key: cord-0792947-hudoe7wf
authors: Eskandarzade, Neda; Ghorbani, Abozar; Samarfard, Samira; Diaz, Jose; Guzzi, Pietro H.; Fariborzi, Niloofar; Tahmasebi, Ahmad; Izadpanah, Keramatollah
title: Network for network concept offers new insights into host- SARS-CoV-2 protein interactions and potential novel targets for developing antiviral drugs
date: 2022-04-30
journal: Comput Biol Med
DOI: 10.1016/j.compbiomed.2022.105575
sha: 2457a3a0c9be3ff3e229c0642b876e6530edc984
doc_id: 792947
cord_uid: hudoe7wf

SARS-CoV-2, the causal agent of COVID-19, is primarily a pulmonary virus that can directly or indirectly infect several organs. Despite many studies carried out during the current COVID-19 pandemic, some pathological features of SARS-CoV-2 have remained unclear. It has been recently attempted to address the current knowledge gaps on the viral pathogenicity and pathological mechanisms via cellular-level tropism of SARS-CoV-2 using human proteomics, visualization of virus-host protein-protein interactions (PPIs), and enrichment analysis of experimental results. The synergistic use of models and methods that rely on graph theory has enabled the visualization and analysis of the molecular context of virus/host PPIs. We review current knowledge on the SARS-COV-2/host interactome cascade involved in the viral pathogenicity through the graph theory concept and highlight the hub proteins in the intra-viral network that create a subnet with a small number of host central proteins, leading to cell disintegration and infectivity. Then we discuss the putative principle of the “gene-for-gene and “network for network” concepts as platforms for future directions toward designing efficient anti-viral therapies.

Drug repositioning is a complicated process that involves multiple steps and requires various kinds 117 of data analysis followed by experimental validation. The explosion of the fast-growing 118 information in the databases pushes the drug repurposing toward using the computational 119 frameworks and bioinformatic tools for collecting and integrating numerous biological data 120 systematically. The first step in the drug repurposing workflow is data mining by searching related 121 databases or articles [21, 33] 

Network mapping is a type of post-genomic analysis in which visual information helps us examine 148 the hidden aspect of underlying connections at the second and third levels for a better 149 understanding of the dynamics of complex systems in biology or any other area of science. In 150 biology, a network is constructed by repositories of interactions data and subjected to statistical 151 analysis using computer-aided models based on the graph theory concept. In such a concept, 152 nodes/vertex represent drugs, genes, proteins, molecules, phenotypes, or any other biological units, 153

and edges represent functional similarities, physical interaction, mode of actions, mechanisms, or 154 any other directional or non-directional relationships [21] . Then various networks can be mapped 155 using different types of nodes and edges and classified into: gene regulatory networks, metabolic 156 networks, protein-protein interaction (PPIs) networks, drug-target interaction networks, drug-drug 157 interaction networks, drug-disease association networks, drug-side effect association networks, 158 disease-disease interaction networks [21] . During network analysis, each node has multiple scores 159 according to the information importance at that point which includes: degree value (the number of 160 edges of a node), clustering coefficient (represents the density of edges connecting to a node), 161 closeness centrality (how much a node is close to all other nodes), betweenness centrality (how 162 many times a node is on the shortest path between two subnetworks) [36] . Then, any drug-target 163 attributed data has been integrated into the network to introduce therapeutic biomarker candidates 164 [37] . Some useful protein-protein interaction databases for network mapping were reviewed by 165 Tanoli Database of Interacting Proteins (DIP) [25] . In a systematic review regarding ranking the protein-170 protein interaction databases from a user's perspective, 16 to predict human binding partners of 26 SARS-CoV-2 proteins using HEK293T cells as hosts for 201 expressed viral proteins [52] . Using affinity-purification followed by mass-spectrometry, they 202 reported 332 SARS-CoV-2-human (CoV-2-Hu) PPIs. Further chemoinformatics searches from the 203 IUPHAR/BPS Guide to pharmacology (2020-3-12) and the ChEMBL25 database revealed 66 204 human druggable proteins that were inhibited by ten different chemotypes, which included 205 inhibitors of mRNA translation (e.g. zotatifin) and regulators of the sigma-1 and sigma-2 receptors 206 (e.g. haloperidol) [52] . The peripheral blood mononuclear cell (PBMC) proteome signature in 207 COVID-19 that has been explored by host-pathogen protein interactome analysis revealed more 208 than 350 host proteins that are significantly perturbed in COVID-19-derived PBMCs and 286 209 human proteins with high degree and high centrality score that are targeted by SARS-CoV-2 [13] . 210

This provided important insights about SARS-CoV-2 pathogenicity and potential novel targets for 211 designing antiviral drugs or repurposing existing ones. Furthermore, in some other cases, the network-based protein signatures may not only identify the potential drug targets but also derive 213 therapy clues for other specific diseases like respiratory, cardiovascular, and immune system 214 diseases [53] . For instance, the antiviral drugs derived from herpes virus PPIs, major-type 215 rhinovirus and minor-type rhinovirus have been confirmed by another study on Ebola [54] . The "gene for gene" hypothesis has been proposed for the description of the viral-host protein 255 interaction under the viral disease process [67]. This hypothesis describes how a viral protein bind 256 to its target in the host for viral diseases. But "gene for gene" just focused on a single protein of 257 the virus and a host protein. In this review, we show that the "gene for gene" theory should be 258 replaced by the network for network concept, especially in SARS-CoV-2/host protein interactions 259 and the network-network concept provides potential platforms for discovering efficient drug 260 targets from viruses and human proteins. As the initial part of this review, we attempted to 261 highlight the SARS-CoV-2 proteome data relevant to intra-viral PPIs that were validated by the 262 experimental studies (Table 3 ). In the next step, we highlighted and discussed the hub proteins in 263 intra-viral PPIs, then we profound current knowledge on the SARS-COV-2/host interactome 264 cascade involved in the viral pathogenicity through graph theory concept and highlighted the hub 265 proteins in the intra-viral network that create a subnet with a small number of host-centered 266 proteins, leading to cell disintegration and infectivity. We hope that this paper may stimulate the 267 identification of novel methodological approaches based on the network for the network concept. PPIs are summarized in Table 3 . 282 Table 3 . Intra-viral interactions of SARS-CoV-2 proteins. All of the SARS-CoV-2 protein 284 sequence identity and similarity percent are in comparison with SARS-CoV [68] . 285 Interfering with cellular processes like heart rhythm and epithelial damaging using its leucine zipper motif. . 2) . 296

The degree value of nodes in the intra-viral network reveals a difference in the contribution of each 297 viral member constructing the SARS-CoV-2 intra-viral network. ORF10 followed by M, NSP16, 298 orf7a and, orf7b has a higher degree among other viral proteins and they are considered hubs in 299 the network (Fig. 2) . This suggests that they have a key role in the SARS-CoV-2 life cycle or 300 pathogenicity. 301

Among these hubs, orf10 has maximum interactions with other SARS-CoV-2 proteins within the 302 intra-viral network (Fig. 2) . This protein is conserved in the SARS-CoV-2 and has no homolog in 303 SARS-CoV; however, deletion of orf10 does not impact the replication and transmission capacity 304 of SARS-CoV-2. Although orf10 was initially supposed to hijack the host protein CRL2 ZYG11B , 305 the proteomic studies showed that the binding of orf10 to the CRL2 ZYG11B had no role in the 306 pathogenicity of the virus [111]. Since the orf10 translation is low in the human cells and non-307 synonymous mutations in the orf10 gene emerge exponentially, some researchers concluded that 308 the orf10 RNA sequence rather than the orf10 protein may play a regulatory role [113] . 309

Considering the high degree of this protein in the intra-viral network (Fig. 2) , it seems that further 310 omics data is needed to determine the exact regulatory role of this protein in SARS-CoV-2 intra-311 viral interactions. 312

In the case of assessing the interaction of the hubs with each other without considering other nodes, 313 M protein has the most interactions with other major hubs. (NSP16, orf7a, orf7b, and orf10) . 314

J o u r n a l P r e -p r o o f using HEK293T cells as hosts for expressed viral proteins [52] . Using affinity-purification 318 followed by mass-spectrometry, they reported 332 SARS-CoV-2-human PPIs. They characterized 319 the viral proteins according to the function of their target proteins. They showed that a specific 320 host cell pathway is not manipulated by a single viral protein and several viral proteins work 321 together to target a pathway. In their study endomembrane compartments or vesicle trafficking 322 pathways were targeted by approximately 40% of SARS-CoV-2-interacting proteins [52] . used as input to the platform. As shown in Fig. 3 , human proteins in some biological processes 376 had the most PPIs (>300) with the viral proteins, which include: cellular process, metabolic 377 process, primary metabolic process, cellular metabolic process, macromolecule metabolic process, 378 localization, transport, the establishment of localization, intra-Golgi vesicle-mediated transport, 379 cellular macromolecule metabolic process, peptidyl-asparagine modification, protein amino acid 380 N-linked glycosylation via asparagine, had the most PPIs (>300) with the viral proteins. The 381 "cellular process" with 981 CoV-2 interacted with host proteins had the most proteins, and it was 382 the first hit on the list (Supplementary file 2) . The drugs which could block the proteins in the 383 mentioned pathways would be more influential and potent against COVID-19/emerging disease. 384

In the next step, SARS-CoV-2 target proteins were examined for their importance in their network, 385 and the 20 proteins that revealed a high centrality score in the host network were depicted in Interestingly, the high degree proteins in the intra-viral network (orf10, NSP16, M, orf7a and 433 orf7b) are different from the high degree viral proteins in the CoV-2-Hu PPI network (orf7b, orf3, 434 M, N and NSP6). This difference revealed that the virus codes for a unique subnetwork to induce 435 pathogenicity (Fig. 6) . Additionally, most of the hub proteins in the host network were targeted by 436 the viral proteins that were hubs in the intra-viral network (Fig. 3) . for efficient interaction mapping to assign the function to uncharacterized gene products that might 537 be involved in response to the viral infections [137, 140] . Furthermore, the topology and pathway 538 enrichment analysis of important host PPI networks can determine the potential key viral 539 interacting host proteins associated with disease pathways, and highly central host proteins that 540 might influence the whole PPI network. Emerging the vaccine-escape and fast-growing mutations 541 including D614G also confers increased efficiency of SARS-CoV-2 cell entry, S494P, Q493L, 542 K417N, F490S, F486L, R403K, E484K, L452R, K417T, F490L, E484Q, and A475S has prompted 543 scientists to investigate the targeting potential of the hub proteins like orf8, M, and NSP7 that have 544 previously shown the most edges (link) within viral/host interactome [141, 142] . 545 broadens our perspective of the need for anti-COVID-19 therapeutic interventions to specifically 547 target the viral/host subnets. Based on recent PPI network studies of SARS-COV2, the future 548 directions toward more efficient vaccine design approaches must focus on targeting and blockage 549 not only the key proteins in the viral network such as the hubs or higher betweenness-value nodes 550 but also proteins that act synergistically within a viral-host PPI network. 551 analysis was performed on CoV-2-interacted host proteins. GO-terms for biological processes 560 were obtained from the STRING database for analysis in the BiNGO tool: a Cytoscape plugin. 561

Significant GO terms (5% FDR) were identified and further refined to select non-redundant terms. 562 Cytoscape 3.8. Red node: hub node that shows a higher degree of human-human PPIs. 565 

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