key: cord-0689108-d2dd5al0
authors: Banerjee, Arinjay; El-Sayes, Nader; Budylowski, Patrick; Jacob, Rajesh Abraham; Richard, Daniel; Maan, Hassaan; Aguiar, Jennifer A.; Demian, Wael L.; Baid, Kaushal; D’Agostino, Michael R.; Ang, Jann Catherine; Murdza, Tetyana; Tremblay, Benjamin J.-M.; Afkhami, Sam; Karimzadeh, Mehran; Irving, Aaron T.; Yip, Lily; Ostrowski, Mario; Hirota, Jeremy A.; Kozak, Robert; Capellini, Terence D.; Miller, Matthew S.; Wang, Bo; Mubareka, Samira; McGeer, Allison J.; McArthur, Andrew G.; Doxey, Andrew C.; Mossman, Karen
title: Experimental and natural evidence of SARS-CoV-2 infection-induced activation of type I interferon responses
date: 2021-04-26
journal: iScience
DOI: 10.1016/j.isci.2021.102477
sha: 0564be62f6675ff8310f0d7d13cebee17c9b2992
doc_id: 689108
cord_uid: d2dd5al0

Type I interferons (IFNs) are our first line of defence against virus infection. Recent studies have suggested the ability of SARS-CoV-2 proteins to inhibit IFN responses. Emerging data also suggest that timing and extent of IFN production is associated with manifestation of COVID-19 severity. In spite of progress in understanding how SARS-CoV-2 activates antiviral responses, mechanistic studies into wildtype SARS-CoV-2-mediated induction and inhibition of human type I IFN responses are scarce. Here we demonstrate that SARS-CoV-2 infection induces a type I IFN response in vitro and in moderate cases of COVID-19. In vitro stimulation of type I IFN expression and signaling in human airway epithelial cells is associated with activation of canonical transcriptions factors, and SARS-CoV-2 is unable to inhibit exogenous induction of these responses. Furthermore, we show that physiological levels of IFNα detected in patients with moderate COVID-19 is sufficient to suppress SARS-CoV-2 replication in human airway cells.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in December 2019 to 27 cause a global pandemic of coronavirus disease (COVID-19) (Zhou et al., 2020a) . SARS-CoV-2 28 causes a respiratory infection, along with acute respiratory distress syndrome in severe cases. 29

Innate antiviral responses, which include type I interferons (IFNs), are the first line of antiviral 30 defense against an invading virus (Kawai and Akira, 2006) . Cellular pattern recognition 31 receptors (PRRs) recognize viral nucleic acids and activate key cellular kinases, such as Inhibitor 32 of nuclear factor Kappa-B Kinase subunit epsilon (IKK) and TANK-Binding Kinase 1 (TBK1). 33

These kinases phosphorylate and activate transcription factors such as interferon regulatory 34 J o u r n a l P r e -p r o o f factor 3 (IRF3) to stimulate downstream production of type I/III IFNs (Koyama et al., 2008) . 35

Type I IFNs interact with interferon alpha/beta receptor (IFNAR) on cells to induce 36 phosphorylation and activation of downstream mediators, such as signal transducer and activator 37 of transcription 1 and 2 (STAT1 and STAT2), which leads to the production of antiviral 38 interferon stimulated genes (ISGs). Similarly, Type III IFNs interact with their cognate receptors, 39 IL-10R2 and IFNLR1 to activate STAT1 and STAT2, followed by the production of ISGs 40 (Mesev et al., 2019) . 41 modulating protein by Gordon et al. (Gordon et al., 2020) , but Lei et al. (Lei et al., 2020) were 58 unable to identify NSP15 as an inhibitor of IFN promoter activation. In addition, both Gordon Table S2 ). Genes 152 associated with structural molecule activity, cell adhesion and exocytosis were downregulated in 153 SARS-CoV-2 infected cells, relative to uninfected cells at 12 hpi (see supplementary Figure S2 ). CoV-2 infection alone is sufficient to induce type I IFN and ISG responses in Calu-3 cells, we 159 infected cells with SARS-CoV-2 and assessed transcript levels of IFNβ, IRF7 and IFIT1 by 160 quantitative polymerase chain reaction (qPCR). IFNβ induction was observed 12 hpi in SARS-161

CoV-2 infected cells, relative to mock-infected cells ( Figure 1G ). Consistent with the 162 upregulation of IFNβ transcripts in SARS-CoV-2 infected cells, transcript levels for ISGs, such 163 as IRF7 and IFIT1 were also significantly upregulated at 12 hpi relative to mock infected cells 164 ( Figures 1H and 1I) . 165

To determine if SARS-CoV-2 is able to inhibit type I IFN responses mounted against an 168 exogenous stimulus, we infected Calu-3 cells with SARS-CoV-2 for 12 hours at a MOI of 1 and 169 stimulated these cells with exogenous double-stranded RNA [poly(I:C)] for 6 hours. We 170 confirmed SARS-CoV-2 replication in Calu-3 cells over 0, 24, 48 and 72 hours of infection by 171 staining for the nucleocapsid (N) protein ( Figure 2A ). We quantified SARS-CoV-2 replication 172 J o u r n a l P r e -p r o o f by qPCR using primers designed to amplify genomic RNA by targeting a region between ORF3a 173 and E genes. We called this region 'upstream of E' (UpE). SARS-CoV-2 UpE levels were higher 174 in SARS-CoV-2 infected cells and in SARS-CoV-2 infected + poly(I:C) treated cells, relative to 175 UpE levels at 0 hpi immediately after removing the inoculum ( Figure 2B ). We also measured the 176 levels of IFNβ transcripts in these cells by qPCR. Poly(I:C) transfection alone induced higher 177 levels of IFNβ transcripts relative to mock transfected cells ( Figure 2C ). SARS-CoV-2 infection 178 alone also induced higher levels of IFNβ transcripts relative to mock infected cells ( Figure 2C ). To evaluate type I IFN and other infection-associated cytokines in COVID-19 patients, we 292 analyzed acute sera (<21 days from symptom onset) from 20 COVID-19 positive patients, of 293 whom 10 were categorized as 'moderate' cases requiring hospital admission, but not admission 294 to intensive care unit (ICU). The remaining 10 samples were from 'severe' cases that required 295 ICU admission or died. For severe cases, 6/10 patients died, and 10/10 moderate cases were 296 discharged (see supplementary Table S4 ). We also included sera from 5 healthy, uninfected 297 individuals. Sera from moderate cases of COVID-19 displayed significantly higher levels of displayed an increasing trend for levels of Interleukin-6 (IL-6), IL-5, macrophage colony-303 stimulating factor 1 (M-CSF), IL-8, tumor necrosis factor  (TNF), TNF and granulocyte 304 colony-stimulating factor 1 (G-CSF) relative to healthy individuals and moderate cases of 305 COVID-19. In addition, both moderate and severe cases of COVID-19 displayed an increasing 306 trend for IL7 and IP-10 relative to healthy controls, although the data were not significant due to 307 wide within patient variation in acute serum samples. Moderate cases of COVID-19 displayed an 308 increasing trend for levels of IFN-2 and IL-10 relative to healthy individuals and severe cases 309 of COVID-19 (Figure 4 and see supplementary Tables S4 and S5) . The physiological relevance of an existing, but dampened type I IFN response to SARS-CoV-2 368 remains to be identified. Emerging data suggest that prolonged and high levels of type I IFNs 369 correlate with COVID-19 disease severity (Lucas et al., 2020) . Thus, a dampened, yet protective 370 early type I IFN response against SARS-CoV-2 may in fact be beneficial for humans (Park and 371 Iwasaki, 2020). However, questions remain about how a low type I IFN response against SARS-372

CoV-2 could play a protective role during infection. One possibility is that low levels of type I 373 IFN production is sufficient to control SARS-CoV-2 replication ( Figure 4E ). This may explain 374 the large number of asymptomatic cases of SARS-CoV-2 where an early IFN response may 375 control virus replication and disease progression. Indeed, in one study, type I IFN (IFN) levels 376

were higher in asymptomatic cases relative to symptomatic cases (n=37) (Long et al., 2020) . 377 to severe cases and healthy controls ( Figure 4A ). In a separate study, IFN- levels were also 462 higher in asymptomatic patients relative to symptomatic COVID-19 patients (Long et al., 2020) . cells. The human respiratory tract is made up of more than one cell type that can be infected with 512 SARS-CoV-2, thus it is important to characterize type I IFN responses in the full range of 513 susceptible human airway and lung cell types. In this study, we did not assess the ability of 514 SARS-CoV-2 to mount a more potent IFN response in the absence of known IFN modulating 515 viral proteins that have been identified in other studies. Future studies will need to assess the full 516

More work is also needed to identify the detailed kinetics of IFN induction by SARS-CoV-2 518 RNA in human cells, followed by subsequent modulation of IFN responses by viral proteins. 519

This will be particularly important to understand why some patients mount a detectable IFN 520 response, while others do not. Timing, intensity and duration of type I IFN responses will be 521 Data are represented as mean  SD, n = 3 or 6, p**<0.01, ***<0.001 and ****<0.0001 645 (Student's t test and Tukey's multiple comparisons test). pTBK1-S172 and pIRF3-S396 protein 646 expression levels are expressed as ratios of pTBK1-S172/TBK1 and pIRF3-S396/IRF3 levels, 647 respectively. Blots were quantified using Image Studio (Li-COR) (n = 3). Ct, cycle threshold. 648

See also supplementary Figure S3 . Further information and requests for resources and reagents should be directed to and will be 745 fulfilled by lead contact, Dr. Karen Mossman (mossk@mcmaster.ca).

Materials availability 748

This study generated recombinant human IFN. The reagent will be made available on request as 749

we are currently trying to secure a commercial partner to commercialize our recombinant 750

proteins.

Data and code availability 753

The only or transfected with varying concentrations of poly(I:C) (InvivoGen) or poly(I:C)-rhodamine 817 (InvivoGen). Recombinant human IFNβ1 was generated using Drosophila Schneider 2 (S2) cells 818

following manufacturer's recommendation and by using ThermoFisher Scientific's Drosophila 819

Expression system (ThermoFisher Scientific). Recombinant IFNβ1 was collected as part of the 820 cell culture supernatant from S2 cells and total protein was measured using Bradford assay. Total 821 protein concentration was used for subsequent experiments. To demonstrate that S2 cell culture 822 media did not contain non-specific stimulators of mammalian antiviral responses, we also 823 generated recombinant green fluorescent protein (GFP) using the same protocol and used the 824 highest total protein concentration (2 mg/ml) for mock treated cells ( Figure S3B ). S2 cell culture 825 supernatant containing GFP did not induce an antiviral response in human cells ( Figure S3B ). 826

For VSV-GFP, HSV-GFP and H1N1-mNeon infections, cells were treated with increasing 827

concentrations of IFNβ1 or GFP (control) containing cell culture supernatant. SARS-CoV-2 828

infected cells were treated with supernatant containing IFNβ1 or GFP. Commercially bought 829 recombinant IFN-2 (Sigma-Aldrich) was used for experiments that utilized IFN-2. 830 831

Quantitative PCR. Calu-3 cells were seeded at a density of 3 x 10 5 cells/well in 12-well plates. 832

Cells were infected with SARS-CoV-2 for 12 hours. Twelve hours post incubation, mock 833 2020b). Primary antibodies used were mouse anti-SARS/SARS-CoV-2 N (ThermoFisher 880

Scientific; Catalogue number: MA5-29981; RRID: AB_2785780) and human anti-SARS-CoV-2 881 N (GenScript; Catalogue number: A02039S). Secondary antibodies used were goat anti-mouse 882

Texas Red-X (ThermoFisher Scientific; Catalogue number: T-6390; RRID: AB_2556778) and 883 rat anti-human FITC (BioLegend; Catalogue number: 410719; RRID: AB_2721575). Images 884

were acquired using an EVOS M5000 imaging system (ThermoFisher Scientific 2014) using the 'DESeqDataSetFromTximport' function. In order to determine time/treatment 908 dependent expression of genes, count data was normalized using the 'estimateSizeFactors' 909 function using the default 'median ratio method' and output using the 'counts' function with the 910 'normalized' option. 911 912

For subsequent differential-expression analysis, a low-count filter was applied prior to 913 normalization, wherein a gene must have had a count greater than 5 in at least three samples in 914 order to be retained. Using all samples, this resulted in the removal of 12,980 genes for a final set were performed using the 'DESeq' function of DESeq2 using all samples, with results 922

subsequently summarized using the 'results' function with the 'alpha' parameter set to 0.05; p-923 values were adjusted using the Benjamini-Hochberg FDR method (Benjamini and Hochberg, 924 1995), with differentially expressed genes filtered for those falling below an adjusted p-value of 925 0.05. For (c), infected/mock samples were subset to individual timepoints, with differential 926 expression calculated using DESeq as described above. Additionally, given the smaller number 927 of samples at individual time-points, differential-expression analysis was also performed with 928 relaxation of the low-count filter described above, with results and p-value adjustments 929 performed as above. subsequently plotted with the ggplot2 package in R (42) ( Figure 1A) . As viral transcript levels 944

increased over time post-infection, we first converted non-normalized transcript counts to a log 2 945 scale, and subsequently compared these across time-points ( Figure 1B and supplementary Table  946 S1). To look at the changes in the expression of viral transcripts relative to total viral expression 947 as a function of post-infection time, normalized transcript counts were used to perform 948 differential-expression analysis with DESeq2. Results and p-value adjustments were performed 949 as described above.

In order to compare host/viral expression patterns, normalized transcript counts from infected 952 samples were compared with either normalized or non-normalized viral transcript counts (from 953 the same sample) across time-points. For each viral transcript (n = 12), all host genes (n = 954 15,760, after filtering described above) were tested for correlated expression changes across 955 matched infected samples (n = 18, across 5 time-points) using Pearson's correlation coefficient 956

(via the cor.test function in R). Correlation test p-values were adjusted across all-by-all 957 comparisons using the Benjamini-Hochberg FDR method, and gene-transcript pairs at adjusted 958 p< 0.05 were retained. To account for possible effects of cellular response to plate incubation, 959 viral transcript abundance was averaged at each time-point and compared to host transcript 960 abundance similarly averaged at each time-point for non-infected samples; correlation testing 961 was done all-by-all for n = 5 data-points. Host genes that correlated with viral transcription in 962 mock samples across time were removed from subsequent analyses; to increase stringency, mock 963 correlation was defined using un-adjusted p< 0.05. Host genes were sorted by correlation 964 coefficient (with any given viral transcript), with the top 100 unique genes retained for 965

visualization. Normalized host transcript counts were z-score transformed per-gene using the 966 'scale' function in R, with normalized/un-normalized viral transcript counts similarly 967 transformed per-transcript. Resulting z-score expression heatmaps were generated using the 968

ComplexHeatmap library in R (version 2.2.0) (Gu et al., 2016) . Heatmaps were generated for 969 normalized/un-normalized viral transcript counts, given the different information revealed by 970 absolute and relative viral expression patterns. 971 Table S5 . Cytokine levels in healthy individuals and COVID-19 patient serum samples, Related 1014

to Figure 4 . 1015 1016

Cells and viruses. Vero E6 cells (African green monkey cells; ATCC) were maintained in 761

Dulbecco's modified Eagle's media (DMEM) supplemented with 10% fetal bovine serum (FBS

Drosophila S2 cells (ThermoFisher Scientific) 767 were cultured in Schneider's Drosophila medium supplemented with 10% FBS (Sigma-Aldrich) 768 as recommended by the manufacturer and cells were incubated at 28C. Sex of THF, Vero E6 769 and S2 cells are unknown as commercial vendors or collaborators did not have that information. 770 Stocks of genetically engineered vesicular stomatitis virus (VSV-GFP) carrying a green 771 fluorescent protein (GFP) cassette

HSV-GFP stocks were generated and maintained as 774 mentioned previously

Virus stocks were thawed once and used for an experiment. A fresh vial was used 777 for each experiment to avoid repeated freeze-thaws. VSV-GFP, HSV-GFP and H1N1 infections 778 were performed at a multiplicity of infection (MOI) of 1. SARS-CoV-2 infections were 779 performed at MOIs of 0.1, 1 or 2. Experiments with SARS-CoV-2 were performed in a BSL3 780 laboratory and all procedures were approved by institutional biosafety committees at

Acute patient sera (<21 days from symptom onset) were acquired from 784 moderate (hospital admission, but no ICU admission) and severe (ICU admission or death) cases 785 of COVID-19 in Toronto, Canada, along with samples from uninfected, healthy individuals (see 786 supplementary Table S4 for details). Work with patient sera was approved by the Sunnybrook

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infected or infected cells were mock stimulated or stimulated with poly(I:C) or IFNβ for 6 hours. 834 RNA extraction was performed using RNeasy ® Immunoblots. Calu-3 cells were seeded at a density of 3 x 10 5 cells/well in 12-well plates. Cells ActivePathways. Only pathways that were enriched at specific time points were considered. Bar 997 plots displaying top ActivePathway GO terms and REACTOME enrichments for infection 998 versus mock were plotted using the ggplot2 R package (version 3.2.1) for 1-, 2-, 3-, and 12-hour 999 time points. Zero and 6-hour time points were omitted due to a lack of sufficient numbers of 1000 differentially expressed genes required for functional enrichment analysis. Table titles  1010   Table S4 . COVID-19 patient serum sample history and sera from healthy controls, Related to 1011 Figure 4 . 1012 1013