Multiple RNA Profiling Reveal Epigenetic Toxicity Effects of Oxidative Stress by Graphene Oxide Silver Nanoparticles in-vitro

Introduction

A wide variety of nanomaterials are either being used or are under investigation for their different industrial and biomedical uses as targeted drug delivery system, and antimicrobial agents against bacteria, fungi and viruses. For example, silver nanoparticles (AgNPs) are being used in different aspects of industrial and medical applications: such as food additives and food packaging, textiles and household appliances, cosmetics, medical devices, water disinfectants, and room sprays.^1,2 The unique surface characteristics of AgNPs is the determining factor behind its antibacterial properties, which helps it to destroy the cell membranes or cell walls. However, application of AgNPs is limited because of its particle aggregation and cytotoxicity resulted in the host mammalian cells. Therefore, the combination of AgNPs and graphene-based nanocomposites such as reduced graphene oxide silver nanoparticles (GO-AgNPs) is being investigation as an alternative to the AgNPs.^3,4 The hybrid GO-AgNPs could help us to avoid the AgNPs mediated cytotoxicity in mammalian cells.⁵ Now, the extensive application and production of these nanoparticles has increased our exposure to them via skin penetration, inhalation, or ingestion: thus, entering human cells either by endosomal uptake or by diffusion and inducing multiple unpredictable and deleterious health effects. Evidences indicate that AgNPs increase the generation of reactive oxygen species (ROS) and malondialdehyde (MDA), result in DNA damage, affect superoxide dismutase (SOD) and catalase (CAT) activity, induce differential expression of DNA replication, cellular senescence, and transcription of mitochondrial respiratory chain-related genes.⁶ Furthermore, surfactant‐coated AgNPs induces epigenetic changes which are associated with oxidative stress, MAPK signaling, and inflammation.⁷ Understanding the mechanism of how AgNPs induces epigenetic changes will help us to improve the design of AgNPs related nanoparticle, both in terms of harnessing their effects and minimizing the side-effects. Thus, conventional toxicity assays are not sufficient and integrated approaches are required to understand the complexities of cellular responses toward GO-AgNPs.

Number of studies showed the possible mechanisms of AgNPs mediated toxicity using both cells and animal models.⁵ AgNPs mediated cytotoxicity is possibly the result of one or a combination of the following effects on the mammalian cells: oxidative stress, inflammation response, DNA and molecular damage, growth inhibition, mitochondrial disruption, and changes in cell morphology.⁸ Although severity and dynamics of responses can vary among the cell types, but overall AgNPs causes a toxic response in varieties of cell types.⁹ However, the existing literature on genotoxicity of graphene family nanomaterials remains limited and conflicting. A few studies showed that graphene family nanomaterials had no adverse effects on genotoxicity.^10,11 In contrast, many researchers have reported that the small size and sharp edges of graphene family nanomaterials can induce genotoxicity on culture cell.^12,13 Compared to the well characterization of the cytotoxic effect of AgNPs, the epigenetic toxicity of AgNPs related nanoparticles on organisms remains poorly elucidated.¹⁴ Therefore, high-throughput analysis of whole transcriptome is necessary to understand the mechanism of GO-AgNPs inducing epigenetic toxicity.

Epigenetic changes could be understood by looking at the DNA methylation, histone tail modifications, and the activity of noncoding RNA, the latter includes different types of large and small non-coding RNAs that control the gene expression through varieties of mechanisms in response to internal or external stimuli.¹⁴ For instance, long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs) are three different categories of ncRNA and are involved in the progression of different diseases.¹⁵ To date, AgNPs mediated epigenetic changes have been assessed to understand the nanosafety of AgNPs related nanoparticles exposure to culture cells and living animals.^3,16–20 However, the changes of circRNAs and lncRNAs expression upon GO-AgNPs exposure to the cells and animals have not been investigated properly, and the nanosafety assessment of GO-AgNPs related nanomaterials is not yet completed.²¹

Knowing the epigenetic aspects of GO-AgNPs mediated toxicity will provide a better understanding of how nanomaterials impact genome integrity, and help us to develop practical strategies for the applications of nanomaterials in biomedicine and evaluations of nanomaterials mediated biotoxicity. We have already shown that 48 h treatment with GO-AgNPs incites DNA hypomethylation, increases the generation of ROS and induces apoptosis in caprine fetal fibroblast cells.²² In addition, long-term sublethal exposure of GO-AgNPs alters the expression of circRNAs and lncRNAs in caprine fetal fibroblast cells.²³ However, we do not know how does the short-term exposure of GO-AgNPs influence the expression of circRNAs and lncRNAs. We expect to establish circRNAs and lncRNAs as biomarkers for GO-AgNPs mediated oxidative stress and toxicity in the cells. In this research, rabbit fetal fibroblast cells (RFFCs) were used as in vitro model to know the potential roles of GO-AgNPs mediated toxicity in cells and its impact on the expressions of circRNAs and lncRNAs.

Materials and Methods

Chemicals

All chemicals and reagents were purchased from Sigma-Aldrich (St. Louis, MO, USA) unless otherwise stated. GO-AgNPs were synthesized and stored in our laboratory.²²

The in-vitro Cell Culture Studies

RFFCs were isolated from 22-day-old fetuses that were recovered surgically from a New Zealand rabbit obtained from the Animal Genetic Engineering Laboratory of Yangzhou University as previously described.²² In brief, the head and internal organs of the fetus were removed and the remaining tissues of were dissociated into small pieces using scissors, washed three times with PBS, and cultured in Dulbecco’s Modified Eagle’s Medium/F12 (DMEM/F12) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin streptomycin (P/S) at 37°C in a humidified 5% CO₂ incubator. For storage, the collected primary cells were frozen and stored in the liquid nitrogen: 3×10⁶ cells/mL of DMEM/F-12 supplemented with 10% dimethyl sulfoxide (DMSO) and 20% FBS. Cells, between passages 2–4 were used for the experiments. The procedures of animal experiments were approved by the Animal Care and Use Committee of Yangzhou University, Yangzhou, China (license number: SYXK(Su)2017-0044 and protocol code 201803212).

Cell Viability Assay

The cell viability of GO-AgNPs treated RFFCs and its control counterpart was measured using the cell counting kit-8 (CCK-8; Rockville, MD, USA) according to the manufacturer’s instruction as described previously.^22,23 In brief, the cell was seeded and cultured in 96-well plate in 100 μL complete medium containing different concentrations of GO-AgNPs (0, 8, 16, 24, 32 and 48 μg/mL) for 24 h with different concentrations (0, 8, 16, 24, 32 and 48 μg/mL) of GO-AgNPs. After that, 10 μL CCK-8 reagent was added to each well and incubated for 30 min at 37°C in dark. The absorbance was measured at 450 nm using a microplate reader (BioTek Synergy 2, USA). The reactions were conducted four times.

Determination of the Enzyme Activity

RFFCs were exposed to 24 μg/mL GO-AgNPs for 24 h and the cells were harvested to detect the activity of reactive oxygen species (ROS), malondialdehyde (MDA), superoxide dismutase (SOD), intracellular ATP, glutathione peroxidase (GPx), and glutathione reductase (Gr) as described in previous study.^24–26 In brief, RFFCs were incubated in 10 μM DCFH-DA for 30 min at 37°C. After that, the cells were rinsed twice with PBS, suspended in 500 μL PBS and then the intracellular accumulation of ROS was measured using flow cytometry (Beckman-Coulter, USA). MDA was measured using a Lipid Peroxidation MDA Assay Kit. ATP levels were determined using the luciferin-luciferase-based ATP Assay Kit. GPx and Gr were determined using the Cellular Glutathione Peroxidase Assay Kit and Glutathione Reductase Assay Kit, respectively. All the assay kits were purchased from Beijing Solarbio Science & Technology, Beijing, China, and the assays were carried out according to the manufacturer’s instructions. The reactions were conducted four times.

Preparation of RNA Samples from RFFCs and Its Quality Assessment

Total RNA was isolated from three GO-AGNPs treated (24 μg/mL for 24 h) RFFC and three non-treated control samples using TRIzol reagent (Invitrogen, Carlsbad, CA, United States), according to the manufacturer’s protocol. The concentration and quality of the isolated total RNA were measured using NanoDrop ND-2000 spectrophotometer (NanaDrop, Wilmington, DE, United States). Furthermore, the integrity of the isolated RNA samples was assessed using standard denaturing agarose gel electrophoresis.

Generation of Illumina RNA-Seq Library and NovaSeq 6000 Sequencing

Construction of RNA-seq library as well as sequencing was performed by using the commercial service from CloudSeq Biotech Inc. (Shanghai, China). To maintain the integrity of RNA samples and deplete rRNAs, total RNAs were treated with RNase R (Epicentre, Madison, WI, USA) and the Ribo-Zero Magnetic Gold Kit (Epicentre, Madison, WI, USA), respectively. TruSeq Stranded Total RNA Library Prep Kit (Illumina, San Diego, CA, USA) was used to construct RNA-seq library from the rRNA-depleted total RNA samples according to the manufacturer’s protocols. The quality and quantity of the libraries were confirmed with the BioAnalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). After that, the libraries were denatured into single-stranded DNA molecules, captured on Illumina Flow Cells (Illumina) and then amplified in situ as clusters, and finally sequenced for 150 cycles using the NovaSeq 6000 Sequencing system (Illumina) as described previously.²⁷

Bioinformatics Analysis

After the trimming of 30 adaptors and removal of low-quality reads, the high-quality reads were aligned to the reference genome with bowtie2 software, and circRNAs were detected and identified with find_circ software. For lncRNA and mRNA, high-quality reads were aligned to the rabbit reference genome (oryCun2) with hisat2 software (v2.0.4). HTSeq software (v0.9.1) was used to detect and identify lncRNA and mRNA. EdgeR software (v3.16.5) was used to normalize the data and perform analysis of differentially expressed circRNAs, lncRNA and mRNA. The raw junction reads were normalized to the number of total mapped reads and were log2 transformed. The differentially expressed circRNAs, lncRNA and mRNA (fold changes ≥ 2.0 and P < 0.05) were identified used for gene ontology (GO) and pathways analysis: Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics tool was applied for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis while Gene Ontology2 was applied to determine the biological roles of the differentially expressed circRNA, lncRNA and mRNA.²³

Construction of circRNA–miRNA–mRNA Networks

The circRNA/miRNA interaction was predicted using Arraystar’s home-made miRNA target prediction software based on TargetScan and miRanda. The circRNA-miRNA network was constructed and visualized using Cytoscape v3.5.1. To explore the underlying mechanisms of top six differentially expressed circRNAs (Table 1 and Table 2), we predicted circRNA–miRNA–mRNA networks of five highest ranking miRNAs matched the potential miRNA binding sites of three down regulated and three upregulated circRNAs as described previously.²³

Table 1 Top 10 Down-Regulated circRNAs

Table 2 Top 10 Significantly Upregulated circRNAs

Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) Analysis

Total RNA samples were reverse-transcribed into cDNA with a random primer using SuperScriptTM III Reverse Transcriptase (Invitrogen) according to the manufacturer’s instructions. The expression of circRNAs was measured using quantitative polymerase chain reaction SYBR Green Master Mix (Takara, Dalian, China) in ABI 7500 thermocycler (Life Technologies, United States). Primers used for the qRT-PCR validation of the six differentially expressed circRNAs are enlisted in Table 3. Expression levels were calculated according to the 2^−ΔΔCt method and GAPDH was used as reference gene. The amplification protocol is as follows: 5 min at 95°C followed by 39 cycles of 15 sec at 95°C and 1 min at 60°C. The reactions were performed in triplicate.

Table 3 Primer Sequences for qRT-PCR Analysis

Statistical Analysis

All of the experimental data are presented as mean ± standard deviation (SD), and the t-test was used for comparisons between two groups. p < 0.05 were considered as significantly different. GraphPad Prism5 software was used for statistical analysis.

Results

GO-AgNPs Induced Toxicity in RFFCs is Dependent on Dose

As shown in Figure 1, 24 h treatment of RFFCs with 8 µg/mL GO-AgNPs did not cause significant difference of its cell viability compared to the wildtype counterpart. However, increasing concentration of GO-AgNPs (16, 24, 32, and 48 µg/mL) significantly reduced (p < 0.05) the viability of RFFCS, indicating the dose-dependent effect of GO-AgNPs on the toxicity of RFFCs. For downstream experiments, 24 h treatment with 24 µg/mL GO-AgNPs was used based on IC50 value (50% inhibitory dose).

Figure 1 Effects of GO-AgNPs on the proliferation of rabbit fetal fibroblast cells (RFFCs). RFFCs were exposed to 0, 8, 16, 24, 32, and 48 µg/mL of GO-AgNPs for 24 h. The cell viability is presented in percentage relative to the control group (0 µg/mL). Values are presented as the mean ± SD of four independent experiments (*p < 0.05).

GO-AgNPs Alters the Metabolic and Enzymatic Activity in RFFCs

As shown in Figure 2A, generation of ROS significantly increased in RFFCs upon treatment with GO-AgNPs (p < 0.05). GO-AgNP treatment also increases the lipid peroxidation of the cell membrane (p < 0.05) compared to the control RFFCs (Figure 2B). These data indicate that GO-AgNPs induce oxidative stress to the cells. In addition, the intracellular ATP production significantly decreased (p < 0.05) in the GO-AgNPs treated RFFCs (Figure 2C), indicating that GO-AgNPs exposure potentially impairs the mitochondrial metabolism in RFFCs. Similarly, the activities of different metabolic enzymes including SOD (Figure 2D), GPx (Figure 2E) and Gr (Figure 2F) significantly decreased (p < 0.05) in the GO-AgNPs treated RFFCs. Overall, these results dictate that exposures to the GO-AgNPs alter activity of the metabolic enzymes that are involved in the detoxification of the cells.

Figure 2 Effects of GO-AgNPs on the activity of metabolic enzymes. Rabbit fetal fibroblast cells (RFFCs) were treated with 0 and 24 µg/mL of GO-AgNPs for 24 h. (A–F) Accumulation of cumulative ROS, MDA content, ATP levels, GPx activity, and Gr activity, respectively. Values are presented as the mean ± SD of four independent experiments (*p < 0.05).

GO-AgNPs Induces Differential Expression of mRNAs, lncRNAs and circRNAs in RFFCs

As shown in Figures 3–5, the expression patterns of hierarchical cluster analysis and differential expressions of mRNAs (Figure 3A–C), lncRNAs (Figure 4A–C) and circRNAs (Figure 5A–C) are detected between GO-AgNPs treated RFFCs and their control counterparts. Further details of the differentially expressed circRNAs are provided in Table 1 and Table 2. In brief, out of 630 differentially expressed mRNAs 444 mRNA upregulated and 186 mRNA downregulated, while fewer numbers of lncRNAs (75 upregulated and 21 downregulated) and circRNAs (57 upregulated and 35 downregulated) showed differential expression. To focus on the most important circRNAs, the top 10 downregulated and upregulated circRNAs in GO-AgNPs treated RFFCs are short-listed in Table 1 and Table 2, respectively. Out of these twenty differentially expressed circRNAs, 3 are intronic while 17 are sense overlapping.

Figure 3 Differentially expressed mRNAs in the GO-AgNPs treated rabbit fetal fibroblast cells. (A) Hierarchical cluster analysis: the mRNAs with the red points and blue points indicate > 2.0-fold changes between control and treated cells. (B) Scatter plot: the red and blue points represent the upregulated and downregulated mRNAs with statistical significance (fold-changes of > 2 and P values of < 0.05), respectively. (C) Volcano plots: the red and green dots represent upregulated and downregulated mRNAs, respectively.

Figure 4 Differentially expressed lncRNAs in the GO-AgNPs treated rabbit fetal fibroblast cells. (A) Hierarchical cluster analysis: the lncRNAs with the red points and blue points indicate > 2.0-fold changes between control and treated cells. (B) Scatter plots: the red and blue points represent the upregulated and downregulated lncRNAs with statistical significance (fold-changes of > 2 and p values of < 0.05), respectively. (C) Volcano plots: the red and green dots upregulated and downregulated lncRNAs, respectively.

Figure 5 Differentially expressed circRNAs in the GO-AgNPs treated rabbit fetal fibroblast cells. (A) Hierarchical cluster analysis: the circRNAs with the red points and blue points indicate > 2.0-fold changes between control and treated cells. (B) Scatter plots: the red and blue points represent the upregulated and downregulated circRNAs with statistical significance (fold-changes of > 2 and p values of < 0.05), respectively. (C) Volcano plots: the red and green dots represent upregulated and downregulated circRNAs, respectively.

Differentially Expressed mRNAs, lncRNAs and circRNAs are Important Parts of Cellular, Biological, and Metabolic Signalings

As shown in Figures 6–8, the differentially expressed genes are part of various biological processes (BP), cellular components (CC), and metabolic functions (MF), they are also playing regulatory roles in different signaling pathways. The down- and up-regulated circRNAs showed highest enrichment scores for intracellular protein transport and macromolecule localization (BP), nucleus and cytosol (CC), and ribosome binding and protein binding (MF). The differentially expressed lncRNA showed highest enrichment for double-strand break repair via nonhomologous end joining (BP), organelle membrane contact site (CC), and protein binding (MF). However, the differentially expressed mRNA molecules are mainly involved in the cellular protein metabolic process and microtubule nucleation (BP), catalytic complex and rough endoplasmic reticulum (CC), beta-tubulin binding and oxidoreductase activity, acting on paired donors with incorporation or reduction of molecular oxygen (MF). On the other hand, the KEGG pathway analysis showed that the differentially expressed circRNAs, lncRNAs and mRNAs are involved in the transcriptional misregulation in cancer and MAPK signaling pathway, non-homologous end-joining pathway, and PPAR and TGF-beta signaling pathways, respectively. It also dictates their potential involvement in the regulation of endometrial cancer as well as in the maintenance of pluripotency of stem cells.

Figure 6 Continued.

Figure 6 Dot plot: function enrichment analysis of differentially expressed mRNAs. (A–C) Gene Ontology (GO) analysis of downregulated mRNAs in the GO-AgNPs treated rabbit fetal fibroblast cells (RFFCs). (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of downregulated mRNAs in the GO-AgNPs treated RFFCs. (E–G) GO analysis of upregulated mRNAs in the GO-AgNPs treated RFFCs. (H) KEGG pathway analysis of upregulated mRNAs in the GO-AgNPs treated RFFCs. The color intensity of the nodes shows the degree of enrichment of this analysis. The enrich-factor is defined as the ratio of the differential genes in the entire genome. The dot size represents the count of genes in a pathway.

Figure 7 Dot plot: function enrichment analysis of differentially expressed lncRNAs. (A–C) Gene Ontology analysis of upregulated lncRNAs in the GO-AgNPs treated rabbit fetal fibroblast cells. (D) Kyoto Encyclopedia of Genes and Genomes pathway analysis of upregulated lncRNAs in the GO-AgNPs treated rabbit fetal fibroblast cells. The color intensity of the nodes shows the degree of enrichment of this analysis. The enrich-factor is defined as the ratio of the differential genes in the entire genome. The dot size represents the count of genes in a pathway.

Figure 8 Continued.

Figure 8 Dot plot: function enrichment analysis of differentially expressed circRNAs. (A–C) Gene Ontology (GO) analysis of downregulated circRNAs in the GO-AgNPs treated rabbit fetal fibroblast cells (RFFCs). (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of downregulated circRNAs in the GO-AgNPs treated RFFCs. (E–G) GO analysis of upregulated circRNAs in the GO-AgNPs treated RFFCs. (H) KEGG pathway analysis of upregulated circRNAs in the GO-AgNPs treated RFFCs. The color intensity of the nodes shows the degree of enrichment of this analysis. The enrich-factor is defined as the ratio of the differential genes in the entire genome. The dot size represents the count of genes in a pathway.

Differentially Expressed circRNAs Potentially Regulates FoxO Signaling, MAPK Signaling, and Oxidative Phosphorylation

To understand the potential mechanism how differentially expressed circRNAs interacts with the miRNAs and mRNAs, sequences of six differentially expressed circRNAs were analyzed using TargetScan and an interacting network of circRNA–miRNA–mRNA was generated. To keep the constructed map comprehensible, it only illustrated the networks of the highest-ranking miRNAs that play regulatory roles on the differentially expressed mRNAs (Figure 9). It could be stipulated from the constructed network that the differentially expressed circRNAs potentially regulate the epigenetic toxicity through FoxO signaling pathway, MAPK signaling pathway, and oxidative phosphorylation.

Figure 9 The expression level of differentially expressed circRNAs detected by the quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). (A) The expression level of downregulated circRNAs. (B) The expression level of upregulated circRNAs. Values are presented as the mean ± SD of three independent experiments (*p < 0.05, **p < 0.01 and ***p < 0.001).

Validation by qRT-PCR Proves the Accuracy of the RNA-Seq Data

Six differentially expressed circRNAs were selected for the validation by qRT-PCR: three downregulated circRNA genes (chrUn0019:1784368-1787145+, chr2:62054752-62070598- and chrUn0145:595264-602252+) and three upregulated circRNA genes (chrUn0017:2492594-2500768+, chrUn0029:1202788-1217961+, and chr13:133595 155-133605230+). As shown in Figure 10, the qRT-PCR expression level of the selected six circRNAs is consistent with the RNA-seq expression level of those circRNAs. This means that the RNA-seq results are accurate and reliable.

Figure 10 Map showing the interaction network contained 6 validated circRNAs and their ~30 target miRNAs with the most stable binding in GO-AgNPs treated RFFCs. Triangles represent circRNAs, oblong represent miRNAs.

Discussion

This research clearly showed that GO-AgNPs may cause epigenetic toxicity by altering the expression of non-coding RNAs including circRNAs and lncRNAs. Oxidative stress mediated toxicity in cells is usually measured by the activity of the endogenous antioxidative factors such as CAT, SOD, GPx, and sulfhydryl form glutathione (GSH).^25,26 Here, we have shown that GO-AgNPs increase oxidative stress in RFFCs by altering its metabolic and enzymatic activity. In particular, the generation of ROS increases which may impair cellular components such as DNA damage, activation of antioxidant enzymes, depletion of antioxidant molecules.^28,29 The data showed that higher GO-AgNPs concentration increases the level of toxicity, this is potentially because of the GO-AgNPs mediated differential regulation of antioxidant activity.^30–32 This means higher doses of GO-AgNPs increase the numbers of oxygen radicals which increases the susceptibility of RFFCs to cytotoxicity. We have also found that GO-AgNPs reduce ATP content and metabolic enzymes which is a clear sign of compromised mitochondrial functions.^2,25 Since ATP is required for a cascade of events requiring phosphorylation of several proteins taking part in repair of DNA damage,³³ it is logical to think that GO-AgNPs mediated depletion of ATP may compromise the DNA repair process in RFFCs.

Now, the question is whether the GO-AgNPs mediated toxicity is regulated at the epigenetic level. Previous studies related to the AgNPs mediated epigenetic toxicity mainly focuses on DNA and histone modifications, or alterations of miRNA expression.^34–37 In this unique research, we have shown that GO-AgNPs cause differential expression of circRNAs and lncRNAs in RFFCs. In addition, we have constructed a comprehensive network map of circRNA–miRNA–mRNA that shows the potential scenario of how circRNAs are regulating gene expression by interacting with the miRNAs. Previous studies also showed that AgNPs related DNA damage could be measured by looking at the expression of related genes,^38–40 lncRNA network,^41,42 and circRNAs network.⁴³ Our analysis (GO and KEGG pathways) indicates that the differentially expressed circRNA–miRNA–mRNA network is connected to the DNA damage and metabolism-related pathways. In particular, non-homologous end-joining, apoptosis, FoxO signaling, MAPK signaling, and oxidative phosphorylation are important pathways that are connected to the top differentially expressed circRNAs. MAPK signaling triggers cell proliferation, pro-inflammatory cytokines, oxidative stress and apoptosis in case of AgNPs induced toxicity.^44–46 This means that the differential expression of circRNAs clearly reflects the cytotoxicity of the GO-AgNPs treated RFFCs. Therefore, we believe that the differentially expressed mRNA, lncRNAs, and circRNAs are associated with GO-AgNPs induced toxicity in cells.

In addition, the constructed circRNA–miRNA–mRNA networks could be connected to certain cellular, biological, and metabolic activity of the cells, including nucleobase-containing compound metabolic process, GTP metabolic process, and protein transports. For example, upregulation of evamiR-N6, eva-miR-N7 and eva-miR-N8 potentially depresses the ATP synthesis,²⁵ which is dependent on the integrity of the mitochondrial membrane.⁴⁷ KEGG pathway analysis also connected several differentially expressed circRNAs to the transcriptional misregulation which is related to the progression of cancers.^25,48 Thus, the differentially expressed circRNAs in the GO-AgNPs treated RFFCs potentially interact with different miRNAs which regulate the metabolic activity and cytotoxicity of cells through multiple pathways.

Conclusions

In conclusion, GO-AgNPs increased the level of ROS, which induces cytotoxicity in RFFCs through lipid peroxidation and mitochondrial dysfunction. However, this cytotoxicity is potentially regulated at the epigenetic level through a complex interacting network of circRNA–miRNA–mRNA. This is a novel insight that will trigger further in vitro and in vivo studies to reveal the mechanisms of GO-AgNPs mediated epigenetic toxicity in cells. In particular, the role of circRNAs in the regulation of MAPK signaling is required to be investigated further.