RESEARCH ARTICLES
Cite as: D. E. Gordon et al., Science
10.1126/science.abe9403 (2020).
Comparative host-coronavirus protein interaction
networks reveal pan-viral disease mechanisms
1QBI
COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA. 2Quantitative Biosciences Institute (QBI), University of California, San Francisco, CA 94158, USA.
of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA. 4J. David Gladstone Institutes, San Francisco, CA 94158, USA.
5Medical Scientist Training Program, University of California, San Francisco, CA 94143, USA. 6Department of Microbiology and Immunology, University of California, San
Francisco, CA 94143, USA. 7Biomedical Sciences Graduate Program, University of California, San Francisco, CA 94143, USA. 8Viral Populations and Pathogenesis Unit,
CNRS UMR 3569, Institut Pasteur, 75724, Paris, cedex 15, France. 9Institute for Clinical and Experimental Pharmacology and Toxicology I, University of Freiburg, 79104
Freiburg, Germany. 10Center for Microbial Pathogenesis, Institute for Biomedical Sciences, Georgia State University, Atlanta, GA 30303, USA. 11Aetion, Inc. 5 Penn Plaza, 7th
Floor New York, NY 10001, USA. 12Quantitative Biosciences Institute (QBI) Coronavirus Research Group Structural Biology Consortium, University of California, San
Francisco, CA 94158, USA. 13Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. 14Department of Pharmaceutical
Chemistry, University of California, San Francisco, CA 94158, USA. 15Howard Hughes Medical Institute. 16European Molecular Biology Laboratory, European Bioinformatics
Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. 17Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle,
WA 98109, USA. 18Beam Therapeutics, Cambridge, MA 02139, USA. 19Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA
94158, USA. 20Department of Biomedical Science, Centre for Membrane Interactions and Dynamics, University of Sheffield, Firth Court, Sheffield S10 2TN, UK.
3Department
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David E. Gordon1,2,3,4*, Joseph Hiatt1,4,5,6,7*, Mehdi Bouhaddou1,2,3,4*, Veronica V. Rezelj8*, Svenja Ulferts9*,
Hannes Braberg1,2,3,4*, Alexander S. Jureka10*, Kirsten Obernier1,2,3,4*, Jeffrey Z. Guo1,2,3,4*, Jyoti Batra1,2,3,4*,
Robyn M. Kaake1,2,3,4*, Andrew R. Weckstein11*, Tristan W. Owens12*, Meghna Gupta12*, Sergei Pourmal12*,
Erron W. Titus12*, Merve Cakir1,2,3,4*, Margaret Soucheray1,2,3,4, Michael McGregor1,2,3,4, Zeynep Cakir1,2,3,4,
Gwendolyn Jang1,2,3,4, Matthew J. O’Meara13, Tia A. Tummino1,2,14, Ziyang Zhang1,2,3,15, Helene Foussard1,2,3,4,
Ajda Rojc1,2,3,4, Yuan Zhou1,2,3,4, Dmitry Kuchenov1,2,3,4, Ruth Hüttenhain1,2,3,4, Jiewei Xu1,2,3,4, Manon
Eckhardt1,2,3,4, Danielle L. Swaney1,2,3,4, Jacqueline M. Fabius1,2, Manisha Ummadi1,2,3,4, Beril Tutuncuoglu1,2,3,4,
Ujjwal Rathore1,2,3,4, Maya Modak1,2,3,4, Paige Haas1,2,3,4, Kelsey M. Haas1,2,3,4, Zun Zar Chi Naing1,2,3,4, Ernst H.
Pulido1,2,3,4, Ying Shi1,2,3,15, Inigo Barrio-Hernandez16, Danish Memon16, Eirini Petsalaki16, Alistair Dunham16,
Miguel Correa Marrero16, David Burke16, Cassandra Koh8, Thomas Vallet8, Jesus A. Silvas10, Caleigh M.
Azumaya12, Christian Billesbølle12, Axel F. Brilot12, Melody G. Campbell12,17, Amy Diallo12, Miles Sasha
Dickinson12, Devan Diwanji12, Nadia Herrera12, Nick Hoppe12, Huong T. Kratochvil12, Yanxin Liu12, Gregory E.
Merz12, Michelle Moritz12, Henry C. Nguyen12, Carlos Nowotny12, Cristina Puchades12, Alexandrea N. Rizo12,
Ursula Schulze-Gahmen12, Amber M. Smith12, Ming Sun12,18, Iris D. Young12, Jianhua Zhao12, Daniel Asarnow12,
Justin Biel12, Alisa Bowen12, Julian R. Braxton12, Jen Chen12, Cynthia M. Chio12, Un Seng Chio12, Ishan
Deshpande12, Loan Doan12, Bryan Faust12, Sebastian Flores12, Mingliang Jin12, Kate Kim12, Victor L. Lam12, Fei
Li12, Junrui Li12, Yen-Li Li12, Yang Li12, Xi Liu12, Megan Lo12, Kyle E. Lopez12, Arthur A. Melo12, Frank R. Moss
III12, Phuong Nguyen12, Joana Paulino12, Komal Ishwar Pawar12, Jessica K. Peters12, Thomas H. Pospiech Jr.12,
Maliheh Safari12, Smriti Sangwan12, Kaitlin Schaefer12, Paul V. Thomas12, Aye C. Thwin12, Raphael Trenker12,
Eric Tse12, Tsz Kin Martin Tsui12, Feng Wang12, Natalie Whitis12, Zanlin Yu12, Kaihua Zhang12, Yang Zhang12,
Fengbo Zhou12, Daniel Saltzberg1,2,19, QCRG Structural Biology Consortium12†, Anthony J. Hodder20, Amber S.
Shun-Shion20, Daniel M. Williams20, Kris M. White21,22, Romel Rosales21,22, Thomas Kehrer21,22, Lisa Miorin21,22,
Elena Moreno21,22, Arvind H. Patel23, Suzannah Rihn23, Mir M. Khalid4, Albert Vallejo-Gracia4, Parinaz
Fozouni4,5,7, Camille R. Simoneau4,7, Theodore L. Roth5,6,7, David Wu5,7, Mohd Anisul Karim24,25, Maya
Ghoussaini24,25, Ian Dunham16,25, Francesco Berardi26, Sebastian Weigang27, Maxime Chazal28, Jisoo Park29,
James Logue30, Marisa McGrath30, Stuart Weston30, Robert Haupt30, C. James Hastie31, Matthew Elliott31, Fiona
Brown31, Kerry A. Burness31, Elaine Reid31, Mark Dorward31, Clare Johnson31, Stuart G. Wilkinson31, Anna
Geyer31, Daniel M. Giesel31, Carla Baillie31, Samantha Raggett31, Hannah Leech31, Rachel Toth31, Nicola
Goodman31, Kathleen C. Keough4, Abigail L. Lind4, Zoonomia Consortium‡, Reyna J. Klesh32, Kafi R.
Hemphill33, Jared Carlson-Stevermer34, Jennifer Oki34, Kevin Holden34, Travis Maures34, Katherine S.
Pollard4,35,36, Andrej Sali1,2,14,19, David A. Agard1,2,12,37, Yifan Cheng1,2,12,15,37, James S. Fraser1,2,12,19, Adam
Frost1,2,12,37, Natalia Jura1,2,3,12,38, Tanja Kortemme1,2,12,19,39, Aashish Manglik1,2,12,14, Daniel R. Southworth1,12,37,
Robert M. Stroud1,2,12,37, Dario R. Alessi31, Paul Davies31, Matthew B. Frieman30, Trey Ideker29,40, Carmen Abate26,
Nolwenn Jouvenet27,28, Georg Kochs27, Brian Shoichet1,2,14, Melanie Ott4,41, Massimo Palmarini23, Kevan M.
Shokat1,2,3,15, Adolfo García-Sastre21,22,42,43§, Jeremy A. Rassen11§, Robert Grosse9,44§, Oren S.
Rosenberg1,2,12,36,37,41§, Kliment A. Verba1,2,12,14§, Christopher F. Basler10§, Marco Vignuzzi8§, Andrew A. Peden20§,
Pedro Beltrao16§, Nevan J. Krogan1,2,3,4§
21
Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 22Global Health and Emerging Pathogens Institute, Icahn School of
Medicine at Mount Sinai, New York, NY 10029, USA. 23MRC-University of Glasgow Centre for Virus Research, 464 Bearsden Road, Glasgow G61 1QH, Scotland, UK.
24
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. 25Open Targets, Wellcome Genome Campus, Hinxton,
Cambridgeshire CB10 1SD, UK. 26Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari ‘ALDO MORO’, Via Orabona, 4 70125, Bari, Italy. 27Institute of
Virology, Medical Center-University of Freiburg, 79104 Freiburg, Germany. 28Département de Virologie, CNRS UMR 3569, Institut Pasteur, Paris 75015, France.
29
Department of Medicine, University of California, San Diego, CA 92093, USA. 30Department of Microbiology and Immunology, University of Maryland School of Medicine,
Baltimore, MD 21201, USA. 31MRC Protein Phosphorylation and Ubiquitylation Unit, College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK. 32HealthVerity,
Philadelphia, PA 19103, USA. 33Department of Neurology, University of California, San Francisco, CA 94143, USA. 34Synthego Corporation, Redwood City, CA 94063, USA.
35Department of Epidemiology & Biostatistics, University of California, San Francisco, CA 94158, USA. 36Chan-Zuckerberg Biohub, San Francisco, CA 94158, USA.
37Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA. 38Cardiovascular Research Institute, University of California, San
Francisco, CA 94158, USA. 39The UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158, USA. 40Department to
Bioengineering, University of California, San Diego, CA 92093, USA. 41Department of Medicine, University of California, San Francisco, CA 94143, USA. 42Department of
Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 43The Tisch Cancer Institute, Icahn School of Medicine at
Mount Sinai, New York, NY 10029, USA. 44Centre for Integrative Biological Signaling Studies (CIBSS), 79104 Freiburg, Germany.
*These authors contributed equally to this work. †The QCRG Structural Biology Consortium collaborators and their affiliations are listed in the supplementary
materials. ‡The Zoonomia Consortium collaborators and their affiliations are listed in the supplementary materials.
§Corresponding author. Email: nevan.krogan@ucsf.edu (N.J.K.); pbeltrao@ebi.ac.uk (P.B.); marco.vignuzzi@pasteur.fr (M.V.); cbasler@gsu.edu (C.F.B.);
verba@msg.ucsf.edu (K.A.V.); oren.rosenberg@ucsf.edu (O.S.R.); a.peden@sheffield.ac.uk (A.A.P.); robert.grosse@pharmakol.uni-freiburg.de (R.G.);
jeremy.rassen@aetion.com (J.A.R.); Adolfo.Garcia-Sastre@mssm.edu (A.G.-S.)
In the past two decades, three deadly human respiratory syndromes associated with coronavirus (CoV) infections have
emerged: Severe Acute Respiratory Syndrome (SARS) in
2002, Middle East Respiratory Syndrome (MERS) in 2012,
and Coronavirus Disease 2019 (COVID-19) in 2019. These
three diseases are caused by the zoonotic CoVs SARS-CoV-1,
MERS-CoV, and SARS-CoV-2 (1), respectively. Before their
emergence, human CoVs were associated with usually mild
respiratory illness. To date, SARS-CoV-2 has sickened millions and killed over one million worldwide. This unprecedented challenge has prompted widespread efforts to develop
new vaccine and antiviral strategies, including repurposed
therapeutics, which offer the potential for treatments with
known safety profiles and short development timelines. The
successful repurposing of the antiviral nucleoside analog
Remdesivir (2), as well as the host-directed anti-inflammatory steroid dexamethasone (3), provide clear proof that existing compounds can be crucial tools in the fight against
COVID-19. Despite these promising examples, there is still no
curative treatment for COVID-19. In addition, as with any virus, the search for effective antiviral strategies could be complicated over time by the continued evolution of SARS-CoV-2
and possible resulting drug resistance (4).
Current endeavors are appropriately focused on SARSCoV-2 due to the severity and urgency of the ongoing pandemic. However, the frequency with which other highly virulent CoV strains have emerged highlights an additional need
First release: 15 October 2020
to identify promising targets for broad CoV inhibitors with
high barriers to resistance mutations and potential for rapid
deployment against future emerging strains. While traditional antivirals target viral enzymes that are often subject to
mutation and thus the development of drug resistance, targeting the host proteins required for viral replication is a
strategy that can avoid resistance and lead to therapeutics
with the potential for broad-spectrum activity as families of
viruses often exploit common cellular pathways and processes.
Here, we identified shared biology and potential drug targets among the three highly pathogenic human CoV strains.
We expanded upon our recently published map of virus-host
protein interactions for SARS-CoV-2 (5) and mapped the full
interactome of SARS-CoV-1 and MERS-CoV. We investigated
the localization of viral proteins across strains, and quantitatively compared the virus-human interactions for each virus.
Using functional genetics and structural analysis of selected
host-dependency factors, we identified drug targets and also
performed real-world analysis on clinical data from COVID19 patient outcomes.
A cross-coronavirus study of protein function
A central goal of this study is to understand, from a systems
level, the conservation of target proteins and cellular processes between SARS-CoV-2, SARS-CoV-1 and MERS-CoV,
and thereby identify shared vulnerabilities that can be
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The COVID-19 (Coronavirus disease-2019) pandemic, caused by the SARS-CoV-2 coronavirus, is a
significant threat to public health and the global economy. SARS-CoV-2 is closely related to the more lethal
but less transmissible coronaviruses SARS-CoV-1 and MERS-CoV. Here, we have carried out comparative
viral-human protein-protein interaction and viral protein localization analysis for all three viruses.
Subsequent functional genetic screening identified host factors that functionally impinge on coronavirus
proliferation, including Tom70, a mitochondrial chaperone protein that interacts with both SARS-CoV-1 and
SARS-CoV-2 Orf9b, an interaction we structurally characterized using cryo-EM. Combining geneticallyvalidated host factors with both COVID-19 patient genetic data and medical billing records identified
important molecular mechanisms and potential drug treatments that merit further molecular and clinical
study.
Conserved coronavirus proteins often retain the same
cellular localization
As protein localization can provide important information regarding function, we assessed the cellular localization of individually expressed coronavirus proteins in addition to
mapping their interactions (Fig. 2A and Methods). Immunofluorescence localization analysis of all 2xStrep-tagged SARSCoV-2, SARS-CoV-1, and MERS-CoV proteins highlights similar patterns of localization for the vast majority of shared protein homologs in HeLaM cells (Fig. 2B), supporting the
hypothesis that conserved proteins share functional similarities. A notable exception is Nsp13, which appears to localize
to the cytoplasm for SARS-CoV-2 and SARS-CoV-1; but to the
mitochondria for MERS-CoV (Fig. 2B, figs. S8 to S13, and table S3). To assess the localization of SARS-CoV-2 proteins in
the context of infected cells, we raised antibodies against 20
of them and validated them with the individually-expressed
2xStrep-tagged proteins (fig. S14). Using the 14 antibodies
with confirmed specificity, we observed that localization of
viral proteins in infected Caco-2 cells sometimes differed
First release: 15 October 2020
from their localization when expressed individually (Fig. 2B,
fig. S15, and table S3). This likely results from recruitment of
viral proteins and complexes into replication compartments,
as well as remodeling of the secretory pathway during viral
infection. Such differences could also be due to miss-localization caused by protein tagging. For example, the localization
of expressed Orf7B does not match the known SARS-CoV-1
Golgi localization seen in the infection state. For proteins
such as Nsp1 and Orf3a, which are not known to be involved
in viral replication, their localization is consistent both when
expressed individually and in the context of viral infection
(Fig. 2, C and D). We have compared the localization of the
expressed viral proteins with the localization of their interaction partners using a cellular compartment Gene Ontology
enrichment analysis (fig. S16). Several examples exist where
the localization of the viral protein is in agreement with the
localization of the interaction partners, including enrichment
of the Nuclear Pore for Nsp9 interactors and ER enrichment
for interactions with Orf8.
Our localization studies suggest that most orthologous
proteins have the same localization across the viruses (Fig.
2B). Moreover, small changes in localization, as observed for
some viral proteins across strains, do not coincide with
strong changes in viral-host protein interactions (Fig. 2E).
Overall, these results suggest that changes in protein localization, as measured by expressed tagged proteins, are not
common and therefore they are unlikely to be a major source
of differences in host targeting mechanisms.
Comparison of host targeted processes identifies conserved mechanisms with divergent implementations
To study the conservation of targeted host factors and processes, we first used a clustering approach (Methods) to compare the overlap in protein interactions for the three viruses
(Fig. 3A). We defined 7 clusters of viral-host interactions corresponding to those that are specific to each or shared among
the viruses. The largest pairwise overlap was observed between SARS-CoV-1 and SARS-CoV-2 (Fig. 3A), as expected
from their closer evolutionary relationship. A functional enrichment analysis (Fig. 3B and table S4) highlighted host processes that are targeted through interactions conserved
across all three viruses including ribosome biogenesis and
regulation of RNA metabolism. Conserved interactions between SARS-CoV-1 and SARS-CoV-2, but not MERS-CoV, were
enriched in endosomal and Golgi vesicle transport (Fig. 3B).
Despite the small fraction (7.1%) of interactions conserved between SARS-CoV-1 and MERS-CoV, but not SARS-CoV-2,
these were strongly enriched in translation initiation and myosin complex proteins (Fig. 3B).
We next asked if the conserved interactions were specific
for certain viral proteins (Fig. 3C), and found that some proteins (M, N, Nsp7/8/13) showed a disproportionately high
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targeted with antiviral therapeutics. All three strains encode
four homologous structural proteins (E, M, N, S) and 16 nonstructural proteins (Nsps). The latter are proteolytically
cleaved from a polyprotein precursor that is expressed from
one large open reading frame (Orf), Orf1ab (Fig. 1A). Additionally, coronaviruses contain a variable number of accessory factors encoded by Orfs. While the genome organization
and sequence of Orf1ab is mainly conserved between the
three viruses under study, it diverges significantly in the region encoding the accessory factors, especially between
MERS-CoV and the two SARS coronaviruses (Fig. 1, A to D,
and table S1). These differences in conservation of genes and
genome organization are linked to differences in host targeting systems that we have studied through large scale protein
localization and interaction profiling (Fig. 1E). Building on
our earlier work on the interactome of SARS-CoV-2 (5), we
identified the host factors physically interacting with each
SARS-CoV-1 and MERS-CoV viral protein. To this end, structural proteins, mature Nsps and predicted Orf proteins were
codon optimized, 2xStrep tagged and cloned into a mammalian expression vector (figs. S1 and S2; see below and Methods
section). Each protein construct was transfected into
HEK293T cells, affinity purified, and high-confidence interactors were identified by mass spectrometry and scored
using SAINTexpress and MiST scoring algorithms (6, 7) (table
S2 and figs. S3 to S6). In addition, we performed mass spectrometry analysis on SARS-CoV-2 Nsp16, which was not analyzed in our earlier work (5) (table S2 and fig. S7). In all, we
now report 389 high-confidence interactors for SARS-CoV-2,
366 interactions for SARS-CoV-1, and 296 interactions for
MERS-CoV (table S2).
Quantitative differential interaction scoring (DIS) identifies interactions conserved between coronaviruses
The identification of virus-host interactions conserved across
pathogenic coronaviruses provides the opportunity to reveal
host targets that may remain essential for these and other
emerging coronaviruses. For a quantitative comparison of
each virus-human interaction from viral baits shared by all
three viruses, we developed a differential interaction score
(DIS). DIS is calculated between any pair of viruses and is
First release: 15 October 2020
defined as the difference between the interaction scores (K)
from each virus (Fig. 4A, table S5, and Methods). This kind of
comparative analysis is beneficial as it permits the recovery
of conserved interactions that may fall just below strict cutoffs. For each comparison, DIS was calculated for interactions residing in certain clusters as defined in the previous
analysis (see Fig. 3A). For example, for the SARS-CoV-2 to
MERS-CoV comparison, a DIS was computed for interactions
residing in all clusters except cluster 3, where interactions are
either not found or scores were very low for both SARS-CoV2 and MERS-CoV. A DIS of 0 indicates that the interaction is
confidently shared between the two viruses being compared,
while a DIS of +1 or -1 indicates that the host protein interaction is specific for the virus listed first or second, respectively.
In agreement with our previous results (Fig. 3A), DIS
scores for the comparison between SARS-CoV-2 and SARSCoV-1 are enriched near zero, indicating a high number of
shared interactions (Fig. 4B, yellow). On the other hand, comparing interactions from either SARS-CoV-1 or SARS-CoV-2
with MERS-CoV resulted in DIS values closer to ±1, indicating
a higher divergence (Fig. 4B, blue and green). The breakdown
of DIS by homologous viral proteins reveals high similarity of
interactions for proteins N, Nsp8, Nsp7, and Nsp13 (Fig. 4C),
reinforcing the observations made by overlapping
thresholded interactions (Fig. 3, C and D). As the greatest dissimilarity was observed between the SARS coronaviruses and
MERS-CoV, we computed a fourth DIS (SARS-MERS) by averaging K from SARS-CoV-1 and SARS-CoV-2 prior to calculating the difference with MERS-CoV (Fig. 4, B and C, purple).
We next created a network visualization of the SARS-MERS
comparison (Fig. 4D), permitting an appreciation of SARSspecific (red; DIS near +1) versus MERS-specific (blue; DIS
near -1) interactions, as well as those conserved between all
three coronavirus species (black; DIS near zero). SARSspecific interactions include: DNA polymerase α interacting
with Nsp1; stress granule regulators interacting with N protein; TLE transcription factors interacting with Nsp13; and
AP2 clathrin interacting with Nsp10. Notable MERS-CoVspecific interactions include: mTOR and Stat3 interacting
with Nsp1; DNA damage response components p53 (TP53),
MRE11, RAD50, and UBR5 interacting with Nsp14; and the
activating signal cointegrator 1 (ASC-1) complex interacting
with Nsp2. Interactions shared between all three coronaviruses include: casein kinase II and RNA processing regulators interacting with N protein; IMP dehydrogenase 2
(IMPDH2) interacting with Nsp14; centrosome, protein kinase A, and TBK1 interacting with Nsp13; and the signal
recognition particle, 7SK snRNP, exosome, and ribosome biogenesis components interacting with Nsp8 (Fig. 4D).
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fraction of shared interactions conserved across the three viruses. This suggests that the processes targeted by these proteins may be more essential and more likely to be required
for other emerging coronaviruses. Such differences in conservation of interactions should be encoded, to some extent, in
the degree of sequence differences. Comparing pairs of homologous proteins shared between SARS-CoV-2 and SARSCoV-1 or MERS-CoV, we observed a significant correlation between sequence conservation and protein-protein interaction
(PPI) similarity (calculated as Jaccard index) (Fig. 3D, r =
0.58, p-value = 0.0001). This shows that the evolution of protein sequences strongly determines the divergence in the host
interactors.
While studying the function of host proteins interacting
with each virus, we noted that some shared cellular processes
were targeted by different interactions across the viruses. To
study this in more detail, we identified the cellular processes
significantly enriched in the interactomes of all three viruses
(fig. S17A and table S4) and ranked them by the degree of
overlapping proteins (Fig. 3E). This identified proteins related to the nuclear envelope, proteasomal catabolism, cellular response to heat and regulation of intracellular protein
transport as biological functions that are hijacked by these
viruses through different human proteins. Additionally, we
found that up to 51% of protein interactions with a conserved
human target occurred via a different (non-orthologous) viral
protein (Fig. 3F) and, in some cases, the overlap of interactions for two non-orthologous virus baits was greater than
that for the orthologous pair (Fig. 3G and fig. S17, B and C).
For example, several interacting proteins of SARS-CoV-2
Nsp8 are also targeted by MERS-CoV Orf4a, and interactions
of MERS-CoV Orf5 share interactors with SARS-CoV-2 Orf3a
(Fig. 3G). In the case of Nsp8, we found some degree of structural homology between the C-terminal region of it and a predicted structural model of Orf4a (Methods and fig. S17D),
indicative of a possible common interaction mechanism.
In summary, we find that sequence differences determine
the degree of changes in viral-host interactions, and that often the same cellular process can be targeted by different viral or host proteins. These results suggest a degree of
plasticity in the way these viruses can control a given biological process in the host cell.
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encoded proteins with SARS-CoV-2, SARS-CoV-1 and MERSCoV proteins (Fig. 5F). Interestingly, we observed an enrichment of genetic hits that encode proteins interacting with viral Nsp7, which has a high degree of interactions shared
across all the three viruses (Fig. 3C). Prostaglandin E synthase 2 (encoded by PTGES2), for example, is a functional interactor of Nsp7 from SARS-CoV-1, SARS-CoV-2 and MERSCoV. Other dependency factors were specific to SARS-CoV-2,
including interleukin-17 receptor A (IL17RA), which interacts
with SARS-CoV-2 Orf8. We also identify dependency factors
that are shared interactors between SARS-CoV-1 and SARSCoV-2, such as the aforementioned sigma receptor 1
(SIGMAR1) which interacts with Nsp6, and the mitochondrial
import receptor subunit Tom70 (TOMM70) which interacts
with Orf9b. We will use these interactions to validate virushost interactions (Orf8-IL17RA and Orf9b-Tom70), connect
our systems biology data to evidence for clinical impact of the
host factors we identified (IL17RA), and analyze outcomes of
COVID-19 patients treated with putative host-directed drugs
against PGES-2 and sigma receptor 1.
SARS Orf9b Interacts with Tom70
Orf9b of SARS-CoV-1 and SARS-CoV-2 was found to be localized to mitochondria upon overexpression as well as in SARSCoV-2 infected cells. In line with this, the mitochondrial outer
membrane protein Tom70 (encoded by TOMM70) is a highconfidence interactor of Orf9b in both SARS-CoV-1 and SARSCoV-2 interaction maps (Fig. 6A) and may act as a host dependency factor for SARS-CoV-2 (Fig. 6B). Tom70 falls below
the scoring threshold as a putative interactor of MERS-CoV
Nsp2, a viral protein not associated with mitochondria. (table
S2). Tom70 is one of the major import receptors in the TOM
complex that recognizes and mediates the translocation of
mitochondrial preproteins from the cytosol into the mitochondria in a chaperone dependent manner (8). Additionally,
Tom70 is involved in the activation of the mitochondrial antiviral signaling (MAVS) protein which leads to apoptosis
upon virus infection (9, 10).
To validate the interaction between viral proteins and
Tom70, we performed a co-immunoprecipitation experiment
in the presence or absence of Strep-tagged Orf9b from SARSCoV-1 and SARS-CoV-2 as well as Strep-tagged Nsp2 from all
three CoVs. Endogenous Tom70, but not other translocase
proteins of the outer membrane including Tom20, Tom22
and Tom40, co-precipitated only in the presence of Orf9b but
not Nsp2 in both HEK293T and A549 cells, confirming our
AP-MS data and suggesting that Orf9b specifically interacts
with Tom70 (Fig. 6C and fig. S19A). Further, upon co-expression in bacterial cells, we were able to co-purify the Orf9bTom70 protein complex, indicating a stable complex (Fig.
6D). We found SARS-CoV-1 and SARS-CoV-2 Orf9b expressed
in HeLaM cells co-localized with Tom70 (Fig. 6E) and
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Cell-based genetic screens identify SARS-CoV-2 host dependency factors
To identify host factors that are critical for infection and
therefore potential targets for host-directed therapies, we
performed genetic perturbations of 332 human proteins, 331
previously identified to interact with SARS-CoV-2 proteins (5)
plus ACE2, and observed their effect on infectivity. To ensure
a broad coverage of potential hits, we carried out two screens
in different cell lines, investigating the effects on infection:
siRNA knockdowns in A549 cells stably expressing ACE2
(A549-ACE2) (Fig. 5A) and CRISPR-based knockouts in Caco2 cells (Fig. 5B). ACE2 was included as positive control in both
screens as were non-targeting siRNAs or non-targeted Caco2 cells as negative controls. After SARS-CoV-2 infection, effects on virus infectivity were quantified by RT-qPCR on cell
supernatants (siRNA) or by titrating virus-containing supernatants on Vero E6 cells (CRISPR) (see Methods for details).
Cells were monitored for viability and knockdown or editing
efficiency was determined as described (Methods and fig.
S18). This revealed that 93% of the genes were knocked down
at least 50% in the A549-ACE2 screen, and 95% of the knockdowns exhibited less than a 20% decrease in viability. In the
Caco-2 assay, we observed an editing efficiency of at least 80%
for 89% of the genes tested (Methods and fig. S18). Of the 332
human SARS-CoV-2 interactors, the final A549-ACE2 dataset
includes 331 gene knockdowns and the Caco-2 dataset includes 286 gene knockouts, with the difference mainly due to
removal of essential genes (Methods). The readouts from
both assays were then separately normalized using robust Zscores (Methods), with negative and positive Z-scores indicating proviral dependency factors (perturbation = decreased infectivity) and antiviral host factors with restrictive activity
(perturbation = increased infectivity), respectively. As expected, negative controls resulted in neutral Z-scores (Fig. 5,
C and D, and tables S6 and S7). Similarly, perturbations of
the positive control ACE2 resulted in strongly negative Zscores in both assays (Fig. 5, C and D). Overall, the Z-scores
did not exhibit any trends related to viability, knockdown efficiency, or editing efficiency (fig. S18). With a cutoff of |Z| >
2 to highlight genes that notably affect SARS-CoV-2 infectivity when perturbed, 31 and 40 dependency factors (Z < -2)
and 3 and 4 factors with restrictive activity (Z > 2) were identified in A549-ACE2 and Caco-2 cells, respectively (Fig. 5E).
Of particular interest are the host dependency factors for
SARS-CoV-2 infection, which represent potential targets for
drug development and repurposing. For example, non-opioid
receptor sigma 1 (sigma-1, encoded by SIGMAR1) was identified as a functional host-dependency factor in both cell systems in agreement with our previous report of antiviral
activity for sigma receptor ligands (5). To provide a contextual view of the genetics results, we generated a network that
integrates the hits from both cell lines and the PPIs of their
observed that SARS-CoV-1 or SARS-CoV-2 Orf9b overexpression led to decreases in Tom70 expression (Fig. 6, E and F).
Similarly, Orf9b was found to co-localize with Tom70 upon
SARS-CoV-2 infection (Fig. 6G). This is in agreement with the
known outer mitochondrial membrane localization of Tom70
(11) and Orf9b localization to mitochondria upon overexpression and during SARS-CoV-2 infection (Fig. 2B). We also saw
decreases in Tom70 expression during SARS-CoV-2 infection
(Fig. 6G) but did not see dramatic changes in expression levels of the mitochondrial protein Tom20 after individual
Strep-Orf9b expression or upon SARS-CoV-2 infection (fig.
S19, B and C).
First release: 15 October 2020
Implications of the Orf8-IL17RA interaction for
COVID-19
As described above, we found that IL-17 receptor A (IL17RA)
physically interacts with Orf8 from SARS-CoV-2, but not
SARS-CoV-1 or MERS-CoV (Fig. 5D, table S2, and Fig. 8A). Interestingly, several recent studies have identified high IL-17
levels or aberrant IL-17 signaling as a correlate of severe
COVID-19 (20–23). We demonstrated the physical interaction
of SARS-CoV-2 Orf8 with IL17RA occurs with or without IL17A treatment, suggesting that signaling through the receptor
does not disrupt the interaction with Orf8 (Fig. 8B). Furthermore, knockdown of IL17RA led to a significant decrease in
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CryoEM structure of Orf9b-Tom70 complex reveals
Orf9b interacting at the substrate binding site of Tom70
Tom70, as part of the Tom complex, is involved in recognition
of mitochondrial pre-proteins from the cytosol (12). To further understand the molecular details of Orf9b-Tom70 interactions, we obtained a 3 Å cryoEM structure of the Orf9bTom70 complex (Fig. 7A and fig. S20). Interestingly, although
purified proteins failed to interact upon attempted in vitro
complex reconstitution, they yielded a stable and pure complex when co-expressed in E. coli (Fig. 6D). This may be due
to the fact that Orf9b alone purifies as a dimer (as inferred
by the apparent molecular weight on size exclusion chromatography) and would need to dissociate to interact with
Tom70 based on our structure. Tom70 preferentially binds
preproteins with internal hydrophobic targeting sequences
(13). It contains an N-terminal transmembrane domain and
tetratricopeptide repeat (TPR) motifs in its cytosolic segment.
The C-terminal TPR motifs recognize the internal mitochondrial targeting signals (MTS) of preproteins, and the N-terminal TPR clamp domain serves as a docking site for multichaperone complexes that contain preprotein (14, 15). Obtained cryoEM density allowed us to build atomic models for
residues 109-600 of human Tom70 and residues 39-76 of
SARS-CoV-2 Orf9b (Fig. 7A and table S8). Orf9b makes extensive hydrophobic interactions at the pocket on Tom70 that
has been implicated in its binding to MTS, with the total buried surface area at the interface being quite extensive, approximately 2000 A2 (Fig. 7B). In addition to the mostly
hydrophobic interface, four salt bridges further stabilize the
interaction (Fig. 7C). Upon interaction with Orf9b, the interacting helices on Tom70 move inward to tightly wrap around
Orf9b as compared to previously crystallized yeast Tom70
homologs (movie S1). No structure for human Tom70 without
a substrate has been reported to date and therefore we cannot rule out that the conformational differences are due to
differences between homologs. However, it is possible that
this conformational change upon substrate binding is conserved across homologs as many of the Tom70 residues interacting with Orf9b are highly conserved, likely indicating
residues essential for endogenous MTS substrate recognition.
Surprisingly, although a previously published crystal
structure of SARS-CoV-2 Orf9b revealed that it entirely consists of beta sheets (PDB:6Z4U) (16), upon binding Tom70
residues 52-68, Orf9b forms a helix (Fig. 7D). This is consistent with the fact that MTS sequences recognized by
Tom70 are usually helical, and analysis with the TargetP MTS
prediction server revealed a high probability for this region
of Orf9b to possess an MTS (Fig. 7E). This shows structural
plasticity in this viral protein where, depending on the binding partner, Orf9b changes between helical and beta strand
folds. Furthermore, we had previously identified two infection-driven phosphorylation sites on Orf9b, S50 and S53 (17),
which map to the region on Orf9b buried deep in the Tom70
binding pocket (Fig. 7B, yellow). S53 contributes two hydrogen bonds to the interaction with Tom70 in this overall hydrophobic region. Therefore, once phosphorylated, it is likely
that the Orf9b-Tom70 interaction is weakened. These residues are surface exposed in the dimeric structure of the
Orf9b, which could potentially allow phosphorylation to partition Orf9b between Tom70-bound and dimeric populations.
The two binding sites on Tom70—the substrate binding
site and the TPR domain that recognizes Hsp70/Hsp90—are
known to be conformationally coupled (17, 18). Tom70’s interaction with a C-terminal EEVD motif of Hsp90 via the TPR
domain is key for its function in the interferon pathway, and
induction of apoptosis upon virus infection (10, 19). Whether
Orf9b, by binding to the substrate recognition site of Tom70,
allosterically inhibits Tom70’s interaction with Hsp90 at the
TPR domain remains to be investigated but interestingly, we
see in our structure that R192, a key residue in the interaction
with Hsp70/Hsp90, is moved out of position to interact with
the EEVD sequence, suggesting that Orf9b may modulate interferon and apoptosis signaling via Tom70 (fig. S21). Alternatively, Tom70 has been described as an essential import
receptor for PTEN induced kinase 1 (PINK1) and therefore
loss of mitochondrial import efficiency as a result of Orf9b
binding to Tom70 substrate binding pocket may induce mitophagy.
First release: 15 October 2020
Investigation of druggable targets identified as interactors of multiple coronaviruses
The identification of druggable host factors provides a rationale for drug repurposing efforts. Given the extent of the
current pandemic, real-world data can now be used to study
the outcome of COVID-19 patients coincidentally treated with
host factor-directed, FDA-approved therapeutics. Using medical billing data, we identified 738,933 patients in the United
States with documented SARS-CoV-2 infection (Methods). In
this cohort, we probed the use of drugs against targets identified here that were shared across coronavirus strains and
found to be functionally relevant in the genetic perturbation
screens. In particular, we analyzed outcomes for an inhibitor
of prostaglandin E synthase type 2 (PGES-2, encoded by
PTGES2) and for potential ligands of sigma non-opioid receptor 1 (sigma-1, encoded by SIGMAR1), and asked whether
these patients fared better than carefully-matched patients
treated with clinically-similar drugs that do not act on coronavirus host factors.
PGES-2, an interactor of Nsp7 from all three viruses (Fig.
4D), is a dependency factor for SARS-CoV-2 (Fig. 5F). It is inhibited by the FDA-approved prescription nonsteroidal antiinflammatory drug (NSAID) indomethacin. Computational
docking of Nsp7 and PGES-2 to predict binding configuration
showed that the dominant cluster of models localizes Nsp7
adjacent to the PGES-2-indomethacin binding site (fig. S23).
However, indomethacin did not inhibit SARS-CoV-2 in vitro
at reasonable antiviral concentrations (fig. S24 and table
S10). A previous study also found that similarly high levels of
the drug were needed for inhibition of SARS-CoV-1 in vitro,
but still showed efficacy for indomethacin against canine
coronavirus in vivo (24). This motivated us to observe outcomes in a cohort of outpatients with confirmed SARS-CoV-2
infection who by happenstance initiated a course of indomethacin, as compared to those who initiated the prescription NSAID celecoxib, which lacks anti-PGES-2 activity. We
compared the odds of hospitalization by risk-set sampling
(RSS) patients treated at the same time and at similar levels
of disease severity and then further matching on propensity
score (PS) (25) (Fig. 9A and table S11). RSS and PS, combined
with a new user, active comparator design that mimics the
interventional component of parallel group randomized studies, are established design and analytic techniques that mitigate biases that can arise in observational studies. A complete
list of risk factors used for matching, which include demographic data, baseline healthcare utilization, comorbidities
and measures of disease severity, are found in table S11.
Among SARS-CoV-2-positive patients, new users of indomethacin in the outpatient setting were less likely than
matched new users of celecoxib to require hospitalization or
inpatient services (Fig. 9B; Odds Ratio (OR) = 0.33, 95% Confidence Interval (CI) 0.03-3.19). The confidence interval of our
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SARS-CoV-2 viral replication in A549-ACE2 cells (Fig. 8C).
These data suggest that the Orf8-IL17RA interaction modulates systemic IL-17 signaling.
One manner in which this signaling is regulated is
through the release of the extracellular domain of the receptor as soluble IL17RA (sIL17RA), which acts as a decoy in circulation by soaking up IL-17A and inhibiting IL-17 signaling
(21). Production of sIL17RA has been demonstrated by alternative splicing in cultured cells (22), but the mechanism by
which IL17RA is shed in vivo remains unclear (23). ADAM
family proteases are known to mediate the release of other
interleukin receptors into their soluble form (24). We found
that SARS-CoV-2 Orf8 physically interacted with both
ADAM9 and ADAMTS1 in our previous study (5). We find that
knockdown of ADAM9, like that of IL17RA, leads to significant decreases in SARS-CoV-2 replication in A549-ACE2 cells
(Fig. 5D and table S2).
In order to test the in vivo relevance of sIL17RA in modulating SARS-CoV-2 infection, we leveraged a genome-wide association study (GWAS) which identified 14 single nucleotide
polymorphisms (SNPs) near the IL17RA gene that causally
regulate sIL17RA plasma levels (25). We then used generalized summary-based Mendelian randomization (GSMR) (25,
26) on the curated GWAS datasets of the COVID-19 Host Genetics Initiative (COVID-HGI) (27) and observed that genotypes that predicted higher sIL17RA plasma levels were
associated with lower risk of COVID-19 when compared to
the population (Fig. 8D and table S9), seemingly consistent
with our molecular data. Similar results were obtained when
comparing only hospitalized COVID-19 patients to the population. However, there was no evidence of association in hospitalized versus non-hospitalized COVID-19 patients. Though
the COVID-HGI dataset is underpowered and this observation needs to be replicated in other cohorts, the clinical observations, functional genetics and clinical genetics all
suggest that SARS-CoV-2 benefits from modulating IL-17 signaling. One potentially contradictory caveat is that we find
high-level IL-17A treatment diminishes SARS-CoV-2 replication in A549-ACE2 cells (fig. S22), however IL-17 is a pleiotropic cytokine and it is likely to play multiple roles during
SARS-CoV-2 infection in the context of a competent immune
system.
Interestingly, infectious and transmissible SARS-CoV-2 viruses with large deletions of Orf8 have arisen during the pandemic and have been associated with milder disease and
lower concentrations of pro-inflammatory cytokines (20). Notably, compared to healthy controls, patients infected with
wildtype, but not Orf8-deleted virus, had three-fold elevated
plasma levels of IL-17A (20). More work will be needed to understand if and how Orf8 manipulates the IL-17 signaling
pathway during the course of SARS-CoV-2 infection.
First release: 15 October 2020
Observing mechanical ventilation outcomes in inpatient cohorts is a proxy for worsening of severe illness, rather than
the progression from mild disease signified by the hospitalization of indomethacin-exposed outpatients above. We again
employed RSS plus PS to build a robust, directly comparable
cohort of inpatients (table S11). In our primary analysis, half
as many new users of the sigma-ligand typical antipsychotics
compared to new users of atypical antipsychotics progressed
to the point of requiring mechanical ventilation, demonstrating significantly lower use with an odds ratio (OR) of 0.46
(95% CI = 0.23-0.93, p = 0.03, Fig. 9D). As above, we conducted a sensitivity analysis in the RSS-only cohort and observed the same trend (OR = 0.56, 95% CI = 0.31-1.02, p =
0.06), emphasizing the primary result of a beneficial effect
for typical versus atypical antipsychotics observed in the RSSplus-PS-matched cohort. Although a careful analysis of the
relative benefits and risks of typical antipsychotics should be
undertaken before considering prospective studies or interventions, these data and analysis demonstrate how molecular
information can be translated into real-world implications
for the treatment of COVID-19, an approach that can ultimately be applied to other diseases in the future.
Discussion
In this study, we generated and compared three different
coronavirus-human protein-protein interaction maps in an
attempt to identify and understand pan-coronavirus molecular mechanisms. The use of a quantitative differential interaction scoring (DIS) approach permitted the identification of
virus-specific as well as shared interactions among distinct
coronaviruses. We also systematically carried out subcellular
localization analysis using tagged viral proteins as well as antibodies targeting specific SARS-CoV-2 proteins. Our results
suggest that protein localization can often differ when comparing individually-expressed viral proteins with the localization of the same protein in the context of infection. This can
be due to factors such as miss-location driven by tagging,
changes in localization due to interaction partners, or cellular
compartments that are specific to the infection state. These
differences are important caveats of viral-host interaction
studies performed by tagged expressed proteins. However,
previous studies and the work performed here shows how
these data can be very powerful for the identification of host
targeted processes and relevant drug targets.
These data were integrated with genetic data where the
interactions uncovered with SARS-CoV-2 were perturbed using RNAi and CRISPR in different cellular systems and viral
assays, an effort that functionally connected many host factors to infection. One of these, Tom70, which we have shown
binds to Orf9b from both SARS-CoV-1 and SARS-CoV-2, is a
mitochondrial outer membrane translocase that has been
previously shown to be important for mounting an interferon
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primary analysis included the null value. In sensitivity analyses, neither using the larger, risk-set-sampled cohort nor relaxing our outcome definition to include any hospital visit
appreciably changed the interpretation of our findings, but it
did narrow the confidence intervals, particularly when both
approaches were combined (OR = 0.25, 95% CI 0.08-0.76).
While it is important to acknowledge that this is a small, noninterventional study, it is nonetheless a powerful example of
how molecular insight can rapidly generate testable clinical
hypotheses and help prioritize candidates for prospective
clinical trials or future drug development.
To create larger patient cohorts, we next grouped drugs
that shared activity against the same target, sigma receptors.
We previously identified sigma-1 and sigma-2 as drug targets
in our SARS-CoV-2-human protein-protein interaction map
and multiple potent, non-selective sigma ligands were among
the most promising inhibitors of SARS-CoV-2 replication in
Vero E6 cells (5). As shown above, knockout and knockdown
of SIGMAR1, but not SIGMAR2 (also known as TMEM97), led
to robust decreases in SARS-CoV-2 replication (fig. S24 and
Fig. 5F), suggesting that sigma-1 may be a key therapeutic target. We analyzed SIGMAR1 sequences across 359 mammals
and observed positive selection of several residues within
beaked whale, mouse, and ruminant lineages, which may indicate a role in host-pathogen competition (fig. S25). Additionally, the sigma ligand drug amiodarone inhibited
replication of SARS-CoV-1 as well as SARS-CoV-2, consistent
with the conservation of the Nsp6-sigma-1 interaction across
the SARS viruses (fig. S24 and Fig. 4D). We then looked for
other FDA-approved drugs with reported nanomolar affinity
for sigma receptors or that fit the sigma ligand chemotype (5,
26–33) and selected 13 such therapeutics. We find that all are
potent inhibitors of SARS-CoV-2 with IC50 values under 10
μM, though it is important to note there is a wide range in
sigma receptor affinity with no clear correlation between
sigma receptor binding affinity and antiviral activity (fig.
S24D). Several clinical drug classes were represented by more
than one candidate, including typical antipsychotics and antihistamines. Over-the-counter antihistamines are not well
represented in medical billing data and are therefore poor
candidates for real-world analysis, but users of typical antipsychotics can be easily identified in our patient cohort. By
grouping these individual drug candidates by clinical indication, we were able to build a better-powered comparison.
We constructed a cohort for retrospective analysis on new,
inpatient users of antipsychotics. In inpatient settings, typical and atypical antipsychotics are used similarly, most commonly for delirium. We compared the effectiveness of typical
antipsychotics, which have sigma activity and antiviral effects
(fig. S24E), versus atypical antipsychotics, which are not predicted to bind sigma receptors and do not have antiviral activity (fig. S24F), for treatment of COVID-19 (Fig. 9C).
First release: 15 October 2020
Materials and Methods
Cells
HEK293T/17 (HEK293T) cells were procured from the UCSF
Cell Culture Facility, and are available through UCSF's Cell
and Genome Engineering Core (https://cgec.ucsf.edu/cellculture-and-banking-services). HEK293T cells were cultured
in Dulbecco’s Modified Eagle’s Medium (DMEM) (Corning)
supplemented with 10% Fetal Bovine Serum (FBS) (Gibco,
Life Technologies) and 1% Penicillin-Streptomycin (Corning)
and maintained at 37°C in a humidified atmosphere of 5%
CO2. STR analysis by the Berkeley Cell Culture Facility on August 8, 2017 authenticates these as HEK293T cells with 94%
probability.
HeLaM cells (RRID: CVCL_R965) were originally obtained from the laboratory of M. S. Robinson (CIMR, University of Cambridge, UK) and routinely tested for mycoplasma
contamination. HeLaM cells were grown in DMEM supplemented with 10% FBS, 100 U/ml penicillin, 100 μg/ml streptomycin and 2 mM glutamine at 37°C in a 5% CO2 humidified
incubator.
A549 cells stably expressing ACE2 (A549-ACE2) were a
kind gift from Dr. Olivier Schwartz. A549-ACE2 cells were
cultured in DMEM supplemented with 10% FBS, blasticidin
(20 μg/ml, Sigma) and maintained at 37°C with 5% CO2. STR
analysis by the Berkeley Cell Culture Facility on July 17, 2020
authenticates these as A549 cells with 100% probability.
Caco-2 cells (ATTC, HTB-37, RRID:CVCL_0025) were cultured in DMEM with GlutaMAX and pyruvate (Gibco,
10569010) and supplemented with 20% FBS (Gibco,
26140079). For Caco-2 cells utilized in Cas9-RNP knockouts,
STR analysis by the Berkeley Cell Culture Facility on April 23,
2020 authenticates these as Caco-2 cells with 100% probability.
Vero E6 cells were purchased from ATCC and thus authenticated (VERO C1008 [Vero 76, clone E6, Vero E6] (ATCC,
CRL-1586). Vero E6 cells tested negative for mycoplasma contamination. Vero E6 cells were cultured in DMEM (Corning)
supplemented with 10% Fetal Bovine Serum (FBS) (Gibco,
Life Technologies) and 1% Penicillin-Streptomycin (Corning)
and maintained at 37°C in a humidified atmosphere of 5%
CO2.
Microbes
LOBSTER E. coli Expression Strain: LOBSTR-(BL21(DE3))
Kerafast # EC1002
Antibodies
Commercially available primary antibodies used in this
study:
rabbit anti-beta-Actin (Cell Signaling Technology #4967,
RRID:AB_330288); mouse anti-beta Tubulin (Sigma-Aldrich
#T8328, RRID:AB_1844090); rabbit anti-BiP (Cell Signaling
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response (34). Our functional data, however, shows that
Tom70 has at least some role in promoting infection rather
than inhibiting it. Using cryoEM, we obtained a 3 Å structure
of a region of Orf9b binding to the active site of Tom70. Remarkably, we find that Orf9b is in a drastically different conformation than previously visualized. This offers the
possibility that Orf9b may partition between two distinct
structural states in the cells, with each possessing a different
function and possibly explaining its potential functional pleiotropy. The exact details of functional significance and regulation of the Orf9b-Tom70 interaction await further
experimental elucidation. This interaction, however, which is
conserved between SARS-CoV-1 and SARS-CoV-2, could have
value as a pan-coronavirus therapeutic target.
Finally, we attempted to connect our in vitro molecular
data to clinical information available for COVID-19 patients
to understand the pathophysiology of COVID-19 and explore
new therapeutic avenues. To this end, using GWAS datasets
of the COVID-19 Host Genetics Initiative (35), we observed
that increased predicted sIL17RA plasma levels were associated with lower risk of COVID-19. Interestingly, we find that
IL17RA physically binds to SARS-CoV-2 Orf8 and genetic disruption results in decreased infection. These collective data
suggest that future studies should be focused on this pathway
as both an indicator and therapeutic target for COVID-19.
Furthermore, using medical billing data, we also observed
trends in COVID-19 patients on specific drugs indicated by
our molecular studies. For example, inpatients prescribed
sigma-ligand typical antipsychotics seemingly have better
COVID-19 outcomes when compared to users of atypical antipsychotics, which do not bind to sigma-1. We cannot be certain that sigma receptor interaction is the mechanism
underpinning this effect, as typical antipsychotics are known
to bind to a multitude of cellular targets. Replication in other
patient cohorts and further work will be needed to see if there
is therapeutic value in these connections, but at the very least
we have demonstrated a strategy wherein protein network
analyses can be used to make testable predictions from realworld, clinical information.
Overall, we have described an integrative and collaborative approach to study and understand pathogenic coronavirus infection, identifying conserved targeted mechanisms
that are likely to be of high relevance for other viruses of this
family, some of which have yet to infect humans. We used
proteomics, cell biology, virology, genetics, structural biology,
biochemistry and clinical and genomic information in an attempt to provide a holistic view of SARS-CoV-2 and other
coronaviruses’ interactions with infected host cells. We propose that such an integrative and collaborative approach
could and should be used to study other infectious agents as
well as other disease areas.
Commercially available secondary antibodies used in this
study:
Alexa Fluor 488 chicken anti-mouse IgG (Invitrogen #A21200,
RRID_AB_2535786, used at 1:400); Alexa Fluor 488 chicken
anti-rabbit IgG (Invitrogen #A21441, RRID_AB_10563745,
used at 1:400); Alexa Fluor 568 donkey anti-sheep IgG (Invitrogen #A21099, RRID_AB_10055702, used at 1:400); Alexa
Fluor Plus 488 goat anti-rabbit (ThermoFisher A32731, used
at 1:500); Alexa Fluor Plus 594 goat anti-mouse (ThermoFisher A32742, used at 1:500); goat anti-mouse IgG-HRP
(BioRad #170-6516, RRID:AB_11125547, used at 1:20000)
Non-commercial antisera
Rabbit anti-SARS-CoV-2-NP antiserum was produced by the
Garcia-Sastre lab and used at 1:10000; for information on polyclonal sheep antibodies targeting SARS-CoV-2 proteins, see
below, table S3 and https://mrcppu-covid.bio/.
Coronavirus annotation and plasmid cloning
SARS-CoV-1 isolate Tor2 (NC_004718) and MERS-CoV
(NC_019843) were downloaded from GenBank and utilized
to design 2x-Strep tagged expression constructs of open reading frames (Orfs) and proteolytically mature nonstructural
proteins (Nsps) derived from Orf1ab (with N-terminal methionines and stop codons added as necessary). Protein termini
were analyzed for predicted acylation motifs, signal peptides,
and transmembrane regions, and either the N- or C terminus
was chosen for tagging as appropriate. Finally, reading
frames were codon optimized and cloned into pLVXEF1alpha-IRES-Puro (Takara/Clontech) including a 5′ Kozak
motif.
First release: 15 October 2020
Immunofluorescence Microscopy of Viral Protein Constructs
Approximately 60,000 HeLaM cells were seeded onto glass
coverslips in a 12-well dish and grown overnight. The cells
were transfected using 0.5 μg of plasmid DNA and either polyethylenimine (Polysciences) or Fugene HD (Promega; 1 part
DNA to 3 parts transfection reagent) and grown for a further
16 hours.
Transfected cells were fixed with 4% paraformaldehyde
(Polysciences) in PBS at room temperature for 15 min. The
fixative was removed and quenched using 0.1 M glycine in
PBS. The cells were permeabilized using 0.1% saponin in PBS
containing 10% FBS. The cells were stained with the indicated primary and secondary antibodies for 1 hour at room
temperature. The coverslips were mounted onto microscope
slides using ProLong Gold antifade reagent (ThermoFisher)
and imaged using a UplanApo 60x oil (NA 1.4) immersion objective on a Olympus BX61 motorized wide-field epifluorescence microscope. Images were captured using a Hamamatsu
Orca monochrome camera and processed using ImageJ.
To gain insight into the intracellular distribution of each
Strep-tagged construct, approximately 100 cells per transfection were manually scored. Each construct was assigned an
intracellular distribution in relation to the plasma membrane, endoplasmic reticulum, Golgi, cytoplasm and mitochondria (scored out of 7). In several instances the viral
proteins were observed on membranes which did not fit any
of the basic categories so were defined as being localized on
undefined membranes. Many of the constructs had several
localizations so this was also reflected in the scoring. The
scoring also took into account the impact of expression level
on the localization of the constructs.
Meta Analysis of immunofluorescence data
We first sorted the data concerning viral protein location for
all Strep-tagged viral proteins expressed individually in three
heatmaps (one per virus) using a custom R script
(“pheatmap” package). The information concerning protein
localization during SARS-CoV-2 infection was added as a
square border color code in the first heatmap, to compare the
two different localization patterns. In order to compare the
predicted versus the experimentally determined locations, for
each protein we took the top scoring sequence based localization prediction from DeepLoc (36) if the score was bigger
than 1. When more than one localization can be assigned to
the same protein, we took as many top scoring ones as experimentally assigned localizations we had for the same protein.
Finally, for each cell compartment, we count the number of
experimentally assigned viral proteins and the subset of them
predicted to that same compartment as “correct predictions”.
To compare changes in protein interactions with changes in
protein localization (Strep-tagged experiment versus
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Technology #3177S, RRID:AB_2119845); mouse anti-EEA1
(BD Biosciences #610457, RRID:AB_397830, used at 1:200);
mouse anti-ERGIC53 (Enzo Life Sciences #ALX-804-602C100, RRID:AB_2051363, used at 1:200); anti-GM130; rabbit
anti-GRP78 BiP (Abcam #Ab21685, RRID:AB_2119834); rabbit anti-SARS-CoV-Nucleocapsid Protein (Rockland #200401-A50, RRID:AB_828403); rabbit anti-PDI (Cell Signaling
Technology #3501, RRID:AB_2156433); mouse anti-Strep tag
(QIAGEN #34850, RRID:AB_2810987, used at 1:5000); Mouse
anti-strepMAB (IBA Lifesciences #2-1507-001, used at 1:1000);
rabbit anti-Strep-tag II (Abcam #ab232586); rabbit antiTom20 (Proteintech #11802-1-AP, RRID:AB_2207530, used at
1:1000); rabbit anti-Tom20 (Cell Signaling Technology
#42406, RRID:AB_2687663); mouse anti-Tom22 (Santa Cruz
Biotechnology #sc-101286, RRID:AB_1130526); rabbit antiTom40
(Santa
Cruz
Biotechnology
#sc-11414,
RRID:AB_793274); mouse anti-Tom70 (Santa Cruz #sc390545, RRID:AB_2714192, used at 1:500); Rabbit anti-STX5
(Synaptic Systems 110 053, used at 1:500); ActinStaining Kit
647-Phalloidin (Hypernol #8817-01, used at 1:400)
sequence-based prediction), we calculated the Jaccard index
of prey overlap for each viral protein (SARS-CoV-2 vs. SARSCoV-1 and SARS-CoV-2 vs. MERS-CoV) and plotted them together, for proteins with the same localization and for proteins with different localization.
Immunofluorescence Microscopy of Infected Caco-2
cells
For infection experiments in human colon epithelial Caco-2
cells (ATCC, HTB-37), SARS-CoV-2 isolate Muc-IMB-1, kindly
provided by the Bundeswehr Institute of Microbiology, Munich, Germany, was used. SARS-CoV-2 was propagated in
Vero E6 cells in DMEM supplemented with 2% FBS. All work
involving live SARS-CoV-2 was performed in the BSL3 facility
of the Institute of Virology, University Hospital Freiburg, and
was approved according to the German Act of Genetic Engineering by the local authority (Regierungspraesidium
Tuebingen, permit UNI.FRK.05.16/05).
Caco-2 human colon epithelial cells seeded on glass coverslips were infected with SARS-CoV-2 (Strain Muc-IMB1/2020, second passage on Vero E6 cells (2x106 PFU/ml)) at
an MOI of 0.1. At 24 hours post-infection, cells were washed
with PBS and fixed in 4% paraformaldehyde in PBS for 20
min at room temperature, followed by 5 min of quenching in
0.1 M glycine in PBS at room temperature. Cells were permeabilized and blocked in 0.1% saponin in PBS supplemented
with 10% fetal calf serum for 45 min at room temperature and
incubated with primary antibodies for 1 hour at room temperature. After washing 15 min with blocking solution,
AF568-labeled donkey-anti-sheep (Invitrogen, #A21099;
1:400) secondary antibody as well as AF4647-labeled Phalloidin (Hypermol, #8817-01, 1:400) were applied for 1 hour at
room temperature. Subsequent washing was followed by embedding in Diamond Antifade Mountant with DAPI (ThermoFisher, #P36971). Fluorescence images were generated
using a LSM800 confocal laser-scanning microscope (Zeiss)
equipped with a 63X, 1.4 NA oil objective and Airyscan detector and the Zen blue software (Zeiss) and processed with Zen
First release: 15 October 2020
Transfection and cell harvest for immunoprecipitation
experiments
For each affinity purification (SARS-CoV-1 baits, MERS-CoV
baits, GFP-2xStrep or empty vector controls), ten million
HEK293T cells were transfected with up to 15 μg of individual
expression constructs using PolyJet transfection reagent
(SignaGen Laboratories) at a 1:3 μg:μl ratio of plasmid to
transfection reagent based on manufacturer’s protocol. After
more than 38 hours, cells were dissociated at room temperature using 10 ml PBS without calcium and magnesium (DPBS) with 10 mM EDTA for at least 5 min, pelleted by centrifugation at 200xg, at 4°C for 5 min, washed with 10 ml D-PBS,
pelleted once more and frozen on dry ice before storage at 80°C for later immunoprecipitation analysis. For each bait,
three independent biological replicates were prepared.
Whole cell lysates were resolved on 4%–20% Criterion
SDS-PAGE gels (Bio-Rad Laboratories) to assess Strep-tagged
protein expression by immunoblotting using mouse antiStrep tag antibody 34850 (QIAGEN) and anti-mouse HRP
secondary antibody (BioRad).
Anti-Strep-Tag affinity purification
Frozen cell pellets were thawed on ice for 15-20 min and suspended in 1 ml Lysis Buffer [IP Buffer (50 mM Tris-HCl, pH
7.4 at 4°C, 150 mM NaCl, 1 mM EDTA) supplemented with
0.5% Nonidet P 40 Substitute (NP-40; Fluka Analytical) and
cOmplete mini EDTA-free protease and PhosSTOP phosphatase inhibitor cocktails (Roche)]. Samples were then freezefractured by refreezing on dry ice for 10-20 min, then rethawed and incubated on a tube rotator for 30 min at 4°C.
Debris was pelleted by centrifugation at 13,000xg, at 4°C for
15 min. Up to 56 samples were arrayed into a 96-well
Deepwell plate for affinity purification on the KingFisher
Flex Purification System (Thermo Scientific) as follows: MagStrep “type3” beads (30 μl; IBA Lifesciences) were equilibrated twice with 1 ml Wash Buffer (IP Buffer supplemented
with 0.05% NP-40) and incubated with 0.95 ml lysate for 2
hours. Beads were washed three times with 1 ml Wash Buffer
and then once with 1 ml IP Buffer. Beads were released into
75 μl Denaturation-Reduction Buffer (2 M urea, 50 mM TrisHCl pH 8.0, 1 mM DTT) in advance of on-bead digestion. All
automated protocol steps were performed at 4°C using the
slow mix speed and the following mix times: 30 s for equilibration/wash steps, 2 hours for binding, and 1 min for final
bead release. Three 10 s bead collection times were used between all steps.
On-bead digestion for affinity purification
Bead-bound proteins were denatured and reduced at 37°C for
30 min, alkylated in the dark with 3 mM iodoacetamide for
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Generation of polyclonal sheep antibodies targeting
SARS-CoV-2 proteins
Sheep were immunized with individual N-terminal GSTtagged SARS-CoV-2 recombinant proteins or N-terminal
MBP-tagged proteins (for SARS-CoV-2 S, S-RBD and Orf7a),
followed by up to 5 booster injections four weeks apart from
each other. Sheep were subsequently bled and IgGs were affinity purified using the specific recombinant N-terminal
maltose binding protein (MBP)-tagged viral proteins. Each
antiserum specifically recognized the appropriate native viral
protein. Characterisation of each antibody can be found at
https://mrcppu-covid.bio/. All antibodies generated can be
requested at https://mrcppu-covid.bio/. Also see table S3.
blue software and ImageJ/Fiji.
45 min at room temperature, and quenched with 3 mM DTT
for 10 min. To offset evaporation, 22.5 μl 50 mM Tris-HCl, pH
8.0 were added prior to trypsin digestion. Proteins were then
incubated at 37°C, initially for 4 hours with 1.5 μl trypsin (0.5
μg/μl; Promega) and then another 1-2 hours with 0.5 μl additional trypsin. All steps were performed with constant shaking at 1,100 rpm on a ThermoMixer C incubator. Resulting
peptides were combined with 50 μl 50 mM Tris-HCl, pH 8.0
used to rinse beads and acidified with trifluoroacetic acid
(0.5% final, pH < 2.0). Acidified peptides were desalted for
MS analysis using a BioPureSPE Mini 96-Well Plate (20 mg
PROTO 300 C18; The Nest Group, Inc.) according to standard
protocols.
High-confidence protein interaction scoring
Identified proteins were then subjected to protein-protein interaction scoring with both SAINTexpress (version 3.6.3) and
MiST (https://github.com/kroganlab/mist) (6, 7). We applied
a two-step filtering strategy to determine the final list of reported interactors, which relied on two different scoring
stringency cut-offs. In the first step, we chose all protein interactions that had a MiST score ≥ 0.7, a SAINTexpress Bayesian false-discovery rate (BFDR) ≤ 0.05 and an average
spectral count ≥ 2. For all proteins that fulfilled these criteria,
we extracted information about the stable protein complexes
First release: 15 October 2020
Hierarchical clustering of virus-human protein interactions
Hierarchical clustering was performed on interactions for (1)
viral bait proteins shared across all three viruses (LIST) and
(2) passed the high-confidence scoring criteria (MiST score ≥
0.6, SAINTexpress BFDR ≤ 0.05 and average spectral counts
≥ 2) in at least one virus. We clustered using a new Interaction Score (K), which we defined as the average between the
MiST and Saint score for each virus-human interaction. This
was done to provide a single score that captured the benefits
from each scoring method. Clustering was performed using
the ComplexHeatmap package in R, using the “average” clustering method and “euclidean” distance metric. K-means
clustering (k=7) was applied to capture all possible combinations of interaction patterns between viruses.
Gene ontology enrichment analysis on clusters
Sets of genes found in 7 clusters were tested for enrichment
of Gene Ontology (GO) terms, which was performed using the
enricher function of clusterProfiler package in R (41). The GO
terms were obtained from the C5 collection of Molecular Signature Database (MSigDBv7.1) and include Biological Process, Cellular Component, and Molecular Function
ontologies. Significant GO terms were identified (adjusted pvalue < 0.05) and further refined to select non-redundant
terms. To select non-redundant gene sets, we first
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Mass spectrometry operation and peptide search
Samples were re-suspended in 4% formic acid, 2% acetonitrile solution, and separated by a reversed-phase gradient
over a nanoflow C18 column (Dr. Maisch). HPLC buffer A was
comprised of 0.1% formic acid, and HPLC buffer B was comprised of 80% acetonitrile in 0.1% formic acid. Peptides were
eluted by a linear gradient from 7 to 36% B over the course of
52 min, after which the column was washed with 95% B, and
re-equilibrated at 2% B. Each sample was directly injected via
a Easy-nLC 1200 (Thermo Fisher Scientific) into a Q-Exactive
Plus mass spectrometer (Thermo Fisher Scientific) and analyzed with a 75 min acquisition, with all MS1 and MS2 spectra
collected in the orbitrap; data were acquired using the
Thermo software Xcalibur (4.2.47) and Tune (2.11 QF1 Build
3006). For all acquisitions, QCloud was used to control instrument longitudinal performance during the project (37).
All proteomic data was searched against the human proteome (uniprot reviewed sequences downloaded February
28th, 2020), EGFP sequence, and the SARS-CoV or MERS protein sequences using the default settings for MaxQuant (version 1.6.12.0) (38). Detected peptides and proteins were
filtered to 1% false discovery rate in MaxQuant. All MS raw
data and search results files have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository
with the dataset (identifier PXD PXD021588, Username: reviewer_pxd021588@ebi.ac.uk, password: B5Ho3HES).
that they participated in from the CORUM (39) database of
known protein complexes. In the second step, we then relaxed the stringency and recovered additional interactors
that (1) formed complexes with interactors determined in filtering step 1 and (2) fulfilled the following criteria: MiST
score ≥ 0.6, SAINTexpress BFDR ≤ 0.05 and average spectral
counts ≥ 2. Proteins that fulfilled filtering criteria in either
step 1 or step 2 were considered to be high-confidence protein–protein interactions (HC-PPIs).
Using this filtering criteria, nearly all of our baits recovered a number of HC-PPIs in close alignment with previous
datasets reporting an average of around 6 PPIs per bait (40).
However, for a subset of baits, we observed a much higher
number of PPIs that passed these filtering criteria. For these
baits, the MiST scoring was instead performed using a larger
in-house database of 87 baits that were prepared and processed in an analogous manner to this SARS-CoV-2 dataset.
This was done to provide a more comprehensive collection of
baits for comparison, to minimize the classification of nonspecifically binding background proteins as HC-PPIs. This
was performed for SARS-CoV-1 baits (M, Nsp12, Nsp13, Nsp8,
and Orf7b), MERS-CoV baits (Nsp13, Nsp2, and Orf4a), and
SARS-CoV-2 Nsp16. SARS-CoV-2 Nsp16 MiST was scored using the in-house database as well as all previous SARS-CoV-2
data (5).
constructed a GO term tree based on distances (1 - Jaccard
Similarity Coefficients of shared genes) between the significant terms. The GO term tree was cut at a specific level (h =
0.99) to identify clusters of non-redundant gene sets. For results with multiple significant terms belonging to the same
cluster, we selected the term with the lowest adjusted p-value.
Sequence similarity analysis
Protein sequence similarity was assessed by comparing the
protein sequences from SARS-CoV-1 and MERS-CoV to SARSCoV-2 for orthologous viral bait proteins. The corresponding
protein-protein interaction similarity was represented by a
Jaccard index, using the high-confidence interactomes for
each virus.
Orthologous versus non-orthologous interactions analysis
For a given pair of viruses, we identified all pairs of baits that
share interactors and categorized these into “orthologous”
and “non-orthologous” groups based on whether the two
baits were orthologs or not. We then summed up the total
number of shared interactors in each group to calculate the
corresponding fractions. This was performed for all pairwise
combinations of the three viruses.
Structural modeling and comparison of MERS-CoV
Orf4a and SARS-CoV-2 Nsp8
To obtain a sensitive sequence comparison between MERSCoV Orf4a and SARS-CoV-2 Nsp8, we took into consideration
their homologs. We first searched for homologs of these proteins in the UniRef30 database using hhblits (1 iteration, Evalue cutoff 1e-3) (42). Subsequently, the resulting alignments
were filtered to include only sequences with at least 80% coverage to the corresponding query sequence, and hidden Markov models (HMMs) were created using hhmake. Finally, the
HMMs of Orf4a andNsp8 homologs were locally aligned using hhalign. The structure of Orf4a was predicted de novo
using trRosetta (43). To provide greater coverage than that
provided by experimental structures, SARS-CoV-2 Nsp8 was
modeled using the structure of its SARS-CoV homolog as template (PDB: 2AHM) (44) using SWISS-MODEL (45). To search
for local structural similarities between Orf4a and Nsp8, we
used Geometricus, a structure embedding tool based on 3D
rotation invariant moments (46). This generates so-called
First release: 15 October 2020
Differential interaction score (DIS) analysis
We computed a differential interaction score (DIS) for interactions that (1) originated from viral bait proteins shared
across all three viruses and (2) passed the high-confidence
scoring criteria (MiST score ≥ 0.6, SAINTexpress BFDR ≤ 0.05
and average spectral counts ≥ 2) in at least one virus. We defined the DIS to be the difference between the interaction
scores (K) from each virus. DIS near 0 indicates that the interaction is confidently shared between the two viruses being
compared, while a DIS near -1 or +1 indicates that the host
protein interaction is specific for one virus or the other. We
computed a fourth DIS (SARS-MERS) by averaging K from
SARS-CoV-1 and SARS-CoV-2 prior to calculating the difference with MERS-CoV. Here, a DIS near +1 indicates SARSspecific interactions (shared between SARS-CoV-1 and SARSCoV-2 but absent in MERS-CoV), a DIS near -1 indicates
MERS-specific interactions (present in MERS-CoV and absent or lowly confident in both SARS-CoVs), and a DIS near
0 indicates interactions shared between all three viruses.
For each pairwise virus comparison, as well as the SARSMERS comparison, DIS was defined based on cluster membership of interactions (Fig. 3A). For the SARS2-SARS1 comparison, interactions from every cluster except 5 were used,
as those interactions are considered absent from both SARSCoV-2 and SARS-CoV-1. For the SARS2-MERS comparison, interactions from all clusters except 3 were used. For the
SARS1-MERS comparison, interactions from all clusters except 6 were used. For the SARS-MERS comparison, only interactions from clusters 2, 4, and 5 were used.
Network generation and visualization
Protein-protein interaction networks were generated in Cytoscape (48) and subsequently annotated using Adobe Illustrator. Host-host physical interactions, protein complex
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Gene ontology enrichment and PPI similarity analysis
The high-confidence interactors of the three viruses were
tested for enrichment of GO terms as described above. We
then identified GO terms that are significantly enriched (adjusted p-value < 0.05) in all 3 viruses. For each enriched term,
we generated the list of its associated genes and computed
the Jaccard Index of pairwise comparisons of 3 viruses.
shape-mers, analogous to sequence k-mers. The structures
were fragmented into overlapping k-mers based on the sequence (k=20) and into overlapping spheres surrounding
each residue (radius=15 Å). To ensure that the similarities
found between these distinct structures were significant, we
used a high resolution of 7 to define the shape-mers. This resulted in the identification of 4 different shape-mers common
to Orf4a and Nsp8. We aligned the entire Orf4a structure
with residues 96 to 191 of the Nsp8 structure (i.e., after removal of the long N-terminal helix) using the Caretta structural alignment algorithm detailed by (47), using 3D rotation
invariant moments (Durairaj et al. 2020) for initial superposition. We optimized parameters to maximize the Caretta
score. The resulting alignment used k = 30, radius = 16 Å, gap
open penalty = 0.05, gap extend penalty = 0.005, and had an
root-mean-square deviation (RMSD) of 7.6 Å across 66 aligning residues.
definitions, and biological process groupings were derived
from CORUM (39), Gene Ontology (biological process), and
manually curated from literature sources. All networks were
deposited in NDEx (49).
Viral infection and quantification assay in A549-ACE2
cells
Cells seeded in a 96-well format were inoculated with a SARSCoV-2 stock (BetaCoV/France/IDF0372/2020 strain, generated and propagated once in Vero E6 cells and a kind gift
from the National Reference Centre for Respiratory Viruses
at Institut Pasteur, Paris, originally supplied through the European Virus Archive goes Global platform) at a MOI of 0.1
PFU per cell. Following a one hour incubation period at 37°C,
the virus inoculum was removed, and replaced by DMEM
containing 2% FBS (Gibco, Thermo Fisher). 72 hours post-infection the cell culture supernatant was collected, heat inactivated at 95°C for 5 min and used for RT-qPCR analysis to
quantify viral genomes present in the supernatant. Briefly,
SARS-CoV-2 specific primers targeting the N gene region: 5′TAATCAGACAAGGAACTGATTA-3′ (Forward) and 5′CGAAGGTGTGACTTCCATG-3′ (Reverse) (50) were used
with the Luna® Universal One-Step RT-qPCR Kit (New England Biolabs) in an Applied Biosystems QuantStudio 6 thermocycler, with the following cycling conditions: 55°C for 10
min, 95°C for 1 min, and 40 cycles of 95°C for 10 s, followed
by 60°C for 1 min. The number of viral genomes is expressed
First release: 15 October 2020
Knockdown validation with qRT-PCR in A549-ACE2
cells
Gene-specific quantitative PCR primers targeting all genes
represented in the OnTargetPlus library were purchased and
arrayed in a 96-well format identical to that of the siRNA library (IDT; table S13). A549-ACE2 cells treated with siRNA
were lysed using the Luna® Cell Ready Lysis Module (New
England Biolabs) following the manufacturer’s protocol. The
lysate was used directly for gene quantification by RT-qPCR
with the Luna® Universal One-Step RT-qPCR Kit (New England Biolabs), using the gene-specific PCR primers and
GAPDH as a housekeeping gene. The following cycling conditions were used in an Applied Biosystems QuantStudio 6
thermocycler: 55°C for 10 min, 95°C for 1 min, and 40 cycles
of 95°C for 10 s, followed by 60°C for 1 min. The fold change
in gene expression for each gene was derived using the 2−ΔΔCT,
2 (Delta Delta CT) method (51), normalized to the constitutively expressed housekeeping gene GAPDH. Relative
changes were generated comparing the control siRNA knockdown transfected cells to the cells transfected with each
siRNA.
sgRNA Selection for Cas9 knockout screen
sgRNAs were designed according to Synthego’s multi-guide
gene knockout (52). Briefly, two or three sgRNAs are bioinformatically designed to work in a cooperative manner to
generate small, knockout-causing, fragment deletions in early
exons (fig. S18). These fragment deletions are larger than
standard indels generated from single guides. The genomic
repair patterns from a multi-guide approach are highly predictable based on the guide-spacing and design constraints
to limit off-targets, resulting in a higher probability protein
knockout phenotype (table S14).
sgRNA Synthesis for Cas9 knockout screen
RNA oligonucleotides were chemically synthesized on
Synthego solid-phase synthesis platform, using CPG solid
support containing a universal linker. 5-Benzylthio-1H-tetrazole (BTT, 0.25 M solution in acetonitrile) was used for
coupling, (3-((Dimethylamino-methylidene)amino)-3H-1,2,4dithiazole-3-thione (DDTT, 0.1 M solution in pyridine)) was
used for thiolation, dichloroacetic acid (DCA, 3% solution in
toluene) was used for detritylation. Modified sgRNA were
chemically synthesized to contain 2’-O-methyl analogs and 3′
phosphorothioate nucleotide interlinkages in the terminal
three nucleotides at both 5′ and 3′ ends of the RNA molecule. After synthesis, oligonucleotides were subject to a series
of deprotection steps, followed by purification by solid phase
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siRNA library and transfection in A549-ACE2 cells
An OnTargetPlus siRNA SMARTpool library (Horizon Discovery) was purchased targeting 331 of the 332 human proteins previously identified to bind SARS-CoV-2 (5) (PDE4DIP
was not available for purchase and excluded from the assay).
This library was arrayed in 96-well format, with each plate
also including two non-targeting siRNAs and one siRNA pool
targeting ACE2 (table S12). The siRNA library was transfected
into A549 cells stably expressing ACE2 (A549-ACE2, kindly
provided by Dr. Olivier Schwartz), using Lipofectamine
RNAiMAX reagent (Thermo Fisher). Briefly, 6 pmoles of each
siRNA pool were mixed with 0.25 μl RNAiMAX transfection
reagent and OptiMEM (Thermo Fisher) in a total volume of
20 μl. After a 5 min incubation period, the transfection mix
was added to cells seeded in a 96-well format. 24 hours posttransfection, the cells were subjected to SARS-CoV-2 infection
as described in ‘Viral infection and quantification assay in
A549-ACE2 cells’, or incubated for 72 hours to assess cell viability using the CellTiter-Glo luminescent viability assay according to the manufacturer’s protocol (Promega).
Luminescence was measured in a Tecan Infinity 2000 plate
reader, and percentage viability calculated relative to untreated cells (100% viability) and cells lysed with 20% ethanol
or 4% formalin (0% viability), included in each experiment.
as PFU equivalents/ml, and was calculated by performing a
standard curve with RNA derived from a viral stock with a
known viral titer.
extraction (SPE). Purified oligonucleotides were analyzed by
ESI-MS.
Quantification of arrayed knockout efficiency
Two days post-nucleofection, genomic DNA was extracted
from cells using DNA QuickExtract (Lucigen, #QE09050).
Briefly, cells were lysed by removal of the spent media followed by addition of 40 μl of QuickExtract solution to each
well. Once the QuickExtract DNA Extraction Solution was
added, the cells were scraped off the plate into the buffer.
Following transfer to compatible plates, DNA extract was
then incubated at 68°C for 15 min followed by 95°C for 10
min in a thermocycler before being stored for downstream
analysis.
Amplicons for indel analysis were generated by PCR amplification with NEBNext polymerase (NEB, #M0541) or AmpliTaq Gold 360 polymerase (Thermo Fisher Scientific,
#4398881) according to the manufacturer’s protocol. The primers were designed to create amplicons between 400 - 800
bp, with both primers at least 100 bp distance from any of the
sgRNA target sites (table S15). PCR products were cleaned-up
and analyzed by Sanger sequencing (Genewiz). Sanger data
files and sgRNA target sequences were input into Inference
of CRISPR Edits (ICE) analysis (ice.synthego.com) to determine editing efficiency and to quantify generated indels (53).
Percentage of alleles edited is expressed as an ice-d score.
This score is a measure of how discordant the sanger trace is
before vs. after the edit. It is a simple and robust estimate of
editing efficiency in a pool, especially suited to highly disruptive editing techniques like multi-guide.
First release: 15 October 2020
Cells, virus, and infections for Caco-2 Cas9 knockout
screen
Wild-type and CRISPR edited Caco-2 cells were grown at
37°C, 5% CO2 in DMEM, 10% FBS. SARS-CoV-2 stocks were
grown and titered on Vero E6 cells as described previously
(54). Wild-type and CRISPR edited Caco-2 cell lines were infected with SARS-CoV-2 at an MOI of 0.01 in DMEM supplemented with 2% FBS. 72 hours post-infection, supernatants
were harvested and stored at -80°C and the Caco-2
WT/CRISPR KO cells were fixed with 10% neutral buffered
formalin (NBF) for 1 hour at room temperature to enable further analysis.
Focus forming assay for Caco-2 Cas9 knockout screen
Vero E6 cells were plated into 96 well plates at confluence
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Arrayed Knockout Generation with Cas9-RNPs
For Caco-2 transfection, 10 pmol Streptococcus Pyogenes
NLS-Sp.Cas9-NLS (SpCas9) nuclease (Aldevron; 9212) was
combined with 30 pmol total synthetic sgRNA (10 pmol each
sgRNA, Synthego) to form ribonucleoproteins (RNPs) in 20
μl total volume with SF Buffer (Lonza V5SC-2002) and allowed to complex at room temperature for 10 min.
All cells were dissociated into single cells using TrypLE
Express (Gibco), resuspended in culture media and counted.
100,000 cells per nucleofection reaction were pelleted by centrifugation at 200xg for 5 min. Following centrifugation, cells
were resuspended in transfection buffer according to cell
type and diluted to 2x104 cells/μl. 5 μl of cell solution was
added to preformed RNP solution and gently mixed. Nucleofections were performed on a Lonza HT 384-well nucleofector system (Lonza, #AAU-1001) using program CM-150
for Caco-2. Immediately following nucleofection, each reaction was transferred to a tissue-culture treated 96-well plate
containing 100 μl normal culture media and seeded at a density of 50,000 cells/well. Transfected cells were incubated following standard protocols.
Identification of essential genes for siRNA and Cas9
knockout screen
Here, we used longitudinal imaging in A549 cells to assess
cell viability (fig. S18). For benchmarking, relative cell viability was measured by CellTiter-Glo Luminescent Cell Viability
Assay (Promega; G7571) as per manufacturer’s instructions.
Briefly, two passages post-nucleofection A549 siRNA pools
cultured in 96-well tissue-culture treated plates (Corning,
#3595) were lysed in the CellTIter-Glo reagent, by removing
spent media and adding 100 μl of the CellTiter-Glo reagent
containing the CellTiter-Glo buffer and CellTiter-Glo Substrate. Cells were placed on an orbital shaker for 2 min on a
SpectraMax iD5 (Molecular Devices) and then incubated in
the dark at room temperature for 10 min. Completely lysed
cells were pipette mixed and 25 μl were transferred to a 384well assay plate (Corning, #3542). The luminescence was recorded on a SpectraMax iD5 (Molecular Devices) with an integration time of 0.25 s per well. Luminescence readings were
all normalized to the without-sgRNA control condition.
To determine cell viability in Caco-2 knockouts we used
longitudinal imaging (fig. S18). All gene knockout pools were
maintained for a minimum of six passages to determine the
effect of loss of protein function on cell fitness prior to viral
infection. Viability was determined through longitudinal imaging and automated image analysis using a Celigo Imaging
Cytometer (Celigo). Each gene knockout pool was split in
triplicate wells on separate plates. Every day, except the day
of seeding, each well was scanned and analyzed using built
in ‘Confluence’ imaging parameters using auto-exposure and
autofocus with an offset of -45 μm. Analysis was performed
with standard settings except for an intensity threshold setting of 8. Confluency was averaged across 3 wells and plotted
over time. Viability genes were determined as pools that were
less than 20% confluent 5 days post seeding following 6 passages. Genes deemed essential were excluded from the knockout screen.
Quantitative analysis and scoring of knockdown and
knockout library screens
Virus readout by qPCR (A549-ACE2, expressed as PFU/ml)
and focus forming assay readouts (Caco-2, FFU/ml) were processed
using
the
RNAither
package
(https://www.bioconductor.org/packages/release/bioc/html/
RNAither.html) in the statistical computing environment R.
The two datasets were normalized separately, using the following method. The readouts were first log transformed (natural logarithm), and robust Z-scores (using median and MAD
“median absolute deviation” instead of mean and standard
deviation) were then calculated for each 96-well plate separately. Z-scores of multiple replicates of the same perturbation were averaged into a final Z-score for presentation in Fig.
5. No filtering was done based on differences in replicate Zscores, but all replicate scores are individually listed in tables
S6 and S7. We suggest consulting the replicate Z-scores for all
genes/perturbations of interest. The A549-ACE2 siRNA
screen includes 3 replicates (or more) of each perturbation,
First release: 15 October 2020
and the Caco-2 CRISPR screen includes 2 replicates (or more)
of each perturbation. The results from the A549-ACE2 screen
cover all 332 screened genes (331 SARS-CoV-2 interactors
plus ACE2). The results from the Caco-2 screen cover 286 of
the screened genes plus ACE2. The remaining Caco-2 genes
were either deemed essential, failed editing, or failed in the
focus forming assay.
Antiviral drug and cytotoxicity assays (A549-ACE2
cells)
2,500 A549-ACE2 cells were seeded into 96- or 384-well plates
in DMEM (10% FBS) and incubated for 24 hours at 37°C, 5%
CO2. Two hours prior to infection, the media was replaced
with 120 μl (96 well format) or 50 μl (384 well format) of
DMEM (2% FBS) containing the compound of interest at the
indicated concentration. At the time of infection, the media
was replaced with virus inoculum (MOI 0.1 PFU/cell) and incubated for 1 hour at 37°C, 5% CO2. Following the adsorption
period, the inoculum was removed, replaced with 120 μl (96
well format) or 50 μl (384 well format) of drug-containing
media, and cells incubated for an additional 72 hours at 37°C,
5% CO2. At this point, the cell culture supernatant was harvested, and viral load assessed by RT-qPCR (as described in
‘Viral infection and quantification assay in A549-ACE2 cells’).
Viability was assayed using the CellTiter-Glo assay following
the manufacturer’s protocol (Promega). Luminescence was
measured in a Tecan Infinity 2000 plate reader, and percentage viability calculated relative to untreated cells (100% viability) and cells lysed with 20% ethanol or 4% formalin (0%
viability), included in each experiment.
Antiviral drug and cytotoxicity assays (Vero E6 cells)
Viral growth and cytotoxicity assays in the presence of inhibitors were performed as previously described (5). 2,000 Vero
E6 cells were seeded into 96-well plates in DMEM (10% FBS)
and incubated for 24 hours at 37°C, 5% CO2. Two hours before
infection, the medium was replaced with 100 μl of DMEM
(2% FBS) containing the compound of interest at concentrations 50% greater than those indicated, including a DMSO
control. SARS-CoV-2 virus (100 PFU; MOI 0.025) was added
in 50 μl of DMEM (2% FBS), bringing the final compound
concentration to those indicated. Plates were then incubated
for 48 hours at 37°C. After infection, supernatants were removed and cells were fixed with 4% formaldehyde for 24
hours prior to being removed from the BSL3 facility. The cells
were then immunostained for the viral NP protein (rabbit
anti-sera produced in the Garcia-Sastre lab; 1:10,000) with a
DAPI counterstain. Infected cells (488 nm) and total cells
(DAPI) were quantified using a Celigo (Nexcelcom) imaging
cytometer. Infectivity is measured by the accumulation of viral NP protein in the nucleus of the cells (fluorescence accumulation). Percent infection was quantified as (Infected cells
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(50,000 cells/well) in DMEM supplemented with 10% heatinactivated FBS (Gibco). Prior to infection, supernatants from
infected Caco-2 WT/CRISPR KO cells were thawed and serially diluted from 10−1 to 10−8. Growth media was removed
from the Vero E6 cells and 40 μl of each virus dilution was
plated. After 1 hour adsorption at 37°C, 5% CO2, 40 μl of 2.4%
microcrystalline cellulose (MCC) overlay supplemented with
DMEM powdered media (Gibco) to a concentration of 1x was
added to each well of the 96 well plate to achieve a final MCC
overlay concentration of 1.2%. Plates were then incubated at
37°C, 5% CO2 for 24 hours. The MCC overlay was gently removed and cells were fixed with 10% NBF for 1 hour at roomtemperature. After removal of NBF, monolayers were washed
with ultrapure water and ice-cold 100% methanol/0.3% H2O2
was added for 30 min to permeabilize the cells and quench
endogenous peroxidase activity. Monolayers were then
blocked for 1 hour in PBS with 5% non-fat dry milk (NFDM).
After blocking, monolayers were incubated with SARS-CoV N
primary antibody (Novus Biologicals; NB100-56576 – 1:2000)
for 1 hour at room temperature in PBS, 5% NFDM. Monolayers were washed with PBS and incubated with an HRPConjugated secondary antibody for 1 hour at room temperature in PBS with 5% NFDM. Secondary was removed, monolayers were washed with PBS, and then developed using
TrueBlue substrate (KPL) for 30 min. Plates were imaged on
a Bio-Rad Chemidoc utilizing a phosphorscreen and foci were
counted by eye to calculate focus forming units per ml
(FFU/ml) for each knockout. The original formalin-fixed
Caco-2 WT/CRISPR KO cells were stained with Dapi (Thermo
Scientific) and imaged on a Cytation 5 plate reader to determine cell viability. Wells containing no cells were excluded
from further analyses.
/ Total cells) - Background) * 100 and the DMSO control was
then set to 100% infection for analysis. The IC50 and IC90 for
each experiment was determined using the Prism (GraphPad
Software) software. Cytotoxicity measurements were performed using the MTT assay (Roche), according to the manufacturer’s instructions. Cytotoxicity was performed in
uninfected Vero E6 cells with same compound dilutions and
concurrent with viral replication assay. All assays were performed in biologically independent triplicates. Sourcing information for all drugs tested may be found in table S10.
Quantification of Tom70 down-regulation in HeLaM
cells overexpressing Orf9b
HeLaM cells were transiently transfected with plasmids encoding GFP-Strep, SARS-CoV-1 Orf9b-Strep or SARS-CoV-2
Orf9b-Strep. The next day, the cells were fixed using 4% paraformaldehyde and immunostained with antibodies against
Strep tag, and Tom20 or Tom70. Representative images for
each construct were captured by acquiring a single optical
section using a Nikon A1 confocal fitted with a CFI Plan Apochromat VC 60x oil objective (NA 1.4). For image quantification multiple fields of view were captured for each
construct using a CFI Super Plan Fluor ELWD 40x objective
(NA 0.6). The mean fluorescent intensity for Tom20 and
Tom70 was measured by manually drawing a region of interest around each cell using ImageJ. Between 30 and 60 cells
were quantified for each construct.
Quantification of Tom70 down-regulation in infected
Caco-2 cells
Caco-2 cells were seeded on glass coverslips in triplicate and
infected with SARS-CoV-2 at an MOI of 0.1 as described
above. At 24 hours post-infection, cells were fixed with 4%
paraformaldehyde and immunostained with antibodies
against Tom70, Tom20 and Orf9b. For signal quantification
images of non-infected and neighboring infected cells were
acquired using a LSM800 confocal laser-scanning microscope
First release: 15 October 2020
Co-expression and Purification of Orf9b-Tom70 (109end) complexes
SARS-CoV-2 Orf9b and Tom70 (residues 109-end) were coexpressed using a pET29-b(+) vector backbone where Orf9b
was tag-less and Tom70 had an N-terminal 10XHis-tag and
SUMO-tag. LOBSTR E. coli cells transformed with the above
construct were grown at 37°C till O.D. (600 nm)=0.8 and the
expression was induced at 37°C with 1 mM IPTG for 4 hours.
Frozen cell pellets were resuspended in 25 ml lysis buffer
(200 mM NaCl, 50 mM Tris-HCl pH 8.0, 10% v/v glycerol, 2
mM MgCl2) per liter cell culture, supplemented with cOmplete protease inhibitor tablets (Roche), 1 mM PMSF (Sigma),
100 μg/ml lysozyme (Sigma), 5 μg/ml DNaseI (Sigma), and
then homogenized with an immersion blender (Cuisinart).
Cells were lysed by 3x passage through an Emulsiflex C3 cell
disruptor (Avestin) at ~15,000psi, and the lysate clarified by
ultracentrifugation at 100,000xg for 30 min at 4°C. The supernatant was collected, supplemented with 20 mM imidazole, loaded into a gravity flow column containing Ni-NTA
superflow resin (Qiagen), and rocked with the resin at 4°C for
1 hour. After allowing the column to drain, resin was rinsed
twice with 5 column volumes (cv) of wash buffer (150 mM
KCl, 30 mM Tris-HCl pH 8.0, 10% v/v glycerol, 20 mM imidazole, 0.5 mM tris(hydroxypropyl)phosphine (THP, VWR))
supplemented with 2 mM ATP (Sigma) and 4 mM MgCl2, then
washed with 5 cv wash buffer with 40 mM imidazole. Resin
was then rinsed with 5 cv Buffer A (50 mM KCl, 30 mM TrisHCl pH 8.0, 5% glycerol, 0.5 mM THP) and protein was eluted
with 2 × 2.5 cv Buffer A + 300 mM imidazole. Elution fractions were combined, supplemented with Ulp1 protease, and
rocked at 4°C for 2 hours. Ulp1-digested Ni-NTA eluate was
diluted 1:1 with additional Buffer A, loaded into a 50 ml Superloop, and applied to a MonoQ 10/100 column on an Äkta
pure system (GE Healthcare) using 100% Buffer A, 0% Buffer
B (1000 mM KCl, 30 mM Tris-HCl pH 8.0, 5% glycerol, 0.5
mM THP). The MonoQ column was washed with 0%-40%
Buffer B gradient over 15 cv, peak fractions were analyzed by
SDS-PAGE and the identity of tagless Tom70(109-end) and
Orf9b proteins confirmed by intact protein mass spectrometry (Xevo G2-XS Mass Spectrometer, Waters). Peak fractions
eluting at ~15% B contained relatively pure Tom70(109-end)
and Orf9b, and these were concentrated using 10kDa Amicon
centrifugal filter (Millipore) and further purified by size exclusion chromatography using a Superdex 200 increase
10/300 GL column (GE healthcare) in buffer containing 150
mM KCl, 20 mM HEPES-NaOH pH 7.5, 0.5 mM THP. The sole
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Co-immunoprecipitation assays for Orf9b and Tom70
HEK293T and A549 cells were transfected with the indicated
mammalian expression plasmids using Lipofectamine 2000
(Invitrogen) and TransIT-X2 (Mirus Bio) respectively. 24
hours post-transfection, cells were harvested and lysed in NP40 lysis buffer (0.5% Nonidet P 40 Substitute (NP-40; Fluka
Analytical), 50 mM Tris-HCl, pH 7.4 at 4°C, 150 mM NaCl, 1
mM EDTA) supplemented with cOmplete mini EDTA-free
protease and PhosSTOP phosphatase inhibitor cocktails
(Roche). Clarified cell lysates were incubated with Streptactin
Sepharose beads (IBA) for 2 hours at 4°C, followed by five
washes with NP-40 lysis buffer. Protein complexes were
eluted in the SDS loading buffer and were analyzed by Western blotting with the indicated antibodies.
(Zeiss) equipped with a 63X, 1.4 NA oil objective and the Zen
blue software (Zeiss). The mean fluorescence intensity of
each cell was measured by ImageJ software. 43 cells were
quantified for each condition, infected or non-infected, from
three independent experiments.
size-exclusion peak contained both Tom70(109-end) and
Orf9b, and the center fraction was used directly for cryo-EM
grid preparation.
Expression and Purification of Tom70(109-end)
Tom70 (109-end) with N-terminal 10XHis-tag and SUMO-tag
and C terminus Spy-tag, HRV-3C protease cleavage site, and
eGFP-tag was expressed using a pET-21(+) vector backbone.
LOBSTR E. coli cells transformed with the above construct
were grown at 37°C till O.D. (600 nm)=0.8 and the expression
was induced at 16°C with 0.5 mM IPTG overnight. The soluble domain of Tom70 (Tom70 (109-end)) was purified as described in (55) with some modifications. Frozen cell pellets of
LOBSTR E. coli transformed with the above construct were
resuspended in 50 ml lysis buffer (500 mM NaCl, 20 mM
KH2PO4 pH 7.5) per liter cell culture, supplemented with 1
mM PMSF (Sigma) and 100 μg/ml, and homogenized. Cells
were lysed by 3x passage through an Emulsiflex C3 cell disruptor (Avestin) at ~15,000psi, and the lysate clarified by ultracentrifugation at 100,000xg for 30 min at 4°C. The
First release: 15 October 2020
Prediction of SARS-CoV-2 Orf9b internal mitochondrial targeting sequence
Orf9b was analyzed for the presence of an internal mitochondrial targeting sequence (i-MTS) as described in (56) using
the TargetP-2.0 server (57). Sequences corresponding to
Orf9b N-terminal truncations of 0 to 62 residues were submitted to the TargetP-2.0 server, and the probability of the
peptides containing an MTS plotted against the numbers of
residues truncated. A similar analysis using the MitoFates
server (58) predicted that Orf9b residues 54-63 were the most
likely to comprise a presequence MTS based on propensity to
form a positively charged amphipathic helix. Notably this
analysis was consistent with the secondary structure prediction from JPRED (59).
CryoEM sample preparation and data collection
3 μL of Orf9b-Tom70 complex (12.5μM) was added to a 400
mesh 1.2/1.3R Au Quantifoil grid previously glow discharged
at 15 mA for 30 s. Blotting was performed with a blot force of
0 for 5 s at 4°C and 100% humidity in a FEI Vitrobot Mark IV
(ThermoFisher) prior to plunge freezing into liquid ethane.
1534 118-frame super-resolution movies were collected with a
3x3 image shift collection strategy at a nominal magnification
of 105,000x (physical pixel size: 0.834 Å/pix) on a Titan Krios
(ThermoFisher) equipped with a K3 camera and a Bioquantum energy filter (Gatan) set to a slit width of 20 eV. Collection dose rate was 8 e-/pixel/second for a total dose of 66 e/Å2. Defocus range was -0.7um to -2.4um. Each collection was
performed with semi-automated scripts in SerialEM (60).
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Expression and Purification of SARS-CoV-2 Orf9b
Orf9b with N-terminal 10XHis-tag and SUMO-tag was expressed using a pET-29b(+) vector backbone. LOBSTR E. coli
cells transformed with the above construct were grown at
37°C until reaching O.D. (600 nm)=0.8 and the expression
was induced at 37°C with 1 mM IPTG for 6 hours. Frozen cell
pellets were lysed, homogenized, clarified, and subject to Ni
affinity purification as described above for Orf9b-Tom70
complexes, with several small changes. Lysis buffers and NiNTA wash buffers contained 500 mM NaCl, and an additional
wash step using 10 cv wash buffer + 0.2% TWEEN20 + 500
mM NaCl was carried out prior to the ATP wash. Orf9b was
eluted from Ni-NTA resin in Buffer A (50 mM NaCl, 25 mM
Tris pH 8.5, 5% glycerol, 0.5 mM THP) supplemented with
300 mM imidazole. This eluate was diluted 1:1 with additional Buffer A, loaded into a 50 ml Superloop, and applied
to a MonoQ 10/100 column on an Äkta pure system (GE
Healthcare) using 100% Buffer A, 0% Buffer B (1000 mM
NaCl, 25mM Tris-HCl pH 8.5, 5% glycerol, 0.5 mM THP). The
MonoQ column was washed with 0%-40% Buffer B gradient
over 15 cv, and relatively pure Orf9b eluted at 20-25% Buffer
B, whereas Orf9b and contaminating proteins eluted at 3035% buffer B. Fractions from these two peaks were combined
and incubated with Ulp1 and HRV3C proteases at 4°C for 2
hours, supplemented with 10 mM imidazole, then thrice
flowed back through 1 ml of Ni-NTA resin equilibrated with
size-exclusion buffer (as above) + 10 mM imidazole. The reverse-Ni purified sample was concentrated using 10kDa
Amicon centrifugal filter and then further purified by size exclusion chromatography using a Superdex 200 increase
10/300 GL column.
supernatant was collected, supplemented with 20 mM imidazole, loaded into a gravity flow column containing Ni-NTA
superflow resin (Qiagen), and rocked with the resin at 4°C for
1 hour. After allowing the column to drain, resin was rinsed
with twice with 5 column volumes (cv) of wash buffer (500
mM KCl, 20 mM KH2PO4 pH 8.0, 20 mM imidazole, 0.5 mM
THP) supplemented with 2 mM ATP - 4 mM MgCl2, then
washed with 5 cv wash buffer with 40 mM imidazole. Bound
Tom70(109-end) was then cleaved from the resin by 2 hour
incubation with Ulp1 protease in 4 cv elution buffer (150 mM
KCl, 20 mM KH2PO4 pH 8.0, 5 mM imidazole, 0.5 mM THP).
After cleavage with Ulp1, the flow through was collected
along with a 2 cv rinse of the resin with additional elution
buffer. These fractions were combined and HRV3C protease
was added to remove the C-terminal EGFP tag (1:20 HRV3C
to Tom70). After 2 hour HRV3C digestion at 4°C, the doubledigested Tom70(109-end) was concentrated using a 30kDa
Amicon centrifugal filter (Millipore) and further purified by
size exclusion chromatography using a Superdex 200 increase 10/300 GL column (GE healthcare) in buffer containing 150 mM KCl, 20 mM HEPES-NaOH pH 7.5, 0.5 mM THP.
Computational human genetics analysis
To look for genetic variants associated with our list of proteins that had a significant impact on SARS-CoV-2 replication, we used the largest proteomic GWAS study to date (70).
We identified IL17RA as one of the proteins assayed in Sun et
al.’s proteomic GWAS and observed that it had multiple cisacting protein quantitative trait loci (pQTLs) at a corrected
p-value 1 × 10−5, where cis-acting is defined as within 1MB of
the transcription start site of IL17RA.
We used the GSMR method (71) to perform MR using
near-independent (linkage disequilibrium or LD r2 = 0.05)
cis-pQTLs for IL17RA. The advantage of GSMR method over
conventional MR methods is two-fold; first, GSMR performs
MR adjusting for any residual correlation between selected
genetic variants by default. Second, GSMR has a built-in
method called HEIDI (heterogeneity in dependent instruments)-outlier that performs heterogeneity tests in the near-
First release: 15 October 2020
independent genetic instruments and remove potentially
pleiotropic instruments (i.e., where there is evidence of heterogeneity at p < 0.01). Details of the GSMR and HEIDI
method have been published previously (71).
Summary statistics generated by COVID-19 Human Genetics
Initiative
(COVID-HGI)
(round
3;
https://www.covid19hg.org/results/) for COVID-19 vs. population, hospitalized COVID-19 vs. population and hospitalized
COVID-19 vs. non-hospitalized COVID-19 were used for
IL17RA MR analysis. We used the 1000 genomes phase 3 European population genotype data to derive the LD correlation
matrix for this analysis. The phenotype definitions as provided by COVID-HGI are as follows. COVID-19 vs. population:
Case, individuals with laboratory confirmation of SARS-CoV2 infection, EHR/ICD coding/Physician-confirmed COVID-19,
or self-reported COVID-19 positive; control, everybody that is
not a case. Hospitalized COVID-19 vs. population: case, hospitalized, laboratory confirmed SARS-CoV-2 infection or hospitalization due to COVID-19-related symptoms; control,
everybody that is not a case, e.g., population. Hospitalized
COVID-19 vs. non-hospitalized COVID-19: case, hospitalized,
laboratory confirmed SARS-CoV-2 infection or hospitalization due to COVID-19-related symptoms; control, laboratory
confirmed SARS-CoV-2 infection and not hospitalized 21 days
after the test.
Infections and treatments for IL17A treatment studies
The WA-1 strain (BEI resources) of SARS-CoV-2 was used for
all experiments. All live virus experiments were performed in
a BSL3 lab. SARS-CoV-2 stocks were passaged in Vero E6 cells
(ATCC) and titer was determined via plaque assay on Vero E6
cells as previously described (72). Briefly, virus was diluted
1:102-1:106 and incubated for 1 hour on Vero E6 cells before
an overlay of Avicel and complete DMEM (Sigma Aldrich,
SLM-241) was added. After incubation at 37°C for 72 hours,
the overlay was removed and cells were fixed with 10% formalin, stained with crystal violet, and counted for plaque formation. SARS-CoV-2 infections of A549-ACE2 cells were done
at a MOI of 0.05 for 24 hours. Inhibitors and cytokines were
added concurrently with virus. All infections were done in
technical triplicate. Cells were treated with the following
compounds: Remdesivir (SELLECK CHEMICALS LLC,
S8932) and IL-17A (Millipore-Sigma, SRP0675).
RNA extraction, RT, and quantitative RT-PCR for IL17A
treatment studies
Total RNA from samples was extracted using the Direct-zol
RNA kit (Zymogen, R2060) and quantified using the
NanoDrop 2000c (ThermoFisher). cDNA was generated using
500 ng of RNA from infected A549-ACE2 cells with Superscript III reverse transcription (ThermoFisher, 18080-044)
and oligo(dT)12-18 (ThermoFisher, 18418-012) and random
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CryoEM Image Processing and Model Building
1534 movies were motion corrected using Motioncor2 (61)
and dose-weighted summed micrographs were imported in
cryosparc (v2.15.0). 1427 micrographs were curated based on
CTF fit (better than 5 Å) from a patch CTF job. Templatebased particle picking resulted in 2,805,121 particles and
1,616,691 particles were selected after 2D-classification. Five
rounds of 3D-classification using multi-class ab-initio reconstruction and heterogeneous refinement yielded 178,373 particles. Homogenous refinement of these final particles led to
a 3.1 Å electron density map which was used for model building. The reconstruction was filtered by the masked FSC and
sharpened with a b-factor of -145.
To build the model of Tom70(109-end), the crystal structure of Saccharomyces cerevisiae Tom71 (PDB ID: 3fp3; sequence identity 25.7%) was first fit into the cryoEM density
as a rigid body in UCSF ChimeraX and then relaxed into the
final density using Rosetta FastRelax mover in torsion space.
This model, along with a BLAST alignment of the two sequences (62), was used as a starting point for manual building using COOT (63). After initial building by hand the
regions with poor density fit/geometry were iteratively rebuilt using Rosetta (64). Orf9b was built de novo into the final
density using COOT, informed and facilitated by the predictions of the TargetP-2.0, MitoFates, and JPRED servers. The
Orf9b-Tom70 complex model was submitted to the Namdinator web server (65) and further refined in ISOLDE 1.0 (66)
using the plugin for UCSF ChimeraX (67). Final model B-factors were estimated using Rosetta. The model was validated
using phenix.validation_cryoem (68). The final model contains residues 109-272, 298-600 of human Tom70, and 39-76
of SARS-CoV-2 Orf9b. Molecular interface between Orf9b and
Tom70 was analyzed using the PISA web server (69). Figures
were prepared using UCSF ChimeraX.
Transfections for IL17A treatment studies
HEK293T cells were seeded 5x105cells/well (in 6 well plate)
or 3x106 cell/10cm2 plates. Next day, 2 μg or 10 μg of plasmids
was transfected using X-tremeGENE 9 DNA Transfection Reagent (Roche) in 6 well plate or 10cm2 plates respectively. For
IL-17A (Millipore-Sigma, SRP0675) incubation in cells, 0.5 μg
of IL-17A was treated either pre- or post-transfection and incubated at 37°C. After 48 hours, cells were collected by trypsinization. For IL-17A incubation with cell lysates, transfected
cell lysates were incubated with presence of 0.5 and 5 μg/ml
IL-17A at 4°C on rotation overnight. Plasmids pLVXEF1alpha-SARS-CoV-2-orf8-2xStrep-IRES-Puro (Orf8) and
pLVX-EF1alpha-eGFP-2xStrep-IRES-Puro (EGFP-Strep) were
a gift from Nevan Krogan. (Addgene plasmid #141390, 141395)
(5). pLVX-EF1alpha- IRES-Puro (Vector) was obtained from
Takara/Clontech.
SARS-CoV-2 Orf8 and IL17RA Co-immunoprecipitation
Transfected and treated HEK293T cells were pelleted and
washed in cold D-PBS and later resuspended in Flag-IP Buffer
(50 mM Tris HCl, pH 7.4, with 150 mM NaCl, 1 mM EDTA,
and 1% NP-40) with 1x HALT (ThermoFisher Scientific,
78429), incubated with buffer for 15 min on ice then centrifuged at 13,000 rpm for 5 min. The supernatant was collected
and 1 mg of protein was used for Immunoprecipitation (IP)
with 100 μl Streptactin Sepharose (IBA, 2-1201-010) on a rotor
overnight at 4°C. Immunoprecipitates were washed 5 times
with Flag-IP buffer and eluted with 1x Buffer E (100 mM TrisCl, 150 mM NaCl, 1 mM EDTA, 2.5 mM Desthiobiotin). Eluate
was diluted with 1x-NuPAGE (ThermoFisher Scientific,
First release: 15 October 2020
#NP0008) LDS Sample Buffer with 2.5% β-Mercaptoethanol
and blotted for targeted antibodies. Antibodies used were
Strep Tag II (Qiagen, #34850), B-Actin (Sigma, #A5316), and
IL17RA (Cell Signaling, #12661S).
Computational docking of mPGES-2 and Nsp7
A model for human mPGES-2 dimer was constructed by homology using MODELER (73) from the crystal structure of
Macaca fascularis mPGES-2 (PDB 1Z9H (74), 98% sequence
identity) bound to indomethacin. Indomethacin was removed from the structure utilized for docking. The structure
of SARS-CoV-2 Nsp7 was extracted from PDB 7BV2 (75).
Docking models were produced using ClusPro (76), ZDock
(77), HDock (78), Gramm-X (79), SwarmDock (80) and PatchDock (81) with SOAP-PP score (82). For each protocol, up to
100 top scoring models were extracted (fewer for those that
do not report > 100 models); for PatchDock, models with
SOAP-PP Z-scores greater than 3.0 were used (fig. S23A). The
420 models were clustered at 4.0 Å RMSD, resulting in 127
clusters. The two largest clusters, comprising 192 models, are
related by the dimer symmetry. All other clusters contain
fewer than 15 models.
Assessment of positive selection signatures in
SIGMAR1
SIGMAR1 protein alignments were generated from whole genome sequences of 359 mammals curated by the Zoonomia
consortium. Protein alignments were generated with TOGA
(https://github.com/hillerlab/TOGA), and missing sequence
gaps were refined with CACTUS (83, 84). Branches undergoing positive selection were detected with the branch-site test
aBSREL (85) implemented in the HyPhy package (85, 86).
PhyloP was used to detect codons undergoing accelerated
evolution along branches detected as undergoing positive selection by aBSREL relative to the neutral evolution rate in
mammals, determined using phyloFit on third nucleotide positions of codons which are assumed to evolve neutrally. Pvalues from phyloP were corrected for multiple tests using
the Benjamini-Hochberg method (87). PhyloFit and phyloP
are both part of the PHAST package v1.4 (88, 89).
Comparative SARS-CoV-1 inhibition by amiodarone
SARS-CoV-1 (Urbani) drug screens were performed with Vero
E6 cells (ATCC# 1568, Manassas, VA) cultured in DMEM
(Quality Biological), supplemented with 10% (v/v) heat inactivated fetal bovine serum (Sigma), 1% (v/v) penicillin/streptomycin (Gemini Bio-products), and 1% (v/v) L-glutamine (2
mM final concentration, Gibco). Cells were plated in opaque
96 well plates one day prior to infection. Drugs were diluted
from stock to 50 μM and an 8-point 1:2 dilution series prepared in duplicate in Vero Media. Every compound dilution
and control was normalized to contain the same
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hexamer primers (ThermoFisher, S0142). Quantitative RTPCR reactions were performed on a CFX384 (BioRad) and
delta cycle threshold (ΔCt) was determined relative to
RPL13A levels. Viral detection levels and target host genes in
treated samples were normalized to water-treated controls.
The SYBR green qPCR reactions contained 5 μl of 2x Maxima
SYBR green/Rox qPCR Master Mix (ThermoFisher; K0221), 2
μl of diluted cDNA, and 1 nmol of both forward and reverse
primers, in a total volume of 10 μl. The reactions were run as
follows: 50°C for 2 min and 95°C for 10 min, followed by 40
cycles of 95°C for 5 s and 62°C for 30 s. Primer efficiencies
were around 100%. Dissociation curve analysis after the end
of the PCR confirmed the presence of a single and specific
product. qRT-PCR primers were used against the SARS-CoV2
E
gene
(PF_042_nCoV_E_F:
ACAGGTACGTTAATAGTTAATAGCGT; PF_042_nCoV_E_R:
ATATTGCAGCAGTACGCACACA), the CXCL8 gene (CXCL8
For: ACTGAGAGTGATTGAGAGTGGAC; CXCL8 Rev:
AACCCTCTGCACCCAGTTTTC), and the RPL13A gene
(RPL13A For: CCTGGAGGAGAAGAGGAAAGAGA; RPL13A
Rev: TTGAGGACCTCTGTGTATTTGTCAA).
concentration of drug vehicle (e.g., DMSO). Cells were pretreated with drug for 2 hours (h) at 37°C (5% CO2) prior to
infection with SARS-CoV-1 at MOI 0.01. In addition to plates
that were infected, parallel plates were left uninfected to
monitor cytotoxicity of drug alone. All plates were incubated
at 37°C (5% CO2) for 3 days before performing CellTiter-Glo
(CTG) assays as per the manufacturer’s instruction (Promega,
Madison, WI). Luminescence was read on a BioTek Synergy
HTX plate reader (BioTek Instruments Inc., Winooski, VT)
using the Gen5 software (v7.07, Biotek Instruments Inc.,
Winooski, VT).
Observation of hospitalization outcomes in outpatient
new users of indomethacin (treatment arm) vs.
celecoxib (active comparator) using real-world data
We used an incident (new) user, active comparator design
(90, 91) to assess the risk of hospitalization among newly diagnosed COVID-19 patients who were subsequently treated
First release: 15 October 2020
Observation of mechanical ventilation outcomes in inpatient new users of typical antipsychotics (treatment
arm) vs. atypical antipsychotics (active comparator)
using real-world data
We used an incident user, active comparator design (90, 91)
to assess the risk of mechanical ventilation among hospitalized COVID-19 patients treated with typical or atypical antipsychotics in an inpatient setting. See table S11 for a list of
drugs included in each category. To permit assessment of
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Real-world data source and analysis
This study used de-identified patient-level records from
HealthVerity’s Marketplace dataset, a nationally representative dataset covering >300 million unique patients with medical and pharmacy records from over 60 healthcare data
sources in the US. The current study used data from 738,933
patients with documented COVID-19 infection between
March 1, 2020 to August 17, 2020, defined as a positive or
presumptive positive viral lab test result or an International
Classification of Diseases, 10th Revision, Clinical Modification
(ICD-10-CM) diagnosis code of U07.1 (COVID-19).
For this population, we analyzed medical claims, pharmacy claims, laboratory data, and hospital chargemaster data
containing diagnoses, procedures, medications and COVID19 laboratory results from both inpatient and outpatient settings. Claims data included open (unadjudicated) claims
sourced in near-real time from practice management and billing systems, claims clearinghouses and laboratory chains, as
well as closed (adjudicated) claims encompassing all major
US payer types (commercial, Medicare, Medicaid). For inpatient treatment evaluations, we used linked hospital chargemaster data containing records of all billable procedures,
medical services and treatments administered in hospital settings. Linkage of patient-level records across these data types
provides a longitudinal view of baseline health status, medication use, and COVID-19 progression for each patient under
study. Data for this study covered the period of December 1,
2018 through August 17th, 2020. All analyses were conducted
with the Aetion Evidence Platform version r4.6.
This study was approved by the New England IRB (#19757-1). Medical records constitute protected health information and can be made available to qualified individuals
upon reasonable request.
with indomethacin or the comparator agent, celecoxib. Patients were required to have COVID-19 infection recorded in
an outpatient setting during the study period of March 1,
2020 to August 17, 2020 and occurring in the 21 days prior to
(and including) the date of indomethacin or celecoxib treatment initiation. Prevalent users of prescription-only NSAIDs
(any prescription fill for indomethacin, celecoxib, ketoprofen,
meloxicam, sulindac, or piroxicam 60 days prior) and patients hospitalized in the 21 days prior to and including the
date of treatment initiation were excluded from this analysis.
Using RSS, patients treated with indomethacin were
matched at a 1:1 ratio to controls randomly selected among
patients treated with celecoxib, with direct matching on calendar date of treatment (±7 days), age (±5 years), sex, Charlson comorbidity index (exact) (92), time since confirmed
COVID-19 (±5 days), and disease severity based on the highest-intensity COVID-19-related health service in the 7 days
prior to and including the date of treatment initiation (lab
service only vs. outpatient medical visit vs. emergency department visit) and symptom profile in the 21 days prior to and
including the date of treatment initiation (recorded symptoms vs. none). This risk set sampled population was further
matched on a propensity score (PS) (25) estimated using logistic regression with 24 demographic and clinical risk factors, including covariates related to baseline medical history
and COVID-19 severity in the 21 days prior to treatment (table
S11). Balance between indomethacin and celecoxib treatment
groups was evaluated by comparison of absolute standardized differences in covariates, with an absolute standardized
difference of less than 0.2 indicating good balance between
the treatment groups (93).
The primary analysis was an intention-to-treat design,
with follow-up beginning 1 day after indomethacin or
celecoxib initiation and ending on the earliest occurrence of
30 days of follow-up reached or end of patient data. Odds ratios for the primary outcome of all-cause inpatient hospitalization were estimated for the RSS+PS matched population as
well as for the RSS matched population. Our primary outcome definition required a record of inpatient hospital admission with a resulting inpatient stay; as a sensitivity, a
broader outcome definition captured any hospital visit (defined with revenue and place of service codes).
REFERENCES AND NOTES
1. J. Liu, W. Xie, Y. Wang, Y. Xiong, S. Chen, J. Han, Q. Wu, A comparative overview of
COVID-19, MERS and SARS: Review article. Int. J. Surg. 81, 1–8 (2020).
doi:10.1016/j.ijsu.2020.07.032 Medline
2. J. H. Beigel, K. M. Tomashek, L. E. Dodd, A. K. Mehta, B. S. Zingman, A. C. Kalil, E.
Hohmann, H. Y. Chu, A. Luetkemeyer, S. Kline, D. Lopez de Castilla, R. W. Finberg,
K. Dierberg, V. Tapson, L. Hsieh, T. F. Patterson, R. Paredes, D. A. Sweeney, W. R.
Short, G. Touloumi, D. C. Lye, N. Ohmagari, M.-d. Oh, G. M. Ruiz-Palacios, T.
Benfield, G. Fätkenheuer, M. G. Kortepeter, R. L. Atmar, C. B. Creech, J. Lundgren,
A. G. Babiker, S. Pett, J. D. Neaton, T. H. Burgess, T. Bonnett, M. Green, M.
Makowski, A. Osinusi, S. Nayak, H. C. Lane, ACTT-1 Study Group Members,
Remdesivir for the treatment of Covid-19—Final report. N. Engl. J. Med.
10.1056/NEJMoa2007764 (2020). doi:10.1056/NEJMoa2007764
3. RECOVERY Collaborative Group, Dexamethasone in Hospitalized Patients with
Covid-19 - Preliminary Report. N. Engl. J. Med. 10.1056/nejmoa2021436 (2020).
doi:10.1056/nejmoa2021436 Medline
4. M. Becerra-Flores, T. Cardozo, SARS-CoV-2 viral spike G614 mutation exhibits
higher case fatality rate. Int. J. Clin. Pract. 74, e13525 (2020).
doi:10.1111/ijcp.13525 Medline
5. D. E. Gordon, G. M. Jang, M. Bouhaddou, J. Xu, K. Obernier, K. M. White, M. J.
O’Meara, V. V. Rezelj, J. Z. Guo, D. L. Swaney, T. A. Tummino, R. Hüttenhain, R. M.
Kaake, A. L. Richards, B. Tutuncuoglu, H. Foussard, J. Batra, K. Haas, M. Modak,
M. Kim, P. Haas, B. J. Polacco, H. Braberg, J. M. Fabius, M. Eckhardt, M.
First release: 15 October 2020
Soucheray, M. J. Bennett, M. Cakir, M. J. McGregor, Q. Li, B. Meyer, F. Roesch, T.
Vallet, A. Mac Kain, L. Miorin, E. Moreno, Z. Z. C. Naing, Y. Zhou, S. Peng, Y. Shi, Z.
Zhang, W. Shen, I. T. Kirby, J. E. Melnyk, J. S. Chorba, K. Lou, S. A. Dai, I. BarrioHernandez, D. Memon, C. Hernandez-Armenta, J. Lyu, C. J. P. Mathy, T. Perica, K.
B. Pilla, S. J. Ganesan, D. J. Saltzberg, R. Rakesh, X. Liu, S. B. Rosenthal, L.
Calviello, S. Venkataramanan, J. Liboy-Lugo, Y. Lin, X.-P. Huang, Y. Liu, S. A.
Wankowicz, M. Bohn, M. Safari, F. S. Ugur, C. Koh, N. S. Savar, Q. D. Tran, D.
Shengjuler, S. J. Fletcher, M. C. O’Neal, Y. Cai, J. C. J. Chang, D. J. Broadhurst, S.
Klippsten, P. P. Sharp, N. A. Wenzell, D. Kuzuoglu-Ozturk, H.-Y. Wang, R. Trenker,
J. M. Young, D. A. Cavero, J. Hiatt, T. L. Roth, U. Rathore, A. Subramanian, J.
Noack, M. Hubert, R. M. Stroud, A. D. Frankel, O. S. Rosenberg, K. A. Verba, D. A.
Agard, M. Ott, M. Emerman, N. Jura, M. von Zastrow, E. Verdin, A. Ashworth, O.
Schwartz, C. d’Enfert, S. Mukherjee, M. Jacobson, H. S. Malik, D. G. Fujimori, T.
Ideker, C. S. Craik, S. N. Floor, J. S. Fraser, J. D. Gross, A. Sali, B. L. Roth, D.
Ruggero, J. Taunton, T. Kortemme, P. Beltrao, M. Vignuzzi, A. García-Sastre, K. M.
Shokat, B. K. Shoichet, N. J. Krogan, A SARS-CoV-2 protein interaction map
reveals targets for drug repurposing. Nature 583, 459–468 (2020).
doi:10.1038/s41586-020-2286-9 Medline
6. G. Teo, G. Liu, J. Zhang, A. I. Nesvizhskii, A.-C. Gingras, H. Choi, SAINTexpress:
Improvements and additional features in Significance Analysis of INTeractome
software. J. Proteomics 100, 37–43 (2014). doi:10.1016/j.jprot.2013.10.023
Medline
7. S. Jäger, P. Cimermancic, N. Gulbahce, J. R. Johnson, K. E. McGovern, S. C. Clarke,
M. Shales, G. Mercenne, L. Pache, K. Li, H. Hernandez, G. M. Jang, S. L. Roth, E.
Akiva, J. Marlett, M. Stephens, I. D’Orso, J. Fernandes, M. Fahey, C. Mahon, A. J.
O’Donoghue, A. Todorovic, J. H. Morris, D. A. Maltby, T. Alber, G. Cagney, F. D.
Bushman, J. A. Young, S. K. Chanda, W. I. Sundquist, T. Kortemme, R. D.
Hernandez, C. S. Craik, A. Burlingame, A. Sali, A. D. Frankel, N. J. Krogan, Global
landscape of HIV-human protein complexes. Nature 481, 365–370 (2011).
doi:10.1038/nature10719 Medline
8. J. C. Young, N. J. Hoogenraad, F. U. Hartl, Molecular chaperones Hsp90 and Hsp70
deliver preproteins to the mitochondrial import receptor Tom70. Cell 112, 41–50
(2003). doi:10.1016/S0092-8674(02)01250-3 Medline
9. R. Lin, S. Paz, J. Hiscott, Tom70 imports antiviral immunity to the mitochondria.
Cell Res. 20, 971–973 (2010). doi:10.1038/cr.2010.113 Medline
10. B. Wei, Y. Cui, Y. Huang, H. Liu, L. Li, M. Li, K.-C. Ruan, Q. Zhou, C. Wang, Tom70
mediates Sendai virus-induced apoptosis on mitochondria. J. Virol. 89, 3804–
3818 (2015). doi:10.1128/JVI.02959-14 Medline
11. A. M. Edmonson, D. K. Mayfield, V. Vervoort, B. R. DuPont, G. Argyropoulos,
Characterization of a human import component of the mitochondrial outer
membrane, TOMM70A. Cell Commun. Adhes. 9, 15–27 (2002).
doi:10.1080/15419060212186 Medline
12. M. J. Baker, A. E. Frazier, J. M. Gulbis, M. T. Ryan, Mitochondrial protein-import
machinery: Correlating structure with function. Trends Cell Biol. 17, 456–464
(2007). doi:10.1016/j.tcb.2007.07.010 Medline
13. J. Brix, K. Dietmeier, N. Pfanner, Differential recognition of preproteins by the
purified cytosolic domains of the mitochondrial import receptors Tom20, Tom22,
and
Tom70.
J.
Biol.
Chem.
272,
20730–20735
(1997).
doi:10.1074/jbc.272.33.20730 Medline
14. J. Brix, G. A. Ziegler, K. Dietmeier, J. Schneider-Mergener, G. E. Schulz, N. Pfanner,
The mitochondrial import receptor Tom70: Identification of a 25 kDa core domain
with a specific binding site for preproteins. J. Mol. Biol. 303, 479–488 (2000).
doi:10.1006/jmbi.2000.4120 Medline
15. R. D. Mills, J. Trewhella, T. W. Qiu, T. Welte, T. M. Ryan, T. Hanley, R. B. Knott, T.
Lithgow, T. D. Mulhern, Domain organization of the monomeric form of the Tom70
mitochondrial import receptor. J. Mol. Biol. 388, 1043–1058 (2009).
doi:10.1016/j.jmb.2009.03.070 Medline
16. S. D. Weeks, S. De Graef, A. Munawar, X-ray Crystallographic Structure of Orf9b
from SARS-CoV-2 (2020); https://doi.org/10.2210/pdb6z4u/pdb.
17. M. Bouhaddou, D. Memon, B. Meyer, K. M. White, V. V. Rezelj, M. Correa Marrero,
B. J. Polacco, J. E. Melnyk, S. Ulferts, R. M. Kaake, J. Batra, A. L. Richards, E.
Stevenson, D. E. Gordon, A. Rojc, K. Obernier, J. M. Fabius, M. Soucheray, L.
Miorin, E. Moreno, C. Koh, Q. D. Tran, A. Hardy, R. Robinot, T. Vallet, B. E. NilssonPayant, C. Hernandez-Armenta, A. Dunham, S. Weigang, J. Knerr, M. Modak, D.
Quintero, Y. Zhou, A. Dugourd, A. Valdeolivas, T. Patil, Q. Li, R. Hüttenhain, M.
www.sciencemag.org
(Page numbers not final at time of first release) 22
Downloaded from http://science.sciencemag.org/ on October 19, 2020
day-level in-hospital confounders and outcomes, this analysis
was restricted to hospitalized patients observable in hospital
chargemaster data. Prevalent users of typical or atypical antipsychotics (any prescription fill or chargemaster-documented use in 60 days prior) and patients with evidence of
mechanical ventilation in the 21 days prior to and including
the date of treatment initiation were excluded from this analysis.
Using RSS, hospitalized patients treated with typical antipsychotics were matched at a 1:1 ratio to controls randomly
selected among patients treated with atypical antipsychotics,
with direct matching (1:1 fixed ratio) on calendar date of
treatment (±7 days), age (±5 years), sex, Charlson comorbidity
index (exact) (92), time since hospital admission, and disease
severity as defined with a simplified version of the World
Health Organization’s ordinal scale for clinical improvement
(94). This risk set sampled population was further matched
on a PS estimated using logistic regression with 36 demographic and clinical risk factors, including covariates related
to baseline medical history, admitting status, and disease severity at treatment (table S11). Balance between typical and
atypical treatment groups was evaluated by comparison of
absolute standardized differences in covariates, with an absolute standardized difference of less than 0.2 indicating
good balance between the treatment groups (93).
The primary analysis was an intention-to-treat design,
with follow-up beginning 1 day after the date of typical or
atypical antipsychotic treatment initiation, and ending on the
earliest occurrence of 30 days of follow-up reached, discharge
from hospital, or end of patient data. Odds ratios for the primary outcome of inpatient mechanical ventilation were estimated for the RSS+PS matched population as well as for the
RSS matched population.
First release: 15 October 2020
Shick, E. Garrison, M. T Karl, D. C. Factor, Z. S. Nevin, J. L. Sax, M. A. Thompson,
Y. Fedorov, J. Jin, W. K. Wilson, M. Giera, F. Bracher, R. H. Miller, P. J. Tesar, D. J.
Adams, Accumulation of 8,9-unsaturated sterols drives oligodendrocyte
formation and remyelination. Nature 560, 372–376 (2018). doi:10.1038/s41586018-0360-3 Medline
33. F. F. Moebius, R. J. Reiter, M. Hanner, H. Glossmann, High affinity of sigma 1binding sites for sterol isomerization inhibitors: Evidence for a pharmacological
relationship with the yeast sterol C8-C7 isomerase. Br. J. Pharmacol. 121, 1–6
(1997). doi:10.1038/sj.bjp.0701079 Medline
34. H.-W. Jiang, H.-N. Zhang, Q.-F. Meng, J. Xie, Y. Li, H. Chen, Y.-X. Zheng, X.-N. Wang,
H. Qi, J. Zhang, P.-H. Wang, Z.-G. Han, S.-C. Tao, SARS-CoV-2 Orf9b suppresses
type I interferon responses by targeting TOM70. Cell. Mol. Immunol. 17, 998–1000
(2020). doi:10.1038/s41423-020-0514-8 Medline
35. The COVID-19 Host Genetics Initiative, The COVID-19 Host Genetics Initiative, a
global initiative to elucidate the role of host genetic factors in susceptibility and
severity of the SARS-CoV-2 virus pandemic. Eur. J. Hum. Genet. 28, 715–718
(2020). doi:10.1038/s41431-020-0636-6 Medline
36. J. J. Almagro Armenteros, C. K. Sønderby, S. K. Sønderby, H. Nielsen, O. Winther,
DeepLoc: Prediction of protein subcellular localization using deep learning.
Bioinformatics 33, 3387–3395 (2017). doi:10.1093/bioinformatics/btx431
Medline
37. C. Chiva, R. Olivella, E. Borràs, G. Espadas, O. Pastor, A. Solé, E. Sabidó, QCloud: A
cloud-based quality control system for mass spectrometry-based proteomics
laboratories. PLOS ONE 13, e0189209 (2018). doi:10.1371/journal.pone.0189209
Medline
38. J. Cox, M. Mann, MaxQuant enables high peptide identification rates,
individualized p.p.b.-range mass accuracies and proteome-wide protein
quantification. Nat. Biotechnol. 26, 1367–1372 (2008). doi:10.1038/nbt.1511
Medline
39. M. Giurgiu, J. Reinhard, B. Brauner, I. Dunger-Kaltenbach, G. Fobo, G. Frishman,
C. Montrone, A. Ruepp, CORUM: The comprehensive resource of mammalian
protein complexes-2019. Nucleic Acids Res. 47, D559–D563 (2019).
doi:10.1093/nar/gky973 Medline
40. E. L. Huttlin, L. Ting, R. J. Bruckner, F. Gebreab, M. P. Gygi, J. Szpyt, S. Tam, G.
Zarraga, G. Colby, K. Baltier, R. Dong, V. Guarani, L. P. Vaites, A. Ordureau, R. Rad,
B. K. Erickson, M. Wühr, J. Chick, B. Zhai, D. Kolippakkam, J. Mintseris, R. A. Obar,
T. Harris, S. Artavanis-Tsakonas, M. E. Sowa, P. De Camilli, J. A. Paulo, J. W.
Harper, S. P. Gygi, The BioPlex Network: A Systematic Exploration of the Human
Interactome. Cell 162, 425–440 (2015). doi:10.1016/j.cell.2015.06.043 Medline
41. G. Yu, L.-G. Wang, Y. Han, Q.-Y. He, clusterProfiler: An R package for comparing
biological themes among gene clusters. OMICS 16, 284–287 (2012).
doi:10.1089/omi.2011.0118 Medline
42. M. Remmert, A. Biegert, A. Hauser, J. Söding, HHblits: Lightning-fast iterative
protein sequence searching by HMM-HMM alignment. Nat. Methods 9, 173–175
(2011). doi:10.1038/nmeth.1818 Medline
43. J. Yang, I. Anishchenko, H. Park, Z. Peng, S. Ovchinnikov, D. Baker, Improved
protein structure prediction using predicted interresidue orientations. Proc. Natl.
Acad. Sci. U.S.A. 117, 1496–1503 (2020). doi:10.1073/pnas.1914677117 Medline
44. Y. Zhai, F. Sun, X. Li, H. Pang, X. Xu, M. Bartlam, Z. Rao, Insights into SARS-CoV
transcription and replication from the structure of the nsp7-nsp8 hexadecamer.
Nat. Struct. Mol. Biol. 12, 980–986 (2005). doi:10.1038/nsmb999 Medline
45. A. Waterhouse, M. Bertoni, S. Bienert, G. Studer, G. Tauriello, R. Gumienny, F. T.
Heer, T. A. P. de Beer, C. Rempfer, L. Bordoli, R. Lepore, T. Schwede, SWISSMODEL: Homology modelling of protein structures and complexes. Nucleic Acids
Res. 46, W296–W303 (2018). doi:10.1093/nar/gky427 Medline
46. J. Durairaj, M. Akdel, D. de Ridder, A. D. J. van Dijk, Geometricus Represents
Protein Structures as Shape-mers Derived from Moment Invariants. bioRxiv
2020.09.07.285569
[Preprint].
8
September
2020.
https://doi.org/10.1101/2020.09.07.285569.
47. M. Akdel, J. Durairaj, D. de Ridder, A. D. J. van Dijk, Caretta - A multiple protein
structure alignment and feature extraction suite. Comput. Struct. Biotechnol. J.
18, 981–992 (2020). doi:10.1016/j.csbj.2020.03.011 Medline
48. P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B.
Schwikowski, T. Ideker, Cytoscape: A software environment for integrated models
of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
www.sciencemag.org
(Page numbers not final at time of first release) 23
Downloaded from http://science.sciencemag.org/ on October 19, 2020
Cakir, M. Muralidharan, M. Kim, G. Jang, B. Tutuncuoglu, J. Hiatt, J. Z. Guo, J. Xu,
S. Bouhaddou, C. J. P. Mathy, A. Gaulton, E. J. Manners, E. Félix, Y. Shi, M. Goff, J.
K. Lim, T. McBride, M. C. O’Neal, Y. Cai, J. C. J. Chang, D. J. Broadhurst, S.
Klippsten, E. De Wit, A. R. Leach, T. Kortemme, B. Shoichet, M. Ott, J. SaezRodriguez, B. R. tenOever, R. D. Mullins, E. R. Fischer, G. Kochs, R. Grosse, A.
García-Sastre, M. Vignuzzi, J. R. Johnson, K. M. Shokat, D. L. Swaney, P. Beltrao,
N. J. Krogan, The Global Phosphorylation Landscape of SARS-CoV-2 Infection.
Cell 182, 685–712.e19 (2020). doi:10.1016/j.cell.2020.06.034 Medline
18. J. Li, X. Qian, J. Hu, B. Sha, Molecular chaperone Hsp70/Hsp90 prepares the
mitochondrial outer membrane translocon receptor Tom71 for preprotein
loading. J. Biol. Chem. 284, 23852–23859 (2009). doi:10.1074/jbc.M109.023986
Medline
19. X.-Y. Liu, B. Wei, H.-X. Shi, Y.-F. Shan, C. Wang, Tom70 mediates activation of
interferon regulatory factor 3 on mitochondria. Cell Res. 20, 994–1011 (2010).
doi:10.1038/cr.2010.103 Medline
20. Y. Liu, C. Zhang, F. Huang, Y. Yang, F. Wang, J. Yuan, Z. Zhang, Y. Qin, X. Li, D. Zhao,
S. Li, S. Tan, Z. Wang, J. Li, C. Shen, J. Li, L. Peng, W. Wu, M. Cao, L. Xing, Z. Xu, L.
Chen, C. Zhou, W. J. Liu, L. Liu, C. Jiang, Elevated plasma levels of selective
cytokines in COVID-19 patients reflect viral load and lung injury. Natl. Sci. Rev. 7,
1003–1011 (2020). doi:10.1093/nsr/nwaa037
21. C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, L. Zhang, G. Fan, J. Xu, X. Gu, Z.
Cheng, T. Yu, J. Xia, Y. Wei, W. Wu, X. Xie, W. Yin, H. Li, M. Liu, Y. Xiao, H. Gao, L.
Guo, J. Xie, G. Wang, R. Jiang, Z. Gao, Q. Jin, J. Wang, B. Cao, Clinical features of
patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497–
506 (2020). doi:10.1016/S0140-6736(20)30183-5 Medline
22. C. Qin, L. Zhou, Z. Hu, S. Zhang, S. Yang, Y. Tao, C. Xie, K. Ma, K. Shang, W. Wang,
D.-S. Tian, Dysregulation of Immune Response in Patients With Coronavirus 2019
(COVID-19) in Wuhan, China. Clin. Infect. Dis. 71, 762–768 (2020).
doi:10.1093/cid/ciaa248 Medline
23. G. Chen, D. Wu, W. Guo, Y. Cao, D. Huang, H. Wang, T. Wang, X. Zhang, H. Chen, H.
Yu, X. Zhang, M. Zhang, S. Wu, J. Song, T. Chen, M. Han, S. Li, X. Luo, J. Zhao, Q.
Ning, Clinical and immunological features of severe and moderate coronavirus
disease 2019. J. Clin. Invest. 130, 2620–2629 (2020). doi:10.1172/JCI137244
Medline
24. C. Amici, A. Di Caro, A. Ciucci, L. Chiappa, C. Castilletti, V. Martella, N. Decaro, C.
Buonavoglia, M. R. Capobianchi, M. G. Santoro, Indomethacin has a potent
antiviral activity against SARS coronavirus. Antivir. Ther. 11, 1021–1030 (2006).
Medline
25. P. R. Rosenbaum, D. B. Rubin, The central role of the propensity score in
observational studies for causal effects. Biometrika 70, 41–55 (1983).
doi:10.1093/biomet/70.1.41
26. C. Abate, P. D. Mosier, F. Berardi, R. A. Glennon, A structure-affinity and
comparative molecular field analysis of sigma-2 (σ2) receptor ligands. Cent. Nerv.
9,
246–257
(2009).
Syst.
Agents
Med.
Chem.
doi:10.2174/1871524910909030246 Medline
27. R. A. Glennon, Sigma receptor ligands and the use thereof, U.S. Patent 6,057,371
(2000);
https://patentimages.storage.googleapis.com/dc/36/68/73f4ccdac4c973/US
6057371.pdf.
28. R. R. Matsumoto, B. Pouw, Correlation between neuroleptic binding to σ1 and σ2
receptors and acute dystonic reactions. Eur. J. Pharmacol. 401, 155–160 (2000).
doi:10.1016/S0014-2999(00)00430-1 Medline
29. M. Dold, M. T. Samara, C. Li, M. Tardy, S. Leucht, Haloperidol versus firstgeneration antipsychotics for the treatment of schizophrenia and other psychotic
disorders. Cochrane Database Syst. Rev. 1, CD009831 (2015).
doi:10.1002/14651858.CD009831.pub2 Medline
30. F. F. Moebius, R. J. Reiter, K. Bermoser, H. Glossmann, S. Y. Cho, Y. K. Paik,
Pharmacological analysis of sterol delta8-delta7 isomerase proteins with
[3H]ifenprodil. Mol. Pharmacol. 54, 591–598 (1998). doi:10.1124/mol.54.3.591
Medline
31. E. Gregori-Puigjané, V. Setola, J. Hert, B. A. Crews, J. J. Irwin, E. Lounkine, L.
Marnett, B. L. Roth, B. K. Shoichet, Identifying mechanism-of-action targets for
drugs and probes. Proc. Natl. Acad. Sci. U.S.A. 109, 11178–11183 (2012).
doi:10.1073/pnas.1204524109 Medline
32. Z. Hubler, D. Allimuthu, I. Bederman, M. S. Elitt, M. Madhavan, K. C. Allan, H. E.
First release: 15 October 2020
Terwilliger, P. D. Adams, A. Urzhumtsev, New tools for the analysis and validation
of cryo-EM maps and atomic models. Acta Cryst. D74, 814–840 (2018).
doi:10.1107/S2059798318009324 Medline
69. E. Krissinel, K. Henrick, Inference of macromolecular assemblies from crystalline
state. J. Mol. Biol. 372, 774–797 (2007). doi:10.1016/j.jmb.2007.05.022 Medline
70. B. B. Sun, J. C. Maranville, J. E. Peters, D. Stacey, J. R. Staley, J. Blackshaw, S.
Burgess, T. Jiang, E. Paige, P. Surendran, C. Oliver-Williams, M. A. Kamat, B. P.
Prins, S. K. Wilcox, E. S. Zimmerman, A. Chi, N. Bansal, S. L. Spain, A. M. Wood, N.
W. Morrell, J. R. Bradley, N. Janjic, D. J. Roberts, W. H. Ouwehand, J. A. Todd, N.
Soranzo, K. Suhre, D. S. Paul, C. S. Fox, R. M. Plenge, J. Danesh, H. Runz, A. S.
Butterworth, Genomic atlas of the human plasma proteome. Nature 558, 73–79
(2018). doi:10.1038/s41586-018-0175-2 Medline
71. Z. Zhu, Z. Zheng, F. Zhang, Y. Wu, M. Trzaskowski, R. Maier, M. R. Robinson, J. J.
McGrath, P. M. Visscher, N. R. Wray, J. Yang, Causal associations between risk
factors and common diseases inferred from GWAS summary data. Nat. Commun.
9, 224 (2018). doi:10.1038/s41467-017-02317-2 Medline
72. A. N. Honko, N. Storm, D. J. Bean, J. H. Vasquez, S. N. Downs, A. Griffiths, Rapid
Quantification and Neutralization Assays for Novel Coronavirus SARS-CoV-2
Using Avicel RC-591 Semi-Solid Overlay. Preprints 2020050264 [Preprint]. 16
May 2020. www.preprints.org/manuscript/202005.0264/v1.
73. A. Šali, T. L. Blundell, Comparative protein modelling by satisfaction of spatial
restraints. J. Mol. Biol. 234, 779–815 (1993). doi:10.1006/jmbi.1993.1626 Medline
74. T. Yamada, J. Komoto, K. Watanabe, Y. Ohmiya, F. Takusagawa, Crystal structure
and possible catalytic mechanism of microsomal prostaglandin E synthase type 2
(mPGES-2). J. Mol. Biol. 348, 1163–1176 (2005). doi:10.1016/j.jmb.2005.03.035
Medline
75. W. Yin, C. Mao, X. Luan, D.-D. Shen, Q. Shen, H. Su, X. Wang, F. Zhou, W. Zhao, M.
Gao, S. Chang, Y.-C. Xie, G. Tian, H.-W. Jiang, S.-C. Tao, J. Shen, Y. Jiang, H. Jiang,
Y. Xu, S. Zhang, Y. Zhang, H. E. Xu, Structural basis for inhibition of the RNAdependent RNA polymerase from SARS-CoV-2 by remdesivir. Science 368, 1499–
1504 (2020). doi:10.1126/science.abc1560 Medline
76. D. Kozakov, D. R. Hall, B. Xia, K. A. Porter, D. Padhorny, C. Yueh, D. Beglov, S. Vajda,
The ClusPro web server for protein-protein docking. Nat. Protoc. 12, 255–278
(2017). doi:10.1038/nprot.2016.169 Medline
77. B. G. Pierce, K. Wiehe, H. Hwang, B.-H. Kim, T. Vreven, Z. Weng, ZDOCK server:
Interactive docking prediction of protein-protein complexes and symmetric
multimers.
Bioinformatics
30,
1771–1773
(2014).
doi:10.1093/bioinformatics/btu097 Medline
78. Y. Yan, H. Tao, J. He, S.-Y. Huang, The HDOCK server for integrated protein-protein
docking. Nat. Protoc. 15, 1829–1852 (2020). doi:10.1038/s41596-020-0312-x
Medline
79. A. Tovchigrechko, I. A. Vakser, GRAMM-X public web server for protein-protein
docking. Nucleic Acids Res. 34, W310–W314 (2006). doi:10.1093/nar/gkl206
Medline
80. M. Torchala, I. H. Moal, R. A. G. Chaleil, J. Fernandez-Recio, P. A. Bates,
SwarmDock: A server for flexible protein-protein docking. Bioinformatics 29,
807–809 (2013). doi:10.1093/bioinformatics/btt038 Medline
81. D. Schneidman-Duhovny, Y. Inbar, R. Nussinov, H. J. Wolfson, PatchDock and
SymmDock: Servers for rigid and symmetric docking. Nucleic Acids Res. 33,
W363–W367 (2005). doi:10.1093/nar/gki481 Medline
82. G. Q. Dong, H. Fan, D. Schneidman-Duhovny, B. Webb, A. Sali, Optimized atomic
statistical potentials: Assessment of protein interfaces and loops. Bioinformatics
29, 3158–3166 (2013). doi:10.1093/bioinformatics/btt560 Medline
83. J. Armstrong, G. Hickey, M. Diekhans, A. Deran, Q. Fang, D. Xie, S. Feng, J. Stiller,
D. Genereux, J. Johnson, V. D. Marinescu, D. Haussler, J. Alföldi, K. Lindblad-Toh,
E. Karlsson, E. D. Jarvis, G. Zhang, B. Paten, Progressive alignment with Cactus: A
multiple-genome aligner for the thousand-genome era. bioRxiv 730531 [Preprint].
15 October 2019. https://doi.org/10.1101/730531.
84. B. Paten, D. Earl, N. Nguyen, M. Diekhans, D. Zerbino, D. Haussler, Cactus:
Algorithms for genome multiple sequence alignment. Genome Res. 21, 1512–1528
(2011). doi:10.1101/gr.123356.111 Medline
85. M. D. Smith, J. O. Wertheim, S. Weaver, B. Murrell, K. Scheffler, S. L. Kosakovsky
Pond, Less is more: An adaptive branch-site random effects model for efficient
detection of episodic diversifying selection. Mol. Biol. Evol. 32, 1342–1353 (2015).
doi:10.1093/molbev/msv022 Medline
www.sciencemag.org
(Page numbers not final at time of first release) 24
Downloaded from http://science.sciencemag.org/ on October 19, 2020
doi:10.1101/gr.1239303 Medline
49. R. T. Pillich, J. Chen, V. Rynkov, D. Welker, D. Pratt, NDEx: A Community Resource
for Sharing and Publishing of Biological Networks. Methods Mol. Biol. 1558, 271–
301 (2017). doi:10.1007/978-1-4939-6783-4_13 Medline
50. D. K. W. Chu, Y. Pan, S. M. S. Cheng, K. P. Y. Hui, P. Krishnan, Y. Liu, D. Y. M. Ng, C.
K. C. Wan, P. Yang, Q. Wang, M. Peiris, L. L. M. Poon, Molecular Diagnosis of a
Novel Coronavirus (2019-nCoV) Causing an Outbreak of Pneumonia. Clin. Chem.
66, 549–555 (2020). doi:10.1093/clinchem/hvaa029 Medline
51. K. J. Livak, T. D. Schmittgen, Analysis of relative gene expression data using realtime quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25, 402–408
(2001). doi:10.1006/meth.2001.1262 Medline
52. R. Stoner, T. Maures, D. Conant, Methods and systems for guide RNA design and
use,
U.S.
Patent
2019/0382797
A1
(2019);
https://patentimages.storage.googleapis.com/95/c7/43/3d48387ce0f116/US
20190382797A1.pdf.
53. T. Hsiau, D. Conant, N. Rossi, T. Maures, K. Waite, J. Yang, S. Joshi, R. Kelso, K.
Holden, B. L. Enzmann, R. Stoner, Inference of CRISPR Edits from Sanger Trace
Data.
bioRxiv
251082
[Preprint].
10
August
2018.
https://doi.org/10.1101/251082.
54. A. S. Jureka, J. A. Silvas, C. F. Basler, Propagation, Inactivation, and Safety Testing
of SARS-CoV-2. Viruses 12, 622 (2020). doi:10.3390/v12060622 Medline
55. A. C. Y. Fan, M. K. Bhangoo, J. C. Young, Hsp90 functions in the targeting and outer
membrane translocation steps of Tom70-mediated mitochondrial import. J. Biol.
Chem. 281, 33313–33324 (2006). doi:10.1074/jbc.M605250200 Medline
56. S. Backes, S. Hess, F. Boos, M. W. Woellhaf, S. Gödel, M. Jung, T. Mühlhaus, J. M.
Herrmann, Tom70 enhances mitochondrial preprotein import efficiency by
binding to internal targeting sequences. J. Cell Biol. 217, 1369–1382 (2018).
doi:10.1083/jcb.201708044 Medline
57. J. J. Almagro Armenteros, M. Salvatore, O. Emanuelsson, O. Winther, G. von
Heijne, A. Elofsson, H. Nielsen, Detecting sequence signals in targeting peptides
using deep learning. Life Sci. Alliance 2, e201900429 (2019).
doi:10.26508/lsa.201900429 Medline
58. Y. Fukasawa, J. Tsuji, S.-C. Fu, K. Tomii, P. Horton, K. Imai, MitoFates: Improved
prediction of mitochondrial targeting sequences and their cleavage sites. Mol.
Cell. Proteomics 14, 1113–1126 (2015). doi:10.1074/mcp.M114.043083 Medline
59. A. Drozdetskiy, C. Cole, J. Procter, G. J. Barton, JPred4: A protein secondary
structure prediction server. Nucleic Acids Res. 43, W389–W394 (2015).
doi:10.1093/nar/gkv332 Medline
60. D. N. Mastronarde, Automated electron microscope tomography using robust
prediction of specimen movements. J. Struct. Biol. 152, 36–51 (2005).
doi:10.1016/j.jsb.2005.07.007 Medline
61. S. Q. Zheng, E. Palovcak, J.-P. Armache, K. A. Verba, Y. Cheng, D. A. Agard,
MotionCor2: Anisotropic correction of beam-induced motion for improved cryoelectron microscopy. Nat. Methods 14, 331–332 (2017). doi:10.1038/nmeth.4193
Medline
62. S. F. Altschul, T. L. Madden, A. A. Schäffer, J. Zhang, Z. Zhang, W. Miller, D. J.
Lipman, Gapped BLAST and PSI-BLAST: A new generation of protein database
search programs. Nucleic Acids Res. 25, 3389–3402 (1997).
doi:10.1093/nar/25.17.3389 Medline
63. P. Emsley, K. Cowtan, Coot: Model-building tools for molecular graphics. Acta
Cryst. D60, 2126–2132 (2004). doi:10.1107/S0907444904019158 Medline
64. R. Y.-R. Wang, Y. Song, B. A. Barad, Y. Cheng, J. S. Fraser, F. DiMaio, Automated
structure refinement of macromolecular assemblies from cryo-EM maps using
Rosetta. eLife 5, e17219 (2016). doi:10.7554/eLife.17219 Medline
65. R. T. Kidmose, J. Juhl, P. Nissen, T. Boesen, J. L. Karlsen, B. P. Pedersen,
Namdinator - automatic molecular dynamics flexible fitting of structural models
into cryo-EM and crystallography experimental maps. IUCrJ 6, 526–531 (2019).
doi:10.1107/S2052252519007619 Medline
66. T. I. Croll, ISOLDE: A physically realistic environment for model building into lowresolution electron-density maps. Acta Cryst. D74, 519–530 (2018).
doi:10.1107/S2059798318002425 Medline
67. T. D. Goddard, C. C. Huang, E. C. Meng, E. F. Pettersen, G. S. Couch, J. H. Morris,
T. E. Ferrin, UCSF ChimeraX: Meeting modern challenges in visualization and
analysis. Protein Sci. 27, 14–25 (2018). doi:10.1002/pro.3235 Medline
68. P. V. Afonine, B. P. Klaholz, N. W. Moriarty, B. K. Poon, O. V. Sobolev, T. C.
ACKNOWLEDGMENTS
The authors acknowledge their partners and families for support in child care and
other matters during this time. The views, opinions, and findings contained in
this study are those of the authors and do not represent the official views,
policies, or endorsement of the Department of Defense or the U.S. Government.
Imaging at the University of Sheffield was performed in the Wolfson Light
Microscopy facility. A549 cells stably expressing ACE2 (A549-ACE2) were kindly
provided by Dr. Olivier Schwartz. We thank Harmit Malik for helpful discussion.
We thank Mehmet Akdel and Janani Durairaj for input on analysis, Randy
Albrecht for support with the BSL3 facility and procedures at the ISMMS, and
Richard Cadagan for technical assistance. We thank the High Containment Core
at Georgia State University for supporting the BSL3 facility and procedures. We
thank Assaf Alon and Andrew C. Kruse for helpful discussions on sigma receptor
biology. We thank Drs. Steven Deeks, Peter J. Hunt, John Gordan, Chiara
Corbetta-Rastelli and Emma Lantos for insight on clinical applications for drug
repurposing. Funding: This research was funded by grants from the National
Institutes of Health, NIH (P01AI063302, P50AI150476, R01AI120694,
R01AI122747, R01AI143292, U19AI135972 and U19AI135990 to N.J.K.;
P01AI120943, R01AI143292 to C.F.B.; U19 AI135990 to T.I.); by National Institute
of Allergy and Infectious Diseases R01AI128214 to O.R.; National Institute of
Neurological Disorders and Stroke R01 NS089713, the NIH Office of the Director
AI150476 and NIGMS R01 GM24485 to R.S.; by a Fast Grant for COVID-19 from
the Emergent Ventures program at the Mercatus Center of George Mason
University (N.J.K.) and a separate Fast Grant for COVID-19 (C.F.B); by
Roddenberry Foundation Gladstone Institutes to K.S.P.; from the Defense
Advanced Research Projects Agency (HR0011-19-2-0020 to B.K.S., N.J.K.,
First release: 15 October 2020
K.A.V., D.A.A., A.G.-S. and K.M.S.; HR0011-20-2-0040 to M.B.F.); NIGMS
R35GM122481 (to B.K.S.); by CRIP (Center for Research for Influenza
Pathogenesis), a NIAID supported Center of Excellence for Influenza Research
and Surveillance (CEIRS, contract # HHSN272201400008C) to A.G.-S.; by
supplements to NIAID grant U19AI135972 and DoD grant W81XWH-20-1-0270 to
A.G.-S.; by the Bill and Melinda Gates Foundation (INV-006099) and BARDA
(ASPR-20-01495) to M.B.F., by Howard Hughes Medical Institute to K.S.; by
Damon Runyon Cancer Research Foundation DRG-2402-20 to C.P.; by
Burroughs Wellcome Fund 1019894 to N.H.; by the Chan Zuckerberg Initiative to
O.S. and T.K.; by Cytoscape: A modeling platform for biomolecular networks
(NHGRI R01 HG009979) to T.I.; by the generous support of the JPB Foundation,
the Open Philanthropy Project (research grant 2020-215611 (5384) and by
anonymous donors to A.G.-S.; a Laboratoire d’Excellence grant ANR-10-LABX62-IBEID and the URGENCE COVID-19 Institut Pasteur fundraising campaign to
M.V and N.J.; by grants from the BBSRC (BB/S009566/1 and BB/L002841/1) to
A.P. and D.W. and by BBSRC White Rose DTP (BB/J014443/1) to A.S-S.; by The
Augusta University-Georgia State University Seed Grant program to C.F.B.; by
the MRC grant MC PC 19026 to M.P.; by the MRC Grant MC_UU_12016/2 to D.A.;
by the Medical Research Council (MC_UU_12016/2) to D.R.A.; by the DFG under
Germany’s Excellence Strategy (EXC-2189, project ID 390939984 to R.G.); and
funding from F. Hoffmann-La Roche and Vir Biotechnology and gifts from The
Ron Conway Family and Vir Biotechnology to the Quantitative Biosciences
Institute Coronavirus Research Group (QCRG). J.H. was supported by the UCSF
Medical Scientist Training Program (T32GM007618); P.F. was supported by the
UCSF Medical Scientist Training Program (T32GM007618) and the NIH/NIAID
(F30AI143401); M.B. was supported by the NCI at the NIH (F32CA239333);
H.T.K. was supported by the NIH (K99GM138753); K.H. was supported by the
National Science Foundation (1650113); B.T. was supported by the NIH (F32
CA239336); U.S.C. was supported by the National Institute of General Medical
Sciences (F32GM137463). The QCRG Structural Biology Consortium has
received support from: Quantitative Biosciences Institute, Defense Advance
Research Projects Agency (HR0011-19-2-0020 to D.A.A. and K.A.V.; B. S. PI),
FastGrants COVID19 grant (K.A.V. PI), Laboratory For Genomics Research (O. S.
R. PI) and Laboratory For Genomics Research (R.M.S. PI). Author contributions:
The following authors designed and conceptualized the study: A.A.P., A.G-S.,
C.F.B., D.E.G., H.B., J.A.R., J.Ba., J.H., K.A.V., K.O., M.B., M.V., N.J.K., O.S.R., P.B.,
R.G., R.M.K. The following authors performed experiments or data acquisition:
A.A.P., A.Du., A.G., A.J.H., A.L.L., A.R., A.R.W., A.S.J., A.S.S-S., A.V-G., B.T.,
C..J.H., C.Ab., C.Ba., C.F.B., C.J., C.K., C.R.S., D.B., D.E.G., D.K., D.L.S., D.M.,
D.M.W., D.R.A., D.S., E.M., E.Pe., E.Pu., E.R., E.W.T., F.Be., F.Br., G.J., G.K., H.F.,
H.L., I.B-H., I.Du., J.A.R., J.A.S., J.Ba., J.C-S., J.H., J.Lo., J.O., J.X., J.Z.G., K.A.B.,
K.C.K., K.Ho., K.M.W., L.M., M.A.K., M.B., M.B.F., M.Ch., M.C.M., M.D., M.El.,
M.Gu., Ma.Mo., M.M.K., Ma.McG., Mi.McG., M.U., M.V., N.G., N.Jo., P.B., P.D., P.F.,
R.H., R.Ha., R.J.K., R.M.K., R.R., R.To., S.G.W., S.P., S.Ra., S.U., S.W., St. W.,
T.Ke., T.M., T.V., T.W.O., V.V.R., Y.S., Z.Z. The following authors conducted
formal data analysis: A.A.P., A.Du., A.J.H., A.L.L., A.R.W., A.S., A.S.J., A.S.S-S.,
A.V-G., B.S., C..J.H., C.Ab., C.F.B., C.K., D.B., D.E.G., D.L.S., D.M., D.M.W., D.S.,
E.M., E.Pe., E.W.T., G.J., G.K., H.B., I.Du., J.A.R., J.A.S., J.Ba., J.H., Ji.P., K.A.V.,
K.C.K., K.M.W., K.O., K.R.H., K.S.P., L.M., M.A.K., M.B., M.B.F., M.Ca., M.C.M.,
M.Ec., M.Gh., M.Gu., M.J.O., M.M.K., Ma.McG., M.V., N.Jo., N.Ju., O.S.R., P.B.,
R.G., R.H., R.M.K., R.R., S.P., S.U., S.W., T.A.T., T.I., T.V., T.W.O., U.R., V.V.R.,
Yu.Z., Z.C., Z.Z. The following authors supervised or managed research: A.A.P.,
A.F., A.G-S., A.M., A.S., B.S., C.Ab., C.F.B., D.A.A., D.E.G., D.L.S., J.A.R., J.H.,
J.M.F., J.S.F., K.A.V., K.Ho., K.M.S., K.M.W., K.O., K.S.P., M.B., M.B.F., M.Ch.,
M.O., M.P., M.V., N.Jo., N.Ju., N.J.K., O.S.R., P.B., R.G., R.H., R.M.K., T.I., T.Ko.,
Y.C.. The following authors raised funds for these efforts: A.A.P., A.G-S., B.S.,
C.F.B., D.A.A., D.E.G., D.R.A., J.M.F., K.M.S., K.O., K.S.P., M.P., M.V., N.Jo., N.J.K.,
O.S.R., P.B., R.G., T.I. The following authors drafted the original manuscript:
A.A.P., B.S., C.Ab., D.E.G., D.L.S., H.B., J.A.R., J.Ba., J.H., K.A.V., K.Ha., K.M.S.,
K.O., K.R.H., K.S.P., M.A.K., M.B., M.Ca., M.Ec., M.M.K., M.O., M.P., M.So., N.J.K.,
O.S.R., P.B., P.H., R.M.K., T.A.T., U.R., V.V.R., Z.Z.C.N. The structural biology
portion of this work was performed by the Quantitative Biosciences Institute
Coronavirus Research Group Structural Biology Consortium. Listed below are
the contributing members of the consortium listed by teams in order of team
relevance to the published work. The team leads are listed first (responsible for
www.sciencemag.org
(Page numbers not final at time of first release) 25
Downloaded from http://science.sciencemag.org/ on October 19, 2020
86. S. L. K. Pond, S. D. W. Frost, S. V. Muse, HyPhy: Hypothesis testing using
phylogenies.
Bioinformatics
21,
676–679
(2005).
doi:10.1093/bioinformatics/bti079 Medline
87. K. S. Pollard, M. J. Hubisz, K. R. Rosenbloom, A. Siepel, Detection of nonneutral
substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).
doi:10.1101/gr.097857.109 Medline
88. M. J. Hubisz, K. S. Pollard, A. Siepel, PHAST and RPHAST: Phylogenetic analysis
with space/time models. Brief. Bioinform. 12, 41–51 (2011).
doi:10.1093/bib/bbq072 Medline
89. R. Ramani, K. Krumholz, Y.-F. Huang, A. Siepel, PhastWeb: A web interface for
evolutionary conservation scoring of multiple sequence alignments using
phastCons and phyloP. Bioinformatics 35, 2320–2322 (2019).
doi:10.1093/bioinformatics/bty966 Medline
90. W. A. Ray, Evaluating medication effects outside of clinical trials: New-user
designs. Am. J. Epidemiol. 158, 915–920 (2003). doi:10.1093/aje/kwg231
Medline
91. S. Schneeweiss, A basic study design for expedited safety signal evaluation based
on electronic healthcare data. Pharmacoepidemiol. Drug Saf. 19, 858–868 (2010).
doi:10.1002/pds.1926 Medline
92. H. Quan, V. Sundararajan, P. Halfon, A. Fong, B. Burnand, J.-C. Luthi, L. D.
Saunders, C. A. Beck, T. E. Feasby, W. A. Ghali, Coding algorithms for defining
comorbidities in ICD-9-CM and ICD-10 administrative data. Med. Care 43, 1130–
1139 (2005). doi:10.1097/01.mlr.0000182534.19832.83 Medline
93. P. C. Austin, Balance diagnostics for comparing the distribution of baseline
covariates between treatment groups in propensity-score matched samples. Stat.
Med. 28, 3083–3107 (2009). doi:10.1002/sim.3697 Medline
94. World Health Organization (WHO), WHO R&D Blueprint, novel Coronavirus:
COVID-19
Therapeutic
Trial
Synopsis
(WHO,
2020);
www.who.int/blueprint/priority-diseases/key-action/COVID19_Treatment_Trial_Design_Master_Protocol_synopsis_Final_18022020.pdf.
95. Y. Perez-Riverol, A. Csordas, J. Bai, M. Bernal-Llinares, S. Hewapathirana, D. J.
Kundu, A. Inuganti, J. Griss, G. Mayer, M. Eisenacher, E. Pérez, J. Uszkoreit, J.
Pfeuffer, T. Sachsenberg, S. Yilmaz, S. Tiwary, J. Cox, E. Audain, M. Walzer, A. F.
Jarnuczak, T. Ternent, A. Brazma, J. A. Vizcaíno, The PRIDE database and related
tools and resources in 2019: Improving support for quantification data. Nucleic
Acids Res. 47, D442–D450 (2019). doi:10.1093/nar/gky1106 Medline
96. J. Li, X. Qian, J. Hu, B. Sha, Crystal structure of Tom71 complexed with Hsp82 Cterminal fragment (2009); https://doi.org/10.2210/pdb3fp2/pdb.
Zoonomia Consortium Author List
Figs. S1 to S25
Tables S1 to S15
Reference (96)
MDAR Reproducibility Checklist
Movie S1
24 September 2020; accepted 12 October 2020
Published online 15 October 2020
10.1126/science.abe9403
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organization and the experimental design utilized within each team), then the
rest of team members are listed alphabetically. Bacterial expression team: A. D.,
M. G., E. W. T., J. C., L. D., S. F., M. J., H. T. K., V. L. L., Y. L., M. L., G. E. M., J. P., A.
C. T., Z. Y., F. Z., Y. Z. Protein purification team: M. M., T. W. O., S. P., C. M. A., C.
M. C., B. F., M. G., K. K., J. P., J. K. P., K. S., T. K. M. T. CryoEM grid
freezing/collection team: C. M. A., A. F. B., G. E. M., C. P., A. N. R., M. S., J. R. B.,
M. G., F. L., K. E. L., A. M., F. M., J. P., T. H. P., Jr., S. P., A. M. S., P. V. T., F. W., Z.
Y. CryoEM data processing team: A. F. B., M. S. D., G. E. M., H. C. N., A. N. R., D.
A., J. R. B., M. G. C., C. M. C., U. S. C., D. D., B. F., M. G., N. H., M. J., F. L., J. L., Y.
L., J. P., T. H. P., Jr., S. P., S. S., R. T., D. T., E. T., K. Z., F. Z.. Mammalian cell
expression team: C. B., M. G. C., D. D., C. N., A. M. S., J. Z., C. M. A., A. B., N. H., Y.
L., P. N., C. P., M. S., S. S., K. S., R. T., T. K. M. T., N. W. Crystallography team: N.
H., H. T. K.l, U. S.-G., I. D. Y., J. B., I. D., X. L. Infrastructure team: D. B., A. J., A. J.,
L. M., M. T., E. T.. Leadership team: O. S. R., K. A. V., D. A. A., Y. C., J. S. F., A. F.,
N. J., T. K., N. J. K., A. M., D. R. S., R. M. S. All authors edited the manuscript.
Competing interests: The Krogan Laboratory has received research support
from Vir Biotechnology and F. Hoffmann-La Roche. Trey Ideker is the co-founder
of Data4Cure, Inc. with an equity interest, and he has a funded sponsored
research agreement from Ideaya BioSciences, Inc., with an equity stake. The
García-Sastre Laboratory has received research support from Pfizer, Senhwa
Biosciences and 7Hills Pharma and Adolfo García-Sastre has consulting
agreements for the following companies involving cash and/or stock: Vivaldi
Biosciences, Contrafect, 7Hills Pharma, Avimex, Valneva, Accurius and
Esperovax. Kevan Shokat has consulting agreements for the following
companies involving cash and/or stock compensation: Black Diamond
Therapeutics, BridGene Biosciences, Denali Therapeutics, Dice Molecules,
eFFECTOR Therapeutics, Erasca, Genentech/Roche, Janssen Pharmaceuticals,
Kumquat Biosciences, Kura Oncology, Merck, Mitokinin, Petra Pharma, Qulab
Inc. Revolution Medicines, Type6 Therapeutics, Venthera, Wellspring
Biosciences (Araxes Pharma). Jeremy A. Rassen is an employee and shareholder
of Aetion, Inc., a company that makes software for the analysis of real-world
data. Andrew R. Weckstein is an employee and shareholder of Aetion, Inc., a
company that makes software for the analysis of real-world data. Reyna J. Klesh
is an employee of HealthVerity, a company that links and de-identifies real-world
data. James Fraser is a founder of Keyhole Therapeutics and a shareholder of
Relay Therapeutics and Keyhole Therapeutics. The Fraser laboratory has
received sponsored research support from Relay Therapeutics. Kevin Holden,
Jared Carlson-Stevermer, Jennifer Oki and Travis Maures are employees and
shareholders of Synthego Corporation. Aetion holds patents including U.S. Pat.
No. 9,378,271; other patents pending. Data and materials availability: Further
information and requests for resources and reagents should be directed to and
will be fulfilled by Nevan J. Krogan (Nevan.Krogan@ucsf.edu). The mass
spectrometry proteomics data have been deposited to the ProteomeXchange
Consortium via the PRIDE partner repository with the dataset identifier
PXD021588 (95). An interactive version of PPI data can be found at
https://kroganlab.ucsf.edu/network-maps. Atomic coordinates and the cryoEM map of the reported Tom70-Orf9b structure have been deposited in the
Protein Data Bank under accession code 7KDT and in the Electron Microscopy
Data Bank under accession code EMD-22829. Expression vectors used in this
study are readily available from the authors for biomedical researchers and
educators in the non-profit sector. The Aetion Evidence Platform used for the
clinical analysis is available under license from Aetion, New York, NY. To protect
patient privacy, data used in real-world analyses are available for inspection by
qualified researchers under confidentiality and third-party agreements with
Aetion and/or HealthVerity. This work is licensed under a Creative Commons
Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original work is
properly cited. To view a copy of this license, visit
https://creativecommons.org/licenses/by/4.0/. This license does not apply to
figures/photos/artwork or other content included in the article that is credited
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material.
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QCRG Structural Biology Consortium Author List
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Fig. 1. Coronavirus genome annotations and integrative analysis overview. (A) Genome annotation of
SARS-CoV-2, SARS-CoV-1 and MERS-CoV with putative protein coding genes highlighted. Intensity of filled
color indicates the lowest sequence identity between SARS-CoV-2 and SARS-CoV-1 or SARS-CoV-2 and
MERS. (B to D) Genome annotation of structural protein genes for SARS-CoV-2 (B), SARS-CoV-1 (C), and
MERS-CoV (D). Color intensity indicates sequence identity to specified virus. (E) Overview of comparative
coronavirus analysis. Proteins from SARS-CoV-2, SARS-CoV-1 and MERS-CoV were analyzed for their
protein interactions and subcellular localization, and these data were integrated for comparative host
interaction network analysis, followed by functional, structural and clinical data analysis for exemplary virusspecific and pan-viral interactions. *The SARS-CoV-2 interactome was previously published in a separate
study (5). SARS = both SARS-CoV-1 and SARS-CoV-2; MERS = MERS-CoV; Nsp = non-structural protein;
Orf = open reading frame.
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Fig. 2. Coronavirus protein localization analysis. (A) Overview of experimental design to
determine localization of Strep-tagged SARS-CoV-2, SARS-CoV-1, and MERS-CoV proteins
in HeLaM cells (left) or of viral proteins upon SARS-CoV-2 infection in Caco-2 cells (right).
(B) Relative localization for all coronavirus proteins across viruses expressed individually
(blue color bar; * indicates viral proteins of high sequence divergence) or in SARS-CoV-2
infected cells (colored box outlines). (C and D) Localization of Nsp1 and Orf3a expressed
individually (C) or during infection (D); for representative images of all tagged constructs and
viral proteins imaged during infection see figs. S8 to S14 and fig. S15 respectively. (E) Prey
overlap per bait measured as Jaccard index comparing SARS-CoV-2 vs. SARS-CoV-1 (red
dots) and SARS-CoV-2 vs. MERS-CoV (blue dots) for all viral baits (All), viral baits found in
the same cellular compartment (Yes) and viral baits found in different compartments (No).
C-D, Scale bars = 10 µm.
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Fig. 3. Comparative analysis of coronavirus-host interactomes. (A) Clustering analysis (k-means) of interactors from
SARS-CoV-2, SARS-CoV-1, and MERS-CoV weighted according to the average between their MIST and Saint scores
(interaction score K) and percentages of total interactions. Included are only viral protein baits represented amongst all three
viruses and interactions that pass the high-confidence scoring threshold for at least one virus. Seven clusters highlight all
possible scenarios of shared versus unique interactions. (B) GO enrichment analysis of each cluster from A, with the top six
most significant terms per cluster. Color indicates -log10(q) and number of genes with significant (q<0.05; white) or nonsignificant enrichment (q>0.05; grey) is shown. (C) Percentage of interactions for each viral protein belonging to each cluster
identified in A. (D) Correlation between protein sequence identity and PPI overlap (Jaccard index) comparing SARS-CoV-2
and SARS-CoV-1 (blue) or MERS-CoV (red). Interactions for PPI overlap are derived from the final thresholded list of
interactions per virus. (E) GO biological process terms significantly enriched (q<0.05) for all three virus PPIs with Jaccard
index indicating overlap of genes from each term for pairwise comparisons between SARS-CoV-1 and SARS-CoV-2 (purple),
SARS-CoV-1 and MERS-CoV (green) and SARS-CoV-2 and MERS-CoV (orange). (F) Fraction of shared preys between
orthologous (blue) versus non-orthologous (red) viral protein baits. (G) Heatmap depicting overlap in PPIs (Jaccard index)
between each bait from SARS-CoV-2 and MERS-CoV. Baits in grey were not assessed, do not exist, or do not have highconfidence interactors in the compared virus. Non-orthologous bait interactions are highlighted with a red square. GO = Gene
Ontology; PPI = protein-protein interaction; SARS2 = SARS-CoV-2; SARS1 = SARS-CoV-1; MERS = MERS-CoV.
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Fig. 4. Comparative differential interaction analysis reveals shared virus-host interactions. (A) Flowchart
depicting calculation of differential interactions scores (DIS) using the average between the Saint and MIST
scores between every bait (i) and prey (j) to derive interaction score (K). The DIS is the difference between
the interaction scores from each virus. The modified DIS (SARS-MERS) compares the average K from SARSCoV-1 and SARS-CoV-2 to that of MERS-CoV (see Methods). Only viral bait proteins shared between all three
viruses are included. (B) Density histogram of the DIS for all comparisons. (C) Dot plot depicting the DIS of
interactions from viral bait proteins shared between all three viruses, ordered left-to-right by the mean DIS
per viral bait. (D) Virus-human protein-protein interaction map depicting the SARS-MERS comparison
(purple in Fig. 4, B and C). The network depicts interactions derived from cluster 2 (all 3 viruses), cluster 4
(SARS-CoV-1 and SARS-CoV-2), and cluster 5 (MERS-CoV only). Edge color denotes DIS: red, interactions
specific to SARS-CoV-1 and SARS-CoV-2 but absent in MERS-CoV; blue, interactions specific to MERS-CoV
but absent from both SARS-CoV-1 and SARS-CoV-2; black, interactions shared between all three viruses.
Human-human interactions (thin dark grey line), proteins sharing the same protein complexes or biological
processes (light yellow or light blue highlighting, respectively) are shown. Host-host physical interactions,
protein complex definitions, and biological process groupings are derived from CORUM (39), Gene Ontology
(biological process), and manually curated from literature sources. Thin dashed grey lines are used to
indicate the placement of node labels when adjacent node labels would have otherwise been obscured. DIS
= differential interactions score; SARS2 = SARS-CoV-2; SARS1 = SARS-CoV-1; MERS = MERS-CoV; SARS =
both SARS-CoV-1 and SARS-CoV-2.
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Fig. 5. Functional interrogation of SARS-CoV-2 interactors using genetic perturbations. (A) A549-ACE2 cells
were transfected with siRNA pools targeting each of the human genes from the SARS-CoV-2 interactome, followed
by infection with SARS-CoV-2 and virus quantification using RT-qPCR. Cell viability and knockdown efficiency in
uninfected cells was determined in parallel. (B) Caco-2 cells with CRISPR knockouts of each human gene from the
SARS-CoV-2 interactome were infected with SARS-CoV-2, and supernatants were serially diluted and plated onto
Vero E6 cells for quantification. Viabilities of the uninfected CRISPR knockout cells after infection were determined
in parallel by DAPI staining. (C and D) Plot of results from the infectivity screens in A549-ACE2 knockdown cells
(C) and Caco-2 knockout cells (D) sorted by Z-score (Z <0, decreased infectivity; Z >0 increased infectivity).
Negative controls (non-targeting control for siRNA, non-targeted cells for CRISPR) and positive controls (ACE2
knockdown/knockout) are highlighted. (E) Results from both assays with potential hits (|Z| > 2) highlighted in red
(A549-ACE2), yellow (Caco-2) and orange (both). (F) Pan-coronavirus This work is licensed under a Creative
Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit
https://creativecommons.org/licenses/by/4.0/. This license does not apply to figures/photos/artwork or other
content included in the article that is credited to a third party; obtain authorization from the rights holder before
using such material.interactome reduced to human preys with significant increase (red nodes) or decrease (blue
nodes) in SARS-CoV2 replication upon knockdown/knockout. Viral proteins baits from SARS-CoV-2 (red), SARSCoV-1 (orange) and MERS-CoV (yellow) are represented as diamonds. The thickness of the edge indicates the
strength of the PPI in spectral counts. KD = Knockdown; KO = Knockout; PPI = protein-protein interaction.
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Fig. 6. Interaction between Orf9b and human Tom70. (A) Orf9b-Tom70 interaction is conserved between SARSCoV-1 and SARS-CoV-2. (B) Viral titers in Caco-2 cells after CRISPR knockout of TOMM70 or controls. (C) Coimmunoprecipitation of endogenous Tom70 with Strep-tagged Orf9b from SARS-CoV-1 and SARS-CoV-2, Nsp2
from SARS-CoV-1, SARS-CoV-2 and MERS-CoV, or vector control in HEK293T cells. Representative blots of whole
cell lysates and eluates after IP are shown. (D) Size exclusion chromatography traces (10/300 S200 Increase) of
Orf9b alone, Tom70 alone and co-expressed Orf9b-Tom70 complex purified from recombinant expression in E. coli.
Insert shows SDS-PAGE of the complex peak indicating presence of both proteins. (E) Immunostainings for Tom70
in HeLaM cells transfected with GFP-Strep and Orf9b from SARS-CoV-1 and SARS-CoV-2 (left) and mean
fluorescence intensity ± SD values of Tom70 in GFP-Strep and Orf9b expressing cells (normalized to nontransfected cells; right). (F) Flag-Tom70 expression levels in total cell lysates of HEK293T cells upon titration of cotransfected Strep-Orf9b from SARS-CoV-1 and SARS-CoV-2. (G) Immunostaining for Orf9b and Tom70 in Caco-2
cells infected with SARS-CoV-2 (left) and mean fluorescence intensity ± SD values of Tom70 in uninfected and
SARS-CoV-2 infected cells (right). SARS2 = SARS-CoV-2; SARS1 = SARS-CoV-1; MERS = MERS-CoV; IP =
immunoprecipitation. **p < 0.05. B, E, G, Student’s t test. E, scale bar = 10 µm.
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Fig. 7. CryoEM structure of Orf9b-Tom70 complex reveals Orf9b adopting a helical fold and binding at
the substrate recognition site of Tom70. (A) Surface representation of the Orf9b-Tom70 structure. Tom70
is depicted as molecular surface in green, Orf9b is depicted as ribbon in orange. Region in charcoal indicates
Hsp70/Hsp90 binding site on Tom70. (B) Magnified view of Orf9b-Tom70 interactions with interacting
hydrophobic residues on Tom70 indicated and shown in spheres. The two phosphorylation sites on Orf9b,
S50 and S53, are shown in yellow. (C) Ionic interactions between Tom70 and Orf9b are depicted as sticks.
Highly conserved residues on Tom70 making hydrophobic interactions with Orf9b are depicted as spheres.
(D) Diagram depicting secondary structure comparison of Orf9b as predicted by JPred server, as visualized
in our structure, or as visualized in the previously-crystallized dimer structure (PDB:6Z4U) (16). Pink tubes
indicate helices, charcoal arrows indicate beta strands, amino acid sequence for the region visualized in the
cryoEM structure is shown on top. (E) Predicted probability of possessing an internal MTS as output by
TargetP server by serially running N-terminally truncated regions of SARS-CoV-2 Orf9b. Region visualized in
the cryoEM structure (amino acids 39-76) overlaps with the highest internal MTS probability region (amino
acids 40-50). MTS = mitochondrial targeting signal.
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Fig. 8. SARS-CoV-2 Orf8 and functional interactor IL17RA are linked to
viral outcomes. (A) IL17RA and ADAM9 are functional interactors of
SARS-CoV-2 Orf8. Only interactors identified in the genetic screening are
shown. (B) Co-immunoprecipitation of endogenous IL17RA with Streptagged Orf8 or EGFP with or without IL-17A treatment at different times.
Overexpression was done in HEK293T cells. (C) Viral titer after IL17RA or
control knockdown in A549-ACE2 cells. (D) Odds ratio of membership in
indicated cohorts by genetically-predicted sIL17RA levels. SARS2 =
SARS-CoV-2; IP = immunoprecipitation; SD = standard deviation; OR =
odds ratio; CI = confidence interval; sIL17RA = soluble IL17RA. * = p <0.05.
C, unpaired t test. Error bars in C indicate SD; in D they indicate 95% CI.
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Fig. 9. Real-world data analysis of drugs identified through molecular investigation support their antiviral
activity. (A) Schematic of retrospective real-world clinical data analysis of indomethacin use for outpatients
with SARS-CoV-2. Plots show distribution of propensity scores for all included patients (red, indomethacin
users; blue, celecoxib users). For a full list of inclusion, exclusion, and matching criteria see Methods and
table S11. (B) Effectiveness of indomethacin vs. celecoxib in patients with confirmed SARS-CoV-2 infection
treated in an outpatient setting. Average standardized absolute mean difference (ASAMD) is a measure of
balance between indomethacin and celecoxib groups calculated as the mean of the absolute standardized
difference for each propensity score factor (table S11); p-value and odds ratios with 95% CI are estimated
using the Aetion Evidence Platform r4.6. No ASAMD was greater than 0.1. (C) Schematic of retrospective
real-world clinical data analysis of typical antipsychotic use for inpatients with SARS-CoV-2. Plots show
distribution of propensity scores for all included patients (red, typical users; blue, atypical users). For a full
list of inclusion, exclusion, and matching criteria see Methods and table S11. (D) Effectiveness of typical vs.
atypical antipsychotics among hospitalized patients with confirmed SARS-CoV-2 infection treated inhospital. Average standardized absolute mean difference (ASAMD) is a measure of balance between typical
and atypical groups calculated as the mean of the absolute standardized difference for each propensity
score factor (table S11); p-value and odds ratios with 95% CI are estimated using the Aetion Evidence
Platform r4.6. No ASAMD was greater than 0.1.
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Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms
published online October 15, 2020
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Manon Eckhardt, Danielle L. Swaney, Jacqueline M. Fabius, Manisha Ummadi, Beril Tutuncuoglu, Ujjwal Rathore, Maya Modak,
Paige Haas, Kelsey M. Haas, Zun Zar Chi Naing, Ernst H. Pulido, Ying Shi, Inigo Barrio-Hernandez, Danish Memon, Eirini
Petsalaki, Alistair Dunham, Miguel Correa Marrero, David Burke, Cassandra Koh, Thomas Vallet, Jesus A. Silvas, Caleigh M.
Azumaya, Christian Billesbølle, Axel F. Brilot, Melody G. Campbell, Amy Diallo, Miles Sasha Dickinson, Devan Diwanji, Nadia
Herrera, Nick Hoppe, Huong T. Kratochvil, Yanxin Liu, Gregory E. Merz, Michelle Moritz, Henry C. Nguyen, Carlos Nowotny,
Cristina Puchades, Alexandrea N. Rizo, Ursula Schulze-Gahmen, Amber M. Smith, Ming Sun, Iris D. Young, Jianhua Zhao, Daniel
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Lopez, Arthur A. Melo, Frank R. Moss III, Phuong Nguyen, Joana Paulino, Komal Ishwar Pawar, Jessica K. Peters, Thomas H.
Pospiech Jr., Maliheh Safari, Smriti Sangwan, Kaitlin Schaefer, Paul V. Thomas, Aye C. Thwin, Raphael Trenker, Eric Tse, Tsz Kin
Martin Tsui, Feng Wang, Natalie Whitis, Zanlin Yu, Kaihua Zhang, Yang Zhang, Fengbo Zhou, Daniel Saltzberg, QCRG Structural
Biology Consortium, Anthony J. Hodder, Amber S. Shun-Shion, Daniel M. Williams, Kris M. White, Romel Rosales, Thomas Kehrer,
Lisa Miorin, Elena Moreno, Arvind H. Patel, Suzannah Rihn, Mir M. Khalid, Albert Vallejo-Gracia, Parinaz Fozouni, Camille R.
Simoneau, Theodore L. Roth, David Wu, Mohd Anisul Karim, Maya Ghoussaini, Ian Dunham, Francesco Berardi, Sebastian
Weigang, Maxime Chazal, Jisoo Park, James Logue, Marisa McGrath, Stuart Weston, Robert Haupt, C. James Hastie, Matthew
Elliott, Fiona Brown, Kerry A. Burness, Elaine Reid, Mark Dorward, Clare Johnson, Stuart G. Wilkinson, Anna Geyer, Daniel M.
Giesel, Carla Baillie, Samantha Raggett, Hannah Leech, Rachel Toth, Nicola Goodman, Kathleen C. Keough, Abigail L. Lind,
Zoonomia Consortium, Reyna J. Klesh, Kafi R. Hemphill, Jared Carlson-Stevermer, Jennifer Oki, Kevin Holden, Travis Maures,
Katherine S. Pollard, Andrej Sali, David A. Agard, Yifan Cheng, James S. Fraser, Adam Frost, Natalia Jura, Tanja Kortemme,
Aashish Manglik, Daniel R. Southworth, Robert M. Stroud, Dario R. Alessi, Paul Davies, Matthew B. Frieman, Trey Ideker, Carmen
Abate, Nolwenn Jouvenet, Georg Kochs, Brian Shoichet, Melanie Ott, Massimo Palmarini, Kevan M. Shokat, Adolfo García-Sastre,
Jeremy A. Rassen, Robert Grosse, Oren S. Rosenberg, Kliment A. Verba, Christopher F. Basler, Marco Vignuzzi, Andrew A. Peden,
Pedro Beltrao and Nevan J. Krogan