Some publications from our partners
Rapid base-specific calling of SARS-CoV-2 variants of concern using combined RT-PCR melting curve screening and SIRPH technology
Tierling Sascha1$, Kattler Kathrin1$, Vogelgesang Markus2, Pfuhl Thorsten2, Lohse Stefan2, Lo Porto Christina1, Schmitt Beate1, Nastasja Seiwert2, Salhab Abdulrahman1, Smola Sigrun2*, Walter Jörn1*
1 Department of Genetics, Saarland University, Saarbrücken, Saarland, 66123, Germany
2 Institute of Virology, Saarland University Medical Center, Homburg, Saarland, 66421, Germany
$ shared first-authorship, * shared senior-authorship
Corresponding author: Prof. Dr. Jörn Walter – Department of Genetics, Saarland University
Abstract
The emergence of novel variants of concern of SARS-CoV-2 demands a fast and reliable detection of such variants in local populations. Here we present a cost-efficient and fast workflow combining a pre-screening of SARS-CoV-2 positive samples using RT-PCR melting curve analysis with multiplexed IP-RP-HPLC-based single nucleotide primer extensions (SIRPH). The entire workflow from positive SARS-CoV-2 testing to base-specific identification of variants requires about 24 h. We applied the sensitive method to monitor the local VOC outbreaks in SARS-CoV-2 positive samples collected in a confined region of Germany.
Characterization of SARS-CoV-2 infection clusters based on integrated genomic surveillance, outbreak analysis and contact tracing in an urban setting
Andreas Walker1,#, Torsten Houwaart2,#, Patrick Finzer2,3,#, Lutz Ehlkes4,#, Alona Tyshaieva2, Maximilian Damagnez1, Daniel Strelow2, Ashley Duplessis1, Jessica Nicolai2, Tobias Wienemann2, Teresa Tamayo2, Malte Kohns Vasconcelos2, Lisanna Hülse2, Katrin Hoffmann3, Nadine Lübke1, Sandra Hauka1, Marcel Andree1, Martin P. Däumer5, Alexander Thielen5, Susanne Kolbe-Busch2, Klaus Göbels4, Rainer Zotz3, Klaus Pfeffer2, Jörg Timm1, Alexander T. Dilthey2,6,7,*, German COVID-19 OMICS Initiative (DeCOI)
1 Institute of Virology, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
2 Institute of Medical Microbiology and Hospital Hygiene, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
3 Zotz | Klimas, Düsseldorf, Germany
4 Düsseldorf Health Department (Gesundheitsamt Düsseldorf), Düsseldorf, Germany
5 SeqIT GmbH, Pfaffplatz 10, 67655 Kaiserslautern
6 Institute of Medical Statistics and Computational Biology, University of Cologne, Cologne, Germany
7 Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
Abstract
Background
Tracing of SARS-CoV-2 transmission chains is still a major challenge for public health authorities, when incidental contacts are not recalled or are not perceived as potential risk contacts. Viral sequencing can address key questions about SARS-CoV-2 evolution and may support reconstruction of viral transmission networks by integration of molecular epidemiology into classical contact tracing.
Methods
In collaboration with local public health authorities, we set up an integrated system of genomic surveillance in an urban setting, combining a) viral surveillance sequencing, b) genetically based identification of infection clusters in the population, c) integration of public health authority contact tracing data, and d) a user-friendly dashboard application as a central data analysis platform.
Results
Application of the integrated system from August to December 2020 enabled a characterization of viral population structure, analysis of four outbreaks at a maximum care hospital, and genetically based identification of five putative population infection clusters, all of which were confirmed by contact tracing. The system contributed to the development of improved hospital infection control and prevention measures and enabled the identification of previously unrecognized transmission chains, involving a martial arts gym and establishing a link between the hospital to the local population.
Conclusions
Integrated systems of genomic surveillance could contribute to the monitoring and, potentially, improved management of SARS-CoV-2 transmission in the population.
Rapid incidence estimation from SARS-CoV-2 genomes reveals decreased case detection in Europe during summer 2020
Maureen Rebecca Smith1, 2,*,+, Maria Trofimova1,2,*, Ariane Weber3, Yannick Duport1,2, Denise K ¨uhnert3,4, and Max von Kleist1,2,4,
1 Systems Medicine of Infectious Disease (P5), Robert Koch Institute, Berlin, Germany
2 Bioinformatics (MF1), Robert Koch Institute, Berlin, Germany
3 Transmission, Infection, Diversification and Evolution Group, Max-Planck Institute for the Science of Human History, Jena, Germany
4 German COVID Omics Initiative (deCOI)
*these authors contributed equally to this work
+smithm@rki.de
+kleistm@rki.de
In May 2021, over 160 million SARS-CoV-2 infections have been reported worldwide. Yet, the true amount of infections is unknown and believed to exceed the reported numbers by several fold, depending on national testing policies that can strongly affect the proportion of undetected cases. To overcome this testing bias and better assess SARS-CoV-2 transmission dynamics, we propose a genome-based computational pipeline, GInPipe, to reconstruct the SARS-CoV-2 incidence dynamics through time. After validating GInPipe against in silico generated outbreak data, as well as more complex phylodynamic analyses, we use the pipeline to reconstruct incidence histories in Denmark, Scotland, Switzerland, and Victoria (Australia) solely from viral sequence data.
The proposed method robustly reconstructs the different pandemic waves in the investigated countries and regions, does not require phylodynamic reconstruction, and can be directly applied to publicly deposited SARS-CoV-2 sequencing data sets. We observe differences in the relative magnitude of reconstructed versus reported incidences during times with sparse availability of
diagnostic tests. Using the reconstructed incidence dynamics, we assess how testing policies may have affected the probability to diagnose and report infected individuals. We find that under-reporting was highest in mid 2020 in all analysed countries, coinciding with liberal testing policies at times of low test capacities. Due to the increased use of real-time sequencing, it is envisaged that GInPipe can complement established surveillance tools to monitor the SARS-CoV-2 pandemic and evaluate testing policies. The method executes within minutes on very large data sets and is freely available as a fully automated pipeline from https://github.com/KleistLab/GInPipe.
COVIDStrategyCalculator: A standalone software to assess testing- and quarantine strategies
for incoming travelers, contact person management and de-isolation
Wiep van der Toorn1,2, Djin-Ye Oh3, Daniel Bourquain4, Janine Michel4, Eva Krause4
, Andreas Nitsche4, *Max von Kleist1,2,5, on behalf of the working group on SARS-CoV-2 Diagnostics at RKI
1,2 Systems Medicine of Infectious Disease (P5) and Bioinformatics (MF1), Methodology and
Research Infrastructure, Robert Koch Institute Berlin, Germany
3 FG17 Influenza and other respiratory viruses, Department of Infectious Diseases, Robert
Koch Institute Berlin, Germany
4 ZBS1 Highly pathogenic viruses, Center for Biological Threats And Special Pathogens, Robert
Koch Institute Berlin, Germany
5 German COVID Omics Initiative (deCOI)
*kleistm@rki.de
In early 2020 COVID-19 turned into a global pandemic. Non-pharmaceutical interventions
(NPIs), including the isolation of infected individuals, tracing and quarantine of exposed
individuals are decisive tools to prevent onwards transmission and curb fatalities. Strategies
that combine NPIs with SARS-CoV-2 testing may help to shorten quarantine durations while
being non-inferior with respect to infection prevention. Thus, combined strategies can help
reducing the socio-economic burden of SARS-CoV2 and generate greater public acceptance.
We developed a software that enables policy makers to calculate the reduction in
transmissibility through quarantine or isolation in combination with arbitrary testing
strategies. The user chooses between three different modi [(i) isolation of infected individuals,
(ii) management of potentially infected contacts and (iii) quarantine of incoming travelers],
while having total flexibility in customizing testing strategies, as well as setting model
parameters. The software enables decision makers to tailor calculations specifically to their
questions and perform an assessment ‘on the fly’, based on current evidence on infection
dynamics.
Underneath, we analytically solve a stochastic transit compartment model of the infection
time course, which captures temporal changes in test sensitivities, incubation- and infectious
periods, as well as times to symptom onset using its default parameters.
Using default parameters, we estimated that testing travelers at the point of entry reduces
the risk about 4.69 (4.19,4.83) fold for PCR vs. 3.59 (3.22, 3.69) fold for based rapid diagnostic
tests (RDT, 87% relative sensitivity) when combined with symptom screening. In comparison
to 14 days of pure quarantine, 8 (PCR) vs. 10 (RDT) days of pre-test quarantine would be
noninferior for incoming travelers as well as for contact person management. De-isolation of
infected individuals 11 days after symptom onset reduces the risk by >99fold (7.68,>1012).
This tool is freely available from:
https://github.com/CovidStrategyCalculator/CovidStrategyCalculator
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SARS-CoV-2-reactive T cells in healthy donors and patients with COVID-19
Julian Braun1,2,16, Lucie Loyal1,2,16, Marco Frentsch3,16, Daniel Wendisch4, Philipp Georg4
, Florian Kurth4,5, Stefan Hippenstiel4, Manuela Dingeldey1,2, Beate Kruse1,2, Florent Fauchere1,2, Emre Baysal1,2, Maike Mangold1,2, Larissa Henze1,2, Roland Lauster1,6, Marcus A. Mall7,13, Kirsten Beyer7, Jobst Röhmel7, Sebastian Voigt8, Jürgen Schmitz9, Stefan Miltenyi9, Ilja Demuth10, Marcel A. Müller11, Andreas Hocke4, Martin Witzenrath4, Norbert Suttorp4, Florian Kern12,13, Ulf Reimer12, Holger Wenschuh12, Christian Drosten11,14, Victor M. Corman11, Claudia Giesecke-Thiel15,17 ✉, Leif Erik Sander4,17 & Andreas Thiel 1,2, 17
1Si-M/ ‘Der Simulierte Mensch’, Technische Universität Berlin and Charité–Universitätsmedizin Berlin, Berlin, Germany, 2 Regenerative Immunology and Aging, BIH Center for Regenerative Therapies, Charité–Universitätsmedizin Berlin, Berlin, Germany, 3 Department of Hematology, Oncology and Tumor Immunology, Charité–Universitätsmedizin Berlin, Berlin, Germany, 4 Department of Infectious Diseases and Respiratory Medicine, Charité–Universitätsmedizin Berlin, Berlin, Germany, 5 Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine and I. Department of Medicine, University Medical Center Hamburg–Eppendorf, Hamburg, Germany, 6 Medical Biotechnology, Institute for Biotechnology, Technische Universität Berlin, Berlin, Germany, 7 Department of Pediatric Pulmonology, Immunology and Critical Care Medicine, Charité -Universitätsmedizin Berlin, Berlin, Germany, 8 Robert Koch Institut, Berlin, Germany, 9 Miltenyi Biotec, Bergisch Gladbach, Germany, 10 Interdisciplinary Metabolism Center, Biology of Aging (BoA) group, Charité–Universitätsmedizin Berlin, Berlin, Germany, 11 Institute of Virology, Charité Universitätsmedizin Berlin, Berlin, Germany, 12 JPT Peptide Technologies GmbH, Berlin, Germany, 13 Brighton and Sussex Medical School, Department of Clinical and Experimental Medicine, Brighton, UK, 14 Berlin Institute of Health (BIH), Berlin, Germany, 15 Max Planck Institute for Molecular Genetics, Berlin, Germany, 16 These authors contributed equally: Julian Braun, Lucie Loyal, Marco Frentsch, 17 These authors jointly supervised this work: Claudia Giesecke-Thiel, Leif Erik Sander, Andreas Thiel. ✉e-mail: giesecke@molgen.mpg.de; leif-erik.sander@charite.de; andreas.thiel@charite.de
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the rapidly unfolding coronavirus disease 2019 (COVID-19) pandemic1,2. Clinical manifestations of COVID-19 vary, ranging from asymptomatic infection to respiratory failure. The mechanisms determining such variable outcomes remain unresolved. Here, we investigated SARS-CoV-2 spike glycoprotein (S)-reactive CD4+ T cells in peripheral blood of patients with COVID-19 and SARS-CoV-2-unexposed healthy donors (HD). We detected SARS-CoV-2 S-reactive CD4+ T cells in 83% of patients with COVID-19 but also in 35% of HD. S-reactive CD4+ T cells in HD reacted primarily to C-terminal S epitopes, which show a higher homology to spike glycoproteins of human endemic coronaviruses, compared to N-terminal epitopes. S-reactive T cell lines generated from SARS-CoV-2-naive HD responded similarly to C-terminal S of human endemic coronaviruses 229E and OC43 and SARS-CoV-2, demonstrating the presence of S-cross-reactive T cells, probably generated during past encounters with endemic coronaviruses. The role of pre-existing SARS-CoV-2 cross-reactive T cells for clinical outcomes remains to be determined in larger cohorts. However, the presence of S-cross-reactive T cells in a sizable fraction of the general population may affect the dynamics of the current pandemic, and has important implications for the design and analysis of upcoming COVID-19 vaccine trials.
Genomewide Association Study of Severe Covid-19 with Respiratory Failure
The authors’ full names and academic degrees are as follows: David Ellinghaus, Ph.D., Frauke Degenhardt, M.Sc., Luis Bujanda, M.D., Ph.D., Maria Buti, M.D., Ph.D., Agustín Albillos, M.D., Ph.D., Pietro Invernizzi, M.D., Ph.D., Javier Fernández, M.D., Ph.D., Daniele Prati, M.D., Guido Baselli, Ph.D., Rosanna Asselta, Ph.D., Marit M. Grimsrud, M.D., Chiara Milani, Ph.D., Fátima Aziz, B.S., Jan Kässens, Ph.D., Sandra May, Ph.D., Mareike Wendorff, M.Sc., Lars Wienbrandt, Ph.D., Florian Uellendahl-Werth, M.Sc., Tenghao Zheng, M.D., Ph.D., Xiaoli Yi, Raúl de Pablo, M.D., Ph.D., Adolfo G. Chercoles, B.S., Adriana Palom, M.S., B.S., Alba-Estela Garcia-Fernandez,
B.S., Francisco Rodriguez-Frias, M.S., Ph.D., Alberto Zanella, M.D., Alessandra Bandera, M.D., Ph.D., Alessandro Protti, M.D., Alessio Aghemo, M.D., Ph.D., Ana Lleo, M.D., Ph.D., Andrea Biondi, M.D., Andrea Caballero-Garralda, M.S., Ph.D., Andrea Gori, M.D., Anja Tanck, Anna Carreras Nolla, B.S., Anna Latiano, Ph.D., Anna Ludovica Fracanzani, M.D., Anna Peschuck, Antonio Julià, Ph.D., Antonio Pesenti, M.D., Antonio Voza, M.D., David Jiménez, M.D., Ph.D., Beatriz Mateos, M.D., Ph.D., Beatriz Nafria Jimenez, B.S., Carmen Quereda, M.D., Ph.D., Cinzia Paccapelo, M.Sc., Christoph Gassner, Ph.D., Claudio Angelini, M.D., Cristina Cea, B.S., Aurora Solier, M.D., David Pestaña, M.D., Ph.D., Eduardo Muñiz-Diaz, M.D., Ph.D., Elena Sandoval, M.D., Elvezia M. Paraboschi, Ph.D., Enrique Navas, M.D., Ph.D., Félix García Sánchez, Ph.D., Ferruccio Ceriotti, M.D., Filippo Martinelli-Boneschi, M.D., Ph.D., Flora Peyvandi, M.D., Ph.D., Francesco Blasi, M.D., Ph.D., Luis Téllez, M.D., Ph.D., Albert Blanco-Grau, B.S., M.S., Georg Hemmrich-Stanisak, Ph.D., Giacomo Grasselli, M.D., Giorgio Costantino, M.D., Giulia Cardamone, Ph.D., Giuseppe Foti, M.D., Serena Aneli, Ph.D., Hayato Kurihara, M.D., Hesham ElAbd, M.Sc., Ilaria My, M.D., Iván Galván-Femenia, M.Sc., Javier Martín, M.D., Ph.D., Jeanette Erdmann, Ph.D., Jose Ferrusquía-Acosta, M.D., Koldo Garcia-Etxebarria, Ph.D., Laura Izquierdo-Sanchez, B.S., Laura R. Bettini, M.D., Lauro Sumoy, Ph.D., Leonardo Terranova, Ph.D., Leticia Moreira, M.D., Ph.D., Luigi Santoro, M.S., Luigia Scudeller, M.D., Francisco Mesonero, M.D., Luisa Roade, M.D., Malte C. Rühlemann, Ph.D., Marco Schaefer, Ph.D., Maria Carrabba, M.D., Ph.D., Mar Riveiro-Barciela, M.D.,
Ph.D., Maria E. Figuera Basso, Maria G. Valsecchi, Ph.D., María Hernandez-Tejero, M.D., Marialbert Acosta-Herrera, Ph.D., Mariella
D’Angiò, M.D., Marina Baldini, M.D., Marina Cazzaniga, M.D., Martin Schulzky, M.A., Maurizio Cecconi, M.D., Ph.D., Michael Wittig, M.Sc., Michele Ciccarelli, M.D., Miguel Rodríguez-Gandía, M.D., Monica Bocciolone, M.D., Monica Miozzo, Ph.D., Nicola Montano, M.D., Ph.D., Nicole Braun, Nicoletta Sacchi, Ph.D., Nilda Martínez, M.D., Onur Özer, M.Sc., Orazio Palmieri, Ph.D., Paola Faverio, M.D., Paoletta Preatoni, M.D., Paolo Bonfanti, M.D., Paolo Omodei, M.D., Paolo Tentorio, M.S., Pedro Castro, M.D., Ph.D., Pedro M. Rodrigues, Ph.D., Aaron Blandino Ortiz, M.D., Rafael de Cid, Ph.D., Ricard Ferrer, M.D., Roberta Gualtierotti, M.D., Rosa Nieto, M.D., Siegfried Goerg, M.D., Salvatore Badalamenti, M.D., Ph.D., Sara Marsal, Ph.D., Giuseppe Matullo, Ph.D., Serena Pelusi, M.D., Simonas Juzenas, Ph.D., Stefano Aliberti, M.D., Valter Monzani, M.D., Victor Moreno, Ph.D., Tanja Wesse, Tobias L. Lenz, Ph.D., Tomas Pumarola, M.D., Ph.D., Valeria Rimoldi, Ph.D., Silvano Bosari, M.D., Wolfgang Albrecht, Wolfgang Peter, Ph.D., Manuel Romero-Gómez,
M.D., Ph.D., Mauro D’Amato, Ph.D., Stefano Duga, Ph.D., Jesus M. Banales, Ph.D., Johannes R Hov, M.D., Ph.D., Trine Folseraas, M.D., Ph.D., Luca Valenti, M.D., Andre Franke, Ph.D., and Tom H. Karlsen, M.D., Ph.D.
The authors’ affiliations are as follows: the Institute of Clinical Molecular Biology, Christian-Albrechts-University (D.E., F.D., J.K., S. May, M. Wendorff, L.W., F.U.-W., X.Y., A.T., A. Peschuck, C.G., G.H.-S., H.E.A., M.C.R., M.E.F.B., M. Schulzky, M. Wittig, N.B., S.J.,T.W., W.A., M. D’Amato, A.F.), and University Hospital Schleswig-Holstein, Campus Kiel (N.B., A.F.), Kiel, the Institute for Cardiogenetics, University of Lübeck, Lübeck (J.E.), the German Research Center for Cardiovascular Research, partner site Hamburg–Lübeck– Kiel (J.E.), the University Heart Center Lübeck (J.E.), and the Institute of Transfusion Medicine, University Hospital Schleswig-Holstein
(S.G.), Lübeck, Stefan-Morsch-Stiftung, Birkenfeld (M. Schaefer, W.P.), and the Research Group for Evolutionary Immunogenomics, Max Planck Institute for Evolutionary Biology, Plön (O.O., T.L.L.) — all in Germany; Novo Nordisk Foundation Center for Protein Research, Disease Systems Biology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen (D.E.); the Department of Liver and Gastrointestinal Diseases, Biodonostia Health Research Institute–Donostia University Hospital–University of the Basque Country (L.B., K.G.-E., L.I.-S., P.M.R., J.M.B.), Osakidetza Basque Health Service, Donostialdea Integrated Health Organization, Clinical Biochemistry Department (A.G.C., B.N.J.), and the Department of Liver and Gastrointestinal Diseases, Biodonostia Health Research Institute (M. D’Amato), San Sebastian, Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas, Instituto de Salud Carlos III (L.B., M. Buti, A. Albillos, A. Palom, F.R.-F., B.M., L. Téllez, K.G.-E., L.I.-S., F.M., L.R., M.R.-B., M. Rodríguez-Gandía, P.M.R., M. Romero-Gómez, J.M.B.), the Departments of Gastroenterology (A. Albillos, B.M., L. Téllez, F.M., M. Rodríguez-Gandía), Intensive Care (R.P., A.B.O.), Respiratory Diseases (D.J., A.S., R.N.), Infectious Diseases (C.Q., E.N.), and Anesthesiology (D. Pestaña, N. Martínez), Hospital Universitario Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria, University of Alcalá, and Histocompatibilidad y Biologia Molecular, Centro de Transfusion de Madrid (F.G.S.), Madrid, the Liver Unit, Department of Internal Medicine, Hospital Universitari Vall d’Hebron, Vall d’Hebron Barcelona Hospital Campus (M. Buti, A. Palom, L.R., M.R.-B.), Hospital Clinic, University of Barcelona, and the August Pi i Sunyer Biomedical Research Institute (J.F., F.A., E.S., J.F.-A., L.M., M.H.-T., P.C.), the European Foundation for the Study of Chronic Liver Failure (J.F.), Vall d’Hebron Institut de Recerca (A. Palom, F.R.-F., A.J., S. Marsal), and the Departments of Biochemistry (A.-E.G.-F., F.R.-F., A.C.-G., C.C., A.B.-G.), Intensive Care (R.F.), and Microbiology (T.P.), University Hospital Vall d’Hebron, the Immunohematology Department, Banc de Sang i Teixits, Autonomous University of Barcelona (E.M.-D.), Catalan Institute of Oncology, Bellvitge Biomedical Research Institute, Consortium for Biomedical Research in Epidemiology and Public Health and University of Barcelona, l’Hospitalet (V. Moreno), and Autonoma University of Barcelona (T.P.), Barcelona, Universitat Autònoma de Barcelona, Bellatera (M. Buti, F.R.-F., M.R.-B.), GenomesForLife–GCAT Lab Group, Germans Trias i Pujol Research Institute (A.C.N., I.G.-F., R.C.), and High Content Genomics and Bioinformatics Unit, Germans Trias i Pujol Research Institute (L. Sumoy), Badalona, Institute of Parasitology and Biomedicine Lopez-Neyra, Granada (J.M., M.A.-H.), the Digestive Diseases Unit, Virgen del Rocio University Hospital, Institute of Biomedicine of Seville, University of Seville, Seville (M. Romero-Gómez), andIkerbasque, Basque Foundation for Science, Bilbao (M. D’Amato, J.M.B.) — all in Spain; the Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milan Bicocca (P.I., C.M.), Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico (D. Prati, G.B., A.Z., A. Bandera, A.G., A.L.F., A. Pesenti, C.P., F.C., F.M.-B., F.P., F.B., G.G.
BACKGROUND
There is considerable variation in disease behavior among patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (Covid-19). Genomewide association analysis may allow for the identification of potential genetic factors involved in the development of Covid-19.
METHODS
We conducted a genomewide association study involving 1980 patients with Covid-19 and severe disease (defined as respiratory failure) at seven hospitals in the Italian and Spanish epicenters of the SARS-CoV-2 pandemic in Europe. After quality control and the exclusion of population outliers, 835 patients and 1255 control participants from Italy and 775 patients and 950 control participants from Spain were included in the final analysis. In total, we analyzed 8,582,968 single-nucleotide polymorphisms and conducted a meta-analysis of the two case–control panels.
RESULTS
We detected cross-replicating associations with rs11385942 at locus 3p21.31 and with rs657152 at locus 9q34.2, which were significant at the genomewide level (P<5×10−8) in the meta-analysis of the two case–control panels (odds ratio, 1.77; 95% confidence interval [CI], 1.48 to 2.11; P=1.15×10−10; and odds ratio, 1.32; 95% CI, 1.20 to 1.47; P=4.95×10−8, respectively). At locus 3p21.31, the association signal spanned the genes SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6 and XCR1. The association signal at locus 9q34.2 coincided with the ABO blood group locus; in this cohort, a blood-group–specific analysis showed a higher risk in blood group A than in other
blood groups (odds ratio, 1.45; 95% CI, 1.20 to 1.75; P=1.48×10−4) and a protective effect in blood group O as compared with other blood groups (odds ratio, 0.65; 95% CI, 0.53 to 0.79; P=1.06×10−5).
CONCLUSIONS
We identified a 3p21.31 gene cluster as a genetic susceptibility locus in patients with Covid-19 with respiratory failure and confirmed a potential involvement of the ABO blood-group system. (Funded by Stein Erik Hagen and others.)
Swarm Learning as a privacy-preserving machine learning approach for disease classification
Stefanie Warnat-Herresthal1,*, Hartmut Schultze2,*, Krishnaprasad Lingadahalli Shastry2,* ,
Sathyanarayanan Manamohan2, Saikat Mukherjee2, Vishesh Garg2, Ravi Sarveswara2,
Kristian Händler3,*, Peter Pickkers4,*, N. Ahmad Aziz5,6,*, Sofia Ktena7,* Christian Siever2 , Michael Kraut3, Milind Desai2, Bruno Monnet2, Maria Saridaki7, Charles Martin Siegel2 , Anna
Drews3, Melanie Nuesch-Germano1, Heidi Theis3, Mihai G. Netea8,9, Fabian Theis10 , Anna C.
Aschenbrenner1,8, Thomas Ulas3, Monique M.B. Breteler5,11,# , Evangelos J. GiamarellosBourboulis7,#, Matthijs Kox4,#, Matthias Becker3,#, Sorin Cheran2,#, Michael S. Woodacre2,# , Eng Lim Goh2,#, Joachim L. Schultze1,3,# , German COVID-19 OMICS Initiative (DeCOI)
Affiliations:
1 Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University
of Bonn, 53115 Bonn, Germany, 2 Hewlett Packard Enterprise, 3 German Center for Neurodegenerative Diseases (DZNE), PRECISE Platform for Single Cell Genomics and Epigenomics at DZNE and the University of Bonn, 53175 Bonn, Germany, 4 Department of Intensive Care Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, 6500HB, The Netherlands, 5 Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), 53175 Bonn, Germany, 6 Department of Neurology, Faculty of Medicine, University of Bonn, 53127 Bonn, Germany, 7 4th Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, 124 62 Athens, Greece, 8 Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen 6500HB, The Netherlands, 9 Immunology & Metabolism, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn 53115, Germany, 10 Institute of Computational Biology, Helmholtz Center Munich (HMGU), 85764 Neuherberg, Germany, 11 Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, 53175 Bonn, Germany
* shared first authors
# shared last authors
corresponding author: joachim.schultze@dzne.de
Identification of patients with life-threatening diseases including leukemias or infections such
as tuberculosis and COVID-19 is an important goal of precision medicine. We recently illustrated that leukemia patients are identified by machine learning (ML) based on their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, blockchain-based peer-to-peer networking and coordination as well as privacy protection without the need for a central coordinator thereby going beyond federated learning. Using more than 14,000 blood transcriptomes derived from over 100 individual studies with non62 uniform distribution of cases and controls and significant study biases, we illustrate the feasibility of SL to develop disease classifiers based on distributed data for COVID-19, tuberculosis or leukemias that outperform those developed at individual sites. Still, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.
Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment
Jonas Schulte-Schrepping1 *, Nico Reusch1 *, Daniela Paclik2 *, Kevin Baßler1 3 *, Stephan Schlickeiser3 *, Bowen Zhang4 *, Benjamin Krämer5 *, Tobias Krammer6 *, Sophia Brumhard7 4 *, Lorenzo Bonaguro1 *, Elena De Domenico8 *, Daniel Wendisch7 *, Martin Grasshoff4 5 , Theodore S. Kapellos1 , Michael Beckstette4 , Tal Pecht1 , Adem Saglam8 , Oliver Dietrich6 , Henrik E. Mei9 6 , Axel R. Schulz9 , Claudia Conrad7 , Désirée Kunkel10, Ehsan Vafadarnejad6 , Cheng-Jian Xu4,11 7 , Arik Horne1 , Miriam Herbert1 , Anna Drews8 , Charlotte Thibeault7 , Moritz Pfeiffer7 8 , Stefan Hippenstiel7,12, Andreas Hocke7,12, Holger Müller-Redetzky7 , Katrin-Moira Heim7 , Felix Machleidt7 9 , Alexander Uhrig7 , Laure Bousquillon de Jarcy7 , Linda Jürgens7 , Miriam Stegemann7 10 , Christoph R. Glösenkamp7 , Hans-Dieter Volk2,3,13, Christine Goffinet14,15, Jan Raabe5 , Kim Melanie Kaiser5 11 , Michael To Vinh5 , Gereon Rieke5 , Christian Meisel14, Thomas Ulas8 , Matthias Becker8 12 , Robert Geffers16, Martin Witzenrath7,12, Christian Drosten14,19, Norbert Suttorp7,12, Christof von Kalle17 13 , Florian Kurth7,18, Kristian Händler8 , Joachim L. Schultze1,8,#,$, Anna C Aschenbrenner20,# 14 , Yang Li4,#, Jacob Nattermann5,19,# , Birgit Sawitzki2,#, Antoine-Emmanuel Saliba6,# , Leif Erik Sander7,12# 15 , 16 Deutsche COVID-19 OMICS Initiative (DeCOI) 17
* shared first authors, # shared last authors, $ corresponding author
1 Life & Medical Sciences (LIMES) Institute, University of Bonn, Germany, 2 Institute of Medical Immunology, Charité, Universitätsmedizin Berlin, Berlin, Germany, 3 Institute of Medical Immunology, Charité, Universitätsmedizin Berlin, Berlin, Germany & BIH Center for Regenerative Therapies, Charité and Berlin Institute of Health, Charité, Universitätsmedizin Berlin, Berlin, Germany, 4 Centre for Individualised Infection Medicine (CiiM) & TWINCORE, joint ventures between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany, 5 Department of Internal Medicine I, University Hospital Bonn, Bonn Germany, 6 Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Center for Infection Research (HZI), Würzburg, Germany, 7 Department of Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany, 8 PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases, Bonn, Germany and University of Bonn, Bonn Germany, 9 Mass Cytometry Lab, DRFZ Berlin, a Leibniz Institute, Berlin, Germany, 10 Flow & Mass Cytometry Core Facility, Charité Universitätsmedizin Berlin and Berlin Institute of Health (BIH), Berlin, Germany, 11 Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands, 12 German Center for Lung Research (DZL), 13 Institute of Medical Immunology, Charité, Universitätsmedizin Berlin, Berlin, Germany, Labor Berlin42 Charité Vivantes, Department of Immunology, Berlin, Germany, 14 Institute of Virology, Charité Universitätsmedizin Berlin, Berlin, Germany, 15 Berlin Institute of Health, 10178 Berlin, Germany, 16 Genome Analytics, Helmholtz-Center for Infection Research (HZI), Braunschweig, Germany, 17 Clinical Study Center (CSC), Berlin Institute of Health (BIH), and Charite Universitätsmedizin Berlin, Germany, 18 Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine & I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 19 German Center for Infection Research (DZIF), 20 Life & Medical Sciences (LIMES) Institute, University of Bonn, Germany and Radboud UMC,Nijmegen, The Netherlands
Coronavirus disease 2019 (COVID-19) is a mild to moderate respiratory tract infection, however, a subset of patients progress to severe disease and respiratory failure. The mechanism of protective immunity in mild forms and the pathogenesis of severe COVID-19 associated with increased neutrophil counts and dysregulated immune responses remain unclear. In a dual-center, two-cohort study, we combined single-cell RNA sequencing and single-cell proteomics of whole-blood and peripheral-blood mononuclear cells to determine changes in immune cell composition and activation in mild versus severe COVID-19 (242 samples from 109 individuals) over time. HLA-DRhiCD11chi inflammatory monocytes with an interferon-stimulated gene signature were elevated in mild COVID-19. Severe COVID-19 was marked by occurrence of neutrophil precursors, as evidence of emergency myelopoiesis, dysfunctional mature neutrophils, and HLA-DRlo monocytes. Our study provides detailed insights into the systemic immune response to SARS-CoV-2 infection and reveals profound alterations in the myeloid cell compartment associated with severe COVID-19.
Genetic structure of SARS-CoV-2 in Western Germany reflects clonal superspreading and multiple independent introduction events
Andreas Walker1,#, Torsten Houwaart2,#, Tobias Wienemann2 , Malte Kohns Vasconcelos2 , Daniel Strelow2 , Tina Senff1 , Lisanna Hülse2 , Ortwin Adams1 , Marcel Andree1 , Sandra Hauka1 , Torsten Feldt3 , Björn-Erik Jensen3 , Verena Keitel3 , Detlef KindgenMilles4 , Jörg Timm1 , Klaus Pfeffer2 , Alexander T Dilthey2,*
# contributed equally * alexander.dilthey@med.uni-duesseldorf.de
1 Institute of Virology, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany 2 Institute of Medical Microbiology and Hospital Hygiene, Heinrich Heine University Düsseldorf, Düsseldorf, Germany 3 Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany 4 Department of Anaesthesiology, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
The whole-genome sequenced 55 SARS-CoV-2 isolates from Western Germany and investigated the genetic structure of SARS-CoV-2 outbreaks in the Heinsberg district and Düsseldorf. While the genetic structure of the Heinsberg outbreak indicates a clonal origin, reflective of superspreading dynamics during the carnival season, distinct viral strains are circulating in Düsseldorf, reflecting the city’s international links. Limited detection of Heinsberg strains in the Düsseldorf area despite geographical proximity may reflect efficient containment and contact tracing efforts.
Clinical classifiers of COVID-19 infection from novel ultra-high-throughput proteomics
Christoph B. Messner1,#, Vadim Demichev1,2,#, Daniel Wendisch3, Laura Michalick4, Matthew White1, Anja Freiwald5, Kathrin Textoris-Taube5, Spyros I. Vernardis1, Anna-Sophia Egger1, Marco Kreidl1, Daniela Ludwig6, Christiane Kilian6, Federica Agostini6, Aleksej Zelezniak1,7, Charlotte Thibeault3, Moritz Pfeiffer3, Stefan Hippenstiel3 Andreas Hocke3, Christof von Kalle8, Archie Campbell9,10, Caroline Hayward11, David J. Porteous9, Riccardo E. Marioni9, Claudia Langenberg1,12, Kathryn S. Lilley2, Wolfgang M. Kuebler4, Michael Mülleder5, Christian Drosten13, Martin Witzenrath3, Florian Kurth3,14, Leif Erik Sander3 and Markus Ralser1,6,15*
1 The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, United Kingdom, 2 Department of Biochemistry, The University of Cambridge, Cambridge, CB21GA, United Kingdom, 3 Charité Universitätsmedizin Berlin, Dept. of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany, 4 Charité Universitätsmedizin Berlin, Institute of Physiology, 10117 Berlin, Germany, 5 Charité Universitätsmedizin Berlin, Core Facility – High Throughput Mass Spectrometry, 10117 Berlin, Germany, 6 Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany, 7 Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-412 96,Sweden, 8 Berlin Institute of Health (BIH), and Charité Universitätsmedizin, Clinical Study Center (CSC), 10117 Berlin, Germany, 9 Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, EH4 2XU, United Kingdom, 10 Usher Institute, University of Edinburgh, Nine, Edinburgh Bioquarter, 9 Little France Road, Edinburgh, EH16 4UX, United Kingdom, 11 MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom, 12 MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, United Kingdom, 13 Charité Universitätsmedizin Berlin, Department of Virology, 10117 Berlin, Germany, 14 Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany, 15 Lead Contact* Correspondence: markus.ralser@charite.de #These authors contributed equally
The COVID-19 pandemic is an unprecedented global challenge. Highly variable in its presentation, spread and clinical outcome, novel point-of-care diagnostic classifiers are urgently required. Here, we describe a set of COVID-19 clinical classifiers discovered using a newly designed low-cost high throughput mass spectrometry-based platform. Introducing a new sample preparation pipeline coupled with short-gradient high-flow liquid chromatography and mass spectrometry, our methodology facilitates clinical implementation and increases sample throughput and quantification precision. Providing a rapid assessment of serum or plasma samples at scale, we report 27 biomarkers that distinguish mild and severe forms of COVID-19, of which some may have potential as therapeutic targets. These proteins highlight the role of complement factors, the coagulation system, inflammation modulators as well as pro-inflammatory signalling upstream and downstream of Interleukin 6. Application of novel methodologies hence transforms proteomics from a research tool into a rapid-response, clinically actionable technology adaptable to infectious outbreaks.
SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes
Waradon Sungnak 1 , Ni Huang1, Christophe Bécavin 2, Marijn Berg3,4, Rachel Queen5,Monika Litvinukova1,6, Carlos Talavera-López1, Henrike Maatz6, Daniel Reichart7,Fotios Sampaziotis 8,9,10, Kaylee B. Worlock11, Masahiro Yoshida 11, Josephine L. Barnes11 and HCA Lung Biological Network
1Wellcome Sanger Institute, Cambridge, UK. 2Université Côte d’Azur, CNRS, IPMC, Sophia-Antipolis, France. 3Department of Pathology and Medical Biology, University Medical Centre Groningen, University of Groningen, Groningen, the Netherlands. 4Groningen Research Institute for Asthma and COPD, University Medical Centre Groningen, University of Groningen, Groningen, the Netherlands. 5Bioinformatics Core Facility, Newcastle University Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle-upon-Tyne, UK. 6Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany. 7Department of Genetics, Harvard Medical School, Boston, MA, USA. 8Wellcome and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK. 9Department of Medicine, Addenbrookes Hospital, Cambridge, UK. 10Cambridge Liver Unit, Cambridge University Hospitals, Cambridge, UK. 11UCL Respiratory, Division of Medicine, University College London, London, UK. *A list of authors and their affiliations appears at the end of the paper. e-mail: ws4@sanger.ac.uk; lung@humancellatlas.org
We investigated SARS-CoV-2 potential tropism by surveying expression of viral entry-associated genes in single-cell RNA-sequencing data from multiple tissues from healthy human donors. We co-detected these transcripts in specific respiratory, corneal and intestinal epithelial cells, potentially explaining the high efficiency of SARS-CoV-2 transmission. These genes are co-expressed in nasal epithelial cells with genes involved in innate immunity, highlighting the cells’ potential role in initial viral infection, spread and clearance. The study offers a useful resource for further lines of inquiry with valuable clinical samples from COVID-19 patients and we provide our data in a comprehensive, open and user-friendly fashion at www.covid19cellatlas.org.
Studying the pathophysiology of coronavirus disease 2019 – a protocol for the Berlin prospective COVID-19 patient cohort (Pa- COVID-19)
Florian Kurth1,2*#, Maria Roennefarth3*, Charlotte Thibeault1, Victor M. Corman4, Holger Müller-Redetzky1,Mirja Mittermaier1, Christoph Ruwwe-Glösenkamp1, Alexander Krannich3, Sein Schmidt3, Lucie Kretzler3, Chantip Dang-Heine3, Matthias Rose5, Michael Hummel6, Andreas Hocke1, Ralf H. Hübner1, Marcus A. Mall7, Jobst Röhmel7, Ulf Landmesser8, Burkert Pieske9, Samuel Knauss10, Matthias Endres10, Joachim Spranger11, Frank P. Mockenhaupt12, Frank Tacke13, Sascha Treskatsch14, Stefan Angermair14, Britta Siegmund 15, Claudia Spies16, Steffen Weber-Carstens16, Kai-Uwe Eckardt17, Alexander Uhrig1, Thomas Zoller1, Christian Drosten4, Norbert Suttorp1, Martin Witzenrath1, Stefan Hippenstiel1, Christof von Kalle3#, Leif Erik Sander1
1 Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany, 2Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine & I. Department of Medicine, University, Medical Center Hamburg-Eppendorf, Hamburg, Germany, 3 Clinical Study Center (CSC), Berlin Institute of Health, and Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany, 4 Institute of Virology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, HumboldtUniversität zu Berlin, and Berlin Institute of Health, Berlin, Germany, 5Department of Psychosomatic Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany, 6 Central Biobank Charité (ZeBanC), Institute of Pathology, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany, 7 Department of Pediatric Pulmonology, Immunology and Critical Care Medicine, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany, 8 Department of Cardiology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, HumboldtUniversität zu Berlin, and Berlin Institute of Health, Berlin, Germany, 9 Medical Department, Division of Cardiology, Campus Virchow-Klinikum, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany, 10 Department of Neurology with Experimental Neurology and Center for Stroke Research Berlin, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin,Germany, 11 Department of Endocrinology and Metabolism, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany, 12 Institute of Tropical Medicine and International Health Berlin, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany, 13 Department of Hepatology and Gastroenterology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany, 14Department of Anaesthesiology and Intensive Care Medicine, Charite Campus Benjamin Franklin, Charité -Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany, 15 Medical Department, Division of Gastroenterology, Infectious Diseases, Rheumatology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany, 16Department of Anesthesiology and Operative Intensive Care Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany, 17 Department of Nephrology and Internal Intensive Care Medicine, Charité – , Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
Purpose Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide causing a global health emergency. Pa-COVID-19 aims to provide comprehensive data on clinical course, pathophysiology, immunology and outcome of COVID-19, in order to identify prognostic biomarkers, clinical scores, and therapeutic targets for improved clinical management and preventive interventions. Methods Pa-COVID-19 is a prospective observational cohort study of patients with confirmed SARS-CoV-2 infection treated at Charite – Universitaetsmedizin Berlin. We collect data on epidemiology, demography, medical history, symptoms, clinical course, pathogen testing and treatment. Systematic, serial blood sampling will allow deep molecular and immunological phenotyping, transcriptomic profiling, and comprehensive biobanking. Longitudinal data and sample collection during hospitalization will be supplemented by long-term follow-up. Results Outcome measures include the WHO clinical ordinal scale on day 15 and clinical, functional and health-related quality of life assessments at discharge and during follow-up. We developed a scalable dataset to (i) suit national standards of care (ii) facilitate comprehensive data collection in medical care facilities with varying resources and (iii) allow for rapid implementation of interventional trials based on the standardized study design and data collection. We propose this scalable protocol as blueprint for harmonized data collection and deep phenotyping in COVID-19 in Germany. Conclusion We established a basic platform for harmonized, scalable data collection, pathophysiological analysis, and deep phenotyping of COVID-19, which enables rapid generation of evidence for improved medical care and identification of candidate therapeutic and preventive strategies. The electronic database accredited for interventional trials allows fast trial implementation for candidate therapeutic agents.
SARS-CoV-2 receptor ACE2 is an interferon-stimulated gene in human airway epithelial cells and is detected in specific cell subsets across tissues
Carly G. K. Ziegler1,2,3,4,5,6*, Samuel J. Allon2,4,5,7,*, Sarah K. Nyquist2,4,5,8,9,*, Ian M. Mbano10,11,*, Vincent N. Miao1,2,4,5, Constantine N. Tzouanas1,2,4,5, Yuming Cao12, Ashraf S. Yousif4, Julia Bals4, Blake M. Hauser4,13, Jared Feldman4,13,14, Christoph Muus5,15, Marc H. Wadsworth II2,3,4,5,7, Samuel W. Kazer2,4,5,7, Travis K. Hughes1,4,5,16, Benjamin Doran2,4,5,7,17,18, G. James Gatter2,4,5, Marko Vukovic2,3,4,5,7, Faith Taliaferro5,18, Benjamin E. Mead2,3,4,5,7, Zhiru Guo12, Jennifer P. Wang12, Delphine Gras19, Magali Plaisant20, Meshal Ansari21,22,23, Ilias Angelidis21,22, Heiko Adler22,24, Jennifer M.S. Sucre25, Chase J. Taylor26, Brian Lin27, Avinash Waghray27, Vanessa Mitsialis18,28, Daniel F. Dwyer29, Kathleen M. Buchheit29, Joshua A. Boyce29, Nora A. Barrett29, Tanya M. Laidlaw29, Shaina L. Carroll30, Lucrezia Colonna31, Victor Tkachev17,32,33, Christopher W. Peterson34,35, Alison Yu17,36, Hengqi Betty Zheng36, Hannah P. Gideon37,38, Caylin G. Winchell37,38,39, Philana Ling Lin38,40,41, Colin D. Bingle42, Scott B. Snapper18,28, Jonathan A. Kropski43,44,45, Fabian J. Theis23, Herbert B. Schiller21,22, Laure-Emmanuelle Zaragosi20, Pascal Barbry20 Alasdair Leslie10,46, Hans-Peter Kiem34,35, JoAnne L. Flynn37,38, Sarah M. Fortune4,5,47, Bonnie Berger9,48, Robert W. Finberg12, Leslie S. Kean17,32,33, Manuel Garber12, Aaron G. Schmidt4,13, Daniel Lingwood4, Alex K. Shalek1-8,16,33,49,#, Jose Ordovas-Montanes5,16,18,49,#, HCA Lung Biological Network
HCA Lung Biological Network Author List: Nicholas Banovich, Pascal Barbry, Alvis Brazma, Tushar Desai, Thu Elizabeth Duong, Oliver Eickelberg, Christine Falk, Michael Farzan, Ian Glass, Muzlifah Haniffa, Peter Horvath, Deborah Hung, Naftali Kaminski, Mark Krasnow, Jonathan A. Kropski, Malte Kuhnemund, Robert Lafyatis, Haeock Lee, Sylvie Leroy, Sten Linnarson, Joakim Lundeberg, Kerstin Meyer, Alexander Misharin, Martijn Nawijn, Marko Z. Nikolic, Jose Ordovas-Montanes, Dana Pe’er, Joseph Powell, Stephen Quake, Jay Rajagopal, Purushothama Rao Tata, Emma L. Rawlins, Aviv Regev, Paul A. Reyfman, Mauricio Rojas, Orit Rosen, Kourosh Saeb-Parsy, Christos Samakovlis, Herbert Schiller, Joachim L. Schultze, Max A. Seibold, Alex K. Shalek, Douglas Shepherd, Jason Spence, Avrum Spira, Xin Sun, Sarah Teichmann, Fabian Theis, Alexander Tsankov, Maarten van den Berge, Michael von Papen, Jeffrey Whitsett, Ramnik Xavier, Yan Xu, Laure-Emmanuelle Zaragosi and Kun Zhang. Pascal Barbry, Alexander Misharin, Martijn Nawijn and Jay Rajagopal serve as the coordinators for the HCA Lung Biological Network
1Program in Health Sciences & Technology, Harvard Medical School & Massachusetts Institute of Technology, Boston, MA 02115, USA, 2 Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA, 3 Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA, 4 Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA, 5 Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA, 6 Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA 02138, USA, 7 Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA, 8 Program in Computational & Systems Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA, 9 Computer Science & Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA, 10 African Health Research Institute, Durban, South Africa, 11 School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa, 12 University of Massachusetts Medical School, Worcester, MA 01655, USA, 13 Department of Microbiology, Harvard Medical School, Boston, MA 02115, USA, 14 Program in Virology, Harvard Medical School, Boston, MA 02115, USA, 15 John A. Paulson School of Engineering & Applied Sciences, Harvard University, Cambridge, MA 02138, USA, 16 Program in Immunology, Harvard Medical School, Boston, MA 02115, USA, 17 Division of Pediatric Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115, USA, 18 Division of Gastroenterology, Hepatology, and Nutrition, Boston Children’s Hospital, Boston, MA 02115, USA 19Aix-Marseille University, INSERM, INRA, C2VN, Marseille, France 20Université Côte d’Azur, CNRS, IPMC, Sophia-Antipolis, France 21Comprehensive Pneumology Center & Institute of Lung Biology and Disease, Helmholtz Zentrum München, Munich, Germany 22German Center for Lung Research, Munich, Germany 23Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany 24Research Unit Lung Repair and Regeneration, Helmholtz Zentrum München, Munich, Germany 25Division of Neonatology, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA 26Divison of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA 27Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA 02114, USA 28Division of Gastroenterology, Brigham and Women’s Hospital, Boston, MA 02115, USA 29Division of Allergy and Clinical Immunology, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA 30University of California, Berkeley, CA 94720, USA 31University of Washington, Seattle, WA 98195, USA 32Dana Farber Cancer Institute, Boston, MA 02115, USA 33Harvard Medical School, Boston, MA 02115, USA 34Stem Cell & Gene Therapy Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA 35Department of Medicine, University of Washington, Seattle, WA 98195, USA 36Seattle Children’s Hospital, Seattle, WA 98145, USA 37Department of Microbiology & Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15219, USA 38Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA 39Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA 40UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
There is pressing urgency to understand the pathogenesis of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) which causes the disease COVID-19. SARS-CoV2 spike (S)-protein binds ACE2, and in concert with host proteases, principally TMPRSS2, promotes cellular entry. The cell subsets targeted by SARS-CoV-2 in host tissues, and the factors that regulate ACE2 expression, remain unknown. Here, we leverage human, non-human primate, and mouse single-cell RNA-sequencing (scRNA-seq) datasets across health and disease to uncover putative targets of SARS-CoV-2 amongst tissue-resident cell subsets. We identify ACE2 and TMPRSS2 co-expressing cells within lung type II pneumocytes, ileal absorptive enterocytes, and nasal goblet secretory cells. Strikingly, we discover that ACE2 is a human interferonstimulated gene (ISG) in vitro using airway epithelial cells, and extend our findings to in vivo viral infections. Our data suggest that SARS-CoV-2 could exploit species-specific interferon-driven upregulation of ACE2, a tissue-protective mediator during lung injury, to enhance infection.