key: cord-0827915-k8mmar6s
authors: Klann, Kevin; Bojkova, Denisa; Tascher, Georg; Ciesek, Sandra; Münch, Christian; Cinatl, Jindrich
title: Growth factor receptor signaling inhibition prevents SARS-CoV-2 replication
date: 2020-05-19
journal: bioRxiv
DOI: 10.1101/2020.05.14.095661
sha: f8f49b1543ba09063af66e59797ea89e867b0be3
doc_id: 827915
cord_uid: k8mmar6s

SARS-CoV-2 infections are rapidly spreading around the globe. The rapid development of therapies is of major importance. However, our lack of understanding of the molecular processes and host cell signaling events underlying SARS-CoV-2 infection hinder therapy development. We employed a SARS-CoV-2 infection system in permissible human cells to study signaling changes by phospho-proteomics. We identified viral protein phosphorylation and defined phosphorylation-driven host cell signaling changes upon infection. Growth factor receptor (GFR) signaling and downstream pathways were activated. Drug-protein network analyses revealed GFR signaling as key pathway targetable by approved drugs. Inhibition of GFR downstream signaling by five compounds prevented SARS-CoV-2 replication in cells, assessed by cytopathic effect, viral dsRNA production, and viral RNA release into the supernatant. This study describes host cell signaling events upon SARS-CoV-2 infection and reveals GFR signaling as central pathway essential for SARS-CoV-2 replication. It provides with novel strategies for COVID-19 treatment.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, has been rapidly spreading around the globe since the beginning of 2020. In people, it causes coronavirus disease 2019 often accompanied by severe respiratory syndrome (Chen et al., 2020) . To conquer the global health crisis triggered by COVID-19, rapidly establishing drugs is 35 required to dampen the disease course and relieve healthcare institutions. Thus, repurposing of already available and (ideally) approved drugs might be essential to rapidly treat COVID-19. Many studies for proposing repurposing of specific drugs have been conducted in the last months, but mostly remain computational without tests in infection models (Smith and Smith, 2020; Wang, 2020) . In addition, they are hindered by the lack of knowledge about the molecular 40 mechanisms of SARS-CoV-2 infection and the resulting host-cell responses required to allow viral replication. To rationally repurpose drugs, a molecular understanding of the infection and the changes within the host cell pathways is essential. Experimentally identifying viral targets in the cell allows candidate drugs to be selected with high confidence for further testing in the clinics to reduce the risks for patients resulting from tests with drugs lacking in vitro validation. 45

Growth factor receptor (GFR) signaling plays important roles in cancer pathogenesis and has also been reported to be crucial for infection with some viruses (Beerli et al., 2019; Kung et al., 2011; Zhu et al., 2009) . GFR activation leads to the modulation of a wide range of cellular processes, including proliferation, adhesion, or differentiation (Yarden, 2001) . Various viruses, such as Epstein-Barr virus, influenza, or hepatitis C, have been shown to use the epidermal 50 growth factor receptor (EGFR) as an entry receptor (Eierhoff et al., 2010; Kung et al., 2011; Lupberger et al., 2011) . In addition, EGFR activation can suppress interferon signaling and thus the antiviral response elicited in respiratory virus diseases, for instance influenza A and rhinovirus (Ueki et al., 2013) . Activation of GFR signaling might play an important role also in other respiratory viruses, such as SARS-CoV-2. 55

In the last years, it has been shown for many viruses that modulation of host cell signaling is crucial for viral replication and might exhibit strong therapeutic potential (Beerli et al., 2019; Pleschka et al., 2001) . However, how SARS-CoV-2 infection changes host cell signaling has remained unclear. We recently established an in vitro cell culture model of SARS-CoV-2 infection using the colon epithelial cell line Caco-2, which is highly permissive for the virus and 60

commonly used for the study of coronaviruses (Herzog et al., 2008; Ren et al., 2006) . Here, we determine changes in the cellular phospho-protein networks upon infection with SARS-CoV-2 to gain insight into infection-induced signaling events. We found extensive rearrangements of cellular signaling pathways, particularly of GFR signaling. Strikingly, inhibiting GFR signaling using prominent (anti-cancer) drugsnamely pictilisib, omipalisib, RO5126766, lonafarnib, and 65

sorafenibprevented SARS-CoV-2 replication in vitro, assessed by cytopathic effect and viral RNA replication and release. These compounds prevented replication at clinically achievable concentrations. Due to their clinical availability, these drugs could be rapidly transitioned towards clinical trials to test their feasibility as COVID-19 treatment option.

To evaluate changes in intracellular signaling networks brought about by SARS-CoV-2 infection, we quantified phospho-proteome changes 24 hours after infection ( Figure 1A ). Caco-2 cells were mock-infected or infected with SARS-CoV-2 patient isolates (in biological quintruplicates) for one hour, washed, and incubated for 24 hours before cell harvest. Extracted proteins were 75 digested and split to 1) carry out whole-cell proteomics of a tandem mass tag (TMT) 10-plex samples using liquid chromatography synchronous precursor selection mass spectromety (LC-SPS-MS 3 ), or 2) use Fe-NTA phosphopeptide enrichment (achieving 98% enrichment) for phospho proteome analyses of a TMT 10-plex analyzed by LC-MS 2 . We identified and quantified 7,150 proteins and 15,392 different phosphopeptides for a total of 15,040 different modification 80 sites ( Figure 1B , C, S1, and Table S1 , S2). The main fraction of phosphopeptides were modified serines (86.4%), followed by threonine (13.4%), and tyrosine (0.2%) ( Figure 1D ). Upon infection, 2,197 and 799 phosphopeptides significantly increased or decreased respectively (log2 FC > 1, p value < 0.05).

Viral proteins are produced in the host cell and underlie (and often require) post-translational 85 modification (PTM) by host cell enzymes (Wu et al., 2009) . Accordingly, we assessed viral proteins phosphorylated in the host cell. We identified 33 modification sites on 6 different viral proteins ( Figure 1E -J). Possible functions of the observed modifications largely remain unclear due to a lack of understanding of their molecular function and regulation. SARS-CoV-2 protein 3a was phosphorylated on the luminal side of this transmembrane protein ( Figure 1E ) Membrane 90 protein M was phosphorylated at three serines in close proximity, at the C-terminal, cytoplasmic region of the protein ( Figure 1F ), suggesting a high-activity modification surface. SARS-CoV-1 protein 6 was described to accelerates infections in murine systems (Tangudu et al., 2007) . We found a single phosphorylation of the SARS-CoV-2 protein homologue non-structural protein 6 in host cells ( Figure 1G ) Protein 9b was modified at two sites ( Figure 1H ). However, its function in 95 SARS-CoV-1 or SARS-CoV-2 remains unknown. Polyprotein 1b is a large 7,096 amino acid protein heavily processed to generate distinct proteins in SARS-CoV-1 (Tangudu et al., 2007) . We found polyprotein 1b to be modified at three residues, two in a region of unknown function and one in the non-structural protein 11 (NSP11) part of the protein ( Figure 1I ). Our data cannot distinguish whether phosphorylation occurred before or after cleavage and whether 100 phosphorylation may affect processing. SARS-CoV-2 nucleoprotein was heavily phosphorylated ( Figure 1J ). Mapping phosphosites to the structure (residues 47 to 173, PDB: 6vyo) revealed a small surface region, suggesting specific regulation and interaction changes ( Figure 1K ). To reveal host kinases potentially phosphorylating viral proteins, we bioinformatically assessed identified phosphorylation motifs using NetPhos 3.1 and GPS5 (Blom et al., 2004; Wang et al., 105 2020) (Table S3 ). Some motifs present in nucleoprotein were predicted to be modified by CMGC kinases. Among several others, casein kinase II (CK2) kinases are part of the CMGC family and have been independently identified as interaction partners of the nucleoprotein, when expressed in cells (Gordon et al., 2020) . Inhibition of CK2 kinases, could be employed to study possible functional interactions between kinase and viral protein. Taken together, we identified extensive changes in phosphorylation of host and viral proteins after SARS-CoV-2 infection. The role of viral protein modifications remain unclear. However, targeting the corresponding host kinases may offer new treatment strategies.

To identify the key host signaling pathway networks modulated by infection, we carried out 115

protein-protein co-regulation analysis on all proteins quantified in phosphorylation and total protein level. We first standardized phosphorylation and total protein levels by individual Zscoring to compare the different datasets. Subsequently, to merge phosphorylation and proteome data, we collapsed all phospho-sites for each protein into one average profile and calculated combined Z-scores. Patterns of co-regulation were identified using protein-protein 120 correlation and hierarchical clustering ( Figure 2A ). The dynamic landscape of the proteome revealed three main clusters of co-regulated proteins, each one representing different sets of pathways (discussed in detail below):

The first cluster was mainly comprised of receptor signaling and endocytic pathways ( Figure 2B ). Prominent among these pathways were platelet derived growth factor receptor (PDGFR), ErbB1 125

(EGFR) signaling, metabolism, and various pathways associated with vesicle trafficking (Table  S4) . As changes in phospho-peptide abundance can represent different ratios in phosphorylated versus non-phosphorylated peptide or a change in protein abundance (with the same ratio of protein being phosphorylated), we integrated our phospho-proteome dataset with total proteome data ( Figure 2C ). In contrast to the extensive changes observed in the phospho-proteome, no 130 general changes were observed for the total proteome ( Figure 2C , Table S2 ). Thus, phosphorylation changes were induced by signaling activity alteration resulting in increased phosphorylation and not due to protein abundance differences.

The second cluster was mainly comprised of proteins decreased in phosphorylation and was highly connected to cell cycle and translation initiation ( Figure 2D and Table S5 ). We reported 135

recently that inhibition of cellular translation prevented SARS-CoV-2 replication in cells (Bojkova et al., 2020) , consistent with regulation of translation by altering phosphorylation patterns. To further distinguish the regulations within this cluster, we correlated protein levels with differential phosphorylation abundance ( Figure 2E ) and found two groups of proteins: The first was contained translation related pathways (identified in Figure 2E ) and was predominantly regulated 140 by decreased modification. The second set of proteins was decreased in phosphorylation and on total protein level. The majority of proteins found in the second cluster belonged to diverse cell cycle pathways. Consistent with these findings, cell cycle pathways were also enriched in the set of proteins significantly decreased on protein level ( Figure 2F , S2, and Table S6 ). Translation pathways were not regulated on protein level to this extent. 145

Analysis of the third cluster revealed signaling events of the splicing machinery (Table S7) possibly explaining previously observed changes in splicing machinery abundance upon SARS-CoV-2 infection (Bojkova et al., 2020) . Consistent with previous literature (Grimmler et al., 2005; Ilan et al., 2017; Mathew et al., 2008; Mermoud et al., 1994) , we therefore hypothesized that the host splicing machinery is extensively reshaped during viral infection. This finding further 150 supports splicing as a potential therapeutic target, in agreement with decreased SARS-CoV-2 pathogenic effects when inhibiting splicing by pladeinolide B. Additionally, we found carbon metabolism among the pathways showing significantly increased phosphorylation upon SARS-CoV-2 infection (Table S4 ) in addition to previously described changes of total protein levels of enzymes part of glycolysis and carbon metabolism (Bojkova et al., 2020 )( Figure S3 ). 155

Taken together, we showed that, during SARS-CoV-2 infection, specific rearrangements of signaling pathways were elicited in the cellular proteome. Regulation was mainly comprised of cellular signaling and translational pathways as well as proteins regulated not only by phosphorylation, but also in total protein abundance. Proteins exhibiting decreased protein levels were significantly enriched in cell cycle proteins. 160

We observed over 2,000 phospho-peptides to be increased in abundance while their protein levels stay constant upon infection ( Figure 2C and S4). This reveals differential modification activity (e.g. signaling events) for these phospho-proteins. For many kinases in cellular signaling pathways there are already approved drugs available. Hence, we investigated the potential to 165

repurpose drugs to treat COVID-19 by mapping already available drugs via ReactomeFI to the set of proteins increased in phosphorylation. We filtered the network for drugs and direct targets and found EGFR as one of the central hits, including a number of regulated proteins in the downstream signaling pathway of EGFR ( Figure 3A ). These downstream targets are also regulated by other GFRs and could thus also be explained by their observed activation upon 170 SARS-CoV-2 infection ( Figure 2 ). 28 clinically approved drugs (largely used in cancer therapy) are already available to target EGFR or downstream targets. Indeed, we found a subnetwork of GFR signaling components remodeled ( Figure 3B ). We mapped identified members of GFR signaling and their respective phosphorylation differences upon SARS-CoV-2 infection ( Figure  3C ) revealing an extensive overall increase in phosphorylation of the whole pathway, including 175 related components for cytoskeleton remodeling and receptor endocytosis. How GFR signaling might regulate SARS-CoV-2 infection is still matter for speculation. However, GFR signaling inhibition might provide a useful approach already implicated in SARS-CoV induced fibrosis therapy and might be a viable strategy to treat COVID-19.

Since GFR signaling seemed to be central during SARS-CoV-2 infection, we examined the use of inhibitors as antiviral agents. Since there are several GFRs integrating their signaling and regulating a number of processes inside the cell, directly targeting downstream signaling components is likely to be more successful to prevent signaling of different GFRs and to avoid mixed effects of multiple pathways. GFR signaling, amongst others, results in activation of 1) the 185 RAF/MEK/ERK MAPK signaling cascade and 2) integrates (via phosphoinositide 3 kinase [PI3K]

and protein kinase B [AKT]) into mTORC1 signaling to regulating proliferation ( Figure 4A ). To explore the antiviral efficiency of targeting proteins downstream of GFRs, we first tested the PI3K inhibitors pictilisib and omipalisib (Ippolito et al., 2016; Sarker et al., 2015; Schmid et al., 2016) . Both compounds inhibited viral replication, based on their propensity to prevent cytopathogenic 190 effect (CPE) and viral RNA production in cells ( Figure 4B -D, S5, and S6).

Our drug-target analyses identified mitogen activated protein kinase kinase (MAP2K2, better known as MEK) and the RAF inhibitor sorafenib (Wilhelm et al., 2006) as promising targets inhibiting downstream signaling of GFRs ( Figure 4A ). Thus, we tested sorafenib and the dual RAF/MEK inhibitor RO5126766 in our viral replication assays. Both compounds inhibited 195 cytopathic effects during infection and the viral replication ( Figure 4B , C, D, S5, and S6). Overall, five compounds, inhibiting downstream signaling of GFRs, prevented SARS-CoV-2 replication at clinically achievable concentrations ( Figure 4B and 5) (Eskens et al., 2001; Fucile et al., 2014; Martinez-Garcia et al., 2012; Munster et al., 2016; Sarker et al., 2015) , emphasizing the importance of GFR signaling during SARS-CoV-2 infection and revealing clinically available 200 treatment options as drug candidates for COVID-19.

With the rapid spreading of the COVID-19 pandemic, investigating the molecular mechanisms underlying SARS-CoV-2 infection are of high importance. In particular, the processes underlying infection and host-cell response remain unclear. These would offer potential avenues for 205

pharmacological treatment of COVID-19. Here, we report global, differential phosphorylation analysis of host cells after infection with intact SARS-CoV-2 virus. We could identify phosphorylation sites on numerous viral proteins in cells, showing that they can undergo efficient modification in infected cells. Until now, we can only speculate about the host kinases involved and the functions driven by PTMs, which will be an important topic for follow-up studies. For 210 SARS-CoV-1, it was shown that modification of viral proteins can lead to regulation of RNA binding of the nucleoprotein (Wu et al., 2009 ) and is needed for viral replication. Although similar effects in SARS-CoV-2 are likely, this remains to be studied in this novel virus. A recent paper analyzed the interaction profile of SARS-CoV-2 proteins expressed in HEK293T cells (Gordon et al., 2020) . For the heavily phosphorylated nucleoprotein they could identify interactions the host 215 casein kinases, which might indicate possible modification events by the latter. Also for the ORF9b/protein 9b that we found modified in cells, interaction mapping identified MARK kinases as interaction partners.

By exploring the signaling changes inside the host cell, we could gain important insights into host cell signaling during infection. We found essential GFR signaling pathways activated such as 220 EGFR or PDGFR, together with a plethora of RhoGTPase associated signaling molecules. We could furthermore show modulation of the splicing machinery, in line with previous results indicating dependency of viral in vitro pathology on the host spliceosome (Bojkova et al., 2020) . The same is true for metabolic reprogramming, where we could find differential post-translational modification of most members of the carbon metabolic pathways, namely glycolysis, pentose 225 phosphate and TCA cycle.

A number of drugs to treat COVID-19 have been suggested, largely based on bioinformatics analyses of genetics or cellular data (Gordon et al., 2020; Li et al., 2020; Wang, 2020) . However, for many of these compounds, studies explaining their working mechanisms in the context of SARS-CoV-2 or viral assays to determine their efficacy of blocking viral replication in cell models 230

of SARS-CoV-2 infection, are missing. While monitoring signaling changes in host cells, we observed activation of GFR signaling cascades after infection, consistent with other viruses relying on the receptors themselves or elicited signal transduction (Eierhoff et al., 2010; Kung et al., 2011; Lupberger et al., 2011; Ueki et al., 2013; Wu et al., 2017; Zhu et al., 2009) . From our data we could not clearly conclude which GFR might be activated and thus tested whether GFR 235 downstream signaling inhibition can prevent SARS-CoV-2 replication, as reported for some other viruses (Baturcam et al., 2019; Pleschka et al., 2001) .Previously, temporal kinome analysis identified antiviral potential of RAS/RAF/MEK and PI3K/AKT/ for MERS-CoV (Kindrachuk et al., 2015) . By targeting the RAS/RAF/MEK and PI3K/AKT/mTOR downstream axes of GFR signaling, we found efficient inhibition of cytopathic effects and cell destruction (Figure 4 ). GFR 240 signaling has already been shown to play a role in diverse virus infections as well as in fibrosis induction by SARS-CoV-1 (Beerli et al., 2019; Kung et al., 2011; Lupberger et al., 2011; Pleschka et al., 2001; Ueki et al., 2013; . Thus, our results in cytopathic effects might indeed indicate cytoprotective roles for GFR signaling axes during SARS-CoV-2 infection and possible development of fibrosis (Luo et al., 2020) . Notably, some 245

inhibitors used in our study such as omipalisib were already shown to suppress fibrosis progression in patients with idiopathic pulmonary fibrosis, which may share deregulation of signaling pathways involved in lung fibrosis of coronavirus patients . These findings suggest that inhibitors of GFR downstream signaling may bring benefit to COVID-19 patients independently of their antiviral activity. 250

Taken together, this study provides new insights into molecular mechanisms elicited by SARS-CoV-2 infection. Proteomic analyses revealed several pathways that are rearranged during infection and showed that targeting of those pathways is a valid strategy to inhibit cytopathic effects triggered by infection. 

The authors filed a patent application on the use of GFR signaling inhibitors for the treatment of COVID-19. (A) Experimental scheme. Caco-2 cells were infected with SARS-CoV-2 for one hour, washed and incubated for additional 24 hours. Proteins were extracted and prepared for bottom-up proteomics. All ten conditions were multiplexed using TMT10 reagents. 250 µg of pooled samples were used for whole cell proteomics (24 Fractions) and the remainder (~1 mg) enriched 280

for phosphopeptides by Fe-NTA. Phosphopeptides were fractionated into 8 fractions and concatenated into 4 fractions. All samples were measured on an Orbitrap Fusion Lumos.

(B) Volcano plot showing fold changes of infected versus mock cells for all 15,392 quantified phosphopeptides. P values were calculated using an unpaired, two-sided student's t-test with equal variance assumed and adjusted using the Benjamini Hochberg FDR method (N = 5). 285

Orange or blue points indicate significantly increased or decreased phosphopeptides, respectively.

(C) Volcano plot showing differences between SARS-CoV-2 and mock infected cells in total protein levels for all 7,150 quantified proteins. P values were calculated using an unpaired, twosided student's t-test with equal variance assumed and adjusted using the Benjamini Hochberg 290

FDR method (N = 5). Orange or blue points indicate significantly increased or decreased phosphopeptides, respectively.

(D) Distribution of phosphorylation sites identified across modified amino acids. See also Figure  S1 and Tables S1 and S2. (E) Scatter plot showing correlation between fold changes of phosphopeptides compared to fold changes of total proteins levels. Two subsets of phosphopeptides were detected: one was 315 mainly regulated by differential modification (indicated in yellow), the second by changes in protein abundance.

(F) STRING network analysis of proteins decreased in total protein levels ( Figure 1C) . Inserts indicate pathways found in the network Fig.3 . Drug-target phosphoprotein network analysis identifies growth factor signaling as 320 central hub for possible intervention by repurposed drugs.

(A) Proteins significantly increased in phosphorylation (FC > 1, FDR < 0.05) were subjected to ReactomeFI pathway analysis and overlaid with a network of FDA-approved drugs. The network was filtered for drugs and drug targets only, to identify pathways that could be modulated by drug repurposing. Red lines indicate drug-target interactions, grey lines protein-protein 325

interactions. Identified drugs are represented with yellow rectangles, while proteins are represented by blue circles. Pictilibsib: 0.625 µM, 2.5 µM, 10 µM; omipalisib: 0.01 µM, 0.625 µM, 2.5 µM; sorafenib: 2.5 µM, 5µM, 10 µM; RO5126766: 2.5 µM, 5 µM, 10 µM; lonafarnib: 0.6 µM, 2.5 µM, 10 µM. N = 3, one representative pictures are shown, two more replicates are shown in Figure S4 . Scale bar represents 100 µm.

Upon infection growth factor signaling is activated and leads among others to the induction of Phosphoinositol 3 Kinase (PI3K) and Mitogen Activated Protein Kinase (MAPK) signaling events. Inhibition of either axis of the two (by Sorafenib, RP5126766, Lonafarnib, Pictilisib or Omapalisib) leads to decreased replication of SARS-CoV-2 inside the host cell.

Human Caco-2 (Caucasian male) cells, derived from colon carcinoma, was obtained from the Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ; Braunschweig, Germany). 365

Cells were grown at 37°C in Minimal Essential Medium (MEM) supplemented with 10% fetal bovine serum (FBS) and containing 100 IU/ml penicillin and 100 µg/ml streptomycin. All culture reagents were purchased from Sigma.

SARS-CoV-2 was isolated from samples of travelers returning from Wuhan (China) to Frankfurt 370 (Germany) using human colon carcinoma cell line CaCo-2 as described previously 12 . SARS-CoV-2 stocks used in the experiments had undergone one passage on CaCo-2 cells and were stored at -80° C. Virus titers were determined as TCID50/ml in confluent cells in 96-well microtiter plates.

Confluent layers of CaCo-2 cells in 96-well plates were infected with SARS-CoV-2 at MOI 0.01.

with different drug concentration in 96-well plates. The viability was measured using the Rotitest Vital (Roth) according to manufacturer's instructions. Data for each condition was collected for at least three biological replicates. For dose response curves, data was fitted with all replicates using OriginPro 2020 with the following equation:

385 IC50 values were generated by OriginPro 2020 together with metrics for curve fits.

Effect of selected compounds on viral replication was assessed by staining of double-stranded RNA, which has been shown to be sufficient for measurement of SARS-CoV-1 replication (Weber et al., 2006) . Briefly, cells were fixed with acetone/methanol (40:60) solution 48 h post infection. 390

Immunostaining was performed using a monoclonal antibody directed against dsRNA (1:150 dilution, SCICONS J2, mouse, IgG2a, kappa chain, English & Scientific Consulting Kft., Szirák, Hungary), which was detected with biotin-conjugated secondary antibody (1:1000 dilution, Jackson ImmunoResearch) followed by application streptavidin, peroxidase conjugate (1:3000 dilution, Sigma Aldrich). Lastly, the dsRNA positive cells were visualized by addition of AEC 395

substrate.

The sample preparation was performed as described previously . Briefly, lysates were precipitated by methanol/chloroform and proteins resuspended in 8 M Urea/10 mM EPPS pH 8.2. Concentration of proteins was determined by Bradford assay and 300 µg of 400 protein per samples was used for digestion. For digestion, the samples were diluted to 1 M Urea with 10mM EPPS pH 8.2 and incubated overnight with 1:50 LysC (Wako Chemicals) and 1:100 Sequencing grade trypsin (Promega). Digests were acidified using TFA and tryptic peptideswere purified by tC18 SepPak (50 mg, Waters). 125 µg peptides per sample were TMT labelled and the mixing was normalized after a single injection measurement by LC-MS/MS to equimolar 405 ratios for each channel. 250 µg of pooled peptides were dried for offline High pH Reverse phase fractionation by HPLC (whole cell proteome) and remaining 1 mg of multiplexed peptides were used for phospho-peptide enrichment by High-Select Fe-NTA Phosphopeptide enrichment kit (Thermo Fisher) after manufacturer`s instructions. After enrichment, peptides were dried and resuspended in 70% acetonitrile/0.1% TFA and filtered through a C8 stage tip to remove 410

contaminating Fe-NTA particles. Dried phospho-peptides then were fractionated on C18 (Empore) stage-tip. For fractionation C18 stagetips were washed with 100% acetonitrile twice, followed by equilibration with 0.1% TFA solution. Peptides were loaded in 0.1% TFA solution and washed with water. Elution was performed stepwise with different acetonitrile concentrations in 0.1% Triethylamine solution (5%, 7.5%, 10%, 12.5%, 15%, 17.5%, 20%, 50%). The eight 415

fractions were concatenated into four fractions and dried for LC-MS.

Peptides were fractionated using a Dionex Ultimate 3000 analytical HPLC. 250 µg of pooled and purified TMT-labeled samples were resuspended in 10 mM ammonium-bicarbonate (ABC), 5% ACN, and separated on a 250 mm long C18 column (X-Bridge, 4.6 mm ID, 3.5 µm particle size; 420

Waters) using a multistep gradient from 100% Solvent A (5% ACN, 10 mM ABC in water) to 60% Solvent B (90% ACN, 10 mM ABC in water) over 70 min. Eluting peptides were collected every 45 s into a total of 96 fractions, which were cross-concatenated into 24 fractions and dried for further processing.

All mass spectrometry data was acquired in centroid mode on an Orbitrap Fusion Lumos mass spectrometer hyphenated to an easy-nLC 1200 nano HPLC system using a nanoFlex ion source (ThermoFisher Scientific) applying a spray voltage of 2.6 kV with the transfer tube heated to 300°C and a funnel RF of 30%. Internal mass calibration was enabled (lock mass 445.12003 m/z). Peptides were separated on a self-made, 32 cm long, 75µm ID fused-silica column, packed in 430 house with 1.9 µm C18 particles (ReproSil-Pur, Dr. Maisch) and heated to 50°C using an integrated column oven (Sonation). HPLC solvents consisted of 0.1% Formic acid in water (Buffer A) and 0.1% Formic acid, 80% acetonitrile in water (Buffer B).

For total proteome analysis, a synchronous precursor selection (SPS) multi-notch MS3 method was used in order to minimize ratio compression as previously described (McAlister et al., 2014) .

Individual peptide fractions were eluted by a non-linear gradient from 7 to 40% B over 90 minutes followed by a step-wise increase to 95% B in 6 minutes which was held for another 9 minutes. Full scan MS spectra (350-1400 m/z) were acquired with a resolution of 120,000 at m/z 200, maximum injection time of 100 ms and AGC target value of 4 x 10 5 . The 20 most intense precursors with a charge state between 2 and 6 per full scan were selected for fragmentation ("Top 20") and isolated 440 with a quadrupole isolation window of 0.7 Th. MS2 scans were performed in the Ion trap (Turbo) using a maximum injection time of 50ms, AGC target value of 1.5 x 10 4 and fragmented using CID with a normalized collision energy (NCE) of 35%. SPS-MS3 scans for quantification were performed on the 10 most intense MS2 fragment ions with an isolation window of 0.7 Th (MS) and 2 m/z (MS2). Ions were fragmented using HCD with an NCE of 65% and analyzed in the Orbitrap 445 with a resolution of 50,000 at m/z 200, scan range of 110-500 m/z, AGC target value of 1.5 x10 5 and a maximum injection time of 120ms. Repeated sequencing of already acquired precursors was limited by setting a dynamic exclusion of 45 seconds and 7 ppm and advanced peak determination was deactivated.

For phosphopeptide analysis, each peptide fraction was eluted by a linear gradient from 5 to 32% 450 B over 120 minutes followed by a step-wise increase to 95% B in 8 minutes which was held for another 7 minutes. Full scan MS spectra (350-1400 m/z) were acquired with a resolution of 120,000 at m/z 200, maximum injection time of 100 ms and AGC target value of 4 x 10 5 . The 20 most intense precursors per full scan with a charge state between 2 and 5 were selected for fragmentation ("Top 20"), isolated with a quadrupole isolation window of 0.7 Th and fragmented 455

via HCD applying an NCE of 38%. MS2 scans were performed in the Orbitrap using a resolution of 50,000 at m/z 200, maximum injection time of 86ms and AGC target value of 1 x 10 5 . Repeated sequencing of already acquired precursors was limited by setting a dynamic exclusion of 60 seconds and 7 ppm and advanced peak determination was deactivated.

Raw files were analyzed using Proteome Discoverer (PD) 2.4 software (ThermoFisher Scientific). Spectra were selected using default settings and database searches performed using SequestHT node in PD. Database searches were performed against trypsin digested Homo Sapiens SwissProt database, SARS-CoV-2 database (Uniprot pre-release). Static modifications were set as TMT6 at the N-terminus and lysines and carbamidomethyl at cysteine residues. 465

Search was performed using Sequest HT taking the following dynamic modifications into account: Oxidation (M), Phospho (S,T,Y), Met-loss (Protein N-terminus), Acetyl (Protein Nterminus) and Met-loss acetyl (Protein N-terminus). For whole cell proteomics, the same settings were used except phosphorylation was not allowed as dynamic modification. For phosphoproteomics all peptide groups were normalized by summed intensity normalization and then 470 analyzed on peptide level. For whole cell proteomics normalized PSMs were summed for each accession and data exported for further use.

Unless otherwise stated significance was tested by unpaired, two-sided students t-tests with 475 equal variance assumed. Resulting P values were corrected using the Benjamini-Hochberg FDR procedure. Adjusted P values smaller/equal 0.05 were considered significant. For phosphoproteomics an additional fold change cutoff was applied (log2 > |1|), while for total protein levels, due to different dynamic range, a fold change cutoff of log 2 > |0.5| was applied.

Kinase motifs of phosphopeptides from SARS-CoV-2 proteins were predicted using NetPhos 3.1 (Blom et al., 1999) and GPS 5.0 (stand-alone version) using the fasta-file of the Uniprot prerelease which was also used for the proteomics data analysis. (Blom et al., 2004; Wang et al., 2020) . For NetPhos, only Kinases with a score above 0.5 were considered as positive hits. For GPS 5.0, sequences were submitted separately for S/T-and Y-kinases and the score threshold 485 was set to "high". For the final list in Supplementary Table 3 , only the top hits with the highest scores were considered.

Z-scores were calculated for each phospho-site and the total protein levels individually. Phosphosites were collapsed by average. For merging phosphorylation and total protein levels 490 Z-scores for collapsed phosphorylation and protein level were added for each condition and replicate. Thus, both negative Z-scores (downregulation) will produce a lower combined Z-score and vice versa two positive Z-scores will produce a larger combined Z-score. Next, Euclidean distance correlation for all possible protein-protein pairs were calculated, taking all conditions and replicates individually into account. Heatmap was then build by Euclidean distance 495 hierarchical clustering of correlation matrix.

Pathway enrichment analysis was performed by ReactomeFI cytoscape plugin or by STRING functional enrichment analysis. Both analysis used Reactome database for pathway annotations.

All proteins were loaded into ReactomeFI cytoscape plugin to visualize protein-protein functional interaction network. Next, drugs were overlaid by ReactomeFI and network was filtered for the drugs and the first interacting partners. Layout was calculated by yFilesLayout algorithm.

All proteins showing significant regulation were loaded by OmicsVisualizer cytoscape plugin and 505 STRING interaction network was retrieved with a confidence cutoff of 0.9. For EGFR subnetwork, EGFR was selected with first interacting neighbors.

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2019) partner repository with the dataset 510 identifiers PXD018357.

Supplementary Shown are Protein UniProt accessions together with gene names and modification sites (numbers in brackets indicate localization confidence). Log2 ratios between mock and SARS-CoV-2 infected cells were calculated together with P values. P values were adjusted using Benjamini Hochberg FDR adjustment. Additionally replicate quantification values are given. Modified amino acid, position in peptide, site probability, peptide sequence, number of modified PSMs, unmodified PSMs, protein accession, protein description, position in protein and modification motifs are given for all identified viral modification sites. Additionally, results of kinase predictions by NetPhos 3.1 and GPS5 are added.

belonging to Cluster I ( Figure 3A /B/C).

Reactome pathway, number of genes found in pathway, enrichment FDR and individual genes in pathway are given. Reactome pathway, number of genes found in pathway, enrichment P values and individual genes in pathway are given. Table 6 : Reactome Pathway enrichment analysis for proteins found belonging to Cluster III ( Figure 3A ). Reactome pathway, number of genes found in pathway, enrichment FDR and individual genes in 540 pathway are given. Table 7 : Reactome Pathway enrichment analysis for proteins found significantly decreased in total protein levels ( Figure 1C ). 

Growth factor receptor Growth factor receptor Growth factor receptor (B) Heatmaps for phosphoproteome (left) and total proteome (right). All quanti ed measurements were Z scored and hierarchical clustering carried out with Euclidean distance measure. Supplementary Fig.2 . Drug-target network analysis of proteins with signi cantly decreased phosphorylation. ReactomeFI network was built from all proteins found signicantly decreased in phosphorylation (log2 < -1, FDR < 0.05) and overlaid with available drugs. Blue circles indicate proteins, yellow rectangles identi ed drugs, and lines functional interactions. 

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We thank the Quantitative Proteomics Unit (IBC2, Goethe University Frankfurt) for support and expertise on LC-MS instrumentation and data analysis and Christiane Pallas and Lena Stegmann for support of with experimental work.