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Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries (vol 55, pg 89, 2023)

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NATURE GENETICS 2023年第3期55卷 519-520页

作者： Fernandez-Rozadilla, Ceres Timofeeva, Maria Chen, Zhishan Law, Philip Thomas, Minta Schmit, Stephanie Diez-Obrero, Virginia Hsu, Li Fernandez-Tajes, Juan Palles, Claire Sherwood, Kitty Briggs, Sarah Svinti, Victoria Donnelly, Kevin Farrington, Susan Blackmur, James Vaughan-Shaw, Peter Shu, Xiao-ou Long, Jirong Cai, Qiuyin Guo, Xingyi Lu, Yingchang Broderick, Peter Studd, James Huyghe, Jeroen Harrison, Tabitha Conti, David Dampier, Christopher Devall, Mathew Schumacher, Fredrick Melas, Marilena Rennert, Gad Obon-Santacana, Mireia Martin-Sanchez, Vicente Moratalla-Navarro, Ferran Oh, Jae Hwan Kim, Jeongseon Jee, Sun Ha Jung, Keum Ji Kweon, Sun-Seog Shin, Min-Ho Shin, Aesun Ahn, Yoon-Ok Kim, Dong-Hyun Oze, Isao Wen, Wanqing Matsuo, Keitaro Matsuda, Koichi Tanikawa, Chizu Ren, Zefang Gao, Yu-Tang Jia, Wei-Hua Hopper, John Jenkins, Mark Win, Aung Ko Pai, Rish Figueiredo, Jane Haile, Robert Gallinger, Steven Woods, Michael Newcomb, Polly Duggan, David Cheadle, Jeremy Kaplan, Richard Maughan, Timothy Kerr, Rachel Kerr, David Kirac, Iva Bohm, Jan Mecklin, Lukka-Pekka Jousilahti, Pekka Knekt, Paul Aaltonen, Lauri Rissanen, Harri Pukkala, Eero Eriksson, Johan Cajuso, Tatiana Hanninen, Ulrika Kondelin, Johanna Palin, Kimmo Tanskanen, Tomas Renkonen-Sinisalo, Laura Zanke, Brent Mannisto, Satu Albanes, Demetrius Weinstein, Stephanie Ruiz-Narvaez, Edward Palmer, Julie Buchanan, Daniel Platz, Elizabeth Visvanathan, Kala Ulrich, Cornelia Siegel, Erin Brezina, Stefanie Gsur, Andrea Campbell, Peter Chang-Claude, Jenny Hoffmeister, Michael Brenner, Hermann Slattery, Martha Potter, John Tsilidis, Konstantinos Schulze, Matthias Gunter, Marc Murphy, Neil Castells, Antoni Castellvi-Bel, Sergi Moreira, Leticia Arndt, Volker Shcherbina, Anna Stern, Mariana Pardamean, Bens Bishop, Timothy Giles, Graham Southey, Melissa Idos, Gregory McDonnell, Kevin Abu-Ful, Zomoroda Greenson, Joel Shulman, Katerina Lejbkowicz, Flavio Offit, Kenneth Su, Yu-Ru Steinfelder, Robert Keku, Temitope van Guelpen, Bethany Hudson, Thomas Hampel, Heather Pearlman, Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA Division of Genetics and Epidemiology Institute of Cancer Research London UK Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA Genomic Medicine Institute Cleveland Clinic Cleveland OH USA Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Department of Biostatistics School of Public Health University of Washington Seattle WA USA Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK Department of Public Health Richard Doll Building University of Oxford Oxford UK Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA Department of Populat

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

关键词： Colorectal cancer Epigenomics Genome-wide association studies Transcriptomics

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MICROBUNDLECOMPUTE: AUTOMATED SEGMENTATION, TRACKING, AND ANALYSIS OF SUBDOMAIN DEFORMATION IN CARDIAC MICROBUNDLES

arXiv

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arXiv 2023年

作者： Kobeissi, Hiba Jilberto, Javiera Karakan, M. Çağatay Gao, Xining DePalma, Samuel J. Das, Shoshana L. Quach, Lani Urquia, Jonathan Baker, Brendon M. Chen, Christopher S. Nordsletten, David Lejeune, Emma Department of Mechanical Engineering Boston University BostonMA02215 United States Center for Multiscale and Translational Mechanobiology Boston University BostonMA02215 United States Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Photonics Center Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Harvard-MIT Program in Health Sciences and Technology Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA02139 United States Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA02215 United States Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY10023 United States Department of Cardiac Surgery University of Michigan MI48109 United States Department of Biomedical Engineering School of Imaging Sciences and Biomedical Engineering King’s College London King’s Health Partners LondonSE1 7EH United Kingdom

Advancing human induced pluripotent stem cell derived cardiomyocyte (hiPSC-CM) technology will lead to significant progress ranging from disease modeling, to drug discovery, to regenerative tissue engineering. Yet, alongside these potential opportunities comes a critical challenge: attaining mature hiPSC-CM tissues. At present, there are multiple techniques to promote maturity of hiPSC-CMs including physical platforms and cell culture protocols. However, when it comes to making quantitative comparisons of functional behavior, there are limited options for reliably and reproducibly computing functional metrics that are suitable for direct cross-system comparison. In addition, the current standard functional metrics obtained from time-lapse images of cardiac microbundle contraction reported in the field (i.e., post forces, average tissue stress) do not take full advantage of the available information present in these data (i.e., full-field tissue displacements and strains). Thus, we present "MicroBundleCompute, " a computational framework for automatic quantification of morphology-based mechanical metrics from movies of cardiac microbundles. Briefly, this computational framework offers tools for automatic tissue segmentation, tracking, and analysis of brightfield and phase contrast movies of beating cardiac microbundles. It is straightforward to implement, runs without user intervention, requires minimal input parameter setting selection, and is computationally inexpensive. In this paper, we describe the methods underlying this computational framework, show the results of our extensive validation studies, and demonstrate the utility of exploring heterogeneous tissue deformations and strains as functional metrics. With this manuscript, we disseminate "MicroBundleCompute" as an open-source computational tool with the aim of making automated quantitative analysis of beating cardiac microbundles more accessible to the *** Codes 92F05, 74A05 © 2023, CC BY-SA.

关键词： Cell culture

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Regulatory T cells constrain T cells of shared specificity to enforce tolerance during infection

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Science (New York, N.Y.) 2025年第6740期387卷 eadk3248页

作者： Klawon, David E J Pagane, Nicole Walker, Matthew T. Ganci, Nicole K. Miller, Christine H. Gai, Eric Rodriguez, Donald M. Ryan-Payseur, Bridgett K. Duncombe, Ryan K. Adams, Erin J. Maienschein-Cline, Mark Freitag, Nancy E. Germain, Ronald N. Wong, Harikesh S. Savage, Peter A. Department of Pathology University of Chicago Chicago IL United States Ragon Institute of Mass General MIT and Harvard Cambridge MA United States Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge MA United States Interdisciplinary Scientist Training Program University of Chicago Chicago IL United States Department of Microbiology and Immunology University of Illinois Chicago Chicago IL United States Department of Biochemistry and Molecular Biology University of Chicago Chicago IL United States Research Informatics Core Research Resources Center University of Illinois Chicago Chicago IL United States Department of Pharmaceutical Sciences University of Illinois Chicago Chicago IL United States Lymphocyte Biology Section Laboratory of Immune System Biology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD United States Department of Biology Massachusetts Institute of Technology Cambridge MA United States

During infections, CD4+ Foxp3+ regulatory T (Treg) cells must control autoreactive CD4+ conventional T (Tconv) cell responses against self-peptide antigens while permitting those against pathogen-derived "nonself" peptides. We defined the basis of this selectivity using mice in which Treg cells reactive to a single prostate-specific self-peptide were selectively depleted. We found that self-peptide-specific Treg cells were dispensable for the control of Tconv cells of matched specificity at homeostasis. However, they were required to control such Tconv cells and prevent autoimmunity toward the prostate after exposure to elevated self-peptide during infection. Notably, the Treg cell response to self-peptide did not affect protective Tconv cell responses to a pathogen-derived peptide. Thus, self-peptide-specific Treg cells promoted self-nonself discrimination during infection by selectively controlling Tconv cells of shared self-specificity.

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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REDUCING INTER-OBSERVER VARIABILITY IN ENDOMYOCARDIAL BIOPSY ASSESSMENT

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Journal of Pathology Informatics 2022年 13卷 100058-100058页

作者： Lipkova, Jana Chen, Tiffany Y. Lu, Ming Y. Chen, Richard J. Shady, Maha Williams, Mane Wang, Jingwen Noor, Zahra mitchell, Richard N. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA USA Broad Institute of Harvard and MIT Cambridge MA USA Data Science Program Dana-Farber Cancer Institute Boston MA USA Harvard-MIT Health Sciences and Technology Cambridge MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA

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Hypoxia in extravillous trophoblasts links maternal obesity and offspring neurobehavior

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iScience 2025年第6期28卷

作者： Fatima Gunter-Rahman Shayna Mallett Frédérique White Pierre-Étienne Jacques Ravikiran M. Raju Marie-France Hivert Eunjung Alice Lee Harvard-MIT Program in Health Sciences and Technology Massachusetts Institute of Technology Cambridge MA USA Division of Genetics and Genomics Boston Children’s Hospital Boston MA USA Department of Pediatrics Harvard Medical School Boston MA USA Département de Biologie Faculté des Sciences Université de Sherbrooke Sherbrooke QC Canada Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke Sherbrooke QC Canada Institut de Recherche sur le Cancer de l’Université de Sherbrooke Sherbrooke QC Canada Division of Newborn Medicine Boston Children’s Hospital Harvard Medical School Boston MA USA Picower Institute for Learning and Memory Massachusetts Institute of Technology Cambridge MA USA Department of Population Medicine Harvard Pilgrim Health Care Institute and Harvard Medical School Boston MA USA Diabetes Unit Endocrine Division Department of Medicine Massachusetts General Hospital Boston MA USA Broad Institute of MIT and Harvard Cambridge MA 02142 USA

While maternal obesity (MO) is associated with neurobehavioral impairment (NBI) in offspring, the underlying mechanisms remain unknown. The placenta is thought to play a role in fetal programming. To elucidate the association between MO and offspring NBI, we performed single-nucleus RNA-seq on maternal- and fetal-facing sides of human term placentas from MO and lean groups. MO placentas showed the upregulation of hypoxia response genes in multiple cell types, and maternal-facing hypoxia gene expression correlated with offspring NBI in an independent birth cohort, Gen3G. Extravillous trophoblasts (EVTs) showed the highest expression of NBI-correlated genes, and EVT NBI-gene expression correlated with hypoxia signatures in two cohorts. Exposing cultured EVTs to hypoxia increased NBI gene expression, and 44% of the association between maternal BMI and NBI-gene expression in EVTs was mediated by hypoxia. Our findings suggest that hypoxia in EVTs is a key process in the neurodevelopmental programming of fetal exposure to MO.

关键词： Molecular biology Neuroscience

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Deep learning-based detection of intravenous contrast in computed tomography scans

arXiv

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arXiv 2021年

作者： Ye, Zezhong Qian, Jack M. Hosny, Ahmed Zeleznik, Roman Plana, Deborah Likitlersuang, Jirapat Zhang, Zhongyi Mak, Raymond H. Aerts, Hugo J.W.L. Kann, Benjamin H. Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Dana-Farber Cancer Institute Brigham and Women’s Hospital Harvard Medical School BostonMA United States Harvard-MIT Division of Health Sciences & Technology CambridgeMA United States Department of Radiology Brigham and Women’s Hospital Dana-Farber Cancer Institute Harvard Medical School BostonMA United States Radiology and Nuclear Medicine CARIM & GROW Maastricht University Maastricht Netherlands

Purpose: Identifying intravenous (IV) contrast use within CT scans is a key component of data curation for model development and testing. Currently, IV contrast is poorly documented in imaging metadata and necessitates manual correction and annotation by clinician experts, presenting a major barrier to imaging analyses and algorithm deployment. We sought to develop and validate a convolutional neural network (CNN)-based deep learning (DL) platform to identify IV contrast within CT scans. Methods: For model development and evaluation, we used independent datasets of CT scans of head, neck (HN) and lung cancer patients, totaling 133,480 axial 2D scan slices from 1,979 CT scans manually annotated for contrast presence by clinical experts. Five different DL models were adopted and trained in HN training datasets for slice-level contrast detection. Model performances were evaluated on a hold-out set and on an independent validation set from another institution. DL models was then fine-tuned on chest CT data and externally validated on a separate chest CT dataset. Results: Initial DICOM metadata tags for IV contrast were missing or erroneous in 1,496 scans (75.6%). The EfficientNetB4-based model showed the best overall detection performance. For HN scans, AUC was 0.996 in the internal validation set (n = 216) and 1.0 in the external validation set (n = 595). The fine-tuned model on chest CTs yielded an AUC: 1.0 for the internal validation set (n = 53), and AUC: 0.980 for the external validation set (n = 402). Conclusion: The DL model could accurately detect IV contrast in both HN and chest CT scans with near-perfect performance. © 2021, CC BY-NC-ND.

关键词： Computerized tomography

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Corrigendum to 'Engineered cell-laden human protein-based elastomer' [34(2013), 5496-5505]

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Biomaterials 2025年 314卷 122850页

作者： Nasim Annabi Suzanne M mithieux Pinar Zorlutuna Gulden Camci-Unal Anthony S Weiss Ali Khademhosseini Center for Biomedical Engineering Department of Medicine Brigham and Women's Hospital Harvard Medical School Boston MA 02139 USA Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge MA 02139 USA Wyss Institute for Biologically Inspired Engineering Harvard University Cambridge MA 02139 USA. School of Molecular Bioscience University of Sydney Sydney 2006 Australia. Biomedical Engineering Program and Mechanical Engineering Department University of Connecticut 191 Auditorium Road Storrs CT 06269-3139 USA. Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge MA 02139 USA. School of Molecular Bioscience University of Sydney Sydney 2006 Australia Bosch Institute University of Sydney Sydney 2006 Australia Charles Perkins Centre University of Sydney Sydney 2006 Australia. Wyss Institute for Biologically Inspired Engineering Harvard University Cambridge MA 02139 USA. Electronic address: khademh@***.

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Impact of Exposure to Per- and Polyfluoroalkyl Substances on Fecal Microbiota Composition in Mother-Infant Dyads

SSRN

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SSRN 2022年

作者： Lamichhane, Santosh Härkönen, Taina Vatanen, Tommi Hyötyläinen, Tuulia Knip, Mikael Orešič, Matej Turku Bioscience Centre University of Turku and Åbo Akademi University Turku20520 Finland Research Program for Clinical and Molecular Metabolism Faculty of Medicine University of Helsinki Finland Broad Institute of MIT and Harvard CambridgeMA02142 United States Liggins Institute University of Auckland New Zealand School of Science and Technology Örebro University Örebro702 81 Sweden Tampere University Hospital Department of Paediatrics Tampere Finland School of Medical Sciences Faculty of Medicine and Health Örebro University Örebro702 81 Sweden

Current evidence suggests that chemical exposure alters gut microbiota composition, with higher exposure to environmental chemicals being associated with reduced microbiome diversity. However, not much is known about the impact of per- and polyfluoroalkyl substances (PFAS) on gut bacteria. Here, in a mother-infant study, we set out to identify the gut bacterial species that associate with chemical exposure before (maternal) and after (maternal, infant) birth. Paired blood and stool samples were collected from mother-infant dyads (n = 30) in a longitudinal setting. PFAS were quantified in maternal blood to examine their associations with the microbial compositions (determined by shotgun metagenomic sequencing) in mothers and infants. High maternal exposure to PFAS was persistently associated with increased abundance of Methanobrevibacter smithii in maternal stool. Among individual PFAS compounds, PFOS and PFHpS showed the strongest connection with M. smithii. However, maternal PFAS exposure associated only weakly with the infant microbiome. Our findings suggest that PFAS exposure contributes to the modulation of the adult gut microbiome composition. © 2022, The Authors. All rights reserved.

关键词： Blood

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Corrigendum to 'Engineered cell-laden human protein-based elastomer' [34 (2013), 5496-5505]

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Biomaterials 2024年 308卷 122572页

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