Abstract

   The rapidly and constantly evolving coronavirus, SARS-CoV-2, imposes a
   great threat to human health causing severe lung disease and
   significant mortality. Cytoplasmic stress granules (SGs) exert
   anti-viral activities due to their involvement in translation
   inhibition and innate immune signaling. SARS-CoV-2 sequesters important
   SG nucleator proteins and impairs SG formation, thus evading the host
   response for efficient viral replication. However, the significance of
   SGs in COVID-19 infection remains elusive. In this study, we utilize a
   protein-protein interaction network approach to systematically dissect
   the crosstalk of human post-translational regulatory networks governed
   by SG proteins due to SARS-CoV-2 infection. We uncovered that 116 human
   SG proteins directly interact with SARS-CoV-2 proteins and are involved
   in 430 different brain disorders including COVID-19. Further, we
   performed gene set enrichment analysis to identify the drugs against
   three important key SG proteins (DYNC1H1, DCTN1, and LMNA) and also
   looked for potential microRNAs (miRNAs) targeting these proteins. We
   identified bexarotene as a potential drug molecule and miRNAs,
   hsa-miR-615-3p, hsa-miR-221-3p, and hsa-miR-124-3p as potential
   candidates for the treatment of COVID-19 and associated manifestations.

   Keywords: SARS-CoV-2, stress granule proteins, protein–protein
   interaction, network, drug, miRNAs

1. Introduction

   The causative agent of COVID-19, severe acute respiratory syndrome
   coronavirus 2 (SARS-CoV-2), is an enveloped, single-stranded ~30 kb RNA
   virus of the family Coronaviridae [[34]1]. Viruses hijack the host
   translation machinery in favor of their needs and accomplish virus
   growth [[35]2,[36]3]. However, to counteract virus growth, host cells
   have highly specific stress sensors that trigger antiviral responses by
   suppressing both host and viral translation. The assembly of stress
   granules (SGs) is a crucial part of host cell stress responses in
   response to viral infection.

   Stress granules (SGs) are membrane-less organelles that store
   translationally silent mRNA when the cell undergoes stress to regulate
   mRNA metabolism [[37]4]. SG assembly and disassembly are tightly
   regulated during viral infection, often reflecting cellular translation
   status [[38]3,[39]5,[40]6,[41]7]. Several studies have shown that viral
   entry can interfere with SG formation [[42]8] through inhibition of
   post-translational modifications [[43]9], sequestration of SG
   components such as T cell-restricted intracellular antigen 1 (TIA-1),
   and Ras GTP activating protein-binding proteins G3BP1/2
   [[44]10,[45]11], and formation of stable viral ribonucleoprotein (RNP)
   complexes with key SG proteins [[46]12]. In the early phase of many
   viral infections, the presence of viral genomic RNAs (gRNAs) activates
   protein kinase R (PKR), resulting in eIF2α phosphorylation, mRNA
   translation inhibition, and the formation of SGs enriched with
   translation initiation factors such as eIF3b. However, in later
   infection stages, many viruses instead suppress SG formation or
   disassemble SGs altogether. The mechanisms underlying this switch, and
   its physiological function, remain unclear. Dysregulation of SG
   formation and disassembly is involved in viral infection, cancer, and
   neurodegeneration [[47]13,[48]14,[49]15,[50]16].

   Coronaviruses such as mouse hepatitis coronavirus and transmissible
   gastroenteritis virus were shown to induce SG assembly [[51]17]. It has
   also been shown that the Zika virus capsid protein hijacks G3BP1 and
   CAPRIN-1 and inhibits the SG formation and thus promotes viral
   replication [[52]18]. Several recent works also reported that
   SARS-CoV-2 nucleocapsid (N) protein undergoes RNA-induced liquid–liquid
   phase separation (LLPS) for its genome packaging and assembly
   [[53]19,[54]20,[55]21,[56]22]. The SARS-CoV-2 N protein interacts and
   sequesters key SG proteins including G3BP which leads to attenuation of
   SG [[57]23,[58]24,[59]25]. These results demonstrate that virus protein
   can interact with different SG proteins and partition into liquid
   phases thus indicating the presence of protein-protein interactions. To
   date, several SARS-CoV-2 human interactomes have been created which aid
   in comprehending the viral entry, infection, and disease development
   mechanisms [[60]23,[61]24,[62]26,[63]27]. Analysis of these networks
   has revealed commonalities and distinctions based on genes and
   molecular pathways associated with viral pathogenicity.

   The mechanisms underlying SARS-CoV-2 mediated SG dynamics are crucial
   to identifying important targetable events in the viral replication
   cycle. We here employed a network-based system biological framework
   approach as described previously [[64]28,[65]29,[66]30,[67]31], to
   investigate the molecular interplay between SARS-CoV-2 proteins and
   human host SG proteins. We created a brain-specific protein–protein
   interaction (PPI) network of 116 human SG genes targeted by SARS-CoV-2
   reported from previous SARS-CoV-2 interactome studies
   [[68]23,[69]24,[70]25]. The disease–gene interaction network revealed
   five key genes linked with the majority of brain-related disorders. The
   gene set enrichment analysis (GSEA) was studied for the identification
   of drugs affecting the gene expression of selected SG genes.

2. Results

2.1. Interaction Network of SARS-CoV-2 Targeted SG Proteins in the Brain

   For identifying the SARS-CoV-2 targeted SG proteins, we first retrieved
   a list of 809 human proteins targeted by viral proteins from three
   different SARS-CoV-2 interactome studies [[71]23,[72]24,[73]25]. A list
   of known mammalian SG proteins was retrieved from the MSGP database. A
   total of 116 SG proteins showing interaction with SARS-CoV-2 proteins
   were identified by comparing the two lists ([74]Figure 1A). We found
   that these 116 proteins interact with 22 SARS-CoV-2 proteins with the
   highest number of interactions to ORF6 (14), N and NSP6 (13), NSP12,
   and NSP13 (11), ORF7 (10), and NSP7 (7) protein ([75]Figure 1B).

Figure 1.

   [76]Figure 1
   [77]Open in a new tab

   SARS-CoV-2 human interactome. (A) Protein–protein interaction network
   of the 116 stress granule proteins (red) with SARS-CoV-2 proteins
   (green). (B) The number of SG proteins showing interaction with
   SARS-CoV-2 proteins is represented as a pie chart.

   The PPI network of the brain was retrieved from the TissuevNet2.0
   database for preparing the interaction network of SARS-CoV-2 target SG
   proteins. Using brain PPI, a network of 12,968 proteins with 165,241
   interactions was prepared. Further, a subnetwork of 116 identified SG
   proteins with their direct neighboring protein was made from the brain
   PPI network. The subnetwork shows 5548 nodes and 13,546 edges
   ([78]Figure 2A). The subnetwork represents how well connected these 116
   identified proteins are in the brain PPI network. The 116 proteins are
   directly connected with 5432 different proteins in the brain, so any
   change in the expression of these proteins may have the ability to
   manipulate the functions of the neighboring proteins directly connected
   to them. The degree distribution of the network indicated the presence
   of a scale-free network ([79]Figure 2B). Most of the real-time network
   follows scale-free property.

Figure 2.

   [80]Figure 2
   [81]Open in a new tab

   SARS-CoV-2-targeted stress granule genes interaction network in the
   brain. (A) SARS-CoV-2-targeted SG gene (yellow) interaction network in
   the human brain with neighboring genes (in pink). (B) Scatterplot
   representing the distribution of degree (k) in the SG genes target
   network.

2.2. Stress Granules-Related Disease–Gene Interaction Network in the Brain

   To understand the role of identified SG genes in the brain-related
   symptoms in COVID-19 patients, we prepared a disease–gene interaction
   network. GeneORGANizer and MalaCards databases were used to retrieve
   the disease–gene-related information for the above-identified 116 SG
   genes. A gene–disease interaction network was made with 453 nodes and
   663 edges ([82]Figure 3A). Four hundred and thirty different brain
   disorders, including COVID-19, showed interaction with 116 SG genes.
   The gene–disease interactions displayed many of the disorders that were
   connected to more than one gene in the network such as seizures (k =
   12), intellectual disability (k = 9), microcephaly (k = 9), ataxia (k =
   8), cognitive impairment (k = 8), dementia (k = 7), developmental
   regression (k = 6), dysarthria (k = 6), spasticity (k = 6), and
   cerebral cortical atrophy (k = 4) ([83]Figure 3B). Similarly, the
   gene-disease interaction network revealed that many disorders share
   common genotypes. The network revealed that the majority of the
   disorders are linked with DYNC1H1 (k = 91), LMNA (k = 86), FMR1 (k =
   74), DCTN1 (k = 57), and ALDH18A1 (k = 54) genes and showed
   interactions with multiple brain disorders ([84]Figure 3C). These genes
   are thus considered key SG genes. The disease–gene interaction
   represents the role of SARS-CoV-2 targeting SGs in brain disorders and
   hence providing a link between COVID-19 and neurological symptoms. It
   is widely known that the SARS-CoV-2 virus majorly affects the lungs as
   compared to other parts of the host body [[85]32,[86]33]. We have also
   prepared a lung/respiratory disease–gene interaction network of the SG
   genes. The corresponding disease–gene interaction network showed a
   total of 40 interactions, in which 36 lung/respiratory-affecting
   disorders were connected with 17 SG genes ([87]Supplementary Figure
   S1A). The respiratory-related disorders in which the identified SGs
   play an important role include hypoventilation, respiration
   insufficiency, aspiration, central hypoventilation, and perry syndrome
   along with some other syndromes. Interestingly, out of five key SG
   genes that showed a high number of associations with brain disorders,
   three genes, namely LMNA (k = 14), DCTN1 (k = 8), and ALDH18A1 (k = 4),
   also play important roles in disorders having a major impact on lungs
   and respiratory ability of the patients ([88]Supplementary Figure S1B).

Figure 3.

   [89]Figure 3
   [90]Open in a new tab

   SG gene–disease interaction network. (A) Interaction of SG genes and
   the associated brain diseases. SARS-CoV-2 target SG genes are shown in
   green, brain-related diseases are represented in pink. (B) Bar plot of
   maximally connected diseases along with the number of SG genes
   connected to the brain in the disease–gene interaction network. (C) Bar
   plot of key SG genes having maximum connections to various brain
   diseases in the network.

   Targeting these SG genes thus could play significant role in brain as
   well as lung/respiratory-related disorders and will provide a dual
   benefit in the process of identifying a potential COVID-19 treatment.

2.3. Functional and Pathways Enrichment Analysis of the Selected Genes

   For determining the function and mechanism of the identified SG genes
   associated with the majority of diseases, a list of these SG genes was
   submitted to DAVID and Enrichr databases for GO and KEGG pathway
   analysis. The GO analysis indicated that the biological process was
   mainly enriched in positive regulation of translation, cell to cell
   adhesion, positive regulation of the apoptotic process, response to
   heat, and response to unfolded proteins. The cellular components are
   significantly enriched in the membrane, extracellular matrix, cell–cell
   adherens junction, cytosol, and cytoplasm. Molecular functions were
   mainly enriched in RNA-binding, cadherin binding involved in cell–cell
   adhesion, ATPase activity, protein binding, ATP binding, and
   translation initiation factor binding ([91]Figure 4A). According to
   KEGG pathway analysis, the SG genes participate in the arrhythmogenic
   right ventricular cardiomyopathy (ARVC) pathway, pathways in cancer,
   amyotrophic lateral sclerosis pathways, protein processing in the
   endoplasmic reticulum, and vasopressin-regulated water absorption
   pathways along with other pathways ([92]Figure 4B).

Figure 4.

   [93]Figure 4
   [94]Open in a new tab

   Functional enrichment analysis. (A) Gene ontology analysis of 116 SG
   genes. (B) KEGG pathways related to 116 SG genes.

2.4. GSEA Based Drug Repurposing

   Using the Enrichr web tool, we identified the expression signatures of
   key SG genes in COVID-19. GSEA of the COVID-19-related gene sets
   indicated that three genes namely DYNC1H1, LMNA, and DCTN1 were
   downregulated in human bronchial epithelial cells in COVID-19 after
   24hr of infection ([95]GSE17400) ([96]Supplementary Figure S2).
   Firstly, the DCTN1 gene is also known as Dynactin-1. It is located on
   chromosome 2p13 and in humans and encodes six different isoforms. The
   dynactin complex acts as a connector of cargos. It is involved in
   multiple cellular functions including ER-to-Golgi transport, the
   centripetal movement of endosomes and lysosomes, chromosomal movements,
   spindle formation, and axonogenesis. The dysregulation of this gene is
   known to cause ALS, perry syndrome, neuropathy, distal hereditary motor
   neuropathy, and other issues related to motor movements
   [[97]34,[98]35]. Secondly, the LMNA gene is known as Lamin A/C and is a
   protein-coding gene. Nuclear lamins are the crucial component of the
   intricate protein mesh that underlies the inner nuclear membrane and
   confers mainly nuclear and cytosolic rigidity. Lamin proteins are
   thought to be involved in nuclear stability, chromatin structure, and
   gene expression. Lamin family proteins make up the matrix and are
   thought to be evolutionarily conserved. Any dysregulation in the LMNA
   gene is known to cause Hutchinson–Gilford progeria syndrome,
   cardiomyopathy, muscular dystrophy, emery-derifusss muscular dystrophy,
   and lipodystrophy [[99]34,[100]36,[101]37]. The third gene was DYNC1H1,
   also known as dynein cytoplasmic-1-heavy chain-1. Dyneins are a group
   of microtubule-activating ATPases that function as molecular motors.
   They are involved in intracellular motility including retrograde axonal
   transport, protein sorting, organelle movement, and spindle dynamics.
   Dysregulation of this gene is known to cause spinal muscular atrophy,
   Charcot-Marie-Tooth disease, mental retardation, and spinal muscular
   atrophy [[102]34].

   Further, the GSEA of the drug perturbations from GEO database records
   of downregulated genes revealed bexarotene, also known as targretin, as
   the top significant enriched candidates showing interaction with the
   three downregulated genes in COVID-19 ([103]Supplementary Figure S3A).
   The search in GEO data sets showed that bexarotene in rats upregulated
   the expression of DYNC1H1, DCTN1, and LMNA genes in the liver, lungs,
   and mammary glands ([104]Supplementary Figure S3B).

   Assuming that bexarotene significantly alters the PPI and would inhibit
   the virus growth, we here studied the drug–protein interactions. Out of
   a total of 809 human proteins prey of SARS-CoV-2, bexarotene interacts
   with 36 (i.e., ~4.4%) human proteins and potentially interferes with 24
   of 27 (i.e., 89%) SARS-CoV-2 proteins ([105]Figure 5A). The 36 proteins
   mostly show at least 1–2 interactions with 24 SARS-CoV-2 proteins,
   totaling 87 interactions ([106]Figure 5B). This finding suggests that
   bexarotene could be considered as a possible drug for drug repurposing
   against COVID-19.

Figure 5.

   [107]Figure 5
   [108]Open in a new tab

   Effect of bexarotene on the SARS-CoV-2 human interactome. (A)
   Bexarotene interacts with 36 (i.e., ~4.4%) of 809 human proteins prey
   of SARS-CoV-2, with a total of 87 interactions. These 36 proteins
   potentially interact with 24 (i.e., ~88.8%) of 27 SARS-CoV-2 proteins.
   (B) Drug–gene interaction network of bexarotene and SARS-CoV-2
   proteins. The yellow node represents SARS-CoV-2 proteins and the red
   node represents human proteins targeted by viral proteins in the human
   host.

2.5. miRNA Based Drug Repurposing

   Apart from chemical-based drug target identification, we also searched
   for miRNAs as a potential target for the key SG genes. A total of
   502,652 miRNA–gene interactions in humans were downloaded from the
   miRTarBase database. For the top five selected key SG genes, a total of
   44 miRNA interactions were identified ([109]Figure 6A). Further, out of
   the 44 identified miRNAs, we selected miRNAs that have anti-viral
   properties and identified that out of five key SG genes, four genes
   interact with at least one antiviral miRNA. DYNC1H1 showed interaction
   with two antiviral miRNAs—namely has-miR-122-5p and
   has-miR-382-5p—whereas the other genes LMNA, DCTN1, and ALDH18A1
   interacted with has-miR-9-5p, has-miR-93-5p, and has-miR-20a-5p,
   respectively. ALDH18A1 gene is also known as aldehyde dehydrogenase 18A
   family member A1 and encodes bifunctional ATP and NADPH mitochondrial
   enzymes. The protein encoded by this gene reduces glutamate into
   delta1-pyrroline-5-carboxylate, a critical step in the biosynthesis of
   proline, ornithine, and arginine. The gene is involved in pathways such
   as the urea cycle, amino acid synthesis pathways, metabolism pathways,
   and peptide chain elongation pathways. Dysregulation in this gene is
   known to cause hyperammonaemia, hyperornithinaemia, hyperargininaemia,
   and is associated with neurodegeneration, cataract, and connective
   tissue disease [[110]34,[111]38,[112]39].

Figure 6.

   [113]Figure 6
   [114]Open in a new tab

   Identification of miRNAs regulating the expression of key five SG
   genes, (A) miRNA-SG genes interaction network. The network displays the
   miRNAs (blue) targeting five key SG genes (green). The antiviral miRNAs
   are highlighted with a yellow border. (B) Gene Ontology analysis of the
   antiviral miRNAs interacting with key SG genes. (C) KEGG pathways
   enrichment analysis of antiviral miRNAs.

   Gene ontology enrichment analysis of the identified antiviral miRNAs
   revealed that the biological process is enriched in craniofacial suture
   morphogenesis, trans-synaptic signaling by endocannabinoid, embryonic
   heart tube left/right pattern formation, and alpha-beta cell
   proliferation. The cellular components were significantly located in
   the endoplasmic reticulum membrane, asymmetric, perinuclear endoplasmic
   reticulum, PML body, cyclin B1-CDK1 complex, and nucleosome. The
   molecular functions were mainly enriched in RNA binding, mRNA binding,
   nucleic acid binding, and organic cyclic compound binding ([115]Figure
   6B). Moreover, the pathway enrichment analysis revealed the role of
   miRNAs in glutathione metabolism, amplification of expansion of
   oncogenic pathways as metastatic traits, molybdenum cofactor
   biosynthesis, IL-6 signaling pathways, pathways in clear cell renal
   cell carcinoma (ccRCC), trans-sulfuration pathways, and regulation of
   Wnt/B-catenin signaling pathways ([116]Figure 6C).

3. Discussion and Conclusions

   The activation of SGs upon viral infection has been considered as a
   host antiviral mechanism [[117]3,[118]5]. Besides blocking viral gene
   expression via translation arrest, SGs also eliminate viral factors to
   inhibit their growth [[119]6,[120]18,[121]40]. Many viruses have
   developed strategies to disrupt SG formation to help their growth
   [[122]18,[123]41]. MERS-CoV protein 4a, HCV NS5A, JEV NS2A protein, and
   Sendai virus C protein target PKR and prevent SG formation
   [[124]42,[125]43,[126]44,[127]45,[128]46]. Enterovirus (EV 71) protease
   3Cpro cleaves G3BP1 and disrupts SGs assembly following EV71 infection
   [[129]47]. The poliovirus, foot-and-mouth disease virus, and feline
   calicivirus adopt similar mechanisms to inhibit SG assembly
   [[130]48,[131]49,[132]50]. Recent studies have shown that the
   SARS-CoV-2 N protein prevents SG formation by preventing PKR
   autophosphorylation and activation, and by sequestering G3BP1
   [[133]22,[134]51]. These observations indicate a fairly conserved
   mechanism of escaping the host defense by beta coronaviruses. Several
   escaping mechanisms from host defense by the SARS-CoV-2 virus have been
   recently described [[135]29,[136]30,[137]31,[138]52,[139]53], but it is
   not clear whether SARS-CoV-2 targets host key SG components.

   Here, we adopted an integrative network biology approach to decipher
   the SG genes-based molecular alliance of COVID-19 with neurological
   disorders. Our findings showed that 116 SG proteins were targeted by 27
   SARS-CoV-2 proteins. The results of the PPI network indicate that these
   SG proteins operate in a highly interconnected network that coordinates
   many activities of the cellular RNA homeostasis. The brain-specific
   disease-genes network showed that 430 different brain disorders
   including COVID-19 interact with 116 SG genes. In this study, diseases
   such as seizures, intellectual disability, microcephaly, ataxia,
   cognitive impairment, dementia, developmental regression, and
   dysarthria represented the most connected diseases based on different
   SG genes—DYNC1H1, LMNA, FMR1, DCTN1, and ALDH18A1. Next, to repurpose a
   drug targeting the most common shared SG genes between SARS-CoV-2 and
   neurological complications, a GSEA analysis was performed. Based on the
   enrichment analysis, bexarotene was identified as the top significant
   enriched candidate interacting with the three downregulated SG genes in
   COVID-19.

   Bexarotene (antineoplastic retinoid) is a synthetic high-affinity
   retinoid X receptor agonist used in the treatment of cutaneous T cell
   lymphoma, non-small cell lung cancer, and breast cancer
   [[140]54,[141]55]. Bexarotene also exerts anti-inflammatory effects by
   downregulating IL-6, IL-8, monocyte chemoattractant protein 1 (MCP-1),
   and high mobility group box-1 [[142]56]. It has been shown previously
   that AM580 and tamibarotene belongs to the same drug class as
   bexarotene, displayed broad-spectrum antiviral activities against
   influenza viruses, enterovirus A71, Zika virus, adenovirus, MERS-CoV,
   and SARS-CoV [[143]57]. Recently, Yuan et al. [[144]58] showed that
   abiraterone acetate and bexarotene effectively inhibit SARS-CoV-2
   replication in vitro. Bexarotene has also been shown as a potential
   drug target of ACE2, TMPRSS2, and AAK1 through bioinformatic analysis
   [[145]59]. Thus, bexarotene could be regarded as a candidate drug for
   repurposing in COVID-19.

   We also identified three miRNAs (hsa-miR-615-3p, hsa-miR-221-3p, and
   hsa-miR-124-3p) which target at least two of the five key SG genes. The
   miRNA, hsa-miR-124-3p, helps in regulating the inflammatory mechanisms
   in viral infection by targeting cytokine regulating immune expressed
   genes and associated transcription factors [[146]60]. Moreover,
   hsa-miR-124-3p was found to be downregulated in JEV-infected human
   neural stem cells [[147]61]. The miR-124-3p agomir reduced
   pro-inflammatory cytokines IL-6 and TNF-α levels and thus was able to
   protect against pulmonary injury [[148]62]. It has been shown that
   SARS-CoV-2 hijacks Ddx58 which is involved in miRNA biogenesis and mRNA
   splicing to help its replication. The miRNA, miR-124-3p, can bind to
   the 3’-UTR of Ddx58 and downregulate the Ddx58. In one study, Arora et
   al. showed that overexpression of miR-124-3p would degrade the Ddx58
   and inhibit the replication of the SARS-CoV- 2 genome [[149]63].

   The miRNA, hsamiR-124, has been shown to inhibit influenza and RSV
   infection by the reduction in mitogen-activated protein
   kinase-activated protein kinase 2 (MAPKAPK2 or MK2) [[150]64].
   Moreover, according to one study, MK2 was predicted to be targeted by
   miR-615-3p and was reduced in the lungs of COVID-19 patients [[151]65].
   The miRNA, hsamiR-221-3p, is found to be upregulated in hamster lung
   tissue infected with SARS-CoV-2. It targets ADAM17 which is involved in
   ACE2-dependent shedding linked with lung pathogenesis [[152]66].

   Our study thus utilizes a comprehensive protein–protein interaction
   network to map the interplay between SARS-CoV-2 proteins with human SG
   proteins along with their functional annotations. Therefore,
   delineating the effect of SARS-CoV-2 infection on human translational
   regulatory networks is central for identifying effective drug targets
   against COVID-19.

4. Methods

4.1. Identification of SARS-CoV-2 Interacting Human SG Proteins from
SARS-CoV-2-Human Interactome

   A list of 809 human target proteins known to interact with SARS-CoV-2
   viral proteins was retrieved from three different SARS-CoV-2
   interactome studies [[153]23,[154]24,[155]25]. The mammalian stress
   granules proteome (MSGP) database [[156]67] was used for retrieving the
   list of SG proteins. The MSGP database curates the information
   regarding the SGs using published literature available on PubMed and
   other sources. Further information regarding each SG protein was then
   obtained from Uniport, GeneDatabase, and OMIM. The database also
   provides the expression profile of SG proteins in the context of
   neurodegenerative diseases. A list of 464 SG proteins was obtained from
   the MSGP database. Out of 809 proteins, a total of 116 SG proteins were
   identified as known to have direct interaction with SARS-CoV-2 viral
   proteins.

4.2. Protein–Protein Interaction of Identified SG Proteins in the Human
Proteome

   The human proteome interaction data were obtained from TissueNet v.2
   databases [[157]68]. For human PPI, the TissueNet database provides the
   quantitative tissue association. For preparing an extensive interaction
   network, protein-based assay profiles and RNA-Seq profiles were
   gathered from the human protein atlas (HPA) and the genotype tissue
   expression project (GTEX), respectively. BioGrid, MINT, DIP, and IntAct
   were the four major databases used for extracting the experimentally
   validated protein interaction information for the PPI network. A list
   of 116 identified SG proteins interacting with SARS-CoV-2 proteins was
   used for creating a subnetwork having interactions between SARS-CoV-2
   proteins and SG proteins and the directly connecting first neighbors.

4.3. Preparation of Disease–Gene Interaction Network Specific to Brain

   After obtaining the interaction network of SARS-CoV-2 target SG
   proteins and their neighboring proteins in the human proteome, the
   MalaCards database [[158]69] along with the GeneORGANizer database
   [[159]70] was used to identify genes playing role in the brain,
   cerebellum, and head-related disorders. GeneORGANizer allows the user
   to identify the organs in which the query genes are expressed along
   with the information related to disorders caused by the query genes in
   these organs. The database delivers organ-specific gene-disease
   information from highly curated DisGeNET [[160]71] and human phenotypes
   ontology (HPO) tools. The MalaCards database scans 74 databases to
   provide disease–gene relationship information regarding the query
   genes. Disease–gene interactions were considered for further study if
   they had HPO identifiers. A total of 1246 disease–gene interactions
   were obtained, of which 430 different brain disorders including
   COVID-19 were linked with 18 SG genes. The brain gene–disease
   interaction network was created using the Cytoscape tool [[161]72].

   Similarly, we identified the role of SG genes in
   lung/respiratory-related disorders by creating a lung disease–gene
   interaction network. The corresponding lungs/respiratory-related
   disease–gene interaction network was prepared with a total of 40
   interactions, in which 36 different lung/respiratory-affecting
   disorders were linked with 17 SG genes.

4.4. Calculation of Topological Properties of the PPI Network

   The topological properties of the network were calculated to identify
   the top genes showing associations with brain-related disorders through
   the network analyzer plugin of Cytoscape, similar to our previous
   studies [[162]28,[163]31]. The calculated network topological
   properties included degree centrality (k) and betweenness centrality (
   [MATH: <mrow><mrow><msub><mi>C</mi><mi>b</mi></msub></mrow></mrow>
   :MATH]
   ) values for identifying the highly connected nodes. Degree centrality
   (k) indicates the number of interactions made by a node with another
   node in the network and thus conveys the significance of that node in
   controlling the network interactions, and is expressed as:
   [MATH:
   <mrow><mrow><mi>Degree</mi><mtext> </mtext><mi>centrality</mi><mtext> <
   /mtext><mo>(</mo><mi
   mathvariant="normal">k</mi><mo>)</mo><mo>=</mo><msub><mstyle
   displaystyle="true"><mo>∑</mo></mstyle><mrow><mi>a</mi><mi>ε</mi><msub>
   <mi>K</mi><mi>b</mi></msub></mrow></msub><mi>w</mi><mrow><mo>(</mo><mro
   w><mi>a</mi><mo>,</mo><mi>b</mi></mrow><mo>)</mo></mrow></mrow></mrow>
   :MATH]
   (1)

   where,
   [MATH: <mrow><mrow><msub><mi>K</mi><mi>a</mi></msub></mrow></mrow>
   :MATH]
   is the node set containing all the neighbors of node a, and w(a,b) is
   the weight of the edge between node a and node b.

   The other parameter, betweenness centrality (
   [MATH: <mrow><mrow><msub><mi>C</mi><mi>b</mi></msub></mrow></mrow>
   :MATH]
   ), indicates the degree to which nodes occur with each other in the
   shortest path. A node with higher betweenness centrality denotes
   stronger control over the information flow in the network. It is
   expressed as:
   [MATH: <mrow><mrow><mrow><msub><mi>C</mi><mi>b</mi></msub><mo>(</mo><mi
   mathvariant="normal">u</mi><mo>)</mo></mrow><mtext> </mtext><mo>=</mo><
   mtext> </mtext><msub><mstyle
   displaystyle="true"><mo>∑</mo></mstyle><mrow><mi>k</mi><mo>≠</mo><mi>u<
   /mi><mo>≠</mo><mi>f</mi></mrow></msub><mfrac><mrow><mi>p</mi><mrow><mo>
   (</mo><mrow><mi>k</mi><mo>,</mo><mi>u</mi><mo>,</mo><mi>f</mi></mrow><m
   o>)</mo></mrow></mrow><mrow><mi>p</mi><mrow><mo>(</mo><mrow><mi>k</mi><
   mo>,</mo><mi>f</mi></mrow><mo>)</mo></mrow></mrow></mfrac></mrow></mrow
   > :MATH]
   (2)

   where, p(k,u,f) is the number of interactions between nodes k and f
   that passes through u, and p(k,f) denotes the total number of shortest
   interactions between node k and f.

4.5. Gene Ontology and Pathway Enrichment Analysis

   Next, the enrichment analysis of the PPI network was explored using the
   DAVID (Database for annotation visualization and integrated discovery)
   tool [[164]73]. DAVID utilizes the Gene Ontology (GO) and Kyoto
   Encyclopedia of Genes and Genomes (KEGG) database for studying the
   functional enrichment of the selected genes. GO analysis includes
   functional annotation of genes at the biological, molecular, and
   cellular level. Functions and pathways with p-values < 0.05 were
   considered significantly enriched and included in the results.

4.6. Identification of Drugs through Gene Set Enrichment Analyses (GSEA)
Analysis

   Further, to identify the drugs modulating the expression of key SG
   genes, GSEA was performed through the Enrichr web server, which stores
   the expression information of almost 200,000 genes from more than 100
   gene set libraries [[165]74,[166]75]. The Enrichr database provides
   multiple drug–gene interaction information along with gene expression
   profiles obtained from the gene expression omnibus (GEO) database.

4.7. Identification of microRNAs as a Gene Expression Regulator

   MicroRNAs (miRNAs) are small non-coding RNAs that can regulate the
   expression of genes by interacting with target messenger RNAs. miRNAs
   play an important role in many viral diseases such as Ebola, SARs, and
   HIV by downregulating the host’s genes [[167]76]. These properties make
   miRNAs a potential therapeutic target. For identifying miRNAs
   interacting with five key SG genes, different miRNA–gene interaction
   databases including miRTarBase, miRbase, miRDB, and miRNet2 were
   screened [[168]77,[169]78,[170]79,[171]80]. A list of miRNAs showing
   antiviral properties was also retrieved from the VIRmiRNA database
   [[172]81]. The GeneTrail [[173]82] database was explored for the GO and
   pathway-based enrichment analysis of the selected antiviral miRNAs.

Acknowledgments