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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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GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Texts

arXiv

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arXiv 2023年

作者： Wu, Da Yang, Jingye Liu, Cong Hsieh, Tzung-Chien Marchi, Elaine Blair, Justin Krawitz, Peter Weng, Chunhua Chung, Wendy Lyon, Gholson J. Krantz, Ian D. Kalish, Jennifer M. Wang, Kai Raymond G. Perelman Center for Cellular and Molecular Therapeutics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Institute for Genomic Statistics and Bioinformatics University Hospital Bonn Rheinische Friedrich-Wilhelms-Universität Bonn Bonn Germany Department of Human Genetics New York State Institute for Basic Research in Developmental Disabilities Staten IslandNY United States Division of Human Genetics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Department of Pediatrics Boston Children’s Hospital Harvard Medical School BostonMA United States Biology PhD Program The Graduate Center The City University of New York New York United States Department of Genetics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pediatrics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States

Individuals with suspected rare genetic disorders often undergo multiple clinical evaluations, imaging studies, laboratory tests and genetic tests, to find a possible answer over a prolonged period of time. Addressing this "diagnostic odyssey" thus has substantial clinical, psychosocial, and economic benefits. Many rare genetic diseases have distinctive facial features, which can be used by artificial intelligence algorithms to facilitate clinical diagnosis, in prioritizing candidate diseases to be further examined by lab tests or genetic assays, or in helping the phenotype-driven reinterpretation of genome/exome sequencing data. Existing methods using frontal facial photos were built on conventional Convolutional Neural Networks (CNNs), rely exclusively on facial images, and cannot capture non-facial phenotypic traits and demographic information essential for guiding accurate diagnoses. Here we introduce GestaltMML, a multimodal machine learning (MML) approach solely based on the Transformer architecture. It integrates facial images, demographic information (age, sex, ethnicity), and clinical notes (optionally, a list of Human Phenotype Ontology terms) to improve prediction accuracy. Furthermore, we also evaluated GestaltMML on a diverse range of datasets, including 528 diseases from the GestaltMatcher Database, several in-house datasets of Beckwith-Wiedemann syndrome (BWS, over-growth syndrome with distinct facial features), Sotos syndrome (overgrowth syndrome with overlapping features with BWS), NAA10-related neurodevelopmental syndrome, Cornelia de Lange syndrome (multiple malformation syndrome), and KBG syndrome (multiple malformation syndrome). Our results suggest that GestaltMML effectively incorporates multiple modalities of data, greatly narrowing candidate genetic diagnoses of rare diseases and may facilitate the reinterpretation of genome/exome sequencing data. © 2023, CC BY.

关键词： Diseases

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Random-effects substitution models for phylogenetics via scalable gradient approximations

arXiv

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arXiv 2023年

作者： Magee, Andrew F. Holbrook, Andrew J. Pekar, Jonathan E. Caviedes-Solis, Itzue W. Matsen, Fredrick A. Baele, Guy Wertheim, Joel O. Ji, Xiang Lemey, Philippe Suchard, Marc A. Department of Biostatistics Jonathan and Karin Fielding School of Public Health University of California Los Angeles Los AngelesCA United States Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Biomedical Informatics University of California San Diega La Jolla CA United States Department of Biology Swarthmore College SwarthmorePA United States Howard Hughes Medical Institute SeattleWA United States Computational Biology Program Fred Hutchinson Cancer Research Center SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Department of Statistics University of Washington SeattleWA United States Department of Microbiology Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium Department of Medicine University of California San Diego La Jolla CA United States Department of Mathematics Tulane University New OrleansLA United States Department of Biomathematics David Geffen School of Medicine at UCLA University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine at UCLA Universtiy of California Los Angeles Los AngelesCA United States

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Phylogenetic

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White Paper on Enabling Global Image Data Sharing in the Life Sciences

arXiv

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arXiv 2024年

作者： Bajcsy, Peter Bhattiprolu, Sreenivas Börner, Katy Cimini, Beth A. Collinson, Lucy Ellenberg, Jan Fiolka, Reto Giger, Maryellen Goscinski, Wojtek Hartley, Matthew Hotaling, Nathan Horwitz, Rick Jug, Florian Kreshuk, Anna Lundberg, Emma Mathur, Aastha Narayan, Kedar Onami, Shuichi Plant, Anne L. Prior, Fred Swedlow, Jason Taylor, Adam Keppler, Antje National Institute of Standards and Technology GaithersburgMD20899 United States ZEISS Microscopy Customer Center DublinCA94568 United States Intelligent Systems Engineering Indiana University BloomingtonIN47408 United States Imaging Platform Broad Institute CambridgeMA United States Electron Microscopy Science Technology Platform Francis Crick Institute 1 Midland Road LondonNW1 1AT United Kingdom European Molecular Biology Laboratory Heidelberg Germany Lyda Hill Department of Bioinformatics UT Southwestern Medical Center DallasTX United States Department of Radiology and Committee on Medical Physics University of Chicago ChicagoIL United States National Imaging Facility Brisbane Australia European Molecular Biology Laboratory European Bioinformatics Institute Hinxton Cambridge United Kingdom National Center for Advancing Translational Science National Institutes of Health Rockville United States Allen Institute for Cell Science Seattle United States Fondazione Human Technopole Milan Italy Stanford University California US and SciLifeLab KTH Royal Institute of Technology Stockholm Sweden Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health BethesdaMD20892 United States Cancer Research Technology Program Frederick National Laboratory for Cancer Research FrederickMD21702 United States RIKEN Center for Biosystems Dynamics Research Kobe Japan Department of Biomedical Informatics Department of Radiology University of Arkansas for Medical Sciences Little RockAR72205 United States Divisions of Computational Biology and Molecular Cell and Developmental Biology University of Dundee United Kingdom Sage Bionetworks SeattleWA United States

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ‘ideal world scenario’ for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled "Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data", which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location. © 2024, CC BY-NC-SA.

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A clonal expression biomarker associates with lung cancer mortality (vol 25, pg 1540, 2019)

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NATURE MEDICINE 2020年第7期26卷 1148-1148页

作者： Biswas, Dhruva Birkbak, Nicolai J. Rosenthal, Rachel Hiley, Crispin T. Lim, Emilia L. Papp, Krisztian Boeing, Stefan Krzystanek, Marcin Djureinovic, Dijana La Fleur, Linnea Greco, Maria Doeme, Balazs Fillinger, Janos Brunnstrom, Hans Wu, Yin Moore, David A. Skrzypski, Marcin Abbosh, Christopher Litchfield, Kevin Al Bakir, Maise Watkins, Thomas B. K. Veeriah, Selvaraju Wilson, Gareth A. Jamal-Hanjani, Mariam Moldvay, Judit Botling, Johan Chinnaiyan, Arul M. Micke, Patrick Hackshaw, Allan Bartek, Jiri Csabai, Istvan Szallasi, Zoltan Herrero, Javier McGranahan, Nicholas Swanton, Charles Cancer Research UK Lung Cancer Centre of Excellence University College London Cancer Institute Paul O’Gorman Building London UK Bill Lyons Informatics Centre University College London Cancer Institute Paul O’Gorman Building London UK Cancer Evolution and Genome Instability Laboratory The Francis Crick Institute London UK Department of Molecular Medicine Aarhus University Aarhus Denmark Bioinformatics Research Centre Aarhus University Aarhus Denmark Department of Physics of Complex Systems ELTE Eötvös Loránd University Budapest Hungary Bioinformatics and Biostatistics The Francis Crick Institute London UK Danish Cancer Society Research Center Copenhagen Denmark Department of Immunology Genetics and Pathology Uppsala University Uppsala Sweden Genomics Equipment Park The Francis Crick Institute London UK Department of Tumor Biology National Korányi Institute of Pulmonology Semmelweis University Budapest Hungary Division of Thoracic Surgery Comprehensive Cancer Center Medical University of Vienna Vienna Austria Department of Thoracic Surgery National Institute of Oncology Semmelweis University Budapest Hungary Department of Pathology National Korányi Institute of Pulmonology Semmelweis University Budapest Hungary Department of Pathology National Institute of Oncology Budapest Hungary Lund University Laboratory Medicine Region Skåne Department of Clinical Sciences Lund Pathology Lund Sweden Department of Pathology UCL Cancer Institute London UK Department of Oncology and Radiotherapy Medical University of Gdansk Gdansk Poland SE-NAP Brain Metastasis Research Group 2nd Department of Pathology Semmelweis University Budapest Hungary Michigan Center for Translational Pathology University of Michigan Ann Arbor USA Department of Pathology University of Michigan Ann Arbor USA Rogel Cancer Center University of Michigan Ann Arbor USA Department of Urology University of Michigan Ann Arbor USA Howard Hughes Medical Institute University of Michigan An

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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Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Research Square

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Research Square 2021年

作者： Zhang, Martin Jinye Hou, Kangcheng Pasaniuc, Bogdan Price, Alkes L. Dey, Kushal K. Jagadeesh, Karthik A. Weinand, Kathryn Sakaue, Saori Taychameekiatchai, Aris Rao, Poorvi Pisco, Angela Oliveira Zou, James Wang, Bruce Gandal, Michael Raychaudhuri, Soumya Harvard University United States Osaka University Japan Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Center for Data Sciences Brigham and Women’s Hospital BostonMA United States Division of Genetics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Division of Rheumatology Inflammation and Immunity Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Medicine and Liver Center University of California San Francisco San FranciscoCA United States Developmental and Stem Cell Biology Graduate Program University of California San Francisco San FranciscoCA United States Chan Zuckerberg Biohub San FranciscoCA United States Department of Electrical Engineering Stanford University Palo AltoCA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Department of Psychiatry David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Program in Neurobehavioral Genetics Semel Institute David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Versus Arthritis Centre for Genetics and Genomic

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution;scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =341K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states;subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region;and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes. © 2021, CC BY.

关键词： RNA

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Development and Validation Of a Highly Sensitive and Specific Gene Expression Classifier To Prospectively Screen and Identify B-Precursor Acute Lymphoblastic Leukemia (ALL) Patients With a Philadelphia Chromosome-Like ("Ph-like" or "BCR-ABL1-Like") Signature For Therapeutic Targeting and Clinical Intervention

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BLOOD 2013年第21期122卷 826-826页

作者： Harvey, Richard C. Kang, Huining Roberts, Kathryn G. Chen, I-Ming L. Atlas, Susan R. Bedrick, Edward J. Gastier-Foster, Julie M. Zhang, Jinghui Gerhard, Daniels S. Smith, Malcolm A. Larsen, Eric C. Raetz, Elizabeth A. Winick, Naomi J. Carroll, William L. Stonerock, Eileen Heerema, Nyla A. Carroll, Andrew J. Chen, Shann-Ching Song, Guangchun Becksfort, Jared Rusch, Michael Li, Yongjin Ma, Jing Ell, Denise Reshmi, Shalini C. Loh, Mignon L. Davidas, Meenakshi Hunger, Stephen P. Mullighan, Charles G. Willman, Cheryl L. Pathology University of New Mexico Albuquerque NM USA Cancer Center University of New Mexico Albuquerque NM USA Pathology St. Jude Children's Research Hospital Memphis TN USA Cancer Center/Pathology University of New Mexico Albuquerque NM USA Physics and Astronomy University of New Mexico Albuquerque NM USA Internal Medicine University of New Mexico Cancer Center Albuquerque NM USA Ohio State University Laboratory Medicine/Pathology Nationwide Childrens Hospital Columbus OH USA Computational Biology St Jude Children's Research Hospital Memphis TN USA Office of Cancer Genomics National Cancer Institute Bethesda MD USA Maine Children's Cancer Program Scarborough ME USA Pedatrics New York University Langone Medical Center New York NY USA Department of Pediatrics University of Texas Southwestern Medical Center Dallas TX USA Cancer Institute New York University New York NY USA Nationwide Childrens Hospital Columbus OH USA Department of Pathology Wexner Medical Center at Ohio State University Columbus OH USA Department of Genetics University of Alabama at Birmingham Birmingham AL USA Department of Pathology St Jude Children's Research Hospital Memphis TN USA Information Sciences Research Informatics Bioinformatics St. Jude Children's Research Hospital Memphis TN USA

826

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The genome-wide association study of serum IgE levels demonstrated a shared genetic background in allergic diseases

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Clinical immunology (Orlando, Fla.) 2024年 260卷 109897页

作者： Hsing-Fang Lu Chen-Hsing Chou Ying-Ju Lin Shunsuke Uchiyama Chikashi Terao Yu-Wen Wang Jai-Sing Yang Ting-Yuan Liu Henry Sung-Ching Wong Sean Chun-Chang Chen Fuu-Jen Tsai Department of Medical Research China Medical University Hospital Taichung 404327 Taiwan Laboratory for Statistical and Translational Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan. PhD Program for Health Science and Industry College of Health Care China Medical University Taichung Taiwan Department of Health Services Administration China Medical University Taichung Taiwan. Genetic Center Department of Medical Research China Medical University Hospital Taichung Taiwan School of Chinese Medicine China Medical University Taichung Taiwan. Laboratory for Statistical and Translational Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Clinical Research Center Shizuoka General Hospital Shizuoka Japan The Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Medical Research China Medical University Hospital Taichung 404327 Taiwan. Department of Medical Research China Medical University Hospital China Medical University Taichung 404327 Taiwan. Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. School of Chinese Medicine China Medical University Taichung Taiwan Department of Biotechnology and Bioinformatics Asia University Taichung Taiwan. Electronic address: d0704@mail.cmuh.org.tw.

Immunoglobulin E (IgE) synthessis is highly related to a variety of atopic diseases, and several genome-wide association studies (GWASs) have demonstrated the association between genes and IgE level. In this study, we conducted the largest genome-wide association study of IgE involving a Taiwanese Han population. Eight independent variants exhibited genome-wide significance. Among them, an intronic SNP of CD28, rs1181388, and an intergenic SNP, rs1002957030, on 11q23.2 were identified as novel signals for IgE. Seven of the loci were replicated successfully in a meta-analysis using data on Japanese population. Among all the human leukocyte antigen (HLA) regions, HLA-DQA1*03:02 - HLA-DQB1*03:03 was the most significant haplotype (OR = 1.25, SE = 0.02, FDR = 1.6 × 10), corresponding to HLA-DQA1 Asp160 and HLA-DQB1 Leu87 amino acid residues. The genetic correlation showed significance between IgE and allergic diseases including asthma, atopic dermatitis, and pollinosis. IgE PRS was significantly correlated with total IgE levels. Furthermore, the top decile IgE polygenic risk score (PRS) group had the highest risk of asthma for the Taiwan Biobank and Biobank Japan cohorts. IgE PRS may be used to aid in predicting the occurrence of allergic reactions before symptoms occur and biomarkers are detectable. Our study provided a more comprehensive understanding of the impact of genomic variants, including complex HLA alleles, on serum IgE levels.

关键词： Allergy genome-wide association study human leukocyte antigen immunoglobulin E

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Democratising Knowledge Representation with BioCypher

arXiv

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arXiv 2022年

作者： Lobentanzer, Sebastian Aloy, Patrick Baumbach, Jan Bohar, Balazs Charoentong, Pornpimol Danhauser, Katharina Doğan, Tunca Dreo, Johann Dunham, Ian Fernandez-Torras, Adrià Gyori, Benjamin M. Hartung, Michael Hoyt, Charles Tapley Klein, Christoph Korcsmaros, Tamas Maier, Andreas Mann, Matthias Ochoa, David Pareja-Lorente, Elena Popp, Ferdinand Preusse, Martin Probul, Niklas Schwikowski, Benno Sen, Bünyamin Strauss, Maximilian T. Turei, Denes Ulusoy, Erva Heidrun Wodke, Judith Andrea Saez-Rodriguez, Julio Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany The Barcelona Institute of Science and Technology Catalonia Barcelona Spain Catalonia Barcelona Spain Institute for Computational Systems Biology University of Hamburg Germany Earlham Institute Norwich United Kingdom Biological Research Centre Szeged Hungary Heidelberg University Im Neuenheimer Feld 267 Heidelberg69120 Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Department of Pediatrics Dr. von Hauner Children’s Hospital University Hospital LMU Munich Germany Biological Data Science Lab Department of Computer Engineering Hacettepe University Ankara Turkey Department of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey Computational Systems Biomedicine Lab Department of Computational Biology Institut Pasteur Université Paris Cité Paris France Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Laboratory of Systems Pharmacology Harvard Medical School Boston United States Imperial College London London United Kingdom Quadram Institute Bioscience Norwich United Kingdom Proteomics Program Novo Nordisk Foundation Centre for Protein Research University of Copenhagen Copenhagen Denmark Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Neuherberg Germany Medical Informatics Laboratory University Medicine Greifswald Germany

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to nonspecialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://***) frees up valuable developer time;we encourage further development and usage by the community. © 2022, CC BY-NC-ND.

关键词： Ontology

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Tumor-selective activity of RAS-GTP inhibition in pancreatic cancer

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Nature 2024年第8013期629卷 927-936页

作者： Urszula N Wasko Jingjing Jiang Tanner C Dalton Alvaro Curiel-Garcia A Cole Edwards Yingyun Wang Bianca Lee Margo Orlen Sha Tian Clint A Stalnecker Kristina Drizyte-Miller Marie Menard Julien Dilly Stephen A Sastra Carmine F Palermo Marie C Hasselluhn Amanda R Decker-Farrell Stephanie Chang Lingyan Jiang Xing Wei Yu C Yang Ciara Helland Haley Courtney Yevgeniy Gindin Karl Muonio Ruiping Zhao Samantha B Kemp Cynthia Clendenin Rina Sor William P Vostrejs Priya S Hibshman Amber M Amparo Connor Hennessey Matthew G Rees Melissa M Ronan Jennifer A Roth Jens Brodbeck Lorenzo Tomassoni Basil Bakir Nicholas D Socci Laura E Herring Natalie K Barker Junning Wang James M Cleary Brian M Wolpin John A Chabot Michael D Kluger Gulam A Manji Kenneth Y Tsai Miroslav Sekulic Stephen M Lagana Andrea Califano Elsa Quintana Zhengping Wang Jacqueline A M Smith Matthew Holderfield David Wildes Scott W Lowe Michael A Badgley Andrew J Aguirre Robert H Vonderheide Ben Z Stanger Timour Baslan Channing J Der Mallika Singh Kenneth P Olive Department of Medicine Vagelos College of Physicians and Surgeons Columbia University Irving Medical Center New York NY USA. Herbert Irving Comprehensive Cancer Center Columbia University Irving Medical Center New York NY USA. Revolution Medicines Redwood City CA USA. Department of Cell Biology and Physiology University of North Carolina at Chapel Hill Chapel Hill NC USA. University of Pennsylvania Perelman School of Medicine Department of Medicine Philadelphia PA USA. Cancer Biology and Genetics Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY 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. Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA. Harvard Medical School Boston MA USA. University of Pennsylvania Perelman School of Medicine Abramson Cancer Center Philadelphia PA USA. The Broad Institute of Harvard and MIT Cambridge MA USA. Department of Systems Biology Columbia University Irving Medical Center New York NY USA. Bioinformatics Core Memorial Sloan Kettering Cancer Center New York NY USA. UNC Michael Hooker Proteomics Center University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Surgery Vagelos College of Physicians and Surgeons Columbia University Irving Medical Center New York NY USA. Department of Pathology H. Lee Moffitt Cancer Center and Research Institute Tampa FL USA. Department of Tumor Microenvironment and Metastasis H. Lee Moffitt Cancer Center and Research Institute Tampa FL USA. Department of Pathology and Cell Biology Columbia University Irving Medical Center New York NY USA. Department of Oncology The Johns Hopkins University School of Medicine Baltimore MD USA. J. P. Sulzberger Columbia Genome Center Columbia University New York NY USA. Department of Biochemistry and Molecular Biop

Broad-spectrum RAS inhibition has the potential to benefit roughly a quarter of human patients with cancer whose tumours are driven by RAS mutations. RMC-7977 is a highly selective inhibitor of the active GTP-bound forms of KRAS, HRAS and NRAS, with affinity for both mutant and wild-type variants. More than 90% of cases of human pancreatic ductal adenocarcinoma (PDAC) are driven by activating mutations in KRAS. Here we assessed the therapeutic potential of RMC-7977 in a comprehensive range of PDAC models. We observed broad and pronounced anti-tumour activity across models following direct RAS inhibition at exposures that were well-tolerated in vivo. Pharmacological analyses revealed divergent responses to RMC-7977 in tumour versus normal tissues. Treated tumours exhibited waves of apoptosis along with sustained proliferative arrest, whereas normal tissues underwent only transient decreases in proliferation, with no evidence of apoptosis. In the autochthonous KPC mouse model, RMC-7977 treatment resulted in a profound extension of survival followed by on-treatment relapse. Analysis of relapsed tumours identified Myc copy number gain as a prevalent candidate resistance mechanism, which could be overcome by combinatorial TEAD inhibition in vitro. Together, these data establish a strong preclinical rationale for the use of broad-spectrum RAS-GTP inhibition in the setting of PDAC and identify a promising candidate combination therapeutic regimen to overcome monotherapy resistance.

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