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Author Correction: GWAS of thyroid stimulating hormone highlights the pleiotropic effects and inverse association with thyroid cancer

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Nature communications 2021年第1期12卷 7354页

作者： Wei Zhou Ben Brumpton Omer Kabil Julius Gudmundsson Gudmar Thorleifsson Josh Weinstock Matthew Zawistowski Jonas B Nielsen Layal Chaker Marco Medici Alexander Teumer Silvia Naitza Serena Sanna Ulla T Schultheiss Anne Cappola Juha Karjalainen Mitja Kurki Morgan Oneka Peter Taylor Lars G Fritsche Sarah E Graham Brooke N Wolford William Overton Humaira Rasheed Eirin B Haug Maiken E Gabrielsen Anne Heidi Skogholt Ida Surakka George Davey Smith Anita Pandit Tanmoy Roychowdhury Whitney E Hornsby Jon G Jonasson Leigha Senter Sandya Liyanarachchi Matthew D Ringel Li Xu Lambertus A Kiemeney Huiling He Romana T Netea-Maier Jose I Mayordomo Theo S Plantinga Jon Hrafnkelsson Hannes Hjartarson Erich M Sturgis Aarno Palotie Mark Daly Cintia E Citterio Peter Arvan Chad M Brummett Michael Boehnke Albert de la Chapelle Kari Stefansson Kristian Hveem Cristen J Willer Bjørn Olav Åsvold Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor Michigan USA. wzhou@***. Analytic and Translational Genetics Unit Massachusetts General Hospital Boston Massachusetts USA. wzhou@***. Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge Massachusetts USA. wzhou@***. Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge Massachusetts USA. wzhou@***. K.G. Jebsen Center for Genetic Epidemiology Department of Public Health and Nursing NTNU Norwegian University of Science and Technology Trondheim Norway. Medical Research Council (MRC) Integrative Epidemiology Unit University of Bristol Bristol UK. Department of Thoracic Medicine St. Olavs Hospital Trondheim University Hospital Trondheim Norway. Department of Biological Chemistry University of Michigan Medical School Ann Arbor Michigan USA. Division of Metabolism Endocrinology and Diabetes University of Michigan Medical School Ann Arbor Michigan USA. deCODE genetics/AMGEN 101 Reykjavik Iceland. Center for Statistical Genetics and Department of Biostatistics University of Michigan School of Public Health Ann Arbor Michigan USA. Department of Internal Medicine Division of Cardiology University of Michigan Medical School Ann Arbor Michigan USA. Department of Epidemiology Research Statens Serum Institute Copenhagen Denmark. Erasmus MC Academic Center for Thyroid Diseases Rotterdam The Netherlands. Department of Epidemiology Erasmus Medical Center Rotterdam The Netherlands. Department of Internal Medicine Erasmus Medical Center Rotterdam The Netherlands. Division of Endocrinology Department of Internal Medicine Radboud University Medical Centre Radboud Institute for Molecular Life Sciences 6500HB Nijmegen The Netherlands. Institute for Community Medicine University Medicine Greifswald Greifswald Germany. DZHK (German Center for Cardiovascular Research) Partner Site Greifswald Greifswald Germany. Istit

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A second update on mapping the human genetic architecture of COVID-19

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Nature 2023年第7977期621卷 E7-E26页

作者： Masahiro Kanai Christine Stevens Benjamin M. Neale Mark Daly Andrea Ganna Amy Trankiem Mary K. Balaconis Samuli Ripatti Lindo Nkambul Mark J. Daly Sam Bryant Matthew Solomonson Shea J. Andrews Mattia Cordioli Juha Karjalainen Juha Mehtonen Matti Pirinen Andrea Corbetta Bradley Jermy Kati Donner Katja Kivinen Aarno Palotie Mari Kaunisto Kumar Veerapen Konrad J. Karczewski Alicia R. Martin Elizabeth G. Atkinson Kristin Tsuo Nikolas Baya Rahul Gupta Raymond K. Walters Duncan S. Palmer Gopal Sarma Nathan Cheng Wenhan Lu Claire Churchhouse Jacqueline I. Goldstein Daniel King Wei Zhou Cotton Seed Hilary Finucane F. Kyle Satterstrom Gita A. Pathak Akiko Iwasaki Karolina Chwialkowska Karolina Chwiałkowska Brooke N. Wolford Hajar Fauzan Ahmad Shea Andrews Kathrin Aprile von Hohenstaufen Puoti Palwende R. Boua Guillaume Butler-Laporte Celia M. T. Greenwood Bartha M. Knoppers Mark Lathrop Carmen L. Cadilla Francesca Colombo Nicole Dueker Chloe Kirk Atanu Kumar Dutta Yasser M. El-Sherbiny Jehan J. El-Jawhari Doaa Shahin Ahmed Elzeiny Tamer A. Mansour Madonna M. Eltoukhy Attia A. S. Mohamed Sahar Esmaeeli Annika Faucon Israel Fernández Cadenas Israel Fernandez-Cadenas Laia Llucià-Carol Natalia Cullell Elena Muiño Jara Cárcel-Márquez Margherita Francescatto Laurent Francioli Lindokuhle Nkambule Jatin Arora Soumya Raychaudhuri Caroline Cusick Lude Franke Macarena Fuentes Macarena Fuentes-Guajardo David Gómez Cabrero Emi N. Harry Philip Jansen József L. Szentpéteri Réka Várnai Elżbieta Kaja Athanasios Kousathanas Dorote Pasko Mark J. Caulfield Alex Stuckey Christopher A. Odhams Susan Walker Daniel Rhodes Richard H. Scott Loukas Moutsianas Afshan Siddiq Peter Goddard Sally Donovan Tala Zainy Fiona Maleady-Crowe Linda Todd Shahla Salehi Greg Elgar Georgia Chan Prabhu Arumugam Christine Patch Augusto Rendon Tom A. Fowler Jose E. Krieger Sanjay K. Patel Audrey Lemaçon Sophie Limou Pietro Lió Eirini Marouli Charles Hinds Minttu M. Marttila Carolina Medina-Gómez Isabelle Migeotte Soumyajit Mondal Andrés Moreno-Estrada Leire Moya Tomok Broad Institute of MIT and Harvard Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland (FIMM) HiLIFE University of Helsinki Helsinki Finland Public Health Faculty of Medicine University of Helsinki Helsinki Finland Analytic & Translational Genetics Unit Massachusetts General Hospital Boston MA USA Stanley Center for Psychiatric Research & Program in Medical and Population Genetics Cambridge UK Institute for Molecular Medicine Finland (FIMM) Univerisity of Helsinki Helsinki Finland University of California San Francisco San Francisco CA USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Department of Psychiatry School of Medicine Yale University New Haven CT USA Veteran Affairs Connecticut Healthcare System West Haven CT USA Yale University New Haven CT USA Centre for Bioinformatics and Data Analysis Medical University of Bialystok Bialystok Poland Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA Faculty of Industrial Sciences and Technology Universiti Malaysia Pahang Pahang Malaysia Icahn School of Medicine at Mount Sinai New York NY USA Genolier Innovation Network and Hub Swiss Medical Network Genolier Healthcare Campus Genolier Switzerland Immediate Independent Multidisciplinary Med & Biotech Research Association Milan Italy Clinical Research Unit of Nanor

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Designing metabolic engineering strategies with genome-scale metabolic flux modeling

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Advances in Genomics and Genetics 2015年 5卷 93页

作者： Yen, Jiun Y Tanniche, Imen Fisher, Amanda K Gillaspy, Glenda E Bevan, David R Senger, Ryan S Department of Biological Systems Engineering Virginia Tech Department of Biochemistry Virginia Tech Genomics Bioinformatics and Computational Biology Interdisciplinary Program Virginia Tech

New in silico tools that make use of genome-scale metabolic flux modeling are improving the design of metabolic engineering strategies. This review highlights the latest developments in this area, explains the interface between these in silico tools and the experimental implementation tools of metabolic engineers, and provides a way forward so that in silico predictions can better mimic reality and more experimental methods can be considered in simulation studies. The several methodologies for solving genome-scale models (eg, flux balance analysis [FBA], parsimonious FBA, flux variability analysis, and minimization of metabolic adjustment) all have unique advantages and applications. There are two basic approaches to designing metabolic engineering strategies in silico, and both have demonstrated success in the literature. The first involves: 1) making a genetic manipulation in a model; 2) testing for improved performance through simulation; and 3) iterating the process. The second approach has been used in more recently designed in silico tools and involves: 1) comparing metabolic flux profiles of a wild-type and ideally engineered state and 2) designing engineering strategies based on the differences in these flux profiles. Improvements in genome-scale modeling are anticipated in areas such as the inclusion of all relevant cellular machinery, the ability to understand and anticipate the results of combinatorial enrichment experiments, and constructing dynamic and flexible biomass equations that can respond to environmental and genetic manipulations.

关键词： genome-scale modeling flux balance analysis flux variability analysis minimization of metabolic adjustment metabolic bottleneck pathway optimization

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Étude métagénomique du microbiome cutané associé à la gale : confirmation moléculaire du lien entre S. scabiei et impétigo

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Annales de Dermatologie et de Vénéréologie 2017年第12期144卷 S284-S284页

作者： C. Bernigaud M. Zakrzewski P. Swe A.T. Papenfuss K.S. Sriprakash D. Holt B. Currie F. Botterel O. Chosidow K. Fischer Dermatologie CHU Henri-Mondor Créteil EA Dynamyc EA 7380 EnvA université Paris-Est Maisons-Alfort & Créteil France Genetics and computational biology department QIMR Berghofer medical research institute Infectious diseases program biology department Scabies Laboratory QIMR Berghofer medical research institute Brisbane Australie Bioinformatics division The Walter and Eliza Hall institute of medical research Parkville Victoria Australie Menzies school of health research Charles Darwin university Darwin Australie

Introduction Au cours de la gale, l’altération de la barrière cutanée, due à la pénétration du parasite dans l’épiderme et le grattage, sert de porte d’entrée aux bactér...

Introduction Au cours de la gale, l’altération de la barrière cutanée, due à la pénétration du parasite dans l’épiderme et le grattage, sert de porte d’entrée aux bactéries, Streptococcus pyogenes et Staphylococcus aureus principalement. Les lésions de gale se compliquent d’impétigo pouvant évoluer vers des infections plus sévères, voire des complications postinfectieuses (RAA, glomérulonéphrite) particulièrement fréquentes dans les pays tropicaux (Fidji, Inde, Australie), mais plus exceptionnelles en France. Si ce lien a pu être démontré par des études épidémiologiques, aucune étude mécanistique n’a jamais été réalisée. Le but de notre étude était d’étudier l’impact du parasite sur le microbiote cutané. Matériel et méthodes Pour étudier la diversité bactérienne et le rôle de cette communauté au cours de l’infection parasitaire, des prélèvements cutanés ont été effectués chez deux patients aborigènes australiens (A, B) ayant une gale hyperkératosique sévère. Le microbiome cutané a été étudié par métagénomique directe ( whole genome profiling/ métagénomique shotgun ). Une analyse fonctionnelle de la communauté bactérienne a été effectuée (facteurs de virulence, voies métaboliques, dégradome). Résultats Deux librairies contenant ∼80 M séquences ( paired-end reads ) ont été construites parmi lesquelles ∼2 M (2,54 %) et ∼0,4 M (0,62 %) séquences microbiennes ont été filtrées après alignement avec le génome humain de référence et l’ébauche du génome de S. scabiei . L’analyse taxonomique a montré une communauté microbienne dominée par l’embranchement des Firmicutes (A : 79 %, B : 59 %). L’embranchement des actinobactéries (A : 11 %, B : 1,8 %), normalement le plus abondant en peau saine, était sous-représenté. Les genres prédominants comprenaient Staphylococcus (principalement S. aureus , dont SARM et S. argenteus ), Acinetobacter (principalement A. baumannii ) et Streptococcus (groupes A et G). De nombreux facteurs de virulence étaient identifiés. Les voies d

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Nat Methods:利用CRISPR-Cas9组合筛选发现癌症弱点

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现代生物医学进展 2017年第17期17卷 I0001-I0001页

作者： John Paul Shen, Ana Bojorquez-Gomez, Katherine Licon, Kristin Klepper, Daniel Pekin, Alex N Beckett, Kyle Salinas Sanchez, Jason F Kreisberg, Trey Ideker John Paul Shen, Trey Ideker, Prashant Mali John Paul Shen, Dongxin Zhao, Dan Du, Assen Roguev, Jason F Kreisberg, Nevan Krogan, Lei Qi, Trey Ideker, Prashant Mali Dongxin Zhao, Chih-Chung Kuo, Prashant Mali Roman Sasik, Amanda Birmingham, Aaron N Chang, Trey Ideker Jens Luebeck, Alex Thomas Alex Thomas, Chih-Chung Kuo, Nathan E Lewis Dan Du Assen Roguev, Nevan Krogan Nathan E Lewis Lei Qi Department of Medicine Division of Genetics University of California San Diego La Jolla California USA Moores UCSD Cancer Center La Jolla California USA The Cancer Cell Map Initiative (CCMI) La Jolla and San Francisco California USA Department of Bioengineering University of California San Diego La Jolla California USA Center for Computational Biology & Bioinformatics University of California San Diego La Jolla California USA Bioinformatics & Systems Biology Program University of California San Diego La Jolla California USA Novo Nordisk Center for Biosustainability at the University of California San Diego La Jolla California USA Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research Department of Cell and Tissue Biology University of California San Francisco San Francisco California USA Department of Cellular and Molecular Pharmacology University of California San Francisco San Francisco California USA Department of Pediatrics University of California San Diego La Jolla California USA Department of Bioengineering Stanford University Stanford California USA.

导致癌症的基因突变也会削弱癌细胞，从而使得人们有机会开发选择性地杀死它们同时不影响正常细胞的药物。这一概念被称作”合成致死性（synthetic lethality）”，这是因为这些药物仅对发生突变的（或者说合成的）细胞是致命性的。在... 详细信息

导致癌症的基因突变也会削弱癌细胞，从而使得人们有机会开发选择性地杀死它们同时不影响正常细胞的药物。这一概念被称作”合成致死性（synthetic lethality）”，这是因为这些药物仅对发生突变的（或者说合成的）细胞是致命性的。在一项新的研究中。来自美国加州大学圣地亚哥分校、加州大学旧金山分校、斯坦福大学和癌细胞图谱项目（Cancer Cell Map Initiative）的研究人员开发出一种新的方法来寻找合成致死性的基因组合。

关键词：组合筛选癌症美国加州大学利用基因突变斯坦福大学癌细胞正常细胞

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Comprehensive Functional Characterization of Germline ETV6 Variants Associated with Inherited Predisposition to Acute Lymphoblastic Leukemia in Children

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BLOOD 2016年第22期128卷 1085-1085页

作者： Nishii, Rina Baskin, Rebekah Moriyama, Takaya Hoshitsuki, Keito Metzger, Monika L. Wu, Gang Devidas, Meenakshi Yang, Wenjian Tedrick, Paige Quinn, Emily Gastier-Foster, Julie M. Raetz, Elizabeth Larsen, Eric Martin, Paul L. Bowman, W. Paul Winick, Naomi Takagi, Masatoshi Pui, Ching-Hon Evans, William E. Zhang, Jinghui Relling, Mary V. Hunger, Stephen P. Mullighan, Charles Loh, Mignon L. Nichols, Kim E. Yang, Jun J. Hematological Malignancies Program and Department of Pharmaceutical Sciences St. Jude Children's Research Hospital Memphis TN Department of Pediatrics and Developmental Biology Tokyo Medical and Dental University Graduate School of Medicine Tokyo Japan Department of Oncology St. Jude Children's Research Hospital Memphis TN School of Pharmacy University of Pittsburgh Pittsburgh PA Department of Computational Biology St. Jude Children's Research Hospital Memphis TN Department of Biostatistics College of Medicine Public Health and Health Professions University of Florida Gainesville FL Department of Pathology and Laboratory Medicine Nationwide Children's Hospital Columbus OH Huntsman Cancer Institute University of Utah Salt Lake City UT Maine Children's Cancer Program Scarborough ME Department of Pediatrics Duke University Durham NC Cook Children's Medical Center Fort Worth TX Pediatric Hematology Oncology University of Texas Southwestern Medical Center Dallas TX Oncology St. Jude Children's Research Hospital Memphis TN Department of Computational Biology and Bioinformatics St. Jude Children's Research Hospital Memphis TN Department of Pediatrics and Center for Childhood Cancer Research Children's Hospital of Philadelphia and University of Pennsylvania Perelman School of Medicine Philadelphia PA Department of Pathology St. Jude Children's Research Hospital Memphis TN Department of Pediatrics Benioff Children's Hospital and Helen Diller Family Comprehensive Cancer Center University of California San Francisco CA

There is increasing evidence for inherited susceptibility to acute lymphoblastic leukemia (ALL) in children. Both common and rare germline genetic variants can significantly influence the risk of developing ALL, often in an age- and/or leukemia subtype-dependent manner (e.g., ARID5B , IKZF1 , GATA3 , PIP4K2A , PAX5, and TP53 ). Recently, we and others reported a novel genetic syndrome characterized by damaging germline ETV6 mutations that co-segregate with hereditary thrombocytopenia and increased propensity for hematologic malignancies (especially ALL). Sequencing 4,405 childhood ALL cases, we subsequently identified 30 germline coding ETV6 variants that were potentially responsible for disease predisposition in 35 patients (Lancet Oncol 2015). These findings unequivocally point to ETV6 as a critical ALL risk gene; however, the exact functional consequences of the ALL-related ETV6 variants are largely unknown and the mechanism by which they contribute to leukemogenesis is unclear. To this end, we first systematically evaluated these 30 ETV6 risk variants for their transcriptional repressor activity, ability to bind to target DNA sequences, localize to the nucleus, and also to homo-dimerize. Using PF4 and MMP3 promoter-driven luciferase transcription in HEK293T cells as the model system, we showed that 18 of these 30 ETV6 variants had loss of transcriptional repression activity (median of 53.2% of wildtype ETV6 activity [range 7.1% to 83.9%]). These include 4 of the 5 frameshift variants, all 4 nonsense variants, and also 10 of 11 missense variants in the ETS/DNA binding domain. None of the missense variants outside the ETS domain had significant effects on transcriptional repression function, and surprisingly the E44fs variant showed enhanced activity. Co-expression of wildtype and each of the 18 loss-of-function variants consistently impaired the normal repressor activity of wildtype ETV6, indicating dominant-negative effects. In cells expressing both wildtype and

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相关性和一致性:这对相仿概念和测量方法的回顾与阐明（英文）

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上海精神医学 2016年第2期28卷 115-120页

作者： Liu JY Tang W Chen GQ Lu Y Feng CY Tu XM Department of Biostatistics and Computational Biology University of Rochester Rochester NY USA Department of Biostatistics & Bioinformatics School of Public Health and Tropical Medicine Tulane University New Orleans LA USA VA Cooperative Studies Program Palo Alto Coordinating Center VA Palo Alto Health Care System Palo Alto CA USA Department of Biomedical Data Science Stanford University Stanford CA USA

一致性(agreement)和相关性(correlation)是两个广泛使用的概念,用来评估变量之间的关联。虽然二者相似且相关,但是它们代表关联完全不同的概念。评估变量之间的一致性假设变量测量的是相同的结构,而在变量测量完全不同的结构时也可以... 详细信息

一致性(agreement)和相关性(correlation)是两个广泛使用的概念,用来评估变量之间的关联。虽然二者相似且相关,但是它们代表关联完全不同的概念。评估变量之间的一致性假设变量测量的是相同的结构,而在变量测量完全不同的结构时也可以评估它们之间的相关性。这种概念上的差异就要求使用不同的统计方法,并且当评估一致性或相关性时,统计方法根据数据的分布和研究者的兴趣可能会有所不同。例如,Pearson相关性,作为评估连续变量之间相关性的一种普遍测量方法,只有用于符合线性关系的变量时才能提供有用的信息;当用于不符合线性关系的变量时就无法提供准确信息甚至会产生误导。同样地,内部相关性,作为一种评估连续变量之间一致性的常用方法,如果一致性不好的实质正好是研究兴趣所在,那么该测量就不能为研究者提供充分的信息。本报告回顾了一致性和相关性的概念,并讨论了几种常用方法在应用中的差异。

关键词：积差相关性内部一致性 Kendall's tau 非线性相关 Pearson's相关性 Spearman's rho

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DOMMINO 2.0: Integrating structurally resolved protein-, RNA-, and DNA-mediated Macromolecular interactions

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Database 2016年第2016期2016卷 bav114页

作者： Kuang, Xingyan Dhroso, Andi Han, Jing Ginger Shyu, Chi-Ren Korkin, Dmitry Informatics Institute University of Missouri Columbia MO United States Department of Computer Science Bioinformatics and Computational Biology Program Worcester Polytechnic Institute Worcester MA United States Department of Electrical and Computer Engineering United States Department of Computer Science University of Missouri Columbia MO United States Ohio State University Comprehensive Cancer Center Columbus OH United States

Macromolecular interactions are formed between proteins, DNA and RNA molecules. Being a principle building block in macromolecular assemblies and pathways, the interactions underlie most of cellular functions. Malfunctioning of macromolecular interactions is also linked to a number of diseases. Structural knowledge of the macromolecular interaction allows one to understand the interaction's mechanism, determine its functional implications and characterize the effects of genetic variations, such as single nucleotide polymorphisms, on the interaction. Unfortunately, until now the interactions mediated by different types of macromolecules, e.g. protein-protein interactions or protein-DNA interactions, are collected into individual and unrelated structural databases. This presents a significant obstacle in the analysis of macromolecular interactions. For instance, the homogeneous structural interaction databases prevent scientists from studying structural interactions of different types but occurring in the same macromolecular complex. Here, we introduce DOMMINO 2.0, a structural Database Of Macro-Molecular INteractiOns. Compared to DOMMINO 1.0, a comprehensive database on protein-protein interactions, DOMMINO 2.0 includes the interactions between all three basic types of macromolecules extracted from PDB files. DOMMINO 2.0 is automatically updated on a weekly basis. It currently includes ∼1 040 000 interactions between two polypeptide subunits (e.g. domains, peptides, termini and interdomain linkers), ∼43 000 RNA-mediated interactions, and ∼12 000 DNA-mediated interactions. All protein structures in the database are annotated using SCOP and SUPERFAMILY family annotation. As a result, protein-mediated interactions involving protein domains, interdomain linkers, C- and N-termini, and peptides are identified. Our database provides an intuitive web interface, allowing one to investigate interactions at three different resolution levels: whole subunit network, binary intera

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A high-resolution transcriptome map of cell cycle reveals novel connections between periodic genes and cancer

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细胞研究（英文版） 2016年第8期26卷 946-962页

作者： Daniel Dominguez Yi-Hsuan Tsai Nicholas Gomez Deepak Kumar Jha Ian Davis Zefeng Wang Department of Pharmacology University of North Carolina at Chapel Hill Chapel Hill NC USA Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Pharmacology University of North Carolina at Chapel Hill Chapel Hill NC USA program in Bioinformatics and Computational Biology University of North Carolina at Chapel Hill Chapel Hill NC USA Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill NC USA Curriculum in Genetics and Molecular Biology University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Biochemistry and Biophysics University of North Carolina at Chapel Hill Chapel Hill NC USA Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Genetics University of North Carolina at Chapel HillChapel Hill NC USA Department of Pediatrics University of North Carolina at Chapel Hill Chapel Hill NC USA Carolina Center for Genome Sciences University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Pharmacology University of North Carolina at Chapel Hill Chapel Hill NC USA Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill NC USA Key Lab of Computational Biology CAS-MPG Partner Institute for Computational Biology Chinese Academy of Science Shanghai China

Progression through the cell cycle is largely dependent on waves of periodic gene expression, and the regulatory networks for these transcriptome dynamics have emerged as critical points of vulnerability in various aspects of tumor biology. Through RNA-sequencing of human cells during two continuous cell cycles (>2.3 billion paired reads), we identified over 1 000 mRNAs, non-coding RNAs and pseudogenes with periodic expression. Periodic transcripts are enriched in functions related to DNA metabolism, mitosis, and DNA damage response, indicating these genes likely represent putative cell cycle regulators. Using our set of periodic genes, we developed a new approach termed “mitotic trait” that can classify primary tumors and normal tissues by their transcriptome similarity to different cell cycle stages. By analyzing >4 000 tumor samples in The Cancer Genome Atlas (TCGA) and other expression data sets, we found that mitotic trait significantly correlates with genetic alterations, tumor subtype and, notably, patient survival. We further defined a core set of 67 genes with robust periodic expression in multiple cell types. Proteins encoded by these genes function as major hubs of protein-protein interaction and are mostly required for cell cycle progression. The core genes also have unique chromatin features including increased levels of CTCF/RAD21 binding and H3K36me3. Loss of these features in uterine and kidney cancers is associated with altered expression of the core 67 genes. Our study suggests new chromatin-associated mechanisms for periodic gene regulation and offers a predictor of cancer patient outcomes.

关键词： cell cycle periodic transcriptome epigenetic regulation mitotic trait

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Author Correction: The power of genetic diversity in genome-wide association studies of lipids

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Nature 2023年第7965期618卷 E19-E20页

作者： Sarah E Graham Shoa L Clarke Kuan-Han H Wu Stavroula Kanoni Greg J M Zajac Shweta Ramdas Ida Surakka Ioanna Ntalla Sailaja Vedantam Thomas W Winkler Adam E Locke Eirini Marouli Mi Yeong Hwang Sohee Han Akira Narita Ananyo Choudhury Amy R Bentley Kenneth Ekoru Anurag Verma Bhavi Trivedi Hilary C Martin Karen A Hunt Qin Hui Derek Klarin Xiang Zhu Gudmar Thorleifsson Anna Helgadottir Daniel F Gudbjartsson Hilma Holm Isleifur Olafsson Masato Akiyama Saori Sakaue Chikashi Terao Masahiro Kanai Wei Zhou Ben M Brumpton Humaira Rasheed Sanni E Ruotsalainen Aki S Havulinna Yogasudha Veturi QiPing Feng Elisabeth A Rosenthal Todd Lingren Jennifer Allen Pacheco Sarah A Pendergrass Jeffrey Haessler Franco Giulianini Yuki Bradford Jason E Miller Archie Campbell Kuang Lin Iona Y Millwood George Hindy Asif Rasheed Jessica D Faul Wei Zhao David R Weir Constance Turman Hongyan Huang Mariaelisa Graff Anubha Mahajan Michael R Brown Weihua Zhang Ketian Yu Ellen M Schmidt Anita Pandit Stefan Gustafsson Xianyong Yin Jian'an Luan Jing-Hua Zhao Fumihiko Matsuda Hye-Mi Jang Kyungheon Yoon Carolina Medina-Gomez Achilleas Pitsillides Jouke Jan Hottenga Gonneke Willemsen Andrew R Wood Yingji Ji Zishan Gao Simon Haworth Ruth E Mitchell Jin Fang Chai Mette Aadahl Jie Yao Ani Manichaikul Helen R Warren Julia Ramirez Jette Bork-Jensen Line L Kårhus Anuj Goel Maria Sabater-Lleal Raymond Noordam Carlo Sidore Edoardo Fiorillo Aaron F McDaid Pedro Marques-Vidal Matthias Wielscher Stella Trompet Naveed Sattar Line T Møllehave Betina H Thuesen Matthias Munz Lingyao Zeng Jianfeng Huang Bin Yang Alaitz Poveda Azra Kurbasic Claudia Lamina Lukas Forer Markus Scholz Tessel E Galesloot Jonathan P Bradfield E Warwick Daw Joseph M Zmuda Jonathan S Mitchell Christian Fuchsberger Henry Christensen Jennifer A Brody Mary F Feitosa Mary K Wojczynski Michael Preuss Massimo Mangino Paraskevi Christofidou Niek Verweij Jan W Benjamins Jorgen Engmann Rachel L Kember Roderick C Slieker Ken Sin Lo Nuno R Zilhao Phuong Le Marcus E Kleber Graciela E Delgado Shaofeng Huo Dai Department of Internal Medicine Division of Cardiology University of Michigan Ann Arbor MI USA. VA Palo Alto Health Care System Palo Alto CA USA. Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Department of Biostatistics and Center for Statistics Genetics University of Michigan Ann Arbor MI USA. Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Clinical Pharmacology William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK. Endocrinology Boston Children's Hospital Boston MA USA. Medical and Population Genetics Broad Institute Cambridge MA USA. Department of Genetic Epidemiology University of Regensburg Regensburg Germany. McDonnell Genome Institute and Department of Medicine Washington University St Louis MO USA. Department of Precision Medicine Division of Genome Science National Institute of Health Cheongju-si South Korea. Tohoku Medical Megabank Organization Tohoku University Sendai Japan. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa. Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA. Blizard Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Wellcome Sanger Institute Hinxton UK. Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA. Atlanta VA Health Care System Decatur GA USA. Malcolm Randall VA Medical Center Gainesville FL USA. Division of Vascular Sur

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