miR-29a and miR-15b Modulate SARS-CoV-2 Beta and Omicron Infection in Human Lung Epithelial Cells

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Manuscript Number: ijms-4341772

Manuscript Title: miR-29a and miR-15b Modulate SARS-CoV-2 Beta and Omicron Infection in Human Lung Epithelial Cells

Summary: In this study, the authors identified miR-29a and miR-15b modulate SARS-CoV-2 Beta and Omicron Infection in Human Lung Epithelial Cells. Using transient transfection and RT-qPCR, the author demonstrated that miR-29a and miR-15b modulate SARS-CoV-2 Beta and Omicron Infection in Human Lung Epithelial Cells. Overall, their data suggest that miR-29a and miR-15b modulate SARS-CoV-2 Beta Infection. However, several mechanistic details remain insufficiently resolved, particularly regarding the direct targets, specificity, and physiological relevance of miR-29a- and miR-15b-mediated effects. In addition, some of the authors’ responses to the previous review comments remain unconvincing or incomplete. Overall, the manuscript has improved since the initial submission, but it requires substantial additional clarification and experimental validation before it can be considered for publication.

Major comments:

1. In Figure 1, the authors show that SARS-CoV-2 WA1 S transcript levels are reduced following co-transfection with miR-15b and miR-29a. Although a scramble control was included, no non-specific viral control (e.g., GFP or an unrelated viral construct such as M or E gene) was included in the assay. Without such controls, it is difficult to determine whether the observed effect is specific to the S transcript or reflects non-specific effects on transfection or viral gene expression. To strengthen the conclusion, the authors should include a non-specific viral control and assess transfection efficiency accordingly. In addition, the author should provide the Western blot to confirm the level of expression.
2. The author’s explanation in response to Reviewer 1 regarding the opposing effects of miRNA mimics and inhibitors on viral replication is not fully convincing. In Figures 3C and 4C, as well as Figures 5C and 6C, the inhibitor experiments do not demonstrate a clear rescue of the phenotype induced by the corresponding mimics. As presented, it remains unclear whether the observed effects are sequence-specific or simply reflect a generalized cellular response to RNA transfection. To strengthen the conclusions, the authors should perform dose-dependent analyses and provide more rigorous functional validation of both the mimics and inhibitors. In addition, cytotoxicity assays should be included to exclude non-specific effects resulting from RNA transfection or treatment-associated toxicity.
3. The authors state that these miRNAs modulate Omicron infection. However, Figure 2 shows that endogenous miRNA expression profiles are not significantly altered at either 6 h or 24 h after Omicron infection. In addition, Figures 5 and 6 indicate increased viral transcripts and titers, suggesting a potential pro-viral effect rather than an antiviral role. This discrepancy raises concerns regarding the physiological relevance and directionality of the proposed antiviral mechanism presented in Figure 1.
4. In Figures 3A, the trends in transcript levels for N and S differ markedly between 6 h and 24 h of infection. N transcript is downregulated at 6 h but upregulated at 24 h, whereas S shows no significant change at 6 h and is downregulated at 24 h. This temporal inconsistency is not adequately explained. Given that the S gene reportedly shows high target complementarity >80%, the authors should justify why differential kinetics are observed and whether this reflects assay variability or true biological divergence. To clarify the time-dependent effects, the analysis should be restricted to the validated S target and extended to additional time points (e.g., 6 h, 12 h, 24 h, and 36 h) to establish a more reliable kinetic profile.
5. In Figure 3B, viral RNA measurements from the cell culture supernatant do not show a significant reduction, whereas Figure 3C TCID50 demonstrates a significant decrease in infectious viral titers at both 6 h and 24 h. This discrepancy suggests that viral genome release may remain unaffected while infectious particle maturation or egress is impaired, potentially resulting in the production of defective viral particles. To clarify this mechanism, the authors should perform Spike cleavage assays and assess the effects of miR-15b and miR-29a on viral maturation and release. In addition, add the statistical significance for Figure 3C.
6. The authors should clarify how miR-15b and miR-29a mechanistically access viral RNA, given that SARS-CoV-2 replication occurs within DMV, whereas miRNAs primarily function in the cytoplasm. The authors should determine whether these miRNAs can localize to or access DMV or otherwise provide a mechanistic explanation supporting how miRNA-mediated regulation occurs during viral replication.
7. The authors should provide the predicted seed sequences and binding sites for miR-15b and miR-29a within both the viral genome and the relevant host target genes to support the specificity and rationale of the proposed interactions.
8. In line 588, the N expression construct was not used in the current study; only the S plasmid was transfected.

 

 

Author Response

Comment 1. In Figure 1, the authors show that SARS-CoV-2 WA1 S transcript levels are reduced following co-transfection with miR-15b and miR-29a. Although a scramble control was included, no non-specific viral control (e.g., GFP or an unrelated viral construct such as M or E gene) was included in the assay. Without such controls, it is difficult to determine whether the observed effect is specific to the S transcript or reflects non-specific effects on transfection or viral gene expression. To strengthen the conclusion, the authors should include a non-specific viral control and assess transfection efficiency accordingly. In addition, the author should provide the Western blot to confirm the level of expression.

Response 1. We thank the reviewer for raising this point, which allows us to clarify the aim of the experiment shown in Fig. 1B (see lines 97–99 of the revised manuscript). The experiment was designed to assess whether the two miRNAs, whose predicted binding sites are present on the transcript and whose physical interaction had already been demonstrated on a chimeric transcript in a reporter assay in our previous work [1], could inhibit Spike (S) expression from the natural sequence.

We chose a plasmid-driven Spike expression system in two human lung adenocarcinoma epithelial cell lines that do not express any other viral genes, thereby avoiding potential confounding effects. Accordingly, S expression levels were normalized to those of the cellular housekeeping gene GAPDH.

The observed inhibition of S expression was strong and statistically significant, providing a clear answer to our experimental question. Therefore, we considered it unnecessary to directly measure transfection efficiency, assuming it was within the medium-to-high range typically achieved under standard conditions following the manufacturer’s protocol (Lipofectamine 2000, Invitrogen/Thermo Fisher Scientific). This point has been clarified in the result section (Lines 104-109).

1. Siniscalchi C, Di Palo A, Russo A, Potenza N. Human MicroRNAs Interacting With SARS-CoV-2 RNA Sequences: Computational Analysis and Experimental Target Validation. Front Genet. 2021;12:678994. Published 2021 Jun 7. doi:10.3389/fgene.2021.678994

Comment 2. The author’s explanation in response to Reviewer 1 regarding the opposing effects of miRNA mimics and inhibitors on viral replication is not fully convincing. In Figures 3C and 4C, as well as Figures 5C and 6C, the inhibitor experiments do not demonstrate a clear rescue of the phenotype induced by the corresponding mimics. As presented, it remains unclear whether the observed effects are sequence-specific or simply reflect a generalized cellular response to RNA transfection. To strengthen the conclusions, the authors should perform dose-dependent analyses and provide more rigorous functional validation of both the mimics and inhibitors. In addition, cytotoxicity assays should be included to exclude non-specific effects resulting from RNA transfection or treatment-associated toxicity.

Response 2. We thank the reviewer for these constructive comments and address each point below.
A) With regard to the reviewer’s comment, “As presented, it remains unclear whether the observed effects are sequence-specific or simply reflect a generalized cellular response to RNA transfection,” we would like to emphasize that all statistical analyses were performed by comparing the experimental conditions with appropriate control transfections (i.e., cells transfected with control molecules). Therefore, any potential background, nonspecific, cytotoxic effects or generalized cellular response to RNA transfection was accounted for and subtracted through these comparisons. The observed effects thus reflect differences specifically associated with the tested miRNAs rather than a generic response to the transfection procedure itself. This is a standard and widely accepted approach in the miRNA functional analysis field [1,2]. Regarding Figures 3C/4C and 5C/6C specifically, while the rescue by the inhibitor is partial rather than complete, this is a commonly observed and biologically expected phenomenon. miRNA inhibitors compete with targets for miRNA binding but do not eliminate the miRNA entirely, and their efficiency is influenced by their abundance. Partial rescue remains valid evidence of sequence-specific activity, as has been documented in multiple miRNA functional studies [3,4].

B) With regard to dose-dependent analyses, all experiments were performed using the most effective dose of both mimic and inhibitor molecules (50 nM) in our hands and in this experimental system. This concentration is consistent with and supported by published literature using analogous approaches [5-7]. Rather than a classical dose-response curve, which is more informative when assessing pharmacological agents targeting protein function, we monitored effects over time (6 and 24 hours post-infection), representing early and later phases of the viral replication cycle. This temporal design is appropriate for studying miRNA-mediated regulation of viral gene expression, which operates at the mRNA level. Longer time points were deliberately avoided, as they would introduce confounding variables independent of the miRNA effect (e.g., secondary transcriptional responses, cell cycle progression, accumulating cytopathic effects). Viral replication was assessed by RT-qPCR targeting viral RNA; MOIs were selected to yield a robust signal at 24 hpi (Ct < 30) compared to uninfected controls (Ct > 45), ensuring a dynamic range suitable for detecting modulatory effects.

C) We acknowledge that dedicated cytotoxicity data were not included in the original manuscript and appreciate the reviewer raising this point.
Lipofectamine 2000 is a widely used and well-characterized lipid-based transfection reagent for delivery of small RNAs into mammalian cells, including epithelial cell lines. While Lipofectamine 2000 is highly effective within recommended concentration ranges, cytotoxic effects can arise at elevated concentrations due to cellular stress. For this reason, careful attention to reagent-to-RNA ratios and cell density is essential — conditions that were respected in the present study, following the manufacturer's recommended protocol. Specifically, transfections were performed at 50 nM using the Lipofectamine 2000 reagent-to-RNA ratio optimized for our cell format, with cells at the recommended confluency, in antibiotic-free medium to minimize compounded toxicity [8].
Importantly, all experimental conditions were compared against negative control transfections carried out under identical conditions: same reagent volume, same RNA concentration (50 nM), same duration, using a non-targeting scramble sequence. This design ensures that any residual cytotoxic or metabolic stress induced by the transfection reagent itself is equally present in both experimental and control groups and is therefore cancelled out in all pairwise comparisons. The effects we report thus reflect sequence-specific biological activity of the tested miRNAs, not reagent-associated toxicity. Furthermore, this approach (comparing miRNA mimic/inhibitor-transfected cells against scramble-transfected controls using Lipofectamine 2000 at 50 nM) is consistent with published methodology in the field and has been employed in multiple studies using lung epithelial cell models without reporting toxicity concerns at this concentration range [7,9,10]. Should the editor consider it necessary, we are prepared to perform cell viability assays (e.g., XTT assay or trypan blue exclusion) under the exact transfection conditions used in this study and provide these data in the revised manuscript.

1. Starega-Roslan J, Galka-Marciniak P, Krzyzosiak WJ. Nucleotide sequence of miRNA precursor contributes to cleavage site selection by Dicer. Nucleic Acids Res. 2015;43(22):10939-10951. doi:10.1093/nar/gkv968
2. Rupaimoole R, Slack FJ. MicroRNA therapeutics: towards a new era for the management of cancer and other diseases. Nat Rev Drug Discov. 2017;16(3):203-222. doi:10.1038/nrd.2016.246
3. Krützfeldt J, Rajewsky N, Braich R, et al. Silencing of microRNAs in vivo with 'antagomirs'. Nature. 2005;438(7068):685-689. doi:10.1038/nature04303
4. Lennox KA, Behlke MA. A direct comparison of anti-microRNA oligonucleotide potency. Pharm Res. 2010;27(9):1788-1799. doi:10.1007/s11095-010-0156-0
5. Siniscalchi C, Di Palo A, Russo A, Potenza N. Human MicroRNAs Interacting With SARS-CoV-2 RNA Sequences: Computational Analysis and Experimental Target Validation. Front Genet. 2021;12:678994. Published 2021 Jun 7. doi:10.3389/fgene.2021.678994
6. De Leo I, Mosca N, Pezzullo M, et al. Transcriptomic-Based Identification of miR-125a Novel Targets in Human Hepatocarcinoma Cells. Biomolecules. 2025;15(1):144. Published 2025 Jan 18. doi:10.3390/biom15010144
7. Mosca N, Pezzullo M, De Leo I, et al. A Novel ceRNET Relying on the lncRNA JPX, miR-378a-3p, and Its mRNA Targets in Lung Cancer. Cancers (Basel). 2024;16(8):1526. Published 2024 Apr 17. doi:10.3390/cancers16081526
8. Ojha R, Timin E, Szirovicza L, Xu W, Hepojoki J. A systematic comparison of in-house prepared transfection reagents in the delivery of mRNA or DNA to a wide range of cultured cells. J Biol Chem. 2025;301(12):110742. doi:10.1016/j.jbc.2025.110742
9. Siniscalchi C, Di Palo A, Russo A, Potenza N. Human MicroRNAs Interacting With SARS-CoV-2 RNA Sequences: Computational Analysis and Experimental Target Validation. Front Genet. 2021;12:678994. Published 2021 Jun 7. doi:10.3389/fgene.2021.678994
10. De Leo I, Mosca N, Pezzullo M, et al. Transcriptomic-Based Identification of miR-125a Novel Targets in Human Hepatocarcinoma Cells. Biomolecules. 2025;15(1):144. Published 2025 Jan 18. doi:10.3390/biom15010144

Comment 3. The authors state that these miRNAs modulate Omicron infection. However, Figure 2 shows that endogenous miRNA expression profiles are not significantly altered at either 6 h or 24 h after Omicron infection. In addition, Figures 5 and 6 indicate increased viral transcripts and titers, suggesting a potential pro-viral effect rather than an antiviral role. This discrepancy raises concerns regarding the physiological relevance and directionality of the proposed antiviral mechanism presented in Figure 1.

Response 3. We thank the reviewer for this important conceptual observation. We have revised the Discussion to address these points explicitly, and provide the rationale below.

Regarding Figure 1 and its relationship to the infection experiments: Figure 1 was obtained in a plasmid-based overexpression system in the absence of live virus, designed specifically to demonstrate the direct molecular capacity of the tested miRNAs to bind and suppress S protein expression in a controlled setting, free from the competing dynamics of productive infection. This is now stated explicitly in the revised Discussion (Lines 350-354). The figure therefore represents mechanistic proof-of-concept at the molecular level and was not intended to predict the net phenotypic outcome during live viral infection, where immune activation, viral counter-regulatory mechanisms, and replication kinetics interact simultaneously.

Regarding the absence of significant endogenous miRNA changes upon Omicron infection (Figure 2): as now clarified in the revised Discussion (Lines 369-374), the lack of infection-induced transcriptional changes in endogenous miRNA levels does not preclude a functional role for these miRNAs, nor does it undermine the rationale for mimic-based overexpression. This reflects a recognized paradigm in the field: endogenous miRNA abundance may be insufficient to exert detectable antiviral pressure at physiological concentrations, yet exogenous delivery of mimics can produce significant effects. An analogous situation has been described during Influenza A virus infection, where endogenous cellular miRNAs at physiological levels are refractory to viral suppression, yet mimic-based overexpression effectively inhibits replication [1].

Regarding the pro-viral directionality in Figures 5 and 6: The apparent increase in viral transcripts and infectious titers observed in certain conditions involving miR-29a and Omicron BA.1 is not a contradiction but a central finding of the study, and is mechanistically explained in the Discussion. Briefly, Omicron BA.1 carries mutations reducing its dependence on Furin-mediated Spike processing and TMPRSS2-mediated entry, shifting replication toward cathepsin and autophagy-lysosomal pathways. Under these conditions, miR-29a-mediated suppression of TFEB and AKT3, which would be restrictive in other contexts, paradoxically limits the cell ability to contain virus-driven exploitation of the autophagy-lysosomal axis, resulting in a net pro-viral outcome. This variant-dependent context-switching is explicitly discussed as the central conceptual finding of the paper, and the discrepancy between Figure 1 and Figures 5–6 reflects the difference in experimental system (virus-free molecular assay vs. productive infection) rather than an inconsistency in the proposed mechanism.

1. Peng S, Wang J, Wei S, et al. Endogenous Cellular MicroRNAs Mediate Antiviral Defense against Influenza A Virus. Mol Ther Nucleic Acids. 2018;10:361-375. doi:10.1016/j.omtn.2017.12.016

Comment 4. In Figure 3A, the trends in transcript levels for N and S differ markedly between 6 h and 24 h of infection. N transcript is downregulated at 6 h but upregulated at 24 h, whereas S shows no significant change at 6 h and is downregulated at 24 h. This temporal inconsistency is not adequately explained. Given that the S gene reportedly shows high target complementarity >80%, the authors should justify why differential kinetics are observed and whether this reflects assay variability or true biological divergence. To clarify the time-dependent effects, the analysis should be restricted to the validated S target and extended to additional time points (e.g., 6 h, 12 h, 24 h, and 36 h) to establish a more reliable kinetic profile.

Response 4. We thank the reviewer for this important comment and apologize for the lack of clarity in our original response. We wish to correct a statement made previously: the N and S primer sets used in this study are entirely independent and gene-specific — the N primers correspond to the validated CDC N2 assay (TTACAAACATTGGCCGCAAA / GCGCGACATTCCGAAGAA), and the S primers target a distinct region of the Spike gene. There is no cross-reactivity or overlapping complementarity between the two assays. The divergent temporal profiles of N and S transcripts therefore reflect genuine biological differences in transcript accumulation kinetics rather than any technical limitation of the assay design.

The differential kinetics between N and S transcripts at 6 h and 24 h post-infection are consistent with the known biology of SARS-CoV-2 subgenomic RNA transcription. SARS-CoV-2 produces a nested set of subgenomic RNAs (sgRNAs) in a discontinuous transcription mechanism, and the abundance of individual sgRNA species is not uniform — it depends on the genomic position of each open reading frame, the efficiency of transcription-regulatory sequences, and the temporal dynamics of the viral replication cycle. N sgRNA is consistently among the most abundant viral transcripts due to its position at the 3' end of the genome and the high efficiency of its transcription-regulatory sequence, whereas S sgRNA is less abundant and may peak at different times post-infection [1,2]. The early downregulation of N at 6 hpi followed by upregulation at 24 hpi, contrasting with the delayed downregulation of S, is therefore compatible with true biological divergence in the kinetics of sgRNA production across the replication cycle rather than assay variability.

We therefore maintain that reporting both targets is informative and provides complementary readouts of the infection dynamics. We revised the manuscript to explicitly state that N and S are measured by independent, gene-specific assays, and to provide the biological rationale for their divergent temporal profiles with reference to SARS-CoV-2 subgenomic transcription kinetics (Lines 170-173).

Regarding the request to add further time points (12 h, 36 h): we acknowledge this would strengthen the kinetic characterization and note it as a valuable future direction. However, given the BSL-3 constraints and the logistical limitations of independent infection sessions already discussed, this falls outside the scope of the current revision.

1. Dagotto G, Mercado NB, Martinez DR, et al. Comparison of Subgenomic and Total RNA in SARS-CoV-2 Challenged Rhesus Macaques. J Virol. 2021;95(8):e02370-20. doi:10.1128/JVI.02370-20
2. Cheng YH, Chen CH, Liu PC, et al. Reverse transcription-quantitative PCR assays for detecting SARS-CoV-2 using subgenomic RNA load. Heliyon. 2025;11(4):e42503. Published 2025 Feb 6. doi:10.1016/j.heliyon.2025.e42503

Comment 5. In Figure 3B, viral RNA measurements from the cell culture supernatant do not show a significant reduction, whereas Figure 3C TCID50 demonstrates a significant decrease in infectious viral titers at both 6 h and 24 h. This discrepancy suggests that viral genome release may remain unaffected while infectious particle maturation or egress is impaired, potentially resulting in the production of defective viral particles. To clarify this mechanism, the authors should perform Spike cleavage assays and assess the effects of miR-15b and miR-29a on viral maturation and release. In addition, add the statistical significance for Figure 3C.

Response 5. We thank the reviewer for this insightful observation. We agree that the discrepancy between extracellular viral RNA (Figure 3B) and infectious titers (Figure 3C) is suggestive of impaired virion maturation or infectivity rather than reduced total particle release, potentially resulting in the production of particles with reduced specific infectivity. We acknowledge that our data do not directly demonstrate altered Spike cleavage on virion surfaces. However, the reduction in Furin mRNA observed upon miR-15b overexpression (Figure 7A) provides a plausible molecular correlate, given that Furin-mediated S1/S2 cleavage is a critical determinant of virion infectivity in Calu-3 cells, which express high levels of Furin and rely on this processing step for efficient Beta variant entry [1,2]. Direct assessment of Spike cleavage status on released virions — for example by western blot quantification of S1/S2 ratios in concentrated supernatants — would be required to formally test this hypothesis and is acknowledged as an important future direction. We have added a sentence to this effect in the Discussion (Lines 419-424).

Regarding the request for statistical analysis of Figure 3C: TCID₅₀ values were calculated by the Reed–Muench method from a single experimental session. This method produces a single point estimate per condition by mathematical interpolation across serial dilution wells; it does not generate independent replicate measurements from which variance or statistical significance can be derived. The addition of formal statistics is therefore not applicable to this dataset, and we respectfully maintain the descriptive presentation of these data. As already noted in the Discussion, the TCID₅₀ results are interpreted in the context of their concordance with the parallel molecular readouts (intracellular transcripts and extracellular viral RNA) and their consistency across independent mimic and inhibitor sessions, which collectively support the reliability of the observed effects.

1. Johnson BA, Xie X, Bailey AL, et al. Loss of furin cleavage site attenuates SARS-CoV-2 pathogenesis. Nature. 2021;591(7849):293-299. doi:10.1038/s41586-021-03237-4
2. Papa G, Mallery DL, Albecka A, et al. Furin cleavage of SARS-CoV-2 Spike promotes but is not essential for infection and cell-cell fusion. PLoS Pathog. 2021;17(1):e1009246. Published 2021 Jan 25. doi:10.1371/journal.ppat.1009246

Comment 6. The authors should clarify how miR-15b and miR-29a mechanistically access viral RNA, given that SARS-CoV-2 replication occurs within DMV, whereas miRNAs primarily function in the cytoplasm. The authors should determine whether these miRNAs can localize to or access DMV or otherwise provide a mechanistic explanation supporting how miRNA-mediated regulation occurs during viral replication.

Response 6. We thank the reviewer for raising this important mechanistic point. We agree that the subcellular compartmentalization of SARS-CoV-2 replication within double-membrane vesicles (DMVs) raises a legitimate question regarding miRNA accessibility to viral RNA.

However, DMVs are not completely sealed compartments. Wolff et al. [1] demonstrated by cryo-electron tomography that DMVs contain ~6 nm membrane pores that connect the vesicle lumen to the cytoplasm, providing a conduit through which viral RNA can exit to the cytoplasm. Critically, a significant fraction of SARS-CoV-2 subgenomic RNAs, including those encoding S, N, E, and M proteins, is present in the cytoplasm and actively translated on ribosomes, which is precisely the compartment where RISC operates. miRNA-mediated regulation therefore does not require access to the DMV interior but instead acts on the cytoplasmic pool of viral transcripts available for translation.

This model is well supported by precedent from other positive-sense RNA viruses that replicate within analogous membrane-associated compartments. The best-characterized example is the miR-122/HCV interaction: Jopling et al. [2] demonstrated that this miRNA binds directly to HCV genomic RNA and promotes replication, despite HCV replicating within membranous web structures. Subsequent mechanistic studies showed that miR-122 activity occurs on cytoplasmic viral RNA rather than on compartment-enclosed replication intermediates [3,4]. Analogous host miRNA–viral RNA interactions have been described for Dengue virus, where miR-548g-3p targets cytoplasmic DENV RNA in infected human cells [5].

Taken together, these data support a model in which miR-15b and miR-29a interact with the cytoplasmic pool of SARS-CoV-2 subgenomic transcripts rather than with replication intermediates enclosed within DMVs. We have added a brief statement to this effect in the Discussion (Lines 357-362).

1. Wolff G, Limpens RWAL, Zevenhoven-Dobbe JC, et al. A molecular pore spans the double membrane of the coronavirus replication organelle. Science. 2020;369(6509):1395-1398. doi:10.1126/science.abd3629
2. Jopling CL, Yi M, Lancaster AM, Lemon SM, Sarnow P. Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science. 2005;309(5740):1577-1581. doi:10.1126/science.1113329
3. Thibault PA, Wilson JA. Targeting miRNAs to treat Hepatitis C Virus infections and liver pathology: Inhibiting the virus and altering the host. Pharmacol Res. 2013;75:48-59. doi:10.1016/j.phrs.2013.03.004
4. Sedano CD, Sarnow P. Hepatitis C virus subverts liver-specific miR-122 to protect the viral genome from exoribonuclease Xrn2. Cell Host Microbe. 2014;16(2):257-264. doi:10.1016/j.chom.2014.07.006
5. Lei L, Cheng A, Wang M, Jia R. The Influence of Host miRNA Binding to RNA Within RNA Viruses on Virus Multiplication. Front Cell Infect Microbiol. 2022;12:802149. Published 2022 Apr 21. doi:10.3389/fcimb.2022.802149

Comment 7. The authors should provide the predicted seed sequences and binding sites for miR-15b and miR-29a within both the viral genome and the relevant host target genes to support the specificity and rationale of the proposed interactions.

Response 7. We thank the reviewer for this suggestion. The predicted seed sequences and binding sites for miR-15b and miR-29a within the viral genome are already provided in Figure 1, which reports the computational prediction of miRNA binding sites across the SARS-CoV-2 genome including target site positions, seed match details, and conservation across variants. Regarding the host target genes, we have revised Figure 7 to include a new panel A showing the predicted miRNA-mRNA pairings for all host targets examined: miR-15b-5p binding sites within ATG9A and Furin 3'UTRs, and miR-29a-3p binding sites within ATG9A, TFEB, and AKT3 3'UTRs, as predicted by TargetScan and miRDB. The seed sequences (nucleotides 2–8 of the miRNA 5' region), which are essential for target recognition, are highlighted in bold and show perfect complementarity with each target site. For miR-29a vs. AKT3 and miR-29a vs. ATG9A, the interactions are further supported by experimental validation in the literature [1,2].

1. Li P, Hao X, Liu J, et al. miR-29a-3p Regulates Autophagy by Targeting Akt3-Mediated mTOR in SiO₂-Induced Lung Fibrosis. Int J Mol Sci. 2023;24(14):11440. Published 2023 Jul 14. doi:10.3390/ijms241411440
2. Xu Y, Yang J, Li F, Lian G, Ouyang M. MiR-29a inhibited intestinal epithelial cells autophagy partly by decreasing ATG9A in ulcerative colitis. Anticancer Drugs. 2018;29(7):652-659. doi:10.1097/CAD.0000000000000636

Comment 8. In line 588, the N expression construct was not used in the current study; only the S plasmid was transfected.

Response 8. We thank the reviewer for this comment and confirm that only the S protein expression plasmid (pSpike) was employed in the plasmid-based overexpression experiments (Figure 1). The N primers reported at line 588 were used exclusively for RT-qPCR quantification of N transcripts in infected cells, where both N and S are naturally transcribed as part of the SARS-CoV-2 subgenomic RNA repertoire. They are therefore unrelated to the plasmid transfection experiments. The N primers and S primers thus serve entirely distinct purposes: quantification of infection-derived transcripts and functional miRNA targeting assay in the absence of virus, respectively, and there is no inconsistency in their inclusion in the Methods section.

Reviewer 2 Report

Comments and Suggestions for Authors

Please see the attached review report.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Can be improved however the manuscript quality is very good.

Author Response

Comments 1. Results:

1. In figure 1, I recommend that A, B, C to be vertically aligned so that it is easier to follow. B and C are on the same plane whilst A is on the top; perhaps they can be aligned differently - soft suggestion, it may be reader dependent.

Response 1. The figure was modified following the reviewer’s comment.

 

Comments 2. Discussion:

1. Check that all abbreviations appear e.g. TFEB was not defined. 2. Consider making the discussion brief to promote readability and also evaluate for English use. 3. Lines 315 and 318; the spacing is not consistent e.g. the “p-values”.

Response 2. We thank the editors for these careful observations and have revised the manuscript accordingly. All abbreviations have been checked throughout the Discussion and are now defined at first use. In particular, TFEB (Transcription Factor EB) and all other previously undefined abbreviations have been introduced in full at their first occurrence.

We have revised the Discussion to improve conciseness and readability, removing redundant statements and streamlining the argumentation where possible, while retaining the key mechanistic and interpretive content. The English throughout has also been reviewed and corrected.

Finally, the spacing inconsistencies at lines 315 and 318 (p-values and surrounding text) have been corrected to ensure uniform formatting throughout the manuscript.

 

Comments 3. Abbreviations:

1. Consult for style guide e.g. has can have varied meanings. 2. Single letter abbreviations may not be standard e.g. “N” and “S” - kindly consult for standard use.

Response 3. We thank the reviewer for the careful observations and insightful comments. In line with these suggestions, we have made the following changes. We consulted the journal style guide and revised abbreviations accordingly. Ambiguous abbreviations, including “has” and similar terms, have been replaced with their full forms or standardized according to the journal’s guidelines.

Single-letter abbreviations for viral proteins, including “N” (Nucleocapsid) and “S” (Spike), have been replaced with their full designations at first use, with the abbreviated form defined in parentheses and used consistently thereafter, in accordance with standard virological nomenclature.

 

Comments 4. References:

1. Referenced paper 2 Xu, D-Y,; Zhou, X.; Ten Y.-Y. RNAi-Based Antiviral Immunity. Yi chan Here’d. 2025 - NOT FOUND (Please provide the article). 2. Kindly check that all references are available and correctly cited. I started to perform a random check.

Response 4. We thank the reviewer for carefully checking the reference list and for pointing out the problem with reference 2. The cited paper indeed corresponds to the review “RNAi-based antiviral immunity” by Xu, Zhou, and Ren published in Yi Chuan / Hereditas (Beijing) in 2025, volume 47(8), pages 876–884. We have corrected the reference to read:

Xu D-Y, Zhou X, Ren Y-J. RNAi-based antiviral immunity. Hereditas (Beijing). 2025;47(8):876–884. doi:10.16288/j.yczz.25-077.

In addition, we have carefully rechecked the entire reference list to ensure that all citations are correctly formatted and that all referenced articles are available in the literature.

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript entitled “miR-29a and miR-15b Modulate SARS-CoV-2 Beta and Omicron Infection in Human Lung Epithelial Cells” presents a well-structured study on the interplay between microRNAs (miR-29a and miR-15b), the viral genome, and host mRNA in the context of SARS-CoV-2 infection, using two viral strains (Beta and Omicron BA.1) in human lung adenocarcinoma cell lines. The study assessed the interactions between miRNAs and the genomes of SARS-CoV-2 variants, as well as how infection modulates the expression of endogenous miRNAs. It also evaluated how viral RNA replication, virion production and infectivity, and host factors associated with SARS-CoV-2 infection are modulated by miRNA overexpression and inhibition. The study presents relevant findings on the dynamics of SARS-CoV-2 infection at the cellular level, highlighting differences in the responses elicited by the two variants. In addition, it provides valuable insights into the role of miRNAs in SARS-CoV-2 infection.

Revisions:

Introduction

In lines 48-49, the authors use the term “microRNAs”, whereas its abbreviation was introduced in line 40. Please, use the abbreviation from line 40.

In line 51 “hsa-miR-29a-3p (miR-29a) e hsa-miR-15b-5p (miR-15b)”, does “e” mean “and”? Please check.

Results

In lines 132-147, the authors introduce “Figure 2 B” before “Figure 2 A”. I suggest that the authors rearrange this paragraph to ensure that the figure titles match the order in which they appear in the text.

In line 151 (Figure 2 B legend), it is not clear whether endogenous miRNA levels were assessed in transfected or non-transfected cells. Please, make it clear.

In section 2.3.1 “miRNA mimic experiments” and Figure 3 C, it is unclear how significant the reduction in virus titration is in miR-15b mimic supernatants. Would it be possible to express it statistically?

In line 196 (Figure 3 legend), the authors mentioned p-values that aren’t displayed in the figure. Only p-values displayed in the figures need to be mentioned in the legends.

In section 2.3.2 “miRNA inhibitor experiments” and Figure 4 C, it is unclear how significant the enhancement of infectious particle production is. Would it be possible to express it statistically?

In line 227 (Figure 4 legend), “**p” isn’t mentioned in the legend, although it is displayed in the figure. Conversely “*p” is mentioned in the legend without been displayed in the figure. Please check.

In section 2.4.1 “miRNA mimic experiments” and Figure 5 C, it is unclear how significant the increase in the infectious titer is. Could the authors express it statistically?

In section 2.4.2 “miRNA inhibitor experiments” and Figure 6 C, it is unclear how significant the changes in viral progeny were. Could the authors express it statistically?

In line 334 (Figure 7 legend), “***p” isn’t mentioned, although it is displayed in the figure. Please check.

Conclusions

In lines 621-623, the authors state that “...  particularly miR-15b overexpression and inhibition of either miRNA, paradoxically enhanced infectious progeny production...”. It seems to contradict the results described in the lines 174-176 “miR-15b mimic-treated supernatants contained markedly reduced infectious titers compared with controls, with a ~2 log₁₀ reduction at 6 hpi and a ~1 log₁₀ reduction at 24 hpi...”and in figure 3 C, as well the discussion (Lines 367-389), where the authors mentioned a reduction of infectious progeny production in miR-15b mimic-treated cells and link this feature to a drop in Furin expression. Please check.

Comments on the Quality of English Language

I suggest that the manuscript be sent for English proofreading by a native speaker or a company specializing in scientific writing.

Author Response

Comments 1. Introduction

In lines 48-49, the authors use the term “microRNAs”, whereas its abbreviation was introduced in line 40. Please, use the abbreviation from line 40.

In line 51 “hsa-miR-29a-3p (miR-29a) e hsa-miR-15b-5p (miR-15b)”, does “e” mean “and”? Please check.

Response 1. We thank the Reviewer for this helpful suggestion. All requested modifications have been incorporated into the revised manuscript.

 

Comments 2. Results

In lines 132-147, the authors introduce “Figure 2 B” before “Figure 2 A”. I suggest that the authors rearrange this paragraph to ensure that the figure titles match the order in which they appear in the text.

Response 2. We thank the reviewer for this observation. The paragraph has been revised to ensure that Figure 2A is introduced before Figure 2B, in accordance with their order of appearance in the text (Lines 137-144).

 

Comments 3. In line 151 (Figure 2 B legend), it is not clear whether endogenous miRNA levels were assessed in transfected or non-transfected cells. Please, make it clear.

Response 3. We thank the reviewer for the comment, the legend was updated to improve its clarity.

 

Comments 4. In section 2.3.1 “miRNA mimic experiments” and Figure 3 C, it is unclear how significant the reduction in virus titration is in miR-15b mimic supernatants. Would it be possible to express it statistically?

Response 4. TCID₅₀ values were calculated by the Reed–Muench method from a single experimental session. This method produces a single point estimate per condition by mathematical interpolation across serial dilution wells; it does not generate independent replicate measurements from which variance or statistical significance can be derived. The addition of formal statistics is therefore not applicable to this dataset, and we respectfully maintain the descriptive presentation of these data. As already noted in the Discussion, the TCID₅₀ results are interpreted in the context of their concordance with the parallel molecular readouts (intracellular transcripts and extracellular viral RNA) and their consistency across independent mimic and inhibitor sessions, which collectively support the reliability of the observed effects.

 

Comments 5. In line 196 (Figure 3 legend), the authors mentioned p-values that aren’t displayed in the figure. Only p-values displayed in the figures need to be mentioned in the legends.

Response 5. We thank the reviewer for the comment, the legend was corrected accordingly.

 

Comments 6. In section 2.3.2 “miRNA inhibitor experiments” and Figure 4 C, it is unclear how significant the enhancement of infectious particle production is. Would it be possible to express it statistically?

Response 6. TCID₅₀ values were calculated by the Reed–Muench method from a single experimental session. This method produces a single point estimate per condition by mathematical interpolation across serial dilution wells; it does not generate independent replicate measurements from which variance or statistical significance can be derived. The addition of formal statistics is therefore not applicable to this dataset, and we respectfully maintain the descriptive presentation of these data. As already noted in the Discussion, the TCID₅₀ results are interpreted in the context of their concordance with the parallel molecular readouts (intracellular transcripts and extracellular viral RNA) and their consistency across independent mimic and inhibitor sessions, which collectively support the reliability of the observed effects.

 

Comments 7. In line 227 (Figure 4 legend), “**p” isn’t mentioned in the legend, although it is displayed in the figure. Conversely “*p” is mentioned in the legend without been displayed in the figure. Please check.

Response 7. We thank the reviewer for the comment, the legend was corrected accordingly.

 

Comments 8. In section 2.4.1 “miRNA mimic experiments” and Figure 5 C, it is unclear how significant the increase in the infectious titer is. Could the authors express it statistically?

Response 8. TCID₅₀ values were calculated by the Reed–Muench method from a single experimental session. This method produces a single point estimate per condition by mathematical interpolation across serial dilution wells; it does not generate independent replicate measurements from which variance or statistical significance can be derived. The addition of formal statistics is therefore not applicable to this dataset, and we respectfully maintain the descriptive presentation of these data. As already noted in the Discussion, the TCID₅₀ results are interpreted in the context of their concordance with the parallel molecular readouts (intracellular transcripts and extracellular viral RNA) and their consistency across independent mimic and inhibitor sessions, which collectively support the reliability of the observed effects.

 

Comments 9. In section 2.4.2 “miRNA inhibitor experiments” and Figure 6 C, it is unclear how significant the changes in viral progeny were. Could the authors express it statistically?

Response 9. TCID₅₀ values were calculated by the Reed–Muench method from a single experimental session. This method produces a single point estimate per condition by mathematical interpolation across serial dilution wells; it does not generate independent replicate measurements from which variance or statistical significance can be derived. The addition of formal statistics is therefore not applicable to this dataset, and we respectfully maintain the descriptive presentation of these data. As already noted in the Discussion, the TCID₅₀ results are interpreted in the context of their concordance with the parallel molecular readouts (intracellular transcripts and extracellular viral RNA) and their consistency across independent mimic and inhibitor sessions, which collectively support the reliability of the observed effects.

 

Comments 10. In line 334 (Figure 7 legend), “***p” isn’t mentioned, although it is displayed in the figure. Please check.

Response 10. We thank the reviewer for the comment, the legend was corrected accordingly.

 

Comments 11. Conclusions

In lines 621-623, the authors state that “...  particularly miR-15b overexpression and inhibition of either miRNA, paradoxically enhanced infectious progeny production...”. It seems to contradict the results described in the lines 174-176 “miR-15b mimic-treated supernatants contained markedly reduced infectious titers compared with controls, with a ~2 log₁₀ reduction at 6 hpi and a ~1 log₁₀ reduction at 24 hpi...”and in figure 3 C, as well the discussion (Lines 367-389), where the authors mentioned a reduction of infectious progeny production in miR-15b mimic-treated cells and link this feature to a drop in Furin expression. Please check.

Response 11. We thank the reviewer for identifying this inconsistency. The statement at lines 621-623 contained an error: as correctly reported in the Results (Lines 180-184 in the revised manuscript) and discussed at lines 377-381, miR-15b mimic treatment was associated with a significant reduction in infectious progeny production during Beta infection. It is miR-29a overexpression that paradoxically enhanced Omicron BA.1 infectivity. The sentence has been corrected accordingly in the revised manuscript.

 

Comments 12. Comments on the Quality of English Language

I suggest that the manuscript be sent for English proofreading by a native speaker or a company specializing in scientific writing.

Response 12. We have carefully revised the manuscript and corrected language issues to improve its fluency and readability, although the other reviewer considered the English “fine”. In particular, we have revised the Discussion to improve conciseness and readability, removing redundant statements and streamlining the argumentation where possible, while retaining the key mechanistic and interpretive content. The English throughout has also been reviewed and corrected.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have not made any substantial improvements to the manuscript. Although a detailed, point-by-point response to the previous critiques has been provided, the revision does not address the central concerns raised during the Major Revision. Despite explicit requests for additional experimental validation to support the claims, no new experiments have been performed. As a result, the major limitations of the original submission remain unresolved.