FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3208113816> ?p ?o ?g. }

• W3208113816 abstract "Article28 October 2021Open Access Transparent process SARS-CoV-2–host proteome interactions for antiviral drug discovery Xiaonan Liu Xiaonan Liu orcid.org/0000-0002-9600-0536 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Sini Huuskonen Sini Huuskonen orcid.org/0000-0003-3857-1805 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Tuomo Laitinen Tuomo Laitinen orcid.org/0000-0003-1539-2142 School of Pharmacy, University of Eastern Finland, Kuopio, Finland Search for more papers by this author Taras Redchuk Taras Redchuk orcid.org/0000-0002-6724-9025 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Mariia Bogacheva Mariia Bogacheva orcid.org/0000-0001-6608-1120 Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland Department of Virology, University of Helsinki, Helsinki, Finland Search for more papers by this author Kari Salokas Kari Salokas orcid.org/0000-0002-4471-6698 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Ina Pöhner Ina Pöhner orcid.org/0000-0002-2801-8902 School of Pharmacy, University of Eastern Finland, Kuopio, Finland Search for more papers by this author Tiina Öhman Tiina Öhman orcid.org/0000-0001-6647-8873 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Arun Kumar Tonduru Arun Kumar Tonduru orcid.org/0000-0001-6986-4198 School of Pharmacy, University of Eastern Finland, Kuopio, Finland Search for more papers by this author Antti Hassinen Antti Hassinen orcid.org/0000-0001-9491-2868 Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland Search for more papers by this author Lisa Gawriyski Lisa Gawriyski orcid.org/0000-0002-1950-4749 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Salla Keskitalo Salla Keskitalo orcid.org/0000-0001-5555-1975 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Maria K Vartiainen Maria K Vartiainen orcid.org/0000-0002-2017-0475 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Vilja Pietiäinen Vilja Pietiäinen orcid.org/0000-0003-3125-2406 Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland Search for more papers by this author Antti Poso Antti Poso orcid.org/0000-0003-4196-4204 School of Pharmacy, University of Eastern Finland, Kuopio, Finland Department of Internal Medicine VIII, University Hospital Tübingen, Tübingen, Germany Search for more papers by this author Markku Varjosalo Corresponding Author Markku Varjosalo [email protected] orcid.org/0000-0002-1340-9732 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Xiaonan Liu Xiaonan Liu orcid.org/0000-0002-9600-0536 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Sini Huuskonen Sini Huuskonen orcid.org/0000-0003-3857-1805 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Tuomo Laitinen Tuomo Laitinen orcid.org/0000-0003-1539-2142 School of Pharmacy, University of Eastern Finland, Kuopio, Finland Search for more papers by this author Taras Redchuk Taras Redchuk orcid.org/0000-0002-6724-9025 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Mariia Bogacheva Mariia Bogacheva orcid.org/0000-0001-6608-1120 Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland Department of Virology, University of Helsinki, Helsinki, Finland Search for more papers by this author Kari Salokas Kari Salokas orcid.org/0000-0002-4471-6698 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Ina Pöhner Ina Pöhner orcid.org/0000-0002-2801-8902 School of Pharmacy, University of Eastern Finland, Kuopio, Finland Search for more papers by this author Tiina Öhman Tiina Öhman orcid.org/0000-0001-6647-8873 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Arun Kumar Tonduru Arun Kumar Tonduru orcid.org/0000-0001-6986-4198 School of Pharmacy, University of Eastern Finland, Kuopio, Finland Search for more papers by this author Antti Hassinen Antti Hassinen orcid.org/0000-0001-9491-2868 Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland Search for more papers by this author Lisa Gawriyski Lisa Gawriyski orcid.org/0000-0002-1950-4749 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Salla Keskitalo Salla Keskitalo orcid.org/0000-0001-5555-1975 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Maria K Vartiainen Maria K Vartiainen orcid.org/0000-0002-2017-0475 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Vilja Pietiäinen Vilja Pietiäinen orcid.org/0000-0003-3125-2406 Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland Search for more papers by this author Antti Poso Antti Poso orcid.org/0000-0003-4196-4204 School of Pharmacy, University of Eastern Finland, Kuopio, Finland Department of Internal Medicine VIII, University Hospital Tübingen, Tübingen, Germany Search for more papers by this author Markku Varjosalo Corresponding Author Markku Varjosalo [email protected] orcid.org/0000-0002-1340-9732 Institute of Biotechnology, University of Helsinki, Helsinki, Finland Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland Search for more papers by this author Author Information Xiaonan Liu1,2, Sini Huuskonen1,2, Tuomo Laitinen3,†, Taras Redchuk1,2,†, Mariia Bogacheva2,4,5,†, Kari Salokas1,2, Ina Pöhner3, Tiina Öhman1,2, Arun Kumar Tonduru3, Antti Hassinen2,4, Lisa Gawriyski1,2, Salla Keskitalo1,2, Maria K Vartiainen1,2, Vilja Pietiäinen2,4, Antti Poso3,6 and Markku Varjosalo *,1,2 1Institute of Biotechnology, University of Helsinki, Helsinki, Finland 2Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland 3School of Pharmacy, University of Eastern Finland, Kuopio, Finland 4Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland 5Department of Virology, University of Helsinki, Helsinki, Finland 6Department of Internal Medicine VIII, University Hospital Tübingen, Tübingen, Germany † These authors contributed equally to this work *Corresponding author. Tel: +358 2941 59413; E-mail: [email protected] Molecular Systems Biology (2021)17:e10396https://doi.org/10.15252/msb.202110396 PDFDownload PDF of article text and main figures.PDF PLUSDownload PDF of article text, main figures, expanded view figures and appendix. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Treatment options for COVID-19, caused by SARS-CoV-2, remain limited. Understanding viral pathogenesis at the molecular level is critical to develop effective therapy. Some recent studies have explored SARS-CoV-2–host interactomes and provided great resources for understanding viral replication. However, host proteins that functionally associate with SARS-CoV-2 are localized in the corresponding subnetwork within the comprehensive human interactome. Therefore, constructing a downstream network including all potential viral receptors, host cell proteases, and cofactors is necessary and should be used as an additional criterion for the validation of critical host machineries used for viral processing. This study applied both affinity purification mass spectrometry (AP-MS) and the complementary proximity-based labeling MS method (BioID-MS) on 29 viral ORFs and 18 host proteins with potential roles in viral replication to map the interactions relevant to viral processing. The analysis yields a list of 693 hub proteins sharing interactions with both viral baits and host baits and revealed their biological significance for SARS-CoV-2. Those hub proteins then served as a rational resource for drug repurposing via a virtual screening approach. The overall process resulted in the suggested repurposing of 59 compounds for 15 protein targets. Furthermore, antiviral effects of some candidate drugs were observed in vitro validation using image-based drug screen with infectious SARS-CoV-2. In addition, our results suggest that the antiviral activity of methotrexate could be associated with its inhibitory effect on specific protein–protein interactions. Synopsis A large-scale proteomics study identifies critical host proteins for SARS-CoV-2 processing. Proteins from these core subnetworks are used for drug repurposing analyses, indicating drugs with antiviral effects. A large-scale proteomics study identifies 4,781 unique high-confidence virus-host protein-protein interactions (PPIs) using 29 viral ORFs, and 4,362 unique PPIs for 18 suggested receptors/proteases/cofactors for SARS-CoV-2. The characterization of 693 hub proteins that connect viral baits and host baits via a dense network reveals critical host pathways used for viral replication. 59 compounds are prioritized that could be repurposed for 15 host protein targets used by the SARS-CoV-2 during the infection. Ten candidate drugs are validated using an image-based drug screen assay, six of them demonstrating antiviral effects. Video Synopsis SARS-CoV-2–host proteome interactions for antiviral drug discovery by Xiaonan Liu, Markku Varjosalo and colleagues Introduction The ongoing global coronavirus disease 2019 (COVID-19) pandemic is caused by severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2) (Wu et al, 2020). Treatment options for COVID-19 are limited, and consequently, coronavirus infections are overwhelming national healthcare systems. The R&D activity to develop a vaccine or drug against COVID-19 is being fast-tracked globally. By July 2021, 32 vaccines had reached phase-three clinical trials, and 11 were approved by at least one country (Dai & Gao, 2020; Creech et al, 2021). Although vaccines are the primary means to prevent COVID-19, antiviral drugs would significantly reduce the disease burden for the early treatment of COVID-19, and long COVID (Schmidt, 2021) suppression. Remdesivir (veklury), an experimental drug that was originally investigated as a potent inhibitor of Ebola virus (EBOV) (Warren et al, 2016), was the first drug approved by the US Food and Drug Administration (FDA) for the treatment of COVID-19 in October 2020 (Rubin et al, 2020). However, the World Health Organization (WHO) panel advised physicians against using remdesivir based on a review of several large clinical trials (preprint: WHO 2020; Harrington et al, 2021). Despite the controversy over whether remdesivir can reduce the duration of COVID-19, it is obvious that fully effective drugs for the prevention or treatment of SARS-CoV-2 are currently not available. Antiviral drugs mainly include direct virus-targeting and host-targeting antiviral drugs. Virus-targeting drugs often inhibit viral polymerases and proteases, while host-targeting drugs aim to disrupt the virus–host protein interactions that are essential for viral replication. Due to the high viral evolutionary rates, resistance to virus-targeting drugs can occur and lead to treatment failure, especially for infections caused by RNA viruses (Heaton, 2019). In contrast, such effects can be avoided by using host-targeting drugs because of the low evolutionary divergence of host proteins. Therefore, it is necessary to construct a comprehensive virus–host proteome interaction atlas that can be used to identify the cellular functions that are mandatory for viral processing and, in turn, to develop effective therapeutic strategies against SARS-CoV-2 and new emergent strains. Four recent proteomic studies (Gordon et al, 2020b; preprint: Laurent et al, 2020; preprint: Samavarchi-Tehrani et al, 2020; preprint: Stukalov et al, 2020) uncovered extensive SARS-CoV-2–host protein–protein interaction (PPI) networks. These studies used known SARS-CoV-2 virus open-reading frames (ORFs) as baits to identify interacting proteins by affinity purification or proximity purification combined with mass spectrometry (AP-MS or BioID-MS) in HEK293 or A549 cells. AP-MS (Varjosalo et al, 2013) is suitable for the study of virus–host multiprotein complexes, while BioID-MS (Roux et al, 2012) has become a complementary method to capture transient interactions that frequently occur throughout viral infection progression. Together, these data provide biochemical insights into how the SARS-CoV-2 hijacks host cells. However, in identifying potential drug targets, studies focusing on viral bait protein interactions are inherently biased, as they neglect to incorporate the cellular context of the host. For example, angiotensin-converting enzyme 2 (ACE2) is a known receptor of SARS-CoV-2 (Gheblawi et al, 2020). Because of its low expression level in most experimental cell lines (Hikmet et al, 2020), none of the abovementioned studies were able to detect the interaction between the spike protein and ACE2 (Wan et al, 2020). Furthermore, the interactions of viral proteins with host proteins have cascading effects on the host interactome, where certain essential proteins necessary for the viral replication cycle are indirectly affected. Therefore, consideration of the downstream host protein interactome is necessary and should be used as an additional criterion for selecting potential targets for therapeutic intervention. As a contribution to this effort, we adopted the Multiple Approaches Combined (MAC)-tag system (Liu et al, 2018, 2020), enabling both AP-MS and BioID-MS analysis with a single construct to perform a comprehensive analysis of all 29 SARS-CoV-2 and 18 host proteins, which include cell surface receptors, proteases, restriction factors, replication factors, and trafficking factors, with known roles in viral infection processes. We first generated a virus–host interactome and compared this PPI network with the results of other studies to address functionally conserved protein interactions. Subsequently, we characterized 693 hub proteins that connect viral baits and host baits via a dense network to reveal critical pathways in the host used for viral replication. Selected hub proteins were then used to propose drug repurposing candidates by a virtual screening approach. Our analysis finally prioritized 59 promising drug candidates for use against SARS-CoV-2. Furthermore, ten candidate drugs were further validated using image-based drug screen with infectious SARS-CoV-2, six of them demonstrating antiviral effects. Results Global analysis of SARS-CoV-2 ORFs and its host protein interactome SARS-CoV-2 is a positive-sense single-stranded (+)ssRNA virus, ~29.9 kb in size, and it contains 14 ORFs encoding 29 proteins (Chan et al, 2020). The first ORF (ORF1a/b) produces two polypeptides, namely, pp1a and pp1ab, which are further processed into 16 nonstructural proteins (NSPs). ORFs 2 to 14 encode four main structural proteins, namely, the spike (S, ORF 2), envelope (E, ORF 4), membrane(M, ORF 5) and nucleocapsid (N, ORF 9), and nine additional accessory factors (ORF 3a, 3b, 6, 7a, 7b, 8a, 8b, 9b, and 9c) (Fig 1A). The viral S, M, and E proteins are embedded in the lipid membrane on the virion surface. The N protein interacts with viral RNA in the core of the virion (Fig 1A). SARS-CoV-2 utilizes ACE2 and transmembrane serine protease 2 (TMPRSS2) as a prime receptor and a critical protease, respectively, to enter target cells (Fig 1A) (Hoffmann et al, 2020). Alternative receptors have also been reported, including CD147 (BSG) (Wang et al, 2020), neuropilin-1(NPR1) (Cantuti-Castelvetri et al, 2020; Daly et al, 2020), transferrin receptor (TFRC) (preprint: Tang et al, 2020), and C-type lectin domain family 4 member D/E (CLEC4D/CLEC4E) (preprint: Singh et al, 2020; Yi & Chuanxin, 2020). In addition to TMPRSS2, several other cellular proteases work as alternative priming factors, including TMPRSS4 (Zang et al, 2020), TMPRSS11A/B (Zhang, Zhang, et al, 2020), FURIN (Xia et al, 2020), and cathepsin B, L, and S (CTSB, CTSL, and CTSS) (Vieira Braga et al, 2019). Furthermore, a genome-scale loss-of-function screen discovered that DNA topoisomerase III beta (TOP3B) is required for efficient replication of a diverse group of (+)ssRNA viruses, including SARS-CoV-2 (Fig 1A) (Prasanth et al, 2020). Some membrane proteins that are known as receptors or regulators of other coronaviruses, such as dipeptidyl peptidase 4 (DPP4) (receptor of hCoV-EMC) (Raj et al, 2013), aminopeptidase N (ANPEP) (receptor of coronavirus TGEV) (Delmas et al, 1992), and interferon-inducible transmembrane proteins (IFITM1 and IFITM3) (Huang, Bailey, et al, 2011; Shi et al, 2021), may not directly bind to SARS-CoV-2 proteins, but can enhance viral entry (Fig 1A) (Li et al, 2020). Figure 1. Workflow for identification of SARS-CoV-2 virus–host PPIs A. SARS-CoV-2 genome annotation. Two-thirds of the viral RNA, mainly located in the first open-reading frame (ORF 1a/b), encodes 16 nonstructural proteins (NSPs). The rest of the viral genome encodes four main structural proteins, namely, the spike (S), envelope (E), nucleocapsid (N), membrane (M) proteins, and several accessory factors. SARS-CoV-2 enters the cell primarily via binding to ACE2, followed by its priming by TMPRSS2. In addition to ACE2 and TMPRSS2, other potential SARS-CoV-2 receptors, proteases, and cofactors for infection are indicated. B. Experimental workflow for the establishment of a SARS-CoV-2–host interactome to identify potential drug targets and treatment. C. Virus–host protein interaction networks were constructed by AP-MS (top) and BioID-MS (bottom). Only protein–protein interactions (PPIs) with a Bayesian false discovery rate (BFDR) ≤ 0.01 (assessed by SAINTexpress) are shown. Download figure Download PowerPoint We aimed to identify host proteins associated with SARS-CoV-2 proteins systematically using both AP-MS and BioID-MS. To achieve this, we cloned the 29 genes corresponding to SARS-CoV-2 proteins (Fig 1A (left) and Dataset EV1) and 18 host proteins (Fig 1A (right) and Dataset EV1) that are functionally relevant for SARS-CoV-2 entry and replication. Each clone was fused to the MAC-tag system (consisting of both StrepIII and BirA* tags) to generate isogenic tetracycline-inducible HEK293 cell lines (Fig 1B). The expression of MAC-tagged host proteins was confirmed by MS analysis. Considering the inefficiency of shotgun proteomics in resolving very small proteins (≤ 50 amino acids), MAC-tagged viral protein expression was confirmed by Western blotting with HA antibodies (Appendix Fig S1). All viral ORFs except NSP3 and NSP6 were successfully detected by Western blotting (Appendix Fig S1). The expression of viral peptides of NSP3 and NSP6 was then confirmed by MS analysis. Viral infection requires the host immune response to create the cellular environment for viral protein processing. Here, a single ORF was expressed in each corresponding cell line, and some cooperative viral interactions were possibly missed. We therefore monitored the immune response of the host cell lines to check whether these stable cell lines reflect the cellular context of viral infection. In total, nine viral ORF-expressing cell lines were randomly selected for transcriptomic profiling (Appendix Fig S2A and Dataset EV2). Five out of nine cell lines showed upregulated expression of FURIN (Appendix Fig S2A and Dataset EV2), which can promote SARS-CoV-2 infectivity and cell-to-cell spread (Papa et al, 2021). In contrast, viral ORF expression suppressed the expression of human leukocyte antigen (HLA)-encoded class II genes (DQA, DPB1, DPA1, DMB) and interferon-inducible transmembrane genes (IFITM1, IFITM2) (Appendix Fig S2A and B), which were previously demonstrated to facilitate matured coronavirus infection in vitro (Josset et al, 2013; Shi et al, 2021). Overall, the stable viral ORF-expressing cell lines created a cellular context mimicking viral infection. All the stable cell lines were used parallelly for both AP and BioID purification to achieve a completed protein interactome, altogether, totaling 366 samples for MS characterization (Dataset EV3). This analysis yielded 5,670 (2,216 for AP-MS; 3454 for BioID-MS) high-confidence interactions (HCIs) for 29 viral bait proteins using both AP-MS and BioID-MS methods (Fig 1C and Dataset EV4). For the host–receptor/cofactor interaction network, we identified 5,351 (2,224 for AP-MS; 3,127 for BioID-MS) HCIs connecting 18 human bait proteins localized in different cellular compartments (Appendix Fig S3 and Dataset EV4). Notably, many viral baits (M, NSP10, NSP16, NSP6, NSP7, NSP9, ORF10, ORF3a, ORF3b, and ORF7a) were detected to interact with TFRC, and few viral baits (N, NSP13, and NSP14) interacted with TOP3B. In general, BioID-MS tends to provide more high-confidence interacting proteins (viral bait-prey pairs: 3,454; host bait-prey pairs: 3,127) (Dataset EV4) than AP-MS (viral bait-prey pairs: 2,216; host bait-prey pairs: 2,224) (Dataset EV4), indicating that BioID-MS can capture highly transient and close-proximity interactions. Proteome interaction data validation To verify host–receptor PPI data, we integrated and compared protein interaction data from seven major databases including Human Cell Map (Go et al, 2021), BioPlex (Huttlin et al, 2021), BioGRID (Oughtred et al, 2021), HuRI (Luck et al, 2020), PINA2 (Cowley et al, 2012), STRING (Szklarczyk et al, 2019), and IntAct (Kerrien et al, 2012). In total, 2,465 known interactions related to 18 host bait proteins were retrieved (Fig EV1A), and more than half of them were seen in only one database highlighting different evidence channels (colocalization, coexpression, literature mining, experimental evidence) of different databases (Fig EV1B). While some baits are well studied with many known interactions (e.g., BSG, TFRC, TOP3B), most have only a few reported interactions (e.g., ACE2, CTSS, CLEC4D) (Fig EV1C), emphasizing the need for a systematic study. Overall, 93 interactions among the 4,362 unique HCIs (~ 2.1%) were previously reported for the host protein interaction network (Fig EV1D and Dataset EV4). Click here to expand this figure. Figure EV1. Defining the host–receptor interaction landscape A. Number of known interactions related to 18 host baits from each of the databases or publications used for constructing the known interaction database. B. Number of previously identified interactions detected in one or more of the databases used. C. Number of known interactions of each human receptor bait. D. The number of high-confidence interactions (HCIs) identified by host baits using AP-MS or BioID-MS methods. E. Human bait protein expression patterns vary greatly in different tissues. Download figure Download PowerPoint Since the tissue distribution and expression of the appropriate receptors determine the tropism of the viral infection, we further investigated receptor bait protein expression in various human organs. Information was obtained from the Human Protein Atlas (Uhlén et al, 2015), and 13 of the 18 host baits were detected at least once in 45 human organs (Fig EV1E). Although their expression patterns vary greatly in different tissues, almost all detected bait proteins are expressed in the kidney (Fig EV1E), further supporting the application of the HEK293 cell model. The renal tropism of these baits is a potential explanation for common kidney injury being observed in patients with COVID-19 (Pei et al, 2020). To validate the virus–host PPIs, we compared our study with four published/preprint studies (Gordon et al, 2020b; preprint: Laurent et al, 2020; preprint: Samavarchi-Tehrani et al, 2020; preprint: Stukalov et al, 2020) that utilized either AP-MS or BioID-MS to map SARS-CoV-2 viral ORFs interacting with host proteins in HEK293 and A549 cells (Dataset EV5). Coverage in terms of the number of bait–prey interaction pairs and the number of unique prey proteins was analyzed. Although each viral protein is expected to have specific interactions accounting for host specificity and pathogenesis, a large portion of host prey proteins are most likely shared across multiple viral baits, considering that the viral proteins are processed by the same replication machinery. Using AP-MS, 103 interactions were characterized by more than one study, and five among those were reported in all independent studies (Fig EV2A). The low overlap between independent AP-MS studies suggests that viral proteins are unlikely to form very stable complexes with host proteins and that virus–host protein interactions are more transient. With BioID-MS, over 10 times more interactions were detected across at least two studies, and 94 PPIs were highly conserved in all studies (Fig EV2B and C). Moreover, prey proteins obtained by AP-MS were mainly detected by one bait protein, while approximately half of the prey proteins were observed to interact with more than one viral bait using the BioID-MS approach (Fig EV2D–G). This implies that viral ORFs may appear in the same subcellular region; therefore, similar proximal proteins were detected. Alternatively, it may also suggest that the virus targets the same host factor in redundant ways, and further investigation is needed. Click here to expand this figure. Figure EV2. Defining the virus–host interaction landscape A, B. Venn diagrams show high-confidence bait–prey interaction pair overlap of three virus–host PPI studies using either AP-MS (A) or BioID-MS (B). Duplicate PPIs were removed from each study. C. The number of HCIs identified by viral baits using AP-MS or BioID-MS. D, E. Frequency of prey proteins detected by baits using AP-MS (D) or BioID-MS (E). F, G. Venn diagrams show high-confidence interactor overlap of three virus–host PPI studies using either AP-MS (F) or BioID-MS (G). Download figure Download PowerPoint Despite the valuable insights provided by the protein interaction network, our interactome data could contain noise and can include some false-positive interactions without biological relevance. To assess the accuracy of the interaction information we provided, 95 protein interaction pairs from each dataset were selected and validated via reverse co-immunoprecipitation (co-IP) using affinity matrix binding prey proteins to pull down bait proteins (Dataset EV6). The positivity ratios were 67% (Fig EV3A) and 77% (Fig EV3B) for the viral interaction dataset and host–receptor interaction dataset, respectively. This is higher than the true-positive ratio of common protein databases (20–40%) (Kuchaiev et al, 2009; Kotlyar et al, 2019). Moreover, different baits targeting the same prey protein were also validated (Fig EV3 and Dataset EV6). For example, we assessed 12 interaction pairs of viral baits interacting with ATP-dependent 6-phosphofructokinase (PFKP), and six out of 12 were confirmed as positive by Co-IP (Fig EV3 and Dataset EV6). Click here to expand this figure. Figure EV3. Validation of interaction data by Co-IP and dot blotting A, B. Proteomics interaction data were evaluated by Co-IP and dot blotting, related to Dataset EV6. In total, 190 randomly selected interaction pairs from both virus–host interac" @default.
• W3208113816 created "2021-11-08" @default.
• W3208113816 creator A5004775674 @default.
• W3208113816 creator A5007120981 @default.
• W3208113816 creator A5010734018 @default.
• W3208113816 creator A5023769869 @default.
• W3208113816 creator A5036215655 @default.
• W3208113816 creator A5043646652 @default.
• W3208113816 creator A5054542807 @default.
• W3208113816 creator A5054623923 @default.
• W3208113816 creator A5060662450 @default.
• W3208113816 creator A5062698572 @default.
• W3208113816 creator A5066409069 @default.
• W3208113816 creator A5073258092 @default.
• W3208113816 creator A5074083544 @default.
• W3208113816 creator A5081860537 @default.
• W3208113816 creator A5088559880 @default.
• W3208113816 creator A5088991911 @default.
• W3208113816 date "2021-10-28" @default.
• W3208113816 modified "2023-10-18" @default.
• W3208113816 title "SARS‐CoV‐2–host proteome interactions for antiviral drug discovery" @default.
• W3208113816 cites W1515293536 @default.
• W3208113816 cites W1547861484 @default.
• W3208113816 cites W1588972287 @default.
• W3208113816 cites W1875899284 @default.
• W3208113816 cites W1879553156 @default.
• W3208113816 cites W1975595673 @default.
• W3208113816 cites W1975875968 @default.
• W3208113816 cites W1979615660 @default.
• W3208113816 cites W1989832105 @default.
• W3208113816 cites W2003856466 @default.
• W3208113816 cites W2006683252 @default.
• W3208113816 cites W2012034410 @default.
• W3208113816 cites W2016921919 @default.
• W3208113816 cites W2025458374 @default.
• W3208113816 cites W2026106960 @default.
• W3208113816 cites W2029358388 @default.
• W3208113816 cites W2029789274 @default.
• W3208113816 cites W2031918554 @default.
• W3208113816 cites W2032639971 @default.
• W3208113816 cites W2036321220 @default.
• W3208113816 cites W2039689649 @default.
• W3208113816 cites W2047077861 @default.
• W3208113816 cites W2050583899 @default.
• W3208113816 cites W2052627122 @default.
• W3208113816 cites W2053186308 @default.
• W3208113816 cites W2054094290 @default.
• W3208113816 cites W2054621186 @default.
• W3208113816 cites W2065360219 @default.
• W3208113816 cites W2076038738 @default.
• W3208113816 cites W2079953901 @default.
• W3208113816 cites W2080752012 @default.
• W3208113816 cites W2087268191 @default.
• W3208113816 cites W2089350319 @default.
• W3208113816 cites W2105047045 @default.
• W3208113816 cites W2105707485 @default.
• W3208113816 cites W2107020066 @default.
• W3208113816 cites W2108492672 @default.
• W3208113816 cites W2110256992 @default.
• W3208113816 cites W2114958077 @default.
• W3208113816 cites W2119111857 @default.
• W3208113816 cites W2125880169 @default.
• W3208113816 cites W2135264287 @default.
• W3208113816 cites W2137052779 @default.
• W3208113816 cites W2140388343 @default.
• W3208113816 cites W2159675211 @default.
• W3208113816 cites W2165253681 @default.
• W3208113816 cites W2167844024 @default.
• W3208113816 cites W2193139301 @default.
• W3208113816 cites W2222303924 @default.
• W3208113816 cites W2238516932 @default.
• W3208113816 cites W2277567666 @default.
• W3208113816 cites W2292021561 @default.
• W3208113816 cites W2399797906 @default.
• W3208113816 cites W2522966839 @default.
• W3208113816 cites W2594844287 @default.
• W3208113816 cites W2613169846 @default.
• W3208113816 cites W2646467224 @default.
• W3208113816 cites W2733321105 @default.
• W3208113816 cites W2789251983 @default.
• W3208113816 cites W2811253857 @default.
• W3208113816 cites W2899942534 @default.
• W3208113816 cites W2900569176 @default.
• W3208113816 cites W2901416019 @default.
• W3208113816 cites W2907157090 @default.
• W3208113816 cites W2948824749 @default.
• W3208113816 cites W2950665405 @default.
• W3208113816 cites W2950891712 @default.
• W3208113816 cites W2953842076 @default.
• W3208113816 cites W2956917138 @default.
• W3208113816 cites W2960692008 @default.
• W3208113816 cites W2989650452 @default.
• W3208113816 cites W3003217347 @default.
• W3208113816 cites W3003464757 @default.
• W3208113816 cites W3004348779 @default.
• W3208113816 cites W3009912996 @default.
• W3208113816 cites W3012991539 @default.
• W3208113816 cites W3013093478 @default.
• W3208113816 cites W3013463737 @default.
• W3208113816 cites W3014571869 @default.

• next