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Natural history of Ebola virus disease in rhesus monkeys shows viral variant emergence dynamics and tissue-specific host responses

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CELL GENOMICS 2023年第12期3卷 100440-100440页

作者： Normandin, Erica Triana, Sergio Raju, Siddharth S. Lan, Tammy C. T. Lagerborg, Kim Rudy, Melissa Adams, Gordon C. DeRuff, Katherine C. Logue, James Liu, David Strebinger, Daniel Rao, Arya Messer, Katelyn S. Sacks, Molly Adams, Ricky D. Janosko, Krisztina Kotliar, Dylan Shah, Rickey Crozier, Ian Rinn, John L. Mele, Marta Honko, Anna N. Zhang, Feng Babadi, Mehrtash Luban, Jeremy Bennett, Richard S. Shalek, Alex K. Barkas, Nikolaos Lin, Aaron E. Hensley, Lisa E. Sabeti, Pardis C. Siddle, Katherine J. Broad Inst Harvard & MIT Cambridge MA 02142 USA Harvard Med Sch Dept Syst Biol Boston MA 02115 USA MIT Harvard MIT Div Hlth Sci & Technol Cambridge MA 02142 USA MIT Inst Med Engn & Sci IMES Cambridge MA 02142 USA MIT Koch Inst Integrat Canc Res Cambridge MA 02142 USA Harvard Ragon Inst MGH Cambridge MA 02139 USA MIT Cambridge MA 02139 USA Harvard Univ Dept Mol & Cellular Biol Boston MA 02138 USA Harvard Med Sch Biol & Biomed Sci Program Boston MA USA Massachusetts Gen Hosp Div Infect Dis Boston MA 02114 USA NIAID Integrated Res Facil Div Clin Res NIH Frederick MD 21702 USA Howard Hughes Med Inst Chevy Chase MD 20815 USA MIT McGovern Inst Brain Res Cambridge MA 02139 USA MIT Dept Brain & Cognit Sci Cambridge MA 02139 USA MIT Dept Biol Engn Cambridge MA 02139 USA Columbia Univ New York NY USA Harvard Med Sch Harvard MIT MD PhD Program Boston MA 02115 USA Frederick Natl Lab Canc Res Clin Monitoring Res Program Directorate Frederick MD 21702 USA Univ Colorado Boulder Dept Biochem Boulder CO 80303 USA Barcelona Supercomp Ctr Life Sci Dept Barcelona 08034 Spain Boston Univ Natl Emerging Infect Dis Labs Boston MA 02118 USA Univ Massachusetts Chan Med Sch Program Mol Med Worcester MA 01655 USA Harvard Med Sch Harvard Program Virol Boston MA 02115 USA Princeton Univ Dept Mol Biol Princeton NJ 08544 USA Princeton Univ Lewis Sigler Inst Integrat Genom Princeton NJ 08544 USA Harvard Univ Dept Organism & Evolutionary Biol Cambridge MA 02138 USA Harvard TH Chan Sch Publ Hlth Dept Immunol & Infect Dis Boston MA 02115 USA Brown Univ Dept Mol Microbiol & Immunol Providence RI 02912 USA Noorda Coll Osteopath Med Provo UT USA Apriori Bio Inc Cambridge MA USA

Ebola virus (EBOV) causes Ebola virus disease (EVD), marked by severe hemorrhagic fever;however, the mechanisms underlying the disease remain unclear. To assess the molecular basis of EVD across time, we performed RNA sequencing on 17 tissues from a natural history study of 21 rhesus monkeys, developing new methods to characterize host-pathogen dynamics. We identified alterations in host gene expression with previously unknown tissue-specific changes, including downregulation of genes related to tissue con-nectivity. EBOV was widely disseminated throughout the body;using a new, broadly applicable deconvolu-tion method, we found that viral load correlated with increased monocyte presence. Patterns of viral variation between tissues differentiated primary infections from compartmentalized infections, and several variants impacted viral fitness in a EBOV/Kikwit minigenome system, suggesting that functionally significant variants can emerge during early infection. This comprehensive portrait of host-pathogen dynamics in EVD illumi-nates new features of pathogenesis and establishes resources to study other emerging pathogens.

关键词： Ebola virus transcriptomics host-virus interactions viral variants Ebola virus disease minigenome hemorrhagic fevers non-human primates deconvolution

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Serological Markers of SARS-CoV-2 Reinfection

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MBIO 2022年第1期13卷 e0214121页

作者： Siddiqui, Sameed M. Bowman, Kathryn A. Zhu, Alex L. Fischinger, Stephanie Beger, Samuel Maron, Jenny S. Bartsch, Yannic C. Atyeo, Caroline Gorman, Matthew J. Yanis, Ahmad Hultquist, Judd F. Lorenzo-Redondo, Ramon Ozer, Egon A. Simons, Lacy M. Talj, Rana Rankin, Danielle A. Chapman, Lindsay Meade, Kyle Steinhart, Jordan Mullane, Sean Siebert, Suzanne Streeck, Hendrik Sabeti, Pardis Halasa, Natasha Musk, Elon R. Barouch, Dan H. Menon, Anil S. Nilles, Eric J. Lauffenburger, Douglas A. Alter, Galit MIT Computat & Syst Biol Program 77 Massachusetts Ave Cambridge MA 02139 USA Broad Inst MIT & Harvard Cambridge MA 02142 USA Ragon Inst MGH MIT & Harvard Cambridge MA 02139 USA Univ Duisburg Essen PhD Program Immunol & Virol Essen Germany Space Explorat Technol Corp Hawthorne CA USA Harvard Univ Div Med Sci PhD Program Virol Boston MA 02115 USA Vanderbilt Univ Med Ctr Dept Pediat Nashville TN 37232 USA Northwestern Univ Dept Med Feinberg Sch Med Div Infect Dis Chicago IL 60611 USA Northwestern Univ Ctr Pathogen Genom & Microbial Evolut Inst Global Hlth Feinberg Sch Med Chicago IL 60611 USA Vanderbilt Univ Sch Med Vanderbilt Epidemiol PhD Program Nashville TN 37212 USA Univ Bonn Univ Hosp Inst Virol Cologne Germany German Ctr Infect Res DZIF Partner Site Bonn Cologne Cologne Germany Harvard TH Chan Sch Publ Hlth Cambridge MA USA Howard Hughes Med Inst Chevy Chase MD USA Massachusetts Consortium Pathogen Readiness Boston MA 02115 USA Harvard Med Sch Beth Israel Deaconess Med Ctr Ctr Virol & Vaccine Res Boston MA 02115 USA Brigham & Womens Hosp Dept Emergency Med 75 Francis St Boston MA 02115 USA Harvard Univ Harvard Med Sch Cambridge MA 02138 USA Harvard Humanitarian Initiat Boston MA USA MIT Dept Biol Engn 77 Massachusetts Ave Cambridge MA 02139 USA

As public health guidelines throughout the world have relaxed in response to vaccination campaigns against SARS-CoV-2 it is likely that SARS-CoV-2 will remain endemic, fueled by the rise of more infectious SARS-CoV-2 variants. Moreover, in the setting of waning natural and vaccine immunity, reinfection have emerged across the globe, even among previously infected and vaccinated individuals. As such, the ability to detect reexposure to and reinfection by SARS-CoV-2 is a key component for global protection against this virus and, more importantly, against the potential emergence of vaccine escape mutations. Accordingly, there is a strong and continued need for the development and deployment of simple methods to detect emerging hot spots of reinfection to inform targeted pandemic response and containment, including targeted and specific deployment of vaccine booster campaigns. In this study, we identify simple, rapid immune biomarkers of reinfection in rhesus macaques, including lgG3 antibody levels against nucleocapsid and Fc gamma R2A receptor binding activity of anti-RBD antibodies, that are recapitulated in human reinfection cases. As such, this cross-species analysis underscores the potential utility of simple antibody titers and function as price-effective and scalable markers of reinfection to provide increased resolution and resilience against new outbreaks. IMPORTANCE As public health and social distancing guidelines loosen in the setting of waning global natural and vaccine immunity, a deeper understanding of the immunological response to reexposure and reinfection to this highly contagious pathogen is necessary to maintain public health. Viral sequencing analysis provides a robust but unrealistic means to monitor reinfection globally. The identification of scalable pathogen-specific biomarkers of reexposure and reinfection, however, could significantly accelerate our capacity to monitor the spread of the virus through naive and experienced hosts, providing k

关键词： SARS-CoV-2 reinfection antibodies humoral immunity diagnostics biomarkers

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Schizophrenia risk from complex variation of complement component 4 (vol 530, pg 177, 2021)

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NATURE 2022年第7892期601卷 E4-E5页

作者： Sekar, Aswin Bialas, Allison R. de Rivera, Heather Davis, Avery Hammond, Timothy R. Kamitaki, Nolan Tooley, Katherine Presumey, Jessy Baum, Matthew Van Doren, Vanessa Genovese, Giulio Rose, Samuel A. Handsaker, Robert E. Daly, Mark J. Carroll, Michael C. Stevens, Beth McCarroll, Steven A. Department of Genetics Harvard Medical School Boston Massachusetts 02115 USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge Massachusetts 02142 USA MD-PhD Program Harvard Medical School Boston Massachusetts 02115 USA Department of Neurology F.M. Kirby Neurobiology Center Boston Children’s Hospital Harvard Medical School Boston Massachusetts 02115 USA Analytical and Translational Genetics Unit Massachusetts General Hospital Boston Massachusetts 02114 USA Program in Cellular and Molecular Medicine Boston Children’s Hospital Boston Massachusetts 02115 USA

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The trajectory of gait development in mice

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BRAIN AND BEHAVIOR 2020年第6期10卷 01636-01636页

作者： Akula, Shyam K. McCullough, Katherine B. Weichselbaum, Claire Dougherty, Joseph D. Maloney, Susan E. Washington Univ Sch Med Dept Psychiat Campus Box 8232660 South Euclid Ave St Louis MO 63110 USA Washington Univ Sch Med Dept Genet St Louis MO 63110 USA Harvard Med Sch Harvard MIT MD PhD Program Boston MA 02115 USA Washington Univ Sch Med Intellectual & Dev Disabil Res Ctr St Louis MO 63110 USA

Objective Gait irregularities are prevalent in neurodevelopmental disorders (NDDs). However, there is a paucity of information on gait phenotypes in NDD experimental models. This is in part due to the lack of understanding of the normal developmental trajectory of gait maturation in the mouse. Materials and methods Using the DigiGait system, we have developed a quantitative, standardized, and reproducible assay of developmental gait metrics in commonly used mouse strains that can be added to the battery of mouse model phenotyping. With this assay, we characterized the trajectory of gait in the developing C57BL/6J and FVB/AntJ mouse lines. Results In both lines, a mature stride consisted of 40% swing and 60% stance in the forelimbs, which mirrors the mature human stride. In C57BL/6J mice, developmental trajectories were observed for stance width, paw overlap distance, braking and propulsion time, rate of stance loading, peak paw area, and metrics of intraindividual variability. In FVB/AntJ mice, developmental trajectories were observed for percent shared stance, paw overlap distance, rate of stance loading, and peak paw area, although in different directions than C57 mice. By accounting for the impact of body length on stride measurements, we demonstrate the importance of considering body length when interpreting gait metrics. Conclusion Overall, our results show that aspects of mouse gait development parallel a timeline of normal human gait development, such as the percent of stride that is stance phase and swing phase. This study may be used as a standard reference for developmental gait phenotyping of murine models, such as models of neurodevelopmental disease.

关键词： development DigiGait gait motor function mouse mouse strains

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Annotations capturing cell type-specific TF binding explain a large fraction of disease heritability

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HUMAN MOLECULAR GENETICS 2020年第7期29卷 1057-1067页

作者： van de Geijn, Bryce Finucane, Hilary Gazal, Steven Hormozdiari, Farhad Amariuta, Tiffany Liu, Xuanyao Gusev, Alexander Loh, Po-Ru Reshef, Yakir Kichaev, Gleb Raychauduri, Soumya Price, Alkes L. Harvard TH Chan Sch Publ Hlth Dept Epidemiol 665 Huntington Ave Boston MA 02115 USA Harvard Med Sch Ctr Data Sci Boston MA 02215 USA Harvard Med Sch Brigham & Womens Hosp Dept Med Div Genet Boston MA 02215 USA Harvard Med Sch Brigham & Womens Hosp Dept Med Div Rheumatol Boston MA 02215 USA Harvard Med Sch Dept Biomed Informat Boston MA 02215 USA Harvard Univ Grad Sch Arts & Sci Boston MA 02215 USA Broad Inst MIT & Harvard Program Med & Populat Genet Cambridge MA 02142 USA Dana Farber Canc Inst Boston MA 02215 USA Brigham & Womens Hosp Boston MA 02215 USA Harvard Univ Dept Comp Sci Cambridge MA 02138 USA Harvard MIT MD PhD Program Boston MA 02215 USA Harvard TH Chan Sch Publ Hlth Dept Biostat Boston MA 02215 USA Univ Calif Los Angeles Bioinformat Interdept Program Los Angeles CA 90095 USA

Regulatory variation plays a major role in complex disease and that cell type-specific binding of transcription factors (TF) is critical to gene regulation. However, assessing the contribution of genetic variation in TF-binding sites to disease heritability is challenging, as binding is often cell type-specific and annotations from directly measured TF binding are not currently available for most cell type-TF pairs. We investigate approaches to annotate TF binding, including directly measured chromatin data and sequence-based predictions. We find that TF-binding annotations constructed by intersecting sequence-based TF-binding predictions with cell type-specific chromatin data explain a large fraction of heritability across a broad set of diseases and corresponding cell types;this strategy of constructing annotations addresses both the limitation that identical sequences may be bound or unbound depending on surrounding chromatin context and the limitation that sequence-based predictions are generally not cell type-specific. We partitioned the heritability of 49 diseases and complex traits using stratified linkage disequilibrium (LD) score regression with the baseline-LD model (which is not cell type-specific) plus the new annotations. We determined that 100 bp windows around MotifMap sequenced-based TF-binding predictions intersected with a union of six cell type-specific chromatin marks (imputed using Chromlmpute) performed best, with an 58% increase in heritability enrichment compared to the chromatin marks alone (11.6x vs. 7.3x, P = 9 x 10(-14) for difference) and a 20% increase in cell type-specific signal conditional on annotations from the baseline-LD model (P = 8 x 10(-11) for difference). Our results show that TF-binding annotations explain substantial disease heritability and can help refine genome-wide association signals.

关键词： chromatin gene expression regulation genome linkage disequilibrium genetics transcription factor imputation chromatin immunoprecipitation sequencing

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Diabetes subgroups and sociodemographic inequalities in Mexico: a cross-sectional analysis of nationally representative surveys from 2016 to 2022

LANCET REGIONAL HEALTH-AMERICAS

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LANCET REGIONAL HEALTH-AMERICAS 2024年 33卷 100732-100732页

作者： Antonio-Villa, Neftali Eduardo Bello-Chavolla, Omar Yaxmehen Fermin-Martinez, Carlos A. Ramirez-Garcia, Daniel Vargas-Vazquez, Arsenio Basile-Alvarez, Martin Roberto Nunez-Luna, Alejandra Sanchez-Castro, Paulina Fernandez-Chirino, Luisa Diaz-Sanchez, Juan Pablo Davila-Lopez, Gael Posadas-Sanchez, Rosalinda Vargas-Alarcon, Gilberto Caballero, A. Enrique Florez, Jose C. Seiglie, Jacqueline A. Inst Nacl Cardiol Ignacio Chavez Dept Endocrinol Juan Badiano 1Belisario Dominguez Secc 16 Mexico City 14080 Mexico Inst Nacl Geriatria Res Div Anillo Perif 2767 Mexico City 10200 Mexico Univ Nacl Autonoma Mexico Fac Med MD PhD PECEM Program Mexico City Mexico Univ Nacl Autonoma Mexico Fac Med Mexico City Mexico Inst Nacl Cardiol Ignacio Chavez Direcc Invest Mexico City Mexico Brigham & Womens Hosp Div Endocrinol Diabet & Hypertens Boston MA USA Harvard Med Sch Boston MA USA Massachusetts Gen Hosp Diabet Unit Boston MA USA Massachusetts Gen Hosp Ctr Genom Med Boston MA USA Broad Inst MIT & Harvard Program Metab Cambridge MA USA Broad Inst MIT & Harvard Program Med & Populat Genet Cambridge MA USA Harvard Med Sch Dept Med Boston MA USA Massachusetts Gen Hosp Diabet Unit Boston MA USA

Background Differences in the prevalence of four diabetes subgroups have been reported in Mexico compared to other populations, but factors that may contribute to these differences are poorly understood. Here, we estimate the prevalence of diabetes subgroups in Mexico and evaluate their correlates with indicators of social disadvantage using data from national representative surveys. Methods We analyzed serial, cross-sectional Mexican National Health and Nutrition Surveys spanning 2016, 2018, 2020, 2021, and 2022, including 23,354 adults (>20 years). Diabetes subgroups (obesity-related [MOD], severe insulin-deficient [SIDD], severe insulin-resistant [SIRD], and age-related [MARD]) were classified using self-normalizing neural networks based on a previously validated algorithm. We used the density-independent social lag index (DISLI) as a proxy of state-level social disadvantage. Findings We identified 4204 adults (median age: 57, IQR: 47-66, women: 64%) living with diabetes, yielding a pooled prevalence of 16.04% [95% CI: 14.92-17.17]. When stratified by diabetes subgroup, prevalence was 6.62% (5.69-7.55) for SIDD, 5.25% (4.52-5.97) for MOD, 2.39% (1.95-2.83) for MARD, and 1.27% (1.00-1.54) for SIRD. SIDD and MOD clustered in Southern Mexico, whereas MARD and SIRD clustered in Northern Mexico and Mexico City. Each standard deviation increase in DISLI was associated with higher odds of SIDD (OR: 1.12, 95% CI: 1.06-1.12) and lower odds of MOD (OR: 0.93, 0.88-0.99). Speaking an indigenous language was associated with higher odds of SIDD (OR: 1.35, 1.16-1.57) and lower odds of MARD (OR 0.58, 0.45-0.74). Interpretation Diabetes prevalence in Mexico is rising in the context of regional and sociodemographic inequalities across distinct diabetes subgroups. SIDD is a subgroup of concern that may be associated with inadequate diabetes management, mainly in marginalized states.

关键词： Diabetes subgroups Mexico Inequalities Insulin de fi ciency Prevalence

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βIV spectrin abundancy, cellular distribution and sensitivity to AKT/GSK3 regulation in schizophrenia

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MOLECULAR PSYCHIATRY 2025年 1-13页

作者： Di Re, Jessica Marini, Michela Hussain, Syed Ibrar Singh, Aditya K. Venkatesh, Akshaya Alshammari, Musaad A. Alshammari, Tahani K. Hamoud, Abdul-Rizaq Ali Imami, Ali Sajid Haghighijoo, Zahra Fularcyzk, Nickolas Stertz, Laura Hawes, Derek Mosebarger, Angela Jernigan, Jordan Chaljub, Claire Nehme, Ralda Walss-Bass, Consuelo Schulmann, Anton Vawter, Marquis P. Mccullumsmith, Robert Damoiseaux, Robert D. Limon, Agenor Labate, Demetrio Wells, Michael F. Laezza, Fernanda Univ Texas Med Branch Galveston Dept Pharmacol & Toxicol Galveston TX USA Univ Houston Dept Math Houston TX USA Univ Texas Med Branch Combined MD PhD Program Galveston TX USA King Saud Univ Coll Pharm Dept Pharmacol & Toxicol Riyadh 11451 Saudi Arabia Univ Toledo Coll Med & Life Sci Dept Neurosci & Psychiat Toledo OH USA Univ Texas Hlth Sci Ctr Houston UTHealth McGovern Med Sch Louis A Faillace MD Dept Psychiat & Behav Sci Houston TX USA Broad Inst MIT & Harvard Stanley Ctr Psychiat Res Cambridge MA 02142 USA Harvard Univ Dept Stem Cell & Regenerat Biol Cambridge MA USA Columbia Univ Irving Med Ctr New York State Psychiat Inst Dept Psychiat New York NY USA Univ Calif Irvine Dept Psychiat & Human Behav Funct Genom Lab Irvine CA USA Promedica Neurosci Inst Toledo OH USA Univ Calif Los Angeles Dept Mol & Med Pharmacol Los Angeles CA USA Univ Calif Los Angeles Dept Bioengn Los Angeles CA USA Univ Calif Los Angeles Calif NanoSyst Inst Los Angeles CA USA Univ Calif Los Angeles Jonsson Comprehens Canc Ctr Los Angeles CA USA Univ Texas Med Branch Galveston Dept Neurol Galveston TX USA Univ Texas Med Branch Galveston Mitchell Ctr Neurodegenerat Dis Galveston TX USA Univ Calif Los Angeles David Geffen Sch Med Dept Human Genet Los Angeles CA USA Univ Calif Los Angeles Mol Biol Inst Los Angeles CA USA

Schizophrenia (SCZ) is a complex psychiatric disorder with unclear biological mechanisms. Spectrins, cytoskeletal proteins linked to neurodevelopmental disorders, are regulated by the AKT/GSK3 pathway, which is implicated in SCZ. However, the impact of SCZ-related dysregulation of this pathway on spectrin expression and distribution remains unexplored. Here, we show that beta IV spectrin protein levels were reduced in neurons of the dorsolateral prefrontal cortex in SCZ postmortem samples compared to healthy control (HC) from the Human Brain Collection Core (HBCC). To investigate potential links between beta IV spectrin and the AKT/GSK3 pathway, we analyzed the PsychEncode dataset, revealing elevated SPTBN4 and AKT2 mRNA levels with correlated gene transcription in both HCs and individuals with SCZ. Next, computational tools were employed to identify potential AKT and GSK3 phosphorylation sites on beta IV spectrin, and two GSK3 sites were validated through in vitro assays. To assess whether beta IV spectrin distribution and sensitivity to AKT/GSK3 are altered in SCZ, we used iPSC-derived neurons from two independent cohorts of patients with significantly increased familial genetic risk for the disorder. Alteration in beta IV spectrin levels and sensitivity to AKT/GSK3 inhibitors were consistently observed across both cohorts. Importantly, a Random Forest classifier applied to beta IV spectrin imaging achieved up to 98% accuracy in classifying cells by diagnosis in postmortem samples, and by diagnosis or diagnosis x perturbation in iPSC samples. These findings reveal altered beta IV spectrin levels and AKT/GSK3 sensitivity in SCZ, identifying beta IV spectrin image-based endophenotypes as robust, generalizable predictive biomarkers of SCZ, with the potential for scalable clinical applications.

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A Framework for Interpretability in Machine Learning for Medical Imaging

arXiv

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arXiv 2023年

作者： Wang, Alan Q. Karaman, Batuhan K. Kim, Heejong Rosenthal, Jacob Saluja, Rachit Young, Sean I. Sabuncu, Mert R. School of Electrical and Computer Engineering Cornell University Cornell Tech New YorkNY10044 United States Department of Radiology Weill Cornell Medical School New YorkNY10065 United States Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program New YorkNY10065 United States Martinos Center for Biomedical Imaging Harvard Medical School BostonMA02129 United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA02139 United States

Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What goals does one actually seek to address when interpretability is needed? To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI. By reasoning about real-world tasks and goals common in both medical image analysis and its intersection with machine learning, we identify five core elements of interpretability: localization, visual recognizability, physical attribution, model transparency, and actionability. From this, we arrive at a framework for interpretability in MLMI, which serves as a step-by-step guide to approaching interpretability in this context. Overall, this paper formalizes interpretability needs in the context of medical imaging, and our applied perspective clarifies concrete MLMI-specific goals and considerations in order to guide method design and improve real-world usage. Our goal is to provide practical and didactic information for model designers and practitioners, inspire developers of models in the medical imaging field to reason more deeply about what interpretability is achieving, and suggest future directions of interpretability research. © 2023, CC0.

关键词： Machine learning

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Massively multiplexed nucleic acid detection with Cas13

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NATURE 2020年第7811期582卷 277-+页

作者： Ackerman, Cheri M. Myhrvold, Cameron Thakku, Sri Gowtham Freije, Catherine A. Metsky, Hayden C. Yang, David K. Ye, Simon H. Boehm, Chloe K. Kosoko-Thoroddsen, Tinna-Solveig F. Kehe, Jared Nguyen, Tien G. Carter, Amber Kulesa, Anthony Barnes, John R. Dugan, Vivien G. Hung, Deborah T. Blainey, Paul C. Sabeti, Pardis C. Broad Inst MIT & Harvard Cambridge MA 02142 USA MIT Dept Biol Engn 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Univ Dept Organism & Evolutionary Biol Cambridge MA 02138 USA Harvard Med Sch Div Hlth Sci & Technol Cambridge MA 02115 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Med Sch PhD Program Virol Div Med Sci Boston MA 02115 USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Ctr Dis Control & Prevent Influenza Div Atlanta GA USA Massachusetts Gen Hosp Mol Biol Dept Boston MA 02114 USA Massachusetts Gen Hosp Ctr Computat & Integrat Biol Boston MA 02114 USA MIT Koch Inst Integrat Canc Res 77 Massachusetts Ave Cambridge MA 02139 USA Howard Hughes Med Inst Chevy Chase MD USA Harvard TH Chan Sch Publ Hearth Dept Immunol & Infect Dis Boston MA USA

The great majority of globally circulating pathogens go undetected, undermining patient care and hindering outbreak preparedness and response. To enable routine surveillance and comprehensive diagnostic applications, there is a need for detection technologies that can scale to test many samples(1-3)while simultaneously testing for many pathogens(4-6). Here, we develop Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (CARMEN), a platform for scalable, multiplexed pathogen detection. In the CARMEN platform, nanolitre droplets containing CRISPR-based nucleic acid detection reagents(7)self-organize in a microwell array(8)to pair with droplets of amplified samples, testing each sample against each CRISPR RNA (crRNA) in replicate. The combination of CARMEN and Cas13 detection (CARMEN-Cas13) enables robust testing of more than 4,500 crRNA-target pairs on a single array. Using CARMEN-Cas13, we developed a multiplexed assay that simultaneously differentiates all 169 human-associated viruses with at least 10 published genome sequences and rapidly incorporated an additional crRNA to detect the causative agent of the 2020 COVID-19 pandemic. CARMEN-Cas13 further enables comprehensive subtyping of influenza A strains and multiplexed identification of dozens of HIV drug-resistance mutations. The intrinsic multiplexing and throughput capabilities of CARMEN make it practical to scale, as miniaturization decreases reagent cost per test by more than 300-fold. Scalable, highly multiplexed CRISPR-based nucleic acid detection shifts diagnostic and surveillance efforts from targeted testing of high-priority samples to comprehensive testing of large sample sets, greatly benefiting patients and public health(9-11).

关键词： Biological techniques Biotechnology Viral infection

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SARS-CoV-2 epitope-specific CD4(+) memory T cell responses across COVID-19 disease severity and antibody durability

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SCIENCE IMMUNOLOGY 2022年第73期7卷 eabl9464页

作者： Nelson, Ryan W. Chen, Yuezhou Venezia, Olivia L. Majerus, Richard M. Shin, Daniel S. Carrington, Mary N. Yu, Xu G. Wesemann, Duane R. Moon, James J. Luster, Andrew D. Harvard Med Sch Ctr Immunol & Inflammatory Dis Massachusetts Gen Hosp Boston MA 02115 USA Harvard Med Sch Div Rheumatol Allergy & Immunol Massachusetts Gen Hosp Boston MA 02115 USA Harvard Med Sch Boston Childrens Hosp Div Immunol Boston MA 02115 USA Harvard Med Sch Brigham & Womens Hosp Dept Med Div Allergy & Clin Immunol Boston MA 02115 USA Harvard Med Sch Brigham & Womens Hosp Div Genet Boston MA 02115 USA Harvard Med Sch Brigham & Womens Hosp Infect Dis Div Boston MA 02115 USA Queens Univ Charlotte Charlotte NC USA Ragon Inst MGH MIT & Harvard Cambridge MA USA NCI Basic Sci Program Frederick Natl Lab Canc Res Frederick MD 21701 USA NCI Lab Integrat Canc Immunol Ctr Canc Res Bethesda MD USA Harvard Med Sch Div Pulm & Crit Care Med Massachusetts Gen Hosp Boston MA 02115 USA Harvard Immunol PhD Program Boston MA USA

CD4(+) T cells are central to long-term immunity against viruses through the functions of T helper 1 (T(H)1) and T follicular helper (TFH) cell subsets. To better understand the role of these subsets in coronavirus disease 2019 (COVID-19) immunity, we conducted a longitudinal study of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific CD4(+) T cell and antibody responses in convalescent individuals who seroconverted during the first wave of the pandemic in Boston, MA, USA, across a range of COVID-19 disease severities. Analyses of spike (S) and nucleocapsid (N) epitope-specific CD4(+) T cells using peptide and major histocompatibility complex class II (pMHCII) tetramers demonstrated expanded populations of T cells recognizing the different SARS-CoV-2 epitopes in most individuals compared with prepandemic controls. Individuals who experienced a milder disease course not requiring hospitalization had a greater percentage of circulating T-FH (cT(FH)) and T(H)1 cells among SARS-CoV-2-specific cells. Analysis of SARS-CoV-2-specific CD4(+) T cells responses in a subset of individuals with sustained anti-S antibody responses after viral clearance also revealed an increased proportion of memory cT(FH) cells. Our findings indicate that efficient early disease control also predicts favorable long-term adaptive immunity.

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