Microarray-based analysis reveals a novel role of the miRNA-613/SNAI2/CXCR4 axis in atrial fibrillation

Zhenyu Zhai; Yiligong Qi; Longlong Hu; Zumao Gan

doi:10.1371/journal.pone.0324324

. 2025 Jun 23;20(6):e0324324. doi: 10.1371/journal.pone.0324324

Microarray-based analysis reveals a novel role of the miRNA-613/SNAI2/CXCR4 axis in atrial fibrillation

Zhenyu Zhai ^1,^*, Yiligong Qi ^2,^‡, Longlong Hu ^1,^‡, Zumao Gan ^1,^‡

Editor: Vincenzo Lionetti³

PMCID: PMC12184911 PMID: 40549746

Abstract

Backgrounds

Atrial fibrillation (AF) can lead to substantial morbidity and mortality in clinic. The previous studies demonstrated that miRNAs were closely associated with several cardiovascular diseases, however, the role of miRNAs in the pathogenesis of AF has not been fully elucidated. In order to investigate the important role of miRNA in the mechanisms of AF, we conducted the study through bioinformatics analysis.

Methods

We downloaded the miRNA expression profile (GSE68475) and mRNA expression profile (GSE31821) from the Gene Expression Omnibus (GEO) database to explore the differentially expressed miRNAs and mRNAs. The criteria for significant differentially expressed miRNA and mRNA using the R limma package were: adjusted P-value < 0.05, log2fold-change ≥ 1. The target mRNAs related to differentially expressed miRNAs of AF were predicted by using Functional enrichment analysis tool. We Screened overlapped mRNAs based on differentially expressed mRNAs and miRNA related mRNAs using Draw Venn Diagram. GO enrichment analysis and KEGG pathway analysis were conducted to explore the role of miRNAs and mRNAs in the pathogenesis of AF.

Results

A total of 70 differentially expressed miRNAs were screened including 33 up-regulated miRNAs and 34 downregulated miRNAs. All of 94 differentially expressed mRNAs were screened including 56 up-regulated mRNAs and 38 downregulated mRNAs. There were three co-expressed up-regulated differentially expressed genes, including CXCR4, SNAI2, and FHL1. We showed the results of GO functional enrichment analysis and KEGG pathway analysis ranked by enrichment score (-log P value) respectively.

Conclusion

Compared with patients of normal sinus rhythm, miRNA-613 was significantly down-regulated in patients with AF. We demonstrated that SNAI2 and CXCR4 may target genes of miRNA-613 for the first time. Our findings may provide new ideas for clarifying the molecular mechanism of atrial fibrillation.

1. Introduction

Atrial fibrillation (AF) is the most widely diagnosed form of arrhythmia, leading to high clinical morbidity and mortality rates. Both male and female AF patients face a 5-fold increase in their odds of ischemic stroke incidence such that this condition imposes a serious economic burden on healthcare systems and societies throughout the globe [1–4]. AF incidence is particularly common in the context of aging, and the rates of AF diagnosis are also rising rapidly in low- and middle-income nations in which there are limited treatments available such that many cases are unreported, and many patients experience premature death. Given these rising rates of AF, this is a growing public health focus on helping to minimize the associated clinical risk [5,6]. Radiofrequency catheter ablation (RFCA) can be an effective treatment option for patients with AF, particularly among individuals with symptomatic drug-refractory AF [7–9]. For this treatment strategy, the pulmonary veins that are the source of AF-related paroxysmal activity are isolated [9,10]. While RFCA is more effective than drug treatment as a means of preventing AF recurrence, but this intervention can fail such that repeat treatment can be required in 20–50% of patients, and many patients nonetheless face a long-term risk of this condition recurring even after experiencing initial positive clinical outcomes [11,12]. Challenges associated with recurrent AF following ablation procedures remain a significant clinical concern. Despite some advances in the management of AF patients, there is still a pressing need for the identification of new diagnostic and prognostic biomarkers associated with this condition and for the establishment of scientifically-validated treatment options that can reduce the rates of AF and associated sequelae. Efforts to study the mechanistic basis for AF onset and persistence may thus offer insights that can aid in the stratification and treatment of affected patients, potentially by aiding the further optimization of current ablation approaches [13]. Several articles have shown that left atrial remodeling is closely related to the onset of AF, but the mechanistic basis for such remodeling remain to be fully determined [14]. Plasma and tissue microRNAs (miRNAs) have been shown to be differentially expressed in middle-aged and elderly atrial arrhythmia patients, suggesting a potential regulatory role for these miRNAs in atrial arrhythmia development [15–20].

As a kind of non-coding RNA sequences 22–25 nucleotides long, miRNAs serve as important post-translational regulators of gene expression owing to their ability to repress the translation or induce the degradation of complementary target RNAs [21–23]. Roughly 2,200 evolutionarily conserved miRNAs have been characterized to date in humans, regulating a diverse array of targets such that they serve as important regulators of a wide range of both pathological and physiological processes [24–27]. Given their ability to regulate vital cardiovascular processes, the dysregulation of miRNAs in vascular tissue or systemic circulation may be linked to the pathogenesis of cardiovascular conditions such as hypertension, cardiomyopathy, atherosclerosis, heart failure, ischemic heart disease, and stroke. However, few reports to date have explored the association between miRNAs and AF in detail, although some reports have suggested that certain miRNAs may offer value as diagnostic or prognostic biomarkers associated with this condition [28,29]. Recent advances in RNA-sequencing, microarray, and bioinformatics technologies have enabled the comprehensive evaluation of miRNAs and the characterization of their ability to silence mRNA expression through destabilization, cleavage, poly(A) tail shortening, and the impairment of ribosome-mediated translation efficiency [30–33]. While there have been several studies of a range of miRNAs over recent years, the roles of many of these miRNAs in humans have yet to be established [34–36]. Although several recent clinical analyses have also suggested that certain circulating miRNAs specific to the cardiovascular system may offer diagnostic or therapeutic value, additional work is required to translate these findings to the clinic. Accordingly, further work assessing the role that miRNAs play in AF development is warranted to guide the detection of new diagnostic biomarkers and therapeutic targets that can help alleviate the burden of AF.

To explore the role of miRNAs as regulators of AF development, bioinformatics analyses were performed using two publicly available AF-related mRNA and miRNA expression datasets from the Gene Expression Omnibus (GEO) database (GSE68475 and GSE31821). Functional enrichment analyses were then used to explore the potential functions of the target genes of miRNAs differentially expressed in AF samples. Co-expressed genes significantly differentially expressed between these two groups were then analyzed, ultimately revealing that miR-163 was downregulated in AF patient samples whereas putative miR-163 target genes including SNAI2 and CXCR4 were upregulated in these patients as compared to control individuals. These results may offer new insight into the pathogenesis of AF, providing a foundation for future efforts to detect biomarkers associated with the diagnosis of AF and to design novel treatments for this debilitating disease.

2. Materials and methods

2.1. AF-related miRNA expression profiling

An AF-related miRNA microarray dataset (GSE68475) was downloaded from the NCBI GEO database (NCBI, http://www.ncbi.nlm.nih.gov/geo/). This dataset included miRNA expression levels in right atrial appendage samples collected from individuals with AF or normal sinus rhythm (NSR) undergoing open-heart surgery at Oita University Hospital. In total, this dataset included 10 AF patient samples and 11 NSR patient samples. Raw microarray data were preprocessed and analyzed using GeneSpring GX software. Briefly, raw data values below 1.0 were set to 1.0 to minimize potential interference from very low-expression miRNAs. Next, the data for each miRNA were normalized to the median expression level of that miRNA across the 11 NSR samples, eliminating inter-array systematic errors and technical bias. Following normalization, we applied additional filtering to retain only miRNAs with raw signal intensities of ≥20 in at least one sample, ensuring the reliability of subsequent analyses. Principal component analysis (PCA) was performed as a quality control measure, and results demonstrated clear separation between AF and NSR groups, supporting the suitability of the dataset for differential expression analysis.

2.2. Differentially expressed miRNA identification

Differentially expressed miRNAs between AF and NSR patient samples were identified using the R limma package. Briefly, moderated t-tests were performed to compare miRNA expression between the two groups, and P values obtained from these tests were subsequently adjusted for multiple comparisons using the Benjamini-Hochberg False Discovery Rate (FDR) method. MiRNAs with adjusted P-value < 0.05 were considered statistically significant and selected for further analyses.

2.3. Functional enrichment analyses

Gene ontology (GO) and enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the miRNAs of interest were performed with the FunRich tool (v 3.1.3). Analyzed GO terms included those in the biological process (BP), cellular component (CC), and molecular function (MF) categories. KEGG pathway analyses provide insight into the pathways associated with particular targets, diseases, or other biological processes of interest [37].

2.4. Identification of overlapping differentially expressed miRNAs and mRNAs associated with AF

The GSE31821 dataset was downloaded from the GEO database and included auricular tissue biopsy samples from 2 control individuals and 4 AF patients from which RNA were extracted for Affymetrix microarray analysis. Raw microarray data were imported into R and preprocessed using the Robust Multi-array Average (RMA) method, including background correction to reduce background noise, quantile normalization to eliminate technical variability among arrays, and probe summarization to obtain gene-level expression values. Principal component analysis was performed as a quality control measure, revealing clear separation between AF and control samples, supporting data reliability for subsequent analyses. The target mRNAs related to differentially expressed miRNAs of AF were predicted by using Functional enrichment analysis tool (FunRich, version 3.1.3). We Screened overlapped mRNAs based on differentially expressed mRNAs and miRNA related mRNAs using Draw Venn Diagram. Those differentially overexpressed mRNAs overlapping with miRNAs identified above were identified using a Venn diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/). The functions of overlapping targets were explored through functional enrichment analyses as above.

2.5. Statistical analysis

Bioinformatics analyses were performed with R v3.6.0 and the ActiveState Perl software programming language. Differential miRNA and mRNA expression were analyzed with t-tests in the R limma package, with adjusted P-value < 0.05 as the threshold for significance in these microarray datasets [38].

2.6. Experimental model and cell culture

Human ventricular cardiomyocytes (AC16; Sigma-Aldrich, Cat# SCC-AC16) were cultured in DMEM/F-12 (Gibco, Cat# 11320−033) supplemented with 10% fetal bovine serum (FBS; Gibco, Cat# 10099−141) and 1% penicillin-streptomycin (Gibco, Cat# 15140−122) at 37 °C in a humidified atmosphere containing 5% CO₂. Cells were maintained mycoplasma-free, authenticated by short-tandem-repeat profiling, and used between passages 4–10 to ensure phenotypic stability. For all experiments, cells were seeded at 2 × 10⁵ cells well ⁻ ¹ in collagen-I-coated 6-well plates (Corning) and allowed to reach ~70% confluence before transfection.

2.7. miRNA mimic/inhibitor transfection

Gain- and loss-of-function studies were performed with synthetic hsa-miR-613 or hsa-miR-339-5p mimics, inhibitors, and matched negative controls (RiboBio, Guangzhou). Oligonucleotides were transfected at a final concentration of 50 nM using Lipofectamine 3000 (Thermo Fisher Scientific, Cat# L3000-015) according to the manufacturer’s instructions. Briefly, Lipofectamine and oligonucleotides were separately diluted in Opti-MEM (Gibco), combined (1:1 vol/vol), incubated for 15 min at room temperature, and added dropwise to the culture medium. Six hours later, the medium was replaced with fresh growth medium. Cells were harvested 48 h post-transfection for RNA extraction.

2.8. RNA isolation and quantitative reverse transcription PCR

Total RNA was extracted with TRIzol™ Reagent (Thermo Fisher Scientific, Cat# 15596−018) and quantified spectrophotometrically (NanoDrop 2000; Thermo Fisher). RNA integrity was verified by agarose-gel electrophoresis. For mRNA analysis, 500 ng RNA were reverse-transcribed with the PrimeScript™ RT Reagent Kit (Takara, Cat# RR037A) using random hexamers; for miRNA analysis, 200 ng RNA were reverse-transcribed with the Mir-X™ miRNA First Strand Synthesis Kit (Takara, Cat# 638313) employing stem-loop primers. Quantitative PCR was performed on a CFX96 Touch™ Real-Time PCR Detection System (Bio-Rad) with TB Green® Premix Ex Taq™ II (Takara, Cat# RR820A). The 20 µL reaction comprised 10 µL 2 × TB Green mix, 0.4 µL of each 10 µM primer, 1 µL cDNA, and 8.2 µL nuclease-free water. Cycling parameters: 95 °C 30 s; 40 cycles of 95 °C 5 s and 60 °C 30 s; followed by melt-curve analysis. Primer sequences for GAPDH, FHL1, CXCR4, SNAI2, and U6 (miRNA reference) are listed in table shown below:

Primer sequences used for qPCR

Target	Forward (5’ → 3’)	Reverse (5’ → 3’)
GAPDH	GGAAGCTTGTCATCAATGGAAATC	TGATGACCCTTTTGGCTCCC
FHL1	TGCTGCCTGAAATGCTTTGAC	GCCAGAAGCGGTTCTTATAGTG
CXCR4	GGGCAATGGATTGGTCATCCT	TGCAGCCTGTACTTGTCCG
SNAI2	GAACTGGACACACATACAGTGATT	GGCTGTATGCTCCTGAGCTG

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2.9. Data analysis

Ct values were averaged from three technical replicates per sample. Relative expression was calculated using the 2^-ΔΔCt method with GAPDH (mRNA) or U6 (miRNA) as internal controls and the corresponding negative-control group (Mimic-NC or Inhibitor-NC) as calibrator. Each experiment was repeated independently three times (n = 3). Data are presented as mean ± SD. Statistical significance between two groups was assessed by unpaired two-tailed Student’s t-test in GraphPad Prism 9; p < 0.05 was considered significant.

3. Results

3.1. Differentially expressed AF-related miRNA identification

An initial analysis of 1,023 miRNAs in a previously published microarray dataset including samples from 10 persistent AF patients and 11 NSR controls was performed, with expression levels in different samples being normalized prior to analysis such that the median values were consistent across all patients (Fig 1A&B). Then, the R limma package was used to identify differentially expressed miRNAs, of which 70 were ultimately detected, including 33 and 34 that were respectively upregulated and downregulated in AF samples as compared to NSR controls. These differential expression profiles were also compared with volcano plots (Fig 1C).

Fig 1 — **(A)** PCA plot of the miRNA expression profiles from dataset GSE68475, showing clear separation between normal sinus rhythm (NSR, blue dots) and atrial fibrillation (AF, red dots) samples. Ellipses represent 90% confidence intervals for each group. **(B)** The box plot showed the normalized expression values of miRNAs between atrial fibrillation group (GSM385-GSM395) and normal sinus rhythm group (GSM396-GSM405). **(C)** Volcano plot displaying the differentially expressed miRNAs identified between AF and NSR samples. Red dots indicate significantly upregulated miRNAs, green dots represent significantly downregulated miRNAs, and black dots represent miRNAs without significant changes.

3.2. Functional enrichment analyses of miRNAs of interest

To evaluate the possible biological effects of the identified differentially expressed miRNAs, miRNAs of interest were subjected to GO, KEGG, and transcription factor enrichment analyses with the FunRich tool (v 3.1.3). For the GO analysis, enriched terms were ranked based on enrichment score values (-logP-value) (Fig 2). The top 3 enriched biological process (BP) terms for these miRNAs were signal transduction (24.8%, P > 0.05), regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolism (19.4%, P > 0.05), and transport (8.6%, P > 0.05) (Fig 2A). Dysregulation of signal transduction pathways is widely recognized as a crucial factor in AF pathogenesis due to their roles in modulating cardiac electrophysiological properties, ion channel activities, and cellular responses. Aberrant nucleotide metabolism and transport processes may influence cellular energy metabolism, transcriptional regulation, and RNA processing, contributing to atrial remodeling and structural alterations frequently observed in AF patients. Regarding cellular components (CC), the top three enriched terms were nucleus (51.8%, P < 0.001), cytoplasm (49.3%, P < 0.05), and lysosome (17.4%, P < 0.05) (Fig 2B). Enrichment of miRNAs targeting genes predominantly located in the nucleus and cytoplasm aligns with their known roles in gene expression regulation, transcriptional control, and signal transduction, which are all relevant to AF pathophysiology. The significant enrichment of lysosomal components suggests potential involvement of autophagy-lysosome pathways in atrial remodeling and fibrosis, mechanisms known to underlie AF development and progression. Additionally, the top three molecular function (MF) terms were transcription factor activity (7.8%, P > 0.05), transporter activity (5.6%, P > 0.05), and RNA binding (4.2%, P > 0.05) (Fig 2C). Altered transcription factor activity and RNA binding processes can directly modulate gene expression programs implicated in AF, such as fibrosis-associated signaling and inflammatory responses. Similarly, transporter activity changes might influence ion channel functions and intracellular signaling, thus directly contributing to AF-associated electrophysiological disturbances. KEGG pathway analysis results were similarly ranked based on enrichment score values (Fig 2D), revealing that these differentially expressed AF-related miRNAs were most closely associated with proteoglycan syndecan-mediated signaling events (38.6%, P < 0.001), syndecan-1-mediated signaling events (37.2%, P < 0.001), the TRAIL signaling pathway (37.2%, P < 0.001), and the sphingosine 1-phosphate (S1P) pathway (36.7%, P < 0.001). Syndecan signaling events are known to regulate cellular adhesion, migration, inflammation, and fibrosis—all central elements involved in atrial remodeling processes observed in AF patients. The TRAIL signaling pathway, typically involved in apoptosis and inflammation, may reflect increased apoptotic and inflammatory activities in atrial tissues affected by AF. Additionally, the S1P signaling pathway is crucial in regulating cardiac electrophysiological stability, cellular proliferation, and migration, suggesting its potential role in AF pathology via promoting structural and electrical atrial remodeling. A transcription factor enrichment analysis was also performed for these miRNAs (Fig 3).

Fig 2 — The plots showed GO enrichment analysis for differentially expressed miRNAs related to atrial fibrillation, including biological processes (BP), cellular component (CC), and molecular functions (MF) terms **(A-C)**. The plots showed KEGG enrichment analysis for differentially expressed miRNAs related to atrial fibrillation **(D)**. GO: Gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes.

3.3. Validation of differentially expressed miRNA target gene expression patterns in a separate AF dataset

A separate GEO dataset (GSE31821) was next analyzed to identify mRNAs that were differentially expressed in AF samples relative to NSR controls, ultimately leading to the detection of 94 differentially expressed genes (adjusted P-value < 0.05, Log₂FC ≥ 1), of which 56 and 38 were respectively upregulated and downregulated in the AF group (Fig 4).

Fig 4 — **(A)** PCA plot of the mRNA expression profiles from dataset GSE31821, demonstrating distinct grouping of AF patient samples (red dots) and control samples (blue dots), confirming suitable data quality for further analyses. **(B)** The heat map showed the differentially expressed mRNAs in atrial fibrillation group, compared to the normal sinus rhythm group. T indicated the atrial fibrillation group; N indicated the normal sinus rhythm group. Green: represented low relative expression; Red: represented high relative expression. **(C)** Volcano plot illustrating the differential expression of mRNAs between AF and control samples. Similarly, red and green dots indicate significantly upregulated and downregulated mRNAs, respectively.

Most miRNAs are capable of binding to complementary sequences for multiple mRNA targets, and the FunRich tool was thus used to predict target mRNAs associated with differentially expressed AF-related miRNAs, leading to the identification of 512 such candidate target genes. A Venn diagram was then used to assess the overlap between these 512 candidate genes and the 94 differentially expressed genes identified in AF samples above (Fig 5). This ultimately identified three overlapping upregulated differentially expressed genes present in both of these datasets (Table 1), including SNAI2, CXCR4, and FHL1. Functional enrichment analyses showed that these genes were enriched in the sensory perception of mechanical stimulus, regulation of metal ion transport, regulation of cell growth, and cell growth BP terms, while no enriched CC terms were detected suggesting these genes to be widely distributed throughout cells; Low levels of MF term enrichment with similar gene ratio values were also observed for these target genes (Fig 6). A miRNA-mRNA network was further constructed to highlight the association between these mRNAs and their associated miRNA regulators (Fig 7).

Table 1. The expression of overlapped mRNAs and their related miRNAs in atrial fibrillation group.

miRNA	mRNA	Target	miRNALogFC	mRNALogFC
hsa-miR-613	CXCR4	target	−1.24	1.05
hsa-miR-339-5p	FHL1	target	2.86	1.38
hsa-miR-613	SNAI2	target	−1.24	1.11

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“Target” indicates overlapped mRNAs were the predicted target genes of related miRNAs. FC, fold change; CXCR4, chemokine receptor 4; FHL1, four- and-a-half LIM domain protein 1; SNAI2, snail family transcriptional repressor 2.

Fig 6 — The size of the circle indicated the count of enriched mRNAs. Red: represented significant gene enrichment; Blue represented relatively insignificant gene enrichment.

Fig 7 — Green: indicated down-regulation in atrial fibrillation group; Orange indicated up-regulation in atrial fibrillation.

3.4. Experimental validation of miRNA-mRNA interactions via quantitative PCR

To experimentally validate the predicted miRNA-mRNA regulatory relationships derived from bioinformatics analyses, quantitative polymerase chain reaction (qPCR) assays were performed. Specifically, we investigated the direct effects of miR-339-5p and miR-613 on the expression levels of their predicted target genes (FHL1, SNAI2, and CXCR4) in vitro (Fig 8).

Fig 8 — Expression level of FHL1 (A), SNAI2 (B) and CXCR4 (C) following transfection with hsa-miR-613 inhibitor or mimic compared to negative controls. Data represent mean ± SD of three independent experiments. **P < 0.01; ***P < 0.001; ****P < 0.0001.

The qPCR results demonstrated that the expression of FHL1 significantly increased when cells were treated with the hsa-miR-339-5p inhibitor compared to the inhibitor negative control (Inhibitor-NC) group (P < 0.0001), indicating a strong negative regulatory relationship. Conversely, treatment with the miR-339-5p mimic markedly decreased the FHL1 expression levels compared with the mimic negative control (Mimic-NC) group (P < 0.01). These findings clearly support the prediction that miR-339-5p negatively regulates FHL1, reinforcing the complexity of miRNA-mRNA interactions in atrial fibrillation.

Similarly, our analysis confirmed that miR-613 significantly modulated the expression of its putative target genes, SNAI2 and CXCR4. Inhibition of miR-613 led to a substantial upregulation of SNAI2 (P < 0.0001) and CXCR4 (P < 0.01), compared with the Inhibitor-NC group. In contrast, overexpression of miR-613 using a miR-613 mimic significantly decreased the expression of SNAI2 (P < 0.01) and CXCR4 (P < 0.001) compared with the Mimic-NC group. These experimental validations confirm the bioinformatics predictions and substantiate our hypothesis that the downregulation of miR-613 observed in atrial fibrillation patients could contribute to disease progression by relieving repression of SNAI2 and CXCR4 expression.

Collectively, these qPCR validation results provide robust experimental evidence confirming the predicted regulatory interactions and strengthen the mechanistic insights into miRNA involvement in atrial fibrillation pathogenesis.

4. Discussion

While many prior studies have explored the etiological basis for AF onset and progression, the precise molecular underpinning for this condition remains incompletely understood [39–43]. Non-coding RNAs have been shown to serve as important regulators of AF pathogenesis, with circular RNAs (circRNAs), for example, controlling relevant processes such as cell function, development, and pathological responses relevant to heart disease [44–46]. In contrast to the relatively complex biogenesis processes and poor interspecies conservation of circRNAs [47], miRNAs are relatively smaller and simpler non-coding RNAs that post-transcriptionally serve to suppress target gene expression [48]. Many of these miRNAs have been shown to specifically control the proliferation, survival, or differentiation of particular cell types [49–54], and several miRNAs have been reported to be differentially regulated in AF including miR-210, miR-30e, and miR-641 [55–61]. This study leveraged extant microarray datasets to further explore the potential roles of miRNAs as relevant AF-associated biomarkers.

Initial analyses for this study utilized the GSE68475 dataset consisting of 11 NSR and 10 AF patient samples, enabling the identification of 70 miRNAs that were differentially expressed between these two groups of which 33 and 34 were respectively upregulated and downregulated in AF patients. To extend these results, differential mRNA expression between 2 AF patients and 4 NSR patients was assessed using a separate publicly available microarray dataset, identifying 56 and 38 mRNAs that were significantly upregulated and downregulated in AF samples. By evaluating the overlap between this latter dataset and the putative targets of the 70 miRNAs identified in the first dataset led to the identification of three overlapping genes (SNAI2, CXCR4, and FHL1). While miR-613 was downregulated in AF samples, the consistent upregulation of its target genes SNAI2 and CXCR4 was also observed. In contrast, both miR-339-5p and its target FHL1 were upregulated in AF samples. At the molecular mechanistic level, we hypothesize that the downregulation of miR-613 observed in AF samples may relieve its inhibitory effect on SNAI2 and CXCR4, thus contributing to the enhanced expression of these genes. SNAI2, a zinc finger transcription factor involved in epithelial-mesenchymal transition, may mediate structural remodeling in atrial tissues by promoting fibrosis and extracellular matrix deposition, both critical factors associated with AF development. Likewise, CXCR4, a chemokine receptor, plays essential roles in inflammatory cell recruitment and tissue remodeling—processes known to contribute significantly to atrial fibrosis and the structural remodeling characteristic of AF pathology.

Moreover, the downregulation of miRNA-613 and subsequent upregulation of SNAI2 and CXCR4 suggest that ventricular myocardium might enter a state analogous to myocardial hibernation. Myocardial hibernation refers to a protective remodeling process in which cardiac myocytes downregulate their contractile function and metabolic demand to maintain viability under conditions of chronic ischemia or hemodynamic stress. Typical characteristics include reduced myocardial contractility despite preserved cell viability, glycogen accumulation, decreased capillary density, downregulated metabolic enzyme activity, and activation of ischemia-related signaling pathways [62]. It has been reported that even in the absence of overt coronary ischemia, persistent rapid pacing and ventricular dyssynchrony can induce myocardial changes mimicking hibernation, such as increased glycogen storage and reduced expression of glycolytic enzymes, representing an adaptive response to sustained hemodynamic stress [63]. The transcriptional repressor SNAI2 promotes cellular survival under stress conditions, while elevated CXCR4 expression is considered a hallmark of protective myocardial responses under ischemic conditions. Therefore, the observed miRNA-613/SNAI2/CXCR4 axis might function as a molecular modulator facilitating a hibernation-like phenotype in ventricular myocardium under chronic AF-induced hemodynamic stress, ultimately protecting myocardial tissue from energetic depletion and stress-induced damage.

FHL1 is associated with cytoskeletal dynamics and structural integrity in cardiomyocytes; thus, its upregulation might reflect compensatory or maladaptive remodeling responses occurring during AF progression. These proposed regulatory relationships underline the potential significance of these miRNA-mRNA interactions in the pathogenesis and maintenance of AF. To experimentally validate these bioinformatics predictions, we performed qPCR experiments to assess the direct regulatory relationships between these miRNAs and their predicted target genes. The results clearly demonstrated that inhibition of miR-613 significantly increased the expression of SNAI2 and CXCR4, while the miR-613 mimic markedly decreased their expression. Similarly, miR-339-5p inhibitor significantly elevated FHL1 expression, whereas the miR-339-5p mimic reduced its expression. These qPCR validation experiments strongly support our hypothesis regarding the involvement of miR-613 in AF pathogenesis through negative regulation of SNAI2 and CXCR4, as well as indicating a direct negative regulatory relationship between miR-339-5p and FHL1.Enrichment analyses of the differentially expressed miRNAs revealed that they were enriched in the signal transduction, regulation of nucleobase, nucleoside, nucleotide, and nucleic acid metabolism, and transport BP terms, the nucleus, cytoplasm, and lysosome CC terms, and the transcription factor activity, transporter activity, and RNA binding MF terms. They were further enriched in the proteoglycan syndecan-mediated signaling events, syndecan-1-mediated signaling events, TRAIL signaling pathway, and S1P pathway KEGG terms. Of particular interest, the TRAIL signaling pathway and S1P signaling pathway have previously been implicated in cardiovascular remodeling processes, inflammation, and apoptotic regulation, suggesting their potential involvement in atrial structural remodeling and electrical instability observed in AF. The syndecan-mediated signaling pathways are also noteworthy, given their known roles in extracellular matrix remodeling and cellular adhesion, both crucial factors influencing cardiac fibrosis and the atrial remodeling processes underlying AF pathogenesis. The most enriched BP terms for the three overlapping target mRNAs were sensory perception of mechanical stimulus, regulation of metal ion transport, regulation of cell growth, and cell growth. Notably, regulation of metal ion transport and cell growth are processes closely related to cellular electrophysiology and myocardial structural remodeling, both of which are critical events in the progression of AF.

Several articles published recently have documented the ability of miR-613 to influence oncogenesis and other diseases. MALAT1 is a lncRNA that has been reported to serve as an inhibitor of miR-613 in hepatocellular carcinoma cells, wherein this miRNA can drive tumor metastasis via the peripheral vascular system. Li et al. further found a role for miR-613 as a suppressor of the proliferation of granulosa cells such that it may serve as a regulator of polycystic ovarian syndrome [64]. miR-613 was also identified as a LINC00460 target in LOVO and HT29 cells that in turn targets SphK1 [65], while a separate analysis revealed that overexpressing miR-613 was sufficient to enhance cisplatin chemosensitivity [66].

By promoting fibronectin 1 downregulation and thereby inactivating AKT signaling activity, miR-613 can suppress nasopharyngeal carcinoma cell angiogenic activity [67], and it can further render gastric cancer cells more sensitive to cisplatin treatment by suppressing Sex-determining region Y-box 9 expression [68]. There is further evidence for the ability of miR-613 to suppress Atonal homolog1, promoting colon cancer cell invasivity, migration, and proliferative activity through JNK1 pathway signaling activation and mucin 2 upregulation [69]. In osteosarcoma, miR-613 has also been shown to suppress CXCR4 expression, thereby driving tumor growth and metastasis to the lungs [70].

Overall, these analyses suggested that in AF patients, significant miR-613 downregulation was evident as compared to NSR controls. This miR-613 downregulation may serve to drive the onset or progression of AF via facilitating CXCR4 upregulation. This study is also the first to have identified SNAI2 as a miR-613 target gene candidate that was upregulated in samples from patients with AF. Both miR-339-5p and its putative target gene FHL1 were also herein found to be upregulated in individuals with AF. Overall the limited number of overlapping miRNA target genes and the inconsistent levels of FHL1 in AF samples may be the result of the limited number of samples in the second analyzed microarray (2 NSR and 4 AF samples). Additional research will also focus in greater detail on the mechanisms through which miR-613 and its target genes shape the pathogenesis of this disease, thereby potentially providing a foundation that will enable researchers to better understand the molecular basis for AF and associated treatment options.

There were key strengths to this study. Firstly, the results of our study showed that compared with NSR patients, AF patients exhibited significant miR-613 downregulation for the first time. Secondly, these analyses also revealed that SNAI2 and CXCR4 were two putative miR-613 target genes upregulated in AF patients in whom miR-613 was downregulated, suggesting a potential regulatory relationship in the pathogenesis of AF. These novel findings indicate that miR-613 may serve as an important regulatory factor in AF progression and merit further investigation.

Nevertheless, some limitations must also be acknowledged. Due to the lack of detailed clinical metadata in publicly available datasets, adjustments for potential confounding factors such as age and clinical characteristics were not possible, which might affect the robustness of our results. Furthermore, the sample size, particularly for the mRNA dataset, was relatively small, which may limit the generalizability and stability of our conclusions. Although our qPCR validation experiments provided direct evidence supporting the bioinformatics predictions, additional in vivo experiments and validation in clinical tissue samples with larger cohorts are necessary to further elucidate the precise molecular roles of these miRNA-mRNA interactions in AF.

In conclusion, compared with patients of normal sinus rhythm, miRNA-613 was significantly down-regulated in patients suffering from atrial fibrillation by using bioinformatics analysis. Our qPCR validation further confirmed that SNAI2 and CXCR4 may be target genes of miRNA-613, and the expression level of these genes was significantly up-regulated in patients with atrial fibrillation, while miRNA-613 was significantly down-regulated in patients suffering from atrial fibrillation. Our findings may provide new ideas for clarifying the molecular mechanism of atrial fibrillation.

Data Availability

All datasets analyzed in this study are publicly available and can be accessed through the NCBI Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Specifically, the miRNA expression profiling dataset (GSE68475) is accessible at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE68475, and the mRNA expression profiling dataset (GSE31821) is available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31821.

Funding Statement

This study was supported by grants from the National Natural Science Foundation of China (No. 82260067), Key Research Project of Jiangxi Provincial Department of Education (GJJ2200105), General Science and Technology Program of Jiangxi Provincial Health Commission (No. 202210627), and Science and Technology Project of Jiangxi Provincial Administration of Traditional Chinese Medicine (No. 2022B903).

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PLoS One. 2025 Jun 23;20(6):e0324324. doi: 10.1371/journal.pone.0324324.r001

Author response to Decision Letter 0

29 Oct 2024

PLoS One. doi: 10.1371/journal.pone.0324324.r002

Decision Letter 0

Vincenzo Lionetti

PONE-D-24-48788Microarray-based analysis and characterization of microRNAs associated with atrial fibrillationPLOS ONE

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Reviewer #1: No

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The study explores key miRNA-mRNA interactions in atrial fibrillation using a bioinformatic approach. While potentially interesting, there are significant shortcomings that affect the interpretability of the results. The methodology was inadequately explained, and there was a lack of reproducible analysis provided. For instance, it is unclear what methods were employed to correct the background and normalize the data. It is also uncertain whether any filters were applied to exclude probes with no expression or if quality control measures such as principal component analysis were conducted. Moreover, confounding corrections do not appear to have been addressed, and it seems that multiple testing corrections (e.g., false discovery rate) were not utilized. In the context of transcriptomics research, this oversight is unacceptable. Furthermore, the lack of independent validation and the small sample sizes of the investigated datasets undermine the validity of their findings.

Reviewer #2: This study, while simple in its approach, leads to important and meaningful conclusions.

I would like to clarify why I believe the manuscript is partially technically valid. To strengthen the conclusions, it would be important to validate the findings observed through bioinformatics analysis with additional techniques, such as IHC, among others. Nevertheless, as preliminary data for further studies, the current study is technically valid.

Additionally, the statistical analysis is somewhat rigorous, but I would recommend using adjusted p-values (p-value adj) < 0.05 (or even 0.10) when selecting differentially expressed miRNAs and mRNAs, rather than relying solely on p-values. A p-value of < 0.05 indicates that the adjusted p-value is likely below 0.05, ensuring that the statistical analysis is both rigorous and appropriate. It would be preferable to consistently use either FC (fold-change) or logFC (log fold-change) throughout the manuscript. In Table 1, logFC is provided, while in the text, FC is used with a threshold of >2 (FC>2). To avoid confusion for readers, I suggest using both terms, perhaps in the table, or consistently using only one of the two throughout the manuscript.

I also suggest expanding the discussion to provide more detail, particularly in explaining the significance of the statistically significant Gene Terms (for BP, CC, MF) in relationship of AF, and what they may represent (Figure 2).

Overall, the results are promising, and I believe it is crucial to validate these findings with additional techniques. Lastly, I recommend reviewing the use of abbreviations throughout the manuscript, ensuring that they are defined in brackets the first time they are mentioned, and thereafter avoiding repetition of the full term.I suggest checking English too, but it seems to be quite fluent. I recommend broadening the discussion on your results by comparing them with what has already been observed in AF or similar conditions. If you don't find any comparison articles (I don't think so) explain in great detail your hypothesis also at the level of molecular mechanisms. This would be very useful for those who will do new in vivo studies and using other techniques to validate this study. So it would give an important strength to this study.

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2025 Jun 23;20(6):e0324324. doi: 10.1371/journal.pone.0324324.r003

Author response to Decision Letter 1

28 Mar 2025

Point-to-point response

We thank all three reviewers for their insights and suggestions, which have helped us greatly improve the manuscript. Below is a point-to-point response embedded in the reviewer’s comments (in blue).

-----------------------------------------------------------------------------------------------------------------

Response to Reviewers’ comments:

Reviewer(s)' Comments to Author:

We sincerely appreciate your thorough review and valuable comments, which have significantly contributed to improving the quality of our manuscript.

We fully acknowledge that our initial submission lacked sufficient detail in describing the data preprocessing, normalization methods, and quality control measures, which could affect the interpretability and reproducibility of our findings. In response to your constructive feedback, we have carefully and thoroughly addressed these issues in the revised manuscript, particularly the methodological concerns you raised.

1. Methodological details and quality control measures:

In the Methods section, we have now provided a detailed description of the preprocessing procedures applied to the publicly available miRNA expression dataset (GSE68475) and mRNA expression dataset (GSE31821). Briefly, For the miRNA expression dataset (GSE68475) downloaded from the public GEO database, raw miRNA microarray data were initially processed using GeneSpring GX software. All raw intensity values lower than 1.0 were first adjusted to 1.0 to minimize interference from extremely low-expressed miRNAs. Subsequently, data normalization was performed by referencing the median expression levels of each miRNA across the 11 normal sinus rhythm (NSR) samples, thereby reducing systematic technical biases between arrays. Additionally, miRNAs with maximum raw signal intensities lower than 20 across all samples were filtered out, ensuring the reliability of the subsequent analyses. Furthermore, we performed principal component analysis (PCA) as an additional quality control measure, and the PCA results demonstrated clear separation between AF and NSR samples in two-dimensional principal component space, indicating good data quality and suitability for subsequent differential expression analyses (Figure1 to reviewer1. A). For another mRNA expression dataset (GSE31821) utilized in this study, the raw microarray data (CEL files) were imported into R software and processed using the Robust Multi-array Average (RMA) method, including background correction, quantile normalization, and probe summarization to generate gene-level expression values. PCA was also performed as a quality control measure, and the results clearly showed distinct clustering of AF and control samples, further confirming data quality and supporting the validity of subsequent analyses (Figure1 to reviewer1. B).

The specific revised Materials and Methods content added is as follows:

2.1 AF-related miRNA expression profiling

2.2 Differentially expressed miRNA identification

2.4 Identification of overlapping differentially expressed miRNAs and mRNAs associated with AF

2. Multiple testing correction:

Concerning multiple testing correction, as recommended, we have now implemented the Benjamini-Hochberg False Discovery Rate (FDR) method in our revised analysis. Differentially expressed miRNAs and mRNAs were re-selected based on the adjusted P-value threshold (p-value adj < 0.05), and these updated analytical results have been clearly included in the revised manuscript. Following this adjustment, we identified a slightly reduced number of differentially expressed miRNAs (33 significantly upregulated and 34 downregulated) (Figure2 to reviewer1. A). Importantly, our key miRNAs of interest (including miR-613 and miR-339-5p) remained significantly differentially expressed, demonstrating that our core conclusions are robust to this statistical refinement. Additionally, the set of differentially expressed mRNAs remained unchanged after adjusting the P-values, possibly due to the relatively small sample size limiting the impact of this correction (Figure2 to reviewer1. B).

3. Adjustment for potential confounding factors and independent experimental validation:

Regarding your concern about potential confounding factors, we did not perform adjustments for possible confounders such as age and other clinical characteristics, mainly due to the lack of sufficiently detailed metadata available from the public datasets used in this study. We fully understand and sincerely accept your criticism, acknowledging this limitation as an inherent constraint of our study design. We have explicitly clarified this limitation in the revised manuscript and committed to addressing this issue through more rigorous and comprehensive experimental designs in future studies, utilizing datasets with more complete clinical information to minimize potential confounding effects and enhance the robustness of our conclusions.

We fully recognize your concerns regarding the absence of independent validation experiments in our initial submission. To address this important limitation, we have now conducted qPCR validation experiments to independently verify the miRNA-mRNA regulatory relationships predicted by our bioinformatic analysis. Specifically, we performed qPCR experiments to validate the regulatory relationships between miR-613 and its predicted targets SNAI2 and CXCR4, as well as between miR-339-5p and its predicted target FHL1.

Our qPCR results demonstrated clear evidence supporting these interactions: inhibition of miR-339-5p significantly increased FHL1 expression (P<0.0001), whereas the miR-339-5p mimic significantly decreased its expression (P<0.01); inhibition of miR-613 significantly elevated the expression levels of SNAI2 (P<0.0001) and CXCR4 (P<0.01), while the miR-613 mimic significantly reduced their expression (SNAI2, P<0.01; CXCR4, P<0.001) (Figure3 to reviewer1. A-C).

These experimental data strongly validate our computational predictions and substantially enhance the robustness and interpretability of our findings. We have clearly presented these new experimental results in the revised manuscript (Figure 8 in the revised manuscript), and thoroughly discussed the implications in the revised Discussion section.

The newly added experimental validation section (Results 3.4) in the revised manuscript is:

3.4 Experimental validation of miRNA-mRNA interactions via quantitative PCR

The updated limitation section in the Discussion now states:

Again, we deeply appreciate your thorough review and insightful suggestions, and we have earnestly and diligently revised the manuscript. We hope our detailed revisions adequately address your concerns and resolve your doubts about the rigor of our data analysis.

Figure1 to reviewer1. Principal component analysis (PCA) of microarray datasets used in this study.

(A) PCA plot of the miRNA expression profiles from dataset GSE68475, showing clear separation between normal sinus rhythm (NSR, blue dots) and atrial fibrillation (AF, red dots) samples. Ellipses represent 90% confidence intervals for each group.

(B) PCA plot of the mRNA expression profiles from dataset GSE31821, demonstrating distinct grouping of AF patient samples (red dots) and control samples (blue dots), confirming suitable data quality for further analyses.

Figure2 to reviewer1. Volcano plots illustrating differential expression analyses for miRNAs and mRNAs.

(A) Volcano plot displaying the differentially expressed miRNAs identified between AF and NSR samples. Red dots indicate significantly upregulated miRNAs, green dots represent significantly downregulated miRNAs, and black dots represent miRNAs without significant changes.

(B) Volcano plot illustrating the differential expression of mRNAs between AF and control samples. Similarly, red and green dots indicate significantly upregulated and downregulated mRNAs, respectively.

Figure3 to reviewer1. Validation of miRNA-mRNA regulatory relationships by qPCR.

qPCR validation of miRNA-mRNA regulatory relationships in atrial fibrillation. Expression level of FHL1 (A), SNAI2 (B) and CXCR4 (C) following transfection with hsa-miR-613 inhibitor or mimic compared to negative controls. Data represent mean ± SD of three independent experiments. **P < 0.01; ***P < 0.001; ****P < 0.0001.

Reviewer #2: This study, while simple in its approach, leads to important and meaningful conclusions.

Attachment

Submitted filename: Response to reviewer.docx

pone.0324324.s002.docx^{(255.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0324324.r004

Decision Letter 1

Vincenzo Lionetti

PONE-D-24-48788R1Microarray-based analysis and characterization of microRNAs associated with atrial fibrillationPLOS ONE

Dear Dr. Zhai,

==============================

ACADEMIC EDITOR: The revised form of the present manuscript suggest potential involvement of miRNA-613/ SNAI2/CXCR4 axis in particular myocardial phenotype. Additional Editor suggestions should be considered.

==============================

Please submit your revised manuscript by May 26 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Vincenzo Lionetti, M.D., PhD

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

1) The revised manuscript highlights a new potential adaptive mechanism involving genes regulated by miRNA613 that deserve attention by the authors. Myocardial hibernation refers to a state of reduced contractile function and metabolism in viable human myocardium due to chronic hypoperfusion or dyssynchrony even if the heart is failing (please see J Cell Mol Med. 2014 Mar;18(3):396-414.;J Card Fail. 2009 Dec;15(10):920-8 ). In the setting of AF, particularly when persistent or accompanied by HF, ventricular myocardial hibernation may occur as an adaptive response to chronic hemodynamic stress and altered perfusion. In patients with AF, the downregulation of miRNA-613 leads to upregulation of SNAI2 and CXCR4, which in turn may contribute to fibrotic and structural remodeling (via SNAI2),enhanced cellular survival signaling and adaptation to chronic stress (via CXCR4), possible dedifferentiation of cardiomyocytes, reducing their metabolic activity and contractile function. These changes are consistent with a myocardial hibernation phenotype—where cells downregulate activity to preserve viability under chronic stress. Thus, the miRNA-613/ SNAI2/CXCR4 axis may act as a molecular switch or modulator of hibernation-like states in ventricular myocardium secondary to chronic AF. The authors are invited to include this perspective in their discussion including abovementioned articles.

2) Include in the title the miRNA-613/ SNAI2/CXCR4 axis.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: I believe that, with the methodological adjustments and revisions made, this study can now be considered valid and suitable for recommendation. The authors have clearly taken my comments into account and have implemented the requested changes appropriately.

Firstly, I appreciated their use of the adjusted p-value, even though some data were modified compared to the previous version. Nevertheless, the key significant results originally highlighted remain evident, thereby further supporting their hypothesis. This consistency demonstrates the robustness of their main conclusions in light of the refined statistical approach.

Moreover, I commend the authors for incorporating qPCR validation, especially as I had suggested using additional techniques to support the bioinformatics findings. This experimental validation has confirmed the observations derived from the bioinformatic analysis, adding substantial value to the study. These results meaningfully reinforce their computational predictions and significantly enhance the credibility and reliability of their conclusions.

Additionally, the revised discussion is more detailed and effectively underscores the authors’ central hypothesis. While in vivo validation would certainly be of interest for future studies, I believe that the analyses presented in this manuscript are sufficient for the current scope of the work.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #2: No

**********

PLoS One. 2025 Jun 23;20(6):e0324324. doi: 10.1371/journal.pone.0324324.r005

Author response to Decision Letter 2

17 Apr 2025

Point-to-point response

We thank all editors and reviewers for their insights and suggestions, which have helped us greatly improve the manuscript. Below is a point-to-point response embedded in the reviewer’s comments (in blue).

-----------------------------------------------------------------------------------------------------------------

Response to Editors’ comments:

Additional Editor Comments:

We sincerely appreciate your thoughtful comments, which have greatly improved the rigor and depth of our manuscript. According to your suggestion, we have expanded the discussion section to include the potential adaptive mechanism involving myocardial hibernation mediated by the miRNA-613/SNAI2/CXCR4 axis, referencing the recommended literature. Specifically, the newly added section is as follows:

2) Include in the title the miRNA-613/ SNAI2/CXCR4 axis.

We sincerely appreciate for your valuable suggestion. We have revised the title of our manuscript to clearly highlight the miRNA-613/SNAI2/CXCR4 axis, as recommended. The revised title is as follows:

Microarray-based analysis reveals a novel role of the miRNA-613/SNAI2/CXCR4 axis in atrial fibrillation

Response to Reivewers’ comments:

Thank you very much for your insightful suggestions and positive evaluation of our manuscript. Your detailed and constructive feedback has significantly enhanced the scientific rigor and clarity of our work. In future studies, we plan to further validate these findings through comprehensive in vivo experiments to deepen our understanding of the underlying molecular mechanisms of atrial fibrillation.

Attachment

Submitted filename: Response to Reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0324324.r006

Decision Letter 2

Vincenzo Lionetti

PONE-D-24-48788R2Microarray-based analysis reveals a novel role of the miRNA-613/SNAI2/CXCR4 axis in atrial fibrillationPLOS ONE

Dear Dr. Zhai,

==============================

ACADEMIC EDITOR: Whilst the authors have included data from qPCR experiments (Figure 8) in the most recent revision, there is no reporting of the qPCR methodology, including the samples used, their origin and the in vitro culture methods to generate them, all of which limit the reproducibility of this study should a reader wish to replicate the findings. As such, the manuscript doesn't currently meet PLOS publication criteria which requires that "Experiments, statistics, and other analyses are performed to a high technical standard and are described in sufficient detail."

The authors are strongly invited to update the reporting of their methodology.

==============================

Please submit your revised manuscript by Jun 27 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Vincenzo Lionetti, M.D., PhD

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

PLoS One. 2025 Jun 23;20(6):e0324324. doi: 10.1371/journal.pone.0324324.r007

Author response to Decision Letter 3

14 May 2025

Point-to-point response

We thank academic editor for insights and suggestions, which have helped us greatly improve the manuscript. Below is a point-to-point response embedded in the editor’s comments (in blue).

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Response to Editor’s comments:

Editor(s)' Comments to Author:

ACADEMIC EDITOR: Whilst the authors have included data from qPCR experiments (Figure 8) in the most recent revision, there is no reporting of the qPCR methodology, including the samples used, their origin and the in vitro culture methods to generate them, all of which limit the reproducibility of this study should a reader wish to replicate the findings. As such, the manuscript doesn't currently meet PLOS publication criteria which requires that "Experiments, statistics, and other analyses are performed to a high technical standard and are described in sufficient detail."

The authors are strongly invited to update the reporting of their methodology.

Thank you for your constructive feedback. We apologize for the earlier omission of key methodological details. We have now added a detailed description of our qPCR procedures to the Materials and Methods section. Thank you again for your guidance in strengthening the scientific rigor and reproducibility of our manuscript.

The newly added content is as follows:

2.6 Experimental Model and Cell Culture

Human ventricular cardiomyocytes (AC16; Sigma-Aldrich, Cat# SCC-AC16) were cultured in DMEM/F-12 (Gibco, Cat# 11320-033) supplemented with 10 % fetal bovine serum (FBS; Gibco, Cat# 10099-141) and 1 % penicillin-streptomycin (Gibco, Cat# 15140-122) at 37 °C in a humidified atmosphere containing 5 % CO₂. Cells were maintained mycoplasma-free, authenticated by short-tandem-repeat profiling, and used between passages 4–10 to ensure phenotypic stability. For all experiments, cells were seeded at 2 × 10⁵ cells well⁻¹ in collagen-I-coated 6-well plates (Corning) and allowed to reach ~70 % confluence before transfection.

2.7 miRNA Mimic/Inhibitor Transfection

2.8 RNA Isolation and Quantitative Reverse Transcription PCR

Total RNA was extracted with TRIzol™ Reagent (Thermo Fisher Scientific, Cat# 15596-018) and quantified spectrophotometrically (NanoDrop 2000; Thermo Fisher). RNA integrity was verified by agarose-gel electrophoresis. For mRNA analysis, 500 ng RNA were reverse-transcribed with the PrimeScript™ RT Reagent Kit (Takara, Cat# RR037A) using random hexamers; for miRNA analysis, 200 ng RNA were reverse-transcribed with the Mir-X™ miRNA First Strand Synthesis Kit (Takara, Cat# 638313) employing stem-loop primers. Quantitative PCR was performed on a CFX96 Touch™ Real-Time PCR Detection System (Bio-Rad) with TB Green® Premix Ex Taq™ II (Takara, Cat# RR820A). The 20 µL reaction comprised 10 µL 2× TB Green mix, 0.4 µL of each 10 µM primer, 1 µL cDNA, and 8.2 µL nuclease-free water. Cycling parameters: 95 °C 30 s; 40 cycles of 95 °C 5 s and 60 °C 30 s; followed by melt-curve analysis. Primer sequences for GAPDH, FHL1, CXCR4, SNAI2, and U6 (miRNA reference) are listed in table shown below:

Primer sequences used for qPCR

Target Forward (5'→3') Reverse (5'→3')

GAPDH GGAAGCTTGTCATCAATGGAAATC TGATGACCCTTTTGGCTCCC

FHL1 TGCTGCCTGAAATGCTTTGAC GCCAGAAGCGGTTCTTATAGTG

CXCR4 GGGCAATGGATTGGTCATCCT TGCAGCCTGTACTTGTCCG

SNAI2 GAACTGGACACACATACAGTGATT GGCTGTATGCTCCTGAGCTG

2.9 Data Analysis

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PLoS One. doi: 10.1371/journal.pone.0324324.r008

Decision Letter 3

Vincenzo Lionetti

<p>Microarray-based analysis reveals a novel role of the miRNA-613/SNAI2/CXCR4 axis in atrial fibrillation

PONE-D-24-48788R3

Dear Dr. Zhai,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Vincenzo Lionetti, M.D., PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0324324.r009

Acceptance letter

Vincenzo Lionetti

PONE-D-24-48788R3

PLOS ONE

Dear Dr. Zhai,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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on behalf of

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PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

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Data Availability Statement

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PERMALINK

Microarray-based analysis reveals a novel role of the miRNA-613/SNAI2/CXCR4 axis in atrial fibrillation

Zhenyu Zhai

Yiligong Qi

Longlong Hu

Zumao Gan

Roles

Abstract

Backgrounds

Methods

Results

Conclusion

1. Introduction

2. Materials and methods

2.1. AF-related miRNA expression profiling

2.2. Differentially expressed miRNA identification

2.3. Functional enrichment analyses

2.4. Identification of overlapping differentially expressed miRNAs and mRNAs associated with AF

2.5. Statistical analysis

2.6. Experimental model and cell culture

2.7. miRNA mimic/inhibitor transfection

2.8. RNA isolation and quantitative reverse transcription PCR

2.9. Data analysis

3. Results

3.1. Differentially expressed AF-related miRNA identification

Fig 1. Identification of differentially expressed atrial fibrillation-related miRNAs.

3.2. Functional enrichment analyses of miRNAs of interest

Fig 2. GO and KEGG enrichment analyses for differently expressed miRNAs in atrial fibrillation group with the FunRich tool (v 3.1.3).

Fig 3. Transcription factor enrichment analyses for differently expressed miRNAs in atrial fibrillation group with the FunRich tool.

3.3. Validation of differentially expressed miRNA target gene expression patterns in a separate AF dataset

Fig 4. Identification of differentially expressed atrial fibrillation-related mRNAs.

Fig 5. Overlapped mRNAs based on target genes of differentially expressed miRNAs and differentially expressed mRNAs in atrial fibrillation group, compared with the normal sinus rhythm group.

Table 1. The expression of overlapped mRNAs and their related miRNAs in atrial fibrillation group.

Fig 6. The bubble chart showed the results of biological process enrichment and molecular function enrichment for overlapped mRNAs in atrial fibrillation group.

Fig 7. The miRNA-mRNA network between overlapped mRNAs and their related miRNAs in atrial fibrillation group.

3.4. Experimental validation of miRNA-mRNA interactions via quantitative PCR

Fig 8. qPCR validation of miRNA-mRNA regulatory relationships in atrial fibrillation.

4. Discussion

Data Availability

Funding Statement

References

Author response to Decision Letter 0

Decision Letter 0

Vincenzo Lionetti

Roles

Author response to Decision Letter 1

Decision Letter 1

Vincenzo Lionetti

Roles

Author response to Decision Letter 2

Decision Letter 2

Vincenzo Lionetti

Roles

Author response to Decision Letter 3

Decision Letter 3

Vincenzo Lionetti

Roles

Acceptance letter

Vincenzo Lionetti

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

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