In silico analysis suggests the RNAi-enhancing antibiotic enoxacin as a potential inhibitor of SARS-CoV-2 infection

doi:10.21203/rs.3.rs-96308/v1

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In silico analysis suggests the RNAi-enhancing antibiotic enoxacin as a potential inhibitor of SARS-CoV-2 infection

https://doi.org/10.21203/rs.3.rs-96308/v1

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published 13 May, 2021

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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 is 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.

Computational Biology

Virology

Drug Discovery, Design, & Development

Enoxacin

RNAi

siRNA

miRNA

coronavirus

TRBP

Since the emergence of SARS-CoV-2 virus in November 2019, COVID-19 has become the biggest challenge in the world¹. 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^2,3. Various strategies including blocking viral entry into the host cells^4-6, inhibiting viral replication^7,8 and reducing cytokine storms^9,10 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 shown to slightly shorten the time to recovery or weakly reduce the rate of death among hospitalized patients^8,11. 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 viruses¹². The IFN pathway is activated by viral components, thereby transcriptionally activating a large number of the so-called IFN-stimulated genes (ISGs)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 infection¹⁴. 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)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 infection¹⁶. This immune overreaction is injurious to the host cells and might be induced by the SARS-CoV-2 infection¹⁷. 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) cells¹⁸. It involves the efficient degradation of the large viral RNAs which form secondary double-stranded structures thereby serving as substrates of the RNAi pathway¹⁸.

Fluoroquinolones such as enoxacin are broad-spectrum synthetic antibiotics used in different clinical conditions like urinary tract-, respiratory-, and systemic infections^19,20. Although quinolones are known to typically inhibit DNA replication by targeting bacterial DNA gyrases²¹, 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 reactions²², and more importantly enhance the RNAi process as an inflammation-free innate immune defense against viral infections²³.

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 interactions²⁴. 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^25,26. miRNAs are short non-coding RNAs processed by DICER which regulate gene expression at the post-transcriptional level, thereby modulating virtually all biological pathways^27-29. 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^30,31. 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^32,33. 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)^23,34,35. 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^36-39, 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 can be a suitable substrate for 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 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.

The RNA genome of SARS-CoV-2 is a suitable substrate for DICER catalytic activity

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^23,33, we first investigated if the single-stranded RNA genome of SARS-CoV-2 might be a suitable substrate for the RNAi machinery. As DICER acts on hairpin 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 strategies⁴⁰, predicted 145 hairpin and miRNA precursors potentially derived from the SARS-CoV-2 genome (Table 1 and Table 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 structure⁴¹, 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 1 and Table S1), which could serve as binding sites for recruiting DICER and stimulating its RNase activity. These results strongly suggest that the SARS-CoV-2 genome intrinsically harbors multiple hairpin structures which could be substrates for DICER catalytic activity, allowing for the efficient degradation of the coronavirus RNA genome through the RNAi pathway.

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 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 S2). Next, we attempted to define miRNAs that could target ACE2 as the main entry receptor of SARS-CoV-2 on cell surface as well as ACE as an alternative entry receptors of the virus (Table S3). Notably, 257 and 461 miRNAs were predicted by TargetScanHuman 7.2 and miRTarBase to repress ACE2 and ACE genes, respectively. Importantly, of miRNAs reported to be induced by enoxacin, 12 and 15 were predicted to target ACE2 (Fig. 1A) and ACE (Fig. 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. Interestingly, our data also revealed that one of the enoxacin-enhanced miRNAs, hsa-miR-582-5p, potentially suppresses both of the receptors ACE2 and ACE (Fig. 1A and B).

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) as well as cathepsin L (CTSL). Judging from the analysis of both predicted and validated miRNAs that target these proteases, we found they can be direct targets of a fraction of the enoxacin-induced miRNAs. Indeed, 21, 5, and 10 miRNAs that putatively target TMPRSS2, CTSL, and TMPRSS11D, respectively, have been previously confirmed to be upregulated by enoxacin treatment (Table 2). These collective results highlight enoxacin's potential to restrict the entry of the novel coronavirus into the cells.

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, putative targets of enoxacin-enhanced miRNAs were determined using ComiR online tool and the PPI network, in which these target proteins were involved, was extracted using STRING database. Totally, 1001 genes were predicted to be targeted by enoxacin-upregulated miRNAs (Table S4). As shown in Fig. 2, the Panther pathway enrichment analysis of these genes revealed that the insulin/IGF-PKB/AKT signaling cascade was the most significant pathway targeted by enoxacin (p-value=0.00003 and combined core=55.21). In addition, we depicted the PPI network of the targeted genes (Fig. 3A) and determined the top four protein modules (M1 to M4) (Fig. 3B). The pathway enrichment analysis of these modules (Table 3) highlighted that M1 was related to MHC class I-mediated antigen processing and TGF-β signaling pathway; M2 was mainly associated with mTOR signaling; M3 was primarily consisted of PI3K-AKT signaling; and the M4 module highlighted the involvement of Rab protein signal transduction (Table 3). Furthermore, as shown in Fig. 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= 1.993e-8 and combined score=389.39), suggesting that enoxacin might modulate the immune system.

On the other hand, we used the BioGRID database to obtain human proteins that interact with SARS-CoV-2 proteins (Table S5) and the PPI network was depicted (Fig. S1A). The results of KEGG pathway enrichment analysis (Fig. S2A) revealed that these 288 proteins were involved in protein processing in endoplasmic reticulum and RNA transport (p-value= 0.0001441 and combined score=37.23), which might be highlighting potential intracellular machineries facilitating SARS-CoV-2 infection. In addition, the top four modules of the interaction network of these human proteins (Fig. 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. S2B). Furthermore, analysis of the nine hub proteins from host cells interacting with SARS-CoV-2 proteins (Fig. S3) demonstrated that they are 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 16 of these 288 proteins (Fig. S4). 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 S6). Venn diagram analysis showed that 28 out of these miRNAs can be upregulated by enoxacin. These 28 miRNAs are predicted to directly bind and target various regions of the SARS-CoV-2 RNA genome which encode key viral components (Table S7); notably, a fraction of these miRNAs appear to target multiple viral components simultaneously (Table 4). Furthermore, we compared the enoxacin-induced miRNAs with the miRNAs previously reported to target SARS-CoV-2 genome via different prediction methods^42-44. Venn diagram analysis revealed that hsa-miR-455-5p, hsa-miR-623, hsa-miR-193a-5p, hsa-miR-602, hsa-miR-1181, hsa-miR-222, hsa-miR-378, hsa-miR-101, and hsa-miR-98 which were reported in these studies to target SARS-CoV-2 genome, are among miRNAs upregulated by enoxacin (Fig. S5). We, therefore, conclude that the RNA genome of the SARS-CoV-2 might be regulated by certain miRNAs that are enoxacin-inducible.

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-19^9,45. 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^46,47. Interestingly, our data showed that 11 out of these 25 miRNAs including hsa-miR-21, hsa-miR-124, hsa-miR-146a, hsa-miR-155, hsa-miR-181b, hsa-miR-181c, hsa-miR-31, hsa-miR-92a, hsa-miR-125b, hsa-miR-132, and hsa-miR-223 could be upregulated by enoxacin (Fig. S6). Of note, we noticed that some of the enoxacin-upregulated miRNAs regulate PIK3A 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 patients⁴⁸. 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 injury⁴⁹. Importantly, these cells appear to be a prime target of SARS-CoV infection⁵⁰. 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 BASCs⁵⁰. Venn diagram analysis showed that 32 out of these miRNAs could be upregulated by enoxacin treatment (Table S8). Particularly, hsa-miR-19b and hsa-miR-106a (which target Sca-1) and hsa-let-7d, hsa-let-7g, and hsa-let-7c (which target CD34) are among the miRNAs upregulated by enoxacin. Thus, enoxacin might induce BASC differentiation (necessary for lung tissue repair upon injury) by upregulating certain miRNAs.

Modeling of the potential SARS-CoV-2 inhibition by enoxacin

According to our results described above, we propose that enoxacin might be beneficial for treatment of COVID-19 through multiple mechanisms. First, the SARS-CoV-2 RNA genome is a suitable substrate for RNAi machinery which is stimulated by the fluoroquinolone antibiotic. Importantly, enoxacin not only enhances the global RNAi activity (which can result in the degradation of the viral genome) but also up-regulates certain miRNAs able to directly target the SARS-CoV-2 genome. In addition, six of the enoxacin-upregulated miRNAs (i.e. hsa-miR-107, hsa-miR-513a-3p, hsa-miR-1231, hsa-miR-2053, hsa-miR-578, and hsa-miR-422a) might target the SARS-CoV-2 nucleocapsid components which might serve as VSRs. Second, some enoxacin-induced miRNAs potentially target entry receptors like ACE2 and membranous proteases such as TMPRSS2 which are necessary for SARS-CoV-2 entry to the host cells. Third, enoxacin might modulate cytokine storms in patients with COVID-19 through upregulation of anti-inflammatory miRNAs and inflammation-related pathways in host cells. Fourth, enoxacin might induce the differentiation of BASCs which is necessary for lung regeneration. Fifth, enoxacin is computationally predicted to upregulate miRNAs that can target the host intracellular proteins which mediate and facilitate the infection of SARS-CoV-2. Fig. 5 illustrates a schematic model of how the SARS-CoV-2 infection might be suppressed by the application of enoxacin. In summary, our collective results strongly suggest the small molecule enoxacin as a therapeutic agent to help inhibit the novel coronavirus infection in COVID-19 patients.

Repurposing of available drugs for COVID-19 treatment is an underway strategy in the world⁵¹. Although recent studies have shown that some fluoroquinolones exert antiviral effects against several types of viruses^36,37,52,53, it has not yet been investigated whether enoxacin, which serves as an efficient RNAi enhancer, could be a drug candidate for COVID-19 treatment. In this study, we first found hundreds of stem-loop structures in the RNA genome of SARS-CoV-2 which could be binding sites for the DICER/TRBP complex on which the antibiotic enoxacin exerts stimulatory effects. This 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^54,55. Thus, our data suggests that the SARS-CoV-2 genome contains stem-loop structures that can trigger the DICER/TRBP enzymatic complex, and therefore the RNAi pathway, in the host cells. Although no experimental evidence still exists regarding RNAi responses to SARS-CoV-2 infection, our in silico findings are supported by a number of evidence suggesting that the RNAi pathway could be an important layer of defense mechanism against coronaviruses. For example, Cui et al.³⁰ demonstrated that the knockdown of Dicer or Ago2 facilitates the replication of mouse hepatitis virus (MHV) which is closely related to SARS-CoV as a member of the family Coronaviridae. The authors also revealed that the nucleocapsid protein of SARS-CoV could directly bind to dsRNAs and sequester RNA duplex from Dicer cleavage in both mammalian and insect cells (HEK293 and Drosophila S2 cells, respectively)³⁰. Furthermore, a Clustal W analysis of nucleocapsid proteins of SARS-CoV and SARS-CoV-2 revealed more than 90% sequence identity between these two sequences which suggest a similar function for the SARS-CoV-2 nucleocapsid proteins in suppression of the RNAi pathway⁵⁶. These findings suggest the RNAi pathway as an effective antiviral mechanism against various viruses including coronaviruses, and supports the application of the RNAi enhancer enoxacin in potential inhibition of the SARS-CoV-2 infection.

Our results also showed that some of enoxacin-induced miRNAs could potentially target the entry receptor ACE2 and cell-membrane-associated proteases such as TMPRSS2, necessary for SARS-CoV-2 infection. We showed that hsa-miR-501-5p, hsa-miR-587 and 10 other miRNAs potentially target ACE2 as the main entry receptor of SARS-CoV-2. Interestingly, Chow et al.⁵⁷ recently reported that hsa-miR-501-5p was significantly upregulated in Calu3 cells infected with SARS-CoV-2, which might be an early response to the presence of the virus, probably needing to be augmented exogenously. In another study, Wicik et al.⁵⁸ reported that hsa-miR-587 interfered with ACE2 signaling network in COVID-19. In addition, our data suggests that hsa-miR-587 also targets TMPRSS2 (see Table 2) which is in line with Wicik et al. study⁵⁸ and highlights miR-587 as a potential therapeutic option for COVID-19 needing further investigation.

We also predicted that hsa-miR-940 and 14 other miRNAs target ACE as an alternate entry receptor (see Fig. 2). hsa-miR-940 has been reported to be downregulated in Calu3 cells infected with SARS-CoV-2⁵⁷. We further found that hsa-miR-199a-5p might target TMPRSS2 and CTSL proteases (see Table 2). This data is supported by an integrative analysis of miRNA and mRNA sequencing profiles showing that hsa-miR-199a-5p regulates the expression of TMPRSS2 in the liver, stomach, and uterine corpus⁵⁹.

In our analyses, 28 enoxacin-induced miRNAs were also predicted to target different regions of SARS-CoV2 genome (Table S7). Nine out of these 28 miRNAs including hsa-miR-455-5p, hsa-miR-623, hsa-miR-193a-5p, hsa-miR-602, hsa-miR-1181, hsa-miR-222, hsa-miR-378, hsa-miR-101, and hsa-miR-98 have also been reported to target SARS-CoV-2 genome in previous studies^42-44, further supporting the reliability of our findings.

Notably, we found that 11 of the enoxacin-induced miRNAs had previously been found to exert immunomodulatory effects⁴⁶. The probable immunomodulatory effect of enoxacin is not surprising given that several reports have independently shown the immunomodulatory activities of fluoroquinolones in diverse contexts²². The enoxacin-enhanced miRNAs hsa-miR-21, hsa-miR-124, hsa-miR-146a, hsa-miR-155, and hsa-miR-181b could induce anti-inflammatory effects in part by decreasing IL-6 expression, as reported previously^60-64. This effect is interesting particularly in view of reports that demonstrated that macrophages in patients with COVID-19 contribute to excessive inflammation through IL-6 secretion^65,66. In addition, the IL-6 inhibitor Tocilizumab is currently being used in patients with acute respiratory distress syndrome⁹, suggesting 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 could contribute to BASC differentiation by targeting certain stem cell markers. BASCs were found to be activated upon different lung injuries, and differentiate into multiple cell lineages for lung regeneration⁶⁷. Since alveolar damage and pulmonary fibrosis is the main pathological findings in patients with severe COVID-19⁶⁸, triggering lung stem cell pools to differentiate may enhance lung repair and regeneration⁶⁷.

In the present study, the target genes of enoxacin-induced miRNAs were also predicted and the related PPI network was depicted. The four top modules in this PPI network were observed to be mainly associated with MHC class I-mediated antigen processing, TGF-β signaling pathway, mTOR signaling, PI3K-AKT signaling, and Rab protein signal transduction. We suggest that the probable effect of enoxacin on MHC class I-mediated antigen processing might affect SARS-CoV-2 infection. This suggestion is supported by the results of Xia et al.⁶⁹ who demonstrated that MHC class I promoted the vesicular stomatitis virus (VSV) replication, 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 particularly in view of reports that show that the sudden and uncontrolled increase in active TGF-β results in rapid and massive edema and fibrosis in patients with COVID-19⁷⁰. In addition, the inhibitors of mTOR and PI3K-AKT signaling pathways, which we suggest to be targeted by enoxacin, have been proposed as drug candidates in COVID-19 treatments⁷¹. The possible effect of enoxacin on Rab protein signal transduction pathway is also notable as some proteins of this pathway are interactors of SARS-CoV-2 proteins⁷². With regard to interactions of the host cell components with the virus, we also suggest that enoxacin-induced miRNAs could downregulate several human proteins which interact with SARS-CoV-2 protein components. Notably, recent studies have introduced some of these proteins such as G3BP1, ZYG11B and MIB1 as targets for drug repurposing in the treatment of COVID-19^72,73.

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. 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 several experimental supports from previously published research. Altogether, our analysis strongly suggests that enoxacin could be a promising drug candidate for COVID-19 treatment.

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 could be a suitable substrate for the DICER activity. We could also found miRNAs, reported to be upregulated by enoxacin, 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 also certain host proteins interacting with the viral components. Therefore, enoxacin might be able to exert antiviral effects against the SARS-CoV-2 infection. The potential antiviral effects of enoxacin could be probably further increased when enoxacin treatment is accompanies 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 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.

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 29903-nucleotide genome of SARS-CoV-2 (NC_045512.2) using miRNAFold with default parameters^41,74. 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 (http://www.regulatoryrna.org/webserver/SSMB/pre-miRNA/home.html). The BLASTN method was also run to predict precursor structures using the miRBase search tool⁷⁵.

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)³³, human prostate cancer cell lines (LNCaP and DU145)³⁴, and the human melanoma cell line A375³⁵. All miRNAs induced by enoxacin across different cell lines were extracted using Venn diagram. The ComiR, as a combinatorial miRNA target prediction web tool, was used to predict the target genes of up-regulated miRNAs as previously described⁷⁶.

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 libraries⁷⁷. 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 (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 network⁷⁸. 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 previously⁷⁹.

Determining the putative miRNAs which target the viral and host components

Prediction of the human miRNAs that target the viral RNA genome was performed using the miRDB custom search web tool⁸⁰. To identify miRNAs that target key viral components and host genes interacting with the virus, TargetScanHuman 7.2⁸¹ and miRTarBase⁸² databases were used, which yield the predicted and validated targeting miRNAs, respectively.

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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 that they have no conflicts of interest.

Table 1. The first 15 stem-loop structures within the SARS-CoV-2 genome predicted by three different methods.

	Ab-initio-based method		SM-based method		BLASTN method
1	5´UUAAAAUCUGUGUGGCUGUCACUCGGCUGCAUGCUUAGUGCACUCACGCAGUAUAAUUAA3´	Location 77 -136	5´GAGCCUUGUCCCUGGUUUCAACGAGAAAACACACGUCCAACUCAGUUUGCCUGUUUUACAGGUUCGCGACGUGCUCGUACGUGGCUUUGGAGACUCCGUG3´	Location 270-370	5´GGUGUGACCGAAAGGUAAGAUGGAGAGCCUUGU3´	Location 247-289
2	5´ACUCGUCUAUCUUCUGCAGGCUGCUUACGGUUUCGUCCGUGUUGCAGCCGAUCAUCAGCACAUCUAGGUUUCGUCCGGGU3´	171-251	5´CGUCAACAUCUUAAAGAUGGCACUUGUGGCUUAGUAGAAGUUGAAAAAGGCGUUUUGCCUCAACUUGAACAGCCCUAUGUGUUCAUCAAACGUUCGGAUGCUC3´	391-494	5´UGUCUAUCCAGUUGCGUCACCAAAUGAAUGCAACCAAAUGUGCCU3´	1171-1215
3	5´UCUAUCUUCUGCAGGCUGCUUACGGUUUCGUCCGUGUUGCAGCCGAUCAUCAGCACAUCUAGGUUUCGUCCGGGUGUGACCGAAAGGUAAGAUGGA3´	176-272	5´GGCAUUCAGUACGGUCGUAGUGGUGAGACACUUGGUGUCCUUGUCCCUCAUGUGGGCGAAAUACCAGUGGCUUACCGCAAGGUUCUUCUUCGUAAGAAC3´	544-643	5´UCUUAUGUUGGUUGCCAUAACAAGUGUGCCUAUUGGGUUCCACGUGCUAGCGCUAA3´	1499-1554
4	5´AGCACAUCUAGGUUUCGUCCGGGUGUGACCGAAAGGUAAGAUGGAGAGCCUUGUCCCUGGUUUCAACGAGAAAACACACGUCCAACUCAGUUUGCCUGUUUUACAGGUUCGCGACGUGCU3´	228-348	5´CUUUGGAGGCUGUGUGUUCUCUUAUGUUGGUUGCCAUAACAAGUGUGCCUAUUGGGUUCCACGUGCUAGCGCUAACAUAGGUUGUAACCAUACAGGUGUUGUU3´	1479-1582	5´AAGUCAACAUCAAUAUUGUUGGUGACUUUAAACUUAAUGAAGAGAUCGCCAUUAUUUUGGCAUC3´	1638-1701
5	5´CAGUUUGCCUGUUUUACAGGUUCGCGACGUGCUCGUACGUGGCUUUGGAGACUCCGUGGAGGAGGUCUUAUCAGAGGCACGUCAACAUCUUAAAGAUGGCACUUG3´	316-421	5´AACAUAGGUUGUAACCAUACAGGUGUUGUUGGAGAAGGUUCCGAAGGUCUUAAUGACAACCUUCUUGAAAUACUCCAAAAAGAGAAAGUCAACAUCAA3´	1552-1650	5´UGACUUUAAACUUAAUGAAGAGAUCGCCAUUAUUUUGGCAUCUU3´	1660-1703
6	5´GGUCUUAUCAGAGGCACGUCAACAUCUUAAAGAUGGCACUUGUGGCUUAGUAGAAGUUGAAAAAGGCGUUUUGCCUCAACUUGAACAGCC3´	380-470	5´UGGAAUAUUGGUGAACAGAAAUCAAUACUGAGUCCUCUUUAUGCAUUUGCAUCAGAGGCUGCUCGUGUUGUACGAUCAAUUUUCUCCCGCACUCUUGAAACUG3´	1825-1928	5´GUGGUAAUUUUAAAGUUACAAAAGGAAAAGCUAAAA3´	1782-1817
7	5´UGAAAAAGGCGUUUUGCCUCAACUUGAACAGCCCUAUGUGUUCAUCAAACGUUCGGAUGCUCGAACUGCACCUCAUGGUCAUGUUAUGGUUGAGCUGGUAGCAGAACUCGAAGGCAUUCA3´	438-558	5´AUGAUGUUCACAUCUGAUUUGGCUACUAACAAUCUAGUUGUAAUGGCCUACAUUACAGGUGGUGUUGUUCAGUUGACUUCGCAGUGGCUAACUAACAUCUUUGGCACUGUU3´	2014-2125	5´CACCACUGGGCAUUGAUUUAGAUGAGUGGAGUAUGG3´	2884-2919
8	5´GCACCUCAUGGUCAUGUUAUGGUUGAGCUGGUAGCAGAACUCGAAGGCAUUCAGUACGGUCGUAGUGGUGAGACACUUGGUGU3´	506-589	5´UGGCCUACAUUACAGGUGGUGUUGUUCAGUUGACUUCGCAGUGGCUAACUAACAUCUUUGGCACUGUUUAUGAAAAACUCAAACCCGUCCUUGAUUGGCUUGAAGA3´	2057-2163	5´CCAACAGUGGUUGUUAAUGCAGCCAAUGUUUACCUUAAACAUGGAGGAGGU3´	3372-3422
9	5´GUUGAGCUGGUAGCAGAACUCGAAGGCAUUCAGUACGGUCGUAGUGGUGAGACACUUGGUGUCCUUGUCCCUCAUGUGGGCGAAAUACCAGUGGCUUACCGCAAGGUUCUUCUUCGUAAGAACGGUAAUAAAGGAGCUGGUGGCCAU3´	527-675	5´AUCUUUGGCACUGUUUAUGAAAAACUCAAACCCGUCCUUGAUUGGCUUGAAGAGAAGUUUAAGGAAGGUGUAGAGUUUCUUAGAGACGGUUGGGAAAUUG3´	2110-2210	5´UGGUAUUUUUGGUGCUGACCCUAUACAUUCUUUAAGAGUUUGUGUAGAUACUGUUC3´	3665-3720
10	5´CCAGUGGCUUACCGCAAGGUUCUUCUUCGUAAGAACGGUAAUAAAGGAGCUGGUGGCCAUAGUUACGGCGCCGAUCUAAAGUCAUUUGACUUAGGCGACGAGCUUGGCACUGAUCCUUAUGAAGAUUUUCAAGAAAACUGG3´	615-757	5´UCUUUAAGCUUGUAAAUAAAUUUUUGGCUUUGUGUGCUGACUCUAUCAUUAUUGGUGGAGCUAAACUUAAAGCCUUGAAUUUAGGUGAAACAUUUGUCACG3´	2297-2398	5´UAAGAAAAUCAAAGCUUGUGUUGAAGAAGUUACAACAACUCU3´	3952-3993
11	5´AGUGGCUUACCGCAAGGUUCUUCUUCGUAAGAACGGUAAUAAAGGAGCUGGUGGCCAUAGUUACGGCGCCGAUCUAAAGUCAUUUGACUUAGGCGACGAGCUUGGCACUGAUCCUUAUGAAGAUUUUCAAGAAAACUGGAACACU3´	617-763	5´AAGUACUUAAUGAGAAGUGCUCUGCCUAUACAGUUGAACUCGGUACAGAAGUAAAUGAGUUCGCCUGUGUUGUGGCAGAUGCUGUCAUAAAAACUUUGCAA3´	2816-2917	5´UCGGUAUAUGAGAUCUCUCAAAGUGCCAGCUACAGUUUCUGUUUCUUCACCUGAUGCUGUUACAG3´	4654-4818
12	5´GAGCUUGGCACUGAUCCUUAUGAAGAUUUUCAAGAAAACUGGAACACUAAACAUAGCAGUGGUGUUACCCGUGAACUCAUGCGUGAGCUU3´	716-806	5´UAUCUGAAUUACUUACACCACUGGGCAUUGAUUUAGAUGAGUGGAGUAUGGCUACAUACUACUUAUUUGAUGAGUCUGGUGAGUUUAAAUUGGCUUCACAU3´	2921-3022	5´CAUAUAUUCUUUUCACUAGGUUUUUCUAUGUACUUGGAUUGGCUGCAAUCAUGCAAUUGUUU3´	7254-7315
13	5´ACAUAGCAGUGGUGUUACCCGUGAACUCAUGCGUGAGCUUAACGGAGGGGCAUACACUCGCUAUGU3´	766-832	5´CUGGGCAUUGAUUUAGAUGAGUGGAGUAUGGCUACAUACUACUUAUUUGAUGAGUCUGGUGAGUUUAAAUUGGCUUCACAUAUGUAUUGUUCUUUCUACCC3´	2941-3042	5´UUCUUGGCUUAUGUGGUUAAUAAUUAAUCUUGUACAAAUGGCCCCGAUUUCAG3´	7348-7400
14	5´ACUCAUGCGUGAGCUUAACGGAGGGGCAUACACUCGCUAUGUCGAUAACAACUUCUGUGGCCCUGAUGGCUACCCUCUUGAGU3´	791-874	5´UGAGUCUGGUGAGUUUAAAUUGGCUUCACAUAUGUAUUGUUCUUUCUACCCUCCAGAUGAGGAUGAAGAAGAAGGUGAUUGUGAAGAAGAAGAGUUUGAGCCAUCAACUCAAUAU3´	2991-3106	5´UUUGAUAAAGCUGGUCAAAAGACUUAUGAAAGACAUUCUCUCUCUCAUU3´	8397-8466
15	5´UGUCGAUAACAACUUCUGUGGCCCUGAUGGCUACCCUCUUGAGUGCAUUAAAGACCUUCUAGCACGUGCUGGUAAAGCUUCAUGCACUUUGUCCGAACAACUGGACUUUAUUGACA3´	830-947	5´ACCAAGGUAAACCUUUGGAAUUUGGUGCCACUUCUGCUGCUCUUCAACCUGAAGAAGAGCAAGAAGAAGAUUGGUUAGAUGAUGAUAGUCAACAAACUGUU3´	3128-3229	5´AAAGGUUCAUUGCCUAUUAAUGUUAUAGUUUUUGAUGGUAAAUCAAA3´	8420-8477

Table 2. Enoxacin-induced miRNAs that target membranous proteases.

Target transcripts	Targeting miRNAs
*TMPRSS2*	hsa-miR-582-5p, hsa-miR-643, hsa-miR-578, hsa-miR-1208, hsa-miR-596, hsa-miR-1207-5p, hsa-miR-612, hsa-miR-486-3p, hsa-miR-875-3p, hsa-miR-587, hsa-miR-588, hsa-miR-516a-3p, hsa-miR-769-5p, hsa-miR-199a-5p, hsa-miR-492, hsa-miR-361-3p, hsa-miR-362-5p, hsa-miR-1181, hsa-miR-512-3p, hsa-miR-518a-5p, hsa-miR-600
*TMPRRS11*	hsa-miR-2053, hsa-miR-600, hsa-miR-671-5p, hsa-miR-337-3p, hsa-miR-384, hsa-miR-193a-5p, hsa-miR-513a-3p, hsa-miR-574-3p, hsa-miR-518a-5p, hsa-miR-582-5p
*CTSL*	hsa-miR-199a-5p, hsa-miR-518a-5p, hsa-miR-501-5p, hsa-miR-513a-3p, hsa-miR-556-3p

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

Biological Pathways or Gene Ontology

p-value

BioPlanet 2019

WikiPathways 2019

KEGG

-Antigen presentation: folding, assembly, and peptide loading of class I MHC proteins

-TGF-beta Signaling Pathway

-TGF-beta signaling pathway

5.036e-9

0.008363

0.003978

BioPlanet 2019

WikiPathways 2019

KEGG

-Signaling by EGFR in cancer

-EGF/EGFR Signaling Pathway

-Focal Adhesion-PI3K-Akt-mTOR-signaling pathway

-mTOR signaling pathway

2.577e-9

3.582e-8

1.566e-7

7.143e-10

BioPlanet 2019

WikiPathways 2019

KEGG

-Signaling by ERBB4

-PI3K-Akt Signaling Pathway

-PI3K-Akt signaling pathway

5.643e-9

1.016e-7

2.922e-9

Gene ontology (Biological process)

-Rab protein signal transduction (GO:0032482)

-Regulation of exocytosis (GO:0017157)

1.475e-15

5.602e-10

Table 4. SARS-CoV-2 components can be targeted by enoxacin-induced miRNAs.

SARS-CoV-2 components	Targeting miRNAs
NSP1	hsa-miR-1181, hsa-miR-649, hsa-miR-578
NSP2	hsa-miR-3065-5p, hsa-miR-603, hsa-miR-513a-3p, hsa-miR-583, hsa-miR-140-3p
NSP3	hsa-miR-603, hsa-miR-485-3p, hsa-miR-647, hsa-miR-520a-3p, hsa-miR-1231, hsa-miR-299-5p, hsa-miR-576-5p, hsa-miR-2053, hsa-miR-587, hsa-miR-1264, hsa-miR-583, hsa-miR-198, hsa-miR-643, hsa-miR-578, hsa-miR-140-3p, hsa-miR-592, hsa-miR-562, hsa-miR-582-5p, hsa-miR-875-5p
NSP4	hsa-miR-548m, hsa-miR-518a-5p, hsa-miR-3065-5p, hsa-miR-107, hsa-miR-513a-3p, hsa-miR-2053, hsa-miR-587, hsa-miR-583, hsa-miR-643, hsa-miR-567, hsa-miR-140-3p, hsa-miR-592
NSP6	hsa-miR-518a-5p, hsa-miR-485-3p, hsa-miR-647, hsa-miR-583, hsa-miR-562, hsa-miR-422a, hsa-miR-875-5p
NSP7	hsa-miR-518a-5p, hsa-miR-198
NSP8	hsa-miR-647, hsa-miR-1231, hsa-miR-576-5p, hsa-miR-649
NSP9	hsa-miR-875-5p
ORF3a	hsa-miR-518a-5p, hsa-miR-3065-5p, hsa-miR-643, hsa-miR-582-5p
ORF5	hsa-miR-107, hsa-miR-562, hsa-miR-198
ORF6	hsa-miR-513a-3p, hsa-miR-1231, hsa-miR-649
ORF7a	hsa-miR-603, hsa-miR-567, hsa-miR-562
ORF8	hsa-miR-513a-3p, hsa-miR-1264, hsa-miR-582-5p
Nucleocapsid	hsa-miR-107, hsa-miR-513a-3p, hsa-miR-1231, hsa-miR-2053, hsa-miR-578, hsa-miR-422a
RdRp	hsa-miR-3065-5p, hsa-miR-107, hsa-miR-520a-3p, hsa-miR-1231, hsa-miR-299-5p, hsa-miR-2053, hsa-miR-1264, hsa-miR-567, hsa-miR-578 hsa-miR-592
Spike	hsa-miR-603, hsa-miR-107, hsa-miR-513a-3p, hsa-miR-485-3p, hsa-miR-1231, hsa-miR-576-5p, hsa-miR-2053, hsa-miR-587, hsa-miR-649 hsa-miR-578, hsa-miR-562
Helicase	hsa-miR-422a, hsa-miR-582-5p, hsa-miR-875-5p, hsa-miR-548m, hsa-miR-518a-5p, hsa-miR-3065-5p, hsa-miR-603, hsa-miR-513a-3p, hsa-miR-1231, hsa-miR-299-5p, hsa-miR-576-5p, hsa-miR-2053, hsa-miR-1264, hsa-miR-198, hsa-miR-567
2OMT	hsa-miR-513a-3p, hsa-miR-576-5p, hsa-miR-2053, hsa-miR-1264, hsa-miR-567, hsa-miR-582-5p
3´-5´ exonuclease	hsa-miR-603, hsa-miR-1231, hsa-miR-299-5p, hsa-miR-587, hsa-miR-1264, hsa-miR-583, hsa-miR-649, hsa-miR-643, hsa-miR-592
3C-like proteinase	hsa-miR-3065-5p, hsa-miR-485-3p, hsa-miR-576-5p, hsa-miR-1264, hsa-miR-583, hsa-miR-140-3p, hsa-miR-592
endoRNAse	hsa-miR-3065-5p, hsa-miR-603, hsa-miR-513a-3p, hsa-miR-587, hsa-miR-1264, hsa-miR-649, hsa-miR-643, hsa-miR-578
3´-UTR	hsa-miR-603, hsa-miR-587

Fig.S1.tif
Figure S1. The PPI network of host proteins potentially interacting with SARS-CoV-2 components together with the top four protein modules derived from the network. (A) The PPI network of host proteins predicted to interact with SARS-CoV-2 components were depicted by Cytoscape (only interactions with the confidence of a combined score >0.400 were included). (B) The top four modules of the PPI network identified by MCODE (cutoff criteria were ‘degree cutoff=2’, ‘k-core=2’, ‘node score cutoff=0.2’, and ‘maximum depth=100) which are shown as distinct blocks outside of the PPI network. M: Module
Fig.S2.TIF
Figure S2. Enrichment analysis of genes and the top modules which are predicted to interact with SARS-CoV-2 components. (A) The KEGG enrichment analysis using Enrichr showed that proteins which interact with SARS-CoV-2 proteins, were mostly involved in protein processing in endoplasmic reticulum. (B) The KEGG pathway enrichment analysis revealed that the top four modules in the PPI network were mostly associated RNA transport. The lighter the red color is, the more significant the p-value.
Fig.S3.TIF
Figure S3. Hub genes in the PPI network of proteins interacting with SARS-CoV-2 proteins and their pathway enrichment analysis. (A) Nine hub genes were identified by Cytohubba and the MCC method. (B) The KEGG pathway enrichment analysis showed that these genes were mostly associated with insulin signaling pathway. The lighter the red color is, the more significant the p-value.
Fig.S4.TIF
Figure S4. Enoxacin might target 16 human proteins predicted to interact with SARS-CoV-2 proteins.
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Figure S5. The nine enoxacin-induced miRNA introduced by our study were previously reported to target SARS-CoV-2 genome.
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Figure S6. Enoxacin-induced miRNAs might diminish the cytokine storm induced by SARS-CoV-2.
TableS1.xlsx
Table S1. Predicted pri-miRNA structures in the SARS-CoV-2 genome.
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Table S2. The list of enoxacin-induced miRNAs in different cell lines.
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Table S3. miRNAs potentially targeting ACE, ACE2, TMPRSS2, CTSL, and TMPRSS11D.
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Table S4. Predicted genes targeted by enoxacin-induced miRNAs.
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Table S5. Human proteins that interact with SARS-CoV-2 proteins (obtained from BioGRID database).
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Table S6. miRNAs targeting the SARS-CoV-2 genome extracted by miRDB custom search tool.
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Table S7. Twenty eight enoxacin-induced miRNAs which might directly target the SARS-CoV-2 genome.
TableS8.xlsx
Table S8. miRNAs which target the developmental stage-specific transcriptional factors and marker genes of BASCs.

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published 13 May, 2021

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In silico analysis suggests the RNAi-enhancing antibiotic enoxacin as a potential inhibitor of SARS-CoV-2 infection

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