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Freshwater microalgae-mediated engineered nanoparticles: Sustainable approach for heavy metals remediation

bioresource technology 2025年 434卷 132761页

作者： Manish Sutradhar Raju Kumar Sharma Gobinda Dey Uttara Sukul Koyeli Das Md Taharia Pin-Yun Lin Shafiqa Ali Chin-Wen Wang Chien-Yen Chen Doctoral Program in Science Technology Environment and Mathematics Department of Earth and Environmental Sciences National Chung Cheng University 168 University Road Min-Hsiung Chiayi County 62102 Taiwan Department of Earth and Environmental Sciences National Chung Cheng University 168 University Road Min-Hsiung Chiayi County 62102 Taiwan. Department of Chemistry and Biochemistry National Chung Cheng University 168 University Road Min-Hsiung Chiayi County 62102 Taiwan Center for Nano Bio-Detection Center for Innovative Research on Aging Society AIM-HI National Chung Cheng University 168 University Road Min-Hsiung Chiayi County 62102 Taiwan. Department of Biomedical Sciences Graduate Institute of Molecular Biology National Chung Cheng University 168 University Road Min-Hsiung Chiayi County 62102 Taiwan. Department of Chemistry and Biochemistry National Chung Cheng University 168 University Road Min-Hsiung Chiayi County 62102 Taiwan. Department of Earth and Environmental Sciences National Chung Cheng University 168 University Road Min-Hsiung Chiayi County 62102 Taiwan Center for Nano Bio-Detection Center for Innovative Research on Aging Society AIM-HI National Chung Cheng University 168 University Road Min-Hsiung Chiayi County 62102 Taiwan You-Cheng Engineering & Technology Co. Ltd 168 University Road Min-Hsiung Chiayi Country 62102 Taiwan. Electronic address: yen@eq.ccu.edu.tw.

Recently, convergence of nanotechnology and sustainable approaches has emerged as promising strategy for advanced remediation of heavy metals (HMs) from water. However, engineered nanoparticles (ENPs)-derived from freshwater microalgae gained attention as viable alternative, owing their inherent metal tolerance, rapid-growth rates, and versatile metabolic capabilities. This review predominantly focuses on freshwater microalgae-mediated-ENPs, highlighting intracellular/extracellular pathways induced by biomolecules such as polysaccharides, proteins, lipids, reductases etc., which govern nanoparticle nucleation, stabilization, and functionalization. Subsequently, the review discusses significant types of ENPs including iron, silver, gold, cadmium-sulfide, iron-oxide, nanocomposites, and bio-nano-hybrids nanoparticles, which reported removal efficiency from 21 to 100% of different HMs (lead, cadmium, chromium, copper, zinc, etc.). Despite ENPs exhibit significant potential, their large-scale implementation is constrained by challenges such as scaling-up, nutrient-variability, and high turbidity of wastewater. Future research recommended to focus on optimizing biosynthesis parameters, investigating diverse microalgal-strains, incorporating hybrid nano-materials to enhance their stability, reusability, and scalability for sustainable green-technology.

关键词： bio-nanotechnology bioremediation efficiency Environmental nano-materials Green synthesis Heavy metal removal Phytoremediation

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Cover Feature: Chemo-biological Upcycling of Poly(ethylene terephthalate) to Multifunctional Coating Materials (ChemSusChem 19/2021)

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ChemSusChem 2021年第19期14卷

作者： Prof. Hee Taek Kim Mi Hee Ryu Ye Jean Jung Sooyoung Lim Hye Min Song Prof. Jeyoung Park Prof. Sung Yeon Hwang Dr. Hoe-Suk Lee Prof. Young Joo Yeon Dr. Bong Hyun Sung Prof. Uwe T. Bornscheuer Prof. Si Jae Park Prof. Jeong Chan Joo Prof. Dongyeop X. Oh Department of Food Science and Technology Chungnam National University Daejeon 34134 (Republic of Korea Research Center for Bio-based Chemicals Korea Research Institute of Chemical Technology Daejeon 34114 & Ulsan 44429 (Republic of Korea Department of Chemical Engineering and Materials Science Graduate Program in System Health Science & Engineering Ewha Womans University Seoul 03760 (Republic of Korea Advanced Materials and Chemical Engineering University of Science and Technology (UST) Daejeon 34113 (Republic of Korea Department of Biochemical Engineering Gangneung-Wonju National University Gangneung-si Gangwon-do 25457 (Republic of Korea Synthetic Biology and Bioengineering Research Center Korea Research Institute of Bioscience and Biotechnology Daejeon 34141 (Republic of Korea Department of Biotechnology & Enzyme Catalysis Institute of Biochemistry University of Greifswald 17487 Greifswald Germany Department of Biotechnology The Catholic University of Korea Bucheon-si Gyeonggi-do 14662 (Republic of Korea

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Skeletal Muscle Regeneration: Functional Skeletal Muscle Regeneration with Thermally Drawn Porous Fibers and Reprogrammed Muscle Progenitors for Volumetric Muscle Injury (Adv. Mater. 14/2021)

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Advanced Materials 2021年第14期33卷

作者： Yoonhee Jin Dena Shahriari Eun Je Jeon Seongjun Park Yi Sun Choi Jonghyeok Back Hyungsuk Lee Polina Anikeeva Seung-Woo Cho Department of Biotechnology Yonsei University Seoul 03722 Republic of Korea Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA 02139 USA McGovern Institute for Brain Research Cambridge MA 02139 USA Department of Biomaterials Science and Engineering Yonsei University Seoul 03722 Republic of Korea Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea KAIST Institute for Health Science and Technology Daejeon 34141 Republic of Korea School of Mechanical Engineering Yonsei University Seoul 03722 Republic of Korea Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA 02139 USA Center for Nanomedicine Institute for Basic Science (IBS) Seoul 03722 Republic of Korea Graduate Program of Nano Biomedical Engineering (NanoBME) Advanced Science Institute Yonsei University Seoul 03722 Republic of Korea

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Robust Assessment of Photoplethysmogram Signal Quality in the Presence of Atrial Fibrillation

Robust Assessment of Photoplethysmogram Signal Quality in th...

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Computers in Cardiology (CinC)

作者： Tania Pereira Kais Gadhoumi Mitchell Ma Rene Colorado Kevin J Keenan Karl Meisel Xiao Hu Department of Physiological Nursing School of Nursing University of California San Francisco CA USA UCSF Center for Physiologic Research USA Department of Neurology School of Medicine USA Core Member of UCB/UCSF Joint Bio-Engineering Graduate Program UCSF USA

ISBN: (纸本)9781728109244

A great deal of algorithms currently available to assess the quality of photoplethysmogram (PPG) signals is based on the similarity between pulses to derive signal quality indices. This approach has limitations when pulse morphology become variable due to the presence of some arrhythmia as in the case of atrial fibrillation (AFib). AFib is a heart arrhythmia characterized in the electrocardiogram mainly by an irregular irregularity. This arrhythmicity is reflected on PPG pulses by the presence of non-uniform pulses and poses challenges in the evaluation of the signal quality. In this work, we first test the performance of few algorithms from the body of methods reported in literature using a dataset of PPG records with AFib, and demonstrate their limitation. Second, we present a novel SVM-based classifier for PPG quality assessment in 30s-long segments of PPG records extracted from pulse oximetry data of 13 stroke patients admitted to the UCSF medical center neuro ICU. 40 time-domain, frequency domain and non-linear features were extracted from all segments. Using an independent test set, the classifier reached a 0.94 accuracy, 0.95 sensitivity and 0.91 specificity. These results demonstrate the robustness of the proposed method in properly evaluating PPG signal quality in the presence of atrial fibrillation.

关键词： Stroke (medical condition) Feature extraction Electrocardiography Standards Quality assessment Sensitivity Support vector machines

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Data-augmented modeling of intracranial pressure

arXiv

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arXiv 2018年

作者： Wang, Jian-Xun Hu, Xiao Shadden, Shawn C. Mechanical Engineering University of California BerkeleyCA United States Aerospace and Mechanical Engineering Center of Informatics and Computational Science University of Notre Dame Notre DameIN United States Department of Physiological Nursing Department of Neurological surgery Institute of Computational Health Sciences UCSF Joint Bio-Engineering Graduate Program University of California San FranciscoCA United States

Precise management of patients with cerebral diseases often requires intracranial pressure (ICP) monitoring, which is highly invasive and requires a specialized ICU setting. The ability to noninvasively estimate ICP is highly compelling as an alternative to, or screening for, invasive ICP measurement. Most existing approaches for noninvasive ICP estimation aim to build a regression function that maps noninvasive measurements to an ICP estimate using statistical learning techniques. These data-based approaches have met limited success, likely because the amount of training data needed is onerous for this complex applications. In this work, we discuss an alternative strategy that aims to better utilize noninvasive measurement data by leveraging mechanistic understanding of physiology. Specifically, we developed a Bayesian framework that combines a multiscale model of intracranial physiology with noninvasive measurements of cerebral blood flow using transcranial Doppler. Virtual experiments with synthetic data are conducted to verify and analyze the proposed framework. A preliminary clinical application study on two patients is also performed in which we demonstrate the ability of this method to improve ICP prediction. Copyright © 2018, The Authors. All rights reserved.

关键词： Diagnosis

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Community-developed checklists for publishing images and image analyses

arXiv

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arXiv 2023年

作者： Schmied, Christopher Nelson, Michael S. Avilov, Sergiy Bakker, Gert-Jan Bertocchi, Cristina Bischof, Johanna Boehm, Ulrike Brocher, Jan Carvalho, Mariana Chiritescu, Catalin Christopher, Jana Cimini, Beth A. Conde-Sousa, Eduardo Ebner, Michael Ecker, Rupert Eliceiri, Kevin Fernandez-Rodriguez, Julia Gaudreault, Nathalie Gelman, Laurent Grunwald, David Gu, Tingting Halidi, Nadia Hammer, Mathias Hartley, Matthew Held, Marie Jug, Florian Kapoor, Varun Koksoy, Ayse Aslihan Lacoste, Judith Le Dévédec, Sylvia Le Guyader, Sylvie Liu, Penghuan Martins, Gabriel G. Mathur, Aastha Miura, Kota Llopis, Paula Montero Nitschke, Roland North, Alison Parslow, Adam C. Payne-Dwyer, Alex Plantard, Laure Ali, Rizwan Schroth-Diez, Britta Schütz, Lucas Scott, Ryan T. Seitz, Arne Selchow, Olaf Sharma, Ved P. Spitaler, Martin Srinivasan, Sathya Strambio-De-Castillia, Caterina Taatjes, Douglas Tischer, Christian Jambor, HelenaKlara Fondazione Human Technopole Viale Rita Levi-Montalcini 1 Milano20157 Italy Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI53706 United States Max Planck Institute of Immunobiology and Epigenetics Freiburg79108 Germany Medical BioSciences department Radboud University Medical Centre Nijmegen Netherlands Laboratory for Molecular mechanics of cell adhesions Pontificia Universidad Católica de Chile Santiago Chile Osaka University Graduate School of Engineering Science Japan Euro-BioImaging ERIC Bio-Hub Meyerhofstr. 1 Heidelberg69117 Germany Carl Zeiss AG Carl-Zeiss-Straße 22 Oberkochen73447 Germany BioVoxxel Scientific Image Processing and Analysis Eugen-Roth-Strasse 8 Ludwigshafen67071 Germany Nanophotonics and BioImaging Facility at INL International Iberian Nanotechnology Laboratory 4715-330 Portugal Phi Optics Inc. 1800 S. Oak St Ste 106 ChampaignIL61820 United States Biochemistry Center Heidelberg Heidelberg University Germany Imaging Platform Broad Institute CambridgeMA02142 United States i3S Instituto de Investigação e Inovação Em Saúde and INEB Instituto de Engenharia Biomédica Universidade do Porto Porto Portugal Translational Research Institute Queensland University of Technology 37 Kent Street WoolloongabbaQLD4102 Australia School of Biomedical Sciences Faculty of Health Queensland University of Technology BrisbaneQLD4059 Australia TissueGnostics GmbH Vienna1020 Austria Department of Medical Physics and Biomedical Engineering University of Wisconsin-Madison MadisonWI53706 United States Centre for Cellular Imaging Core Facility Sahlgrenska Academy University of Gothenburg Sweden Allen Institute for Cell Science SeattleWA United States Friedrich Miescher Institute for Biomedical Research Basel Switzerland RNA Therapeutics Institute University of Massachusetts Chan Medical School WorcesterMA01605 United States University of Oklahoma NormanOK United States Advanced Light Microscopy Unit Ce

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However for scientists wishing to publish the obtained images and image analyses results, there are to date no unified guidelines. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here we present community-developed checklists for preparing light microscopy images and image analysis for publications. These checklists offer authors, readers, and publishers key recommendations for image formatting and annotation, color selection, data availability, and for reporting image analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby heighten the quality and explanatory power of microscopy data is in publications. © 2023, CC BY-SA.

关键词： Image analysis

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A consensus protocol for functional connectivity analysis in the rat brain (vol 26, pg 673, 2023)

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NATURE NEUROSCIENCE 2023年第6期26卷 1127-1128页

作者： Grandjean, Joanes Desrosiers-Gregoire, Gabriel Anckaerts, Cynthia Angeles-Valdez, Diego Ayad, Fadi Barriere, David A. Blockx, Ines Bortel, Aleksandra Broadwater, Margaret Cardoso, Beatriz M. Celestine, Marina Chavez-Negrete, Jorge E. Choi, Sangcheon Christiaen, Emma Clavijo, Perrin Colon-Perez, Luis Cramer, Samuel Daniele, Tolomeo Dempsey, Elaine Diao, Yujian Doelemeyer, Arno Dopfel, David Dvorakova, Lenka Falfan-Melgoza, Claudia Fernandes, Francisca F. Fowler, Caitlin F. Fuentes-Ibanez, Antonio Garin, Clement M. Gelderman, Eveline Golden, Carla E. M. Guo, Chao C. G. Henckens, Marloes J. A. G. Hennessy, Lauren A. Herman, Peter Hofwijks, Nita Horien, Corey Ionescu, Tudor M. Jones, Jolyon Kaesser, Johannes Kim, Eugene Lambers, Henriette Lazari, Alberto Lee, Sung-Ho Lillywhite, Amanda Liu, Yikang Liu, Yanyan Y. Lopez-Castro, Alejandra Lopez-Gil, Xavier Ma, Zilu MacNicol, Eilidh Madularu, Dan Mandino, Francesca Marciano, Sabina McAuslan, Matthew J. McCunn, Patrick McIntosh, Alison Meng, Xianzong Meyer-Baese, Lisa Missault, Stephan Moro, Federico Naessens, Daphne M. P. Nava-Gomez, Laura J. Nonaka, Hiroi Ortiz, Juan J. Paasonen, Jaakko Peeters, Lore M. Pereira, Mickael Perez, Pablo D. Pompilus, Marjory Prior, Malcolm Rakhmatullin, Rustam Reimann, Henning M. Reinwald, Jonathan Del Rio, Rodrigo Triana Rivera-Olvera, Alejandro Ruiz-Perez, Daniel Russo, Gabriele Rutten, Tobias J. Ryoke, Rie Sack, Markus Salvan, Piergiorgio Sanganahalli, Basavaraju G. Schroeter, Aileen Seewoo, Bhedita J. Selingue, Erwan Seuwen, Aline Shi, Bowen Sirmpilatze, Nikoloz Smith, Joanna A. B. Smith, Corrie Sobczak, Filip Stenroos, Petteri J. Straathof, Milou Strobelt, Sandra Sumiyoshi, Akira Takahashi, Kengo Torres-Garcia, Maria E. Tudela, Raul van den Berg, Monica van der Marel, Kajo van Hout, Aran T. B. Vertullo, Roberta Vidal, Benjamin Vrooman, Roel M. Wang, Victora X. Wank, Isabel Watson, David J. G. Yin, Ting Zhang, Yongzhi Zurbruegg, Stefan Achard, Sophie Alcauter, Sarael Auer, Dorothee P. Barbier, Emmanuel L. Baudewig, Juergen Beckmann, Chri Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada Integrated Program in Neuroscience McGill University Montreal QC Canada Bio-imaging Lab University of Antwerp Antwerp Belgium µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico Biological and Biomedical Engineering McGill University Montreal QC Canada McConnell Brain Imaging Centre McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France Department of Neurology and Neurosurgery McGill University Montreal QC Canada Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tuebingen Germany Graduate Training Centre of Neuroscience International Max

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

关键词： Databases Neural circuits

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

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Scientific reports 2019年第1期9卷 18041页

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

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

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Publisher Correction: Paternal restraint stress affects offspring metabolism via ATF-2 dependent mechanisms in Drosophila melanogaster germ cells

Communications biology

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Communications biology 2020年第1期3卷 251页

作者： Ki-Hyeon Seong Nhung Hong Ly Yuki Katou Naoko Yokota Ryuichiro Nakato Shinnosuke Murakami Akiyoshi Hirayama Shinji Fukuda Siu Kang Tomoyoshi Soga Katsuhiko Shirahige Shunsuke Ishii RIKEN Cluster for Pioneering Research Tsukuba Ibaraki Japan. ki-hyeon.seong@riken.jp. PRESTO Japan Science and Technology Agency Kawaguchi Japan. ki-hyeon.seong@riken.jp. RIKEN Cluster for Pioneering Research Tsukuba Ibaraki Japan. Laboratory of Genome Structure and Function Institute for Quantitative Biosciences University of Tokyo Tokyo Japan. Laboratory of Computational Genomics Institute for Quantitative Biosciences University of Tokyo Tokyo Japan. Institute for Advanced Biosciences Keio University Tsuruoka Yamagata Japan. Systems Biology Program Graduate School of Media and Governance Keio University Fujisawa Kanagawa Japan. PRESTO Japan Science and Technology Agency Kawaguchi Japan. Transborder Medical Research Center University of Tsukuba Tsukuba Ibaraki Japan. Department of Bio-Systems Engineering Graduate School of Science and Engineering Yamagata University Yonezawa Yamagata Japan. RIKEN Cluster for Pioneering Research Tsukuba Ibaraki Japan. sishii@riken.jp.

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

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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

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Nature 2024年第8003期627卷 347-357页

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

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

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