Skip to main content
NCBI home page
Immunity, Inflammation and Disease logoLink to Immunity, Inflammation and Disease
. 2024 Aug 2;12(8):e1314. doi: 10.1002/iid3.1314

Identification of key genes with abnormal RNA methylation modification and selected m6A regulators in ankylosing spondylitis

Fengqing Wu 1, Hongbin Huang 2, Deyang Sun 3, Bingbing Cai 4, Huateng Zhou 4, Renfu Quan 4,✉, Huan Yang 5,✉
PMCID: PMC11295096  PMID: 39092763

Abstract

Background

N6‐methyladenosine (m6A) has been identified as the most abundant modification of RNA molecules and the aberrant m6A modifications have been associated with the development of autoimmune diseases. However, the role of m6A modification in ankylosing spondylitis (AS) has not been adequately investigated. Therefore, we aimed to explore the significance of m6A regulator‐mediated RNA methylation in AS.

Methods

The methylated RNA immunoprecipitation sequencing (meRIP‐seq) and digital RNA sequencing (Digital RNA‐seq) were conducted using the peripheral blood mononuclear cells from three AS cases and three healthy controls, to identify genes affected by abnormal RNA methylation. The genes associated with different peaks were cross‐referenced with AS‐related genes obtained from the GeneCards Suite. Subsequently, the expression levels of shared differentially expressed genes (DEGs) and key m6A regulators in AS were evaluated using data from 68 AS cases and 36 healthy controls from two data sets (GSE25101 and GSE73754). In addition, the results were validated through quantitative polymerase chain reaction (qPCR).

Results

The meRIP‐seq and Digital RNA‐seq analyses identified 28 genes with upregulated m6A peaks but with downregulated expression, and 52 genes with downregulated m6A peaks but with upregulated expression. By intersecting the genes associated with different peaks with 2184 AS‐related genes from the GeneCards Suite, we identified a total of five shared DEGs: BCL11B, KAT6B, IL1R1, TRIB1, and ALDH2. Through analysis of the data sets and qPCR, we found that BCL11B and IL1R1 were differentially expressed in AS. Moreover, two key m6A regulators, WTAP and heterogeneous nuclear ribonucleoprotein C, were identified.

Conclusions

In conclusion, the current study revealed that m6A modification plays a crucial role in AS and might hence provide a new treatment strategy for AS disease.

Keywords: ankylosing spondylitis, bioinformatics, N6‐methyladenosine (m6A), RNA sequence analyses

This article reveals that aberrant expression of m6A regulators contributes to AS development. This relation may be conducted by genes BCL11B and IL1R1, as well as m6A regulators WTAP and HNRNPC. Considering the importance of m6A modification as a critical regulator on AS, blocking or inhibiting certain m6A enzymes may reveal previously unidentified strategies for therapeutic interventions against AS disease.

graphic file with name IID3-12-e1314-g002.jpg

1. INTRODUCTION

Ankylosing spondylitis (AS) is a common, chronic, and autoimmune disease that involves axial joints and entheses. 1 Globally, the prevalence of AS is estimated to range between 0.1% and 1.4% and has a gender ratio of 2:1 (male: female). 2 However, currently, there is no effective cure for AS, although immunosuppressants especially interleukin (IL)‐17 inhibitors have shown good potential to alleviate disease progression, 3 but with a corresponding increase in the risk of nonserious infections, which range from mild to moderate, and hence increasing medical costs of the disease management. 4 Therefore, it is imperative to investigate the pathogenesis and establish better treatment methods of AS. Both environmental factors and genetic susceptibility contribute to the development of AS disease. 5 Furthermore, through genome wide association studies, it has been shown that 90% of all AS cases are associated with HLA‐B27, 6 and ERAP1 mutations only affect the susceptibility of the patients with HLA‐B27 alleles. 7 , 8 Through high‐throughput sequencing methods, multiple susceptibility genes have been found to participate in the genetic pathogenesis of AS. 9

Recently, an increasing number of studies have been conducted with a focus on the field of epigenetics to explain how genes interact with the environment and thus affect the process of AS disease. 10 , 11 , 12 Epigenetics mainly includes DNA methylation, histone marks, and RNA modification whereby the methylation of the N (6) position of adenosine (m6A) is the most common RNA modification on eukaryotic messenger RNA (mRNA). In addition, the RNA methylation is involved in translation, degradation, splicing, and export of mRNA metabolism, that extensively affect mammalian development, cell differentiation, immunity, metabolism, and tumors among other life processes. Besides, m6A modification has also been identified for its important biological functions in regulating noncoding RNAs such as microRNAs, circular RNAs, and long noncoding RNAs (lncRNAs). 13 The N6‐methyladenosine (m6A) methylated‐related enzymes are divided into methyltransferases (writers), demethylases (erasers), and methylated reading proteins (readers). 14 The main function of writers is to “enter” m6A methylation into a specific RNA site to achieve RNA methylation modification. In contrast, the role of “erasers” is to demethylate the RNA that have been modified with m6A. On the other hand, “readers” are required for m6A‐modified RNA to perform specific biological functions through specifically recognizing m6A modified mRNA, promoting RNA translation, or maintaining the stability of RNA.

Accumulating evidence show that m6A modification participates in a variety of disease processes and particularly plays vital role in autoimmune diseases. 15 Furthermore, several studies of m6A have been applied on various autoimmune diseases and immune related cells with the goal of discovering new therapies. 16 , 17 Currently, several studies have explored the significance of m6A regulators. 18 , 19 , 20 Moreover, it has been found that METTL14‐dependent m6A modification of ELMO1 contributes to the directed migration of mesenchymal stem cells in AS. 21 Furthermore, METTL14 was found to be downregulated in T cells from patients with AS, resulting in impaired autophagic flux and severe inflammation due to reduced FOXO3a expression. 22 However, the relationship between AS and m6A is not well understood. Therefore, this study preliminarily attempted to evaluate the potential role and mechanism of m6A methylation regulators in AS to generate ideas for guiding clinical diagnosis and treatment of the disease.

2. MATERIALS AND METHODS

2.1. Sample collection and RNA extraction

Samples were selected from The Affiliated Jiangnan Hospital of Zhejiang Chinese Medical University using a random sampling method. The current study was reviewed by the Ethics Committee of Hangzhou Xiaoshan District Chinese Medicine Hospital and also met all the requirements of the Declaration of Helsinki of the World Medical Assembly. All participants provided signed informed consent forms to participate in the study. Peripheral blood mononuclear cells (PBMCs) were acquired from whole blood of the participants through density gradient centrifugation and total RNA of PBMCs were extracted with Invitrogen's TRizolTM Reagent.

Three AS cases and three controls were then referred to Wuhan Kangce Technology Co., Ltd, for Nano methylated RNA immunoprecipitation sequencing (MeRIP‐seq) and digital mRNA sequencing (Digital mRNA‐seq), while nine AS cases and nine controls were used for quantitative polymerase chain reaction (qPCR) validation.

2.2. Nano meRIP‐seq and m6A methylation peak determination

In this study, 5 μg total RNAs were used for meRIP experiment. Total RNAs was extracted using TRIzol Reagent (Invitrogen, cat. no. 15596026). Briefly, 20 mM ZnCl2 was added to total RNA and incubated at 94°C for 5 min until the RNA fragments were mainly distributed in 100 nt. Fragmented RNA was precipitated with anti‐m6A antibody (Synaptic Systems, 202203) for m6A immunoprecipitation (m6A‐IP). The stranded RNA sequencing library was constructed by using KC‐DigitalTM Stranded mRNA Library Prep Kit for Illumina® (Catalog no. DR08502, Wuhan Seqhealth Co., Ltd). Then, ribosomal complementary DNA (cDNA) was removed by SMARTer Stranded Total RNA‐Seq Kit version 2 (Pico Input Mammalian; 634413; Takara/Clontech). The quality control of the completed libraries was performed on Novaseq. 6000 sequencer (Illumina) with PE150 model.

Raw sequencing data were filtered by Trimmomatic (version 0.36) and then clustered to eliminate any errors and biases introduced by PCR amplification or sequencing. Package “exomePeak” (Version 3.8) software was used for peak calling and differential analysis. The m6A‐binded regions (also called peaks in each m6A‐IP sample) were later detected using the corresponding m6A‐input sample being the control. 23 Significant peaks with p < .05 and |logFC| > 1 were identified and annotated to the RefSeq database (hg19) using STAR software (version 2.5.3a) with default parameters. Furthermore, HOMER (version 4.9) was utilized to perform peak annotation and to analyze the binding motif. The m6A methylation peak was displayed by integrative genomics viewer (IGV) software according to the BW file of sequencing samples.

2.3. Digital mRNA‐seq and differential expressed m6A regulators verification

Two micrograms of total RNAs were used for digital mRNA‐seq library preparation using Ribo‐off rRNA Depletion Kit (Human/Mouse/Rat) (Catalog no. MRZG12324, Illumina) and KC‐DigitalTM Stranded mRNA Library Prep Kit for Illumina® (Catalog no. DR08502, Wuhan Seqhealth Co., Ltd) following the manufacturer's instruction. The library products corresponding to 200–500 bp were enriched, quantified, and finally sequenced on NovaSeq. 6000 sequencer (Illumina) with PE150 model. In the present study, t test was used to examine the differential expressed genes with a cutoff of p < .05 between the AS group and the normal control group.

Heatmaps were then constructed for 27 m6A regulators and box plots for the potential m6A regulators in AS.

The present study annotated the differential peak with gene symbol and compared it with the DEGs of mRNA‐seq to identify the shared DEGs using the “ggscatter” package.

2.4. Data acquisition and preprocessing

The identified gene set was overlapped with the ankylosing‐sondylitis‐related gene list extracted from GeneCards 24 with the “VennDiagram” package.

Two gene expression matrices were obtained from the Gene Expression Omnibus (GEO) data sets (https://www.ncbi.nlm.nih.gov/geo/). The GSE25101 data set comprises 16 AS cases and 16 healthy controls, whereas GSE73754 consists of 52 AS samples and 20 healthy donors (controls). Both matrices contain samples from peripheral blood. Probe names were converted to gene names using the “affy” package and the “removeBatchEffect” function of the “limma” package was employed to eliminate batch effects in the RStudio environment.

2.5. Differential expression analysis of m6A regulators

A total of 27 widely recognized m6A regulators were collected from previously published literatures, 25 , 26 including 10 writers (METTL3, METTL4, METTL14, CBLL1, WTAP, KIAA1429, RBM15B, RBM15, ZC3H13, and ZNF217), two erasers (ALKBH5 and FTO), and 15 readers (YTHDF1, YTHDF2, YTHDF3, IGF2BP1, IGF2BP2, IGF2BP3, YTHDC1, YTHDC2, EIF3A, EIF3B, HNRNPA2B1, heterogeneous nuclear ribonucleoprotein C (HNRNPC), LRPPRC, FMR1, and ELAVL1).

Differential expression analysis for these regulators between two groups was performed using wilcox.test and visualized using “ggplot2” package. The level of significance difference was set at p < .05. The correlation between the m6A regulators was identified using “corrplot” package. To identify potential m6A RNA methylation regulators in patients with AS disease, univariate logistic regression was conducted and interrupted at p < .05. Dimension reduction and variable selection was performed using the least absolute shrinkage and selection operator (LASSO). Further, the risk scores of selected AS‐related genes were also calculated, whereby the box plot and receiver operating characteristic (ROC) curve analysis were adopted, to analyze the prediction effect of the risk model.

2.6. qPCR

The quality and purity of the isolated total RNA were tested by Nanodrop one (Thermo Scientific). RNA samples with A260/280 values between 1.8 and 2.1 were used for subsequent analysis. Using Thermo Scientific Revert Aid First Strand cDNA Synthesis Kit (Thermo Scientific), a total of 1 μg of RNA was reverse‐transcribed into cDNA. All primers were supplied by Beijing Liuhe Huada gene technology company. The primers are as follows:

GAPDH_F 5′‐GGAGCGAGATCCCTCCAAAAT‐3′,

GAPDH_R 5′‐GGCTGTTGTCATACTTCTCATGG‐3′;

KAT6B_F 5′‐GCCTTGCCTCCTATAAGGACC‐3′, KAT6B_R 5′‐TCCACATTGCGGAGATCATTAC‐3′;

TRIB1_F 5′‐GCTGCAAGGTGTTTCCCATTA‐3′, TRIB1_R 5′‐TCCCCAAAGTCCTTCTCAAAGA‐3′;

BCL11B_F 5′‐TCCAGCTACATTTGCACAACA‐3′, BCL11B_R 5′‐GCTCCAGGTAGATGCGGAAG‐3′;

IL1R1_F 5′‐ATGAAATTGATGTTCGTCCCTGT‐3′, IL1R1_R 5′‐ACCACGCAATAGTAATGTCCTG‐3′;

WTAP_F 5′‐CTTCCCAAGAAGGTTCGATTGA‐3′, WTAP_R 5′‐TCAGACTCTCTTAGGCCAGTTAC‐3′;

HNRNPC_F 5′‐GGAGATGTACGGGTCAGTAACA‐3′, KAT6B_R 5′‐CCCGAGCAATAGGAGGAGGA‐3′.

R GAPDH was used as an endogenous constant control. Use △△C t method to determine the relative difference of mRNA expression between AS patients and healthy controls. 27

3. RESULTS

3.1. Conjoint analyses of MeRIP‐seq and RNA‐seq

To determine the difference in RNA methylation modification by m6A between AS and healthy controls, the meRIP‐seq method was employed to sequence the methylated RNA of PBMCs from three AS cases and three healthy donors. Results showed that the methylation peaks of AS were significantly different from those in the control group. Moreover, although there was no difference in the overall distribution of methylation peaks between AS cases and the control samples, it was evident that the proportion of differential peaks distributed in 3′‐untranslated region and coding sequence regions was significantly increased (Figure 1A). Furthermore, the current study identified the top five motifs related to differential peaks (Figure 1B). The AS samples had 1337 dysregulated m6A peaks, with 619 m6A peaks significantly upregulated and 718 m6A peaks significantly downregulated. Moreover, we identified 2952 DEGs from UID mRNA‐seq, with 1546 genes significantly upregulated and 1406 genes significantly downregulated. Through conjoint analyses of MeRIP‐seq and RNA‐seq data, we got 28 genes with upregulated m6A peaks but with downregulated expression, referred to as “hyper‐down” genes, and 52 genes with downregulated m6A peaks but with upregulated expression, referred to as “hypo‐up” genes (Figure 1C).

Figure 1.

Figure 1

Open in a new tab

Conjoint analyses of methylated RNA immunoprecipitation sequencing (MeRIP‐seq) and RNA sequencing (RNA‐seq). (A) Distribution of peaks in 5′‐untranslated region (UTR), CDS, 3′‐UTR area (the upper), and distribution statistics of peaks in each functional area of gene (the lower). The proportion of differential peaks distributed in 3′‐UTR and CDS region was a little higher than AS peaks and healthy control (HC) peaks (cds is the CDS region of the gene, utr5 and utr3 are the 5′‐UTR and 3′‐UTR of the gene, respectively. NP_exon is the exon region of noncoding genes, NP_intron is the intron region of noncoding genes, and PR_intron is the intron region) (B). The top five motifs related to differential peaks. (C) Volcano map demonstrated 28 “hyper‐down” genes and 52 “hypo‐up” genes. CDS, coding sequence.

3.2. BCL11B and IL1R1 were identified as key genes of AS

After intersecting the differentially associated peak genes with 2184 genes related to AS from the GeneCards Suite, a set of five shared DEGs was identified (Figure 2A), namely BCL11B, KAT6B, IL1R1, TRIB1, and ALDH2. To corroborate these results, the expression levels of these five shared DEGs were assessed in 68 AS cases and 36 healthy controls across two data sets (GSE25101 and GSE73754) using qPCR. The results showed that BCL11B and IL1‐receptor 1 (IL1R1) were differentially expressed in AS (Figure 2B,C). IGV plots showing different m6A methylated peaks for BCL11B and IL1R1 between AS and healthy control cases (Figure 3A,B).

Figure 2.

Figure 2

Open in a new tab

BCL11B and IL1R1 were identified as key genes of ankylosing spondylitis (AS). (A) Venn diagram showed five deifferentially expressed genes (DEGs) shared by different peak related genes and 2184 DEGs from messenger RNA sequencing. (B) The expression of five shared DEGs in Gene Expression Omnibus data sets. (C) The relative expression of four shared DEGs in quantitative polymerase chain reaction.

Figure 3.

Figure 3

Open in a new tab

m6A methylation peaks of BCL11B and IL1R1. (A) Integrative genomics viewer (IGV) plots showing m6A methylated peaks for BCL11B. (B) IGV plots showing m6A methylated peaks for IL1R1. Blue boxes represent exons and blue lines represent introns.

3.3. Abnormal expression of selected m6A regulators in AS

By analyzing the differential expression of 27 m6A RNA methylation regulators (22 m6A regulators were actually found in the expression data) between the two groups, a total of 12 m6A RNA methylation regulators were found to be significantly related to AS disease (Figure 4C). The upregulated genes included YTHDF3, YTHDC1, WTAP, IGF2BP2, and IGF2BP3, whereas the downregulated genes included EIF3A, EIF3B, HNRNPC, CBLL1, ELAVL1, ALKBH5, and FTO (Figure 4D).

Figure 4.

Figure 4

Open in a new tab

Abnormal expression of selected m6A regulators in ankylosing spondylitis (AS). (A) The Heatmap of the expression differences of 22 m6A RNA methylation regulators between AS cases and healthy controls. (B) Correlations among the expression of 22 m6A regulators in all samples. (C, D) The box plot and Volcano map demonstrated the expression differences of 22 m6A RNA methylation regulators between AS cases and healthy controls. Compared with the healthy controls, the expression of WTAP, YTHDC1, YTHDF3, IGFBP2, and IGFBP3 were upregulated and HNRNPC, EIF3A, EIF3B, CBLL1, ELAVL1, FTO, and ALKBH5 were downregulated in AS (*p < .05; **p < .01; ***p < .001; ****p < .0001).

3.4. WTAP and HNRNPC identified as potential m6A regulators of AS

In this study, univariate logistic regression was performed to analyze the expression data of 22 m6A regulators. The findings indicated that WTAP, RBM15B, and HNRNPC were potential m6A regulators associated with AS disease (Figure 5A). Further, results of LASSO Cox regression indicated that the three m6A regulators were also significant in AS disease (Figure 5B,C). In addition, it was found that the risk scores of the three regulators significantly increased in the AS group with the p value being 1.4e−09 (Figure 5D,E). Moreover, the plotted ROC curve revealed that the m6A regulators had significant potential in distinguishing between the AS and control cases (Figure 5F). Ultimately, the WTAP and HNRNPC were identified as the potential m6A regulators of AS, as their expression had been found to be significantly related in AS.

Figure 5.

Figure 5

Open in a new tab

Identification of crucial m6A regulators in ankylosing spondylitis (AS). (A) Univariate logistic regression investigated the relationship between 22 m6A regulators and AS cases, revealing three AS‐related m6A regulators: WTAP, RBM15B, and heterogeneous nuclear ribonucleoprotein C (HNRNPC) (p < .05). (B) Least absolute shrinkage and selection operator (LASSO) coefficient profiles of WTAP, RBM15B, and HNRNPC. (C) 10‐fold cross‐validation for tuning parameter selection in the LASSO regression. (D) The risk scores of WTAP and HNRNPC. (E) The risk distribution between healthy and AS cases showed that AS cases have a much higher risk score than healthy controls. (F) The discrimination ability of m6A regulators in healthy and AS cases analyzed by receiver operating characteristic (ROC) curve and evaluated by area under the curve value.

3.5. Verification of WTAP and HNRNPC as potential m6A regulators of AS

After getting the differential expressed genes between the three AS cases and three healthy control samples, heatmaps (for 27 m6A regulators) and box plots (for WTAP and HNRNPC) were constructed (Figure 6A). It was evident that WTAP was significantly upregulated in AS, whereas HNRNPC was significantly downregulated, and the findings were in agreement with results of previous studies (Figure 6B) and qPCR results (Figure 6C).

Figure 6.

Figure 6

Open in a new tab

Differentially expressed m6A regulators between three ankylosing spondylitis (AS) cases and three healthy controls. (A) Heatmap for 27 differentially expressed m6A regulators (p < .05). (B) Box plots for the expression of WTAP and heterogeneous nuclear ribonucleoprotein C (HNRNPC) in Gene Expression Omnibus data sets. WTAP was significantly upregulated in AS with HNRNPC being significantly downregulated. (C) Box plots for the relative expression of WTAP and HNRNPC in quantitative polymerase chain reaction. WTAP was significantly upregulated in AS with HNRNPC being significantly downregulated. (*p < .05; **p < .01; ***p < .001; ****p < .0001).

4. DISCUSSION

Recently, research studies have reported that abnormal m6A modifications contribute to the occurrence of autoimmune diseases. 28 , 29 Research has demonstrated that m6A‐related enzymes regulate responses to nonmicrobial double‐stranded DNA in uninfected cells, thereby influencing host immunity and potentially contributing to autoimmune diseases. 30 Further, it is evident that the m6A reader, IMP2 can direct autoimmune inflammation through an IL‐17‐ and tumor necrosis factor‐α (TNF‐α)‐dependent C/EBP transcription factor axis. 31 Moreover, aberrant m6A modifications has been linked to the initiation of several autoimmune diseases, including rheumatoid arthritis, 32 systemic lupus erythematosus (SLE), 33 , 34 multiple sclerosis, 35 and AS.

In this study, through conjoint analyses of meRIP‐seq and RNA‐seq, we got 28 “hyper‐down” genes and 52 “hypo‐up” genes, among which BCL11B and IL1R1 were identified as key genes of AS. In addition, two potential m6a regulators, WTAP and HNRNPC, were identified through bioinformatics analysis and confirmed using qPCR. This indicate that m6A modification contributes to the the occurrence of AS, probably by targeting BCL11B and IL1R1.

BCL11B is a C2H2 zinc finger domain‐containing transcription factor, 36 which is first expressed at the early stage of thymocytes and modulates the the survival of double‐negative and double‐positive thymocytes, β selection, and positive selection of CD4 and CD8 single‐positive thymocytes. 37 It has been shown to block the TH2 effector program in T‐helper 1 (TH1) and TH17 cells. 38 BCL11B is essential for maintaining the identity and function of innate lymphoid cells type 2, 39 and for controlling the antigen‐specific clonal expansion and cytolytic function of CD8+ T lymphocytes. Mice lacking BCL11B in double‐positive thymocytes develop inflammatory bowel disease, with massive pro‐inflammatory cytokine‐producing CD4+ T cells infiltrating in the colon. 37 This phenomenon was similarly observed in mice deficient in BCL11B expression within Treg cells, where a decline in suppressor function was observed, accompanied by decreased levels of Foxp3 and IL‐10, and increased production of pro‐inflammatory cytokines TNF, interferon‐γ, and IL‐17. 40 BCL11B/Treg knockout mice presented fatal systemic autoimmunity and died at an early age. 41 In this study, we identified decreased mRNA levels of the BCL11B gene in AS patients, which is consist with previous research. 42

IL1R1 is located in the IL‐1 gene cluster. It can affect nuclear factor‐κB signaling and lead to the upregulation of inflammatory by combining with IL‐1 on the cell surface. 43 Cytokines have been implicated in the pathogenesis of AS, 44 including IL‐1, which has been reported to be associated with AS. 45 IL1R1 polymorphisms was also found to be an increased risk factor for AS in a Northwest Chinese Han population. 46 Our research revealed increased mRNA levels of IL1R1 gene in AS patients, further confirming its relationship with AS.

WTAP and HNRNPC, essential regulators of m6A methylation, can regulate m6A modifications. Analysis of DEGs in the GSE73754 data set revealed that WTAP was upregulated in AS. 20 The WTAP is necessary for nuclear speckle localization and formation of METTL3 and METTL14 complex formation, which functions as an m6A methyltransferase and is essential for alternative splicing and gene expression. 47 The HNRNPC protein, which is part of the large family of ubiquitously expressed heterogeneous nuclear ribonucleoproteins, binds to nascent RNA transcripts and affects pre‐mRNA stability, splicing, export, and translation. 48 M6A modification can alter the local structure of mRNA or lncRNA by facilitating the interaction with specific reader proteins, such as HNRNPC. This interaction, in turn, regulates mRNA splicing patterns. 49 The mRNA levels of WTAP has been found to be decreased in peripheral blood from SLE patients, as well as primary Sjogren's syndrome patients. 50 Our study found that WTAP was upregulated in peripheral blood from AS patients, whereas HNRNPC was downregulated, indicating abnormal m6A modification in AS may be due to the two regulators. Unfortunately, we did not detect other significant m6A regulators, such as METTL14, which have been reported in previous studies. 20 , 22 Nonetheless, our study presents the first attempt to integrate multiple analyses, including MeRIP‐seq, Digital RNA‐seq, GeneCards Suite, GEO data sets, and qPCR, thereby identifying crucial genes associated with AS. Therefore, our study provides innovative insights meant to improve the clinical diagnosis and treatment of AS. However, our study had several limitations. First, the sample size used for sequencing and qPCR tests was limited. In addition, we did not confirm the overall methylation level of AS nor the functional role of m6A in AS pathogenesis. Therefore, further investigation and validation are necessary to elucidate the precise association and exact mechanism of m6A modification in AS.

In conclusion, this work reveals that aberrant expression of m6A regulators contributes to AS development. This relation may be conducted by genes BCL11B and IL1R1, as well as m6A regulators WTAP and HNRNPC. Therefore, the precise association and exact mechanism of m6A modification in AS disease still needs further investigation and verification. Furthermore, considering the importance of m6A modification as a critical regulator on AS, blocking or inhibiting certain m6A enzymes may reveal previously unidentified strategies for therapeutic interventions against AS disease.

AUTHOR CONTRIBUTIONS

Fengqing Wu: Conceptualization; methodology; data curation; formal analysis; validation; writing—original draft; writing—review and editing; visualization. Hongbin Huang: Methodology; resources; data curation; supervision; writing—review and editing. Deyang Sun: Software; formal analysis; visualization. Bingbing Cai, Huateng Zhou: Resources; data curation. Renfu Quan: Conceptualization; project administration; funding acquisition; supervision; resources. Huan Yang: Validation; writing—review and editing. All authors revised and approved the manuscript.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

ETHICS STATEMENT

Ethics committee approval was received for this study from the Ethics Committee of Hangzhou Xiaoshan District Chinese Medicine Hospital, review batch number: 202012KL‐012. This study conforms to Declaration of Helsinki standards and has obtained the prior informed consent to participate in research as well as publish from all participants.

ACKNOWLEDGMENTS

We thank Zhenhua Yang (Seqhealth Technology Co., LTD, Wuhan, China) for his assistance in bioinformatic analysis. We appreciate the great help/technical support/experimental support from the Public Platform of Pharmaceutical Research Center, Academy of Chinese Medical Science, Zhejiang Chinese Medical University. No funding was received for this study.

Wu F, Huang H, Sun D, et al. Identification of key genes with abnormal RNA methylation modification and selected m6A regulators in ankylosing spondylitis. Immun Inflamm Dis. 2024;12:e1314. 10.1002/iid3.1314

Fengqing Wu and Hongbin Huang contributed equally to this work and share first authorship.

Contributor Information

Renfu Quan, Email: quanrenfu@126.com.

Huan Yang, Email: 0622766@zju.edu.cn.

DATA AVAILABILITY STATEMENT

The raw data used and analyzed during the current study are available from the first author or the corresponding author on reasonable request. The microarray data generated in this study have been uploaded in the SRA data sets database, with the BioProject ID being PRJNA833263 and PRJNA833405, and will be released on May 31, 2024.

REFERENCES

  • 1. Pedersen SJ, Maksymowych WP. The pathogenesis of ankylosing spondylitis: an update. Curr Rheumatol Rep. 2019;21:58. [DOI] [PubMed] [Google Scholar]
  • 2. Dean LE, Jones GT, MacDonald AG, Downham C, Sturrock RD, Macfarlane GJ. Global prevalence of ankylosing spondylitis. Rheumatology. 2014;53(4):650‐657. [DOI] [PubMed] [Google Scholar]
  • 3. Jethwa H, Bowness P. The interleukin (IL)‐23/IL‐17 axis in ankylosing spondylitis: new advances and potentials for treatment. Clin Exp Immunol. 2016;183:30‐36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Shirley M, Scott LJ. Secukinumab: a review in psoriatic arthritis. Drugs. 2016;76:1135‐1145. [DOI] [PubMed] [Google Scholar]
  • 5. Brown MA. Genetics and the pathogenesis of ankylosing spondylitis. Curr Opin Rheumatol. 2009;21:318‐323. [DOI] [PubMed] [Google Scholar]
  • 6. Sieper J, Poddubnyy D. Axial spondyloarthritis. Lancet. 2017;390:73‐84. [DOI] [PubMed] [Google Scholar]
  • 7. Evans DM, Spencer CCA, Pointon JJ, et al. Interaction between ERAP1 and HLA‐B27 in ankylosing spondylitis implicates peptide handling in the mechanism for HLA‐B27 in disease susceptibility. Nat Genet. 2011;43:761‐767. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. International Genetics of Ankylosing Spondylitis Consortium (IGAS) , Cortes A, Hadler J, et al. Identification of multiple risk variants for ankylosing spondylitis through high‐density genotyping of immune‐related loci. Nat Genet. 2013;45:730‐738. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. O'Rielly DD, Uddin M, Rahman P. Ankylosing spondylitis: beyond genome‐wide association studies. Curr Opin Rheumatol. 2016;28:337‐345. [DOI] [PubMed] [Google Scholar]
  • 10. Hammaker D, Firestein GS. Epigenetics of inflammatory arthritis. Curr Opin Rheumatol. 2018;30:188‐196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Whyte JM, Ellis JJ, Brown MA, Kenna TJ. Best practices in DNA methylation: lessons from inflammatory bowel disease, psoriasis and ankylosing spondylitis. Arthritis Res Ther. 2019;21:133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Tavasolian F, Inman RD. Gut microbiota‐microRNA interactions in ankylosing spondylitis. Autoimmun Rev. 2021;20:102827. [DOI] [PubMed] [Google Scholar]
  • 13. Yi YC, Chen XY, Zhang J, Zhu JS. Novel insights into the interplay between m(6)A modification and noncoding RNAs in cancer. Mol Cancer. 2020;19(1):121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Wu S, Li XF, Wu YY, Yin SQ, Huang C, Li J. 6) ‐methyladenosine and rheumatoid arthritis: a comprehensive review. Front Immunol. 2021;12:731842. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Zhang L, Hou C, Chen C, et al. The role of N(6)‐methyladenosine (m(6)A) modification in the regulation of circRNAs. Mol Cancer. 2020;19:105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Wardowska A. m6A RNA methylation in systemic autoimmune diseases‐a new target for epigenetic‐based therapy? Pharmaceuticals. 2021;14(3):218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Zhou J, Zhang X, Hu J, et al. m(6)A demethylase ALKBH5 controls CD4(+) T cell pathogenicity and promotes autoimmunity. Sci Adv. 2021;7:eabg0470. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Luan Z, Wang Y. Association between ankylosing spondylitis and m6A methylation. J Orthop Surg. 2023;18:757. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Luo Q, Guo Y, Xiao Q, et al. Expression and clinical significance of the m6A RNA‐binding proteins YTHDF2 in peripheral blood mononuclear cells from new‐onset ankylosing spondylitis. Front Med (Lausanne). 2022;7(9):922219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Zhong C, Liang J, Chen Z, et al. Analysis of N6‐methyladenosine RNA methylation regulators in diagnosis and distinct molecular subtypes of ankylosing spondylitis. Dis Markers. 2022;2022:1‐23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Xie Z, Yu W, Zheng G, et al. TNF‐α‐mediated m6A modification of ELMO1 triggers directional migration of mesenchymal stem cell in ankylosing spondylitis. Nat Commun. 2021;12:5373. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Chen Y, Wu Y, Fang L, et al. METTL14‐m6A‐FOXO3a axis regulates autophagy and inflammation in ankylosing spondylitis. Clin Immunol. 2023;257:109838. [DOI] [PubMed] [Google Scholar]
  • 23. Liu L, Zhang SW, Huang Y, Meng J. QNB: differential RNA methylation analysis for count‐based small‐sample sequencing data with a quad‐negative binomial model. BMC Bioinformatics. 2017;18:387. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Stelzer G, Rosen N, Plaschkes I, et al. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics. 2016;54:1.30.31‐31.30.33. [DOI] [PubMed] [Google Scholar]
  • 25. Zhang B, Wu Q, Li B, Wang D, Wang L, Zhou YL. m(6)A regulator‐mediated methylation modification patterns and tumor microenvironment infiltration characterization in gastric cancer. Mol Cancer. 2020;19(1):53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Sun D, Yang H, Fan L, Shen F, Wang Z. m6A regulator‐mediated RNA methylation modification patterns and immune microenvironment infiltration characterization in severe asthma. J Cell Mol Med. 2021;25(21):10236‐10247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real‐time quantitative PCR and the 2−ΔΔCT method. Methods. 2001;25:402‐408. [DOI] [PubMed] [Google Scholar]
  • 28. Paramasivam A, Priyadharsini JV, Raghunandhakumar S. Implications of m6A modification in autoimmune disorders. Cell Mol Immunol. 2020;17:550‐551. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Tang L, Wei X, Li T, et al. Emerging perspectives of RNA N (6)‐methyladenosine (m(6)A) modification on immunity and autoimmune diseases. Front Immunol. 2021;12:630358. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Rubio RM, Depledge DP, Bianco C, Thompson L, Mohr I. RNA m(6) A modification enzymes shape innate responses to DNA by regulating interferon β. Genes Dev. 2018;32(23‐24):1472‐1484. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Bechara R, Amatya N, Bailey RD, et al. The m(6)A reader IMP2 directs autoimmune inflammation through an IL‐17‐ and TNF‐α‐dependent C/EBP transcription factor axis. Sci Immunol. 2021;6(61):eabd1287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Luo Q, Gao Y, Zhang L, et al. Decreased ALKBH5, FTO, and YTHDF2 in peripheral blood are as risk factors for rheumatoid arthritis. BioMed Res Int. 2020;2020:1‐9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Luo Q, Fu B, Zhang L, Guo Y, Huang Z, Li J. Decreased peripheral blood ALKBH5 correlates with markers of autoimmune response in systemic lupus erythematosus. Dis Markers. 2020;2020:1‐11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Luo Q, Rao J, Zhang L, et al. The study of METTL14, ALKBH5, and YTHDF2 in peripheral blood mononuclear cells from systemic lupus erythematosus. Mol Genet Genomic Med. 2020;8(9):e1298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Mo XB, Lei SF, Qian QY, Guo YF, Zhang YH, Zhang H. Integrative analysis revealed potential causal genetic and epigenetic factors for multiple sclerosis. J Neurol. 2019;266(11):2699‐2709. [DOI] [PubMed] [Google Scholar]
  • 36. Avram D, Fields A, Top KPO, Nevrivy DJ, Ishmael JE, Leid M. Isolation of a novel family of C(2)H(2) zinc finger proteins implicated in transcriptional repression mediated by chicken ovalbumin upstream promoter transcription factor (COUP‐TF) orphan nuclear receptors. J Biol Chem. 2000;275:10315‐10322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Albu DI, Feng D, Bhattacharya D, et al. BCL11B is required for positive selection and survival of double‐positive thymocytes. J Exp Med. 2007;204:3003‐3015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Califano D, Sweeney KJ, Le H, et al. Diverting T helper cell trafficking through increased plasticity attenuates autoimmune encephalomyelitis. J Clin Invest. 2013;124:174‐187. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Califano D, Cho JJ, Uddin MN, et al. Transcription factor Bcl11b controls identity and function of mature type 2 innate lymphoid cells. Immunity. 2015;43:354‐368. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Liu P, Li P, Burke S. Critical roles of Bcl11b in T‐cell development and maintenance of T‐cell identity. Immunol Rev. 2010;238:138‐149. [DOI] [PubMed] [Google Scholar]
  • 41. Drashansky TT, Helm E, Huo Z, et al. Bcl11b prevents fatal autoimmunity by promoting T(reg) cell program and constraining innate lineages in T(reg) cells. Sci Adv. 2019;5:eaaw0480. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Karami J, Mahmoudi M, Amirzargar A, et al. Promoter hypermethylation of BCL11B gene correlates with downregulation of gene transcription in ankylosing spondylitis patients. Genes Immunity. 2017;18:170‐175. [DOI] [PubMed] [Google Scholar]
  • 43. Rhodes DM, Smith SA, Holcombe M, Qwarnstrom EE. Computational modelling of NF‐kappaB activation by IL‐1RI and its co‐receptor TILRR, predicts a role for cytoskeletal sequestration of IkappaBalpha in inflammatory signalling. PLoS One. 2015;10:e0129888. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Braun J, Bollow M, Neure L, et al. Use of immunohistologic and in situ hybridization techniques in the examination of sacroiliac joint biopsy specimens from patients with ankylosing spondylitis. Arthritis Rheum. 1995;38:499‐505. [DOI] [PubMed] [Google Scholar]
  • 45. Lea W, Lee YH. The associations between interleukin‐1 polymorphisms and susceptibility to ankylosing spondylitis: a meta‐analysis. Joint Bone Spine. 2012;79:370‐374. [DOI] [PubMed] [Google Scholar]
  • 46. Na Y, Bai R, Ren Y, et al. IL1R1 polymorphisms are associated with ankylosing spondylitis in the Han Chinese population: a case‐control study. Int J Clin Exp Pathol. 2018;11:3759‐3764. [PMC free article] [PubMed] [Google Scholar]
  • 47. Ping XL, Sun BF, Wang L, et al. Mammalian WTAP is a regulatory subunit of the RNA N6‐methyladenosine methyltransferase. Cell Res. 2014;24:177‐189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Liu N, Dai Q, Zheng G, He C, Parisien M, Pan T. N6‐methyladenosine‐dependent RNA structural switches regulate RNA‐protein interactions. Nature. 2015;518:560‐564. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Dreyfuss G, Kim VN, Kataoka N. Messenger‐RNA‐binding proteins and the messages they carry. Nat Rev Mol Cell Biol. 2002;3:195‐205. [DOI] [PubMed] [Google Scholar]
  • 50. Xiao Q, Wu X, Deng C, et al. The potential role of RNA N6‐methyladenosine in primary Sjogren's syndrome. Front Med (Lausanne). 2022;9:959388. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Data Availability Statement

The raw data used and analyzed during the current study are available from the first author or the corresponding author on reasonable request. The microarray data generated in this study have been uploaded in the SRA data sets database, with the BioProject ID being PRJNA833263 and PRJNA833405, and will be released on May 31, 2024.

Articles from Immunity, Inflammation and Disease are provided here courtesy of Wiley

RESOURCES