenic and are targets of mismatch repair and RNases H | Full Text - Mispaired rNMPs in DNA are mutagenic and are targets of mismatch repair and RNases H | PDF (445 KB) - Mispaired rNMPs in DNA are mutagenic and are targets of mismatch repair and RNases H | Supplementary information A cis-antisense RNA acts in trans in Staphylococcus aureus to control translation of a human cytolytic peptide - pp105 - 112 Nour Sayed, Ambre Jousselin & Brice Felden doi:10.1038/nsmb.2193 Cis-encoded antisense RNAs (asRNAs) are transcribed from the DNA strand opposite another gene and function by pairing with RNAs expressed from the complementary strand. A new study provides evidence that a bacterial cis-asRNA acts in trans, using a domain outside of its target complementarity sequence, suggesting the need for a mechanistic re-evaluation of asRNA-based gene regulation. Abstract - A cis-antisense RNA acts in trans in Staphylococcus aureus to control translation of a human cytolytic peptide | Full Text - A cis-antisense RNA acts in trans in Staphylococcus aureus to control translation of a human cytolytic peptide | PDF (1,611 KB) - A cis-antisense RNA acts in trans in Staphylococcus aureus to control translation of a human cytolytic peptide | Supplementary information Brief Communication Single-molecule studies reveal the function of a third polymerase in the replisome - pp113 - 116 Roxana E Georgescu, Isabel Kurth & Mike E O'Donnell doi:10.1038/nsmb.2179 Recent work has indicated that the Escherichia coli replisome contains three DNA polymerases that are used to replicate two parental strands. A single-molecule approach is now used to compare replisomes reconstituted with two or three polymerases, revealing that the presence of a third polymerase ensures higher processivity overall and more efficient replication of the lagging strand. Abstract - Single-molecule studies reveal the function of a third polymerase in the replisome | Full Text - Single-molecule studies reveal the function of a third polymerase in the replisome | PDF (728 KB) - Single-molecule studies reveal the function of a third polymerase in the replisome | Supplementary information Technical Reports Fluorescent fusion protein knockout mediated by anti-GFP nanobody - pp117 - 121 Emmanuel Caussinus, Oguz Kanca & Markus Affolter doi:10.1038/nsmb.2180 The combination of an F-box domain with a single-domain antibody that recognizes green fluorescent protein (GFP) now allows controlled depletion of GFP fusions in mammalian cells and in flies. This system, called deGradFP, should be widely useful, as GFP fusions are available for many proteins in model organisms. Abstract - Fluorescent fusion protein knockout mediated by anti-GFP nanobody | Full Text - Fluorescent fusion protein knockout mediated by anti-GFP nanobody | PDF (1,208 KB) - Fluorescent fusion protein knockout mediated by anti-GFP nanobody | Supplementary information A metal switch for controlling the activity of molecular motor proteins - pp122 - 127 Jared C Cochran, Yu Cheng Zhao, Dean E Wilcox & F Jon Kull doi:10.1038/nsmb.2190 * PDB code * 3PXN * 3D view * 3PXN NTPases use a metal ion, typically Mg2+, coordinated by a conserved serine or threonine residue, to enable phosphate binding and catalysis. Now cysteine substitutions at the switch 1 motif of different kinesins render them able to use Mn2+ instead of Mg2+, allowing their enzymatic and motor activities to be modulated by the ratio of Mg2+ to Mn2+. Abstract - A metal switch for controlling the activity of molecular motor proteins | Full Text - A metal switch for controlling the activity of molecular motor proteins | PDF (1,145 KB) - A metal switch for controlling the activity of molecular motor proteins | Supplementary information </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_6cb0c80b0aa373e2f1aec62d04498831"> Claims and counterclaims of X-chromosome compensation</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_6cb0c80b0aa373e2f1aec62d04498831">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_6cb0c80b0aa373e2f1aec62d04498831"><strong>19</strong>(1):3-5</a> (2012)<br /> Article preview View full access options Nature Structural & Molecular Biology | News and Views Claims and counterclaims of X-chromosome compensation * James A Birchler1Journal name:Nature Structural & Molecular BiologyVolume: 19,Pages:3–5Year published:(2012)DOI:doi:10.1038/nsmb.2218Published online 05 January 2012 Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Is there upregulation of the single active X chromosome in mammals or not? Recent studies take different points of view. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Structural & Molecular Biology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Affiliations * James A. Birchler is in the Division of Biological Sciences, University of Missouri, Columbia, Missouri, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * James A Birchler Author Details * James A Birchler Contact James A Birchler Search for this author in: * NPG journals * PubMed * Google Scholar Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_ad05bf0e647dd2635b33c94d49c0c6a6"> Thresholds of replication stress signaling in cancer development and treatment</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_ad05bf0e647dd2635b33c94d49c0c6a6">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_ad05bf0e647dd2635b33c94d49c0c6a6"><strong>19</strong>(1):5-7</a> (2012)<br /> Article preview View full access options Nature Structural & Molecular Biology | News and Views Thresholds of replication stress signaling in cancer development and treatment * Jiri Bartek1, 2 * Martin Mistrik2 * Jirina Bartkova1 * Affiliations * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:5–7Year published:(2012)DOI:doi:10.1038/nsmb.2220Published online 05 January 2012 Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Oncogene-induced replication stress and DNA damage are among the hallmarks of cancer. A recent study explores how different levels of replication stress affect animal development and tumorigenesis, and how targeting of the replication stress–signaling pathway of ATR and Chk1 kinases can be exploited for selective killing of cancer cells. Figures at a glance * Figure 1: Impact of different levels of ATR signaling on organismal development and tumorigenesis. Whereas wild-type and heterozygous mice develop normally, both human patients17 and the corresponding mouse model18 with pronounced genetic defects in ATR suffer from a complex developmental disorder (Seckel syndrome). The role of ATR in tumorigenesis also depends on a signaling threshold, as heterozygous mice are haploinsufficient for tumor suppression22, 23. Murga et al.4 show that excessive replication stress under in vivo conditions of very low ATR with concomitant overexpression of the Myc oncogene leads to synthetic lethality at the cellular level, resulting in exacerbation of the Seckel syndrome (*) and the virtual absence of tumors (**). * Figure 2: Distinct roles of the ATR-Chk1 pathway during multistep tumorigenesis. Oncogene activation in early lesions leads to replication stress and DNA damage, consequently triggering the DDR machinery and leading to checkpoint-imposed senescence or death of nascent tumor cells3, 4, 5, 6, 7. In tumors where the DDR barrier is overcome (for example, by selection for p53 mutations), disease progression may ensue. The advanced tumors still experience the oncogene-induced replication stress and genetic instability and often adapt to such challenge. In the context of disabled cell-cycle checkpoints and apoptosis, the abilities of the ATR-Chk1 signaling module to support the replication machinery and promote DNA repair can thus be 'hijacked' to boost the overall fitness of the malignant tumor. * Figure 3: Potential exploitation of replication stress as a target for cancer therapy. Many, but not all, tumors feature enhanced replication stress (RS)4, 12, 24. Subsets of cancers of diverse tissue origin might therefore be examined for markers of replication stress and/or activated RSR in order to select individuals who might benefit from treatment with drugs that inhibit the ATR or Chk1 kinases. Examples of preclinical results of such a treatment approach are reported by Murga et al.4. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Structural & Molecular Biology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Affiliations * Jiri Bartek and Jirina Bartkova are at the Centre for Genotoxic Stress Research, Danish Cancer Society, Copenhagen, Denmark. * Martin Mistrik and Jiri Bartek are at the Institute of Molecular and Translational Medicine, Palacky University, Olomouc, Czech Republic. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jiri Bartek Author Details * Jiri Bartek Contact Jiri Bartek Search for this author in: * NPG journals * PubMed * Google Scholar * Martin Mistrik Search for this author in: * NPG journals * PubMed * Google Scholar * Jirina Bartkova Search for this author in: * NPG journals * PubMed * Google Scholar Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_9f7b4e97db5f98c81563fa6480d4330a"> New approaches for dissecting protease functions to improve probe development and drug discovery</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_9f7b4e97db5f98c81563fa6480d4330a">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_9f7b4e97db5f98c81563fa6480d4330a"><strong>19</strong>(1):9-16</a> (2012)<br /> Nature Structural & Molecular Biology | Review New approaches for dissecting protease functions to improve probe development and drug discovery * Edgar Deu1 * Martijn Verdoes1 * Matthew Bogyo1, 2 * Affiliations * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:9–16Year published:(2012)DOI:doi:10.1038/nsmb.2203Published online 05 January 2012 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Proteases are well-established targets for pharmaceutical development because of their known enzymatic mechanism and their regulatory roles in many pathologies. However, many potent clinical lead compounds have been unsuccessful either because of a lack of specificity or because of our limited understanding of the biological roles of the targeted protease. In order to successfully develop protease inhibitors as drugs, it is necessary to understand protease functions and to expand the platform of inhibitor development beyond active site–directed design and in vitro optimization. Several newly developed technologies will enhance assessment of drug selectivity in living cells and animal models, allowing researchers to focus on compounds with high specificity and minimal side effects in vivo. In this review, we highlight advances in the development of chemical probes, proteomic methods and screening tools that we feel will help facilitate this paradigm shift in drug discovery. View full text Figures at a glance * Figure 1: Mechanism of substrate hydrolysis by the primary families of proteases. () Protease substrates bind through interactions of the side chain residues (P and P′ residues) with the substrate pockets of the protease (S and S′ pockets). The red dashed line indicates a scissile bond. (–) The architecture of the active site and mechanism of hydrolysis for N-terminal threonine, serine and cysteine proteases that use an acyl-enzyme intermediate formed through nucelophilic attack by the catalytic side chain residue. (,) In the case of zinc metalloproteases (), aspartate proteases () and glutamate proteases (not depicted), a carboxylic acid group or metal ion activates a water molecule, leading to acid-base catalysis. The seventh and newest protease family, the asparagine peptide lyases, cleave themselves using an asparagine residue as the nucleophile2 (not depicted). * Figure 2: Schematic presentation of the hit-to-lead process. () In a classical protease drug discovery approach, the emphasis of screening and optimization is on maximizing the potency of a hit compound for a recombinant protease. Off-target effects and efficacy are usually tested after the optimization process, and problems encountered when testing the compounds in cultures and in vivo require either modifying the structure of the lead inhibitor to solve a particular issue or selecting a different chemotype for further optimization. () In this review, we propose a holistic approach, in which the emphasis is on identifying hits in a more complex and relevant context (intact cells), incorporating the specificity profile of hits to identify and optimize lead compounds. We believe that placing the emphasis of the hit-to-lead optimization process on selectivity instead of just on potency will help prevent off-target effects and thus increase the chances for developing protease inhibitor drugs with minimum side effects. * Figure 3: Activity-based probes report on tightly regulated protease activity. () Proteases are not only regulated on the transcription and translation levels but also highly regulated on the protein level. Expressed as zymogens, proteases are activated in a variety of ways and by a variety of factors, depending on the protease, including allosteric activation, environmental changes, localization, protein-protein interactions and processing by upstream proteases. Endogenous inhibitors and targeted degradation form yet another layer of regulation. () Activity-based probes are small-molecule reporters that use the active protease's own chemistry to distinguish it from its zymogen or inhibited form. Most ABPs consist of three parts: a warhead (an electrophilic moiety that reacts with the active site nucleophile to result in a covalent and irreversible adduct), a spacer and/or recognition element that targets the probe to a specific target protease and a tag (usually a fluorescent dye and/or an affinity handle, like biotin). * Figure 4: Chemical tools to study protease function and to measure target inhibition. () Forward chemical genetics allows for target identification through the introduction of an affinity tag to the hit compound. () Near-infrared fluorescently labeled ABPs can be applied to top-down characterization of a target protease. Whole-animal noninvasive imaging techniques allow the visualization of target distribution, and extracted tissue can be analyzed ex vivo. Histology shows target distribution on a microscopic level, FACS analysis identifies the types of cells that contain active protease and biochemical analysis allows characterization at the protein level. Treatment with a lead compound before labeling provides information on target inhibition. Mouse images are from our previous publication39. () Broad-spectrum protease probes enable a readout of the inhibition profile of a lead compound for an entire protease family in a proteome. Members of the targeted family can be resolved on a gel, and inhibition results in diminished labeling of individual proteases. (! ) Global profiling of all reactive cysteines in a proteome; iodoacetamide-based reporter molecules will react primarily with the more reactive cysteines. Using isotopically labeled reporter molecules, this method can be used to predict functional cysteines in proteomes as well as to identify targets. When the methods described here are used to evaluate the specificity profile of a reversible inhibitor, the labeling conditions should be adjusted so that the covalent probe does not outcompete the inhibitor. Because these methods have a good dynamic range, this can be accomplished by lowering the probe concentration or decreasing the labeling times. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Pathology, Stanford University School of Medicine, Stanford, California, USA. * Edgar Deu, * Martijn Verdoes & * Matthew Bogyo * Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA. * Matthew Bogyo Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Matthew Bogyo Author Details * Edgar Deu Search for this author in: * NPG journals * PubMed * Google Scholar * Martijn Verdoes Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew Bogyo Contact Matthew Bogyo Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (74K) Supplementary Box 1 Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_77216bd20a8afc6e58b6eb7197c2c92b"> RAD51- and MRE11-dependent reassembly of uncoupled CMG helicase complex at collapsed replication forks</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_77216bd20a8afc6e58b6eb7197c2c92b">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_77216bd20a8afc6e58b6eb7197c2c92b"><strong>19</strong>(1):17-24</a> (2012)<br /> Nature Structural & Molecular Biology | Review New approaches for dissecting protease functions to improve probe development and drug discovery * Edgar Deu1 * Martijn Verdoes1 * Matthew Bogyo1, 2 * Affiliations * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:9–16Year published:(2012)DOI:doi:10.1038/nsmb.2203Published online 05 January 2012 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Proteases are well-established targets for pharmaceutical development because of their known enzymatic mechanism and their regulatory roles in many pathologies. However, many potent clinical lead compounds have been unsuccessful either because of a lack of specificity or because of our limited understanding of the biological roles of the targeted protease. In order to successfully develop protease inhibitors as drugs, it is necessary to understand protease functions and to expand the platform of inhibitor development beyond active site–directed design and in vitro optimization. Several newly developed technologies will enhance assessment of drug selectivity in living cells and animal models, allowing researchers to focus on compounds with high specificity and minimal side effects in vivo. In this review, we highlight advances in the development of chemical probes, proteomic methods and screening tools that we feel will help facilitate this paradigm shift in drug discovery. View full text Figures at a glance * Figure 1: Mechanism of substrate hydrolysis by the primary families of proteases. () Protease substrates bind through interactions of the side chain residues (P and P′ residues) with the substrate pockets of the protease (S and S′ pockets). The red dashed line indicates a scissile bond. (–) The architecture of the active site and mechanism of hydrolysis for N-terminal threonine, serine and cysteine proteases that use an acyl-enzyme intermediate formed through nucelophilic attack by the catalytic side chain residue. (,) In the case of zinc metalloproteases (), aspartate proteases () and glutamate proteases (not depicted), a carboxylic acid group or metal ion activates a water molecule, leading to acid-base catalysis. The seventh and newest protease family, the asparagine peptide lyases, cleave themselves using an asparagine residue as the nucleophile2 (not depicted). * Figure 2: Schematic presentation of the hit-to-lead process. () In a classical protease drug discovery approach, the emphasis of screening and optimization is on maximizing the potency of a hit compound for a recombinant protease. Off-target effects and efficacy are usually tested after the optimization process, and problems encountered when testing the compounds in cultures and in vivo require either modifying the structure of the lead inhibitor to solve a particular issue or selecting a different chemotype for further optimization. () In this review, we propose a holistic approach, in which the emphasis is on identifying hits in a more complex and relevant context (intact cells), incorporating the specificity profile of hits to identify and optimize lead compounds. We believe that placing the emphasis of the hit-to-lead optimization process on selectivity instead of just on potency will help prevent off-target effects and thus increase the chances for developing protease inhibitor drugs with minimum side effects. * Figure 3: Activity-based probes report on tightly regulated protease activity. () Proteases are not only regulated on the transcription and translation levels but also highly regulated on the protein level. Expressed as zymogens, proteases are activated in a variety of ways and by a variety of factors, depending on the protease, including allosteric activation, environmental changes, localization, protein-protein interactions and processing by upstream proteases. Endogenous inhibitors and targeted degradation form yet another layer of regulation. () Activity-based probes are small-molecule reporters that use the active protease's own chemistry to distinguish it from its zymogen or inhibited form. Most ABPs consist of three parts: a warhead (an electrophilic moiety that reacts with the active site nucleophile to result in a covalent and irreversible adduct), a spacer and/or recognition element that targets the probe to a specific target protease and a tag (usually a fluorescent dye and/or an affinity handle, like biotin). * Figure 4: Chemical tools to study protease function and to measure target inhibition. () Forward chemical genetics allows for target identification through the introduction of an affinity tag to the hit compound. () Near-infrared fluorescently labeled ABPs can be applied to top-down characterization of a target protease. Whole-animal noninvasive imaging techniques allow the visualization of target distribution, and extracted tissue can be analyzed ex vivo. Histology shows target distribution on a microscopic level, FACS analysis identifies the types of cells that contain active protease and biochemical analysis allows characterization at the protein level. Treatment with a lead compound before labeling provides information on target inhibition. Mouse images are from our previous publication39. () Broad-spectrum protease probes enable a readout of the inhibition profile of a lead compound for an entire protease family in a proteome. Members of the targeted family can be resolved on a gel, and inhibition results in diminished labeling of individual proteases. (! ) Global profiling of all reactive cysteines in a proteome; iodoacetamide-based reporter molecules will react primarily with the more reactive cysteines. Using isotopically labeled reporter molecules, this method can be used to predict functional cysteines in proteomes as well as to identify targets. When the methods described here are used to evaluate the specificity profile of a reversible inhibitor, the labeling conditions should be adjusted so that the covalent probe does not outcompete the inhibitor. Because these methods have a good dynamic range, this can be accomplished by lowering the probe concentration or decreasing the labeling times. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Pathology, Stanford University School of Medicine, Stanford, California, USA. * Edgar Deu, * Martijn Verdoes & * Matthew Bogyo * Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA. * Matthew Bogyo Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Matthew Bogyo Author Details * Edgar Deu Search for this author in: * NPG journals * PubMed * Google Scholar * Martijn Verdoes Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew Bogyo Contact Matthew Bogyo Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (74K) Supplementary Box 1 Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_f0ccfb57f1fc324b0fe50ff0f9548539"> A unique H2A histone variant occupies the transcriptional start site of active genes</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_f0ccfb57f1fc324b0fe50ff0f9548539">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_f0ccfb57f1fc324b0fe50ff0f9548539"><strong>19</strong>(1):25-30</a> (2012)<br /> Nature Structural & Molecular Biology | Article RAD51- and MRE11-dependent reassembly of uncoupled CMG helicase complex at collapsed replication forks * Yoshitami Hashimoto1 * Fabio Puddu1 * Vincenzo Costanzo1 * Affiliations * Contributions * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:17–24Year published:(2012)DOI:doi:10.1038/nsmb.2177Received 16 May 2011 Accepted 29 September 2011 Published online 04 December 2011 Highlighting tool Genes and ProteinsUpdate Highlighting Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg In higher eukaryotes, the dynamics of replisome components during fork collapse and restart are poorly understood. Here we have reconstituted replication fork collapse and restart by inducing single-strand DNA lesions that create a double-strand break in one of the replicated sister chromatids after fork passage. We found that, upon fork collapse, the active CDC45–MCM–GINS (CMG) helicase complex loses its GINS subunit. A functional replisome is restored by the reloading of GINS and polymerase ɛ onto DNA in a fashion that is dependent on RAD51 and MRE11 but independent of replication origin assembly and firing. PCNA mutant alleles defective in break-induced replication (BIR) are unable to support restoration of replisome integrity. These results show that, in higher eukaryotes, replisomes are partially dismantled after fork collapse and fully re-established by a recombination-mediated process. View full text Figures at a glance * Figure 1: RAD51 is required for DNA replication in the presence of forks collapsed by a single-strand break in the template. () The requirement of RAD51 and PCNA modification at Lys164 for replication of undamaged sperm DNA (control) or MMS- or UV-treated sperm DNA was tested using GST-labeled BRC4, which sequesters RAD51, and PCNA K164R mutant. Replication products (labeled with [32P]dATP) were resolved by neutral agarose gel electrophoresis and subjected to autoradiography (left). The quantification of the signal is shown in the graph as photon emission intensity expressed in arbitrary units (AU) (right). The data shown here and hereafter represent typical findings of three or more experiments. () A model for ssDNA-specific endonuclease-dependent fork collapse and RAD51-dependent restart. () The requirement of RAD51 for DNA replication in the presence of S1 nuclease was tested using sperm nuclei (4,000 nuclei per microliter) incubated for 80 min in the presence or absence of 1 μg ml−1 aphidicolin (Low aph) and S1 nuclease (0, 2.92, 1.46, 0.73, 0.37 U μl−1). Replication products were detect! ed by autoradiography with quantification shown on the right, as in . SYBR Green staining shows total DNA. * Figure 2: RAD51 is required for stable chromatin association of fork proteins in the presence of template breakage. () Association of fork proteins to chromatin isolated from extracts treated with GST or GST-BRC4 and 0, 2.92, 0.97 and 0.32 U μl−1 S1 nuclease in the presence of 1 μg ml−1 aphidicolin (Low aph). () Chromatin status of fork proteins, histone H2AX and PCNA in extracts treated with GST or GST-BRC4 2.92 (1/100) and 0.37 (1/800) U μl−1 S1 nuclease and aphidicolin. () Chromatin binding of PSF2 and CDC45 in the presence of 0.97 U μl−1 S1 nuclease in mock- or RAD51-depleted (–RAD51) extracts. () Chromatin binding of the indicated proteins over time in extracts treated with GST or GST-BRC4 and 1.46 U μl−1 of S1 nuclease and aphidicolin. () Nuclear CHK1 phosphorylation (P-CHK1) on Ser345 in extracts treated with 1 μg ml−1 aphidicolin alone or in combination with 1.46 U μl−1 of S1 nuclease. In –, western blotting was performed using antibodies against the indicated chromatin binding factors. 'Ext' in and indicates lanes containing 0.5 μl egg extract loaded as ! a control. Chromatin and nuclear fractions were isolated 60 min after the addition of sperm DNA to egg extracts unless otherwise indicated. * Figure 3: RAD51 is required for origin-independent fork restart and reloading of replisome components after fork collapse. (,) Replication fork restart was monitored following incubation of sperm nuclei in the first extract for 60 min with or without 10 μg ml−1 aphidicolin, and then nuclear fractions that were untreated or briefly incubated with mung bean nuclease were transferred to a second extract containing 320 nM geminin, 1 mM roscovitine and GST or GST-BRC4 () or to mock- or RAD51-depleted (–RAD51) extracts containing recombinant RAD51 (rRAD51) (). Replication products were monitored by incorporation of [32P]dATP added to the second extract and resolved by alkaline () or neutral () agarose gel electrophoresis followed by autoradiography. Quantification of signals is shown at the bottom of the gel in and in the graph in . () Chromatin binding of RAD51 and CDC45 was monitored in egg extracts that were mock- or RAD51-depleted and supplemented with the indicated amount of rRAD51. () The status of replication fork proteins bound to chromatin isolated from extracts treated as in . * Figure 4: MRE11 nuclease activity is required for DNA replication upon fork collapse. (,) Effects of the MRE11 nuclease inhibitor mirin on replication of sperm nuclei that were untreated or treated with MMS in the presence or absence of GST-BRC4 () or on sperm nuclei incubated in extracts treated with 0, 0.73, 0.37 or 0.18 U μl−1 S1 nuclease and aphidicolin (). Replication products were monitored by [32P]dATP incorporation and resolved on neutral agarose gels, which were subjected to autoradiography. Signal intensities are reported in the graphs. () Effect of mirin on replication proteins bound to chromatin isolated after a 50-min incubation in extracts treated with 0, 1.46, 0.73, 0.37 or 0.18 U μl−1 S1 nuclease. () Binding of the indicated fork proteins to chromatin incubated for 45 min in egg extracts that were untreated or supplemented with 0.73 U μl−1 S1 nuclease and mirin following protein cross-linking, sonication-induced DNA fragmentation and immunoprecipitation with control and anti-CDC45 serum. 'Ext' indicates 0.5 μl egg extract loaded as a! control in and . * Figure 5: The role of PCNA in DNA replication and chromatin association of replication proteins upon fork collapse. () Replication of sperm nuclei incubated in extracts for 80 min in the presence of 1 μg ml−1 aphidicolin and 0, 0.73, 0.37 or 0.18 U μl−1 S1 nuclease and wild-type PCNA (WT), PCNA K164R (KR), PCNA Y249A Y250A (YA) or PCNA K164R Y249A Y250A (KR YA) recombinant proteins. Replication products were resolved by neutral agarose gel electrophoresis and subjected to autoradiography (left). Signal intensities are shown in the graph (right). () Binding to chromatin of the indicated proteins was monitored by immunoblotting of chromatin treated with 200 J m−2 UV or incubated in extracts treated with 1 μg ml−1 aphidicolin, 0.97 U μl−1 S1 nuclease or EcoRI and recombinant wild-type PCNA, PCNA K164R or PCNA Y249A Y250A as indicated. 'Ext' indicates 0.5 μl egg extract loaded as a control. () The interaction of PCNA and replication proteins in egg extract was monitored by incubation of His6-tagged wild type and mutant PCNA proteins followed by pull-down with Ni-NTA–Sepharose! . The interacting proteins were detected by immunoblotting as indicated. * Figure 6: A model of replication fork collapse and restart. The presence of a ssDNA lesion in the template creates a one-sided DSB upon passage of the replisome (1), leading to the dissociation of the GINS and Pol ɛ from the fork, whereas MCM and CDC45 remain stably bound to collapsed fork (2). The one-sided DSB undergoes MRE11-mediated nuclease resection and RAD51-dependent strand annealing and invasion of the intact template. The MRE11 complex might also tether the broken DNA strand to the intact one (3). This process requires BIR-proficient PCNA, which promotes Pol η–dependent strand extension (4). Reloading of the GINS and Pol ɛ in an origin-independent fashion promotes reassembly of a functional replisome (5). Author information * Abstract * Author information * Supplementary information Affiliations * Genome Stability Unit, Clare Hall Laboratories, London Research Institute, South Mimms, Hertfordshire, UK. * Yoshitami Hashimoto, * Fabio Puddu & * Vincenzo Costanzo Contributions Y.H. and F.P. performed experiments. Y.H. and V.C. analyzed the results and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Vincenzo Costanzo Author Details * Yoshitami Hashimoto Search for this author in: * NPG journals * PubMed * Google Scholar * Fabio Puddu Search for this author in: * NPG journals * PubMed * Google Scholar * Vincenzo Costanzo Contact Vincenzo Costanzo Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (3.5 MB) Supplementary Figures 1–6 Additional data Entities in this article * DNA repair protein RAD51 homolog A rad51-a Xenopus laevis * View in UniProt * View in Entrez Gene * Double-strand break repair protein MRE11 mre11a Xenopus laevis * View in UniProt * View in Entrez Gene * Cell division control protein 45 homolog cdc45 Xenopus laevis * View in UniProt * View in Entrez Gene * Proliferating cell nuclear antigen pcna Xenopus laevis * View in UniProt * View in Entrez Gene * Proliferating cell nuclear antigen PCNA Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * E3 ubiquitin-protein ligase RAD18 RAD18 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Protein RecA recA Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * DNA repair protein RAD51 homolog 1 RAD51 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Double-strand break repair protein MRE11A MRE11A Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * DNA repair protein RAD50 RAD50 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Nibrin NBN Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * DNA polymerase delta subunit 3 POL32 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Origin recognition complex subunit 1 ORC1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Origin recognition complex subunit 6 ORC6 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Cell division control protein 6 CDC6 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Cell division cycle protein CDT1 TAH11 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * DNA replication licensing factor MCM2 MCM2 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * DNA replication licensing factor MCM7 MCM7 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Cell division control protein 45 CDC45 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * DNA replication complex GINS protein SLD5 SLD5 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * DNA replication complex GINS protein PSF1 PSF1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * DNA replication complex GINS protein PSF2 PSF2 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * DNA replication complex GINS protein PSF3 PSF3 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Breast cancer type 2 susceptibility protein BRCA2 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Geminin GMNN Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * GINS complex subunit 4 (Sld5 homolog) gins4 Xenopus laevis * View in UniProt * View in Entrez Gene * DNA replication complex GINS protein PSF2 gins2 Xenopus laevis * View in UniProt * View in Entrez Gene * DNA replication licensing factor mcm2 mcm2 Xenopus laevis * View in UniProt * View in Entrez Gene * Histone H2A type 1 h2afx Xenopus laevis * View in UniProt * View in Entrez Gene * Serine/threonine-protein kinase Chk1 chek1 Xenopus laevis * View in UniProt * View in Entrez Gene * DNA replication licensing factor mcm7-A mcm7-a Xenopus laevis * View in UniProt * View in Entrez Gene * Proliferating cell nuclear antigen POL30 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Ubiquitin-like-specific protease 1 ULP1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Maternal DNA replication licensing factor mcm3 mcm3 Xenopus laevis * View in UniProt * View in Entrez Gene * DNA replication licensing factor mcm5-A mcm5-a Xenopus laevis * View in UniProt * View in Entrez Gene * Serine/threonine-protein kinase Chk1 CHEK1 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Origin recognition complex subunit 2 orc2 Xenopus laevis * View in UniProt * View in Entrez Gene </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_931a19e39541df73fa7264ba923fa8b9"> Signal-dependent dynamics of transcription factor translocation controls gene expression</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_931a19e39541df73fa7264ba923fa8b9">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_931a19e39541df73fa7264ba923fa8b9"><strong>19</strong>(1):31-39</a> (2012)<br /> Nature Structural & Molecular Biology | Article A unique H2A histone variant occupies the transcriptional start site of active genes * Tatiana A Soboleva1 * Maxim Nekrasov1 * Anuj Pahwa1 * Rohan Williams1, 2 * Gavin A Huttley1 * David J Tremethick1 * Affiliations * Contributions * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:25–30Year published:(2012)DOI:doi:10.1038/nsmb.2161Received 02 June 2011 Accepted 21 September 2011 Published online 04 December 2011 Corrected online11 December 2011 Highlighting tool Genes and ProteinsUpdate Highlighting Abstract * Abstract * Accession codes * Change history * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Transcriptional activation is controlled by chromatin, which needs to be unfolded and remodeled to ensure access to the transcription start site (TSS). However, the mechanisms that yield such an 'open' chromatin structure, and how these processes are coordinately regulated during differentiation, are poorly understood. We identify the mouse (Mus musculus) H2A histone variant H2A.Lap1 as a previously undescribed component of the TSS of active genes expressed during specific stages of spermatogenesis. This unique chromatin landscape also includes a second histone variant, H2A.Z. In the later stages of round spermatid development, H2A.Lap1 dynamically loads onto the inactive X chromosome, enabling the transcriptional activation of previously repressed genes. Mechanistically, we show that H2A.Lap1 imparts unique unfolding properties to chromatin. We therefore propose that H2A.Lap1 coordinately regulates gene expression by directly opening the chromatin structure of the TSS at ge! nes regulated during spermatogenesis. View full text Figures at a glance * Figure 1: H2A.Lap1 dynamically loads onto the sex chromosomes in late round spermatids. () Seminiferous tubule sections were indirectly immunostained with Lap1 antibodies (red) and stained with peanut agglutinin Alexa Fluor 488 conjugate (LectinPNA, green) to allow identification of the various spermatid stages on the basis of acrosome maturation9. DNA was costained with DAPI (blue). P, mid-pachytene spermatocytes; LP, late-pachytene spermatocytes; RS, round spermatids. Arrow marks residual bodies (RB; Supplementary Fig. 8). Scale bars, 25 μm. () Surface spreads of leptotene and zygotene cells were prepared from pre-pachytene testes of 12-day-old mice. Surface spreads of pachytene spermatocytes, round spermatids and elongating spermatids were prepared from adult mice. Different cell types were immunostained with SCP3 (a marker for the progression of chromosome synapsis), γX (a component of the XY body in pachytene spermatocytes) or macroH2A1.2 (enriched in centromeric heterochromatin of the Y chromosome in round spermatids). Filled white arrow, XY body. Unfil! led white arrow, Y chromosome centromeric heterochromatin. Scale bar, 10 μm. () Fluorescence in situ hybridization analyses of round spermatids using specific chromosome-X or chromosome-Y paints (reproduced with permission from our previous published study5). White lines in merged images indicate the paths used to determine fluorescence intensity across the sex chromosomes and the chromocenter, graphed at right. C, hromocenter. Scale bars, 10 μm. () Round spermatids were immunostained with Lap1 antibodies and stained with DAPI. Round spermatids were also stained with LectinPNA to distinguish whether a round spermatid was at an early or late stage of development. White lines in merged images indicate the paths used to determine the fluorescence intensity of Lap1 across the DAPI stained sex chromosome, graphed at right. Scale bar, 10 μm. * Figure 2: H2A.Lap1 is located at the TSS of active genes. () H2A.Lap1 ChIP profiles on genes active in round-spermatid X-chromosomes. Each line represents 50 genes, grouped by expression level using published gene expression data6; coloring indicates average gene expression rank in the group. The sum of frequency tag counts in the group is plotted at each base pair relative to the TSS. () Lap1 ChIP profile showing sum of frequency tag counts on 44 X-linked Group C genes, aligned between −1 kb and +1 kb from the TSS. (,) Lap1 ChIP profiles for genes on chromosome 1 () and for the whole genome (), grouped by expression level using global expression of all mouse genes in the 30-day-old testis. We separated 23 groups of 50 genes on chromosome 1 and 201 groups of 100 genes in the whole genome; coloring is as in . Note that for the whole-genome plot, the sum of all shown frequency tag counts equals 1. () Lap1 ChIP profile for the 1,000 most highly expressed mouse genes in the 30-day-old testis, aligned between −5 and +5 kb from the T! SS. () Lap1 ChIP profiles for all mouse genes expressed at the pachytene stage, grouped by expression level (164 groups of 100 genes) using published pachytene expression data6. Coloring is as in . * Figure 3: Targeting of H2A.Lap1 to X chromosome–linked genes occurs in late round spermatids. Six round spermatid–specific X-linked genes were chosen for H2A.Lap1 ChIP and gene expression analyses. () H2A.Lap1 enrichment for each gene in mouse testes at 18, 24, or 30 d of development, relative to Dusp21 at 18 d. H2A.Lap1 signal was assayed by ChIP. ChIP-seq libraries, normalized to the same DNA concentration, were analyzed by quantitative PCR using gene-specific primers that target the TSS. Data shown are means and s.d. of three repeats. () The mRNA level of each gene at each stage of testes development, determined by real-time quantitative PCR, relative to β-actin. Data shown are means and s.d. of three repeats. () H2A.Z ChIP profiles at TSS of all mouse genes expressed in the 30-day-old testis; genes are grouped by average gene expression rank as in Figure 2d (201 groups of 100 genes). () Normalized ChIP profiles of H2A.Z and H2A.Lap1 (with confidence intervals estimated by resampling) for the 1,000 most highly expressed genes in the 30-day-old mouse testis. () ! Cartoon depicting the location of H2A.Z- and H2A.Lap1-containing nucleosomes at the −2 and −1 positions, respectively, relative to the TSS. * Figure 4: H2A.Lap1 has gained a single acidic amino acid residue, which enables nucleosome arrays to partially fold. () Sedimentation coefficient distribution plots of arrays containing human Bbd in the absence or presence of 0.3 mM MgCl2 (reproduced from our previous published study3). (,) Sedimentation coefficient distribution plots of arrays containing wild-type (WT) H2A or Lap1 in the absence or presence of 1.2 mM MgCl2. (,) Sedimentation coefficient distribution plots of arrays containing WT H2A, Lap1 or mutant Lap1 (D100T) in the absence or presence of 0.4 mM MgCl2. Accession codes * Abstract * Accession codes * Change history * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE29781 Change history * Abstract * Accession codes * Change history * Author information * Supplementary informationCorrected online 11 December 2011In the version of this article initially published, the incorrect PDB code for the MthK open channel structure was provided in the legend to Figure 1. The correct PDB code for this structure is 1LNQ. The error has been corrected in the HTML and PDF versions of the article. Author information * Abstract * Accession codes * Change history * Author information * Supplementary information Affiliations * The John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia. * Tatiana A Soboleva, * Maxim Nekrasov, * Anuj Pahwa, * Rohan Williams, * Gavin A Huttley & * David J Tremethick * Present address: Singapore Centre on Environmental Life Sciences Engineering, National University of Singapore, Singapore. * Rohan Williams Contributions T.A.S. helped design the experiments, cloned H2A.Lap, conducted all spermatogenesis experiments, prepared chromatin for ChIP-seq experiments and conducted the gene expression and ChIP experiments on individual X-chromosome genes. M.N. conducted the biochemical and biophysical experiments on the nucleosome arrays and prepared DNA ChIP libraries for high-throughput sequencing. R.W. developed and did data analysis of global mouse gene expression data. G.A.H. designed and executed the analysis of the Illumina short-read data. A.P. assisted with the analyses of Illumina short-read data. D.J.T. conceived the project, helped design the experiments and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * David J Tremethick Author Details * Tatiana A Soboleva Search for this author in: * NPG journals * PubMed * Google Scholar * Maxim Nekrasov Search for this author in: * NPG journals * PubMed * Google Scholar * Anuj Pahwa Search for this author in: * NPG journals * PubMed * Google Scholar * Rohan Williams Search for this author in: * NPG journals * PubMed * Google Scholar * Gavin A Huttley Search for this author in: * NPG journals * PubMed * Google Scholar * David J Tremethick Contact David J Tremethick Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Change history * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–8, Supplementary Table 1 and Supplementary Methods Additional data Entities in this article * Histone H2A.Z H2afz Mus musculus * View in UniProt * View in Entrez Gene * Histone H2A.x H2afx Mus musculus * View in UniProt * View in Entrez Gene * Core histone macro-H2A.1 H2afy Mus musculus * View in UniProt * View in Entrez Gene * Dual specificity phosphatase 21 Dusp21 Mus musculus * View in UniProt * View in Entrez Gene * MCG52127 4930557A04Rik Mus musculus * View in UniProt * View in Entrez Gene </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_c4dd77a54d71afdaa5eaf184a6722af5"> Intrinsic tethering activity of endosomal Rab proteins</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_c4dd77a54d71afdaa5eaf184a6722af5">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_c4dd77a54d71afdaa5eaf184a6722af5"><strong>19</strong>(1):40-47</a> (2012)<br /> Nature Structural & Molecular Biology | Article Signal-dependent dynamics of transcription factor translocation controls gene expression * Nan Hao1, 2, 3 * Erin K O'Shea1, 2, 3, 4 * Affiliations * Contributions * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:31–39Year published:(2012)DOI:doi:10.1038/nsmb.2192Received 13 June 2011 Accepted 13 October 2011 Published online 18 December 2011 Highlighting tool Genes and ProteinsUpdate Highlighting Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Information about environmental stimuli is often transmitted using common signaling molecules, but the mechanisms that ensure signaling specificity are not entirely known. Here we show that the identities and intensities of different stresses are transmitted by modulation of the amplitude, duration or frequency of nuclear translocation of the Saccharomyces cerevisiae general stress response transcription factor Msn2. Through artificial control of the dynamics of Msn2 translocation, we reveal how distinct dynamical schemes differentially affect reporter gene expression. Using a simple model, we predict stress-induced reporter gene expression from single-cell translocation dynamics. We then demonstrate that the response of natural target genes to dynamical modulation of Msn2 translocation is influenced by differences in the kinetics of promoter transitions and transcription factor binding properties. Thus, multiple environmental signals can trigger qualitatively different dyna! mics of a single transcription factor and influence gene expression patterns. View full text Figures at a glance * Figure 1: Msn2 translocates to the nucleus with different dynamics in response to different stresses. (–) Time traces of YFP-tagged Msn2 nuclear translocation are shown for glucose limitation (), osmotic stress () and oxidative stress (). In each panel, top row: averages of single-cell time traces of Msn2-YFP translocation in response to the indicated stresses (solid circles, averages of single-cell experimental data; solid lines, s.d. of single-cell responses of ~60 cells, from at least two independent experiments); bottom row: representative single-cell time traces of Msn2-YFP nuclear translocation. AU, arbitrary units of fluorescence. Additional single-cell traces are shown in Supplementary Figure 1a,b. * Figure 2: Quantification of single-cell Msn2-YFP translocation traces. () A schematic defines the initial peak of Msn2 nuclear translocation and subsequent sporadic bursts in a single-cell time trace. () Duration (top row) and amplitude (middle row) of the initial peak are quantified for the indicated stress conditions (open circles, mean value of single cells; error bars, s.d. of single-cell responses of ~60 cells, from at least two independent experiments). Duration is not quantified for the H2O2 treatment, because a sustained translocation event was observed under this condition. () Frequency, amplitude, burst duration and interval durations of sporadic bursts in response to glucose limitation. Frequency of sporadic bursts under osmotic stress is also quantified (right). The distributions of amplitude, duration, and frequency of sporadic nuclear burst in response to glucose limitation are shown in Supplementary Figure 2a–c. Autocorrelation analysis of Msn2 localization traces upon glucose limitation are presented in Supplementary Figure 2d. * Figure 3: Experimental and computational analysis of gene expression in response to modulation of Msn2 nuclear translocation dynamics. () A diagram describes the analog-sensitive system used to control Msn2 nuclear translocation. () Gene expression model. A detailed description of the model construction and fitting procedure are included in Supplementary Results. () Averages of single-cell time traces of Msn2-YFP nuclear localization and reporter gene expression (cyan fluorescent protein, CFP) measured in the same cells in response to inhibitor treatments (black solid circles, averages of time-trace data; black solid lines, s.d. of single-cell data of ~50 cells, from at least two independent experiments; green solid line, curve fitting of Msn2 translocation traces; red solid line, model simulation). The time traces of Msn2 nuclear localization were fit with a piecewise exponential function (Supplementary Fig. 3) to produce continuous time-dependent profiles, TF(t), which served as input for the model. The model in was fit to the averages of single-cell time traces of reporter gene expression (Supplementary ! Results). The complete dataset is included in Supplementary Figures 3–5. * Figure 4: The dynamics of Msn2 nuclear translocation influences target gene expression. () The relationship between gene expression and the area under the curve (AUC) of Msn2 inputs (open circles, experimental data; solid lines, model simulation; error bars, s.d. of single-cell data of ~50 cells, from at least two independent experiments). The integrals of Msn2 inputs were quantified from the data in Supplementary Figures 3–5. Single Msn2 inputs with 10 min (blue), 20 min (red) or 40 min (black) durations were compared with oscillatory Msn2 inputs for 5-min pulse duration (orange). () Relationship between dynamic of Msn2 nuclear inputs and reporter gene expression. Left, gene expression versus Msn2 input amplitude (input duration: black = 40 min; red = 20 min; blue = 10 min); center, gene expression versus Msn2 input duration (input amplitude: yellow green = 2,190 AU; orange = 1,751 AU; black = 1,309 AU; green = 1,010 AU; red = 672 AU; blue = 406 AU); right, gene expression versus Msn2 input frequency (input amplitude = 1,751 AU; pulse duration = 5 min). () M! odel simulations reproduce the measured expression responses to natural stresses. For the indicated stress conditions, each single-cell trace of Msn2 translocation was used as input for the gene expression model. The simulated single-cell expression traces were averaged to generate the simulation curves (solid lines, top row) and compared with averages of measured single-cell expression (solid circles, bottom row). * Figure 5: The model predicts that target genes have distinct responses to different input regimes. Two sets of parameters were varied, and alterations in gene expression output were predicted using the expression model: parameters that govern transcription factor binding, Kd and n; and parameters that govern kinetics of promoter transition, k1 and k2. The inputs are selected to be in the physiological ranges of the natural stress responses (Fig. 2). () The expression curves upon amplitude modulation (AM), duration modulation (DM) and frequency modulation (FM) (left column) and the expression ratios (the ratio of gene expression level upon low stimulus to expression level upon high stimulus) calculated from the expression curves (right column, same genes use same colors for curves and ratios) are shown for hypothetical genes with different binding parameters (Kd, n) and the same promoter kinetics (k1, k2). The values below the bar graph represent the fold changes from the parameter values obtained from fitting the reporter response data (Fig. 3). () Model predictions are s! hown for hypothetical genes with the same binding parameters and different promoter kinetics. () Natural target genes may differ in both binding parameters and promoter kinetics. Model predictions for four hypothetical genes with different binding parameters and different promoter kinetics. Genes 1 and 2 have the same slow promoter kinetics, whereas Genes 3 and 4 have the same fast promoter kinetics. Genes 1 and 3 have the same high transcription factor binding, whereas Genes 2 and 4 have the same low transcription factor binding. * Figure 6: Analysis of a simplified model. () Schematic of the simplified model. () The model behaviors in response to duration modulation inputs when the timescale of promoter transition is longer () and shorter () than input duration. The input durations are Ta and Tb. () The model behaviors in response to frequency modulation inputs. () The responses when the timescales of promoter activation and deactivation are longer than pulse duration and pulse interval, respectively. (–) The responses when the timescale of promoter activation is shorter than pulse duration or when the timescale of promoter inactivation is shorter than pulse interval. Pulse duration is Ton; the interval durations are Toff_a and Toff_b. For and , ω, inputs; P2, promoter activity; R, gene product. () The simulated relationship between gene expression output and input duration. Equations used in simulations are indicated; these relationships are calculated with the median value of the input duration we used in the simulation. Black dashed lin! es, the curve of RAUC = T; red dashed lines, the curve of RAUC = T2. () The simulated relationship between gene expression output and oscillatory input pulse number (n). Black dashed lines, the curve of RAUC = n; red dashed lines, the curve of RAUC = n2. Left: we set k2 = ω · k1 and changed k2 + ω · k1 to the equations used in simulations as indicated. With increasing frequency (pulse number), the interval duration changes from smaller than 1/Ton to larger than 1/Ton. Right: we set k2 + ω · k1 = 0.1 × (1/Ton) and changed Toff to the equations used in simulations as indicated. The variables k2, ω, k1 and Ton are fixed. In this case, pulse number does not correlate with pulse frequency. * Figure 7: Microarray analysis to evaluate the model predictions. () Msn2 nuclear localization response to different 1-NM-PP1 treatments: red line, 120 nM 1-NM-PP1, 20 min; green line, 3 μM 1-NM-PP1, 20 min; blue line, 3 μM 1-NM-PP1, 40 min; orange line, 750 nM 1-NM-PP1, 5 min × 3; black line, 750 nM 1-NM-PP1, 5 min × 6. () Measured time courses of mRNA levels from representative target genes (solid circles: normalized fold change of mRNA level with baseline subtracted). The inputs in panel were used experimentally to produce the measured mRNA time traces (the input and the corresponding response use the same color). () Distributions of Msn2 binding sites (red) relative to the experimentally determined nucleosome profile (blue, data not shown) within promoters of target genes. The averaged nucleosome profiles were obtained by dividing the sum of nucleosome positioning signals of all genes in one group by the gene numbers. The distribution of Msn2 binding sites (5′-AGGGG-3′ or 5′-CCCCT-3′) is represented by bars corresponding to! the sum of the numbers of Msn2 binding sites in each ten-base-pair window. () mRNA ratios of target genes in different encoding regimes (blue, Group I genes; red, Group II genes). The mRNA ratio of each gene is calculated by dividing the area under the curve of the mRNA time course (which correlates with gene expression level, Supplementary Fig. 7) at low transcription factor inputs by the area under the curve at high inputs. Accession codes * Abstract * Accession codes * Author information * Supplementary information Primary accessions Gene Expression Omnibus * GSE32703 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Howard Hughes Medical Institute, Harvard University, Cambridge, Massachusetts, USA. * Nan Hao & * Erin K O'Shea * Faculty of Arts and Sciences Center for Systems Biology, Harvard University, Cambridge, Massachusetts, USA. * Nan Hao & * Erin K O'Shea * Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA. * Nan Hao & * Erin K O'Shea * Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA. * Erin K O'Shea Contributions N.H. and E.K.O. designed the project. N.H. carried out the experiments and analyzed the data. N.H. and E.K.O. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Erin K O'Shea Author Details * Nan Hao Search for this author in: * NPG journals * PubMed * Google Scholar * Erin K O'Shea Contact Erin K O'Shea 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 Figures 1–15, Supplementary Results and Supplementary Methods Audio files * Supplementary Video 1 (3M) Time-lapse video of Msn2-YFP in response to 0.1% glucose limitation. * Supplementary Video 2 (3M) Time-lapse video of Msn2-YFP in response to 0.375 M KCl. * Supplementary Video 3 (4M) Time-lapse video of Msn2-YFP in response to 0.01 mM H2O2. * Supplementary Video 4 (2M) Time-lapse video of Msn2-YFP in response to oscillatory 1-NM-PP1 treatment. Additional data Entities in this article * Zinc finger protein MSN2 MSN2 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Transcriptional regulator CRZ1 CRZ1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Zinc finger protein MSN4 MSN4 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * cAMP-dependent protein kinase type 1 TPK1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * cAMP-dependent protein kinase type 2 TPK2 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * cAMP-dependent protein kinase type 3 TPK3 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Glutaredoxin-1 GRX1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Uncharacterized membrane protein YLR312C YLR312C Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Cellular tumor antigen p53 TP53 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_0539380f9173810c980517dfd96cc0b3"> Ndc10 is a platform for inner kinetochore assembly in budding yeast</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_0539380f9173810c980517dfd96cc0b3">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_0539380f9173810c980517dfd96cc0b3"><strong>19</strong>(1):48-55</a> (2012)<br /> Nature Structural & Molecular Biology | Article Intrinsic tethering activity of endosomal Rab proteins * Sheng-Ying Lo1, 2 * Christopher L Brett1, 5 * Rachael L Plemel1 * Marissa Vignali3 * Stanley Fields3, 4 * Tamir Gonen1, 4, 5 * Alexey J Merz1 * Affiliations * Contributions * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:40–47Year published:(2012)DOI:doi:10.1038/nsmb.2162Received 30 May 2010 Accepted 22 September 2011 Published online 11 December 2011 Highlighting tool Genes and ProteinsUpdate Highlighting Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Rab small G proteins control membrane trafficking events required for many processes including secretion, lipid metabolism, antigen presentation and growth factor signaling. Rabs recruit effectors that mediate diverse functions including vesicle tethering and fusion. However, many mechanistic questions about Rab-regulated vesicle tethering are unresolved. Using chemically defined reaction systems, we discovered that Vps21, a Saccharomyces cerevisiae ortholog of mammalian endosomal Rab5, functions in trans with itself and with at least two other endosomal Rabs to directly mediate GTP-dependent tethering. Vps21-mediated tethering was stringently and reversibly regulated by an upstream activator, Vps9, and an inhibitor, Gyp1, which were sufficient to drive dynamic cycles of tethering and detethering. These experiments reveal a previously undescribed mode of tethering by endocytic Rabs. In our working model, the intrinsic tethering capacity Vps21 operates in concert with convent! ional effectors and SNAREs to drive efficient docking and fusion. View full text Figures at a glance * Figure 1: GTP-bound Vps21 tethers liposomes. () Experimental configuration. Full details are in Online Methods and Supplementary Methods. () Liposome particle size distributions were measured by QLS after 60-min incubation in the presence of the indicated Rab-His10 proteins, preloaded with GDP or GTP. Error bars indicate mean and s.e.m. for three independent experiments. () TEM images of negatively stained samples taken from experiment in . () Liposomes were prepared as in –, except that fluorescent lipid was incorporated. Liposomes were incubated for 20 or 40 min, then a drop of the suspension was imaged by epifluorescence microscopy (200 ms exposure). Brightness and contrast adjustments are identical for the panels shown. Traces below the images show pixel intensities along the indicated dashed lines (AU, arbitrary units). Untethered liposomes are small and move rapidly and appear as diffuse fluorescence. As tethering proceeds, clusters grow in size and the fluorescent background markedly decreases indicating that ! most individual liposomes in the population have tethered. Liposomes with GDP- His10-Vps21 are shown at 20 min incubation and were indistinguishable from those at 40 min incubation. * Figure 2: Vps21 surface density and tethering activity. (,) Liposome tethering, measured by QLS, was examined as a function of Vps21 membrane surface density. Vps21 was loaded with GTP () or GDP (). Insets, onset of tethering at low Vps21 surface densities. Additional surface density data for Vps21 and Ypt7 are in Supplementary Figure 1. () To test effect of ionic strength, liposomes were decorated with Vps21-GDP or Vps21-GTP at two different surface densities, and tethering was monitored by QLS in buffers containing indicated salt concentrations. As in ,, indicated Vps21 surface densities are upper-bound estimates. Data are mean and s.e.m. from three independent experiments. * Figure 3: Vps21 interactions in trans are required for efficient tethering. () Schematic of two possible mechanisms of Rab-mediated tethering. () Schematic of bead-liposome tethering assay. () GTP-loaded GST–Vps21 beads were photographed after incubation for 20 or 60 min in presence of fluorescent liposomes containing 6 mol% Ni2+-NTA-DOGS and GTP-loaded Vps21-His10. Images are representative of nine independent experiments. () As in (inset) but without GTP-loaded Vps21-His10. () As in (inset), but liposomes were prepared without Ni2+-NTA-DOGS. () As in , except that after 20 min incubation, 10 mM reduced glutathione was added (left), or buffer without glutathione was added (right). Samples were then incubated for 4 min more, then photographed. Scale bars, 15 μm () and 75 μm (–). * Figure 4: The Vps21 C-terminal linker is not required for tethering. () Vps21-His10 fusion proteins lacking last 10, 20 or 30 residues of the Vps21 C-terminal linker were prepared. Purified proteins (5 μg) were analyzed by SDS-PAGE. () Liposomes bearing these proteins were assayed by QLS for the ability to drive tethering over indicated range of surface densities. Each construct was loaded with either GTP (filled symbols) or GDP (open symbols). Liposomes contained 4.5 mol% Ni2+-NTA-DOGS. Error bars indicate mean and s.e.m. from four independent experiments. * Figure 5: Vps21-GTP interacts with known effectors and with itself in yeast two-hybrid assays. A positive interaction in the yeast two-hybrid assay is indicated by yeast colony growth on medium lacking tryptophan, leucine and histidine, and supplemented with 1.5 mM 3-aminotriazole. The Vps21 effectors Vac1 (also known as Pep7), Vps3 and Vps8 are positive controls for interaction selectivity with Vps21-GTP, whereas Vps9 is a control for interaction selectivity with Vps21-GDP. * Figure 6: Vps21 interacts with Ypt53 and Ypt10 to drive GTP-dependent heterotypic tethering. () Heterotypic Rab-Rab tethering was assayed as in Figure 3 except that beads were decorated with various GTP-loaded GST-Rab fusion proteins, as indicated. Bottom, representative fields of beads under epifluorescence illumination. Top, fluorescence intensity profile plots of representative beads. () Assays were done as in , except that the Rabs were preloaded with either GTP or GDP. Ypt6, which does not interact with Vps21, was a negative control. Scale bars, 75 μm. * Figure 7: Regulation and reversibility of Vps21-mediated liposome tethering. () GEF-stimulated tethering. Tethering by GDP-loaded Vps21-decorated liposomes was measured by QLS after addition of 0.2 mM GTP and varying concentrations of Vps9. Data are mean and s.e.m.; data points from three independent experiments were binned into 10-min intervals. () GAP-mediated reversal of tethering. GTP-loaded Vps21-decorated liposome tethering was measured by QLS after addition of Gyp1TBC or Gyp1TBC-R343K. Error bars indicate mean and s.e.m.; data from three independent experiments were binned into 2-min intervals. () Regulated cycle of tethering and detethering. Vps21-mediated liposome tethering, measured by QLS, was examined during sequential addition of 20 μM GTP, 5 μM Vps9 and 10 μM Gyp1TBC. Data are representative of three independent experiments. () Histograms of Vps21-decorated liposome size distributions, derived from QLS, at time points indicated in . () TEM images of negatively stained samples withdrawn at indicated time points from experiment analyze! d in ,. * Figure 8: Model for Rab-Rab driven tethering in endosome docking and fusion. In this working model, three representative Rab functions are shown: classical effector-mediated tethering, Rab-Rab tethering and coordination of trans-SNARE complex assembly by a Rab-mediated recruitment of a SNARE-binding regulator. Together, these mechanisms could in principle coordinate an ordered tethering, docking and fusion sequence. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Biochemistry, University of Washington School of Medicine, Seattle, Washington, USA. * Sheng-Ying Lo, * Christopher L Brett, * Rachael L Plemel, * Tamir Gonen & * Alexey J Merz * Department of Chemistry, University of Washington, Seattle, Washington, USA. * Sheng-Ying Lo * Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington, USA. * Marissa Vignali & * Stanley Fields * Howard Hughes Medical Institute, University of Washington School of Medicine, Seattle, Washington, USA. * Stanley Fields & * Tamir Gonen * Present addresses: Department of Biology, Concordia University, Montreal, Quebec, Canada (C.L.B.); and Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA (T.G.). * Christopher L Brett & * Tamir Gonen Contributions S.Y.L. and A.J.M. conceived the project. S.Y.L. developed and validated the QLS-based tethering system; expressed, purified and characterized proteins; prepared liposomes and carried out and interpreted all QLS tethering experiments. C.L.B. and A.J.M. conceived and C.L.B. and S.Y.L. implemented the fluorescence microscopy-based tethering assays. T.G. did the E M. S.F. and M.V. developed the high-throughput yeast two-hybrid technology, and R.L.P. and M.V. executed and interpreted yeast two-hybrid screens and assays. S.Y.L. and A.J.M. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Alexey J Merz Author Details * Sheng-Ying Lo Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher L Brett Search for this author in: * NPG journals * PubMed * Google Scholar * Rachael L Plemel Search for this author in: * NPG journals * PubMed * Google Scholar * Marissa Vignali Search for this author in: * NPG journals * PubMed * Google Scholar * Stanley Fields Search for this author in: * NPG journals * PubMed * Google Scholar * Tamir Gonen Search for this author in: * NPG journals * PubMed * Google Scholar * Alexey J Merz Contact Alexey J Merz 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 1–4, Supplementary Tables 1–4 and Supplementary Methods Additional data Entities in this article * Vacuolar protein sorting-associated protein 21 VPS21 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Ras-related protein Rab-5A RAB5A Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Vacuolar protein sorting-associated protein 9 VPS9 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * GTPase-activating protein GYP1 GYP1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Early endosome antigen 1 EEA1 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Thyroid receptor-interacting protein 11 TRIP11 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * ADP-ribosylation factor 1 ARF1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * GTP-binding protein YPT7 YPT7 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Ras-related protein Rab-3A RAB3A Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Rab GDP-dissociation inhibitor GDI1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Rab proteins geranylgeranyltransferase component A MRS6 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Protein transport protein YIF1 YIF1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * GTP-binding protein YPT52 YPT52 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * GTP-binding protein YPT53 YPT53 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * GTP-binding protein YPT10 YPT10 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Ras-related protein Rab-6A RAB6A Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * GTP-binding protein YPT6 YPT6 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Ras-related protein Rab-7a RAB7A Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Vacuolar protein sorting-associated protein 3 VPS3 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Vacuolar protein sorting-associated protein 8 VPS8 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Vacuolar segregation protein PEP7 PEP7 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Small COPII coat GTPase SAR1 SAR1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Ras-related protein Rab-5B RAB5B Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Ras-related protein Rab-5C RAB5C Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Ras-related protein Rab-9A RAB9A Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Ras-related protein Rab-11A RAB11A Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Carboxypeptidase Y PRC1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Vacuolar protein sorting-associated protein 45 VPS45 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Rabenosyn-5 ZFYVE20 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_2dd829c128828edce9a02165435c5b8e"> X-chromosome hyperactivation in mammals via nonlinear relationships between chromatin states and transcription</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_2dd829c128828edce9a02165435c5b8e">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_2dd829c128828edce9a02165435c5b8e"><strong>19</strong>(1):56-61</a> (2012)<br /> Nature Structural & Molecular Biology | Article Ndc10 is a platform for inner kinetochore assembly in budding yeast * Uhn-Soo Cho1 * Stephen C Harrison1 * Affiliations * Contributions * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:48–55Year published:(2012)DOI:doi:10.1038/nsmb.2178Received 14 June 2010 Accepted 20 September 2011 Published online 04 December 2011 Highlighting tool Genes and ProteinsUpdate Highlighting Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Kinetochores link centromeric DNA to spindle microtubules and ensure faithful chromosome segregation during mitosis. In point-centromere yeasts, the CBF3 complex Skp1–Ctf13–(Cep3)2–(Ndc10)2 recognizes a conserved centromeric DNA element through contacts made by Cep3 and Ndc10. We describe here the five-domain organization of Kluyveromyces lactis Ndc10 and the structure at 2.8 Å resolution of domains I–II (residues 1–402) bound to DNA. The structure resembles tyrosine DNA recombinases, although it lacks both endonuclease and ligase activities. Structural and biochemical data demonstrate that each subunit of the Ndc10 dimer binds a separate fragment of DNA, suggesting that Ndc10 stabilizes a DNA loop at the centromere. We describe in vitro association experiments showing that specific domains of Ndc10 interact with each of the known inner-kinetochore proteins or protein complexes in budding yeast. We propose that Ndc10 provides a central platform for inner-kinetocho! re assembly. View full text Figures at a glance * Figure 1: Domains of K. lactis Ndc10 and crystal structure of DI–II. () Domain organization of Ndc10; numbers show residues at the domain boundaries and are derived either from limited proteolysis or from the crystal structure. () Structure of K. lactis Ndc10 (DI–II; 1–402) with 30-bp poly(dA-dT) DNA. Domain I (N domain, residues 1–100) is in cyan, and domain II (DNA-binding domain, residues 101–402) is in dark blue. Dashed lines represent disordered residues 36–39 and 283–292. A second, symmetry-related, 15-bp DNA fragment is shown in gray. The DNA has been modeled as poly(dA-dT) (see text), with the sequences of 5′-TTAATTTATAAAATT-3′ (1–15) and 5′-AAATTTTATAAATTA-3′ (1′–15′), as indicated. () Sequence conservation of Ndc10 among point-centromere yeasts. Location of insertions (red) in S. cerevisiaeNdc10 DI–II with respect to K. lactis Ndc10 DI–II, shown on a schematic representation of the primary sequence and on a ribbon representation of the structure. All molecular illustrations were made with PyMOL (Delan! o Scientific). * Figure 2: Surface charge distribution and DNA contacts of Ndc10 DI–II. () Two views of the surface charge distribution of Ndc10 DI–II; bound DNA is shown in worm representation. () Sugar-phosphate backbone interactions. Residues involved in DNA contacts are labeled and shown in stick representation. Colors as in Figure 1b. () EMSA of wild-type and mutant Ndc10 (10% (w/v) TBE acrylamide gel stained sequentially with ethidium bromide and Coomassie blue). * Figure 3: Structural alignment of K. lactis Ndc10 DI–II with Flp recombinases. () Monomer structure of Flp (PDB 1M6X) aligned with the K. lactis Ndc10 DI–II. The N domain and the DNA-binding domain of Flp recombinase are colored in orange and yellow, respectively. In Flp, the DNA structure of the Holliday junction was replaced by 30-bp CDEIII DNA for simple comparison. () Folding diagrams of K. lactis Ndc10 DI–II and Flp recombinase. Secondary-structure elements are labeled according to their position in the polypeptide chain; domains are colored as in panel . * Figure 4: Dimerization of K. lactis Ndc10 DI–III. () Views of the probable Ndc10 DI–II dimer (symmetry axis along b in the C2221 space group). The subunits of the dimer contact different pseudocontinuous DNA duplexes. Domains I and II of the Ndc10 dimer are colored in cyan and blue for the one molecule, and green and orange for the other. () EMSA of Ndc10 DI–III with increasing amounts of 30-bp CDEIII DNA. Color code for proteins is the same as in . () DNA-capture assay with two different labels. Either Ndc10 DI–III or Ndc10 DI–II was incubated with a mixture of equal amounts of biotinylated and unmodified CDEIII DNA, the latter including 32P-labeled product (10%). (–) Ratio of Ndc10 DI–III and CDEIII DNA, as determined by analytical size-exclusion chromatography. * Figure 5: Interactions of Ndc10-associated proteins or protein complexes in the inner kinetochore. (–) Ni2+ affinity pulldown of 35S-labeled, in vitro translated prey proteins with purified, His-tagged bait protein, analyzed by SDS-PAGE and visualized by phosphoimaging. Each panel includes a lane loaded with 10% of the in vitro translation reaction mixture (to monitor extent of synthesis) and either in vitro translated maltose-binding protein as a prey or purified His-tagged MBP as a bait (negative controls). * Figure 6: Interaction of Ndc10 domain IV–V with N-terminal Scm3. () Ni2+ affinity pulldown of 35S-labeled, in vitro translated Ndc10 proteins with purified, His-tagged Scm3 proteins, analyzed by SDS-PAGE and visualized by phosphoimaging. () In vitro amylose pulldown of purified MBP-tagged Scm3 proteins with Ndc10 domain IV–V. () Schematic overview of domain association of K. lactis Ndc10 with other kinetochore proteins. Ndc10 DI interacts with CBF3 core; Ndc10 DI–II, with Cbf1 (229–359) and Bir1p (1–328). Scm3 N (1–28) associates with Ndc10 DIV–V but not with DV. Interaction of Cbf1 with Ndc10 was confirmed by analytical size-exclusion chromatography using purified proteins (Supplementary Fig. 6). * Figure 7: Schematic model of Ndc10 interactions on budding yeast centromeres. Cbf1 and CBF3 core recognize CDEI and CDEIII, respectively. Ndc10 does not have sequence-specific DNA contacts, but it binds in defined register through its interactions with Cbf1 and CBF3 core. We propose that these contacts bring CDEI and CDEIII together to form a loop. Two potential loop configurations are shown. The Scm3–Cse4–H4 heterotrimeric complex can be recruited through Scm3–Ndc10 interaction. Accession codes * Abstract * Accession codes * Author information * Supplementary information Primary accessions Protein Data Bank * 3SQI * 3T79 * 3SQI * 3T79 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Jack and Eileen Connors Structural Biology Laboratory and Howard Hughes Medical Institute, Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA. * Uhn-Soo Cho & * Stephen C Harrison Contributions U.-S.C. designed and conducted experiments, determined and refined the structures, analyzed data and wrote the manuscript; S.C.H directed the project, analyzed data and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Stephen C Harrison Author Details * Uhn-Soo Cho Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen C Harrison Contact Stephen C Harrison 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 (10.1 MB) Supplementary Figures 1–8 and Supplementary Methods Additional data Entities in this article * Suppressor of kinetochore protein 1 SKP1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Centromere DNA-binding protein complex CBF3 subunit C CTF13 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Centromere DNA-binding protein complex CBF3 subunit B CEP3 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Centromere DNA-binding protein complex CBF3 subunit A CBF2 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Histone H3-like centromeric protein A CENPA Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Histone H4 Homo sapiens * View in UniProt * View in Antibodypedia * Histone H3-like centromeric protein CSE4 CSE4 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Protein MIF2 MIF2 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Centromere protein C 1 CENPC1 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Centromere-binding protein 1 CBF1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Protein SCM3 SCM3 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * S-phase kinase-associated protein 1 SKP1 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * S-phase kinase-associated protein 2 SKP2 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Cullin-1 CUL1 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Spindle assembly checkpoint kinase IPL1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Inner centromere protein-related protein SLI15 SLI15 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Protein BIR1 BIR1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * N-terminal-borealin-like protein NBL1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Aurora kinase B AURKB Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Inner centromere protein INCENP Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Baculoviral IAP repeat-containing protein 5 BIRC5 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Borealin CDCA8 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Histone H3-like centromeric protein CSE4 KLLA0C12529g Kluyveromyces lactis (strain ATCC 8585 / CBS 2359 / DSM 70799 / NBRC 1267 / NRRL Y-1140 / WM37) * View in UniProt * View in Entrez Gene * Site-specific recombinase Flp Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * Recombinase cre cre Enterobacteria phage P1 * View in UniProt * View in Entrez Gene * Centromere-associated factor Kluyveromyces lactis * View in UniProt * Centromere-binding protein 1 Kluyveromyces lactis (strain ATCC 8585 / CBS 2359 / DSM 70799 / NBRC 1267 / NRRL Y-1140 / WM37) * View in UniProt * Holliday junction recognition protein HJURP Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Histone H4 Kluyveromyces lactis (strain ATCC 8585 / CBS 2359 / DSM 70799 / NBRC 1267 / NRRL Y-1140 / WM37) * View in UniProt </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_e8b8859f52a84d21ef639b6ff0000cb1"> An ankyrin-repeat ubiquitin-binding domain determines TRABID's specificity for atypical ubiquitin chains</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_e8b8859f52a84d21ef639b6ff0000cb1">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_e8b8859f52a84d21ef639b6ff0000cb1"><strong>19</strong>(1):62-71</a> (2012)<br /> Nature Structural & Molecular Biology | Article X-chromosome hyperactivation in mammals via nonlinear relationships between chromatin states and transcription * Eda Yildirim1, 2, 3, 4 * Ruslan I Sadreyev1, 2, 3, 4 * Stefan F Pinter1, 2, 3 * Jeannie T Lee1, 2, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:56–61Year published:(2012)DOI:doi:10.1038/nsmb.2195Received 11 October 2011 Accepted 08 November 2011 Published online 04 December 2011 Highlighting tool Genes and ProteinsUpdate Highlighting Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Dosage compensation in mammals occurs at two levels. In addition to balancing X-chromosome dosage between males and females via X inactivation, mammals also balance dosage of Xs and autosomes. It has been proposed that X-autosome equalization occurs by upregulation of Xa (active X). To investigate mechanism, we perform allele-specific ChIP-seq for chromatin epitopes and analyze RNA-seq data. The hypertranscribed Xa demonstrates enrichment of active chromatin marks relative to autosomes. We derive predictive models for relationships among Pol II occupancy, active mark densities and gene expression, and we suggest that Xa upregulation involves increased transcription initiation and elongation. Enrichment of active marks on Xa does not scale proportionally with transcription output, a disparity explained by nonlinear quantitative dependencies among active histone marks, Pol II occupancy and transcription. Notably, the trend of nonlinear upregulation also occurs on autosomes. Th! us, Xa upregulation involves combined increases of active histone marks and Pol II occupancy, without invoking X-specific dependencies between chromatin states and transcription. View full text Figures at a glance * Figure 1: Allele-specific ChIP-seq. () Profiles for Pol II-S2P, H3K4me3 and H3K36me3 are mapped to M. castaneus (cast) or M. musculus (mus) alleles for two imprinted loci, Zim1 (ref. 47) and Peg3 (ref. 48) on chromosome 7. Composite tracks (comp) represent combination of cast, mus and neutral reads. Coverage values are normalized by input and are indicated on the y axis. () X chromosome shows a strong allelic skew in the occupancy of active histone marks and Pol II at the TSS and across the gene body. Bar plots show mean composite densities of Pol II-S5P, Pol II-S2P, H3K4me3 and H3K36me3 on autosomes (A) and X chromosome (X), with proportion of allelic coverage indicated by red (cast; active X) and blue (mus; inactive X) fractions. * Figure 2: Distributions of coverage densities for Pol II and active histone modifications on X chromosome and autosomes. Coverage density values are shown for H3K4me3 and Pol II-S5P at the TSS and for H3K36me3 and Pol II-S2P across the gene bodies as indicated. Distributions are plotted for actively transcribed (HCP+LCP, HCP and LCP) genes. Black line, autosomal genes; red line, X-linked genes. * Figure 3: Relationships between levels of gene expression, Pol II and active histone modifications. M. castaneus alleles of actively expressed autosomal HCP genes are represented as points, with point density shown by colored contour. Black line contour represents active HCP X-linked M. castaneus alleles (Xa). Expression, Pol II and histone modification levels are positively correlated, the relationships are nonlinear, and X-linked genes follow autosomal trends of dependency, albeit with a shift to higher values. (,) Pol II densities at the TSS () and across the gene body () versus expression (log-log scale). () H3K4me3 densities at the TSS versus expression (log-log scale). () H3K36me3 densities across the gene body versus expression (linear-log scale). () H3K4me3 versus Pol II densities at the TSS (log-log scale). (f H3K36me3 densities across the gene body versus Pol II densities at the TSS (linear-log scale). () H3K4me3 densities at the TSS versus Pol II densities across the gene body (log-log scale). () H3K36me3 versus Pol II densities across the gene body (linear-log ! scale). Decimal logarithms are used. * Figure 4: Autosomal relationships between active histone modifications, Pol II and expression are predictive of X-linked gene expression. () Actively expressed X-linked and autosomal genes show similar patterns of correlation between the levels of active marks and expression. Pearson correlation coefficients between the levels of all marks and expression (FPKM) are shown as heat maps for actively expressed (HCP+LCP, HCP and LCP) genes. In each plot, autosomal and X chromosome correlations are shown above and below diagonal, respectively. () Active X chromosome loci (X) and the corresponding set of autosomal loci (A) show similar nonlinear relationship between active marks and expression (blue curve), which produces a large average expression change in response to smaller changes in the mark occupancy (schematic). () Scatter plot of X-linked gene expression values predicted from autosome-based full linear model versus observed X-linked expression (decimal log-log scale). Shades of blue indicate point density. Identity line y = x is shown in red. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE33823 Sequence Read Archive * SRA010053 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Eda Yildirim & * Ruslan I Sadreyev Affiliations * Howard Hughes Medical Institute, Boston, Massachusetts, USA. * Eda Yildirim, * Ruslan I Sadreyev, * Stefan F Pinter & * Jeannie T Lee * Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA. * Eda Yildirim, * Ruslan I Sadreyev, * Stefan F Pinter & * Jeannie T Lee * Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA. * Eda Yildirim, * Ruslan I Sadreyev, * Stefan F Pinter & * Jeannie T Lee Contributions E.Y. and J.T.L. designed the research; E.Y. and S.F.P. conducted ChIP-seq experiments; R.I.S. performed the bioinformatics analysis; S.F.P. designed the allele-specific ChIP-seq strategy and performed allele-specific alignments; E.Y., R.I.S., S.F.P., and J.T.L. analyzed the data; and E.Y., R.I.S., and J.T.L. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jeannie T Lee Author Details * Eda Yildirim Search for this author in: * NPG journals * PubMed * Google Scholar * Ruslan I Sadreyev Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan F Pinter Search for this author in: * NPG journals * PubMed * Google Scholar * Jeannie T Lee Contact Jeannie T Lee 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 (16.4 MB) Supplementary Figures 1–4 and Supplementary Tables 1–3 Additional data Entities in this article * Zinc finger, imprinted 1 Zim1 Mus musculus * View in UniProt * View in Entrez Gene * Paternally-expressed gene 3 protein Peg3 Mus musculus * View in UniProt * View in Entrez Gene * Males-absent on the first protein mof Drosophila melanogaster * View in UniProt * View in Entrez Gene * RNA on the X 1 roX1 Drosophila melanogaster * View in Entrez Gene * RNA on the X 2 roX2 Drosophila melanogaster * View in Entrez Gene * Inactive X specific transcripts Xist Mus musculus * View in Entrez Gene * Proto-oncogene c-Fos Fos Mus musculus * View in UniProt * View in Entrez Gene * Transcription factor AP-1 Jun Mus musculus * View in UniProt * View in Entrez Gene </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_f611c6c8bccffb0f23d3d508a5b25123"> Mechanism of mismatch recognition revealed by human MutSβ bound to unpaired DNA loops</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_f611c6c8bccffb0f23d3d508a5b25123">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_f611c6c8bccffb0f23d3d508a5b25123"><strong>19</strong>(1):72-78</a> (2012)<br /> Nature Structural & Molecular Biology | Article An ankyrin-repeat ubiquitin-binding domain determines TRABID's specificity for atypical ubiquitin chains * Julien D F Licchesi1 * Juliusz Mieszczanek1 * Tycho E T Mevissen1 * Trevor J Rutherford1 * Masato Akutsu1 * Satpal Virdee1, 3 * Farid El Oualid2 * Jason W Chin1 * Huib Ovaa2 * Mariann Bienz1 * David Komander1 * Affiliations * Contributions * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:62–71Year published:(2012)DOI:doi:10.1038/nsmb.2169Received 07 April 2011 Accepted 29 September 2011 Published online 11 December 2011 Highlighting tool Genes and ProteinsUpdate Highlighting Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Eight different types of ubiquitin linkages are present in eukaryotic cells that regulate diverse biological processes. Proteins that mediate specific assembly and disassembly of atypical Lys6, Lys27, Lys29 and Lys33 linkages are mainly unknown. We here reveal how the human ovarian tumor (OTU) domain deubiquitinase (DUB) TRABID specifically hydrolyzes both Lys29- and Lys33-linked diubiquitin. A crystal structure of the extended catalytic domain reveals an unpredicted ankyrin repeat domain that precedes an A20-like catalytic core. NMR analysis identifies the ankyrin domain as a new ubiquitin-binding fold, which we have termed AnkUBD, and DUB assays in vitro and in vivo show that this domain is crucial for TRABID efficiency and linkage specificity. Our data are consistent with AnkUBD functioning as an enzymatic S1′ ubiquitin-binding site, which orients a ubiquitin chain so that Lys29 and Lys33 linkages are cleaved preferentially. View full text Figures at a glance * Figure 1: Structure and specificity of an extended TRABID OTU domain. () Schematic representation of the functional domains of TRABID (top) and species conservation derived from a multiple sequence alignment (http://www.ensembl.org) (middle). An extended catalytic OTU domain was analyzed (residues 245–697, bottom). () Linkage specificity of the extended catalytic OTU domain of TRABID using diubiquitin molecules of all eight linkage types, analyzed as reported before26. TRABID was incubated with diubiquitin for the indicated times, and the reaction mixtures were resolved on an SDS-PAGE gel and silver stained. Ub, ubiquitin. () Structure of the extended TRABID OTU domain. The catalytic core is colored in shades of blue, where dark blue indicates the minimal OTU core domain, and the lighter blue indicates additional secondary structure elements found in the A20-like subfamily of OTU DUBs. The ankyrin repeats are shown in two shades of orange. The catalytic triad residues are indicated in ball-and-stick representation. () Structure of A20 (green! , left, PDB 2VFJ23) and superposition with TRABID (blue, right). () Catalytic triad residues of TRABID are shown in ball-and-stick representation with yellow sulfur, red oxygen and blue nitrogen atoms. A red sphere indicates a water molecule, and yellow dotted lines indicate hydrogen bonds. A 2Fo – Fc electron density map contoured at 1σ covers relevant residues. () The A20 catalytic triad is shown, with atoms colored as in . * Figure 2: TRABID contains two ankyrin repeats with roles in ubiquitin binding. () Structure of the ankyrin domain in TRABID showing the two repeats. () Structure of RNase L (PDB 1WDY47), the ankyrin-repeat protein with highest similarity to the TRABID ankyrin domain in a DALI search (Z score 8.4). The eight ankyrin repeats are numbered. () Superposition of the TRABID ankyrin domain and RNase L. () The minimal OTU domain of yeast Otu1 (green) with ubiquitin (yellow) bound at the S1 site (PDB 3BY4 (ref. 25)). The orientation matches that of the minimal OTU domain core indicated in Figure 1c. Ub, ubiquitin. () Superposition of TRABID and yeast Otu1 reveals the relative position of the S1 ubiquitin-binding site on TRABID, and this suggests that the ankyrin domain may constitute an S1′ ubiquitin-binding site. * Figure 3: A conserved hydrophobic surface on AnkUBD binds ubiquitin. () 1H-15N HSQC spectrum of 13C-15N–labeled TRABID ankyrin domain. () Closeup of the region within the red box in , showing resonances of the doubly labeled ankyrin domain (blue) and their shifts upon addition of 250 μM (yellow) or 1 mM (red) unlabeled ubiquitin (Ub). Arrows indicate the shift of individual resonances. () Weighted chemical shift perturbation map of the AnkUBD binding to ubiquitin. () AnkUBD residues are colored according to the degree of perturbation from blue (unperturbed) to red (strongly perturbed), and crucial residues are shown in stick representation. () The AnkUBD surface is shown colored as in and key residues are labeled. () Invariant residues derived from a species sequence alignment (Supplementary Fig. 2b) are shown in red on a white AnkUBD surface. * Figure 4: AnkUBD binds the ubiquitin hydrophobic patch. Ubiquitin (Ub) binding to the AnkUBD was confirmed by NMR shift mapping experiments, for which spectra of 15N ubiquitin were recorded in the absence and presence of AnkUBD (Supplementary Fig. 3). () Perturbation of a selected resonance (that of ubiquitin Leu43, yellow) by titration of increasing concentrations of AnkUBD (colored from red to cyan) is shown as an example. The complete spectra can be found in Supplementary Figure 3. () The resulting weighted chemical shift perturbation map reveals a familiar pattern seen when proteins bind to the ubiquitin hydrophobic patch. () Ubiquitin residues are colored according to the degree of perturbation from blue (unperturbed) to red (strongly perturbed) and crucial residues are shown in stick representation. () The ubiquitin surface is shown colored as in and crucial residues are labeled. () Titration experiments using indicated concentrations of AnkUBD mutants H317A (left), I320D (middle) and L332E (right) were conducted. The same ! resonance as in is shown. Mutant L332E did not perturb any resonances, whereas H317A and I320D perturbed a few resonances to a lesser degree (Supplementary Fig. 3). * Figure 5: Analysis of TRABID DUB activity. () Bacterial TRABID variants were incubated with polyubiquitin substrates for indicated times and visualized by silver staining. Comparison of activity and specificity of the isolated OTU domain (above, [E] 1.2 μM) with TRABID AnkOTU (below, [E] 0.2 μM, reproduced from Fig. 1b to allow comparison). Input enzyme levels are shown in OTU panel (see also Supplementary Fig. 4a). Ub, ubiquitin. (–) Mammalian TRABID variants were incubated with polyubiquitin substrates for indicated times and visualized by silver staining. Flag-tagged TRABID variants were purified from HEK293 cells and used in DUB assays. () Specificity of mammalian full-length (FL), AnkOTU and OTU TRABID against the diubiquitin panel after overnight (O/N, 16 h) incubation. () Time-course analysis of mammalian TRABID variants against its substrate linkages. FL ΔAnk means full-length, lacking AnkUBD. Input (Inp) controls highlight the stability of ubiquitin substrates in the absence of enzyme in the reaction mi! xture. () Activity of full-length TRABID with point mutations in the AnkUBD against its preferred diubiquitin substrates. Full-length C443S, catalytic mutant. DUB activity assays carried out with material obtained from Flag-empty vector (ev) immunoprecipitation showed no activity. () Time-course activity of TRABID variants against Lys63-linked hexaubiquitin. * Figure 6: Role of the NZF domains in cleaving longer ubiquitin chains. Mammalian TRABID variants were incubated with polyubiquitin substrates for indicated times and visualized by silver staining. () Activity of TRABID variants against Lys29, Lys33 and Lys63-linked diubiquitin (Ub2) at indicated time point. Full-length C443S, catalytic mutant; full-length NZFmut, full-length with mutations in all three NZF domains; FL ΔAnk, full-length, lacking AnkUBD; AnkOTU, crystallized fragment; OTU, OTU domain. () Time-course analysis of mammalian full-length TRABID, full-length NZFmut and AnkOTU activity toward Lys63-linked hexaubiquitin. () Model for the role of the AnkUBD as an S1′ ubiquitin-binding site in TRABID. () Model for the additional contribution of the NZF domains in cleaving longer polyubiquitin chains. * Figure 7: In vivo DUB assay NZF and AnkUBD are essential for TRABID puncta. () Localization studies with GFP-TRABID in COS-7 cells 18 h after transfection (left). Nuclei are stained using DAPI (middle). The right image is a merge of the channels. The domain structure of TRABID is shown above. () A GFP-tagged full-length TRABID catalytic mutant (C443S; a yellow star in the domain representation indicates the mutation) adopts a punctate localization in COS-7 (shown) and other cell types35. () FRAP experiments on a control (black) or puncta-containing volume (C443S, blue). Fluorescent recovery is recorded over time. () Localization studies of TRABID GFP-tagged AnkOTU C443S and GFP-tagged full-length NZFmut C443S, colored as in . The domain structure is shown (left), and the GFP fluorescence of either construct (right) shows that no puncta are formed. Ub, ubiquitin. () Puncta-forming GFP-tagged C443S (left image) was coexpressed with Flag-ubiquitin or single-lysine ('Konly') ubiquitin mutants (middle image). The merged image is shown to the right. Furth! er ubiquitin mutants are shown in Supplementary Figure 6a. (–) Dissolving TRABID assemblies requires the AnkUBD. () Puncta-forming GFP-tagged full-length TRABID C443S (left) was expressed in COS-7 cells and TRABID assemblies were visualized (left). Nuclei are stained using DAPI (middle). The merged image is shown to the right. (–) As in , but in addition, Flag-tagged full-length WT TRABID constructs (), full-length NZFmut () or full-length ΔAnk () were coexpressed (far left), and the presence of GFP puncta was assessed. Additional data are shown in Supplementary Figure 6b. Accession codes * Abstract * Accession codes * Author information * Supplementary information Primary accessions Protein Data Bank * 3ZRH * 3ZRH Referenced accessions Protein Data Bank * 2VFJ * 1WDY * 3BY4 * 2VFJ * 1WDY * 3BY4 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Medical Research Council Laboratory of Molecular Biology, Cambridge, UK. * Julien D F Licchesi, * Juliusz Mieszczanek, * Tycho E T Mevissen, * Trevor J Rutherford, * Masato Akutsu, * Satpal Virdee, * Jason W Chin, * Mariann Bienz & * David Komander * Division of Cell Biology, Netherlands Cancer Institute, Amsterdam, The Netherlands. * Farid El Oualid & * Huib Ovaa * Present address: Scottish Institute for Cell Signalling, Protein Ubiquitylation Unit, University of Dundee, Dundee, UK. * Satpal Virdee Contributions D.K., M.B. and J.D.F.L. designed the research. J.D.F.L., D.K., J.M., T.E.T.M., T.J.R. and M.A. conducted the experiments. F.E., H.O., S.V. and J.W.C. contributed reagents. D.K. wrote the manuscript, with help from all authors. Competing financial interests H.O. and F.E. are cofounders of UbiQ Bio BV. Corresponding author Correspondence to: * David Komander Author Details * Julien D F Licchesi Search for this author in: * NPG journals * PubMed * Google Scholar * Juliusz Mieszczanek Search for this author in: * NPG journals * PubMed * Google Scholar * Tycho E T Mevissen Search for this author in: * NPG journals * PubMed * Google Scholar * Trevor J Rutherford Search for this author in: * NPG journals * PubMed * Google Scholar * Masato Akutsu Search for this author in: * NPG journals * PubMed * Google Scholar * Satpal Virdee Search for this author in: * NPG journals * PubMed * Google Scholar * Farid El Oualid Search for this author in: * NPG journals * PubMed * Google Scholar * Jason W Chin Search for this author in: * NPG journals * PubMed * Google Scholar * Huib Ovaa Search for this author in: * NPG journals * PubMed * Google Scholar * Mariann Bienz Search for this author in: * NPG journals * PubMed * Google Scholar * David Komander Contact David Komander 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 (5M) Supplementary Figures 1–6 and Supplementary Methods Additional data Entities in this article * Ubiquitin thioesterase ZRANB1 ZRANB1 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Catenin beta-1 CTNNB1 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Ubiquitin thioesterase OTUB1 OTUB1 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Ubiquitin thioesterase OTUB2 OTUB2 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Tumor necrosis factor alpha-induced protein 3 TNFAIP3 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Ubiquitin thioesterase OTU1 OTU1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Transitional endoplasmic reticulum ATPase VCP Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * OTU domain-containing protein 7B OTUD7B Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * AMSH-like protease STAMBPL1 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * OTU domain-containing protein 5 OTUD5 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Ubiquitin carboxyl-terminal hydrolase 5 USP5 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * E3 ubiquitin-protein ligase UBR5 UBR5 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * 2-5A-dependent ribonuclease RNASEL Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_763dc3e1ba5c325c4f00d92e81dd84ac"> The extracellular chaperone clusterin sequesters oligomeric forms of the amyloid-β1−40 peptide</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_763dc3e1ba5c325c4f00d92e81dd84ac">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_763dc3e1ba5c325c4f00d92e81dd84ac"><strong>19</strong>(1):79-83</a> (2012)<br /> Nature Structural & Molecular Biology | Article Mechanism of mismatch recognition revealed by human MutSβ bound to unpaired DNA loops * Shikha Gupta1 * Martin Gellert1 * Wei Yang1 * Affiliations * Contributions * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:72–78Year published:(2012)DOI:doi:10.1038/nsmb.2175Received 09 August 2011 Accepted 12 October 2011 Published online 18 December 2011 Highlighting tool Genes and ProteinsUpdate Highlighting Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg DNA mismatch repair corrects replication errors, thus reducing mutation rates and microsatellite instability. Genetic defects in this pathway cause Lynch syndrome and various cancers in humans. Binding of a mispaired or unpaired base by bacterial MutS and eukaryotic MutSα is well characterized. We report here crystal structures of human MutSβ in complex with DNA containing insertion-deletion loops (IDL) of two, three, four or six unpaired nucleotides. In contrast to eukaryotic MutSα and bacterial MutS, which bind the base of a mismatched nucleotide, MutSβ binds three phosphates in an IDL. DNA is severely bent at the IDL; unpaired bases are flipped out into the major groove and partially exposed to solvent. A normal downstream base pair can become unpaired; a single unpaired base can thereby be converted to an IDL of two nucleotides and recognized by MutSβ. The C-terminal dimerization domains form an integral part of the MutS structure and coordinate asymmetrical ATP hyd! rolysis by Msh2 and Msh3 with mismatch binding to signal for repair. View full text Figures at a glance * Figure 1: Overall structures of MutSβ–DNA complexes. () Orthogonal views of Loop3 structure in ribbon diagrams, with MSH2 in green and MSH3 in blue. The DNA is shown in a space-filling model with the backbone in red, bases in light pink, and the unpaired nucleotides in yellow and orange. ADP bound to MSH2 is shown in purple sticks. () Side views of DNA-binding domains and DNA in Loop2, Loop4 and Loop6 structures. Each unpaired CA dinucleotide repeat is shown in yellow and orange. Domain I of MSH3 is the MBD. MSH2 and MSH3 subunits are indicated by circled numbers 2 and 3, respectively. Domains I–V and dimerization domains are indicated. * Figure 2: Comparison of MutSα and MutSβ proteins. () Ribbon diagram of MSH2 from MutSβ. Domains I, II, III, IV, V and the DMDs are shown in blue, green, yellow, orange, pink and red, respectively. MSH2 of MutSα is superimposed and shown in gray. Domain interfaces between I and II and between II, III and V are highlighted in magenta. () Ribbon diagram of MSH3 in the same orientation and same color codes. Domains I (MBD) and IV (clamp) interact as indicated by the dotted oval. () Superposition of the mismatch-binding subunits in MutSα, MutSβ, E. coli and TaqMutS in the same orientation as in and . Except for domain IV, they superimpose very well. () A ribbon diagram of the interface between domains I in MutSβ. Protein residues in all figures are labeled in one-letter code for clarity. () On the left is a ribbon diagram of MSH3 MBD decorated by residues conserved among MutS homologs (shown as yellow-blue-red stick-and-ball models) and residues unique among MSH3 homologs (pink-blue-red stick-and-ball models). On the right ! is the superposition of MSH3 (blue) and MSH6 (gray) MBDs. The r.m.s. deviation between them is 0.7 Å over 87 pairs of Cα atoms. * Figure 3: IDL recognition by MutSβ. () A closeup comparison of MSH3–IDL interaction and TaqMutS with a single unpaired base (ΔT). () DNA-binding domains and DNA in Loop4. Domains I and IV of MSH2 are shown in green and yellow, and MBD and clamp domains of MSH3 in blue and orange, respectively. () Diagram of the protein-DNA interactions using the same color scheme as in . () Space-filling model of four IDLs and their interaction with domain I of MSH2 (green) and MBD of MSH3 (blue). The base pairs surrounding the IDL are shown in light (upstream) and dark (downstream) pink. For Loop2 and Loop4, a back view looking into the minor groove is also shown. * Figure 4: Dimerization domains (DMD) of MutSβ. (,) Orthogonal views of the C-terminal halves of DMDs. The hydrophobic side chains at the interface, and polar residues forming salt bridges that stabilize intrasubunit interactions, are shown as sticks with carbon in light gray, nitrogen in blue and oxygen in red. Glu901 and Lys912 of MSH2 form N- and C-caps of the MSH3 helices. () The ATPase domain (light green) and DMD (green) of MSH2 are shown with the trans-acting N2 (red and cyan) and DMD (blue) of MSH3. The ADP bound to MSH2 is shown as purple sticks. The two shaded ovals indicate the enlarged areas shown in , and . () A closeup view of the interactions between MSH3 DMD(N) and the ATPase domain of MSH2. Interactions between hydrophobic residues dominate, and two pairs of salt bridges (red dashes) may have alternative interacting partners (black dashes) if the two subunits slide relative to each other. * Figure 5: Asymmetric ATPase sites of MutSβ. () Ribbon diagram of the ATPase and dimerization domains. MSH2 is shown in light and dark green, and MSH3 in light and dark blue. The trans-acting N2 regions of MSH2 and MSH3 are highlighted in red. The aromatic side chains connecting the ATPase site to the DMD are shown as blue (MSH3) and green sticks (MSH2). () A view 180° from showing the asymmetric DMDs, biased toward the MSH2 ATPase site. () Comparison of the connection between the ATP binding site and DMD in MSH3 (blue), MSH6 with ADP (pink) and without (yellow) after superposition. The critical aromatic side chains are shown as sticks. * Figure 6: Mechanism of mismatch recognition. Msh2 is drawn in green and Msh3 and Msh6 in blue. The ATPase activities of the two subunits are indicated by the curved arrows: the thicker the arrow, the higher the activity. The DNA-binding domains are flexible in the apo form. Binding to normal DNA, which is resistant to bending, induces conformational changes in the DNA-binding domains and ATP hydrolysis. But the protein-DNA association is not stable, and MutS(α or β) can dissociate from or slide along DNA. Binding to a mismatched DNA, which is readily bent, leads to stable association of Msh3 or Msh6 with DNA and inhibition of its ATPase activity. ATP binding by Msh3 or Msh6, however, leads to release of the mismatched DNA. When Msh2 is bound to ATP and Msh3 or Msh6 to the mismatched DNA, MutS(α or β) can recruit MutLα to form a 'repairosome', thus initiating the repair process. Accession codes * Abstract * Accession codes * Author information * Supplementary information Primary accessions Protein Data Bank * 3THY * 3THX * 3THW * 3THZ * 3THY * 3THX * 3THW * 3THZ Referenced accessions Protein Data Bank * 1EWQ * 2O8E * 2O8B * 1EWQ * 2O8E * 2O8B Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Laboratory of Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, US National Institutes of Health, Bethesda, Maryland, USA. * Shikha Gupta, * Martin Gellert & * Wei Yang Contributions S.G. conducted all experiments and collected X-ray data. W.Y. determined and refined the structures. S.G., M.G. and W.Y. contributed to the experimental design, data interpretation and manuscript preparation. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Wei Yang Author Details * Shikha Gupta Search for this author in: * NPG journals * PubMed * Google Scholar * Martin Gellert Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Yang Contact Wei Yang 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 (13M) Supplementary Figures 1–7 Additional data Entities in this article * DNA mismatch repair protein mutS mutS Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * DNA mismatch repair protein Msh2 MSH2 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * DNA mismatch repair protein Msh3 MSH3 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * DNA mismatch repair protein mutL mutL Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * DNA mismatch repair protein Msh6 MSH6 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * DNA mismatch repair protein Msh3 Msh3 Mus musculus * View in UniProt * View in Entrez Gene * DNA mismatch repair protein Msh6 Msh6 Mus musculus * View in UniProt * View in Entrez Gene * DNA mismatch repair protein MSH3 MSH3 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * DNA mismatch repair protein MSH6 MSH6 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * DNA mismatch repair protein mutS Thermus aquaticus * View in UniProt * DNA mismatch repair protein MSH2 MSH2 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_a10c9e5a6cd0b3f94e50051b601b7e4f"> Structural basis of pre-let-7 miRNA recognition by the zinc knuckles of pluripotency factor Lin28</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_a10c9e5a6cd0b3f94e50051b601b7e4f">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_a10c9e5a6cd0b3f94e50051b601b7e4f"><strong>19</strong>(1):84-89</a> (2012)<br /> Nature Structural & Molecular Biology | Article The extracellular chaperone clusterin sequesters oligomeric forms of the amyloid-β1−40 peptide * Priyanka Narayan1 * Angel Orte1, 2 * Richard W Clarke1 * Benedetta Bolognesi1 * Sharon Hook1 * Kristina A Ganzinger1 * Sarah Meehan1 * Mark R Wilson3 * Christopher M Dobson1 * David Klenerman1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:79–83Year published:(2012)DOI:doi:10.1038/nsmb.2191Received 27 May 2011 Accepted 14 October 2011 Published online 18 December 2011 Highlighting tool Genes and ProteinsUpdate Highlighting Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg In recent genome-wide association studies, the extracellular chaperone protein, clusterin, has been identified as a newly-discovered risk factor in Alzheimer's disease. We have examined the interactions between human clusterin and the Alzheimer's disease–associated amyloid-β1−40 peptide (Aβ1−40), which is prone to aggregate into an ensemble of oligomeric intermediates implicated in both the proliferation of amyloid fibrils and in neuronal toxicity. Using highly sensitive single-molecule fluorescence methods, we have found that Aβ1−40 forms a heterogeneous distribution of small oligomers (from dimers to 50-mers), all of which interact with clusterin to form long-lived, stable complexes. Consequently, clusterin is able to influence both the aggregation and disaggregation of Aβ1−40 by sequestration of the Aβ oligomers. These results not only elucidate the protective role of clusterin but also provide a molecular basis for the genetic link between clusterin and Al! zheimer's disease. View full text Figures at a glance * Figure 1: Bulk and single-molecule studies of Aβ1−40. () Appearance and disappearance of species populated during the aggregation of Aβ1−40 (2 μM at 37 °C with agitation). Fibril formation monitored by thioflavin (ThT) fluorescence (top). The inset is a transmission electron microscopy (TEM) image of the fibrils present after 24 h of incubation (scale bar, 200 nm). The concentration of soluble oligomers (dimers to 50-mers; middle) and of monomeric species (bottom) are both tracked using cTCCD. The data are averaged from multiple experimental repetitions (2 μM Aβ1−40, n = 3; error bars are s.e.m.). () A representative distribution of apparent sizes of oligomers formed during Aβ1−40 aggregation and disaggregation (error bars are s.d.). Insets are zoomed into regions of dimers to 15-mers and 16-mers to 50-mers to show greater detail. () A comparison of the distributions of apparent oligomer sizes during aggregation and disaggregation experiments (2 μM Aβ1−40 aggregation, n = 3; disaggregation, n = 12; 10−30 nM A�! �1−40 aggregation, n = 4; error bars are s.e.m.). () Time dependence of the concentration of soluble species released from a pellet of fibrils (n = 12; error bars are s.e.m.). * Figure 2: The effects of clusterin on the aggregation of Aβ1−40. () Fraction of oligomers detected in solution during the aggregation of Aβ1−40 with and without clusterin (Aβ1−40 and clusterin are both at a concentration of 600 nM; n = 3 and error bars are s.e.m.). () TIRFM image of the species present after 24 h of aggregation of a 2-μM solution of Aβ1−40 without clusterin (left). TIRFM image of a 2 μM solution of Aβ1−40 after 24 h of aggregation, but with clusterin added at a concentration of 2 μM 4 h after the start of the reaction, during the fibril growth phase (right). An approximately 50% reduction in the average dimensions of species present is observed in the presence of clusterin (from 1,400 ± 200 nm without clusterin to 780 ± 60 nm with clusterin, s.e.m., P-value is 0.01, two-sample independent, two-tailed t-test). Scale bars, 5 μm. () Fractions of species formed during the aggregation of a 2 μM solution that are oligomeric and that are in Aβ–clusterin complexes. (n = 3, error bars are s.e.m.). () Proporti! on of Aβ–clusterin complexes persisting at 10−20 nM (total peptide concentration) at 21 °C. Complexes were formed between clusterin and oligomers formed in both aggregation and disaggregation reactions. For both traces, n = 3 and error bars are s.e.m. There is no statistically significant change in the proportion of complexes with oligomers formed during either the disaggregation experiments (P value of 0.77, analysis of variance (ANOVA) single-factor) or the aggregation experiments (P value of 0.99, ANOVA single-factor). * Figure 3: The effects of clusterin on the disaggregation of Aβ1−40 fibrils. () Distributions of apparent sizes of oligomers formed during aggregation and disaggregation reactions with and without clusterin. (Aggregation without clusterin, n = 2 and error bars are range; aggregation with clusterin, n = 3; disaggregation without clusterin, n = 10; disaggregation with clusterin, n = 3 and error bars are s.e.m.). () Time dependence of the release of soluble species during the disaggregation experiments in the presence and absence of clusterin (top), increased oligomer concentration in the presence of clusterin in the concentration plateau region (significant, with a P value of 0.002) (bottom left) and decreased monomer concentration in the presence of clusterin, in the concentration plateau region (significant, with a P value of 0.0003) (bottom right). Both correlations were analyzed using a two-sample independent, two-tailed t-test; n = 8 and error bars are s.e.m. () TIRFM images of HiLyte488Fluor-labeled Aβ1−40 fibrils incubated overnight at 21 °C! with AlexaFluor647-labeled clusterin. Aβ1−40 fluorescence only (left), clusterin fluorescence only (middle) and colocalization of the two species (right). Scale bars, 5 μm. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Chemistry, University of Cambridge, Cambridge, UK. * Priyanka Narayan, * Angel Orte, * Richard W Clarke, * Benedetta Bolognesi, * Sharon Hook, * Kristina A Ganzinger, * Sarah Meehan, * Christopher M Dobson & * David Klenerman * Department of Physical Chemistry, Faculty of Pharmacy, University of Granada, Campus Cartuja, Granada, Spain. * Angel Orte * School of Biological Sciences, University of Wollongong, Wollongong, New South Wales, Australia. * Mark R Wilson Contributions P.N., A.O., S.M., M.R.W., C.M.D. and D.K. designed the experiments. P.N. conducted the cTCCD experiments. P.N., A.O. and R.W.C. refined analysis methods. A.O. and R.W.C. developed instrumentation. R.W.C. wrote the analysis software, and designed, built and calibrated the scanning stage used for cTCCD experiments. P.N. and B.B. conducted the bulk scale experiments. P.N. and K.A.G. conducted the TIRFM experiments. P.N., B.B., K.A.G. and A.O. analyzed the data. S.H. labeled the clusterin that was purified and provided by M.R.W. All authors discussed and interpreted results and contributed to the writing of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Mark R Wilson or * Christopher M Dobson or * David Klenerman Author Details * Priyanka Narayan Search for this author in: * NPG journals * PubMed * Google Scholar * Angel Orte Search for this author in: * NPG journals * PubMed * Google Scholar * Richard W Clarke Search for this author in: * NPG journals * PubMed * Google Scholar * Benedetta Bolognesi Search for this author in: * NPG journals * PubMed * Google Scholar * Sharon Hook Search for this author in: * NPG journals * PubMed * Google Scholar * Kristina A Ganzinger Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah Meehan Search for this author in: * NPG journals * PubMed * Google Scholar * Mark R Wilson Contact Mark R Wilson Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher M Dobson Contact Christopher M Dobson Search for this author in: * NPG journals * PubMed * Google Scholar * David Klenerman Contact David Klenerman Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (979K) Supplementary Figures 1–6, Supplementary Methods and Supplementary Discussion Additional data Entities in this article * Clusterin CLU Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Amyloid beta A4 protein APP Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_035ef09a8ea6c3e0aa37024bc5e70ec0"> Tudor domain ERI-5 tethers an RNA-dependent RNA polymerase to DCR-1 to potentiate endo-RNAi</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_035ef09a8ea6c3e0aa37024bc5e70ec0">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_035ef09a8ea6c3e0aa37024bc5e70ec0"><strong>19</strong>(1):90-97</a> (2012)<br /> Nature Structural & Molecular Biology | Article Structural basis of pre-let-7 miRNA recognition by the zinc knuckles of pluripotency factor Lin28 * Fionna E Loughlin1 * Luca F R Gebert2 * Harry Towbin2 * Andreas Brunschweiger2 * Jonathan Hall2 * Frédéric H-T Allain1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:84–89Year published:(2012)DOI:doi:10.1038/nsmb.2202Received 20 May 2011 Accepted 10 November 2011 Published online 11 December 2011 Highlighting tool Genes and ProteinsUpdate Highlighting Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Lin28 inhibits the biogenesis of let-7 miRNAs through a direct interaction with the terminal loop of pre-let-7. This interaction requires the zinc-knuckle domains of Lin28. We show that the zinc knuckle domains of Lin28 are sufficient to provide binding selectivity for pre-let-7 miRNAs and present the NMR structure of human Lin28 zinc knuckles bound to the short sequence 5′-AGGAGAU-3′. The structure reveals that each zinc knuckle recognizes an AG dinucleotide separated by a single nucleotide spacer. This defines a new 5′-NGNNG-3′ consensus motif that explains how Lin28 selectively recognizes pre-let-7 family members. Binding assays in cell lysates and functional assays in cultured cells demonstrate that the interactions observed in the solution structure also occur between the full-length protein and members of the pre-let-7 family. The consensus sequence explains several seemingly disparate previously published observations on the binding properties of Lin28. View full text Figures at a glance * Figure 1: The ZnFs of Lin28 bind single-stranded regions of pre-let-7 terminal loops. () Domain structure of Lin28 and of the construct containing the two ZnFs used in this study. () Chemically synthesized biotinylated pre-miRNAs bound by immobilized, recombinant, purified Lin28 ZnF12. Pre-let-7 family members and pre-miRNAs that are known not to be regulated by Lin28 are shown. Error bars represent s.d. of triplicate determinations. One of two assays is shown. () Upper, secondary structure of the terminal loop of pre-let-7f-1, as predicted with Mfold29. Lower left, overlay of section of 15N HSQC spectra of Lin28 ZnF12, free (gray) and bound (green) to the terminal loop of pre-let-7f-1. Arrows show the changes of selected resonances. Lower right, overlay of section of 15N HSQC spectra of Lin28 ZnF12 bound to the terminal loops of pre-let-7f-1 (green) and bound to the single-stranded 5′-AGGAGAU-3′ (purple) sequence. TL, terminal loop. * Figure 2: The solution structure of Lin28 ZnF12 bound to 5′-AGGAGAU-3′. () Representative structures of the Lin28 ZnF12–5′-AGGAGAU-3′ complex. The ZnF ribbon is shown in green, the zinc atom in purple and the RNA in yellow. () G2 recognition by ZnF2. The hydrogen bonds are indicated by dotted black lines. () G5 recognition by ZnF1. Figures were generated by MOLMOL30. Arrows identify residues; bb indicates amino acid backbone. * Figure 3: Affinity of Lin28 variants for single-stranded and pre-let-7g RNAs and processing of pre-let-7g point mutants in Huh-7 cells. () Representative ITC data obtained by titration of Lin28 ZnF12 WT and point mutant into 5′-AGGAGAU-3′ (left). Kd of Lin28 ZnF12 WT and single–amino acid mutant binding to ssRNA (right). Error bars indicate s.d. of two measurements; for details see Supplementary Table 1. () Representative inhibition curves for full-length Myc-tagged Lin28 WT and single-point mutations in HeLa cell lysates (left). Relative binding affinities (Kd) of Myc-Lin28 for pre-let-7g in HeLa cell lysates (right, average of two replicate experiments, error bars indicate s.d.). () Representative ITC data obtained upon titration of Lin28 ZnF12 into ssRNA (left). Kd of Lin28 ZnF12 binding different RNAs (right). For details see Supplementary Table 1. () Levels of mature microRNAs (let-7g, mir-16 and mir-191) in Huh-7 cells transfected with graded concentrations (10, 5 and 2.5 nM) of in vitro transcribed pre-let-7g WT or point mutants, as determined by stem-loop RT-PCR after 24 h. Mir-191 was used for! normalization and error bars indicate s.e.m. of quadruplicate determinations. * Figure 4: Comparison between the structure of Lin28 ZnFs bound to 5′-AGGAGAU-3′ (this study) and HIV nucleocapsid (NC) bound to stem-loops of the RNA packaging signal. () Lin28 bound to 5′-AGGAGAU-3′ (green) overlaid with HIV NC bound to SL3 containing a GAG loop26 (purple; PDB 1A1T). () Lin28 bound to 5′-AGGAGAU-3′ (green) overlaid with HIV NC bound to SL2 containing a GUG loop27 (blue; PDB 1F6U). () Sequence alignment of Lin28 and HIV NC, indicating the insertion of Pro158. () Representative structures of the Lin28 ZnF12–5′-AGGAGAU-3′ complex. The ZnF ribbon is shown in green, the zinc atom in purple and the RNA in yellow. () Representative structures of HIV NC–bound SL3 containing a GAG sequence in the loop. The ZnF ribbon is shown in gray, the zinc atom in purple and the RNA in yellow. Accession codes * Abstract * Accession codes * Author information * Supplementary information Primary accessions Biological Magnetic Resonance Data Bank * 17883 Protein Data Bank * 2LI8 * 2LI8 Referenced accessions Protein Data Bank * 2CQF * 1A1T * 1F6U * 2CQF * 1A1T * 1F6U Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Institute of Molecular Biology and Biophysics, ETH Zürich, Zürich, Switzerland. * Fionna E Loughlin & * Frédéric H-T Allain * Institute of Pharmaceutical Sciences, ETH Zürich, Zürich, Switzerland. * Luca F R Gebert, * Harry Towbin, * Andreas Brunschweiger & * Jonathan Hall Contributions F.H.-T.A., F.E.L. and J.H. designed the project; F.E.L. prepared protein and RNA samples for structural studies; F.E.L. and F.H.-T.A. collected and analyzed NMR data; F.E.L. carried out the structure calculations and the ITC measurements; H.T. and A.B. did the Lin28 binding assay with miRNAs and L.F.R.G. did the quantitative PCR in cell assays. All authors discussed the results, wrote and approved the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Jonathan Hall or * Frédéric H-T Allain Author Details * Fionna E Loughlin Search for this author in: * NPG journals * PubMed * Google Scholar * Luca F R Gebert Search for this author in: * NPG journals * PubMed * Google Scholar * Harry Towbin Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Brunschweiger Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan Hall Contact Jonathan Hall Search for this author in: * NPG journals * PubMed * Google Scholar * Frédéric H-T Allain Contact Frédéric H-T Allain 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 (8M) Supplementary Figures 1–6, Supplementary Table 1 and Supplementary Methods Additional data Entities in this article * Protein lin-28 homolog A LIN28A Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * GTPase KRas KRAS Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Myc proto-oncogene protein MYC Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * High mobility group protein HMGI-C HMGA2 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Ribonuclease 3 DROSHA Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Endoribonuclease Dicer DICER1 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Terminal uridylyltransferase 4 ZCCHC11 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * POU domain, class 5, transcription factor 1 POU5F1 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Homeobox protein NANOG NANOG Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Transcription factor SOX-2 SOX2 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Protein lin-28 homolog B LIN28B Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Protein lin-28 lin-28 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * microRNA let-7 C05G5.6 Caenorhabditis elegans * View in Entrez Gene * microRNA let-7g MIRLET7G Homo sapiens * View in Entrez Gene * microRNA let-7c MIRLET7C Homo sapiens * View in Entrez Gene * microRNA let-7f-1 MIRLET7F1 Homo sapiens * View in Entrez Gene * microRNA let-7a-1 MIRLET7A1 Homo sapiens * View in Entrez Gene * microRNA 191 MIR191 Homo sapiens * View in Entrez Gene * microRNA let-7a-3 MIRLET7A3 Homo sapiens * View in Entrez Gene * Gag-Pol polyprotein Human immunodeficiency virus type 1 group M subtype B (isolate YU-2) * View in UniProt </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_ec59196d18faa2f1dd5fc939f0da57e0"> Mispaired rNMPs in DNA are mutagenic and are targets of mismatch repair and RNases H</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_ec59196d18faa2f1dd5fc939f0da57e0">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_ec59196d18faa2f1dd5fc939f0da57e0"><strong>19</strong>(1):98-104</a> (2012)<br /> Nature Structural & Molecular Biology | Article Tudor domain ERI-5 tethers an RNA-dependent RNA polymerase to DCR-1 to potentiate endo-RNAi * Caroline Thivierge1, 2, 6 * Neetha Makil1, 2, 6 * Mathieu Flamand1, 2 * Jessica J Vasale3 * Craig C Mello3, 4 * James Wohlschlegel5 * Darryl Conte Jr3 * Thomas F Duchaine1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:90–97Year published:(2012)DOI:doi:10.1038/nsmb.2186Received 25 June 2011 Accepted 14 October 2011 Published online 18 December 2011 Corrected online09 January 2012 Highlighting tool Genes and ProteinsUpdate Highlighting Abstract * Abstract * Change history * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Endogenous RNA interference (endo-RNAi) pathways use a variety of mechanisms to generate siRNA and to mediate gene silencing. In Caenorhabditis elegans, DCR-1 is essential for competing RNAi pathways—the ERI endo-RNAi pathway and the exogenous RNAi pathway—to function. Here, we demonstrate that DCR-1 forms exclusive complexes in each pathway and further define the ERI–DCR-1 complex. We show that the tandem tudor protein ERI-5 potentiates ERI endo-RNAi by tethering an RNA-dependent RNA polymerase (RdRP) module to DCR-1. In the absence of ERI-5, the RdRP module is uncoupled from DCR-1. Notably, EKL-1, an ERI-5 paralog that specifies distinct RdRP modules in Dicer-independent endo-RNAi pathways, partially compensates for the loss of ERI-5 without interacting with DCR-1. Our results implicate tudor proteins in the recruitment of RdRP complexes to specific steps within DCR-1-dependent and DCR-1-independent endo-RNAi pathways. View full text Figures at a glance * Figure 1: Distinct DCR-1 complexes initiate endo- and exo-RNAi. () Gel filtration on wild-type embryonic extract. DRH-1, RDE-4, DCR-1, RRF-3, ERI-5, ERI-1 and DRH-3 proteins were detected by western blot on fractions from a Superose S6 column. The fractionation of molecular weight (MW) standards is indicated. The asterisk (*) labels in DRH-1 (in the low molecular weight fractions) and RDE-4 filtration panels indicate non-specific bands. () Immunoprecipitation (IP) of DCR-1, DRH-1 and RDE-4 from WT, dcr-1, rde-4 or rde-1 mutant embryos. DCR-1, RDE-1, DRH-1 and RDE-4 proteins were detected in total lysate (LOAD) and IP by western blot. Tubulin was used as a loading control. The asterisk (*) to the right of the RDE-4 panels indicates background signal from the IgG heavy chains used for immunoprecipitation, which migrate with RDE-4 around 50 kDa. () Immunoprecipitation of DRH-1 in WT and drh-1 mutant embryos. DRH-1, DCR-1, DRH-3, ERI-5 and ERI-1 were detected by western blot. The asterisks (*) to the right and left of the DRH-1 panel indicat! e non-specific bands in the loading and DRH-1 IP lanes, respectively. () Immunoprecipitation of ERI-5 in WT and eri-5 mutant embryos. DRH-3, DRH-1 and ERI-5 proteins were detected by western blot. The asterisk (*) indicates the non-specific band detected in the input lanes (LOAD) of the DRH-1 blot, as in panel . () Interaction map of the proteins detected by MuDPIT analyses in WT embryonic extracts. Proteins circled in bold (DCR-1, ERI-5, ERI-1 and RDE-4) represent immunoprecipitation targets. See Online Methods section for details on the epitope targeted. Arrowheads indicate interactions detected. The interactions of ERI-5 and ERI-1 in RDE-4 immunoprecipitation included in the diagram were only detected by western blotting. The number of interactions detected exclusively in DCR-1 or ERI-1 MuDPIT experiments is indicated (17 or 11 single target hits) and may reflect divergent functions for these proteins. * Figure 2: ERI-5 promotes the association of an RdRP module to the DCR-1 N terminus. (,) Immunoprecipitation (IP) of DCR-1 and ERI-5 in WT, eri-5, rrf-3 del (deletion mutant, pk1426), rrf-3 pm (point mutant, mg373), eri-3 and eri-1 mutant embryos. DCR-1, RRF-3, DRH-3 and ERI-5 were detected by western blotting. Tubulin was used as a loading control. () Map of the DCR-1–GST constructs used for the GST pulldown of recombinant (r) ERI-5 or ERI-3 (top). The ability of each DCR-1–GST fusion to interact with rERI-5 or rERI-3 was assessed by western blot (bottom panel) to detect recombinant rERI-5-CBP or rERI-3–Flag. The results are summarized to the right of the DCR-1 map; the minus sign denotes weak or no interaction, and the plus sign denotes an interaction (see Supplementary Fig. 2c for Coomassie blue gel staining). Percentage (%) of the loading (bottom panel) represents the fraction of rERI-5 and rERI-3 used in the GST pulldown. () ERI-3 and ERI-5 bind to DCR-1 (272–1045) simultaneously. An increasing amount of rERI-3 was incubated with DCR-1 (272–10! 45) before addition of rERI-5 and pull-down of the DCR-1 fragment. * Figure 3: ERI-5 potentiates ERI endo-RNAi small RNA biogenesis. (,) Northern () and qrtPCR analysis () of C40A11.10 26G-RNA siRNA species (siR26-1) as indicated in WT, eri-5 and rrf-3 (pk1426) mutant embryos. The C40A11.10 probe detected both 26G and 22G RNAs. 5S ribosomal RNA (rRNA) ethidium bromide staining is shown as a loading control in . The mean of at least three independent experiments is depicted as the ratio of siR26-1 or X-cluster relative to actin. Error bars indicate s.d. () Box and whisker plots show the enrichment or depletion of small RNAs targeting 26G-RNA coding genes (red) and non-annotated 26G-RNA clusters (yellow) in the indicated mutant. The left panel is an analysis of 26-nt antisense reads from embryo small RNA libraries that target the 26G-RNA loci. The right panel is an analysis of all antisense reads from adult small RNA libraries that target the 26G-RNA loci. The majority of reads in the adult samples are 22G RNAs. Values approaching 1 indicate enrichment of small RNA; values approaching 0 indicate depletion. ! Relative enrichment was calculated as the ratio of mutant per (mutant plus wild type). The top and bottom of each box represent the 75th and 25th percentiles, respectively. The horizontal line within each box represents the median value. The number of loci used to generate box and whisker plots is indicated above each plot, and the data are provided in Supplementary Data 1 and 2. * Figure 4: Tandem tudor domain proteins are required for ERI endo-siRNA biogenesis. (,) Northern () and qrtPCR () analysis of C40A11.10 26G RNAs (siR26-1) in sel-1 (RNAi) (a negative control, marked with (−)), ekl-1(RNAi), eri-5 and eri-5, and in ekl-1(RNAi) embryos. The mean of at least three independent experiments is depicted as the ratio of siR26-1 relative to actin. Error bars indicate s.d. () qrtPCR analysis of C40A11.10 26G RNAs (siR26-1) in WT, eri-5, eri-4 and double eri-5, and in eri-4 mutant embryos. The mean of at least three independent experiments is depicted as the ratio of siR26-1 relative to actin. Error bars indicate s.d. () IP of EKL-1 and DCR-1 in WT and eri-5 mutant embryos. EKL-1 and DCR-1 proteins were detected by western blot. () Immunoprecipitation of RRF-3 in WT and eri-5 mutant embryos. DCR-1, RRF-3, EKL-1 and ERI-5 proteins were detected by western blot. Tubulin was used as a loading control. * Figure 5: Roles and paralog organization of RdRP modules in ERI endo-RNAi. () IP of EKL-1 in WT and ekl-1(RNAi) (ekl-1 lanes) embryos, and immunoprecipitation of ERI-5 in WT and eri-5 mutant embryos. The RdRPs EGO-1, RRF-1 and RRF-3, and the tudor domain proteins EKL-1 and ERI-5 were detected by western blot. Asterisk (*) indicates a non-specific band. () Model of the molecular compensation of ERI-5 by EKL-1. Interactions between the RdRP module and the N-terminal helicase domain of DCR-1 couple the generation of dsRNA by RRF-3 with processive DCR-1 activity. In the eri-5 mutant, this coupling is lost and the autoinhibitory function of the helicase domain predominates, resulting in inefficient 26G-RNA production. () Paralogous RdRP modules function sequentially in ERI endo-RNAi. An RdRP module comprised of RRF-3, DRH-3 and ERI-5 together with DCR-1 function at the initial step to generate 26G RNAs, the primary siRNAs of the ERI pathway that programs ERGO-1. A paralogous RdRP module comprised of RRF-1, DRH-3 and EKL-1 is responsible for secondary si! RNA generation that is independent of DCR-1. This abundant pool of small RNAs programs the WAGO Argonautes to effect endo-RNAi silencing. Paralogous EGO-1 complexes may be involved in this and other RNAi pathways. Some of the ERIC components were omitted from the model for clarity. Change history * Abstract * Change history * Author information * Supplementary informationCorrected online 09 January 2012In the version of this article initially published, information in Table 1 was inaccurate. "Newly described" should have been "novel" and "Argonaute protein domain" should have read "Argonaute protein." The errors have been corrected in the HTML and PDF versions of the article. Author information * Abstract * Change history * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Caroline Thivierge & * Neetha Makil Affiliations * Department of Biochemistry, McGill University, Montreal, Quebec, Canada. * Caroline Thivierge, * Neetha Makil, * Mathieu Flamand & * Thomas F Duchaine * Goodman Cancer Center, McGill University, Montreal, Quebec, Canada. * Caroline Thivierge, * Neetha Makil, * Mathieu Flamand & * Thomas F Duchaine * Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA. * Jessica J Vasale, * Craig C Mello & * Darryl Conte Jr * Howard Hughes Medical Institute, University of Massachusetts Medical School, Worcester, Massachusetts, USA. * Craig C Mello * Department of Biological Chemistry, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA. * James Wohlschlegel Contributions C.T. conducted the experiments presented in Figures 1c, 2c,d, 3a,b, 4a,b,d and 5a, prepared the figures and assisted with the preparation of the manuscript. N.M. conducted the experiments presented in Figure 1a,d, the ERI-5 samples in 1e and 2a,b. M.F. conducted the experiments presented in Figure 4b,d, and assisted with the model. J.W. carried out the MuDPIT analyses of IP samples. D.C. and J.J.V. conducted the experiments in Figure 3c, under C.C.M.'s direction. D.C. provided scientific advice, and assisted with the redaction of the manuscript. T.F.D. conducted the experiments in Figure 1b, wrote the manuscript and directed the project. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Thomas F Duchaine Author Details * Caroline Thivierge Search for this author in: * NPG journals * PubMed * Google Scholar * Neetha Makil Search for this author in: * NPG journals * PubMed * Google Scholar * Mathieu Flamand Search for this author in: * NPG journals * PubMed * Google Scholar * Jessica J Vasale Search for this author in: * NPG journals * PubMed * Google Scholar * Craig C Mello Search for this author in: * NPG journals * PubMed * Google Scholar * James Wohlschlegel Search for this author in: * NPG journals * PubMed * Google Scholar * Darryl Conte Jr Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas F Duchaine Contact Thomas F Duchaine Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Change history * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–4, Supplementary Results and Supplementary Methods Excel files * Supplementary Data 1 (123K) Supplementary data of the coding loci targeted by 26G-RNAs in eri-5 and rrf-3 embryos. * Supplementary Data 2 (2M) Complement to Supplementary Figure 3: small RNA defect of eri-5 mutant. Additional data Entities in this article * Enhanced RNAi (RNA interference) protein 5 eri-5 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * Endoribonuclease dcr-1 dcr-1 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * Enhancer of Ksr-1 Lethality ekl-1 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * RNAi DEfective family member (rde-4) rde-4 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * Dicer related helicase protein 1 drh-1 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * RNAi DEfective family member (rde-1) rde-1 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * RNA-dependent RNA polymerase family member (rrf-1) rrf-1 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * RNA-directed RNA polymerase related EGO-1 ego-1 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * ALG-3 T22B3.2 Caenorhabditis elegans * View in Entrez Gene * ALG-4 tag-76 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * Endogenous-RNAi deficient argonaute protein 1 ergo-1 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * RNA-dependent RNA polymerase family member (rrf-3) rrf-3 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * Dicer Related Helicase family member (drh-3) drh-3 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * 3'-5' exonuclease eri-1 eri-1 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * Enhanced RNAi (RNA interference) protein 3 eri-3 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * Enhanced RNAi (RNA interference) protein 9 C26E6.7 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * Phosphatase Interacting with RNA/RNP family member (pir-1) pir-1 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * Argonaute (plant)-Like Gene family member (alg-1) alg-1 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * Argonaute (plant)-Like Gene alg-2 Caenorhabditis elegans * View in Entrez Gene * Protein T06A10.3 Caenorhabditis elegans * View in UniProt * Protein B0001.2 B0001.2 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * Ribonuclease 3 DROSHA Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Microprocessor complex subunit DGCR8 DGCR8 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * Dicer-2 Dcr-2 Drosophila melanogaster * View in UniProt * View in Entrez Gene * R2D2 r2d2 Drosophila melanogaster * View in UniProt * View in Entrez Gene * Protein C40A11.10 C40A11.10 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * Suppressor/Enhancer of Lin-12 family member (sel-1) sel-1 Caenorhabditis elegans * View in UniProt * View in Entrez Gene * Protein Dicer dcr1 Schizosaccharomyces pombe (strain 972 / ATCC 24843) * View in UniProt * View in Entrez Gene * Endoribonuclease Dicer DICER1 Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_3f3cb55b8473ae64f9a1708e95eef8c9"> A cis-antisense RNA acts in trans in Staphylococcus aureus to control translation of a human cytolytic peptide</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_3f3cb55b8473ae64f9a1708e95eef8c9">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_3f3cb55b8473ae64f9a1708e95eef8c9"><strong>19</strong>(1):105-112</a> (2012)<br /> Nature Structural & Molecular Biology | Article Mispaired rNMPs in DNA are mutagenic and are targets of mismatch repair and RNases H * Ying Shen1 * Kyung Duk Koh1 * Bernard Weiss2 * Francesca Storici1 * Affiliations * Contributions * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:98–104Year published:(2012)DOI:doi:10.1038/nsmb.2176Received 21 December 2010 Accepted 16 September 2011 Published online 04 December 2011 Highlighting tool Genes and ProteinsUpdate Highlighting Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Numerous studies have shown that ribonucleoside monophosphates (rNMPs) are probably abundant among all nonstandard nucleotides occurring in genomic DNA. Therefore, it is important to understand to what extent rNMPs may alter genome integrity and what factors affect their stability. We developed oligonucleotide-driven gene correction assays in Escherichia coli and Saccharomyces cerevisiae to show that mispaired rNMPs embedded into genomic DNA, if not removed, serve as templates for DNA synthesis and produce a genetic change. We discovered that isolated mispaired rNMPs in chromosomal DNA are removed by the mismatch repair system in competition with RNase H type 2. However, a mismatch within an RNA-DNA heteroduplex region requires RNase H type 1 for removal. In the absence of mismatch repair and RNases H, ribonucleotide-driven gene modification increased by a factor of 47 in yeast and 77,000 in E. coli. View full text Figures at a glance * Figure 1: Diagrams and sequences of the loci targeted by the RNA-containing oligonucleotides. () The lacZ locus containing a two-base deletion and a substitution mutation targeted by the LacZ.R6I2, LacZ.R2.47I2, LacZ.R1S1 or LacZ.R5S1 oligonucleotide. () The lacZ locus containing a substitution mutation targeted by the LacZ.R1S1 or LacZ.R5S1 oligonucleotide. () The rpsL locus targeted by the RpsL.R1S1 oligonucleotide. (–) The trp5 locus containing a two-base deletion and substitution mutations targeted by the TRP5.R2_R1I2_S1 oligonucleotide (), containing just a substitution mutation targeted by the TRP5.R1S1 oligonucleotide () or containing only a two-base deletion mutation targeted by the TRP5.R2I2 oligonucleotide (). In the name of the RNA-containing oligonucleotides, substitutions are indicated by a subscript capital 'S' and insertions by a subscript capital 'I'. The letters 'S' and 'I' are followed by a subscript number indicating the number of bases that are substituted or inserted, respectively. * Figure 2: RNase HII cleavage specificity. () Structural presentation of 5′-radiolabeled (32P, indicated by a purple asterisks) substrates (S1–S11) and cleavage percentage for each substrate, expressed as median and range (in parentheses) from three independent samples. Inverted triangles indicate the cleavage sites. () Denaturing polyacrylamide gels showing fragments resulting from cleavage using RNase HII. M, 20- to 100-nt oligonucleotide marker. The gel images were cropped above the 50-nt band of the marker. S1–S11, substrates used; nt, nucleotide. () Substrates used in the experiment shown in panel and their cleavage percentage, expressed as mean and range (in parentheses) from two independent samples. () Denaturing polyacrylamide gel showing fragments resulting from cleavage using reduced amount of RNase HII and shorter incubation time. M, 20- to 100-nt oligonucleotide marker. The gel image was cropped as in . S2, S4, S6, S11 are substrates used. Author information * Abstract * Author information * Supplementary information Affiliations * School of Biology, Georgia Institute of Technology, Atlanta, Georgia, USA. * Ying Shen, * Kyung Duk Koh & * Francesca Storici * Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, Georgia, USA. * Bernard Weiss Contributions Y.S. conducted most of the experiments on E. coli, all yeast experiments and statistical analyses of the data. K.D.K. carried out the RNase HII cleavage experiments, analyzed biochemical data and helped with the E. coli experiments. B.W. helped to design the experiments, conducted initial tests on E. coli and analyzed data. F.S. designed most of experiments, analyzed data and wrote the manuscript, with input from all authors. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Francesca Storici Author Details * Ying Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Kyung Duk Koh Search for this author in: * NPG journals * PubMed * Google Scholar * Bernard Weiss Search for this author in: * NPG journals * PubMed * Google Scholar * Francesca Storici Contact Francesca Storici 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 1–2 and Supplementary Tables 1–6 Additional data Entities in this article * DNA topoisomerase 1 TOP1 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * DNA mismatch repair protein mutS mutS Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * Beta-galactosidase lacZ Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * Ribonuclease HI rnhA Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * Ribonuclease HII rnhB Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * 30S ribosomal protein S12 rpsL Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * Tryptophan synthase TRP5 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * DNA mismatch repair protein MSH2 MSH2 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * Ribonuclease H2 subunit A RNH201 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * DNA polymerase epsilon catalytic subunit A POL2 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * DNA mismatch repair protein MSH6 MSH6 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene * DNA-directed DNA/RNA polymerase mu POLM Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * DNA polymerase beta POLB Homo sapiens * View in UniProt * View in Entrez Gene * View in Antibodypedia * DNA polymerase III subunit epsilon dnaQ Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * DNA polymerase III subunit alpha dnaE Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * 3-isopropylmalate dehydrogenase LEU2 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) * View in UniProt * View in Entrez Gene </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_061ae3046068e1f680ee7bbf85ebb3f3"> Single-molecule studies reveal the function of a third polymerase in the replisome</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_061ae3046068e1f680ee7bbf85ebb3f3">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_061ae3046068e1f680ee7bbf85ebb3f3"><strong>19</strong>(1):113-116</a> (2012)<br /> Nature Structural & Molecular Biology | Article A cis-antisense RNA acts in trans in Staphylococcus aureus to control translation of a human cytolytic peptide * Nour Sayed1 * Ambre Jousselin1 * Brice Felden1 * Affiliations * Contributions * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:105–112Year published:(2012)DOI:doi:10.1038/nsmb.2193Received 09 July 2011 Accepted 31 October 2011 Published online 25 December 2011 Highlighting tool Genes and ProteinsUpdate Highlighting Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Antisense RNAs (asRNAs) pair to RNAs expressed from the complementary strand, and their functions are thought to depend on nucleotide overlap with genes on the opposite strand. There is little information on the roles and mechanisms of asRNAs. We show that a cis asRNA acts in trans, using a domain outside its target complementary sequence. SprA1 small regulatory RNA (sRNA) and SprA1AS asRNA are concomitantly expressed in S. aureus. SprA1AS forms a complex with SprA1, preventing translation of the SprA1-encoded open reading frame by occluding translation initiation signals through pairing interactions. The SprA1 peptide sequence is within two RNA pseudoknots. SprA1AS represses production of the SprA1-encoded cytolytic peptide in trans, as its overlapping region is dispensable for regulation. These findings demonstrate that sometimes asRNA functional domains are not their gene-target complementary sequences, suggesting there is a need for mechanistic re-evaluation of asRNAs ex! pressed in prokaryotes and eukaryotes. View full text Figures at a glance * Figure 1: Genomic location, lengths, boundaries and expression of sprA1 and sprA1AS. () Location of sprA1-sprA1AS in S. aureus pathogenicity island SaPIn3 of the S. aureus strain Newman (NWMN) genome. () Right panels: northern-blot detection of SprA1 and SprA1AS in a wild-type Newman strain (lane 1) and in an isogenic sprA1-sprA1AS double deletion strain (lane 2). Left panels: length evaluation of SprA1 and SprA1AS adjoining synthetic labeled RNAs of known lengths combined to 5′ end determinations by RACE mapping. The nucleotide numberings of the SprA1 and SprA1AS ends refer to positions in the S. aureus Newman genomic sequence12. (,) SprA1 and SprA1AS expression profiles during S. aureus growth. The expression levels of SprA1 and SprA1AS during a 10-h growth of S. aureus Newman strain detected by northern blots. For loading controls, the blots were also probed for tmRNA. The growth curves of the Newman strains are presented, with the quantification of SprA1 (black triangles) and SprA1AS (gray diamonds) levels relative to the amount of tmRNA from the same ! RNA extraction. AU, arbitrary units. () Determination of the in vivo concentrations of SprA1 and SprA1AS in a wild-type S. aureus Newman strain during growth detected by northern blots. The quantification of SprA1 and SprA1ASin vivo levels (left panels) was carried out relative to increasing amounts of synthetic, gel-purified SprA1 and SprA1AS RNAs (the two right panels). In vivo, the ratios between the SprA1 and SprA1AS RNAs are 1:35, 1:92, 1:63 and 1:50 at A600nm levels of 4, 7, 10 and 12, respectively. * Figure 2: Detection of the interaction between SprA1 and SprA1ASin vivo and assessment of their binding constants. () Northern blot analysis of SprA1 (wild-type or tagged with a StreptoTag (ST) expression at mid-exponential (A600nm = 3) and stationary (A600nm = 11) phases in wild-type Newman (lane 3), isogenic Newman ΔsprA1-sprA1AS deletion mutant (lane 2) and Newman ΔsprA1-ΔsprA1AS pCN35Ω STsprA1-sprA1AS strain (lane 1). () Northern blot analysis of the affinity purification fractions from either Newman ΔsprA1-ΔsprA1AS pCN35ΩSTsprA1-sprA1AS extracts, or Newman wild-type pCN35ΩsprA1AS extracts, as a negative control. Labeled DNA probes were used for SprA1 (WT and tagged), for SprA1AS and for tmRNA used as an internal negative control. FT, flow through; W4, wash 4, W5, wash 5; E, elution. (,) Complex formation between purified SprA1 and SprA1AS by native gel retardation assays. Purified, labeled (asterisks) SprA1AS () or SprA1 () with increasing amounts of unlabeled SprA1 () or unlabeled SprA1AS (). The diamonds indicate the molar ratios used to perform the competition assays with! a 1,000-fold molar excess of yeast (Saccharomyces cerevisiae) total tRNAs or with a 20-fold molar excess of the indicated unlabeled RNA. The apparent binding constant between SprA1AS and SprA1 was inferred from these data: Kd = 15 ± 5 nM. * Figure 3: Experimental and phylogenetic evidence for the pairings between SprA1AS and SprA1. () Proposed pairings between SprA1 and SprA1AS. Shine-Dalgarno and 5′-GUG-3′ or 5′-AUG-3′ start codons are in red, and the SprA1AS and SprA1 interacting domains are boxed in red. Blue minus signs indicate the disappearance of the cleavages triggered by the structural probes in the RNA duplex. Triangles are the V1 cuts, arrows capped by circles are the S1 cuts, and uncapped arrows are the lead cleavages. The intensity of the cleavages is proportional to the darkness of the symbols. The blue S1 cut appears when the duplex forms. () Phylogenetic support for the proposed interaction between SprA1 and SprA1AS, when comparing the sequences of the two RNAs located in genomes and plasmids. Covariations are shown in gray, Shine-Dalgarno and start codons are boxed. (,) Experimentally supported structure of SprA1 and SprA1AS, emphasizing the 3′ overlapping sequence (yellow) as well as the experimentally and phylogenetically supported interaction region (red box). Covariations! are shown in gray. The other symbols are similar to those in panel . Structural changes detected upon complex formation are indicated in blue. The domains of the RNAs are indicated (SprA1: H1–H6, H1-H2 junction, L1–L6, PK1-PK2; SprA1AS: H1-H2AS, H1AS-H2AS junction and L1AS-L2AS) (see also Supplementary Figs. 3–5). * Figure 4: SprA1 and SprA1AS interact by their 5′ non-overlapping domains. Complex formation between labeled SprA1AS with increasing amounts of unlabeled 5′ SprA1 () or 3′ SprA1 () and between labeled SprA1 with increasing amounts of unlabeled 5′ SprA1AS () or 3′ SprA1AS (), as detected by native gel retardation assays. The apparent binding constants between the RNAs were inferred from these data. For SprA1AS–5′ SprA1, the Kd is 16 ± 5 nM. For SprA1–5′ SprA1AS, the Kd is 300 ± 50 nM. There is no duplex formation between SprA1 and 3′ SprA1AS or between 3′ SprA1 and SprA1AS. The black diamonds indicate the molar ratios used to carry out the competition assays with a 2,000-fold molar excess of poly(U) RNAs or with a 20-fold molar excess of the indicated unlabeled RNA. Asterisks indicate the 32P-radiolabeled RNAs (see also Supplementary Fig. 6). * Figure 5: SprA1 recruits the S. aureus ribosomes and is translated in vitro, and SprA1AS hinders SprA1 translation by its 5′ non-overlapping domain. () SprA1 structure indicating the ribosome toeprints (oval), the reverse transcriptase (RT) pause in the presence of sprA1AS (black arrows) and the mutated nucleotides in the SD–mutated SprA1 construct (rectangles, 12 mutated nucleotides to maintain H1 while modifying the Shine-Dalgarno sequence). The predicted initiation and termination codons are framed, and the nucleotide sequence overlapping with sprA1AS is in gray. () S. aureus ribosome toeprint assay of SprA1 (WT SprA1) and the disappearance of the toeprints in the SD-mutated SprA1. In the presence of SprA1AS at a 2:1 molar ratio, there are no toeprints, indicating that the asRNA impairs ribosome loading onto SprA1. The toeprints are indicated with a black bullet and the reverse transcriptase pause for SprA1, in the presence of SprA1AS, is indicated by an arrowhead. T, A, G and C are the SprA1 sequencing ladders. () In vitro translation of SprA1 (lane 1), of SprA1 in the presence of SprA1AS at a 1:1 molar ratio (lane! 2), of SprA1 in the presence of 5′ SprA1AS at a 1:10 molar ratio (lane 3), of SprA1 in the presence of 3′ SprA1AS at a 1:10 molar ratio (lane 4) and of SD-mutated SprA1 (lane 5). The translated SprA1-encoded polypeptide of ~3 kDa is indicated by an arrowhead. () Northern blot analysis of SprA1 and SprA1AS in Newman pCN35 and isogenic Newman pCN35ΩsprA1AS during growth. The 5S rRNAs are the controls. * Figure 6: SprA1AScis-RNA acts in trans to downregulate SprA1-encoded peptide expression in vivo. () Detection of the ~5 kDa SprA1-encoded flagged peptide at early (A600nm = 1) and mid-exponential (A600nm = 5) phases of growth in strains Newman ΔsprA1-ΔsprA1AS pCN34ΩsprA1tag pCN35 (lanes 1 and 3) and in isogenic Newman ΔsprA1-ΔsprA1AS pCN34ΩsprA1tag pCN35ΩsprA1AS strain (lanes 2 and 4) by immunoblots using anti-Flag antibodies. () Northern blot analysis for monitoring SprA1-Flag RNA (upper panel) and SprA1AS RNA (lower panel) expression levels at identical phases of growth. The 5S rRNAs are the internal loading controls. * Figure 7: The SprA1-encoded peptide is lytic for human cells. () Hemolytic activity of synthetic SprA1-encoded peptide compared to a non-hemolytic peptide used as a negative control. Controls: the minus sign indicates that PBS was added to the red blood cells (RBC), the plus sign indicates hypotonic solution was added to RBCs. RBC sedimentation indicates the absence of hemolysis, whereas a red supernatant implies hemolysis. () Differential hemolytic activity of the synthetic SprA1 peptide for human and sheep RBCs. The peptide induces a strong hemolysis on the human RBCs but a weak hemolysis on the sheep RBCs. () Proposed model for the downregulation of SprA1 sRNA internal translation in trans by the cis-encoded SprA1AS. The SprA1 internal ORF is shown in green and the SprA1 and SprA1AS 5′ non-overlapping interacting domains are in red. Their 3′ overlapping domains are in yellow. Upon duplex formation, the SprA1AS 5′ domain pairs at and around the SprA1 internal translation initiation signals (SD-sequence and start codon, red) by ! unfolding pseudoknot PK1. During S. aureus growth, translation of the SprA1-encoded peptide is repressed by base pairings in trans with SprA1AS RNA (see also Supplementary Fig. 7). Author information * Abstract * Author information * Supplementary information Affiliations * Laboratoire de Biochimie Pharmaceutique Inserm U835 Upres EA2311 Université de Rennes, Rennes, France. * Nour Sayed, * Ambre Jousselin & * Brice Felden Contributions N.S. and B.F. designed experiments, prepared samples, analyzed the data and wrote the manuscript. A.J. constructed the sRNA double mutant, did the Hfq experiment and participated in discussions and writing of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Brice Felden Author Details * Nour Sayed Search for this author in: * NPG journals * PubMed * Google Scholar * Ambre Jousselin Search for this author in: * NPG journals * PubMed * Google Scholar * Brice Felden Contact Brice Felden 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 1–7 and Supplementary Tables 1–2 Additional data Entities in this article * Beta-lactamase blaZ Staphylococcus aureus * View in UniProt * View in Entrez Gene * RNA chaperone, host factor-1 protein NWMN_1212 Staphylococcus aureus (strain Newman) * View in UniProt * View in Entrez Gene * Delta-hemolysin Staphylococcus aureus * View in UniProt </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_c0164852e6a58a53979a8e9bcd171d0b"> Fluorescent fusion protein knockout mediated by anti-GFP nanobody</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_c0164852e6a58a53979a8e9bcd171d0b">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_c0164852e6a58a53979a8e9bcd171d0b"><strong>19</strong>(1):117-121</a> (2012)<br /> Nature Structural & Molecular Biology | Brief Communication Single-molecule studies reveal the function of a third polymerase in the replisome * Roxana E Georgescu1 * Isabel Kurth1 * Mike E O'Donnell1 * Affiliations * Contributions * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:113–116Year published:(2012)DOI:doi:10.1038/nsmb.2179Received 16 June 2011 Accepted 29 September 2011 Published online 11 December 2011 Highlighting tool Genes and ProteinsUpdate Highlighting Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The Escherichia coli replisome contains three polymerases, one more than necessary to duplicate the two parental strands. Using single-molecule studies, we reveal two advantages conferred by the third polymerase. First, dipolymerase replisomes are inefficient at synthesizing lagging strands, leaving single-strand gaps, whereas tripolymerase replisomes fill strands almost to completion. Second, tripolymerase replisomes are much more processive than dipolymerase replisomes. These features account for the unexpected three-polymerase-structure of bacterial replisomes. View full text Figures at a glance * Figure 1: TriPol replisomes are more processive than DiPol replisomes. () Scheme of single-molecule experiments. For clarity, only the DiPol replisome is illustrated. () DNA products from either the DiPol (left) or TriPol (right) replisome, using 250 nM primase. The endpoints of two representative DNA products are marked with arrowheads. () DNA length distribution histograms. Numbers represent the single-exponential fit ± s.e.m. of the total number (N) of molecules analyzed. Gray bars represent DNA strand lengths below 15 kb that were undersampled because they were obscured by the width of the diffusion barrier. Left, DiPol replisomes, right, TriPol replisomes. () Processivity of DiPol and TriPol replisomes, where the indicated polymerase is present or absent from the buffer flow. * Figure 2: TriPol replisomes are more efficient on the lagging strand than DiPol replisomes are. () Scheme of the bead-based assay; the DiPol replisome is illustrated for simplicity. () Dipol and Tripol replisomes replicate DNA with similar rates. Left, autoradiogram of 0.8% alkaline agarose gel analysis of reactions, using either Tripol III* (20 nM) or DiPol III* (80 nM); DnaG primase concentration was 200 nM. Right, plot of DNA length versus time. () Left, leading- and lagging-strand replication products from bead-based reactions, resolved on denaturing agarose gels, using 320 nM DnaG primase. Right, quantitation of leading- and lagging-strand synthesis, normalized to the products of the TriPol replisome. * Figure 3: Analysis of ssDNA gaps in lagging strand products. () Magnified view of DNA products generated by DiPol and TriPol replisomes; the light and dark regions correspond to dsDNA segments and ssDNA gaps. () Comparative histogram showing the percentage of DNA strands with gaps (green) and without gaps (purple). () Histograms showing the distribution of gap length (in μm) using DiPol (red) and TriPol (blue) replisomes. () Model of TriPol and DiPol replisome action. Pol III cores are represented as right hands; with the β-clamp (red), clamp loader (dark green), DnaB helicase (blue hexamer), primase (light green) and SSB (purple). The τ-subunit C-terminal domains (IV and V) are illustrated as jointed lines that mediate connections to DnaB helicase and Pol III cores. The χψψ subunits of the clamp loader are omitted for clarity. The TriPol replisome depicts two Pol III cores extending two Okazaki fragments simultaneously, although there are other ways a TriPol replisome can be used (see text). The left illustration depicts one la! gging Pol III extending an RNA primer (red) to produce a DNA strand (yellow), and the other lagging Pol III core extends the DNA (blue) to fill a ssDNA gap. Author information * Author information * Supplementary information Affiliations * The Rockefeller University, Howard Hughes Medical Institute, New York, New York, USA. * Roxana E Georgescu, * Isabel Kurth & * Mike E O'Donnell Contributions R.E.G. and I.K. carried out experiments; R.E.G., I.K. and M.E.O. designed the experiments. R.E.G., I.K. and M.E.O. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Mike E O'Donnell Author Details * Roxana E Georgescu Search for this author in: * NPG journals * PubMed * Google Scholar * Isabel Kurth Search for this author in: * NPG journals * PubMed * Google Scholar * Mike E O'Donnell Contact Mike E O'Donnell Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (324K) Supplementary Figures 1–4 and Supplementary Methods Movies * Supplementary Video 1 (1M) Replication performed by Dipol replisomes. An example of a movie depicting real-time observation of coupled leading/lagging strand replication of a mini-rolling circle substrate by E. coli Dipol replisomes. The force of the hydrodynamic flow pushes the DNA-lipid complex to a diffusion barrier etched in the glass surface and concentrates numerous DNA molecules in the visual field shown here. The width of the visible area in the direction of the flow is 73 μm (equivalent to 220 kb) and the flow direction is from top to the bottom. Individual DNA molecules visualized with the fluorescent dye Yo-Pro1 are stretched by the buffer flow (100 μl/min) and imaged through Total Internal Reflection Fluorescence (TIRF) microscopy. Toward the end of the movie, the buffer-flow is stopped, letting the strands recoil, then the buffer flow is started again. Movie contains circa 7' 30″ of experimental data rendered at 20 frames per second (original data acquisition is 1 frame/s at 100 ms ex! posure per frame). * Supplementary Video 2 (332K) Replication performed by Tripol replisomes. The video depicts the recording of a replication reaction performed by Tripol replisomes. The movie contains circa 5' 30″ of experimental data rendered at 20 frames per second (original data acquisition is 1 frame/s at 100 ms exposure for each frame). * Supplementary Video 3 (451K) DNA molecules that harbor duplex regions contain gaps on the same molecule. The video depicts a recording at the end of a replication reaction using a DiPol replisome. The flow of the buffer solution is stopped then restarted, allowing the DNA strands to stretch and then recoil to their point of origin. * Supplementary Video 4 (2M) Use of fluorescent SSB to identify ssDNA in DNA products. The video depicts three successive recordings of different DNA products of DiPol replisomes, in which reactions contained fluorescently labeled SSB. The three successive recordings are easy to identify since they have different dimensions. The videos show that DNA products contain fluorescently labeled E. coli SSB (with Oregon Green488 Maleimide). The duplex DNA is not visualized because Yo-Pro1 is omitted from the buffer flow for these experiments. To distinguish SSB bound to DNA from SSB that binds non-specifically to the surface of the flow cell, the buffer-flow is alternatively stopped and restarted in order to observe the recoiling of the DNA strands. Fluorescent SSB bound to DNA recoils and re-extends in synchrony with the changes in buffer flow (while non-specifically bound SSB does not change position). Additional data Entities in this article * DNA polymerase III subunit tau dnaX Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * Replicative DNA helicase dnaB Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * Single-stranded DNA-binding protein ssb Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * DNA polymerase I polA Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * DNA polymerase II polB Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * DNA polymerase IV dinB Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * DNA primase dnaG Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Struct%20Mol%20Biol%5Blatest%5D&highlight=pgtmp_270976f7cbca49c49580da401d0c389d"> A metal switch for controlling the activity of molecular motor proteins</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Structural+%26+Molecular+Biology&amp;from=pgtmp_270976f7cbca49c49580da401d0c389d">Nat Struct Mol Biol</a> <a class="journal" onclick="journalSearch();" title="Get volume 19, issue 1" href="/search?q=issn%3A1545-9993+vol%3A19+issue%3A1&amp;from=pgtmp_270976f7cbca49c49580da401d0c389d"><strong>19</strong>(1):122-127</a> (2012)<br /> Nature Structural & Molecular Biology | Brief Communication Single-molecule studies reveal the function of a third polymerase in the replisome * Roxana E Georgescu1 * Isabel Kurth1 * Mike E O'Donnell1 * Affiliations * Contributions * Corresponding authorJournal name:Nature Structural & Molecular BiologyVolume: 19,Pages:113–116Year published:(2012)DOI:doi:10.1038/nsmb.2179Received 16 June 2011 Accepted 29 September 2011 Published online 11 December 2011 Highlighting tool Genes and ProteinsUpdate Highlighting Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The Escherichia coli replisome contains three polymerases, one more than necessary to duplicate the two parental strands. Using single-molecule studies, we reveal two advantages conferred by the third polymerase. First, dipolymerase replisomes are inefficient at synthesizing lagging strands, leaving single-strand gaps, whereas tripolymerase replisomes fill strands almost to completion. Second, tripolymerase replisomes are much more processive than dipolymerase replisomes. These features account for the unexpected three-polymerase-structure of bacterial replisomes. View full text Figures at a glance * Figure 1: TriPol replisomes are more processive than DiPol replisomes. () Scheme of single-molecule experiments. For clarity, only the DiPol replisome is illustrated. () DNA products from either the DiPol (left) or TriPol (right) replisome, using 250 nM primase. The endpoints of two representative DNA products are marked with arrowheads. () DNA length distribution histograms. Numbers represent the single-exponential fit ± s.e.m. of the total number (N) of molecules analyzed. Gray bars represent DNA strand lengths below 15 kb that were undersampled because they were obscured by the width of the diffusion barrier. Left, DiPol replisomes, right, TriPol replisomes. () Processivity of DiPol and TriPol replisomes, where the indicated polymerase is present or absent from the buffer flow. * Figure 2: TriPol replisomes are more efficient on the lagging strand than DiPol replisomes are. () Scheme of the bead-based assay; the DiPol replisome is illustrated for simplicity. () Dipol and Tripol replisomes replicate DNA with similar rates. Left, autoradiogram of 0.8% alkaline agarose gel analysis of reactions, using either Tripol III* (20 nM) or DiPol III* (80 nM); DnaG primase concentration was 200 nM. Right, plot of DNA length versus time. () Left, leading- and lagging-strand replication products from bead-based reactions, resolved on denaturing agarose gels, using 320 nM DnaG primase. Right, quantitation of leading- and lagging-strand synthesis, normalized to the products of the TriPol replisome. * Figure 3: Analysis of ssDNA gaps in lagging strand products. () Magnified view of DNA products generated by DiPol and TriPol replisomes; the light and dark regions correspond to dsDNA segments and ssDNA gaps. () Comparative histogram showing the percentage of DNA strands with gaps (green) and without gaps (purple). () Histograms showing the distribution of gap length (in μm) using DiPol (red) and TriPol (blue) replisomes. () Model of TriPol and DiPol replisome action. Pol III cores are represented as right hands; with the β-clamp (red), clamp loader (dark green), DnaB helicase (blue hexamer), primase (light green) and SSB (purple). The τ-subunit C-terminal domains (IV and V) are illustrated as jointed lines that mediate connections to DnaB helicase and Pol III cores. The χψψ subunits of the clamp loader are omitted for clarity. The TriPol replisome depicts two Pol III cores extending two Okazaki fragments simultaneously, although there are other ways a TriPol replisome can be used (see text). The left illustration depicts one la! gging Pol III extending an RNA primer (red) to produce a DNA strand (yellow), and the other lagging Pol III core extends the DNA (blue) to fill a ssDNA gap. Author information * Author information * Supplementary information Affiliations * The Rockefeller University, Howard Hughes Medical Institute, New York, New York, USA. * Roxana E Georgescu, * Isabel Kurth & * Mike E O'Donnell Contributions R.E.G. and I.K. carried out experiments; R.E.G., I.K. and M.E.O. designed the experiments. R.E.G., I.K. and M.E.O. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Mike E O'Donnell Author Details * Roxana E Georgescu Search for this author in: * NPG journals * PubMed * Google Scholar * Isabel Kurth Search for this author in: * NPG journals * PubMed * Google Scholar * Mike E O'Donnell Contact Mike E O'Donnell Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (324K) Supplementary Figures 1–4 and Supplementary Methods Movies * Supplementary Video 1 (1M) Replication performed by Dipol replisomes. An example of a movie depicting real-time observation of coupled leading/lagging strand replication of a mini-rolling circle substrate by E. coli Dipol replisomes. The force of the hydrodynamic flow pushes the DNA-lipid complex to a diffusion barrier etched in the glass surface and concentrates numerous DNA molecules in the visual field shown here. The width of the visible area in the direction of the flow is 73 μm (equivalent to 220 kb) and the flow direction is from top to the bottom. Individual DNA molecules visualized with the fluorescent dye Yo-Pro1 are stretched by the buffer flow (100 μl/min) and imaged through Total Internal Reflection Fluorescence (TIRF) microscopy. Toward the end of the movie, the buffer-flow is stopped, letting the strands recoil, then the buffer flow is started again. Movie contains circa 7' 30″ of experimental data rendered at 20 frames per second (original data acquisition is 1 frame/s at 100 ms ex! posure per frame). * Supplementary Video 2 (332K) Replication performed by Tripol replisomes. The video depicts the recording of a replication reaction performed by Tripol replisomes. The movie contains circa 5' 30″ of experimental data rendered at 20 frames per second (original data acquisition is 1 frame/s at 100 ms exposure for each frame). * Supplementary Video 3 (451K) DNA molecules that harbor duplex regions contain gaps on the same molecule. The video depicts a recording at the end of a replication reaction using a DiPol replisome. The flow of the buffer solution is stopped then restarted, allowing the DNA strands to stretch and then recoil to their point of origin. * Supplementary Video 4 (2M) Use of fluorescent SSB to identify ssDNA in DNA products. The video depicts three successive recordings of different DNA products of DiPol replisomes, in which reactions contained fluorescently labeled SSB. The three successive recordings are easy to identify since they have different dimensions. The videos show that DNA products contain fluorescently labeled E. coli SSB (with Oregon Green488 Maleimide). The duplex DNA is not visualized because Yo-Pro1 is omitted from the buffer flow for these experiments. To distinguish SSB bound to DNA from SSB that binds non-specifically to the surface of the flow cell, the buffer-flow is alternatively stopped and restarted in order to observe the recoiling of the DNA strands. Fluorescent SSB bound to DNA recoils and re-extends in synchrony with the changes in buffer flow (while non-specifically bound SSB does not change position). Additional data Entities in this article * DNA polymerase III subunit tau dnaX Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * Replicative DNA helicase dnaB Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * Single-stranded DNA-binding protein ssb Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * DNA polymerase I polA Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * DNA polymerase II polB Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * DNA polymerase IV dinB Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene * DNA primase dnaG Escherichia coli (strain K12) * View in UniProt * View in Entrez Gene </li> </ul> <div class="blogger-post-footer"><img width='1' height='1' src='https://blogger.googleusercontent.com/tracker/5969181590050102457-5037398153394291044?l=pubget.blogspot.com' alt='' /></div>ian connorhttp://www.blogger.com/profile/17012291553690617903noreply@blogger.com0tag:blogger.com,1999:blog-5969181590050102457.post-41481349217254779142012-01-25T03:27:00.001-08:002012-01-25T03:27:12.213-08:00The Feb 01 issue of the <a href="http://pubget.com/search?q=Nat%20Rev%20Immunol[latest]" ><i>Nat Rev Immunol</i></a> is now up on <a href="http://pubget.com/">Pubget</a> (<a href="http://pubget.com/profile/journal/Nat%20Rev%20Immunol"><i>About Nat Rev Immunol</i></a>): if you're at a subscribing institution, just click the link in the latest link at the home page. (Note you'll only be able to get all the PDFs in the issue if your institution <a href="http://pubget.com/site/contact/contact_box">subscribes to Pubget</a>.) <p>Latest Articles Include:</p> <ul> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Rev%20Immunol%5Blatest%5D&highlight=pgtmp_2c2adc48413e3274cdd91603d779fa77"> Antibody responses: Neutrophils zone in to help B cells | PDF (280 KB)</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Reviews%3A+Immunology&amp;from=pgtmp_2c2adc48413e3274cdd91603d779fa77">Nat Rev Immunol</a> <a class="journal" onclick="journalSearch();" title="Get volume 12, issue 2" href="/search?q=issn%3A1474-1733+vol%3A12+issue%3A2&amp;from=pgtmp_2c2adc48413e3274cdd91603d779fa77"><strong>12</strong>(2):73</a> (2012)<br /> Neutrophils are key effector cells of the innate immune system that are rapidly recruited to infected tissues to clear pathogens. Recently, researchers have shown that neutrophils also shape adaptive immune responses by interacting with T cells and dendritic cells (DCs). </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Rev%20Immunol%5Blatest%5D&highlight=pgtmp_db305e6f8e842d08555a64d0b15aca09"> Trafficking: Effector T cells cross the line | PDF (158 KB)</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Reviews%3A+Immunology&amp;from=pgtmp_db305e6f8e842d08555a64d0b15aca09">Nat Rev Immunol</a> <a class="journal" onclick="journalSearch();" title="Get volume 12, issue 2" href="/search?q=issn%3A1474-1733+vol%3A12+issue%3A2&amp;from=pgtmp_db305e6f8e842d08555a64d0b15aca09"><strong>12</strong>(2):74</a> (2012)<br /> Effector T cells migrate through the endothelial cell wall of blood vessels into inflamed tissues. Selectins, integrins and chemokine receptors have a central role in T cell extravasation, which involves the steps of cell arrest, spreading, crawling and transendothelial migration. </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Rev%20Immunol%5Blatest%5D&highlight=pgtmp_dc15118042638b83af42499f0fb5cb0f"> T cells: The TFH-like transition of TH1 cells | PDF (254 KB)</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Reviews%3A+Immunology&amp;from=pgtmp_dc15118042638b83af42499f0fb5cb0f">Nat Rev Immunol</a> <a class="journal" onclick="journalSearch();" title="Get volume 12, issue 2" href="/search?q=issn%3A1474-1733+vol%3A12+issue%3A2&amp;from=pgtmp_dc15118042638b83af42499f0fb5cb0f"><strong>12</strong>(2):74</a> (2012)<br /> The extent to which T helper (TH) cell subsets — including T follicular helper (TFH) cells — are distinct cell lineages has been the subject of much debate in recent years. Now, new evidence suggests that early during their development TH1 cells pass through a TH1–TFH cell stage, which involves a dynamic balance of signals mediated by the transcription factors signal transducer and activator of transcription 4 (STAT4), T-bet and B cell lymphoma 6 (BCL-6). </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Rev%20Immunol%5Blatest%5D&highlight=pgtmp_5130ec4e0a6546f1260aee2ac54c4f29"> Mucosal immunology: Multifunctional gut IgA+ plasma cells | PDF (262 KB)</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Reviews%3A+Immunology&amp;from=pgtmp_5130ec4e0a6546f1260aee2ac54c4f29">Nat Rev Immunol</a> <a class="journal" onclick="journalSearch();" title="Get volume 12, issue 2" href="/search?q=issn%3A1474-1733+vol%3A12+issue%3A2&amp;from=pgtmp_5130ec4e0a6546f1260aee2ac54c4f29"><strong>12</strong>(2):75</a> (2012)<br /> The production of polyreactive IgA by plasma cells in the gastrointestinal tract is important for maintaining mucosal homeostasis. 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An acquired loss of expression of the signalling adaptor TNFR-associated factor 1 (TRAF1) from virus-specific CD8+ T cells during HIV infection in humans and during chronic lymphocytic choriomeningitis virus (LCMV) infection in mice decreases their ability to control the virus. </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Rev%20Immunol%5Blatest%5D&highlight=pgtmp_9e39c001a37ae57e7aa2513cb7c89d5d"> Immunometabolism: IL-15 provides breathing space for memory | PDF (269 KB)</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Reviews%3A+Immunology&amp;from=pgtmp_9e39c001a37ae57e7aa2513cb7c89d5d">Nat Rev Immunol</a> <a class="journal" onclick="journalSearch();" title="Get volume 12, issue 2" href="/search?q=issn%3A1474-1733+vol%3A12+issue%3A2&amp;from=pgtmp_9e39c001a37ae57e7aa2513cb7c89d5d"><strong>12</strong>(2):76</a> (2012)<br /> Memory T cells promote long-term resistance to infection, and uncovering the mechanisms that control their development and function is an important goal for immunologists. Van der Windt et al.+ T cells possess greater mitochondrial spare respiratory capacity (SRC) than naive or effector T cells. </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Rev%20Immunol%5Blatest%5D&highlight=pgtmp_1ba9f2c21f3234240e8a9469468a6d84"> Autoimmunity: Interfering with brain inflammation | PDF (161 KB)</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Reviews%3A+Immunology&amp;from=pgtmp_1ba9f2c21f3234240e8a9469468a6d84">Nat Rev Immunol</a> <a class="journal" onclick="journalSearch();" title="Get volume 12, issue 2" href="/search?q=issn%3A1474-1733+vol%3A12+issue%3A2&amp;from=pgtmp_1ba9f2c21f3234240e8a9469468a6d84"><strong>12</strong>(2):77</a> (2012)<br /> Interferon-β (IFNβ) is a first-line therapy for patients with relapsing–remitting multiple sclerosis. 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The authors found that the enhanced clearance of the intracellular pathogen Listeria monocytogenes following re-infection was associated with increased reactive oxygen species (ROS)-mediated bacterial killing. </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Rev%20Immunol%5Blatest%5D&highlight=pgtmp_726a1de42521338ea66f7a226a712f53"> </a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Reviews%3A+Immunology&amp;from=pgtmp_726a1de42521338ea66f7a226a712f53">Nat Rev Immunol</a> <a class="journal" onclick="journalSearch();" title="Get volume 12, issue 2" href="/search?q=issn%3A1474-1733+vol%3A12+issue%3A2&amp;from=pgtmp_726a1de42521338ea66f7a226a712f53"><strong>12</strong>(2):74</a> (2012)<br /> </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Rev%20Immunol%5Blatest%5D&highlight=pgtmp_4aa656ad88055772965444ba414ae31f"> </a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Reviews%3A+Immunology&amp;from=pgtmp_4aa656ad88055772965444ba414ae31f">Nat Rev Immunol</a> <a class="journal" onclick="journalSearch();" title="Get volume 12, issue 2" href="/search?q=issn%3A1474-1733+vol%3A12+issue%3A2&amp;from=pgtmp_4aa656ad88055772965444ba414ae31f"><strong>12</strong>(2):74</a> (2012)<br /> </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Rev%20Immunol%5Blatest%5D&highlight=pgtmp_4e587d11fd0f9e21470dc747d4e1c427"> Viral infection and the evolution of caspase 8-regulated apoptotic and necrotic death pathways</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Reviews%3A+Immunology&amp;from=pgtmp_4e587d11fd0f9e21470dc747d4e1c427">Nat Rev Immunol</a> <a class="journal" onclick="journalSearch();" title="Get volume 12, issue 2" href="/search?q=issn%3A1474-1733+vol%3A12+issue%3A2&amp;from=pgtmp_4e587d11fd0f9e21470dc747d4e1c427"><strong>12</strong>(2):79</a> (2012)<br /> Pathogens specifically target both the caspase 8-dependent apoptotic cell death pathway and the necrotic cell death pathway that is dependent on receptor-interacting protein 1 (RIP1; also known as RIPK1) and RIP3 (also known as RIPK3). 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Recent developments point towards a multilayered plant innate immune system comprised of self-surveillance, systemic signalling and chromosomal changes that together establish effective immunity. </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Rev%20Immunol%5Blatest%5D&highlight=pgtmp_8ce3e90c57585119491f23cf2a2c40f9"> Transcriptional programming of the dendritic cell network</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Reviews%3A+Immunology&amp;from=pgtmp_8ce3e90c57585119491f23cf2a2c40f9">Nat Rev Immunol</a> <a class="journal" onclick="journalSearch();" title="Get volume 12, issue 2" href="/search?q=issn%3A1474-1733+vol%3A12+issue%3A2&amp;from=pgtmp_8ce3e90c57585119491f23cf2a2c40f9"><strong>12</strong>(2):101</a> (2012)<br /> Specialized subsets of dendritic cells (DCs) provide a crucial link between the innate and adaptive immune responses. The genetic programme that coordinates these distinct DC subsets is controlled by both cytokines and transcription factors. The initial steps in DC specification occur in the bone marrow and result in the generation of precursors committed to either the plasmacytoid or conventional DC pathways. DCs undergo further differentiation and lineage diversification in peripheral organs in response to local environmental cues. In this Review, we discuss new evidence regarding the coordination of the specification and commitment of precursor cells to different DC subsets and highlight the ensemble of transcription factors that control these processes. </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Rev%20Immunol%5Blatest%5D&highlight=pgtmp_bf591e59344c6dce8bbca892466f5b44"> Early immune events in the induction of allergic contact dermatitis</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Reviews%3A+Immunology&amp;from=pgtmp_bf591e59344c6dce8bbca892466f5b44">Nat Rev Immunol</a> <a class="journal" onclick="journalSearch();" title="Get volume 12, issue 2" href="/search?q=issn%3A1474-1733+vol%3A12+issue%3A2&amp;from=pgtmp_bf591e59344c6dce8bbca892466f5b44"><strong>12</strong>(2):114</a> (2012)<br /> The skin is a barrier site that is exposed to a wide variety of potential pathogens. As in other organs, pathogens that invade the skin are recognized by pattern-recognition receptors (PRRs). Recently, it has been recognized that PRRs are also engaged by chemical contact allergens and, in susceptible individuals, this elicits an inappropriate immune response that results in allergic contact dermatitis. In this Review, we focus on how contact allergens promote inflammation by activating the innate immune system. We also examine how innate immune cells in the skin, including mast cells and dendritic cells, cooperate with each other and with T cells and keratinocytes to initiate and drive early responses to contact allergens. </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Rev%20Immunol%5Blatest%5D&highlight=pgtmp_2c9edb0b426805dcd934616dcc2898b0"> Immunomodulatory functions of type I interferons</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Reviews%3A+Immunology&amp;from=pgtmp_2c9edb0b426805dcd934616dcc2898b0">Nat Rev Immunol</a> <a class="journal" onclick="journalSearch();" title="Get volume 12, issue 2" href="/search?q=issn%3A1474-1733+vol%3A12+issue%3A2&amp;from=pgtmp_2c9edb0b426805dcd934616dcc2898b0"><strong>12</strong>(2):125</a> (2012)<br /> Interferon-α (IFNα) and IFNβ, collectively known as type I IFNs, are the major effector cytokines of the host immune response against viral infections. However, the production of type I IFNs is also induced in response to bacterial ligands of innate immune receptors and/or bacterial infections, indicating a broader physiological role for these cytokines in host defence and homeostasis than was originally assumed. The main focus of this Review is the underappreciated immunomodulatory functions of type I IFNs in health and disease. We discuss their function in the regulation of innate and adaptive immune responses, the response to bacterial ligands, inflammasome activation, intestinal homeostasis and inflammatory and autoimmune diseases. </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Rev%20Immunol%5Blatest%5D&highlight=pgtmp_ddb065ec1d085058b4cc4009fa864222"> Expanding roles for CD4+ T cells in immunity to viruses</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Reviews%3A+Immunology&amp;from=pgtmp_ddb065ec1d085058b4cc4009fa864222">Nat Rev Immunol</a> <a class="journal" onclick="journalSearch();" title="Get volume 12, issue 2" href="/search?q=issn%3A1474-1733+vol%3A12+issue%3A2&amp;from=pgtmp_ddb065ec1d085058b4cc4009fa864222"><strong>12</strong>(2):136</a> (2012)<br /> Viral pathogens often induce strong effector CD4+ T cell responses that are best known for their ability to help B cell and CD8+ T cell responses. However, recent studies have uncovered additional roles for CD4+ T cells, some of which are independent of other lymphocytes, and have described previously unappreciated functions for memory CD4+ T cells in immunity to viruses. Here, we review the full range of antiviral functions of CD4+ T cells, discussing the activities of these cells in helping other lymphocytes and in inducing innate immune responses, as well as their direct antiviral roles. We suggest that all of these functions of CD4+ T cells are integrated to provide highly effective immune protection against viral pathogens. </li> </ul> <div class="blogger-post-footer"><img width='1' height='1' src='https://blogger.googleusercontent.com/tracker/5969181590050102457-4148134921725477914?l=pubget.blogspot.com' alt='' /></div>ian connorhttp://www.blogger.com/profile/17012291553690617903noreply@blogger.com0tag:blogger.com,1999:blog-5969181590050102457.post-48748651252298226632012-01-24T21:41:00.001-08:002012-01-24T21:41:36.005-08:00The Jan 01 issue of the <a href="http://pubget.com/search?q=Nat%20Genet[latest]" ><i>Nat Genet</i></a> is now up on <a href="http://pubget.com/">Pubget</a> (<a href="http://pubget.com/profile/journal/Nat%20Genet"><i>About Nat Genet</i></a>): if you're at a subscribing institution, just click the link in the latest link at the home page. (Note you'll only be able to get all the PDFs in the issue if your institution <a href="http://pubget.com/site/contact/contact_box">subscribes to Pubget</a>.) <p>Latest Articles Include:</p> <ul> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_b58a55a6cb42b42992d0c0042761d045"> Full spectrum genetics</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_b58a55a6cb42b42992d0c0042761d045">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_b58a55a6cb42b42992d0c0042761d045"><strong>44</strong>(1):1</a> (2012)<br /> ARTICLE NAVIGATION - ISSUE Previous January 2012, Volume 44 No 1 pp1-110 * Editorial * Correspondence * News and Views * Research Highlights * Brief Communications * Articles * LettersAbout the cover Editorial Full spectrum genetics - p1 doi:10.1038/ng.1057 Every instance of a variant in the human genome causing or correlated with a trait deserves to be databased and analyzed. As a consequence of rapidly evolving technology and strategies, more of the mutational spectrum of human disease is now accessible to research. Advised by our referees' progressively higher standards, we continue to select the most informative and useful results. Abstract - Full spectrum genetics | Full Text - Full spectrum genetics | PDF (70 KB) - Full spectrum genetics Correspondence Rare and functional SIAE variants are not associated with autoimmune disease risk in up to 66,924 individuals of European ancestry - pp3 - 5 Karen A Hunt, Deborah J Smyth, Tobias Balschun, Maria Ban, Vanisha Mistry, Tariq Ahmad, Vidya Anand, Jeffrey C Barrett, Leena Bhaw-Rosun, Nicholas A Bockett, Oliver J Brand, Elisabeth Brouwer, Patrick Concannon, Jason D Cooper, Kerith-Rae M Dias, Cleo C van Diemen, Patrick C Dubois, Sarah Edkins, Regina Fölster-Holst, Karin Fransen, David N Glass, Graham A R Heap, Sylvia Hofmann, Tom W J Huizinga, Sarah Hunt, Cordelia Langford, James Lee, John Mansfield, Maria Giovanna Marrosu, Christopher G Mathew, Charles A Mein, Joachim Müller-Quernheim, Sarah Nutland, Suna Onengut-Gumuscu, Willem Ouwehand, Kerra Pearce, Natalie J Prescott, Marcel D Posthumus, Simon Potter, Giulio Rosati, Jennifer Sambrook, Jack Satsangi, Stefan Schreiber, Corina Shtir, Matthew J Simmonds, Marc Sudman, Susan D Thompson, Rene Toes, Gosia Trynka, Timothy J Vyse, Neil M Walker, Stephan Weidinger, Alexandra Zhernakova, Magdalena Zoledziewska, Type 1 Diabetes Genetics Consortium , UK Inflammatory Bowel Disea! se (IBD) Genetics Consortium , Wellcome Trust Case Control Consortium , Rinse K Weersma, Stephen C L Gough, Stephen Sawcer, Cisca Wijmenga, Miles Parkes, Francesco Cucca, Andre Franke, Panos Deloukas, Stephen S Rich, John A Todd & David A van Heel doi:10.1038/ng.1037 Full Text - Rare and functional SIAE variants are not associated with autoimmune disease risk in up to 66,924 individuals of European ancestry | PDF (105 KB) - Rare and functional SIAE variants are not associated with autoimmune disease risk in up to 66,924 individuals of European ancestry | Supplementary information Improved imputation of common and uncommon SNPs with a new reference set - pp6 - 7 Zhaoming Wang, Kevin B Jacobs, Meredith Yeager, Amy Hutchinson, Joshua Sampson, Nilanjan Chatterjee, Demetrius Albanes, Sonja I Berndt, Charles C Chung, W Ryan Diver, Susan M Gapstur, Lauren R Teras, Christopher A Haiman, Brian E Henderson, Daniel Stram, Xiang Deng, Ann W Hsing, Jarmo Virtamo, Michael A Eberle, Jennifer L Stone, Mark P Purdue, Phil Taylor, Margaret Tucker & Stephen J Chanock doi:10.1038/ng.1044 Full Text - Improved imputation of common and uncommon SNPs with a new reference set | PDF (124 KB) - Improved imputation of common and uncommon SNPs with a new reference set | Supplementary information News and Views Spliceosome mutations in hematopoietic malignancies - pp9 - 10 Christopher N Hahn & Hamish S Scott doi:10.1038/ng.1045 Full Text - Spliceosome mutations in hematopoietic malignancies | PDF (222 KB) - Spliceosome mutations in hematopoietic malignancies See also:Letter by Quesada et al. | Letter by Graubert et al. Following evolution of bacterial antibiotic resistance in real time - pp11 - 13 Adam Z Rosenthal & Michael B Elowitz doi:10.1038/ng.1048 Full Text - Following evolution of bacterial antibiotic resistance in real time | PDF (329 KB) - Following evolution of bacterial antibiotic resistance in real time See also:Letter by Toprak et al. | Letter by Comas et al. Dnmt3a silences hematopoietic stem cell self-renewal - pp13 - 14 Jennifer J Trowbridge & Stuart H Orkin doi:10.1038/ng.1043 Full Text - Dnmt3a silences hematopoietic stem cell self-renewal | PDF (343 KB) - Dnmt3a silences hematopoietic stem cell self-renewal See also:Article by Challen et al. Research Highlights * Hedgehog in the blood-brain barrier * Intestinal stem cell interconversion * Joint-rank for Mendelian sequencing * Lamarckian viral defense in worms * Unifying antipsychotic drugs Brief Communications Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma - pp17 - 19 Guangwu Guo, Yaoting Gui, Shengjie Gao, Aifa Tang, Xueda Hu, Yi Huang, Wenlong Jia, Zesong Li, Minghui He, Liang Sun, Pengfei Song, Xiaojuan Sun, Xiaokun Zhao, Sangming Yang, Chaozhao Liang, Shengqing Wan, Fangjian Zhou, Chao Chen, Jialou Zhu, Xianxin Li, Minghan Jian, Liang Zhou, Rui Ye, Peide Huang, Jing Chen, Tao Jiang, Xiao Liu, Yong Wang, Jing Zou, Zhimao Jiang, Renhua Wu, Song Wu, Fan Fan, Zhongfu Zhang, Lin Liu, Ruilin Yang, Xingwang Liu, Haibo Wu, Weihua Yin, Xia Zhao, Yuchen Liu, Huanhuan Peng, Binghua Jiang, Qingxin Feng, Cailing Li, Jun Xie, Jingxiao Lu, Karsten Kristiansen, Yingrui Li, Xiuqing Zhang, Songgang Li, Jian Wang, Huanming Yang, Zhiming Cai & Jun Wang doi:10.1038/ng.1014 Huanming Yang, Zhiming Cai, Jun Wang and colleagues report whole-exome sequencing of 10 clear cell renal cell carcinomas followed by a screen of ~1,100 genes in a total of 98 tumors. They found 12 new disease-associated genes and detected frequent alterations in the ubiquitin-mediated proteolysis pathway. Abstract - Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma | Full Text - Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma | PDF (219 KB) - Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma | Supplementary information GATA6 haploinsufficiency causes pancreatic agenesis in humans - pp20 - 22 Hana Lango Allen, Sarah E Flanagan, Charles Shaw-Smith, Elisa De Franco, Ildem Akerman, Richard Caswell, the International Pancreatic Agenesis Consortium, Jorge Ferrer, Andrew T Hattersley & Sian Ellard doi:10.1038/ng.1035 Andrew Hattersley and colleagues report an exome sequencing study that identifies de novo heterozygous inactivating mutations in GATA6 as a common cause of pancreatic agenesis. This suggests an essential function for GATA6 in human pancreas development. Abstract - GATA6 haploinsufficiency causes pancreatic agenesis in humans | Full Text - GATA6 haploinsufficiency causes pancreatic agenesis in humans | PDF (384 KB) - GATA6 haploinsufficiency causes pancreatic agenesis in humans | Supplementary information Articles Dnmt3a is essential for hematopoietic stem cell differentiation - pp23 - 31 Grant A Challen, Deqiang Sun, Mira Jeong, Min Luo, Jaroslav Jelinek, Jonathan S Berg, Christoph Bock, Aparna Vasanthakumar, Hongcang Gu, Yuanxin Xi, Shoudan Liang, Yue Lu, Gretchen J Darlington, Alexander Meissner, Jean-Pierre J Issa, Lucy A Godley, Wei Li & Margaret A Goodell doi:10.1038/ng.1009 Margaret Goodell, Wei Li and colleagues report conditional ablation of the Dnmt3a DNA methyltransferase in hematopoietic stem cells (HSCs) in mice. They show that Dnmt3a is critical for epigenetic silencing of HSC regulatory genes and for HSC differentiation. Abstract - Dnmt3a is essential for hematopoietic stem cell differentiation | Full Text - Dnmt3a is essential for hematopoietic stem cell differentiation | PDF (1,845 KB) - Dnmt3a is essential for hematopoietic stem cell differentiation | Supplementary information See also:News and Views by Trowbridge & Orkin Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm - pp32 - 39 Xuehui Huang, Yan Zhao, Xinghua Wei, Canyang Li, Ahong Wang, Qiang Zhao, Wenjun Li, Yunli Guo, Liuwei Deng, Chuanrang Zhu, Danlin Fan, Yiqi Lu, Qijun Weng, Kunyan Liu, Taoying Zhou, Yufeng Jing, Lizhen Si, Guojun Dong, Tao Huang, Tingting Lu, Qi Feng, Qian Qian, Jiayang Li & Bin Han doi:10.1038/ng.1018 Bin Han and colleagues report low-coverage sequencing for 950 diverse rice accessions. They develop a framework for haplotype-based de novo assembly, phenotyped the 950 lines for 11 agronomic traits and used this information to conduct genome-wide association studies. They identify 32 new loci associated with these traits. Abstract - Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm | Full Text - Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm | PDF (1,103 KB) - Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm | Supplementary information Letters Regions of focal DNA hypermethylation and long-range hypomethylation in colorectal cancer coincide with nuclear lamina–associated domains - pp40 - 46 Benjamin P Berman, Daniel J Weisenberger, Joseph F Aman, Toshinori Hinoue, Zachary Ramjan, Yaping Liu, Houtan Noushmehr, Christopher P E Lange, Cornelis M van Dijk, Rob A E M Tollenaar, David Van Den Berg & Peter W Laird doi:10.1038/ng.969 Peter Laird and colleagues performed whole-genome bisulfite sequencing in a human colorectal tumor and a matched normal sample. They find regions of methylation variation that coincide with domains associated with the nuclear lamina. First Paragraph - Regions of focal DNA hypermethylation and long-range hypomethylation in colorectal cancer coincide with nuclear lamina-associated domains | Full Text - Regions of focal DNA hypermethylation and long-range hypomethylation in colorectal cancer coincide with nuclear lamina–associated domains | PDF (1,679 KB) - Regions of focal DNA hypermethylation and long-range hypomethylation in colorectal cancer coincide with nuclear lamina–associated domains | Supplementary information Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia - pp47 - 52 Víctor Quesada, Laura Conde, Neus Villamor, Gonzalo R Ordóñez, Pedro Jares, Laia Bassaganyas, Andrew J Ramsay, Sílvia Beà, Magda Pinyol, Alejandra Martínez-Trillos, Mónica López-Guerra, Dolors Colomer, Alba Navarro, Tycho Baumann, Marta Aymerich, María Rozman, Julio Delgado, Eva Giné, Jesús M Hernández, Marcos González-Díaz, Diana A Puente, Gloria Velasco, José M P Freije, José M C Tubío, Romina Royo, Josep L Gelpí, Modesto Orozco, David G Pisano, Jorge Zamora, Miguel Vázquez, Alfonso Valencia, Heinz Himmelbauer, Mónica Bayés, Simon Heath, Marta Gut, Ivo Gut, Xavier Estivill, Armando López-Guillermo, Xose S Puente, Elías Campo & Carlos López-Otín doi:10.1038/ng.1032 Carlos López-Otín, Elías Campo and colleagues report exome sequencing of tumor and normal samples from 105 individuals with chronic lymphocytic leukemia (CLL). They identify 1,246 somatic mutations predicted to affect gene function and 78 genes with recurrent predicted functional mutations. They find recurrent mutations in the gene encoding the SF3B1 splicing factor, which was mutated in 10% of the CLL samples. First Paragraph - Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia | Full Text - Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia | PDF (936 KB) - Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia | Supplementary information See also:News and Views by Hahn & Scott Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes - pp53 - 57 Timothy A Graubert, Dong Shen, Li Ding, Theresa Okeyo-Owuor, Cara L Lunn, Jin Shao, Kilannin Krysiak, Christopher C Harris, Daniel C Koboldt, David E Larson, Michael D McLellan, David J Dooling, Rachel M Abbott, Robert S Fulton, Heather Schmidt, Joelle Kalicki-Veizer, Michelle O'Laughlin, Marcus Grillot, Jack Baty, Sharon Heath, John L Frater, Talat Nasim, Daniel C Link, Michael H Tomasson, Peter Westervelt, John F DiPersio, Elaine R Mardis, Timothy J Ley, Richard K Wilson & Matthew J Walter doi:10.1038/ng.1031 Matthew Walter and colleagues report the whole-genome sequencing of a secondary acute myeloid leukemia sample and a matched normal tissue sample. Further analysis of additional subjects identified recurrent mutations in U2AF1 in 13/150 (8.7%) individuals with myelodysplastic syndrome. First Paragraph - Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes | Full Text - Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes | PDF (540 KB) - Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes | Supplementary information See also:News and Views by Hahn & Scott Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk - pp58 - 61 Peter Broderick, Daniel Chubb, David C Johnson, Niels Weinhold, Asta Försti, Amy Lloyd, Bianca Olver, Yussanne P Ma, Sara E Dobbins, Brian A Walker, Faith E Davies, Walter A Gregory, J Anthony Child, Fiona M Ross, Graham H Jackson, Kai Neben, Anna Jauch, Per Hoffmann, Thomas W Mühleisen, Markus M Nöthen, Susanne Moebus, Ian P Tomlinson, Hartmut Goldschmidt, Kari Hemminki, Gareth J Morgan & Richard S Houlston doi:10.1038/ng.993 Richard Houlston, Gareth Morgan, Kari Hemminki and colleagues report the results of a genome-wide association study of multiple myeloma. They identify two regions influencing susceptibility to this hematological malignancy. First Paragraph - Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk | Full Text - Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk | PDF (391 KB) - Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk | Supplementary information Genome-wide association study identifies five loci associated with susceptibility to pancreatic cancer in Chinese populations - pp62 - 66 Chen Wu, Xiaoping Miao, Liming Huang, Xu Che, Guoliang Jiang, Dianke Yu, Xianghong Yang, Guangwen Cao, Zhibin Hu, Yongjian Zhou, Chaohui Zuo, Chunyou Wang, Xianghong Zhang, Yifeng Zhou, Xianjun Yu, Wanjin Dai, Zhaoshen Li, Hongbing Shen, Luming Liu, Yanling Chen, Sheng Zhang, Xiaoqi Wang, Kan Zhai, Jiang Chang, Yu Liu, Menghong Sun, Wei Cao, Jun Gao, Ying Ma, Xiongwei Zheng, Siu Tim Cheung, Yongfeng Jia, Jian Xu, Wen Tan, Ping Zhao, Tangchun Wu, Chengfeng Wang & Dongxin Lin doi:10.1038/ng.1020 Dongxin Lin and colleagues report a genome-wide association study for pancreatic cancer in Chinese populations. The authors identify five new genetic loci associated with risk of pancreatic cancer. First Paragraph - Genome-wide association study identifies five loci associated with susceptibility to pancreatic cancer in Chinese populations | Full Text - Genome-wide association study identifies five loci associated with susceptibility to pancreatic cancer in Chinese populations | PDF (874 KB) - Genome-wide association study identifies five loci associated with susceptibility to pancreatic cancer in Chinese populations | Supplementary information Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians - pp67 - 72 Yoon Shin Cho, Chien-Hsiun Chen, Cheng Hu, Jirong Long, Rick Twee Hee Ong, Xueling Sim, Fumihiko Takeuchi, Ying Wu, Min Jin Go, Toshimasa Yamauchi, Yi-Cheng Chang, Soo Heon Kwak, Ronald C W Ma, Ken Yamamoto, Linda S Adair, Tin Aung, Qiuyin Cai, Li-Ching Chang, Yuan-Tsong Chen, Yutang Gao, Frank B Hu, Hyung-Lae Kim, Sangsoo Kim, Young Jin Kim, Jeannette Jen-Mai Lee, Nanette R Lee, Yun Li, Jian Jun Liu, Wei Lu, Jiro Nakamura, Eitaro Nakashima, Daniel Peng-Keat Ng, Wan Ting Tay, Fuu-Jen Tsai, Tien Yin Wong, Mitsuhiro Yokota, Wei Zheng, Rong Zhang, Congrong Wang, Wing Yee So, Keizo Ohnaka, Hiroshi Ikegami, Kazuo Hara, Young Min Cho, Nam H Cho, Tien-Jyun Chang, Yuqian Bao, Åsa K Hedman, Andrew P Morris, Mark I McCarthy, DIAGRAM Consortium, MuTHER Consortium, Ryoichi Takayanagi, Kyong Soo Park, Weiping Jia, Lee-Ming Chuang, Juliana C N Chan, Shiro Maeda, Takashi Kadowaki, Jong-Young Lee, Jer-Yuarn Wu, Yik Ying Teo, E Shyong Tai, Xiao Ou Shu, Karen L Mohlke, Norihiro Kato, Bok-Ghe! e Han & Mark Seielstad doi:10.1038/ng.1019 Yoon Shin Cho, Mark Seielstad and colleagues report a meta-analysis of genome-wide association studies for type 2 diabetes in individuals of east Asian ancestry. They identify eight new loci associated with type 2 diabetes. First Paragraph - Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians | Full Text - Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians | PDF (713 KB) - Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians | Supplementary information A genome-wide association study in Han Chinese identifies new susceptibility loci for ankylosing spondylitis - pp73 - 77 Zhiming Lin, Jin-Xin Bei, Meixin Shen, Qiuxia Li, Zetao Liao, Yanli Zhang, Qing Lv, Qiujing Wei, Hui-Qi Low, Yun-Miao Guo, Shuangyan Cao, Mingcan Yang, Zaiying Hu, Manlong Xu, Xinwei Wang, Yanlin Wei, Li Li, Chao Li, Tianwang Li, Jianlin Huang, Yunfeng Pan, Ou Jin, Yuqiong Wu, Jing Wu, Zishi Guo, Peigen He, Shaoxian Hu, Husheng Wu, Hui Song, Feng Zhan, Shengyun Liu, Guanmin Gao, Zhangsuo Liu, Yinong Li, Changhong Xiao, Juan Li, Zhizhong Ye, Weizhen He, Dongzhou Liu, Lingxun Shen, Anbin Huang, Henglian Wu, Yi Tao, Xieping Pan, Buyun Yu, E Shyong Tai, Yi-Xin Zeng, Ee Chee Ren, Yan Shen, Jianjun Liu & Jieruo Gu doi:10.1038/ng.1005 Jieruo Gu, Jianjun Liu and colleagues report the results of a genome-wide association study of ankylosing spondylitis in Han Chinese. They identify two new susceptibility loci for this inflammatory disease and confirm strong associations with variants in the HLA-B region. First Paragraph - A genome-wide association study in Han Chinese identifies new susceptibility loci for ankylosing spondylitis | Full Text - A genome-wide association study in Han Chinese identifies new susceptibility loci for ankylosing spondylitis | PDF (399 KB) - A genome-wide association study in Han Chinese identifies new susceptibility loci for ankylosing spondylitis | Supplementary information Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention deficit hyperactivity disorder - pp78 - 84 Josephine Elia, Joseph T Glessner, Kai Wang, Nagahide Takahashi, Corina J Shtir, Dexter Hadley, Patrick M A Sleiman, Haitao Zhang, Cecilia E Kim, Reid Robison, Gholson J Lyon, James H Flory, Jonathan P Bradfield, Marcin Imielinski, Cuiping Hou, Edward C Frackelton, Rosetta M Chiavacci, Takeshi Sakurai, Cara Rabin, Frank A Middleton, Kelly A Thomas, Maria Garris, Frank Mentch, Christine M Freitag, Hans-Christoph Steinhausen, Alexandre A Todorov, Andreas Reif, Aribert Rothenberger, Barbara Franke, Eric O Mick, Herbert Roeyers, Jan Buitelaar, Klaus-Peter Lesch, Tobias Banaschewski, Richard P Ebstein, Fernando Mulas, Robert D Oades, Joseph Sergeant, Edmund Sonuga-Barke, Tobias J Renner, Marcel Romanos, Jasmin Romanos, Andreas Warnke, Susanne Walitza, Jobst Meyer, Haukur Pálmason, Christiane Seitz, Sandra K Loo, Susan L Smalley, Joseph Biederman, Lindsey Kent, Philip Asherson, Richard J L Anney, J William Gaynor, Philip Shaw, Marcella Devoto, Peter S White, Struan F A Grant, Jos! eph D Buxbaum, Judith L Rapoport, Nigel M Williams, Stanley F Nelson, Stephen V Faraone & Hakon Hakonarson doi:10.1038/ng.1013 Hakon Hakonarson and colleagues report a genome-wide copy number variation study in 3,506 cases of attention-deficit hyperactivity disorder. The authors identify a statistically significant enrichment of CNVs impacting metabotropic glutamate receptor genes. First Paragraph - Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention deficit hyperactivity disorder | Full Text - Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention deficit hyperactivity disorder | PDF (650 KB) - Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention deficit hyperactivity disorder | Supplementary information Mutations at a single codon in Mad homology 2 domain of SMAD4 cause Myhre syndrome - pp85 - 88 Carine Le Goff, Clémentine Mahaut, Avinash Abhyankar, Wilfried Le Goff, Valérie Serre, Alexandra Afenjar, Anne Destrée, Maja di Rocco, Delphine Héron, Sébastien Jacquemont, Sandrine Marlin, Marleen Simon, John Tolmie, Alain Verloes, Jean-Laurent Casanova, Arnold Munnich & Valérie Cormier-Daire doi:10.1038/ng.1016 Valérie Cormier-Daire and colleagues report the identification of mutations in SMAD4 that cause Myhre syndrome, a developmental disorder characterized by short stature, short hands and feet, facial dysmorphism, muscular hypertrophy, deafness and cognitive delay. All of the mutations alter a single codon in the Mad Homology 2 domain of SMAD4. First Paragraph - Mutations at a single codon in Mad homology 2 domain of SMAD4 cause Myhre syndrome | Full Text - Mutations at a single codon in Mad homology 2 domain of SMAD4 cause Myhre syndrome | PDF (696 KB) - Mutations at a single codon in Mad homology 2 domain of SMAD4 cause Myhre syndrome | Supplementary information Large-scale discovery of enhancers from human heart tissue - pp89 - 93 Dalit May, Matthew J Blow, Tommy Kaplan, David J McCulley, Brian C Jensen, Jennifer A Akiyama, Amy Holt, Ingrid Plajzer-Frick, Malak Shoukry, Crystal Wright, Veena Afzal, Paul C Simpson, Edward M Rubin, Brian L Black, James Bristow, Len A Pennacchio & Axel Visel doi:10.1038/ng.1006 Len Pennacchio, Axel Visel and colleagues use an epigenomic approach to identify a large number of candidate enhancers from human heart tissue. This work will facilitate further studies into the role of enhancers in human cardiac development and disease. First Paragraph - Large-scale discovery of enhancers from human heart tissue | Full Text - Large-scale discovery of enhancers from human heart tissue | PDF (668 KB) - Large-scale discovery of enhancers from human heart tissue | Supplementary information A chromatin-modifying function of JNK during stem cell differentiation - pp94 - 100 Vijay K Tiwari, Michael B Stadler, Christiane Wirbelauer, Renato Paro, Dirk Schübeler & Christian Beisel doi:10.1038/ng.1036 Dirk Schübeler, Michael Stadler and colleagues show that the c-Jun NH2-terminal kinase (JNK) binds directly to active promoters during the differentiation of stem cells to neurons and targets histone H3 serine 10 for phosphorylation. First Paragraph - A chromatin-modifying function of JNK during stem cell differentiation | Full Text - A chromatin-modifying function of JNK during stem cell differentiation | PDF (896 KB) - A chromatin-modifying function of JNK during stem cell differentiation | Supplementary information Evolutionary paths to antibiotic resistance under dynamically sustained drug selection - pp101 - 105 Erdal Toprak, Adrian Veres, Jean-Baptiste Michel, Remy Chait, Daniel L Hartl & Roy Kishony doi:10.1038/ng.1034 Roy Kishony and colleagues develop a device for the continuous culture of bacterial populations under constant antibiotic selection pressure. They use this morbidostat, together with whole-genome sequencing of E. coli strains, to follow evolutionary paths leading to high levels of resistance to three individual drugs. First Paragraph - Evolutionary paths to antibiotic resistance under dynamically sustained drug selection | Full Text - Evolutionary paths to antibiotic resistance under dynamically sustained drug selection | PDF (1,036 KB) - Evolutionary paths to antibiotic resistance under dynamically sustained drug selection | Supplementary information See also:News and Views by Rosenthal & Elowitz Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes - pp106 - 110 Iñaki Comas, Sonia Borrell, Andreas Roetzer, Graham Rose, Bijaya Malla, Midori Kato-Maeda, James Galagan, Stefan Niemann & Sebastien Gagneux doi:10.1038/ng.1038 Sebastien Gagneux and colleagues identify a set of compensatory mutations in the RNA polymerase of rifampicin-resistant M. tuberculosis by comparing the whole-genome sequences of ten paired clinical isolates and strains evolved in vitro. These mutations are associated with high competitive fitness in vitro and occur with increased clinical frequency in affected populations with a high burden of drug-resistant tuberculosis. First Paragraph - Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes | Full Text - Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes | PDF (647 KB) - Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes | Supplementary information See also:News and Views by Rosenthal & Elowitz </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_de5ebfff124647f71fd0b10074b9a28d"> Rare and functional SIAE variants are not associated with autoimmune disease risk in up to 66,924 individuals of European ancestry</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_de5ebfff124647f71fd0b10074b9a28d">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_de5ebfff124647f71fd0b10074b9a28d"><strong>44</strong>(1):3-5</a> (2012)<br /> Nature Genetics | Editorial Full spectrum genetics Journal name:Nature GeneticsVolume: 44,Page:1Year published:(2012)DOI:doi:10.1038/ng.1057Published online 27 December 2011 Every instance of a variant in the human genome causing or correlated with a trait deserves to be databased and analyzed. As a consequence of rapidly evolving technology and strategies, more of the mutational spectrum of human disease is now accessible to research. Advised by our referees' progressively higher standards, we continue to select the most informative and useful results. View full text Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_e7c07a00815ae57ea5f965b006067ad0"> Improved imputation of common and uncommon SNPs with a new reference set</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_e7c07a00815ae57ea5f965b006067ad0">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_e7c07a00815ae57ea5f965b006067ad0"><strong>44</strong>(1):6-7</a> (2012)<br /> Nature Genetics | Correspondence Rare and functional SIAE variants are not associated with autoimmune disease risk in up to 66,924 individuals of European ancestry * Karen A Hunt1 * Deborah J Smyth2 * Tobias Balschun3 * Maria Ban4 * Vanisha Mistry1 * Tariq Ahmad5 * Vidya Anand6 * Jeffrey C Barrett7 * Leena Bhaw-Rosun8 * Nicholas A Bockett1 * Oliver J Brand9 * Elisabeth Brouwer10 * Patrick Concannon11 * Jason D Cooper2 * Kerith-Rae M Dias8 * Cleo C van Diemen12 * Patrick C Dubois1 * Sarah Edkins7 * Regina Fölster-Holst13 * Karin Fransen12 * David N Glass14 * Graham A R Heap1 * Sylvia Hofmann3 * Tom W J Huizinga15 * Sarah Hunt7 * Cordelia Langford7 * James Lee16 * John Mansfield17 * Maria Giovanna Marrosu18 * Christopher G Mathew19 * Charles A Mein8 * Joachim Müller-Quernheim20 * Sarah Nutland2 * Suna Onengut-Gumuscu11 * Willem Ouwehand7, 21 * Kerra Pearce22 * Natalie J Prescott19 * Marcel D Posthumus10 * Simon Potter7 * Giulio Rosati23 * Jennifer Sambrook21 * Jack Satsangi24 * Stefan Schreiber3 * Corina Shtir2 * Matthew J Simmonds9 * Marc Sudman14 * Susan D Thompson14 * Rene Toes15 * Gosia Trynka12 * Timothy J Vyse6 * Neil M Walker2 * Stephan Weidinger13, 25 * Alexandra Zhernakova12, 15, 26, 27 * Magdalena Zoledziewska28 * Type 1 Diabetes Genetics Consortium 29 * UK Inflammatory Bowel Disease (IBD) Genetics Consortium 29 * Wellcome Trust Case Control Consortium 29 * Rinse K Weersma30 * Stephen C L Gough9 * Stephen Sawcer4 * Cisca Wijmenga12 * Miles Parkes16 * Francesco Cucca28, 31 * Andre Franke3 * Panos Deloukas7 * Stephen S Rich11 * John A Todd2 * David A van Heel1 * Affiliations * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:3–5Year published:(2012)DOI:doi:10.1038/ng.1037Published online 27 December 2011 To the Editor: Recently, rare loss-of-function genetic variants in the SIAE gene, which encodes sialic acid acetylesterase, were reported to predispose to multiple autoimmune diseases1. In a pooled analysis of samples from ten autoimmune diseases, Surolia et al. identified 12 distinct nonsynonymous SIAE risk variant genotypes present in 24 of 923 affected persons (2.60%) versus 2 of 648 controls (0.31%; P = 0.0002; odds ratio of 8.6) that the authors considered to be "functionally defective SIAE alleles" because they result in defects in either esterase activity or secretion1. These nonsynonymous markers comprised one common allele frequency variant (rs78778622; encoding a p.Met89Val substitution in SIAE) and 11 rare allele frequency variants. Homozygosity for the SIAE variant (rs78778622, GG) causing the p.Met89Val alteration that resulted in a secretion-defective mutant was reported in 8 of 923 affected individuals (0.87%) but in none of the 648 control subjects1. To date, in contras! t to the numerous genome-wide association studies for common variants, there have been only a few studies reporting rare variants of large effect predisposing individuals to clinically typical autoimmune disease phenotypes, despite much recent enthusiasm for exome sequencing in these genetically complex conditions. View full text Author information * 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 Affiliations * Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK. * Karen A Hunt, * Vanisha Mistry, * Nicholas A Bockett, * Patrick C Dubois, * Graham A R Heap & * David A van Heel * Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. * Deborah J Smyth, * Jason D Cooper, * Sarah Nutland, * Corina Shtir, * Neil M Walker & * John A Todd * Institute of Clinical Molecular Biology, Christian-Albrechts-Universität zu Kiel, Kiel, Germany. * Tobias Balschun, * Sylvia Hofmann, * Stefan Schreiber & * Andre Franke * Department of Clinical Neurosciences, Addenbrookes Hospital, University of Cambridge, Cambridge, UK. * Maria Ban & * Stephen Sawcer * Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, UK. * Tariq Ahmad * Division of Genetics and Molecular Medicine, King's College London, London, UK. * Vidya Anand & * Timothy J Vyse * Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. * Jeffrey C Barrett, * Sarah Edkins, * Sarah Hunt, * Cordelia Langford, * Willem Ouwehand, * Simon Potter & * Panos Deloukas * Genome Centre, Barts and the London School of Medicine and Dentistry, John Vane Science Centre, London, UK. * Leena Bhaw-Rosun, * Kerith-Rae M Dias & * Charles A Mein * Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford and Oxford National Institute for Health Research (NIHR) Biomedical Centre, Oxford, UK. * Oliver J Brand, * Matthew J Simmonds & * Stephen C L Gough * Department of Rheumatology and Clinical Immunology, University Medical Centre Groningen and University of Groningen, Groningen, The Netherlands. * Elisabeth Brouwer & * Marcel D Posthumus * Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA. * Patrick Concannon, * Suna Onengut-Gumuscu & * Stephen S Rich * Genetics Department, University Medical Centre Groningen and University of Groningen, Groningen, The Netherlands. * Cleo C van Diemen, * Karin Fransen, * Gosia Trynka, * Alexandra Zhernakova & * Cisca Wijmenga * Department of Dermatology, University Clinic Schleswig-Holstein, Campus Kiel, Kiel, Germany. * Regina Fölster-Holst & * Stephan Weidinger * Division of Rheumatology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. * David N Glass, * Marc Sudman & * Susan D Thompson * Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands. * Tom W J Huizinga, * Rene Toes & * Alexandra Zhernakova * IBD Genetics Research Group, Addenbrooke's Hospital, Cambridge, UK. * James Lee & * Miles Parkes * Department of Gastroenterology & Hepatology, University of Newcastle upon Tyne, Royal Victoria Infirmary, Newcastle upon Tyne, UK. * John Mansfield * Centro Sclerosi Multipla, Dipartimento di Scienze Neurologiche e Cardiovascolari, Università di Cagliari, Cagliari, Italy. * Maria Giovanna Marrosu * Department of Medical and Molecular Genetics, King's College London School of Medicine, Guy's Hospital, London, UK. * Christopher G Mathew & * Natalie J Prescott * Department of Pneumology, University Medical Center, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany. * Joachim Müller-Quernheim * Department of Haematology, University of Cambridge & National Health Service (NHS) Blood and Transplant, Cambridge, UK. * Willem Ouwehand & * Jennifer Sambrook * University College London Genomics, Institute of Child Health, University College London, London, UK. * Kerra Pearce * Istituto di Neurologia Clinica, Università di Sassari, Sassari, Italy. * Giulio Rosati * Gastrointestinal Unit, Division of Medical Sciences, School of Molecular and Clinical Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK. * Jack Satsangi * Department of Dermatology and Allergy, Technical University Munich, Munich, Germany. * Stephan Weidinger * Complex Genetics Section, Department of Medical Genetics, University Medical Centre Utrecht, Utrecht, The Netherlands. * Alexandra Zhernakova * Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, Massachusetts, USA. * Alexandra Zhernakova * Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy. * Magdalena Zoledziewska & * Francesco Cucca * A full list of consortium members is provided in the Supplementary Note. * Type 1 Diabetes Genetics Consortium , * UK Inflammatory Bowel Disease (IBD) Genetics Consortium & * Wellcome Trust Case Control Consortium * Department of Gastroenterology and Hepatology, University Medical Centre Groningen and University of Groningen, Groningen, The Netherlands. * Rinse K Weersma * Istituto di Neurogenetica e Neurofarmacologia, Consiglio Natzionale delle Richerche (CNR), Monserrato, Italy. * Francesco Cucca Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * David A van Heel Author Details * Karen A Hunt Search for this author in: * NPG journals * PubMed * Google Scholar * Deborah J Smyth Search for this author in: * NPG journals * PubMed * Google Scholar * Tobias Balschun Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Ban Search for this author in: * NPG journals * PubMed * Google Scholar * Vanisha Mistry Search for this author in: * NPG journals * PubMed * Google Scholar * Tariq Ahmad Search for this author in: * NPG journals * PubMed * Google Scholar * Vidya Anand Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey C Barrett Search for this author in: * NPG journals * PubMed * Google Scholar * Leena Bhaw-Rosun Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas A Bockett Search for this author in: * NPG journals * PubMed * Google Scholar * Oliver J Brand Search for this author in: * NPG journals * PubMed * Google Scholar * Elisabeth Brouwer Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick Concannon Search for this author in: * NPG journals * PubMed * Google Scholar * Jason D Cooper Search for this author in: * NPG journals * PubMed * Google Scholar * Kerith-Rae M Dias Search for this author in: * NPG journals * PubMed * Google Scholar * Cleo C van Diemen Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick C Dubois Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah Edkins Search for this author in: * NPG journals * PubMed * Google Scholar * Regina Fölster-Holst Search for this author in: * NPG journals * PubMed * Google Scholar * Karin Fransen Search for this author in: * NPG journals * PubMed * Google Scholar * David N Glass Search for this author in: * NPG journals * PubMed * Google Scholar * Graham A R Heap Search for this author in: * NPG journals * PubMed * Google Scholar * Sylvia Hofmann Search for this author 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journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (188K) Supplementary Note and Supplementary Tables 1 and 2. Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_8b673cfb29dfa9196d402c8070632905"> Spliceosome mutations in hematopoietic malignancies</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_8b673cfb29dfa9196d402c8070632905">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_8b673cfb29dfa9196d402c8070632905"><strong>44</strong>(1):9-10</a> (2012)<br /> Nature Genetics | Correspondence Improved imputation of common and uncommon SNPs with a new reference set * Zhaoming Wang1, 2 * Kevin B Jacobs1, 2 * Meredith Yeager1, 2 * Amy Hutchinson1, 2 * Joshua Sampson2 * Nilanjan Chatterjee2 * Demetrius Albanes2 * Sonja I Berndt2 * Charles C Chung2 * W Ryan Diver3 * Susan M Gapstur3 * Lauren R Teras3 * Christopher A Haiman4 * Brian E Henderson4 * Daniel Stram4 * Xiang Deng1, 2 * Ann W Hsing2 * Jarmo Virtamo5 * Michael A Eberle6 * Jennifer L Stone6 * Mark P Purdue2 * Phil Taylor2 * Margaret Tucker2 * Stephen J Chanock2 * Affiliations * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:6–7Year published:(2012)DOI:doi:10.1038/ng.1044Published online 27 December 2011 Statistical imputation of genotype data is an important statistical technique that uses patterns of linkage disequilibrium observed in a reference set of haplotypes to computationally predict genetic variants in silico1. Currently, the most popular reference sets are the publicly available International HapMap2 and 1000 Genomes data sets3. Although these resources are valuable for imputing a sizeable fraction of common SNPs, they may not be optimal for imputing data for the next generation of genome-wide association studies (GWAS) and SNP arrays, which explore a fraction of uncommon variants. We have built a new resource for the imputation of SNPs for existing and future GWAS, known as the Division of Cancer Epidemiology and Genetics (DCEG) Reference Set. The data set has genotypes for cancer-free individuals, including 728 of European ancestry from three large prospectively sampled studies4, 5, 6, 98 African-American individuals from the Prostate, Lung, Colon and Ovary Cancer Screening Trial (PLCO), 74 Chinese individuals from a clinical trial in Shanxi, China (SHNX)7 and 349 individuals from the HapMap Project (Table 1). The final harmonized data set includes 2.8 million autosomal polymorphic SNPs for 1,249 individuals after rigorous quality control metrics were applied (see Supplementary Methods and Supplementary Tables 1 and 2). View full text Author information * 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 Affiliations * Core Genotyping Facility, SAIC-Frederick, National Cancer Institute (NCI)-Frederick, Frederick, Maryland, USA. * Zhaoming Wang, * Kevin B Jacobs, * Meredith Yeager, * Amy Hutchinson & * Xiang Deng * Division of Cancer Epidemiology and Genetics, NCI, US National Institutes of Health (NIH), Bethesda, Maryland, USA. * Zhaoming Wang, * Kevin B Jacobs, * Meredith Yeager, * Amy Hutchinson, * Joshua Sampson, * Nilanjan Chatterjee, * Demetrius Albanes, * Sonja I Berndt, * Charles C Chung, * Xiang Deng, * Ann W Hsing, * Mark P Purdue, * Phil Taylor, * Margaret Tucker & * Stephen J Chanock * Epidemiology Research Program, American Cancer Society, Atlanta, Georgia, USA. * W Ryan Diver, * Susan M Gapstur & * Lauren R Teras * Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California, USA. * Christopher A Haiman, * Brian E Henderson & * Daniel Stram * Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland. * Jarmo Virtamo * Illumina, San Diego, California, USA. * Michael A Eberle & * Jennifer L Stone Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Stephen J Chanock Author Details * Zhaoming Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin B Jacobs Search for this author in: * NPG journals * PubMed * Google Scholar * Meredith Yeager Search for this author in: * NPG journals * PubMed * Google Scholar * Amy Hutchinson Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua Sampson Search for this author in: * NPG journals * PubMed * Google Scholar * Nilanjan Chatterjee Search for this author in: * NPG journals * PubMed * Google Scholar * Demetrius Albanes Search for this author in: * NPG journals * PubMed * Google Scholar * Sonja I Berndt Search for this author in: * NPG journals * PubMed * Google Scholar * Charles C Chung Search for this author in: * NPG journals * PubMed * Google Scholar * W Ryan Diver Search for this author in: * NPG journals * PubMed * Google Scholar * Susan M Gapstur Search for this author in: * NPG journals * PubMed * Google Scholar * Lauren R Teras Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher A Haiman Search for this author in: * NPG journals * PubMed * Google Scholar * Brian E Henderson Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Stram Search for this author in: * NPG journals * PubMed * Google Scholar * Xiang Deng Search for this author in: * NPG journals * PubMed * Google Scholar * Ann W Hsing Search for this author in: * NPG journals * PubMed * Google Scholar * Jarmo Virtamo Search for this author in: * NPG journals * PubMed * Google Scholar * Michael A Eberle Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer L Stone Search for this author in: * NPG journals * PubMed * Google Scholar * Mark P Purdue Search for this author in: * NPG journals * PubMed * Google Scholar * Phil Taylor Search for this author in: * NPG journals * PubMed * Google Scholar * Margaret Tucker Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen J Chanock Contact Stephen J Chanock Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (779K) Supplementary Methods, Supplementary Figures 1–3 and Supplementary Tables 1 and 2. Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_95b00e8402a94435cb4280e8622c0221"> Following evolution of bacterial antibiotic resistance in real time</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_95b00e8402a94435cb4280e8622c0221">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_95b00e8402a94435cb4280e8622c0221"><strong>44</strong>(1):11-13</a> (2012)<br /> </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_da23e45fb85af7bcbf6ed1a6949a22da"> Dnmt3a silences hematopoietic stem cell self-renewal</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_da23e45fb85af7bcbf6ed1a6949a22da">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_da23e45fb85af7bcbf6ed1a6949a22da"><strong>44</strong>(1):13-14</a> (2012)<br /> Article preview View full access options Nature Genetics | News and Views Following evolution of bacterial antibiotic resistance in real time * Adam Z Rosenthal1 * Michael B Elowitz1 * Affiliations * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:11–13Year published:(2012)DOI:doi:10.1038/ng.1048Published online 27 December 2011 Article tools * 日本語要約 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg A new study reports the development of the 'morbidostat', a device that allows for continuous culture of bacteria under a constant drug selection pressure using computer feedback control of antibiotic concentration. This device, together with bacterial whole-genome sequencing, allowed the authors to follow the evolution of resistance-conferring mutations in Escherichia coli populations in real time, providing support for deterministic evolution of resistance in some situations. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Genetics for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Affiliations * Adam Z. Rosenthal and Michael B. Elowitz are at the Howard Hughes Medical Institute, Division of Biology, California Institute of Technology, Pasadena, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Michael B Elowitz Author Details * Adam Z Rosenthal Search for this author in: * NPG journals * PubMed * Google Scholar * Michael B Elowitz Contact Michael B Elowitz Search for this author in: * NPG journals * PubMed * Google Scholar Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_387cde570a47ef59e01782d9ccb982b6"> Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_387cde570a47ef59e01782d9ccb982b6">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_387cde570a47ef59e01782d9ccb982b6"><strong>44</strong>(1):17-19</a> (2012)<br /> Article preview View full access options Nature Genetics | News and Views Dnmt3a silences hematopoietic stem cell self-renewal * Jennifer J Trowbridge1 * Stuart H Orkin1 * Affiliations * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:13–14Year published:(2012)DOI:doi:10.1038/ng.1043Published online 27 December 2011 Article tools * 日本語要約 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg DNA methylation is an epigenetic mark stably directing gene expression throughout development. A new study uncovers a role for the DNA methyltransferase Dnmt3a in silencing self-renewal genes in hematopoietic stem cells (HSCs) to permit efficient hematopoietic differentiation. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Genetics for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Affiliations * Jennifer J. Trowbridge and Stuart H. Orkin are at the Department of Pediatric Oncology, Dana-Farber Cancer Institute and Division of Hematology/Oncology, Children's Hospital Boston, Boston, Massachusetts, USA. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Jennifer J Trowbridge or * Stuart H Orkin Author Details * Jennifer J Trowbridge Contact Jennifer J Trowbridge Search for this author in: * NPG journals * PubMed * Google Scholar * Stuart H Orkin Contact Stuart H Orkin Search for this author in: * NPG journals * PubMed * Google Scholar Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_d4c380eba18e85773f20b4abb7812d40"> GATA6 haploinsufficiency causes pancreatic agenesis in humans</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_d4c380eba18e85773f20b4abb7812d40">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_d4c380eba18e85773f20b4abb7812d40"><strong>44</strong>(1):20-22</a> (2012)<br /> Nature Genetics | Brief Communication Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma * Guangwu Guo1, 10 * Yaoting Gui2, 10 * Shengjie Gao1, 10 * Aifa Tang2, 3, 10 * Xueda Hu1, 10 * Yi Huang2, 3, 10 * Wenlong Jia1 * Zesong Li2, 3 * Minghui He1 * Liang Sun2 * Pengfei Song1 * Xiaojuan Sun3 * Xiaokun Zhao4 * Sangming Yang1 * Chaozhao Liang5 * Shengqing Wan1 * Fangjian Zhou6 * Chao Chen1 * Jialou Zhu1, 7 * Xianxin Li2 * Minghan Jian1 * Liang Zhou2 * Rui Ye1 * Peide Huang1 * Jing Chen2 * Tao Jiang1 * Xiao Liu1 * Yong Wang2 * Jing Zou1 * Zhimao Jiang2 * Renhua Wu1 * Song Wu2 * Fan Fan1 * Zhongfu Zhang2 * Lin Liu1 * Ruilin Yang2 * Xingwang Liu1 * Haibo Wu1 * Weihua Yin2 * Xia Zhao1 * Yuchen Liu2 * Huanhuan Peng1 * Binghua Jiang2 * Qingxin Feng2 * Cailing Li2 * Jun Xie2 * Jingxiao Lu2 * Karsten Kristiansen1, 8 * Yingrui Li1 * Xiuqing Zhang1 * Songgang Li1 * Jian Wang1 * Huanming Yang1 * Zhiming Cai2, 3 * Jun Wang1, 8, 9 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:17–19Year published:(2012)DOI:doi:10.1038/ng.1014Received 03 June 2011 Accepted 28 October 2011 Published online 04 December 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We sequenced whole exomes of ten clear cell renal cell carcinomas (ccRCCs) and performed a screen of ~1,100 genes in 88 additional ccRCCs, from which we discovered 12 previously unidentified genes mutated at elevated frequencies in ccRCC. Notably, we detected frequent mutations in the ubiquitin-mediated proteolysis pathway (UMPP), and alterations in the UMPP were significantly associated with overexpression of HIF1α and HIF2α in the tumors (P = 0.01 and 0.04, respectively). Our findings highlight the potential contribution of UMPP to ccRCC tumorigenesis through the activation of the hypoxia regulatory network. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Guangwu Guo, * Yaoting Gui, * Shengjie Gao, * Aifa Tang, * Xueda Hu & * Yi Huang Affiliations * Shenzhen Key Laboratory of Transomics Biotechnologies, BGI-Shenzhen, Shenzhen, China. * Guangwu Guo, * Shengjie Gao, * Xueda Hu, * Wenlong Jia, * Minghui He, * Pengfei Song, * Sangming Yang, * Shengqing Wan, * Chao Chen, * Jialou Zhu, * Minghan Jian, * Rui Ye, * Peide Huang, * Tao Jiang, * Xiao Liu, * Jing Zou, * Renhua Wu, * Fan Fan, * Lin Liu, * Xingwang Liu, * Haibo Wu, * Xia Zhao, * Huanhuan Peng, * Karsten Kristiansen, * Yingrui Li, * Xiuqing Zhang, * Songgang Li, * Jian Wang, * Huanming Yang & * Jun Wang * Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Institute of Urology, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen, China. * Yaoting Gui, * Aifa Tang, * Yi Huang, * Zesong Li, * Liang Sun, * Xianxin Li, * Liang Zhou, * Jing Chen, * Yong Wang, * Zhimao Jiang, * Song Wu, * Zhongfu Zhang, * Ruilin Yang, * Weihua Yin, * Yuchen Liu, * Binghua Jiang, * Qingxin Feng, * Cailing Li, * Jun Xie, * Jingxiao Lu & * Zhiming Cai * Shenzhen Second People′s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China. * Aifa Tang, * Yi Huang, * Zesong Li, * Xiaojuan Sun & * Zhiming Cai * Department of Urology, The Second Xiangya Hospital of Central-Southern University, Changsha, China. * Xiaokun Zhao * Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China. * Chaozhao Liang * Department of Urology, Sun Yat-Sen University Cancer Center, Guangzhou, China. * Fangjian Zhou * College of Life Science, Wuhan University, Wuhan, China. * Jialou Zhu * Department of Biology, University of Copenhagen, Copenhagen, Denmark. * Karsten Kristiansen & * Jun Wang * The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark. * Jun Wang Contributions Jun Wang, Z.C., Jian Wang, H.Y., S.L. and Y. Li managed the project. Xiaokun Zhao, C. Liang, F.Z., Z.L., X. Li, L.Z., J.C., Y.W., Z.J., S. Wu, Z.Z., R. Yang, W.Y., Y. Liu, B.J., J.L. and Q.F. prepared the samples. X. Zhang, X.H., Xiao Liu, R.W., L.L., Xia Zhao, H.P. and K.K. performed the sequencing. G.G., Y.G., S.G., A.T., Y.H., W.J., M.H., S. Wan, C.C., M.J., T.J. and R. Ye performed the bioinformatic analysis. S.Y., P.S., P.H., J. Zou, F.F., Xingwang Liu and H.W. performed the validation of somatic mutations. L.S. and C. Li performed the immunohistochemistry analysis. Z.L., J.X. and J. Zhu performed the methylation analysis. G.G., Y.G., Y. Li, A.T. and Y.H. wrote the manuscript, and Y.H., Y.G., G.G., Y. Li and X.S. revised the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Jun Wang or * Zhiming Cai or * Huanming Yang Author Details * Guangwu Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Yaoting Gui Search for this author in: * NPG journals * PubMed * Google Scholar * Shengjie Gao Search for this author in: * NPG journals * PubMed * Google Scholar * Aifa Tang Search for this author in: * NPG journals * PubMed * Google Scholar * Xueda Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Yi Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Wenlong Jia Search for this author in: * NPG journals * PubMed * Google Scholar * Zesong Li Search for this author in: * NPG journals * PubMed * Google Scholar * Minghui He Search for this author in: * NPG journals * PubMed * Google Scholar * Liang Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Pengfei Song Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaojuan Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaokun Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Sangming Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Chaozhao Liang Search for this author in: * NPG journals * PubMed * Google Scholar * Shengqing Wan Search for this author in: * NPG journals * PubMed * Google Scholar * Fangjian Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Chao Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Jialou Zhu Search for this author in: * NPG journals * PubMed * Google Scholar * Xianxin Li Search for this author in: * NPG journals * PubMed * Google Scholar * Minghan Jian Search for this author in: * NPG journals * PubMed * Google Scholar * Liang Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Rui Ye Search for this author in: * NPG journals * PubMed * Google Scholar * Peide Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Tao Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Xiao Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Yong Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Zou Search for this author in: * NPG journals * PubMed * Google Scholar * Zhimao Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Renhua Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Song Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Fan Fan Search for this author in: * NPG journals * PubMed * Google Scholar * Zhongfu Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Lin Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Ruilin Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Xingwang Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Haibo Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Weihua Yin Search for this author in: * NPG journals * PubMed * Google Scholar * Xia Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Yuchen Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Huanhuan Peng Search for this author in: * NPG journals * PubMed * Google Scholar * Binghua Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Qingxin Feng Search for this author in: * NPG journals * PubMed * Google Scholar * Cailing Li Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Xie Search for this author in: * NPG journals * PubMed * Google Scholar * Jingxiao Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Karsten Kristiansen Search for this author in: * NPG journals * PubMed * Google Scholar * Yingrui Li Search for this author in: * NPG journals * PubMed * Google Scholar * Xiuqing Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Songgang Li Search for this author in: * NPG journals * PubMed * Google Scholar * Jian Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Huanming Yang Contact Huanming Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Zhiming Cai Contact Zhiming Cai Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Wang Contact Jun Wang Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (958K) Supplementary Methods, Supplementary Figures 1–4 and Supplementary Tables 1, 2 and 6–9. Excel files * Supplementary Table 3 (45K) Supplementary Table 3 * Supplementary Table 4 (29K) Supplementary Table 4 * Supplementary Table 5 (57K) Supplementary Table 5 Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_8d8a832267995292b6648e4ee4e75f20"> Dnmt3a is essential for hematopoietic stem cell differentiation</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_8d8a832267995292b6648e4ee4e75f20">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_8d8a832267995292b6648e4ee4e75f20"><strong>44</strong>(1):23-31</a> (2012)<br /> Nature Genetics | Brief Communication Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma * Guangwu Guo1, 10 * Yaoting Gui2, 10 * Shengjie Gao1, 10 * Aifa Tang2, 3, 10 * Xueda Hu1, 10 * Yi Huang2, 3, 10 * Wenlong Jia1 * Zesong Li2, 3 * Minghui He1 * Liang Sun2 * Pengfei Song1 * Xiaojuan Sun3 * Xiaokun Zhao4 * Sangming Yang1 * Chaozhao Liang5 * Shengqing Wan1 * Fangjian Zhou6 * Chao Chen1 * Jialou Zhu1, 7 * Xianxin Li2 * Minghan Jian1 * Liang Zhou2 * Rui Ye1 * Peide Huang1 * Jing Chen2 * Tao Jiang1 * Xiao Liu1 * Yong Wang2 * Jing Zou1 * Zhimao Jiang2 * Renhua Wu1 * Song Wu2 * Fan Fan1 * Zhongfu Zhang2 * Lin Liu1 * Ruilin Yang2 * Xingwang Liu1 * Haibo Wu1 * Weihua Yin2 * Xia Zhao1 * Yuchen Liu2 * Huanhuan Peng1 * Binghua Jiang2 * Qingxin Feng2 * Cailing Li2 * Jun Xie2 * Jingxiao Lu2 * Karsten Kristiansen1, 8 * Yingrui Li1 * Xiuqing Zhang1 * Songgang Li1 * Jian Wang1 * Huanming Yang1 * Zhiming Cai2, 3 * Jun Wang1, 8, 9 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:17–19Year published:(2012)DOI:doi:10.1038/ng.1014Received 03 June 2011 Accepted 28 October 2011 Published online 04 December 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We sequenced whole exomes of ten clear cell renal cell carcinomas (ccRCCs) and performed a screen of ~1,100 genes in 88 additional ccRCCs, from which we discovered 12 previously unidentified genes mutated at elevated frequencies in ccRCC. Notably, we detected frequent mutations in the ubiquitin-mediated proteolysis pathway (UMPP), and alterations in the UMPP were significantly associated with overexpression of HIF1α and HIF2α in the tumors (P = 0.01 and 0.04, respectively). Our findings highlight the potential contribution of UMPP to ccRCC tumorigenesis through the activation of the hypoxia regulatory network. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Guangwu Guo, * Yaoting Gui, * Shengjie Gao, * Aifa Tang, * Xueda Hu & * Yi Huang Affiliations * Shenzhen Key Laboratory of Transomics Biotechnologies, BGI-Shenzhen, Shenzhen, China. * Guangwu Guo, * Shengjie Gao, * Xueda Hu, * Wenlong Jia, * Minghui He, * Pengfei Song, * Sangming Yang, * Shengqing Wan, * Chao Chen, * Jialou Zhu, * Minghan Jian, * Rui Ye, * Peide Huang, * Tao Jiang, * Xiao Liu, * Jing Zou, * Renhua Wu, * Fan Fan, * Lin Liu, * Xingwang Liu, * Haibo Wu, * Xia Zhao, * Huanhuan Peng, * Karsten Kristiansen, * Yingrui Li, * Xiuqing Zhang, * Songgang Li, * Jian Wang, * Huanming Yang & * Jun Wang * Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Institute of Urology, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen, China. * Yaoting Gui, * Aifa Tang, * Yi Huang, * Zesong Li, * Liang Sun, * Xianxin Li, * Liang Zhou, * Jing Chen, * Yong Wang, * Zhimao Jiang, * Song Wu, * Zhongfu Zhang, * Ruilin Yang, * Weihua Yin, * Yuchen Liu, * Binghua Jiang, * Qingxin Feng, * Cailing Li, * Jun Xie, * Jingxiao Lu & * Zhiming Cai * Shenzhen Second People′s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China. * Aifa Tang, * Yi Huang, * Zesong Li, * Xiaojuan Sun & * Zhiming Cai * Department of Urology, The Second Xiangya Hospital of Central-Southern University, Changsha, China. * Xiaokun Zhao * Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China. * Chaozhao Liang * Department of Urology, Sun Yat-Sen University Cancer Center, Guangzhou, China. * Fangjian Zhou * College of Life Science, Wuhan University, Wuhan, China. * Jialou Zhu * Department of Biology, University of Copenhagen, Copenhagen, Denmark. * Karsten Kristiansen & * Jun Wang * The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark. * Jun Wang Contributions Jun Wang, Z.C., Jian Wang, H.Y., S.L. and Y. Li managed the project. Xiaokun Zhao, C. Liang, F.Z., Z.L., X. Li, L.Z., J.C., Y.W., Z.J., S. Wu, Z.Z., R. Yang, W.Y., Y. Liu, B.J., J.L. and Q.F. prepared the samples. X. Zhang, X.H., Xiao Liu, R.W., L.L., Xia Zhao, H.P. and K.K. performed the sequencing. G.G., Y.G., S.G., A.T., Y.H., W.J., M.H., S. Wan, C.C., M.J., T.J. and R. Ye performed the bioinformatic analysis. S.Y., P.S., P.H., J. Zou, F.F., Xingwang Liu and H.W. performed the validation of somatic mutations. L.S. and C. Li performed the immunohistochemistry analysis. Z.L., J.X. and J. Zhu performed the methylation analysis. G.G., Y.G., Y. Li, A.T. and Y.H. wrote the manuscript, and Y.H., Y.G., G.G., Y. Li and X.S. revised the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Jun Wang or * Zhiming Cai or * Huanming Yang Author Details * Guangwu Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Yaoting Gui Search for this author in: * NPG journals * PubMed * Google Scholar * Shengjie Gao Search for this author in: * NPG journals * PubMed * Google Scholar * Aifa Tang Search for this author in: * NPG journals * PubMed * Google Scholar * Xueda Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Yi Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Wenlong Jia Search for this author in: * NPG journals * PubMed * Google Scholar * Zesong Li Search for this author in: * NPG journals * PubMed * Google Scholar * Minghui He Search for this author in: * NPG journals * PubMed * Google Scholar * Liang Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Pengfei Song Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaojuan Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaokun Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Sangming Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Chaozhao Liang Search for this author in: * NPG journals * PubMed * Google Scholar * Shengqing Wan Search for this author in: * NPG journals * PubMed * Google Scholar * Fangjian Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Chao Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Jialou Zhu Search for this author in: * NPG journals * PubMed * Google Scholar * Xianxin Li Search for this author in: * NPG journals * PubMed * Google Scholar * Minghan Jian Search for this author in: * NPG journals * PubMed * Google Scholar * Liang Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Rui Ye Search for this author in: * NPG journals * PubMed * Google Scholar * Peide Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Tao Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Xiao Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Yong Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Zou Search for this author in: * NPG journals * PubMed * Google Scholar * Zhimao Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Renhua Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Song Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Fan Fan Search for this author in: * NPG journals * PubMed * Google Scholar * Zhongfu Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Lin Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Ruilin Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Xingwang Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Haibo Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Weihua Yin Search for this author in: * NPG journals * PubMed * Google Scholar * Xia Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Yuchen Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Huanhuan Peng Search for this author in: * NPG journals * PubMed * Google Scholar * Binghua Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Qingxin Feng Search for this author in: * NPG journals * PubMed * Google Scholar * Cailing Li Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Xie Search for this author in: * NPG journals * PubMed * Google Scholar * Jingxiao Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Karsten Kristiansen Search for this author in: * NPG journals * PubMed * Google Scholar * Yingrui Li Search for this author in: * NPG journals * PubMed * Google Scholar * Xiuqing Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Songgang Li Search for this author in: * NPG journals * PubMed * Google Scholar * Jian Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Huanming Yang Contact Huanming Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Zhiming Cai Contact Zhiming Cai Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Wang Contact Jun Wang Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (958K) Supplementary Methods, Supplementary Figures 1–4 and Supplementary Tables 1, 2 and 6–9. Excel files * Supplementary Table 3 (45K) Supplementary Table 3 * Supplementary Table 4 (29K) Supplementary Table 4 * Supplementary Table 5 (57K) Supplementary Table 5 Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_cc5c621f7b49b3b53f2cdd1929d893c4"> Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_cc5c621f7b49b3b53f2cdd1929d893c4">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_cc5c621f7b49b3b53f2cdd1929d893c4"><strong>44</strong>(1):32-39</a> (2012)<br /> Nature Genetics | Brief Communication GATA6 haploinsufficiency causes pancreatic agenesis in humans * Hana Lango Allen1, 6 * Sarah E Flanagan1, 6 * Charles Shaw-Smith1, 6 * Elisa De Franco1, 6 * Ildem Akerman2, 3, 4 * Richard Caswell1 * the International Pancreatic Agenesis Consortium * Jorge Ferrer2, 3, 4 * Andrew T Hattersley1 * Sian Ellard1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:20–22Year published:(2012)DOI:doi:10.1038/ng.1035Received 03 August 2011 Accepted 15 November 2011 Published online 11 December 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Understanding the regulation of pancreatic development is key for efforts to develop new regenerative therapeutic approaches for diabetes. Rare mutations in PDX1 and PTF1A can cause pancreatic agenesis, however, most instances of this disorder are of unknown origin. We report de novo heterozygous inactivating mutations in GATA6 in 15/27 (56%) individuals with pancreatic agenesis. These findings define the most common cause of human pancreatic agenesis and establish a key role for the transcription factor GATA6 in human pancreatic development. View full text Figures at a glance * Figure 1: GATA6 mutations causing pancreatic agenesis. () Genomic and protein positions of the 14 GATA6 mutations. () Electrophoretic mobility shift assay showing that mutations abolish the binding to a predicted GATA6 binding sequence in the pancreatic HNF4A proximal promoter. We used nuclear extracts from cells transfected with a control vector or vectors expressing GATA6 wild-type (WT) or mutant (Mut) proteins (described at the level of the protein changes shown in Fig. 1a). Only wild-type GATA6 formed a retardation complex (arrow) that disappeared after preincubation with unlabeled wild-type but not mutated double-stranded oligonucleotide probes (competitor) and with GATA6 antiserum. Identical results were observed with in vitro translated wild-type and mutant GATA6 proteins using the TFF2 GATA6 binding site (data not shown). () Mutated GATA6 does not activate the GATA6-responsive WNT2 promoter-luciferase gene in HeLa cells. *Statistically significant difference in activity as compared to wild type (P < 0.0001). () Protein b! lot showing comparable expression of wild-type and mutant GATA6 proteins. * Figure 2: Clinical characteristics of the pancreatic agenesis cohort. In addition to pancreatic agenesis, GATA6 mutations cause several other phenotypes. The precise malformations seen in each subject are listed in Supplementary Table 4. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Hana Lango Allen, * Sarah E Flanagan, * Charles Shaw-Smith & * Elisa De Franco Affiliations * Institute of Biomedical and Clinical Science, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, UK. * Hana Lango Allen, * Sarah E Flanagan, * Charles Shaw-Smith, * Elisa De Franco, * Richard Caswell, * Andrew T Hattersley & * Sian Ellard * Genomic Programming of Beta-cells Laboratory, Institut d'Investigacions August Pi i Sunyer (IDIBAPS), Barcelona, Spain. * Ildem Akerman & * Jorge Ferrer * Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Barcelona, Spain. * Ildem Akerman & * Jorge Ferrer * Department of Endocrinology and Nutrition, Hospital Clínic de Barcelona, Barcelona, Spain. * Ildem Akerman & * Jorge Ferrer Consortia * the International Pancreatic Agenesis Consortium Contributions S.E., S.E.F., J.F. and A.T.H. designed the study. R.C. performed the exome sequencing and the structural modeling. H.L.A. did the bioinformatic analyses. E.D.F. and S.E.F. did the Sanger sequencing analysis and the interpretation of the resulting data. C.S.-S. and A.T.H. analyzed the clinical data. I.A. and J.F. performed the functional studies. H.L.A., C.S.-S., J.F., A.T.H. and S.E. prepared the draft manuscript. All authors contributed to the discussion of the results and the manuscript preparation. A full list of members is provided in the Supplementary Note. the International Pancreatic Agenesis Consortium Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Andrew T Hattersley Author Details * Hana Lango Allen Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah E Flanagan Search for this author in: * NPG journals * PubMed * Google Scholar * Charles Shaw-Smith Search for this author in: * NPG journals * PubMed * Google Scholar * Elisa De Franco Search for this author in: * NPG journals * PubMed * Google Scholar * Ildem Akerman Search for this author in: * NPG journals * PubMed * Google Scholar * Richard Caswell Search for this author in: * NPG journals * PubMed * Google Scholar * the International Pancreatic Agenesis Consortium * Jorge Ferrer Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew T Hattersley Contact Andrew T Hattersley Search for this author in: * NPG journals * PubMed * Google Scholar * Sian Ellard Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Note, Supplementary Methods, Supplementary Figures 1–3 and Supplementary Tables 1–4. Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_8ac108b1c293125f0ff035bcfda52c46"> Regions of focal DNA hypermethylation and long-range hypomethylation in colorectal cancer coincide with nuclear lamina–associated domains</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_8ac108b1c293125f0ff035bcfda52c46">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_8ac108b1c293125f0ff035bcfda52c46"><strong>44</strong>(1):40-46</a> (2012)<br /> Nature Genetics | Article Dnmt3a is essential for hematopoietic stem cell differentiation * Grant A Challen1, 2, 3 * Deqiang Sun4, 5, 15 * Mira Jeong1, 2, 6, 15 * Min Luo6, 15 * Jaroslav Jelinek7, 15 * Jonathan S Berg8, 9, 15 * Christoph Bock10, 11 * Aparna Vasanthakumar12 * Hongcang Gu7 * Yuanxin Xi4, 5 * Shoudan Liang13 * Yue Lu7 * Gretchen J Darlington6 * Alexander Meissner10, 11 * Jean-Pierre J Issa7 * Lucy A Godley12 * Wei Li4, 5 * Margaret A Goodell1, 2, 14 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:23–31Year published:(2012)DOI:doi:10.1038/ng.1009Received 06 July 2011 Accepted 25 October 2011 Published online 04 December 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Loss of the de novo DNA methyltransferases Dnmt3a and Dnmt3b in embryonic stem cells obstructs differentiation; however, the role of these enzymes in somatic stem cells is largely unknown. Using conditional ablation, we show that Dnmt3a loss progressively impairs hematopoietic stem cell (HSC) differentiation over serial transplantation, while simultaneously expanding HSC numbers in the bone marrow. Dnmt3a-null HSCs show both increased and decreased methylation at distinct loci, including substantial CpG island hypermethylation. Dnmt3a-null HSCs upregulate HSC multipotency genes and downregulate differentiation factors, and their progeny exhibit global hypomethylation and incomplete repression of HSC-specific genes. These data establish Dnmt3a as a critical participant in the epigenetic silencing of HSC regulatory genes, thereby enabling efficient differentiation. View full text Figures at a glance * Figure 1: Dnmt3a is highly expressed in HSCs and its ablation has profound functional effects. () Real-time PCR analysis of Dnmt3a mRNA in LT-HSCs, short-term HSCs (ST-HSCs) and representative committed progenitors and differentiated cells. MPPs, multi-potential progenitors; CLPs, common lymphoid progenitors; CMPs, common myeloid progenitors; MEPs, megakaryocyte-erythroid progenitors; GMPs, granulocyte-macrophage progenitors (see Online Methods for purification schemes). Mean ± s.e.m. values are shown for three biological replicates. () Contribution of control (Dnmt3afl/fl;Mx1-Cre−) and Dnmt3a-null (Dnmt3afl/fl;Mx1-Cre+) HSCs to recipient mouse peripheral blood in secondary competitive transplants, measured at monthly intervals. Mean ± s.e.m. values are shown. () Lineage differentiation in secondary recipients of transplanted control and Dnmt3a-null HSCs. Shown are percentages of donor-derived (CD45.2+) myeloid cells (Gr1+ or Mac1+), B cells (B220+) and T cells (CD4+ or CD8+) in peripheral blood analyzed 16 weeks after transplantation. Differences that are signifi! cant between control and Dnmt3a-null HSCs are indicated. Mean ± s.e.m. values are shown (N = 15–37 mice). () Hoechst staining and flow cytometry analysis of the bone marrow of secondary recipient mice. Top, the boxed region shows the percentage of side population (SP) cells from mice transplanted with HSCs of the indicated genotypes. Bottom, SP cells were further gated using c-Kit+, lineage− and Sca-1+ (KLS) markers to reveal the proportion of test (CD45.2+) versus competitor (CD45.1+) HSCs. () Alternative HSC phenotype schemes for test cells gated first by KLS show similar expansion of the Dnmt3a-null HSC compartment. () Quantification of total HSC frequency in the bone marrow of secondary recipient mice by three phenotypic definitions. Hatched area indicates the proportion of CD45.2+ test cells. SLAM is CD150+, CD48−, KLS gating. Mean ± s.e.m. values are shown. () Analysis of progenitor frequencies in secondary recipient mice. Hatched area indicates the contributi! on of CD45.2+ test cells. Mean ± s.e.m. values are shown. **P! < 0.001; ***P < 0.001. * Figure 2: Cellular kinetics of Dnmt3a-null HSCs. () HSC gating scheme for the analysis of proliferation and apoptosis in secondary recipient mice transplanted with control or Dnmt3a-null HSCs. () Ki67 staining shows a significant reduction in the proliferative index of Dnmt3a-null HSCs relative to control HSCs; *P < 0.05. () Annexin V staining shows no difference in the apoptotic rate between control and Dnmt3a-null HSCs. Bars in and indicate the mean values for each genotype. * Figure 3: Dnmt3a-null HSCs show inhibition of long-term differentiation in serial competitive transplantation of HSCs. () The proportion of peripheral blood generated from the test cells in recipient mice 16 weeks after transplantation. () Quantification of donor-derived HSCs in the bone marrow of recipient mice 18 weeks after transplantation, defined as CD45.2+, SPKLS cells. Data are representative of at least three individual transplantation experiments for each stage of serial transfer (N = 15–37 mice per group). Mean ± s.e.m. values are shown. () Flow cytometry data of quaternary recipient mice transplanted with control or Dnmt3a-null HSCs showing virtually all continuously amplified HSCs in bone marrow were derived from Dnmt3a-null HSCs (CD45.2+). () Differentiation and self-renewal quotients, calculated at the end of each round of transplantation with Dnmt3a-null and control HSCs. ***P < 0.001. * Figure 4: Dnmt3a loss in HSCs results in both hyper- and hypo-methylation. () HPLC-MS analysis of global 5mc levels as a proportion of the total cytosine in purified HSCs from secondary recipient mice (N = 2). () RRBS analysis of tertiary recipient mice transplanted with control or Dnmt3a-null HSCs. Plots show the degree of differential methylation (between Dnmt3a-null and control HSCs) and its relationship to local CpG density (blue). Left, all hypomethylated (red, CpGs ≤33% methylated) and hypermethylated (green, CpGs ≥33% methylated) DMCs in Dnmt3a-null HSCs. Right, DMCs located within CGIs. () Independent bisulfite sequencing analysis of selected hypermethylated CGIs in Dnmt3a-null HSCs. () Bisulfite sequencing analysis of selected hypomethylated genes in Dnmt3a-null HSCs. Schematic diagrams for each gene are shown in and (not to scale). Exons are represented by vertical rectangles. White horizontal bars indicate CGIs, and black horizontal bars show the tested region. Open and filled circles represent unmethylated and methylated CpGs, respe! ctively. Differences in methylation between control and Dnmt3a-null cells that were statistically significant are indicated; *P < 0.05, ***P < 0.001. * Figure 5: Dnmt3a loss in HSCs leads to higher expression of HSC multipotency genes. () Relative expression levels of select multipotency, HSC fingerprint18 and differentiation genes measured by real-time PCR analysis. Mean ± s.e.m. values are from three replicates in Dnmt3a-null HSCs normalized to the expression levels in control HSCs (dashed line). () Bisulfite sequencing analysis of multipotency and HSC fingerprint genes in control and Dnmt3a-null HSCs. Open horizontal bars indicate CGIs, and black horizontal bars show the tested region. Open and filled circles represent unmethylated and methylated CpGs, respectively. Differences in methylation between control and Dnmt3a-null HSCs that were statistically significant are indicated. () H3K4me3 ChIP analysis of DMRs in control and Dnmt3a-null HSCs. Mean ± s.e.m. values are shown (N = 3 replicate experiments). () Dnmt3a ChIP analysis of DMRs in wild-type hematopoietic progenitors (KLS cells, N = 2 replicate experiments) reveals Dnmt3a binding to CGIs in Runx1 and Gata3 but not in Nr4a2. Mean ± s.e.m. value! s are shown. *P < 0.05, **P < 0.01, ***P < 0.001. * Figure 6: Dnmt3a is required to suppress the stem cell program in HSCs to permit differentiation. () HPLC-MS analysis of global 5mc levels as a proportion of the total cytosine in B cells from secondary recipient mice. Mean ± s.e.m. values are shown (N = 7 mice). () DREAM analysis of B cells in secondary recipient mice. SmaI sites with at least 20 sequence tags in control B cells are plotted showing the methylation ratio between the genotypes. The red triangle indicates sites of hypomethylation in Dnmt3a-null B cells in 1.4% of all CpGs (FDR = 0.07%). () Bisulfite sequencing across the Vasn and Runx1 CGIs in control (top) and Dnmt3a-null (bottom) B cells. Differences in methylation between control and Dnmt3a-null cells that were statistically significant are indicated. () Cognate gene expression for cells analyzed in . Diamonds indicate control cells, and squares indicate Dntm3a-null cells. HSCs are represented by filled symbols and B cells by open symbols. Bars indicate the average gene expression for each cell population. Differences in expression between control and ! Dnmt3a-null cells that were statistically significant are indicated. () H3K4me3 ChIP analysis for Runx1 and Vasn in control and Dnmt3a-null B cells. Mean ± s.e.m. values are shown (N = 4 replicate experiments). () Expression of Dnmt3a-responsive genes at day 0 (d0) and day 6 (d6) after 5-FU exposure measured by real-time PCR. Expression levels are relative to normalized expression of that gene in d0 control HSCs. Mean ± s.e.m. values are shown, and statistically significant differences in expression at the two time points are indicated. () Proportion of methylated CpGs as detected by bisulfite sequencing across CGIs. Mean ± s.e.m. values are shown for three biological replicates, and statistically significant differences in methylation at the two time points are indicated. *P < 0.05, **P < 0.01, ***P < 0.001. * Figure 7: Exogenous Dnmt3a partially restores function and methylation patterns. () Analysis of HSC (CD150+, CD48−, KLS gated) frequency 18-weeks after transplantation. Sca-1+ cells from secondary recipient mice transplanted with Dnmt3a-null HSCs were transduced with either MSCV-Dnmt3a or control (MSCV-GFP) retroviruses and transplanted into tertiary recipients. Also shown are HSC frequencies in tertiary recipients transplanted with non-transduced control or Dnmt3a-null HSCs. Mean ± s.e.m. values are shown (N = 7–12 mice), and statistically significant differences are indicated. () Colony-forming capacity of Dnmt3a-null HSCs transduced with MSCV-GFP or MSCV-Dnmt3a. Also shown are colony formation from non-transduced control and Dnmt3a-null HSCs after the third serial transplantation. Mean ± s.e.m. values are shown (N = 4 replicate plates), and statistically significant differences are indicated. () Bisulfite sequencing in B cells across Vasn and Runx1 CGIs after forced exogenous Dnmt3a expression in Dnmt3a-null HSCs. Statistically significant diffe! rences in methylation are indicated. *P < 0.05, **P < 0.01, ***P < 0.001. * Figure 8: Model for Dnmt3a action in HSCs. HSC-specific genes are mostly unmethylated and expressed in normal HSCs (left). Upon receiving a signal to differentiate, Dnmt3a methylates and silences these regions to permit lineage commitment. This is associated with a loss of H3K4me3 and gene repression in B cells. Dnmt3a-null HSCs (right) cannot silence HSC genes, so upon receiving a stimulus to differentiate, HSC-specific genes remain expressed due to a lack of methylation and elevated H3K4me3. Upon cell division, the HSC self-renewal pathway remains active in Dnmt3a-null HSCs, resulting in their accumulation in the bone marrow. Of the few Dnmt3a-null HSCs that do differentiate, their progeny show incomplete methylation and partial repression of HSC genes. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE27322 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Deqiang Sun, * Mira Jeong, * Min Luo, * Jaroslav Jelinek & * Jonathan S Berg Affiliations * Stem Cells and Regenerative Medicine Center, Baylor College of Medicine, Houston, Texas, USA. * Grant A Challen, * Mira Jeong & * Margaret A Goodell * Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, Texas, USA. * Grant A Challen, * Mira Jeong & * Margaret A Goodell * Department of Pathology, Baylor College of Medicine, Houston, Texas, USA. * Grant A Challen * Division of Biostatistics, Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, Texas, USA. * Deqiang Sun, * Yuanxin Xi & * Wei Li * Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas, USA. * Deqiang Sun, * Yuanxin Xi & * Wei Li * Huffington Center for Aging, Baylor College of Medicine, Houston, Texas, USA. * Mira Jeong, * Min Luo & * Gretchen J Darlington * Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. * Jaroslav Jelinek, * Hongcang Gu, * Yue Lu & * Jean-Pierre J Issa * Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA. * Jonathan S Berg * Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA. * Jonathan S Berg * Broad Institute, Harvard University, Cambridge, Massachusetts, USA. * Christoph Bock & * Alexander Meissner * Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA. * Christoph Bock & * Alexander Meissner * Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, Illinois, USA. * Aparna Vasanthakumar & * Lucy A Godley * Department of Bioinformatics & Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. * Shoudan Liang * Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA. * Margaret A Goodell Contributions G.A.C. designed and performed experiments, analyzed data and wrote the manuscript. Experiments were also designed by J.S.B., J.-P.J.I., L.A.G., H.G., C.B., W.L. and M.A.G. and were performed by M.J., M.L., A.V. and J.J. Data were additionally analyzed and interpreted by M.J., D.S., M.L., C.B., A.V., J.J., S.L., Y.L., A.M., J.-P.J.I., L.A.G., W.L. and M.A.G. D.S., C.B., Y.X., S.L. and Y.L. developed critical software. The manuscript was written or edited by G.J.D., W.L., L.A.G., J.-P.J.I., J.S.B., C.B. and M.A.G. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Margaret A Goodell or * Wei Li Author Details * Grant A Challen Search for this author in: * NPG journals * PubMed * Google Scholar * Deqiang Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Mira Jeong Search for this author in: * NPG journals * PubMed * Google Scholar * Min Luo Search for this author in: * NPG journals * PubMed * Google Scholar * Jaroslav Jelinek Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan S Berg Search for this author in: * NPG journals * PubMed * Google Scholar * Christoph Bock Search for this author in: * NPG journals * PubMed * Google Scholar * Aparna Vasanthakumar Search for this author in: * NPG journals * PubMed * Google Scholar * Hongcang Gu Search for this author in: * NPG journals * PubMed * Google Scholar * Yuanxin Xi Search for this author in: * NPG journals * PubMed * Google Scholar * Shoudan Liang Search for this author in: * NPG journals * PubMed * Google Scholar * Yue Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Gretchen J Darlington Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Meissner Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-Pierre J Issa Search for this author in: * NPG journals * PubMed * Google Scholar * Lucy A Godley Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Li Contact Wei Li Search for this author in: * NPG journals * PubMed * Google Scholar * Margaret A Goodell Contact Margaret A Goodell 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 (5M) Supplementary Figures 1–12 and Supplementary Tables 1–5 and 9. Excel files * Supplementary Table 6 (512K) Annotation of differentially methylated regions (DMRs) * Supplementary Table 7 (31M) Microarray transcriptional profiling comparison of secondarily-transplanted control and Dnmt3a-KO HSCs * Supplementary Table 8 (158K) DREAM sequencing of secondary transplant control and Dnmt3a-KO B-cells Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_b25be020ca582c88c5a151ad4f20ee1b"> Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_b25be020ca582c88c5a151ad4f20ee1b">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_b25be020ca582c88c5a151ad4f20ee1b"><strong>44</strong>(1):47-52</a> (2012)<br /> Nature Genetics | Article Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm * Xuehui Huang1, 2, 5 * Yan Zhao1, 2, 5 * Xinghua Wei3, 5 * Canyang Li1 * Ahong Wang1 * Qiang Zhao1 * Wenjun Li1 * Yunli Guo1 * Liuwei Deng1 * Chuanrang Zhu1 * Danlin Fan1 * Yiqi Lu1 * Qijun Weng1 * Kunyan Liu1 * Taoying Zhou1 * Yufeng Jing1 * Lizhen Si1 * Guojun Dong1, 3 * Tao Huang1 * Tingting Lu1 * Qi Feng1 * Qian Qian3 * Jiayang Li4 * Bin Han1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:32–39Year published:(2012)DOI:doi:10.1038/ng.1018Received 03 June 2011 Accepted 02 November 2011 Published online 04 December 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg A high-density haplotype map recently enabled a genome-wide association study (GWAS) in a population of indica subspecies of Chinese rice landraces. Here we extend this methodology to a larger and more diverse sample of 950 worldwide rice varieties, including the Oryza sativa indica and Oryza sativa japonica subspecies, to perform an additional GWAS. We identified a total of 32 new loci associated with flowering time and with ten grain-related traits, indicating that the larger sample increased the power to detect trait-associated variants using GWAS. To characterize various alleles and complex genetic variation, we developed an analytical framework for haplotype-based de novo assembly of the low-coverage sequencing data in rice. We identified candidate genes for 18 associated loci through detailed annotation. This study shows that the integrated approach of sequence-based GWAS and functional genome annotation has the potential to match complex traits to their causal polymor! phisms in rice. View full text Figures at a glance * Figure 1: Genetic structure and population differentiation in 950 rice accessions. () Neighbor-joining tree of 950 rice accessions constructed from a simple matching distance of 4.1 million SNPs. The five divergent groups, indica (Ind), aus (Aus), temperate japonica (TeJ), tropical japonica (TrJ) and intermediate (Int), are colored in red, purple, blue, cyan and black, respectively. The scale bar indicates the simple matching distance. () The distributions of the pairwise population-differentiation statistic (Fst) across the rice genomes between indica and temperate japonica (in black), between temperate japonica and tropical japonica (in blue) and between indica and aus (in red). * Figure 2: Causal variant detection in six genes previously identified by GWAS in the indica population. The top of each panel shows the genomic location of the known gene and its gene structure. Exons and introns are depicted as rectangles and lines, respectively. Coding regions and untranslated regions are shown in black and gray, respectively. The points indicate the orientation of the genes. The red triangles indicate the location of causal variants detected in the gene. The bottom of each panel shows the contigs of two alleles (with major allele shown as a brown line and the minor allele shown as a green line) from local assembly, where the genotypes in the causal variant site are indicated. () Waxy is responsible for amylase content. () ALK is responsible for starch gelatinization temperature. () Rc is responsible for pericarp color. () OsC1 is responsible for apiculus color. () GS3 is responsible for grain length. () qSW5 is responsible for grain width. The dark red triangle indicates the location of 1.2-kb deletion in the reference genome. The indica cv. 93-11, which ha! s sequences from whole-genome shotgun and belongs to the haplotype group with the minor allele in qSW5, was used as a template to compare to assembly results. * Figure 3: Illustration of haplotype-based local assembly. () For each gene, genotypes for 55 SNP sites around the gene were retrieved from the genotype dataset. We calculated the simple matching distances for the 55 SNP sites and performed hierarchical cluster analysis by using the single linkage algorithm, which generated several haplotype groups (Hap1 through Hap4). The haplotype groups with a frequency of < 0.02 were excluded. () For each haplotype group, paired-end reads (shown as thick lines, with dashed lines connecting the read pairs) that were uniquely mapped onto the local region were collected together. In the case that only one of the paired-end reads was aligned, we picked up both of them. The sequence assembly was performed for each haplotype group separately, generating one (no gaps) or several contigs. () The contigs of different haplotype groups were aligned with the reference genome for sequence variation detection. The sequence variants detected, including SNPs and indels, were then used to predict the potential e! ffects on the gene. * Figure 4: Genome-wide association study of heading date in the indica population, the japonica population and the full population using the compressed MLM. For the significant loci identified, known loci are shown in purple and newly discovered loci are shown in green. Of these loci, those in Hd3a, Hd1 and Ghd7 had their causal variants detected through the haplotype-based assembly method, and there were no variants detected in the coding regions of RCN1 and OsGI (Supplementary Note). () Manhattan plots for heading date in indica population. The −log10P values from a genome-wide scan are plotted against the position on each of the 12 chromosomes. The horizontal dashed line indicates the genome-wide significance threshold (P = 5 × 10−8). () Quantile-quantile plot for heading date in the indica population. The horizontal axis shows −log10 transformed expected P values, and the vertical axis indicates −log10 transformed observed P values. () Manhattan plots for heading date in the japonica population and the genome-wide significance threshold (P = 2 × 10−7, shown as a dashed line). () Quantile-quantile plot for heading! date in japonica population. () Manhattan plots for heading date in the full population and the genome-wide significance threshold (P = 1 × 10−9, shown as a dashed line). () Quantile-quantile plot for heading date in the full population. Clear association signals around known genes that did not meet the genome-wide significance threshold are shown in orange. * Figure 5: Regions of the genome showing association signals and the expression profiles of candidate genes. () An associated locus for starch gelatinization temperature. The top of the panel shows the region on each side of the peak SNP. −log10 transformed P values from the compressed MLM are plotted on the vertical axis. The bottom of the panel shows a narrow region, with the candidate genes indicated by dark gray. () The expression pattern of a candidate gene (OsRAL6) in the amylase inhibitor gene cluster from public microarray data. DAP, days after pollination. Error bars, s.d. of three replicates. () Functional variants detected through de novo local assembly. The triangles indicate the location of causal variants detected in the gene. The colored lines show the contigs of two alleles (with the major allele shown as a brown line and the minor allele shown as a green line) from local assembly, where the genotypes of four non-synonymous SNPs are indicated. () An associated locus for hull color. () Expression pattern of the candidate gene (OsFBX310). () The ratio of read depths! (the ratio of the number of mapped reads from 41 indica rice lines with the minor allele to those from 41 indica lines with the major allele) are plotted against the local genomic region, and the positions of the candidate gene and the potential deletion are indicated. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Xuehui Huang, * Yan Zhao & * Xinghua Wei Affiliations * National Center for Gene Research, National Center for Plant Gene Research (Shanghai), Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China. * Xuehui Huang, * Yan Zhao, * Canyang Li, * Ahong Wang, * Qiang Zhao, * Wenjun Li, * Yunli Guo, * Liuwei Deng, * Chuanrang Zhu, * Danlin Fan, * Yiqi Lu, * Qijun Weng, * Kunyan Liu, * Taoying Zhou, * Yufeng Jing, * Lizhen Si, * Guojun Dong, * Tao Huang, * Tingting Lu, * Qi Feng & * Bin Han * Chinese Academy of Sciences Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China. * Xuehui Huang, * Yan Zhao & * Bin Han * State Key Laboratory of Rice Biology, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, China. * Xinghua Wei, * Guojun Dong & * Qian Qian * National Center for Plant Gene Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China. * Jiayang Li Contributions B.H. conceived of the project and its components. J.L. and B.H. contributed to the original concept of the project. W.L., Y.G., L.D., D.F., Y.L., Q.W. and Q.F. performed the genome sequencing. X.H., Q.Z., Y.Z., C.Z., K.L., L.S., T.H. and T.L. performed the genome data analyses. Y.Z., C.Z., Q.Z. and X.H. improved the imputation program for the data analyses. X.H., Q.Z. and Y.Z. developed an analytical framework for de novo assembly of the low-coverage sequencing data. X.W., C.L., A.W., T.Z., Y.J., G.D. and Q.Q. collected samples and performed the phenotyping. Y.Z. and X.H. performed the GWAS and statistical analyses. X.H. and B.H. analyzed all of the data together and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Bin Han Author Details * Xuehui Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Yan Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Xinghua Wei Search for this author in: * NPG journals * PubMed * Google Scholar * Canyang Li Search for this author in: * NPG journals * PubMed * Google Scholar * Ahong Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Qiang Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Wenjun Li Search for this author in: * NPG journals * PubMed * Google Scholar * Yunli Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Liuwei Deng Search for this author in: * NPG journals * PubMed * Google Scholar * Chuanrang Zhu Search for this author in: * NPG journals * PubMed * Google Scholar * Danlin Fan Search for this author in: * NPG journals * PubMed * Google Scholar * Yiqi Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Qijun Weng Search for this author in: * NPG journals * PubMed * Google Scholar * Kunyan Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Taoying Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Yufeng Jing Search for this author in: * NPG journals * PubMed * Google Scholar * Lizhen Si Search for this author in: * NPG journals * PubMed * Google Scholar * Guojun Dong Search for this author in: * NPG journals * PubMed * Google Scholar * Tao Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Tingting Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Qi Feng Search for this author in: * NPG journals * PubMed * Google Scholar * Qian Qian Search for this author in: * NPG journals * PubMed * Google Scholar * Jiayang Li Search for this author in: * NPG journals * PubMed * Google Scholar * Bin Han Contact Bin Han Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (14M) Supplementary Note, Supplementary Tables 4, 6, 7 and 9–12 and Supplementary Figures 1–31. Excel files * Supplementary Table 1 (188K) The list of 950 accessions sampled in the collection. * Supplementary Table 2 (492K) The levels of sequence diversity (π) in each group across the rice genome. * Supplementary Table 3 (393K) The levels of pariwise population differentiation (Fst) across the rice genome. * Supplementary Table 5 (66K) The list of SNP sites with population-special alleles. * Supplementary Table 8 (811K) The detailed list of all the large-effect variations in rice genome. * Supplementary Table 13 (164K) The detailed list of the microarrays used in the study and their related descriptions. * Supplementary Table 14 (37K) The genotype dataset of indica accessions on the causal polymorphic sites of Hd3a. * Supplementary Table 15 (57K) The genotype dataset of indica accessions on the causal polymorphic sites of OsFBX310. * Supplementary Table 16 (29K) The genotype dataset of japonica accessions on the causal polymorphic sites of OsRAL6. Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_8b34f10cfb655e8698b4a91299a5d6ab"> Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_8b34f10cfb655e8698b4a91299a5d6ab">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_8b34f10cfb655e8698b4a91299a5d6ab"><strong>44</strong>(1):53-57</a> (2012)<br /> Nature Genetics | Letter Regions of focal DNA hypermethylation and long-range hypomethylation in colorectal cancer coincide with nuclear lamina–associated domains * Benjamin P Berman1 * Daniel J Weisenberger1 * Joseph F Aman1 * Toshinori Hinoue1 * Zachary Ramjan1 * Yaping Liu1 * Houtan Noushmehr1 * Christopher P E Lange2, 3 * Cornelis M van Dijk4 * Rob A E M Tollenaar3 * David Van Den Berg1 * Peter W Laird1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:40–46Year published:(2012)DOI:doi:10.1038/ng.969Received 02 March 2011 Accepted 13 September 2011 Published online 27 November 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Extensive changes in DNA methylation are common in cancer and may contribute to oncogenesis through transcriptional silencing of tumor-suppressor genes1. Genome-scale studies have yielded important insights into these changes2, 3, 4, 5 but have focused on CpG islands or gene promoters. We used whole-genome bisulfite sequencing (bisulfite-seq) to comprehensively profile a primary human colorectal tumor and adjacent normal colon tissue at single-basepair resolution. Regions of focal hypermethylation in the tumor were located primarily at CpG islands and were concentrated within regions of long-range (>100 kb) hypomethylation. These hypomethylated domains covered nearly half of the genome and coincided with late replication and attachment to the nuclear lamina in human cell lines. We confirmed the confluence of hypermethylation and hypomethylation within these domains in 25 diverse colorectal tumors and matched adjacent tissue. We propose that widespread DNA methylation changes! in cancer are linked to silencing programs orchestrated by the three-dimensional organization of chromatin within the nucleus. View full text Figures at a glance * Figure 1: Bisulfite-seq of a colon tumor and adjacent normal mucosa. Individual sequencing reads and summary methylation levels are shown within a 10-kb region around the STK33 gene promoter for the normal adjacent colon tissue (top) and matched colon tumor (bottom). Reads are shown without respect to strand orientation and are colored to indicate the percentage of CpG dinucleotides methylated within the read (reads with no CpGs are indicated in yellow). The percent methylation tracks summarize the percentage of reads methylated for each CpG dinucleotide (black dots) as well as the average methylation within sliding windows of five CpGs (solid brown graph). The methylation difference track at the bottom shows the average methylation difference between tumor and normal tissue within sliding windows of five CpGs, with red indicating tumor hypermethylation and green indicating tumor hypomethylation. * Figure 2: Three distinct methylation classes at focal elements. () Density plot of the average DNA methylation within all windows of five adjacent CpG dinucleotides on chromosome 4. Distinct subsets of methylation-prone (MP) and methylation-resistant (MR) windows are visible as high-density clusters, whereas the methylation-loss (ML) region is low density. () Comparison of each methylation class to ENCODE protein-DNA binding (ChIP-seq) data9 and other genomic features (for the full version, see Supplementary Fig. 6). We determined genomic enrichment by dividing the proportion of overlapping elements within each methylation class by the proportion of overlapping elements within size-matched, randomly generated genomic locations (shown as fold changes). All transcription factors are shown in a boxplot (left), and selected genomic features are shown as individual bars (right). * Figure 3: Focal methylation classes correspond to distinct epigenomic and sequence signatures. () UCSC Genome Browser plots of two downregulated (MGMT and MAF) and two upregulated (B3GNTL1 and TACSTD2) genes reveal that elements of the methylation-prone (MP), methylation-resistant (MR) and methylation-loss (ML) classes often coincide with a combination of promoter or enhancer histone modifications (H3K4 methylation), DNase I hypersensitivity (HS) and transcription-factor binding. In the enhancer and promoter tracks, each color represents an individual ENCODE cell line, and all cell lines are combined in the DNase HS and transcription factor tracks. () Significant results from HOMER19 sequence motif searches within each of the three methylation classes (for the full results, see the Supplementary Figs. 13–15). Because methylation-prone and methylation-resistant elements most often corresponded to CGI TSS, alignments for these two classes are relative to the oriented TSS, whereas those for the methylation-loss class (right) show alignments relative to the center of th! e unoriented methylation-loss element. Matches to known motifs from the HOMER database are shown below the de novo motif they match (Nrf1 and AP-1). * Figure 4: Hypermethylated CGIs fall within long, tumor-specific PMDs. () Density plot of average DNA methylation within all 20-kb windows on chromosome 4 showing a distinct subset of windows representing PMDs in the tumor but not normal colon tissue. () We identified PMDs for four cell types by searching for 100-kb partially methylated windows (see text), and we compared the percentage of the genome contained within PMDs between the tumor and normal colon tissue along with two other cell types7. () The average methylation change is shown as a function of distance from CGI promoters for all promoters that were unmethylated in the normal colon (with mean methylation <0.2). We divided promoters into methylation-prone (MP; with mean tumor methylation >0.3) and methylation-resistant (MR; with mean tumor methylation <0.2), and the plots are oriented to show the transcribed region toward the right side. () UCSC Genome Browser plot of a representative 10-Mb region on chromosome 3q showing substantial overlap between colon tumor and IMR-90 PMDs, Lamin-! B1 marks and focal hypermethylation (methylation-resistant elements are visible as red spikes in the methylation change track). Lamin-B1 and ENCODE enhancer and promoter tracks are from the UCSC annotation database. * Figure 5: Properties of PMD boundaries. () UCSC Genome Browser plot of a 13-Mb region with several PMD boundaries specific to either the colon tumor or IMR-90 fibroblasts7. Tumor-specific PMD regions are annotated, showing that the two epithelial tumor suppressors NRG1 and SFRP1 fall within these regions. () A higher resolution view of the highlighted area surrounding SFRP1 showing that the gene promoter is hypermethylated in the tumor and defines a cell-type–specific PMD boundary in IMR-90 cells. (,) Average genomic density of a number of annotation features is plotted for 10-kb bins relative to colon tumor () and IMR-90 () PMD boundaries. Plots are oriented with regions outside the PMD to the left of the midpoint and regions inside the PMD to the right of the midpoint, as shown in the diagrams below each plot. We normalized the genomic density by dividing the value within each bin by the average density within bins lying outside of PMDs. For complete boundary plots, see Supplementary Figure 11. * Figure 6: Tumor-specific hypermethylation and hypomethylation are correlated and are strongly enriched within PMDs in a diverse set of 25 colon tumors. () Infinium HumanMethylation27k array values (β values) for five representative tumors, each compared to adjacent normal colon mucosa from the same individual. The tumor sequenced using bisulfite-seq (from individual 14838) is shown alongside one tumor of each methylation subtype from ref. 6, and colored points indicate probes identified as one of four methylation classes: methylation prone (MP, red), methylation resistant (MR, cyan), partial methylation loss (PML, green) and constitutively methylated (CM, purple). Probes not clearly falling into one of these categories are shown in orange. () The mean hypermethylation of methylation-prone probes (tumor β minus normal β) and the mean hypomethylation of methylation-loss probes (normal β minus tumor β) show a strong linear correlation (Pearson r = 0.80) across all samples. Colored lines indicate the best robust linear regression fit for each methylation subtype. () For each tumor-normal comparison, the fraction of microar! ray features falling within different genomic regions (H3K27me3, bisulfite-seq PMDs, and so on) is shown, with features separated by methylation class (methylation resistant, methylation prone, methylation loss and constitutively methylated). Shapes indicate tumor subtype as in panel , with the bisulfite-seq data colored solid black. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE25070 * GSE25062 * GSE18199 Author information * Accession codes * Author information * Supplementary information Affiliations * University of Southern California Epigenome Center, University of Southern California, Keck School of Medicine, Los Angeles, California, USA. * Benjamin P Berman, * Daniel J Weisenberger, * Joseph F Aman, * Toshinori Hinoue, * Zachary Ramjan, * Yaping Liu, * Houtan Noushmehr, * David Van Den Berg & * Peter W Laird * Department of Surgery, Groene Hart Hospital, Gouda, The Netherlands. * Christopher P E Lange * Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands. * Christopher P E Lange & * Rob A E M Tollenaar * Department of Pathology, Groene Hart Hospital, Gouda, The Netherlands. * Cornelis M van Dijk Contributions The project was conceived and the experiments were designed by P.W.L., D.J.W., B.P.B., D.V.D.B. and T.H. The Bisulfite-seq library construction and Genome Analyzer sequencing were performed by D.J.W., J.F.A. and D.V.D.B. The Infinium genotyping and data analysis was performed by B.P.B., motif analysis by H.N. and pipeline automation by B.P.B. and Z.R. Bisulfite-seq data processing and analysis were performed by B.P.B., Z.R. and H.N. Validation samples were collected and analyzed by C.P.E.L., C.M.v.D., R.A.E.M.T., B.P.B., D.J.W. and T.H. The manuscript was prepared by B.P.B. and P.W.L., and the study was supervised by P.W.L. Competing financial interests P.W.L. is scientific advisory board member and consultant for Epigenomics, AG, which has a commercial interest in DNA methylation biomarkers. The work described in this manuscript was not supported by nor will it benefit Epigenomics, AG. Corresponding author Correspondence to: * Peter W Laird Author Details * Benjamin P Berman Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel J Weisenberger Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph F Aman Search for this author in: * NPG journals * PubMed * Google Scholar * Toshinori Hinoue Search for this author in: * NPG journals * PubMed * Google Scholar * Zachary Ramjan Search for this author in: * NPG journals * PubMed * Google Scholar * Yaping Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Houtan Noushmehr Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher P E Lange Search for this author in: * NPG journals * PubMed * Google Scholar * Cornelis M van Dijk Search for this author in: * NPG journals * PubMed * Google Scholar * Rob A E M Tollenaar Search for this author in: * NPG journals * PubMed * Google Scholar * David Van Den Berg Search for this author in: * NPG journals * PubMed * Google Scholar * Peter W Laird Contact Peter W Laird Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (18M) Supplementary Note, Supplementary Figures 1–16 Excel files * Supplementary Tables 1 and 2 (94K) Bisulfite-seq summary statistics and Bisulfite-seq detailed statistics by chromosome Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_587ef2780396f9c416b585503f3be331"> Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_587ef2780396f9c416b585503f3be331">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_587ef2780396f9c416b585503f3be331"><strong>44</strong>(1):58-61</a> (2012)<br /> Nature Genetics | Letter Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia * Víctor Quesada1 * Laura Conde2 * Neus Villamor2 * Gonzalo R Ordóñez1 * Pedro Jares2 * Laia Bassaganyas3 * Andrew J Ramsay1 * Sílvia Beà2 * Magda Pinyol4 * Alejandra Martínez-Trillos5 * Mónica López-Guerra2 * Dolors Colomer2 * Alba Navarro2 * Tycho Baumann5 * Marta Aymerich2 * María Rozman2 * Julio Delgado5 * Eva Giné5 * Jesús M Hernández6 * Marcos González-Díaz6 * Diana A Puente1 * Gloria Velasco1 * José M P Freije1 * José M C Tubío3 * Romina Royo7 * Josep L Gelpí7 * Modesto Orozco7 * David G Pisano8 * Jorge Zamora8 * Miguel Vázquez8 * Alfonso Valencia8 * Heinz Himmelbauer9 * Mónica Bayés10 * Simon Heath10 * Marta Gut10 * Ivo Gut10 * Xavier Estivill3 * Armando López-Guillermo5 * Xose S Puente1 * Elías Campo2, 11 * Carlos López-Otín1, 11 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:47–52Year published:(2012)DOI:doi:10.1038/ng.1032Received 12 July 2011 Accepted 10 November 2011 Published online 11 December 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Here we perform whole-exome sequencing of samples from 105 individuals with chronic lymphocytic leukemia (CLL)1, 2, the most frequent leukemia in adults in Western countries. We found 1,246 somatic mutations potentially affecting gene function and identified 78 genes with predicted functional alterations in more than one tumor sample. Among these genes, SF3B1, encoding a subunit of the spliceosomal U2 small nuclear ribonucleoprotein (snRNP), is somatically mutated in 9.7% of affected individuals. Further analysis in 279 individuals with CLL showed that SF3B1 mutations were associated with faster disease progression and poor overall survival. This work provides the first comprehensive catalog of somatic mutations in CLL with relevant clinical correlates and defines a large set of new genes that may drive the development of this common form of leukemia. The results reinforce the idea that targeting several well-known genetic pathways, including mRNA splicing, could be useful i! n the treatment of CLL and other malignancies. View full text Figures at a glance * Figure 1: Somatic mutation profiles of 105 CLL exomes. () Chromosomal distribution and location of protein-coding mutations (dots) and insertions and deletions (indels; Xs) identified by exome sequencing of 60 CLL samples with IGHV region mutations (blue) and 45 without IGHV region mutations (red). RM-CLL genes are highlighted with vertical bars and summarized for each individual with orange dots. () Box plot representation comparing the frequency of coding, nonsynonymous somatic mutations in CLL samples with and without IGHV region mutations. Error bars, range of the data set (*P = 0.038). () Frequency of substitutions in the 105 CLL samples for the six possible mutation classes. Error bars, s.d. (***P = 0.0017). * Figure 2: Structural impact of SF3B1 alterations. () Protein sequence alignments of the SF3B1 C-terminal domain around the altered residues (arrows) in evolutionarily diverse species. () Schematic representation of the human SF3B1 protein with the primary structural domains highlighted. The locations of the different somatic alterations determined to be encoded in CLL samples (top) and the frequencies of each alteration (bottom) are shown. () Molecular model of the C-terminal portion of the human SF3B1 protein and detailed view of the altered amino acids identified in CLL cases. * Figure 3: Novel alternative splicing of FOXP1 in CLL cases. () An expanded view of the protein interval subject to truncation as a result of alternative splicing is shown. The alternative splicing event that generates the novel transcript encoding this protein, FOXP1w, is shown as a red line, and the primers used for RT-PCR amplification of FOXP1w and full-length FOXP1 (control) as arrows. Q-rich, glutamine-rich region; C2H2-Zf, Cys2His2 zinc finger. () Quantitative RT-PCR analysis of truncated FOXP1w levels in CLL samples with and without SF3B1 somatic mutations. Error bars, s.d. * Figure 4: Clinical analysis of SF3B1 in CLL. () Distribution of disease stage (Binet), IGHV region mutational status and ZAP-70 expression in individuals with (MUT) or without (WT) mutations in SF3B1 (***P = 0.004, *P = 0.03). () Actuarial probability of disease progression of CLL cases with mutated or wild-type SF3B1 (P = 0.0001). () Actuarial probability of overall survival of CLL cases with mutated or wild-type SF3B1 (P = 0.002). Author information * Author information * Supplementary information Affiliations * Departamento de Bioquímica y Biología Molecular, Instituto Universitario de Oncología, Universidad de Oviedo, Oviedo, Spain. * Víctor Quesada, * Gonzalo R Ordóñez, * Andrew J Ramsay, * Diana A Puente, * Gloria Velasco, * José M P Freije, * Xose S Puente & * Carlos López-Otín * Unidad de Hematopatología, Servicio de Anatomía Patológica, Hospital Clínic, Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. * Laura Conde, * Neus Villamor, * Pedro Jares, * Sílvia Beà, * Mónica López-Guerra, * Dolors Colomer, * Alba Navarro, * Marta Aymerich, * María Rozman & * Elías Campo * Genes and Disease Programme, Center for Genomic Regulation, Pompeu Fabra University (CRG-UPF), Barcelona, Spain. * Laia Bassaganyas, * José M C Tubío & * Xavier Estivill * Unidad de Genómica, IDIBAPS, Barcelona, Spain. * Magda Pinyol * Servicio de Hematología, Hospital Clínic, Universidad de Barcelona, Barcelona, Spain. * Alejandra Martínez-Trillos, * Tycho Baumann, * Julio Delgado, * Eva Giné & * Armando López-Guillermo * Servicio de Hematología, Hospital Universitario, Centro de Investigación del Cáncer, Universidad de Salamanca, Salamanca, Spain. * Jesús M Hernández & * Marcos González-Díaz * Programa Conjunto de Biología Computacional, Barcelona Supercomputing Center (BSC), Institut de Reçerca Biomèdica (IRB), Spanish National Bioinformatics Institute, Universitat de Barcelona, Barcelona, Spain. * Romina Royo, * Josep L Gelpí & * Modesto Orozco * Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Spanish National Bioinformatics Institute, Madrid, Spain. * David G Pisano, * Jorge Zamora, * Miguel Vázquez & * Alfonso Valencia * Ultrasequencing Unit, CRG-UPF, Barcelona, Spain. * Heinz Himmelbauer * Centro Nacional de Análisis Genómico, Parc Científic de Barcelona, Barcelona, Spain. * Mónica Bayés, * Simon Heath, * Marta Gut & * Ivo Gut * These authors jointly directed this work. * Elías Campo & * Carlos López-Otín Contributions V.Q., G.R.O., A.J.R., G.V., J.M.P.F. and X.S.P. developed the bioinformatic algorithms and performed the analysis of sequence data. L.C., P.J., M.P., M.L.-G., D.C. and A.N. were responsible for downstream validation analysis and functional studies. L.B., S.B. and J.M.C.T. studied structural variants. D.A.P., H.H., M.B., S.H. and M.G. were responsible for generating libraries, performing exome capture and running sequencers. M.A. prepared and supervised the bioethics requirements. N.V., A.M.-T., T.B., J.D., E.G., A.L.-G. and E.C. performed clinical and biological studies. M.R., M.G.-D., N.V. and J.M.H. reviewed the pathologic data and confirmed the diagnosis. R.R., J.L.G., M.O., D.G.P., J.Z., M.V. and A.V. were in charge of bioinformatics data management. I.G. coordinated the sequencing efforts and performed primary data analysis. V.Q., X.S.P., X.E., A. L.-G., E.C. and C.L.-O. directed the research and wrote the manuscript, which all authors have approved. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Carlos López-Otín or * Elías Campo Author Details * Víctor Quesada Search for this author in: * NPG journals * PubMed * Google Scholar * Laura Conde Search for this author in: * NPG journals * PubMed * Google Scholar * Neus Villamor Search for this author in: * NPG journals * PubMed * Google Scholar * Gonzalo R Ordóñez Search for this author in: * NPG journals * PubMed * Google Scholar * Pedro Jares Search for this author in: * NPG journals * PubMed * Google Scholar * Laia Bassaganyas Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew J Ramsay Search for this author in: * NPG journals * PubMed * Google Scholar * Sílvia Beà Search for this author in: * NPG journals * PubMed * Google Scholar * Magda Pinyol Search for this author in: * NPG journals * PubMed * Google Scholar * Alejandra Martínez-Trillos Search for this author in: * NPG journals * PubMed * Google Scholar * Mónica López-Guerra Search for this author in: * NPG journals * PubMed * Google Scholar * Dolors Colomer Search for this author in: * NPG journals * PubMed * Google Scholar * Alba Navarro Search for this author in: * NPG journals * PubMed * Google Scholar * Tycho Baumann Search for this author in: * NPG journals * PubMed * Google Scholar * Marta Aymerich Search for this author in: * NPG journals * PubMed * Google Scholar * María Rozman Search for this author in: * NPG journals * PubMed * Google Scholar * Julio Delgado Search for this author in: * NPG journals * PubMed * Google Scholar * Eva Giné Search for this author in: * NPG journals * PubMed * Google Scholar * Jesús M Hernández Search for this author in: * NPG journals * PubMed * Google Scholar * Marcos González-Díaz Search for this author in: * NPG journals * PubMed * Google Scholar * Diana A Puente Search for this author in: * NPG journals * PubMed * Google Scholar * Gloria Velasco Search for this author in: * NPG journals * PubMed * Google Scholar * José M P Freije Search for this author in: * NPG journals * PubMed * Google Scholar * José M C Tubío Search for this author in: * NPG journals * PubMed * Google Scholar * Romina Royo Search for this author in: * NPG journals * PubMed * Google Scholar * Josep L Gelpí Search for this author in: * NPG journals * PubMed * Google Scholar * Modesto Orozco Search for this author in: * NPG journals * PubMed * Google Scholar * David G Pisano Search for this author in: * NPG journals * PubMed * Google Scholar * Jorge Zamora Search for this author in: * NPG journals * PubMed * Google Scholar * Miguel Vázquez Search for this author in: * NPG journals * PubMed * Google Scholar * Alfonso Valencia Search for this author in: * NPG journals * PubMed * Google Scholar * Heinz Himmelbauer Search for this author in: * NPG journals * PubMed * Google Scholar * Mónica Bayés Search for this author in: * NPG journals * PubMed * Google Scholar * Simon Heath Search for this author in: * NPG journals * PubMed * Google Scholar * Marta Gut Search for this author in: * NPG journals * PubMed * Google Scholar * Ivo Gut Search for this author in: * NPG journals * PubMed * Google Scholar * Xavier Estivill Search for this author in: * NPG journals * PubMed * Google Scholar * Armando López-Guillermo Search for this author in: * NPG journals * PubMed * Google Scholar * Xose S Puente Search for this author in: * NPG journals * PubMed * Google Scholar * Elías Campo Contact Elías Campo Search for this author in: * NPG journals * PubMed * Google Scholar * Carlos López-Otín Contact Carlos López-Otín Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (651K) Supplementary Note, Supplementary Figures 1–3 and Supplementary Tables 1–3 and 5–16 Excel files * Supplementary Table 4 (201K) Somatic mutations in CLL patients Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_541b5d922991ea33a5b7bd92f1404e90"> Genome-wide association study identifies five loci associated with susceptibility to pancreatic cancer in Chinese populations</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_541b5d922991ea33a5b7bd92f1404e90">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_541b5d922991ea33a5b7bd92f1404e90"><strong>44</strong>(1):62-66</a> (2012)<br /> Nature Genetics | Letter Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes * Timothy A Graubert1, 2, 3, 9 * Dong Shen4, 9 * Li Ding4, 5, 9 * Theresa Okeyo-Owuor1 * Cara L Lunn1 * Jin Shao1 * Kilannin Krysiak1 * Christopher C Harris4 * Daniel C Koboldt4 * David E Larson4 * Michael D McLellan4 * David J Dooling4 * Rachel M Abbott4 * Robert S Fulton4 * Heather Schmidt4 * Joelle Kalicki-Veizer4 * Michelle O'Laughlin4 * Marcus Grillot1 * Jack Baty6 * Sharon Heath1 * John L Frater3 * Talat Nasim7, 8 * Daniel C Link1, 2 * Michael H Tomasson1, 2 * Peter Westervelt1, 2 * John F DiPersio1, 2 * Elaine R Mardis2, 4, 5 * Timothy J Ley1, 2, 4, 5 * Richard K Wilson2, 4, 5 * Matthew J Walter1, 2, 5 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:53–57Year published:(2012)DOI:doi:10.1038/ng.1031Received 03 August 2011 Accepted 09 November 2011 Published online 11 December 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Myelodysplastic syndromes (MDS) are hematopoietic stem cell disorders that often progress to chemotherapy-resistant secondary acute myeloid leukemia (sAML). We used whole-genome sequencing to perform an unbiased comprehensive screen to discover the somatic mutations in a sample from an individual with sAML and genotyped the loci containing these mutations in the matched MDS sample. Here we show that a missense mutation affecting the serine at codon 34 (Ser34) in U2AF1 was recurrently present in 13 out of 150 (8.7%) subjects with de novo MDS, and we found suggestive evidence of an increased risk of progression to sAML associated with this mutation. U2AF1 is a U2 auxiliary factor protein that recognizes the AG splice acceptor dinucleotide at the 3′ end of introns, and the alterations in U2AF1 are located in highly conserved zinc fingers of this protein1, 2. Mutant U2AF1 promotes enhanced splicing and exon skipping in reporter assays in vitro. This previously unidentified, re! current mutation in U2AF1 implicates altered pre-mRNA splicing as a potential mechanism for MDS pathogenesis. View full text Figures at a glance * Figure 1: U2AF1 mutations found in individuals with MDS. () Missense mutations were detected in codons 34 and 157 of U2AF1. The ZnF1 (zinc finger 1), UHM (U2AF homology motif), ZnF2 (zinc finger 2) and RS (arginine-serine rich) domains are shown. The amino acid sequence of the ZnF1 domain is highly conserved (gray shaded area). The zinc coordinating and mutated residues are shown in blue (asterisks) and red (arrow), respectively. () Deep sequencing of U2AF1 using DNA collected from paired normal, MDS or sAML samples. Mutant allele frequencies represent the proportion of sequencing reads supporting the mutant allele compared to the total reads. Total read counts are shown below as gray bars (with a mean of 5,651 reads per sample). The mutation was present in the majority of cells (mutant allele frequency of 31.4–48.2%) in all of the samples. Two subjects with MDS had a second bone marrow sample harvested and analyzed (MDS (2nd)) () Deep sequencing of cDNA from MDS or sAML samples. The mutant allele is expressed in all the samples! tested. * Figure 2: Impact of U2AF1 mutations on clinical outcome. (,) Overall () and disease-free () survival are not affected by U2AF1 genotype. () The probability of sAML progression is increased in individuals with MDS with U2AF1 mutations (P = 0.03). * Figure 3: U2AF1 p.Ser34Phe alteration induces splicing alterations in 293T cells. () In the absence of the Tra2α splicing enhancer or the hnRNPG splicing inhibitor, transient coexpression of mutated U2AF1 increases splicing of the pTN24 construct, resulting in an increase in the ratio of luciferase expression relative to β-galactosidase expression, compared to expression of wild-type U2AF1 (P < 0.001). Tra2α (positive control) and hnRNPG (negative control) cause increased or decreased splicing efficiency, respectively. A protein blot of U2AF1 using extracts from the same cells used for the luciferase assays is shown below each combination of plasmids. () Expression of the Ser34Phe mutant results in an increase in the splicing of the pTN24 construct and an increase in the luciferase expression compared to the control plasmid (P < 0.001). This result is independent of endogenous U2AF1 expression. A protein blot of U2AF1 using lysates from the same cells is shown below each condition. () The GH1 minigene was transiently transfected into 293T cells with a ! control plasmid, wild-type U2AF1 cDNA or mutant p.Ser34Phe U2AF1 cDNA in the presence of a control siRNA (siControl) or siRNA targeting endogenous U2AF1 (siU2AF1). RT-PCR using the indicated primers resulted in a fully spliced 505-bp amplicon or a 386-bp amplicon that skips the middle exon, which is shaded in black (exon skipping). A representative PCR gel image is shown, and the ratio of the lower band (amplicon b, representing exon skipping) relative to the normally spliced upper band (amplicon a) is shown above each condition. Expression of the Ser34Phe mutant results in an increase in exon skipping compared to expression of control or wild-type U2AF1 (P < 0.02). WT, wild type; Mut, mutant; T7, T7 primer. Error bars, s.d. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE30195 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Timothy A Graubert, * Dong Shen & * Li Ding Affiliations * Department of Internal Medicine, Division of Oncology, Washington University, St. Louis, Missouri, USA. * Timothy A Graubert, * Theresa Okeyo-Owuor, * Cara L Lunn, * Jin Shao, * Kilannin Krysiak, * Marcus Grillot, * Sharon Heath, * Daniel C Link, * Michael H Tomasson, * Peter Westervelt, * John F DiPersio, * Timothy J Ley & * Matthew J Walter * Siteman Cancer Center, Washington University, St. Louis, Missouri, USA. * Timothy A Graubert, * Daniel C Link, * Michael H Tomasson, * Peter Westervelt, * John F DiPersio, * Elaine R Mardis, * Timothy J Ley, * Richard K Wilson & * Matthew J Walter * Department of Pathology and Immunology, Washington University, St. Louis, Missouri, USA. * Timothy A Graubert & * John L Frater * The Genome Institute, Washington University, St. Louis, Missouri, USA. * Dong Shen, * Li Ding, * Christopher C Harris, * Daniel C Koboldt, * David E Larson, * Michael D McLellan, * David J Dooling, * Rachel M Abbott, * Robert S Fulton, * Heather Schmidt, * Joelle Kalicki-Veizer, * Michelle O'Laughlin, * Elaine R Mardis, * Timothy J Ley & * Richard K Wilson * Department of Genetics, Washington University, St. Louis, Missouri, USA. * Li Ding, * Elaine R Mardis, * Timothy J Ley, * Richard K Wilson & * Matthew J Walter * Division of Biostatistics, Washington University, St. Louis, Missouri, USA. * Jack Baty * Department of Medical and Molecular Genetics, King's College, Guy's Hospital, London, UK. * Talat Nasim * National Institute for Health Research (NIHR), Biomedical Research Centre, Guy's and St. Thomas' National Health Service (NHS) Foundation Trust and King's College London, London, UK. * Talat Nasim Contributions The project leaders were T.A.G., D.S., L.D. and M.J.W. Study design and project conception were performed by T.A.G., L.D., D.C.L., M.H.T., P.W., J.F.D., E.R.M., T.J.L., R.K.W. and M.J.W. Sequence and data analysis were performed by D.S., L.D., C.C.H., D.C.K., D.E.L., M.D.M., D.J.D., R.M.A., R.S.F., H.S., J.K.-V. and M.O. In vitro splicing assays, PCR or gene expression analyses were performed by T.O.-O., C.L.L., J.S., K.K. and T.N. Clinical data management, specimen acquisition or statistical analyses were performed by M.G., S.H. and J.B. Hematopathology was performed by J.L.F. Manuscript preparation was performed by T.A.G., D.S., L.D., D.C.L., J.F.D., E.R.M., T.J.L., R.K.W. and M.J.W. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Matthew J Walter Author Details * Timothy A Graubert Search for this author in: * NPG journals * PubMed * Google Scholar * Dong Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Li Ding Search for this author in: * NPG journals * PubMed * Google Scholar * Theresa Okeyo-Owuor Search for this author in: * NPG journals * PubMed * Google Scholar * Cara L Lunn Search for this author in: * NPG journals * PubMed * Google Scholar * Jin Shao Search for this author in: * NPG journals * PubMed * Google Scholar * Kilannin Krysiak Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher C Harris Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel C Koboldt Search for this author in: * NPG journals * PubMed * Google Scholar * David E Larson Search for this author in: * NPG journals * PubMed * Google Scholar * Michael D McLellan Search for this author in: * NPG journals * PubMed * Google Scholar * David J Dooling Search for this author in: * NPG journals * PubMed * Google Scholar * Rachel M Abbott Search for this author in: * NPG journals * PubMed * Google Scholar * Robert S Fulton Search for this author in: * NPG journals * PubMed * Google Scholar * Heather Schmidt Search for this author in: * NPG journals * PubMed * Google Scholar * Joelle Kalicki-Veizer Search for this author in: * NPG journals * PubMed * Google Scholar * Michelle O'Laughlin Search for this author in: * NPG journals * PubMed * Google Scholar * Marcus Grillot Search for this author in: * NPG journals * PubMed * Google Scholar * Jack Baty Search for this author in: * NPG journals * PubMed * Google Scholar * Sharon Heath Search for this author in: * NPG journals * PubMed * Google Scholar * John L Frater Search for this author in: * NPG journals * PubMed * Google Scholar * Talat Nasim Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel C Link Search for this author in: * NPG journals * PubMed * Google Scholar * Michael H Tomasson Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Westervelt Search for this author in: * NPG journals * PubMed * Google Scholar * John F DiPersio Search for this author in: * NPG journals * PubMed * Google Scholar * Elaine R Mardis Search for this author in: * NPG journals * PubMed * Google Scholar * Timothy J Ley Search for this author in: * NPG journals * PubMed * Google Scholar * Richard K Wilson Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew J Walter Contact Matthew J Walter Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (9M) Supplementary Note, Supplementary Figures 1–4 and Supplementary Tables 1–9 Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_ac64c0511d6e2fe2db615c895f8b1740"> Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_ac64c0511d6e2fe2db615c895f8b1740">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_ac64c0511d6e2fe2db615c895f8b1740"><strong>44</strong>(1):67-72</a> (2012)<br /> Nature Genetics | Letter Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk * Peter Broderick1, 15 * Daniel Chubb1, 15 * David C Johnson2, 15 * Niels Weinhold3, 15 * Asta Försti4 * Amy Lloyd1 * Bianca Olver1 * Yussanne P Ma1 * Sara E Dobbins1 * Brian A Walker2 * Faith E Davies2 * Walter A Gregory5 * J Anthony Child5 * Fiona M Ross6 * Graham H Jackson7 * Kai Neben3 * Anna Jauch8 * Per Hoffmann9 * Thomas W Mühleisen9 * Markus M Nöthen9, 10 * Susanne Moebus11 * Ian P Tomlinson12 * Hartmut Goldschmidt3, 13 * Kari Hemminki4, 14, 16 * Gareth J Morgan2, 16 * Richard S Houlston1, 2, 16 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:58–61Year published:(2012)DOI:doi:10.1038/ng.993Received 08 September 2011 Accepted 03 October 2011 Published online 27 November 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg To identify risk variants for multiple myeloma, we conducted a genome-wide association study of 1,675 individuals with multiple myeloma and 5,903 control subjects. We identified risk loci for multiple myeloma at 3p22.1 (rs1052501 in ULK4; odds ratio (OR) = 1.32; P = 7.47 × 10−9) and 7p15.3 (rs4487645, OR = 1.38; P = 3.33 × 10−15). In addition, we observed a promising association at 2p23.3 (rs6746082, OR = 1.29; P = 1.22 × 10−7). Our study identifies new genomic regions associated with multiple myeloma risk that may lead to new etiological insights. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Peter Broderick, * Daniel Chubb, * David C Johnson & * Niels Weinhold Affiliations * Molecular and Population Genetics, Division of Genetics and Epidemiology, Institute of Cancer Research, Surrey, UK. * Peter Broderick, * Daniel Chubb, * Amy Lloyd, * Bianca Olver, * Yussanne P Ma, * Sara E Dobbins & * Richard S Houlston * Haemato-Oncology Research Unit, Division of Molecular Pathology, Institute of Cancer Research, Surrey, UK. * David C Johnson, * Brian A Walker, * Faith E Davies, * Gareth J Morgan & * Richard S Houlston * Department of Internal Medicine V, University of Heidelberg, Heidelberg, Germany. * Niels Weinhold, * Kai Neben & * Hartmut Goldschmidt * German Cancer Research Center, Heidelberg, Germany. * Asta Försti & * Kari Hemminki * Clinical Trials Research Unit, University of Leeds, Leeds, UK. * Walter A Gregory & * J Anthony Child * Cytogenetics Group, Wessex Regional Cytogenetic Laboratory, Salisbury, UK. * Fiona M Ross * Royal Victoria Infirmary, Newcastle upon Tyne, UK. * Graham H Jackson * Institute of Human Genetics, University of Heidelberg, Heidelberg, Germany. * Anna Jauch * Institute of Human Genetics, University of Bonn, Bonn, Germany. * Per Hoffmann, * Thomas W Mühleisen & * Markus M Nöthen * German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany. * Markus M Nöthen * Institute for Medical Informatics, Biometry and Epidemiology, University Hospital Essen, University of Duisburg–Essen, Essen, Germany. * Susanne Moebus * Molecular and Population Genetics, Wellcome Trust Centre for Human Genetics, Oxford, UK. * Ian P Tomlinson * National Centre of Tumour Diseases, Heidelberg, Germany. * Hartmut Goldschmidt * Center for Primary Health Care Research, Lund University, Malmo, Sweden. * Kari Hemminki * These authors jointly directed this work. * Kari Hemminki, * Gareth J Morgan & * Richard S Houlston Contributions R.S.H. designed the study. R.S.H. and G.J.M. obtained financial support in the UK, and K.H. and H.G. obtained funding in Germany. D.C. performed the main statistical and bioinformatic analyses, and Y.P.M. and S.E.D. performed additional related analyses. P.B. coordinated laboratory studies. A.L. and B.O. performed genotyping of the UK samples. P.H., T.W.M. and M.M.N. performed and coordinated genotyping of the German controls; K.H. and A.F. coordinated genotyping of the German cases. D.C.J. managed and prepared the Myeloma-VII and Myeloma-IX case study DNA samples. H.G., K.N. and N.W. coordinated and managed the German DNA samples. G.J.M., F.E.D., W.A.G., G.H.J. and J.A.C. ascertained and collected case study samples from the UK Myeloma-VII and Myeloma-IX studies. S.M. obtained and managed the HNR samples. I.P.T. acquired colorectal cancer control samples. B.A.W. performed expression analyses on the UK samples. F.M.R. performed FISH analyses on the UK samples, and A.J. perfo! rmed these analyses on the German samples. R.S.H. drafted the manuscript, and all authors contributed to the final version. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Richard S Houlston Author Details * Peter Broderick Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Chubb Search for this author in: * NPG journals * PubMed * Google Scholar * David C Johnson Search for this author in: * NPG journals * PubMed * Google Scholar * Niels Weinhold Search for this author in: * NPG journals * PubMed * Google Scholar * Asta Försti Search for this author in: * NPG journals * PubMed * Google Scholar * Amy Lloyd Search for this author in: * NPG journals * PubMed * Google Scholar * Bianca Olver Search for this author in: * NPG journals * PubMed * Google Scholar * Yussanne P Ma Search for this author in: * NPG journals * PubMed * Google Scholar * Sara E Dobbins Search for this author in: * NPG journals * PubMed * Google Scholar * Brian A Walker Search for this author in: * NPG journals * PubMed * Google Scholar * Faith E Davies Search for this author in: * NPG journals * PubMed * Google Scholar * Walter A Gregory Search for this author in: * NPG journals * PubMed * Google Scholar * J Anthony Child Search for this author in: * NPG journals * PubMed * Google Scholar * Fiona M Ross Search for this author in: * NPG journals * PubMed * Google Scholar * Graham H Jackson Search for this author in: * NPG journals * PubMed * Google Scholar * Kai Neben Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Jauch Search for this author in: * NPG journals * PubMed * Google Scholar * Per Hoffmann Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas W Mühleisen Search for this author in: * NPG journals * PubMed * Google Scholar * Markus M Nöthen Search for this author in: * NPG journals * PubMed * Google Scholar * Susanne Moebus Search for this author in: * NPG journals * PubMed * Google Scholar * Ian P Tomlinson Search for this author in: * NPG journals * PubMed * Google Scholar * Hartmut Goldschmidt Search for this author in: * NPG journals * PubMed * Google Scholar * Kari Hemminki Search for this author in: * NPG journals * PubMed * Google Scholar * Gareth J Morgan Search for this author in: * NPG journals * PubMed * Google Scholar * Richard S Houlston Contact Richard S Houlston Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (6M) Supplementary Tables 1–3 and Supplementary Figures 1–4. Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_51f5f96c5527676d33aa02665cabd7d4"> A genome-wide association study in Han Chinese identifies new susceptibility loci for ankylosing spondylitis</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_51f5f96c5527676d33aa02665cabd7d4">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_51f5f96c5527676d33aa02665cabd7d4"><strong>44</strong>(1):73-77</a> (2012)<br /> Nature Genetics | Letter Genome-wide association study identifies five loci associated with susceptibility to pancreatic cancer in Chinese populations * Chen Wu1, 22 * Xiaoping Miao2, 22 * Liming Huang1 * Xu Che3 * Guoliang Jiang4 * Dianke Yu1 * Xianghong Yang5 * Guangwen Cao6 * Zhibin Hu7 * Yongjian Zhou8 * Chaohui Zuo9 * Chunyou Wang10 * Xianghong Zhang11 * Yifeng Zhou12 * Xianjun Yu13 * Wanjin Dai5 * Zhaoshen Li14 * Hongbing Shen7 * Luming Liu15 * Yanling Chen16 * Sheng Zhang17 * Xiaoqi Wang18 * Kan Zhai1 * Jiang Chang1 * Yu Liu1 * Menghong Sun19 * Wei Cao5 * Jun Gao14 * Ying Ma5 * Xiongwei Zheng20 * Siu Tim Cheung18 * Yongfeng Jia21 * Jian Xu1 * Wen Tan1 * Ping Zhao3 * Tangchun Wu2 * Chengfeng Wang3, 23 * Dongxin Lin1, 23 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:62–66Year published:(2012)DOI:doi:10.1038/ng.1020Received 06 April 2011 Accepted 03 November 2011 Published online 11 December 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Pancreatic cancer has the lowest survival rate among human cancers, and there are no effective markers for its screening and early diagnosis. To identify genetic susceptibility markers for this cancer, we carried out a genome-wide association study on 981 individuals with pancreatic cancer (cases) and 1,991 cancer-free controls of Chinese descent using 666,141 autosomal SNPs. Promising associations were replicated in an additional 2,603 pancreatic cancer cases and 2,877 controls recruited from 25 hospitals in 16 provinces or cities in China. We identified five new susceptibility loci at chromosomes 21q21.3, 5p13.1, 21q22.3, 22q13.32 and 10q26.11 (P = 2.24 × 10−13 to P = 4.18 × 10−10) in addition to 13q22.1 previously reported in populations of European ancestry. These results advance our understanding of the development of pancreatic cancer and highlight potential targets for the prevention or treatment of this cancer. View full text Figures at a glance * Figure 1: Manhattan plot of genome-wide P values of association. Association was assessed using an additive model in logistic regression analysis with adjustment for age, sex and the top three principal components of population stratification analysis. The −log10P values of 666,141 SNPs in 981 cases and 1,991 controls (y axis) are shown relative to their chromosomal positions (x axis). The 33 loci with P < 1 × 10−6 are above the blue horizontal line, and the smallest P value is 2.10 × 10−10. * Figure 2: Regional plots of association results and recombination rates within six significantly associated susceptibility loci . (–) –log10P values of SNPs relative to their positions at chromosomes 21q21.3 (), 5p13.1 (), 21q22.3 (), 13q22.1 (), 22q13.32 () and 10q26.11 () . Estimated recombination rates (cM/Mb) from HapMap Project (NCBI Build 36) are light blue lines, and the genomic locations of genes within the regions of interest on the NCBI Build 36 human assembly were annotated from the UCSC Genome Browser (arrows). For each locus, both genotyped (diamond) and imputed (circle) SNPs are shown, and the top SNP is labeled by rs ID. SNPs in red, orange, yellow and white have r2 of ≥0.8, ≥0.5, ≥0.2 and <0.2 with the top SNP, respectively. Blue diamonds are SNPs that showed association in the combined genome-wide association and replication samples. * Figure 3: Odds ratio for pancreatic cancer versus number of risk genotypes. Bars indicate 95% confidence intervals. Dotted line, null value (OR = 1.0). Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Chen Wu & * Xiaoping Miao Affiliations * State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. * Chen Wu, * Liming Huang, * Dianke Yu, * Kan Zhai, * Jiang Chang, * Yu Liu, * Jian Xu, * Wen Tan & * Dongxin Lin * Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan, China. * Xiaoping Miao & * Tangchun Wu * Department of Abdominal Surgery, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. * Xu Che, * Ping Zhao & * Chengfeng Wang * Department of Radiation Oncology, Cancer Hospital, Fudan University, Shanghai, China. * Guoliang Jiang * Department of Pathology, Shengjing Hospital, China Medical University, Shenyang, China. * Xianghong Yang, * Wanjin Dai, * Wei Cao & * Ying Ma * Department of Epidemiology, Second Military Medical University, Shanghai, China. * Guangwen Cao * Department of Epidemiology and Biostatistics, Cancer Center, Nanjing Medical University, Nanjing, China. * Zhibin Hu & * Hongbing Shen * Department of Gastrointestinal Surgery, Union Hospital of Fujian Medical University, Fuzhou, China. * Yongjian Zhou * Department of Gastroduodenal and Pancreatic Surgery, Hunan Province Tumor Hospital, Changsha, China. * Chaohui Zuo * Union Hospital, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan, China. * Chunyou Wang * Department of Experimental Pathology, Hebei Medical University, Shijiazhuang, China. * Xianghong Zhang * Laboratory of Cancer Molecular Genetics, Medical College of Soochow University, Suzhou, China. * Yifeng Zhou * Department of Pancreas and Hepatobiliary Surgery, Cancer Hospital, Fudan University, Shanghai, China. * Xianjun Yu * Department of Gastroenterology, First Affiliated Hospital, Second Military Medical University, Shanghai, China. * Zhaoshen Li & * Jun Gao * Department of Integrative Oncology, Cancer Hospital, Fudan University, Shanghai, China. * Luming Liu * Department of Hepatobiliary and Pancreatic Surgery, Union Hospital of Fujian Medical University, Fuzhou, China. * Yanling Chen * Department of Pathology, First Affiliated Hospital of Fujian Medical University, Fuzhou, China. * Sheng Zhang * Department of Surgery, The University of Hong Kong, Hong Kong, China. * Xiaoqi Wang & * Siu Tim Cheung * Department of Pathology, Cancer Hospital, Fudan University, Shanghai, China. * Menghong Sun * Department of Pathology, Fujian Provincial Cancer Hospital, Fuzhou, China. * Xiongwei Zheng * Department of Pathology, Affiliated Hospital, Inner Mongolia School of Medicine, Huhhot, Inner Mongolia. * Yongfeng Jia * These authors jointly directed this work. * Chengfeng Wang & * Dongxin Lin Contributions D.L. conceived, designed and oversaw the study, obtained financial support, interpreted the results and wrote parts of and synthesized the paper. C. Wu managed the project, oversaw laboratory and statistical analyses and drafted the initial manuscript. Chengfeng Wang designed the study and oversaw pancreatic cancer patient recruitment. X.M. designed the study and carried out statistical analyses, subject recruitment and sample preparation of Zhejiang samples. L.H., K.Z., J.C. and J.X. prepared samples and did TaqMan genotyping. X.C., D.Y., Y.L., W.T. and P.Z. recruited subjects from Beijing, Shandong, Sichuan and Chongqing. Various authors recruited subjects and samples from Shanghai (G.J., G.C., X.Y., Z.L., L.L., M.S. and J.G.), Liaoning (X.Y., W.D., W.C. and Y.M.), Jiangsu (Z.H., H.S. and Yifeng Zhou), Fujian (Yongjian Zhou, Y.C., S.Z. and X. Zheng), Hunan (C.Z.), Hubei (Chunyou Wang and T.W.), Hebei (X. Zhang and Y.J.) and Hong Kong (X.W. and S.T.C.). Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Dongxin Lin or * Chengfeng Wang Author Details * Chen Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaoping Miao Search for this author in: * NPG journals * PubMed * Google Scholar * Liming Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Xu Che Search for this author in: * NPG journals * PubMed * Google Scholar * Guoliang Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Dianke Yu Search for this author in: * NPG journals * PubMed * Google Scholar * Xianghong Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Guangwen Cao Search for this author in: * NPG journals * PubMed * Google Scholar * Zhibin Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Yongjian Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Chaohui Zuo Search for this author in: * NPG journals * PubMed * Google Scholar * Chunyou Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Xianghong Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Yifeng Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Xianjun Yu Search for this author in: * NPG journals * PubMed * Google Scholar * Wanjin Dai Search for this author in: * NPG journals * PubMed * Google Scholar * Zhaoshen Li Search for this author in: * NPG journals * PubMed * Google Scholar * Hongbing Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Luming Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Yanling Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Sheng Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaoqi Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Kan Zhai Search for this author in: * NPG journals * PubMed * Google Scholar * Jiang Chang Search for this author in: * NPG journals * PubMed * Google Scholar * Yu Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Menghong Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Cao Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Gao Search for this author in: * NPG journals * PubMed * Google Scholar * Ying Ma Search for this author in: * NPG journals * PubMed * Google Scholar * Xiongwei Zheng Search for this author in: * NPG journals * PubMed * Google Scholar * Siu Tim Cheung Search for this author in: * NPG journals * PubMed * Google Scholar * Yongfeng Jia Search for this author in: * NPG journals * PubMed * Google Scholar * Jian Xu Search for this author in: * NPG journals * PubMed * Google Scholar * Wen Tan Search for this author in: * NPG journals * PubMed * Google Scholar * Ping Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Tangchun Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Chengfeng Wang Contact Chengfeng Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Dongxin Lin Contact Dongxin Lin Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (659K) Supplementary Figures 1–3 and Supplementary Tables 1–7 Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_7a71a4ba47b61fe9f09e52498cd9fdb2"> Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention deficit hyperactivity disorder</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_7a71a4ba47b61fe9f09e52498cd9fdb2">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_7a71a4ba47b61fe9f09e52498cd9fdb2"><strong>44</strong>(1):78-84</a> (2012)<br /> Nature Genetics | Letter Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians * Yoon Shin Cho1, 46 * Chien-Hsiun Chen2, 3, 46 * Cheng Hu4, 46 * Jirong Long5, 46 * Rick Twee Hee Ong6, 46 * Xueling Sim7, 46 * Fumihiko Takeuchi8, 46 * Ying Wu9, 46 * Min Jin Go1, 46 * Toshimasa Yamauchi10, 46 * Yi-Cheng Chang11, 46 * Soo Heon Kwak12, 46 * Ronald C W Ma13, 46 * Ken Yamamoto14, 46 * Linda S Adair15 * Tin Aung16, 17 * Qiuyin Cai5 * Li-Ching Chang2 * Yuan-Tsong Chen2 * Yutang Gao18 * Frank B Hu19 * Hyung-Lae Kim1, 20 * Sangsoo Kim21 * Young Jin Kim1 * Jeannette Jen-Mai Lee22 * Nanette R Lee23 * Yun Li9, 24 * Jian Jun Liu25 * Wei Lu26 * Jiro Nakamura27 * Eitaro Nakashima27, 28 * Daniel Peng-Keat Ng22 * Wan Ting Tay16 * Fuu-Jen Tsai3 * Tien Yin Wong16, 17, 29 * Mitsuhiro Yokota30 * Wei Zheng5 * Rong Zhang4 * Congrong Wang4 * Wing Yee So13 * Keizo Ohnaka31 * Hiroshi Ikegami32 * Kazuo Hara10 * Young Min Cho12 * Nam H Cho33 * Tien-Jyun Chang11 * Yuqian Bao4 * Åsa K Hedman34 * Andrew P Morris34 * Mark I McCarthy34, 35 * DIAGRAM Consortium * MuTHER Consortium * Ryoichi Takayanagi37, 47 * Kyong Soo Park12, 38, 47 * Weiping Jia4, 47 * Lee-Ming Chuang11, 39, 47 * Juliana C N Chan13, 47 * Shiro Maeda39, 47 * Takashi Kadowaki10, 47 * Jong-Young Lee1, 47 * Jer-Yuarn Wu2, 3, 47 * Yik Ying Teo6, 7, 22, 25, 41, 47 * E Shyong Tai22, 42, 43, 47 * Xiao Ou Shu5, 47 * Karen L Mohlke9, 47 * Norihiro Kato8, 47 * Bok-Ghee Han1, 47 * Mark Seielstad25, 44, 45, 47 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:67–72Year published:(2012)DOI:doi:10.1038/ng.1019Received 12 April 2011 Accepted 02 November 2011 Published online 11 December 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We conducted a three-stage genetic study to identify susceptibility loci for type 2 diabetes (T2D) in east Asian populations. We followed our stage 1 meta-analysis of eight T2D genome-wide association studies (6,952 cases with T2D and 11,865 controls) with a stage 2 in silico replication analysis (5,843 cases and 4,574 controls) and a stage 3 de novo replication analysis (12,284 cases and 13,172 controls). The combined analysis identified eight new T2D loci reaching genome-wide significance, which mapped in or near GLIS3, PEPD, FITM2-R3HDML-HNF4A, KCNK16, MAEA, GCC1-PAX4, PSMD6 and ZFAND3. GLIS3, which is involved in pancreatic beta cell development and insulin gene expression1, 2, is known for its association with fasting glucose levels3, 4. The evidence of an association with T2D for PEPD5 and HNF4A6, 7 has been shown in previous studies. KCNK16 may regulate glucose-dependent insulin secretion in the pancreas. These findings, derived from an east Asian population, provide ! new perspectives on the etiology of T2D. View full text Figures at a glance * Figure 1: Genome-wide Manhattan plot for the east Asian T2D stage 1 meta-analysis. Shown are the –log10P values using the trend test for SNPs distributed across the entire autosomal genome. The red dots at each locus indicate the signals with P < 10−6 detected in the genome-wide meta-analysis. A total of 1,934,619 SNPs that were present in at least five stage 1 studies were used to generate the plot. * Figure 2: Regional association plots for new T2D loci. – At the top, the positions of SNPs are shown, and in the middle, the regional association results from the genome-wide meta-analysis are shown. The trend test –log10P values are shown for SNPs distributed in a 0.8-Mb genomic region centered on the most strongly associated signal, which is depicted as a purple diamond for the stage 1 results and a red diamond for the combined stage 1, 2 and 3 results. At the bottom, the locations of known genes in the region are shown. The genetic information was from the Human Genome build hg18, and the LD structure was based on the 1000 Genomes Project JPT+CHB data (June 2010). Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Yoon Shin Cho, * Chien-Hsiun Chen, * Cheng Hu, * Jirong Long, * Rick Twee Hee Ong, * Xueling Sim, * Fumihiko Takeuchi, * Ying Wu, * Min Jin Go, * Toshimasa Yamauchi, * Yi-Cheng Chang, * Soo Heon Kwak, * Ronald C W Ma & * Ken Yamamoto Affiliations * Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Cheongwon-gun, Gangoe-myeon, Yeonje-ri, Korea. * Yoon Shin Cho, * Min Jin Go, * Hyung-Lae Kim, * Young Jin Kim, * Jong-Young Lee & * Bok-Ghee Han * Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, Taiwan. * Chien-Hsiun Chen, * Li-Ching Chang, * Yuan-Tsong Chen & * Jer-Yuarn Wu * School of Chinese Medicine, China Medical University, Taichung, Taiwan. * Chien-Hsiun Chen, * Fuu-Jen Tsai & * Jer-Yuarn Wu * Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China. * Cheng Hu, * Rong Zhang, * Congrong Wang, * Yuqian Bao & * Weiping Jia * Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. * Jirong Long, * Qiuyin Cai, * Wei Zheng & * Xiao Ou Shu * Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore. * Rick Twee Hee Ong & * Yik Ying Teo * Centre for Molecular Epidemiology, National University of Singapore, Singapore, Singapore. * Xueling Sim & * Yik Ying Teo * Research Institute, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan. * Fumihiko Takeuchi & * Norihiro Kato * Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA. * Ying Wu, * Yun Li & * Karen L Mohlke * Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. * Toshimasa Yamauchi, * Kazuo Hara & * Takashi Kadowaki * Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan. * Yi-Cheng Chang, * Tien-Jyun Chang & * Lee-Ming Chuang * Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea. * Soo Heon Kwak, * Young Min Cho & * Kyong Soo Park * Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China. * Ronald C W Ma, * Wing Yee So & * Juliana C N Chan * Division of Genome Analysis, Research Center for Genetic Information, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan. * Ken Yamamoto * Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina, USA. * Linda S Adair * Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore. * Tin Aung, * Wan Ting Tay & * Tien Yin Wong * Department of Ophthalmology, National University of Singapore, Singapore, Singapore. * Tin Aung & * Tien Yin Wong * Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China. * Yutang Gao * Department of Nutrition and Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. * Frank B Hu * Department of Biochemistry, School of Medicine, Ewha Womans University, Seoul, Korea. * Hyung-Lae Kim * School of Systems Biomedical Science, Soongsil University, Dongjak-gu, Seoul, Korea. * Sangsoo Kim * Department of Epidemiology and Public Health, National University of Singapore, Singapore, Singapore. * Jeannette Jen-Mai Lee, * Daniel Peng-Keat Ng, * Yik Ying Teo & * E Shyong Tai * Office of Population Studies Foundation Inc., University of San Carlos, Cebu City, Philippines. * Nanette R Lee * Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA. * Yun Li * Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore. * Jian Jun Liu, * Yik Ying Teo & * Mark Seielstad * Shanghai Institute of Preventive Medicine, Shanghai, China. * Wei Lu * Division of Endocrinology and Diabetes, Department of Internal Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan. * Jiro Nakamura & * Eitaro Nakashima * Department of Diabetes and Endocrinology, Chubu Rosai Hospital, Nagoya, Japan. * Eitaro Nakashima * Centre for Eye Research Australia, University of Melbourne, East Melbourne, Victoria, Australia. * Tien Yin Wong * Department of Genome Science, Aichi-Gakuin University, School of Dentistry, Nagoya, Japan. * Mitsuhiro Yokota * Department of Geriatric Medicine, Graduate School of Medical Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan. * Keizo Ohnaka * Department of Endocrinology, Metabolism and Diabetes, Kinki University School of Medicine, Osaka-sayama, Osaka, Japan. * Hiroshi Ikegami * Department of Preventive Medicine, Ajou University School of Medicine, Suwon, Korea. * Nam H Cho * Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. * Åsa K Hedman, * Andrew P Morris & * Mark I McCarthy * Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK. * Mark I McCarthy * Department of Internal Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan. * Ryoichi Takayanagi * World Class University program, Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology and College of Medicine, Seoul National University, Seoul, Korea. * Kyong Soo Park * Graduate Institute of Clinical Medicine, National Taiwan University School of Medicine, Taipei, Taiwan. * Lee-Ming Chuang & * Shiro Maeda * Laboratory for Endocrinology and Metabolism, RIKEN Center for Genomic Medicine, Yokohama, Japan. * Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore. * Yik Ying Teo * Department of Medicine, National University of Singapore, Singapore, Singapore. * E Shyong Tai * Duke-National University of Singapore Graduate Medical School, Singapore, Singapore. * E Shyong Tai * Institute for Human Genetics, University of California, San Francisco, California, USA. * Mark Seielstad * Blood Systems Research Institute, San Francisco, California, USA. * Mark Seielstad * These authors jointly directed this work. * Ryoichi Takayanagi, * Kyong Soo Park, * Weiping Jia, * Lee-Ming Chuang, * Juliana C N Chan, * Shiro Maeda, * Takashi Kadowaki, * Jong-Young Lee, * Jer-Yuarn Wu, * Yik Ying Teo, * E Shyong Tai, * Xiao Ou Shu, * Karen L Mohlke, * Norihiro Kato, * Bok-Ghee Han & * Mark Seielstad Consortia * DIAGRAM Consortium * MuTHER Consortium Contributions The study was supervised by E.S.T., B.-G.H., N.K., Y.S.C., Y.Y.T., W.Z., Q.C., X.O.S., Y.-T.C., J.-Y.W., L.S.A., K.L.M., T.K., C.H., W.J., L.-M.C., Y.M.C., K.S.P., J.-Y.L. and J.C.N.C. The experiments were conceived of and designed by Y.S.C., E.S.T., N.K., D.P.-K.N., J.J.-M.L., M.S., T.Y.W., Y.Y.T., W.Z., F.B.H., X.O.S., C.-H.C., F.-J.T., Y.-T.C., J.-Y.W., L.S.A., K.L.M., S.M., C.H., L.-M.C., K.S.P., M.J.G., M.I.M. and R.C.W.M. The experiments were performed by J.L., M.S., J.J.L., J.-Y.W., S.M., R.Z., K.Y., Y.-C.C., T.-J.C., L.-M.C. and S.H.K. Statistical analyses was performed by M.J.G., X.S., Y.J.K., R.T.H.O., W.T.T., Y.Y.T., F.T., J.L., C.-H.C., L.-C.C., Y.W., Y.L., K.H., C.H., Y.-C.C., S.H.K., A.P.M. and R.C.W.M. The data were analyzed by M.J.G., X.S., Y.J.K., R.T.H.O., W.T.T., Y.Y.T., J.L., C.-H.C., L.-C.C., Y.W., N.R.L., Y.L., L.S.A., K.L.M., T.Y., C.H., Y.-C.C., S.H.K., Y.S.C., S.K., Å.K.H. and R.C.W.M. The reagents, materials and analysis tools were contributed by E! .S.T., B.-G.H., N.K., D.P.-K.N., J.J.-M.L., J.L., M.S., T.A., T.Y.W., E.N., M.Y., J.N., J.J.L., W.Z., Q.C., Y.G., W.L., F.B.H., X.O.S., F.-J.T., Y.-T.C., J.-Y.W., N.R.L., Y.L., K.O., H.I., R.T., C.W., Y.B., T.-J.C., L.-M.C., K.S.P., H.-L.K., N.H.C., J.-Y.L., W.Y.S. and J.C.N.C. The manuscript was written by Y.S.C., M.S. and E.S.T. All authors reviewed the manuscript. A list of full members is provided in the Supplementary Note. DIAGRAM Consortium MuTHER Consortium Competing financial interests The authors declare no competing financial interests. 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Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_d9a3f1f48e8b193a5a906a82a70a16b0"> Mutations at a single codon in Mad homology 2 domain of SMAD4 cause Myhre syndrome</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_d9a3f1f48e8b193a5a906a82a70a16b0">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_d9a3f1f48e8b193a5a906a82a70a16b0"><strong>44</strong>(1):85-88</a> (2012)<br /> Nature Genetics | Letter A genome-wide association study in Han Chinese identifies new susceptibility loci for ankylosing spondylitis * Zhiming Lin1, 24 * Jin-Xin Bei2, 24 * Meixin Shen3 * Qiuxia Li1 * Zetao Liao1 * Yanli Zhang1 * Qing Lv1 * Qiujing Wei1 * Hui-Qi Low4 * Yun-Miao Guo2 * Shuangyan Cao1 * Mingcan Yang1 * Zaiying Hu1 * Manlong Xu1 * Xinwei Wang1 * Yanlin Wei1 * Li Li1 * Chao Li1 * Tianwang Li1 * Jianlin Huang1 * Yunfeng Pan1 * Ou Jin1 * Yuqiong Wu1 * Jing Wu1 * Zishi Guo1 * Peigen He5 * Shaoxian Hu5 * Husheng Wu6 * Hui Song6 * Feng Zhan7 * Shengyun Liu8 * Guanmin Gao8 * Zhangsuo Liu8 * Yinong Li9 * Changhong Xiao10 * Juan Li11 * Zhizhong Ye12 * Weizhen He12 * Dongzhou Liu13 * Lingxun Shen14 * Anbin Huang14 * Henglian Wu15 * Yi Tao16 * Xieping Pan17 * Buyun Yu1 * E Shyong Tai18, 19 * Yi-Xin Zeng2 * Ee Chee Ren20, 21 * Yan Shen22 * Jianjun Liu4, 23, 25 * Jieruo Gu1, 25 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:73–77Year published:(2012)DOI:doi:10.1038/ng.1005Received 31 May 2011 Accepted 17 October 2011 Published online 04 December 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg To identify susceptibility loci for ankylosing spondylitis, we performed a two-stage genome-wide association study in Han Chinese. In the discovery stage, we analyzed 1,356,350 autosomal SNPs in 1,837 individuals with ankylosing spondylitis and 4,231 controls; in the validation stage, we analyzed 30 suggestive SNPs in an additional 2,100 affected individuals and 3,496 controls. We identified two new susceptibility loci between EDIL3 and HAPLN1 at 5q14.3 (rs4552569; P = 8.77 × 10−10) and within ANO6 at 12q12 (rs17095830; P = 1.63 × 10−8). We also confirmed previously reported associations in Europeans within the major histocompatibility complex (MHC) region (top SNP, rs13202464; P < 5 × 10−324) and at 2p15 (rs10865331; P = 1.98 × 10−8). We show that rs13202464 within the MHC region mainly represents the risk effect of HLA-B*27 variants (including HLA-B*2704, HLA-B*2705 and HLA-B*2715) in Chinese. The two newly discovered loci implicate genes related to bone format! ion and cartilage development, suggesting their potential involvement in the etiology of ankylosing spondylitis. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Zhiming Lin & * Jin-Xin Bei Affiliations * Department of Rheumatology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. * Zhiming Lin, * Qiuxia Li, * Zetao Liao, * Yanli Zhang, * Qing Lv, * Qiujing Wei, * Shuangyan Cao, * Mingcan Yang, * Zaiying Hu, * Manlong Xu, * Xinwei Wang, * Yanlin Wei, * Li Li, * Chao Li, * Tianwang Li, * Jianlin Huang, * Yunfeng Pan, * Ou Jin, * Yuqiong Wu, * Jing Wu, * Zishi Guo, * Buyun Yu & * Jieruo Gu * State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China. * Jin-Xin Bei, * Yun-Miao Guo & * Yi-Xin Zeng * Department of Microbiology, National University of Singapore, Singapore. * Meixin Shen * Human Genetics, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore. * Hui-Qi Low & * Jianjun Liu * Department of Immunology and Rheumatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Hubei, China. * Peigen He & * Shaoxian Hu * Department of Rheumatology & Immunology, Beijing Jishuitan Hospital, Beijing, China. * Husheng Wu & * Hui Song * Department of Rheumatology, Hainan Provincial People's Hospital, Hainan, China. * Feng Zhan * Department of Urology and Rheumatology, The First Affiliated Hospital of Zhengzhou University, Henan, China. * Shengyun Liu, * Guanmin Gao & * Zhangsuo Liu * Department of Rheumatology, Fuzhou General Hospital of Nanjing Military Command, Fuzhou, China. * Yinong Li * Rheumatology & Immunology Department, Hospital of Integrated Traditional Chinese Medicine & Western Medicine of Southern Medical University, Guangzhou, China. * Changhong Xiao * Department of Rheumatology, Nanfang Hospital, Southern Medical University, Guangzhou, China. * Juan Li * Xiangmihu Rheumatology Branch, Fourth People's Hospital of Shenzhen, Shenzhen Rheumatology Institute of Guangdong Medical College, Shenzhen, China. * Zhizhong Ye & * Weizhen He * Department of Rheumatology and Immunology, Jinan University Second Clinical Medical College, Shenzhen People's Hospital, Shenzhen, China. * Dongzhou Liu * Department of Immunology and Rheumatology, Union Hospital, Tongji Medical College, Huazhong University of Science & Technology, Hubei, China. * Lingxun Shen & * Anbin Huang * Department of Urology, Dongguan People's Hospital, Guangdong, China. * Henglian Wu * Department of Rheumatology and Immunology, Second Affiliated Hospital of Guangzhou Medical College, Guangzhou, China. * Yi Tao * Department of Rheumatology and Immunology, The First People′s Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, Jiangsu, China. * Xieping Pan * Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. * E Shyong Tai * Department of Epidemiology and Public Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. * E Shyong Tai * Singapore Immunology Network, A*STAR, Singapore. * Ee Chee Ren * Department of Microbiology, National University of Singapore, Singapore. * Ee Chee Ren * National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Science and Peking Union Medical College, Tsinghua University, Beijing, China. * Yan Shen * The School of Life Sciences, Anhui Medical University, Hefei, China. * Jianjun Liu * These authors jointly directed this work. * Jianjun Liu & * Jieruo Gu Contributions J.G. and J. Liu conceived of the study and obtained financial support. J.G., J. Liu and J.-X.B. designed the study. Z. Lin, Z. Liao, Y.Z., Q. Lv, S.C., M.Y., Z.H., M.X., X.W., Y. Wei, L.L., C.L., T.L., J.H., Y.P., O.J., Y. Wu, J.W., Z.G., P.H., S.H., Husheng Wu, H.S., F.Z., S.L., G.G., Z. Liu, Y.L., C.X., J. Li, Z.Y., W.H., D.L., L.S., A.H., Henglian Wu, Y.T., X.P. and B.Y. coordinated recruitment and obtained biological samples. Z. Lin, Q. Li and Q.W. undertook sample preparation and storage. M.S. and E.C.R. were involved in HLA genotyping. E.S.T. and Y.-X.Z. provided genotypes. J.-X.B. conducted the statistical analyses with help from H.-Q.L., Z. Lin, Y.-M.G., Y.S. and J. Liu. J.-X.B. drafted the manuscript with contributions from J. Liu, Z. Lin, Y.Z., E.C.R. and J.G. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Jianjun Liu or * Jieruo Gu Author Details * Zhiming Lin Search for this author in: * NPG journals * PubMed * Google Scholar * Jin-Xin Bei Search for this author in: * NPG journals * PubMed * Google Scholar * Meixin Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Qiuxia Li Search for this author in: * NPG journals * PubMed * Google Scholar * Zetao Liao Search for this author in: * NPG journals * PubMed * Google Scholar * Yanli Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Qing Lv Search for this author in: * NPG journals * PubMed * Google Scholar * Qiujing Wei Search for this author in: * NPG journals * PubMed * Google Scholar * Hui-Qi Low Search for this author in: * NPG journals * PubMed * Google Scholar * Yun-Miao Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Shuangyan Cao Search for this author in: * NPG journals * PubMed * Google Scholar * Mingcan Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Zaiying Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Manlong Xu Search for this author in: * NPG journals * PubMed * Google Scholar * Xinwei Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Yanlin Wei Search for this author in: * NPG journals * PubMed * Google Scholar * Li Li Search for this author in: * NPG journals * PubMed * Google Scholar * Chao Li Search for this author in: * NPG journals * PubMed * Google Scholar * Tianwang Li Search for this author in: * NPG journals * PubMed * Google Scholar * Jianlin Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Yunfeng Pan Search for this author in: * NPG journals * PubMed * Google Scholar * Ou Jin Search for this author in: * NPG journals * PubMed * Google Scholar * Yuqiong Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Zishi Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Peigen He Search for this author in: * NPG journals * PubMed * Google Scholar * Shaoxian Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Husheng Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Hui Song Search for this author in: * NPG journals * PubMed * Google Scholar * Feng Zhan Search for this author in: * NPG journals * PubMed * Google Scholar * Shengyun Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Guanmin Gao Search for this author in: * NPG journals * PubMed * Google Scholar * Zhangsuo Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Yinong Li Search for this author in: * NPG journals * PubMed * Google Scholar * Changhong Xiao Search for this author in: * NPG journals * PubMed * Google Scholar * Juan Li Search for this author in: * NPG journals * PubMed * Google Scholar * Zhizhong Ye Search for this author in: * NPG journals * PubMed * Google Scholar * Weizhen He Search for this author in: * NPG journals * PubMed * Google Scholar * Dongzhou Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Lingxun Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Anbin Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Henglian Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Yi Tao Search for this author in: * NPG journals * PubMed * Google Scholar * Xieping Pan Search for this author in: * NPG journals * PubMed * Google Scholar * Buyun Yu Search for this author in: * NPG journals * PubMed * Google Scholar * E Shyong Tai Search for this author in: * NPG journals * PubMed * Google Scholar * Yi-Xin Zeng Search for this author in: * NPG journals * PubMed * Google Scholar * Ee Chee Ren Search for this author in: * NPG journals * PubMed * Google Scholar * Yan Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Jianjun Liu Contact Jianjun Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Jieruo Gu Contact Jieruo Gu Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Tables 1–9 and Supplementary Figures 1–5. Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_1bb36269a46601ce556a70950074f94c"> Large-scale discovery of enhancers from human heart tissue</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_1bb36269a46601ce556a70950074f94c">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_1bb36269a46601ce556a70950074f94c"><strong>44</strong>(1):89-93</a> (2012)<br /> Nature Genetics | Letter Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention deficit hyperactivity disorder * Josephine Elia1, 2, 46 * Joseph T Glessner3, 46 * Kai Wang3 * Nagahide Takahashi4 * Corina J Shtir5 * Dexter Hadley3 * Patrick M A Sleiman3 * Haitao Zhang3 * Cecilia E Kim3 * Reid Robison6 * Gholson J Lyon6 * James H Flory3 * Jonathan P Bradfield3 * Marcin Imielinski3 * Cuiping Hou3 * Edward C Frackelton3 * Rosetta M Chiavacci3 * Takeshi Sakurai4 * Cara Rabin7 * Frank A Middleton8 * Kelly A Thomas3 * Maria Garris3 * Frank Mentch3 * Christine M Freitag9 * Hans-Christoph Steinhausen10, 11, 12 * Alexandre A Todorov13 * Andreas Reif14 * Aribert Rothenberger15 * Barbara Franke16, 17 * Eric O Mick18 * Herbert Roeyers19 * Jan Buitelaar20 * Klaus-Peter Lesch14 * Tobias Banaschewski21 * Richard P Ebstein22 * Fernando Mulas23 * Robert D Oades24 * Joseph Sergeant25 * Edmund Sonuga-Barke26, 27, 28 * Tobias J Renner29 * Marcel Romanos29, 30 * Jasmin Romanos29 * Andreas Warnke29 * Susanne Walitza10, 29 * Jobst Meyer31 * Haukur Pálmason31 * Christiane Seitz32 * Sandra K Loo33 * Susan L Smalley33 * Joseph Biederman18 * Lindsey Kent34 * Philip Asherson10 * Richard J L Anney35 * J William Gaynor36 * Philip Shaw7 * Marcella Devoto37, 38, 39, 40 * Peter S White41, 42 * Struan F A Grant3, 37, 38 * Joseph D Buxbaum4 * Judith L Rapoport7 * Nigel M Williams43 * Stanley F Nelson5 * Stephen V Faraone8, 44 * Hakon Hakonarson3, 36, 37, 45 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:78–84Year published:(2012)DOI:doi:10.1038/ng.1013Received 24 June 2011 Accepted 28 October 2011 Published online 04 December 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Attention deficit hyperactivity disorder (ADHD) is a common, heritable neuropsychiatric disorder of unknown etiology. We performed a whole-genome copy number variation (CNV) study on 1,013 cases with ADHD and 4,105 healthy children of European ancestry using 550,000 SNPs. We evaluated statistically significant findings in multiple independent cohorts, with a total of 2,493 cases with ADHD and 9,222 controls of European ancestry, using matched platforms. CNVs affecting metabotropic glutamate receptor genes were enriched across all cohorts (P = 2.1 × 10−9). We saw GRM5 (encoding glutamate receptor, metabotropic 5) deletions in ten cases and one control (P = 1.36 × 10−6). We saw GRM7 deletions in six cases, and we saw GRM8 deletions in eight cases and no controls. GRM1 was duplicated in eight cases. We experimentally validated the observed variants using quantitative RT-PCR. A gene network analysis showed that genes interacting with the genes in the GRM family are enriche! d for CNVs in ~10% of the cases (P = 4.38 × 10−10) after correction for occurrence in the controls. We identified rare recurrent CNVs affecting glutamatergic neurotransmission genes that were overrepresented in multiple ADHD cohorts. View full text Figures at a glance * Figure 1: A deletion directly affecting GRM5 that is exclusive to cases with ADHD and that was replicated in the IMAGE and PUWMa studies. Four hemizygous deletions in GRM5 in cases with ADHD from the CHOP study that were replicated by two deletions and three larger deletions found in the IMAGE study and one deletion found in the PUWMa study. The SNP coverage of the Illumina 550K, Perlegen 600K, Illumina 1M and Affymetrix 5.0 arrays is shown by vertical blue lines. M.Of.M.Cs., Massachusetts General Hospital offspring male case; W.Fa.M.Cn., Washington University father male control. * Figure 2: GRM receptor gene interaction networks affected in ADHD. () GRM receptor genes are shown as large diamond-shaped nodes, and other genes within two degrees of interaction with GRM genes are shown as smaller circular nodes. Nodes are colored to represent the enrichment of the CNVs: dark red represents deletions enriched in cases, light red represents deletions enriched in controls, dark turquoise represents duplications enriched in cases, light turquoise represents duplications enriched in controls, and gray represents diploids that are devoid of CNVs. Thick blue dashed lines highlight edges that are connected to at least one GRM gene, and thin gray lines represent all other gene interactions. Highly connected modules enriched for significant functional annotations are highlighted by blue shaded ellipses. Details on the gene-based CNV observations are included in Supplementary Table 16, and the respective gene functional clusters are listed in Supplementary Table 17. () A schematic overview showing the interaction of GRM receptors a! ffected in ADHD with modules of genes enriched for functional significance. GRM receptor genes are shown as diamonds colored either turquoise or red to represent duplications and deletions, respectively, that were enriched in cases. Boxes highlight the functional modules defined by the network of interacting genes () that are significantly enriched for Gene Ontology annotations. The functional modules describe significant functional annotations and are labeled with the cluster name and the number of component genes in parenthesis. Functional annotations that may be particularly pertinent to the underlying pathophysiology of ADHD are shown in bold. The edges of the network connect GRM receptor genes to functional modules: solid lines indicate membership of the GRM-interacting gene in the functional module, and dotted lines indicate a first-degree relationship between GRM receptor genes and at least one component gene of a functional module. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Josephine Elia & * Joseph T Glessner Affiliations * Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Josephine Elia * Department of Child and Adolescent Psychiatry, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Josephine Elia * Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Joseph T Glessner, * Kai Wang, * Dexter Hadley, * Patrick M A Sleiman, * Haitao Zhang, * Cecilia E Kim, * James H Flory, * Jonathan P Bradfield, * Marcin Imielinski, * Cuiping Hou, * Edward C Frackelton, * Rosetta M Chiavacci, * Kelly A Thomas, * Maria Garris, * Frank Mentch, * Struan F A Grant & * Hakon Hakonarson * Laboratory of Molecular Neuropsychiatry, Department of Psychiatry, Mount Sinai School of Medicine, New York, New York, USA. * Nagahide Takahashi, * Takeshi Sakurai & * Joseph D Buxbaum * Department of Human Genetics and Psychiatry, University of California Los Angeles, Los Angeles, California, USA. * Corina J Shtir & * Stanley F Nelson * Department of Psychiatry, University of Utah, Salt Lake City, Utah, USA. * Reid Robison & * Gholson J Lyon * Child Psychiatry Branch, National Institute of Mental Health, Bethesda, Maryland, USA. * Cara Rabin, * Philip Shaw & * Judith L Rapoport * Department of Neuroscience and Physiology, State University of New York Upstate Medical University, Syracuse, New York, USA. * Frank A Middleton & * Stephen V Faraone * Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Goethe University, Frankfurt am Main, Germany. * Christine M Freitag * Department of Child and Adolescent Psychiatry, University of Zurich, Zurich, Switzerland. * Hans-Christoph Steinhausen, * Susanne Walitza & * Philip Asherson * Aalborg Psychiatric Hospital, Aarhus University Hospital, Aarhus, Denmark. * Hans-Christoph Steinhausen * Institute of Psychology, University of Basel, Basel, Switzerland. * Hans-Christoph Steinhausen * Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA. * Alexandre A Todorov * Attention Deficit Hyperactivity Disorder Clinical Research Network, Unit of Molecular Psychiatry, Department of Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany. * Andreas Reif & * Klaus-Peter Lesch * Child and Adolescent Psychiatry, University of Göttingen, Göttingen, Germany. * Aribert Rothenberger * Department of Human Genetics, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. * Barbara Franke * Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Centre for Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. * Barbara Franke * Department of Psychiatry, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA. * Eric O Mick & * Joseph Biederman * Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium. * Herbert Roeyers * Radboud University Nijmegen Medical Centre, Donders Institute for Brain Cognition and Behaviour, Department of Cognitive Neuroscience, Nijmegen, The Netherlands. * Jan Buitelaar * Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany. * Tobias Banaschewski * Department of Psychology, National University of Singapore, Queenstown, Singapore. * Richard P Ebstein * Department of Neuropaediatrics, La Fe University Hospital, Faculty of Medicine, Valencia, Spain. * Fernando Mulas * Clinic for Child and Adolescent Psychiatry, University of Duisburg-Essen, Essen, Germany. * Robert D Oades * Vrije Universiteit, De Boelelaan, Amsterdam, The Netherlands. * Joseph Sergeant * School of Psychology, Institute for Disorder on Impulse and Attention, University of Southampton, Highfield, Southampton, UK. * Edmund Sonuga-Barke * Department of Experimental Clinical and Health Psychology, Ghent University, Dunantlaan, Ghent, Belgium. * Edmund Sonuga-Barke * Child Study Center, New York University, New York, New York, USA. * Edmund Sonuga-Barke * Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany. * Tobias J Renner, * Marcel Romanos, * Jasmin Romanos, * Andreas Warnke & * Susanne Walitza * Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Munich, Munich, Germany. * Marcel Romanos * Institute of Psychobiology, Department of Neurobehavioral Genetics, University of Trier, Trier, Germany. * Jobst Meyer & * Haukur Pálmason * Department of Child and Adolescent Psychiatry, Saarland University, Homburg, Germany. * Christiane Seitz * Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California, USA. * Sandra K Loo & * Susan L Smalley * Bute Medical School, St. Andrews, Scotland, UK. * Lindsey Kent * Department of Psychiatry, Trinity Centre for Health Sciences, St. James's Hospital, Dublin, Ireland. * Richard J L Anney * Division of Cardiothoracic Surgery, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * J William Gaynor & * Hakon Hakonarson * Division of Genetics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Marcella Devoto, * Struan F A Grant & * Hakon Hakonarson * Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Marcella Devoto & * Struan F A Grant * Dipartimento di Medicina Sperimentale, University La Sapienza, Rome, Italy. * Marcella Devoto * Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Marcella Devoto * Center for Biomedical Informatics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Peter S White * Division of Oncology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Peter S White * Department of Psychological Medicine, School of Medicine, Cardiff University, Cardiff, Wales, UK. * Nigel M Williams * Department of Psychiatry, State University of New York Upstate Medical University, Syracuse, New York, USA. * Stephen V Faraone * Division of Pulmonary Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Hakon Hakonarson Contributions H.H. and J.E. designed the CHOP study and supervised the data analyses and interpretation. S.V.F., M.G., P.A. and J. Buitelaar designed the IMAGE and IMAGE II studies. S.V.F. designed the PUWMa study and coordinated the analyses for the IMAGE, IMAGE II and PUWMa studies. J.T.G. and K.W. conducted the statistical analyses. C.E.K. and E.C.F. directed the stage 1 genotyping. J.D.B. coordinated the validation analyses. N.T. performed the qRT-PCR validation of the CNVs. J.T.G. and H.H. drafted the manuscript. J.E. collected the CHOP samples. C.R., P.S. and J.L.R. collected the NIMH samples. C.M.F., H.-C.S., A.A.T., A. Reif, A. Rothenberger, B.F., E.O.M., H.R., J. Buitelaar, K.-P.L., L.K., T.B., R.P.E., F.M., R.D.O., J.S., E.S.-B., T.J.R., M.R., J.R., A.W., S.W., J.M., H.P., C.S., S.K.L., S.L.S., J. Biederman, L.K., P.A. and R.J.L.A. collected data for the IMAGE, IMAGE II and PUWMa projects. J. Biederman, E.O.M., S.V.F., S.K.L., S.L.S. and A.A.T. collected samples for the PUWMa st! udy. F.A.M. genotyped the IMAGE II data. H.H. directed and D.H. and J.T.G. performed the gene interaction network and functional enrichment analyses. All authors contributed to the manuscript preparation. S.F.A.G. accessed the public domain data, assisted with the interpretation of the data and edited the manuscript. All other authors contributed samples and/or were involved with data mining and processing. Competing financial interests For the last 3 years, M.R. has been in the speakers' bureau for Janssen-Cilag. In previous years, he was on the speakers' bureau for MEDICE. 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Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph D Buxbaum Search for this author in: * NPG journals * PubMed * Google Scholar * Judith L Rapoport Search for this author in: * NPG journals * PubMed * Google Scholar * Nigel M Williams Search for this author in: * NPG journals * PubMed * Google Scholar * Stanley F Nelson Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen V Faraone Search for this author in: * NPG journals * PubMed * Google Scholar * Hakon Hakonarson Contact Hakon Hakonarson Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Note, Supplementary Figures 1–11 and Supplementary Tables 1–17. Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_3034bf8369e58d01b79fafa25f391fef"> A chromatin-modifying function of JNK during stem cell differentiation</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_3034bf8369e58d01b79fafa25f391fef">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_3034bf8369e58d01b79fafa25f391fef"><strong>44</strong>(1):94-100</a> (2012)<br /> Nature Genetics | Letter Mutations at a single codon in Mad homology 2 domain of SMAD4 cause Myhre syndrome * Carine Le Goff1 * Clémentine Mahaut1 * Avinash Abhyankar2 * Wilfried Le Goff3 * Valérie Serre1 * Alexandra Afenjar4 * Anne Destrée5 * Maja di Rocco6 * Delphine Héron7 * Sébastien Jacquemont8 * Sandrine Marlin9 * Marleen Simon10 * John Tolmie11 * Alain Verloes12 * Jean-Laurent Casanova2, 13 * Arnold Munnich1 * Valérie Cormier-Daire1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:85–88Year published:(2012)DOI:doi:10.1038/ng.1016Received 04 August 2011 Accepted 31 October 2011 Published online 11 December 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Myhre syndrome (MIM 139210) is a developmental disorder characterized by short stature, short hands and feet, facial dysmorphism, muscular hypertrophy, deafness and cognitive delay. Using exome sequencing of individuals with Myhre syndrome, we identified SMAD4 as a candidate gene that contributes to this syndrome on the basis of its pivotal role in the bone morphogenetic pathway (BMP) and transforming growth factor (TGF)-β signaling. We identified three distinct heterozygous missense SMAD4 mutations affecting the codon for Ile500 in 11 individuals with Myhre syndrome. All three mutations are located in the region of SMAD4 encoding the Mad homology 2 (MH2) domain near the site of monoubiquitination at Lys519, and we found a defect in SMAD4 ubiquitination in fibroblasts from affected individuals. We also observed decreased expression of downstream TGF-β target genes, supporting the idea of impaired TGF-β–mediated transcriptional control in individuals with Myhre syndrome. View full text Figures at a glance * Figure 1: Clinical and radiological manifestations of individuals with Myhre syndrome. (–) Photographs of affected individuals at ages 4 years (case 8) (), 16 years (case 5) () and 8 years (case 1) (). Note the short palpebral fissures, maxillary hypoplasia, prognathism, muscular build and short extremities. (,) Hand X rays are shown for affected individuals at ages 4 years () and 14 years (). Note the generalized brachydactyly and delayed carpal ossification at age 4 years. () Spine X ray of a case at age 14 years. Note the large vertebrae with short and large pedicles. (,) Skull X rays of cases at age 10 years () and age 16 years (). Note the thickened calvarium. Informed consent was obtained from all individuals or the legal guardians of minors. * Figure 2: Functional consequences of SMAD4 mutations in fibroblasts from individuals with Myhre syndrome. () Characterization of wild-type and mutant SMAD4 protein expression. Increased levels of SMAD4 were seen for mutant SMAD4 (from cases 1 and 4) compared to wild-type SMAD4. () Ubiquitination of wild-type and mutant SMAD4. SMAD4 was immunoprecipitated from cell lysates, and protein blots were performed with an antibody that recognizes ubiquitinated proteins. Mutated SMAD4 (in case 1) was ubiquitinated to a lesser extent than wild-type SMAD4 proteins. * Figure 3: Levels of phosphorylated SMAD proteins in skin fibroblasts from individuals with Myhre syndrome and age- and passage-matched controls. Enhanced levels of phosphorylation of SMAD2/3 and of SMAD/5/8 were seen in cells from subjects with Myhre syndrome (cases 1 and 4) compared to controls. The levels of phosphorylated SMADs were normalized to total SMAD protein. * Figure 4: Cellular localization of phosphorylated SMAD proteins. Phosphorylated SMAD2/3 and phosphorylated SMAD1/5/8 localized to the nucleus in fibroblasts cultured from case 1. * Figure 5: Expression analysis of TGF-β– and BMP-driven target genes in fibroblasts from control and case subjects. (,) Quantification of the mRNA levels of COL1A1, CTGF and SERPINE1 (TGF-β signaling pathway) () and ID3 and SMAD6 (BMP signaling pathway) () was performed by quantitative RT-PCR in fibroblasts from controls (controls 1 and 2) and a case (case 1). mRNA levels were normalized to the expression of housekeeping genes (HSP90AA1 and NONO) and to 18S rRNA levels. Values are expressed as mean ± s.e.m. (N = 6; #P < 0.01 compared to control 2; *P < 0.05, **P < 0.01 compared to control 1.) Author information * Author information * Supplementary information Affiliations * Département de Génétique, Unité INSERM U781, Université Paris Descartes, Sorbonne Paris Cité, Hôpital Necker Enfants Malades, Paris, France. * Carine Le Goff, * Clémentine Mahaut, * Valérie Serre, * Arnold Munnich & * Valérie Cormier-Daire * St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, New York, USA. * Avinash Abhyankar & * Jean-Laurent Casanova * INSERM, Unité Mixte de Recherche (UMR) S939, Dyslipidemia, Inflammation and Atherosclerosis in Metabolic Diseases, University of Pierre and Marie Curie (UPMC)–Université Paris VI, Paris, France. * Wilfried Le Goff * Service de Neuropédiatrie, Centre de Référence Anomalies du Développement, Hôpital Armand Trousseau, Paris, France. * Alexandra Afenjar * Institut de Génétique Humaine, Insitute de Pathologie et de Génétique (IPG), Charleroi, Belgium. * Anne Destrée * Unit of Rare Diseases, Department of Pediatrics, Gaslini Institute, Genoa, Italy. * Maja di Rocco * Unité de Génetique Clinique, Hôpital La Pité Salpétrière, Paris, France. * Delphine Héron * Service de Génétique Médicale, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland. * Sébastien Jacquemont * Unité de Génétique Clinique, Hôpital Armand Trousseau, Paris, France. * Sandrine Marlin * Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands. * Marleen Simon * Department of Medical Genetics, Ferguson Smith Centre, Yorkhill Hospital, Glasgow, UK. * John Tolmie * Département de Génétique, INSERM U676, Hôpital Robert Debré, Paris, France. * Alain Verloes * Laboratory of Human Genetics of Infectious Diseases, University Paris Descartes and INSERM U980, Necker Medical School, Paris, France. * Jean-Laurent Casanova Contributions C.L.G. designed the experiments, analyzed the exome sequencing data, performed protein blot analysis and wrote the manuscript. C.M. performed Sanger sequencing analysis. A. Abhyankar and J.-L.C. performed exome capture. W.L.G. performed quantitative RT-PCR analysis. V.S. performed three-dimensional structure analysis. A. Afenjar, A.D., M.d.R., D.H., S.J., S.M., M.S., J.T. and A.V. provided clinical data. A.M. wrote the manuscript. V.C.-D. provided clinical data, analyzed the exome sequencing data, oversaw all aspects of the research and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Valérie Cormier-Daire Author Details * Carine Le Goff Search for this author in: * NPG journals * PubMed * Google Scholar * Clémentine Mahaut Search for this author in: * NPG journals * PubMed * Google Scholar * Avinash Abhyankar Search for this author in: * NPG journals * PubMed * Google Scholar * Wilfried Le Goff Search for this author in: * NPG journals * PubMed * Google Scholar * Valérie Serre Search for this author in: * NPG journals * PubMed * Google Scholar * Alexandra Afenjar Search for this author in: * NPG journals * PubMed * Google Scholar * Anne Destrée Search for this author in: * NPG journals * PubMed * Google Scholar * Maja di Rocco Search for this author in: * NPG journals * PubMed * Google Scholar * Delphine Héron Search for this author in: * NPG journals * PubMed * Google Scholar * Sébastien Jacquemont Search for this author in: * NPG journals * PubMed * Google Scholar * Sandrine Marlin Search for this author in: * NPG journals * PubMed * Google Scholar * Marleen Simon Search for this author in: * NPG journals * PubMed * Google Scholar * John Tolmie Search for this author in: * NPG journals * PubMed * Google Scholar * Alain Verloes Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-Laurent Casanova Search for this author in: * NPG journals * PubMed * Google Scholar * Arnold Munnich Search for this author in: * NPG journals * PubMed * Google Scholar * Valérie Cormier-Daire Contact Valérie Cormier-Daire Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (315K) Supplementary Figures 1 and 2 and Supplementary Tables 1–4. Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_07bc6ac0ab6d4d6bd6ccfb55e00f46bf"> Evolutionary paths to antibiotic resistance under dynamically sustained drug selection</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_07bc6ac0ab6d4d6bd6ccfb55e00f46bf">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_07bc6ac0ab6d4d6bd6ccfb55e00f46bf"><strong>44</strong>(1):101-105</a> (2012)<br /> Nature Genetics | Letter Large-scale discovery of enhancers from human heart tissue * Dalit May1 * Matthew J Blow1, 2 * Tommy Kaplan3, 4 * David J McCulley5 * Brian C Jensen6, 8 * Jennifer A Akiyama1 * Amy Holt1 * Ingrid Plajzer-Frick1 * Malak Shoukry1 * Crystal Wright2 * Veena Afzal1 * Paul C Simpson5, 7 * Edward M Rubin1, 2 * Brian L Black5 * James Bristow1, 2 * Len A Pennacchio1, 2 * Axel Visel1, 2 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:89–93Year published:(2012)DOI:doi:10.1038/ng.1006Received 13 April 2011 Accepted 20 October 2011 Published online 04 December 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Development and function of the human heart depend on the dynamic control of tissue-specific gene expression by distant-acting transcriptional enhancers. To generate an accurate genome-wide map of human heart enhancers, we used an epigenomic enhancer discovery approach and identified ~6,200 candidate enhancer sequences directly from fetal and adult human heart tissue. Consistent with their predicted function, these elements were markedly enriched near genes implicated in heart development, function and disease. To further validate their in vivo enhancer activity, we tested 65 of these human sequences in a transgenic mouse enhancer assay and observed that 43 (66%) drove reproducible reporter gene expression in the heart. These results support the discovery of a genome-wide set of noncoding sequences highly enriched in human heart enhancers that is likely to facilitate downstream studies of the role of enhancers in development and pathological conditions of the heart. View full text Figures at a glance * Figure 1: ChIP-Seq identification of candidate enhancer regions from human fetal and adult heart. Human fetal heart was obtained at gestational week 16, and adult heart tissue was obtained from the septum of an adult failing heart. () Overview of strategy and results of ChIP-Seq analysis. In total, 5,047 regions from fetal heart and 2,233 from adult heart were significantly enriched in p300/CBP-binding sites and were considered as candidate human heart enhancers (distal: ≥2.5 kb from the nearest transcript start site; proximal or promoter associated: <2.5 kb from the nearest TSS). () Overlap of candidate enhancers identified in fetal and adult heart tissues. () ChIP-Seq profiles of p300/CBP in the genomic region of the tested hs1763 element (thin black bar). Thick black bars indicate two regions significantly enriched for p300/CBP binding in introns of the INPP5A gene. The thin black line represents a read depth of 10; maximum read depth shown is 50. * Figure 2: Human p300/CBP candidate enhancers are enriched near genes expressed in human heart. (,) Frequency of human fetal heart candidate enhancers (red) compared to matched random regions (black) near genes that are overexpressed () or underexpressed () in fetal heart relative to other human tissues (see Online Methods). Error bars indicate 95% confidence intervals. * Figure 3: In vivo testing of predicted human heart enhancer activities in transgenic mice. () In vivo enhancer activity of the 65 tested elements. () Proportion of reproducible enhancers by extent of sequence constraint (+, phastCons > 350; −, phastCons ≤ 350). () Proportion of reproducible enhancers by binding conservation to the mouse (+, p300/CBP binding significant or subsignificant but above background; −, p300/CBP binding not above background or in non-alignable peaks). Pairwise comparison for each subcategory was calculated with two-tailed Fisher's exact test; P > 0.05 in all cases. * Figure 4: In vivo activity of human cardiac enhancers in embryonic and 4-week-old transgenic mice. (–) From left to right: whole-mount stained E11.5 embryo, close-up and histological section of heart at E11.5, whole-mount stained heart at P28 and longitudinal section of heart at P28. All specimens were stained for LacZ enhancer reporter activity (dark blue). Element ID, reproducibility in E11.5 embryos and flanking genes are indicated. LV, left ventricle; RV, right ventricle; LA, left atrium; RA, right atrium; OFT, outflow tract; PA, pulmonary artery; Ao, aorta. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE1789 * GSE32587 Author information * Accession codes * Author information * Supplementary information Affiliations * Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA. * Dalit May, * Matthew J Blow, * Jennifer A Akiyama, * Amy Holt, * Ingrid Plajzer-Frick, * Malak Shoukry, * Veena Afzal, * Edward M Rubin, * James Bristow, * Len A Pennacchio & * Axel Visel * United States Department of Energy Joint Genome Institute, Walnut Creek, California, USA. * Matthew J Blow, * Crystal Wright, * Edward M Rubin, * James Bristow, * Len A Pennacchio & * Axel Visel * Department of Molecular and Cell Biology, California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, California, USA. * Tommy Kaplan * School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel. * Tommy Kaplan * Cardiovascular Research Institute, University of California, San Francisco, San Francisco, California, USA. * David J McCulley, * Paul C Simpson & * Brian L Black * Division of Cardiology, University of California, San Francisco, San Francisco, California, USA. * Brian C Jensen * Cardiology Division, Virginia Medical Center, San Francisco, California, USA. * Paul C Simpson * Current address: Division of Cardiology, University of North Carolina, Chapel Hill, North Carolina, USA. * Brian C Jensen Contributions D.M., E.M.R., J.B., L.A.P. and A.V. conceived of and designed the experiments. D.M., M.J.B., T.K., D.J.M., B.C.J., J.A.A., A.H., I.P.-F., M.S., C.W. and V.A. performed experiments and data analysis. P.C.S. and B.L.B. provided reagents and materials and performed data analysis. All authors contributed to the writing of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Len A Pennacchio or * Axel Visel Author Details * Dalit May Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew J Blow Search for this author in: * NPG journals * PubMed * Google Scholar * Tommy Kaplan Search for this author in: * NPG journals * PubMed * Google Scholar * David J McCulley Search for this author in: * NPG journals * PubMed * Google Scholar * Brian C Jensen Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer A Akiyama Search for this author in: * NPG journals * PubMed * Google Scholar * Amy Holt Search for this author in: * NPG journals * PubMed * Google Scholar * Ingrid Plajzer-Frick Search for this author in: * NPG journals * PubMed * Google Scholar * Malak Shoukry Search for this author in: * NPG journals * PubMed * Google Scholar * Crystal Wright Search for this author in: * NPG journals * PubMed * Google Scholar * Veena Afzal Search for this author in: * NPG journals * PubMed * Google Scholar * Paul C Simpson Search for this author in: * NPG journals * PubMed * Google Scholar * Edward M Rubin Search for this author in: * NPG journals * PubMed * Google Scholar * Brian L Black Search for this author in: * NPG journals * PubMed * Google Scholar * James Bristow Search for this author in: * NPG journals * PubMed * Google Scholar * Len A Pennacchio Contact Len A Pennacchio Search for this author in: * NPG journals * PubMed * Google Scholar * Axel Visel Contact Axel Visel Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (6M) Supplementary Note, Supplementary Figures 1–12 and Supplementary Tables 1–12. Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Genet%5Blatest%5D&highlight=pgtmp_5e98eb5e9186a583f3b8fa97a7ff6773"> Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Genetics&amp;from=pgtmp_5e98eb5e9186a583f3b8fa97a7ff6773">Nat Genet</a> <a class="journal" onclick="journalSearch();" title="Get volume 44, issue 1" href="/search?q=issn%3A1061-4036+vol%3A44+issue%3A1&amp;from=pgtmp_5e98eb5e9186a583f3b8fa97a7ff6773"><strong>44</strong>(1):106-110</a> (2012)<br /> Nature Genetics | Letter A chromatin-modifying function of JNK during stem cell differentiation * Vijay K Tiwari1 * Michael B Stadler1, 2 * Christiane Wirbelauer1 * Renato Paro3, 4 * Dirk Schübeler1, 4 * Christian Beisel3 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:94–100Year published:(2012)DOI:doi:10.1038/ng.1036Received 12 July 2011 Accepted 15 November 2011 Published online 18 December 2011 Article tools * Full text * 日本語要約 * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Signaling mediates cellular responses to extracellular stimuli. The c-Jun NH2-terminal kinase (JNK) pathway exemplifies one subgroup of the mitogen-activated protein (MAP) kinases, which, besides having established functions in stress response, also contribute to development by an unknown mechanism1, 2, 3, 4. We show by genome-wide location analysis that JNK binds to a large set of active promoters during the differentiation of stem cells into neurons. JNK-bound promoters are enriched with binding motifs for the transcription factor NF-Y but not for AP-1. NF-Y occupies these predicted sites, and overexpression of dominant-negative NF-YA reduces the JNK presence on chromatin. We find that histone H3 Ser10 (H3S10) is a substrate for JNK, and JNK-bound promoters are enriched for H3S10 phosphorylation. Inhibition of JNK signaling in post-mitotic neurons reduces phosphorylation at H3S10 and the expression of target genes. These results establish MAP kinase binding and function on! chromatin at a novel class of target genes during stem cell differentiation. View full text Figures at a glance * Figure 1: JNK is upregulated during stem cell differentiation and directly binds promoters. () Analysis of Mapk8, Mapk9 and Mapk10 transcript levels by real-time PCR during neuronal differentiation of stem cells, revealing that Mapk8 and Mapk10 levels are low in ES cells but are upregulated upon differentiation. mRNA levels are plotted on the y axis and are normalized to Gapdh levels. Error bars represent s.e.m. () Protein blot detection of JNK1/3 in protein extracts isolated from ES, NP and TN cells showing protein upregulation during differentiation. Lamin B1 serves as loading control. () Protein blot analysis of samples in using an antibody specific to phosphorylated JNK (p-JNK), revealing the presence of the active form in TNs. () Immunofluorescence with DAPI staining of the nucleus (blue) and an antibody specific to JNK1/3 (red). An overlay of DAPI and JNK staining shows that a substantial fraction of JNK localizes to the nucleus. Scale bar, 80 μm. () Genome browser view of enrichment for JNK binding at the G3bp1 locus in the three stages of ES cell different! iation as determined by ChIP. Tag densities are normalized to the total number of reads in each sample. () Peaks of JNK binding are enriched at promoters but not in exons, introns or intergenic regions in all three stages of ES cell differentiation. Enrichments are calculated relative to the genomic size of each type of region. () Averaged JNK coverage around TSSs in neurons normalized to the input chromatin control sample and grouped into ten classes of decreasing JNK1/3 ChIP-Seq signal. * Figure 2: JNK target promoters are active and show increased JNK binding during terminal differentiation. () ChIP-qPCR validation of the enrichment of JNK binding at various identified targets and non-target controls using JNK1/3 ChIP samples derived from all three stages of ES cell differentiation. Average enrichments from separate assays are plotted on the y axis as the ratio of precipitated DNA relative to the total input DNA and are further normalized to a control region. Error bars show s.e.m. () ChIP-qPCR validation of select JNK targets in JNK1/3 ChIP samples derived from adult mouse brain plotted as in , showing the in vivo targeting of chromatin by JNK. () Genes were classified as JNK positive (enrichment at TSS ≥ 0.7, solid lines) or negative (enrichment at TSS < 0.7, dotted lines) on the basis of JNK levels at the TSS of each gene. The distribution of signals in TN cells for each group of genes is shown for RNA Pol II binding, mRNA levels and H3K4me2 and H3K27me3 chromatin modifications. * Figure 3: NF-Y mediates JNK recruitment to chromatin. () Sequence logos of motifs identified in JNK target genes. () Performance (adjusted r2) of different linear models in predicting JNK binding at promoters in TNs as a function of chromatin features (RNA Pol II binding and H3K27me3 and H3K4me2 marks), sequence features (number of NF-Y–, SP-1– or AP-1–like motifs), mRNA levels, or a combination of all (full) or two (RNA Pol II binding and a given motif) of these features. RNA Pol II + NF-Y–like motif almost achieve the performance of the full model, explaining 40% of the observed variance in JNK binding (left). Models including the NF-YA binding data derived from ChIP-Seq (right) explain more than 60% of the variance. () Genome browser screenshot comparing JNK and NF-YA binding at the G3bp1 locus showing similar binding dynamics during neuronal differentiation. () NF-YA enrichment as determined using ChIP-qPCR for various JNK targets in ES, NP and TN stages. NF-Y occupies all tested JNK targets and shows similar bindin! g dynamics during differentiation. Error bars show s.e.m. () Comparison of enrichment in JNK and NF-YA binding at promoters in TN cells. Promoters with proximal JNK peaks (within 1 kb of the TSS) are shown in red. Dotted lines indicate the cut-off values used in this study to define promoters positive for JNK and NF-YA binding (0.7 and 0.5, respectively). () Dominant-negative NF-YA reduces JNK binding to chromatin. HEK293 cells were stably transfected with constructs encoding wild-type (WT) or dominant-negative (DN) NF-YA, and NF-YA expression was induced by the addition of tetracycline (Tet) to cells. Subsequently, JNK binding was determined by ChIP-qPCR using primers specific for JNK target genes. Error bars represent s.e.m. * Figure 4: Inhibition of JNK signaling blocks differentiation and reduces H3S10 phosphorylation. () Exposure of cells to the JNK inhibitor SP600125 (JNKi) during the transition from neuronal progenitors to neurons abrogates neurogenesis. NPs were treated with either DMSO (control) or with SP600125 for 2 h after plating, and light-field microscopy images were taken 24 h later. Scale bar, 70 μm. () TNs were exposed to either DMSO or SP600125, and the levels of phosphorylated JNK were detected by protein blotting. The levels of total JNK1/3 and Lamin B1 (loading control) are also shown. Exposure to SP600125 leads to a substantial reduction in the levels of phosphorylated JNK. () Total histones were isolated from TNs exposed to either DMSO or SP600125, and levels of phosphorylated H3S10 (p-H3S10), H3T3 (p-H3T3), H3T11 (p-H3T3) and H3S28 (p-H3S28), as well as acetylated H3 (H3Ac), H3K4me2, H3K9me3 and total histone H4 were detected by protein blotting. Total histone H3 levels are also shown as a loading control. Inhibition of JNK signaling leads to a substantial reduction i! n the levels of phosphorylated H3S10. () JNK phosphorylates H3S10 in vitro. Recombinant active JNK was incubated with ATP and recombinant H3.1 and kinase reactions were performed, followed by protein blotting with the indicated antibodies. () Phosphorylated H3S10 is preferentially enriched at JNK target genes. ChIP-qPCR for phosphorylated H3S10 presence at various JNK target and non-target control genes using phosphorylated H3S10 ChIP samples derived from the TN cells. Error bars represent s.e.m. * Figure 5: Blocking JNK kinase activity downregulates target gene expression. () Effect of JNK inhibition on the transcriptome. Genes were divided into ten equally sized bins according to expression levels, median-centered and SP600125-induced expression changes were compared between JNK target and non-target genes. Bins with significant differences in expression between the two gene classes are indicated by asterisks (P value < 0.001, two-sided Wilcoxon rank-sum test with continuity correction). These data show preferential downregulation of JNK targets with moderate expression levels. Dotted lines show the minimum and maximum range of data. () Analysis performed as in for NF-YA target genes illustrating the same preferential downregulation by the inhibition of JNK kinase activity. () Real-time PCR analysis of transcript levels of two JNK targets (Mcm10 and Traf2) in TNs exposed to either DMSO or SP600125. Relative mRNA levels were determined by normalization to Gapdh expression, and average data from independent assays are plotted on the y axis. Err! or bars show s.e.m. () Correlation between expression changes caused by the inhibition of JNK kinase activity (x axis) and changes in JNK binding at the TSS of relevant genes (y axis). The red line corresponds to a local smoothed line fitted to the values for individual genes (loess fit with smoothing parameter α = 0.15). Changes in expression and JNK binding are significantly correlated (Spearman's rank correlation ρ = 0.255, P < 2.2 × 10−16). Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE25533 Author information * Accession codes * Author information * Supplementary information Affiliations * Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland. * Vijay K Tiwari, * Michael B Stadler, * Christiane Wirbelauer & * Dirk Schübeler * Swiss Institute of Bioinformatics, Basel, Switzerland. * Michael B Stadler * Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zürich, Basel, Switzerland. * Renato Paro & * Christian Beisel * Faculty of Science, University of Basel, Basel, Switzerland. * Renato Paro & * Dirk Schübeler Contributions V.K.T. initiated and designed the study, performed experiments, analyzed data and wrote the manuscript. M.B.S. designed and performed the computational analysis and wrote the manuscript. C.W. performed experiments and analyzed data. R.P. provided input during the study and comments on the manuscript. C.B. initiated the study, performed experiments, analyzed data and wrote the manuscript. D.S. designed the study, analyzed data and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Dirk Schübeler or * Michael B Stadler Author Details * Vijay K Tiwari Search for this author in: * NPG journals * PubMed * Google Scholar * Michael B Stadler Contact Michael B Stadler Search for this author in: * NPG journals * PubMed * Google Scholar * Christiane Wirbelauer Search for this author in: * NPG journals * PubMed * Google Scholar * Renato Paro Search for this author in: * NPG journals * PubMed * Google Scholar * Dirk Schübeler Contact Dirk Schübeler Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Beisel 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 Figures 1–10. Additional data </li> </ul> <div class="blogger-post-footer"><img width='1' height='1' src='https://blogger.googleusercontent.com/tracker/5969181590050102457-4874865125229822663?l=pubget.blogspot.com' alt='' /></div>ian connorhttp://www.blogger.com/profile/17012291553690617903noreply@blogger.com0tag:blogger.com,1999:blog-5969181590050102457.post-67998579741104682732012-01-24T21:40:00.000-08:002012-01-24T21:41:58.115-08:00The Jan 01 issue of the <a href="http://pubget.com/search?q=Nat%20Methods[latest]" ><i>Nat Methods</i></a> is now up on <a href="http://pubget.com/">Pubget</a> (<a href="http://pubget.com/profile/journal/Nat%20Methods"><i>About Nat Methods</i></a>): if you're at a subscribing institution, just click the link in the latest link at the home page. (Note you'll only be able to get all the PDFs in the issue if your institution <a href="http://pubget.com/site/contact/contact_box">subscribes to Pubget</a>.) <p>Latest Articles Include:</p> <ul> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Methods%5Blatest%5D&highlight=pgtmp_2126a590f513d6346bac54b47299af80"> Method of the Year 2011</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Methods&amp;from=pgtmp_2126a590f513d6346bac54b47299af80">Nat Methods</a> <a class="journal" onclick="journalSearch();" title="Get volume 9, issue 1" href="/search?q=issn%3A1548-7091+vol%3A9+issue%3A1&amp;from=pgtmp_2126a590f513d6346bac54b47299af80"><strong>9</strong>(1):1</a> (2012)<br /> Nature Methods | Editorial Method of the Year 2011 Journal name:Nature MethodsVolume: 9,Page:1Year published:(2012)DOI:doi:10.1038/nmeth.1852Published online 28 December 2011 The ability to introduce targeted, tailored changes into the genomes of several species will make it feasible to ask more precise biological questions. View full text Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Methods%5Blatest%5D&highlight=pgtmp_b54efab95af347b94520b6a1ba5be8a3"> The author file: Khalid Salaita</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Methods&amp;from=pgtmp_b54efab95af347b94520b6a1ba5be8a3">Nat Methods</a> <a class="journal" onclick="journalSearch();" title="Get volume 9, issue 1" href="/search?q=issn%3A1548-7091+vol%3A9+issue%3A1&amp;from=pgtmp_b54efab95af347b94520b6a1ba5be8a3"><strong>9</strong>(1):3</a> (2012)<br /> Nature Methods | This Month The author file: Khalid Salaita * Monya BakerJournal name:Nature MethodsVolume: 9,Page:3Year published:(2012)DOI:doi:10.1038/nmeth.1816Published online 28 December 2011 Measuring single-molecule forces with light. 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 Author Details * Monya Baker Search for this author in: * NPG journals * PubMed * Google Scholar Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Methods%5Blatest%5D&highlight=pgtmp_9a980bc438479c4051752ea3e8954cff"> Points of view: Data exploration</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Methods&amp;from=pgtmp_9a980bc438479c4051752ea3e8954cff">Nat Methods</a> <a class="journal" onclick="journalSearch();" title="Get volume 9, issue 1" href="/search?q=issn%3A1548-7091+vol%3A9+issue%3A1&amp;from=pgtmp_9a980bc438479c4051752ea3e8954cff"><strong>9</strong>(1):5</a> (2012)<br /> Nature Methods | This Month Points of view: Data exploration * Noam Shoresh1 * Bang Wong2 * AffiliationsJournal name:Nature MethodsVolume: 9,Page:5Year published:(2012)DOI:doi:10.1038/nmeth.1829Published online 28 December 2011 Enhancement of pattern discovery through graphical representation of data. View full text Figures at a glance * Figure 1: Anscombe's quartet. () The four sets of numbers that form Anscombe's quartet. () The highly distinctive graphs that result from plotting the data in . * Figure 2: Small multiples. () A stack graph showing the relative proportions of 24 cell lines over time. () Individual growth curves for the data graphed in . 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 * Noam Shoresh is a senior computational biologist at the Broad Institute. * Bang Wong is the creative director of the Broad Institute of the Massachusetts Institute of Technology and Harvard and an adjunct assistant professor in the Department of Art as Applied to Medicine at The Johns Hopkins University School of Medicine. Competing financial interests The authors declare no competing financial interests. Author Details * Noam Shoresh Search for this author in: * NPG journals * PubMed * Google Scholar * Bang Wong Search for this author in: * NPG journals * PubMed * Google Scholar Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Methods%5Blatest%5D&highlight=pgtmp_d38c4618a2cf84ac97e3090c46c8b65d"> GeneProf: analysis of high-throughput sequencing experiments</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Methods&amp;from=pgtmp_d38c4618a2cf84ac97e3090c46c8b65d">Nat Methods</a> <a class="journal" onclick="journalSearch();" title="Get volume 9, issue 1" href="/search?q=issn%3A1548-7091+vol%3A9+issue%3A1&amp;from=pgtmp_d38c4618a2cf84ac97e3090c46c8b65d"><strong>9</strong>(1):7-8</a> (2012)<br /> Nature Methods | Correspondence GeneProf: analysis of high-throughput sequencing experiments * Florian Halbritter1 * Harsh J Vaidya1 * Simon R Tomlinson1 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:7–8Year published:(2012)DOI:doi:10.1038/nmeth.1809Published online 28 December 2011 To the Editor: The huge volume and complexity of data produced by high-throughput sequencing make it difficult for researchers in many laboratories to fully harness the potential of these data for the study of biological processes and human disease. Data processing rather than generation is now often the bottleneck for biological experiments1, and the efficient use of high-throughput sequencing data submitted to public databases such as the Sequence Read Archive remains a challenging goal for many. Workflow-based software2, 3 offers an attractive approach for dealing with complex data because it allows the visual organization of software components into ordered 'workflows' (Supplementary Note). This enables complicated analyses without any need to write custom computer scripts. However, workflow engines focus on the mechanics of the computational processes involved; the primary goal is to achieve computation rather than a particular biological result. Therefore, setting up a workflow can b! e a daunting task for many life scientists, especially those lacking experience in the visual programming paradigm. Existing tools are hence not sufficient to make high-throughput sequencing data fully accessible to the entire research community. View full text Subject terms: * Bioinformatics * Gene Expression * Epigenetics * Sequencing Author information * 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 Affiliations * Institute for Stem Cell Research, Centre for Regenerative Medicine, School of Biological Sciences, University of Edinburgh, Edinburgh, UK. * Florian Halbritter, * Harsh J Vaidya & * Simon R Tomlinson Competing financial interests Edinburgh Research and Innovation (University of Edinburgh) is currently investigating the commercial potential of GeneProf. Corresponding author Correspondence to: * Simon R Tomlinson Author Details * Florian Halbritter Search for this author in: * NPG journals * PubMed * Google Scholar * Harsh J Vaidya Search for this author in: * NPG journals * PubMed * Google Scholar * Simon R Tomlinson Contact Simon R Tomlinson Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (5.2M) Supplementary Figures 1–9, Supplementary Note, Supplementary Discussion, Supplementary Methods, Supplementary Data Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Methods%5Blatest%5D&highlight=pgtmp_7fca2e017a4e96b4e9141e385eb0b3f1"> Gene expression deconvolution in linear space</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Methods&amp;from=pgtmp_7fca2e017a4e96b4e9141e385eb0b3f1">Nat Methods</a> <a class="journal" onclick="journalSearch();" title="Get volume 9, issue 1" href="/search?q=issn%3A1548-7091+vol%3A9+issue%3A1&amp;from=pgtmp_7fca2e017a4e96b4e9141e385eb0b3f1"><strong>9</strong>(1):8-9</a> (2012)<br /> </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Methods%5Blatest%5D&highlight=pgtmp_46e3553aee513dd9df6d87c3209e4e45"> Reply to "Gene expression deconvolution in linear space"</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Methods&amp;from=pgtmp_46e3553aee513dd9df6d87c3209e4e45">Nat Methods</a> <a class="journal" onclick="journalSearch();" title="Get volume 9, issue 1" href="/search?q=issn%3A1548-7091+vol%3A9+issue%3A1&amp;from=pgtmp_46e3553aee513dd9df6d87c3209e4e45"><strong>9</strong>(1):9</a> (2012)<br /> Nature Methods | Correspondence Gene expression deconvolution in linear space * Yi Zhong1, 2 * Zhandong Liu1, 2 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:8–9Year published:(2012)DOI:doi:10.1038/nmeth.1830Published online 28 December 2011 To the Editor: In the April 2010 issue, Shen-Orr et al. proposed an algorithm for cell type–specific significance analysis of microarrays (csSAM) that can deconvolve cell type–specific expression profiles from complex tissues1. The authors tested the relationships between computationally reconstructed signals from pure cell types and the signal measured from physically mixed samples. They found that a large fraction of the reconstructed (~10%) and deconvolved (~25%) signals deviated from the true gene-expression values1. The authors mixed complementary RNA from the tissues and observed similar off-diagonal effects. They concluded that the off-diagonal effects are due to technical reasons, such as nonlinear sample amplification or probe cross-hybridization, rather than statistical deconvolution. View full text Subject terms: * Gene Expression * Bioinformatics * Genomics * Systems Biology Author information * 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 Affiliations * Department of Pediatrics, Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Baylor College of Medicine, Houston, Texas, USA. * Yi Zhong & * Zhandong Liu * Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas, USA. * Yi Zhong & * Zhandong Liu Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Zhandong Liu Author Details * Yi Zhong Search for this author in: * NPG journals * PubMed * Google Scholar * Zhandong Liu Contact Zhandong Liu Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3.3M) Supplementary Figures 1–3 Additional data </li> <li> <a class="title" href="http://pubget.com/search?q=Nat%20Methods%5Blatest%5D&highlight=pgtmp_2cba9441775948d466c5840f317bb4ad"> Optimal enzymes for amplifying sequencing libraries</a><br /> - <a class="journal" title="Get latest issue" onclick="journalSearch();" href="/search?q=latest%3ANature+Methods&amp;from=pgtmp_2cba9441775948d466c5840f317bb4ad">Nat Methods</a> <a class="journal" onclick="journalSearch();" title="Get volume 9, issue 1" href="/search?q=issn%3A1548-7091+vol%3A9+issue%3A1&amp;from=pgtmp_2cba9441775948d466c5840f317bb4ad"><strong>9</strong>(1):10-11</a> (2012)<br /> Nature Methods | Correspondence Gene expression deconvolution in linear space * Yi Zhong1, 2 * Zhandong Liu1, 2 * Affiliations * Corresponding authorJournal name:Nature MethodsVolume: 9,Pages:8–9Year published:(2012)DOI:doi:10.1038/nmeth.1830Published online 28