Redundant and distinct mechanisms suppress innate immune activation during SARS-CoV-2 infection

This is an uncorrected proof. Figures Abstract Several SARS-CoV-2 proteins have been shown to counteract the host innate immune response, mostly using in vitro protein expression, which may not fully reflect their role in the context of viral infection. In addition, while each viral protein was characterized in a different experimental system, its relative contribution to immunosuppression remains unclear. Here we used a SARS-CoV-2 bacterial artificial chromosome with en passant mutagenesis to recover a panel of 12 infectious recombinant SARS-CoV-2 viruses, each with mutations in either NSP1, NSP2, NSP3, NSP6, NSP12, NSP13, NSP14, NSP15, NSP16, ORF3a, ORF6, or ORF8. We used the interferon-stimulated response element (ISRE)-driven luciferase assay in 293T-ACE2/TMPRSS2 cells to test the panel, demonstrating that mutations in many proteins, especially in NSP1 and NSP15, increased the type I interferon response relative to the parental wild-type virus. RNA-seq analysis of mutant-virus infected Calu-3 cells showed that the mutations in NSP1 or NSP15 lead to higher expression of multiple genes involved in innate immune response, cytokine-mediated signaling, and regulation of lymphocyte proliferation. Furthermore, mutations in either NSP1 or NSP15 resulted in a greater maturation of human monocyte-derived dendritic cells in vitro. Infection of K18 hACE2 transgenic mice with either NSP1 or NSP15 mutated viruses demonstrated attenuated respiratory tract replication. Analysis of lung immune cells from infected mice by single-cell RNA-seq identified 15 populations of major myeloid and lymphoid cells with changes in the pattern of their activation associated with viral infection. The effects of mutations in NSP1 or NSP15 on these responses are consistent with differences in the immunosuppressive mechanisms utilized by the two proteins. Overall, these data demonstrate different and redundant mechanisms of innate immune antagonism by SARS-CoV-2 and suppression of activation of antigen-presenting cells and T and B lymphocytes mediated by multiple viral proteins. Author summary The mechanisms by which SARS-CoV-2 and its proteins modulate host immunity, specifically the interferon response, are still not clear. We generated 12 infectious SARS-CoV-2 viruses with mutations in individual proteins and demonstrated that many of them have interferon-antagonizing activity and immunosuppressive effects in human cells and in the K18 hACE mouse model of infection. We identified distinct and redundant mechanisms of immunosuppression of SARS-CoV-2 mediated by multiple individual viral proteins, with 9 out of the 12 tested proteins showing some immunosuppressive effect in at least one experimental system. The demonstrated immunosuppressive effects extend from the innate response to immune cells to pathologic changes in vivo. Importantly, this work shows, for the first time, a comparison of the effects of multiple viral proteins in the context of authentic viral infection, rather than in a surrogate system, and shows the relative contribution of each viral protein under identical experimental conditions. Overall, our data indicates that SARS-CoV-2 antagonizes multiple immune mechanisms, particularly type I interferon signaling, activation of innate immune cells and T and B lymphocyte functions, with the greatest effects due to NSP1 and NSP15. Citation: Zhou F, Periasamy S, Jackson ND, Cheng WS, Soto Acosta R, Tripathi A, et al. (2026) Redundant and distinct mechanisms suppress innate immune activation during SARS-CoV-2 infection. PLoS Biol 24(5): e3003808. https://doi.org/10.1371/journal.pbio.3003808 Academic Editor: Frank Kirchhoff, Ulm University Medical Center, GERMANY Received: June 30, 2025; Accepted: May 5, 2026; Published: May 20, 2026 Copyright: © 2026 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the paper and its Supporting information files. The data underlying Figures can be found in GEO database, accession number GSE254699. Deposition of flow cytometry data shown in Fig 3C can be found in Zenodo, DOI: https://doi.org/10.5281/zenodo.19840470. Funding: This project was partially funded by UTMB (https://www.utmb.edu) intramural funds and partially by Defense Advanced Research Project Agency (DARPA) (https://www.darpa.mil) contract N6600119C4022 (to S.C.S and A.B.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Abbreviations: 3CLpro, 3C-like protease; ABSL-3, animal biosafety level 3; ACE2, angiotensin-converting enzyme 2; ARDS, acute respiratory distress syndrome; DCs, dendritic cells; DEG, Differentially expressed genes; FC, fold change; GEX, gene expression; GEMs, Gel beads-in-emulsion; hACE-2, human angiotensin receptor 2; IFN, Interferons; IFN-I, I IFNs; LPS, lipopolysaccharide; MEM, minimal essential medium; MFI, mean fluorescent intensity; NOD, nucleoside-binding oligomerization domain; NYGC, New York Genome Center; ORF, open reading frames; PBMC, peripheral blood mononuclear cells; PCA, Principal component analysis; PFU, plaque-forming units; PLpro, papain-like protease; PSG, penicillin-streptomycin-glutamine; PBS, phosphate buffered saline; rRNA, ribosomal RNA; RT, reverse transcription; TMPRSS2, transmembrane serine protease 2; UMI, unique molecular identification; VOC, variants of concern; WT, wild-type. Introduction The COVID-19 pandemic, caused by SARS-CoV-2, has caused more than 776 million confirmed cases of human disease, including more than 7 million deaths (October 2024) [1]. Since the first report on the occurrence and rise of COVID-19 cases, SARS-CoV-2 has shown a powerful transmission potential that has contributed to the deadly pandemic. This virus has continuously evolved into variants of concern (VOC) with a higher ability for transmission and continuing the pandemic beyond 2 years [2]. SARS-CoV-2, similarly to SARS-CoV, enters mammalian cells using the angiotensin-converting enzyme 2 (ACE2) receptor, which is abundant in the respiratory and intestinal epithelial cells [3]. The virus causes diffuse alveolar damage and COVID-19 associated acute respiratory distress syndrome (ARDS) through dysfunctional immune responses [4]. In addition, it also causes acute tissue injury, particularly in the liver and kidneys, coagulopathies (disseminated intravascular coagulation and fibrin thrombi formation), thrombocytopathy, and pulmonary embolism [4]. The large RNA genome of SARS-CoV-2 contains multiple open reading frames (ORFs) which encode 4 structural proteins (S, E, M, and N), 16 nonstructural proteins (NSP1-16), derived from ORF1a and ORF1b polyproteins, and several accessory proteins, including ORF3a, ORF3b, ORF3c, ORF3d, ORF6, ORF7a, ORF7b, ORF8, ORF9b, ORF9c, and ORF10. The 16 NSPs are from the processing of the polyprotein precursors by the viral NSP3 and NSP5, which have the activity of papain-like protease (PLpro) and the 3C-like protease (3CLpro), respectively. NSP3 is responsible for the proteolytic cleavage of NSP1-44, and NSP5 for the processing of other cleavage sites that result in NSP5-16 [5]. While NSPs play a critical role in viral replication, the accessory proteins do not, but both groups of proteins play an important role in viral pathogenesis and modulating the host immune response [6]. Interferons (IFN) are important secreted host proteins with strong anti-viral functions. Type I IFNs (IFN-I), which include IFNα and IFNβ, are produced by many cells in response to viral infections, including SARS-CoV-2 [7]. Data from clinical patients indicate that low induction of IFN-I at the local and systemic levels correlates with the severity of COVID-19 [8–10]. In some patients with severe COVID-19, high nasal viral titers correlate with low IFN-I levels, and in some cases with high titers of autoantibodies against IFNs. This suggests that an appropriate induction of high titers of IFNs enables epithelial cells to inhibit SARS-CoV-2 replication and growth at nasopharyngeal and other mucosal sites [11]. However, SARS-CoV-2 antagonizes the innate host defense mechanisms, including the inhibition of IFN-I production and signaling [12], allowing the virus to replicate exponentially, causing severe tissue pathologies. Multiple coronavirus NSPs and accessory proteins antagonize IFN-I signaling (Table 1). Specifically, SARS-CoV-2 NSP1, NSP2, NSP3, NSP5, NSP6, NSP8, NSP9, NSP12, NSP13, NSP14, NSP15, NSP16, and accessory proteins ORF3a, ORF6, ORF8 modulate host innate immune response at several levels, including disruption of host RNA splicing and translation, interference of protein trafficking and modification of host protein-protein interactions [13–46]. Most of the immune antagonism studies described above relied in vitro cell cultures transfected with plasmids expressing SARS-CoV-2 proteins [13–24,26,29,30,36,38,41,43–46] and some used purified recombinant proteins [14,28,31,32,42]. However, this system most likely does not recapitulate the biological effects of the proteins in the context of authentic SARS-CoV-2 infection. Importantly, the previous studies characterized the effects of innate immune-antagonizing viral proteins individually and in different experimental systems, making assessment of their relative importance impossible. Here, we investigated inhibition of IFN-I signaling by viral proteins in the context of replication-competent SARS-CoV-2. We generated 12 infectious SARS-CoV-2 viruses with mutations in individual proteins and demonstrated that many of them have IFN-I-antagonizing activity and immunosuppressive effects in human cells and in the K18 hACE mouse model of infection. Our data indicate that SARS-CoV-2 antagonizes multiple immune mechanisms, particularly IFN-I signaling, activation of innate immune cells and T and B lymphocyte functions, with the greatest effects due to NSP1 and NSP15. Results Generation of SARS-CoV-2 mutant viruses To investigate the contribution of SARS-CoV-2 proteins or domains on viral pathogenesis and protective immune mechanisms, we selected 13 genes and specific mutations based on the available literature on several coronaviruses [13,16,19,23,25,26,29,30,32,45,50,58,59] (Table 1). To generate SARS-CoV-2 mutants, we first introduced mutations into the viral genes in an infectious cDNA clone of strain USA-WA1/2020 pBeloBAC11-SARS-CoV-2 [60], which was driven by the human cytomegalovirus immediate early gene promoter, using en passant mutagenesis [61] (the primers for mutagenesis are shown in S1 Table). After the mutations were confirmed in the cDNA clones by Sanger DNA sequencing, Vero E6 cells were transfected with the cDNA clones containing the desired mutations for recombinant virus recovery. We generated a total of 16 mutated cDNA clones (Table 1, Fig 1A). To recover viruses from the mutated cDNA clones, 80% confluent monolayers of Vero E6 cells in 12-well plates were transfected with 1.0 μg per well of infectious SARS-CoV-2 BAC DNA WT or its mutated derivatives. At 48 h post-transfection, transfected cells were split and seeded into 25 cm2 flasks with 50% confluent monolayers of Vero E6 cells. The cell cultures were observed daily until the appearance of cytopathic effect to collect viral supernatants. Viruses were recovered from the wild-type (WT) nonmutated control and the 12 cDNA clones containing mutations in NSP1, NSP2, NSP3, NSP6, NSP12, NSP13, NSP14, NSP15, NSP16, ORF3a, ORF6, or ORF8 (Table 1). The recovered viruses were expanded by an additional 1 or 2 passages, and the viral genomic RNA was isolated from the cell culture supernatants. Sequencing of the viral genomes confirmed the expected mutations (S1 Fig). We could not recover viruses from the cDNA clones carrying two separate mutations in NSP3, or a mutation in NSP5, or a deletion in NSP6 (Table 1). All the viruses were titrated in Vero E6 cells by plaque assay, and their plaque sizes were compared. We found that all the mutant viruses, except the NSP2 and NSP3 mutants, have a smaller plaque phenotype when compared to the WT virus (Fig 1B). Growth kinetics experiment in human epithelial Calu-3 cells demonstrated the most pronounced attenuation of the NSP1, NSP2, NSP6, NSP14, NSP16, ORF3, and ORF6 mutants (S2A Fig). Generally, attenuation of replication corresponded to reduced plaque phenotype (Fig 1B) with two notable exceptions. The NSP2 mutant demonstrated WT virus-like plaque size, yet attenuated replication, while the NSP12 mutant demonstrated reduced plaque phenotype yet WT-like replication. We also compared replication of the NSP1 and NSP15 mutants selected for in-depth investigation (below) with that of WT virus in Vero-AT cells, which express human angiotensin receptor 2 (hACE-2) and transmembrane serine protease 2 (TMPRSS2), and A549-hACE2 cells, which express hACE-2, that demonstrated their attenuation (S2B Fig). The attenuation of the NSP1 and NSP15 mutants in Vero-AT cells, which are deficient in IFN-I production [62], is consistent with the competence of Vero cells in type III interferon production [63]. A. Schematic representation of the SARS-CoV-2 genome. The positions of the mutations are indicated by arrows with mutation numbers. B. Comparison of plaques of WT SARS-CoV-2 and the mutants in Vero E6 cells on day 2 post infection. Multiple SARS-CoV-2 NSP proteins contribute to inhibition of IFN-I induction Using the panel of 12 mutated SARS-CoV-2, we investigated the impact of NSP and ORF proteins on IFN-I signaling. We transfected 293T-ACE2/TMPRSS2 cells, constitutively expressing SARS-CoV-2 receptors ACE-2 and TMPRSS2 (ACE2/TMPRSS2) [64], with pISRE-luc firefly luciferase reporter plasmid and pTK-RL renilla luciferase reporter plasmid for control of transfection efficiency. The next day, cells were mock-infected (phosphate buffered saline, PBS) or infected with SARS-CoV-2 WT or mutant viruses at 0.3 plaque-forming units (PFU) per cell. After 24 h of infection, cells were either left untreated or treated with IFN-α (100 U/ml) and incubated for an additional 24 h, and then the ISRE-induced luciferase activity was measured (Fig 2A). As expected, WT SARS-CoV-2 inhibited the ISRE-luc activity when compared to mock-infected cells, presumably due to the combined effect of multiple IFN-I-antagonizing proteins of SARS-CoV-2 (Fig 2B). Importantly, significantly higher ISRE-luc activities were observed in cells infected with NSP1, NSP15, and NSP16 mutant viruses as compared to WT SARS-CoV-2 (Fig 2B). Even with IFN-α treatment, WT SARS-CoV-2 inhibited ISRE-luc activity (Fig 2C). In contrast, most of the mutant viruses – NSP1, NSP2, NSP3, NSP6, NSP14, NSP15, ORF3a, ORF6, and ORF8 – showed higher ISRE-luc activity than WT SARS-CoV-2. A. Flowchart of the experiment. 293T-ACE2/TMPRSS2 cells were transfected with pISRE-luc and pTK-RL. At 24 h after the transfection, the cells were mock-infected or infected with WT SARS-CoV-2 or the 12 mutants. At 24 h after the infection, cells were mock-treated or treated with IFN-α for an additional 24 h, and the luciferase activity of the cell lysates was measured. Created in BioRender. Zhou, F. (2026) https://BioRender.com/x94u104. B, C. Relative fold change compared to mock-infected cells in luciferase activity for WT and mutated viruses in 293T-ACE2/TMPRSS2 cells without (B) and with (C) IFN-α treatment. Mock, uninfected cells. Mean values ± SD based on triplicate samples. Significance of differences for each mutant vs. WT are shown by asterisks and separately shown for WT compared to mock-infected cells (Mock). The significance was determined by one way ANOVA. * p 2) and 79 genes were down-regulated (Log2FC > 2) in WT SARS-CoV-2-infected Calu-3 cells compared with mock-infected cells. E. Volcano plot for DGE in NSP1 vs. WT. Ninety-four genes were up-regulated (Log2FC > 2) and 174 genes were down-regulated (Log2FC > 2) in NSP1 mutant-infected Calu-3 cells compared with mock-infected cells. F. Volcano plot for DGE in NSP15 vs. WT. 129 genes were up-regulated (Log2FC > 2) and 14 genes were down-regulated (Log2FC > 2) in NSP15 mutant-infected Calu-3 cells compared with mock-infected cells. G. Gene Ontology (GO) term analysis showing pathway enrichment for NSP1 and NSP15 over WT. NSP1, NSP15 inhibit several pathways, including innate immune response and cytokine-mediate signaling pathways. The data underlying this Figure can be found in GEO database, accession number GSE254699. Infection of Calu-3 cells with WT SARS-CoV-2 up-regulated more than 1,500 genes compared to mock-infected cells (Fig 4D). The upregulated genes included those involved in innate immune response and are known responders of IFN-I stimulation [72]: IFNB1, CXCL10, OAS2, TNFAIP3 RSAD2, IFNL2, USP18, MX1, CXCL11, NFKBIA, IFIT2, ATF3, IFIT1, IFNL3, and IFI44L. Among these, OAS2 is an important antiviral protein involved in innate immune response against viruses, including SARS-CoV-2 [73]. IFIT2 (ISG54) is an anti-viral protein that inhibits the expression of viral mRNA and acts as a mediator of cell apoptosis [74]. Infection with the NSP1, NSP12, NSP13, NSP14, NSP15, NSP16, NSP2, NSP3, NSP6, ORF3a, ORF6, or ORF8 mutant resulted in 94, 3, 47, 35, 129, 18, 3, 60, 9, 19, 305, and 4 cellular genes, respectively, upregulated (log2FC ≥ 2 and p-value 25% loss of their initial body weight were defined as reaching the experimental endpoint and humanely euthanized. - Infection of mice to assess viral load and change in gene expression in lungs. Six-week-old female K18 hACE2 transgenic mice at 8 animals per group, a total of 32 mice, were either mock-infected with medium or infected as described above. The mice were euthanized at 2 and 4 days post-infection, and the lungs were isolated for assessment of gross changes. The whole lungs were excised and photographed. Next, the left lungs were fixed in 10% neutral buffered formalin for histopathology, and the right lungs were processed for virus titration and RNA isolation as follows. The right lung was homogenized in Precellys tubes, then frozen in a −80 °C freezer. After thawing, the tubes were centrifuged at 12,000 x g at 4 °C for 5 min, and supernatants were collected for virus titration. The pellets were lysed in 1 ml Trizol for RNA isolation according to the manufacturer’s instructions. - Infection of mice for single cell RNA sequencing of lung immune cells. Five- to seven-week-old K18 hACE2 transgenic mice (4 mice per group, total of 16 mice) were either mock-infected with medium or infected as described above. On day 3 mice were euthanized as described above, and the lungs were isolated for immune cell isolation. Single cell isolation and RNA sequencing Freshly collected whole lungs were dissected into single lobes to isolate single cells using a mouse lung dissociation kit (Miltenyi Biotec) and a gentleMACS Dissociator (Miltenyi Biotec), and red blood cells were lysed with 1X RBC lysis buffer (eBioscience). Cell suspensions were processed for single-cell sequencing following the protocol for Chromium Next GEM Single Cell 5′ Version 2 (dual index). 10,000 cells were targeted. The transcriptome of each cell was indexed with a pool of 750,000 barcodes by partitioning each cell into Gel beads-in-emulsion (GEMs) combined with a Master Mix containing reverse transcription (RT) reagents and poly(dT) RT primers. The emulsion was generated using Next GEM chips and the Chromium Controller device (10x Genomics). The RT reaction to the generated emulsion produced 10x barcoded full-length cDNA from poly-adenylated mRNA. This initial cDNA was PCR amplified to produce material for 5′ gene expression sequencing. After PCR amplification, bioanalyzer quality control was performed for all the samples using the Agilent Bioanalyzer High Sensitivity DNA assay in the 2,100 expert software (Agilent). All the samples passed the initial quality control with a cDNA size 700−1,500 bp. Amplified full-length cDNA from polyadenylated mRNA was used to generate 5′ gene expression (GEX) libraries. The cDNA was enzymatically fragmented, and size-selected to optimize the cDNA amplicon size. P5, P7, i5, and i7 sample indexes, and Illumina R2 sequences (read 2 primer sequences) were added via End Repair, A-tailing, Adaptor ligation, and sample index PCR. A second quality control was performed for each library before sequencing, with an expected library size 500–900 bp. Finally, libraries were pooled and sequenced by the New York Genome Center (NYGC) using a NovaSeq sequencer and S2 flowcell with a minimum of 20,000 read pairs per cell. scRNA sequencing data processing To enable quantification of both viral and host genes from single-cell data, we first merged the mm10(GRCm38.p6) and SARS-CoV-2 reference genomes (ASM993790v1). Cell Ranger v7.0.0 (10× Genomics) was then used to demultiplex cellular barcodes and align the reads to the combined genome. The filtered feature-barcode matrix per sample was then read into a Seurat object [113]. Putative doublets from each sample were identified using scds [114]. Any cell with the scds hybrid doublet score of 0.8 (doublet scoring based on co-expression and binary classification) was considered a putative doublet and was removed. Additionally, cells with unusually high expression levels (nFeature over 4,000 or nCount over 10,000) were also considered putative doublets and were removed from further analysis. Poor quality cells (nFeature of less than 1,000 or over 5% mitochondrial content) were also filtered out. The merged Seurat dataset was examined for the presence of intrinsic batches. All the samples were then integrated following the Seurat integration pipeline while correcting for the identified batch labels. Initial cell-type annotation was performed with SingleR using the reference from the Immunologic Genome Project, accessed via celldex [115]. Cell cycle (G2M and S phases) scores were calculated using the Seurat function CellCycleScoring. A single cluster with high G2M and S phase scores was identified and labeled as “Proliferating”. Putative cell type annotation of each cluster was also validated using canonical marker expression (Figs 8C and S7). Major clusters corresponding to 15 cell types were identified among single cells which were isolated from the lung tissue. The average expression of SARS-CoV-2 viral genes in each cell type/sample pair was also calculated (Fig 9). Within each cell type, differential expression analysis was performed between the following sample groups: WT versus mock, NSP1 mutant versus WT, and NSP15 mutant versus WT. Differentially expressed genes (DEG) at the single-cell level were identified using the Wilcoxon test. Those passing the FDR-adjusted p-value 0.3 in expression were selected as significant. We also performed a DEG analysis at the pseudo-bulk per cell-type level. Because of the small sample sizes, this analysis did not have sufficient power to detect differentially expressed genes and was not used further. Pathway enrichment analysis was performed on the sets of DEG with EnrichR [116] using the Gene Ontology biological process terms. All terms with FDR-adjusted p-value 0.3 and are represented by orange dots. Downregulated genes are defined as those with FDR adjusted p-value < 0.05 and log2FC < −0.3 and are represented by purple dots. Genes with adjusted p-value < 0.05 and log2FC between −0.3 and 0.3 are represented by gray dots. All p-values are calculated using the Wilcoxon Rank Sum test and adjusted for multiple testing correction (BH). Numbers of upregulated and downregulated genes per cell type are displayed in parentheses. A. Comparison for WT SARS-CoV-2 versus mock. B. Comparison for NSP1 mutant versus WT. C. Comparison for NSP15 mutant versus WT. The data underlying this Figure can be found in GEO database, accession number GSE 255483. https://doi.org/10.1371/journal.pbio.3003808.s009 (PDF) S10 Fig. Dot plot showing GO terms from pathway enrichment analyses by EnrichR among differentially expressed genes (DEG) for the WT versus mock. Dot size represents the fraction of DEG within the GO term. Dot color represents the direction of the regulation of the term in the corresponding cell type (up-regulation: yellow; down-regulation: purple) and the color scale indicates the adjusted p-value (shown are only terms with FDR-adjusted enrichment p-value < 0.1). Terms with substantial gene overlap are filtered out, with terms remaining only if there is a difference of at least two regulated genes from every other term. The direction of regulation for each enriched term is determined by the proportion of upregulated DEG versus the downregulated DEG across all cell types. The data underlying this Figure can be found in GEO database, accession number GSE 255483. https://doi.org/10.1371/journal.pbio.3003808.s010 (PDF) S11 Fig. Activation of DCs following infection with WT SARS-CoV-2, NSP1 mutant, or NSP15 mutant assessed by single-cell sequencing. A. Violin plots showing expression levels of markers of DC activation CD80 and CD86. B. Violin plots showing expression levels of IFN-I inducible genes ISG15 and OAS1g. The data underlying this Figure can be found in GEO database, accession number GSE 255483. https://doi.org/10.1371/journal.pbio.3003808.s011 (PDF) S12 Fig. Activation of DC subpopulations characterized by single-cell sequencing. A. Integrated re-clustered UMAP of all mock (n = 3), WT (n = 4), NSP1 mutant (n = 4), and NSP15 mutant (n = 4) cells in the DC population colored by cluster number (top) and DC subtype (bottom, based on markers in panel C). B. Dot plot showing canonical marker expression in the DC sub-clusters. Relevant marker sets for each DC sub-population are highlighted in the same color as the corresponding sub-populations in the UMAP (B, bottom). C. Box plots comparing cell-type proportions of the DC sub-populations observed in Mock, WT, NSP1, and NSP15 groups. The limits of the box reflect the interquartile range (IQR: Q3–Q1) with the medians shown as horizontal bars. Whiskers extend to 1.5 times the IQR of the box. For each cell type, pairwise t test comparisons of the wild type (WT) proportion with every other group are shown (*p-value < 0.05, ***p-value < 0.001, ns - nonsignificant). The data underlying this Figure can be found in GEO database, accession number GSE 255483. https://doi.org/10.1371/journal.pbio.3003808.s012 (PDF) S13 Fig. Contribution of individual SARS-CoV-2 proteins in viral immunosuppression and pathogenicity. Summary of the effects of the 12 mutated proteins on the viral phenotype and innate and adaptive immune responses shown in Figs 1, 2, 3, and 5. The darker colors reflect the greater effects of the mutations on the biological effects indicated at the left, as compared to WT SARS-CoV-2, and therefore a greater contribution of the corresponding proteins in these biological effects. The transcriptional effects in human cells and in mice are summarized in Figs 4, 6, 7, and 9 and are not included in this heat map. The data underlying this Figure can be found in S2 Data. https://doi.org/10.1371/journal.pbio.3003808.s013 (PDF) S2 Table. Criteria used for histopathology scoring. https://doi.org/10.1371/journal.pbio.3003808.s015 (PDF) S3 Table. Differentially expressed genes in mice infected with WT SARS-CoV-2. https://doi.org/10.1371/journal.pbio.3003808.s016 (PDF) S1 Data. Quantitative data underlying Figs 2B, 2C, 3C, 5B, 6B, 6C, 6E, 6G, 6I, and S2A, S2B. https://doi.org/10.1371/journal.pbio.3003808.s017 (XLSX) S2 Data. Quantitative data underlying S13 Fig. https://doi.org/10.1371/journal.pbio.3003808.s018 (XLSX) Acknowledgments We thank the UTMB Animal Resource Center veterinary staff for the technical support of mouse experiments. We thank Dr. Gregory A. Smith at Northwestern University for providing E.coli strain GS1783 and Dr. Mohsan Saeed at Boston University for providing 293T-ACE2/TMPRSS2 cells. The following reagents were obtained through BEI Resources, NIAID, NIH: Cercopithecus aethiops kidney epithelial cells expressing transmembrane protease, serine 2 and human angiotensin-converting enzyme 2 (Vero E6-TMPRSS2-T2A-ACE2) (cat #NR-54970) and human lung carcinoma cells (A549) expressing human angiotensin-converting enzyme 2 (cat #NR-53821). We thank Dr. Natalia Kuzmina for help with the isolation of immune cells from mouse lungs and work with human DCs, and Tarani Barman for help with the preparation of human DCs. 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