Abstract

   COVID-19 has currently become the biggest challenge in the world. There
   is still no specific medicine for COVID-19, which leaves a critical gap
   for the identification of new drug candidates for the disease. Recent
   studies have reported that the small-molecule enoxacin exerts an
   antiviral activity by enhancing the RNAi pathway. The aim of this study
   is to analyze if enoxacin can exert anti-SARS-CoV-2 effects. We exploit
   multiple computational tools and databases to examine (i) whether the
   RNAi mechanism, as the target pathway of enoxacin, could act on the
   SARS-CoV-2 genome, and (ii) microRNAs induced by enoxacin might
   directly silence viral components as well as the host cell proteins
   mediating the viral entry and replication. We find that the RNA genome
   of SARS-CoV-2 might be a suitable substrate for DICER activity. We also
   highlight several enoxacin-enhanced microRNAs which could target
   SARS-CoV-2 components, pro-inflammatory cytokines, host cell components
   facilitating viral replication, and transcription factors enriched in
   lung stem cells, thereby promoting their differentiation and lung
   regeneration. Finally, our analyses identify several enoxacin-targeted
   regulatory modules that were critically associated with exacerbation of
   the SARS-CoV-2 infection. Overall, our analysis suggests that enoxacin
   could be a promising candidate for COVID-19 treatment through enhancing
   the RNAi pathway.

   Subject terms: Data integration, Data processing, Gene ontology, Gene
   regulatory networks, Viral infection, Molecular biology

Introduction

   Since the emergence of SARS-CoV-2 virus in November 2019, COVID-19 has
   become the biggest challenge in the world^[26]1. Although several
   efforts are currently underway to develop COVID-19 vaccines, there is
   an urgent need to find new, effective treatments in order to decrease
   the mortality rate especially in regions and countries with the highest
   number of COVID-19 cases and deaths^[27]2,[28]3. Various strategies
   including blocking viral entry into the host cells^[29]4–[30]6,
   inhibiting viral replication^[31]7,[32]8 and reducing cytokine
   storms^[33]9,[34]10 have been proposed to relieve patients from
   COVID-19 symptoms. Based on these strategies, hundreds of clinical
   trials have been conducted and only a few drugs have been shown to
   slightly shorten the time to recovery or weakly reduce the rate of
   death among hospitalized patients^[35]8,[36]11. However, there is still
   no specific efficacious medicine for COVID-19 that clearly reduces the
   death rate, making it critically inevitable to look for, and evaluate,
   new drug candidates for the effective treatment of COVID-19. As a
   potent antiviral strategy, the innate immune system could be exploited
   to fight the deadly infection caused by SARS-CoV-2.

   The innate immune system, which functions as the first line of defense
   against viruses in the majority of mammalian cells, consists of the
   interferon (IFN) and the RNA interference (RNAi) pathways as major
   immune mechanisms against various viruses^[37]12. The IFN pathway is
   activated by viral components, thereby transcriptionally activating a
   large number of the so-called IFN-stimulated genes (ISGs)^[38]13. The
   activation of ISGs induces the production and secretion of various
   cytokines and chemokines which in turn recruit a large number of immune
   cells to the site of infection^[39]14. This pathway appears to be more
   active in mature cells (i.e. less active in stem and progenitor cells
   which are frequently infected by many viruses)^[40]15. Moreover, it can
   lead to a potentially life-threatening immune reaction called the
   cytokine release syndrome or cytokine storm which is resulted from an
   exaggerated immune response (i.e. a hyperactive IFN-mediated response)
   to the viral infection^[41]16. This immune overreaction is injurious to
   the host cells and might be induced by the SARS-CoV-2 infection^[42]17.
   In contrast, the RNAi pathway is an IFN-independent process of fighting
   viruses (therefore, does not induce a cytokine storm) and is typically
   more active in embryonic and non-mature (e.g. stem and progenitor)
   cells^[43]18. It involves the efficient degradation of the large viral
   RNAs which form secondary double-stranded structures thereby serving as
   substrates of the RNAi pathway^[44]18.

   Fluoroquinolones such as enoxacin are broad-spectrum synthetic
   antibiotics used in different clinical conditions like urinary tract-,
   respiratory-, and systemic infections^[45]19,[46]20. Although
   quinolones are known to typically inhibit DNA replication by targeting
   bacterial DNA gyrases^[47]21, a growing body of evidence has revealed
   that some members of this family of antibiotics could also inhibit
   viral helicases, attenuate cytokine production and pro-inflammatory
   reactions^[48]22, and more importantly enhance the RNAi process as an
   inflammation-free innate immune defense against viral
   infections^[49]23.

   RNAi acts as a sequence-specific gene silencing process in which
   double-stranded RNAs (dsRNAs) such as short hairpin RNAs (shRNAs),
   viral RNAs, and microRNA (miRNA) precursors are cleaved by the RNase
   enzyme DICER to yield small interfering RNA (siRNA) duplexes. One
   strand of these duplexes is then preferentially incorporated into the
   so-called RNA‐induced silencing complex (RISC) to target complementary
   transcripts through Watson–Crick base‐pairing interactions^[50]24. The
   initiating dsRNAs can be either exogenous (e.g. viral RNAs or shRNAs)
   or endogenous (e.g. pre-miRNA transcripts) which are processed to
   generate siRNAs and miRNAs^[51]25,[52]26. miRNAs are short non-coding
   RNAs processed by DICER which regulate gene expression at the
   post-transcriptional level, thereby modulating virtually all biological
   pathways^[53]27–[54]29. Importantly, the RNAi pathway appears to be a
   particularly potent antiviral process, as many viruses have evolved
   several RNAi-suppressing strategies including encoding the viral
   suppressor of RNAi (VSR) proteins to minimize the RNAi
   pathway^[55]30,[56]31. Therefore, compounds such as enoxacin which
   could serve as RNAi enhancers might be ideal candidates for antiviral
   therapy of COVID-19.

   Studies have shown that enoxacin could enhance RNAi activity through
   its binding to, and stimulating, TAR RNA binding protein (TRBP), as the
   main cofactor of DICER, thereby facilitating the binding of DICER to
   target RNAs^[57]32,[58]33. This enhancement in the RNAi pathway
   subsequently leads to a more potent RNAi (i.e. siRNA and miRNA) effect
   on the target RNAs, either mRNAs (targets of miRNAs) or viral RNAs
   (targets of the virally-derived siRNAs)^[59]23,[60]34,[61]35.
   Interestingly, enoxacin has recently been shown to exert a potent
   antiviral activity against several types of viruses such as Zika virus,
   Dengue virus, human immunodeficiency virus (HIV/AIDS), and langat virus
   in in vitro, organoid, and animal models by enhancing the RNAi
   pathway^[62]36–[63]39, suggesting that the RNAi-enhancing activity of
   enoxacin could serve as a general antiviral strategy including against
   the novel coronavirus. Here, we exploited several in silico analyses to
   predict if enoxacin could exert anti-SARS-CoV-2 effects. Our results
   indicated that the RNA genome of SARS-CoV-2 might be processed by the
   endonuclealytic activity of DICER. Moreover, we determined a set of
   putative miRNA targets of enoxacin and found that a fraction of these
   miRNAs could restrict the entry of SARS-CoV-2 into the host cells by
   targeting key cell surface proteins. Another fraction of
   enoxacin-responsive miRNAs showed the potential to repress both host
   transcripts mediating the replication of the virus and viral
   transcripts encoding important viral proteins. Finally, other
   enoxacin-induced miRNAs appeared to potentially silence the cytokine
   storm driven by the SARS-CoV-2 infection as well as promote bronchiolar
   stem cell differentiation, thereby empowering the regeneration of the
   lung parenchyma. Overall, our findings strongly suggest that enoxacin
   and possibly other RNAi-enhancing members of the fluoroquinolones might
   serve as antiviral drugs able to be repositioned for effective COVID-19
   therapy.

Results

The RNA genome of SARS-CoV-2 might be a suitable substrate for DICER

   Since enoxacin has been reported to exert its anti-viral activity by
   means of enhancing the RNAi pathway through binding and stimulating the
   activity of TRBP, the physical partner of DICER^[64]23,[65]33, we first
   investigated if the single-stranded RNA genome of SARS-CoV-2 might be
   processed by the RNAi machinery. As DICER acts on hairpin
   RNA structures, we used three methods to predict these precursor
   structures in the viral genome. The SM-based method, which predicts
   pre-miRNAs from given sequences using sequence-structure motif
   strategies^[66]40, predicted 145 hairpin structures and miRNA
   precursors potentially derived from the SARS-CoV-2 genome (Table
   [67]S1), suggesting that DICER could act on the SARS-CoV-2 genome and
   gradually degrade it down to siRNAs. Moreover, we utilized two other
   hairpin-predicting tools, i.e. the Ab-initio and the BLASTN method (a
   miRBase feature), to examine if other different methods of hairpin
   prediction similarly identify potential DICER-acting genomic regions.
   Of note, in the Ab-initio method an approximation of miRNA hairpin
   structure is first searched for, before reconstituting the pre-miRNA
   structure^[68]41, while the BLASTN approach searches for a
   human miRNA homolog in the viral genome. Using these two approaches, we
   could predict 518 and 69 hairpin/miRNA precursors, respectively (Table
   [69]S1), which could serve as binding sites for recruiting DICER and
   stimulating its RNase activity. Notably, comparing the results obtained
   with the three hairpin-predicting tools revealed that 12 hairpin
   structures were commonly predicted by all these approaches in nearly
   the same regions of the SARS-CoV-2 genome (Table [70]1). These results
   strongly suggest that the SARS-CoV-2 genome intrinsically harbors
   multiple hairpin structures which could be processed by DICER/TRBP
   complex, allowing for the efficient degradation of the coronavirus RNA
   genome through the RNAi pathway.

Table 1.

   Stem-loop structures co-predicted by three different methods in nearly
   the same genomic regions of the SARS-CoV-2.
   Ab-initio-based method SM-based method BLASTN method
   1 Sequences Location Sequences Location Sequences Location
   2
   5′GCCUUUGGAGGCUGUGUGUUCUCUUAUGUUGGUUGCCAUAACAAGUGUGCCUAUUGGGUUCCACGUGCU
   AGCGCUAACAUAGGUUGUAACCAUACAGGUGUUGUUGGAGAAGGUUCCGAAGGU 3′ 1498–1621
   5′CUUUGGAGGCUGUGUGUUCUCUUAUGUUGGUUGCCAUAACAAGUGUGCCUAUUGGGUUCCACGUGCUAG
   CGCUAACAUAGGUUGUAACCAUACAGGUGUUGUU 3′ 1479–1582
   5′UCUUAUGUUGGUUGCCAUAACAAGUGUGCCUAUUGGGUUCCACGUGCUAGCGCUAA 3′ 1499–1554
   3
   5′UGAACUUGAUGAAAGGAUUGAUAAAGUACUUAAUGAGAAGUGCUCUGCCUAUACAGUUGAACUCGGUAC
   AGAAGUAAAUGAGUUCGCCUGUGUUGUGGCAGAUGCUGUCAUAAAAACUUUGCAACCAGUAUCUGAAUUA
   3′ 2832–2972
   5′AAGUACUUAAUGAGAAGUGCUCUGCCUAUACAGUUGAACUCGGUACAGAAGUAAAUGAGUUCGCCUGUG
   UUGUGGCAGAUGCUGUCAUAAAAACUUUGCAA 3′ 2816–2917
   5′CACCACUGGGCAUUGAUUUAGAUGAGUGGAGUAUGG 3′ 2884–2919
   4
   5′GUGAUACAUUCUGUGCUGGUAGUACAUUUAUUAGUGAUGAAGUUGCGAGAGACUUGUCACUACAGUUUA
   AAAGACCAAUAAAUCCUACUGACCAGUCUUCUUACAUCGUUGAUAGUGUUACAGUGAAGAAUGGUUCCAU
   3′ 7736–7876
   5′CUGACCAGUCUUCUUACAUCGUUGAUAGUGUUACAGUGAAGAAUGGUUCCAUCCAUCUUUACUUUGAUA
   AAGCUGGUCAAAAGACUUAUGAAAGACAUUCUCU 3′ 7715–7718
   5′UUUGAUAAAGCUGGUCAAAAGACUUAUGAAAGACAUUCUCUCUCUCAUU 3′ 7778–7826
   5
   5′AGUCUUCUUACAUCGUUGAUAGUGUUACAGUGAAGAAUGGUUCCAUCCAUCUUUACUUUGAUAAAGCUG
   GUCAAAAGACU 3′ 7831–7911
   5′UAAUAACACUAAAGGUUCAUUGCCUAUUAAUGUUAUAGUUUUUGAUGGUAAAUCAAAAUGUGAAGAAUC
   AUCUGCAAAAUCAGCGUCUGUUUACUACA 3′ 7851–7949
   5′AAAGGUUCAUUGCCUAUUAAUGUUAUAGUUUUUGAUGGUAAAUCAAA 3′ 7862–7908
   6
   5′UAUUUUAGUGGAGCAAUGGAUACAACUAGCUACAGAGAAGCUGCUUGUUGUCAUCUCGCAAAGGCUCUC
   AAUGACUUCAGUA 3′ 10051–10133
   5′AACCACCACAAACCUCUAUCACCUCAGCUGUUUUGCAGAGUGGUUUUAGAAAAAUGGCAUUCCCAUCUG
   GUAAAGUUGAGGGUUGUAUGGUACAAGUAACUUG 3′ 10016–10119
   5′CUGGUAAAGUUGAGGGUUGUAUGGUACAAGUAACUUGUGGUACAACU 3′ 10083–10129
   7
   5′CAGCUGAUGCACAAUCGUUUUUAAACGGGUUUGCGGUGUAAGUGCAGCCCGUCUUACACCGUGCGGCAC
   AGGCACUAGUACUGAUGUCGUAUACAGGGCUUUUGACAUCUACAAUGAUAAAGUAGCUG 3′
   13660–13710
   5′AGGACGAAGAUGACAAUUUAAUUGAUUCUUACUUUGUAGUUAAGAGACACACUUUCUCUAACUACCAAC
   AUGAAGAAACAAUUUAUAAUUUACUUAAGGAUUGU 3′ 13615–13719
   5′AGACACACUUUCUCUAACUACCAACAUGAAGAAACAAUUUAUAAUUUACUU 3′ 13660–13710
   8
   5′CAGCUGAUGCACAAUCGUUUUUAAACGGGUUUGCGGUGUAAGUGCAGCCCGUCUUACACCGUGCGGCAC
   AGGCACUAGUACUGAUGUCGUAUACAGGGCUUUUGACAUCUACAAUGAUAAAGUAGCUG 3′
   13660–13710
   5′AGGACGAAGAUGACAAUUUAAUUGAUUCUUACUUUGUAGUUAAGAGACACACUUUCUCUAACUACCAAC
   AUGAAGAAACAAUUUAUAAUUUACUUAAGGAUUGU 3′ 13615–13719
   5′ACUUUCUCUAACUACCAACAUGAAGAA 3′ 13366–13692
   9
   5′UGGCUUAUACCCAACACUCAAUAUCUCAGAUGAGUUUUCUAGCAAUGUUGCAAAUUAUCAAAAGGUUGG
   UAUGCAAAAGUAUUCUACACUCCAGGGACCACCUGGUACUGGUAAGAGUCA3′ 17227–17348
   5′GUAGUAGAAUUAUACCUGCACGUGCUCGUGUAGAGUGUUUUGAUAAAUUCAAAGUGAAUUCAACAUUAG
   AACAGUAUGUCUUUUGUACUGUAAA3′ 17224–17318
   5′GUUUUGAUAAAUUCAAAGUGAAUUCAACAUUAGAACAGUAUGUCUUUUGUACUGUA 3′
   17261–17316
   10
   5′GAGGGUUUUUUCACUUACAUUUGUGGGUUUAUACAACAAAAGCUAGCUCUUGGAGGUUCCGUGGCUAUA
   AAGAUAACAGAACAUUCUUGGAAUGCUGAUCUUUAUAAGCUCAUGGGACACUUCGCAUGGUGGACAGCCUU
   U3′ 21397–21539
   5′CUUAAAUUAAGGGGUACUGCUGUUAUGUCUUUAAAAGAAGGUCAAAUCAAUGAUAUGAUUUUAUCUCUU
   CUUAGUAAAGGUAGACUUAUAAUUAGAGAAAA3′ 21411–21512
   5′GUGACUAUUGACUAUACAGAAAUUUCAUUUAUGCUUUGGUGUAAAGAUG 3′ 21433–21471
   11
   5′AGAAUGUUCUCUAUGAGAACCAAAAAUUGAUUGCCAACCAAUUUAAUAGUGCUAUUGGCAAAAUUCAAG
   ACUCACUUUCU3′ 24646–24726
   5′GUACUUGGACAAUCAAAAAGAGUUGAUUUUUGUGGAAAGGGCUAUCAUCUUAUGUCCUUCCCUCAGUCA
   GCACCUCAUGGUGUAGUCUUCUUG3′ 24658–24751
   5′CAAAAAGAGUUGAUUUUUGUGGAAAGGGCUAUCAUCUUAUGUCCUUC 3′ 24672–24718
   12
   5′UACAUUUGGCUAGGUUUUAUAGCUGGCUUGAUUGCCAUAGUAAUGGUGACAAUUAUGCUUUGCUGUAUG
   ACCAGUUGCUGUAGUUGUCUCAAGGGCUGUUGUUCUUGUGGAUCCUGCUGCAAAUUUG3′
   25564–25691
   5′CCUCAAAAAGAGAUGGCAACUAGCACUCUCCAAGGGUGUUCACUUUGUUUGCAACUUGCUGUUGUUGUU
   UGUAACAGUUUACUCACACCUUUUGCUCGUUGCUGCUGGCCUUGAAGCCC3′ 25583–25702
   5′UGUUGUUGUUUGUAACAGUUUACUCACACCUUUUGCUCGUUGCUGCU 3′ 25643–25689
   [71]Open in a new tab

Enoxacin might target the proteins mediating SARS-CoV-2 entry into host cells

   To predict if enoxacin-stimulated miRNAs could target SARS-CoV-2 entry
   receptors, we first sought to determine the miRNAs that are reported in
   several published research to be upregulated by enoxacin (Table
   [72]S2). Analysis of enoxacin-induced miRNAs across multiple types of
   cell lines (i.e. prostate cancer cells, HEK cells, and melanoma cells)
   revealed that enoxacin could upregulate a large set of mature miRNAs.
   Indeed, 268 miRNAs were found to be upregulated under enoxacin
   treatment (Table [73]S2). Comparing these 268 miRNAs with the miRNA
   expression profile of the lung tissue (see the list of miRNAs in Table
   [74]S3) showed that 137 of enoxacin-upregulated miRNAs were also
   expressed in the lung tissue. To minimize the false positives, we used
   these 137 miRNAs (that were both enoxacin-induced and lung
   tissue-expressed) for our subsequent analyses. Next, we attempted to
   define miRNAs that could potentially target ACE2 as the main entry
   receptor of SARS-CoV-2 on cell surface as well as ACE as an alternative
   entry receptor of the virus (Table [75]S4). To this end, we considered
   only those miRNAs which were co-predicted by three different miRNA
   target prediction tools (i.e. miRanda, TargetScan, and miRDB) in
   addition to the miRNAs on miRTarBase that have previously been
   experimentally verified to regulate these two cell-surface receptors.
   This stringent approach could considerably increase the reliability of
   our miRNA target analyses. Using this strategy, 25 and 14 miRNAs were
   found to potentially repress ACE2 and ACE genes, respectively.
   Importantly, two of the miRNAs reported to be induced by enoxacin, i.e.
   hsa-miR-362-5p and hsa-miR-582-5p, were among these miRNAs that can
   target ACE2 (Fig. [76]1A) and ACE (Fig. [77]1B), respectively,
   suggesting that enoxacin might be able to reduce the efficiency with
   which SARS-CoV-2 enters the host cells through cell surface receptors.

Figure 1.

   [78]Figure 1
   [79]Open in a new tab

   Venn diagram analysis of enoxacin-induced miRNAs and miRNAs targeting
   cell entry receptors necessary for SARS-CoV-2 infection. Entry
   receptors including ACE2 and ACE could be targeted by two
   enoxacin-induced miRNAs.

   SARS-CoV-2 cell entry is reported to also depend on the activity of
   certain cell proteases particularly the transmembrane serine proteinase
   2 (TMPRSS2) and 11D (TMPRSS11D), cathepsin L (CTSL), and FURIN. Judging
   from the analysis of miRNAs co-predicted or validated to target these
   proteases, we found they can be direct targets of a fraction of the
   enoxacin-induced miRNAs. Indeed, seven, two, three, and seven of the
   enoxacin-induced miRNAs were found to putatively target TMPRSS2, CTSL,
   TMPRSS11D, and FURIN, respectively (Table [80]2). These collective
   results highlight enoxacin's potential to restrict the entry of the
   novel coronavirus into the cells.

Table 2.

   Enoxacin-induced miRNAs that can target membranous proteases
   facilitating SARS-CoV-2 entry.
   Target transcripts Targeting miRNAs
   TMPRSS2 hsa-miR-582-5p, hsa-miR-452-5p, hsa-miR-214-3p, hsa-miR-1208,
   hsa-miR-181b-5p, hsa-miR-181c-5p, hsa-miR-98-5p
   TMPRSS11D hsa-miR-574-3p, hsa-miR-186-5p, hsa-miR-23a-3p
   CTSL hsa-miR-501-5p, hsa-miR-518a-5p
   FURIN hsa-miR-20a-5p, hsa-miR-483-3p, hsa-miR-17-5p, hsa-miR-4286,
   hsa-miR-140-3p, hsa-miR-497-5p, hsa-miR-107
   [81]Open in a new tab

Enoxacin might target intracellular proteins interacting with SARS-CoV-2
components

   To perform enrichment analysis on the SARS-CoV-2-interacting host genes
   that might be affected by enoxacin, we used the union of co-predicted
   (from miRanda, PITA, TargetScan, and miRDB) and validated targets (from
   miRTarBase) of the miRNAs upregulated by enoxacin. Next, to find out if
   these targets are also expressed in the lung tissue (to enhance the
   reliability of our in silico analysis), the intersection of these
   target genes and the genes expressed in the lung tissue was determined
   (see the list of mRNA transcripts in Table [82]S3). Then, the PPI
   network of these targets was extracted using STRING database. Totally,
   3893 genes were predicted to be targeted by enoxacin-upregulated miRNAs
   in the lung (Table [83]S5). As shown in Fig. [84]2, the pathway
   enrichment analysis of these genes revealed that the TGF-β signaling
   pathway was the most significant pathway targeted by enoxacin
   (p-value = 5.782e−20). In addition, we determined the top four protein
   modules (M1 to M4) in the PPI network of the targeted genes
   (Fig. [85]3). The pathway enrichment analysis of these modules (Table
   [86]3) highlighted that M1 was related to MHC class I-mediated antigen
   processing and TGF-β signaling pathway; M2 was mainly associated with M
   phase pathway and PI3K-Akt signaling; M3 was primarily consisted of
   endocytosis and EGF/EGFR signaling; and the M4 module highlighted the
   involvement of VEGF/VEGFR2 and PI3K-Akt pathways (Table [87]3).
   Furthermore, as shown in Fig. [88]4, the enrichment analysis of the top
   25 hub genes identified by Cytohubba showed that hub genes mostly
   belonged to MHC class I-mediated antigen presentation
   (p-value = 7.684e−25), suggesting that enoxacin might modulate the
   immune system. It is also noteworthy that we depicted the PPI network
   for 386 genes which were observed to be simultaneously co-targeted by
   10 or more enoxacin-induced miRNAs and repeated all the above analyses.
   Our data revealed a highly similar set of results (data not shown),
   suggesting that the overall impact of enoxacin might be mostly mediated
   by a fraction of its target miRNAs.

Figure 2.

   [89]Figure 2
   [90]Open in a new tab

   Enrichment analysis of genes which are predicted to be targeted by
   enoxacin-induced miRNAs. The Panther enrichment analysis using Enrichr
   showed that genes putatively targeted by enoxacin-induced miRNAs were
   mostly involved in TGF-β signaling (p-value = 5.782e−20). The lighter
   the red color is, the more significant the p-value.

Figure 3.

   [91]Figure 3
   [92]Open in a new tab

   The PPI network (top four modules) of genes potentially targeted by
   enoxacin-induced miRNAs. The PPI network of genes predicted to be
   targeted by enoxacin-induced miRNAs were depicted by Cytoscape (only
   interactions with the confidence of a combined score > 0.400 were
   included) and protein modules were identified by MCODE (cutoff criteria
   were ‘degree cutoff = 2’, ‘k-core = 2’, ‘node score cutoff = 0.2’, and
   ‘maximum depth = 100). M: Module.

Table 3.

   GO and pathway enrichment analysis of top four modules in the PPI
   network of genes predicted to be targeted by enoxacin-induced miRNAs.
   Modules Classification system Pathways p-value
   1 Bioplanet 2019 Antigen presentation: folding, assembly, and peptide
   loading of class I MHC proteins 1.698e−86
   WikiPathways 2019 TGF-β signaling pathway 3.646e−6
   KEGG Ubiquitin-mediated proteolysis 1.510e−60
   2 Bioplanet 2019 M phase pathway 1.226e−39
   WikiPathways 2019 PI3K-Akt signaling pathway 1.170e−17
   KEGG Splicesome 2.026e−23
   3 Bioplanet 2019 Endocytosis 2.710e−14
   WikiPathways 2019 EGF/EGFR signaling pathway 3.439e−7
   KEGG Endocytosis 2.291e−13
   4 Bioplanet 2019 Pathways in cancer 4.469e−17
   WikiPathways 2019 VEGF/VEGFR2 signaling pathway 4.704e−18
   KEGG PI3K-Akt signaling pathway 2.333e−19
   [93]Open in a new tab

Figure 4.

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

   Hub genes in the PPI network could be targeted by enoxacin-induced
   miRNAs. (A) Twenty-five hub genes were identified by Cytohubba and MCC
   method. (B) The KEGG pathway enrichment analysis showed that these
   genes were mostly associated with MHC class I-mediated antigen
   processing (p-value = 1.698e−86). The lighter the red color is, the
   more significant the p-value.

   On the other hand, we used the BioGRID database to obtain human
   proteins that interact with SARS-CoV-2 proteins (Table [96]S6) for
   which the PPI network was depicted (Fig. [97]S1A). The results of KEGG
   pathway enrichment analysis (Fig. [98]S2A) revealed that these 321
   proteins were involved in protein processing in endoplasmic reticulum
   and RNA transport (p-value = 0.0001441), which highlights potential
   intracellular machineries facilitating SARS-CoV-2 infection. In
   addition, the top four modules of the interaction network of these
   human proteins (Fig. [99]S1B) were observed to be mostly involved in
   RNA transport and AMPK signaling pathway (enrichment analysis by KEGG,
   p-value = 2.651e−5 and 1.956e−4 respectively). (Fig. [100]S2B).
   Furthermore, analysis of the nine hub proteins from host cells
   interacting with SARS-CoV-2 proteins (Fig. [101]S3) demonstrated that
   they were associated with insulin signaling and cell junction
   (enrichment analysis by KEGG, p-value = 0.001625 and 0.002486,
   respectively).

   The proteins and cellular pathways described above appear to interact
   with SARS-CoV-2 viruses, thereby facilitating its infection. We,
   therefore, hypothesized that enoxacin might down-regulate some of these
   host proteins to perturb the viral life cycle. Our analysis revealed
   that enoxacin-upregulated miRNAs could target 103 of these 321 proteins
   (Table [102]S7). Taken together, the induction of certain miRNAs by
   enoxacin could lead to the repression of host proteins mediating the
   infection of the novel coronavirus.

A set of miRNAs targeting SARS-CoV-2 components are upregulated by enoxacin

   A fraction of enoxacin-upregulated miRNAs might directly inhibit the
   viral RNA genome. The miRDB search tool predicted 900 miRNAs that might
   target the SARS-CoV-2 RNA genome (Table [103]S8). Venn diagram analysis
   showed that 26 out of these miRNAs (with target score > 70) can be
   upregulated by enoxacin. These 26 miRNAs were predicted to directly
   bind and target various regions of the SARS-CoV-2 RNA genome which
   encode key viral components (Table [104]S9); notably, a fraction of
   these miRNAs appear to target multiple viral components simultaneously
   (Table [105]4). Furthermore, we compared the enoxacin-induced miRNAs
   with the miRNAs previously reported to target SARS-CoV-2 genome via
   different prediction methods^[106]42–[107]44. Venn diagram analysis
   revealed that hsa-miR-455-5p, hsa-miR-623, hsa-miR-193a-5p,
   hsa-miR-602, hsa-miR-222, hsa-miR-378a-3p, hsa-miR-34a-5p, and
   hsa-miR-98-5p which were reported in these studies to target SARS-CoV-2
   genome, were among miRNAs upregulated by enoxacin (Fig. [108]S4). We,
   therefore, conclude that the RNA genome of the SARS-CoV-2 might be
   regulated by certain human miRNAs that are enoxacin-inducible.

Table 4.

   SARS-CoV-2 components can be targeted by enoxacin-induced miRNAs.
   SARS-CoV-2 components Targeting miRNAs
   NSP1 hsa-miR-125b-2-3p, hsa-miR-382-5p
   NSP2 hsa-miR-513a-3p, hsa-miR-376a-3p, hsa-miR-583, hsa-miR-186-5p,
   hsa-miR-495-3p, hsa-miR-3065-5p, hsa-miR-125-2-3p
   NSP3 hsa-miR-485-3p, hsa-miR-20a-3p, hsa-miR-23a-3p, hsa-miR-520a-3p,
   hsa-miR-376a-3p, hsa-miR-452-5p, hsa-miR-382-5p, hsa-miR-576-5p,
   hsa-miR-583, hsa-miR-181c-5p, hsa-miR-181b-5p, hsa-miR-497-5p,
   hsa-miR-186-5p, hsa-miR-545-3p, hsa-miR-30b-5p, hsa-miR-505-3p,
   hsa-miR-518a-5p, hsa-miR-29c-3p, hsa-miR-29b-3p, hsa-miR-495-3p,
   hsa-miR-29a-3p, hsa-miR-194-5p, hsa-miR-3065-5p, hsa-miR-125b-2-3p
   NSP4 hsa-miR-3065-5p, hsa-miR-125b-2-3p, hsa-miR-107, hsa-miR-513a-3p,
   hsa-miR-382-5p, hsa-miR-583, hsa-miR-181c-5p, hsa-miR-181b-5p,
   hsa-miR-186-5p, hsa-miR-30b-5p, hsa-miR-518a-5p, hsa-miR-29a-3p,
   hsa-miR-29b-3p, hsa-miR-495-3p, hsa-miR-29a-3p, hsa-miR-194-5p
   NSP6 hsa-miR-485-3p, hsa-miR-583, hsa-miR-181c-5p, hsa-miR-181b-5p,
   hsa-miR-30b-5p, hsa-miR-518a-5p, hsa-miR-29c-3p, hsa-miR-29b-3p,
   hsa-miR-495-3p, hsa-miR-29a-3p, hsa-miR-194-5p
   NSP7 hsa-miR-518a-5p
   NSP8 hsa-miR-20a-3p, hsa-miR-382-5p, hsa-miR-576-5p, hsa-miR-181c-5p,
   hsa-miR-181b-5p
   NSP9 hsa-miR-495-3p, hsa-miR-194-5p
   ORF3a hsa-miR-3065-5p, hsa-miR-497-5p, hsa-miR-545-3p, hsa-miR-518a-5p
   ORF5 hsa-miR-107
   ORF6 hsa-miR-513a-3p
   ORF7a hsa-miR-452-5p, hsa-miR-186-5p, hsa-miR-125b-2-3p
   ORF8 hsa-miR-181c-5p, hsa-miR-181b-5p, hsa-miR-30b-5p, hsa-miR-513a-3p,
   hsa-miR-376a-3p
   Nucleocapsid hsa-miR-497-5p, hsa-miR-545-3p, hsa-miR-29c-3p,
   hsa-miR-29b-3p, hsa-miR-29a-3p, hsa-miR-107, hsa-miR-513a-3p,
   hsa-miR-20a-3p, hsa-miR-382-5p
   RdRp hsa-miR-186-5p, hsa-miR-545-3p, hsa-miR-505-3p, hsa-miR-495-3p,
   hsa-miR-3065-5p, hsa-miR-125b-2-3p, hsa-miR-107, hsa-miR-23a-3p,
   hsa-miR-520a-3p, hsa-miR-376a-3p hsa-miR-452-5p, hsa-miR-382-5p,
   hsa-miR-497-5p
   Spike hsa-miR-518a-5p, hsa-miR-29c-3p, hsa-miR-29b-3p, hsa-miR-495-3p,
   hsa-miR-29a-3p, hsa-miR-3065-5p, hsa-miR-125b-2-3p, hsa-miR-107,
   hsa-miR-513a-3p, hsa-miR-485-3p, hsa-miR-20a-3p, hsa-miR-23a-3p,
   hsa-miR-376a-3p, hsa-miR-382-5p, hsa-miR-576-5p, hsa-miR-497-5p,
   hsa-miR-186-5p, hsa-miR-545-3p
   Helicase hsa-miR-29c-3p, hsa-miR-29b-3p, hsa-miR-29a-3p,
   hsa-miR-194-5p, hsa-miR-3065-5p, hsa-miR-513a-3p, hsa-miR-452-5p,
   hsa-miR-382-5p, hsa-miR-576-5p, hsa-miR-30b-5p, hsa-miR-505-3p,
   hsa-miR-518a-5p
   2OMT hsa-miR-29c-3p, hsa-miR-29b-3p, hsa-miR-495-3p, hsa-miR-29a-3p,
   hsa-miR-194-5p, hsa-miR-513a-3p, hsa-miR-20a-3p, hsa-miR-576-5p,
   hsa-miR-181c-5p, hsa-miR-181b-5p, hsa-miR-186-5p
   3′-5′ exonuclease hsa-miR-29c-3p, hsa-miR-29b-3p, hsa-miR-495-3p,
   hsa-miR-29a-3p, hsa-miR-376a-3p, hsa-miR-181c-5p, hsa-miR-181b-5p,
   hsa-miR-30b-5p, hsa-miR-505-3p
   3C-like proteinase hsa-miR-495-3p, hsa-miR-194-5p, hsa-miR-3065-5p,
   hsa-miR-125b-2-3p, hsa-miR-485-3p, hsa-miR-376a-3p, hsa-miR-576-5p,
   hsa-miR-583, hsa-miR-30b-5p, hsa-miR-505-3p
   endoRNAse hsa-miR-513a-3p, hsa-miR-376a-3p, hsa-miR-497-5p,
   hsa-miR-30b-5p, hsa-miR-495-3p, hsa-miR-3065-5p, hsa-miR-513a-3p
   5′-UTR hsa-miR-505-3p
   [109]Open in a new tab

Enoxacin-enhanced miRNAs may exert immunomodulatory effects against the
SARS-CoV-2-induced cytokine storm

   An exaggerated immune response in the respiratory system to the
   SARS-CoV-2 infection has been suggested to contribute to the high
   mortality rate seen in patients with COVID-19^[110]9,[111]45. To
   predict if enoxacin could attenuate such cytokine storms in patients
   with COVID-19, we extracted the anti-inflammatory miRNAs through
   literature review and found 25 miRNAs frequently reported to exert
   immunomodulatory effects^[112]46,[113]47. Interestingly, our data
   showed that nine out of these 25 miRNAs including hsa-miR-21,
   hsa-miR-17, hsa-miR-146a, hsa-miR-155, hsa-miR-181b, hsa-miR-181c,
   hsa-miR-31, hsa-miR-92a, and hsa-miR-223 could be upregulated by
   enoxacin (Fig. [114]S5). Of note, we noticed that some of the
   enoxacin-upregulated miRNAs targeted PIK3CA and GSK3B as major
   components of PI3K signaling which plays a key role in driving
   inflammatory responses (data not shown). In conclusion, enoxacin might
   prove beneficial in fighting the SARS-CoV-2 infection via promoting
   immunomodulatory effects.

Enoxacin might promote bronchiolar stem cell differentiation, reversing viral
negative effects on lung parenchyma

   As with SARS-CoV infection (which can promote the severe acute
   respiratory syndrome, SARS), the SARS-CoV-2 infection triggers and
   promotes lung injury as a typical symptom of hospitalized COVID-19
   patients^[115]48. Cumulative evidence suggests that the
   bronchio-alveolar stem cells (BASCs), which are characterized by
   “Sca-1^+ CD34^+ CD45^− Pecam^−” markers, participate in tissue
   regeneration after lung injury^[116]49. Importantly, these cells appear
   to be a prime target of SARS-CoV infection^[117]50. To predict if
   miRNAs upregulated by enoxacin might play a role in lung regeneration,
   we extracted 95 previously reported miRNAs targeting the developmental
   stage-specific transcription factors and key marker genes of
   BASCs^[118]50. Venn diagram analysis showed that 27 out of these miRNAs
   could be upregulated by enoxacin treatment (Table [119]S10).
   Particularly, hsa-let-7d, hsa-let-7g, and hsa-let-7c (predicted to
   target CD34^[120]50) were among the miRNAs upregulated by enoxacin.
   Thus, enoxacin might induce BASC differentiation (necessary for lung
   tissue repair upon injury) by upregulating certain miRNAs.

Discussion

   The RNAi enhancer enoxacin has been proposed as a repurposed drug
   candidate for targeting multiple cancers^[121]51,[122]52 and several
   viral diseases^[123]36,[124]37,[125]53,[126]54. For example, Yan-Peng
   et al. reported that enoxacin augmented virus-derived siRNA levels in
   Zika virus-infected human neural progenitor cells and brain organoids,
   highlighting that enoxacin can promote viral RNA-genome
   degradation^[127]36. We hypothesized that the RNAi-enhancing activity
   of enoxacin might similarly interfere with SARS-CoV-2 infection. In
   line with this, Bartoszewski et al. suggested that SARS-CoV-2 may act
   through the depletion of specific host miRNAs^[128]55, explaining, at
   least partly, why enhancing the host-cell miRNA activity might be a
   viable therapeutic option against SARS-CoV-2 replication. In addition,
   Chow et al. found that most of the differentially expressed miRNAs in
   Calu3 cells infected with SARS-CoV-2 were downregulated^[129]56.
   Interestingly, we found that some of these down-regulated miRNAs such
   as hsa-miR-194-5p, hsa-miR-21-5p, and hsa-miR-940 can be upregulated by
   enoxacin (see Table [130]S2).

   Moreover, we found hundreds of stem-loop structures in the RNA genome
   of SARS-CoV-2 which could be binding sites for the DICER/TRBP complex
   as the direct target of enoxacin's stimulatory effect. Several small
   RNAs have experimentally been found to be derived from our predicted
   stem-loop regions of the SARS-CoV-2 genome. Merino et al. reported
   eight SARS-CoV-2-derived small RNA molecules experimentally confirmed
   by small-RNA sequencing in the SARS-CoV-2-infected human Calu-3
   cells^[131]57. Interestingly, six of these genuine SARS-CoV-2 small
   RNAs were predicted using the SM-based or Ab-initio methods (see Table
   [132]S1) to be derived from the SARS-CoV-2 genomic regions which
   included the regions 396–496, 555–634, 26742–26873,27002–27104
   (predicted by the SM-based approach), 26903–27016, and 29541–29625
   (predicted by the Ab-initio approach). The identification of these
   putative DICER binding sites in the SARS-CoV-2 genome is important for
   the targeting of viral RNA genome, as intramolecular stem-loop
   structures in RNA molecules are known to be typical substrates for
   Dicer enzymes^[133]58,[134]59. Finally, the fact that the SARS-CoV-2
   genome encodes VSRs (i.e. nucleocapsid and SARS-CoV-2-7a proteins)
   further supports the importance of the RNAi pathway as a crucial
   antiviral defense mechanism in mammalian cells^[135]60,[136]61.

   In addition to the direct action of DICER/TRBP complex on the
   SARS-CoV-2 genome, our results suggested that 26 enoxacin-induced
   miRNAs could target different regions of the SARS-CoV-2 genome (see
   Table [137]S9). Experimentally, eight out of these 26 miRNAs have
   previously been reported to target SARS-CoV-2 genome^[138]42–[139]44,
   providing further support for our in silico findings. Altogether, these
   findings suggest the RNAi pathway as an effective antiviral mechanism
   against various viruses including coronaviruses, and support the
   application of the RNAi enhancer enoxacin in potential inhibition of
   the SARS-CoV-2 infection.

   Targeting viral entry receptors and cell-membrane-associated proteases
   necessary for SARS-CoV-2 infection has been proposed as a rational
   strategy to treat COVID-19^[140]62. Our results showed that some of
   enoxacin-induced miRNAs could potentially target the cell-surface
   receptors and cell-membrane-associated proteases necessary for
   SARS-CoV-2 infection. In this regard, hsa-miR-214 and has-miR-98, which
   we found to potentially target TMPRSS2 using several miRNA target
   prediction and validation databases, have been experimentally verified
   to target TMPRSS2 in Caco-2, HMVEC-L, and HUVEC cells^[141]63,[142]64.
   Finally, microarray analyses^[143]65 support our data regarding the
   high likelihood of hsa-miR-107 to repress FURIN, which codes for
   another cell-membrane-associated protease involved in SARS-CoV-2
   pathogenesis^[144]66.

   Following the entry of SARS-CoV-2 into the lung epithelial cells, these
   cells start secreting inflammatory factors to recruit various
   leukocytes to the lung tissue, helping to suppress the
   infection^[145]67,[146]68. However, the over-production of inflammatory
   mediators might lead to the so-called acute respiratory distress
   syndrome (ARDS) which promotes the destruction of the lung parenchyma
   in patients with severe COVID-19^[147]69. Among these inflammatory
   mediators, IL-6 is strikingly upregulated in the blood samples from
   non-survivor individuals^[148]69, explaining why the IL-6 inhibitor
   Tocilizumab is currently being used in patients with ARDS^[149]9.

   Notably, we found that nine of the enoxacin-induced miRNAs had
   previously been found to exert immunomodulatory effects^[150]46. The
   probable immunomodulatory effect of enoxacin is not surprising given
   that several reports have independently shown the immunomodulatory
   activities of fluoroquinolones in diverse contexts^[151]22. It is
   worthwhile to note that the enoxacin-enhanced miRNAs hsa-miR-21,
   hsa-miR-146a, hsa-miR-92a, hsa-miR-181b, and hsa-miR-223 have been
   reported to induce anti-inflammatory effects in part by decreasing IL-6
   expression^[152]70–[153]74. Moreover, overexpression of hsa-miR-21 in
   lipopolysaccharide-induced macrophages was reported to significantly
   decrease IL-6 and increase anti-inflammatory IL-10 secretion^[154]70.
   Further, hsa-miR-146a overexpression in human retinal endothelial cells
   or in lipopolysaccharide-induced macrophages reduced the IL-6
   secretion^[155]75,[156]76. Furthermore, hsa-miR-146s can negatively
   regulate TNFα-induced inflammatory pathway in macrophages^[157]77 and
   reduce IL-6 secretion in primary human small airway epithelial
   cells^[158]78. Another enoxacin-induced miRNA, hsa-miR-92a, was found
   to directly target mitogen-activated protein kinase kinase 4,
   decreasing TNFα and IL-6 production in macrophages^[159]79.
   Interestingly, hsa-miR-181b was found to decrease IL-6 expression in
   lipopolysaccharide-induced macrophages^[160]80. Finally, hsa-miR-223, a
   crucial regulator of the innate immune responses, was found to directly
   target poly (adenosine diphosphatase-ribose) polymerase-1
   (PARP-1)^[161]81 and NLRP^[162]82, thereby suppressing inflammation.
   Taken together, these collective evidence strongly suggest that the
   miRNAs induced by enoxacin are functionally involved in dampening
   inflammation in various biological contexts, and that the potential
   enoxacin-driven immunomodulation might play an important part in the
   effective treatment of COVID-19 patients.

   We also found that a fraction of the enoxacin-induced miRNAs
   particularly hsa-let-7c, hsa-let-7g, and hsa-let-7d could contribute to
   BASC differentiation possibly by targeting the stem cell marker
   CD34^[163]50. In accordance with this result, over-expression of
   hsa-let-7 family members has frequently been reported to inhibit
   various stem cell states and promote multi-lineage
   differentiation^[164]27,[165]83,[166]84. BASCs were found to be
   activated upon different lung injuries, and differentiate into multiple
   cell lineages for lung regeneration^[167]85. Since alveolar damage and
   pulmonary fibrosis are the main pathological findings in patients with
   severe COVID-19^[168]86, triggering lung stem cell pools to
   differentiate may enhance lung repair and regeneration^[169]85. In
   addition, Mallick et al. reported that 15 miRNAs were downregulated in
   SARS-CoV-infected BASCs^[170]50, of which the expression of hsa-let-7c,
   hsa-let-7d, hsa-let-7g, hsa-miR-186, hsa-miR-98, and hsa-miR-223 can be
   restored by enoxacin-induced miRNAs (see Table [171]S2).

   The PPI network the target genes of enoxacin-induced, lung-expressed
   miRNAs revealed that these genes were mainly associated with MHC class
   I-mediated antigen processing, TGF-β signaling pathway, PI3K-AKT
   signaling, and endocytosis. In this regard, Xia et al.^[172]87
   demonstrated in mice that MHC class I exacerbated the vesicular
   stomatitis virus (VSV)-induced infection in the lung by disrupting the
   type I IFN signaling, and that the viral load in MHC class I-deficient
   macrophages was decreased. Moreover, the possible inhibitory effect of
   enoxacin on TGF-β signaling is interesting because TGF-β signaling (i)
   drives the chronic adaptive immune responses in patients with
   ARDS^[173]88, resulting in rapid and massive edema and fibrosis in
   these patients (for this reason, its blockade has been suggested as a
   potential treatment of COVID-19)^[174]89 and (ii) increases FURIN
   expression in well-differentiated primary human bronchial epithelial
   cells^[175]90 (therefore, its blockade may attenuate the SARS-CoV-2
   entry into the cells). In addition, the inhibitors of PI3K-AKT
   signaling, which we suggest to be targeted by enoxacin, have been
   proposed as drug candidates in COVID-19 treatments^[176]91. This
   argument is supported by the point that PI3K-AKT signaling is required
   for establishing persistent SARS-CoV infection in Vero E6
   cells^[177]92. The possible effect of enoxacin on endocytosis pathway
   is also notable as some proteins of this pathway are interactors of
   SARS-CoV-2 proteins^[178]93. Overall, it seems that enoxacin
   upregulates a specific set of miRNAs in lung cells which can restrict
   the entry, replication, and infection of SARS-CoV-2 through suppressing
   several intracellular pathways.

   It is also noteworthy that our study has a number of limitations.
   First, enoxacin-induced miRNA profiles were extracted from studies on
   cancer cells which may not completely reflect the actual effects of
   enoxacin on SARS-CoV-2-infected cells, although the molecular
   interaction of enoxacin with its target, TRBP, does not appear to be
   mechanistically different in different types of cell. To minimize this
   limitation, we considered only those enoxacin-induced miRNAs that were
   also expressed by lung cells. Second, our in silico results need to be
   investigated experimentally in both in vitro and animal models of
   SARS-CoV-2 infection, although we provided a large body of experimental
   evidence from previously published research for our in silico findings.
   Altogether, our analysis strongly suggests that enoxacin could be a
   promising drug candidate for COVID-19 treatment.

Conclusion

   Enoxacin belongs to the fluoroquinolone family of synthetic antibiotics
   which was recently found to enhance the maturation of
   TRBP/DICER-dependent small RNAs, leading to a global increase in the
   concentration of these regulatory RNAs. The enhancement of the RNAi
   pathway by enoxacin has been frequently found to exert detrimental
   effects on the replication of several types of viruses, suggesting that
   enoxacin might similarly inhibit the infection caused by the novel
   coronavirus. Using several in silico analyses, we observed that
   enoxacin could promote the DICER/TRBP-mediated degradation of the
   SARS-CoV-2 RNA genome, as our data suggested that the viral genomic RNA
   can be a suitable substrate for the DICER activity. We could also
   find several enoxacin-upregulated miRNAs that are predicted to directly
   target the viral genome. Importantly, several enoxacin-induced miRNAs
   were suggested to inhibit not only the viral components mediating viral
   entry into host cells and its intracellular replication but could also
   target certain host proteins interacting with the viral components.
   Therefore, enoxacin might be able to exert antiviral effects against
   the SARS-CoV-2 infection. Figure [179]5 illustrates a schematic model
   of how the SARS-CoV-2 infection might be suppressed by the application
   of enoxacin. The potential antiviral effects of enoxacin could be
   probably further increased when enoxacin treatment is accompanied by
   the delivery of a shRNA sequence directly targeting the SARS-CoV-2
   genome. In this way, not only enoxacin could restrict the viral
   replication per se as suggested in this study but also can enhance the
   processing of the delivered shRNA in order to provide a more potent
   inhibitory effect on the novel coronavirus. Finally, since there are
   other fluoroquinolones which similarly exhibit RNAi enhancing effects,
   it might be possible to also use those fluoroquinolone members for
   targeting the novel coronavirus infection. Further investigations are
   needed to examine how enoxacin or other family members might modulate
   the infection caused by SARS-CoV-2.

Figure 5.

   [180]Figure 5
   [181]Open in a new tab

   Modeling of the potential SARS-CoV-2 inhibition by enoxacin. Enoxacin
   enhances the RNAi pathway through binding to TRBP, the physical partner
   of DICER. This interaction enhances the dicing of viral RNA genome
   directly by DICER as well as upregulates certain mature miRNAs which
   could target SARS-CoV-2 RNA genome and viral transcripts including VSRs
   through RISC complexes. Enoxacin-induced miRNAs might also target entry
   receptors and membranous proteases in host cells, pro-inflammatory
   genes in the immune cells, and stem cell markers in BASCs. It might
   also suppress the interactions between certain viral and host
   RNA molecules which mediate and facilitate viral replication and
   infection. ( Inline graphic ) designates mutual interaction. miRISC:
   miRNA-induced silencing complex; siRISC: siRNA-induced silencing
   complex.

Materials and Methods

Prediction of miRNA- and shRNA/siRNA precursors in the SARS-CoV-2 genome

   The shRNA/miRNA precursor structures in the SARS-CoV-2 genome were
   predicted by three web server tools. First, the ab initio method was
   used to predict miRNA hairpin structures in the entire
   29,903-nucleotide genome of SARS-CoV-2 ([182]NC_045512.2) using
   miRNAFold with default parameters^[183]41,[184]94. The entire
   SARS-CoV-2 genome was also analyzed by the sequence-structure
   motif-based (SM-based) miRNA prediction method using the Regulatory RNA
   web tool with default parameters
   ([185]http://www.regulatoryrna.org/webserver/SSMB/pre-miRNA/home.html).
   The BLASTN method was also run to predict precursor structures using
   the miRBase search tool^[186]95.

Obtaining the list of enoxacin-induced miRNAs and prediction of their target
genes

   The lists of enoxacin-induced miRNAs were obtained from previous
   experiments analyzing the effects of enoxacin on human embryonic kidney
   cells (HEK293)^[187]33, human prostate cancer cell lines (LNCaP and
   DU145)^[188]34, and the human melanoma cell line A375^[189]35. All
   miRNAs induced by enoxacin across different cell lines were extracted
   using Venn diagram. The expression profiles of miRNAs and mRNAs of the
   human lung tissue were obtained from the IMOTA database, an interactive
   multi-omics-tissue atlas^[190]96. The target genes of miRNAs were
   co-predicted by four databases (miRDB^[191]97, PITA^[192]98,
   miRanda^[193]99, and TargetScan^[194]100) provided by miRWalk^[195]101.
   To further increase the likelihood of obtaining true-positive miRNA
   targets, only transcripts with at least two binding sites for any given
   miRNA were extracted from the miRWalk 2.0 atlas^[196]101. Validated
   target genes (FDR < 0.05) of enoxacin-induced miRNAs were obtained from
   the miRTarBase database^[197]102 via the MIENTURNET web tool^[198]103.

Gene ontology (GO) and biological pathway analyses

   Gene set enrichment analysis of the predicted target genes for
   up-regulated miRNAs was performed using Enrichr as an enrichment
   analysis web application which provides access to 35 gene-set
   libraries^[199]104. Several features of the Enrichr database including
   KEGG, Wikipathways, and BioPlanet were used for GO analysis. P < 0.05
   was considered to indicate statistical significance and the results
   were ranked by P-value.

Protein–protein interaction (PPI) network analysis, module selection, and
identification of hub genes

   The PPI networks were constructed to infer interaction among proteins
   using the online STRING database ([200]http://string-db.org/).
   Interactions with the confidence of a combined score > 0.400 were
   imported into Cytoscape to construct the PPI network. We used MCODE to
   identify the modules in the PPI network^[201]105. The cutoff criteria
   were ‘degree cutoff = 2’, ‘k-core = 2’, ‘node score cutoff = 0.2’, and
   ‘maximum depth = 100’. Hub genes were identified using the Cytoscape
   plugin cytoHubba by MCC method, as described previously^[202]106.

Determining the putative miRNAs which target the viral and host components

   Prediction of the human miRNAs that could target the viral RNA genome
   was performed using the miRDB custom search web tool^[203]107. This
   database allows for submission of the entire SARS-CoV-2 genome and
   provides the list of human miRNAs potentially targeting different
   regions of the viral genome. Prediction of the human miRNAs that target
   host genes interacting with the virus was performed using miRDB,
   miRanda, and TargetScan databases. Validated miRNA targets were
   obtained from miRTarBase and considered in combination with
   co-predicted gene targets for gene ontology and biological pathway
   analyses.

Supplementary Information

   [204]Supplementary Information 1.^ (1.3MB, docx)
   [205]Supplementary Table S1.^ (106KB, xlsx)
   [206]Supplementary Table S2.^ (19.6KB, xlsx)
   [207]Supplementary Table S3.^ (258.1KB, xlsx)
   [208]Supplementary Table S4.^ (24KB, xlsx)
   [209]Supplementary Table S5.^ (114.3KB, xlsx)
   [210]Supplementary Table S6.^ (15.5KB, xlsx)
   [211]Supplementary Table S7.^ (10.3KB, xlsx)
   [212]Supplementary Table S8.^ (28.6KB, xlsx)
   [213]Supplementary Table S9.^ (11.7KB, xlsx)
   [214]Supplementary Table S10.^ (11.1KB, xlsx)

Author contributions

   A.A. performed the majority of the in silico analyses, interpreted and
   discussed the results, and wrote the manuscript. S.M. designed and
   supervised the study, interpreted and discussed the results, and wrote
   and approved the manuscript for submission.

Competing interests

   The authors declare no competing interests.

Footnotes

   Publisher's note

   Springer Nature remains neutral with regard to jurisdictional claims in
   published maps and institutional affiliations.

Supplementary Information

   The online version contains supplementary material available at
   10.1038/s41598-021-89605-6.

References