Skip to main content

RNA m6A Alterations Induced by Biomineralization Nanoparticles: A Proof-of-Concept Study of Epitranscriptomics for Nanotoxicity Evaluation


Although various strategies have been included in nanotoxicity evaluation, epitranscriptomics has rarely been integrated into this field. In this proof-of-concept study, N6-methyladenosine (m6A) changes of mRNA in HEK293T cells induced by three bovine serum albumin (BSA)-templated Au, CuS and Gd2O3 nanoparticles are systematically explored, and their possible biological mechanisms are preliminarily investigated. It has been found that all the three BSA-templated nanoparticles can reduce m6A levels, and the genes with reduced m6A are enriched for TGF-beta signaling, which is critical for cell proliferation, differentiation and apoptosis. Further results indicate that abnormal aggregation of m6A-related enzymes at least partly account for the nanoparticle-induced epitranscriptomic changes. These findings demonstrate that epitranscriptomics analysis can provide an unprecedented landscape of the biological effect induced by nanomaterials, which should be involved in the nanotoxicity evaluation to promote the potential clinical translation of nanomaterials.


Nanosafety is attracting considerable attention with the booming development and extensive applications of nanotechnology in the field of biomedicine [1, 2]. Although versatile nanomaterials with unique physiochemical characteristics are promising for diagnosis and therapy of diverse diseases, few nanoagents have been approved for clinical use due to the indistinct biosafety [3]. As a result, a standard and comprehensive nanotoxicological evaluation framework is highly desired for clarifying biosafety of nanoagents to guide their controlled synthesis and promote their clinical translation [4,5,6].

In the past decade, various strategies have been developed to evaluate nanotoxicity from in vitro to in vivo (Fig. 1a) [7,8,9,10,11,12,13,14,15,16,17,18], and the adverse outcome pathway (AOP) framework and the quantitative structure–activity relationship (QSAR) modeling have been commonly used at the cellular level due to their simplified process, high efficiency, good logicality and predictability [8, 19,20,21]. However, these methods generally focus on specific genes, proteins, organelles and biological processes, which cannot provide a full picture of the nano-biological interactions [7]. In contrast, the emerging omics analysis can provide full characterization and quantification of biological effects of nanomaterials at a given molecular level, such as DNA, RNA, proteins, and lipids [22]. By integrating with bioinformatic analysis, the omics-based approaches are capable of mapping the toxicity-related molecular pathways and biological processes [23]. To date, nanotoxicology has entered into a new era following the development of various omics-based methods, such as genomics [24], epigenomics [25], transcriptomics [26, 27], proteomics [28], lipidomics [29, 30], and metabolomics [22, 31, 32].

Fig. 1
figure 1

Global view of nanotoxicology. a Illustration of current nanotoxicity evaluation frameworks (QSAR: quantitative structure–activity relationship). b Schematic diagram of the epitranscriptomic nanotoxicity evaluation of BSA-templated NPs

Epitranscriptomics describes post-transcriptional RNA modifications that can dynamically regulate gene expression and control cell fate [33]. N6-methyladenosine (m6A) is the most abundant internal mRNA modification in eukaryotic cells, which functionally modulates the eukaryotic transcriptome to influence mRNA splicing, export, localization, translation and stability [34,35,36,37], as well as to regulate the expression of genes controlling extensive biological processes, such as development, reproduction, metabolism, immunity, and tumorigenesis [34, 38, 39]. Aberrant m6A as crucial drivers of multiple diseases (especially cancers) can provide an epitranscriptomic indicator of cellular responses and pathogenetic effects induced by nanomaterials, of which field remains largely unknown [40,41,42,43]. Although several omics-based approaches have been used to assess biological effects of nanomaterials, the epitranscriptomics has rarely been applied in the field of nanosafety evaluations so far [44]. Therefore, it is of great significance to investigate the nanomaterial-induced epitranscriptomics scenery.

Protein-templated biomineralization nanomaterials have attracted considerable interests in the field of biomedicine, and shown great potential in clinical translations due to their facile synthesis process, ultra-small and uniform size, remarkable colloidal stability and water solubility, good biocompatibility and favorable theranostics capability [45, 46]. In this proof-of-concept study, three types of bovine serum albumin (BSA)-templated nanoparticles (NPs) for different biological applications (BSA-Au NPs for fluorescent imaging, BSA-CuS NPs for photothermal therapy, and BSA-Gd2O3 NPs for magnetic resonance imaging) were employed to study the nanotoxicity in terms of epitranscriptomics. These NPs were synthesized by the biomineralization method, and m6A changes induced by these NPs were measured by the immunoprecipitation sequencing (m6A-seq). Functional annotation of genes with m6A changes were performed using the bioinformatic analysis, and biological mechanisms accounting for m6A changes were investigated via cell biological techniques (Fig. 1b). These results indicated that BSA-templated NPs could interfere the processing of m6A-related enzymes to reduce m6A level of diverse genes, which are relevant to multiple cellular pathways and biological processes. Therefore, epitranscriptomic effects (e.g., the m6A changes) induced by NPs were nonnegligible biological events, which should be integrated into the biosafety evaluation of nanomaterials for their potential clinical translation.


Synthesis of BSA-Templated NPs

For the synthesis of BSA-Au NPs, 2 mL of aqueous HAuCl4 solution (25 mM, 37 °C) was added to 8 mL of BSA solution (250 mg, 37 °C) under vigorous stirring. Five minutes later, 0.5 mL of NaOH solution (1 M) was added, and the reaction was kept under vigorous stirring at 37 °C for 12 h.

For the synthesis of BSA-CuS NPs, 1 mL of aqueous Cu(NO3)2 solution (200 mM) was added to 7.5 mL of BSA solution (250 mg) under vigorous stirring. Five minutes later, 0.5 mL of NaOH solution (1 M) was quickly added. Then, 2 mL of Na2S (200 mM) was added and the reaction was kept under vigorous stirring at 90 °C for 0.5 h.

For the synthesis of BSA-Gd2O3 NPs, 0.5 mL of aqueous Gd(NO3)3 solution (100 mM) was added to 9.5 mL of BSA solution (250 mg) under vigorous stirring. Five minutes later, 0.5 mL of NaOH solution (1 M) was added, and the reaction was kept under vigorous stirring at room temperature for 2 h.

All these obtained BSA-templated NPs were purified by dialysis (molecular weight cut off: 8–14 kDa), and then freeze-dried and stored in dark at 4 °C for further use.

Characterization of NPs

The size and morphology of BSA-templated NPs were determined on a Philips Tecnai G2 F20 (Philips, Holland) field emission transmission electron microscopy (TEM). The Fourier transform infrared (FT-IR) spectra (650–4000 cm−1) of BSA, BSA-Au, BSA-CuS, and BSA-Gd2O3 NPs were measured on a Nicolet iS10 spectrometer (Nicolet, USA) with background of pure KBr. Fluorescent spectra were recorded on a F7000 spectrofluorometer (Hitachi, Japan) equipped with a plotter unit and a quartz cell (1 cm × 1 cm). The absorption spectra were obtained via a UV-3600 plus spectrophotometer (Hitachi, Japan). The hydrodynamic size and zeta potential were determined on a Malvern Zetasizer (Nano series ZS, UK). The metal elements in nanoparticles were identified through the inductively coupled plasma-atomic emission spectrometer (ICP-AES, Thermo Fisher, ICAP 7400, USA).

Storage Stability of NPs

To investigate the storage stability of BSA-templated metallic nanoparticles, the three NPs in freeze-dried powder state and aqueous state (4 mg/mL) were stored at 4 °C for 2 weeks, and their common state, optical absorption property, fluorescence property, magnetic resonance imaging capability, and hydrodynamic size were systematically evaluated at different timepoints (0, 7, and 14 days).

Protein Corona Analysis

We preliminarily evaluated the protein corona formation in vitro by mixing the BSA-templated NPs (5 mg/mL) with 10% fetal bovine serum (FBS). Then, the mixing solutions were put into an oscillator under 37 °C, and the hydrodynamic sizes of solutions were monitored at different timepoints (0, 0.5, 1, and 3 h) post-mixing.


The pFLAG-CMV vectors were provided by Sigma. Human METTL3 was cloned into pFLAG-CMV vectors by Gibson clone with forward primer ACAAGCTTGCGG CCGCGAATTCAatgtcggacacgtggag and reverse primer GGTCACAGGGATGCCACCCG GGATCCtaaattcttaggtttagag.

Cell Culture and Transfection

HEK293T cells were obtained from American Type Culture Collection (ATCC) and grown in DMEM medium (Invitrogen) containing 10% FBS and 1% penicillin–streptomycin. Plasmids were transfected into cells with Lipofectamine 3000 (Invitrogen) according to manufacturer’s instructions.

Cytotoxicity of NPs

The cytotoxicity of BSA-templated NPs was measured with HEK293T cells and 3T3-L1 cells via the standard Methyl Thiazolyl Tetrazolium (MTT) assays. Cells were seeded in 96-well culture plates at density of 1 × 104 cells/well in 2 mL of DMEM supplemented with 10% FBS and 1% penicillin–streptomycin at 37 °C under 5% CO2 and cultured for 24 h. Then, the stale medium in each well was replaced with 2 mL of fresh medium containing different concentrations (50, 100, and 200 μg/mL) of NPs (BSA-Au, BSA-CuS, and BSA-Gd2O3 NPs), respectively. After another 24-h incubation, the cells were washed with PBS and treated with fresh medium containing MTT (0.25 mg/mL). Four hours later, the supernatant in each well was replaced with 120 μL of DMSO. After a mild shake for 10 min, the absorbance of each well at 490 nm was measured on a microplate reader (Bio-tek, USA). Then, the cell viability under the exposure of BSA-templated NPs was calculated.

To evaluate the influences of NPs on the apoptosis of cells, HEK293T cells were incubated in six-well culture plates at a density of 1 × 105 cells per well in 200 μL of DMEM supplemented with 10% FBS and 1% penicillin–streptomycin at 37 °C under 5% CO2 and cultured for 24 h. Then, the stale medium in each well was replaced with 200 μL of fresh medium containing different concentrations (50, 100, and 200 μg/mL) of NPs (BSA-Au, BSA-CuS, and BSA-Gd2O3 NPs), respectively. After incubation for another 24 h, the apoptosis of HEK293T cells was evaluated by using Annexin V-FITC/PI Apoptosis Detection Kit (KeyGEN, Shanghai, China).

Nanoparticles Uptaken by Cells

The nanoparticles uptaken rates by HEK293T cells were identified through the ICP-AES. Briefly, HEK293T cells were seeded in a 10 cm-culture dish at a density of 1 × 106 cells/dish in 8 mL of DMEM supplemented with 10% FBS and 1% penicillin–streptomycin at 37 °C under 5% CO2, and cultured for 24 h. Then, 8 mL of fresh culture medium containing 200 μg/mL nanoparticles (BSA-Au, BSA-Gd2O3, and BSA-CuS NPs) was used to replace the old culture medium in each dish. After another 24-h incubation, the cells were washed with PBS, digested from the dish bottom with trypsin, and dispersed in PBS. After centrifugation at 1000 rpm for 3 min, and the supernatant was discarded. This washing process was repeated 3 times. Finally, these cells were fully dissolved with aqua regia, and the metal elements were quantified by ICP-AES. Cells cultured with 8 mL of fresh medium without nanoparticles were processed in the same way as the control group.

m6A-seq and Data Analysis

m6A-seq was performed following previously reported protocol [47]. Total RNA was extracted by homogenizing cells in TRIzol reagent. mRNA was further purified using GenElute™ mRNA Miniprep Kit (Sigma). RNA fragmentation and m6A-immunoprecipitation were performed with Magna MeRIP™ m6A Kit according to the instructions. The library preparation and sequencing were carried out on Illumina HiSeq 2000 according to the manufacturer’s instructions. Samples were sequenced by with single-end 50-bp read length. All reads were mapped to human genome version hg19 by tophat v2.0.13 with default settings. The m6A level changes for nanoparticles/control were calculated by using exomePeak. Gene expression level changes for input and treatment were analyzed using Cuffdiff. The sequence reads were visualized with Integrative Genomics Viewer [48].

Redox-Western Blotting

Cells were lysed using high KCl lysis buffer and sonicated [49]. Equal amounts of proteins were loaded and separated by SDS-PAGE, transferred to polyvinylidene fluoride membranes, and detected by immunoblotting with the Millipore Immobilon Western Chemiluminescent HRP Substrate. Antibodies used for western blotting were as follows unless otherwise specified: METTL3, METTL14, WTAP, and FTO were bought from Cell Signaling Technology (CST), ALKBH5, YTHDC1, YTHDF2, and YTHDF3 were obtained from (ProteinTech), and FLAG (M2, F3165) was provided by Sigma-Aldrich. β-actin and β-tubulin (Santa Cruz) were used as loading controls.

RNA Isolation and Quantitative RT-PCR

Total RNA was isolated from cultured cells using TRIzol reagent. First-strand complementary DNA (cDNA) was synthesized by reverse transcription of 1 μg RNA using HiScript Q RT SuperMix for qPCR (+ gDNA wiper) (Vazyme). QPCR was carried out using ChamQ Universal SYBR qPCR Master Mix (Vazyme) and mRNA expressions were normalized to reference genes GAPDH. The primers used in all qPCR assays are listed in Additional file 1: Table S1 [50, 51].


The MeRIP-qPCR was conducted as previous reported [52]. Briefly, total RNA was isolated with Trizol reagent, and then mRNA was further purified using GenElute™ mRNA Miniprep Kit (Sigma). Two μg of the purified mRNA was fragmentized into 100–200 nt length with fragmentation buffer at 94 °C for 5 min. The mRNA fragments were purified with Rneasy Mini Kit (QiaGen) and then subjected to immunoprecipitation with m6A antibody. After extensive wash, the immunoprecipitated fragments were eluted by competition using free N6-methyladenosine and then used for cDNA construction and qPCR analysis. The primers used in m6A-qPCR assays are listed in Additional file 1: Table S2.

Statistical Analysis

For gene expression, statistical comparisons were performed by using one-way ANOVA as indicated in the figure legends. P < 0.05 was considered significant. For GO and KEGG analysis, the Benjamini and Hochberg method for false discovery rate (FDR-BH correction) was applied to correct for multiple comparisons. The number of biological (non-technical) replicates for each experiment was indicated in the figure legends.

Results and Discussion

Synthesis and Characterization of BSA-Templated NPs

Three BSA-templated NPs were synthesized through a classical biomineralization method [53,54,55], in which BSA as a nanoreactor enables entrapping metal ions based on the interaction between functional groups (e.g., –SH, –NH2, and –COOH) and metal ions, and controlling the growth of NPs. These NPs showed ultra-small size in both TEM images (~ 3 nm for BSA- Au NPs, ~ 3 nm for BSA-Gd2O3 NPs, and ~ 10 nm for BSA-CuS NPs) (Fig. 2a) and hydrodynamic size measurement (5–20 nm) (Fig. 2b), which is benefited from BSA-directed controlled synthesis. BSA-Au NPs exhibited a strong fluorescent emission peaked at 664 nm (Fig. 2c), which can serve as an excellent fluorescent nanoprobe for biosensing and bioimaging [55]. The d–d transition of Cu2+ guarantees the strong near-infrared absorption of BSA-CuS NPs (Fig. 2d), making it a promising photothermal therapy agent for tumor ablation and antimicrobial treatment [53, 56]. BSA-Gd2O3 NPs owned a stronger magnetic resonance imaging capability than Gd-DTPA (Fig. 2e), and have been widely used for in vivo MR imaging [57, 58]. The characteristic FT-IR absorption bands of BSA confirmed the presence of BSA in these NPs (Additional file 1: Fig. S1), and all these NPs showed similar zeta potential with that of BSA solution, indicating BSA serves as the encapsulation layer (Additional file 1: Fig. S2). As shown in Additional file 1: Fig. S3, the appearance of both solid and solution of BSA-templated NPs exhibited no obvious changes, and no precipitate was observed in all solutions. The hydrodynamic sizes of these BSA-templated metallic nanoparticles did not change significantly (Additional file 1: Fig. S4). Consistently, the optical absorption spectra of BSA-CuS NPs (Additional file 1: Fig. S5), the fluorescence spectra of BSA-Au NPs (Additional file 1: Fig. S6), and the MR signal intensity of BSA-Gd2O3 NPs (Additional file 1: Fig. S7) did not change dramatically within 2 weeks no matter in solid or aqueous storage conditions. These results verified the good storage stability of BSA-templated metallic nanoparticles both in solid or aqueous conditions. We also preliminarily evaluated the protein corona formation in vitro by mixing the BSA-templated NPs with 10% FBS under 37 °C and monitoring the hydrodynamic sizes of solutions at different timepoints post-mixing. As shown in Additional file 1: Fig. S8, the hydrodynamic sizes of nanoparticles only and mixed solutions remained relatively stable (approximately 5–20 nm), which did not significantly alter as the incubation time increased. This indicated that BSA-templated NPs did not readily adsorb the proteins probably due to the own albumin template, which is consistent with previous report [59]. Despite the good storage stability and generally accepted biocompatibility of BSA-templated NPs [60,61,62], the potential effects of biomineralization NPs on the epitranscriptomic changes remain largely unknown.

Fig. 2
figure 2

Characterization of BSA-templated NPs. a TEM images of BSA-Au, BSA-CuS, and BSA-Gd2O3 NPs. b Hydrodynamic sizes of BSA-Au, BSA-CuS, and BSA-Gd2O3 NPs. Inset: The photographs of obtained BSA-templated NPs dispersed in water (4 mg/mL). c Fluorescence spectra of BSA-Au NPs with different concentrations. Inset: The photographs of BSA-Au NPs solution (4 mg/mL) under ultraviolet light. d Absorption spectra of BSA-CuS NPs with different concentrations. e T1-weighted magnetic resonance images of BSA-Gd2O3 NPs and Gd-DTPA aqueous solutions with different concentrations of Gd

Global m6A Changes in HEK293T Cells Induced by BSA-Templated NPs

In the assessment of cell viability upon the exposure of NPs, neither MTT analysis nor apoptosis assay exhibited obvious cell death when HEK293T cells were incubated with BSA-Au, BSA-CuS and BSA-Gd2O3 NPs at the concentrations of 50, 100, and 200 μg/mL (Additional file 1: Figs. S9 and S10). Similarly, no obvious cytotoxicity was observed on 3T3-L1 cells (a mouse embryonic fibroblastic cell line) when incubated with different BSA-templated NPs at different concentrations (Additional file 1: Fig. S11). We also quantitated the uptaken rates of BSA-Au, BSA-Gd2O3, and BSA-CuS NPs by HEK293T cells, which were 0.32%, 0.43%, and 2.11%, respectively (Additional file 1: Fig. S12). Then, the m6A-seq was performed to explore the NPs-induced RNA methylation changes. As shown in Fig. 3a, b, all the three NPs could induce both upregulated and downregulated m6A level. However, the number of genes with downregulated m6A level was much greater than that with upregulated m6A level, which led to the overall reduced m6A landscape. For example, BSA-CuS NPs induced downregulated m6A level in 1198 genes, which was far beyond that (156 genes) with upregulated m6A level. The genomic landscape of m6A distribution indicated that the m6A-binding sites of three NPs were similar (Fig. 3c, d). Most of the m6A-binding sites were located in protein-coding sequence and are highly enriched for stop codon and 3’UTR, which was consistent with the control group. Moreover, all the three NPs shared highly conserved m6A targets “GGACU” sequence (Fig. 3e), which matched well with the previously reported m6A consensus sequence “RRACH” (R = G or A, H = A, C or U) [63]. These findings indicated that BSA-templated NPs could reduce the global m6A landscape, but could not affect the distribution of m6A and consensus motif. In addition, it has been reported that m6A residues can be selectively recognized by the reader protein YTHDF2 to regulate the mRNA degradation [36]. Therefore, the significantly decreased m6A level hinted that BSA-templated NPs may own the ability to reprogram epitranscriptome for stability regulation of targeted mRNA.

Fig. 3
figure 3

Global m6A changes induced by BSA-templated NPs. a Scatter plots showing the variation of m6A coverage of individual m6A sites in cells treated with BSA-templated NPs. Pair-wise comparison was shown between BSA-Au NPs (left), BSA-CuS NPs (middle), BSA-Gd2O3 NPs (right), and controls, respectively. The dashed lines indicated cut-off of log2FC(BSA-templated NPs/Control) (1 or −1). The numbers of genes with downregulated (log2FC < -1) or upregulated (log2FC > 1) m6A level was shown. FC, fold change. b Cumulative frequency of m6A targeted genes upon BSA-templated NPs exposure. P values were calculated using two-sided Wilcoxon and Mann–Whitney test. c Metagene profile of enrichment of m6A-targeted sites across mRNA transcriptome. 5’UTR, 5’ untranslated region; CDS, coding sequence; 3’UTR, 3’ untranslated region. d The distribution (left) and enrichment (right) of m6A peaks within different gene regions. Enrichment was determined by the proportion of m6A peaks normalized by the length of the region. e Top consensus sequences of m6A-targeted motif

Functional Annotation of Genes with NPs-Induced m6A Changes

Given the important role of m6A in various biological processes, the biological functions of genes with NPs-induced m6A changes were further investigated by enrichment analysis. As shown in Fig. 4a, b, the cells treated with BSA-Au, BSA-CuS and BSA-Gd2O3 NPs displayed both some overlapped peaks (genes with m6A) and unique ones no matter with upregulated or downregulated m6A level. Considering that different m6A peak patterns are related to different cellular functions, functional modeling for genes with upregulated or downregulated m6A level under treatment of different BSA-templated NPs was respectively conducted with Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) analysis. The numbers of genes with downregulated m6A were 1062, 1198 and 1249 under treatment of BSA-Au, BSA-CuS and BSA-Gd2O3 NPs, respectively. In the KEGG pathway analysis, these genes with downregulated m6A level were enriched in multiple pathways, but all pointed to the TGF-beta signaling (Fig. 4c–e). In the annotation of GO, the genes with downregulated m6A level were enriched in diverse biological processes, such as RNA metabolic process, nucleic acid process and transcription regulation (Additional file 1: Fig. S13). The differences among BSA-Au, BSA-CuS, and BSA-Gd2O3 NPs may be attributed to their distinct physicochemical properties and released metal ions. In spite of this, some common pathways and biological processes could be found among the three NPs-treated HEK293T cells. Particularly, there were 622 common genes with downregulated m6A level among the three NPs-treated cells (Fig. 4b). The KEGG analysis of the 622 genes showed that TGF-beta signaling was the most enriched pathway, which was associated with multiple genes like BMP6, SMAD7, CDKN2B, GDF7, and PPP2CB (Fig. 4f). In the meantime, the transcription regulation was the most relevant process as indicated by the GO analysis of the 622 common genes (Fig. 4f). Then, we used MeRIP-qPCR to quantify the m6A level of representative genes associated with TGF-beta signaling and transcription regulation process. The results showed that the m6A level of BMP6, CDKN2B, GDF7, PPP2CB, TASOR and NAB1 were attenuated by BSA-templated NPs with different patterns (Additional file 1: Figs. S14 and S15), which was consistent with m6A-seq results. It is worth mentioning that TGF-beta signaling plays vital roles in a diverse set of cellular processes, such as cell proliferation, recognition, differentiation, apoptosis, and specification of developmental fate [64]. The KEGG and GO analysis of genes with upregulated m6A level induced by the NPs were also displayed (Additional file 1: Figs. S16 and S17). These functional annotations suggested that the genes with m6A variations induced by BSA-templated NPs were related to multiple pathways and biological processes, particularly the genes with reduced m6A level were enriched for TGF-beta signaling.

Fig. 4
figure 4

Functional annotation of the genes with NPs-induced m6A changes. a Venn diagram of genes with upregulated m6A treated with various NPs. b Venn diagram of genes with downregulated m6A treated with various NPs. c–e KEGG pathway analysis of genes with downregulated m6A level treated with different NPs. The axis refers to the -log10FDR(p value). f The most prominent pathway and biological process from the KEGG and GO analysis of three NPs-shared 622 genes with downregulated m6A level, respectively

Potential Reasons for m6A Variations Induced by the NPs

The m6A mRNA methylation is regulated and exerts its functions by three groups of “m6A RNA modifiers” including m6A methyltransferases (writers), m6A demethylases (erasers), and m6A binding proteins (readers) (Fig. 5a). The writers (e.g., METTL3, METTL14 and WTAP) facilitate the synthesis of m6A [65, 66], the erasers (e.g., ALKBH5 and FTO) catalyze the demethylation of m6A [67, 68], and the readers (e.g., YTHDC1, YTHDF2 and YTHDF3) specifically recognize m6A and regulate its functions, such as splicing and translation [63, 69]. According to the functions of writers, erasers and readers, we hypothesized that the m6A alteration induced by BSA-templated NPs was resulted from the dysregulation of the m6A-related writers and erasers. To test the hypothesis, we measured gene expression and protein levels of these modifiers in HEK293T cells after being exposed to BSA-templated NPs. As shown in Fig. 5b, c and Additional file 1: S18, these genes did not exhibit a significantly regular expression pattern after exposure to these NPs. This result indicated that the BSA-templated NPs may not induce the m6A variations by directly affecting the expression level of m6A-related enzymes, and possible interpretations need to be further explored.

Fig. 5
figure 5

Expression changes of m6A-related writers and erasers upon BSA-templated NPs exposure. a Schematic representation of the m6A manipulation and function. b Western blot analysis of m6A-related protein expression in HEK293T cells upon BSA-templated NPs treatment for 24 h. Left panel: exposure to BSA-Au NPs, middle panel: exposure to BSA-CuS NPs, right panel: exposure to BSA-Gd2O3 NPs. A total of 30 μg protein was loaded (β-actin was used as a control for loading). c Relative mRNA levels of m6A-related genes in HEK293T cells upon NPs exposure for 24 h. Left panel: exposure to BSA-Au NPs; middle panel: exposure to BSA-CuS NPs; right panel: exposure to BSA-Gd2O3 NPs. Data were shown as the mean ± SD

It has been found that glutathione-based nanoclusters can cause Ten-Eleven-Translocation (TET) proteins aggregation, which affects DNA methylation and hydroxyl-methylation [49]. This inspires us to suppose that if NPs could induce abnormal aggregation of m6A-related enzymes, leading to the m6A distribution reconstruction. To verify the hypothesis, the writer METTL3, one of the earliest discovered m6A-related enzymes but with no significant expression change in previous assays [70], was taken into investigation. Flag-tagged METTL3 was transfected into HEK293T cells before exposure to BSA-CuS NPs, which induced the most significant m6A change (Fig. 6a). As shown in Fig. 5b, BSA-CuS NPs (200 μg/mL) did not induce significant change in METTL3 expression within 24 h-exposure. Next, to validate if BSA-CuS NPs could affect METTL3, a redox-western blot assay was performed. METTL3 protein complex could be observed under non-reducing condition (Fig. 6b), and more complex formed as incubation time extended. This suggested that BSA-CuS NPs could induce abnormal aggregation of m6A writer METTL3. METTL3 is the catalytic subunit in m6A methyltransferase complex that transfers a methyl group from S-adenosylmethionine (SAM) to an adenosine in RNA, and the abnormal structure of METTL3 may directly affect its enzymatic activity for transmethylation. Thus, the aggregation of m6A writer METTL3 induced by the NPs may be another underlying mechanism for reprogramming m6A enrichment and epitranscriptome.

Fig. 6
figure 6

Possible mechanism for m6A changes induced by the BSA-templated NPs. a Schematic illustration of transfection of Flag-tagged human METTL3 (Flag-hMETTL3) into HEK293T cells, followed by exposure to 200 μg/mL BSA-CuS NPs for 24 h. b Redox-western blot analysis of Flag-hMETTL3 in reduced and non-reduced conditions. A total of 50 μg protein was loaded (β-tubulin was used as a control for loading). c Schematic representation of the potential mechanism for m6A changes induced by the BSA-templated NPs

Based on the above results, despite no obviously detected apoptosis in cells upon exposure of BSA-templated NPs, these NPs could indeed induce the m6A changes and potentially affect the cellular biological functions, showing an unprecedented epitranscriptomic scenery induced by NPs. In addition, the potential mechanism for m6A changes induced by the BSA-templated NPs has been preliminarily revealed. As shown in Fig. 6c, BSA-templated NPs could result in the abnormality of m6A-related enzymes, such as aggregation of m6A writers, which at least partly contributed to the alterations of m6A landscape.


In this proof-of-concept study, we studied the epitranscriptomic impact (m6A) of biomineralization nanoparticles and explored its potential biological mechanisms. We found that BSA-templated NPs could induce epitranscriptomic abnormalities (e.g., reduced m6A level), which cannot be detected by conventional biotoxicity assessments. The possible mechanism could be at least partly deduced that the BSA-templated NPs may induce the aggregation of m6A-related enzymes to affect the m6A distribution. However, the underlying mechanisms of how NPs affect these m6A-related enzymes still need further investigation in the future. Taken together, epitranscriptomics analysis could provide an unprecedented finding of the biological effect induced by NPs, which should be integrated into the nanotoxicity evaluation systems for nanomaterials for their potential clinical translation.

Availability of Data and Materials

All data supporting the conclusions of this article are included within the article.





Bovine serum albumin


Adverse outcome pathway


Quantitative structure–activity relationship




Transmission electron microscopy


Fourier transform infrared


Methyl Thiazolyl Tetrazolium


Complementary DNA


  1. Soenen SJ, Parak WJ, Rejman J, Manshian B (2015) (Intra)Cellular stability of inorganic nanoparticles: effects on cytotoxicity, particle functionality, and biomedical applications. Chem Rev 115:2109–2135

    CAS  Google Scholar 

  2. Sharifi S, Behzadi S, Laurent S, Forrest ML, Stroeve P, Mahmoudi M (2012) Toxicity of nanomaterials. Chem Soc Rev 41:2323–2343

    CAS  Google Scholar 

  3. Min Y, Caster JM, Eblan MJ, Wang AZ (2015) Clinical translation of nanomedicine. Chem Rev 115:11147–11190

    CAS  Google Scholar 

  4. Hofmann-Amtenbrink M, Grainger DW, Hofmann H (2015) Nanoparticles in medicine: current challenges facing inorganic nanoparticle toxicity assessments and standardizations. Nanomedicine 11:1689–1694

    CAS  Google Scholar 

  5. Nystrom AM, Fadeel B (2012) Safety assessment of nanomaterials: Implications for nanomedicine. J Control Release 161:403–408

    Google Scholar 

  6. Scott-Fordsmand JJ, Pozzi-Mucelli S, Tran L, Aschberger K, Sabella S, Vogel U et al (2014) A unified framework for nanosafety is needed. Nano Today 9:546–549

    CAS  Google Scholar 

  7. Chen R, Qiao J, Bai R, Zhao Y, Chen C (2018) Intelligent testing strategy and analytical techniques for the safety assessment of nanomaterials. Anal Bioanal Chem 410:6051–6066

    CAS  Google Scholar 

  8. Fadeel B, Farcal L, Hardy B, Vazquez-Campos S, Hristozov D, Marcomini A et al (2018) Advanced tools for the safety assessment of nanomaterials. Nat Nanotech 13:537–543

    CAS  Google Scholar 

  9. Kanakia S, Toussaint JD, Chowdhury SM, Tembulkar T, Lee S, Jiang Y-P et al (2014) Dose ranging, expanded acute toxicity and safety pharmacology studies for intravenously administered functionalized graphene nanoparticle formulations. Biomaterials 35:7022–7031

    CAS  Google Scholar 

  10. Hall S, Bradley T, Moore JT, Kuykindall T, Minella L (2009) Acute and chronic toxicity of nano-scale TiO2 particles to freshwater fish, cladocerans, and green algae, and effects of organic and inorganic substrate on TiO2 toxicity. Nanotoxicology 3:91–97

    CAS  Google Scholar 

  11. Ruiz A, Ali LMA, Caceres-Velez PR, Cornudella R, Gutierrez M, Moreno JA et al (2015) Hematotoxicity of magnetite nanoparticles coated with polyethylene glycol: in vitro and in vivo studies. Toxicol Res 4:1555–1564

    CAS  Google Scholar 

  12. Ema M, Kobayashi N, Naya M, Hanai S, Nakanishi J (2010) Reproductive and developmental toxicity studies of manufactured nanomaterials. Reprod Toxicol 30:343–352

    CAS  Google Scholar 

  13. Dobrovolskaia MA, Germolec DR, Weaver JL (2009) Evaluation of nanoparticle immunotoxicity. Nat Nanotech 4:411–414

    CAS  Google Scholar 

  14. Chen R, Huo L, Shi X, Bai R, Zhang Z, Zhao Y et al (2014) Endoplasmic reticulum stress induced by zinc oxide nanoparticles is an earlier biomarker for nanotoxicological evaluation. ACS Nano 8:2562–2574

    CAS  Google Scholar 

  15. Kao Y-Y, Chen Y-C, Cheng T-J, Chiung Y-M, Liu P-S (2012) Zinc oxide nanoparticles interfere with zinc ion homeostasis to cause cytotoxicity. Toxicol Sci 125:462–472

    CAS  Google Scholar 

  16. Andon FT, Fadeel B (2013) Programmed cell death: molecular mechanisms and implications for safety assessment of nanomaterials. Acc Chem Res 46:733–742

    CAS  Google Scholar 

  17. Singh N, Manshian B, Jenkins GJS, Griffiths SM, Williams PM, Maffeis TGG et al (2009) NanoGenotoxicology: the DNA damaging potential of engineered nanomaterials. Biomaterials 30:3891–3914

    CAS  Google Scholar 

  18. Toyokuni S (2013) Genotoxicity and carcinogenicity risk of carbon nanotubes. Adv Drug Deliver Rev 65:2098–2110

    CAS  Google Scholar 

  19. Vinken M (2013) The adverse outcome pathway concept: a pragmatic tool in toxicology. Toxicology 312:158–165

    CAS  Google Scholar 

  20. Puzyn T, Jeliazkova N, Sarimveis H, Robinson RLM, Lobaskin V, Rallo R et al (2018) Perspectives from the nanosafety modelling cluster on the validation criteria for (Q)SAR models used in nanotechnology. Food Chem Toxicol 112:478–494

    CAS  Google Scholar 

  21. Puzyn T, Rasulev B, Gajewicz A, Hu X, Dasari TP, Michalkova A et al (2011) Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat Nanotech 6:175–178

    CAS  Google Scholar 

  22. Shin TH, Lee DY, Lee H-S, Park HJ, Jin MS, Paik M-J et al (2018) Integration of metabolomics and transcriptomics in nanotoxicity studies. BMB Rep 51:14–20

    CAS  Google Scholar 

  23. Revel M, Chatel A, Mouneyrac C (2017) Omics tools: New challenges in aquatic nanotoxicology? Aquat Toxicol 193:72–85

    CAS  Google Scholar 

  24. Lee T-L, Chan W-Y, Rennert OM (2009) Assessing the safety of nanomaterials by genomic approach could be another alternative. ACS Nano 3:3830–3830

    CAS  Google Scholar 

  25. Chen Y, Xu M, Zhang J, Ma J, Gao M, Zhang Z et al (2017) Genome-wide DNA methylation variations upon exposure to engineered nanomaterials and their implications in nanosafety assessment. Adv Mater 29:1604580

    Google Scholar 

  26. Kinaret P, Marwah V, Fortino V, Ilves M, Wolff H, Ruokolainen L et al (2017) Network analysis reveals similar transcriptomic. Responses to intrinsic properties of carbon nanomaterials in vitro and in vivo. ACS Nano 11:3786–3796

    CAS  Google Scholar 

  27. Du C, Zhang B, He Y, Hu C, Ng QX, Zhang H et al (2017) Biological effect of aqueous C-60 aggregates on Scenedesmus obliquus revealed by transcriptomics and non-targeted metabolomics. J Hazard Mater 324:221–229

    CAS  Google Scholar 

  28. Matysiak M, Kapka-Skrzypczak L, Brzoska K, Gutleb AC, Kruszewski M (2016) Proteomic approach to nanotoxicity. J Proteomics 137:35–44

    CAS  Google Scholar 

  29. Soria NGC, Aga DS, Atilla-Gokcumen GE (2019) Lipidomics reveals insights on the biological effects of copper oxide nanoparticles in a human colon carcinoma cell line. Mol Omics 15:30–38

    Google Scholar 

  30. Lee S-H, Tang C-H, Lin W-Y, Chen K-H, Liang H-J, Cheng T-J et al (2018) LC-MS-based lipidomics to examine acute rat pulmonary responses after nano- and fine-sized ZnO particle inhalation exposure. Nanotoxicology 12:439–452

    CAS  Google Scholar 

  31. Lv M, Huang W, Chen Z, Jiang H, Chen J, Tian Y et al (2015) Metabolomics techniques for nanotoxicity investigations. Bioanalysis 7:1527–1544

    CAS  Google Scholar 

  32. Boyles MSP, Ranninger C, Reischl R, Rurik M, Tessadri R, Kohlbacher O et al (2016) Copper oxide nanoparticle toxicity profiling using untargeted metabolomics. Part Fibre Toxicol 13:49

    Google Scholar 

  33. Li XY, Xiong XS, Yi CQ (2017) Epitranscriptome sequencing technologies: decoding RNA modifications. Nat Methods 14:23–31

    CAS  Google Scholar 

  34. Deng X, Su R, Weng H, Huang H, Li Z, Chen J (2018) RNA N(6)-methyladenosine modification in cancers: current status and perspectives. Cell Res 28:507–517

    CAS  Google Scholar 

  35. Liu N, Dai Q, Zheng G, He C, Parisien M, Pan T (2015) N-6-methyladenosine-dependent RNA structural switches regulate RNA-protein interactions. Nature 518:560–564

    CAS  Google Scholar 

  36. Wang X, Lu Z, Gomez A, Hon GC, Yue Y, Han D et al (2014) N6-methyladenosine-dependent regulation of messenger RNA stability. Nature 505:117–120

    Google Scholar 

  37. Meyer KD, Jaffrey SR (2014) The dynamic epitranscriptome: N-6-methyladenosine and gene expression control. Nat Rev Mol Cell Bio 15:313–326

    CAS  Google Scholar 

  38. Zhang CX, Chen YS, Sun BF, Wang L, Yang Y, Ma DY et al (2017) m(6)A modulates haematopoietic stem and progenitor cell specification. Nature 549:273–276

    CAS  Google Scholar 

  39. Huang HL, Weng HY, Chen JJ (2020) The biogenesis and precise control of RNA m(6)A methylation. Trends Genet 36:44–52

    CAS  Google Scholar 

  40. Ma JZ, Yang F, Zhou CC, Liu F, Yuan JH, Wang F et al (2017) METTL14 suppresses the metastatic potential of hepatocellular carcinoma by modulating N(6) -methyladenosine-dependent primary MicroRNA processing. Hepatology 65:529–543

    CAS  Google Scholar 

  41. Min KW, Zealy RW, Davila S, Fomin M, Cummings JC, Makowsky D et al (2018) Profiling of m6A RNA modifications identified an age-associated regulation of AGO2 mRNA stability. Aging Cell e12753

  42. Yang Y, Hsu PJ, Chen YS, Yang YG (2018) Dynamic transcriptomic m(6)A decoration: writers, erasers, readers and functions in RNA metabolism. Cell Res 28:616–624

    CAS  Google Scholar 

  43. Su R, Dong L, Li C, Nachtergaele S, Wunderlich M, Qing Y et al (2018) R-2HG Exhibits Anti-tumor Activity by Targeting FTO/m(6)A/MYC/CEBPA Signaling. Cell 172:90–105

  44. Ruan F, Zeng J, Yin H, Jiang S, Cao X, Zheng N et al (2021) RNA m6A modification alteration by black phosphorus quantum dots regulates cell ferroptosis: implications for nanotoxicological assessment. Small Methods 5:2001045

    CAS  Google Scholar 

  45. Chen Q, Liu Z (2016) Albumin carriers for cancer theranostics: a conventional platform with new promise. Adv Mater 28:10557–10566

    CAS  Google Scholar 

  46. Zan G, Wu Q (2016) Biomimetic and bioinspired synthesis of nanomaterials/nanostructures. Adv Mater 28:2099–2147

    CAS  Google Scholar 

  47. Dominissini D, Moshitch-Moshkovitz S, Salmon-Divon M, Amariglio N, Rechavi G (2013) m6A-seq: transcriptome-wide mapping of N(6)-methyladenosine by m(6)A-seq based on immunocapturing and massively parallel sequencing. Nat Protoc 8:176–189

    CAS  Google Scholar 

  48. Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G et al (2011) Integrative genomics viewer. Nat Biotechnol 29:24–26

    CAS  Google Scholar 

  49. Ma Y, Fu H, Zhang C, Cheng S, Gao J, Wang Z et al (2016) Chiral antioxidant-based gold nanoclusters reprogram DNA epigenetic patterns. Sci Rep 6:33436

    CAS  Google Scholar 

  50. Zhao J, Ma XL, Ma JX, Sun L, Lu B, Wang Y et al (2017) TET3 mediates alterations in the epigenetic marker 5hmC and Akt pathway in steroid-associated osteonecrosis. J Bone Miner Res 32:319–332

    CAS  Google Scholar 

  51. Li B, Zhao J, Ma J, Chen W, Zhou C, Wei W et al (2021) Cross-talk between histone and DNA methylation mediates bone loss in hind limb unloading. J Bone Miner Res 36:956–967

    CAS  Google Scholar 

  52. Zhao J, Li B, Ma J, Jin W, Ma X (2020) Photoactivatable RNA N(6)-methyladenosine editing with CRISPR-Cas13. Small 16:e1907301

  53. Zhang C, Fu Y-Y, Zhang X, Yu C, Zhao Y, Sun S-K (2015) BSA-directed synthesis of CuS nanoparticles as a biocompatible photothermal agent for tumor ablation in vivo. Dalton Trans 44:13112–13118

    CAS  Google Scholar 

  54. Zhang BB, Jin HT, Li Y, Chen BD, Liu SY, Shi DL (2012) Bioinspired synthesis of gadolinium-based hybrid nanoparticles as MRI blood pool contrast agents with high relaxivity. J Mater Chem 22:14494–14501

    CAS  Google Scholar 

  55. Xie JP, Zheng YG, Ying JY (2009) Protein-directed synthesis of highly fluorescent gold nanoclusters. J Am Chem Soc 131:888–889

    CAS  Google Scholar 

  56. Yang WT, Guo WS, Le WJ, Lv GX, Zhang FH, Shi L et al (2016) Albumin-bioinspired Gd:CuS nanotheranostic agent for in vivo photoacoustic/magnetic resonance imaging-guided tumor-targeted photothermal therapy. ACS Nano 10:10245–10257

    CAS  Google Scholar 

  57. Sun S-K, Dong L-X, Cao Y, Sun H-R, Yan X-P (2013) Fabrication of multifunctional Gd2O3/Au hybrid nanoprobe via a one-step approach for near-infrared fluorescence and magnetic resonance multimodal imaging in vivo. Anal Chem 85:8436–8441

    CAS  Google Scholar 

  58. Wang Y, Yang T, Ke H, Zhu A, Wang Y, Wang J et al (2015) Smart Albumin-biomineralized nanocomposites for multimodal imaging and photothermal tumor ablation. Adv Mater 27:3874–3882

    CAS  Google Scholar 

  59. Peng Q, Zhang S, Yang Q, Zhang T, Wei XQ, Jiang L et al (2013) Preformed albumin corona, a protective coating for nanoparticles based drug delivery system. Biomaterials 34:8521–8530

    CAS  Google Scholar 

  60. Elzoghby AO, Samy WM, Elgindy NA (2012) Albumin-based nanoparticles as potential controlled release drug delivery systems. J Control Release 157:168–182

    CAS  Google Scholar 

  61. Wang J, Zhang BB (2018) Bovine serum albumin as a versatile platform for cancer imaging and therapy. Curr Med Chem 25:2938–2953

    CAS  Google Scholar 

  62. Liu XW, Wang C, Liu Z (2018) Protein-engineered biomaterials for cancer theranostics. Adv Healthc Mater 7:e1800913

  63. Patil DP, Pickering BF, Jaffrey SR (2018) Reading m(6)A in the transcriptome: m(6)A-binding proteins. Trends Cell Biol 28:113–127

    CAS  Google Scholar 

  64. Shi YG, Massague J (2003) Mechanisms of TGF-beta signaling from cell membrane to the nucleus. Cell 113:685–700

    CAS  Google Scholar 

  65. Liu J, Yue Y, Han D, Wang X, Fu Y, Zhang L et al (2014) A METTL3-METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation. Nat Chem Biol 10:93–95

    CAS  Google Scholar 

  66. Yue Y, Liu J, Cui X, Cao J, Luo G, Zhang Z et al (2018) VIRMA mediates preferential m(6)A mRNA methylation in 3’UTR and near stop codon and associates with alternative polyadenylation. Cell Discov 4:10

    Google Scholar 

  67. Zheng G, Dahl JA, Niu Y, Fedorcsak P, Huang CM, Li CJ et al (2013) ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility. Mol Cell 49:18–29

    CAS  Google Scholar 

  68. Yu J, Chen M, Huang H, Zhu J, Song H, Park J et al (2018) Dynamic m6A modification regulates local translation of mRNA in axons. Nucleic Acids Res 46:1412–1423

    CAS  Google Scholar 

  69. Meyer KD, Patil DP, Zhou J, Zinoviev A, Skabkin MA, Elemento O et al (2015) 5’ UTR m(6)A promotes cap-independent translation. Cell 163:999–1010

    CAS  Google Scholar 

  70. Liu JZ, Yue YN, Han DL, Wang X, Fu Y, Zhang L et al (2014) A METTL3-METTL14 complex mediates mammalian nuclear RNA N-6-adenosine methylation. Nat Chem Biol 10:93–95

    CAS  Google Scholar 

Download references


Thanks to all the peer reviewers and editors for their opinions and suggestions.


This work was supported by the National Natural Science Foundation of China (81801829, 81572154, 81871777, 82102639), Natural Science Foundation of Tianjin (18JCQNJC14300, 20JCQNJC01170), and the Science & Technology Development Fund of Tianjin Education Commission for Higher Education (2017KJ192) and Tianjin Health Science and Technology Project (TJWJ2021QN047).

Author information

Authors and Affiliations



JP and JZ designed the study. JZ and JW performed experiments and analyzed the data. KF and XM supervised the data collection. WH and BL interpreted the data. JP and JZ prepared the manuscript for publication and reviewed the draft of the manuscript. All authors have read and approved the manuscript.

Corresponding authors

Correspondence to Jinbin Pan, Jie Zhao or Xinlong Ma.

Ethics declarations

Competing interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

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

Supplementary Information

Additional file 1

. Sequences of PCR primers; FT-IR spectra, Zeta potential, hydrodynamic sizes, absorption spectra, fluorescence spectra, and magnetic resonance imaging of NPs; cell viabilities; cellular uptaken rates of NPs; GO and KEGG analysis; PCR analysis, and Western blot analysis.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pan, J., Wang, J., Fang, K. et al. RNA m6A Alterations Induced by Biomineralization Nanoparticles: A Proof-of-Concept Study of Epitranscriptomics for Nanotoxicity Evaluation. Nanoscale Res Lett 17, 23 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Epitranscriptomics
  • Nanotoxicity
  • RNA N6-methyladenosine
  • Nanomaterials
  • Biomineralization