Antiviral immune responses, cellular metabolism and adhesion are differentially modulated by SARS-CoV-2 ORF7a or ORF7b

1 Title 1 2 Antiviral immune responses, cellular metabolism and adhesion are 3 differentially modulated by SARS-CoV-2 ORF7a or ORF7b 4 5 Tránsito García-García1,2, Raúl Fernández-Rodríguez1,2, Natalia Redondo3, Ana de Lucas-Rius3, Sara 6 Zaldívar-López1,2, Blanca Dies López-Ayllón3, José M. Suárez-Cárdenas1,2, Ángeles Jiménez-Marín1,2, 7 María Montoya3* and Juan J. Garrido1,2*. 8 9 1Immunogenomics and Molecular Pathogenesis BIO365 Group, Department of Genetics, University of Córdoba, Córdoba, 10 Spain. 11 2Maimónides Biomedical Research Institute of Córdoba (IMIBIC), GA-14 Research Group, Córdoba, Spain. 12 3Molecular Biomedicine Department, Centro de Investigaciones Biológicas Margarita Salas (CIB), CSIC, Madrid 28040, 13 Spain. 14 15 * These authors have contributed equally to this work and share senior and corresponding 16 authorship. 17 18 19 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 2 Abstract 20 SARS-CoV-2, the causative agent of the present COVID-19 pandemic, possesses eleven 21 accessory proteins encoded in its genome, and some have been implicated in facilitating 22 infection and pathogenesis through their interaction with cellular components. Among these 23 proteins, accessory protein ORF7a and ORF7b functions are poorly understood. In this study, 24 A549 cells were transduced to express ORF7a and ORF7b, respectively, to explore more in 25 depth the role of each accessory protein in the pathological manifestation leading to COVID-26 19. Bioinformatic analysis and integration of transcriptome results identified defined 27 canonical pathways and functional groupings revealing that after expression of ORF7a or 28 ORF7b, the lung cells are potentially altered to create conditions more favorable for SARS-29 CoV-2, by inhibiting the IFN-I response, increasing proinflammatory cytokines release, and 30 altering cell metabolic activity and adhesion. Based on these results, it is reasonable to 31 suggest that ORF7a and ORF7b could be targeted by new therapies or used as future 32 biomarkers during this pandemic. 33 34 Keywords: SARS-COV-2, ORF7a, ORF7b, COVID-19 35 36 37 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 3 Introduction 38 There is an urgent need to better understand the molecular mechanisms governing severe 39 acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the ongoing 40 coronavirus disease 2019 (COVID-19) pandemic. SARS-CoV-2 belongs to the family 41 Coronaviridae, subfamily Orthocoronavirinae, genus Betacoronavirus, subgenus 42 Sarbecovirus(Gorbalenya et al., 2020). Since the 2019/2020 outbreak, SARS-CoV-2 has 43 infected more than 500 million people, causing more than 6 million deaths worldwide 44 (https://covid19.who.int/), and unleashing a serious global health problem. COVID-19 45 exhibits a broad spectrum of severity and progression patterns from mild upper respiratory 46 disease or even asymptomatic sub-clinical infection to severe and fatal pneumonia (Rajarshi 47 et al., 2021). 48 SARS-CoV-2 genome consists of a single-stranded positive-sense RNA of 29,903 bp 49 containing 14 open reading frames (ORFs) encoding 31 viral proteins (Ellis et al., 2021). 50 Although much of the research on this virus is focused on the Spike protein (Walls et al., 51 2020; Yang et al., 2020; Zost et al., 2020), recent reports demonstrate that SARS-CoV-2 52 accessory proteins are involved in COVID-19 pathogenesis by modulating antiviral host 53 responses (Jiang et al., 2020; Konno et al., 2020; Miorin et al., 2020; Wang et al., 2021; Wu 54 et al., 2021; Xia et al., 2020; Zhang et al., 2021). Eleven accessory proteins are encoded in the 55 SARS-CoV-2 genome (Redondo et al., 2021) and some of them have been involved in 56 facilitating the infection process by interacting with cell components (Gordon et al., 2020; 57 Stukalov et al., 2021). Among these proteins, accessory protein ORF7a and ORF7b are less 58 studied and their functions have not been fully resolved. ORF7a is a type-I transmembrane 59 protein of 121 amino acid residues with an N-terminal signal peptide (residues 1–15), an 60 Immunoglobulin (Ig)-like ectodomain (residues 16–96), a transmembrane domain (residues 61 97–116), and an endoplasmic reticulum (ER) retention motif (residues 117–121) (Zhou et al., 62 2021) (Figure 1a). It exhibits 95.9% sequence similarity with ORF7a protein from SARS-63 CoV (Yoshimoto, 2020). SARS-CoV-2 ORF7a Ig-like ectodomain has recently been 64 identified as an immunomodulating factor able to interact with CD14+ monocytes, leading to 65 a decrease in their antigen-presenting ability and triggering a dramatic inflammatory response 66 (Zhou et al., 2021). In addition, ORF7a is one of SARS-CoV-2 proteins able to antagonize 67 IFN-I responses (Xia et al., 2020) by promoting inhibition of IFN-I signaling via STAT2 68 phosphorylation (Cao et al., 2021). ORF7b is a 43 amino acid transmembrane protein, one 69 less than in SARS-CoV (Figure 1a). Although less well studied than ORF7a, some authors 70 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 4 identified that ORF7b is able to multimerize through a leucine zipper and hypothesized that it 71 could interfere with some cellular processes involving leucine zipper formation and epithelial 72 cell-cell adhesion that might underlie some common COVID-19 symptoms such as heart rate 73 dysregulation and odor loss (Fogeron et al., 2021). Recently, ORF7b has been involved in 74 host immune responses by promoting IFNβ , TNFα and IL-6 expression, activating IFN-I 75 signaling pathways and eventually accelerating TNF-induced apoptosis (Yang et al., 2021). 76 As there is still a lack of knowledge regarding the role of ORF7a and ORF7b in the 77 pathogenesis of SARS-CoV-2, the response of A549 human epithelial cells expressing 78 ORF7a or ORF7b separately was analyzed using transcriptomic approaches combined with 79 bioinformatic analysis and functional assays. Overexpression of ORF7a or ORF7b induced 80 specific and differential alteration on metabolic cascades via UGT1A9, PTGS2 and CYP1A1; 81 interferon responses via OASL, IFIT1 and IFIT2; inflammation via IL-8, IL-11 and CXCL1 82 and cell adhesion via ICAM-1, ZO-1 and γ-catenin. Overall, we found that the expression of 83 either ORF7a or ORF7b was sufficient to alter cellular networks in a manner similar to full 84 SARS-CoV-2 virus infection. 85 86 Results 87 ORF7a and ORF7b overexpression in A549 cells. 88 SARS-CoV-2 uses several strategies to interact with and interfere with the host cellular 89 machinery. To investigate the function of ORF7a and ORF7b in such interactions, A549 90 human lung carcinoma cell line was used since infection of lung epithelial cells is a hallmark 91 of SARS-CoV-2 infection in the humans. Lentiviruses expressing individual viral proteins 92 ORF7a or ORF7b were transduced in A549 cells with a 2xStrep-tag at C-terminus to allow 93 their detection. In western blot analysis of A549-ORF7a and A549-ORF7b cells, proteins 94 bands of 15 kDa were detected using an ant-Strep-tag antibody in agreement with previous 95 results (Xia et al., 2020) (Figure 1b). For ORF7a, an additional band of 10 kDa was also 96 detected, which may due to protein cleavage. Protein overexpression was confirmed by 97 immunofluorescence and highlighted different patterns of localization in A549 cells. 98 According to a heatmaps of signal intensities in a single cell, ORF7a is highly concentrated in 99 the perinuclear region while ORF7b was diffused through the cytoplasm with an enrichment 100 adjacent to the nucleus (Figure 1c). Because protein localization can provide important 101 information on their function, immunofluorescence confocal microscopy was assessed to 102 examine the subcellular co-localization of proteins with different organelles. The A549 cells 103 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 5 were stained with anti-Strep-tag and the organelle markers GM130 to visualize Golgi, Tom20 104 to visualize Mitochondria and Rab4 and Rab7 to visualize early and late Endosomes, 105 respectively. Cytosolic ORF7a C-terminal contains a di-lysine ER retrieval signal (KRKTE) 106 that mediates protein trafficking to the ER-Golgi intermediate compartment (Figure 1a). 107 Thus, ORF7a localized predominantly at the Golgi apparatus (Figure 2a) in agreement with 108 previous studies (Gordon et al., 2020; Lee et al., 2021; Zhang et al., 2020) and with early 109 Endosomes. Likewise, ORF7b has been associated with ER in SARS-CoV-2 (Lee et al., 2021; 110 Zhang et al., 2020) and with Golgi in SARS-CoV (Schaecher et al., 2007, 2008). However, 111 our results showed that SARS-CoV-2 ORF7b partially localizes with Golgi (Pearson’s 112 coefficient of 0,58) and predominantly colocalized with Mitochondria (Pearson’s coefficient 113 of 0,74) (Figure 2b). 114 115 RNA sequencing identified genes altered on A549-ORF7a and A549-ORF7b cells. 116 Differential gene expression analysis was performed for A549 cells and A549 cells expressing 117 either ORF7a or ORF7a (Figure S1a). A principal component analysis (PCA) based on 118 normalized counts from DESeq2 was used to explore the similarity of our samples. High 119 quality was achieved since samples were well clustered (Figure S1b). We identified the 120 overall upand down-regulated differentially expressed genes (DEGs) in A549-ORF7a and 121 A549-ORF7b cells compared to A549 non-transduced cells (adjusted p-value <0,05 and log2 122 fold chance >1). In total, 882 genes were up-regulated, and 457 genes were down-regulated 123 by ORF7a (Figure 3a, Table S1). Likewise, 652 genes were up-regulated and 500 genes were 124 down-regulated in A549-ORF7b cells (Figure 3a, Table S2). To delineate the potential 125 functions of SARS ‐ CoV ‐ 2 ORF7a and ORF7b proteins, gene ontology (GO) and pathways 126 (KEGG) analysis was conducted based on their respective DEGs. The most significantly 127 enriched biological processes in cells expressing ORF7a were cell-cell adhesion, extracellular 128 structure and extracellular matrix organization (Figure S2a, Table S1) and pathways such as 129 steroid hormone biosynthesis, ascorbate and aldarate metabolism, bile secretion and ECM-130 receptor interaction (Figure S2c, Table S1). In A549-ORF7b expressing cells, processes like 131 extracellular structure, extracellular matrix organization and metabolism pathways were also 132 the most significant ones (Figure S2b and S2d, Table S2). These data indicated that 133 identified DEGs are mainly enriched in ECM-related items and metabolism genes, suggesting 134 that both proteins could be interacting in similar pathways. The overlap of DEGs in ORF7a135 and ORF7b-regulated genes was analyzed using the Venn diagram visualization, showing 751 136 genes in common of which 311 genes were up-regulated and 440 genes were down-regulated 137 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 6 (Figure 3b). Next, the MCODE enrichment analysis based on this PPI network was applied 138 to the common genes. Out of the eight PPI modules presented, three of them were discarded 139 since they contained only 3 genes (Figure 3c, Table S3). Among the top list of enriched terms 140 of MCODE 1 (22 genes), three major Reactome pathways were extracellular matrix, integrin 141 cell surface interactions and collagen chain trimerization, most of which were down-142 regulated (red and green colors). The top list of MCODE 2 enriched categories (15 genes) 143 included GO-term positive regulation of vasculature development, positive regulation of 144 angiogenesis and the reactome pathway adherens junctions interactions (JUP, CDH7, CDH5, 145 PDZD3 down-regulated and CDH6 up-regulated). MCODE 3 (11 genes) was associated with 146 post-translational protein phosphorylation, regulation of Insulin-like Growth Factor (IGF) 147 transport and uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs) and NABA 148 CORE MATRISOME pathways, most of them up-regulated (blue and violet colors). MCODE 149 4 (10 genes) included the KEGG pathway steroid hormone biosynthesis and the metapathway 150 biotransformation Phase I and II. Finally, MCODE 5 (5 genes: WNT5A, IL7R, VAMP8, 151 STON1, SYT1) was associated with the Reactome pathway Cargo recognition for clathrin-152 mediated endocytosis, Clathrin-mediated endocytosis and Membrane Trafficking (Figure 3c). 153 154 Expression of ORF7a and ORF7b induced metabolic disfunctions in A549 cells. 155 The uridin diphosphate (UDP)-glucuronosyltransferase (UGT) family of enzymes catalyzes 156 the attachment of a glucuronic acid (glucuronidation) to certain drugs and xenobiotics, as well 157 as to endogenous compounds such as bilirubin to facilitate their elimination from the body 158 (Mano et al., 2018). We found genes coding for several UDP-glucuronosyltransferases 159 (UGT1A1, UGT1A3, UGT1A6, UGT1A7, UGT1A9, UGT2B7) highly overexpressed in 160 transduced cells with ORF7a and ORF7b (Figure 3c and 4a). Using qRT-PCR, the 161 expression of accessory proteins ORF7a and ORF7b induced an 18-fold increase in UGT1A9 162 mRNA levels as compared with control cells (Figure 4b). 163 On the other hand, cytochromes P450 (P450s or CYPs) comprise a superfamily of 164 monooxygenase enzymes that catalyze oxygen insertion into a large array of different 165 substrates such as lipids and steroids, as well as drug compounds and other xenobiotics. A 166 prominent member of the P450 superfamily is CYP1A1, which metabolizes a variety of 167 substrates including fatty acids such as arachidonic acid, the fluoroquinolone antibiotic 168 difloxacin, and the drug theophylline. CYP1A1 also acts on several procarcinogenic 169 molecules (Munro, 2018). Thus, CYP´s system is the most important drug-metabolizing 170 enzyme family existing amongst species(Stipp and Acco, 2021). Generally, CYPs’ expression 171 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 7 or activity decrease during viral infections, chronic inflammation and in presence of 172 proinflammatory cytokines (e.g. IL-6, TNFα , IFNγ TGFβ and IL-1 β ), which can result in 173 alterations of the pharmacological effects of substances in inflammatory diseases (Christmas, 174 2015; Wang et al., 2022). Surprisingly, ORF7a and ORF7b overexpression led to significant 175 down-regulation of genes coding for CYP enzymes such as CYP1B1, CYP2F1, CYP2T3P, 176 CYP4F3, CYP27C1 and CYP26A1 (Figure 4a). We also observed a reduction in CYP1A1 177 expression by qRT-PCR in A549-ORF7a and A549-ORF7b cells (Figure 4c). These results 178 indicate that both ORF7a and ORF7b perturb the UGT and CYP expression suggesting a 179 metabolic dysfunction that might facilitate the metabolic inactivation of drug therapies in 180 COVID-19 patients. 181 Interestingly, we found that the gene coding for the prostaglandin-endoperoxidase synthase 2 182 (PTGS2), also known as cyclooxygenase 2 (COX-2) was significantly up-regulated in A549-183 ORF7a and ORF7b cells (Figure 4d). COX-2 enzyme is critical for the generation of 184 prostaglandins, lipid molecules with diverse roles in maintaining homeostasis as well as in 185 mediating pathogenic mechanisms, including the inflammatory response (Ricciotti and 186 FitzGerald, 2011). Stimulation of PTSG2 has been previously detected in SARS-CoV via its 187 spike and nucleocapsid proteins (Liu et al., 2007; Yan et al., 2006). More recently it was 188 found that SARS-CoV-2 induced COX-2 upregulation in human lung epithelial cells (Blanco-189 Melo et al., 2020) as observed in the present study. 190 191 ORF7a and ORF7b disrupt inflammatory responses balance in A549 cells. 192 SARS-CoV-2 infection induces unbalanced inflammatory responses, characterized by weak 193 production of type I interferon (IFN-I) and overexpression of proinflammatory cytokines, 194 both of which are linked to severe clinical outcomes (Hojyo et al., 2020; Sa Ribero et al., 195 2020). Data from our RNAseq study showed a down-regulation of several Interferon-196 Stimulated Genes (ISGs) as well as an up-regulation of cytokines and chemokines (Figure 197 5a). As previously reported, SARS-CoV-2 ORF7a antagonizes the production of IFN-I by 198 blocking the phosphorylation of STAT2 thereby suppressing the transcriptional activation of 199 antiviral ISGs (Martin-Sancho et al., 2021; Xia et al., 2020). As expected, we found reduced 200 expression of OASL, IFIT1, IFIT2 and TRIM22 genes in A549-ORF7a cells (Figure 5a). 201 However, in ORF7b-expressing cells we observed a decrease of IFIT1 and TRIM22 202 expression bur overexpression of IFITM1 and RSAD2 genes, suggesting that both proteins 203 may interfere with IFN-I responses but employing different strategies. To confirm this, we 204 examined by qRT-PCR whether ORF7a or ORF7b expression affected ISG-associated gene 205 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 8 expression in A549 cells. The results showed that while ORF7a inhibited the expression of 206 OASL, IFIT1 and IFIT2, ORF7b did not (Figure 5b), indicating that ORF7a is an IFN-I 207 antagonist whereas ORF7b is not. 208 ORF7a is also thought to activate the NFkB pathway and promote the production of 209 inflammatory cytokines, which play a significant role in the clinical severity of COVID-19 210 (Su et al., 2021). Conversely, little is known about ORF7b’s role in the inflammatory 211 response. It has been described that ORF7b may promote the IFN-I signaling pathways and 212 eventually accelerate TNF-induced apoptosis (Yang et al., 2021). An increase in several 213 genes encoding cytokines was observed here, including IL-8, IL-11, IL-15, IL-32 and IL-12A 214 in A549-ORF7a and IL-8 and IL-15 in A549-ORF7b (Figure 5a). In addition, genes encoding 215 chemokines such as ACKR3, CXCL1 and CXCL12 were strongly overexpressed in ORF7a 216 cells whereas only CXCL1 was observed overexpressed in ORF7b cells. To further 217 corroborate this data, expression of IL-8, IL-11 and CXCL1 was measured in A549-ORF7a 218 and A549-ORF7b by qRT-PCR. Increased levels of these genes were detected in both 219 transduced cell lines (Figure 5c). All in all, our results showed that accessory proteins 220 ORF7a and ORF7b play key roles in regulating the host immune responses to SARS-CoV-2 221 infection, inhibiting the IFN-I production by ORF7a and increasing proinflammatory 222 cytokines release by both ORF7a and ORF7b. 223 224 ORF7a and ORF7b expression alter cell-ECM and cell-cell interactions 225 Most of the dysregulated genes associated with the enriched process ECM organization 226 (Figure 3c) were down-regulated. Among them, we found genes coding for integrins 227 (ITGA2B, ITGB3, ITGB6, ITGA10, ITGA11), collagens (COL4A4, COL9A3), tenascins 228 (TNC), vitronectin (VTN), laminins (LAMB2, LAMA3, LAMB3, LAMC3), nephronectin 229 (NPNT) and thrombospondin-3 (THBS3-AS1) and cell adhesion molecules (CAMs) such as 230 immunoglobulin superfamily members (ICAM-1, IGSF11), integrins (ITGAL), cadherins 231 (CDH5) and claudins (CLDN2), most of them down-regulated (Figure 6a). Integrins are 232 integral cell-surface proteins composed of an alpha chain and beta chain that participate in 233 cell adhesion as well as in cell-surface mediated signaling. Interestingly, ITGA2B and ITGB3 234 genes encoding alpha and beta chains of the alpha-IIb/beta-3 integrin were observed down-235 regulated. This integrin is highly expressed in platelet and plays a crucial role in the blood 236 coagulation system by mediating platelet aggregation (Ma et al., 2007). Integrin alpha-237 IIb/beta-3 binds specific adhesive proteins as fibrinogen/fibrin, plasminogen, prothrombin, 238 thrombospondin and vitronectin (Huang et al., 2019; Ma et al., 2007). A down-regulation of 239 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 9 ITGA10 and ITGA1, the genes encoding the beta chains of the collagen-binding integrins 240 alpha-10/beta-1 and the alpha-11/beta-1, respectively (White et al., 2004), were also observed. 241 Conversely, we observed an increase in the expression of the gene encoding beta-6 integrin 242 subunit (ITGB6), which forms exclusively a dimer with the alpha v chain (alpha-v/beta-6) 243 (Bandyopadhyay and Raghavan, 2009). This integrin plays a role in modulating the innate 244 immune response in lungs, being able to bind ligands such as fibronectin and transforming 245 growth factor beta-1 (TGF-1). Integrin alpha-v/beta-6 is predominantly expressed in 246 epithelial cells and is highly expressed in inflamed and injured lung tissue (Horan et al., 247 2008). In summary, our results suggest that ORF7a and ORF7b expression is responsible for 248 critical changes in the expression of important components of the cell adhesion machinery, 249 which can significantly alter cell–ECM and cell-cell interactions. To test this hypothesis, 250 transduced and control cells were evaluated for their adhesion ability to different ECM 251 components (Figure 6b). According to transcriptomic data, A549-ORF7a cells showed 252 weaker binding capacity to fibrinogen, collagen IV, and fibronectin whereas A549-ORF7b 253 exhibited lower adhesion to fibrinogen and fibronectin. This could be due to the inhibition of 254 ITGA2B, ITGB3, ITGA10 and ITGA11 expression observed in both transduced cells. 255 Integrins typically bind to the ECM while immunoglobulin members and cadherins are 256 associated with cell adhesion and cell-cell signaling. In fact, previous studies have suggested 257 that SARS-CoV-2 ORF7a may play a vital role in recognizing macromolecules on the cellular 258 surface since its structural homology with the human intercellular adhesion family of 259 molecules ( ICAM) (Nizamudeen et al., 2021; Zhou et al., 2021). Interaction between ICAM 260 and integrins regulates immune cell migration, activation, and target cell recognition, which 261 are important host defense mechanisms against infectious agents. Interestingly, our 262 transcriptomic results revealed a down-regulation of ICAM1 as well as ITGAL and ITGB2, the 263 genes encoding the integrin alpha-L and beta-2 chains for the leukocyte function-associated 264 antigen-1 (LFA-1) (Figure 6a). Significant reductions in ICAM1 and ITGB2 expression were 265 further confirmed by qRT-PCR in A549-ORF7a and A549-ORF7b cells (Figure 6c). Next, 266 flow cytometry was used to analyze ICAM-1 expression on cell surfaces. As shown in Figure 267 6d, ICAM-1 expression was significantly reduced in direct relation to ORF7a and ORF7b 268 expression in A549 cells. 269 Cadherins are the major cell adhesion molecules (CAMs) responsible for Ca2+-dependent cell-270 cell adhesion and they are therefore crucial for promoting diverse morphogenetic processes 271 (Hirano and Takeichi, 2012). The transcriptomic analyses of A549-ORF7a and ORF7b 272 revealed a reduction in transcripts encoding adherens junctions proteins including cadherins 273 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 16 Western blot 476 Transduced cells were harvested and lysed in ice-cold RIPA lysis buffer containing complete 477 protease inhibitor and phosphatase inhibitors cocktails (Sigma-Aldrich) at 4º C for 30 min. 478 Cell lysates was mixed with SDS-Laemli sample buffer and heated at 95º C for 5 min. Protein 479 samples were resolved by SDS polyacrylamide gel electrophoresis and transferred onto a 480 nitrocellulose membrane using Trans-Blot Turbo Transfer System (Bio-Rad, Hercules, CA), 481 followed by blocking for 1 h with 5% nonfat milk in Tris-buffered saline-Tween 20 buffer 482 and probing with antibodies against Strep Tag (SAB2702215, Sigma-Aldrich), γ-catenin (sc-483 514115, Santa Cruz Biotechnology) and GAPDH (A00084, GenScript) (Supplementary Table 484 6). The washed membranes were incubated with secondary antibody StarBright Blue 700 485 Goat anti-mouse IgG (Bio-rad). The proteins were visualized by fluorescence using 486 ChemiDoc MP Imaging Systems (Bio-Rad). 487 488 Immunofluorescence microscopy 489 Cells were seeded on 24-well plates containing glass coverslips coated with poly-lysine 490 solution (100.000 cells per well). Cells were fixed with 4% PFA in PBS for 15 min, washed 491 twice in PBS, and then permeabilized for 10 min with 0.1% Triton X-100 in PBS. Primary 492 antibodies incubation was carried out for 1h in PBS containing 3% BSA and 0.1% Triton X-493 100 at 1:100 dilution. Coverslips were washed three times with PBS before secondary anti-494 mouse antibodies incubation (1:1000 dilution). The antibodies used for immunofluorescence 495 are shown in Supplementary Table 5. Anti-phalloidin was used as a cytoplasmic marker at 496 1:200, and DAPI (4’6-diamidino-2-phenylindole) (Molecular Probes) was used as a nuclear 497 marker. Coverslips were mounted in Mowiol 4-88 (Sigma-Aldrich). Images were acquired 498 with a confocal laser microscope Leica TCS SP8 STED 3X. 499 500 RNA isolation and sequencing 501 WT A549, A549-ORF7a and A549-ORF7b cells were seeded (3x10E5) in 6-well plates and 502 lysed using RLT buffer for RNA isolation (RNeasy mini kit, Qiagen). Each sample was 503 performed in triplicate. RNA was isolated following manufacturer protocol, quantified by 504 Nanodrop 1000 (Thermo Scientific) and quality controlled by Bioanalyzer (Agilent). All 505 samples sent for sequencing had a RIN (RNA integrity number) over 9.90. cDNA libraries 506 and sequencing were performed by Novogene Europe, using 400 ng of RNA per sample for 507 library preparation. Samples were sequenced in an Illumina platform using a PE150 strategy. 508 509 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 17 Gene sets and expression analysis 510 Sequencing raw data was quality controlled (error rate, GC content distribution) and filtered, 511 removing bad quality and N-containing sequences and adaptors. Clean data were mapped 512 (HISAT2) to reference genome GRCh38.p13, and gene expression was quantified using 513 FPKM (Fragments Per Kilobase of transcript sequence per Millions of base pairs sequenced). 514 Differential expression analysis was performed using DESeq2 R package (Anders and Huber, 515 2010). 516 Raw counts were transformed with the vst function in the DESeq2 package (Love et al., 2014) 517 of the R software version 3.6.3 (R Core Team, 2020), and subsequent PCA was performed 518 with the prcomp function. The 500 genes with the highest variance among samples were 519 considered. Finally, the PCA graph was made with Graphad Prism software. All sequencing 520 data sets are available in the NCBI BioProject database under accession number 521 PRJNA841835. 522 523 GO and Pathways Enrichment Analysis 524 The annotation function of GO analysis is comprised of three categories: BP, CC, and MF. 525 Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database resource for understanding 526 high-level functions and utilities of the genes or proteins (Kanehisa and Goto, 2000; Kanehisa 527 et al., 2012). GO analysis and KEGG pathway enrichment analysis of candidate DEGs were 528 performed using the R package and using NovoSmart Software. An adjusted p-value less than 529 0.05 was considered as the cut-off criterion for both GO analysis and pathway enrichment 530 analysis. 531 532 Pathway Enrichment Analysis, Network and PPI Module Reconstruction 533 To perform the pathway enrichment analysis and the gene network reconstruction, we used 534 the online Metascape tool (http://metascape.org)(Zhou et al., 2019) with the default 535 parameters set. Enrichment analyses were carried out by selecting the genomics sources: 536 KEGG Pathway, GO Biological Processes, Reactome Gene Sets, Canonical Pathways, and 537 CORUM. Terms with p < 0.01, minimum count 3, and enrichment factor >1.5 were collected 538 and grouped into clusters based on their membership similarities. p values were calculated 539 based on accumulative hypergeometric distribution, and q values were calculated using the 540 Benjamini-Hochberg procedure to account for multiple testing. To further capture the 541 relationship among terms, a subset of enriched terms was selected and rendered as a network 542 plot, where terms with similarity >0.3 are connected by edges. Based on PPI enrichment 543 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 18 analysis, we ran a module network reconstruction based on the selected genomics databases. 544 The resulting network was constructed containing the subset of proteins that form physical 545 interactions with at least one other list member. Subsequently, employing Molecular Complex 546 Detection (MCODE) algorithm, we first identified connected network components, then a 547 pathway and process enrichment analysis was applied to each MCODE component 548 independently and the three best-scoring (by p-value) terms were retained as the functional 549 description of the resulting modules. 550 Real time qPCR analysis 551 RNA samples (500 ng) were reverse transcribed using qScript™ cDNA synthesis kit (Quanta 552 Biosciences Inc.), following manufacturer’s instructions. Primer sequences are available in 553 Supplementary Table 6. The final 15 µL PCR reaction included 2 μ L of 1:5 diluted cDNA as 554 template, 3 µL of 5x PyroTaq EvaGreen qPCR Mix Plus with ROX (Cultek Molecular 555 Bioline, Madrid, Spain), and transcript-specific forward and reverse primers at a 10 μ M final 556 concentration. Real time PCR was carried out in a QuantStudio 12K Flex system (Applied 557 Biosystems) under the following conditions: 15 min at 95 °C followed by 35 cycles of 30 s at 558 94 °C, 30 s at 57 °C and 45 s at 72 °C. Melting curve analyses were performed at the end, in 559 order to ensure the specificity of each PCR product. Relative expression results were 560 calculated using GenEx6 Pro software (MultiDGöteborg, Sweden), based on the Cq values 561 obtained. Statistical differences in expression among groups were assessed using Student’s t-562 test, setting statistical significance at P <0.05. 563 564 Flow cytometric analysis 565 Flow cytometry was used to analyze the expression levels of ICAM-1. Cells were harvested, 566 washed in PBS and blocked with goat serum for 20 minutes at 4ºC. For staining, cells were 567 resuspended in PBS 0.1% BSA 0.01% NaN3 containing the monoclonal antibody PE mouse 568 anti-human CD54 (BD Pharmingen) or its isotype control at a concentration of 1/500. Both 569 antibodies were previously titrated to determine their concentration. Live/Dead Fixable Aqua 570 dye (Invitrogen) was used at 1/1000 to assess cell viability. Cells were incubated for 30 min at 571 4ºC protected from the light, washed with PBS 0.1% BSA 0.01% NaN3 and resuspended in 572 PBS. For these experiments, a CytoFLEX flow cytometer (Beckman Coulter) was used and 573 data was analyzed using FlowJo v10 (BD Biosciences). 574 575 Cell Adhesion Assays 576 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 19 Cell to ECM adhesion was tested using the CytoSelect™ 48-well Cell Adhesion Assay 577 (CBA-070, Cell Biolabs) according to the manufacturer's protocol. Briefly, cells were seeded 578 at 0.12 × 106 onto the 48-well plate and were left to incubate for 90 min at 37 °C, with 5% 579 CO2. The unbound cells were washed off and the adherent cells were stained for 10 min at 580 RT. Any residual stain was removed with 5 washes using dH2O and the plate was left to air 581 dry. Using an orbital shaker, the plate was incubated for a final 10 min at RT with extraction 582 solution. The optical density (OD) was read at 595 nm. Bovine serum albumin (BSA) was 583 used as a negative control. 584 The cell-cell adhesion assay was performed as follow. Briefly, 5 × 104 A549 cells were 585 seeded into a 96-well plate for 24 hours. After 24 h incubation, A549 cells were labeled with 586 calcein-AM. They were co-cultured with the monolayer of A549 cells for 2 h at 37 °C, 5% 587 CO2. After the indicated period, non-adherent cells were removed, and then calcein-AM 588 measured the fluorescence using a fluorescein filter set (absorbance maximum of 480/20 nm 589 and an emission maximum of 530/25 nm) to calculate the number of adherent cells. 590 591 ZO-1 staining 592 For the optical characterization of tight junctions, cells were grown on Transwell membranes 593 (ref. 3401, Costar, 0.4 μ m pore size). Seeding density was 70  000 cells. ZO-1 staining was 594 performed after 8 days as described below. Cells were washed three times with PBS and fixed 595 with -20ºC methanol for 5 minutes at 4ºC. Afterward, the samples were permeabilized with 596 0.1% Triton X-100 for 5 minutes at RT. Subsequently, a blocking step with PBS containing 597 1% BSA was performed. The primary anti-ZO-1 antibody (ref. 61-7300, Invitrogen) was 598 diluted at 1:100 in PBS containing 1% BSA and incubated for 3 h at RT. The secondary 599 antibody (anti-rabbit IgG-FITC, ref. 9887, Sigma) was diluted 1:200 in PBS and incubated for 600 1 h at RT. Cell nuclei were counterstained with DAPI (0,5 μ g/mL) for 2 min at RT. Transwell 601 membranes were then sliced into strips and mounted onto glass slides with the cell side up, 602 and treated with anti-fade reagent overnight at 4ºC with coverslips on top. Samples were 603 analyzed by a confocal laser scanning microscopy. 604 605 Acknowledgments 606 The authors wish to acknowledge Bioinformatics & Biostatistics Service at CIB and Dr 607 Aurora Gómez-Durán. This research work was funded by: Junta de Andalucía and the 608 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 20 European Commission – NextGenerationEU (Regulation EU 2020/2094), through CSIC's 609 Global Health Platform (PTI Salud Global). 610 611 Author contributions 612 Conceptualization: MM and JJG; Methodology: TGG, RFR, NR, SZL, BDLA, JMSC and 613 AJM; Investigation: TGG, RFR, NR, SZL, BDLA, JMSC and AJM; Writing Original Draft: 614 TGG; WritingReview & Editing: MM and JJG; Supervision: MM and JJG; Project 615 administration: MM and JJG; Funding acquisition: MM and JJG. 616 617 Competing interests 618 Authors declare that they have no competing interests. 619 620 Figures 621 Figure 1. Expression of SARS-CoV-2 ORF7a and ORF7b in A459 epithelial cells. a, 622 Schematic representation of SARS-CoV-2 ORF7a and ORF7b proteins. Domains are 623 highlighted in couleur (SP, signal peptide; TM, transmembrane domain; ER, endoplasmic 624 reticulum retention signal) and numbers denote the residues sites. b, Expression of SARS-625 CoV-2 proteins ORF7a and ORF7b. C-terminally Strep-tagged viral proteins were transduced 626 in A459 cells and analyzed by western blotting using anti-Strep-tag and anti-GAPDH 627 antibodies. c, Cellular localization of ORF7a and ORF7b. A459 transduced cells with Strep-628 tagged SARS-CoV-2 proteins were imaged by confocal microscopy. Right panels are 629 heatmaps of signal intensity detected in a representative cell. Scale bar is 25 µm. 630 631 Figure 2. Cellular localization of SARS-CoV-2 ORF7a and ORF7b. Confocal analysis of 632 SARS-CoV-2 protein ORF7a and ORF7b localization in A549 cells transduced with Strep-633 tagged-ORF7a (a) or ORF7b (b) and organelle markers: GM130 (Golgi), Tom20 634 (Mitochondria) Rab4 (Early endosome) and Rab7 (Late endosome). Red: Strep-tag antibody 635 signal; yellow: organelle markers; Blue: DAPI (nuclei staining); Green: Phalloidin. Scale bar, 636 25 μ m. All experiments were done at least twice, and one representative is shown. PCC 637 indicates the Pearson’s coefficient for co-localization of each organelle with the Strep-tag 638 ORF7a or ORF7b. 639 640 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 21 Figure 3. a, Identification of DEGs in A549 cells expressing ORF7a or ORF7b. a, 641 Volcano plot of DEGs in A549 cells transduced with SARS-CoV-2 ORF7a (left) or ORF7b 642 (right) compared with A549 WT. b, Venn diagram of DEGs in A549 cells expressing ORF7a 643 and ORF7b. c, MCODE enrichment analysis by Metascape. MCODE algorithm was applied 644 to clustered enrichment ontology terms to identify neighborhoods where proteins are densely 645 connected and GO enrichment analysis was applied to each MCODE network to assign 646 biological meanings. The color code for pie sector represents a gene list. 647 648 Figure 4. SARS-CoV-2 ORF7a and ORF7b alter the metabolic process. a, Log2 649 Foldchange Heatmaps of DEGs involved in metabolism pathways. b-c, Expression of 650 UGT1A9 (b) and CYP1A1 (c) genes were calculated with 2ΔΔ CT method by normalizing to 651 that of GADPH. The fold changes were calculated with respect to the level of A549 WT. 652 Error bars represent mean ± SD (n=3). Statistical significance is as follows: **p  <  0.01, 653 ***p  <  0.001. 654 655 Figure 5. Interferon and inflammatory responses to SARS-CoV-2 ORF7a and ORF7b. 656 a, Heatmaps showing expression of genes related to antiviral and inflammatory response 657 compared to A549 cells. Genes shown in red are significantly increased, genes in green 658 significantly decreased and genes in black indicate no change in expression. b-c, Expression 659 of ISGs, OASL, IFIT1 and IFIT2 (b) and cytokines IL8, IL11 and CXCL1 (c) were calculated 660 with 2ΔΔ CT method by normalizing to that of GADPH. The fold changes were calculated with 661 respect to the level of A549 WT. Error bars represent mean ± SD (n=3). Statistical 662 significance is as follows: *p  <  0.05, **p  <  0.01, ***p  <  0.001. 663 664 Figure 6. Effect of SARS-CoV-2 ORF7a and SARS-CoV-2 ORF7b in cell adhesion. a, 665 Log2 Foldchange Heatmaps of DEGs involved in ECM-receptor interaction (hsa04512) and 666 cell adhesion molecules (CAMS) (hsa04514). Red for up-regulated, green for down-regulated 667 and asterisk (*) for significant in both cell lines. b, Quantification of A549 cells adhering to 668 the extracellular matrix (ECM) components fibronectin, collagen I, collagen IV, laminin I, 669 and fibrinogen, and BSA (control). c, RT-qPCR for ICAM1 and ITGB2 genes. d, Analysis by 670 flow cytometry of ICAM-1 expression in A549 cells expressing ORF7a or ORF7b (left panel) 671 and quantification of the percentage of ICAM-1 positive cells (right panel). Data are 672 represented as mean  ±  SD (n=3). Statistical significance is given as follows: *p  <  0.05, 673 **p  <  0.01, ***p  <  0.001 to the control group A549 WT. 674 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint 22 675 Figure 7. Effect of SARS-CoV-2 ORF7a and SARS-CoV-2 ORF7b in cell junctions. a, 676 Log2 Foldchange Heatmaps of DEGs related to cell-cell adhesion interaction. b, Western 677 blotting showing the expression of ࢽ -catenin, the major protein in cell-cell adhesion at the 678 desmosomes (top panel) and quantification for ࢽ -catenin (bottom panel). GAPDH was used 679 as a control. c, Cell-cell adhesion assay showing the percentage of adherent cells. Data are 680 represented as mean  ±  SD (n=3). Statistical significance is given as follows: *p  <  0.05, 681 **p  <  0.01, ***p  <  0.001 to the control group A549 WT. d, Immunostaining for the tight 682 junction protein ZO-1 after 8 days of culture on transwell inserts. Scale bar, 10 μ m. 683 684 Figure 8. Comparison of host transcriptome responses of ORF7a and ORF7b to SARS-685 CoV-2 infection. a-b, Venn diagrams of the intersection between differentially expressed 686 genes in A549 cells expressing ORF7a or ORF7b and SARS-CoV-2-regulated genes in A549-687 ACE2 cells (a) and COVID-19 lung biopsies (b). Data are from Blanco-Melo et al., 2020. c, 688 MCODE components identified from the PPI network. Network nodes are displayed as pies. 689 The color code for the pie sector represents a gene list (metascape.org). 690 691 References 692 Ackermann, M., Verleden, S.E., Kuehnel, M., Haverich, A., Welte, T., Laenger, F., 693 Vanstapel, A., Werlein, C., Stark, H., Tzankov, A., et al. (2020). Pulmonary Vascular 694 Endothelialitis, Thrombosis, and Angiogenesis in Covid-19. N. Engl. J. Med. 383, 120–128. 695 https://doi.org/10.1056/NEJMoa2015432. 696 Al-Kuraishy, H.M., Al-Gareeb, A.I., Abdullah, S.M., Cruz-Martins, N., and Batiha, G.E.-S. 697 (2021). 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It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint WT ORF 7a ORF 7b 0.0 0.5 1.0 1.5 Relative Expression ✱✱ ✱✱✱ CYP1A1 WT ORF7a ORF 7b 0 1 2 3 4 Relative Expression PTGS2 ✱✱✱ ✱✱✱ WT ORF 7a ORF 7b 0 5 10 15 20 25 Relative Expression UGT1A9 ✱✱✱ ✱✱✱ a b c d Figure 4 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint a b ORF7a ORF7b IL8 IL11 IL15 IL32 IL12A IL34 IL1RL1 IL7R IL18R1 ACKR3 CXCL1 CXCL12 CXCR4 CX3CL1 CCL26 OASL IFIT1 IFIT2 TRIM22 IFITM1 RSAD2 TMEM233 GBP1 IRF8 CAMK2A -5 Cytokines Chemokines ISGs 0 5 Log. Fold Change c WT ORF7a ORF7b 0 1 2 3 4 IL8 Relative Expression ✱ ✱✱ WT ORF7a ORF7b 0 1 2 3 4 5 6 7 Relative Expression CXCL1 ✱ ✱✱✱ ✱✱ WT ORF7a ORF7b 0.0 0.5 1.0 1.5 IFIT1 Relative Expression WT ORF7a ORF7b 0.0 0.5 1.0 1.5 IFIT2 Relative Expression ✱ ✱ ns WT ORF7a ORF7b 0.0 0.5 1.0 1.5 OASL Relative Expression ✱ ✱ ns Figure 5 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted June 1, 2022. ; https://doi.org/10.1101/2022.06.01.494101doi: bioRxiv preprint b a ICAM-1 c WT SARS-CoV-2 Orf7a SARS-CoV-2 Orf7b BSA Fibrinogen Laminin I Collagen IV Collagen I Fibronectin WT ORF7a ORF7b d ORF7a ORF7b ITGA2B ITGB3 ITGB6 AC092153,1 ITGB8 ITGA10 ITGA11 COL4A4 COL9A3 DELEC1 TNC VTN LAMA3 LAMB2 LAMB3 LAMC3 NPNT THBS3-AS1 ECM-receptor interaction -2 -1 0 1 2 * * * * * * * * * Log. Fold Change ORF7a ORF7b CDH5 CLDN2 CLDN7 NLGN1 NLGN3 NLGN4Y NRXN3 CNTNAP2 ICAM1 ITGAL ITGB2 ITGB8 SDC2 VCAN VTCN1 CD40 ESAM IGSF11 CAMs -4 -2 0 2 4 * * * * * * * * * * Log. Fold Change ICAM1 ITGB2 0.0 0.5 1.0 1.5 Relative Expression WT ORF7a ORF7b *** *** *** ** * Figure 6 ab c WT ORF7a ORF7b 0 50 100 150 % Cell-Cell Adhesion ns ✱✱✱ WT ORF7a ORF7b 36 - 72 - 95 - GAPDH !-catenin WT ORF7a ORF7b 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Relative signal intensity (a.u) ✱ ✱✱ ZO-1/DAPI WT d ORF7a ORF7b Figure 7 A549 ACE2_SARS-CoV-2 (Blanco-Melo et al., 2020) 3270 505 79 A549_ORF7a (this study) 345 125 629 52 A549_ORF7b (this study) COVID-19 Lung Biopsies (Blanco-Melo et al., 2020) 1787 540 44 A549_ORF7a (this study) 371 53 701 26 A549_ORF7b (this study) a Integrin cell surface interactions Leukocyte activation involved in immune response Post-translational protein phosphorylation Neutrophil degranulation Interferon alpha/beta signaling b c Figure 8