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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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Micellar Hybrid Nanoparticles for Simultaneous Magnetofluorescent Imaging and Drug Delivery†

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Angewandte Chemie 2008年第38期120卷

作者： Ji‐Ho Park Geoffrey von Maltzahn Erkki Ruoslahti Sangeeta N. Bhatia Michael J. Sailor Materials Science and Engineering Program Department of Chemistry and Biochemistry University of California San Diego 9500 Gilman La Jolla CA 92093 (USA) Harvard‐MIT Division of Health Sciences and Technology Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 (USA) Burnham Institute for Medical Research at UCSB University of California Santa Barbara 1105 Life Sciences Technology Bldg Santa Barbara CA 93106 (USA)

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The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system (vol 74, pg 879, 2020)

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GENETICS IN MEDICINE 2020年第2期22卷 449-449页

作者： Mandl, Kenneth D. Glauser, Tracy Krantz, Ian D. Avillach, Paul Bartels, Anna Beggs, Alan H. Biswas, Sawona Bourgeois, Florence T. Corsmo, Jeremy Dauber, Andrew Devkota, Batsal Fleisher, Gary R. Heath, Allison P. Helbig, Ingo Hirschhorn, Joel N. Kilbourn, Judson Kong, Sek Won Kornetsky, Susan Majzoub, Joseph A. Marsolo, Keith Martin, Lisa J. Nix, Jeremy Schwarzhoff, Amy Stedman, Jason Strauss, Arnold Sund, Kristen L. Taylor, Deanne M. White, Peter S. Marsh, Eric Grimberg, Adda Hawkes, Colin Computational Health Informatics Program Boston Children’s Hospital Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Pediatrics Harvard Medical School Boston MA USA Division of Neurology Cincinnati Children’s Hospital Medical Center Cincinnati OH USA Department of Pediatrics University of Cincinnati College of Medicine Cincinnati OH USA Division of Human Genetics at the Children’s Hospital of Philadelphia Philadelphia PA USA Department of Pediatrics Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA Cincinnati Children’s Hospital Medical Center Cincinnati OH USA The Manton Center for Orphan Disease Research Division of Genetics and Genomics Boston Children’s Hospital Boston MA USA Department of Pediatrics Boston Children’s Hospital Boston MA USA Office of Research Compliance and Regulatory Affairs Cincinnati Children’s Hospital Medical Center Cincinnati OH USA Division of Endocrinology Children’s National Health System Washington DC USA Center for Data-Driven Discovery in Biomedicine The Children’s Hospital of Philadelphia Philadelphia PA USA Department of Biomedical and Health Informatics The Children’s Hospital of Philadelphia Philadelphia PA USA Division of Neurology Children’s Hospital of Philadelphia Philadelphia PA USA Division of Endocrinology Boston Children’s Hospital Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Research Administration Boston Children’s Hospital Boston MA USA Department of Population Health Sciences Duke University School of Medicine Durham NC USA Division of Human Genetics Cincinnati Children’s Hospital Medical Center Cincinnati OH USA Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center Cincinnati OH USA Department of Biomedical Informatics University of Cincinnati College of Medicine Cincinnati OH USA Children’s Hospital of Philadelphia Philadelphia PA USA Division o

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

关键词： genomic medicine federated networks electronic health records biobanking information technology

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Cover Picture: Magnetic Iron Oxide Nanoworms for Tumor Targeting and Imaging (Adv. Mater. 9/2008)

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Advanced Materials 2008年第9期20卷

作者： Ji‐Ho Park Geoffrey von Maltzahn Lianglin Zhang Michael P. Schwartz Erkki Ruoslahti Sangeeta N. Bhatia Michael J. Sailor Materials Science and Engineering Program Department of Chemistry and Biochemistry University of California San Diego 9500 Gilman La Jolla CA 92093 (USA) Harvard–MIT Division of Health Sciences and Technology Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 (USA) Burnham Institute for Medical Research at UCSB University of California Santa Barbara 1105 Life Sciences Technology Bldg. Santa Barbara CA 93106 (USA)

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Benchmarking Emergency Department Triage Prediction Models with Machine Learning and Large Public Electronic health Records

arXiv

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arXiv 2021年

作者： Xie, Feng Zhou, Jun Lee, Jin Wee Tan, Mingrui Li, Siqi Rajnthern, Logasan S.O. Chee, Marcel Lucas Chakraborty, Bibhas Wong, An-Kwok Ian Dagan, Alon Ong, Marcus Eng Hock Gao, Fei Liu, Nan Centre for Quantitative Medicine and Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Faculty of Medicine Nursing and Health Sciences Monash University VIC Australia Department of Statistics and Data Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Division of Pulmonary Allergy and Critical Care Medicine Duke University DurhamNC United States Department of Emergency Medicine Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States MIT Critical Data Laboratory for Computational Physiology Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Department of Emergency Medicine Singapore General Hospital Singapore Singapore SingHealth AI Health Program Singapore Health Services Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop predictive models and decision support systems to address these challenges. To date, however, there are no widely accepted benchmark ED triage prediction models based on large-scale public EHR data. An open-source benchmarking platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. In this paper, based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400,000 ED visits from 2011 to 2019. We introduced three ED-based outcomes (hospitalization, critical outcomes, and 72-hour ED reattendance) and implemented a variety of popular methodologies, ranging from machine learning methods to clinical scoring systems. We evaluated and compared the performance of these methods against benchmark tasks. Our codes are open-source, allowing anyone with MIMIC-IV-ED data access to perform the same steps in data processing, benchmark model building, and experiments. This study provides future researchers with insights, suggestions, and protocols for managing raw data and developing risk triaging tools for emergency care. © 2021, CC BY.

关键词： Learning systems

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Controlled Continuous Evolution of Enzymatic Activity Screened at Ultrahigh Throughput Using Drop-Based Microfluidics

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Angewandte Chemie 2023年第24期135卷

作者： R. G. Rosenthal X. Diana Zhang K. Ilić Đurđić J. J. Collins D. A. Weitz School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA Institute for Medical Engineering and Science Massachusetts Institute of Technology 77 Massachusetts Ave Cambridge MA 02139 USA Wyss Institute for Biologically Inspired Engineering Harvard University 3 Blackfan Circle Boston MA 02115 USA University of Belgrade-Faculty of Chemistry Studentski trg 12–16 11000 Belgrade Serbia Department of Biological Engineering Massachusetts Institute of Technology 77 Massachusetts Ave Cambridge MA 02139 USA Synthetic Biology Center Massachusetts Institute of Technology 77 Massachusetts Ave Cambridge MA 02139 USA Harvard-MIT Program in Health Sciences and Technology Massachusetts Institute of Technology 77 Massachusetts Ave Cambridge MA 02139 USA Broad Institute of MIT and Harvard 415 Main St Cambridge MA 02142 USA Department of Physics Harvard University Cambridge MA 02138 USA

Enzymes are highly specific catalysts delivering improved drugs and greener industrial processes. Naturally occurring enzymes must typically be optimized which is often accomplished through directed evolution; however, this is still a labor- and capital-intensive process, due in part to multiple molecular biology steps including DNA extraction, in vitro library generation, transformation, and limited screening throughput. We present an effective and broadly applicable continuous evolution platform that enables controlled exploration of fitness landscape to evolve enzymes at ultrahigh throughput based on direct measurement of enzymatic activity. This drop-based microfluidics platform cycles cells between growth and mutagenesis followed by screening with minimal human intervention, relying on the nCas9 chimera with mutagenesis polymerase to produce in vivo gene diversification using sgRNAs tiled along the gene. We evolve alditol oxidase to change its substrate specificity towards glycerol, turning a waste product into a valuable feedstock. We identify a variant with a 10.5-fold catalytic efficiency.

关键词： CRISPR-Directed Evolution Continuous Evolution Droplet Microfluidics Protein Engineering Sustainable Chemistry

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Influence of Magnetic Fields on Electrochemical Reactions of Redox Cofactor Solutions

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Angewandte Chemie 2021年第33期133卷

作者： Jimin Park Florian Koehler Georgios Varnavides Marc-Joseph Antonini Prof. Dr. Polina Anikeeva Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA Research Laboratory of Electronics and McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge MA 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02139 USA Harvard/MIT Health Science & Technology Graduate Program Cambridge MA 02139 USA Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA 02139 USA

Redox cofactors mediate many enzymatic processes and are increasingly employed in biomedical and energy applications. Exploring the influence of external magnetic fields on redox cofactor chemistry can enhance our understanding of magnetic-field-sensitive biological processes and allow the application of magnetic fields to modulate redox reactions involving cofactors. Through a combination of experiments and modeling, we investigate the influence of magnetic fields on electrochemical reactions in redox cofactor solutions. By employing flavin mononucleotide (FMN) cofactor as a model system, we characterize magnetically induced changes in Faradaic currents. We find that radical pair intermediates have negligible influence on current increases in FMN solution upon application of a magnetic field. The dominant mechanism underlying the observed current increases is the magneto-hydrodynamic effect. We extend our analyses to other diffusion-limited electrochemical reactions of redox cofactor solutions and arrive at similar conclusions, highlighting the opportunity to use this framework in redox cofactor chemistry.

关键词： electrochemistry magnetic field effects magneto-hydrodynamic effect radicals redox cofactors

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Click‐Assembled, Oxygen‐Sensing Nanoconjugates for Depth‐Resolved, Near‐Infrared Imaging in a 3 D Cancer Model†

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Angewandte Chemie 2014年第14期126卷

作者： Alexander J. Nichols Emmanuel Roussakis Oliver J. Klein Conor L. Evans Wellman Center for Photomedicine Massachusetts General Hospital CNY 149‐3210 13th Street Charlestown MA 02129 (USA) Harvard University Program in Biophysics Building C2 Room 112 240 Longwood Avenue Boston MA 02115 (USA) Harvard‐MIT Division of Health Sciences and Technology 77 Massachusetts Avenue E25‐519 Cambridge MA 02139 (USA)

Hypoxia is an important contributing factor to the development of drug‐resistant cancer, yet few nonperturbative tools exist for studying oxygenation in tissues. While progress has been made in the development of chemical probes for optical oxygen mapping, penetration of such molecules into poorly perfused or avascular tumor regions remains problematic. A click‐assembled oxygen‐sensing (CAOS) nanoconjugate is reported and its properties demonstrated in an in vitro 3D spheroid cancer model. The synthesis relies on the sequential click‐based ligation of poly(amidoamine)‐like subunits for rapid assembly. Near‐infrared confocal phosphorescence microscopy was used to demonstrate the ability of the CAOS nanoconjugates to penetrate hundreds of micrometers into spheroids within hours and to show their sensitivity to oxygen changes throughout the nodule. This proof‐of‐concept study demonstrates a modular approach that is readily extensible to a wide variety of oxygen and cellular sensors for depth‐resolved imaging in tissue and tissue models.

关键词： Bildgebungsagentien Dendrimere Klick‐Chemie Krebs Nanotechnologie

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Bright, “Clickable” Porphyrins for the Visualization of Oxygenation under Ambient Light

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Angewandte Chemie 2015年第49期127卷

作者： Emmanuel Roussakis Zongxi Li Nicholas H. Nowell Alexander J. Nichols Conor L. Evans Wellman Center for Photomedicine Massachusetts General Hospital CNY 149‐3210 13thStreet Charlestown MA 02129 (USA) Harvard University Program in Biophysics Building C2 Room 112 240 Longwood Avenue Boston MA 02115 (USA) Harvard–MIT Division of Health Sciences and Technology 77 Massachusetts Avenue E25‐519 Cambridge MA 02139 (USA)

A new group of “clickable” and brightly emissive metalloporphyrins has been developed for the visualization of oxygenation under ambient light with the naked eye. These alkynyl‐terminated compounds permit the rapid and facile synthesis of oxygen‐sensing dendrimers through azide–alkyne click chemistry. With absorption maxima overlapping with the wavelengths of common commercial laser sources, they are readily applicable to biomedical imaging of tissue oxygenation. An efficient synthetic methodology, featuring the stable trimethylacetyl (pivaloyl) protecting group, is described for their preparation. A paint‐on liquid bandage containing a new, click‐synthesized porphyrin dendrimer has been used to map oxygenation across an ex vivo porcine skin burn model.

关键词： Biotechnologie Dendrimere Lumineszenz Porphyrine Sensoren

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Cover Picture: The Binding of Fluorophores to Proteins Depends on the Cellular Environment (Angew. Chem. Int. Ed. 12/2011)

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Angewandte Chemie International Edition 2011年第12期50卷

作者： Dr. Yun Kyung Kim Dr. Jun-Seok Lee Dr. Xuezhi Bi Dr. Hyung-Ho Ha Shin Hui Ng Dr. Young-hoon Ahn Dr. Jae-Jung Lee Dr. Bridget K. Wagner Dr. Paul A. Clemons Prof. Dr. Young-Tae Chang Korea Institute of Science and Technology (KIST) Life/Health Division Integrated Omics Center Seoul (Korea) Laboratory of Bioimaging Probe Development Singapore Bioimaging Consortium A*STAR Biopolis Singapore NUS MedChem Program of the Office of Life Sciences National University of Singapore Singapore Department of Pharmacology Johns Hopkins University Baltimore MD (USA) Chemical Biology Program Broad Institute of Harvard and MIT Cambridge MA (USA)

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