Biomedical Engineering; Molecular Medicine; Applied Microbiology and Biotechnology; Bioengineering; Biotechnology
Abstract :
[en] Understanding the mechanisms of coronavirus disease 2019 (COVID-19) disease severity to efficiently design therapies for emerging virus variants remains an urgent challenge of the ongoing pandemic. Infection and immune reactions are mediated by direct contacts between viral molecules and the host proteome, and the vast majority of these virus–host contacts (the ‘contactome’) have not been identified. Here, we present a systematic contactome map of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with the human host encompassing more than 200 binary virus–host and intraviral protein–protein interactions. We find that host proteins genetically associated with comorbidities of severe illness and long COVID are enriched in SARS-CoV-2 targeted network communities. Evaluating contactome-derived hypotheses, we demonstrate that viral NSP14 activates nuclear factor κB (NF-κB)-dependent transcription, even in the presence of cytokine signaling. Moreover, for several tested host proteins, genetic knock-down substantially reduces viral replication. Additionally, we show for USP25 that this effect is phenocopied by the small-molecule inhibitor AZ1. Our results connect viral proteins to human genetic architecture for COVID-19 severity and offer potential therapeutic targets.
F.R.S.-FNRS - Fonds de la Recherche Scientifique BAEF - Belgian American Educational Foundation Télévie
Funding number :
Fonds de la Recherche Scientifique (FRS-FNRS) grant PER-40003579
Funding text :
We thank P. Charneau for the hACE2 lentivirus. This work was supported by a Canadian Institutes for Health Research Foundation Grant (F.P.R.), the Canada Excellence Research Chairs Program (F.P.R.), the Thistledown Foundation (F.P.R.); the LabEx Integrative Biology of Emerging Infectious Diseases (10-LABX-0062; Y.J., C.D.) and Platform for European Preparedness Against (Re-)emerging Epidemics, EU (602525; Y.J. and C.D.), the European Union’s Horizon 2020 Research and Innovation Programme (Project ID 101003633, RiPCoN; P.F.-B., C.B., P.A.), HDHL-INTIMIC ‘Interrelation of the Intestinal Microbiome, Diet and Health’ (BMBF Project ID 01EA1803; P.F.-B.), the Free State of Bavaria’s AI for Therapy (AI4T) Initiative through the Institute of AI for Drug Discovery (AID) (P.F.-B.) and Fonds de la Recherche Scientifique (FRS-FNRS) grant PER-40003579 (J.-C.T., L.W.). F.L. was supported by a Belgian American Educational Foundation doctoral research fellowship, a Wallonia-Brussels International (WBI)-World Excellence fellowship and Fonds de la Recherche Scientifique (FRS-FNRS)-Télévie grant FC31747 (Crédit n° 7459421F). M.V. is a Chercheur Qualifié Honoraire from the Fonds de la Recherche Scientifique (FRS-FNRS, Wallonia-Brussels Federation, Belgium). C.P. was supported by a Ramon y Cajal fellowship (RYC-2017–22959). G.D. was supported by the Ministère de l’Education Nationale, de la Recherche et de l’Innovation with a fellowship from Université Paris Cité.
Nalbandian, A. et al. Post-acute COVID-19 syndrome. Nat. Med. 27, 601–615 (2021). DOI: 10.1038/s41591-021-01283-z
Yu, H. et al. High-quality binary protein interaction map of the yeast interactome network. Science 322, 104–110 (2008). DOI: 10.1126/science.1158684
Altmann, M. et al. Extensive signal integration by the phytohormone protein network. Nature 583, 271–276 (2020). DOI: 10.1038/s41586-020-2460-0
Yachie, N. et al. Pooled-matrix protein interaction screens using Barcode Fusion Genetics. Mol. Syst. Biol. 12, 863 (2016). DOI: 10.15252/msb.20156660
Braun, P. et al. An experimentally derived confidence score for binary protein-protein interactions. Nat. Methods 6, 91–97 (2009). DOI: 10.1038/nmeth.1281
Choi, S. G. et al. Maximizing binary interactome mapping with a minimal number of assays. Nat. Commun. 10, 3907 (2019). DOI: 10.1038/s41467-019-11809-2
Li, Y. et al. SARS-CoV-2 induces double-stranded RNA-mediated innate immune responses in respiratory epithelial-derived cells and cardiomyocytes. Proc. Natl. Acad. Sci. U. S. A. 118, e2022643118 (2021). DOI: 10.1073/pnas.2022643118
Orchard, S. et al. The MIntAct project–IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42, D358–D363 (2014). DOI: 10.1093/nar/gkt1115
Stukalov, A. et al. Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV. Nature 594, 246–252 (2021). DOI: 10.1038/s41586-021-03493-4
Bouhaddou, M. et al. The global phosphorylation landscape of SARS-CoV-2 infection. Cell 182, 685–712.e19 (2020). DOI: 10.1016/j.cell.2020.06.034
Kamel, W. et al. Global analysis of protein-RNA interactions in SARS-CoV-2 infected cells reveals key regulators of infection. Mol. Cell 81, 2851–2867 (2021). DOI: 10.1016/j.molcel.2021.05.023
Gordon, D. E. et al. Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms. Science 370, eabe9403 (2020b). DOI: 10.1126/science.abe9403
Gordon, D. E. et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature 583, 459–468 (2020a). DOI: 10.1038/s41586-020-2286-9
Li, J. et al. Virus-host interactome and proteomic survey reveal potential virulence factors influencing SARS-CoV-2 pathogenesis. Med (N Y) 2, 99–112.e7 (2021).
Nabeel-Shah, S. et al. SARS-CoV-2 nucleocapsid protein binds host mRNAs and attenuates stress granules to impair host stress response. iScience 25, 103562 (2022). DOI: 10.1016/j.isci.2021.103562
Laurent, E. M. N. et al. Global BioID-based SARS-CoV-2 proteins proximal interactome unveils novel ties between viral polypeptides and host factors involved in multiple COVID19-associated mechanisms. Preprint at bioRxiv https://doi.org/10.1101/2020.08.28.272955 (2020).
St-Germain, J. R. et al. A SARS-CoV-2 BioID-based virus-host membrane protein interactome and virus peptide compendium: new proteomics resources for COVID-19 research. Preprint at bioRxiv https://doi.org/10.1101/2020.08.28.269175 (2020).
Samavarchi-Tehrani, P. et al. A SARS-CoV-2–host proximity interactome. Preprint at bioRxiv https://doi.org/10.1101/2020.09.03.282103 (2020).
Wierbowski, S. D. et al. A 3D structural SARS-CoV-2–human interactome to explore genetic and drug perturbations. Nat. Methods 18, 1477–1488 (2021). DOI: 10.1038/s41592-021-01318-w
Callard, F. & Perego, E. How and why patients made long covid. Soc. Sci. Med. 268, 113426 (2021). DOI: 10.1016/j.socscimed.2020.113426
Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015). DOI: 10.1126/science.1260419
Dorward, D. A. et al. Tissue-specific immunopathology in fatal COVID-19. Am. J. Respir. Crit. Care Med. 203, 192–201 (2021). DOI: 10.1164/rccm.202008-3265OC
Zhao, X. et al. LY6E restricts entry of human coronaviruses, including currently pandemic SARS-CoV-2. J. Virol. 94, e00562-20 (2020). DOI: 10.1128/JVI.00562-20
Garcia-Moreno, M. et al. System-wide profiling of RNA-binding proteins uncovers key regulators of virus infection. Mol. Cell 74, 196–211 (2019). DOI: 10.1016/j.molcel.2019.01.017
Zanzoni, A., Spinelli, L., Ribeiro, D. M., Tartaglia, G. G. & Brun, C. Post-transcriptional regulatory patterns revealed by protein-RNA interactions. Sci. Rep. 9, 4302 (2019). DOI: 10.1038/s41598-019-40939-2
Luck, K. et al. A reference map of the human binary protein interactome. Nature 580, 402–408 (2020). DOI: 10.1038/s41586-020-2188-x
Kruse, T. et al. Large scale discovery of coronavirus-host factor protein interaction motifs reveals SARS-CoV-2 specific mechanisms and vulnerabilities. Nat. Commun. 12, 6761 (2021). DOI: 10.1038/s41467-021-26498-z
Ferrari, S. et al. Mutations of CD40 gene cause an autosomal recessive form of immunodeficiency with hyper IgM. Proc. Natl. Acad. Sci. U. S. A. 98, 12614–12619 (2001). DOI: 10.1073/pnas.221456898
de Vries, L., Gat-Yablonski, G., Dror, N., Singer, A. & Phillip, M. A novel MKRN3 missense mutation causing familial precocious puberty. Hum. Reprod. 29, 2838–2843 (2014). DOI: 10.1093/humrep/deu256
Zhong, Q. et al. An inter-species protein-protein interaction network across vast evolutionary distance. Mol. Syst. Biol. 12, 865 (2016). DOI: 10.15252/msb.20156484
Sahni, N. et al. Widespread macromolecular interaction perturbations in human genetic disorders. Cell 161, 647–660 (2015). DOI: 10.1016/j.cell.2015.04.013
Pairo-Castineira, E. et al. Genetic mechanisms of critical illness in COVID-19. Nature 591, 92–98 (2021). DOI: 10.1038/s41586-020-03065-y
COVID-19 Host Genetics Initiative. Mapping the human genetic architecture of COVID-19. Nature 600, 472–477 (2021). DOI: 10.1038/s41586-021-03767-x
Whyte, P. et al. Association between an oncogene and an anti-oncogene: the adenovirus E1A proteins bind to the retinoblastoma gene product. Nature 334, 124–129 (1988). DOI: 10.1038/334124a0
Weßling, R. et al. Convergent targeting of a common host protein-network by pathogen effectors from three kingdoms of life. Cell Host Microbe 16, 364–375 (2014). DOI: 10.1016/j.chom.2014.08.004
Ostaszewski, M. et al. COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms. Mol. Syst. Biol. 17, e10387 (2021).
Soveg, F. W. et al. Endomembrane targeting of human OAS1 p46 augments antiviral activity. eLife 10, e71047 (2021). DOI: 10.7554/eLife.71047
Cifuentes-Muñoz, N., Dutch, R. E. & Cattaneo, R. Direct cell-to-cell transmission of respiratory viruses: the fast lanes. PLoS Pathog 14, e1007015 (2018). DOI: 10.1371/journal.ppat.1007015
Zhu, Y. et al. A genome-wide CRISPR screen identifies host factors that regulate SARS-CoV-2 entry. Nat. Commun. 12, 961 (2021). DOI: 10.1038/s41467-021-21213-4
Daniloski, Z. et al. Identification of required host factors for SARS-CoV-2 infection in human cells. Cell 184, 92–105.e16 (2021). DOI: 10.1016/j.cell.2020.10.030
Barbeira, A. N. et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol 22, 49 (2021). DOI: 10.1186/s13059-020-02252-4
Bliddal, S. et al. Acute and persistent symptoms in non-hospitalized PCR-confirmed COVID-19 patients. Sci. Rep. 11, 13153 (2021). DOI: 10.1038/s41598-021-92045-x
Whiting, A., Reyes, J. V. M., Ahmad, S. & Lieber, J. Post-COVID-19 fatigue: a case of infectious hypothyroidism. Cureus 13, e14815 (2021).
Mohan, M., Perry, B. I., Saravanan, P. & Singh, S. P. COVID-19 in people with schizophrenia: potential mechanisms linking schizophrenia to poor prognosis. Front. Psychiatry 12, 666067 (2021). DOI: 10.3389/fpsyt.2021.666067
VanderWeele, T. J. Genetic self knowledge and the future of epidemiologic confounding. Am. J. Hum. Genet. 87, 168–172 (2010). DOI: 10.1016/j.ajhg.2010.08.007
Li, T. et al. SARS-CoV-2 Nsp14 activates NF-κB signaling and induces IL-8 upregulation. Preprint at bioRxiv https://doi.org/10.1101/2021.05.26.445787 (2021).
Hadjadj, J. et al. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science 369, 718–724 (2020). DOI: 10.1126/science.abc6027
Sun, G. et al. Comparative transcriptomic analysis of SARS-CoV-2 infected cell model systems reveals differential innate immune responses. Sci. Rep. 11, 17146 (2021). DOI: 10.1038/s41598-021-96462-w
Costela-Ruiz, V. J., Illescas-Montes, R., Puerta-Puerta, J. M., Ruiz, C. & Melguizo-Rodríguez, L. SARS-CoV-2 infection: the role of cytokines in COVID-19 disease. Cytokine Growth Factor Rev. 54, 62–75 (2020). DOI: 10.1016/j.cytogfr.2020.06.001
Hayden, M. S. & Ghosh, S. Regulation of NF-κB by TNF family cytokines. Semin. Immunol. 26, 253–266 (2014). DOI: 10.1016/j.smim.2014.05.004
Lin, D. et al. Induction of USP25 by viral infection promotes innate antiviral responses by mediating the stabilization of TRAF3 and TRAF6. Proc. Natl. Acad. Sci. U. S. A. 112, 11324–11329 (2015). DOI: 10.1073/pnas.1509968112
Wrigley, J. D. et al. Identification and characterization of dual inhibitors of the USP25/28 deubiquitinating enzyme subfamily. ACS Chem. Biol. 12, 3113–3125 (2017). DOI: 10.1021/acschembio.7b00334
Xie, X. et al. An infectious cDNA clone of SARS-CoV-2. Cell Host Microbe 27, 841–848.e3 (2020). DOI: 10.1016/j.chom.2020.04.004
Hou, Y. J. et al. SARS-CoV-2 reverse genetics reveals a variable infection gradient in the respiratory tract. Cell 182, 429–446 (2020). DOI: 10.1016/j.cell.2020.05.042
Grodzki, M. et al. Genome-scale CRISPR screens identify host factors that promote human coronavirus infection. Genome Med. 14, 10 (2022). DOI: 10.1186/s13073-022-01013-1
Chang, L.-J. & Chen, T.-H. NSP16 2’-O-MTase in coronavirus pathogenesis: Possible prevention and treatments strategies. Viruses 13, 538 (2021). DOI: 10.3390/v13040538
Alshiraihi, I. M., Klein, G. L. & Brown, M. A. Targeting NSP16 methyltransferase for the broad-spectrum clinical management of coronaviruses: managing the next pandemic. Diseases 9, 12 (2021). DOI: 10.3390/diseases9010012
Li, Q. et al. The impact of mutations in SARS-CoV-2 spike on viral infectivity and antigenicity. Cell 182, 1284–1294.e9 (2020). DOI: 10.1016/j.cell.2020.07.012
Syed, A. M. et al. Rapid assessment of SARS-CoV-2 evolved variants using virus-like particles. Science 374, 1626–1632 (2021). DOI: 10.1126/science.abl6184
Kim, D.-K. et al. A comprehensive, flexible collection of SARS-CoV-2 coding regions. G3 10, 3399–3402 (2020). DOI: 10.1534/g3.120.401554
Wu, F. et al. A new coronavirus associated with human respiratory disease in China. Nature 579, 265–269 (2020). DOI: 10.1038/s41586-020-2008-3
Wu, A. et al. Genome composition and divergence of the novel coronavirus (2019-nCoV) originating in China. Cell Host Microbe 27, 325–328 (2020). DOI: 10.1016/j.chom.2020.02.001
Jungreis, I. et al. Conflicting and ambiguous names of overlapping ORFs in the SARS-CoV-2 genome: A homology-based resolution. Virology 558, 145–151 (2021). DOI: 10.1016/j.virol.2021.02.013
Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009). DOI: 10.1038/nmeth.1318
Altmann, M., Altmann, S., Falter, C. & Falter-Braun, P. High-quality yeast-2-hybrid interaction network mapping. Curr. Protoc. Plant Biol. 3, e20067 (2018). DOI: 10.1002/cppb.20067
Weile, J. et al. A framework for exhaustively mapping functional missense variants. Mol. Syst. Biol. 13, 957 (2017). DOI: 10.15252/msb.20177908
The ORFeome Collaboration. The ORFeome collaboration: a genome-scale human ORF-clone resource. Nat. Methods 13, 191–192 (2016). DOI: 10.1038/nmeth.3776
Fisher, Y. & Koltun, V. Multi-Scale Context Aggregation by Dilated Convolutions. CoRR abs/1511.07122 (JMLR.org, 2016): n. pag.
Maas, A. L, Hannun, A. Y & Ng, A. Y. Rectifier nonlinearities improve neural network acoustic models. Proceedings of the 30th International Conference on Machine Learning, 30 (Atlanta, GA, 2013).
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at arXiv https://arxiv.org/abs/1412.6980 (2014).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012). DOI: 10.1038/nmeth.1923
Chen, C. et al. CoV-Spectrum: analysis of globally shared SARS-CoV-2 data to identify and characterize new variants.Bioinformatics 38, 1735–1737 (2021). DOI: 10.1093/bioinformatics/btab856
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13, 2498–2504 (2003). DOI: 10.1101/gr.1239303
Reimand, J., Kull, M., Peterson, H., Hansen, J. & Vilo, J. g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res 35, W193 (2007). DOI: 10.1093/nar/gkm226
Carbon, S. et al. AmiGO: online access to ontology and annotation data. Bioinformatics 25, 288 (2009). DOI: 10.1093/bioinformatics/btn615
Mistry, J. et al. Pfam: the protein families database in 2021. Nucleic Acids Res. 49, D412 (2021). DOI: 10.1093/nar/gkaa913
Shin, C. et al. MKRN2 is a novel ubiquitin E3 ligase for the p65 subunit of NF-κB and negatively regulates inflammatory responses. Sci. Rep. 7, 46097 (2017). DOI: 10.1038/srep46097
Götte, B. et al. Separate domains of G3BP promote efficient clustering of alphavirus replication complexes and recruitment of the translation initiation machinery. PLoS Pathog. 15, e1007842 (2019). DOI: 10.1371/journal.ppat.1007842
Hosmillo, M. et al. Noroviruses subvert the core stress granule component G3BP1 to promote viral VPg-dependent translation. eLife 8, e46681 (2019). DOI: 10.7554/eLife.46681
Liu, S., Dominska-Ngowe, M. & Dykxhoorn, D. M. Target silencing of components of the conserved oligomeric Golgi complex impairs HIV-1 replication. Virus Res. 192, 92–102 (2014). DOI: 10.1016/j.virusres.2014.08.015
Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 25, 402–408 (2001). DOI: 10.1006/meth.2001.1262
Meinhardt, J. et al. Olfactory transmucosal SARS-CoV-2 invasion as a port of central nervous system entry in individuals with COVID-19. Nat. Neurosci. 24, 168–175 (2020). DOI: 10.1038/s41593-020-00758-5
Becker, E., Robisson, B., Chapple, C. E., Guénoche, A. & Brun, C. Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics 28, 84–90 (2012). DOI: 10.1093/bioinformatics/btr621
Kalinka, A. T. & Tomancak, P. linkcomm: an R package for the generation, visualization, and analysis of link communities in networks of arbitrary size and type. Bioinformatics 27, 2011–2012 (2011). DOI: 10.1093/bioinformatics/btr311
Chapple, C. E. et al. Extreme multifunctional proteins identified from a human protein interaction network. Nat. Commun. 6, 7412 (2015). DOI: 10.1038/ncomms8412
Barbeira, A. N. et al. GWAS and GTEx QTL integration. Zenodo 10.5281/ZENODO.3518299 (2019).
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015). DOI: 10.1371/journal.pcbi.1004219
Coutant, E. P. et al. Bioluminescence profiling of NanoKAZ/NanoLuc luciferase using a chemical library of coelenterazine analogues. Chemistry 26, 948–958 (2020). DOI: 10.1002/chem.201904844
Kim, D.K. et al. IM-28880. IMEx. https://www.ebi.ac.uk/legacy-intact/query/pubid:unassigned2933;jsessionid=E9D9D501AAC618B88078DBD0BD47AEFA?conversationContext=1 (2022).
Kim, D.K. et al. SARS-CoV-2-contactome. GitHub. https://github.com/INET-HMGU/SARS-CoV-2-contactome (2022).
Barron, E. et al. Associations of type 1 and type 2 diabetes with COVID-19-related mortality in England: a whole-population study. Lancet Diabetes Endocrinol. 8, 813–822 (2020). DOI: 10.1016/S2213-8587(20)30272-2
Leong, A. et al. Cardiometabolic risk factors for COVID-19 susceptibility and severity: a Mendelian randomization analysis. PLoS Med. 18, e1003553 (2021). DOI: 10.1371/journal.pmed.1003553
Nikniaz, Z., Somi, M. H., Dinevari, M. F., Taghizadieh, A. & Mokhtari, L. Diabesity associates with poor COVID-19 outcomes among hospitalized patients. J. Obes. Metab. Syndr. 30, 149–154 (2021). DOI: 10.7570/jomes20121
Aung, N., Khanji, M. Y., Munroe, P. B. & Petersen, S. E. Causal inference for genetic obesity, cardiometabolic profile and COVID-19 susceptibility: a Mendelian randomization study. Front. Genet. 11, 586308 (2020). DOI: 10.3389/fgene.2020.586308
Freuer, D., Linseisen, J. & Meisinger, C. Impact of body composition on COVID-19 susceptibility and severity: a two-sample multivariable Mendelian randomization study. Metabolism 118, 154732 (2021). DOI: 10.1016/j.metabol.2021.154732
Wang, C. et al. Red cell distribution width (RDW): a prognostic indicator of severe COVID-19. Ann. Transl. Med. 8, 1230 (2020). DOI: 10.21037/atm-20-6090
Ouyang, S.-M. et al. Temporal changes in laboratory markers of survivors and non-survivors of adult inpatients with COVID-19. BMC Infect. Dis. 20, 952 (2020). DOI: 10.1186/s12879-020-05678-0
Kearns, S. M. et al. Reduced adiponectin levels in patients with COVID-19 acute respiratory failure: a case-control study. Physiol Rep. 9, e14843 (2021). DOI: 10.14814/phy2.14843
Hypothyroidism is associated with prolonged COVID-19-induced anosmia: a case-control study. J. Neurol. Neurosurg. Psychiatry 20, jnnp–2021–326587 (2021).
Brancatella, A. et al. Subacute thyroiditis after SARS-CoV-2 infection. J. Clin. Endocrinol. Metab. 105, dgaa276 (2020). DOI: 10.1210/clinem/dgaa276
Nemani, K. et al. Association of psychiatric disorders with mortality among patients with COVID-19. JAMA Psychiatry 78, 380–386 (2021). DOI: 10.1001/jamapsychiatry.2020.4442
Zhu, Z. et al. Association of obesity and its genetic predisposition with the risk of severe COVID-19: analysis of population-based cohort data. Metabolism 112, 154345 (2020). DOI: 10.1016/j.metabol.2020.154345
Derikx, L. A. A. P. et al. Clinical outcomes of COVID-19 in patients with inflammatory bowel disease: a nationwide cohort study. J. Crohns. Colitis 15, 529–539 (2021). DOI: 10.1093/ecco-jcc/jjaa215
Dar, H. Y., Azam, Z., Anupam, R., Mondal, R. K. & Srivastava, R. K. Osteoimmunology: the between bone and immune system. Front. Biosci. 23, 464–492 (2018). DOI: 10.2741/4600
