Epigenetic combined with transcriptomic analysis of the m6A methylome after spared nerve injury-induced neuropathic pain in mice

Abstract

Epigenetic changes in the spinal cord play a key role in the initiation and maintenance of nerve injury-induced neuropathic pain. N6-methyladenosine (m6A) is one of the most abundant internal RNA modifications and plays an essential function in gene regulation in many diseases. However, the global m6A modification status of mRNA in the spinal cord at different stages after neuropathic pain is unknown. In this study, we established a neuropathic pain model in mice by preserving the complete sural nerve and only damaging the common peroneal nerve. High-throughput methylated RNA immunoprecipitation sequencing results showed that after spared nerve injury, there were 55 m6A methylated and differentially expressed genes in the spinal cord. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway results showed that m6A modification triggered inflammatory responses and apoptotic processes in the early stages after spared nerve injury. Over time, the differential gene function at postoperative day 7 was enriched in “positive regulation of neurogenesis” and “positive regulation of neural precursor cell proliferation.” These functions suggested that altered synaptic morphological plasticity was a turning point in neuropathic pain formation and maintenance. Results at postoperative day 14 suggested that the persistence of neuropathic pain might be from lipid metabolic processes, such as “very-low-density lipoprotein particle clearance,” “negative regulation of cholesterol transport” and “membrane lipid catabolic process.” We detected the expression of m6A enzymes and found elevated mRNA expression of Ythdf2 and Ythdf3 after spared nerve injury modeling. We speculate that m6A reader enzymes also have an important role in neuropathic pain. These results provide a global landscape of mRNA m6A modifications in the spinal cord in the spared nerve injury model at different stages after injury.

Introduction

Neuropathic pain (NP) is defined by the International Association for the Study of Pain as pain caused by a lesion or disease of the somatosensory nervous system (Scholz et al., 2019). The prevalence of chronic pain in adults in the USA ranges between 11% and 40%, and approximately 15–25% of chronic pain cases involve NP (Cohen et al., 2021). NP frequently results in disabilities and reduced quality of life for patients and has become a major contributor to the global disease burden (Rice et al., 2016). NP is generally caused by noxious stressors to the peripheral or central nervous system, including traumatic, infectious, neurodegenerative, vascular, autoimmune, tumor, and metabolic conditions (Baron et al., 2010; Scholz et al., 2019; Garcia-Pallero et al., 2022). The treatment of NP is still a challenge for pain clinicians, mainly owing to the poor understanding of the mechanism underlying NP (Baron et al., 2010; Finnerup et al., 2021). The spinal cord serves as the hub that connects the central and peripheral nervous systems and has an essential effect on the pathophysiological process of NP by regulating immunomodulation, inflammation, metabolism, oxidative stress, and mitochondria (Finnerup et al., 2021; Chu et al., 2022). However, the underlying mechanism requires further exploration.

Epigenetic modifications regulate chromatin remodeling and alter gene expression (Zhu et al., 2021). RNA methylation affects mRNA synthesis and degradation and protein binding by affecting mRNA structural stability. Methylation of RNA is achieved by methylesterases, called readers, writers and erasers. Readers, including YTH N6-methyladenosine RNA binding protein 1 (encoded by Ythdf1), YTH N6-methyladenosine RNA binding protein 2 (Ythdf2) and YTH domain containing 1 (Ythdc1), recognize RNA bases with methylation modifications. Writers, such as methyltransferase like 3 (encoded by Mettl3), methyltransferase like 14 (Mettl14) and WT1 associated protein (Wtap), catalyze methylation of adenosine. Demethylation of m6A-modified bases is mediated by two “eraser” enzymes, fat-mass and obesity-associated protein (encoded by Fto) and AlkB homolog 5 (Alkbh5).

Recently, increasing numbers of studies have suggested that RNA N⁶-methyladenosine (m6A) modification may participate in the transition and maintenance of chronic pain (Albik and Tao, 2021). In a previous study, spinal nerve ligation promoted the binding of the m6A eraser Fto to the matrix metallopeptidase 24 gene (Mmp24), which subsequently facilitated the translation of MMP24 in the spinal cord and ultimately contributed to NP genesis (Ma et al., 2021). Fto expression level was increased in the dorsal root ganglion after peripheral nerve injury (Li et al., 2020). Methyltransferase like 3, an m6A writer, was significantly decreased in an NP model and acted on primiR-150 maturation during NP progression (Zhang et al., 2022). Pan et al. (2021) reported that the down-regulation of spinal YTH N6-methyladenosine RNA binding protein 2, an m6A reader, coordinated with Mettl3 and contributed to the regulation of inflammatory pain in the spinal cord. Other studies showed that changes in RNA m6A modification in the spinal cord after peripheral nerve injury play a pivotal role in the initiation and maintenance of NP (Baron et al., 2010; Jiang et al., 2016; Lee et al., 2018; Ma et al., 2020; Wang et al., 2021). These studies have mostly focused on the role of m6A enzymes and their target genes in the spinal cord during NP. To the best of our knowledge, the global m6A modification status of mRNA in the spinal cord at different stages of NP remains poorly understood.

Therefore, in the current study, we conducted high-throughput methylated RNA immunoprecipitation sequencing (MeRIP-Seq) to examine the RNA m6A modification dynamics and RNA profiles of the spinal cord in a mouse model of spared nerve injury (SNI) at different time points. We also applied bioinformatics to explore the relationship between mRNA expression and m6A methylation in the spinal cord at different NP stages. Our results suggest that RNA m6A modification may participate in the regulation of different functions in the spinal cord during NP. These findings may help further the understanding of the mechanism of RNA m6A modification in NP.

Methods

Animals

All experimental procedures were approved by the Southern Medical University Administrative Panel on Laboratory Animal Care (approval No. NFYY-2020-0631, approval date: November 6, 2020), and experiments were conducted in accordance with the guidelines of Animal Use and Care of Southern Medical University. Efforts were made to minimize the stress and suffering of animals.

Seventy-six 6- to 8-week-old specific-pathogen-free wild-type C57BL/6J male mice (20–30 g) were purchased from the Experimental Animal Center of Southern Medical University (license No. SCXK (Yue) 2021-0041). The mice were maintained under standard laboratory conditions (12-hour circadian rhythm, adequate water and food, humidity 60 ± 10% and temperature 24 ± 2°C) unless otherwise indicated. The experimental animals were adapted to the environment in 1 week. The 76 mice were randomly assigned into SNI (n = 38) or sham modeling groups (n = 38).

SNI-induced NP model

The SNI-induced NP model was generated as previously reported (Decosterd and Woolf, 2000). Briefly, after mice were anesthetized by inhaling 3–5% sevoflurane (RWD, Shenzhen, Guangdong, China) and maintained at 3%, a longitude incision proximal to the left knee was made. The skin and biceps femoris were separated bluntly until the sciatic nerve and its three peripheral branches (the sural nerve, common peroneal nerve and tibial nerve) were exposed. The common peroneal and tibial nerve were carefully separated and then tightly ligated with 6-0 silk. The distal end of the ligated nerve was cut off, and 2–4 mm of the distal nerve stump was removed. Caution was made to avoid touching or stretching the intact sural nerve during the procedures. The muscles and skin were gently closed in two layers. In the sham group, the sciatic nerve and its three peripheral branches were exposed and then the muscles and skin were sutured. The above operations were performed on the left hind limb. The experimental overview is shown in Figure 1A.

Figure 1.

The expression levels of m6A writers, readers and erasers after SNI.

(A) Flow chart of the experimental analysis. (B) von Frey test after SNI at the 3^rd, 7^th and 14^th days. (C) Quantitative RT-PCR showing the upregulated levels of m6A readers (Ythdf2 and Ythdf3) at the indicated time points after SNI. (D–F) Quantitative RT-PCR showing the expression levels of m6A erasers (D), readers (E) and writers (F) at the indicated time points after SNI. Data are expressed as mean ± SD. *P < 0.05, **P < 0.01 (two-way analysis of variance analysis of variance (B) or one-way ANOVA analysis of variance followed by Dunnett’s multiple comparisons test (C–E). Three mice were used for qRT-qPCR and a total of 14 replicates were performed for all samples for each enzyme. MeRIP-Seq: Methylated RNA immunoprecipitation sequencing; qRT-PCR: quantitative reverse transcription-polymerase chain reaction; RNA-Seq: RNA sequencing; SNI: spared nerve injury.

Behavioral test

To determine the success of the model, we quantified the mechanical pain threshold of mice using the von Frey test. The following behavioral measurements were performed blindly (between 6 a.m. and 6 p.m.). For at least 3 days before the baseline test, the animals were acclimatized to the test environment every day. The mice were placed on an elevated wire mesh in a box and their hind paws were stimulated with a series of von Frey hairs. The hardness of the hairs increased logarithmically (0.02–2.00 g), perpendicular to the outside of the plantar surface. Each stimulation was separated by more than 30 seconds. The 50 % paw withdrawal threshold was determined using the up-and-down method (Chaplan et al., 1994).

Quantitative reverse transcription-polymerase chain reaction

mRNAs were analyzed in spinal cord samples at 3, 7 and 14 days after SNI and sham group samples. After deep anesthesia with 5% sevoflurane, mice were decapitated, and lumbar segments of the spinal cord were collected and stored with TRIzol (Invitrogen, Carlsbad, CA, USA) at –80°C. The sample was homogenized for 60 seconds at a frequency of 60 Hz for five times (Tuohe Technology, Shanghai, China). Next, 1 µg mRNA was reverse transcribed with an RT-Kit (Hiscript III Reverse Transcriptase, R302-01, Vazyme, Nanjing, China). mRNA expression was determined by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) using the TB Green Premix Ex Taq II (TaKaRa, Beijing, China) performed on the ABI QuantStudio 6 flex (Applied Biosystems, Carlsbad, CA, USA). The primers are shown in Table 1. The PCR reaction was performed as follows: cycling conditions began with an initial DNA denaturation step at 95°C for 20 seconds, followed by 40 cycles at 94°C for 15 seconds, 56°C for 30 seconds and 72°C for 25 seconds. Gapdh mRNA was used to normalize gene expression using the ΔΔCt method (Schmittgen and Livak, 2008).

Table 1.

Primers used for quantitative polymerase chain reaction

Gene	Description	Forward primer sequence (5’–3’)	Reverse primer sequence (5’–3’)
Mettl3	Methyltransferase like 3	CTG GGC ACT TGG ATT TAA GGA A	TGA GAG GTG TAG CAA CTT
Mettl14	Methyltransferase like 14	CTG AGA GTG CGG ATA GCA TTG	GAG CAG ATG TAT CAT AGG AAG CC
Fto	Fat mass and obesity associated	TTC ATG CTG GAT GAC CTC AATG	GCC AAC TGA CAG CGT TCT AAG
Alkbh5	alkB homolog 5, RNA demethylase	CGC GGT CAT CAA CGA CTA CC	ATG GGC TTG AAC TGG AAC TTG
Wtap	WT1 associating protein	TAG ACC CAG CGA TCA ACT TGT	CCT GTT TGG CTA TCA GGC GTA
Ythdc1	YTH domain containing 1	ATC TTC CGT TCG TGC TGT CC	GCA AGA GAC ACA TTC TCA TGG T
Ythdc2	YTH domain containing 2	GAA GAT CGC CGT CAA CAT CG	GCT CTT TCC GTA CTG GTC AAA
Ythdf1	YTH N6-methyladenosine RNA binding protein 1	ACA GTT ACC CCT CGA TGA GTG	GGT AGT GAG ATA CGG GAT GGG A
Ythdf2	YTH N6-methyladenosine RNA binding protein 2	GAG CAG AGA CCA AAA GGT CAA G	CTG TGG GCT CAA GTA AGG TTC
Ythdf3	YTH N6-methyladenosine RNA binding protein 3	GAT CAG CCT ATG CCA TAT CTG AC	CCC CTG GTT GAC TAA AAA CAC C
Rbm15	RNA binding motif protein 15	GCG AGT CCG CTG TGT GAA A	TCC CCA CGA GAA CTG GAG TC
Gapdh	Glyceraldehyde-3-phosphate dehydrogenase	CAG TGG CAA AGT GGA GAT TGT TG	TCG CTC CTG GAA GAT GGT GAT
Nlrp1b	NLR family, pyrin domain containing 1B	AGT AAT CTG GAG GGG TTG GAC	GTT GGC AGC CAG GGT ATA TCA
Asc	PYD and CARD domain containing	CGA CTC CAG ATA GTA GCT GAC AA	GAC AGT GCA ACT GCG AGA AG
CASP1	Caspase1	AGT CAC AAG ACC AGG CAT ATT CT	CTT GGA GAC ATC CTG TCA GGG

MeRIP-seq and RNA-seq

The spinal lumbar segments of 10 mice in each group were prepared for sequencing. Total RNA was isolated and purified using TRIzol reagent (Invitrogen) following the manufacturer’s instructions. The RNA amount and purity of each sample were quantified using a NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA). The RNA integrity was assessed on a Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA) with RNA integrity number > 7.0 and confirmed by electrophoresis on a denaturing agarose gel. Poly(A) RNA was purified from 50 µg total RNA using Dynabeads Oligo (dT)25-61005 (Thermo Fisher, Waltham, MA, USA) using two rounds of purification. The poly(A) RNA was fragmented into small pieces using the Magnesium RNA Fragmentation Module (NEB, Franklin Lake, NJ, USA, Cat# e6150) at 86°C for 7 minutes. The fragmented RNA was divided into two parts. One part was used for MeRIP-seq and incubated with immunomagnetic beads with m6A antibody to enrich for mRNA fragments containing m6A methylation. The cleaved RNA fragments were incubated for 2 hours at 4°C with m6A-specific antibody (rabbit, 1:1000, Synaptic Systems, Gottingen, Germany, Cat# 202003, RRID: AB_2279214) in immunoprecipitation buffer (50 mM Tris-HCl, 750 mM NaCl and 0.5% Igepal CA-630). The other part was used to directly construct a conventional transcriptome sequencing library. The immunoprecipitated (IP) RNA and mRNA were then reverse-transcribed to create cDNA using SuperScript™ II Reverse Transcriptase (Invitrogen, Cat# 1896649), and cDNA was used to synthesize U-labeled second-stranded DNAs with Escherichia coli DNA polymerase I (NEB, Cat# m0209), RNase H (NEB, Cat# m0297) and dUTP Solution (Thermo Fisher, Cat# R0133). An A-base was then added to the blunt ends of each strand, preparing them for ligation to the indexed adapters. Each adapter contained a T-base overhang for ligating the adapter to the A-tailed fragmented DNA. Single- or dual-index adapters were ligated to the fragments, and size selection was performed with AMPureXP beads (Beckman, Pasadena, CA, USA, Cat# A63880). After treatment of U-labeled second-strand DNAs with thermally degraded uracil-DNA glycosylase enzyme (NEB, Cat# m0280), the ligated products were amplified with PCR using the following conditions: initial denaturation at 95°C for 3 minutes, followed by eight cycles of denaturation at 98°C for 15 seconds, annealing at 60°C for 15 seconds and extension at 72°C for 30 seconds, with a final extension at 72°C for 5 minutes. The average insert size for the final cDNA library was 300 ± 50 bp. We then performed 2× 150 bp paired-end sequencing on an Illumina Novaseq™ 6000 (LC-Bio Technology Co., Ltd., Hangzhou, China) following the vendor’s recommended protocol.

Biological information analysis

Fastp software (https://github.com/OpenGene/fastp) (Chen et al., 2018) was used to remove the reads that contained adaptor contamination, the low quality bases and undetermined bases with default parameters. The sequence quality of IP (MeRIP-seq) and Input (RNA-seq) samples were also verified using Fastp.

We used HISAT2 (http://daehwankimlab.github.io/hisat2) (Kim et al., 2019) to map reads to the reference genome Mus musculus (Version: v101). Mapped reads of IP and input libraries were provided for R package exomePeak (v2.13.2) (https://bioconductor.org/packages/exomePeak) (Meng et al., 2014), which identifies m6A peaks (a region with higher m6A methylation) with the bed or bigwig format that can be adapted for visualization using IGV software (http://www.igv.org) (Robinson et al., 2011, 2017; Thorvaldsdóttir et al., 2013). MEME (http://meme-suite.org) (Bailey et al., 2015) and HOMER (http://homer.ucsd.edu/homer/motif) were used for de novo and known motif finding followed by localization of the motif with respect to peak summit. Called peaks were annotated by intersection with gene architecture using the R package ChIPseeker (https://bioconductor.org/packages/ChIPseeker) (Yu et al., 2015). StringTie (https://ccb.jhu.edu/software/stringtie) (Perez et al., 2016; Pertea et al., 2016; Kovaka et al., 2019; Shumate et al., 2022) was used to assess expression level for all mRNAs from input libraries by calculating FPKM (total exon fragments/mapped reads (millions) × exon length (kB)). The differentially expressed mRNAs were selected by |fold-change| ≥ 2.0 and P < 0.05 by R package edgeR (https://bioconductor.org/packages/edgeR) (McDermaid et al., 2019). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for the differentially methylated genes and differentially expressed genes using the R package Clusterprofile (Wu et al., 2021).

Conjoint analysis of MeRIP-seq and RNA-seq

From the results of differential genes and differential peak analysis, genes with altered methylation and altered mRNA expression were merged. We screened for both methylation and expression that met |fold-change| ≥ 2.0 and P < 0.05 and were presented in a four-quadrant diagram. GO and KEGG pathway analyses were performed using both differentially methylated and differentially expressed genes using the R package Clusterprofile.

Western blotting

Lumbar segments of spine cord samples obtained from sham and SNI (3, 7 and 14 days after injury) groups were ground using an automatic grinder (Tuohe Technology, Shanghai, China) and then lysed with radio immunoprecipitation assay lysis buffer (P0013C, Beyotime, Shanghai, China). A bicinchoninic acid protein assay kit (P0012, Beyotime) was used to measure the concentration of total protein. The protein samples were separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis and then transferred onto polyvinylidene difluoride membranes (Millipore, Merck KgaA, Darmstadt, Germany). After blocking with 5% non-fat milk for 1 hour, the membranes were incubated with primary antibodies PYD and CARD domain containing (ASC) (rabbit, 1:1000; Adipogen, San Diego, CA, USA, Cat# AG-25B-0006, RRID AB_2885200), Caspase1 (mouse, 1:1000; Adipogen, Cat#AG-20B-0042, RRID AB_2490248) or β-actin (rabbit, 1:1000; Cell Signaling Technology, Shanghai, China, Cat# 4970, RRID AB_2223172) overnight at 4°C. The membranes were then incubated with horseradish peroxidase-conjugated goat anti-rabbit IgG secondary antibody (1:10,000, Dia-an, Wuhan, Hubei, China, Cat# Q1002, RRID AB_2916184) or horseradish peroxidase-conjugated goat anti-mouse IgG secondary antibody (1:10,000, Dia-an, Cat# Q1001, RRID: AB 10888011) for 1 hour at 37°C. Enhanced chemiluminescent substrate (WBKLS0500, Millipore) was used to visualize the protein bands and ImageJ software 1.8.0 (National Institutes of Health, Bethesda, MA, USA) (Schneider et al., 2012) was used to quantify protein expression relative to β-actin level.

Enzyme linked immunosorbent assay

Lumbar segments of the spine cord samples obtained from sham and SNI (3, 7 and 14 days after injury) groups were ground using an automatic grinder and assayed using sandwich enzyme linked immunosorbent assay (ELISA) with well-matched antibodies. Tumor necrosis factor alpha (TNF-α) and interleukin-1beta (IL-1β) were detected using ELISA kits (Invitrogen Life Technologies, Frederick, MD, USA) following the manufacturer’s instructions. The Multimode Plate Reader (Spectrmax® i3x, CLARIOstar, Offenburg, Germany) was used for detection.

Statistical analysis

Data are expressed as the mean ± standard deviation (SD). The von Frey test data were statistically analyzed with two-way analysis of variance (ANOVA) followed by Dunnett’s multiple comparisons test. The qRT-PCR, western blot and ELISA results were statistically analyzed with one-way analysis of variance (Dunn’s multiple comparisons test or Tukey’s multiple comparisons test). P < 0.05 was considered statistically significant. Statistical analyses were performed using GraphPad Prism 8.4.2 (GraphPad Software, San Diego, CA, USA, www.graphpad.com).

Results

SNI modeling and expression of m6A writers, readers and erasers in the spinal cord lumbar segment after injury

After SNI modeling, mice were evaluated for plantar lateral pain using the von Frey test on the 3^rd, 7^th and 14^th days after surgery. The results showed that the mechanical pain threshold of mice was markedly decreased on the 3^rd, 7^th and 14^th days after surgery compared with the sham group (Figure 1B). These results confirm the successful establishment of the SNI model.

To investigate the m6A modification modulators in lumbar spinal cord after SNI, we examined the mRNA expression profiles of m6A writers (Mettl3, Mettl14, Rbm15 and Wtap), readers (Ythdc1, Ythdc2, Ythdf1, Ythdf2 and Ythdf3) and erasers (Fto and Alkbh5) (Figure 1C–F). The mRNA expression levels of Ythdf2 (P = 0.0040 at the 3^rd day; P = 0.0222 at the 14^th day) and Ythdf3 (P = 0.0037 at the 3^rd day; P = 0.0143 at the 14^th day) were markedly higher in the SNI group than in the sham group on the 3^rd and 14^th days after SNI modeling (Figure 1C). The other enzymes did not show marked changes after SNI (Figure 1D–F). These results suggested that m6A might be involved in disease mechanisms after SNI.

M6A methylation profile of spinal cord after SNI

We analyzed the level and distribution of m6A methylation in the lumbar spinal cord at different time points after SNI using MeRIP-seq (GEO accession No. GSE202166). We performed MIP-seq to sequence the enriched mRNA fragments with m6A methylation; RNA-seq of the samples was performed separately. Regions with high m6A methylation were obtained by exomePeak, and these mRNA regions were called m6A peaks. First, we determined the position of peaks in the mRNA coding region. The results demonstrated that the curve of the m6A peaks was mostly located in the coding sequence (CDS) and 3′ untranslated (3′UTR) region of mRNAs. We compared the curve distribution of each group within the CDS and 3′UTR regions and found that the curve in the sham group was lower than that in the SNI group, suggesting that the m6A peak density in the SNI group was higher than that in the sham group (Figure 2A and B). The percentage of the m6A peaks is shown in Figure 2B. Approximately 50% of m6A peaks were related to the 3′UTR in both the sham and SNI groups, and the proportions of m6A peaks related to exons were 36.33%, 36.46%, 31.82%, and 31.25% in the sham group and on the 3^rd, 7^th, and 14^th days after SNI, respectively (Figure 2B). The m6A enzymes including “erasers,” “writers” and “readers” bind to specific sequences of RNA containing the DRACH motif (in which D = G, A, or U; R = G or A; and H = C, A, or U), which is the consensus RNA sequence bound by m6A enzymes (Wei et al., 1976; Wei and Moss, 1977; Linder et al., 2015; Meyer and Jaffrey, 2017). Motif enrichment analysis indicated that the m6A peaks had different RNA sequences in the SNI and sham groups and the motifs in each group contained DRACH sequences (Figure 2C). Motif analysis verified the involvement of m6A methylesterases in the spinal cord at all time points after SNI. Comparison of the m6A peaks between the sham and SNI groups revealed 1978 mRNA peaks (1236 hypermethylation, 742 hypomethylation), 1142 mRNA peaks (631 hypermethylation, 510 hypomethylation) and 2107 mRNA peaks (1362 hypermethylation, 744 hypomethylation) at the 3^rd, 7^th and 14^th days after SNI, respectively (Additional Table 1 ^{(49.3MB, pdf)} ). These results indicate that m6A methylation was dynamic and ubiquitous after SNI.

Figure 2.

The degree and pattern of m6A modifications in the SNI and sham groups.

(A) Priority region and average distribution of m6A peaks at the indicated time points after SNI. (B) The distribution of m6A peaks at the 3^rd, 7^th and 14^th days after SNI and in the sham group. (C) m6A peak motif analysis includes DRACH in each group. Samples from ten mice were used for sequencing. DRACH: D = G, A, or U; R = G or A; and H = C, A, or U sequences. CDS: Coding sequence; m6A: N⁶-methyladenosine; SNI: spared nerve injury; UTR: untranslated region.

GO and KEGG analyses and genomes of differentially methylated genes

To elucidate the biological functions and signaling pathways of the genes modified by m6A during SNI, GO enrichment and KEGG pathway analyses were performed on genes with regions of m6A hypermethylation. In GO analysis, the biological functions of differentially methylated genes are divided into three categories: biological process, cellular component and molecular function. On the 3^rd, 7^th and 14^th days after SNI, regulation of transcription, regulation of transcription by RNA polymerase II, and cell cycle were simultaneously involved. Cell differentiation, oxidation-reduction process and apoptotic process were involved in the 3^rd and 14^th day groups after SNI. The GO terms of the 7^th day group included nervous system development, protein phosphorylation, and signal transduction. The GO terms of the 14^th group included protein phosphorylation, lipid metabolic process and transmembrane transport. The enriched GO terms are shown in Figure 3A–C.

Figure 3.

GO and KEGG pathway analyses of the differentially methylated genes after SNI.

(A–F) Significantly enriched GO terms and KEGG pathways of the differentially methylated genes at the 3^rd (A, D), 7^th (B, E) and 14^th (C, F) days after SNI. GO: Gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; SNI: spared nerve injury.

KEGG pathway analysis was performed to identify the key pathways of differential m6A-modified genes. The differentially methylated mRNAs on the 3^rd day after SNI correlated with the cyclic adenosine monophosphate (cAMP) signaling pathway, GABA, γ-aminobutyric acid (GABAergic) synapse, Notch signaling pathway and retrograde endocannabinoid signaling (Figure 3D). Transforming growth factor beta (TGF-β) signaling pathway, protein digestion and absorption, axon guidance and glycolysis/gluconeogenesis were involved on the 7^th day after SNI (Figure 3E). Retrograde endocannabinoid signaling, amino acid metabolism and degradation were involved on the 14^th day after SNI (Figure 3F). These results indicated that the spinal cord had a durative, pronounced inflammatory response after SNI modeling. The enrichment of protein digestion and absorption, glycolysis/gluconeogenesis, and axon guidance pathways on the 7^th day after SNI suggested that the early inflammatory response may cause morphological changes in neurological cells, especially neurons. The group on the 14^th day after SNI showed enrichment in lipid metabolic process; we conjectured that alterations in the lipid metabolism may play crucial roles in the late stage of pain induced by SNI. Together these results suggest that the inflammatory response dominated in the early stage after SNI, and this inflammatory response may induce metabolic abnormalities and morphological and functional changes in the cells of the nervous system.

Conjoint analysis of MeRIP-seq and RNA-seq after SNI

To explore the association between gene expression and m6A methylation, we performed RNA sequencing of the mRNAs obtained at each time point. Among the differentially expressed transcripts in the SNI compared with sham groups, 87, 60 and 81 transcripts were upregulated and 68, 66 and 95 transcripts were downregulated on the 3^rd, 7^th and 14^th days after surgery, respectively (fold-change ≥ 2.0 and P < 0.05) (Figure 4A). The results also showed that 21 genes were differentially expressed at all three-time points after SNI, which are shown in detail in the heatmap (Figure 4B and C). The top 10 differentially expressed genes of the SNI groups at the 3^rd, 7^th and 14^th days are listed in Table 2.

Figure 4.

Conjoint analysis of m6A methylation and mRNA expression after SNI.

(A) Histogram displaying the differentially expressed genes at the indicated time points after SNI. (B, C) Venn diagram and heatmap (|fold-change| ≥ 2.0 and P < 0.05) of differentially expressed genes at three different time points after SNI. Nlrp1 (red) was selected for subsequent validation. (D–F) Four-quadrant graph showing the association between mRNA m6A methylation and transcriptome level at 3, 7 and 14 days after SNi. diff. log2.fc: mRNA m6A methylation regulation; log2(fc): gene regulation. Down: downregulation; Hyper: hypermethylation; Hypo: hypomethylation; UP: upregulation. Dots indicate |fold-change|≥ 2.0, of which with P < 0.05 highlighted by red (Hyper-UP), yellow (Hypo-UP), blue (Hyper-Down) and green (Hypo-Down). Adgrd1: Adhesion G protein-coupled receptor D1; Atf3: activating transcription factor 3; CT010467: 18s RNA, related sequence 5; Gal: galanin and GMAP prepropeptide; Gm10222: predicted gene 10222; Gm15564: predicted gene 15564; Gm23935: dihydrolipoyllysine-residue succinyltransferase component of 2-oxoglutarate dehydrogenase complex, mitochondrial; Gm24270: predicted gene 15564; Gm5898: predicted gene 5898; Gpr151: G protein-coupled receptor 151; Ifi44: interferon-induced protein 44; Ifit1: interferon induced protein with tetratricopeptide repeats 1; Lars: leucyl-TRNA synthetase 1; Mettl7a3: methyltransferase like 7A3; Ppp1r3g: protein phosphatase 1, regulatory subunit 3G; Rn7sk: RNA component of 7SK nuclear ribonucleoprotein; Rps13-ps: ribosomal protein S13; SNI: spared nerve injury; Sprr1a: small proline-rich protein 1A; Tgtp1: interferon gamma induced GTPase; Tubb6: tubulin, beta 6 class V.

Table 2.

The top 10 altered genes at the 3^rd, 7^th and 14^th days after spared nerve injury (SNI)

Group	Gene name	Description	Fold change	Regulation	P-value
SNI Day 3	Gm26920	Predicted gene, 26920	–5.85	Down	1.00E-10
	Gm42878	Predicted gene 42878	–5.4	Down	0.0011
	Ccl7	Chemokine (C-C motif) ligand 7	5.17	Up	7.00E-11
	Rps12l1	Ribosomal protein S12-like 1	5.02	Up	1.00E-05
	Sprr1a	Small proline-rich protein 1A	4.41	Up	4.00E-13
	Ppp1r3g	Protein phosphatase 1, regulatory subunit 3G	4.2	Up	2.00E-12
	Cyp2f2	Cytochrome P450, family 2, subfamily f, polypeptide 2	4.07	Up	0.0003
	Snhg4	Small nucleolar RNA host gene 4	4.04	Up	0.0002
	Atf3	Activating transcription factor 3	3.89	Up	6.00E-28
	Gm5763	Predicted pseudogene 5763	3.11	Up	9.00E-05
SNI Day 7	Serpina1e	Serine (or cysteine) peptidase inhibitor, clade A, member 1E	–7.75	Down	8.00E-06
	Gm42893	Predicted gene 42893	–6.35	Down	9.00E-14
	Sprr1a	Small proline-rich protein 1A	5.53	Up	2.00E-19
	Fabp1	Fatty acid binding protein 1, liver	–5.27	Down	0.0007
	Ccl7	Chemokine (C-C motif) ligand 7	5.21	Up	4.00E-10
	Serpina3k	Serine (or cysteine) peptidase inhibitor, clade A, member 3K	–4.79	Down	1.00E-06
	Apob	Apolipoprotein B	–4.56	Down	0.0073
	Atf3	Activating transcription factor 3	4.37	Up	4.00E-30
	Serpina1a	Serine (or cysteine) peptidase inhibitor, clade A, member 1A	–4.32	Down	0.0045
	Gm24270	Predicted gene, 24270	–4.16	Down	0.0025
SNI Day 14	Fabp1	Fatty acid binding protein 1, liver	–6.99	Down	1.00E-05
	Serpina1e	Serine (or cysteine) peptidase inhibitor, clade A, member 1E	–6.43	Down	8.00E-05
	Sprr1a	Small proline-rich protein 1A	5.64	Up	1.00E-22
	Serpina3k	Serine (or cysteine) peptidase inhibitor, clade A, member 3K	–5.49	Down	0.0001
	Apoa5	Apolipoprotein A-V	–5.32	Down	0.0019
	Serpina1d	Serine (or cysteine) peptidase inhibitor, clade A, member 1D	–5.27	Down	6.00E-05
	Pigr	Polymeric immunoglobulin receptor	–4.69	Down	0.0018
	Pzp	PZP, alpha-2-macroglobulin like	–4.57	Down	0.0004
	Fga	Fibrinogen alpha chai	–4.53	Down	0.0103
	Cyp2a5	Cytochrome P450, family 2, subfamily a, polypeptide 5	–4.43	Down	0.0015

From the combined analysis of MeRIP-seq and RNA-seq data, differential m6A peaks with differential mRNA levels were divided into four groups: hypermethylation-upregulation, hypomethylation-upregulation, hypermethylation-downregulation and hypomethylation-downregulation. On days 3, 7 and 14 after SNI, 19, 11 and 14 differentially expressed genes with altered methylation were identified (|fold-change| ≥ 2.0 and P < 0.05), respectively (Figure 4D–F). The alterations of detailed gene expression and methylation are listed in Additional Table 2 ^{(52.7MB, pdf)} . These results suggested that methylation alterations after SNI modeling might cause changes in RNA expression.

Next, GO enrichment and KEGG pathway analyses were performed on the differential expressed genes with altered methylation (Figure 4D–F) that were identified from the conjoint analysis. The GO terms of the differential genes on the 3^rd day after SNI were immune system process, innate immune response, NLRP1 inflammasome complex assembly, interleukin-1 alpha/beta production and pyroptosis (Figure 5A). The NOD-like receptor signaling pathway, phosphoinositide 3-kinase (PI3K)-protein kinase B (Akt)-mammalian target of rapamycin (mTOR) signaling pathway and forkhead box protein O (FoxO) signaling pathway were enriched in KEGG (Figure 5B). These results suggested that on day 3 after SNI, the regulation of m6A methylation was associated with the inflammatory response and pyroptosis in the acute phase. On the 7^th day, GO terms were correlated with positive regulation of neurogenesis, positive regulation of neural precursor cell proliferation and lipid metabolic process (Figure 5C). KEGG pathways included the peroxisome proliferator-activated receptor (PPAR) signaling pathway, fatty acid degradation and AMP-activated protein kinase (AMPK) signaling pathway (Figure 5D). The results on day 7 after SNI showed that the quantity and morphology of the cells in the nervous system were affected. GO terms of the differential genes on the 14^th day after surgery included very-low-density lipoprotein particle clearance, negative regulation of cholesterol transport, membrane lipid catabolic process and negative regulation of lipoprotein lipase activity (Figure 5E). Calcium signaling pathway, cholesterol metabolism and metabolism of xenobiotics by cytochrome P450 were enriched in KEGG pathway analysis on the 14^th day after SNI (Figure 5F). These results suggested that altered m6A methylation was involved in maintaining long-term chronic pain through lipid metabolism on day 14 after SNI.

Figure 5.

GO and KEGG pathway analyses of the differentially expressed genes combined with analysis of m6A methylation and mRNA expression after SNI.

(A–F) Significantly enriched GO terms and KEGG pathways of the differentially expressed genes at the 3^rd (A, B), 7^th (C, D) and 14^th (E, F) days after SNI. GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; SNI: spared nerve injury.

Nlrp1b elicits an inflammatory response in the spinal cord after SNI

Nlrp1b was identified as a differentially expressed gene in groups on the 3^rd, 7^th and 14^th days after SNI in RNA-Seq; Nlrp1b was also identified by MeRIP-Seq and RNA-Seq in the 3^rd-day group. Nlrp1b is a member of the inflammasome of the NOD family and is involved in inflammation and apoptosis. Previous studies showed that Nlrp1b plays a vital role in the occurrence of NP (Li et al., 2019). This gene was up-regulated on the 3^rd day after SNI and its methylation level was decreased. We found that Nlrp1b mRNA increased on the 3^rd and 14^th days after SNI, with a more than two-fold increase on the 3^rd day (Figure 6A).

Figure 6.

The expression levels of Nlrp1b and inflammatory factors in the spinal cord after SNI.

(A) Quantitative RT-PCR showing the upregulated levels of Nlrp1b, Asc and Caspase1 (Casp1) mRNAs at the indicated time points after SNI. (B) Upregulation of Asc and Casp1 protein levels at the indicated time points after SNI. (C) Elevation of TNF-α and IL-1β protein levels at the indicated time points after SNI as detected by ELISA. Data are expressed as mean ± SD (n = 3). *P < 0.05, **P < 0.01, ***P < 0.001 (one-way analysis of variance followed by Tukey’s multiple comparisons test). ELISA: Enzyme linked immunosorbent assay: IL-1β: interleukin-1beta; RT-PCR: reverse transcription polymerase chain reaction; SNI: spared nerve injury; TNF-α: tumor necrosis factor-alpha.

To demonstrate whether the Nlrp1 inflammasome (composed of Nlrp1, Asc and Caspase1) elicited an inflammatory response as well as apoptosis, we detected the mRNA expressions of ASC and Caspase1. The mRNA levels of Asc and Caspase1 were up-regulated on the 3^rd and 7^th days after SCI (Figure 6A), and the protein expressions of ASC and Caspase1 were also increased on the 3^rd day after SNI (Figure 6B). IL-1β, a downstream effector molecule of the Nlrp1 inflammasome, induces apoptosis. Thus, we next examined IL-1β and TNF-α. The inflammatory cytokines IL-1β and TNF-α were up-regulated after SNI and gradually decreased with time (Figure 6C). In conclusion, our results found that the expression of Nlrp1b was up-regulated after SNI.

Discussion

In the present study, we performed MeRIP-seq and identified 1978, 1142 and 2107 peaks at the 3^rd, 7^th and 14^th days after SNI modeling, which represented different stages of NP. Approximately 80% of m6A peaks were related to the 3′UTR and exon in both sham and SNI groups. From the analysis of MeRIP-Seq and RNA-Seq, we found that (i) m6A modification may regulate the early inflammatory response during the acute stage of NP (0–3 days after SNI), (ii) m6A modification is involved in the synaptic and metabolic variation during the transitional stage of NP (3–7 days after SNI) and (iii) during the maintenance stage (7–14 days after SNI), m6A modification may interfere with lipid metabolism and voltage-gated calcium channels of synaptic strength. We also found that the mRNA expression levels of Ythdf2 and Ythdf3, two m6A readers, changed significantly after SNI, which suggested that m6A readers may play an essential role during the NP pathophysiology.

M6A readers have been shown to be involved in the splicing, exportation, decay and translation initiation of coding RNA in cancer (Huang et al., 2020). Ythdf2, one of the up-regulated readers identified in this study, accelerates the decay of m6A-modified transcripts (Wang et al., 2015). A previous study found that spinal Ythdf2 knockdown via intrathecal injection induced hypersensitivity to mechanical and thermal stimulation in a complete Freund’s adjuvant-induced inflammatory pain model (Pan et al., 2021). The differences in the Ythdf2 expression pattern between the study of Pan et al. and our group is likely because we used the SNI model, in which pain is induced through nerve injury. Our results suggested that the upregulation of Ythdf2 on day 3 after SNI might be involved in the inflammatory regulation of the acute stage of NP, and the increase in Ythdf2 on day 14 after SNI might be involved in the regulation of lipid metabolism during the maintenance stage. However, the detailed mechanism needs further investigation. Ythdf3, which was differentially upregulated on the 3^rd and 14^th days after surgery, is involved in the regulation of proinflammatory cytokine secretion (Xiao et al., 2022) and hepatic lipid homeostasis disorder (Wu et al., 2020). Our enrichment analysis suggested that Ythdf3 was involved in inflammation on the 3^rd after surgery and in lipid metabolism on the 14^th day after surgery. Recent studies have shown that the m6A readers Ythdf1–3 and Mettl3 exhibit synergistic effects on mRNA methylation regulation, which regulates mRNA stability and protein synthesis (Wang et al., 2015; Shi et al., 2017; Cai et al., 2022). The above studies suggest that m6A readers play a key role in mRNA regulation, protein expression and lipid metabolism. In our enrichment analysis of MeRIP-seq analysis and MeRIP-seq and RNA-seq co-analysis results at different time points, the changes in mRNA methylation were correlated with potential functional mechanisms at different disease stages of NP. Therefore, changes in m6A readers may play an important role in SNI-induced NP.

In the current study, we selected the 3^rd, 7^th and 14^th days after surgery to perform MeRIP-Seq and RNA-Seq, which correspond to the time points used in previous investigations and may represent the acute, transitional and maintenance stages of NP, respectively (Castro et al., 2017; Scholz et al., 2019; Li et al., 2020; Ma et al., 2021). NP is often described as occurring in stages, with the acute stage lasting approximately 7 days in rodents and the maintenance stage being permanent. From our functional enrichment results, we assumed three stages after SNI, using day 7 as the transitional time point between the acute and maintenance stages. GO enrichment and KEGG pathway analyses of MeRIP-seq revealed distinct mechanisms at different time points after SNI modeling. (i) During the acute stage (represented by the 3^rd day after SNI), our analysis indicated that the early inflammatory response may play a vital role to induce NP. The cAMP signaling pathways and the Notch signaling pathway enriched in our results were reported to induce microglial activation through pro-inflammatory signaling (Kim et al., 2002; Woo et al., 2003) and microglia M1 polarization in the spinal cord of different NP models (Jin et al., 2021). (ii) During the transitional stage (represented by the 7^th day after SNI), GO and KEGG enrichment analysis results, including axon guidance, nervous system development, protein digestion and absorption and glycolysis/gluconeogenesis, suggested that m6A modification may regulate synaptic plasticity, dendritic spine and metabolism through related genes (e.g. EphB3 (Henkemeyer et al., 2003; Perez et al., 2016), Rock1 (Swanger et al., 2015; Yan et al., 2019) and Trpc1 (Chu et al., 2020)). These results and studies indicated that the changes in synaptic plasticity and substance metabolism began to dominate the course of the NP during the transitional stage. Previous studies found that synaptic plasticity plays a vital role in the NP transition stage (Liu et al., 2017; Zhang et al., 2019). (iii) During the maintenance stage (represented by the 14^th day after SNI), amino acid metabolism, lipid metabolic process and negative regulation of apoptotic process suggested that substance metabolism was related to the NP mechanism after SNI. The role of lipids in pain signaling is well established (Wagner et al., 2020), whereas the correlation between m6A epigenetic modification and lipid metabolism in NP is not clear. In summary, our MeRIP-seq results indicate that inflammation may play an initial role in the acute pain stage and that it is then involved in the transition of NP formation, while in the maintenance stage, inflammation-induced axon guidance and metabolism alternations may more profoundly regulate chronic pain formation and maintenance.

Furthermore, by combining MeRIP- and RNA-seq data, we found differentially methylated m6A peaks and differentially expressed genes, such as Nlrp1b (Al Mamun et al., 2021) and Procr (Healy et al., 2021) on the 3^rd day after SNI, suggesting that the inflammatory response and pyroptosis were involved in the initiation and formation of NP. On the 14^th day after SNI, our results identified Cyp1b1 (Li et al., 2017; Yu et al., 2019; Singh et al., 2020), which suggested that lipid metabolism-related genes were involved in the maintenance of NP. KEGG and GO enrichment analyses in samples at the acute phase were mainly enriched in inflammation-related pathways, such as the Nlrp1 inflammasome. GO and KEGG enrichment analysis of transitional and maintenance stages were mostly enriched in the metabolic pathway and synaptic-related pathway, respectively. In general, the analysis of differential m6A peaks and the conjoint analysis of MeRIP and mRNA-seq suggest that the inflammatory process is involved in the acute phase of NP, whereas synaptic and metabolism changes may act as a turning point of NP, and eventually lipid metabolism affects synaptic function, resulting in NP consolidation and persistence.

We further verified that Nlrp1b, Asc and the related inflammatory cytokines like IL-1β were increased at all examined time points after SNI. Nlrp1b forms inflammasomes by interacting with Asc, increasing the expression of IL-1β. Previous studies demonstrated that Nlrp1 regulates NP through the IL-1β-induced inflammatory response, which substantially modulates the onset, formation, maintenance and persistence of chronic pain through an m6A epigenetic manner (Xiang et al., 2019; Sun et al., 2021; Yan et al., 2021; Yi et al., 2021). However, we only performed a simple validation of one of the target genes. Validation of the other target genes and conclusive evidence of m6A enzyme action on target genes still need to be examined in further studies. Our results suggest that m6A modification regulates Nlrp1 and inflammasome-related protein expression after SNI-induced NP, indicating the importance of studying m6A modification and the NOD family inflammasome in pain mechanisms and therapeutic strategies.

Previous studies showed that the sex of the mouse has a significant effect on the course of pain (Mogil, 2020). Given that most of the current studies on pain in mice still focus on male animals, we conducted this study on male mice. Subsequent experiments with female mice should be performed.

In conclusion, the present study depicted the m6A RNA methylation landscape at different time points after SNI surgery through MeRIP-Seq, which provided detailed epigenetic information during the NP pathophysiological process. Combined MeRIP-Seq and RNA-Seq analysis, screening for both differentially expressed and methylation-altered genes, demonstrated the regulatory effect of m6A methylation on gene expression while suggesting different underlying functional mechanisms in the development of NP disease. Our results indicated that the m6A readers may be closely related to the pathophysiological development of NP through an inflammasome-modulated mechanism. The limitation of the current study is that we did not validate the specific enzymes acting on NP or the binding of target genes and enzymes to elucidate the molecular biological mechanisms at different time points. Further study on these points is required.

Additional files:

Additional Table 1 ^{(49.3MB, pdf)} : Differential m6A peaks.

Additional Table 1

Differential m6A peaks.

Additional Table 2 ^{(52.7MB, pdf)} : Methylation-associated and differentially expressed genes.

Additional Table 2

Methylation-associated and differentially expressed genes.