Multi-Level Proteomics Reveals Host-Perturbation Strategies Of

Multi-level proteomics reveals host-perturbation strategies of

SARS-CoV-2 and SARS-CoV

Alexey Stukalov1*, Virginie Girault1*, Vincent Grass1*, Valter Bergant1*, Ozge Karayel2*, Christian Urban1*, Darya A. Haas1*, Yiqi Huang1, Lila Oubraham1, Anqi Wang1, Sabri M. Hamad1, Antonio Piras1, Maria Tanzer2, Fynn M. Hansen2, Thomas Enghleitner3, Maria Reinecke4, 5, Teresa M. Lavacca1, Rosina Ehmann6, 7, Roman Wölfel6, 7, Jörg Jores8, Bernhard Kuster4, 5, Ulrike Protzer1, 7, Roland Rad3, John Ziebuhr9, Volker Thiel10, Pietro Scaturro1,11, Matthias Mann2 and Andreas Pichlmair1, 7, §

1Technical University of Munich, School of Medicine, Institute of Virology, 81675 Munich, Germany, 2Department of Proteomics and Signal transduction, Max-Planck Institute of Biochemistry, Martinsried/Munich, 82152, Germany, 3Institute of Molecular Oncology and Functional Genomics and Department of Medicine II, School of Medicine, Technical University of Munich, 81675 Munich, Germany, 4Chair of Proteomics and Bioanalytics, Technical University of Munich, 85354 Freising, Germany, 5German Cancer Consortium (DKTK), Munich partner site and German Cancer Research Center, (DKFZ) Heidelberg, Germany, 6Bundeswehr Institute of Microbiology, 80937 Munich, Germany, 7German Center for Infection Research (DZIF), Munich partner site, Germany, 8Institute of Veterinary Bacteriology, Department of Infectious Diseases and Pathobiology, University of Bern, Bern, Switzerland, 9Justus Liebig University Giessen, Institute of Medical Virology, 35392 Giessen, Germany, 10Institute of Virology and Immunology (IVI), Bern, Switzerland & Department of Infectious Diseases and Pathobiology, University of Bern, Bern, Switzerland, 11Systems Arbovirology, Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany.

* these authors contributed equally

§Corresponding author:

Andreas Pichlmair, PhD, DVM Technical University Munich, Faculty of Medicine Institute of Virology - Viral Immunopathology Schneckenburger Str. 8 D-81675 Munich, Germany bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 available under aCC-BY 4.0 International license.

Abstract:

The sudden global emergence of SARS-CoV-2 urgently requires an in-depth understanding of

molecular functions of viral proteins and their interactions with the host proteome. Several omics

studies have extended our knowledge of COVID-19 pathophysiology, including some focused on

proteomic aspects1–3. To understand how SARS-CoV-2 and related coronaviruses manipulate the host

we here characterized interactome, proteome and signaling processes in a systems-wide manner. This

identified connections between the corresponding cellular events, revealed functional effects of the

individual viral proteins and put these findings into the context of host signaling pathways. We

investigated the closely related SARS-CoV-2 and SARS-CoV viruses as well as the influence of

SARS-CoV-2 on transcriptome, proteome, ubiquitinome and phosphoproteome of a lung-derived

human cell line. Projecting these data onto the global network of cellular interactions revealed

relationships between the perturbations taking place upon SARS-CoV-2 infection at different layers

and identified unique and common molecular mechanisms of SARS coronaviruses. The results

highlight the functionality of individual proteins as well as vulnerability hotspots of SARS-CoV-2,

which we targeted with clinically approved drugs. We exemplify this by identification of kinase

inhibitors as well as MMPase inhibitors with significant antiviral effects against SARS-CoV-2.

Main text:

To identify interactions of SARS-CoV-2 and SARS-CoV with cellular proteins, we transduced A549

lung carcinoma cells with lentiviruses expressing individual HA-tagged viral proteins (Figure 1a;

Extended data Fig. 1a; Supplementary Table 1). Affinity purification followed by mass spectrometry

analysis (AP-MS) and statistical modelling of the MS1-level quantitative data allowed identification

of 1484 interactions between 1086 cellular proteins and 24 SARS-CoV-2 and 27 SARS-CoV bait

proteins (Figure 1b; Extended data Fig. 1b; Supplementary Table 2). The resulting virus-host

interaction network revealed a wide range of cellular activities intercepted by SARS-CoV-2 and

SARS-CoV (Figure 1b; Extended data Table 1; Supplementary Table 2). In particular, we discovered bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

that SARS-CoV-2 targets a number of key cellular regulators involved in innate immunity (ORF7b-

MAVS, -UNC93B1), stress response components (N-HSPA1A) and DNA damage response mediators

(ORF7a-ATM, -ATR) (Figure 1b; Extended data Fig. 1c - e). Overall, SARS-CoV-2 interacts with

specific protein complexes contributing to a range of biological processes (Supplementary Table 2).

To evaluate the consequences of these interactions on cellular proteostasis, we proceeded with the total

proteome analysis of A549 cells expressing the 54 individual viral proteins (Figure 1a, d;

Supplementary Table 3). The analysis of the proteome changes induced by each viral protein and

consideration of the interactions of this respective protein provided direct insights into their functions.

For instance, we confirmed that ORF9b of SARS-CoV-2 leads to a dysregulation of mitochondrial

functions (Figure 1d; Supplementary Table 3), as was previously reported for SARS-CoV4, correlating

with the binding of ORF9b of both viruses to TOMM70 (Figure 1b; Supplementary Table 2)1, a

known regulator of mitophagy5, which was not yet known for SARS-CoV ORF9b. Importantly, this

approach identified novel SARS-CoV-2 activities, such as the regulation of proteins involved in

cholesterol metabolism by NSP6 (Figure 1d; Supplementary Table 3). Despite the high similarity of

SARS-CoV-2 and SARS-CoV6, these datasets allow to discriminate the commonalities and differences

of both viruses, which may in part explain the characteristics in pathogenicity and transmission

capabilities. By comparing the AP-MS data of homologous SARS-CoV-2 and SARS-CoV proteins,

we identified significant differences in the enrichment of individual host targets, highlighting potential

virus-specific interactions (Figure 1b (edge color); Figure 1c; Extended data Fig. 1f - h;

Supplementary Table 2). For instance, we could recapitulate the known interaction between SARS-

CoV NSP2 and prohibitins (PHB, PHB2)7, whereas their enrichment was not observed for NSP2 of

SARS-CoV-2 (Extended data Fig. 1g). Alternatively, we found that ORF8 of SARS-CoV-2, but not its

SARS-CoV homolog, binds specifically to the TGFB1-LTBP1 complex (Extended data Fig. 1f, h).

To obtain information on the concerted activity of the viral proteins during infection, we infected

ACE2-expressing A549 cells (Extended data Fig. 2a-b) with SARS-CoV-2, and profiled the impact of

viral infection on mRNA transcription, protein abundance, ubiquitination and phosphorylation in a

time-resolved manner (Figure 2a – e; Supplementary Tables 4 - 7; Methods). In line with previous

reports8,9, we did not observe major upregulation of type-I interferons and related genes at the mRNA bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

level (e.g. IFNB, IFIT3, MX1; Extended data Fig. 2c – d; Supplementary Table 4), suggesting active

viral inhibition of this system. In contrast, SARS-CoV-2 upregulated NF-κB and stress responses, as

inferred from the induction of IL6, CXCL2 and JUNs and transcription factor enrichment analysis

(Extended data Fig. 2c – e; Supplementary Tables 4, 8; Methods). At the proteome level, we found

1053 regulated proteins (Figure 2a - b). Most notably, SARS-CoV-2 infection failed to induce an

interferon response pattern indicative for an appropriate cellular antiviral response in this dataset

(Extended data Fig. 2f; Supplementary Table 5). We complemented these data with global MS

analysis of protein ubiquitination, which revealed 884 sites that were differentially regulated after

SARS-CoV-2 infection (Figure 2a and c, Extended data Fig. 2f; Supplementary Table 6). A number of

proteins displayed both differential abundance and dynamic ubiquitination patterns in an infection-

dependent manner (e.g. mediators of caveolar-mediated endocytosis signaling) (Figure 2c; Extended

data Fig. 2f; Supplementary Tables 5, 6, 8). Notably, EFNB1, POLR2B, TYMS and DHFR showed

concomitant ubiquitination and a decrease at the protein level (Figure 2c; Extended data Fig. 2f;

Supplementary Tables 5, 6). Moreover, we identified two upregulated ubiquitination sites on ACE2,

including one previously unknown (K702) (Figure 2c; Extended data Fig. 2f - i), suggesting an

alternative post-translational mechanism of its degradation upon SARS-CoV-2 infection besides the

cleavage by matrix metalloproteinases10,11. We identified multiple yet undescribed ubiquitination sites

on viral proteins, which may be tied to the interactions with several E3 ligases observed in the

interactome (e.g. ORF3 and TRIM47, WWP1/2, STUB1; M and TRIM7; NSP13 and RING1)

(Extended data Fig. 2j; Supplementary Table 2, 6) and likely indicative of cross-talks between

ubiquitination and viral protein functions. Moreover, in the phosphoproteomic analysis, we mapped

multiple novel phosphorylation sites on viral proteins (M, N, S and ORF9b), which correspond to

known recognition motifs of GSK3, CSNKs, GPCR, AKT, CAMKs, and ERKs (Extended data Fig.

2k; Supplementary Table 7). Of 11,847 total quantified phosphorylation sites, 1483 showed significant

changes after SARS-CoV-2 infection (Extended data Fig. 2l; Supplementary Table 7). The regulation

of known phosphosites suggests an involvement of central kinases known to modulate key cellular

pathways, e.g. EPHA2 – focal adhesion, RPS6Ks – cell survival, CDKs – cell cycle progression, AKT

– cell growth, survival and motility, p38, JNK, ERK – stress responses, ATM, and CHEK1/2 – DNA bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

damage response, during virus infection (Extended data Fig. 2l, m; Supplementary Tables 7, 8).

Intriguingly, we could also observe an interplay of phosphorylation and ubiquitination on YAP1, a

downstream regulatory target of Hippo signaling (Figure 2d), underlining the value of testing different

post-translational modifications simultaneously. Combining these datasets, describing different aspects

of SARS-CoV-2 infection, allowed us to determine the key pathways perturbed during the infection,

such as stress and DNA damage response, regulation of transcription and cell junction organization, at

various levels (Figure 2e, see Methods).

The systematic interactome and proteome profiling of individual viral proteins provided us with the

opportunity to gain deeper understanding of their molecular mechanisms. For each viral protein, we

mapped the collected data onto the global network of cellular interactions12 and applied a network

diffusion approach13. Such analysis identifies short links of known protein-protein interactions,

signaling and regulation events that connect the interactors of the viral protein with the proteins

affected by its expression (Figure 3a, Extended data Fig. 3a, b; Supplementary Data 1).The

connections predicted using the real data were significantly shorter than for the randomized data,

confirming both relevance of the approach and the data quality (Extended data Fig. 3a, b). Amongst

many other findings, this approach pointed towards the potential mechanisms of autophagy regulation

by ORF3 and NSP6; the modulation of innate immunity by M, ORF3 and ORF7b; and the Integrin-

TGFβ-EGFR-RTK signaling perturbation by ORF8 of SARS-CoV-2 (Figure 3b - d, Supplementary

Data 1). Enriching these subnetworks with the SARS-CoV-2 infection-dependent mRNA abundance,

protein abundance, phosphorylation and ubiquitination (Figure 3a) allowed us to gain unprecedented

insights into the regulatory mechanisms employed by SARS-CoV-2 (Figure 3e, Extended data Fig. 3c,

e). For instance, this analysis confirmed a role of NSP6 in autophagy14 and revealed a significant

inhibition of autophagic flux by ORF3 leading to the accumulation of autophagy receptors (SQSTM1,

GABARAPL2, NBR1, CALCOCO2, MAP1LC3B, TAX1BP1), also observed in virus-infected cells

(SQSTM1, MAP1LC3B) (Figure 3e - i). This inhibition may be explained by the interaction of ORF3

with the HOPS complex (VPS11, -16, -18, -39, -41), which is essential for autophagosome-lysosome

fusion, as well as by the differential phosphorylation of regulatory sites of key components (AKT1,

AKT1S1, SQSTM1, RPS6). The inhibition of the interferon response observed at transcriptional and bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

proteome levels was similarly explained by the network diffusion analysis (Extended data Fig. 3c),

demonstrating that multiple proteins of SARS-CoV-2 are employed in the disruption of antiviral

immunity. Additional functional experiments corroborated the inhibition of interferon induction or

signaling by ORF3, ORF6, ORF7a, ORF7b, ORF9b (Extended data Fig. 3d). Upon virus infection, we

observed upregulation of TGFβ and EGFR pathways, which modulate cell survival, motility and

innate immune responses (Extended data Fig. 3e - g). Besides promoting virus replication, activation

of these pathways has been implicated in fibrosis15,16, one of the hallmarks of COVID-1917.

Specifically, our network diffusion analysis revealed the connection between the binding of ORF8 and

ORF3 to TGFβ-associated factors (TGFB1, TGFB2, LTBP1, TGFBR2, FURIN, BAMBI) and the

virus-induced upregulation of fibrinogens, fibronectin, SERPINE1 and integrin(s) (Extended data Fig.

3e, h)18. The phosphorylation of SMAD1/5, and ERK, JNK, p38 cascade activation, as well as an

increased expression of MMPs, DUSPs, JUN, and EGR1 are indicative of TGFβ and EGFR pathway

regulation. In turn, they are known to be potentiated by the increased integrin signaling and activation

of YAP-dependent transcription19, which we observed upon SARS-CoV-2 infection (Extended data

Fig. 3e).

Taken together, the viral-host protein-protein interactions and pathway regulations observed at

multiple levels identify potential vulnerability points of SARS-CoV-2 that could be targeted by well-

characterized selective drugs for antiviral therapies. To test their antiviral efficacy, we established

time-lapse fluorescent microscopy of SARS-CoV-2 GFP-reporter virus infection20. Inhibition of virus

replication by type-I interferon treatment corroborated the necessity for SARS-CoV-2 to block this

pathway and confirmed the reliability of this screening approach (Figure 4a)9,21. We tested a panel of

48 drugs modulating the pathways perturbed by the virus for their effects on SARS-CoV-2 replication

(Figure 4b, Supplementary Table 9). Notably, B-RAF (Sorafenib, Regorafenib, Dabrafenib), JAK1/2

(Baricitinib) and MAPK (SB 239063) inhibitors, among others, led to a significant increase of virus

growth in our in vitro infection setting (Figure 4b, Extended data Fig. 4, Supplementary Table 9). In

contrast, inducers of DNA damage (Tirapazamine, Rabusertib) or the mTOR inhibitor (Rapamycin)

led to suppression of the virus. The highest antiviral effect was seen for Gilteritinib (a designated

FLT3/AXL inhibitor), Ipatasertib (AKT inhibitor), Prinomastat and Marimastat (matrix bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

metalloproteinases inhibitors) (Figure 4b - e, Extended data Fig. 4, Supplementary Table 9).

Remarkably, these compounds profoundly inhibited replication of SARS-CoV-2 while having no or

minor influence on cell growth (Extended data Fig. 4, Supplementary Table 9). These inhibitors may

perturb host pathways required by the virus or influence viral protein activity through post-

translational modifications. Notably, we identified AKT as a potential kinase phosphorylating SARS-

CoV-2 protein N (Extended data Fig. 2k), indicating the possibility of a direct influence of Ipatasertib

on the viral protein.

This drug screen demonstrates the value of our combined dataset that profiles the infection of SARS-

CoV-2 at multiple levels. Further exploration of these rich data by the scientific community and

investigating the interplay between different -omics levels will substantially advance our knowledge of

coronavirus biology, in particular on the pathogenicity caused by highly virulent strains such as

SARS-CoV-2 and SARS-CoV. Moreover, this resource may streamline the search for antiviral

compounds and serve as a base for intelligent design of combination therapies that aim at targeting the

virus from multiple, synergistic angles, thus potentiating the effect of individual drugs while

minimizing side-effects on healthy tissues.

Acknowledgements

We thank Stefan Pöhlmann for sharing ACE2 plasmids and Robert Baier for technical assistance, Juan

Pancorbo and Johannes Albert-von der Gönna from the Leibniz Supercomputing Centre (www.lrz.de)

for technical assistance. We further thank the Karl Max von Bauernfeind - Verein for support for the

screening microscope. Work in the author’s laboratories was supported by an ERC consolidator grant

(ERC-CoG ProDAP, 817798), the German Research Foundation (PI 1084/3, PI 1084/4) and the

German Federal Ministry of Education and Research (COVINET) to A.P. This work was also

supported by the German Federal Ministry of Education and Research (CLINSPECT-M) to BK. AW

was supported by the China Scholarship Council (CSC). The work of J.Z. was supported by the

German Research Foundation (SFB1021, A01 and B01; KFO309, P3), the State of Hessen through the

LOEWE Program (DRUID, B02) and the German Ministry for Education and Research (COVINET).

Figure legends:

Figure 1 | Joint analysis of SARS-CoV-2 and SARS-CoV protein-protein virus-host

interactomes. (a) Experimental design to systematically compare the AP-MS interactomes and

induced host proteome changes of the homologous SARS-CoV-2 and SARS-CoV viral proteins, with

ORF3 homologs of HCoV-NL63 and HCoV-229E as reference for pan coronavirus specificity. (b)

Combined virus-host protein interaction network of SARS-CoV-2 and SARS-CoV measured by

affinity-purification coupled to mass spectrometry. Homolog viral proteins are displayed as one node.

Shared and virus-specific interactions are denoted by the edge color. (c) The numbers of unique and

shared host interactions between the homologous proteins of SARS-CoV-2 and SARS-CoV. (d) Gene

Ontology Biological Processes enriched among the cellular proteins that are up- (red arrow) or down-

(blue arrow) regulated upon overexpression of individual viral proteins.

Figure 2 | Orthogonal profiling of SARS-CoV-2 infection. (a) Time-resolved profiling of SARS-

CoV-2 infection by multiple -omics methods. The plot shows normalized MS intensities of three

SARS-CoV-2 viral proteins over time. (b) Numbers of distinct transcripts, proteins, ubiquitination and

phosphorylation sites, up- or down-regulated at the indicated time points after infection, as identified

using data independent (DIA) or dependent (DDA) acquisition methods. (c) Volcano plot showing

ubiquitination sites regulated at 24h after SARS-CoV-2 infection. Viral proteins are marked in orange.

Selected significant ubiquitination sites (Student’s t-test, two-tailed, permutation-based FDR < 0.05,

S0 = 0.1, n = 4) are marked in black. (d) Scatter plot of phosphorylation and ubiquitination sites on

Yes-associated protein (YAP1) regulated upon SARS-CoV-2 infection. Presented are fold changes

compared to mock at 6, 24, and 30 hours after infection. S61 and S127 dephosphorylation lead to

nuclear translocation, S131 phosphorylation regulates protein stability. Phosphorylation of S289 and

S367 is involved in cell cycle regulation. K321 deubiquitination leads to a decrease in YAP1

activation. (e) Reactome pathways enriched in up- (red arrow) or downregulated (blue arrow)

transcripts, proteins, ubiquitination- and phosphorylation sites (Fisher’s exact test, unadjusted). DIA

MS measurements are marked in grey, DDA in black. bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

Figure 3 | Network diffusion approach identifies molecular pathways linking protein-protein

interactions with downstream changes in the host proteome. (a) Network diffusion approach to

identify functional connections between the host targets of a viral protein and downstream proteome

changes followed by the integration of RNA expression, protein abundance, ubiquitination and

phosphorylation changes upon SARS-CoV-2 infection to streamline the identification of affected host

pathways. (b-d) Subnetworks of the network diffusion predictions linking host targets of (b) SARS-

CoV-2 ORF3 to the accumulation of factors involved in autophagy, (c) ORF7b to the factors involved

in innate immunity and (d) ORF8 to the factors involved in TGFβ signaling. (e) Overview of

perturbations to host-cell autophagy, induced by distinct proteins of SARS-CoV-2, derived from the

network diffusion model and overlaid with the changes in protein levels, ubiquitination and

phosphorylation induced by SARS-CoV-2 infection. (f-i) Western blot of autophagy-associated factors

MAP1LC3B-II and SQSTM1 accumulation upon SARS-CoV-2 ORF3 expression in (f) HEK293R1

and (g-i) SARS-CoV-2 infection of A549-ACE2 cells. (h) Profile plot of SQSTM1 MS intensity and

(i) line diagram showing SQSTM1 mRNA level relative to RPLP0 tested by qRT-PCR upon SARS-

CoV-2 infection.

Figure 4 | SARS-CoV-2-targeted pathways, revealed by multi-omics profiling approach, allow

systematic testing of novel antiviral therapies. (a) A549-ACE2 cells, exposed for 6h to the specified

concentrations of interferon alpha and infected with SARS-CoV-2-GFP reporter virus (MOI 3). GFP

signal and cell confluency were analysed by live-cell imaging for 48h. Line diagrams show virus

growth over time of GFP-positive vs total cell area with indicated mean of four biological replicates.

(b) A549-ACE2 cells were treated with the indicated drugs 6h prior to infection with SARS-CoV-2-

GFP (MOI 3). Scatter plot represents GFP vs total cell area signal (y-axis) versus cell confluency in

uninfected control treatments (x-axis) at 48h after infection. A confluence cutoff of -0.2 log2 fold

change was applied to remove cytotoxic compounds. (c-e) as (a) but line diagrams showing virus

replication after (c) Prinomastat, (d) Ipatasertib and (e) Gilteritinib pre-treatment. Asterisks indicate

significance to control treatment (Wilcoxon test; p-value ≤ 0.01).

Extended data Figure 1 | Expression of viral proteins in transduced A549 cells induces changes

to the host proteome.

(a) Expression of HA-tagged viral proteins, in stably transduced A549 cells, used in AP-MS and

proteome expression measurements. (b) The extended version of the virus-host protein-protein

interaction network with 24 SARS-CoV-2 and 27 SARS-CoV proteins, as well as ORF3 of HCoV-

NL63 and ORF4 and 4a of HCoV-229E, used as baits. Host targets regulated upon viral protein

overexpression or SARS-CoV-2 infection (based on the analysis of all data of this study) are

highlighted (see the in-plot legend). (c-f) Co-precipitation experiments in HEK 293T cells showing a

specific enrichment of (c) endogenous MAVS co-precipitated with c-term HA-tagged ORF7b of

SARS-CoV-2 and SARS-CoV (negative controls: SARS-CoV-2 ORF6-HA, ORF7a-HA), (d) ORF7b-

HA of SARS-CoV-2 and SARS-CoV co-precipitated with SII-HA-UNC93B1 (control precipitation:

SII-HA-RSAD2), (e) endogenous HSPA1A co-precipitated with N-HA of SARS-CoV-2 and SARS-

CoV (control: SARS-CoV-2 ORF6-HA) and (f) endogenous TGFβ with ORF8-HA of SARS-CoV-2

vs ORF8-HA, ORF8a-HA, ORF8b-HA of SARS-CoV or ORF9b-HA of SARS-CoV-2. (g, h)

Differential enrichment of proteins in (g) NSP2 and (h) ORF8 of SARS-CoV-2 (x-axis) vs SARS-CoV

(y-axis) AP-MS experiments.

Extended data Table 1 | Functional annotations of the protein-protein interaction network of

SARS-CoV-2 and SARS-CoV (AP-MS). Proteins identified as SARS-CoV-2 and/or SARS-CoV host

binders via AP-MS (Figure 1b) were assemble in functional groups based on functional enrichment

analysis of GOBP, GPCC, GPMF and Reactome terms (Supplementary table 2)

Extended data Figure 2 | Validation of the in vitro SARS-CoV-2 infection model and tracking of

virus-specific changes at the multi-omics level. (a) Western blot showing ACE2-HA expression

levels in A549 cells untransduced (wt) or transduced with ACE2-HA encoding lentivirus. (b) mRNA

expression levels of SARS-CoV-2 N relative to RPLP0 as measured by qRT-PCR upon infection of wt

A549 and A549-ACE2 cells at the indicated MOIs. Mean +/- standard deviation of three biological

replicates are shown. (c) Volcano plot of mRNA expression changes of A549-ACE2 cells, infected

with SARS-CoV-2 at an MOI of 3, shown as a fold change versus mock at 24 h.p.i.. Selected bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

significant hits are marked in black (Wald test, n=3). (d) Expression levels, as measured by qRT-PCR,

of SARS-CoV-2 N and host transcripts relative to RPLP0 after infection of A549-ACE2 cells at MOI

of 3 at indicated time points after infection with indicated mean +/- standard deviation (n=3). ND: not

detectable. (e) Transcription factor enrichment analysis of up- (red arrow) and down- (blue arrow)

regulated genes in A549-ACE2 cells infected with SARS-CoV-2 for indicated time periods (Fisher’s

exact test, unadjusted). (f) Volcano plot of protein abundance changes at 24 h.p.i. in comparison to

mock measured by proteome profiling (DDA MS). Viral proteins are highlighted in orange, selected

significant hits are marked in black (Student’s t-test, two-tailed, permutation-based FDR < 0.05, S0 =

0.1, n = 4). (g) Total levels of ACE2 protein at 6 and 24 hours (top) and its ubiquitination at indicated

sites (bottom) at 24 hours after infection with SARS-CoV-2 as measured by proteome and diGly

proteome profiling (DDA MS). (h) Western blot showing the expression levels of SARS-CoV-2

proteins, ACE2-HA and ACTB in A549-ACE2 cells at indicated time points post-infection with

SARS-CoV-2 compared to mock. (i) Stable expression of ACE2 mRNA transcript relative to RPLP0,

as measured by qRT-PCR, after SARS-CoV-2 infection (MOI 3) of A549-ACE2 cells at indicated

time points post-infection with indicated mean +/- standard deviation of three biological replicates. (j)

Mapping the ubiquitination sites of SARS-CoV-2 proteins and measuring their regulation (DDA MS

of diGly PTMs) in SARS-CoV-2-infected A549-ACE2 cells (MOI 3) at 24 h.p.i., highlighting the

binding of TRIM47 to SARS-CoV-2 ORF3 and TRIM7 to SARS-CoV-2 M as examples of potential

E3 ubiquitin ligases driving ubiquitination and associated with the shown proteins. (k) Mapping the

phosphorylation sites of SARS-CoV-2 N and measuring their regulation (DDA MS) in SARS-CoV-2-

infected A549-ACE2 cells (MOI 3) at 24 h.p.i.. The host kinases potentially driving the

phosphorylation on the shown sites are indicated. (l) Volcano plot of phosphorylation sites regulation

indicated as a log2 -fold change compared to mock as measured by phosphoproteome profiling (DDA

MS). Viral proteins are marked in orange. Selected significant hits (Student’s t-test, two-tailed,

permutation-based FDR < 0.05, S0 = 0.5, n = 3) are marked in black. (m) The enrichment of host

kinases known to regulate the phosphorylation sites identified by phosphoproteome profiling (DIA and

DDA MS) of infected A549-ACE2 cells (MOI 3) at the indicated time points after infection (Fisher’s

exact test, unadjusted). DIA measurements are marked in grey, DDA in black. bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

Extended data Figure 3 | SARS-CoV-2 uses a multi-pronged approach to perturb host-pathways

at multiple levels. (a) The host subnetwork perturbed by SARS-CoV-2 ORF7a, as predicted by the

network diffusion approach. (b) Selection of the optimal cutting threshold for the network diffusion

graph of SARS-CoV-2 ORF7a-induced proteome changes. The plot shows the correlation between the

minimal allowed edge weight (X axis), and the average path length from the regulated proteins to the

host targets of the viral protein along the edges of the filtered subnetwork (Y axis). The red curve

represents the path length for the network diffusion analysis of the actual data. The grey band shows

50% confidence interval, and dashed lines correspond to 95% confidence interval for the path lengths

of 1000 randomised datasets. Optimal edge weight threshold that maximises the difference between

the median path length in randomised data and the path length in the real data is highlighted by the

blue vertical line. (c) Overview of perturbations to host-cell innate immunity related pathways,

induced by distinct proteins of SARS-CoV-2, derived from the network diffusion model and overlaid

with transcriptional, protein abundance, ubiquitination and phosphorylation changes upon SARS-

CoV-2 infection. (d) Heatmap showing effects of the indicated SARS-CoV-2 proteins on type-I IFN

expression levels, ISRE and GAS promoter activation in HEK293-R1. Accumulation of type-I IFN in

the supernatant was evaluated by testing supernatants of PPP-RNA (IVT4) stimulated cells on MX1-

luciferase reporter cells, ISRE promoter activation - by luciferase assay after IFN-α stimulation and

GAS promoter activation - by luciferase assay after INF-γ stimulation in cells expressing SARS-CoV-

2 proteins as compared to the controls (ZIKV NS5 and SMN1). Average of three independent

experiments is shown. (e) Overview of perturbations to host-cell Integrin-TGFβ-EGFR-RTK

signaling, induced by distinct proteins of SARS-CoV-2, derived from the network diffusion model and

overlaid with transcriptional, protein abundance, ubiquitination and phosphorylation changes upon

SARS-CoV-2 infection. (f) Profile plots showing intensities of indicated phosphosites and total protein

levels of EGFR, EPHA2 and AKAP12 in SARS-CoV-2 infected A549-ACE2 cells at the indicated

time points post infection. Points are normalized intensities of individual replicates, solid line is

median, filled area corresponds to 25–75 percentiles, dashed lines mark 2.5–97.5 percentiles of the

posterior distribution. n = 3 independent experiments; Bayesian statistical modelling. (g) Western blot

showing phosphospecies and total protein levels of p38 (T180/Y182, MAPK14) and JNK bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

(T183/Y185, MAPK8) in SARS-CoV-2 infected A549-ACE2 cells. (h) Profile plots of total protein

levels of ITGA3, SERPINE1 and FN1 in SARS-CoV-2 infected A549-ACE2 cells at 6 and 24 hours

post infection with indicated median and confidence intervals. n = 4 independent experiments.

Extended data Figure 4 | Drug screen, focusing on pathways perturbed by SARS-CoV-2 on

several levels, reveals potential candidates for use in antiviral therapy. (a) A549-ACE2 cells were

pre-treated for 6h or treated at the time of infection with SARS-CoV-2-GFP reporter virus (MOI 3).

GFP signal and cell growth were evaluated for 48h by live cell imaging using an Incucyte S3 platform.

Heatmap show the cell growth rate over time in uninfected conditions, and GFP signal vs total cell

confluency and normalized to the signal measured in control treatment (water, DMSO), over time.

Only treatments with significant effects on SARS-CoV-2-GFP are shown. Asterisks indicate

significance to control treatment (Wilcoxon test; p-value ≤ 0.05).

References

1. Gordon, D. E. et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature 1–13 (2020) doi:10.1038/s41586-020-2286-9.

2. Bojkova, D. et al. Proteomics of SARS-CoV-2-infected host cells reveals therapy targets. Nature 1–8 (2020) doi:10.1038/s41586-020-2332-7.

3. Messner, C. B. et al. Ultra-high-throughput clinical proteomics reveals classifiers of COVID- 19 infection. Cell Systems (2020) doi:10.1016/j.cels.2020.05.012.

4. Shi, C.-S. et al. SARS-Coronavirus Open Reading Frame-9b Suppresses Innate Immunity by Targeting Mitochondria and the MAVS/TRAF3/TRAF6 Signalosome. The Journal of Immunology 193, 3080–3089 (2014).

5. Heo, J.-M. et al. Integrated proteogenetic analysis reveals the landscape of a mitochondrial- autophagosome synapse during PARK2-dependent mitophagy. Science Advances 5, eaay4624 (2019).

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7. Cornillez-Ty, C. T., Liao, L., Yates, J. R., Kuhn, P. & Buchmeier, M. J. Severe Acute Respiratory Syndrome Coronavirus Nonstructural Protein 2 Interacts with a Host Protein Complex Involved in Mitochondrial Biogenesis and Intracellular Signaling. Journal of Virology 83, 10314– 10318 (2009).

8. Chu, H. et al. Comparative replication and immune activation profiles of SARS-CoV-2 and SARS-CoV in human lungs: an ex vivo study with implications for the pathogenesis of COVID-19. Clin Infect Dis doi:10.1093/cid/ciaa410. bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

9. Blanco-Melo, D. et al. Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19. Cell 181, 1036-1045.e9 (2020).

10. Heurich, A. et al. TMPRSS2 and ADAM17 Cleave ACE2 Differentially and Only Proteolysis by TMPRSS2 Augments Entry Driven by the Severe Acute Respiratory Syndrome Coronavirus Spike Protein. J Virol 88, 1293–1307 (2014).

11. Lambert, D. W. et al. Tumor Necrosis Factor-α Convertase (ADAM17) Mediates Regulated Ectodomain Shedding of the Severe-acute Respiratory Syndrome-Coronavirus (SARS-CoV) Receptor, Angiotensin-converting Enzyme-2 (ACE2). J. Biol. Chem. 280, 30113–30119 (2005).

12. Wu, G., Dawson, E., Duong, A., Haw, R. & Stein, L. ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis. F1000Res 3, 146 (2014).

13. Reyna, M. A., Leiserson, M. D. M. & Raphael, B. J. Hierarchical HotNet: identifying hierarchies of altered subnetworks. Bioinformatics 34, i972–i980 (2018).

14. Cottam, E. M., Whelband, M. C. & Wileman, T. Coronavirus NSP6 restricts autophagosome expansion. Autophagy 10, 1426–1441 (2014).

15. Andrianifahanana, M. et al. ERBB receptor activation is required for profibrotic responses to transforming growth factor beta. Cancer Res. 70, 7421–7430 (2010).

16. Pittet, J.-F. et al. TGF-β is a critical mediator of acute lung injury. J Clin Invest 107, 1537– 1544 (2001).

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19. Qin, Z., Xia, W., Fisher, G. J., Voorhees, J. J. & Quan, T. YAP/TAZ regulates TGF-β/Smad3 signaling by induction of Smad7 via AP-1 in human skin dermal fibroblasts. Cell Commun. Signal 16, 18 (2018).

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Material and Methods

Cell lines and reagents

HEK293T, A549, Vero E6 and HEK293R1 cells and their respective culturing conditions were described previously22. All cell lines were tested to be mycoplasma-free. Expression constructs for C- terminal HA tagged viral ORFs were synthesised (Twist Bioscience and BioCat) and cloned into pWPI vector as described previously23 with the following modifications: starting ATG codon was added, internal canonical splicing sites were replaced with synonymous mutations and C-terminal HA- tag, followed by amber stop codon, was added to individual viral open reading frames. C-terminally hemagglutinin(HA)-tagged ACE2 sequence was amplified from an ACE2 expression vector (kindly provided by Stefan Pöhlmann)24 into the lentiviral vector pWPI-puro. A549 cells were transduced twice, and ACE2-expressing A549 (ACE2-A549) cells were selected with puromycin. Lentiviruses bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

production, transduction of cells and antibiotic selection were performed as described previously23. RNA-isolation (Macherey-Nagel NucleoSpin RNA plus), reverse transcription (TaKaRa Bio PrimeScript RT with gDNA eraser) and RT-qPCR (Thermo-Fisher Scientific PowerUp SYBR green) were performed as described previously25. RNA-isolation for NGS applications was performed according to manufacturer’s protocol (Qiagen RNeasy mini kit, RNase free DNase set). For detection of protein abundance by western blotting, HA-HRP (Sigma-Aldrich), ACTB-HRP (Santa Cruz), ATM, MAP1LC3B, MAVS, HSPA1A, TGFβ and SQSTM1, phospho-JNK (T183/Y185), JNK, phospho-p38 (T180/Y182), p38 (Cell Signaling), SARS-CoV-2 (Sino Biological) antibodies were used. For AP-MS and AP-WB applications, HA-beads (Sigma-Aldrich and Thermo Fisher Scientific) and Streptactin II beads (IBA Lifesciences) were used. Secondary Abs: HRP and WB imaging was performed as described previously25. For the stimulation of cells in the reporter assay, recombinant human interferon-α (IFN-α) was a kind gift from Peter Stäheli, recombinant human IFN-γ were purchased from PeproTech and IVT4 was produced as described before26. All compounds tested during the viral inhibitor assay are listed in Suppl. Table 9.

Virus strains, stock preparation, plaque assay and in vitro infection

SARS-CoV-2-MUC-IMB-1 and SARS-CoV-2-GFP strains20 were produced by infecting Vero E6 cells cultured in DMEM medium (10% FCS, 100 ug/ml Streptomycin, 100 IU/ml Penicillin) for 2 days (MOI 0,01). Viral stock was harvested and spun twice (1000g/10min) before storage at - 80°C.Titer of viral stock was determined by plaque assay. Confluent monolayers of VeroE6 cells were infected with serial five-fold dilutions of virus supernatants for 1 h at 37°C. The inoculum was removed and replaced with serum-free MEM (Gibco, Life Technologies) containing 0.5% carboxymethylcellulose (Sigma-Aldrich). Two days post-infection, cells were fixed for 20 minutes at room temperature with formaldehyde directly added to the medium to a final concentration of 5%. Fixed cells were washed extensively with PBS before staining with H2O containing 1% crystal violet and 10% ethanol for 20 minutes. After rinsing with PBS, the number of plaques was counted and the virus titer was calculated.

A549-ACE2 cells were infected with SARS-CoV-2-MUC-IMB-1 strain (MOI 3) for the subsequent experiments. At each time point, the samples were washed once with 1x TBS buffer and harvested in SDC lysis buffer (100 mM Tris HCl pH 8.5; 4% SDC) or 1x SSB lysis buffer (62.5 mM Tris HCl pH 6.8; 2% SDS; 10% glycerol; 50 mM DTT; 0.01% bromophenol blue) or RLT (Qiagen) for proteome- phosphoproteome-ubiquitinome,western blot, and transcriptome analyses, respectively. The samples were heat-inactivated and frozen at -80°C until further processing, as described in the following sections.

Affinity purification mass spectrometric analyses of SARS-COV-2, SARS-COV and HCoV protein expressing A549 cells

For the determination of SARS-COV-2, SARS-COV and partial HCoV interactomes, four replicate affinity purifications were performed for each HA-tagged viral protein. A549 cells (6×106 cells per 15- cm dish) were transduced with HA-tagged SARS-COV-2, SARS-COV or HCoV protein coding lentivirus and harvested three days post transduction. Cell pellets of two 15-cm dishes were lysed in lysis buffer (50 mM Tris-HCl pH 7.5, 100 mM NaCl, 1.5 mM MgCl2, 0.2% (v/v) NP-40, 5% (v/v) glycerol, cOmplete protease inhibitor cocktail (Roche), 0.5% (v/v) 750 U/µl Sm DNAse) and sonicated (5 min, 4°C, 30 sec on, 30 sec off, low settings; Bioruptor, Diagenode SA). Following normalization of protein concentrations cleared lysates, virus protein-bound host proteins were enriched by adding 50 µl anti-HA-agarose slurry (Sigma-Aldrich, A2095) with constant agitation for 3h at 4°C. Non-specifically bound proteins were removed by four subsequent washes with lysis buffer followed by three detergent-removal steps with washing buffer (50 mM Tris-HCl pH 7.5, 100 mM NaCl, 1.5 mM MgCl2, 5% (v/v) glycerol). Enriched proteins were denatured, reduced, alkylated and digested by addition of 200 µl digestion buffer (0.6 M GdmCl, 1 mM TCEP, 4 mM CAA, 100 mM Tris-HCl pH 8, 0.5 µg LysC (WAKO Chemicals), 0.5 µg trypsin (Promega) at 30°C overnight. Peptide purification on StageTips with three layers of C18 Empore filter discs (3M) and subsequent bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

mass spectrometry analysis was performed as described previously22,23. Briefly, purified peptides were loaded onto a 20cm reverse-phase analytical column (75µm diameter; ReproSil-Pur C18-AQ 1.9µm resin; Dr. Maisch) and separated using an EASY-nLC 1200 system (Thermo Fisher Scientific) with a 90 min gradient (80% acetonitrile, 0.1% formic acid; 5% (80% acetonitrile) to 30% for 65min, 30% to 95% for 10min, wash out at 95% for 5min, readjustment to 5% in 10min) at a flow rate of 300 nlper min. Eluting peptides were directly analyzed on a Q-Exactive HF mass spectrometer (Thermo Fisher Scientific) with data-dependent acquisition including repeating cycles of one MS1 full scan (150-2,000m/z, R=60,000 at 200m/z) followed by 15 MS2 scans of the highest abundant isolated and higher-energy collisional dissociation fragmented peptide precursors.

Proteome analyses of SARS-COV-2, SARS-COV and HCoV protein expressing cells

For the determination of proteome changes in A549 cells expressing SARS-COV-2, SARS-COV or HCoV proteins, a fraction of 1×106 lentivirus-transduced cells from the affinity purification samples were lysed in guanidinium chloride buffer (6 M GdmCl, 10 mM TCEP, 40 mM CAA, 100 mM Tris- HCl pH 8), boiled at 95°C for 8 min and sonicated (10 min, 4°C, 30 sec on, 30 sec off, high settings). Protein concentrations of cleared lysates were normalized to 50 µg and proteins were pre-digested with 1 µg LysC at 37°C for 1h followed by a 1:10 dilution (100 mM Tris-HCl pH 8) and overnight digestion with 1 µg trypsin at 30°C. Peptide purification on StageTips with three layers of C18 Empore filter discs (3M) and subsequent mass spectrometry analysis was performed as described previously22,23. Briefly, 300 ng of purified peptides were loaded onto a 50 cm reversed phase column (75 μm inner diameter, packed in house with ReproSil-Pur C18-AQ 1.9 μm resin [Dr. Maisch GmbH]). The column temperature was maintained at 60°C using a homemade column oven. A binary buffer system, consisting of buffer A (0.1% formic acid (FA)) and buffer B (80% ACN, 0.1% FA), was used for peptide separation, at a flow rate of 300 nl/min. An EASY-nLC 1200 system (Thermo Fisher Scientific), directly coupled online with the mass spectrometer (Q Exactive HF-X, Thermo Fisher Scientific) via a nano-electrospray source, was employed for nano-flow liquid chromatography. Peptides were eluted by a linear 80 min gradient from 5% to 30% buffer B (0.1% v/v formic acid, 80% v/v acetonitrile), followed by a 4 min increase to 60% B, a further 4 min increase to 95% B, a 4 min plateau phase at 95% B, a 4 min decrease to 5% B and a 4 min wash phase of 5% B. To acquire MS data, the data-independent acquisition (DIA) scan mode operated by the XCalibur software (Thermo Fisher) was used. DIA was performed with one full MS event followed by 33 MS/MS windows in one cycle resulting in a cycle time of 2.7 seconds. The full MS settings included an ion target value of 3 x 106 charges in the 300 – 1650 m/z range with a maximum injection time of 60 ms and a resolution of 120,000 at m/z 200. DIA precursor windows ranged from 300.5 m/z (lower boundary of first window) to 1649.5 m/z (upper boundary of 33rd window). MS/MS settings included an ion target value of 3 x 106 charges for the precursor window with an Xcalibur-automated maximum injection time and a resolution of 30,000 at m/z 200.

To generate the proteome library for DIA measurements purified peptides from the first replicates and the fourth replicates of all samples were pooled separately and 25 µg of peptides from each pool were fractionated into 24 fractions by high pH reversed-phase chromatography as described earlier27. During each separation, fractions were concatenated automatically by shifting the collection tube every 120 seconds. In total 48 fractions were dried in a vacuum centrifuge, resuspended in buffer A* (0.3% TFA/ 2% ACN) and subsequently analyzed by a top12 data-dependent acquisition (DDA) scan mode using the same LC gradient and settings. The mass spectrometer was operated by the XCalibur software (Thermo Fisher). DDA scan settings on full MS level included an ion target value of 3 x 106 charges in the 300 – 1650 m/z range with a maximum injection time of 20 ms and a resolution of 60,000 at m/z 200. At the MS/MS level the target value was 105 charges with a maximum injection time of 60 ms and a resolution of 15,000 at m/z 200. For MS/MS events only, precursor ions with 2-5 charges that were not on the 20 s dynamic exclusion list were isolated in a 1.4 m/z window. Fragmentation was performed by higher-energy C-trap dissociation (HCD) with a normalized collision energy of 27eV.

Infected proteome-phosphoproteome time-course bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

Frozen cell lysates of infected A549-ACE2 harvested at 3, 6, 12, 18, 24 and 30h post infection were thawed on ice and boiled for 5 min at 95 degrees. Lysates were transferred to a 96-well plate (Covaris) and sonicated for 5 min. Protein concentrations were estimated by tryptophan assay28 and protein material was equalized to 200 µg per sample. CAA (10 mM) and TCEP (40 mM) along with trypsin (1:100 w/w, Sigma-Aldrich) and LysC (1/100 w/w, Wako) were added to the samples that were digested at 37°C overnight. Peptides were desalted using SDB-RPS cartridges (PreOmics).

Briefly, samples were mixed with 300 µl 1% TFA in isopropanol, loaded onto cartridges and washed with 200 µl 1% TFA in isopropanol and 200 µl 0.2% TFA. Peptides were eluted with 150 µl of 1.25% Ammonium hydroxide (NH4OH)/ 80% ACN and 10 µl aliquots were taken and dried separately for global proteome analysis. The rest was dried using a SpeedVac centrifuge (Eppendorf, Concentrator plus) and resuspended in 105 µl of equilibration buffer (1% TFA/ 80% ACN) for phosphopeptide enrichment. The AssayMAP Bravo robot (Agilent) performed the enrichment for phosphopeptides by priming AssayMAP cartridges (packed with 5 µl Fe(III)-NTA) with 1 % TFA in 99 % ACN followed by equilibration in equilibration buffer and loading of peptides. Enriched phosphopeptides were eluted with 1 % Ammonium hydroxide, dried in a vacuum centrifuge and resuspended in 1 % FA buffer. Evotips were activated by wetting in 40 ml 1-propanol in Evotipbox and subsequently washed with 100 µl of 0.1% FA. Peptides were loaded onto tips which were subsequently washed with 100 µl of 0.1 % FA. The tips were then loaded with 100 µl 0.1 % FA and centrifuged very shortly.

To generate the proteome library for DIA measurements cells were lysed in 4 % SDC and 100 mM Tris pH 8.4, followed by sonication, protein quantification, reduction, and alkylation and desalting using SDB-RPS cartridges (see above). 100 µg of peptides were fractionated into 24 fractions by high pH reversed-phase chromatography as described earlier27. Fractions were concatenated automatically by shifting the collection tube every 120 seconds and subsequently dried in a vacuum centrifuge and resuspended in buffer A* (0.3% TFA/ 2% ACN).

To generate the library for phosphoproteome DIA measurements A549 cells were treated with 100 ng/ml Calyculin and 2 mM Sodium orthovanadate for 20 min. Cells were lysed and treated as for the proteome library generation. After overnight digestion, peptides were desalted using Sepax Extraction columns. 5.5 mg of desalted peptides were fractionated into 84 fractions on a C18 reversed-phase column (4.6 x 150 mm, 3.5 µm bead size) under basic conditions using a Shimadzu UFLC operating at 1 ml/minute. Buffer A (2.5 mM Ammoniumbicarbonat in MQ) and Buffer B (2.5 mM ABC in 80% CAN) were used to separate peptides on a linear gradient of 2.5% B to 44% B for 64 minutes and 44% B to 75% B for 5 minutes before a rapid increase to 100% B which was kept for 5 minutes. Fractions were subsequently concatenated into 24 fractions and lyophilized. Phosphopeptides of these 24 fractions were enriched using the AssayMAP Bravo robot and loaded on Evotips. Another 5.5 mg of desalted peptides were split into 24 samples and enriched for phosphopeptides by the AssayMAP Bravo robot. Eluted phoshopeptides were combined, dried and fractionated into 24 fractions by neutral pH reversed-phase chromatography27. Fractions were dried and loaded on Evotips as described above.

Infected proteome-phosphoproteome-diGly-proteome (6 and 24hr)

Frozen cell lysates of infected A549-ACE2 harvested at 6 and 24h post infection were thawed on ice and sonicated for 1 min (Branson Sonifierer). Protein concentrations were estimated by tryptophan assay28. To reduce and alkylate proteins, samples were incubated for 5 min at 45°C with CAA and TCEP, final concentrations of 10 mM and 40 mM, respectively. Samples were digested overnight at 37°C using trypsin (1:100 w/w, Sigma-Aldrich) and LysC (1/100 w/w, Wako).

For proteome analysis, 10 µg of peptide material were desalted using SDB-RPS StageTips (Empore) (2). Briefly, samples were diluted with 1% TFA in isopropanol to a final volume of 200 µl and loaded onto StageTips, subsequently washed with 200 µl of 1% TFA in isopropanol and 200 µl 0.2% TFA/ 2% ACN. Peptides were eluted with 60 µl of 1.25% Ammonium hydroxide (NH4OH)/ 80% ACN and dried using a SpeedVac centrifuge (Eppendorf, Concentrator plus). They were resuspended in buffer A* prior to LC-MS/MS analysis. Peptide concentrations were measured optically at 280nm (Nanodrop bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

2000, Thermo Scientific) and subsequently equalized using buffer A*. 500ng peptide was subjected to LC-MS/MS analysis.

To generate phosphoproteome data, the EasyPhos protocol was used for the enrichment of phosphopeptides29. Briefly, samples were adjusted with the lysis buffer to a volume of 300 µl, transferred into a 96-deep-well plate and mixed with 100 µl 48% TFA, 8 mM KH2PO4. Phosphopeptides were captured by 5 min incubation at 40°C with 5 mg TiO2 beads. Thereafter, beads were washed 5 times with 5% TFA/ 60% isopropanol, followed by a transfer in 0.1% TFA/ 60% isopropanol into C8 StageTips. Phosphopeptides were eluted twice with 30 µl 20% NH4OH/ 40% ACN and concentrated for 30 min at 45°C using a SpeedVac centrifuge. Concentrated samples were immediately diluted with 100 µl 1% TFA in isopropanol and transferred into SDB-RPS StageTips. Peptides were washed, eluted and dried as described above. Dried peptides were resuspended in 6 µl buffer A* and 5 µl was subjected to LC-MS/MS analysis.

For diGly peptide enrichment, samples were four-fold diluted with 1% TFA in isopropanol and loaded onto SDB-RPS cartridges (Strata™-X-C, 30 mg/ 3 ml, Phenomenex Inc), pre-equilibrated with 4 ml 30% MeOH/1% TFA and washed with 4 ml 0.2% TFA. Samples were washed twice with 4 ml 1% TFA in isopropanol, once with 0.2% TFA/ 2% ACN and eluted twice with 2 ml 1.25% NH4OH/ 80% ACN. Eluted peptides were diluted with ddH2O to a final ACN concentration of 35%, snap frozen and lyophilized. Lyophilized peptides were reconstituted in IAP buffer (50 mM MOPS, pH 7.2, 10 mM Na2HPO4, 50 mM NaCl) and the peptide concentration was estimated by tryptophan assay. K--GG remnant containing peptides were enriched using the PTMScan® Ubiquitin Remnant Motif (K--GG) Kit (Cell Signaling Technology). Crosslinking of antibodies to beads and subsequent immunopurification was performed with slight modifications as previously described30. Briefly, two vials of crosslinked beads were combined and equally split into 16 tubes (~31 µg of antibody per tube). Equal peptide amounts (800 µg) were added to crosslinked beads and the volume was adjusted with IAP buffer to 1 ml. After 1h of incubation at 4°C and gentle agitation, beads were washed twice with cold IAP and 5 times with cold ddH2O. Thereafter, peptides were eluted twice with 50 µl 0.15% TFA. Eluted peptides were desalted and dried as described for proteome analysis with the difference that 0.2% TFA instead of 1%TFA in isopropanol was used for the first wash. Eluted peptides were resuspended in 9 µl buffer A* and 4 µl was subjected to LC-MS/MS analysis.

DDA Measurements

Samples were loaded onto a 50 cm reversed phase column (75 μm inner diameter, packed in house with ReproSil-Pur C18-AQ 1.9 μm resin [Dr. Maisch GmbH]). The column temperature was maintained at 60°C using a homemade column oven. A binary buffer system, consisting of buffer A (0.1% formic acid (FA)) and buffer B (80% ACN plus 0.1% FA), was used for peptide separation, at a flow rate of 300 nl/min. An EASY-nLC 1200 system (Thermo Fisher Scientific), directly coupled online with the mass spectrometer (Q Exactive HF-X, Thermo Fisher Scientific) via a nano- electrospray source, was employed for nano-flow liquid chromatography.

For proteome measurements, we used a gradient starting at 5% buffer B and stepwise increasing to 30% in 95 min, 60% in 5 min and 95% in 5 min. The mass spectrometer was operated in Top12 data- dependent mode (DDA) with a full scan range of 300-1650 m/z at 60,000 resolution with an automatic gain control (AGC) target of 3e6 and a maximum fill time of 20ms. Precursor ions were isolated with a width of 1.4 m/z and fragmented by higher-energy collisional dissociation (HCD) (NCE 27%). Fragment scans were performed at a resolution of 15,000, an AGC of 1e5 and a maximum injection time of 60 ms. Dynamic exclusion was enabled and set to 30 s.

For phosphopeptide samples 5µl were loaded and eluted with a gradient starting at 3% buffer B and stepwise increased to 19% in 60 min, 41% in 30 min, 36% in 39 min and 95% in 5 min. The mass spectrometer was operated in Top10 DDA with a full scan range of 300-1600 m/z at 60,000 resolution with an AGC target of 3e6 and a maximum fill time of 120 ms. Precursor ions were isolated with a width of 1.4 m/z and fragmented by HCD (NCE 28%). Fragment scans were performed at a resolution bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

of 15,000, an AGC of 1e5 and a maximum injection time of 50 ms. Dynamic exclusion was enabled and set to 30 s.

For the analysis of K--GG peptide samples, we use a gradient starting at 3% buffer B and stepwise increased to 7% in 6 min, 20% in 49 min, 36% in 39 min, 45% in 10 min and 95% in 4 min. The mass spectrometer was operated in Top12 DDA with a full scan range of 300-1350 m/z at 60,000 resolution with an AGC target of 3e6 and a maximum fill time of 20ms. Precursor ions were isolated with a width of 1.4 m/z and fragmented by HCD (NCE 28%). Fragment scans were performed at a resolution of 30,000, an AGC of 1e5 and a maximum injection time of 110 ms. Dynamic exclusion was enabled and set to 15 s.

DIA Measurements

Samples were loaded onto a 15 cm reversed phase column (150 μm inner diameter, packed in house with ReproSil-Pur C18-AQ 1.9 μm resin [Dr. Maisch GmbH]), which was kept in a homemade column oven at 60°C. Peptides were separated by the Evosep One LC system using the pre- programmed 44 minutes gradient for proteome samples and the 21 minutes gradient for phosphoproteome samples. The same gradients were used for the acquisition of proteome and phosphoproteome library fractions. The Evosep One system was coupled to a Q Exactive HF-X Orbitrap (Thermo Fisher Scientific) via a nano-electrospray source.

The proteome and phosphoproteome fractions we used to build the libraries were measured in DDA mode. To acquire proteome fractions the mass spectrometer was operated in Top15 data-dependent mode with a full scan range of 300-1650 m/z at 60,000 resolution, an automatic gain control (AGC) target of 3e6 and a maximum fill time of 20ms. For the generation of the phosphoproteome library the mass spectrometer was operated in Top12 data-dependent mode (DDA) with a full scan range of 300- 1650 m/z at 60,000 resolution, an automatic gain control (AGC) target of 3e6 and a maximum fill time of 25ms. For both libraries precursor ions were isolated with a width of 1.4 m/z and fragmented by higher-energy collisional dissociation (HCD) (NCE 27%). Fragment scans were performed at a resolution of 15,000, an AGC of 1e5 and a maximum fill time of 28 ms. Dynamic exclusion was enabled and set to 30 s for the proteome library and 20 s for the phosphoproteome library.

For proteome and phosphoproteome DIA measurements, full MS resolution was set to 60,000 with a full scan range of 300-1650 m/z, a maximum fill time of 60 ms and an automatic gain control (AGC) target of 3e6. One full scan was followed by 32 windows with a resolution of 30,000 and a maximum fill time of 54 ms for proteome measurements and 40 windows for phosphoproteome measurements with a resolution of 15,000 and maximum fill time of 28 ms in profile mode. Precursor ions were fragmented by higher-energy collisional dissociation (HCD) (NCE 27%).

Data-processing of affinity purification, (phospho)proteome and ubiquitinome LC-MS/MS analyses

Raw MS data files of experiments conducted in DDA mode were processed with MaxQuant (version 1.6.14) using the standard settings and label-free quantification enabled (LFQ min ratio count 1, normalization type none, stabilize large LFQ ratios disabled). For profiling of post-translational modifications, additional variable modifications for ubiquitination (GlyGly(K)) and phosphorylation (Phospho(STY)) were added. Spectra were searched against forward and reverse sequences of the reviewed human proteome including isoforms (UniprotKB, release 10.2019) and SARS-COV-2, SARS-COV and HCoV proteins by the built-in Andromeda search engine31.

For AP-MS data, the alternative protein group definition was used: only the peptides identified in AP- MS samples could be regarded as protein group-specific, protein groups that differed by the single specific peptide or had less than 25% different specific peptides were merged to extend the set of peptides used for protein group quantitation and reduce the number of protein isoform-specific interactions. bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

For the experiments conducted in DIA mode, Spectronaut version 13 (Biognosys) was used to generate the proteome and phosphoproteome libraries from DDA runs by combining files of respective fractionations using the human fasta file (Uniprot, 2019, 42,431 entries). For the generation of the proteome library default settings were left unchanged. For the phosphoproteome library generation 2 x 24 files received by both fractionation strategies were combined and phosphorylation at Serine/Threonine/Tyrosine was added as variable modification to default settings. Maximum number of fragment ions per peptide was increased from 6 to 25. Proteome DIA files were analyzed using the proteome library with default settings and disabled cross run normalization. Phospho DIA files were analyzed using the phosphoproteome library using default settings with disabled PTM localization filter and cross run normalization. To search for viral proteins, we also generated the “hybrid” spectral library by merging DDA proteome library with a direct-DIA library generated from the DIA analysis of DIA proteome samples. For this search, the sequences of viral proteins were added to the human fasta file.

Bioinformatic analysis

Unless otherwise specified, the bioinformatic analysis was done in R (version 3.6), Julia (version 1.4) and Python (version 3.8) using a collection of in-house scripts (available upon request).

Statistical analysis of MS data

For all MS datasets, except DDA phosphoproteome and ubiquitinome data, the Bayesian linear random effects models were used to define how the abundances of proteins change between the conditions. To specify and fit the models we employed msglm R package (https://github.com/innatelab/msglm), which depends on rstan package (version 2.19)32 for inferring the posterior distribution of the model parameters. In all the models, the effects corresponding to the experimental conditions have regularized horseshoe+ priors33, while the batch effects have normally distributed priors. Laplacian distribution was used to model the instrumental error of MS intensities. For each MS instrument used, the heteroscedastic intensities noise model was calibrated with the technical replicate MS data of the instrument. These data were also used to calibrate the logit-based model of missing MS data (the probability that the MS instrument will fail to identify the protein given its expected abundance in the sample). The model was fit using unnormalized MS intensities data. Instead of transforming the data by normalization, the inferred protein abundances were scaled by the normalization multiplier of each individual MS sample to match the expected MS intensity of that sample. This allows taking the signal-to-noise variation between the samples into account when fitting the model. Due to high computational intensity, the model was applied to each protein group separately. For all the models, 4000 iterations (2000 warmup + 2000 sampling) of the No-U-Turn Markov Chain Monte Carlo were performed in 7 or 8 independent chains, every 4th sample was collected for posterior distribution of the model parameters. For estimating the statistical significance of protein abundance changes between the two experimental conditions, the P-value was defined as the probability that a random sample from the posterior distribution of the first condition would be smaller (larger) than a random sample drawn from the second condition. No multiple hypothesis testing corrections were applied, since this is handled by the choice of the model priors.

Statistical analysis of AP-MS data and filtering for specific interactions Given the sparsity of the AP-MS data (each peptide is quantified in a small fraction of experiments), to take advantage of the missing data modeling by msglm, the statistical model was applied directly to the MS1 intensities of protein group-specific LC peaks (evidence.txt table of MaxQuant output). In R GLM formula language, the model could be specified as bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

log ~ 1 : 1 , where APMS effect models the average shift of intensities in AP-MS data in comparison to full proteome samples, Bait is the average enrichment of a protein in AP-MS experiments of homologous proteins of both SARS-CoV and SARS-CoV-2, and Bait:Virus corresponds to the virus-specific changes in protein enrichment. MS1peak is the log-ratio between the intensity of a given peak and the total protein abundance (the peak is defined by its peptide sequence, PTMs and the charge; it is assumed that the peak ratios do not depend on experimental conditions34), and MSbatch accounts for batch-specific variations of protein intensity. APMS, Bait and Bait:Virus effects were used to reconstruct the batch effect-free abundance of the protein in AP-MS samples. The modeling provided the enrichment estimates for each protein in each AP experiment. Specific AP- MS interactions had to pass the two tests. In the first test, the enrichment of the candidate protein in a given bait AP was compared against the background, which was dynamically defined for each interaction to contain the data from all other baits, where the abundance of the candidate was within 50%-90% percentile range (excluding top 10% baits from the background allowed the protein to be shared by a few baits in the resulting AP-MS network). The non-targeting control and Gaussia luciferase baits were always preserved in the background. Similarly, to filter out any potential side- effects of very high bait protein expression, the ORF3 homologs were always present in the background of M interactors and vice versa. To rule out the influence of the batch effects, the second test was applied. It was defined similarly to the first one, but the background was constrained to the baits of the same batch, and 40%-80% percentile range was used. In both tests, the protein has to be 4 times enriched against the background (16 times for highly expressed baits: ORF3, M, NSP13, NSP5, NSP6, ORF3a, ORF7b, ORF8b, HCoV ORF4a) with the P-value ≤ 1E-3. Additionally, we excluded the proteins that, in the viral protein expression data, have shown upregulation, and their enrichment in AP-MS data was less than 16 times stronger than observed upregulation effects. Finally, to exclude the carryover of material between the samples sequentially analyzed by MS, we removed the putative interactors, which were also enriched at higher levels in the samples of the preceding bait, or the one before it. For the analysis of interaction specificity between the homologous viral proteins, we estimated the significance of interaction enrichment difference (corrected by the average difference between the enrichment of the shared interactors to adjust for the bait expression variation). Specific interactions have to be 4 times enriched in comparison to the homolog with P-value ≤ 1E-3.

Statistical analysis of DIA proteome effects upon viral protein overexpression

The statistical model of the viral protein overexpression data set was similar to AP-MS data, except that protein-level intensities provided by Spectronaut were used. The PCA analysis of the protein intensities has identified that the 2nd principal component is associated with the batch-dependent variations between the samples. To exclude their influence, this principal component was added to the experimental design matrix as an additional batch effect. As with AP-MS data, the two statistical tests were used to identify the significantly regulated proteins. First, the absolute value of log2-fold change of protein abundance upon overexpression of a given viral protein in comparison to the control samples had to be above 0.25 with P-value ≤ 1E-3. Second, the

protein had to be significantly regulated (same log2-fold change and P-value applied) against the background distribution of its abundance in the selected experiments of the same batch (experiments, where the tested protein abundance was within the 20%-80% percentile range of the whole batch, were dynamically selected for each protein).

Statistical analysis of DDA proteome data of virus infection

For DDA proteome data the following linear model was used: bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

log~ $ :$,

/Ĝ/ where after(24h) effect corresponds to the protein abundance changes in mock-infected samples that

happened between 6h and 24h, and treatment:after(ti) (ti=6,24) is the effect of interaction between the SARS-CoV-2 infection and the timepoint, and corresponds to the changes in the virus-infected cells (in comparison to the mock-infected samples) within the first 6h hours after infection, and between 6h

and 24h after infection, respectively. The absolute value of log2-fold change between the conditions below 0.25 and the corresponding P-value ≤ 1E-3 criteria were used to define the significant changes.

Statistical analysis of DIA proteome and phosphoproteome data of virus infection

For DIA phosphoproteome data, to convert peptide-level output of Spectronaut into PTM site-level report, the Peptide Collapse Perseus plugin was used (https://github.com/AlexHgO/Perseus_Plugin_Peptide_Collapse)35. Phosphosites with less than 0.75 localization probability were excluded. For DIA proteome and phosphoproteome datasets, the following linear model was used:

log~ $ :$,

/Ĝ/

where after(ti) effect corresponds to the protein abundance changes in mock-infected samples that

happened between ti-1 and ti (ti=6,12,18,24,30), and treatment:after(ti) (ti=3,6,12,18,24,30) is the effect

of interaction between the SARS-CoV-2 infection and the timepoint. The absolute value of log2-fold change between the conditions below 0.25 and the corresponding P-value ≤ 1E-3 criteria were used to

define the significant changes for proteome data, and |log2-fold change| ≥ 0.5, P-value ≤ 1E-2 for phosphoproteome data.

Statistical analysis of DDA total proteome, phosphoproteome and ubiquitinome data 6 and 24 hours post SARS-CoV-2 infection of A549-ACE2 cells

The output of MaxQuant was analyzed with Perseus (version 1.6.14.0)36 and visualized with R (version 3.6.0) and RStudio (version 1.2.1335). For total proteome analysis, detected protein groups within the proteinGroups output table identified as known contaminants, reverse sequence matches, only identified by site or quantified in less than 3 out of 4 replicates in at least one condition were excluded. Following log2 transformation, missing values were imputed for each replicate individually by sampling values from a normal distribution calculated from the original data distribution (width = 0.3×s.d., downshift = -1.8×s.d.). Differentially regulated protein groups between mock and SARS- CoV-2 infection at 6 and 24 h.p.i. were identified via two-sided Student’s T-tests corrected for multiple hypothesis testing applying a permutation-based FDR (S0 = 0.1; FDR < 0.05, 250 randomizations). Protein groups were further removed for statistical testing if not at least one T-test condition contained a minimum of three non-imputed values. For phosphoproteome analysis, phosphosites within the Phospho (STY)Sites output table identified as known contaminants, reverse sequence matches or less than 0.75 localization probability were excluded. Following log2 transformation, phosphosite intensities were normalized based on sites that were quantified in at least 90% of all samples to account for technical variations. In detail, the median of phosphosite-specific intensities across samples was subtracted from individual intensities to normalize for different phosphosite abundances (row-wise normalization). Next, the median of normalized phosphosite intensities per sample was used as final normalization factor and subtracted from individual non- normalized phosphosite intensities (column-wise normalization). Phosphosites were further filtered for quantification in at least 3 replicates and missing values were imputed for each replicate individually by sampling values from a normal distribution calculated from the original data distribution (width = bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

0.3 × s.d., downshift = -1.8 × s.d.). Differentially regulated phosphosites between mock and SARS- CoV-2 infection at 6 and 24 h.p.i. were identified via two-sided Student’s T-tests corrected for multiple hypothesis testing applying a permutation-based FDR (S0 = 0.5; FDR < 0.05, 250 randomizations). Phosphosites were further removed from statistical testing if not at least one T-test condition contained a minimum of 3 non-imputed values. For ubiquitinome analysis, ubiquitination sites within the GlyGly (K)Sites output table were processed as mention for phosphosites, but normalization for technical variation was based on ubiquitination sites quantified in more than 60% of all samples. Differentially regulated ubiquitination sites between mock and SARS-CoV-2 infection at 6 and 24 h.p.i. were identified via two-sided Student’s T-tests corrected for multiple hypothesis testing applying a permutation-based FDR (S0 = 0.1; FDR < 0.05, 250 randomizations) and removed from statistical testing if not at least one T-test condition contained a minimum of 3 non-imputed values. Annotation of detected protein groups, phosphosites and ubiquitination sites with GOBP, -MF, -CC, KEGG, Pfam, GSEA, Keywords and Corum as well as PhosphoSitePlus kinase-substrate relations and regulatory sites (version May 1st 2020)37 was performed in Perseus.

Transcriptomic analysis of SARS-COV-2 infected A549-ACE2 cells

A549-ACE2 cells used for transcriptional profiling of SARS-CoV-2 infection were cultured and infected as described above. RNA isolation was performed using RNeasy Mini kit (Qiagen) according to the manufacturer's protocol with addition of a heat inactivation step after cell lysis. Library preparation for bulk 3’-sequencing of poly(A)-RNA was done as described previously38. Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (Thermo Fisher) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adapter. 5’ ends of the cDNAs were extended by a template switch oligo (TSO) and after pooling of all samples full- length cDNA was amplified with primers binding to the TSO-site and the adapter. cDNA was fragmented and TruSeq-Adapters ligated with the NEBNext® Ultra™ II FS DNA Library Prep Kit for Illumina® (NEB) and 3’-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to Parekh et al.38 the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read1 and barcodes and UMIs in read2 to achieve better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 75 cycles for the cDNA in read1 and 16 cycles for the barcodes and UMIs in read2.

As for the analysis of the transcriptome data, Gencode gene annotations v28 and the human reference genome GRCh38 were derived from the Gencode homepage (EMBL-EBI). Dropseq tool v1.1239 was used for mapping raw sequencing data to the reference genome. The resulting UMI filtered count matrix was imported into R v3.4.4. CPM (counts per million) values were calculated for the raw data and genes having a mean cpm value less than 1 were removed from the dataset. Prior differential expression analysis with DESeq2 v1.18.140, dispersion of the data was estimated with a parametric fit using a multiplicative model where infection status (MOCK, virus infected) and time were incorporated as covariates in the model matrix. The Wald test was used for determining differentially regulated genes across timepoints in individual infection states and shrunk log2 fold changes were calculated afterwards. Transcripts with low mean normalized count that were flagged by the independent filtering procedure of DESeq2 were removed and those with absolute apeglm shrunk log2 fold change > 0.5 and the p-value < 0.05 were considered differentially expressed in distinct conditions.

The data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB38744 (https://www.ebi.ac.uk/ena/data/view/PRJEB38744).

qRT-PCR analysis

RNA isolation from SARS-CoV-2 infected A549-ACE2 cells was performed as described above (Qiagen). 500 ng total RNA was used for reverse transcription with PrimeScript RT with gDNA eraser bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

(Takara). For relative transcript quantification PowerUp SYBR Green (Applied Biosystems) was used. Primer sequences can be provided upon request.

Gene Set Enrichment Analysis

We have used Gene Ontology, Reactome and other EnrichmentMap gene sets of human proteins41 as well as protein complexes annotations from IntAct Complex Portal (version 2019.11)42 and CORUM (version 2019)43. To find the nonredundant collection of annotations describing the unique and shared features of multiple experiments in a dataset, we have used Julia package OptEnrichedSetCover.jl (https://github.com/alyst/OptEnrichedSetCover.jl), which employs evolutionary multi-objective optimization technique to find a collection of annotation terms that have both significant enrichments in the individual experiments and minimal pairwise overlaps.

For transcription factor enrichment analysis the significantly regulated transcripts were submitted to ChEA3 web-based application44. Transcription factor – target gene set libraries from ENCODE were used45. Transcriptome, proteome, ubiquitinome and phosphoproteome changes along with unchanged transcripts/proteins/sites were submitted to the core ingenuity pathway analysis (IPA) (www.ingenuity.com). The following cut-offs were used for differentially expressed transcripts: the absolute values of

apeglm-shrunk log2 fold change > 0.5, the p-value < 0.05. Transcripts with low mean normalized count that were flagged by the independent filtering procedure of DESeq2 were removed prior pathway analysis. The following cut-offs were used for differentially expressed proteins or regulated

sites: p-value <0.05 and absolute log2 fold change ≥ 0.5. Ingenuity knowledge base was used as a reference dataset, only experimentally observed findings were used for confidence filtering, additionally human species and A549-ATCC cell line filters were set. Input datasets were used to identify the most significant canonical pathways and upstream regulators (in case of transcriptome). Right-tailed Fisher’s exact test with Benjamini-Hochberg’s correction was used to calculate p-values, which are presented in Supplementary Table 3.

Prediction of Functional Links between AP-MS and viral protein overexpression data

To systematically detect functional interactions, which may connect the cellular targets of each viral protein with the downstream changes it induces on proteome level, we have used the network diffusion-based HierarchicalHotNet method13 as implemented in Julia package HierarchicalHotNet.jl (https://github.com/alyst/HierarchicalHotNet.jl). Specifically, for network diffusion with restart, we used the ReactomeFI network (version 2019)12 of cellular functional interactions, reversing the direction of functional interaction (e.g. replacing kinase→substrate interaction with substrate→kinase). The proteins with significant abundance changes upon bait overexpression

(|median(log2-fold change)| ≥ 0.25, P-value ≤ 1E-3 both in the comparison against the controls and against the baits of the same batch) were used as the sources of signal diffusion with weights set to

medianlogͦ fold-change · logͥͤ P-value, and the restart probability was set to 0.4. To find the optimal cutting threshold of the resulting hierarchical tree of strongly connected components (SCCs) of the weighted graph corresponding to the stationary distribution of signal diffusion and to confirm the relevance of predicted functional connections, the same procedure was applied to 1000 random permutations of vertex weights as described in Reyna et al.13 (vertex weights are randomly shuffled between the vertices with similar in- and out-degrees). Since cutting the tree of SCCs at any threshold t (keeping only the edges with weights above t) and collapsing each resulting SCC into a single node bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

produces the directed acyclic graph of connections between SCCs, it allowed efficient enumeration of the paths from the “source” nodes (proteins perturbed by viral protein expression with vertex weight w, w ≥ 1.5) to the “sink” nodes (interactors of the viral protein). We have used this property of the tree to calculate the average source-to-sink path length at each cutting threshold of the network diffusion weighted graph. At each threshold t, the average path from source to sink nodes was calculated as:

1" $. · 1 .- ·.$)&, +

where Nsrc is the number of “sources”, Nsink is the number of “sinks”, Ndis is the number of

disconnected pairs of sources and sinks, NSCC is the number of SCC at given threshold, LSCC(p) is the number of SCCs that the given path p from source to sink goes through, and the sum is for all paths

from sources to sinks. For the generation of the diffusion network we have selected the topt threshold

that maximized the difference between the median of Lavg(t) for randomly shuffled data and Lavg(t) for the real data.

Co-immunoprecipitation and western blot analysis

HEK293T cells were transfected with pWPI plasmid encoding single HA-tagged viral proteins, alone or together with pTO-SII-HA expressing host factor of interest. 48 hours after transfection, cells were washed in PBS, flash frozen in liquid nitrogen and kept at -80°C until further processing. Co- immunoprecipitation experiments were performed as described previously22,23. Briefly, cells were lysed in lysis buffer (50 mM Tris-HCl pH 7.5, 100 mM NaCl, 1.5 mM MgCl2, 0.2% (v/v) NP-40, 5% (v/v) glycerol, cOmplete protease inhibitor cocktail (Roche), 0.5% (v/v) 750 U/µl Sm DNAse) and sonicated (5 min, 4°C, 30 sec on, 30 sec off, low settings; Bioruptor, Diagenode SA). HA or Streptactin beads were added to cleared lysates and samples were incubated for 3h at 4°C under constant rotation. Beads were washed six times in the lysis buffer and resuspended in 1x SDS sample buffer 62,5 mM Tris-HCl pH 6.8, 2% SDS, 10% glycerol, 50 mM DTT, 0.01% bromophenol blue). After boiling for 5 minutes at 95°C, a fraction of the input lysate and elution were loaded on NuPAGE™ Novex™ 4-12% Bis-Tris (Invitrogen), and further submitted to western blotting using Amersham Protran nitrocellulose membranes. Imaging was performed by HRP luminescence (ECL, Perkin Elmer).

SARS-CoV-2 infected A549-ACE2 cell lysates were sonicated (10 min, 4°C, 30 sec on, 30 sec off, low settings; Bioruptor, Diagenode SA). Protein concentration was adjusted based on Pierce660 assay supplemented with ionic detergent compatibility reagent. After boiling for 5 min at 95°C and brief max g centrifugation, the samples were loaded on NuPAGE™ Novex™ 4-12% Bis-Tris (Invitrogen), and blotted onto 0,22 µm Amersham™ Protran® nitrocellulose membranes (Merck). Primary and secondary antibody stainings were performed according to the manufacturer’s recommendations. Imaging was performed by HRP luminescence using Femto kit (ThermoFischer Scientific) or Western Lightning PlusECL kit (Perkin Elmer).

Reporter Assay and IFN Bioassay

The following reporter constructs were used in this study: pISRE-luc was purchased from Stratagene, EF1-α-ren from Engin Gürlevik, pCAGGS-Flag-RIG-I from Chris Basler, pIRF1-GAS-ff-luc, pWPI- SMN1-flag and pWPI-NS5 (ZIKV)-HA was described previously23,46.

For the reporter assay, HEK293RI cells were plated in 24-well plates 24 hours prior to transfection. Firefly reporter and Renilla transfection control were transfected together with plasmids expressing viral proteins using polyethylenimine (PEI, Polysciences) for untreated and treated conditions. In 18 hours cells were stimulated for 8 hours with a corresponding inducer and harvested in the passive lysis buffer (Promega). Luminescence of Firefly and Renilla luciferases was measured using dual- bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

luciferase-reporter assay (Promega) according to the manufacturer’s instructions in a microplate reader (Tecan). Total amounts of IFN-α/β in cell supernatants were measured by using 293T cells stably expressing the firefly luciferase gene under the control of the mouse Mx1 promoter (Mx1-luc reporter cells)47. Briefly, HEK293RI cells were seeded, transfected with pCAGGS-flag-RIG-I plus viral protein constructs and stimulated as described above. Cell supernatants were harvested in 8 hour. Mx1-luc reporter cells were seeded into 96-well plates in triplicates and were treated 24 hours later with supernatants. At 16 hours post incubation, cells were lysed in the passive lysis buffer (Promega), and luminescence was measured with a microplate reader (Tecan). The assay sensitivity was determined by a standard curve. Viral inhibitors assay

A549-ACE2 cells were seeded into 96-well plates in DMEM medium (10% FCS, 100 ug/ml Streptomycin, 100 IU/ml Penicillin) one day before infection. Six hours before infection, or at the time of infection, the medium was replaced with 100ul of DMEM medium containing either the compounds of interest or DMSO as a control. Infection was performed by adding 10ul of SARS-CoV-2-GFP (MOI 3) per well and plates were placed in the IncuCyte S3 Live-Cell Analysis System where whole well real-time images of mock (Phase channel) and infected (GFP and Phase channel) cells were captured every 4h for 48h. Cell viability (mock) and virus growth (mock and infected) were assessed as the cell confluence per well (Phase area) and GFP area normalized on cell confluence per well (GFP area/Phase area) respectively using IncuCyte S3 Software (Essen Bioscience; version 2019B Rev2).

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Figure 1 Coronaviruses Lentiviral transduction LC-MS/MS Proteins Viral-host interactions identified a ORFsbioRxiv library cloningpreprint doi:Quadruplicates https://doi.org/10.1101/2020.06.17.156455580 runs identified ; this bversion posted June 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder,6288 who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International293 license. 853 169 SARS SARS Joint CoV-2 CoV interactome SARS-CoV-2 Shared SARS-CoV 24 27 specific CoV 7839 specific HCoV protein 3 Proteome A549 cells effects -10 0 +10

Endocytosis Viral proteins (baits) ER to cytosol SARS-CoV-2 c via AP-2 complex NSP15 trafficking tRNA SARS-CoV splicing ER stress Combined SARS-CoV-2 / -CoV HSPA1A Proteasome Host targets (prey) core ORF9b G3BP2 TOMM70 Interactions (AP/MS) G3BP1 E NSP13 NSP8 SARS-CoV-2-specific N SARS-CoV-specific Transcription Shared TGFb and integrins elongation Glycolysis signaling NSP7 known host complexes STAT6 LTBP1 NSP9 TAX1BP1 Septin NSP10 TGFB1 mRNA complex ER quality Endolysosomal TGFB2 control processing GSK3A trafficking NSP14 NSP12 EphrinB-EPHB FURIN RAB2A RHEB NSP3 pathway MD VPS39 Proteasome ORF8 ECM regulators and TREX1 regulatory proteins metalloproteases VPS41 Oxidoreduction HOPS complex Integrins TRIM47 Glycosylation MHC-I ORF8b complex tRNA GABARAPL2 Ubiquitin-like Mitochondrial charging ligase activity metalloproteases ORF3a ORF3b TP53 Notch NSP4 ORF3 signaling ORF8a NSP16 EPHA2 Lipid Inflammatory p53signaling ITCH response Glycolysis oxidation Ions transport NEDD4-ITCH BAMBI Mitochondrial by ATPases Receptor tyrosine complex TGFBR1 respiratory chain phosphatases TNF receptor TGFBR2 M superfamilly JAK1 Solute carriers TNFAIP2 Cytokine receptors signaling

IRAK1 SNARE complex JAK2 CAV1 ITGA6

CAV2 Cell adhesion TRIM7 Sarcoglycan ATM SARM1 and motility Condensin complex Golgi II complex membrane Palmitoylation ATR COG complex Peroxisome

MAVS ORF7b

ER membrane ErbB receptor S Integrator ORF7a signaling complex complex NSP6 ER-Golgi trafficking GPCRs Nuclear pore signaling

Nuclear GPI anchor UNC93B1 import/export Glycosylation NSP2

NSP1

ATP synthase CTNNA1 PHB2 PHB Nuclear CTNNA2 membrane

d mitochondrial translational termination 0 mitochondrial ATP synthesis coupled electron transport early endosome to Golgi transport cellular response to thyroid hormone stimulus −2 keratan sulfate catabolic process actomyosin contractile ring organization tricuspid valve morphogenesis −4 cholesterol biosynthetic process protein K29-linked ubiquitination ( P-value )

polyketide metabolic process −6 10 negative regulation of proteasomal ubiquitin-dependent protein catabolic process nucleosome assembly log nuclear-transcribed mRNA catabolic process −8 DNA replication initiation DNA unwinding involved in DNA replication xylulose biosynthetic process −10 ORF7aORF8a N NSP6 ORF3 NSP4 ORF4 NSP13E M NSP6 ORF7aORF7b NSP12 ORF3 ORF3 NSP13ORF3b ORF9b ORF9b NSP6 N CoV2 CoV2 CoV2 CoV2 ▲ ▲ ▲ ▼ CoV CoV2 CoV HCoV CoV2 HCoV CoV2 CoV2 CoV CoV CoV CoV2 CoV2 CoV CoV2 CoV CoV2 CoV ▼ ▼ ▲ ▼ ▲ ▲ ▼ ▲ ▼ ▲ ▼ ▼ ▼ ▼ ▲ ▼ ▼ ▼ FigurebioRxiv 2 preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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

available under aCC-BY 4.0 International up license. a b 316 no. of regulated SARS-CoV-2 identified regulated 67 102 A549 cells time (h) 16 7 14 transcripts 3 6 12 18 24 30 13472 509 genes 2 17 18 proteins 20 Transcriptome 33 39 mock down 101 N / NSP8 / S 7462 1053 proteins 15 89

en sity MOI 3 Proteome up 20 21 26 13 31 37 19 nt N 10 NSP8 S 4659 884 sites 14 35 50 13 5 Ubiquitinome 111 113

down 216 217 0 11847 1483 sites 750 Phosphoproteome up 220 172 5 158 490 NormMS I ali z ed x10 144 5 26 58108 111 113 3 18 41 72 2 c d 15 27 47 78 6 2 15 70 62 87 SQSTM1 30 61 361 CAV1 down 98 90 K157 505 K39 TRAF2 K27 ACE2 10 YAP1 3 6 12 18 24 30 h CAV1 K625 581 4 K47 ACE2 S131 phosphosites up 420 CAV1 K702 126 5 CREBBP K8 112 p-value FC

10 TRAF2 K1014 2 S127 S289 S367 30 h phosphoproteins K165 24 h log -log 2 POLR2B 0 6 h 191 S138 ubi sites 244 RPL24 K934 253 214 down K97 S61 K321 S340 SQSTM1 -5 K281 ubi proteins phosphosites 6 24 h 0 ubi sites -5 0 5 10

log2 fold change

e trans-Golgi Network Vesicle Budding 0 Packaging Of Telomere Ends XAV939 inhibits tankyrase, stabilizing AXIN Apoptotic execution phase Eukaryotic Translation Initiation −2 RHO GTPases activate PAKs NS1 Mediated Effects on Host Pathways mTORC1-mediated signalling pre-mRNA splicing −4 Resolution of Sister Chromatid Cohesion HDL remodeling

GRB2:SOS provides linkage to p-value MAPK signaling for Integrins 10

Unwinding of DNA -log −6 Telomere C-strand synthesis initiation Loss of MECP2 binding ability to 5mC-DNA p75NTR negatively regulates cell cycle via SC1 ATF4 activates genes in response to ER stress −8 Chemokine receptors bind chemokines TNFSF members mediating non-canonical NF-kB pathway Hydrolysis of LPE RAF-independent MAPK1 3 activation −10 3 18 24 30 3 6 12 18 24 24 30 6 6 18 24 24 30 6 24 6 24 6 6 12 18 24 24 30 6 6 12 18 24 24 30 h ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▼ ▼ ▼ ▼ ▼ ▼ ▲ ▲ ▼ ▼ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▼ ▼ ▼ ▼ ▼ ▼ ▼

RNA-Seq Proteome Ubiquitinome Phosphoproteome Figure 3

bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint a (which 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 available under aCC-BY 4.0 International license. 1 Network Diffusion Model 2 functional connections 3 SARS-CoV-2 infection

CoV-2 Transcriptome SARS-CoV-2 protein A549 cells Proteome protein + protein Ubiquitinome interactions effects Phosphoproteome

4 AFFECTED PATHWAYS

global cellular interactor regulated protein linking network up down protein b c d TAX1BP1 IL13RA1 TGFB2 SERPINB1 SQSTM1 WWP2 IL10RB TGFB1 GABARAP MAP1LC3B FGA PROS1 NFX1 ASAH1 ALDH1A1 HLA-A PROCR NBR1 PFN1 PLAU CALCOCO2 MAP1LC3B2 SERPINE1 PDE8A NFKB2 IL6ST CTSS FGB GABARAPL2 ATG12 PPIA FKBP1A VPS35 S100A10 CD44 UNC93B1 VPS29 RAB7A PPP2R2D ORF3 ENO3 ORF7b CTSB VPS26A ITGA3 RAB2A MAVS LGALS3 ORF8 HSP90B1 VPS45 YWHAB RAB2B BAX PARP16 VPS39 MFN2 TNFRSF10D VCAN VPS18 STX2 UBE2G2 UQCRH UBE2V2 PPP5C PTPRF VPS16 VPS11 VPS41 e f + SARS-CoV-2 ORF3-HA SARS-CoV-2 infection SARS-CoV-2 proteins + SARS-CoV ORF3a-HA

Ubi(K) site unknown known site log2 fold change interaction MAP1LC3B Phospho(S/T) site site regulatory site -2 -1 0 1 2 3 4 5 up-/downregulated HA

PKM AKTS1 AMPK ORF3 AKT S477 ACTB S202 S203 MLST8 RPTOR RHEB TSC2 S1341 g P70S6K MTOR T1162 mock SARS-CoV-2 DEPTOR RAB7A mTORC1 GSK3 h.p.i.30241812633024181263 VPS S236 VPS VPS 39 SQSTM1 S240 RPS6 LARP1 S766 41 VPS 11 K143 ATG13 FIP200 18 MAP1LC3B K149 ULK1 Late VPS HOPS S479 endosome Autophagosome VPS 16 complex ACTB S638 33A cargo sequestration Phagophore SNAREs T525 NSP6 ORF3 NBR1 h SQSTM1 i SQSTM1 OPTN 2.5e5 0 ATG9A TAX1BP1 ATG3 CALCOCO2 S18 ATG7 fusion 2.0e5 −5 ORF3 S735 −10 S738 ATG4 SQSTM1 1.5e5 Ubi Ubi Intensity −15 K157 T269 Autophago- mock −dCt [log2] mock ATG16L GABARAPL2 MAP1LC3B K281 S272 lysosome SARS-CoV-2 −20 SARS-CoV-2 K82 ATG12 K435 S355 Lysosome 3 6 12 18 24 30 3 6 12 18 24 30 K97 K74 ATG5 S361 h.p.i. h.p.i. S366 K24 K46 Figure 4 a bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455b 2 ; this version posted June 17, 2020. The copyright holder for this preprint 5 (which 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 vehicle available under aCC-BYSorafenib 4.0 International license. 4 IFNα 10U Regorafenib 3 IFNα 50U IFNα 250U Dabrafenib 2 1 Baricitinib 1 SB239063

virus growth (norm. GFP x 1000) 0 0 8 16 24 32 40 48 t [h] 0

vehicle ** ** c Prinomastat 0.5uM ** Prinomastat 2µM Prinomastat 10µM -1 Rapamycin Ipatasertib Tirapazamine virus growth (Δ norm. GFP; log2) Ribavirin Gilteritinib Acyclovir 5 -2 IFNγ Marimastat Prinomastat IFNα

virus growth (norm. GFP x 1000) -3 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0 0 8 16 24 32 40 48 t [h] cell viability (Δ confluency; log2) d e ** vehicle vehicle ** ** **

Ipatasertib 0.5µM Gilteritinib 0.5µM ** Ipatasertib 1µM Gilteritinib 1µM Gilteritinib 5µM

5 5 virus growth (norm. GFP x 1000) virus growth (norm. GFP x 1000)

0 0 0 8 16 24 32 40 48 t [h] 0 8 16 24 32 40 48 t [h] bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

Extended data Table 1: functionnal annotation of the protein-protein interactions network of SARS-CoV-2 and SARS-CoV (AP/MS) Gene identified as SARS-CoV-2 and/or SARS-CoV protein host binders via AP/MS (Figure 1b) were assemble in functional groups base on enrichment analysis of GOBP, GPCC, GPMF, Reactome terms (Supplementary table 2) annotation_label annotation_category annotation_genes Cell adhesion and motility cellular_process AMIGO2 CDH16 CDH17 CLDN12 DSC3 EPCAM FAT1 LRFN4 NECTIN2 NECTIN3 PCDH9 PCDHA12 PCDHA4 PCDHAC2 PCDHGC3 PTPRF PTPRS PVR NRP2 PLXNA1 PLXND1 SEMA4B SEMA4C Endolysosomal trafficking cellular_process RAB13 RAB14 RAB1A RAB21 RAB2A RAB31 RAB32 RAB34 RAB3D RAB5A RAB5B RAB7A RAB8A RAB9A ER quality control cellular_process CANX ERLEC1 FBXO6 OS9 UGGT1 UGGT2 ER stress cellular_process HSPA1A HSPA2 HSPA6 HSPA8 HSPA9 HSPH1 G3BP1 G3BP2 CAPRIN1 ER to cytosol trafficking cellular_process FAF2 NPLOC4 UFD1 ER-Golgi protein trafficking cellular_process AREG KDELR1 LMAN1 LMAN2 PIEZO1 TMED2 TMED7 TMED9 TMEM199 ARFIP1 SCAMP1 SCAMP2 SCAMP3 SCAMP4 CUX1 GOLIM4 Glycolysis cellular_process L2HGDH OGDH PDHX PDPR Glycolysis cellular_process ACO2 FH MDH1 GPI anchor cellular_process GPAA1 PIGS PIGU Ion transport by ATPases cellular_process ATP11C ATP12A ATP13A1 ATP13A3 ATP2A3 ATP2B4 ATP6AP1 ATP6V0A2 ATP6V1B1 ATP7B ATP8B1 ATP8B2 Lipid oxidation cellular_process ACAD10 ACADS ACSF2 PCCA PCCB ECI1 mRNA processing cellular_process HNRNPM MYEF2 DICER1 TARBP2 MBNL1 Nuclear import/export cellular_process IPO8 TNPO1 TNPO2 XPO5 XPO6 XPO7 XPOT Oxidoreduction cellular_process ALDH2 ALDH5A1 Glycosylation cellular_process B4GALT7 POMGNT1 ALG11 ALG13 ALG14 B3GALT6 B3GAT3 EXT1 EXTL2 EXTL3 GLCE XXYLT1 DAD1 TMEM258 GALNT1 GALNT10 GALNT12 ALG5 ALG8 FUT8 LMAN1 OSTC STT3A Glycosylation cellular_process FUCA2 GANAB GBA GUSB Palmitoylation cellular_process SELENOK ZDHHC20 SPTLC2 ZDHHC13 ZDHHC18 ZDHHC21 ZDHHC3 ZDHHC6 ZDHHC9 GOLGA7 ZDHHC5 Transcription elongation cellular_process GTF2F2 SETD2 tRNA charging cellular_process IARS2 NARS2 PPA2 SARS2 TARS2 HARS2 tRNA splicing cellular_process FAM98A RTCB RTRAF Ubiquitin-like ligase activity cellular_process MGRN1 RNF130 RNF149 RNF19A STUB1 WWP1 WWP2 ZNRF3 HUWE1 MDM2 TRIM47 ATP synthase complex_compartiment ATP5F1B ATP5F1D ATP5F1E ATP5PB ATP5PD MT-ATP6 ATP5PF COG complex complex_compartiment COG1 COG2 COG3 COG4 COG5 COG6 COG7 COG8 Condensin II complex complex_compartiment NCAPD3 NCAPH2 NCAPG2 ECM regulators and metalloproteases complex_compartiment ADAM17 ADAM9 CLTRN CNDP2 CPD ECE1 MMP15 RNPEP ADAM10 ADAM15 Endocytosis via AP-2 complex complex_compartiment AP2A1 AP2M1 AP2S1 EPN2 ER membrane protein complex complex_compartiment EMC10 EMC2 EMC3 EMC4 EMC8 Golgi membrane complex_compartiment B4GAT1 CSGALNACT1 ENTPD4 QSOX1 QSOX2 SAMD8 STEAP2 TVP23C HOPS complex complex_compartiment HOOK3 VPS11 VPS16 VPS18 VPS39 VPS41 Integrator complex complex_compartiment INTS1 INTS12 INTS2 INTS4 INTS5 INTS8 Integrins complex_compartiment ITGA3 ITGB4 ITGB5 MHC-I complex complex_compartiment B2M HLA-A HLA-C HLA-E HLA-G HFE Mitochondrial metalloproteases complex_compartiment NLN PITRM1 PMPCA PMPCB Mitochondrial respiratory chain complex_compartiment NDUFA10 NDUFS2 NDUFS8 Nuclear inner membrane complex_compartiment DPY19L2 DPY19L3 DPY19L4 LEMD3 PSEN2 ZMPSTE24 Nuclear pore complex_compartiment NUP188 NUP205 NUP93 Peroxisome complex_compartiment GNPAT MAVS MGST1 PEX10 PEX13 PEX2 Proteasome core complex_compartiment PSMA4 PSMA5 Proteasome regulatory proteins complex_compartiment PSMC2 PSMC4 PSMC5 PSMD11 PSMD12 PSMD4 PSME3 Sarcoglycan complex complex_compartiment SGCB SGCD SGCE Septin complex complex_compartiment SEPTIN10 SEPTIN11 SEPTIN2 SEPTIN7 SEPTIN8 SEPTIN9 SNARE complex complex_compartiment BET1 GOSR1 GOSR2 NAPA NAPG SNAP25 STX10 STX12 STX16 STX2 STX4 STX5 STX6 STX7 VAMP2 VAMP3 VAMP4 VAMP7 VTI1A Solute carriers complex_compartiment SLC12A4 SLC12A6 SLC12A7 SLC15A4 SLC16A4 SLC16A6 SLC18B1 SLC19A2 SLC20A1 SLC22A5 SLC23A2 SLC25A2 SLC25A52 SLC12A9 SLC26A2 SLC29A3 SLC25A24 SLC2A6 SLC29A4 SLC30A1 SLC35D2 SLC35F5 SLC30A5 SLC33A1 SLC35A1 SLC35A2 SLC23A1 SLC30A7 SLC35F2 SLC35F6 SLC39A1 SLC39A14 SLC6A6 SLC7A6 SLC35B4 SLC37A4 SLC38A2 SLC45A1 SLC46A1 SLC47A2 SLC4A10 SLC4A2 SLC4A4 SLC6A15 SLC9A1 Cytokine receptors signaling signaling CD44 IFNGR1 IL10RB IL13RA1 IL6ST OSMR JAK1 ACVR1 ACVR1B ACVR2A BAMBI BMPR1A BMPR2 FKBP1A TGFBR1 TGFBR2 EIF2A FKBP1A SHC1 EphrinB-EPHB pathway signaling EPHB2 EPHB3 ErbB receptor signaling signaling ERBB2 ERBB3 NRG1 GPCRs signaling signaling GPR39 OPN3 S1PR3 S1PR5 GNA13 Inflammatory response signaling AHR AXL CD70 DCBLD2 IFITM1 LDLR LPAR1 SELENOS TNFSF15 TNFSF9 TPBG NEDD4-ITCH complex signaling NDFIP1 NDFIP2 ITCH Notch signaling signaling NOTCH1 NOTCH2 NOTCH3 p53 signaling signaling FAS TP53 BNIP3L EPHA2 FAS STEAP3 MET NDRG1 MDM2 IGF2R BAG3 Receptor tyrosine phosphatases signaling PTPMT1 PTPN11 PTPRA PTPRF PTPRJ PTPRM PTPRS TGFb and integrins signaling signaling FGA FGB PROS1 SERPINE1 TGFB1 TGFB2 LTBP1 TGFBI IGFBP3 IGFBP4 SERPINE1 PLAU TNF receptors superfamilly signaling TNFRSF10A TNFRSF10B TNFRSF10D TNFRSF1A Extended data Figure 1

bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint (which 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 a available under aCC-BY 4.0 International license. CoV-2 E-HA CoV-2 CoV E-HA ORF6-HA CoV-2 CoV ORF6-HA ORF7a-HA CoV-2 CoV ORF7a-HA ORF7b-HA CoV-2 CoV ORF7b-HA NT HCV NS4B-HA Gaussia-luc-HA ZIKV Capsid-HA M-HA CoV-2 CoV M-HA N-HA CoV-2 CoV N-HA ORF3-HA CoV-2 CoV ORF3a-HA CoV ORF3b-HA HCoV ORF3-HA HCoV ORF4-HA HCoV ORF4a-HA NSP2-HA CoV-2 CoV NSP2-HA NSP1-HA CoV-2 CoV NSP1-HA NSP3(MD)-HA CoV-2 CoV NSP3(MD)-HA NSP4-HA CoV-2 CoV NSP4-HA NSP6-HA CoV-2 CoV NSP6-HA NSP7-HA CoV-2 CoV NSP7-HA NSP8-HA CoV-2 CoV NSP8-HA NSP9-HA CoV-2 CoV NSP9-HA NSP10-HA CoV-2 CoV NSP10-HA NSP12-HA CoV-2 CoV NSP12-HA NSP13-HA CoV-2 CoV NSP13-HA NSP14-HA CoV-2 CoV NSP14-HA NSP15-HA CoV-2 CoV NSP15-HA NSP16-HA CoV-2 CoV NSP16-HA ORF8-HA CoV-2 CoV ORF8-HA CoV ORF8a-HA CoV ORF8b-HA ORF9b-HA CoV-2 CoV ORF9b-HA S-HA CoV-2 CoV S-HA

140 - 80 -

50 -

30 - HA 25 - 15 -

10 -

42 - ACTB

NUP205 Viral proteins (baits) NSF HAUS6 NUP93 SMPD4 RTN4 KIAA0355 ELF3 SGSM3 MCM3AP b NUP188 TTI1 NCAPG2 SARS-CoV-2 SARS-CoV ATP13A1 RBFOX3 MDN1 FADS2 P3H4 NDUFA12 ATM NCAPH2 TOR1AIP2 GUF1 STARD3NL HOOK2 FANCD2 COQ8B UNC50 NCAPD3 HCoV-229E HCoV-NL63 TUT1 STK11IP FANCI ATP7B MT-CO2 PFKP FAM20B SLC37A4 IRAK1 NF1 MED24 RANBP6 PRKDC GPR37 NR5A2 NDUFS8NDUFA10 MROH1 CDK5RAP3 SERPINE2 ELMO2 USP34 GBF1 RNF145 RUFY2 MAIP1 SLC18B1 RIF1 DYNC2H1 INTS1 NDUFS2 DOCK5 TTC27 NR2C2 CALU Host targets (prey) ORF7a INTS12 IPO13 PHB TMEM168 AK4 MACO1 NUBP2 MTFR1L SLC22A5 ECI1 TRIM7 PHB2 IPO11 INTS8 INTS2 TMTC4 STOML2 BTAF1 UBE3C RAP1GDS1 TBC1D8 NLRX1 POLD3 NFE2L2 SCAI INTS4 SLC45A1 ATL2 SLC46A1 PYCR3 SLC16A4 DNAJB2 HAX1 ERGIC2 OAT INTS5 TNFAIP2 RNF213 FASTKD1 ORF7a ARFGEF2 NSP2 LTN1 ARFGEF1 ORC4 Regulated upon bait overexpression JAK2 ATP8B2 C2CD2ATP6V0A2 ACADSB METTL7B NSP2 SPG11 WDR72 C6orf120 NUBPL

MIPEP SDF4 WDFY3 PRPF39 Regulated upon SARS-CoV-2 infection HARS2 RBFOX2 PEX6 FANCG SLC29A3 ORF6 MCFD2 ATP2A3 Ubiqutine-regulated upon SARS-CoV-2 infection TIMM10B BLM PC ABCC10 M NSP10 THADA XPO5 PLEKHG4 L2HGDH ALDH1A2 SLC19A2 HACD2 Phospho-regulated upon SARS-CoV-2 infection DOLK SARM1 MFSD3 AGMAT ORF6 NSP10 XPOT DERL3 DRAM2 COG3 EVA1C VMP1 RHBDD3 FAM8A1 POMT1 Interactions (AP/MS) YIPF4 BRI3BP ATP13A3 ATP12A SLC25A2 GFM1 COG1 HSD17B8 COG2 ODR4 IARS2 SARS-CoV-2-specific ABCB8 ACADS PDS5B LPCAT3 EMC8 CERS2 YIF1B COG4 DENND11VPS13A FPGS EMC10 TARS2 SARS-CoV-specific VPS53 PDS5A COG5 COG7 TNPO1 SLC9A1 LMBRD2 SARS2 HPS6 EMC3 DGLUCY Shared STAG2 EMC2 COPG2 ALDH5A1 PDPR NPC1 COG6 MALL TM4SF1 COG8 DHCR24 CAV2 CAV1 PMPCB HDHD5 HM13 UPK1B HIBADH APOB ABHD10 NUDT19 PIGS ENDOD1 MTHFD1L TBC1D20 DYM TYW1 known host complexes SOAT1 BRAT1 EMC4 ACSF2 SLC39A1 GPAA1 PYROXD2 ALDH2 ECHDC3 PCCA PCCB NLN SLC35F2 ATRIP UBIAD1 PIGU CRYZ DNAAF5 ATG9A CAV2 ACAD10 TMEM245 ATXN10 XPO7 M NCDN ATR HMGCR GNAL PDHX CAV1 SLC35B4 PMPCA DGAT1 DOCK1 NARS2 OGDH SPCS1 SCFD2 SLC35F6 SLC6A15 ANKIB1 TMEM41A PITRM1 HSPE1 FBXO46 TACO1 SIRT3 PTDSS1 TNPO3 CASC4 IL31RA EVI5 XPO4 BZW2 PPP4R2 EXOC8 CIP2A EBP SIGMAR1 PPA2 RAD17 NBAS CHPT1 ALG6 COQ6 SLC23A1 WFS1 PALM EXOC6B PTDSS2 UNC13D BZW1 UBE4A SAAL1 ORF8b NSP3 PPP2R1B ZW10 SLC7A6 NME3 IPO8 RINT1 SURF4 MD SLC39A14 NSP6 FSCN1 NSP3 PPP2R5A GLMN NSP6 UHRF1BP1L TBRG4 SLC30A7 ORF8a MD EPG5 C19orf25 GEMIN6 RMND1 FBXO2 TTYH3 MSMO1 PDLIM1 CTSB GUSB NBEAL2 GEMIN8 TNPO2 ATP5PF ATP5F1B TGFBI VDAC2 LTBP1 NSP14 ATP5F1D CXCL5 ZNF250 REEP6 ATP5PD NSP16 CNTNAP3B CANX MUC13 TGFB2 ZNF148 ATP5F1E FGA ATP6AP1 MT-ATP6 ATP5PB MAN2A1 ST8SIA4 EGFL7 IGFBP3 NSP1 FGB NSP14 NSP16 PEG10 ADPGK ITGA3 IGFBP4 NTS TGFB1 CANT1 HLA-DMB SELENOF

REEP5 TIMELESS UGGT1 SDF2 NSP1 VEZT NDUFB5 C5 PLAU TRIM59 AP2S1 CLINT1 TLCD4 ORF8 ADAM10 AP2M1 PTGES ORF4a UGGT2 TWSG1 SERPINE1 MFSD5 XPO6 PDIA3 OS9 FUCA2 RETREG2 DPAGT1 FDFT1 AP2A1 VTI1B LMO7 TMEM135 NPTX1 MARK2 COTL1 PROS1 TMEM97 CYB5D2 MT-ND4 GNS ERLEC1 RETREG3 FBXO6 CLU TLCD1 DIPK1A EPN1 TCEAL4 CLASP2 SEC16A SLC25A52 FKBP9 PSEN2 EPN2 GCNT3 TOR3A PON3 C11orf24 APH1A HSPA1L NDUFB11 CLGN CLASP1 LSG1 VKORC1L1 RBM33 ORF8 UGT1A9 PON2 TRIR DTWD2 OCIAD2 SLC33A1 CSGALNACT1 SLC35A2 PISD GANAB EXTL2 EFHD2 PKN1 WDR83OS JAGN1 LSM14AARHGEF2 CHD8 RABL3 PEX2 PAQR5 EXT2 ACAT1 BICC1 CAMSAP2 TMPPE SCAMP3 DPY19L2 PLIN2 AGPAT3 PIGO FUT8 TMX1 DAGLB GPR89A CUX1 PPP1R12B UPK3B ADAM15 TM9SF3 S1PR5 FXR1 CYP1B1 BSCL2 TMEM159 GBA NEMF ORF9b SYMPK TOR1B ITGB5 TAPT1 GGT7 GPX8 FITM2 GGH LIMCH1 ORAI1 MAN1B1 MTX1 SCAMP4 DOCK6 DPY19L4 SLC30A5 ZDHHC21 ADAM9 SCAP RFT1 SPIRE1 FKRP SGCB TOR1A TMEM248 IMMP1L RAB32 PTPMT1 TMEM254 GNPAT ORF9b SELENOS PCDHA4 PIGT NSP15 MOB4 RHBDD2 MFSD10 TSKU LGR4 GALNT1 SGCD TMED10 TMEM221 ENTPD6 MARC1TOMM70 VSIR TMUB1 CNNM3 TMED2 ARSE TAMM41 TUSC3 DAD1 CYP4F8 BNIP3L RBPMS CEP170B NSP15 MTFP1 DHRS7 ACP2 ADGRG6 PRKCSH BMPR1B CACNG6 STT3A CYB5A TMEM164 NSP4 STEAP3 TCTN3 ANGPT4 ABCB10 TMEM70 GTF2F2 SYPL1 STEAP1B HFE TMED9 ANXA4 SMCHD1 CHST14 HSPA5 PSAT1 FKBP8 LPAR1 G3BP2 RDH14 SSBP1 OPA3 ANO6 SCAMP1 B3GAT3 TMED7 CCDC115 HSPA1A TMEM258 HTATIP2 TVP23C NSP4 CAVIN2 NSP9 TMEM177 NCEH1 ERBB3 SQLE LETMD1 NEK9 C1QL1 ALG13 B4GALT7 SLC2A6 ECE1 CLDN1 GPR39 LDAH TMEM199NT5C3A HSPA8 ELOVL6 HMOX2 PCNP N STEAP2 IGF2R HSPA2 GALNT10 CAVIN1 TRPM4 SETD2 MGST1 ERMP1 TMEM223 GORASP2 SEPTIN8 SLC35A1 ELOVL7 STX5 STRA6 SEC61G GLCE NSP9 PLXND1 GOSR2 LRRC8D FAM234A G3BP1 HSPA9 PFDN2 HIGD2A QSOX1 SEPTIN11 ADGRG1 TM9SF4 SLC47A2METTL16 GOLIM4 SEPTIN2 CNTNAP3 LEMD3 MARCKS NIPA1 LRRC8A ERBB2 CLDN12 CAPRIN1 MTHFD1 STAT6 NBR1 SEC23IP TM9SF2 LRP12 SLC26A2 ITGB4 SEPTIN7 PLD3 SMIM1TMEM256 COMT ALG11 ORF7b GOLGB1 SEPTIN9 AREG EXOG PEX10 NRG1 USO1 SPTLC2 TMEM106C GABARAPL2 CSDE1 CCNT1 EXT1 PCDHAC2 HDGF MEAK7 PROCR BAG4 S1PR3 UNC93B1GOLM1LMAN2L CD70 DPY19L3 DEGS1 BSCL2 SEMA4C RAB9A PLIN3 TPBG ABHD16A STRAP CYBRD1 SYNGR3 PNKD PVR TPD52 FAM50A ORF7b CD38 USP9X TM9SF1 FAM20CCELSR2 SEMA4B LRFN1 EFNB1 MET EIF2S3 PRXL2A ZFPL1 MTCH1 MMP15 RELL1 GJA1 ABHD5 HLA-C CD99 RAB13 GSK3A N HSPA6 SPNS1 ARFIP1 GNA13 RAB34 CLIP1 SEPTIN10 RAD23B ZDHHC3 VPS45 PASK CEMIP2 SUGT1 SRPRA C16orf58 ALG5 UBE2G2TMEM192 BCAM PTPRA TNFSF15 PRAF2 PMEPA1 NACA FLRT3 NEO1 CRIM1 SLC4A2 B2M HSPH1 OSTC GALNT12 MPZL1 SLC30A1 EPHA2 DYNC1LI1 HMGN1 TSPAN14 LMAN1 CKAP4ZMPSTE24 PTPN11 ITGA6 CTTN RTRAF C4orf3 FXYD2 SPINT2 TNIP1 SPAG9 PDE4DIP CCDC9 ST7 STX2 PTPRJ RTCB MAVS B4GAT1 LMAN2 TMEM156 SHC1 SAMD8 TNFRSF1A MAPRE1 EML4 NDFIP2 ACVR1 IL6ST B3GALT6 PTPRM HMGA1 ZDHHC6 FAM98A SLC38A2 MUL1 PACC1 SCARB1 TRO ZDHHC18 NAPG ATP11C TAX1BP1 SHTN1 KIF5C XXYLT1 GPD2 TMEM87A TMED4 VPS26A SNX2 LTBR ORF4 JAK1 MAP4 STX16 DDR1 NDFIP1 PGAP2 ENTPD4 CTNNA1 TNFRSF21 APP CALD1 POMGNT1 LAPTM4A STX6 LDLR GAL3ST1 FKBP3 VASP SEC31A TIMM17B USP8 SNX1 QSOX2 SORT1 VTI1A HOOK3 VAMP4 MYRF TGOLN2 ALG14 STX12 SNX17 ACVR1B LCLAT1 HLA-G PNPLA2 CTNNA2 BET1 VAMP3 SCFD1 LPCAT4 ALG8 STX10 TNFRSF10B LRP10 BAG3 NAPA RAB7A VPS8 VPS41 STUB1 TRAFD1 PTPRF CCAR2 PEX13 KLHDC2 SNAP25 IFNGR1 HLA-A CD97 TNFRSF10D MICOS13 STAM2 C1orf159 GOSR1 SLC16A6 DCBLD2 MLF2 HAVCR1 FAF2 STX7 VPS16 IFITM1 VAMP2 VPS18 TMEM30B ZDHHC13 STX4 RNF19A BAG2 SCCPDH APOOL RNF149 TRIM9 TMEM68 MFSD8 ORF3b NOVA1 TMEM214 ORF3 TNFRSF10A LRRC47 FAM241B SLC6A6 VPS11 VAMP7 TNFSF9 IL13RA1 ZBTB20 GINM1 VPS39 SEC61B CYP2S1 RBM4B DNAJA2 ITPRIPL2 TCIRG1 ORF3 SNX27 GLO1 UBL3 E CALML5 SLC12A7 BAG5 MGRN1 RPL39 SCARA5 FAS FAM49B RAB5A EEA1 WBP2 RHBDF2 BAG6 GET4 NPLOC4 RNF130 PCDH9 CD44 ORF3a BMPR2 FAT1 SGPP1 GPAT4 SLC15A4 HINT1 BAMBI HNRNPM ZNRF3 WWP2 PRPF40A OPN3 CLCC1 NOTCH3 FGFR1 MBNL1 UFD1 BCAP31 CMIP SLC4A10 ACO1 LRRN2 TTF1 PTRH2 SGCE OSMR BROX MDM2 TP53 MAFG E SAYSD1 PTGR1 CD81 TMF1 NOTCH2 ITM2C EXTL3 CTDNEP1 CA12 CS NOP53 MSN TOM1L1 LSR TARBP2 TKFC ADAM17 DDX39B TIMM17A PSME3 SLC35F5 HLA-A BLVRA C15orf61 SLC23A2 DICER1 STIP1 DIS3 KCT2 ADCY9 BMPR1A TGFBR1 NDC1 SCAMP2 FURIN DLG1 OBI1 IL10RB ATP8B1 PSMD4 MAGED1 CAAP1 HLA-E SMC6 PSMD11 KDELR1 NECTIN2 NETO2 TGFBR2 MDH1 NRSN2 FKBP1A TBCE ELOVL5 GOLGA5 LRBA ZDHHC9 AMIGO2 PSMC4 PSMC5 CNEP1R1 AKR1B1 RAB1A SLC12A6 MOSPD2 AHR PSMC2 SELENOK MFAP3L GGT1 EPHB3 CDH17 RHOB KIAA0232 MICA ADGRA3 TBCB PLP2 PSMD12 RAB5B DSC3 HUWE1 RAB21 PMM2 LGALS3 ITCH RAB8A ANXA5 PTPA NEDD1 PTPRS PLXNA1 TAF4 ZDHHC5 WWP1 EZR KIAA0754 PRPF40B ADIPOR1 RAB14 NRP2 TRAPPC5 SS18 CNDP2 RNPEP KYNU RBM5 FKBP4 TREX1 EEF1A2 NOTCH1 CLTRN COPS4 S NECTIN3 NECTIN3 EPCAM BICRA SELENBP1 GOLGA7 TMEM11 CPD HLA-C PI4K2A RAB31 MYEF2 PHPT1 KIAA0930 RHEB EPHB2 DNAJB1 MRC2 LSS ATP6V1B1 UCHL1 CD99L2 NSP7 NSP13 PLPP6 TAZ PXMP4UBQLN1 HMGCS2 IMPA2 ACO2 AHCYL1 SLK DHCR7 CDH16 BMERB1 ITM2B SLC20A1 HSPA13 LRP8 S MRPL1 RABGAP1L AGO3 PRRC1 CBR1 PCDHA12 PCDHGC3 ANXA6 PIP4P2 NSP13 PSMA5 PSMA4 GCLC NDRG1 SLC25A24 NPEPPS APOL2 MRPL28 LRP11 KIAA0319 ADCY6 SLC29A4 AXL STXBP3 PRPS2 ROR1 B4GALT1 FADS3 SUMF2 EIF2A PGM1 RAB34 SEPHS1 ZDHHC20 NSP7 RB1CC1 DCAKD SLC12A9 GPI RNF8 SLC35D2 DTNB SMS WDR83 HSD17B12 MGLL ACVR2A TRIM16 NCOA4 LRFN4 SFXN5 ATP2B4 GNAQ TSN PPIC DDT CYP51A1 TRIM47 IDH1 CLPTM1L CAP1 HPCAL1 PPA1 CALCOCO1 CLCN7 HNRNPDL TMEM237 LMCD1 HSP90AB4P YKT6 FOCAD SULT1A3 SRXN1 FH SLC12A4 HERC1 TBL2 MYOF NAA10 DLGAP4 MTPN LRIG3 AKR1A1 NSP8 NSP12 CALCOCO2 AKR7A2 NSP12 RAB2A RAB3D SLC4A4 UGCG DTYMK NSP8 ATE1 ExtendedbioRxiv preprint data doi: https://doi.org/10.1101/2020.06.17.156455 Figure 1 ; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license. c d e f

CoV-2 N-HACoV N-HACoV-2 ORF6-HA CoV-2 CoVORF7b-HA ORF7b-HACoV-2CoV-2 ORF6-HA ORF7a-HA CoV-2 ORF7b-HACoV ORF7b-HA CoV-2 CoVORF8-HA ORF8-HACoV ORF8a-HACoV ORF8b-HACoV-2 ORF9b-HA 70 - 70 - HSPA1A co-IP MAVS co-IP + + SII-HA-UNC93B1 50 - + + SII-HA-RSAD2 40 - 15 - N-HA TGFB co-IP 15 - IP 30 - HA IP HA co-IP 55 - 25 - ORF6-HA SII-HA-UNC93B1 100 - IP 25 - 70 - SII-HA-RSAD2 70 - HSPA1A MAVS 25 - 15 - HA IP N-HA 15 - 15 - HA-ORF7b 55 - input HA input SII-HA-UNC93B1 input ORF6-HA 50 - 100 - 40 - SII-HA-RSAD2 TGFB 25 - 42 - ACTB 30 - 42 - ACTB 42 - ACTB input 25 - HA 15 -

42 - ACTB

12 12 g SARS_CoV_NSP2 h SARS_CoV_ORF8 C5 STOML2 ITGB4 ARSE ERLEC1 ADAM10 ACAT1 PRKCSH OS9 ITGA3 8 ECE1 NPTX1 UGT1A9 F ADAM9 SP 2 GBA IGF2R R

N MET ENTPD6 ADAM15 O FBXO6 8 FAM234A FUCA2 CLGN V _ 8 GUSB TWSG1 EXT2 BMPR1B PROS1 V _ o GNS GGH UGGT2 CSGALNACT1 o HFE DIPK1A

C DSC3 C LRP11 HLA-DMB FGB TSKU S _ PCDHA4 IGFBP4 S _ ITGB5 R CYB5D2 FKBP9... TOR3A CNNM3

SA R TOR1B

SA TMX1 ) SELENOF ) e PHB PON3

4 ng e an g

PHB2 a 4 PON2 h

-c h PDIA3

-c CANX d l d UGGT1 l

o CLU o ( f f

( SDF2 TGFBI 2 TGFB2 g ITGA6 o GANAB l

og 2 SARS_CoV2_ORF8

l NME3 RAP1GDS1 NTS CXCL5 IGFBP3 0 TCTN3 SARS_CoV2_NSP2 ST8SIA4 FGA LTBP1 MAN2A1 0 CNTNAP3B EGFL7 TGFB1

SERPINE1

0 4 8 12 0.0 2.5 5.0 7.5 10.0 12.5 log2(fold-change) SARS_CoV2_NSP2 log2(fold-change) SARS_CoV2_ORF8 ExtendedbioRxiv preprint data doi: https://doi.org/10.1101/2020.06.17.156455 Figure 2 ; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0SARS-CoV-2 Internationalmock license. N IFNB1 Mx1 IFIT3 a c d 10 0 0 0

wt ACE2-HA 25 NFKBIA PROZ -5 -5 -5 ) 0 HA 2 CCL2 ACTB CDKN2A CXCL2 20 -10 -10 -10 -10 −ΔCt (log EGR1 -15 -15 -15 b A549−ACE2 ATF3 A549 15 -20 NDND -20 -20 -20 p-value )

2 5 10 3 6 12 18 24 30 3 6 12 18 24 30 3 6 12 18 24 30 3 6 12 18 24 30 t (h) 10

-log SMARCA4 0 TGM2 IL6 CXCL2 JUN JUNB 0 0 0 0 5 HMGN5 - ΔCt (N) (log

-5 ) 2 -5 -5 -5 -5 0 -10 -10 -10 -10 -10

mock 0.001 0.01 MOI 0 2 4 −ΔCt (log

log2 fold change -15 -15 -15 -15

-20 NDND -20 -20 -20 e 3 6 12 18 24 30 3 6 12 18 24 30 3 6 12 18 24 30 3 6 12 18 24 30 t (h) 6 h ▼ 0 12 h ▼ 18 h ▼ 24 h ▼ 30 h ▼ −5 3 h ▲ p-value

6 h ▲ 10 12 h ▲ 18 h ▲ −10 -log 24 h ▲ 30 h ▲ TEAD4NR3C1TFAP2ARELANFICJUNDMEF2AGATA2PAX5TCF3BHLHE40RESTSTAT3FOSMAZETS1CEBPDBCL11AJUNGATA3STAT1MYOGTCF12BACH1MYOD1ZNF274ZNF384TBPZBTB7AFOXM1MEF2CATF3NFE2SRFFOSL1HNF4AESR1EGR1IKZF1RXRAMAFKUSF2CTCFRFX5CEBPBFOSL2FOXA2PBX3USF1NR2F2TCF7L2PRDM1E2F1TFAP2CMAXGTF2BARID3AIRF1MXI1ZNF143

ORF9b f N g h i ACE2 7.5 ORF7a S 1e+09 NSP7 M mock mock SARS-CoV-2 ACE2 7e+08 SARS-CoV-2 0 ORF3 30241812633024181263 h.p.i. ACE2 Intensity 5e+08 mock 5.0 * SARS-CoV-2 NSP1 −5

6 h.p.i. 24 ) 2 FN1 p-value CHEK1 SARS-CoV-2 10 SERPINE1 4.4 −10 625 POLR2B 4.2 * -log FC

NSP6 2

2.5 IRAK1 TGM2 4.0 − ∆ Ct ( log * −15 log 3.8 702 ITGA3 3.6 CASP2 ACE2-HA 3.4 CAV1 ACE2 ACTB −20 0.0 3 6 12 18 24 30 t (h) -5 0 5 10

log2 fold change

10 TRIM28 j k l 4 S681 1269 15 MAPK1 8 SMAD1 41 417 825 Y187 835 1028 S206 TRIM28S465 6 795 MAPK1 FC 535 1266 SMAD1 2 304 444 790 933 10 3 S473 T185 97 206 921 947 1038 T198 S463

310 FC log 4 786 1255 2 195 386 529 776 S197 MAPK8

150 1086 log S79 S194 T183 2 S2 S202 5 p-value TRIM28 S176 S201 10 2 MAP2K2 S21 0 YAP1 S23 T541 -log S S23 S61 MAPK8 0 Y185 N 1 375 AKT3 4.5 8 162 205 8 347 CSNK2 CSNK2 AKT CSNK2 16 TRIM47 TRIM7 169 361 S472/S476 4.0 6 6 102 248 DNA-PK ERK1/2 CAMK2 GPCR FC JUN FC FC 355 2 3.5 2 4 180 2 4 233 3.0 21 2 2 388 GSK3 ERK1/2 MK2 S243 log log log 0 2.5 0 0 CDK5 PKA ORF3 M N -5 0 5 10 log fold change m 2 3 h ▼ 0 6 h ▼ 6 h ▼ 12 h ▼ −1 18 h ▼ 24 h ▼ 24 h ▼ −2 30 h ▼ 3 h ▲ p-value 6 h ▲ −3 10

6 h ▲ -log 12 h ▲ 18 h ▲ −4 24 h ▲ 24 h ▲ 30 h ▲ −5 MKK7CDK19CRIKPKMCSNK2A2Src TAK1DNAPKCDK14Ret MAPKAPK5FynMEK1PKCBAurA iso2BMPR1BATRCK1APKCEP38APKCDAKT1RSK2p90RSKCHUKERK3Chk2Chk1P38DERK2MAPKAPK2PRKG1ABL1MAP2K2MAPK3PKG1MAP2K1 iso2P70S6KBJNK2CAMK2GPKCBCDK6PRKCIEIF2AK2CDK16AURKBPDPK1STK38LPLK2STK3CSNK1EMTORCK2A1CAMKK2HIPK2TTKBRSK1NDR1 AMPKA1iso2Akt2IKKB Extended data Figure 3 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.17.156455; this version posted June 17, 2020. The copyright holder for this preprint BRD4 (which was not certified by peer review) is the author/funder,ECHS1 who has granted bioRxiv a license to display the preprint in perpetuity. It is made Target of Viral Protein MORC2 FH NDUFB11 ARHGAP35 NCK1 PEX10 a EFNB1 b PIP4P1 PEX13 NDUFA3 PEX2 available under aCC-BY 4.0 International license. CALML5 CTPS1 MET Regulated Protein EPHA2 PTPN11 GAMT NDUFV2 PTPRJ S1PR3 FBXL2 TNFRSF10D SPTLC2 AHNAK2 Intermediate Link MYOF DPYSL4 DDX39B UQCRH TECR MUC5AC PLXND1 DPYSL5 DDX6 DIS3 CTNNA2 ASAH1 14k CTNNA1 IL6ST FXYD2 ZCCHC8 SYMPK NES PTK7 DCP1A IGF2BP3 PVR CELSR2 IL13RA1 U2SURP ARIH1 ALAD TGOLN2 CDH1 ReactomeFI LDLR ELF3 NOTCH3 SMG8

PTPRM PCDHAC2 PKM AGO3 Interaction RIOK3 NIF3L1 12k ERBB2 VAMP4 TRIP10 EIF4G1 NOTCH2 METTL14 MYL6B COL28A1 NFX1 PFKM GFPT2 RPS15 Network AP1M1 PRR11 VAMP3 STX4 SRPRA Diffusion TLN2 SEC61G FASN POLR3C TSR2 RAB5A ITGA6 Flow PDE8A 10k MFGE8 PTPRF CTIF PPT1 LGR4 SEC61B NFKB2 VAMP7 STK11IP CD44

NAPB VAMP8 VCAN ASNS PRKDC SNAP25 AREG TXNDC5 FAM20C FAS APOB STX2 GNA13 CA12 ADCY9 PGAM5 8k SLC35B2 KANSL3 SELENOS NAPG GOSR2 HLA-A STX7 SORT1 PARP16 SCARB1 PSEN2 STX10 NAPA NUCB1 KXD1 LPAR1 RIC8B STX12 DERL2 B2M STX6 ABCC3 ANPEP UBE2G2 SEC23B SEC24B STRA6 STX16 SCAP ABHD5 SLC4A2 TNFRSF1A ZMPSTE24 SGCD GPR39 FUNDC2 TCIRG1 SCFD1 6k VTI1A GLB1 PNPLA2 SLC26A2 NBR1 UBE2S VPS45 ACVR1 SGCB STX5 SEC24C IL10RB GJA1 PGM5 TMED9 LAMP2 ATP6V1G1 NTHL1 BARD1 GOSR1 IGF2R TMX1 SQSTM1 SGCE TMED10 GABARAPL2 CTSA HUS1 ATP6AP1 CTSS STARD3 TIMELESS DPP7 LMAN1 PLIN2 MAVS BNIP3L CUX1 TMED2 KLF16 4k CTSC CTSB COG8 CTSD TOR1B MLH1 CD70 ATM BET1 RAB1A BCAP31 MANBA MVB12A UBTF HMGN5 Path Length Average permuted GOLGA5 LGALS3 BAX GOLGB1 ERCC3 MFN2 UNC93B1 NCEH1 YIPF1 BCHE TMED7 GGH FLOT1 DCAF1 NR4A1 CTSH TGIF1 GOLIM4 CAPZA1 JAGN1 STUB1 FBXO30 KDELR1 2k TARDBP ZNRF2 BMPR1A TVP23B TUSC3 RAB7A CKAP4 FEM1A real GALNT1 NRCAM RNF19A BCAM FEM1C HDAC6 STK25 TBC1D10A HIST2H2AB DDI2 LTBR RMND5A HEXA CETN3 HEXB HIST1H2AH RAB13 0.001 0.002 0.003 DUSP3 MAEA SCARA5 PTRH2 SH3GLB2 NPC2 CNP PLK1 FTL ORAI1 Minimal Edge Weight PMEPA1 ITCH FOXA2 NDFIP2 GORASP2 AKAP9 WWP2 LAPTM4A NDFIP1 c d SARS-CoV-2 infection SARS-CoV-2 proteins Type I Ubi(K) site known site log fold change interaction 2 IFN ISRE GAS Phospho(S/T) site regulatory site -3 -2 -1 0 1 2 3 4 up-/downregulated 120% NSP1 IL6 SARS-CoV-2 100% IL6 IFN NSP13

IL6R IFN IL6ST ENDOSOME IFNAR ORF3 MDA5 RIG-I ORF3 ORF7b JAK1/2 ORF7b 50% M JAK2 ORF6 JAK1 TLRs MAVS S222 UNC93B1 STAT1/3 M SARS-CoV-2 ORF7a % of stimulated control MYD88 IRAK1 K446 STAT1 STAT2 0% TRAF6 MITOCHONDRION ITCH ORF7b STAT3 S727 ORF9b ORF3 TNF IKBKG TANK IFN IRF9 TBK1 TRAF2 IL ZIKV-NS5 IRF3 TBK1 ORF7b CXCL K27 K165 NFKB control: SMN1 IRF3 IRF3 control: SMN1 CYTOPLASM unstimulated STAT1 STAT2

IL6 ISGs IFN-α TNF IFIT1 IFN-β NFKB CXCL2 IRF3 IRF9 MX1 NUCLEUS

e f SARS-CoV-2 mock EGFR S991 EPHA2 S897 AKAP12 S286 SARS-CoV-2 infection SARS-CoV-2 proteins 1e6 3e6 1e5 3e6 Ubi(K) site known site log2 fold change interaction 1e4 1e6 1e6 Phospho(S/T/Y) site regulatory site -4 -2 0 2 4 6 8 10 up-/downregulated 1e3 Intensity 1e2 3e5 3e5 ECM S991 ECM EGFR EPHA2 AKAP12 TGFB TGFB S995 1e4 SERPINE1 LAP ORF3 1e5 3e3 TGFBR EGFR K708 ITGA3 1e3 5e3 RTKs 5e4 Intensity TGFB TGFB ITGA/ITGB 3e2 3e3 LTBP1 BAMBI 3e4 RAS ORF8 S1225 3024181263 3024181263 3024181263 S864 PLCG1 TLN1 PTK2 h.p.i. h.p.i. h.p.i. PI3K S910 S1248 PXN VCL RAF K802 GOLGI ORF3 S126 g mock SARS-CoV-2 AKT S130 TRAF6 S137 30241812633024181263 h.p.i. LAP S477 S23 p38-T180/Y182 MAP2K2 p38 TGFB TGFB FURIN MAP2K1 LTBP1 JNK-T183/Y185 T180 T202 Y182 T207 JNK MAPK3 YAP MAPK14 MAPK8 ACTB S463 SMAD1 T183 MAPK1 S61 S138 S465 SMAD5 NFKB Y185 T185 S127 S340 S463 Y187 S131 K321 S465 h SARS-CoV-2 mock JUN CYTOPLASM COL7A1 MMP24 ITGA3 SERPINE1 FN1 FOS 1e8 FGG AMOTL2 1e8 FGB EGR1 5e7 1e7 YAP LATS2 3e7 1e7 FN1 NFKB AP-1 DUSPs 1e6 SMAD TEAD COL7A1 NUCLEUS 1e6 SERPINE1 MMPs 1e5

TEAD1 Intensity PTGS2 1e7 1e5 1e4 ITGA3 IER2 6 h.p.i. 24 6 h.p.i. 24 6 h.p.i 24 ExtendedbioRxiv preprint data doi: https://doi.org/10.1101/2020.06.17.156455 Figure 4 ; this version posted June 17, 2020. The copyright holder for this preprint (which 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 available under aCC-BY 4.0 International license.

a virus growth virus growth cell growth rate; uninfected (6h pre-treatment) (treatment at time of infection)

Z-VAD-FMK 5uM IFNb 250U Chloroquine 10uM water MG-132 1µM 2 Autophagy 14 Rapamycin 1µM 1 12 Tirapazamine 2µM DNA damage 0 Rabusertib 1µM 10 -1 Prinomastat 2µM MMPs 8 Marimastat 2µM -2 Gilteritinib 0.5µM 6 -3 cell growth rate (%) Ribavirin 100µM 4 -4 Ipatasertib 5µM -5 Erastin 1µM 2 virus growth (Δ norm. GFP; log2) -6 Ferrostatin-1 50µM Bosutinib 1µM Neratinib 0.5µM SB239063 10µM Baricitinib 1µM Dabrafenib 1µM B-RAF Regorafenib 5µM Sorafenib 10µM DMSO

time after infection 8h 12h 16h 20h 24h 28h 32h 36h 40h 44h 48h 8h 12h 16h 20h 24h 28h 32h 36h 40h 44h 48h 8h 12h 16h 20h 24h 28h 32h 36h 40h 44h 48h significance significance mock 0h