The DOI matches its Crossref record; 51 of the 53 references it cites were found in a registry or an index.
Proteomics of SARS-CoV-2-infected host cells reveals therapy targets
Open accessPublished version of a preprint
Abstract
A new coronavirus was recently discovered and named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infection with SARS-CoV-2 in humans causes coronavirus disease 2019 (COVID-19) and has been rapidly spreading around the globe 1,2. SARS-CoV-2 shows some similarities to other coronaviruses; however, treatment options and an understanding of how SARS-CoV-2 infects cells are lacking. Here we identify the host cell pathways that are modulated by SARS-CoV-2 and show that inhibition of these pathways prevents viral replication in human cells. We established a human cell-culture model for infection with a clinical isolate of SARS-CoV-2. Using this cell-culture system, we determined the infection profile of SARS-CoV-2 by translatome 3 and proteome proteomics at different times after infection. These analyses revealed that SARS-CoV-2 reshapes central cellular pathways such as translation, splicing, carbon metabolism, protein homeostasis (proteostasis) and nucleic acid metabolism. Small-molecule inhibitors that target these pathways prevented viral replication in cells. Our results reveal the cellular infection profile of SARS-CoV-2 and have enabled the identification of drugs that inhibit viral replication. We anticipate that our results will guide efforts to understand the molecular mechanisms that underlie the modulation of host cells after infection with SARS-CoV-2. Furthermore, our findings provide insights for the development of therapies for the treatment of COVID-19.
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“Clinical isolate infection models have been developed using Caco-2 cells154 with temporal proteome changes identified during infection using multiplexed MS by combining metabolic labelling with tandem mass tagging methods.”
From A high-stringency blueprint of the human proteome (2020) · cites this paper as [154]
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“Building on this knowledge of coronavirus infection, recent proteomics studies have focused on SARS-CoV-2, uncovering additional potential therapeutic targets154,155.”
From A high-stringency blueprint of the human proteome (2020) · cites this paper as [154]
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Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
A novel coronavirus from patients with pneumonia in China, 2019
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001017 · Cited by 28,244
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
Martínez González, M. (2020) · DSPACE System - Metalibrary (University of the Coast) · Cited by 18,463
Data Structures for Statistical Computing in Python
McKinney, W. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-00a · Cited by 12,930
The NumPy Array: A Structure for Efficient Numerical Computation
van der Walt, S., Colbert, S. C., & Varoquaux, G. (2011) · Computing in Science & Engineering · doi:10.1109/mcse.2011.37 · Cited by 9,381
The Perseus computational platform for comprehensive analysis of (prote)omics data
Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M. Y., Geiger, T., Mann, M., & Cox, J. (2016) · Nature Methods · doi:10.1038/nmeth.3901 · Cited by 8,803
Chan, J., Yuan, S.-F., Kok, K.-H., To, K., Chu, H., Yang, J., Xing, F., Liu, J.-L., Yip, C., Poon, R., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30154-9 · Cited by 8,450
A scaling normalization method for differential expression analysis of RNA-seq data
Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876
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Metadata
- Authors
- Bojkova, D., Klann, K., Koch, B., Widera, M., Krause, D., Ciesek, S., Cinatl, J., & Münch, C.
- Affiliations
- Goethe University Frankfurt (DE)
- University Hospital Frankfurt (DE)
- Corresponding author
- Cinatl, Jindrich
- Münch, Christian
- ORCID
- Venue
- Nature
- Published
- 2020-05-14
- Volume
- 583
- Issue
- 7816
- Pages
- 469-472
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC7616921
- OpenAlex
- W3026002191
- Cited by
- 1,010
- References
- 53
- Citations by year
- 2026: 25 · 2025: 58 · 2024: 86 · 2023: 118 · 2022: 198 · 2021: 347 · 2020: 177
- FWCI
- 27.46
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Funders
- Frankfurter Stiftung für krebskranke Kinder
- Hilfe für krebskranke Kinder Frankfurt
- European Commission
- Deutsche Forschungsgemeinschaft (ERC StG 803565, MU 4216/1-1, CRC 1177)
- Aventis Foundation
- Topics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- SARS-CoV-2, COVID-19 pandemic, viral replication, host-pathogen interaction, translation (biology), splicing, carbon metabolism, proteostasis, nucleic acid metabolism, small molecule inhibitors, antiviral drug targets, proteome, translatome
- MeSH terms
- Betacoronavirus, Proteostasis, COVID-19, SARS-CoV-2, COVID-19 Drug Treatment, Carbon, Drug Evaluation, Preclinical, Humans, Kinetics, Pneumonia, Viral, RNA Splicing, Time Factors, Protein Biosynthesis, Virus Replication, Coronavirus Infections, Caco-2 Cells, Proteome, Proteomics, Host-Pathogen Interactions, Pandemics, Molecular Targeted Therapy, In Vitro Techniques
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