Phosphorylation Signaling Represents a Primary Host Response to SARS-CoV-2 Infection
To determine how SARS-CoV-2 hijacks host-protein signaling, a global phosphoproteomics experiment was performed in Vero E6 cells, a cell line originating from the kidney of a female African green monkey (Chlorocebus sabaeus) (Osada et al., 2014). This cell line was selected because of its high susceptibility to SARS-CoV-2 infection (Harcourt et al., 2020). Cells were harvested in biological triplicate at 6 time points after SARS-CoV-2 infection (0, 2, 4, 8, 12, or 24 h) or after mock infection at 0 or 24 h (Figure 1 A). Using a data-independent acquisition (DIA) proteomics approach, each sample was then partitioned and analyzed for changes in global protein abundance or phosphorylation (data available in Table S1). Chlorocebus sabaeus and human protein sequences were aligned, and phosphorylation sites and protein identifiers were mapped to their respective human protein orthologs. Phosphorylation fold changes calculated using the 0- or 24-h mock control were highly comparable (correlation coefficient r = 0.77); therefore, the 0-h mock control was used for all subsequent comparisons.
Figure 1 Global Proteomics of Phosphorylation and Abundance Changes upon SARS-CoV-2 Infection
(A) Vero E6 cells were infected with SARS-CoV-2 (MOI 1.0). After 1 h of viral uptake, cells were harvested (0 h) or, subsequently, after 2, 4, 8, 12, or 24 h. As a control, Vero E6 cells were also mock infected for 1 h and harvested immediately thereafter (0 h) or after 24 h of mock infection. All conditions were performed in biological triplicate. Following cell harvest, cells were lysed, and proteins were digested into peptides. Aliquots of all samples were analyzed by mass spectrometry (MS) to measure changes in protein abundance upon infection, whereas the remaining sample was enriched for phosphorylated peptides and subsequently analyzed to measure changes in phosphorylation signaling. A DIA approach was used for all MS acquisitions. Last, all phosphorylation sites and protein identifiers were mapped to their respective human protein orthologs.
(B) Principal-component analysis (PCA) of phosphorylation replicates after removing outliers. See also Figure S1.
(C) Correlation of protein abundance (AB) and phosphorylation sites (PHs) between replicates within a biological condition (red) and across biological conditions (black). Boxplots depict median (horizonal lines), interquartile range (boxes), maximum and minimum values (vertical lines), and outliers (solid circles).
(D) Median AB of individual SARS-CoV-2 proteins in the protein AB analysis.
(E) The number of significantly regulated PH groups across the infection time course.
(F) Volcano plot of PH group quantification 24 h after infection.
(G) The number of significantly regulated proteins across the infection time course.
(H) Volcano plot of protein AB quantification 24 h after infection.
(I) Gene Ontology enrichment analysis of all significantly changing proteins in terms of AB divided into two sets: downregulated (blue) and upregulated (red).
(J) Proportion of significantly regulated PH groups with a correlated (i.e., same direction, AB match) or anticorrelated (i.e., opposite direction, AB mismatch) significant or insignificant (gray) change in protein AB.
In (E)–(H), all infection time points are compared with the mock infection at 0 h, and significantly regulated proteins are defined as (absolute value of log2(infection/mock) > 1 and adjusted p < 0.05 or when only detected in infected or mock based on replicate and MS feature counts; STAR Methods).
See also Figure S1.
Quality control filtering of the data was performed, and two samples from each of the phosphorylation and protein abundance datasets were removed because of poor correlation with their respective replicates (Figures S1 A and S1B). Principal component analysis (PCA) of the remaining samples revealed good separation of mock and infected samples as well as high quantitative reproducibility between biological replicates (Figures 1B, 1C, and S1C). In total, high-quality quantification of 4,624 human-orthologous phosphorylation sites and 3,036 human-orthologous proteins was obtained (Figure S1D). Successful infection was confirmed by the observation of a dramatic increase in viral protein abundance over the course of a 24-h infection period (Figures 1D and S1E).
Figure S1 Proteomics: Quality Control (QC), Orthology, Enrichments, and Viral Proteins, Related to Figure 1
(A) Principal component analysis computed on intensities summarized by MSstats at the level of phosphorylation site groups within (from left to right) all runs, with one outlier run removed, and with two outlier runs removed. Outlier runs are labeled 00Hr.2 and 02.Hr.2. (B) Principal components analysis computed on protein intensities as summarized by MSstats (from left to right) within all runs, with one outlier run removed, and with two outlier runs removed. Outlier runs are both labeled 00Hr.2; one is mock and the other is infected. (C) Coefficient of variance boxplot for each condition. Black lines depict the median and their values are indicated above each boxplot. (D) Mapping detected and quantifiable proteins and phosphorylation sites from the green monkey (Chlorocebus sabaeus) protein sequences to human genes. Proteins and sites were considered quantifiable if MSstats computed a non-infinite fold change for any time point or if an infinite log2 fold change passes criteria for inclusion in any time point. (E) Intensities of viral proteins as summarized over all peptide ion fragments by MSstats, averaged across replicates. The MSstats summarization is based on the median intensity of all fragments after data pre-processing (STAR Methods). (F) Gene Ontology enrichment analysis for proteins significantly regulated in terms of abundance upon infection, separated by time point and direction of phosphorylation regulation. All terms with significant over-representation (adjusted p value < 0.01) in the regulated gene set are kept, and redundant terms are removed (see STAR Methods). Numbers in cells indicate the number of genes that match the term for a given time point and direction. (G) Gene Ontology enrichment analysis for significantly phosphorylated proteins upon infection, separated by time point and direction of protein regulation. Details same as for (F).
As expected, an increase was observed in the number of significantly regulated phosphorylation sites and proteins over the infection time course, with the majority of regulation occurring at the level of phosphorylation (Figures 1E and 1F) as opposed to protein abundance (Figures 1G and 1H). Of the few proteins that significantly increased in abundance upon infection, the vast majority were SARS-CoV-2 viral proteins (Figure 1H). In contrast, the majority of host proteins decreased in abundance. This finding is consistent with mechanisms of host mRNA nuclear export and/or host mRNA translation inhibition, which are common in viral infections (Kuss et al., 2013; Walsh and Mohr 2011). Gene Ontology enrichment analysis of significantly downregulated proteins revealed several terms related to platelet regulation (Figures 1I and S1F). Several downregulated host proteins are known to be involved in platelet regulation, thrombosis, and prevention of blood coagulation, including APOH, CD9, TSPAN14, AHSG, SERPINA1, and A2M (Mather et al., 2016; Mangin et al., 2009; Taggart et al., 2000). The downregulation of these proteins suggests that they may mechanistically contribute to symptoms of blood coagulation and stroke in COVID-19 patients (Han et al., 2020).
Lastly, the contribution of protein abundance to phosphorylation level changes was evaluated. For nearly all cases of a significantly changed phosphorylation site, no corresponding significant change in protein abundance was observed (Figure 1J), further suggesting that phosphorylation signaling represents a primary host response over this time course of infection as opposed to transcriptional regulation, which would influence protein abundance.