Abstract

   In the last months, many studies have clearly described several
   mechanisms of SARS-CoV-2 infection at cell and tissue level, but the
   mechanisms of interaction between host and SARS-CoV-2, determining the
   grade of COVID-19 severity, are still unknown. We provide a network
   analysis on protein–protein interactions (PPI) between viral and host
   proteins to better identify host biological responses, induced by both
   whole proteome of SARS-CoV-2 and specific viral proteins. A host-virus
   interactome was inferred, applying an explorative algorithm (Random
   Walk with Restart, RWR) triggered by 28 proteins of SARS-CoV-2. The
   analysis of PPI allowed to estimate the distribution of SARS-CoV-2
   proteins in the host cell. Interactome built around one single viral
   protein allowed to define a different response, underlining as ORF8 and
   ORF3a modulated cardiovascular diseases and pro-inflammatory pathways,
   respectively. Finally, the network-based approach highlighted a
   possible direct action of ORF3a and NS7b to enhancing Bradykinin Storm.
   This network-based representation of SARS-CoV-2 infection could be a
   framework for pathogenic evaluation of specific clinical outcomes. We
   identified possible host responses induced by specific proteins of
   SARS-CoV-2, underlining the important role of specific viral accessory
   proteins in pathogenic phenotypes of severe COVID-19 patients.

   Subject terms: Protein-protein interaction networks, Viral infection

Introduction

   Whilst COVID-19 predominantly affects the respiratory system, it is a
   multisystem disease, with a wide spectrum of clinical presentations
   from asymptomatic, mild and moderate, to severe, fulminant
   disease^[46]1. Host conditions and comorbidities (age, obesity,
   diabetes, hypertension, organ damages, inflammation and coagulation
   dysfunctionality), represented risk factors for severe and fatal
   disease courses^[47]2, but the mechanisms of host-SARS-CoV-2
   interaction, activating pathological pathways and influencing severity,
   are still unknown.

   Recently, several studies described many mechanisms of SARS-CoV-2
   infection at cell and tissue level. It was observed that the
   replication of SARS-CoV-2, as well as all +RNA viruses, occurs in the
   cytoplasm of the host cell, inducing a membrane rearrangement of rough
   endoplasmic reticulum (ER) membranes into double-membrane
   vesicles^[48]3,[49]4. NSP8, NSP7 and NSP12, yield the RNA polymerase
   activity of NSP8, and are assembled into the replicase–transcriptase
   complex, generating anti-sense (−) RNAs, templates for positive-sense
   genome (+) and mRNA transcripts^[50]5,[51]6. During virus entry, a
   well-known process is the cleavage of S protein by FURIN on the cell
   membrane, which lead to the split S protein into two subunits, S1 and
   S2, which the last can interact with ACE2 (refs. ^[52]7,[53]8).
   However, the SARS-CoV-2–host interaction is not restricted to local
   infection, but it triggers a systemic reaction, including the
   activation of the Bradykinin Storm, as described in many severe
   COVID-19 patients^[54]9. Indeed, SARS-CoV-2 infection causes from one
   side a decrease of ACE level in the lung cells and, on the other side,
   an increase of ACE2 level, leading to increase Bradykinin (BK)
   level^[55]9. BK is produced from an inactive pre-protein Kininogen-1
   (KNG1) through the activation by the serine protease kallikrein^[56]10.
   Furthermore, the excess of BK can lead to vasodilatation, hypotension
   and hypokalaemia^[57]9,[58]11, which is associated with
   arrhythmia^[59]12,[60]13. All these clinical conditions have been
   widely reported in COVID-19 patients^[61]14–[62]16. Furthermore,
   microvascular injuries, due to systemic inflammatory response and
   endothelial dysfunction, were frequently found in severe COVID-19
   patients^[63]17. Moreover, myocardial injuries were found to be linked
   to the risk of fatal outcome in COVID-19 patients^[64]15,[65]18,[66]19.
   43% of the patients with severe COVID-19 (21% of all COVID-19 patients)
   presented myocardial injury, showing an increased risk of myocardial
   injury of 4.74-fold in severe compared with non-severe patients^[67]20.
   Although microvascular injuries and microthrombi formation frequently
   occurred in severe COVID-19 patients, the role of SARS-CoV-2 in this
   phenotype is not completely explained yet.

   Although many aspects of COVID-19 pathogenesis and mechanisms of
   SARS-CoV-2 have been investigated, only few papers describe the
   interactions among SARS-CoV-2 proteins by wet experiments. The
   influence of SARS-CoV-2 on transcriptome, proteome, ubiquitinome and
   phosphoproteome of a lung-derived human cell line, was described
   through a multi-omics approach.

   Virus–host interactome by the computational approach has been applied
   to COVID-19 for drug repurposing^[68]21, allowing the identification of
   new drug targets^[69]22 and contributing to explain clinical
   manifestations^[70]23,[71]24. The structural information on SARS-CoV-2
   proteins and their interactions with human proteins and other viral
   proteins, allowed to better understand the mechanisms of SARS-CoV-2
   infection, also comparing it with SARS-CoV^[72]25.

   One of the most interesting models of SARS-CoV-2–host interactions was
   carried out on Spike-receptor interactions in other Human Coronaviruses
   (H-CoV). This network-based analysis of H-CoV–host interaction has
   provided a theoretic model for H-CoV infections applicable to
   SARS-CoV-2 pathogenesis, revealing biologically and clinically relevant
   molecular targets of three H-CoV infections^[73]26.

   The interactome based on PPI and gene expression data, have been
   applied also to uncover the molecular origins of phenotypes of other
   complex diseases^[74]27,[75]28, in order to better define the
   mechanisms of COVID-19 pathogenesis.

   The understanding of all mechanisms of SARS-CoV-2 infection also passes
   by overall visualization of biological reactions and pathways involved
   in COVID-19 and H-CoV infections^[76]29–[77]31.

   In this context, defining the host response induced by specific viral
   proteins would be of great importance and can guide the identification
   of functional viral targets, helping to better define the pathologic
   phenotypes of the infection.

   Here, we carry out a network analysis on PPI to better identify host
   biological response induced by SARS-CoV-2. Furthermore, the interactome
   analysis was applied to design network of SARS-CoV-2–host proteins that
   could lead to a Bradykinin Storm. We used three different applications
   to identify interacting proteins: 200 proteins that interact closely
   with 28 SARS-CoV-2 proteins, 50 protein interactomes around each
   SARS-CoV-2 protein and 200 protein associated with KNG1, the pre-cursor
   of BK. The proteins found in all three approaches were analysed by gene
   enrichment and pathways associated with proteins were identified
   analysis (Fig. [78]1).

Fig. 1. Workflow to describe SARS-CoV-2 host interactome analysis based on
PPI data.

   [79]Fig. 1
   [80]Open in a new tab

   Human-human PPI database has been merged with Host-SARS-CoV-2 PPIs
   databases and then an explorative algorithm (Random Walk with Restart)
   was applied in order to identify host response to SARS-CoV-2 infection,
   pathways modulations induced by specific SARS-CoV-2 protein and link
   with pathogenic conditions in COVID-19.

Results

Interactions with the whole proteome of SARS-CoV-2

   To define the massive effect of SARS-CoV-2 infection on the host cell,
   an interactome between the entire set of SARS-CoV-2 proteins and host
   was carried out. Observing the colours’ distribution, it is possible to
   distinguish three different areas, corresponding to as many subcellular
   districts, where SARS-CoV-2 proteins seem to be distributed (Fig.
   [81]2). In fact, N and some non-structural proteins (nsp1, nsp4 and
   nsp15) are posed around nuclear proteins, while S, along with nsp5,
   nsp10, nsp12, nsp13, nsp14, nsp16, ORF1a and ORF9b, were among
   cytosolic and membrane proteins, at the bounder of the interactome.
   Protein M and many accessory and not-structural proteins (NS7b, nsp6,
   nsp7, ORF3a, ORF7a, ORF8, ORF10, and ORF14) are around mitochondrial
   and endoplasmic proteins. At first, Gene Enrichment Analysis on Gene
   Ontology databases highlighted the processes DNA replication, synthesis
   of RNA primer (GO:0006269) and peptide antigen transport (GO:0046968)
   were the most enriched (66.33 fold enrichment and False Discovery Rate,
   FDR < 5%). To further dissect the interactions with the entire proteome
   of SARS-CoV-2 and to better understand which pathways could be
   involved, enrichment analysis was carried out on proteins reported in
   the interactome, using WikiPathways and Kyoto Encyclopaedia of Genes
   and Genomes (KEGG) databases. WikiPathways gene enrichment analysis
   revealed biological pathways of DNA replication, ubiquitination, and
   proteasome, with high significance (FDR < 0.01%). KEGG pathway
   enrichment analysis revealed DNA and RNA replication pathways as the
   most significant pathways (FDR < 0.01%), as well as signalling pathways
   and viral infection pathways (Supplementary Fig. [82]1). We described
   the high values of betweenness centrality and degree for nine host
   proteins: two proteins multi-organelle amyloid-beta precursor protein
   (APP) and dual-specificity protein phosphatase (PTEN); the membrane
   protein Sodium/potassium-transporting ATPase subunit alpha-1 (ATP1A1);
   two cytoplasmic proteins, 14-3-3 protein theta (YWHAQ) and Ubiquitin
   (UBC); two proteins of ER and Golgi apparatus, Sarcoplasmic/ER calcium
   ATPase 2 (ATP2A2) and Unconventional myosin-VI (MYO6); the
   mitochondrial ATP synthase subunit alpha (ATP5F1A); one nuclear
   receptor for export of RNAs, Exportin-1 (XPO1), suggesting their main
   role in this infection (Supplementary Fig. [83]2, Supplementary Table
   [84]1).

Fig. 2. PPI interactome, based on human PPI and SARS-CoV-2-host interactions,
with top 200 closest proteins identified by RWR, using together 28 proteins
of SARS-CoV-2 as seeds for only one RWR run.

   [85]Fig. 2
   [86]Open in a new tab

   Different colours of node and edges represent different locations in
   the cell, which are reported in the legend and in Supplementary Table
   [87]1. This network was represented by Force Atlas algorithm.

Host response induced by the specific protein of SARS-CoV-2

   To define the effect of specific viral proteins on host response,
   interactomes were built, choosing one specific viral protein as seed,
   imposing to find the 50 closest proteins. These analyses allowed to
   produce 28 restricted interactomes, which defined the strictest
   biological interactions associated to seed proteins both among human
   proteins and other viral proteins. In Supplementary Fig. [88]3 the
   interactomes for structural proteins were reported, while in
   Supplementary Figs. [89]4 and [90]5 the interactomes for accessory and
   non-structural proteins were plotted.

   Most of the interactomes have few viral proteins into the reconstruct
   PPI network (i.e., M, nsp1, and ORF3a), while into other interactomes
   the viral seed protein shared own human target proteins with other
   viral proteins or interacts directly with other viral proteins, such as
   nsp3, nsp10, ORF6 and ORF10.

   The lists of proteins for each reduced interactome were submitted to
   gene enrichment analysis on WikiPathways and KEGG pathway databases
   (Supplementary Tables [91]2 and [92]3, respectively). For each single
   viral protein interactome, the top 5% with the smallest p values of
   gene enrichment analysis was selected, reporting for KEGG and
   WikiPathways in Fig. [93]3A and B, respectively.

Fig. 3. Heatmap reporting top 5% smallest P values of Gene Enrichment
analysis on list of proteins, obtained by every interactome of single viral
seed.

   [94]Fig. 3
   [95]Open in a new tab

   The values were reported as −Log10 (p value). A P values of Gene
   Enrichment based on KEGG 2019. B P values of Gene Enrichment based on
   WikiPathways 2019.

   For KEGG database the gene enrichment analysis on interactomes of NS7b,
   ORF1a, ORF3a, and ORF8 showed pathway clusters highly significant and
   consistent with possible pathogenic mechanisms, such as the activation
   of the complement and of the coagulative cascade^[96]32, and the
   TGF-β-dominated immune response^[97]33. In particular, the NS7b
   interactome revealed SNARE interactions in the vesicular transport
   pathway, describing the mechanisms of intracellular vesicle trafficking
   and secretion, as the most significant pathway among all interactome
   (FDR < 0.00001%). The ORF1a interactome revealed a cluster of pathways,
   composed by Adherent junctions, Bacteria host infection mechanisms,
   Regulation of actin cytoskeleton and phagocytosis, all showing the
   direct involvement of these accessory proteins in a viral entry in host
   cells (FDR < 0.001%). The ORF8 interactome showed a pathway cluster
   involving complement and coagulation cascades and cardiovascular
   pathology (FDR < 0.3%). The ORF3a interactome revealed the TGF-β and
   Cytokine signalling, endocytosis, and Vasopressin-regulation pathways
   (FDR < 0.01%). From pathway point of view, Proteasome pathway was
   involved in the interactome of M protein as well as for ORF7a, ORF8
   (FDR < 0.05%) (Fig. [98]3A).

   For WikiPathways database the gene enrichment analysis showed results
   consistent with KEGG gene enrichment for ORF1a, ORF3a and ORF8, and
   added new pathway cluster for nsp1 (FDR < 0.00001%) with DNA
   replication and modification pathways (Fig. [99]3B).

Computational investigation on Bradykinin Storm in COVID-19

   As the virus-host interactome has been efficient to describe the
   response to specific viral proteins, this method was applied to
   reconstruct the possible involvement of SARS-CoV-2 proteins in
   triggering the Bradykinin Storm during the infection. In this case,
   KNG1, precursor of bradykinin made by proteolysis cleavage, was
   considered as seed for RWR, imposing 200 closest proteins as limit to
   stop the algorithm. The resulting interactome showed NS7b, ORF3a, ORF8,
   and S as proximal to the seed protein, suggesting their role in the
   modulation of biological processes around KNG1. Indeed, it would
   provide a direct influence of NS7b and ORF3a proteins to activation of
   BK. In fact, both NS7b and ORF3a interact with the same target,
   endothelin converter enzyme (ECE1), and cell surface endopeptidase that
   converts big endothelin-1 to pressure peptide endothelin-1 (ET-1) and
   inactivates BK (Fig. [100]4A). The enrichment analysis on WikiPathways
   and KEGG databases revealed biological pathways of Complement and
   coagulation cascades (FDR < 0.000001%). Finally, to quantify the direct
   effect of S, ORF8, ORF3a, NS7b on Complement and coagulation cascades
   pathways, all proteins that belonged to the most significantly enriched
   pathways, were highlighted on the interactome: Human Complement System
   WP2806 for WikiPathways (22 of 97 genes; adj P value 2.9E−21) and
   Complement and coagulation cascades for KEGG (24 of 79 genes; adj P
   value 5.96E−27). ORF8 showed the direct interactions with Fibrinogen
   alpha chain (FGA), described in both pathways, and urokinase-type
   plasminogen activator (PLAU), tissue-type plasminogen activator (PLAT)
   and Plasminogen activator inhibitor 1 (SERPINE1).

Fig. 4. PPI interactome based on human PPI and SARS-CoV-2-host interactions,
with top 200 closest proteins identified by RWR, using Kininogen-1 as seed.

   [101]Fig. 4
   [102]Open in a new tab

   A PPI Intercatome, where larger nodes and edges in red represent
   interactions via NS7b and ORF3a-ECE1-KNG1 (BK). The nodes in red are
   human proteins, while the nodes in green are virus proteins. B KEGG
   human pathway and WikiPathways Gene Enrichment analyses for 200
   proteins closest to KNG-1. C PPI interactome, where proteins belonged
   to Human Complement System WP2806 (modes in purple) and, Complement and
   coagulation cascades (nodes in blue) pathways were highlighted. The
   proteins that belonged to both pathways were marked in light blue. The
   nodes in red are human proteins, while the nodes in green are virus
   proteins.

Discussion

   In this study, we built a functional interactome between SARS-CoV-2 and
   human proteome through RWR algorithm, to identify biological mechanisms
   and cell responses during SARS-CoV-2 infection and to propose a model
   of infection contributing to a better understanding of COVID-19
   pathogenesis.

   SARS-CoV-2–host interactome allowed to mirror likely mechanisms of
   infections in different subcellular districts.

   The high likelihood of our model to in vivo real mechanisms strengthens
   the representative power of the interactome and the explorative
   algorithm to simulate biological processes.

   Moreover, the proximity among specific viral proteins into the network,
   such as nsp7, nsp8, nsp9, nsp10, nsp12, nsp13, nsp14 involved in the
   viral RNA replication, can be a good way to investigate their possible
   function in viral infection. The closeness among S, nsp5, nsp16, ORF9b
   in the network is difficult to interpret, but S, nsp5 and ORF9b, along
   with N, had significant positive responses of IgG antibody in sera of
   COVID-19 patients^[103]34. In SARS-CoV infected cell culture, the
   location of nsp2 in the cytoplasm and to some extent in the nucleus, as
   well as ORF3a, ORF7b, ORF6 and M in ER, seems to be consistent with
   their location among nuclear and cytoplasmatic, and ER proteins
   respectively^[104]35.

   The involvement of proteasome and ubiquitination pathways, along with
   RNA replication, represent the principal pathways activated for
   assembly and replication of SARS-CoV-2 (ref. ^[105]36). In fact, strong
   increases in RNA-modifying proteins were revealed in cell culture after
   infection with SARS-CoV-2 (ref. ^[106]37). The ubiquitin-proteasome
   system deletes viral proteins to control the infection, but the virus
   can use them for its propagation^[107]38.

   The interactomes, built around a single protein of SARS-CoV-2, allowed
   to draw effects on cell, involving specific pathways. Vesicular
   transport mechanism by SNARE interactions, identified for NS7b, is used
   by pathogens to penetrate host cells through their membranes and in
   particular in SARS-CoV^[108]39,[109]40.

   The pathways showed in ORF3a and ORF8 interactomes suggested a possible
   modulation of the coagulation cascade and cardiovascular pathology in
   COVID-19 and the involvement in Cytokine storm and water homoeostasis
   in endothelial tissue, respectively.

   Binding of ORF3a and ORF8 to TGF-β-associated factors (TGFB1, TGFB2,
   LTBP1, TGFBR2, FURIN, BAMBI) supports a strong involvement to begging
   pro-inflammation state, while the direct interactions between ORF8 and
   components of the Coagulation pathway, such as FGB, FGA, C5, PLAU,
   PROS1, SERPINE1, PLAT and CLU^[110]21,[111]36 suggest a modulation of
   coagulation cascade by SARS-CoV-2. Moreover, a multi-omics analysis
   highlighted the role of ORF3a and ORF8 during SARS-CoV-2 infection:
   autophagy mechanisms regulation by ORF3a and nsp6; the modulation of
   innate immunity by M, ORF3a and NS7b; the Integrin-TGF-β-EGFR-RTK
   signalling perturbation by ORF8 (ref. ^[112]36).

   Although the biological function of the ORF8 protein of SARS-CoV-2
   remains unclear, the role of ORF8 in severe COVID-19 outcome might be
   supported by SARS-CoV-2 variant with a 382-nucleotide deletion (Δ382)
   found in Singapore in January–February 2020 linked to mild forms of
   COVID-19. This deletion truncates ORF 7b and locks ORF8 transcription
   and would be associated to less cytokine releasing during the acute
   phase of infection^[113]41,[114]42.

   The systemic inflammatory response and the relied endothelial
   dysfunction might be responsible for the microvascular injuries
   resulting in the formation of microthrombi, as well as a featuring
   microvascular thrombosis and haemorrhage pathology, viewed in the lung
   of COVID-19 patients^[115]43. The main pathways described in ORF3a and
   ORF8 interactomes, are consistent with the microvascular injuries and
   the development of cardiovascular complications, i.e., heart failure,
   myocarditis, pericarditis, vasculitis and cardiac arrhythmias observed
   in COVID-19 patients^[116]44. Such featuring symptoms, suggest specific
   mechanisms of host-virus interactions, and reinforce the role of ORF3a
   and ORF8 in the COVID-19 pathogenesis.

   To study the pathogenesis of COVID-19 in a systemic context, Bradykinin
   Storm was investigated by the interactome approach. We showed NS7b and
   ORF3a interacting with ECE1, which can inactivate BK^[117]45. Such
   observation suggests specific mechanisms of host-virus interactions,
   occurring in severe COVID-19 patients and reinforce the role of ORF3a
   to enhance the bradykinin dysregulation.

   These host-virus interactions could enhance a better viral fitness
   during the infection, as suggested by the interaction already observed
   with SARS-CoV between NS7b in ECE1 (ref. ^[118]36).

   This interaction system might suggest a hypothesis that can explain the
   possible enhancing effect on Bradykinin Storm in COVID-19: the locking
   of ECE1 activity due to NS7b and especially ORF3a interaction, could
   reduce the activation of the vasoconstrictor endotelin-1, and amplify
   the vasodilatation effect of BK.

   The ECE1 gene is much more expressed in all the tissues than ACE2 and
   ACE, especially in the lungs, where ECE1 is 128 fold and 3.8 fold
   expressed compared to ACE2 and ACE, respectively^[119]46. Significant
   down-expression of ECE1 protein was reported in endothelial cells
   infected by SARS-CoV-2 in vitro, resulting clearly consistent with
   effect of SARS-CoV-2 infection on BK modulation^[120]47.

   BK activation is associated with coagulation disorders: F12 activates
   plasma kallikrein (PK), which releases bradykinin by cleavage^[121]48.

   The results of our network-based model for the pathogenesis of
   SARS-CoV-2 mirror likely clinical manifestations in severe COVID-19
   patients, noting that analysis of published data about COVID-19 allows
   to synthesise and describe complex aspect of pathogenesis, as reported
   for inflammatory cytokines storm in COVID-19-induced respiratory
   failure^[122]49.

   This theoretical approach, combined with proteomic analysis in COVID-19
   patients, might suggests new targets for specific treatments, as
   described for interaction between nsp10 and NKRF to mediate IL-8
   expression, proposing this molecular mechanism as possible therapeutic
   target for anti-IL-8 and JUK inhibitor monoclonal antibodies^[123]50.

   In conclusion, we developed a network-based model for SARS-CoV-2
   infection, which could be a framework for pathogenic evaluation of
   specific clinical outcomes.

   Here, the PPI interactomes were used to identify mechanistic processes
   of the viral molecular machinery during SARS-CoV-2 infection, for viral
   survival and replication within the host. With this knowledge, proteins
   interactions crucial for pathogenesis could be discerned. We identified
   different host response induced by specific proteins of SARS-CoV-2,
   underlining the important role of ORF3a and ORF8 in phenotypes of
   severe COVID-19 patients. The interactome approach applied to the
   identification of biological reactions around KNG1, allowed to view
   NS7b and ORF3a interactions with ECE1, which might have a role to
   enhance the Bradykinin storm. This network-based model of
   SARS-CoV-2–host interaction could guide to develop novel treatments
   against specific viral proteins.

Materials and methods

   The virus–host interactome was made by merging SARS-CoV-2–host
   interaction data (PPI) from Intact^[124]51, with data from human PPI
   databases, such as BioGrid, InnateDB-All, IMEx, IntAct, MatrixDB,
   MBInfo, MINT, Reactome, Reactome-FIs, UniProt, VirHostNet, BioData,
   obtained by R packages PSICQUIC and biomaRt^[125]52,[126]53. For
   SARS-CoV-2–host interaction experimentally obtained, 1407 protein
   interactors and 2305 interactions were downloads from IntAct at 12
   September 2020. 28 SARS-CoV-2 proteins are used for all the analyses:
   E, M, N, S, nsp1, nsp2, nsp3, nsp4, nsp5, nsp6, nsp7, nsp8, nsp9,
   nsp10, nsp12, nsp13, nsp14, nsp15, nsp16, ORF1a, ORF3a, ORF6, ORF7a,
   NS7b, ORF8, ORF9b, ORF10, ORF14.

   To obtain functional information about PPI in vitro, to simulate
   biological reactions in infected cells and to explore cell response
   against viral infection, we employed a RWR algorithm, a
   state-of-the-art guilt-by-association approach^[127]54. This algorithm
   allows to establish a proximity network from a given protein (seed), to
   study its functions, based on the premise that nodes related to similar
   functions tend to lie close to each other in the network. For this
   study, two types of interactome were performed: the whole
   SARS-CoV-2–host interactome and an interactome per single SARS-CoV-2
   protein.

   In the first imputation, every protein of SARS-CoV-2 proteome was used
   as seeds, with the limit of 200 closest host’s proteins to every
   SARS-CoV-2 protein, every protein of SARS-CoV-2 proteome was chosen as
   seeds for RWR algorithm, imposing the limit of 200 closest host
   proteins to every SARS-CoV-2 protein. This analysis allowed to design
   SARS-CoV-2–host interactome, where the different colours correspond to
   the localization of the proteins within the cell.

   For the second kind of interactome, one SARS-CoV-2 per time was used as
   seed, lowering to the closest 50 proteins to define the induced
   biological response as better as possible.

   For each node, a score was computed as a measure of proximity to the
   seed protein^[128]26. In total, a large PPI interaction database was
   assembled, including 13334 nodes and 73584 interactions. Graphical
   representations of networks were performed by GEPHI 0.9.2 (ref.
   ^[129]55). To identify hub protein in the SARS-CoV-2–host interactome,
   the values of betweenness centrality and degree were plotted.
   Betweenness centrality score measure how a specific node is in-between
   other nodes and then can be considered a hub, while the degree of node
   corresponds to number of connections.

   Pathways of proteins involved in host response were tested by gene
   enrichment analysis on KEGG human pathways and WikiPathways
   databases^[130]56, the biological processes were identified by PANTHER
   GO^[131]57. To allow gathering of results for every running, the R
   package enrichR was used, an R interface to web-based tool ‘Enrichr’
   for analysing gene sets^[132]58. The Enrichr analysis was performed
   using these statistical parameters: p-value (Fisher exact test),
   q-value (adjusted p-value for False Discovery Rate, FDR). Results for
   KEGG and WikiPathways were considered significant with a revised
   p-value < 0.05. To infer pathways involved in a single viral
   interactome, gene enrichment analyses for each viral interactome were
   collected along with p-values, as reported in enrichR package output.
   P-values of every single enrichment analyses were transformed by the
   function x = −log10 (p-value) and the 5% of x values were plotted on
   heatmap by R package pheatmap^[133]59.

Limitations

   There are many experimental platforms for deriving such physical
   interactions, such as affinity purification mass-spectrometry (AP-MS)
   and yeast-two-hybrid (Y2H), which enable the accurate identification of
   interactions with a relatively long time.

   The scenario reported in this study refers to few experimental data
   available on public databases and could be different respect to real
   phenotypes of COVID-19 patients.

   The pathways’ analysis did not consider tissue and cell-type diversity.
   Finally, the low threshold established for the number of nodes found by
   RWR (200) limited the reconstruction of the entire pathways. However,
   this was a software-imposed threshold. Although such a network-based
   approach showed great potential in identifying mechanisms not yet
   observed, experimental tests will be necessary to confirm what we have
   described. This study does not report clinical data or the outcomes of
   patients, but our results resulted highly consistent with clinical
   cases reported in literature^[134]43,[135]44,[136]49 and in vitro
   studies^[137]21,[138]36,[139]37,[140]47. Moreover, this approach should
   be used to design adaptive clinical or experimental trials.

Supplementary information

   [141]Supplementary Figure Legends^ (13.2KB, docx)
   [142]Supplementary Figure 1^ (585.9KB, tif)
   [143]Supplementary Figure 2^ (136.2KB, tif)
   [144]Supplementary Figure 3^ (336.3KB, tif)
   [145]Supplementary Figure 4^ (1MB, tif)
   [146]Supplementary Figure 5^ (462KB, tif)
   [147]supplementary_table_1^ (43.5KB, xlsx)
   [148]supplementary_table_2^ (150.6KB, xlsx)
   [149]supplementary_table_3^ (133KB, xlsx)
   [150]supplementary_table_4^ (261.7KB, xlsx)

Acknowledgements