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Genome-wide association study of long COVID

Nature Genetics 2025年 1-16页

作者： Vilma Lammi Tomoko Nakanishi Samuel E. Jones Juha Karjalainen Martin Broberg Hele H. Haapaniemi Matti Pirinen Mari E. K. Niemi Mattia Cordioli Mark J. Daly Andrea Ganna Hanna M. Ollila J. Brent Richards Yukinori Okada Shea J. Andrews Beatriz Cortés Rafael de Cid Susana Iraola-Guzmán Natalia Blay Xavier Farré Heath E. O’Brien Thompson Hannah Patrick J. Short Ana Ochoa-Guzman Michelle Duran-Gomez Brian E. Fulton-Howard Masahiro Kanai Axel Schmidt Daniella Balla Julia Heggemann Sonja Schultz Pari Behzad Markus M. Nöthen Abigail Miller Kerstin U. Ludwig Ruth E. Mitchell George Davey Smith Nicholas J. Timpson Abdou Mousas Evangelos Evangelou Evangelia Ntzani Konstantinos K. Tsilidis Massimo Mangino Alicia Huerta-Chagoya Nasa Sinnott-Armstrong Elizabeth T. Cirulli Francisco Tanudjaja Efren Sandoval Nicole L. Washington Simon White Alexandre Bolze Kelly M. Schiabor Barrett Marc Vaudel Alex S. F. Kwong Amit K. Maiti Minttu M. Marttila Daniel C. Posner Alexis A. Rodriguez Chiara Batini Beatriz Guillen-Guio Olivia C. Leavy Anna L. Guyatt Catherine John Louise V. Wain Francesca Minnai Anna R. Dearman Benedict Hignell Meena Kumari C. A. Robert Warmerdam Lude H. Franke Celia B. Sequeros Søren Brunak David Westergaard Karina Banasik Thomas W. Winkler Iris M. Heid Daniel M. Jordan Ryan C. Thompson Alexander W. Charney Laura G. Sloofman Nicole W. Simons Noam D. Beckmann Raimonds Rešcenko Vita Rovite Peculis Raitis Monta Briviba Janis Klovinš Laura Ansone Lorenzo Miano Flora Peyvandi Francesco Malvestiti Nicola Montano Alessandra Bandera Francesco Bruno Arturo Blasi Jacqueline M. Lane Jakob M. Cherry Ryan K. Chung Laura Carvajal-Silva Kevin Aguilar-Valdés Leslie C. Cerpa Tamara V. Arévalo Eduardo Lamoza Alicia Colombo Ricardo A. Verdugo Erika Frangione Xu Xinyi Jennifer Taher Jordan Lerner-Ellis Lindsay Guare Shefali S. Verma Ekaterina Vergasova Michil Trofimov Layal Shaheen Nikolay Plotnikov Anna Kim Dmitrii Kharitonov Alexei Kamelin Alexander Rakitko Eirini Marouli Pasquale Striano Ummu Afeera Zainulabid Ashutosh Kumar Hajar Fa Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Department of Human Genetics McGill University Montreal Quebec Canada Centre for Clinical Epidemiology Department of Medicine Lady Davis Institute Jewish General Hospital McGill University Montreal Quebec Canada Kyoto-McGill International Collaborative Program in Genomic Medicine Graduate School of Medicine Kyoto University Kyoto Japan Department of Genome Informatics Graduate School of Medicine the University of Tokyo Tokyo Japan Research Fellow Japan Society for the Promotion of Science Tokyo Japan Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Department of Mathematics and Statistics University of Helsinki Helsinki Finland Department of Public Health University of Helsinki Helsinki Finland Broad Institute Cambridge MA USA Analytical and Translational Genetics Unit Massachusetts General Hospital Harvard Medical School Boston MA USA Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Anesthesia Critical Care and Pain Medicine Massachusetts General Hospital and Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Twin Research King’s College London London UK 5 Prime Sciences Inc Montreal Quebec Canada Department of Epidemiology Biostatistics and Occupational Health McGill University Montreal Quebec Canada Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan Laboratory for Systems Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan Departmen

Infections can lead to persistent symptoms and diseases such as shingles after varicella zoster or rheumatic fever after streptococcal infections. Similarly, severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection can result in long coronavirus disease (COVID), typically manifesting as fatigue, pulmonary symptoms and cognitive dysfunction. The biological mechanisms behind long COVID remain unclear. We performed a genome-wide association study for long COVID including up to 6,450 long COVID cases and 1,093,995 population controls from 24 studies across 16 countries. We discovered an association ofFOXP4with long COVID, independent of its previously identified association with severe COVID-19. The signal was replicated in 9,500 long COVID cases and 798,835 population controls. Given the transcription factor FOXP4’s role in lung physiology and pathology, our findings highlight the importance of lung function in the pathophysiology of long COVID.

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Artificial intelligence in gastrointestinal radiology: A review with special focus on recent development of magnetic resonance and computed tomography

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Artificial Intelligence in Gastroenterology 2021年第2期2卷 27-41页

作者： Kai-Po Chang Shih-Huan Lin Yen-Wei Chu PhD Program in Medical Biotechnology National Chung Hsing UniversityTaichung 40227Taiwan Department of Pathology China Medical University HospitalTaichung 40447Taiwan Institute of Genomics and Bioinformatics National Chung Hsing UniversityTaichung 40227Taiwan Institute of Molecular Biology National Chung Hsing UniversityTaichung 40227Taiwan Agricultural Biotechnology Center National Chung Hsing UniversityTaichung 40227Taiwan Biotechnology Center National Chung Hsing UniversityTaichung 40227Taiwan PhD Program in Translational Medicine National Chung Hsing UniversityTaichung 40227Taiwan Rong Hsing Research Center for Translational Medicine Taichung 40227Taiwan

Artificial intelligence(AI),particularly the deep learning technology,have been proven influential to radiology in the recent *** ability in image classification,segmentation,detection and reconstruction tasks have substantially assisted diagnostic radiology,and has even been viewed as having the potential to perform better than radiologists in some *** radiology,an important subspecialty dealing with complex anatomy and various modalities including endoscopy,have especially attracted the attention of AI researchers and engineers ***,recently many tools have been developed for lesion detection and image construction in gastrointestinal radiology,particularly in the fields for which public databases are available,such as diagnostic abdominal magnetic resonance imaging(MRI)and computed tomography(CT).This review will provide a framework for understanding recent advancements of AI in gastrointestinal radiology,with a special focus on hepatic and pancreatobiliary diagnostic radiology with MRI and *** fields where AI is less developed,this review will also explain the difficulty in AI model training and possible strategies to overcome the technical *** authors’insights of possible future development will be addressed in the last section.

关键词： Artificial intelligence Deep learning Image diagnosis Radiology Magnetic resonance imaging Computed tomography

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Erratum to: triple-negative, basal-like, and quintuple-negative breast cancers: better prediction model for survival

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BMC Cancer 2011年第1期11卷 1-2页

作者： Yoon-La Choi Eun Yoon Cho Ensel Oh Kyug Song Young Kee Shin Sarah Park Yeonju Kim Yeon-Hee Park Young-Hyuck Lim Yun-Chul Hong Jong Sun Choi Jeong Eon Lee Jung Han Kim Seok Jin Nam Jung-Hyun Yang Laboratory of Cancer Genomics and Molecular Pathology South Korea Department of Pathology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul South Korea Research Institute of Pharmaceutical Science South Korea Interdiciplinary Program of Bioinformatics Department of Pharmacy Seoul National University College of Pharmacy Seoul South Korea Division of Medical Oncology Department of Internal Medicine Seoul St. Mary's hospital Catholic University Seoul South Korea Cancer Early Detection Branch National Cancer Control Research Institute National Cancer Center Goyang South Korea Department of Internal Medicine Samsung Medical Center Sungkyunkwan University School of Medicine Seoul South Korea Department of Preventive Medicine Seoul National University College of Medicine Seoul South Korea Department of Pathology Dongguk University Ilsan Hospital Dongguk University College of Medicine Goyang South Korea Department of Surgery Samsung Medical Center Sungkyunkwan University School of Medicine Seoul South Korea

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Multiplexed Antibody Glycosylation Profiling Using Dual Enzyme Digestion and Liquid Chromatography-Triple Quadrupole Mass Spectrometry Method

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Molecular and Cellular proteomics 2024年第2期23卷 100710页

作者： Cheng, Yu-Hsuan Lee, Chih-Hsin Wang, San-Yuan Chou, Chia-Yi Yang, Yun-Jung Kao, Chih-Chin Wu, Hsin-Yi Dong, Yushi Hung, Wen-Ying Su, Ching-Yi Tseng, Shih-Ting Tsai, I-Lin Department of Biochemistry and Molecular Cell Biology School of Medicine Taiwan Graduate Institute of Medical Sciences College of Medicine Taiwan Pulmonary Research Center Taiwan Department of Internal Medicine Wan Fang Hospital Taiwan Division of Pulmonary Medicine Department of Internal Medicine School of Medicine College of Medicine Taiwan Master Program in Clinical Genomics and Proteomics College of Pharmacy Taiwan Division of Nephrology Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Division of Nephrology Department of Internal Medicine Taipei Medical University Hospital Taipei Taiwan Taipei Medical University Research Center of Urology and Kidney (TMU-RCUK) Taipei Medical University Taipei Taiwan Instrumentation Center National Taiwan University Taipei Taiwan International PhD Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei Taiwan

Antibody glycosylation plays a crucial role in the humoral immune response by regulating effector functions and influencing the binding affinity to immune cell receptors. Previous studies have focused mainly on the immunoglobulin G (IgG) isotype owing to the analytical challenges associated with other isotypes. Thus, the development of a sensitive and accurate analytical platform is necessary to characterize antibody glycosylation across multiple isotypes. In this study, we have developed an analytical workflow using antibody-light-chain affinity beads to purify IgG, IgA, and IgM from 16 μL of human plasma. Dual enzymes, trypsin and Glu-C, were used during on-bead digestion to obtain enzymatic glycopeptides and protein-specific surrogate peptides. Ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry was used in order to determine the sensitivity and specificity. Our platform targets 95 glycopeptides across the IgG, IgA, and IgM isotypes, as well as eight surrogate peptides representing total IgG, four IgG classes, two IgA classes, and IgM. Four stable isotope-labeled internal standards were added after antibody purification to calibrate the preparation and instrumental bias during analysis. Calibration curves constructed using serially diluted plasma samples showed good curve fitting (R² > 0.959). The intrabatch and interbatch precision for all the targets had relative standard deviation of less than 29.6%. This method was applied to 19 human plasma samples, and the glycosylation percentages were calculated, which were comparable to those reported in the literature. The developed method is sensitive and accurate for Ig glycosylation profiling. It can be used in clinical investigations, particularly for detailed humoral immune profiling. © 2023 THE AUTHORS. Published by Elsevier Inc on behalf of American Society for Biochemistry and Molecular Biology.

关键词： liquid chromatography mass spectrometry human immunoglobulin N-glycosylation antibody profiling

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Custom CRISPR-Cas9 PAM variants via scalable engineering and machine learning

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Nature 2025年 2025 Apr 22页

作者： Rachel A Silverstein Nahye Kim Ann-Sophie Kroell Russell T Walton Justin Delano Rossano M Butcher Martin Pacesa Blaire K Smith Kathleen A Christie Leillani L Ha Ronald J Meis Aaron B Clark Aviv D Spinner Cicera R Lazzarotto Yichao Li Azusa Matsubara Elizabeth O Urbina Gary A Dahl Bruno E Correia Debora S Marks Shengdar Q Tsai Luca Pinello Suk See De Ravin Qin Liu Benjamin P Kleinstiver PhD Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. Department of Pathology Massachusetts General Hospital Boston MA USA. Department of Pathology Harvard Medical School Boston MA USA. Institute of Pharmacy and Molecular Biotechnology (IPMB) Faculty of Engineering Sciences Heidelberg University Heidelberg Germany. Department of Biomedical Informatics Harvard Medical School Boston MA USA. Molecular Pathology Unit Massachusetts General Hospital Charlestown MA USA. Krantz Family Center for Cancer Research Massachusetts General Hospital Charlestown Charlestown MA USA. Broad Institute of Harvard and MIT Cambridge MA USA. Ocular Genomics Institute Massachusetts Eye and Ear Boston MA USA. Department of Ophthalmology Harvard Medical School Boston MA USA. Laboratory of Protein Design and Immunoengineering École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics Lausanne Switzerland. CELLSCRIPTTM Madison WI USA. Department of Systems Biology Harvard Medical School Boston MA USA. Department of Hematology St. Jude Children's Research Hospital Memphis TN USA. Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. bkleinstiver@mgh.harvard.edu. Department of Pathology Massachusetts General Hospital Boston MA USA. bkleinstiver@mgh.harvard.edu. Department of Pathology Harvard Medical School Boston MA USA. bkleinstiver@mgh.harvard.edu.

Engineering and characterizing proteins can be time-consuming and cumbersome, motivating the development of generalist CRISPR-Cas enzymes to enable diverse genome editing applications. However, such enzymes have caveats such as an increased risk of off-target editing. To enable scalable reprogramming of Cas9 enzymes, here we combined high-throughput protein engineering with machine learning (ML) to derive bespoke editors more uniquely suited to specific targets. Via structure/function-informed saturation mutagenesis and bacterial selections, we obtained nearly 1,000 engineered SpCas9 enzymes and characterized their protospacer-adjacent motif (PAM) requirements to train a neural network that relates amino acid sequence to PAM specificity. By utilizing the resulting PAM ML algorithm (PAMmla) to predict the PAMs of 64 million SpCas9 enzymes, we identified efficacious and specific enzymes that outperform evolution-based and engineered SpCas9 enzymes as nucleases and base editors in human cells while reducing off-targets. An in silico directed evolution method enables user-directed Cas9 enzyme design, including for allele-selective targeting of the RHO P23H allele in human cells and mice. Together, PAMmla integrates ML and protein engineering to curate a catalog of SpCas9 enzymes with distinct PAM requirements, and motivates the use of efficient and safe bespoke Cas9 enzymes instead of generalist enzymes for various applications.

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Differential regulation of JAK1 expression by ETS1 associated with predisposition to primary biliary cholangitis

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Journal of Genetics and genomics 2023年第10期50卷 807-812页

作者： Peng Jiang Chan Wang Mingming Zhang Ye Tian Weifeng Zhao Junyi Xin Yexi Huang Zhibin Zhao Wenjuan Sun Jie Long Ruqi Tang Fang Qiu Xingjuan Shi Yi Zhao Li Zhu Na Dai Lei Liu Xudong Wu Jinshan Nie Bo Jiang Youlin Shao Yueqiu Gao Jianjiang Yu Zhigang Hu Zhidong Zang Yuhua Gong Yaping Dai Lan Wang Ningling Ding Ping Xu Sufang Chen Lu Wang Jing Xu Luyao Zhang Junyan Hong Ruonan Qian Hu Li Xuan Jiang Congwei Chen Wenyan Tian Jian Wu Yuzhang Jiang Chongxu Han Kui Zhang Hong Qiu Li Li Hong Fan Liming Chen Jianqiong Zhang Zhongsheng Sun Xiao Han Zhenhua Dai Erguang Li M.Eric Gershwin Zhexiong Lian Xiong Ma Michael F.Seldin Weichang Chen Meilin Wang Xiangdong Liu Key Laboratory of Developmental Genes and Human Diseases School of Life Science and TechnologySoutheast UniversityNanjingJiangsu 210096China Institute of Translational Medicine Yangzhou University Medical CollegeYangzhouJiangsu 225009China Department of Radiology&Oncology The Second Affiliated Hospital of Soochow UniversitySuzhouJiangsu 215004China The First Affiliated Hospital of Soochow University SuzhouJiangsu 215006China Department of Bioinformatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjingJiangsu 210029China Author links open overlay panelYexi Huang Guangdong Provincial People's Hospital Guangdong Academy of Medical SciencesGuangzhouGuangdong 510080China Department of Gastroenterology and Hepatology Shanghai Institute of Digestive DiseasesShanghai Jiao Tong University School of Medicine Affiliated Renji HospitalShanghai 200001China Department of Laboratory Medicine The Fourth Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsu 210031China Department of Gastrointestinal Endoscopy Eastern Hepatobiliary Surgery HospitalShanghai 201800China The Fifth People's Hospital Soochow UniversitySuzhouJiangsu 215007China Department of Gastroenterology Jiangsu University Affiliated Kunshan HospitalKunshanJiangsu 215300China Department of Gastroenterology Yixing People's HospitalYixinJiangsu 214200China Department of Gastroenterology Yancheng First People's HospitalYanchengJiangsu 224005China Department of Gastroenterology Taicang First People's HospitalSoochow UniversityTaicangJiangsu 215400China Department of Hepatology Jingjiang Second People's HospitalJingjiangJiangsu 214500China Department of Hepatology The Third People's Hospital of ChangzhouChangzhouJiangsu 213001China Department of Liver Diseases Shuguang HospitalShanghai University of Traditional Chinese MedicineShanghai 201203China Department of Laboratory Medicine Jiangyin People's HospitalSoutheast UniversityJiangyinJiangsu 214400China Department of Labora

Primary biliary cholangitis(PBC)is an autoimmune liver disease characterized by the destruction of intrahepatic small bile ducts and progressive cholestasis,eventually leading to liver cirrhosis and hepatic failure wi... 详细信息

关键词： cholangitis biliary

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Molecular landscape of sex- and modality-specific exercise adaptation in human skeletal muscle through large-scale multi-omics integration

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Cell Reports 2025年第6期44卷

作者： Macsue Jacques Shanie Landen Adam P. Sharples Andrew Garnham Ralf Schittenhelm Joel Steele Aino Heikkinen Elina Sillanpää Miina Ollikainen James Broatch Navabeh Zarekookandeh Ola Hanson Ola Ekström Olof Asplund Séverine Lamon Sarah E. Alexander Cassandra Smith Carlie Bauer Mary N. Woessner Itamar Levinger Andrew E. Teschendorff Linn Gillberg Ida Blom Jørn Wulff Helge Nicholas R. Harvey Larisa M. Haupt Lyn R. Griffiths Atul S. Deshmukh Kirsi H. Pietiläinen Päivi Piirilä Robert A.E. Seaborne Marie Klevjer Anja Bye Ulrik Wisløff Bernadette Jones-Freeman Nir Eynon Australian Regenerative Medicine Institute Monash University Clayton VIC Australia Institute for Health and Sport (iHeS) Victoria University Footscray VIC Australia Centre for Endocrinology and Metabolism Hudson Institute of Medical Research Melbourne VIC Australia Institute of Physical Performance Norwegian School of Sport Sciences Oslo Norway Monash Proteomics & Metabolomics Platform Monash Biomedicine Discovery Institute Monash University Melbourne VIC Australia Institute for Molecular Medicine Finland (FIMM) Helsinki University Helsinki Finland Faculty of Sport and Health Sciences University of Jyväskylä Jyväskylä Finland Wellbeing Services County of Central Finland Jyväskylä Finland Minerva Foundation Institute for Medical Research Helsinki Finland Department of Clinical Sciences Translational Muscle Research Unit Lund University Diabetes Center Lund University Lund Sweden Institute for Physical Activity and Nutrition (IPAN) School of Exercise and Nutrition Sciences Deakin University 221 Burwood Highway Burwood VIC 3125 Australia Cardiometabolic Health and Exercise Physiology Laboratory Baker Heart and Diabetes Institute 75 Commercial Road Melbourne VIC 3004 Australia Nutrition & Health Innovation Research Institute Edith Cowan University Joondalup WA Australia Medical School The University of Western Australia Perth WA Australia Australian Institute for Musculoskeletal Science (AIMSS) Victoria University Melbourne VIC Australia CAS Key Lab of Computational Biology Shanghai Institute of Nutrition and Health Shanghai China Department of Biomedical Sciences University of Copenhagen Copenhagen Denmark Faculty of Health Sciences and Medicine Bond University Gold Coast QLD Australia Genomics Research Centre Centre for Genomics and Personalised Health School of Biomedical Sciences Queensland University of Technology Brisbane QLD Australia ARC Training Centre for Cell and Tissue Engineering Technologies Queensland University of Technology (Q

We investigated the molecular mechanisms of exercise adaptations in human muscle by integrating genome, methylome, transcriptome, and proteome data from over 1,000 participants (2,340 muscle samples). We identified distinctive signatures associated with maximal oxygen consumption (VO2max), and multi-omics integration uncovered five key genes as robust exercise markers across layers, with transcription factors functioning as activators, synergizing with DNA methylation to regulate gene expression. Minimal sex differences were observed, while modality-specific analysis highlighted distinct pathways for aerobic and resistance exercise, contrasting with muscle disuse patterns. Finally, we created a webtool, OMAx, featuring our individual omics and integration analysis. These findings provide a comprehensive multi-omics framework for understanding exercise-induced molecular adaptations, offering insights into muscle health, cardiorespiratory fitness, and their roles in aging and disease prevention.

关键词： skeletal muscle Multi Omics epigenetics DNA methylation exercise VO2 max gene expression transcriptomic proteomic

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Meeting abstracts from the Annual Conference on Hereditary Cancers 2015 Abstracts

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HEREDITARY CANCER IN CLINICAL PRACTICE 2017年第SUPPL 1期15卷 17-17页

作者： [Anonymous] Cancer Genetics Laboratory Peter MacCallum Cancer Centre East Melbourne Victoria Australia Department of Oncology University of Melbourne Melbourne Victoria Australia The University of Newcastle and Hunter Medical Research Institute Newcastle Australia Division of Genetics Hunter Area Pathology Service Newcastle Australia Hereditary Cancer Program BC Cancer Agency Vancouver Canada European Centre for Bioinformatics and Genomics Institute of Bioorganic Chemistry Polish Academy of Sciences Poznan Poland Department of Genetics and Pathology Pomeranian Medical University Szczecin Poland Oncology Institute Riga Stradins University Riga Latvia Center for Translational Research and Molecular Biology of Cancer Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch Gliwice Poland Regional Oncology Center Opole Poland Department of Genetics and Pathology International Hereditary Cancer Center Pomeranian Medical University Szczecin Poland Department of Oncology Disctict Specialist Hospital Świdnica Poland Department of Nuclear Medicine and Endocrine Oncology Maria Skłodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch Gliwice Poland Genomic Medicine Medical University of Warsaw Warsaw Poland III Department of Radiotherapy and Chemotherapy Maria Skłodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch Gliwice Poland Institute of Human Genetics Polish Academy of Sciences Poznan Poland Department of Genetics Holycross Cancer Centre Kielce Poland Read – Gene S.A. Grzepnica Poland Department of Ophthalmology Pomeranian Medical University Szczecin Poland

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SCALE RELIANT INFERENCE

arXiv

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arXiv 2022年

作者： Nixon, Michelle Pistner McGovern, Kyle C. Letourneau, Jeffrey David, Lawrence A. Lazar, Nicole A. Mukherjee, Sayan Silverman, Justin D. College of Information Sciences and Technology The Pennsylvania State University United States Program in Bioinformatics and Genomics The Pennsylvania State University United States Department of Molecular Genetics and Microbiology Duke University United States Department of Statistics The Pennsylvania State University United States Huck Institutes for the Life Sciences The Pennsylvania State University United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University United States Center for Scalable Data Analytics and Artificial Intelligence University of Leipzig Germany Max Planck Institute for Mathematics in the Natural Sciences Germany Department of Medicine The Pennsylvania State University United States

Many scientific fields, including human gut microbiome science, collect multivariate count data where the sum of the counts is unrelated to the scale of the underlying system being measured (e.g., total microbial load in a subject’s colon). This disconnect complicates downstream analyses such as differential analysis in case-control studies. This article is motivated by a novel study of in vitro human gut microbiome models. Popular tools for analyzing these data led to dramatically elevated rates of both false positives and false negatives. To understand those failures, we provide a formal problem statement that frames these challenges of scale in terms of the classical theory of identifiability. We call this the problem of Scale Reliant Inference (SRI). We use this formulation to prove fundamental limits on SRI in terms of criteria such as consistency and type-I error control. We show that the failures of existing methods stem from a fundamental failure to properly quantify uncertainty in the system scale. We demonstrate that a particular type of Bayesian model called a Bayesian Partially Identified Model (PIMs) can correctly quantify uncertainty in SRI. We introduce Scale Simulation Random Variables (SSRVs) as a flexible and efficient approach to specifying and inferring Bayesian PIMs. In the context of both real and simulated data, we find SSRVs drastically decrease type-I and type-II error rates. © 2022, CC BY.

关键词： Bayesian networks

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The proteogenomic subtypes of acute myeloid leukemia

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Cancer Cell 2022年第3期40卷 301-317.e12页

作者： Jayavelu, Ashok Kumar Wolf, Sebastian Buettner, Florian Alexe, Gabriela Häupl, Björn Comoglio, Federico Schneider, Constanze Doebele, Carmen Fuhrmann, Dominik C. Wagner, Sebastian Donato, Elisa Andresen, Carolin Wilke, Anne C. Zindel, Alena Jahn, Dominique Splettstoesser, Bianca Plessmann, Uwe Münch, Silvia Abou-El-Ardat, Khali Makowka, Philipp Acker, Fabian Enssle, Julius C. Cremer, Anjali Schnütgen, Frank Kurrle, Nina Chapuy, Björn Löber, Jens Hartmann, Sylvia Wild, Peter J. Wittig, Ilka Hübschmann, Daniel Kaderali, Lars Cox, Jürgen Brüne, Bernhard Röllig, Christoph Thiede, Christian Steffen, Björn Bornhäuser, Martin Trumpp, Andreas Urlaub, Henning Stegmaier, Kimberly Serve, Hubert Mann, Matthias Oellerich, Thomas Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Department of Medicine II Hematology/Oncology University Hospital Frankfurt Goethe University Theodor-Stern-Kai 7 Frankfurt Germany German Cancer Consortium (DKTK) Partner Site Frankfurt/Mainz and German Cancer Research Center (DKFZ) Heidelberg Germany Clinical Cooperation Unit Pediatric Leukemia DKFZ and Department of Pediatric Oncology Hematology and Immunology University of Heidelberg Germany Department of Medicine University Hospital Frankfurt Goethe University Frankfurt Germany Division of Hematology/Oncology Department of Pediatric Oncology Dana-Farber Cancer Institute and Boston Children's Hospital Boston MA United States enGene Statistics GmbH Basel Switzerland Department of Biochemistry I Goethe University Frankfurt Germany Division of Stem Cells and Cancer German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance Heidelberg Germany Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH) Heidelberg Germany Bioanalytical Mass Spectrometry Group Max Planck Institute for Biophysical Chemistry Göttingen Germany Department of Medical Hematology and Oncology University Medical Center Göttingen Göttingen Germany Dr. Senckenberg Institute of Pathology University Hospital Frankfurt Frankfurt Germany Functional Proteomics Institute of Cardiovascular Physiology Goethe University Frankfurt Germany Pattern Recognition and Digital Medicine Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM) Heidelberg Germany Computational Oncology Molecular Precision Oncology Program National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Bioinformatics University Medicine Greifswald Greifswald Germany Computational Systems Biochemistry Research Group Max Planck Institute of Biochemistry Martinsried Germany Department of Internal Medicine

Acute myeloid leukemia (AML) is an aggressive blood cancer with a poor prognosis. We report a comprehensive proteogenomic analysis of bone marrow biopsies from 252 uniformly treated AML patients to elucidate the molecular pathophysiology of AML in order to inform future diagnostic and therapeutic approaches. In addition to in-depth quantitative proteomics, our analysis includes cytogenetic profiling and DNA/RNA sequencing. We identify five proteomic AML subtypes, each reflecting specific biological features spanning genomic boundaries. Two of these proteomic subtypes correlate with patient outcome, but none is exclusively associated with specific genomic aberrations. Remarkably, one subtype (Mito-AML), which is captured only in the proteome, is characterized by high expression of mitochondrial proteins and confers poor outcome, with reduced remission rate and shorter overall survival on treatment with intensive induction chemotherapy. Functional analyses reveal that Mito-AML is metabolically wired toward stronger complex I-dependent respiration and is more responsive to treatment with the BCL2 inhibitor venetoclax. © 2022 Elsevier Inc.

关键词： acute myeloid leukemia BCL-2 inhibitor chemotherapy mitochondrial oxidative phosphorylation multi-omics data integration proteogenomics proteomics venetoclax

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