Open Access

m6A‑SNP: From genetics to epigenetics (Review)

  • Authors:
    • Chaoxu Niu
    • Rongmiao Zhou
  • View Affiliations

  • Published online on: November 21, 2022     https://doi.org/10.3892/ije.2022.13
  • Article Number: 4
  • Copyright: © Niu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

N6‑methyladenosine (m6A), the most abundant RNA modification, can participate in various physiological functions and pathological processes by regulating the expression or structure of genes due to its involvement in all aspects of RNA metabolism. Thus, the genetic variant that influences m6A, such as m6A‑associated single nucleotide polymorphism (m6A‑SNP), which is in close proximity to or in the methylation site, may be related to various pathological processes by increasing or decreasing the m6A methylation level due to the alteration of the nucleotide. The present review summarizes the recent advances made in m6A‑SNPs. Both the mining of genome‑wide association studies and the combined analysis of the m6Avar database with expression quantitative trait loci datasets have identified functional variants and causal genes associated with various diseases and have provided new direction for future studies on disease pathogenesis. In particular, some studies have indicated that base change in m6A‑SNPs lead to alterations in m6A modification levels, a conversion from genetics to epigenetics, and the expression variation of corresponding genes, which may affect the biological behavior of cells and explain the association of m6A‑SNPs with the risk or prognosis of diseases. In bladder cancer, colorectal cancer, coeliac disease, and pancreatic ductal adenocarcinoma, the overexpression of a specific allele alone can significantly modify the function of corresponding genes. On the whole, m6A‑SNPs play a pivotal role in all stages of diseases. In the future, the identification of m6A‑SNPs as disease biomarkers and ascertaining the functions of these m6A‑SNPs may prove beneficial. This would help to identify susceptible individuals in a timely manner and clarify the roles of corresponding genes in the occurrence and progression of diseases, and would also aid in the development of novel treatment strategies, ultimately improving patients' survival.

1. Introduction

N6-methyladenosine (m6A), the most abundant RNA modification, refers to the methylation at the N6 position of adenosine mainly located in the RRACH sequence (R=A or G, H=A, C, or U) and is a dynamic and reversible process, in which the addition, removal, and recognition of methyl is responsible by methyltransferases, demethylases, and m6A RNA binding proteins, respectively (1,2). m6A can participate in various physiological functions, such as tissue development, heat shock response, DNA damage response, and circadian clock control. It can also participate in pathological process by regulating the expression or structure of genes due to its involvement in all aspects of RNA metabolism, including RNA processing, nuclear export, stability, translation, and degradation (3-17).

A proper m6A level is necessary for sustaining normal bioprocesses, which mainly relies on the appropriate expression and function of methyltransferases and demethylases. m6A can be found not only in mRNAs, but also in non-coding RNAs, such as microRNAs (miRNAs/miRs) and long non-coding RNAs (lncRNAs). The fate of m6A-modified RNA is dependent on the protein that binds to it. By recognizing and binding to target mRNAs in an m6A-dependent manner, YTH m6A-binding protein 1 (YTHDF1) facilitates translation initiation and protein synthesis (8), while YTH m6A-binding protein 2 (YTHDF2) enhances the degradation of target mRNA (7,18). YTH m6A-binding protein 3 (YTHDF3) interacts with YTHDF1 or YTHDF2 to promote mRNA translation or increase mRNA degradation (19,20). YTH domain-containing 1 (YTHDC1) and YTH domain-containing 2 (YTHDC2) promotes the translocation of m6A-modified mRNA from the nucleus to the cytoplasm and elevates the translation efficiency of m6A-modified mRNA, respectively (21-25). Insulin like growth factor 2 mRNA binding protein (IGF2BP)1, IGF2BP2 and IGF2BP3 enhance mRNA stability (26). Heterogeneous nuclear ribonucleoprotein (HNRNP)C and HNRNPG regulate the alternative splicing of mRNAs in an m6A-dependent manner (11,27). HNRNPA2B1 promotes the maturation of miRNAs by recognizing m6A on pri-miRNAs and interacting with DROSHA and DDGCR8(28). m6A modification on lncRNAs can influence the interaction of lncRNAs with RNA binding proteins through an ‘m6A switch’ mechanism or the interaction between lncRNAs and miRNAs, which may lead to alterations in the gene expression of target RNAs (11,29). Thus, the genetic variant that influences m6A, such as m6A-associated single nucleotide polymorphism (m6A-SNP) may be related to various pathological processes.

m6A-SNP, which is in close proximity to or in the methylation site, results in the gain or loss of the m6A methylation site due to the alteration of the nucleotide (30). It is generally acknowledged that SNPs in various regions of genes, such as the regulatory and coding regions may affect the expression, structure, or function of genes through disparate patterns. For example, SNPs in the regulatory region, including the promoter region, 5'-untranslated region (UTR), and 3'-UTR can influence the binding of transcription factors or miRNAs, and in turn alter the expression of genes (31-33). The coding region consists of exons and introns. Non-synonymous coding SNPs alter the composition of amino acids of the protein that the gene encodes and affect the structure and/or function of the protein (34-36). Although synonymous coding SNPs do not modify the amino acid sequence of the protein, they exert an effect on mRNA conformation, protein folding, and subcellular localization (37-39). SNPs in introns play a crucial role in regulating the functions of genes by affecting the activity of the splice site (40). The function of m6A-SNP is not confined to the aforementioned layers, as the base substitution of m6A-SNP causes the gain or loss of the methylation site. Furthermore, previous studies have demonstrated that m6A-SNPs have a stronger association with diseases or clinical manifestations than non-m6A-SNPs (41-43). Therefore, it is valuable to explore the role of m6A-SNPs in the occurrence, progression, treatment, and prognosis of diseases. This may prove to be helpful in identifying susceptible individuals, determining patients' survival, clarifying disease pathogenesis, discovering new treatment targets, and improving patients' prognosis. The present review summarizes recent findings on m6A-SNPs.

2. Data mining of genome-wide association studies

The development of the majority of diseases is attributed to the interaction of genetic and environmental factors. Genome-wide association studies (GWAS) have identified numerous disease-associated genetic variants and revolutionized the understanding of the genetic architecture of diseases. However, a major challenge that needs to be combatted is the identification of functional or causative variants among those disease-associated genetic variants.

A number of studies have identified some genes related to various diseases or traits based on the combined analysis of GWAS and other public data (Table I) (41-57). Firstly, disease-associated m6A-SNPs were selected from GWAS according to the m6Avar database. Secondly, on the basis of expression quantitative trait loci (eQTL) datasets, those m6A-SNPs with eQTL signals were selected. Thirdly, the expression of the corresponding genes harboring m6A-SNPs that exhibited eQTL signals was further evaluated by means of expression datasets and differently expressed genes were ascertained. The base change of m6A-SNP caused by germline variants modulates the m6A level, alters the binding of protein and regulatory motifs, and affects the expression of genes, and is consequently linked to the development of diseases (Table II). For instance, rs3818978 is located in the 5'-UTR of MRPS21 and is predicted to change the binding of 33 protein and four regulatory motifs. Furthermore, rs3818978 is associated with the expression of MRPS21 and ADAMTSL4 in the aorta and with the plasma levels of seven proteins, which are enriched in the extracellular region and cytoplasmic vesicle. ADAMTSL4 has been reported to be associated with arterial fragility. Thus, rs3818978 may play a critical role in the occurrence of spontaneous coronary artery dissection (44). rs4829 in the 3'-UTR of TOM1L1 is near the m6A modification site, according to sequence-based RNA adenosine methylation site predictor (SRAMP) and can interact with polyadenylate-binding protein cytoplasmic 1, which is considered to participate in the occurrence of breast cancer induced by small nucleolar RNA host gene 14 (SNHG14). Therefore, rs4829 may regulate the expression of TOM1L1 to be involved in the development of breast cancer by altering the m6A modification level and protein binding (45).

Table I

Disease-associated genes identified by mining the data of GWAS.

Table I

Disease-associated genes identified by mining the data of GWAS.

Disease/conditionNo. of m6A-SNPsP<0.05P<0.001P<0.0001 P<1.25x10-5 P<5x10-5No. of eQTLsNo. of DEGs(Refs.)
SCAD11,4645197   7 (44)
Breast cancer (2021)     113866(45)
Colorectal cancer  402   983(46)
Type 2 diabetes15,124    715611(47)
Periodontitis1,938104    6548(48)
Adiposity20,993    23021588(49)
IS (2021)4,216305    15884(50)
Blood lipids1,655HDL-C, 139;    93HDL-C, 22;(41)
  LDL-C, 162;     LDL-C, 33; 
  TC, 150;     TC, 31; 
  TG, 147     TG, 27 
CAD4,390304    5045(51)
IS (2019)4,000AIS, 310;  AIS, 9; 64(52)
  LAS, 305;  LAS, 4;    
  CES, 279;  CES, 1;    
  SVS, 205;  SVS, 1;    
BMDBMD, 1,919;FN-BMD, 138; FN-BMD, 8;  4726(42)
 eBMD, 9,854LS-BMD, 125; LS-BMD, 6;     
  eBMD, 993 eBMD, 88     
RA3,883Asians, 227; Asians, 26;  Asians, 13;20(53)
  Europeans, 308 Europeans, 42  Europeans, 20  
BP1,236SBP, 761;    SBP, 217; (43)
  DBP, 799;    DBP, 246  
Oral ulcer 7,490   302519(54)
Breast cancer (2022) 981    43(55)
Parkinson's disease65712    93(56)
Bladder cancer 673    22111(57)

[i] GWAS, Genome-wide association studies; m6A-SNPs, m6A-associated single nucleotide polymorphisms; eQTLs, expression quantitative loci; DEGs, differentially expressed genes; SCAD, spontaneous coronary artery dissection; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, cholesterol; TG, total triglycerides; CAD, coronary artery disease; IS, ischemic stroke; AIS, arterial ischemic stroke; LAS, large artery stroke; CES, cardioembolic stroke; SVS, small vessel stroke; BMD, bone mineral density; FN, femoral neck; LS, lumbar spine; eBMD, quantitative heel ultrasounds; RA, rheumatoid arthritis; BP, blood pressure; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index.

Table II

Disease-associated m6A-SNPs affecting the m6A modification level and the binding of proteins, and altering regulatory motifs.

Table II

Disease-associated m6A-SNPs affecting the m6A modification level and the binding of proteins, and altering regulatory motifs.

Disease/condition m6A-SNPRef baseAlt baseGeneSNP function annotationm6A functionProteins boundMotifs changed(Refs.)
SCADrs3818978TAMRPS215'-UTRFunctional loss334(44)
 rs12758270AGRPRD23'-UTRFunctional loss 2 
Breast cancer (2021)rs4829CTTOM1L13'-UTRFunctional loss 11(45)
 rs9610915CGMAFF3'-UTRFunctional loss24 
Colorectal cancerrs178184TGNOVA1IntronicFunctional loss 2(46)
 rs35782901CTHTR4IntronicFunctional gain   
 rs60571683GASLCO1B3SynonymousFunctional loss   
Type 2 diabetesrs4993986CGHLA-DQB13'-UTRFunctional loss36(47)
Periodontitisrs2723183AGIL-37MissenseFunctional loss 1(48)
Adiposityrs8024CAIPO93'-UTRFunctional loss3 (49)
IS (2021)rs1803439AGDYRK1A3'-UTRFunctional loss  (50)
 rs8124907AGLAMA5SynonymousFunctional loss 1 
Blood lipidsrs6859AGPVRL23'-UTRFunctional loss34(41)
CADrs12286GAADAMTS73'-UTRFunctional gain 5(51)
IS (2019)rs2013162CAIRF6SynonymousFunctional loss12(52)
 rs2273235TGNDST1SynonymousFunctional loss 3 
BMDrs1110720GAESPL1SynonymousFunctional gain 5(42)
 rs11614913CTMIR196A2 Functional gain 1 
BPrs9847953AGZNF589MissenseFunctional loss321(43)
 rs197922GAGOSR2MissenseFunctional loss 4 
 rs1801253GCADRB1MissenseFunctional loss21 
 rs7398833TCCUX23'-UTRFunctional gain 1 
Oral ulcerrs11266744ACCCRL2SynonymousFunctional loss 3(54)
Breast cancer (2022)rs76563149GTZNF354A5'-UTRFunctional loss112(55)
 rs11614913CTMIR196A2 Functional gain 1 
 rs1801270CACDKN1AMissenseFunctional loss24 
Parkinson's diseasers75072999GAGAKSynonymousFunctional gain 2(56)
 rs1033500GAC6orf10MissenseFunctional gain 5 
Bladder cancerrs3088107GARNFT23'-UTRFunctional loss 2(57)
 rs9418589TCPDSS1IntronicFunctional loss 4 
 rs1550116AGCENPOMissenseFunctional loss 2 
 rs7611841TCCRTAPIntronicFunctional loss42 
 rs4385847TCBDNFIntronicFunctional loss 6 
 rs4147971CTABCA8IntronicFunctional loss 8 
 rs1053433TGKCTD123'-UTRFunctional loss 2 
 rs2466791TCFBN1IntronicFunctional loss 2 
 rs12275254TCDLG2IntronicFunctional loss 1 
 rs7070678GTSVILSynonymousFunctional gain   
 rs3748338ATRNASE4MissenseFunctional loss 3 

[i] m6A-SNP, m6A-associated single nucleotide polymorphism; Ref base, reference base; Alt base, alternative base; SCAD, spontaneous coronary artery dissection; CAD, coronary artery disease; IS, ischemic stroke; BMD, bone mineral density; BP, blood pressure; BMI, body mass index.

Mo et al (58) jointly analyzed GWAS data of multiple sclerosis (MS) with eQTL data from four studies using summary data-based mendelian randomization (SMR) and found that the expression of 29 genes was significantly associated with MS (PSMR<5x10-6). Among the SNPs in these genes, m6A-SNP rs923829 in methyltransferase-like protein (METTL)21B was not only associated with the risk of MS (P=1.35x10-10), but was associated with the expression of METTL21B in 37 tissues (58). Moreover, the association of rs923829 with the expression of METTL21B was confirmed in 40 unrelated Chinese Han individuals. These results suggested the critical role of m6A-SNP rs923829 in the development of MS by regulating the expression of METTL21B (58).

Following an integrated analysis of GWAS data, m6A peaks of HeLa and HepG2 epithelial cell lines, and expression data, five genes harboring m6A-SNPs including C6orf47 and SNAPC4 were identified to be involved in inflammatory bowel disease (IBD) (59). Subsequent research indicated that the total m6A modification levels and the expression of METTL3 and YTHDF1 were enhanced in HCT116 cells treated with IFNγ, and cytokine levels were upregulated in the intestinal mucosa of patients with IBD (59). Moreover, the overexpression of METTL3 and the interference of YTHDH1 led to the altered expression of C6orf47 and SNAPC4, respectively. Therefore, m6A modification played key roles in the occurrence of IBD (59).

Ruan et al (60) identified acyl-CoA synthetase medium chain family member 5 (ACSM5) as a candidate gene for thyroid cancer by jointly analyzing GWAS data, thyroid eQTL data and m6A-SNPs. ACSM5 was found to be downregulated in thyroid cancer tissues, which was associated with the poor prognosis of patients with thyroid cancer. According to prediction by SRAMP, m6A modification sites with very high confidence all contained an SNP site, the base change of which resulted in the loss of m6A modification sites (60). Furthermore, the knockdown of METTL3 decreased the m6A modification level and the expression of ACSM5. Hence, it was suggested that m6A-SNPs participated in disease development and progression by affecting expression of ACSM5(60).

3. Combined analysis of m6Avar database and eQTLs

The combined analysis of the m6Avar database and eQTLs associated with sepsis has revealed 15,720 m6A-cis-eQTLs in 1,321 genes. Among these genes, 17 genes were enriched in platelet degranulation process, a typical biomarker of sepsis, and 12 genes gathered in the pathway of Staphylococcus aureus infection, the most common pathogenic bacterium in sepsis, which suggested that m6A-SNPs played key roles in sepsis (61).

Both the mining of GWAS and the combined analysis of the m6Avar database with eQTLs have facilitated the identification of functional variants and causal genes associated with various diseases, and have helped to provide new direction for future studies on disease pathogenesis. Moreover, a relatively broad significance threshold was adopted to analyze the association between m6A-SNP and diseases, which avoided missing valuable information. However, there are some limitations in the aforementioned studies. Firstly, the associations between m6A-SNPs and diseases were not validated in additional independent studies. Secondly, the inconsistency of samples used in eQTL analysis and in the analysis of differentially expressed genes might have some degree of influence on the results due to the tissue specificity of gene expression. Thirdly, whether m6A-SNPs affected m6A modification levels and the expression of corresponding genes were not examined experimentally. Recently, some studies on the effects of m6A SNP and the underlying mechanisms were conducted, as described below.

4. Effects and mechanisms of m6A SNPs

The A allele of rs5746136 in superoxide dismutase 2 (SOD2) was previously found to be associated with a reduced risk of bladder cancer. A mechanistic analysis demonstrated that the transition of base from G to A led to an increased m6A modification level of SOD2 and increased the binding of HNRNPC with SOD2 followed by the upregulation of SOD2. The overexpression of SOD2 inhibited the proliferation, migration, and invasion of bladder cancer cells, which suggested that SOD2 functioned as tumor suppressor gene for bladder cancer (62).

Another study demonstrated that the transversion of the C to the G allele of rs3088107 resulted in a decreased m6A modification level of ring finger protein, transmembrane 2 (RNFT2) in 293T cells and reduced the expression of RNFT2 in bladder cancer cells (57). Moreover, the G allele of rs3088107 inhibited the proliferation and migration of bladder cancer cells compared to the C allele of rs3088107(57).

The A allele of rs1800241 in ankyrin repeat and LEM domain containing 1 (ANKLE1) has been linked to a decreased risk of colorectal cancer, which may be attributed to the enhanced expression of ANKLE1 by elevating the m6A modification level of ANKLE1 mediated by METTL3, METTL14, or WTAP and increasing binding of YTHDF1 with ANKLE1. Furthermore, the overexpression of ANKLE1[A] repressed the proliferation of colorectal cancer cells and promoted genomic stability more effectively than the overexpression of ANKLE1[G] (63).

Individuals carrying the exportin 1 (XPO1) gene rs3087898 T allele were previously shown to be more susceptible to coeliac disease (CD) than those carrying the rs3087898 C allele (64). The XPO1 mRNA transcript with the T allele was shown to have a higher m6A modification level and higher translation efficiency by increasing the binding of YTHDF1 with the XPO1 mRNA than transcription with the C allele. Subsequent experiments indicated that the allele-specific increase of XPO1 activated the NF-κB signaling pathway to facilitate the development of CD (64).

The GT genotype of rs142933486 in phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta (PIK3CB) was previously found to predicted the poor survival of patients with pancreatic ductal adenocarcinoma (PDAC) (65). Cell biology experiments revealed that the overexpression of the T allele reduced the m6A modification level of PIK3CB, decreased the binding of YTHDF2 with PIK3CB, and in turn, increased the mRNA stability and expression of PIK3CB. Consistently, a higher PIK3CB expression was associated with the poor prognosis of patients with PDAC, particularly in patients with PTEN deficiency. Further analyses demonstrated that the overexpression of PIK3CB[T] activated the AKT signaling pathway to promote the proliferation and migration of PTEN-deficient PDAC cells, with a more prominent effect than the overexpression of PIK3CB[G] (65).

The aforementioned studies have indicated that the base change of m6A-SNPs leads to alterations in m6A modification levels, a conversion from genetics to epigenetics, and the expression variation of corresponding genes, which is dependent on the RNA binding proteins that recognize m6A methylation. Furthermore, the expression variation of genes affects the biological behavior of cells, which explains the association of m6A-SNPs with the risk or prognosis of diseases. In particular, in bladder cancer, colorectal cancer, CD, and PDAC, the overexpression of a specific allele alone can significantly modify the function of corresponding genes. All these findings may provide an experimental foundation for the development of novel therapeutic strategies. For example, XPO1 and PIK3CB have the potential to function as therapeutic targets for CD and PDAC, respectively.

5. Conclusion

In conclusion, m6A-SNPs play a pivotal role in all stages of diseases. In the future, the identification of m6A-SNPs as disease biomarkers and ascertaining the functions of these m6A-SNPs may prove beneficial, as it may help to identify susceptible individuals in a timely manner. It may also clarify the role of corresponding genes in the occurrence and progression of diseases, and may thus aid the development of novel treatment strategies, ultimately improving the survival of patients.

Acknowledgements

Not applicable.

Funding

Funding: The present study was supported by the Hebei Province Medical Science Research Key Project (grant no. 20180533).

Availability of data and materials

Data sharing is not applicable to this article, as no datasets were generated or analyzed during the current study.

Authors' contributions

CN participated in the literature collection, reading and analysis, and drafted the manuscript. RZ participated in the design of the study and in the revision of the manuscript. Data authentication is not applicable. Both authors have read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Bokar JA, Shambaugh ME, Polayes D, Matera AG and Rottman FM: Purification and cDNA cloning of the AdoMet-binding subunit of the human mRNA (N6-adenosine)-methyltransferase. RNA. 3:1233–1247. 1997.PubMed/NCBI

2 

Wei CM and Moss B: Nucleotide sequences at the N6-methyladenosine sites of HeLa cell messenger ribonucleic acid. Biochemistry. 16:1672–1676. 1977.PubMed/NCBI View Article : Google Scholar

3 

Dominissini D, Moshitch-Moshkovitz S, Schwartz S, Salmon-Divon M, Ungar L, Osenberg S, Cesarkas K, Jacob-Hirsch J, Amariglio N, Kupiec M, et al: Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature. 485:201–206. 2012.PubMed/NCBI View Article : Google Scholar

4 

Meyer KD, Saletore Y, Zumbo P, Elemento O, Mason CE and Jaffrey SR: Comprehensive analysis of mRNA methylation reveals enrichment in 3' UTRs and near stop codons. Cell. 149:1635–1646. 2012.PubMed/NCBI View Article : Google Scholar

5 

Wang Y, Li Y, Toth JI, Petroski MD, Zhang Z and Zhao JC: N6-methyladenosine modification destabilizes developmental regulators in embryonic stem cells. Nat Cell Biol. 16:191–198. 2014.PubMed/NCBI View Article : Google Scholar

6 

Zheng G, Dahl JA, Niu Y, Fedorcsak P, Huang CM, Li CJ, Vågbø CB, Shi Y, Wang WL, Song SH, et al: ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility. Mol Cell. 49:18–29. 2013.PubMed/NCBI View Article : Google Scholar

7 

Wang X, Lu Z, Gomez A, Hon GC, Yue Y, Han D, Fu Y, Parisien M, Dai Q, Jia G, et al: N6-methyladenosine-dependent regulation of messenger RNA stability. Nature. 505:117–120. 2014.PubMed/NCBI View Article : Google Scholar

8 

Wang X, Zhao BS, Roundtree IA, Lu Z, Han D, Ma H, Weng X, Chen K, Shi H and He C: N(6)-methyladenosine modulates messenger RNA translation efficiency. Cell. 161:1388–1399. 2015.PubMed/NCBI View Article : Google Scholar

9 

Zhao X, Yang Y, Sun BF, Shi Y, Yang X, Xiao W, Hao YJ, Ping XL, Chen YS, Wang WJ, et al: FTO-dependent demethylation of N6-methyladenosine regulates mRNA splicing and is required for adipogenesis. Cell Res. 24:1403–1419. 2014.PubMed/NCBI View Article : Google Scholar

10 

Chen T, Hao YJ, Zhang Y, Li MM, Wang M, Han W, Wu Y, Lv Y, Hao J, Wang L, et al: m(6)A RNA methylation is regulated by microRNAs and promotes reprogramming to pluripotency. Cell Stem Cell. 16:289–301. 2015.PubMed/NCBI View Article : Google Scholar

11 

Liu N, Dai Q, Zheng G, He C, Parisien M and Pan T: N(6)-methyladenosine-dependent RNA structural switches regulate RNA-protein interactions. Nature. 518:560–564. 2015.PubMed/NCBI View Article : Google Scholar

12 

Alarcón CR, Lee H, Goodarzi H, Halberg N and Tavazoie SF: N6-methyladenosine marks primary microRNAs for processing. Nature. 519:482–485. 2015.PubMed/NCBI View Article : Google Scholar

13 

Geula S, Moshitch-Moshkovitz S, Dominissini D, Mansour AA, Kol N, Salmon-Divon M, Hershkovitz V, Peer E, Mor N, Manor YS, et al: Stem cells. m6A mRNA methylation facilitates resolution of naïve pluripotency toward differentiation. Science. 347:1002–1006. 2015.PubMed/NCBI View Article : Google Scholar

14 

Zhou J, Wan J, Gao X, Zhang X, Jaffrey SR and Qian SB: Dynamic m(6)A mRNA methylation directs translational control of heat shock response. Nature. 526:591–594. 2015.PubMed/NCBI View Article : Google Scholar

15 

Meyer KD, Patil DP, Zhou J, Zinoviev A, Skabkin MA, Elemento O, Pestova TV, Qian SB and Jaffrey SR: 5' UTR m(6)A promotes cap-independent translation. Cell. 163:999–1010. 2015.PubMed/NCBI View Article : Google Scholar

16 

Xiang Y, Laurent B, Hsu CH, Nachtergaele S, Lu Z, Sheng W, Xu C, Chen H, Ouyang J, Wang S, et al: RNA m6A methylation regulates the ultraviolet-induced DNA damage response. Nature. 543:573–576. 2017.PubMed/NCBI View Article : Google Scholar

17 

Zhao BS, Wang X, Beadell AV, Lu Z, Shi H, Kuuspalu A, Ho RK and He C: m6A-dependent maternal mRNA clearance facilitates zebrafish maternal-to-zygotic transition. Nature. 542:475–478. 2017.PubMed/NCBI View Article : Google Scholar

18 

Du H, Zhao Y, He J, Zhang Y, Xi H, Liu M, Ma J and Wu L: YTHDF2 destabilizes m(6)A-containing RNA through direct recruitment of the CCR4-NOT deadenylase complex. Nat Commun. 7(12626)2016.PubMed/NCBI View Article : Google Scholar

19 

Li A, Chen YS, Ping XL, Yang X, Xiao W, Yang Y, Sun HY, Zhu Q, Baidya P, Wang X, et al: Cytoplasmic m6A reader YTHDF3 promotes mRNA translation. Cell Res. 27:444–447. 2017.PubMed/NCBI View Article : Google Scholar

20 

Shi H, Wang X, Lu Z, Zhao BS, Ma H, Hsu PJ, Liu C and He C: YTHDF3 facilitates translation and decay of N6-methyladenosine-modified RNA. Cell Res. 27:315–328. 2017.PubMed/NCBI View Article : Google Scholar

21 

Xiao W, Adhikari S, Dahal U, Chen YS, Hao YJ, Sun BF, Sun HY, Li A, Ping XL, Lai WY, et al: Nuclear m(6)A reader YTHDC1 regulates mRNA splicing. Mol Cell. 61:507–519. 2016.PubMed/NCBI View Article : Google Scholar

22 

Roundtree IA, Luo GZ, Zhang Z, Wang X, Zhou T, Cui Y, Sha J, Huang X, Guerrero L, Xie P, et al: YTHDC1 mediates nuclear export of N6-methyladenosine methylated mRNAs. Elife. 6(e31311)2017.PubMed/NCBI View Article : Google Scholar

23 

Lesbirel S, Viphakone N, Parker M, Parker J, Heath C, Sudbery I and Wilson SA: The m6A-methylase complex recruits TREX and regulates mRNA export. Sci Rep. 8(13827)2018.PubMed/NCBI View Article : Google Scholar

24 

Wojtas MN, Pandey RR, Mendel M, Homolka D, Sachidanandam R and Pillai RS: Regulation of m6A transcripts by the 3'→5' RNA helicase YTHDC2 is essential for a successful meiotic program in the mammalian germline. Mol Cell. 68:374–387.e12. 2017.PubMed/NCBI View Article : Google Scholar

25 

Hsu PJ, Zhu Y, Ma H, Guo Y, Shi X, Liu Y, Qi M, Lu Z, Shi H, Wang J, et al: Ythdc2 is an N6-methyladenosine binding protein that regulates mammalian spermatogenesis. Cell Res. 27:1115–1127. 2017.PubMed/NCBI View Article : Google Scholar

26 

Huang H, Weng H, Sun W, Qin X, Shi H, Wu H, Zhao BS, Mesquita A, Liu C, Yuan CL, et al: Recognition of RNA N6-methyladenosine by IGF2BP proteins enhances mRNA stability and translation. Nat Cell Biol. 20:285–295. 2018.PubMed/NCBI View Article : Google Scholar

27 

Liu N, Zhou KI, Parisien M, Dai Q, Diatchenko L and Pan T: N6-methyladenosine alters RNA structure to regulate binding of a low-complexity protein. Nucleic Acids Res. 45:6051–6063. 2017.PubMed/NCBI View Article : Google Scholar

28 

Alarcón CR, Goodarzi H, Lee H, Liu X, Tavazoie S and Tavazoie SF: HNRNPA2B1 is a mediator of m(6)A-dependent nuclear RNA Processing events. Cell. 162:1299–1308. 2015.PubMed/NCBI View Article : Google Scholar

29 

Yang D, Qiao J, Wang G, Lan Y, Li G, Guo X, Xi J, Ye D, Zhu S, Chen W, et al: N6-Methyladenosine modification of lincRNA 1281 is critically required for mESC differentiation potential. Nucleic Acids Res. 46:3906–3920. 2018.PubMed/NCBI View Article : Google Scholar

30 

Zheng Y, Nie P, Peng D, He Z, Liu M, Xie Y, Miao Y, Zuo Z and Ren J: m6AVar: A database of functional variants involved in m6A modification. Nucleic Acids Res. 46 (D1):D139–D145. 2018.PubMed/NCBI View Article : Google Scholar

31 

Hoogendoorn B, Coleman SL, Guy CA, Smith SK, O'Donovan MC and Buckland PR: Functional analysis of polymorphisms in the promoter regions of genes on 22q11. Hum Mutat. 24:35–42. 2004.PubMed/NCBI View Article : Google Scholar

32 

He H, Jazdzewski K, Li W, Liyanarachchi S, Nagy R, Volinia S, Calin GA, Liu CG, Franssila K, Suster S, et al: The role of microRNA genes in papillary thyroid carcinoma. Proc Natl Acad Sci USA. 102:19075–19080. 2005.PubMed/NCBI View Article : Google Scholar

33 

Mishra PJ, Humeniuk R, Mishra PJ, Longo-Sorbello GS, Banerjee D and Bertino JR: A miR-24 microRNA binding-site polymorphism in dihydrofolate reductase gene leads to methotrexate resistance. Proc Natl Acad Sci USA. 104:13513–13518. 2007.PubMed/NCBI View Article : Google Scholar

34 

Kubo M, Hata J, Ninomiya T, Matsuda K, Yonemoto K, Nakano T, Matsushita T, Yamazaki K, Ohnishi Y, Saito S, et al: A nonsynonymous SNP in PRKCH (protein kinase C eta) increases the risk of cerebral infarction. Nat Genet. 39:212–217. 2007.PubMed/NCBI View Article : Google Scholar

35 

Wenzlau JM, Liu Y, Yu L, Moua O, Fowler KT, Rangasamy S, Walters J, Eisenbarth GS, Davidson HW and Hutton JC: A common nonsynonymous single nucleotide polymorphism in the SLC30A8 gene determines ZnT8 autoantibody specificity in type 1 diabetes. Diabetes. 57:2693–2697. 2008.PubMed/NCBI View Article : Google Scholar

36 

Colacios C, Casemayou A, Dejean AS, Gaits-Iacovoni F, Pedros C, Bernard I, Lagrange D, Deckert M, Lamouroux L, Jagodic M, et al: The p.Arg63Trp polymorphism controls Vav1 functions and Foxp3 regulatory T cell development. J Exp Med. 208:2183–2191. 2011.PubMed/NCBI View Article : Google Scholar

37 

Shen LX, Basilion JP and Stanton VP Jr: Single-nucleotide polymorphisms can cause different structural folds of mRNA. Proc Natl Acad USA. 96:7871–7876. 1999.PubMed/NCBI View Article : Google Scholar

38 

Kimchi-Sarfaty C, Oh JM, Kim IW, Sauna ZE, Calcagno AM, Ambudkar SV and Gottesman MM: A ‘silent’ polymorphism in the MDR1 gene changes substrate specificity. Science. 315:525–528. 2007.PubMed/NCBI View Article : Google Scholar

39 

Komar AA: Genetics. SNPs, silent but not invisible. Science. 315:466–467. 2007.PubMed/NCBI View Article : Google Scholar

40 

Thi Tran HT, Takeshima Y, Surono A, Yagi M, Wada H and Matsuo M: A G-to-A transition at the fifth position of intron-32 of the dystrophin gene inactivates a splice-donor site both in vivo and in vitro. Mol Genet Metab. 85:213–219. 2005.PubMed/NCBI View Article : Google Scholar

41 

Mo X, Lei S, Zhang Y and Zhang H: Genome-wide enrichment of m6A-associated single-nucleotide polymorphisms in the lipid loci. Pharmacogenomics J. 19:347–357. 2019.PubMed/NCBI View Article : Google Scholar

42 

Mo XB, Zhang YH and Lei SF: Genome-wide identification of m6A-associated SNPs as potential functional variants for bone mineral density. Osteoporos Int. 29:2029–2039. 2018.PubMed/NCBI View Article : Google Scholar

43 

Mo XB, Lei SF, Zhang YH and Zhang H: Examination of the associations between m6A-associated single-nucleotide polymorphisms and blood pressure. Hypertens Res. 42:1582–1589. 2019.PubMed/NCBI View Article : Google Scholar

44 

Chai T, Tian M, Yang X, Qiu Z, Lin X and Chen L: Genome-wide identification of RNA modifications for spontaneous coronary aortic dissection. Front Genet. 12(696562)2021.PubMed/NCBI View Article : Google Scholar

45 

Xuan Z, Zhang Y, Jiang J, Zheng X, Hu X, Yang X, Shao Y, Zhang G and Huang P: Integrative genomic analysis of N6-methyladenosine-single nucleotide polymorphisms (m6A-SNPs) associated with breast cancer. Bioengineered. 12:2389–2397. 2021.PubMed/NCBI View Article : Google Scholar

46 

Zhao H, Jiang J, Wang M and Xuan Z: Genome-wide identification of m6A-associated single-nucleotide polymorphisms in colorectal cancer. Pharmgenomics Pers Med. 14:887–892. 2021.PubMed/NCBI View Article : Google Scholar

47 

Chen M, Lin W, Yi J and Zhao Z: Exploring the epigenetic regulatory role of m6A-associated SNPs in type 2 diabetes pathogenesis. Pharmgenomics Pers Med. 14:1369–1378. 2021.PubMed/NCBI View Article : Google Scholar

48 

Lin W, Xu H, Wu Y, Wang J and Yuan Q: In silico genome-wide identification of m6A-associated SNPs as potential functional variants for periodontitis. J Cell Physiol. 235:900–908. 2020.PubMed/NCBI View Article : Google Scholar

49 

Lin W, Xu H, Yuan Q and Zhang S: Integrative genomic analysis predicts regulatory role of N 6-methyladenosine-associated SNPs for adiposity. Front Cell Dev Biol. 8(551)2020.PubMed/NCBI View Article : Google Scholar

50 

Zhu R, Tian D, Zhao Y, Zhang C and Liu X: Genome-wide detection of m6A-associated genetic polymorphisms associated with ischemic stroke. J Mol Neurosci. 71:2107–2115. 2021.PubMed/NCBI View Article : Google Scholar

51 

Mo XB, Lei SF, Zhang YH and Zhang H: Detection of m6A-associated SNPs as potential functional variants for coronary artery disease. Epigenomics. 10:1279–1287. 2018.PubMed/NCBI View Article : Google Scholar

52 

Mo XB, Lei SF, Zhang YH and Zhang H: Integrative analysis identified IRF6 and NDST1 as potential causal genes for ischemic stroke. Front Neurol. 10(517)2019.PubMed/NCBI View Article : Google Scholar

53 

Mo XB, Zhang YH and Lei SF: Genome-wide identification of N6-methyladenosine (m6A) SNPs associated with rheumatoid arthritis. Front Genet. 9(299)2018.PubMed/NCBI View Article : Google Scholar

54 

Wu Z, Lin W, Yuan Q and Lyu M: A genome-wide association analysis: m6A-SNP related to the onset of oral ulcers. Front Immunol. 13(931408)2022.PubMed/NCBI View Article : Google Scholar

55 

Kleinbielen T, Olasagasti F, Azcarate D, Beristain E, Viguri-Díaz A, Guerra-Merino I, García-Orad Á and de Pancorbo MM: In silico identification and in vitro expression analysis of breast cancer-related m6A-SNPs. Epigenetics. 17:2144–2156. 2022.PubMed/NCBI View Article : Google Scholar

56 

Qiu X, He H, Huang Y, Wang J and Xiao Y: Genome-wide identification of m6A-associated single-nucleotide polymorphisms in Parkinson's disease. Neurosci Lett. 737(135315)2020.PubMed/NCBI View Article : Google Scholar

57 

Lv J, Song Q, Bai K, Han J, Yu H, Li K, Zhuang J, Yang X, Yang H and Lu Q: N6-methyladenosine-related single-nucleotide polymorphism analyses identify oncogene RNFT2 in bladder cancer. Cancer Cell Int. 22(301)2022.PubMed/NCBI View Article : Google Scholar

58 

Mo XB, Lei SF, Qian QY, Guo YF, Zhang YH and Zhang H: Integrative analysis revealed potential causal genetic and epigenetic factors for multiple sclerosis. J Neurol. 266:2699–2709. 2019.PubMed/NCBI View Article : Google Scholar

59 

Sebastian-delaCruz M, Olazagoitia-Garmendia A, Gonzalez-Moro I, Santin I, Garcia-Etxebarria K and Castellanos-Rubio A: Implication of m6A mRNA methylation in susceptibility to inflammatory bowel disease. Epigenomes. 4(16)2020.PubMed/NCBI View Article : Google Scholar

60 

Ruan X, Tian M, Kang N, Ma W, Zeng Y, Zhuang G, Zhang W, Xu G, Hu L, Hou X, et al: Genome-wide identification of m6A-associated functional SNPs as potential functional variants for thyroid cancer. Am J Cancer Res. 11:5402–5414. 2021.PubMed/NCBI

61 

Sun X, Dai Y, Tan G, Liu Y and Li N: Integration analysis of m6A-SNPs and eQTLs associated with sepsis reveals platelet degranulation and Staphylococcus aureus infection are mediated by m6A mRNA methylation. Front Genet. 11(7)2020.PubMed/NCBI View Article : Google Scholar

62 

Liu H, Gu J, Jin Y, Yuan Q, Ma G, Du M, Ge Y, Qin C, Lv Q, Fu G, et al: Genetic variants in N6-methyladenosine are associated with bladder cancer risk in the Chinese population. Arch Toxicol. 95:299–309. 2021.PubMed/NCBI View Article : Google Scholar

63 

Tian J, Ying P, Ke J, Zhu Y, Yang Y, Gong Y, Zou D, Peng X, Yang N, Wang X, et al: ANKLE1 N6-methyladenosine-related variant is associated with colorectal cancer risk by maintaining the genomic stability. Int J Cancer. 146:3281–3293. 2020.PubMed/NCBI View Article : Google Scholar

64 

Olazagoitia-Garmendia A, Zhang L, Mera P, Godbout JK, Sebastian-DelaCruz M, Garcia-Santisteban I, Mendoza LM, Huerta A, Irastorza I, Bhagat G, et al: Gluten-induced RNA methylation changes regulate intestinal inflammation via allele-specific XPO1 translation in epithelial cells. Gut. 71:68–76. 2022.PubMed/NCBI View Article : Google Scholar

65 

Tian J, Zhu Y, Rao M, Cai Y, Lu Z, Zou D, Peng X, Ying P, Zhang M, Niu S, et al: N6-methyladenosine mRNA methylation of PIK3CB regulates AKT signalling to promote PTEN-deficient pancreatic cancer progression. Gut. 69:2180–2192. 2020.PubMed/NCBI View Article : Google Scholar

Related Articles

Journal Cover

October-December 2022
Volume 2 Issue 4

Print ISSN: 2752-5406
Online ISSN:2752-5414

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Niu C and Niu C: m6A‑SNP: From genetics to epigenetics (Review). Int J Epigen 2: 4, 2022
APA
Niu, C., & Niu, C. (2022). m6A‑SNP: From genetics to epigenetics (Review). International Journal of Epigenetics, 2, 4. https://doi.org/10.3892/ije.2022.13
MLA
Niu, C., Zhou, R."m6A‑SNP: From genetics to epigenetics (Review)". International Journal of Epigenetics 2.4 (2022): 4.
Chicago
Niu, C., Zhou, R."m6A‑SNP: From genetics to epigenetics (Review)". International Journal of Epigenetics 2, no. 4 (2022): 4. https://doi.org/10.3892/ije.2022.13