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arXiv 2017年

作者： Pavel, Ana B. Korolev, Kirill S. Graduate Program in Bioinformatics Boston University 44 Cummington Mall BostonMA02215 United States Department of Physics Boston University 590 Commonwealth Avenue BostonMA02215 United States

Genetic alterations initiate tumors and enable the evolution of drug resistance. The pro-cancer view of mutations is however incomplete, and several studies show that mutational load can reduce tumor fitness. Given its negative effect, genetic load should make tumors more sensitive to anticancer drugs. Here, we test this hypothesis across all major types of cancer from the Cancer Cell Line Encyclopedia, that provides genetic and expression data of 496 cell lines together with their response to 24 common anticancer drugs. We found that the efficacy of 9 out of 24 drugs showed significant association with genetic load in a pan-cancer analysis. The associations for some tissue-drug combinations were remarkably strong with genetic load explaining up to 83% of the variance in the drug response. Overall, the role of genetic load depended on both the drug and the tissue type with 10 tissues being particularly vulnerable to genetic load. We also identified changes in gene expression associated with increased genetic load, which included cell-cycle checkpoints, DNA damage and apoptosis. Our results show that genetic load is an important component of tumor fitness and can predict drug sensitivity. Beyond being a biomarker, genetic load might be a new, unexplored vulnerability of cancer. Copyright © 2017, The Authors. All rights reserved.

关键词： Tumors

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The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2

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Cell 2025年第12期188卷 3167-3183.e18页

作者： Pekar, Jonathan E. Lytras, Spyros Ghafari, Mahan Magee, Andrew F. Parker, Edyth Wang, Yu Ji, Xiang Havens, Jennifer L. Katzourakis, Aris Vasylyeva, Tetyana I. Suchard, Marc A. Hughes, Alice C. Hughes, Joseph Rambaut, Andrew Robertson, David L. Dellicour, Simon Worobey, Michael Wertheim, Joel O. Lemey, Philippe Institute of Ecology and Evolution University of Edinburgh Edinburgh United Kingdom Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla 92093 CA United States Department of Biomedical Informatics University of California San Diego La Jolla 92093 CA United States Department of Medicine University of California San Diego La Jolla 92093 CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Medical Research Council University of Glasgow Centre for Virus Research Glasgow United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States Department of Immunology and Microbiology The Scripps Research Institute La Jolla 92037 CA United States Institute of Genomics and Global Health Redeemer's University Osun State Ede Nigeria Department of Computer Science and Engineering University of California San Diego La Jolla 92093 CA United States Department of Mathematics School of Science and Engineering Tulane University New Orleans LA United States Department of Population Health and Disease Prevention University of California Irvine Irvine 92617 CA United States Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles 90095 CA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States School of Biological Sciences University of Hong Kong Hong Kong Hong Kong China Biodiversity Green Development Foundation Beijing China Spatial Epidemiology Lab (SpELL) Université Libre de Bruxelles CP160/12 50 av. FD Roosevelt Bruxelles 1050 Belgium Department of Microbiology Immunology and Transplantation Rega Institute

The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 led to increased sampling of sarbecoviruses circulating in horseshoe bats. Employing phylogenetic inference while accounting for recombination of bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed less than a decade prior to their emergence in humans. Phylogeographic analyses show bat sarbecoviruses traveled at rates approximating their horseshoe bat hosts and circulated in Asia for millennia. We find that the direct ancestors of SARS-CoV and SARS-CoV-2 are unlikely to have reached their respective sites of emergence via dispersal in the bat reservoir alone, supporting interactions with intermediate hosts through wildlife trade playing a role in zoonotic spillover. These results can guide future sampling efforts and demonstrate that viral genomic regions extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses. © 2025 The Authors

关键词： molecular epidemiology phylogeography sarbecoviruses virus evolution

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A VLPs based vaccine protects against Zika virus infection and prevents cerebral and testicular damage

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NPJ vaccines 2025年第1期10卷 107页

作者： Nelson Côrtes Aline Lira Jaqueline D Q Silva Evelyn Carvalho Wasim A Prates-Syed Barbara Hamaguchi Ricardo Durães-Carvalho Andrea Balan Niels O S Câmara Otavio Cabral-Marques Norbert Pardi Ester C Sabino José E Krieger Gustavo Cabral-Miranda Department of Infectious Diseases and Tropical Medicine Laboratory of Medical Investigation 46 Faculty of Medicine University of São Paulo São Paulo Brazil. Department of Immunology Institute of Biomedical Sciences University of São Paulo São Paulo Brazil. Department of Clinical and Toxicological Analyses School of Pharmaceutical Sciences University of Sao Paulo São Paulo Brazil. Department of Biophysics São Paulo School of Medicine Federal University of São Paulo São Paulo SP Brazil. Department of Microbiology Immunology and Parasitology São Paulo School of Medicine Federal University of São Paulo (UNIFESP) São Paulo SP Brazil. Department of Morphology and Genetics Federal University of São Paulo São Paulo Brazil. Interunit Bioinformatics Graduate Program Institute of Chemistry University of Sao Paulo Sao Paulo Brazil. Applied Structural Biology Laboratory Institute of Biomedical Sciences University of São Paulo São Paulo Brazil. Department of Medicine Division of Molecular Medicine Laboratory of Medical Investigation 29 Faculty of Medicine University of São Paulo São Paulo Brazil. DO'R Institute for Research São Paulo Brazil. Department of Microbiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology Laboratory of Medical Investigation 46 Faculty of Medicine University of São Paulo São Paulo Brazil. Laboratory of Genetics and Molecular Cardiology Clinical Hospital Faculty of Medicine University of São Paulo Heart Institute São Paulo Brazil. Department of Infectious Diseases and Tropical Medicine Laboratory of Medical Investigation 46 Faculty of Medicine University of São Paulo São Paulo Brazil. gcabral.miranda@usp.br. Department of Immunology Institute of Biomedical Sciences University of São Paulo São Paulo Brazil. gcabral.miranda@usp.br.

Still, Zika virus (ZIKV) infection poses a substantial public health risk, especially for pregnant women and their fetuses, as it can result in congenital abnormalities and fetal mortality during pregnancy. Despite significant advances in understanding and combating ZIKV, considerable challenges remain in the fight against this flavivirus. A crucial component of this effort is the development of vaccines, none of which have yet been licensed for human use. Here, we present a comprehensive study of a novel ZIKV vaccine candidate based on virus-like particles (VLPs), designed to provide broad immunological protection against viral infection combined with safety, without the need for additional adjuvants. A self-adjuvanted VLPs-based vaccine displaying the envelope protein domain III (EDIII) of ZIKV was built. The EDIII protein was expressed in E. coli and chemically conjugated to QβVLPs. Immunization of C57BL/6 mice with two doses of the EDIII-QβVLPs vaccine elicited strong EDIII-specific Th1-based immune response. Notably, the vaccine induced neutralizing antibodies and conferred protection in type I IFN receptor-deficient (G129) mice against ZIKV challenge. Furthermore, vaccinated male mice were protected from ZIKV-induced cerebral and testicular damage, critical concerns for ZIKV pathogenesis. These findings suggest that the EDIII-QβVLP vaccine is a promising candidate for preventing ZIKV infection, with potential applications in combatting this and other emerging flaviviruses.

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Correction to: Authors’ Reply to Letter to the Editor: Continued improvement to genetic diversity indicator for CBD

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Conservation Genetics 2021年第4期22卷 537-539页

作者： Laikre, Linda Hohenlohe, Paul A. Allendorf, Fred W. Bertola, Laura D. Breed, Martin F. Bruford, Michael W. Funk, W. Chris Gajardo, Gonzalo González-Rodríguez, Antonio Grueber, Catherine E. Hedrick, Philip W. Heuertz, Myriam Hunter, Margaret E. Johannesson, Kerstin Liggins, Libby MacDonald, Anna J. Mergeay, Joachim Moharrek, Farideh O’Brien, David Ogden, Rob Orozco-terWengel, Pablo Palma-Silva, Clarisse Pierson, Jennifer Paz-Vinas, Ivan Russo, Isa-Rita M. Ryman, Nils Segelbacher, Gernot Sjögren-Gulve, Per Waits, Lisette P. Vernesi, Cristiano Hoban, Sean Division of Population Genetics Department of Zoology Stockholm University Stockholm Sweden Institute for Bioinformatics and Evolutionary Studies Department of Biological Sciences University of Idaho Moscow USA Division of Biological Sciences University of Montana Missoula USA City College of New York New York USA College of Science and Engineering Flinders University Bedford Park Australia School of Biosciences Cardiff University Cardiff Wales UK Graduate Degree Program in Ecology Department of Biology Colorado State University Fort Collins USA Universidad de Los Lagos Lab Genetics Aquaculture & Biodiversity Osorno Chile Instituto de Investigaciones en Ecosistemas y Sustentabilidad Universidad Nacional Autónoma de México Morelia Mexico School of Life and Environmental Sciences Faculty of Science The University of Sydney Sydney Australia School of Life Sciences Arizona State University Tempe USA INRAE Univ. Bordeaux Biogeco Cestas France U.S. Geological Survey Wetland and Aquatic Research Center Gainesville USA Department of Marine Sciences University of Gothenburg Tjärnö Strömstad Sweden School of Natural and Computational Sciences Massey University Auckland New Zealand The John Curtin School of Medical Research/Research School of Biology The Australian National University Acton Australia Research Institute for Nature and Forest Geraardsbergen Belgium Aquatic Ecology Evolution and Conservation KULeuven Leuven Belgium Novo Nordisk Foundation Center for Basic Metabolic Research Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Faculty of Biological Sciences Tarbiat Modares University Tehran Iran Scottish Natural Heritage Inverness UK Royal (Dick) School of Veterinary Studies and the Roslin Institute University of Edinburgh Midlothian UK Department of Plant Science Institute of Biology University of Campinas Campinas Brazil Australian Wildlife Conservancy Subiaco East Australia Laboratoire Evolution & Div

A correction to this paper has been published: https://***/10.1007/s10592-021-01376-9

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Polygenic risk modeling for prediction of epithelial ovarian cancer risk (vol 30, pg 349, 2021)

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EUROPEAN JOURNAL OF HUMAN GENETICS 2022年第5期30卷 630-631页

作者： Dareng, Eileen O. Tyrer, Jonathan P. Barnes, Daniel R. Jones, Michelle R. Yang, Xin Aben, Katja K. H. Adank, Muriel A. Agata, Simona Andrulis, Irene L. Anton-Culver, Hoda Antonenkova, Natalia N. Aravantinos, Gerasimos Arun, Banu K. Augustinsson, Annelie Balmana, Judith Bandera, Elisa V. Barkardottir, Rosa B. Barrowdale, Daniel Beckmann, Matthias W. Beeghly-Fadiel, Alicia Benitez, Javier Bermisheva, Marina Bernardini, Marcus Q. Bjorge, Line Black, Amanda Bogdanova, Natalia V. Bonanni, Bernardo Borg, Ake Brenton, James D. Budzilowska, Agnieszka Butzow, Ralf Buys, Saundra S. Cai, Hui Caligo, Maria A. Campbell, Ian Cannioto, Rikki Cassingham, Hayley Chang-Claude, Jenny Chanock, Stephen J. Chen, Kexin Chiew, Yoke-Eng Chung, Wendy K. Claes, Kathleen B. M. Colonna, Sarah Cook, Linda S. Couch, Fergus J. Daly, Mary B. Dao, Fanny Davies, Eleanor de la Hoya, Miguel de Putter, Robin Dennis, Joe DePersia, Allison Devilee, Peter Diez, Orland Ding, Yuan Chun Doherty, Jennifer A. Domchek, Susan M. Dork, Thilo du Bois, Andreas Durst, Matthias Eccles, Diana M. Eliassen, Heather A. Engel, Christoph Evans, Gareth D. Fasching, Peter A. Flanagan, James M. Fortner, Renee T. Machackova, Eva Friedman, Eitan Ganz, Patricia A. Garber, Judy Gensini, Francesca Giles, Graham G. Glendon, Gord Godwin, Andrew K. Goodman, Marc T. Greene, Mark H. Gronwald, Jacek Hahnen, Eric Haiman, Christopher A. Hakansson, Niclas Hamann, Ute Hansen, Thomas V. O. Harris, Holly R. Hartman, Mikael Heitz, Florian Hildebrandt, Michelle A. T. Hogdall, Estrid Hogdall, Claus K. Hopper, John L. Huang, Ruea-Yea Huff, Chad Hulick, Peter J. Huntsman, David G. Imyanitov, Evgeny N. Isaacs, Claudine Jakubowska, Anna James, Paul A. Janavicius, Ramunas Jensen, Allan Johannsson, Oskar Th. John, Esther M. Jones, Michael E. Kang, Daehee Karlan, Beth Y. Karnezis, Anthony Kelemen, Linda E. Khusnutdinova, Elza Kiemeney, Lambertus A. Kim, Byoung-Gie Kjaer, Susanne K. Komenaka, Ian Kupryjanczyk, Jolanta Kurian, Allison W. Kwong, Ava Lambrechts, Diether Larson, Melissa C. Lazaro, Conxi L University of Cambridge Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care Cambridge UK University of Cambridge Centre for Cancer Genetic Epidemiology Department of Oncology Cambridge UK Center for Bioinformatics and Functional Genomics Cedars-Sinai Medical Center Los Angeles CA USA Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen The Netherlands Netherlands Comprehensive Cancer Organisation Utrecht The Netherlands The Netherlands Cancer Institute—Antoni van Leeuwenhoek hospital Family Cancer Clinic Amsterdam The Netherlands Veneto Institute of Oncology IOV—IRCCS Immunology and Molecular Oncology Unit Padua Italy Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital Fred A. Litwin Center for Cancer Genetics Toronto ON Canada University of Toronto Department of Molecular Genetics Toronto ON Canada University of California Irvine Department of Epidemiology Genetic Epidemiology Research Institute Irvine CA USA N.N. Alexandrov Research Institute of Oncology and Medical Radiology Minsk Belarus ‘Agii Anargiri’ Cancer Hospital Athens Greece University of Texas MD Anderson Cancer Center Department of Breast Medical Oncology Houston TX USA Lund University Department of Cancer Epidemiology Clinical Sciences Lund Sweden Vall d’Hebron Institute of Oncology Hereditary cancer Genetics Group Barcelona Spain University Hospital of Vall d’Hebron Department of Medical Oncology Barcelona Spain Rutgers Cancer Institute of New Jersey Cancer Prevention and Control Program New Brunswick NJ USA Landspitali University Hospital Department of Pathology Reykjavik Iceland University of Iceland BMC (Biomedical Centre) Faculty of Medicine Reykjavik Iceland University Hospital Erlangen Friedrich-Alexander-University Erlangen-Nuremberg Department of Gynecology and Obstetrics Comprehensive Cancer Center ER-EMN Erlangen Germany Vanderbilt University School of Medicine Division of Epidemiology Department

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

关键词： Clinical genetics Genetic markers Risk factors Biomedicine general Human Genetics bioinformatics Gene Expression Cytogenetics

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Void distributions reveal structural link between jammed packings and protein cores

arXiv

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arXiv 2018年

作者： Treado, John D. Mei, Zhe Regan, Lynne O'Hern, Corey S. Department of Mechanical Engineering & Materials Science Yale University New HavenCT06520 United States Integrated Graduate Program in Physical & Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Molecular Biophysics & Biochemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Dense packing of hydrophobic residues in the cores of globular proteins determines their stability. Recently, we have shown that protein cores possess packing fraction φ ≈ 0.56, which is the same as dense, random packing of amino acid-shaped particles. In this article, we compare the structural properties of protein cores and jammed packings of amino acid-shaped particles in much greater depth by measuring their local and connected void regions. We find that the distributions of surface Voronoi cell volumes and local porosities obey similar statistics in both systems. We also measure the probability that accessible, connected void regions percolate as a function of the size of a spherical probe particle and show that both systems possess the same critical probe size. By measuring the critical exponent τ that characterizes the size distribution of connected void clusters at the onset of percolation, we show that void percolation in packings of amino acid-shaped particles and protein cores belong to the same universality class, which is different from that for void percolation in jammed sphere packings. We propose that the connected void regions of proteins are a defining feature of proteins and can be used to differentiate experimentally observed proteins from decoy structures that are generated using computational protein design software. This work emphasizes that jammed packings of amino acid-shaped particles can serve as structural and mechanical analogs of protein cores, and could therefore be useful in modeling the response of protein cores to cavity-expanding and -reducing mutations. Copyright © 2018, The Authors. All rights reserved.

关键词： Amino acids

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Author Correction: Effect of de novo transcriptome assembly on transcript quantification

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Scientific reports 2019年第1期9卷 18041页

作者： Ping-Han Hsieh Yen-Jen Oyang Chien-Yu Chen Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 10617 Taiwan. Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan. Department of Bio-Industrial Mechatronics Engineering National Taiwan University Taipei 10617 Taiwan. chienyuchen@ntu.edu.tw. Genome and Systems Biology Program National Taiwan University and Academia sinica Taipei 10617 Taiwan. chienyuchen@ntu.edu.tw.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

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Nature 2024年第8003期627卷 347-357页

作者： Ken Suzuki Konstantinos Hatzikotoulas Lorraine Southam Henry J Taylor Xianyong Yin Kim M Lorenz Ravi Mandla Alicia Huerta-Chagoya Giorgio E M Melloni Stavroula Kanoni Nigel W Rayner Ozvan Bocher Ana Luiza Arruda Kyuto Sonehara Shinichi Namba Simon S K Lee Michael H Preuss Lauren E Petty Philip Schroeder Brett Vanderwerff Mart Kals Fiona Bragg Kuang Lin Xiuqing Guo Weihua Zhang Jie Yao Young Jin Kim Mariaelisa Graff Fumihiko Takeuchi Jana Nano Amel Lamri Masahiro Nakatochi Sanghoon Moon Robert A Scott James P Cook Jung-Jin Lee Ian Pan Daniel Taliun Esteban J Parra Jin-Fang Chai Lawrence F Bielak Yasuharu Tabara Yang Hai Gudmar Thorleifsson Niels Grarup Tamar Sofer Matthias Wuttke Chloé Sarnowski Christian Gieger Darryl Nousome Stella Trompet Soo-Heon Kwak Jirong Long Meng Sun Lin Tong Wei-Min Chen Suraj S Nongmaithem Raymond Noordam Victor J Y Lim Claudia H T Tam Yoonjung Yoonie Joo Chien-Hsiun Chen Laura M Raffield Bram Peter Prins Aude Nicolas Lisa R Yanek Guanjie Chen Jennifer A Brody Edmond Kabagambe Ping An Anny H Xiang Hyeok Sun Choi Brian E Cade Jingyi Tan K Alaine Broadaway Alice Williamson Zoha Kamali Jinrui Cui Manonanthini Thangam Linda S Adair Adebowale Adeyemo Carlos A Aguilar-Salinas Tarunveer S Ahluwalia Sonia S Anand Alain Bertoni Jette Bork-Jensen Ivan Brandslund Thomas A Buchanan Charles F Burant Adam S Butterworth Mickaël Canouil Juliana C N Chan Li-Ching Chang Miao-Li Chee Ji Chen Shyh-Huei Chen Yuan-Tsong Chen Zhengming Chen Lee-Ming Chuang Mary Cushman John Danesh Swapan K Das H Janaka de Silva George Dedoussis Latchezar Dimitrov Ayo P Doumatey Shufa Du Qing Duan Kai-Uwe Eckardt Leslie S Emery Daniel S Evans Michele K Evans Krista Fischer James S Floyd Ian Ford Oscar H Franco Timothy M Frayling Barry I Freedman Pauline Genter Hertzel C Gerstein Vilmantas Giedraitis Clicerio González-Villalpando Maria Elena González-Villalpando Penny Gordon-Larsen Myron Gross Lindsay A Guare Sophie Hackinger Liisa Hakaste Sohee Han Andrew T Hattersley Christian Herder Momoko Horikoshi Annie-Green Howard Willa Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research Division of Musculoskeletal and Dermatological Sciences University of Manchester Manchester UK. Department of Diabetes and Metabolic Diseases Graduate School of Medicine University of Tokyo Tokyo Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. konstantinos.hatzikotoulas@helmholtz-munich.de. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. Center for Precision Health Research National Human Genome Research Institute National Institutes of Health Bethesda MD USA. British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Heart and Lung Research Institute University of Cambridge Cambridge UK. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor MI USA. Corporal Michael J. Crescenz VA Medical Center Philadelphia PA USA. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Genetics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Programs in Metabolism and Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Diabetes Unit and Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. TIMI Study Group Division of Cardiovascular Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London L

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

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Chronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts

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The Lancet Respiratory Medicine 2020年第7期8卷 696-708页

作者： Moll, Matthew Sakornsakolpat, Phuwanat Shrine, Nick Hobbs, Brian D DeMeo, Dawn L John, Catherine Guyatt, Anna L McGeachie, Michael J Gharib, Sina A Obeidat, Ma'en Lahousse, Lies Wijnant, Sara R A Brusselle, Guy Meyers, Deborah A Bleecker, Eugene R Li, Xingnan Tal-Singer, Ruth Manichaikul, Ani Rich, Stephen S Won, Sungho Kim, Woo Jin Do, Ah Ra Washko, George R Barr, R Graham Psaty, Bruce M Bartz, Traci M Hansel, Nadia N Barnes, Kathleen Hokanson, John E Crapo, James D Lynch, David Bakke, Per Gulsvik, Amund Hall, Ian P Wain, Louise Soler Artigas, María Jackson, Victoria E Strachan, David P Hui, Jennie James, Alan L Kerr, Shona M Polasek, Ozren Vitart, Veronique Marten, Jonathan Rudan, Igor Kähönen, Mika Surakka, Ida Gieger, Christian Karrasch, Stefan Rawal, Rajesh Schulz, Holger Deary, Ian J Harris, Sarah E Enroth, Stefan Gyllensten, Ulf Imboden, Medea Probst-Hensch, Nicole M Lehtimäki, Terho Raitakari, Olli T Langenberg, Claudia Luan, Jian'an Wareham, Nick Zhao, Jing Hua Hayward, Caroline Murray, Alison Porteous, David J Smith, Blair H Jarvelin, Marjo-Riitta Wielscher, Matthias Joshi, Peter K Kentistou, Katherine A Timmers, Paul RHJ Wilson, James F Cook, James P Lind, Lars Mahajan, Anubha Morris, Andrew P Ewert, Ralf Homuth, Georg Stubbe, Beate Weiss, Stefan Zeggini, Eleftheria Weiss, Scott T Silverman, Edwin K Dudbridge, Frank Tobin, Martin D Cho, Michael H Channing Division of Network Medicine Brigham and Women's Hospital Boston MA United States Division of Pulmonary and Critical Care Medicine Brigham and Women's Hospital Boston MA United States Department of Medicine Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand Genetic Epidemiology Group Department of Health Sciences University of Leicester Leicester United Kingdom Computational Medicine Core Center for Lung Biology Department of Medicine University of Washington Seattle WA United States Division of Pulmonary Critical Care and Sleep Medicine Department of Medicine University of Washington Seattle WA United States Cardiovascular Health Research Unit Department of Medicine University of Washington Seattle WA United States University of British Columbia Center for Heart Lung Innovation St Paul's Hospital Vancouver BC Canada Department of Epidemiology Erasmus Medical Centre Rotterdam Netherlands Department of Respiratory Medicine Erasmus Medical Centre Rotterdam Netherlands Department of Bioanalysis Faculty of Pharmaceutical Sciences Ghent University Ghent Belgium Department of Respiratory Medicine Ghent University Hospital Ghent Belgium Department of Medicine University of Arizona Tucson AZ United States GlaxoSmithKline Research and Development Collegeville PA United States Center for Public Health Genomics University of Virginia Charlottesville VA United States Department of Public Health Sciences University of Virginia Charlottesville VA United States Department of Public Health Sciences Graduate School of Public Health Seoul National University Seoul South Korea Interdisciplinary Program of Bioinformatics College of National Sciences Seoul National University Seoul South Korea Institute of Health and Environment Seoul National University Seoul South Korea Department of Internal Medicine Kangwon National University Chuncheon South Korea Department of Medicine and Department of Epidemiology Columbia Universit

Background: Genetic factors influence chronic obstructive pulmonary disease (COPD) risk, but the individual variants that have been identified have small effects. We hypothesised that a polygenic risk score using additional variants would predict COPD and associated phenotypes. Methods: We constructed a polygenic risk score using a genome-wide association study of lung function (FEV₁ and FEV₁/forced vital capacity [FVC]) from the UK Biobank and SpiroMeta. We tested this polygenic risk score in nine cohorts of multiple ethnicities for an association with moderate-to-severe COPD (defined as FEV₁/FVC <0·7 and FEV₁ <80% of predicted). Associations were tested using logistic regression models, adjusting for age, sex, height, smoking pack-years, and principal components of genetic ancestry. We assessed predictive performance of models by area under the curve. In a subset of studies, we also studied quantitative and qualitative CT imaging phenotypes that reflect parenchymal and airway pathology, and patterns of reduced lung growth. Findings: The polygenic risk score was associated with COPD in European (odds ratio [OR] per SD 1·81 [95% CI 1·74–1·88] and non-European (1·42 [1·34–1·51]) populations. Compared with the first decile, the tenth decile of the polygenic risk score was associated with COPD, with an OR of 7·99 (6·56–9·72) in European ancestry and 4·83 (3·45–6·77) in non-European ancestry cohorts. The polygenic risk score was superior to previously described genetic risk scores and, when combined with clinical risk factors (ie, age, sex, and smoking pack-years), showed improved prediction for COPD compared with a model comprising clinical risk factors alone (AUC 0·80 [0·79–0·81] vs 0·76 [0·75–0·76]). The polygenic risk score was associated with CT imaging phenotypes, including wall area percent, quantitative and qualitative measures of emphysema, local histogram emphysema patterns, and destructive emphysema subtypes. The po

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Prognostic Impacts and Dynamic Changes of Cohesin Complex Gene Mutations in De Novo Acute Myeloid Leukemia

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BLOOD 2017年第Sup1期130卷 2582-2582页

作者： Tsai, Cheng-Hong Hou, Hsin-An Tang, Jih-Luh Kuo, Yuan-Yeh Chiu, Yu-Chiao Lin, Chien-Chin Lin, Liang-In Yao, Ming Li, Chi-Cheng Huang, Shang-Yi Ko, Bor-Sheng Hsu, Szu Chun Lin, Chien-Ting Wu, Shang-Ju Chen, Chien-Yuan Tsai, Woei Chou, Wen-Chien Tien, Hwei-Fang Tai-Cheng Stem Cell Therapy Center National Taiwan University Taipei Taiwan Division of Hematology Department of Internal Medicine National Taiwan University Hospital Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University Taipei Taiwan Graduate Institute of Oncology National Taiwan University Taipei Taiwan Graduate institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Clinical Laboratory Sciences and Medical Biotechnology National Taiwan University Taipei Taiwan Department of Laboratory Medicine National Taiwan University Hospital Taipei Taiwan Division of Hematology Department of Laboratory Medicine National Taiwan University Hospital Taipei Taiwan

Background Patients with acute myeloid leukemia (AML) have remarkable heterogeneity in the pathogenesis, clinical features, and treatment outcomes. Risk-stratification is one of the most critical factors in the treatment of these patients. Cohesin is a multimeric protein complex, composed of four core subunits, including SMC1A, SMC3, RAD21 and either STAG1 or STAG2 proteins. Studies regarding the clinical and prognostic relevance and dynamic changes of cohesin gene mutations in de novo AML patients are limited and the findings are controversial. In this study, we aimed to investigate the clinical, biological and prognostic implications of cohesin gene mutations in a large cohort of de novo AML patients. Method We analyzed cohesin gene mutations and their clinico-biological associations in 391 adult patients with newly diagnosed de novo non-M3 AML. To evaluate the dynamic changes of cohesin and co-occurring gene mutations, sequential analyses of gene mutations by targeted next generation sequencing using HiSeq platform (Illumina) were performed in 386 samples from 116 patients during follow-ups. Furthermore, to investigate the pathophysiological pathways associated with cohesin gene mutations, gene expression microarray analysis was performed in 173 patients, including 10 cohesin-mutated and 163 wild-type patients, who had enough mRNA for analysis. Results We found 37 patients (9.5%) had cohesin gene mutations, most prevalent in RAD21 (3.8%) and STAG2 (3.1%). Except for one patient with concurrent mutations in STAG2 and RAD21, the mutations in these component genes were mutually exclusive, suggesting a convergence of biological effects of these mutations (Figure 1). Mutations in STAG2 and RAD21 were mainly truncations or frameshift mutations (10/12, 83.3% and 12/15, 80%, respectively) while those in SMC1A and SMC3 were mostly missense mutations (7/8, 87.5% and 2/2, 100%, respectively). Cohesin mutations were inversely associated with complex karyotype (P=0.023) and u

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