Multi-level proteomics reveals host-perturbation …...2020/06/17 · Extended data Figure 1 a b CoV-2 E-HA CoV E-HA CoV-2 ORF6-HA HA CoV ORF6-HA 80 - 140 - 50 - 30 - 25 - 15 - 10

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

.CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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

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https://doi.org/10.1101/2020.06.17.156455

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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



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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



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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



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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



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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



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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.



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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).



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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.



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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).



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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



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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.



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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



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(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).

6. Lu, R. et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. The Lancet 395, 565–574 (2020).

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.



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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).

17. Mo, X. et al. Abnormal pulmonary function in COVID-19 patients at time of hospital discharge. European Respiratory Journal (2020) doi:10.1183/13993003.01217-2020.

18. Zavadil, J. et al. Genetic programs of epithelial cell plasticity directed by transforming growth factor-β. Proc Natl Acad Sci U S A 98, 6686–6691 (2001).

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).

20. Thao, T. T. N. et al. Rapid reconstruction of SARS-CoV-2 using a synthetic genomics platform. Nature 1–8 (2020) doi:10.1038/s41586-020-2294-9.

21. Mantlo, E., Bukreyeva, N., Maruyama, J., Paessler, S. & Huang, C. Antiviral activities of type I interferons to SARS-CoV-2 infection. Antiviral Res 179, 104811 (2020).

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



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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



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mass spectrometry analysis was performed as described previously22,23. Briefly, purified peptides were loaded onto a 20�cm reverse-phase analytical column (75�µm diameter; ReproSil-Pur C18-AQ 1.9�µm 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 65�min, 30% to 95% for 10�min, wash out at 95% for 5�min, readjustment to 5% in 10�min) at a flow rate of 300 nl�per 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 (150-2,000�m/z, R�=�60,000 at 200�m/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



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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



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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



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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.



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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



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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:



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log!��" ~ #!�$�� %��: �$��"��

, 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!��" ~ #!�$�� %��: �$��"��

, 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 =



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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



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(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

&'median�log� fold-change�' · 'log�� 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



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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:

/�� 0 12�� · �2� � 1� � # /� ��

3 �2�� · 2��4 , 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-



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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 1a

c

d

b293 853 169

Viral-host interactions identified

SARS-CoV-2specific

SARS-CoVspecific

Shared

ORF7a CoV ▼

ORF8a CoV ▲

N CoV2 ▲NSP6 CoV ▼

ORF3 CoV2 ▼

NSP4 CoV ▲

ORF4 HCoV ▼

NSP13 CoV ▲

E CoV2 ▲M CoV2 ▲

NSP6 CoV2 ▼

ORF7a CoV2 ▼

ORF7b CoV2 ▼

NSP12 CoV ▼

ORF3 HCoV ▼

ORF3 CoV2 ▲

NSP13 CoV2 ▲

ORF3b CoV ▼

ORF9b CoV2 ▼

ORF9b CoV ▼

NSP6 CoV2 ▲

N CoV2 ▼

xylulose biosynthetic processDNA unwinding involved in DNA replication

DNA replication initiationnuclear-transcribed mRNA catabolic process

nucleosome assemblynegative regulation of proteasomal

ubiquitin-dependent protein catabolic processpolyketide metabolic process

protein K29-linked ubiquitinationcholesterol biosynthetic process

tricuspid valve morphogenesisactomyosin contractile ring organization

keratan sulfate catabolic processcellular response to thyroid hormone stimulus

early endosome to Golgi transportmitochondrial ATP synthesis coupled electron transport

mitochondrial translational termination

−10

−8

−6

−4

−2

0

log 1

0(P-v

alue

)

ORF7b

ORF7a

NSP1

ATM

RHEB

TRIM7

ORF8b

M

NSP7

ITGA6

GSK3A

CTNNA2

NSP6

FURIN

CAV1

ORF3a

IRAK1

NSP10

NSP16

ORF8

NSP12

UNC93B1

STAT6

ATR

TREX1

CAV2

PHB2

NSP9

N

TAX1BP1

CTNNA1

NSP14

NSP13

ORF9b

E

S

PHB

NSP2

SARM1

ORF8a

TOMM70

ORF3b

ORF3

NSP8

GABARAPL2

NSP15

NSP3MD

TNFAIP2

NSP4

JAK2

Solute carriers

Ions transportby ATPases

Peroxisome

MAVS

SNARE complex

GPCRssignaling

ER stressHSPA1A

G3BP2G3BP1

MHC-Icomplex

Notchsignaling

Endolysosomaltrafficking

RAB2A

HOPScomplex

VPS41

VPS39 Proteasome regulatory proteins

Proteasomecore

Ubiquitin-likeligase activity

TRIM47

Mitochondrialmetalloproteases

tRNAcharging

ECM regulators andmetalloproteases

ATP synthase

ER membrane complex

Endocytosisvia AP-2 complex

GPI anchor

Integratorcomplex

Septincomplex

Glycolysis

Glycolysis

Nuclearimport/export

tRNAsplicing

Mitochondrialrespiratory chain

CondensinII complex

Glycosylation

Glycosylation

Lipidoxidation

Palmitoylation

mRNAprocessing

Oxidoreduction

Nuclear pore

Nuclearmembrane

Transcriptionelongation

ER qualitycontrol

Inflammatoryresponse

Cytokine receptorssignaling

JAK1

TGFBR2TGFBR1

BAMBI

TGFb and integrinssignaling

TGFB2TGFB1

LTBP1

Integrins

TNF receptorsuperfamilly

p53signaling

TP53

EPHA2

Receptor tyrosinephosphatases

COG complex

Golgimembrane

ER to cytosoltrafficking

Sarcoglycancomplex

ErbB receptorsignaling

EphrinB-EPHBpathway

NEDD4-ITCHcomplex

ITCH

Cell adhesionand motility

ER-Golgitrafficking

Viral proteins (baits)

Interactions (AP/MS)

Host targets (prey)Combined SARS-CoV-2 / -CoV

SARS-CoV

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known host complexes

SARS-CoV-specificSARS-CoV-2-specific

SARS-CoV-2

CoVprotein

A549 cells0 +10-10

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Proteomeeffects

6288

7839

Lentiviral transductionQuadruplicates

LC-MS/MS580 runs

CoronavirusesORFs library cloning

Proteinsidentified

SARSCoV

SARSCoV-2

HCoV

24

3

27



https://doi.org/10.1101/2020.06.17.156455


Figure 2a b

cx105

identified regulated

Transcriptome

Proteome

Ubiquitinome

Phosphoproteome

13472 509 genes

7462 1053 proteins

4659 884 sites

11847 1483 sites

time (h)3 6 12 18 24 30

0

5

10

15

20

Nor

mal

ized

MS

Inte

nsity N / NSP8 / S

NNSP8S

mock

MOI 3

SARS-CoV-2A549 cells

e

POLR2BK934RPL24

K97

CAV1K39

CAV1K47

CAV1K8

TRAF2K165

TRAF2K27

SQSTM1K157

SQSTM1K281

CREBBPK1014

ACE2K625

ACE2K702

0

2

4

6

log2 fold change

-log 10

p-v

alue

-5 0 5 10

d

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▼

12▼

18▼

24▼

24▼

30▼

RNA-Seq Proteome Ubiquitinome Phosphoproteome

h

-log 10

p-v

alue

RAF-independent MAPK1 3 activationHydrolysis of LPE

TNFSF members mediatingnon-canonical NF-kB pathway

Chemokine receptors bind chemokinesATF4 activates genes in response to ER stress

p75NTR negatively regulates cell cycle via SC1Loss of MECP2 binding ability to 5mC-DNA

Telomere C-strand synthesis initiationUnwinding of DNA

GRB2:SOS provides linkage toMAPK signaling for Integrins

HDL remodelingResolution of Sister Chromatid Cohesion

pre-mRNA splicingmTORC1-mediated signalling

NS1 Mediated Effects on Host PathwaysRHO GTPases activate PAKs

Eukaryotic Translation InitiationApoptotic execution phase

XAV939 inhibits tankyrase, stabilizing AXINPackaging Of Telomere Ends

trans-Golgi Network Vesicle Budding

−10

−8

−6

−4

−2

0

6▼

transcripts16 7 14 67 102316

2 17 18 33 39 101

20 21 26 13 31 37

14 35 50 111216 217

1989

13113

3 6 12 18 24 30

3 18 41108 144 1115 26 58 158 220 172

215 27 47 70 622 15

30 61 98 90

72

490113

750

7836187

505

6 24

112

420126

581

244 191253 214

proteins

phosphosites

phosphoproteins

ubi sites

ubi proteins

h

h

24 h

-5

0

5

10

K321 S340

S367

S138

S131

S61

S289S127

log 2 F

C

30 h6 h

YAP1

phosphositesubi sites

updo

wn

updo

wn

updo

wn

updo

wn

no. of regulated.CC-BY 4.0 International licenseavailable under 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


https://doi.org/10.1101/2020.06.17.156455


Figure 3

db c

fe

HA

MAP1LC3B

ACTB

SARS-CoV ORF3a-HA+SARS-CoV-2 ORF3-HA+

MAP1LC3B

h.p.i.30241812633024181263

SARS-CoV-2mock

SQSTM1

ACTB

i

g

−20

−15

−10−5

0

3 6 1218 24 30

−dC

t [lo

g2]

mockSARS-CoV-2

SQSTM1

h.p.i.

a

CoV-2protein

protein interactions

protein effects

+Ubiquitinome

Phosphoproteome

Proteome

SARS-CoV-2

A549 cells

Network Diffusion Model1 functional connections2 SARS-CoV-2 infection3

AFFECTED PATHWAYS4

Lysosome

Autophagosome

Autophago-lysosome

fusion

Phagophore

UbiUbi

cargo sequestration

VPS41 VPS

18

VPS11

VPS39

VPS16VPS

33A

HOPS complex

mTORC1

-2 -1 0 1 2 3 4 5

S479S638

T269S272S355S361S366

K157K281K435 K82

K97K24

K74K46

S1341

S477S202S203

S18S735S738

S766

PKM AKTS1 AMPK

GABARAPL2

ATG4

ATG7ATG3

SQSTM1

NBR1

ATG16LATG12

ATG5

ATG9A

ULK1FIP200ATG13

LARP1

P70S6K

RPTORMLST8MTOR

DEPTOR

RHEB TSC2

AKT

GSK3

SNAREs

RAB7A

ORF3

RPS6

Late endosome

MAP1LC3B

T1162

S236S240

ORF3

CALCOCO2

OPTN

TAX1BP1

ORF3

NSP6T525

log2 fold changePhospho(S/T) site regulatory site

known siteUbi(K) site unknownsite up-/downregulated

interaction

SARS-CoV-2 infection SARS-CoV-2 proteins

K143K149

hSQSTM1

1.5e5

2.0e5

2.5e5

Inte

nsity

mockSARS-CoV-2

3 6 12 18 24 30h.p.i.

interactor regulated proteinup down

linkingprotein

global cellularnetwork

Transcriptome

ORF3

ORF8

ORF7b ENO3

FKBP1A

HSP90B1

PFN1

PPIA

PPP5C

S100A10

UBE2V2

YWHAB

PPP2R2D

ALDH1A1

PTPRF

VCAN

PROCR

SERPINB1

FGA

FGB

ITGA3

PLAU

PROS1

SERPINE1

TGFB1

TGFB2

ATG12

CALCOCO2

GABARAP

GABARAPL2

MAP1LC3B

MAP1LC3B2NBR1

RAB2B

RAB7A

SQSTM1

STX2

TAX1BP1

VPS18

VPS26A

VPS29

VPS35

VPS39

RAB2A

VPS11VPS16 VPS41

VPS45 BAX

CTSB

CTSS

MFN2

NFKB2PDE8A

UQCRH

ASAH1NFX1

PARP16

CD44

HLA-A

IL10RBIL13RA1

IL6ST

LGALS3MAVS

TNFRSF10D

UBE2G2

UNC93B1

WWP2



https://doi.org/10.1101/2020.06.17.156455


Figure 4b

IFNγ

IFNα

Acyclovir Tirapazamine

Sorafenib

SB239063

Ribavirin

Regorafenib

Rapamycin

Prinomastat Marimastat

Ipatasertib Gilteritinib

Dabrafenib

Baricitinib

-0.2 -0.15 -0.1 -0.05 0 0.05 0.1-3

-2

-1

0

1

2

cell viability (Δ confluency; log2)

viru

s gr

owth

(Δ n

orm

. GFP

; log

2)

0

5

0 8 16 24 32 40 48 t [h]

d

a

0 8 16 24 32 40 480

1

2

3

4

5

IFNα 50UIFNα 250U

IFNα 10Uvehicle

t [h]

c

0 8 16 24 32 40 48 t [h]0

5

Prinomastat 10µMPrinomastat 2µMPrinomastat 0.5uMvehicle ** ** **

0 8 16 24 32 40 48 t [h]0

5

viru

s gr

owth

(nor

m. G

FP x

100

0)

e

Gilteritinib 5µMGilteritinib 1µMGilteritinib 0.5µMvehicle ** ** **

**

Ipatasertib 1µMIpatasertib 0.5µM

**

vehicle

viru

s gr

owth

(nor

m. G

FP x

100

0)vi

rus

grow

th (n

orm

. GFP

x 1

000)

viru

s gr

owth

(nor

m. G

FP x

100

0)



https://doi.org/10.1101/2020.06.17.156455


annotation_label annotation_category annotation_genesCell 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 SEMA4CEndolysosomal trafficking cellular_process RAB13 RAB14 RAB1A RAB21 RAB2A RAB31 RAB32 RAB34 RAB3D RAB5A RAB5B RAB7A RAB8A RAB9AER quality control cellular_process CANX ERLEC1 FBXO6 OS9 UGGT1 UGGT2ER stress cellular_process HSPA1A HSPA2 HSPA6 HSPA8 HSPA9 HSPH1 G3BP1 G3BP2 CAPRIN1ER to cytosol trafficking cellular_process FAF2 NPLOC4 UFD1ER-Golgi protein trafficking cellular_process AREG KDELR1 LMAN1 LMAN2 PIEZO1 TMED2 TMED7 TMED9 TMEM199 ARFIP1 SCAMP1 SCAMP2 SCAMP3 SCAMP4 CUX1 GOLIM4Glycolysis cellular_process L2HGDH OGDH PDHX PDPRGlycolysis cellular_process ACO2 FH MDH1GPI anchor cellular_process GPAA1 PIGS PIGUIon transport by ATPases cellular_process ATP11C ATP12A ATP13A1 ATP13A3 ATP2A3 ATP2B4 ATP6AP1 ATP6V0A2 ATP6V1B1 ATP7B ATP8B1 ATP8B2Lipid oxidation cellular_process ACAD10 ACADS ACSF2 PCCA PCCB ECI1mRNA processing cellular_process HNRNPM MYEF2 DICER1 TARBP2 MBNL1Nuclear import/export cellular_process IPO8 TNPO1 TNPO2 XPO5 XPO6 XPO7 XPOTOxidoreduction cellular_process ALDH2 ALDH5A1Glycosylation cellular_process B4GALT7 POMGNT1 ALG11 ALG13 ALG14 B3GALT6 B3GAT3 EXT1 EXTL2 EXTL3 GLCE XXYLT1 DAD1 TMEM258 GALNT1 GALNT10 GALNT12 ALG5 ALG8 FUT8 LMAN1 OSTC STT3AGlycosylation cellular_process FUCA2 GANAB GBA GUSBPalmitoylation cellular_process SELENOK ZDHHC20 SPTLC2 ZDHHC13 ZDHHC18 ZDHHC21 ZDHHC3 ZDHHC6 ZDHHC9 GOLGA7 ZDHHC5Transcription elongation cellular_process GTF2F2 SETD2tRNA charging cellular_process IARS2 NARS2 PPA2 SARS2 TARS2 HARS2tRNA splicing cellular_process FAM98A RTCB RTRAFUbiquitin-like ligase activity cellular_process MGRN1 RNF130 RNF149 RNF19A STUB1 WWP1 WWP2 ZNRF3 HUWE1 MDM2 TRIM47ATP synthase complex_compartiment ATP5F1B ATP5F1D ATP5F1E ATP5PB ATP5PD MT-ATP6 ATP5PFCOG complex complex_compartiment COG1 COG2 COG3 COG4 COG5 COG6 COG7 COG8Condensin II complex complex_compartiment NCAPD3 NCAPH2 NCAPG2ECM regulators and metalloproteases complex_compartiment ADAM17 ADAM9 CLTRN CNDP2 CPD ECE1 MMP15 RNPEP ADAM10 ADAM15Endocytosis via AP-2 complex complex_compartiment AP2A1 AP2M1 AP2S1 EPN2ER membrane protein complex complex_compartiment EMC10 EMC2 EMC3 EMC4 EMC8Golgi membrane complex_compartiment B4GAT1 CSGALNACT1 ENTPD4 QSOX1 QSOX2 SAMD8 STEAP2 TVP23CHOPS complex complex_compartiment HOOK3 VPS11 VPS16 VPS18 VPS39 VPS41Integrator complex complex_compartiment INTS1 INTS12 INTS2 INTS4 INTS5 INTS8Integrins complex_compartiment ITGA3 ITGB4 ITGB5MHC-I complex complex_compartiment B2M HLA-A HLA-C HLA-E HLA-G HFEMitochondrial metalloproteases complex_compartiment NLN PITRM1 PMPCA PMPCBMitochondrial respiratory chain complex_compartiment NDUFA10 NDUFS2 NDUFS8Nuclear inner membrane complex_compartiment DPY19L2 DPY19L3 DPY19L4 LEMD3 PSEN2 ZMPSTE24Nuclear pore complex_compartiment NUP188 NUP205 NUP93Peroxisome complex_compartiment GNPAT MAVS MGST1 PEX10 PEX13 PEX2Proteasome core complex_compartiment PSMA4 PSMA5Proteasome regulatory proteins complex_compartiment PSMC2 PSMC4 PSMC5 PSMD11 PSMD12 PSMD4 PSME3Sarcoglycan complex complex_compartiment SGCB SGCD SGCESeptin 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 VTI1ASolute 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 SHC1EphrinB-EPHB pathway signaling EPHB2 EPHB3ErbB receptor signaling signaling ERBB2 ERBB3 NRG1GPCRs signaling signaling GPR39 OPN3 S1PR3 S1PR5 GNA13 Inflammatory response signaling AHR AXL CD70 DCBLD2 IFITM1 LDLR LPAR1 SELENOS TNFSF15 TNFSF9 TPBGNEDD4-ITCH complex signaling NDFIP1 NDFIP2 ITCHNotch signaling signaling NOTCH1 NOTCH2 NOTCH3p53 signaling signaling FAS TP53 BNIP3L EPHA2 FAS STEAP3 MET NDRG1 MDM2 IGF2R BAG3Receptor tyrosine phosphatases signaling PTPMT1 PTPN11 PTPRA PTPRF PTPRJ PTPRM PTPRSTGFb and integrins signaling signaling FGA FGB PROS1 SERPINE1 TGFB1 TGFB2 LTBP1 TGFBI IGFBP3 IGFBP4 SERPINE1 PLAUTNF receptors superfamilly signaling TNFRSF10A TNFRSF10B TNFRSF10D TNFRSF1A

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)



https://doi.org/10.1101/2020.06.17.156455


Extended data Figure 1a

b

CoV

-2 O

RF6

-HA

CoV

E-H

AC

oV-2

E-H

A

HA

CoV

OR

F6-H

A

80 -140 -

50 -

30 -25 -

15 -

10 -

CoV

-2 O

RF7

b-H

AC

oV O

RF7

a-H

AC

oV-2

OR

F7a-

HA

CoV

OR

F7b-

HA

Gau

ssia

-luc-

HA

HC

V N

S4B-

HA

NT

ZIKV

Cap

sid-

HA

CoV

-2 N

-HA

CoV

M-H

AC

oV-2

M-H

A

CoV

N-H

A

CoV

OR

F3b-

HA

CoV

OR

F3a-

HA

CoV

-2 O

RF3

-HA

HC

oV O

RF3

-HA

CoV

-2 N

SP2-

HA

HC

oV O

RF4

a-H

AH

CoV

OR

F4-H

A

CoV

NSP

2-H

A

CoV

-2 N

SP3(

MD

)-HA

CoV

NSP

1-H

AC

oV-2

NSP

1-H

A

CoV

NSP

3(M

D)-H

A

CoV

-2 N

SP6-

HA

CoV

NSP

4-H

AC

oV-2

NSP

4-H

A

CoV

NSP

6-H

A

CoV

-2 N

SP8-

HA

CoV

NSP

7-H

AC

oV-2

NSP

7-H

A

CoV

-2 N

SP10

-HA

CoV

NSP

9-H

AC

oV-2

NSP

9-H

A

CoV

NSP

10-H

A

CoV

-2 N

SP13

-HA

CoV

NSP

12-H

AC

oV-2

NSP

12-H

A

CoV

NSP

13-H

A

CoV

NSP

14-H

AC

oV-2

NSP

14-H

A

CoV

-2 N

SP16

-HA

CoV

NSP

15-H

AC

oV-2

NSP

15-H

A

CoV

NSP

16-H

A

CoV

OR

F8a-

HA

CoV

OR

F8-H

AC

oV-2

OR

F8-H

A

CoV

OR

F8b-

HA

CoV

-2 S

-HA

CoV

OR

F9b-

HA

CoV

-2 O

RF9

b-H

A

CoV

S-H

A

CoV

NSP

8-H

A

42 - ACTB

Interactions (AP/MS)

Shared

known host complexes

SARS-CoV-specificSARS-CoV-2-specific

Viral proteins (baits)

HCoV-229E

SARS-CoVSARS-CoV-2

HCoV-NL63

Host targets (prey)

Ubiqutine-regulated upon SARS-CoV-2 infection

Regulated upon SARS-CoV-2 infection

Phospho-regulated upon SARS-CoV-2 infection

Regulated upon bait overexpression

ORF7b

ORF7a

NSP1

NSP1

NF1

IPO11

PPP1R12B

SS18

FKBP9

GPR89A

TRIM47

ADCY6

ABCC10

SLK

B4GALT1

ATM

ATP8B1

RNF8

PCCA

IGFBP3

GOLM1

RAP1GDS1

TMUB1

SEC31A

SEPTIN10SRPRA

CCNT1

DENND11

TMEM237

ABCB8

RHEB

ACVR2A

UBIAD1

PROCR

TGOLN2

STOML2

NSF

CD99L2

ADGRA3

PLEKHG4

RFT1

SEPTIN9

TRIM7

ORF8b

LDLR

NRG1

VPS13A

SLC4A2

ADAM15

KLHDC2

SEPTIN2

SLC4A10

AP2A1

BMPR1A

TPD52

MPZL1

LSS

FKBP8

CLU

M

PMEPA1

SHC1

HPCAL1

SLC29A3

RNF19A

SLC35D2

TCEAL4

FAS

ATP2B4

ATP7B

SPNS1 GNA13

ACSF2

CAMSAP2

KIAA0355

PRPF40B

TGFB2

TMEM254

PTPA

MARC1

MOB4

VAMP4

TNIP1

MYEF2

USP9X

B4GALT7

SELENOK

HOOK2

TM9SF3

FOCAD

XPO6

PGAP2

ZDHHC3 SUGT1

LSM14A

LRP12

APP

NCAPH2

SELENOF

COG5

NSP7

HNRNPDL

C19orf25

MAGED1

ACVR1B

CAVIN2

NACA

ALDH5A1

STX2

TVP23C

HSP90AB4P

ATP2A3

ITGA6

RHBDD3

RNF145

TP53

CYB5A

TM9SF1

NCDN

DTNB

FGA

TGFB1

STRAP

SYMPK

CLTRN

ACADS

NCAPG2

MICOS13

CYBRD1

SEPHS1

BMPR2

VPS18

LCLAT1

MGLL

ACO2

EVI5

ABCB10

REEP6 ZNF250

ANXA6

GSK3A

TMEM248

HMOX2

AP2M1

SCARB1

ITGB4

SEMA4C

NDUFB5

RINT1

DPAGT1

PSME3

EPHB2

MCFD2

DPY19L3

EVA1C

MET

NEMF

TBC1D20

TGFBI

APOL2

CSGALNACT1PKN1

EMC10 FPGS

BCAP31

MYOF

NRSN2

UBQLN1

CTNNA2

TMTC4

FAM49B

TSKU

TMED7

MTFR1L

NFE2L2

NOVA1

ATP12A

NSP6

HFE

METTL16

SLC39A14

DLG1

ADIPOR1

VAMP3

BSCL2

VPS41

ATP13A3

NR2C2

ERBB2

MBNL1

PRRC1

CIP2A

GINM1

LRIG3

SLC4A4

IMPA2

FLRT3

USP34

IMMP1L

ZDHHC5

TNFRSF10B

ERGIC2

EPN2

DDT

PPA2

SPINT2

MTCH1

TAZ

CLIP1

NEO1

FURIN

SLC37A4

LMAN1

DICER1

USO1GOLGB1

CAV1

CNEP1R1

CAPRIN1ADGRG1

L2HGDHTHADA

RUFY2

DAD1

SHTN1

PDS5B

PXMP4

LTBR

ATG9A

TNPO3

AGMAT

PCDH9

RIF1

ENTPD4

SLC38A2

CCAR2

PVR

ORF3a

KIAA0930

TTF1

ZMPSTE24

MGRN1

LMO7

DGLUCY

RAB3D

DNAJB1

MICA

LMCD1

SNX27

GALNT12

VPS26A

UGCG

SLC16A4

AGPAT3

NDFIP2

NCOA4

IRAK1

EPHA2

MARCKS

SEPTIN8

VDAC2

HDGF

TNPO1

SGSM3

SCAMP1

ATE1

SORT1

NEK9

MEAK7

SAMD8

SLC35B4

NEDD1

MTX1

STAM2

IGFBP4

HMGCR

TMEM106C

PTDSS2

UGGT2

PLAU

DSC3

PPIC

BNIP3L

TTYH3

LAPTM4A

RBFOX2

CRIM1

PPP2R1B

HSPA9

LRP10

GPI

GPX8

PLP2

HSPA1A

ORC4

DYNC2H1

ATL2

CYP51A1

ITM2C

ADPGK

ZNRF3

ATP11C

STIP1

GNAQ

AP2S1

HSPE1

SLC29A4

MALL

GUSB

NSP10

HSPA6

UPK3B

PDHX

PCDHAC2

ATP5F1E

MDN1

EIF2A

EIF2S3

PI4K2A

NUP93

GNPAT

GALNT10

UNC13D

PIGU

PLPP6

TMF1

C2CD2

MDH1

ATP5F1B

DLGAP4

SARS2

NETO2

NSP16

CD97

TWSG1

MTPN

ORF8

CYP4F8

TBL2

IFNGR1

POMT1

CSDE1

TUSC3

ENTPD6

MDM2

JAK1

NAA10

EPG5

VTI1A

UBE3C

CASC4

ODR4

LDAH

NDC1

MED24

SERPINE1

MRPL1

LPAR1

BICRA

TOM1L1

ALG5

MIPEP

ANO6

PTPRF

DHRS7

TRO

PASK

PIGO

LRP11

VEZT

CDH16

PRXL2A

NSP12

ORF4a

CCDC115

NDUFS2

UNC93B1

CA12

CD38

QSOX1

ZDHHC20

CYP2S1

SGCE

ATP8B2

TM9SF4

EZR

GFM1

STEAP3

CERS2

MGST1

PSEN2

TMEM135

PALM

MOSPD2

ALG11

COPG2

NBEAL2

EXOC6B

RAB21

EMC2

PEX2

PTPRA

SEPTIN7

POLD3

MAPRE1

TNFRSF21

EFHD2

ATXN10

PRPF39

CNDP2

GTF2F2

FBXO2

TSN

GGH

LRRC8D

BAG2

C5

TMEM70

RAB34

NDUFB11

ADAM17

SERPINE2

BTAF1

RAB14

PPP4R2

COG3

ORAI1

RTCB

HIGD2A

AKR1B1

NPTX1

SNAP25

UFD1

TRIM9

TGFBR2

MTHFD1

MACO1

MAP4

TGFBR1

DRAM2

ADGRG6

FASTKD1

TCTN3

ZDHHC18

COQ8B

MAN2A1

DEGS1

PCNP

S1PR3

GEMIN6

PRKCSH

SETD2

UNC50

METTL7B

SLC6A15

CALU

FDFT1

HSD17B12 LRFN4

SCAMP2

PSMC5

PLIN3

MT-ATP6

TNFRSF1A

RAB32

TBRG4

BMERB1

YIPF4

HLA-DMB

TMEM87A

EXOC8

CLASP1

GOLGA7

ARSE

CTTN

PCDHGC3

IARS2

ELOVL6

RELL1

YKT6

SLC16A6

GALNT1

TMEM156

BAG5

LPCAT3

TRIM59

RAD17

SLC39A1

KIAA0319

SPG11

PDS5A

PSMD11

PNKD

SELENBP1

VTI1B

FADS2

MAN1B1

INTS5

PSMA4

GBA

DYM

SCAMP4

MTHFD1L

TMED10

PMM2

ACP2

XPO4

UBL3

TOR3A

GLMN

ATP13A1

AHR

STAT6

TTI1

C1QL1

ANGPT4

ATR

CNTNAP3B

AXL

SLC12A4

AKR7A2

PNPLA2

ANKIB1

RMND1

PIGT

DDX39BCS

P3H4

ITCH

TREX1

SPIRE1

SLC45A1

LRP8

RBM4B

ELOVL5

BCAMUBE2G2

NTS

FH

TSPAN14

CAV2

RB1CC1

EXTL2

IL6ST

CD99

IL13RA1

UPK1B

SMCHD1

ARHGEF2

HACD2

HOOK3

OPN3

ERBB3

AMIGO2

TARS2

ACAT1

RNF213

OPA3

RNF149

ADAM10

TKFC

CCDC9

PHB2

DCAKDPGM1

MFSD3

SGCD

PEG10

NSP9

TMED4

ACVR1

TRIM16

N

NPLOC4

PTGR1

EMC4

TAX1BP1

ZDHHC21

TOR1A

TPBG

FADS3

AKR1A1

ZDHHC6

FKRP

CANX

APOOL

FSCN1

STX10

SLC2A6

CTNNA1

B2M

PRPS2

STT3A

NSP14

EBP

ECE1

ERLEC1

INTS2

RABL3

HSPA2

NSP13

FANCI

SCAMP3

CELSR2

NBAS

VPS53

IGF2R

SIGMAR1

BZW1

PFKP

GCNT3

ORF9b

DTYMK

PPA1

SLC46A1

SLC22A5

NUBPL

STX4

GOLGA5

USP8

TMEM41A

E

NDFIP1

DYNC1LI1

UBE4A

RAB5B

HIBADH

VMP1

LMAN2

NIPA1CNTNAP3

SELENOS

TMEM30B

MTFP1

CHD8

ERMP1

DIS3

VASP

APH1A

ORF6

GAL3ST1

CEMIP2

MUC13

ATP6V0A2

WDFY3

COG1

POMGNT1

PLXND1

NBR1

BLM

INTS8

S

COG2

CTDNEP1

RAB34

HARS2

DOCK5

PDIA3

ATRIP

CBR1

PTGES

CDH17

VPS45

LRRN2

CYP1B1

APOB

ACAD10

MMP15

ALDH1A2

PSMC4

CD70

PCDHA12ITM2B

CLCN7

ORF3

PACC1

WBP2

FAM20C

VPS8

C15orf61

KIAA0232

TMEM168

PTPMT1

TMEM164

PSMA5

RBM5

DCBLD2

TTC27

CNNM3

HNRNPM

PHPT1

ZBTB20

CALCOCO1

PYCR3

DOLK

MRPL28

NUDT19

BMPR1B

C6orf120

ELMO2

BZW2

NSP14

DHCR7

SNX17

HDHD5

GLO1

ST8SIA4

RNF130

MFSD5

MFSD10

AGO3

IL31RA

PON3

FAM8A1

TIMM10B

ITPRIPL2

ZDHHC9

SLC23A2

SLC30A5

FKBP4

FXYD2

SLC7A6

XPOT

ZNF148

HSD17B8

SMC6

STEAP2

SLC23A1

BRI3BP

TMEM68SLC6A6

SLC35A2

HLA-G

GNS

CACNG6

PITRM1

UCHL1

SLC25A24

LEMD3

TNFSF15

KIAA0754

SGPP1

BROX

PHB

ORF4

BLVRA

TMEM192

CLPTM1L

WWP2

FAM98A

CHST14

FKBP3

STX7

SCAP

SLC35F2

NSP2

TOR1AIP2

QSOX2

FAM50A

G3BP2

SLC25A2

DOCK1

TMX1

NR5A2

PLD3

STUB1

IPO13

FBXO6

SLC30A1

DTWD2

ECI1

RHBDD2

PMPCA

SLC12A6

TMEM11

MFSD8

PTPRJ

SLC19A2

COG8

SLC30A7

GPR37

ZFPL1

TMED2

MFAP3L

SLC35F5

CLDN1

MROH1

ALDH2

PROS1

PSMD4

BRAT1

RPL39

LMAN2L

STAG2

FAM234A

MAFG

HSPA8

TBCB

NAPG

HINT1 BAMBI

EPN1

SARM1

ARFGEF2

GET4

NUP188

RTRAF

WDR83

SLC35F6

REEP5

LTN1

ORF8a

SYNGR3

WFS1

ATP5PB

VPS11

RAB8A

SLC47A2

HLA-C

OGDH

PYROXD2

OBI1

UHRF1BP1L

NUP205

SLC26A2

PLIN2 BICC1

RAB5A

AHCYL1

TOMM70

IFITM1

EGFL7

EMC8

HAX1

TRAPPC5

ATP5PF

DIPK1A

ATP5PD

CD81

EPHB3

TMPPE

RBPMSTAMM41

TLCD4

TMEM177

PEX10

ARFIP1

RAB1A

SNX2

ZW10

EMC3

GOSR1

ENDOD1

TARBP2

TUT1

UGGT1

LSG1

PCDHA4

ORF3b

ORF3

SCFD1

SFXN5

PFDN2

FANCD2

CANT1

MYRF

OAT

NDUFA12

LIMCH1

SLC35A1

PC

C1orf159

B3GALT6

DGAT1

PISD

NOTCH1

TACO1

SLC20A1

SPCS1

STX5

NSP8

HERC1

CALCOCO2

TIMELESS

GPD2

OSTC

GABARAPL2

MLF2

FAT1

SMS

OCIAD2

TRPM4

ORF6

BSCL2

XPO7

DERL3

TMEM199

CLDN12

DAGLB

SDF4

CYB5D2

FUT8

GPR39

COMT

M

TOR1B

GOLIM4

IPO8

SEC61G

HAUS6

NECTIN3

LTBP1

C16orf58

COG4

CTSB

LETMD1

EFNB1

TMEM258

NSP15

PMPCB

ALG6

ALG8

PDLIM1

EXT2

LRRC47

RDH14

COQ6

PRPF40A

SLC18B1

VPS39TCIRG1

FAM241B

DNAJA2

ORF9b

TNPO2

CLGN

NSP6

TMEM245

PRAF2

MSMO1

NSP3MD

TBCE

NSP16

DHCR24ABHD10

ATP6AP1

ABHD5

PIGS

SMIM1

ZDHHC13

HSPA1L

BAG3

SRXN1

TIMM17A

MCM3AP

SDF2

TBC1D8

SLC9A1

ACO1

SAYSD1

PEX13

STEAP1B

TMEM223

B4GAT1PTPRM

CEP170B

GORASP2

DNAAF5

LRFN1

COPS4

SOAT1

OSMR

MUL1

RETREG2

VSIR

NCEH1

TNFRSF10D

PDE4DIP

PLXNA1

TNFSF9

LRRC8A

NSP3MD

TAPT1

EEA1

STARD3NL

NME3

SLC12A7

DPY19L4

XXYLT1

PSAT1

RBFOX3

ADAM9

SURF4

ADCY9

SLC15A4

PTPN11

WDR83OS

S

NT5C3A

EXOG

CDK5RAP3

SCAI

INTS1

NPC1

COTL1

ANXA5

RAB13

TYW1

HAVCR1

RAD23B

TMEM159

NCAPD3

SSBP1

FUCA2

ACADSB

ELF3

ARFGEF1

IL10RB

C4orf3

NSP8

PPP2R5A

SUMF2

CMIP

CALD1

PDPR

SLC33A1

GPAT4

SCARA5

SQLE

ANXA4

RHBDF2

CXCL5

MT-ND4

VAMP7

HMGN1

RAB31

KCT2

ELOVL7

FKBP1A

WDR72

TM4SF1

XPO5

SEC61B

JAGN1

MSN

N

TMEM214

NOTCH3

VKORC1L1

NUBP2

SCCPDH

FAM20B

CAVIN1

LSR

TM9SF2

ABHD16A

G3BP1SEPTIN11

NSP12

TNFAIP2

SEMA4B

EXTL3

ORF7b

TMEM97

BAG6

S1PR5

GUF1

SCFD2

STK11IP

CPD

KYNU

NARS2

PTDSS1

ROR1

CALML5

C11orf24

HLA-E

SEC23IP

EEF1A2

SNX1

RETREG3

ATP6V1B1

KDELR1

HLA-A

NOTCH2

TIMM17B

RABGAP1L

NSP4

DOCK6

FBXO46

INTS4

GEMIN8

VAMP2

CKAP4

PSMC2

CHPT1

NLRX1

GJA1

STXBP3

FAF2

ECHDC3

COG6

RAB7ABET1

MT-CO2

PIP4P2

CAV2

TMEM221

RAB9A

RBM33

GNAL

RHOB

FXR1

RANBP6

PCCB

NECTIN3

TAF4

LPCAT4

E

NSP13

HLA-A

NSP9

CRYZ

LGALS3

IDH1CAP1

PEX6

STX6

CAAP1

NOP53

SYPL1

FGB

STRA6

PTRH2

HSPA13

AREG

SLC12A9

PSMD12

COG7LMBRD2

SPTLC2

INTS12

EXT1

NDUFS8

STX12

SAAL1

LRBA

DNAJB2

TNFRSF10A

ALG14

HMGCS2

MAIP1

EML4

CAV1

GCLC

NDUFA10

EPCAM

NSP7

HSPA5

TLCD1

NSP4

NDRG1

MRC2

VPS16

RNPEP

BAG4

TRIR

GLCE

JAK2

SIRT3

NLN

TMED9

NSP10

AK4

HPS6

NSP15

WWP1

B3GAT3

FANCG

TMEM256

ATP5F1D

NSP2

GOSR2

ITGA3

FITM2

PAQR5

ITGB5

TRAFD1

GPAA1

ORF8

PRKDC

GGT7

SGCB

NAPA

CLINT1

FGFR1

DDR1

GBF1

ALG13

GGT1

HUWE1

STX16

CD44

SLC25A52

MARK2

NECTIN2

SMPD4

OS9

RAB2A

NPEPPS

YIF1B

NRP2

PTPRS

HSPH1

HM13

CUX1

HLA-C

GANAB

ORF7a

MAVS

PON2

SULT1A3

SPAG9ST7

CLASP2

UGT1A9

CLCC1

HTATIP2

LGR4

RTN4

HMGA1

SEC16A

KIF5C

DPY19L2



https://doi.org/10.1101/2020.06.17.156455


Extended data Figure 1

c

g

d e f

SII-HA-RSAD2SII-HA-UNC93B1

42 -

CoV O

RF7b-H

A

CoV-2

ORF7b-H

A

input

IP

++++

100 -

25 -

15 -

15 -

100 -

25 -

HA co-IPSII-HA-UNC93B1SII-HA-RSAD2

HA-ORF7bSII-HA-UNC93B1SII-HA-RSAD2

ACTB

CoV-2

ORF6-HA

CoV N

-HA

CoV-2

N-HA

input

HSPA1A co-IP70 -

70 - HSPA1A

ORF6-HA

ORF6-HA55 -

N-HA

55 -N-HA

42 -

IP

ACTB

CoV-2

ORF6-HA

CoV O

RF7b-H

A

CoV-2

ORF7b-H

A

input

70 -MAVS

MAVS co-IP70 -

HA15 -

15 -HA IP

42 -

CoV-2

ORF7a-H

A

ACTB

h

CoV O

RF8a-H

A

CoV O

RF8-HA

CoV-2

ORF8-HA

input

TGFB co-IP

HA IP

CoV O

RF8b-H

A

ACTB

CoV-2

ORF9b-H

A

50 -40 -30 -25 -

50 -40 -30 -

25 -15 -

25 -

15 -

42 -

TGFB

HA

RAP1GDS1

STOML2

PHB2

PHB

SARS_CoV2_NSP2

SARS_CoV_NSP2

0

4

8

12

0 4 8 12log2(fold-change) SARS_CoV2_NSP2

log2

(fold

-cha

nge)

SA

RS_C

oV_N

SP2

FKBP9...

IGFBP3

ADAM15

CLU

TGFB2

SELENOF

ITGA6

FGA

TGFB1

ITGB4

MET

TGFBI

CSGALNACT1

TSKU

HFE

IGFBP4

DSC3

GUSB TWSG1

SARS_CoV_ORF8

ENTPD6

SERPINE1

LRP11

GGH

C5

NPTX1

TCTN3 MAN2A1

PRKCSH

HLA-DMB

ARSE

TOR3A

CNTNAP3B

NTS

ACAT1ADAM10

CANX

ECE1

ERLEC1

IGF2R

PDIA3

CNNM3

BMPR1B

PON3

GNS

TMX1

FBXO6

PROS1FAM234A

EGFL7

DIPK1A

UGGT1

PCDHA4

CYB5D2TOR1B

LTBP1

EXT2CLGN

SDF2

NME3

ADAM9

FUCA2

CXCL5

FGB

ITGA3

ITGB5

SARS_CoV2_ORF8

OS9

GANAB

PON2

UGT1A9

0

4

8

12

0.0 2.5 5.0 7.5 10.0 12.5log2(fold-change) SARS_CoV2_ORF8

log2

(fold

-cha

nge)

SA

RS_C

oV_O

RF8

ST8SIA4

GBA

UGGT2



https://doi.org/10.1101/2020.06.17.156455


log2 fold change

NSP7

CHEK1 FN1SERPINE1

S

ORF3M

NSP6

ORF7a

N

NSP1

ORF9b

TGM2

ITGA3

POLR2B

CASP2

IRAK1

CAV1

ACE2

0.0

2.5

5.0

7.5

-5 0 5 10

-log 10

p-v

alue

a mockSARS-CoV-2

b −ΔC

t (lo

g 2)

-10

-5

0

5

mock 0.001 0.01 MOI

-ΔC

t (N

) (lo

g 2)

A549A549−ACE2

NDND

-20

-10

0

10

3 6 12 18 24 30

N

-20

-15

-10

-5

0 IFNB1

3 6 12 18 24 30

Mx1

-20

-15

-10

-5

0

3 6 12 18 24 30

IFIT3

-20

-15

-10

-5

0

3 6 12 18 24 30

IL6

-20

-15

-10

-5

0

3 6 12 18 24 30

CXCL2

-20

-15

-10

-5

0

3 6 12 18 24 30 t (h)

ACTBHA

wt ACE2-HA


k

i

j

SARS-CoV-2

ACTBACE2-HA

30241812633024181263 h.p.i.mock SARS-CoV-2

*

**

f g h

6 24

5e+087e+08

1e+09

h.p.i.

Inte

nsity mock

SARS-CoV-2

ACE2

l

m

e

t (h)

−ΔC

t (lo

g 2)

PROZ

EGR1

CXCL2

ATF3

NFKBIA

TGM2

CCL2

SMARCA4

HMGN5

CDKN2A

0

5

10

15

20

25

log2 fold change

-log 10

p-v

alue

0 2 4

JUNB

3 6 12 18 24 30-20

-15

-10

-5

0

3 6 12 18 24 30-20

-15

-10

-5

0JUN

dc

NDND

−20

−15

−10

−5

0

3 6 12 18 24 30

−∆C

t (lo

g 2)

mockSARS-CoV-2

t (h)

ACE2

3.43.63.84.04.24.4

625

702log 2 F

C

MAP2K2S23YAP1

S61

TRIM28S473

TRIM28T541

JUNS243

TRIM28S681

SMAD1S463

SMAD1S465

AKT3S472/S476

MAPK1T185

MAPK8T183

MAPK1Y187

MAPK8Y185

0

1

2

3

4

log2 fold change

-log 10

p-v

alue

-5 0 5 10

S0

2

4

6

8

10

41

97

150 195206 304

310386

417

444

529

535

776786790795

825835

921933

947

1028

1038

10861255

1266

1269

log 2 F

C

ORF3

TRIM47

2.53.03.54.04.5 16

21log 2 F

C

ACE2

N02468

102 169233 248

347355361375

388log 2 F

C

M

TRIM7

02468 162

180

205

log 2 F

C

N

DNA-PKCSNK2CSNK2 AKT

CAMK2ERK1/2CDK5

CSNK2GPCRMK2PKA

ERK1/2GSK3

0

5

10

15

S176S79

S21 S201S2

S23

S206

S194S197

S202

T198

log 2 F

C

TEAD4

NR3C1

TFAP2A

RELANFIC

JUNDMEF2A

GATA2

PAX5TCF3

BHLHE40

RESTSTAT3

FOSMAZ

ETS1CEBPD

BCL11A

JUNGATA3

STAT1MYOG

TCF12

BACH1

MYOD1

ZNF274

ZNF384

TBPZBTB7A

FOXM1

MEF2C

ATF3NFE2

SRFFOSL1

HNF4A

ESR1EGR1

IKZF1RXRA

MAFKUSF2

CTCFRFX5

CEBPB

FOSL2

FOXA2

PBX3USF1

NR2F2

TCF7L2

PRDM1

E2F1TFAP2C

MAXGTF2B

ARID3A

IRF1MXI1

ZNF143

30 h ▲24 h ▲18 h ▲12 h ▲

6 h ▲3 h ▲

30 h ▼24 h ▼18 h ▼12 h ▼

6 h ▼

−10

−5

0

-log 10

p-v

alue

MKK7CDK19

CRIKPKM

CSNK2A2

SrcTAK1

DNAPK

CDK14

RetMAPKAPK5

FynMEK1

PKCB iso2

AurABMPR1B

ATRCK1A

PKCEP38A

PKCDAKT1

RSK2p90RSK

CHUKERK3

Chk2Chk1

P38DERK2

MAPKAPK2

PRKG1

ABL1MAP2K2

MAPK3

PKG1 iso2

MAP2K1

P70S6KB

JNK2CAMK2G

PKCBCDK6

PRKCI

EIF2AK2

CDK16

AURKB

PDPK1

STK38L

PLK2STK3

CSNK1E

MTORCK2A1

CAMKK2

HIPK2TTK

BRSK1 iso2

NDR1AMPKA1

Akt2IKKB

30 h ▲ 24 h ▲ 24 h ▲ 18 h ▲ 12 h ▲ 6 h ▲ 6 h ▲ 3 h ▲ 30 h ▼ 24 h ▼ 24 h ▼ 18 h ▼ 12 h ▼ 6 h ▼ 6 h ▼ 3 h ▼

−5

−4

−3

−2

−1

0

-log 10

p-v

alue



https://doi.org/10.1101/2020.06.17.156455



c

e

d

f

gh.p.i.30241812633024181263

SARS-CoV-2mock

p38-T180/Y182p38JNK-T183/Y185

JNK

ACTB

h

1e7

3e75e7

1e51e61e71e8

1e41e51e61e71e8

6 24h.p.i. 6 24h.p.i. 6 24h.p.i

ITGA3 SERPINE1 FN1mockSARS-CoV-2

Inte

nsity

3e5

1e6

3e6

3e35e3

1e4

AKAP12 S286

3e2

1e33e3

3e5

1e63e6

EPHA2 S897

1e21e31e41e51e6

EGFR S991

Type IIFN ISRE GAS

NSP1

NSP13

ORF3

ORF6

ORF7a

ORF7b

ORF9b

ZIKV-NS5

control: SMN1

control: SMN1unstimulated

0%

50%

100%

120%

% o

f stim

ulat

ed c

ontro

l

AKAP12EPHA2EGFR

3024181263h.p.i.

3024181263h.p.i.

3024181263h.p.i.

NUCLEUS

IL6TNFCXCL2

IFN-αIFN-β

ILIFN

CXCL

TNF

CYTOPLASM

ISGsIFIT1MX1

IL6

IFN

IFN

IFNAR

SARS-CoV-2

IL6STIL6R

IL6

IRF9

STAT2STAT1

IRF9

STAT2STAT1

IRF3 IRF3

IRF3

IRF3 TBK1

MAVS

RIG-IMDA5

TLRs

IRAK1TRAF6

MYD88

ORF3

M

ENDOSOME

MITOCHONDRION

JAK1/2

TANK

STAT1/3

STAT3

IKBKGTRAF2TBK1

ORF7b

NFKB

NFKB

M JAK2 JAK1

ORF3

ITCH

S222

S727

K27K165

UNC93B1

K446

ORF7b

ORF7b

NUCLEUS

COL7A1FGGFGBFN1SERPINE1ITGA3

SMAD

RAS

RAF

SMAD1

TLN1PXN

PTK2VCL

YAP

RTKsITGA/ITGB

CYTOPLASMMMP24AMOTL2LATS2COL7A1TEAD1IER2

YAP

TRAF6

NFKB

MAPK8MAPK14

GOLGI

JUNFOSEGR1DUSPsMMPsPTGS2

NFKB

ECM

LTBP1

TGFB

TGFB

ORF8

FURIN

LTBP1

TGFB

TGFB

LAP

LAP

TGFB

TGFB

ORF3

ORF3

TGFBR

BAMBI

EGFRECM

MAPK3MAPK1

MAP2K1MAP2K2

PLCG1PI3K

AP-1

SMAD5S465

S463

S463

S465

T180Y182

T183Y185 T185

Y187

T202T207

S23

S1248

S991S995

S1225

K802

S864S910

S126S130S137

S61S340S127

S131

S138

K708

S477AKT

SERPINE1ITGA3

-2 -1 0 1 2 3 4log2 fold change

Phospho(S/T) site regulatory siteknown siteUbi(K) site

-3


up-/downregulatedinteraction

-4 -2 0 2 4 6 8 10

log2 fold changePhospho(S/T/Y) site regulatory site

known siteUbi(K) siteup-/downregulatedinteraction


Inte

nsity

Inte

nsity

a b

1e5

5e4

3e4

TEAD

mockSARS-CoV-2

K321

SAR

S-C

oV-2

0.001 0.002 0.003

2k

4k

6k

8k

10k

12k

14k

Minimal Edge Weight

Aver

age

Path

Len

gth

permuted

real

BRD4

EPHA2

NCK1

CDH1

ARHGAP35

AP1M1

APOB

CTSC

SEC23B SEC24B

SEC24C

STX12

STX7

TRIP10

VAMP8

CAPZA1

COG8ATM

BARD1

BAX

CTSA

CTSBCTSD

CTSS

ERCC3

GABARAPL2

GLB1

HDAC6

HIST1H2AH

HIST2H2AB

HUS1

LAMP2

MFN2

MLH1

MVB12A

NBR1

NFKB2

NR4A1

PDE8A

PFKM

PKM

PRKDC

SQSTM1

STARD3

TARDBP

UBTF

CTSH

FEM1A

FEM1C

DCAF1

ATP6V1G1

FBXO30

ZNRF2

ARIH1

DCP1A

DDX6

EIF4G1

RIOK3

RPS15

SMG8

DDX39B

U2SURP

POLR3C

CETN3

MAEA

RMND5A

STK25

MFGE8

VCAN

CTPS1

FASN

GFPT2

AKAP9

PLK1

NRCAM

ASNS

ELF3

KXD1

TXNDC5

FLOT1

MYL6B

NDUFA3

NDUFV2

DPYSL4

DPYSL5

NES

DUSP3

UBE2S

FOXA2

TGIF1

TECR

FUNDC2

PGAM5

TLN2

KANSL3

UQCRH

PIP4P1

IGF2BP3

ASAH1

NUCB1

FTL

ECHS1

GAMT

DERL2

SH3GLB2

MUC5AC

FBXL2

DPP7

MANBA

GGH

TOR1B

TSR2

DDI2

AGO3

STK11IP

RIC8B

JAGN1

HEXA HEXB

BCHE

NFX1

PARP16

YIPF1

AHNAK2 MYOF

PTK7

NPC2

PGM5

CA12

ANPEP

SLC35B2

KLF16

TBC1D10A

ZCCHC8

MORC2

PPT1

HMGN5

NTHL1

COL28A1

NIF3L1

CNP

ALAD

CTIF

METTL14

SLC4A2

PRR11

LAPTM4A

ABCC3

ABHD5

ACVR1

ADCY9

AREG

ATP6AP1

B2M

BCAM

BCAP31BET1

BMPR1A

BNIP3L

CALML5

CD44

CD70

CELSR2

CKAP4

CTNNA1 CTNNA2

CUX1

DIS3

EFNB1

ERBB2

FAM20C FAS

FH

FXYD2

GALNT1

GJA1

GNA13

GOLGA5 GOLGB1

GOLIM4

GORASP2

GOSR1

GOSR2

GPR39

HLA-A

IGF2R

IL10RB

IL13RA1

IL6ST

ITCH

ITGA6

KDELR1

LDLR

LGALS3

LGR4

LMAN1

LPAR1

LTBR

MAVS

MET

NAPA

NAPB

NAPG

NCEH1

NDFIP1

NDFIP2

NDUFB11

NOTCH2

NOTCH3

ORAI1

PCDHAC2

PEX10

PEX13PEX2

PLIN2

PLXND1

PMEPA1

PNPLA2

PSEN2

PTPN11

PTPRF

PTPRJ

PTPRM

PTRH2

PVR

RAB13

RAB1A

RAB5A

RAB7A

RNF19A

S1PR3

SCAP

SCARA5

SCARB1

SCFD1

SEC61B

SEC61G

SELENOS

SGCB

SGCD

SGCE

SLC26A2

SNAP25

SORT1

SPTLC2

SRPRA

STRA6

STUB1

STX10

STX16

STX2

STX4

STX5

STX6

SYMPK

TCIRG1

TGOLN2

TIMELESSTMED10

TMED2

TMED7

TMED9

TMX1

TNFRSF10D

TNFRSF1A

TUSC3 TVP23B

UBE2G2

UNC93B1

VAMP3

VAMP4

VAMP7

VPS45VTI1A

WWP2

ZMPSTE24

ReactomeFIInteractionNetworkDiffusionFlow

Target of Viral ProteinRegulated ProteinIntermediate Link



https://doi.org/10.1101/2020.06.17.156455


Extended data Figure 4a

8h

cell growth rate; uninfectedcell growth rate; uninfected

12h

16h

20h

24h

28h

32h

36h

40h

44h

48h

moc

k 0h

Z-VAD-FMK 5uM

IFNb 250U

Chloroquine 10uM

water

MG-132 1µM

Rapamycin 1µM

Tirapazamine 2µM

Rabusertib 1µM

Prinomastat 2µM

Marimastat 2µM

Gilteritinib 0.5µM

Ribavirin 100µM

Ipatasertib 5µM

Erastin 1µM

Ferrostatin-1 50µM

Bosutinib 1µM

Neratinib 0.5µM

SB239063 10µM

Baricitinib 1µM

Dabrafenib 1µM

Regorafenib 5µM

Sorafenib 10µM

DMSO

sign

ifica

nce

8h 12h

16h

20h

24h

28h

32h

36h

40h

44h

48h

sign

ifica

nce

8h 12h

16h

20h

24h

28h

32h

36h

40h

44h

48h

2

4

6

8

10

12

14

cell

grow

th ra

te (%

)

-6

-5

-4

-3

-2

-1

0

1

2Autophagy

DNA damage

MMPs

B-RAF

viru

s gr

owth

(Δ n

orm

. GFP

; log

2)

cell growth rate; uninfectedvirus growth

(6h pre-treatment)virus growth

(treatment at time of infection)

time after infection



https://doi.org/10.1101/2020.06.17.156455


Documents

Multi-level proteomics reveals host-perturbation …...2020/06/17 · Extended data Figure 1 a b CoV-2 E-HA CoV E-HA CoV-2 ORF6-HA HA CoV ORF6-HA 80 - 140 - 50 - 30 - 25 - 15 - 10