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Reconstruction of context-specific genome-scale metabolic models using multiomics data to study metabolic rewiring

Current Opinion in Systems Biology 2019年 15卷 1-11页

作者： Cho, Jae Sung Gu, Changdai Han, Tae Hee Ryu, Jae Yong Lee, Sang Yup Metabolic and Biomolecular Engineering National Research Laboratory Department of Chemical and Biomolecular Engineering (BK21 Plus Program) Institute for the BioCentury Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory KAIST Daejeon 34141 South Korea BioProcess Engineering Research Center KAIST Daejeon South Korea BioInformatics Research Center KAIST Daejeon South Korea

metabolic rewiring or reprogramming is the alteration of metabolism in living organisms, leading to disordered states aberrant from homeostasis. As large amounts of omics data become available, complex mechanisms leading to or driven by metabolic rewiring of cells can be better understood using reconstructed context-specific genome-scale metabolic models (GEMs). Here, we review recent advances in reconstructing context-specific GEMs for studying metabolic rewiring of human cells or tissues, from generic GEMs and omics databases to multiomics data integration methods. Also, we review recent studies that use context-specific GEMs to obtain insights such as identifying key regulators or therapeutic targets. Analyses of recent trends indicate the importance of integrating context-specific GEMs with multiscale networks for understanding metabolic diseases and advancing precision medicine. © 2019 Elsevier Ltd

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Bacterial Polyesters: Microbial Polyhydroxyalkanoates and Nonnatural Polyesters (Adv. Mater. 35/2020)

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Advanced Materials 2020年第35期32卷

作者： So Young Choi In Jin Cho Youngjoon Lee Yeo‐Jin Kim Kyung‐Jin Kim Sang Yup Lee Metabolic and Biomolecular Engineering National Research Laboratory Systems Metabolic Engineering and Systems Healthcare Cross‐Generation Collaborative Laboratory Department of Chemical and Biomolecular Engineering (BK21 Plus Program) Institute for the BioCentury Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak‐ro Yuseong‐gu Daejeon 34141 Republic of Korea School of Life Sciences (KNU Creative BioResearch Group) KNU Institute for Microorganisms Kyungpook National University 80 Daehak‐ro Buk‐gu Daegu 41566 Republic of Korea BioProcess Engineering Research Center and Bioinformatics Research Center Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak‐ro Yuseong‐gu Daejeon 34141 Republic of Korea

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Corrigendum to "metabolic engineering of Escherichia coli for high-level astaxanthin production with high productivity" [Metab. Eng. 49 (2018) 105-115]

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metabolic engineering 2019年 54卷 285页

作者： Seon Young Park Robert M Binkley Won Jun Kim Mun Hee Lee Sang Yup Lee Metabolic and Biomolecular Engineering National Research Laboratory Department of Chemical and Biomolecular Engineering (BK21 plus program) Institute for the BioCentury Korea Advanced Institute of Science and Technology (KAIST) 34141 Daejeon Republic of Korea Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory KAIST Daejeon 34141 Republic of Korea. Metabolic and Biomolecular Engineering National Research Laboratory Department of Chemical and Biomolecular Engineering (BK21 plus program) Institute for the BioCentury Korea Advanced Institute of Science and Technology (KAIST) 34141 Daejeon Republic of Korea. Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory KAIST Daejeon 34141 Republic of Korea BioProcess Engineering Research Center and BioInformatics Research Center KAIST 34141 Daejeon Republic of Korea. Electronic address: leesy@kaist.ac.kr.

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Author Correction: Differential peripheral immune signatures elicited by vegan versus ketogenic diets in humans

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Nature medicine 2024年第6期30卷 1785页

作者： Verena M Link Poorani Subramanian Foo Cheung Kyu Lee Han Apollo Stacy Liang Chi Brian A Sellers Galina Koroleva Amber B Courville Shreni Mistry Andrew Burns Richard Apps Kevin D Hall Yasmine Belkaid Metaorganism Immunity Section Laboratory of Host Immunity and Microbiome National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. verena.link@nih.gov. NIH Center for Human Immunology National Institutes of Health Bethesda MD USA. verena.link@nih.gov. Bioinformatics and Computational Biosciences Branch Office of Cyber Infrastructure and Computational Biology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. NIH Center for Human Immunology National Institutes of Health Bethesda MD USA. Center for Cellular Engineering Department of Transfusion Medicine Clinical Center National Institutes of Health Bethesda MD USA. Metaorganism Immunity Section Laboratory of Host Immunity and Microbiome National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. Department of Cardiovascular and Metabolic Sciences Lerner Research Institute Cleveland Clinic Cleveland OH USA. National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health Bethesda MD USA. NIAID Microbiome Program National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health Bethesda MD USA. kevinh@niddk.nih.gov. Metaorganism Immunity Section Laboratory of Host Immunity and Microbiome National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. ybelkaid@niaid.nih.gov. NIH Center for Human Immunology National Institutes of Health Bethesda MD USA. ybelkaid@niaid.nih.gov.

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Current state and applications of microbial genome-scale metabolic models

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Current Opinion in Systems Biology 2017年 2卷 10-18页

作者： Kim, Won Jun Kim, Hyun Uk Lee, Sang Yup Metabolic and Biomolecular Engineering National Research Laboratory Department of Chemical and Biomolecular Engineering (BK21 Plus Program) Institute for the BioCentury Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 South Korea BioInformatics Research Center KAIST Daejeon 34141 South Korea BioProcess Engineering Research Center KAIST Daejeon 34141 South Korea The Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Hørsholm Denmark

Genome-scale metabolic models (GEMs) are valuable in describing an organism's entire metabolism using genomic information and have made huge impact on systems biology and metabolic engineering. Here we review current state of microbial GEMs and their use in various applications. There has recently been much interest in reconstructing GEMs for microbial communities, reflecting the increasing importance of microbiome. Analyses of recent work suggest that the GEMs for the best studied microorganisms, new or less studied microorganisms, and also microbial communities need to be thoroughly validated with experimental data beyond simple gene essentiality and nutrient utilization capabilities. In this paper, computational modeling resources and frameworks developed recently for reconstructing GEMs of microorganisms and microbial communities together with their applications are reviewed. © 2017 Elsevier Ltd.

关键词： Genome-scale metabolic models (GEMs) metabolic modeling resources/frameworks Microbial community Microorganisms Model and non-model organisms

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Inside Front Cover: Biotechnology Journal 9/2019

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Biotechnology Journal 2019年第9期14卷

作者： Youngjoon Lee In J. Cho So Y. Choi Sang Y. Lee Metabolic and Biomolecular Engineering National Research Laboratory Department of Chemical and Biomolecular Engineering (BK21 Plus Program) Institute for the BioCentury Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak‐ro Yuseong‐gu Daejeon 34141 Republic of Korea Systems Metabolic Engineering and Systems Healthcare Cross‐Generation Collaborative Laboratory Korea Advanced Institute of Science and Technology Daejeon 34141 Republic of Korea Applied Science Research Institute Korea Advanced Institute of Science and Technology Daejeon 34141 Republic of Korea BioInformatics Research Center Korea Advanced Institute of Science and Technology Daejeon 34141 Republic of Korea BioProcess Engineering Research Center Korea Advanced Institute of Science and Technology Daejeon 34141 Republic of Korea

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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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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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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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Whole-genome and Transcriptome Sequencing of Prostate Cancer Identify New Genetic Alterations Driving Disease Progression

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European urology 2018年第3期73卷 322-339页

作者： Shancheng Ren Gong-Hong Wei Dongbing Liu Liguo Wang Yong Hou Shida Zhu Lihua Peng Qin Zhang Yanbing Cheng Hong Su Xiuqing Zhou Jibin Zhang Fuqiang Li Hancheng Zheng Zhikun Zhao Changjun Yin Zengquan He Xin Gao Haiyen E Zhau Chia-Yi Chu Jason Boyang Wu Colin Collins Stanislav V Volik Robert Bell Jiaoti Huang Kui Wu Danfeng Xu Dingwei Ye Yongwei Yu Lianhui Zhu Meng Qiao Hang-Mao Lee Yuehong Yang Yasheng Zhu Xiaolei Shi Rui Chen Yang Wang Weidong Xu Yanqiong Cheng Chuanliang Xu Xu Gao Tie Zhou Bo Yang Jianguo Hou Li Liu Zhensheng Zhang Yao Zhu Chao Qin Pengfei Shao Jun Pang Leland W K Chung Jianfeng Xu Chin-Lee Wu Weide Zhong Xun Xu Yingrui Li Xiuqing Zhang Jian Wang Huanming Yang Jun Wang Haojie Huang Yinghao Sun Department of Urology Shanghai Changhai Hospital Second Military Medical University Shanghai China. Biocenter Oulu Faculty of Biochemistry and Molecular Medicine University of Oulu Oulu Finland. BGI-Shenzhen Shenzhen China China National GeneBank-Shenzhen BGI-Shenzhen Shenzhen China. Division of Biomedical Statistics and Informatics Mayo Clinic College of Medicine Rochester MN USA. China National GeneBank-Shenzhen BGI-Shenzhen Shenzhen China State Key Laboratory of Genetic Engineering School of Life Sciences Fudan University Shanghai China. Division of Genomics and Bioinformatics CUHK-BGI Innovation Institute of Trans-Omics The Chinese University of Hong Kong Hong Kong China. BGI Education Center University of Chinese Academy of Sciences Shenzhen China. BGI-Shenzhen Shenzhen China. School of Biological Science and Medical Engineering Southeast University Nanjing China State Key Laboratory of Bioelectronics Southeast University Nanjing China. Department of Urology The First Affiliated Hospital of Nanjing Medical University Nanjing China. Department of Urology The Third Affiliated Hospital of Sun Yat-Sen University Guangzhou China. Uro-Oncology Research Program Department of Medicine Samuel Oschin Comprehensive Cancer Institute Cedars-Sinai Medical Center Los Angeles CA USA. Vancouver Prostate Centre and Department of Urologic Sciences University of British Columbia Vancouver BC Canada. Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles CA USA. Department of Urology Changzheng Hospital Second Military Medical University Shanghai China. Department of Urology Fudan University Shanghai Cancer Center Shanghai China. Department of Pathology Shanghai Changhai Hospital Second Military Medical University Shanghai China. State Key Laboratory of Genetic Engineering School of Life Sciences Fudan University Shanghai China Program for Personalized Cancer Care NorthShore University HealthS

BACKGROUND:Global disparities in prostate cancer (PCa) incidence highlight the urgent need to identify genomic abnormalities in prostate tumors in different ethnic populations including Asian men. OBJECTIVE:To systematically explore the genomic complexity and define disease-driven genetic alterations in PCa. DESIGN, SETTING, AND PARTICIPANTS:The study sequenced whole-genome and transcriptome of tumor-benign paired tissues from 65 treatment-naive Chinese PCa patients. Subsequent targeted deep sequencing of 293 PCa-relevant genes was performed in another cohort of 145 prostate tumors. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS:The genomic alteration landscape in PCa was analyzed using an integrated computational pipeline. Relationships with PCa progression and survival were analyzed using nonparametric test, log-rank, and multivariable Cox regression analyses. RESULTS AND LIMITATIONS:We demonstrated an association of high frequency of CHD1 deletion with a low rate of TMPRSS2-ERG fusion and relatively high percentage of mutations in androgen receptor upstream activator genes in Chinese patients. We identified five putative clustered deleted tumor suppressor genes and provided experimental and clinical evidence that PCDH9, deleted/loss in approximately 23% of tumors, functions as a novel tumor suppressor gene with prognostic potential in PCa. Furthermore, axon guidance pathway genes were frequently deregulated, including gain/amplification of PLXNA1 gene in approximately 17% of tumors. Functional and clinical data analyses showed that increased expression of PLXNA1 promoted prostate tumor growth and independently predicted prostate tumor biochemical recurrence, metastasis, and poor survival in multi-institutional cohorts of patients with PCa. A limitation of this study is that other genetic alterations were not experimentally investigated. CONCLUSIONS:There are shared and salient genetic characteristics of PCa in Chinese and Caucasian men. Novel genetic alterations i

关键词： AR coactivator mutation Axon guidance pathway CHD1 deletion Chinese prostate cancer Clustered deleted tumor suppressor genes Personalized medicine Prognosis RNA-seq TMPRSS2-ERG fusion Whole genome sequencing

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