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Proteomics of SARS-CoV-2-infected host cells reveals therapy targets

Abstract

A novel coronavirus was recently discovered and termed SARS-CoV-2. Human infection can cause coronavirus disease 2019 (COVID-19), which has been rapidly spreading around the globe1,2. SARS-CoV-2 shows some similarities to other coronaviruses. However, treatment options and a cellular understanding of SARS-CoV-2 infection are lacking. Here we identify the host cell pathways modulated by SARS-CoV-2 infection and show that inhibition of these pathways prevent viral replication in human cells. We established a human cell culture model for infection with SARS-CoV-2 clinical isolate. Employing this system, we determined the SARS-CoV-2 infection profile by translatome3 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 and nucleic acid metabolism. Small molecule inhibitors targeting these pathways prevented viral replication in cells. Our results reveal the cellular infection profile of SARS-CoV-2 and led to the identification of drugs inhibiting viral replication. We anticipate our results to guide efforts to understand the molecular mechanisms underlying host cell modulation upon SARS-CoV-2 infection. Furthermore, our findings provide insight for the development of therapy options for COVID-19.

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Correspondence to Jindrich Cinatl or Christian Münch.

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Supplementary Table 1 | Data of translatome measurements by MS

Contains Uniprot Accession, Species annotation, Gene Symbol and normalized translation data for each replicate (Data was normalized using summed intensity normalisation for sample loading, followed by internal reference scaling and Trimmed mean of M normalisation). Log2 ratios and P values were computed for each group comparison (two-sided, unpaired t-test with equal variance assumed, n = 3 independent biological samples). See Fig. 2.

Supplementary Table 2 | Data of proteome measurements by MS

Contains UniProt Accession, Gene Symbol and normalized protein abundances for each sample. (Data was normalized using summed intensity normalisation for sample loading, followed by internal reference scaling and Trimmed mean of M normalisation). Log2 ratios and P values were computed for each group comparison (two-sided, unpaired t-test with equal variance assumed, n = 3 independent biological samples). See Fig. 3, 4.

Supplementary Table 3 | Results of Reactome pathway analysis of genes decreased in protein level during infection (Fig. 3a, S3)

Pathway names, size of pathway, proteins found in dataset, P values and FDR are given (P values by binomial test and FDR by binomial test and Benjamini-Hochberg correction).

Supplementary Table 4 | Results of Reactome pathway analysis of genes increased in protein level during infection (Fig. 3a, b)

Pathway names, size of pathway, proteins found in dataset, P values and FDR are given (P values by binomial test and FDR by binomial test and Benjamini-Hochberg correction).

Supplementary Table 5 | Results of gene ontology (biological process) analysis of genes following viral gene expression (Fig. 4)

GO term, proteins found in dataset, GO size, P value and FDR are given (P values by binomial test and FDR by binomial test and Benjamini-Hochberg correction).

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Bojkova, D., Klann, K., Koch, B. et al. Proteomics of SARS-CoV-2-infected host cells reveals therapy targets. Nature (2020). https://doi.org/10.1038/s41586-020-2332-7

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