Dysregulated N⁶-methyladenosine modification in peripheral immune cells contributes to the pathogenesis of amyotrophic lateral sclerosis

Abstract

Amyotrophic lateral sclerosis (ALS) is a progressive neurogenerative disorder with uncertain origins. Emerging evidence implicates N6-methyladenosine (m⁶A) modification in ALS pathogenesis. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) and liquid chromatography–mass spectrometry were utilized for m⁶A profiling in peripheral immune cells and serum proteome analysis, respectively, in patients with ALS (n = 16) and controls (n = 6). The single-cell transcriptomic dataset (GSE174332) of primary motor cortex was further analyzed to illuminate the biological implications of differentially methylated genes and cell communication changes. Analysis of peripheral immune cells revealed extensive RNA hypermethylation, highlighting candidate genes with differential m⁶A modification and expression, including C-X3-C motif chemokine receptor 1 (CX3CR1). In RAW264.7 macrophages, disrupted CX3CR1 signaling affected chemotaxis, potentially influencing immune cell migration in ALS. Serum proteome analysis demonstrated the role of dysregulated immune cell migration in ALS. Cell type-specific expression variations of these genes in the central nervous system (CNS), particularly microglia, were observed. Intercellular communication between neurons and glial cells was selectively altered in ALS CNS. This integrated approach underscores m⁶A dysregulation in immune cells as a potential ALS contributor.

Keywords

amyotrophic lateral sclerosis / N ⁶-methyladenosine / epi-transcriptome / proteome / single cell RNA sequencing analysis / CX3CR1

1 Introduction

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that affects upper and lower motor neurons, normally culminating in respiratory failure and death within 5 years of disease onset [1–4]. Despite recent expansion of the disease-associated gene list, the etiology of ALS remains largely unclear in sporadic patients without known genetic causes [5–7]. Several common ALS mutant genes, such as C9orf72 [8], TARDBP [9], and SOD1 [10], are notably implicated in aberrant immune responses of the central nervous system (CNS), and mutations in OPTN, TBK1, and TNIP1 can similarly compromise immune function and induce neuroinflammation [11]. However, the complex interplay between genetics and dysregulated inflammatory pathways in ALS is not fully understood, partly due to a lack of evidence that ALS is primarily an immunogenic neurodegenerative disorder because immunoregulatory drugs have so far failed to impede disease progression in most clinical trials [12,13]. Therefore, investigating the potential role of immune dysregulation in the disease is crucial to advance the understanding of the intricate mechanisms underlying ALS pathogenesis, possibly necessitating the identification and characterization of novel regulatory mechanisms.

The N⁶-methyladenosine (m⁶A) modification of eukaryotic mRNA is the most common post-transcriptional modification to regulate mRNA maturation, stability, nuclear export, and translation efficiency [14–16]. It influences biological processes via m⁶A methyltransferases (writers), m⁶A demethylases (erasers), and m⁶A-specific binding proteins (readers) [17,18]. By screening for RNA binding proteins (RBPs) that preferentially bind to m⁶A-quantitative trait loci (QTL) regions, researchers have identified the TDP-43 encoding gene TARDBP as a candidate m⁶A reader gene [19]. This finding was supported by recent findings indicating that TDP-43 may act as an indirect m⁶A reader [20]. Mechanistically, the aggregation of TDP-43 may possibly bind to the m⁶A-modified mRNA via interacting with RBPs HNRNPA2B1 or YTHDF2, further leading to neurotoxic type I interferon responses [20–22]. Given that the mRNA transcripts encoding type I interferons are heavily m⁶A-modified [23], and that several genes encoding m⁶A binding proteins are ALS-implicated risk genes, impaired mRNA metabolism and immune dysregulation are likely to converge on ALS pathogenesis.

Previous research on ALS has focused on non-cell autonomous neuronal loss caused by aberrant glial cell activation, whereas the roles played by infiltrated peripheral myeloid cells following blood–brain barrier impairment have not been fully explored [24–27]. Motor neurons are constantly exposed to cytokines produced by circulating immune cells via their distal motor axon and neuromuscular junction. In fact, the incidence of ALS with prior diagnosis of autoimmune disorders is significantly higher than expected, indicating a potential link between dysfunction in peripheral immune system and the development of ALS [28]. This fact is consistent with the observation that the onset and progression of ALS are associated with innate and adaptive immunity, likely reflecting the unique pro-inflammatory gene expression profiles adopted by peripheral monocytes [29], macrophages [30], and T cells [31]. Moreover, m⁶A QTLs appear to be enriched for variants of complex traits related to blood cell and autoimmunity [32]. Thus, clarifying the regulatory mechanism of dynamic mRNA methylation in peripheral immune cells could be a promising approach to elucidate ALS pathophysiology.

This study aims to examine the role of m⁶A methylation in peripheral immune cells in the pathogenesis of ALS through the implementation of an integrated approach that combines epi-transcriptomic and proteomic analyses. Specifically, the study aims to identify genes with differential methylation and expression patterns in peripheral immune cells and investigate their expression profiles in CNS by utilizing single-cell sequencing data derived from the primary motor cortex of patients with ALS and healthy controls. CellChat was employed to explore the altered intercellular communication network in ALS (Fig.1). The investigation revealed distinctive patterns of m⁶A modification in the peripheral immune cells of patients with ALS, which primarily affect cell migration. The results also suggested altered intercellular signaling between neurons and glial cells in ALS. These preliminary discoveries provide insights into the potential involvement of immune cell dysfunction in the pathogenesis of ALS and advocate for further functional validation in the direction of m⁶A-associated regulatory mechanisms. Overall, the integrated approach employed in this study represents a valuable basis for exploring the potential pathological significance of m⁶A methylation in ALS, shedding light on the aspects of this devastating disease that were previously underestimated.

2 Materials and methods

2.1 Subjects and sample collection

The study design and sample collection were approved by the Research Ethical Boards of Peking Union Medical College Hospital (PUMCH, No. JS-2624). Prior to participation, all subjects were informed, and they signed a written consent form or had a relative sign on their behalf. Blood samples were obtained from 16 patients diagnosed with clinically definite, probable, or probable laboratory-supported ALS in accordance with El Escorial criteria and from six matched healthy controls (Table S1). Only patients without signs of cognitive impairment were recruited to limit the effects of phenotypic heterogeneity, and the patients were stratified into two subgroups on the basis of the presence of bulbar symptoms at the time of sample collection (ALS_b for bulbar (n = 7) and ALS_s for spinal (n = 9)). Peripheral blood (10 mL) was collected using PAXgene® Blood RNA Tubes. Serum was separated through centrifugation at 3500 rpm for 10 min at 4 °C and then divided into aliquots. All samples were stored at −80 °C within 1 h of collection until use. The amount and purity of total RNA were measured with a NanoDrop ND-1000 spectrophotometer, and RNA integrity was assessed using denaturing agarose gel electrophoresis and Agilent Bioanalyzer 2100.

2.2 Immortalized cell lines and cell migration scratch assay

RAW264.7 murine macrophage cell line was obtained from ATCC and cultured in complete high-glucose Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin solution. The cells were maintained in a humidified atmosphere with 5% CO₂ at 37 °C and passaged every 2–3 days. For CX3CR1 stimulation and inhibition, RAW264.7 cells were seeded overnight onto six-well plates at a density of 5 × 10⁵ cells/well and then treated with 1 μg/mL lipopolysaccharide (LPS, Solarbio, #L8880) for 4 h. Afterwards, the cells were incubated for 24 h in the presence of 0.2 μg/mL CX3CR1 ligand fractalkine (FKN, OriCell, #HECXP-01031) and 10 μM high-affinity selective CX3CR1 inhibitor AZD8797 (Chemegen, #C105220) [33]. For cell migration scratch assay, RAW264.7 macrophages were seeded in six-well plates and grown to confluence. Scratches were made with a sterile 20 μL pipette tip after 4 h stimulation with LPS. The stimulated cells were rinsed two times with PBS and then subjected to serum-free medium with/without AZD8797. Images were taken at 0 and 12 h following the scratch, and the scratch areas were measured using ImageJ software [34].

2.3 MeRIP-seq and MeRIP-qPCR

Peripheral blood cells were used to extract the total RNA, with 2.5 μg of the RNA fragmented into 100-nucleotide fragments through 5 min incubation at 94 °C by using NEBNext Magnesium RNA Fragmentation Module (NEB, #E6150S). Then, 10% of the fragmented RNA was set aside as input control, whereas the remainder was incubated with 0.2 μg anti-m⁶A polyclonal rabbit antibody (Synaptic Systems, #202003) that was conjugated to pre-washed Dynabeads Protein A (Thermo Fisher Scientific, #10002D) for 2 h at 4 °C with gentle rotation. After the incubation, the beads were washed three times with 1× RIP buffer (150 mM NaCl, 10 mM Tris-HCl (pH 7.5), and 0.05% Triton X-100), and the bound RNA was eluted from the beads and purified with TRIzol reagents (Thermo Fisher Scientific, #15596018) in accordance with the manufacturer’s instructions. Sequencing libraries were prepared from the input and immunoprecipitated RNA by using the SMARTer Stranded Total RNA-Seq Kit (Takara, #634413) and then sequenced on an Illumina NovaSeq 6000 platform in paired-end 150-base mode. After adapter sequences at the 3′ ends and low-quality bases (Q < 15) were removed with fastp (version 0.19.5), the remaining reads were evaluated with FastQC (version 0.11.4) and aligned to the human reference genome (GRCh38) by using STAR (version 2.7.7a) and HISAT2 (version 2.2.1). Peaks were identified using exomePeak2 (version 1.0.0) and visualized using Integrative Genomics Viewer (IGV). Homer (version 4.11) was used to perform motif enrichment with a motif length of six nucleotides. R package DESeq2 (version 1.10.1) was used to analyze differential peaks between groups. The calculated significance threshold (P≤ 0.05) of Fisher’s test was used to select significantly enriched GO terms on the basis of biological process and KEGG pathways by using clusterProfiler (version 2.4.2).

MeRIP-qPCR analysis was conducted on the basis of the predicted m⁶A sites by using SRAMP, and the input and immunoprecipitated RNA were prepared using the same protocol as described above but with scaled-down reagents. RT-qPCR analysis was then performed in accordance with the experimental procedure described below to validate the modification of m⁶A in CX3CR1.

2.4 RT-qPCR

A qScript cDNA Synthesis Kit (Quantabio, #95048-025) was used to prepare the cDNA, followed by RT-qPCR on a QuantStudio 12K Flex Real-Time PCR System (Life Technologies) using the SYBR Premix Ex Taq (Takara, # 4472903). The results were normalized to GAPDH, and the relative expression of mRNAs was quantified using the 2^–∆∆Ct method. Details of the primers used are shown in Table S2.

2.5 RNA-seq

The total RNA from LPS-stimulated RAW264.7 cells, with or without FKN and AZD8797 priming, was extracted using TRI Reagent and fragmented. The BGISEQ platform was utilized to generate sequencing libraries, and the libraries with 150 bp paired-end reads (PE150) were sequenced to achieve a depth of around 45 million reads per sample. After the reads were filtered by removing adapter sequences and low-quality reads, the remaining reads were aligned to the “Mus musculus” reference genome (GCF_000001635.27_GRCm39) by using HISAT (version 2.0.4). RSEM (version 1.2.8) was used for all samples to quantify gene expression. DEseq2 was used for differential expression analysis. Pathway enrichment analysis of genes with significantly differential expression was conducted using Gene Set Enrichment Analysis (GSEA) [35].

2.6 Serum FKN quantification (ELISA)

Blood samples were collected in Vacutainer Serum Tubes (BD Biosciences) from patients with ALS and healthy controls (n = 6). They were allowed to clot at room temperature before being separated by centrifugation (1000 g at room temperature for 10 min) within 4 h of collection and then stored at –80 °C until batch analysis. Soluble FKN levels were measured via ELISA (Human CX3CL1 ELISA KIT, #SEKH-0067, Solarbio) in accordance with the manufacturer’s instructions.

2.7 Liquid chromatography–mass spectrometry (LC–MS)

Serum samples were processed for protein analysis in the following manner. High-abundance proteins were depleted using High-Select Top 14 Abundant Protein Depletion Resin (Thermo Fisher, #A36369). The protein concentration was determined using the Bradford method. The protein extracts were then subjected to reduction with 25 mmol/L dithiothreitol at 37 °C for 60 min, followed by alkylation with 50 mmol/L iodoacetamide at room temperature for 30 min in the dark. The mixture was transferred to a 10 kDa ultrafiltration filter and washed with 20 and 50 mmol/L triethylammonium bicarbonate buffer. Trypsin digestion was performed overnight at 37 °C at a trypsin-to-protein mass ratio of 1:50. The resulting tryptic peptides were dried, resuspended in mobile phase A (2% (v/v) acetonitrile, 98% (v/v) ddH₂O, pH 10), and centrifuged. The peptides were loaded onto a XBridge peptide BEH C18 column (4.6 × 15 mm, 3.5 μm, 130 Å) and eluted using stepwise injections of mobile phase B (98% acetonitrile, 2% ddH₂O, pH 10) in the RIGOL L-3000 system (RIGOL, China). The chromatographic run lasted for 46 min with a flow rate of 1 mL/min, starting with 5% mobile phase B for 5 min, followed by a gradient from 5% to 30% mobile phase B over 31 min; a gradient from 30% to 95% mobile phase B over 5 min; and finally, a 5 min equilibration with 5% mobile phase B. Fractions were collected using the step gradients of mobile phase B at 1 min intervals.

For MS analysis, 1 μg of the collected samples was loaded onto an EASY-nLC1000 system connected to a Q Exactive HF mass spectrometer (Thermo Scientific). Peptides were eluted using a binary solvent system consisting of 99.9% H₂O, 0.1% formic acid (phase A), 99.9% ACN, and 0.1% formic acid (phase B) at a flow rate of 0.6 μL/min. The elution gradient comprised 6%–30% phase B over 69 min, followed by 30%–42% phase B over 12 min, 42%–100% phase B over 4 min, and a 5 min wash at 100% phase B. The eluent was introduced to an Orbitrap Exploris 240 mass spectrometer via an EASY-Spray ion source with specific ionization parameters (spray voltage, 2.2 kV; capillary temperature, 320 °C; declustering potential, 100 V). For data-dependent acquisition (DDA) MS, a full-scan MS was conducted with specific settings (scan range, 300–1500 m/z; isolation window, 2 m/z; max injection time, 20 ms; AGC target, 3 × 10⁶; collision energy, 30%; resolution, 60 k), followed by 20 MS/MS scans (max injection time, 22 ms; dynamic exclusion duration, 45 s; AGC target, 7.5 × 10⁴; included charge states, 2–6; resolution, 15 k). The DDA spectra were characterized using Proteome Discoverer (version 2.3.0.523) analysis software with default settings. The search criteria included carbamidomethylation (cysteine) as a fixed modification, and oxidation (methionine) and acetyl (protein N terminus) as variable modifications. The precursor mass tolerance was 10 ppm, and the fragment-ion mass tolerance was 0.02 Da. For data-independent acquisition (DIA) MS, an MS1 scan was conducted with specific settings (scan range, 300–1300 m/z; AGC target, 3 × 10⁶; collision energy, 32%; resolution, 30 k), followed by a MS2 scan with multiple scan windows. The DIA data were analyzed using a Spectronaut pulsar (Biognosys, version 15.0.210615.50606) with default settings, including retention time prediction and correction, mass calibration, decoy generation, interference correction, and FDR estimation. The raw files were converted into Spectronaut file format to analyze the DIA runs with the spectral library, calibrated in the retention time dimension, and then used for targeted data analysis with the spectral library without additional recalibration.

2.8 Parallel reaction monitoring (PRM)

In the independent cohort of eight patients with ALS and eight healthy controls (Table S3), selected proteins from the discovery cohort were selected. Peptide samples were separated using a reverse-phase column (Dr. Maisch GmbH) with a 250 mL/min flow rate of increasing buffer B concentration (2%–60%) over 98 min on an HPLC system (Thermo Fisher Scientific). The separated peptides were analyzed on a Q-Exactive HFx (Thermo Fisher Scientific) using PRM settings of 30 000 resolutions, 2 × 10⁵ AGC target, 1.6 m/z isolation window, and 60 ms maximum ion injection time. Data analysis was conducted using Skyline software. Peaks were selected manually on the basis of retention time and dot product, and the total intensity value of the correct peak area was extracted for peptide quantification.

2.9 Analysis of single-cell mRNA sequencing data

The gene-barcode matrix of the primary motor cortex’s single-cell sequencing data derived from 17 patients with ALS and 17 pathologically normal (PN) controls (GSE174332) [36] was analyzed using Seurat R toolkit (version 4.2.1) [37]. The quality control parameters included < 50 genes/cell, < 3 cells per genes, and > 7% mitochondrial genes. PCA was performed on the top 2000 most variable genes, and the first 15 principal components were used for k-means clustering and k-nearest neighbor graph construction with a clustering resolution parameter of 0.5. Cell clusters were visualized using the Uniform Approximation and Projection (UMAP) method and annotated on the basis of cell type-specific markers. Differential gene testing was conducted using the FindMarkers function with non-parametric Wilcoxon rank sum test. GO and KEGG enrichment analysis was performed to summarize the differential genes functionally. The DM_score was calculated using the AddModuleScore function in Seurat R package. Random Forest regression and LASSO regression were used to analyze the data by using the randomForestSRC and glmnet R packages, respectively. CellChat (version 1.6.1) was used to infer intercellular signaling networks on the basis of the curated ligand–receptor interaction database [38].

2.10 Statistical analysis

Data were statistically analyzed using GraphPad Prism (version 7.0) and R (version 4.2.2). Unpaired two-tailed Student’s t-test or two-way ANOVA was used to compare groups as appropriate. The results were presented as means ± standard deviation (SD) with at least three biological replicates. A P value of less than 0.05 was considered statistically significant.

3 Results

3.1 Widespread mRNA hypermethylation in peripheral immune cells from patients with ALS

Transcriptome-wide m⁶A modification profiling of peripheral blood cells from patients with ALS and healthy controls was conducted to explore the potential role of m⁶A methylation in ALS pathogenesis. A total of 36.38–62.23 million reads were generated from each m⁶A-seq library after quality control. The results revealed that the distribution pattern of m⁶A modification across genes was similar between the two groups, with most m⁶A peaks preferentially located in the transcription start site and stop codon site (Fig.2), and most methylated mRNAs containing one or two peaks (Fig.2). The peak motif observed in this study was consistent with the known consensus m⁶A methylation motif (Fig.2). Moreover, the ALS samples had a greater number of hypermethylated peaks than the controls, whereas fewer hypomethylated peaks were identified (log₂|FC| > log₂(1.5), adjusted P < 0.05; Fig.2). The candidate genes that were potentially modulated by RNA methylation were assessed through the integration of m⁶A methylation and RNA expression profiles (Fig.2). Specifically, 109 and 160 deferentially expressed genes with differential m⁶A methylation peaks were identified in the ALS_b and ALS_s samples, respectively (Fig.2 and Table S4). These results demonstrated the probable involvement of m⁶A methylation in the regulation of gene expression, thus highlighting the significance of investigating RNA modification in disease pathogenesis.

3.2 Candidate m⁶A-regulated genes screened by differential methylation and expression

GO and KEGG enrichment analyses were conducted to further explore the potential biological implications of differential methylation in the context of ALS. The results unveiled that the differentially methylated genes are associated with various biological processes, including chromatin modification, histone modification, and endocytosis and inflammatory signaling pathways (Fig.3 and 3B). The functional pathway analyses did not reveal any significant differences between the two ALS subgroups, thus allowing this study to focus on common differentially methylated and expressed genes that may be relevant to ALS pathogenesis. A total of 15 genes that were differentially methylated and expressed in both ALS subgroups were identified, some of which are crucial regulators of immune cells and neurons (Fig.3). Next, qRT–PCR to verify the expression levels of several genes selected on the basis of their biological relevance (Fig.3). This study focused on CX3CR1 given the evidence supporting its involvement in ALS pathogenesis. The examination of the read density by using IGV showed enhanced m⁶A methylation on the CX3CR1 transcript in ALS samples (Fig.3). Consistent with this finding, the SRAMP browser tool [39] predicted several m⁶A modification sites located on the CX3CR1 RNA sequence, with high confidence scores, which overlapped with the sequencing results. The MeRIP–qPCR analysis further confirmed the differential methylation of CX3CR1 at the selected region (P = 0.0037, Fig.3). Meanwhile, the online m⁶A target database suggested that Mettl3 knockout and Mettl14 knockdown are associated with increased CX3CR1 gene expression in different cell lines [40]. The serum levels of the CX3CR1 ligand FKN were assessed, and no statistically significant distinctions between patients with ALS and controls was found (Fig.3). These findings underscored the probable involvement of m⁶A-mediated regulation of CX3CR1 in the pathogenesis of ALS.

3.3 CX3CR1 signaling is involved in regulating macrophage migration

Due to the prevalence of immune dysregulation characterized by increased proinflammatory macrophages in patients with ALS [29] and the active involvement of m⁶A modification in regulating macrophage functions [41], further investigations into how the macrophage cell line RAW264.7 respond to stimulation and inhibition of CX3CR1 signaling in vitro were conducted. LPS-primed RAW264.7 cells were treated with either FKN or AZD8797 for 24 h before subjecting them to mRNA-seq to detect transcriptomic changes (log₂|FC| > log₂(1.5), adjusted P < 0.05) for pathway enrichment analyses. Consistent with the known biological functions of CX3CR1, the KEGG results indicated that activating and blocking CX3CR1 mainly affected cytokine–cytokine receptor interaction (Fig.4). In line with this finding, GSEA using GO gene sets suggested that CX3CR1 signaling was associated with cell mobility, chemotaxis, and cellular response to interferons (Fig.4 and 4C). The differential expression of several immune-associated genes selected based on the sequencing results and literature were verified by RT-qPCR (Fig.4). The scratch assays showed that cell migration was reduced by AZD8797 treatment (Fig.4). Collectively, these findings suggested that CX3CR1 signaling is likely involved in the pathogenesis of ALS by regulating the migration function of peripheral immune cells.

3.4 Serum proteomic analyses supported the altered cell migration function in ALS

Previous studies suggested that proteome homeostasis disturbance is a key factor underlying cellular dysfunction in ALS pathogenesis [42]. LC–MS was employed to analyze the serum proteome of patients with ALS to investigate the association between altered immune cell phenotypes and differential m⁶A modification at the protein level. A total of 3122 distinct peptides (detectable in at least 50% of all samples) representing 711 proteins were identified via DIA. Specifically, 33 proteins were significantly altered in ALS_b (27 downregulated and six upregulated), and 39 proteins were significantly altered in ALS_s (32 downregulated and seven upregulated) compared with controls (log₂|FC| > log₂(1.3), adjusted P value < 0.05; Table S5). The differential proteins accurately clustered the subjects, thereby validating the arbitrary thresholds imposed for filtering (Fig.5). Consistent with previous findings in ALS and frontotemporal dementia (FTD) serum samples, the present study identified downregulated APOA2, COMP, and CRTAC1 and upregulated S1008A8 [43,44]. By integrating the results with m⁶A-seq data, several identified proteins were observed to be differentially m⁶A-modified, thus supporting the potential regulatory functions of m⁶A modification at the protein level (Fig.5). The STRING web tool was used to investigate the possible protein–protein interactions (PPIs) of the differential proteins (Fig.5). The analysis revealed that the differentially expressed proteins were enriched for extracellular matrix organization, cell adhesion, and regulation of collagen biosynthetic process. A notable detail that several network nodes were differentially m⁶A-modified, suggesting a potential involvement of m⁶A modification in regulating cell motility, although such observation alone did not establish a direct causal link. The LC–MS results were validated in an independent cohort via PRM. This assay enabled the determination of protein levels for 23 specific proteins in serum samples, with selection guided by the results of PPI pathway analysis. Among these proteins, statistically significant differences were observed in the serum levels of ENG, CDH13, TNXB, CLEC3B, and DEFA1 (Fig.5), consistent with the MS results.

3.5 Expression patterns of differentially methylated genes in ALS motor cortex

The observation of widespread RNA hypermethylation in post-mortem spinal cords of patients with ALS led to the further investigation of whether the differentially methylated genes identified in peripheral immune cells may affect cellular functions within CNS. ScRNA-seq data derived from the human primary motor cortex of patients with ALS and controls were analyzed [37]. Neurons (excitatory and inhibitory), glial cells, and vascular cells were identified on the basis of canonical CNS cell markers before further sub-clustering (Fig.6). The average expression levels of the differentially methylated genes identified in peripheral immune cells (DM_score) were calculated using Seurat’s AddModuleScore function. The DM_score was highly heterogenous in glial cell populations (Fig.6), with microglia displaying the highest degree of heterogeneity (Fig.6). The DM_score was significantly lower in ALS-derived cells, particularly for microglia (Fig.6), indicating that on average, these genes were differentially expressed in ALS primary motor cortex. Random Forest and LASSO regression analyses were further conducted to refine the candidate genes most likely involved in ALS pathogenesis (Fig.6 and 6F). Eleven overlapping genes were identified, several of which were involved in regulating cell mobility and immune cell infiltration (CORO1B [45], ARHGEF18 [46], and ISYNA1 [47]), as shown in Fig.6. Notably, these genes were top-ranked in the Random Forest regression model, supporting the critical role of cell–cell interaction in ALS pathogenesis. The findings suggested that differential m⁶A modification may similarly regulate immune cell characteristics in CNS and the periphery, and intercellular communication in CNS is an important aspect requiring further clarification.

3.6 Altered intercellular signaling network between neuronal and non-neuronal cells in ALS

Given the potential involvement of m⁶A-mediated regulation in influencing the migration of peripheral immune cells in the context of ALS pathogenesis, the perturbed patterns of intercellular communication existing between neuronal and immune cell populations within CNS were explored. The CellChat algorithm was utilized to conduct a comparative analysis of signaling patterns in the motor cortex of ALS and control subjects. The global communication between neuronal and non-neuronal cell clusters was quantified. The inferred number of interactions in ALS generally increased, whereas the interaction strength in inhibitory neurons with oligodendrocytes and excitatory neurons decreased (Fig.7). By calculating the information flow for each signaling pathway, which is defined as the sum of communication probability among all pairs of cell groups in the inferred network, prominent changes in the pathway information flow in ALS were observed as follows: turned off (e.g., ENHO, KIT, and CCK), decreased (e.g., TGFb, GAS, and CX3C), or increased (e.g., ANGPT, EDN, and VEGF; Fig.7). Then, the Euclidean distance was computed to measure dissimilarity between pairs of signaling pathways shared by ALS and PN. Significant differences were found in the communication network architecture of signaling pathways for ANGPT, PROS, VEGF, CX3C, and TGFb in ALS compared with PN (Fig.7), implying that these pathways are greatly altered in ALS (larger Euclidean distance). Heatmaps of the pathway-specific signaling flows further indicated that the inferred alterations mainly involved excitatory and inhibitory neurons (Fig.7). Excitatory neuron subtypes were the dominant sources of CX3C ligand to microglia in ALS and PN, but a minor contribution of signals from inhibitory basket cells was found in PN, which was absent from ALS cells (Fig.7 and 7F). Similarly, decreased GAS6-AXL signaling was observed between microglia and inhibitory neurons in ALS (Fig.7 and 7H). A pattern recognition method based on nonnegative matrix factorization was applied to further explore how these implicated signaling pathways and cell groups coordinate to function. The resulting Sankey plots revealed four patterns for incoming signaling (Fig.7) and four patterns for outgoing signaling (Fig.7). The communication patterns of target cells showed that the incoming microglia and oligodendrocyte signals were dominated by pattern #3, which was characterized by multiple signaling pathways regulating CNS inflammatory responses, such as CX3C, PSAP, PROS, and VISFATIN, that were found to be reduced in ALS. The outgoing communication pattern of these pathways was featured by pattern #1, which was contributed almost exclusively by excitatory neurons. In summary, while most incoming microglia signaling was contributed by excitatory neurons, reduced levels of signaling, such as CX3C from inhibitory neurons, may play critical roles in regulating microglia phenotype during disease pathogenesis. These results highlighted the altered intercellular communication between neuronal and non-neuronal cells in ALS, although direct evaluation of m⁶A methylation in CNS is warranted to confirm its involvement in the decreased intercellular signaling between neurons and glial cells.

4 Discussion

RNA destabilization and dysregulated induction of immune cells are recurrent themes in the pathogenesis of ALS [48,49]. RNA m⁶A modification has notably emerged as a prominent player in the regulation of innate and adaptive immune responses, particularly in an interferon signaling-dependent manner [50]. The MeRIP-seq profiling of peripheral immune cells in the present study revealed that most differentially m⁶A-modified genes were hypermethylated in ALS, aligning with the recent findings of widespread m⁶A RNA hypermethylation in post-mortem spinal cord samples from patients with ALS [20]. A notable detail that conflicting results were reported in iPSC-differentiated neurons derived from patients with C9orf72-ALS/FTD. In this context, global m⁶A hypomethylation has been observed to lead to an increased accumulation of C9orf72 repeat expansions and poly-dipeptides [51]. These findings suggest that dysregulated m⁶A modification, which actively participates in ALS pathogenesis, may exert its effect on mRNA metabolism in a context-dependent manner. For example, m⁶A-mediated RNA regulation exhibits distinct outcomes, with the binding of m⁶A reader YTHDF2 promoting mRNA degradation [52], whereas IGF2BPs stabilize methylated transcripts [53]. Therefore, m⁶A modification may serve as an upstream regulator of multiple immune pathways in ALS, thus highlighting its potential role in the modulation of immune responses associated with the disease. However, determining the predominant causal role among these pathways, either individually or in combination, remains a challenge.

The integrated transcriptomic analyses of peripheral blood samples provided some preliminary insights. Among the genes that were differentially methylated and expressed in both ALS subgroups, several were implicated in pathways relevant to ALS pathophysiology. For example, m⁶A methylation is actively involved in the homeostasis of peripheral immune cells by modulating the expression of key regulators on JAK–STAT signaling pathways, such as SOCS family members [54]. Overexpression of SOCS genes attenuates IL-7- dependent CD4⁺ T cell proliferation and induces defective regulatory T cell (Treg) activity [55]. Myeloid lineage-restricted deletion of METTL3 results in a significant increase in DDIT4 mRNA levels, which is associated with immunity-driven nonalcoholic fatty liver disease [56,57]. Overexpression of TSC22D3 in mature Tregs can impair their immunoregulatory function during intestinal inflammation [58], and CYP4F3 is involved in inflammatory processes by specifically metabolizing the neutrophil chemoattractant LTB4 [59]. In CNS, DAAM2 plays an essential role in glial cell morphogenesis and myelination by regulating the Wnt signaling pathway [60–62], and CDK5R1 (also known as p35) is associated with neuron tauopathies and axon outgrowth impairment [63].

Previous studies suggested that CX3CR1 polymorphisms may serve as disease-modifying factors in ALS [64,65], and cells expressing CX3CR1^V249I/T280M exhibited defective signaling and increased adhesive capacity to FKN [66]. As an exclusive receptor for FKN, CX3CR1 is primarily expressed by microglia in CNS and certain peripheral immune cells such as perivascular macrophages and monocytes [66,67]. Its primary role in CNS is to suppress proinflammatory activities, as disruption of CX3CR1 signaling in systemic LPS-primed SOD1^G93A mouse models leads to aberrant microglial activation and neurotoxicity [68]. In the periphery, CX3CR1 plays a critical role in cell adhesion, ensuring the retention of leukocytes within tissues [69]. Reduced CX3CR1 expression is likely to disrupt immune cell trapping and exacerbate neuronal loss in ALS. This finding aligns with the altered immune cell migration function observed in the network-based proteomic analysis of ALS serum samples. The experiments involving the activation and blockade of CX3CR1 in LPS-stimulated RAW264.7 cells in the present study resulted in transcriptomic changes associated with chemokine-mediated signaling pathways and cell mobility. There is a notable detail that LPS treatment was found to reduce CX3CR1 expression [70,71] and enhance METTL3 expression and overall m⁶A levels in macrophages [72], dendritic cells [73], and dental pulp cells [74]. Given that m⁶A methylation regulates RNA stability, the observed hypermethylation of CX3CR1 mRNA in ALS samples may indicate accelerated decay and decreased expression. Therefore, although further functional validation is necessary, m⁶A modification may serve as a remarkable regulator of macrophage activation in response to LPS.

Apart from macrophages, infiltration of monocytes [75] and NK cells [76] into the motor cortex of TDP-43^A315T mice and patients with TDP-43 pathology has been noted, although the extent to which the peripheral immune cell phenotypes may influence ALS neuroinflammation remains inconclusive. In this study, the methylation patterns of CNS immune cells were not directly quantified, so the average expression of differentially methylated genes was correlated to the scRNA-seq data set of primary motor cortex. The results indicated that these m⁶A-modified genes were differentially expressed in ALS samples, with microglia demonstrating the most prominent differences. CellChat further suggested that the alteration of intricate cellular communication between neuronal and non-neuronal cells may be a crucial aspect of ALS pathogenesis. These results confirmed that CX3C signaling was reduced between microglia and inhibitory neurons in ALS motor cortex. In addition to CX3C signaling, disturbance of other signaling pathways sharing similar communication pattern may induce neurodegeneration, possibly via immune-associated pathways. For example, reduced GAS6-AXL signaling was observed between microglia and inhibitory neurons in ALS, which is a critical regulator of microglial inflammatory responses in Parkinson’s disease [77] and Alzheimer’s disease [78] mouse models. Similarly, reduced PSAP signaling was observed in ALS, the diminished trafficking of which underlies FTD pathogenesis [79]. Therefore, the differential methylation detected in the peripheral immune cells may serve as a reflection of the CNS transcriptome, the alteration of which could likely induce dysregulated intercellular communication in the motor cortex (Fig.8).

An important limitation of this study is the lack of direct evidence demonstrating the biological consequences of differential m⁶A methylation in CNS tissue. Cellular models combined with m⁶A methyltransferase gene knockout assays using induced pluripotent stem cell (iPSC) from patients with ALS may provide useful insights, because neuron–glial cell crosstalk is essential for inducing neurotoxicity in ALS [80]. However, in-vitro cells display significant transcriptional changes compared with in-vivo cells, thus making direct evidence derived from animal models necessary to firmly establish any causal links, although trans-species differences could be another issue that needs to be addressed. Nevertheless, the bioinformatic analyses of the human motor cortex provides encouraging results for carrying out further functional validation. Given the relatively small sample size of this study, sequencing data from a larger cohort for validation in the future are desirable.

In conclusion, although significant progress has been made in understanding the role of m⁶A modification in immune phenotypes, further efforts are needed to understand how aberrant m⁶A-mediated gene regulation contributes to the etiology of complex diseases like ALS. In this study, the m⁶A methylation pattern of peripheral immune cells in ALS was profiled, and the differential methylation was correlated with serum proteome and CNS transcriptome. The integrated analyses highlighted the previously underexplored m⁶A-mediated RNA regulation in ALS pathogenesis, which may have important therapeutic implications in the future.

References

[1] Hardiman O, Al-Chalabi A, Chio A, Corr EM, Logroscino G, Robberecht W, Shaw PJ, Simmons Z, van den Berg LH. Amyotrophic lateral sclerosis. Nat Rev Dis Primers 2017; 3(1): 17071

[2] van Es MA, Hardiman O, Chio A, Al-Chalabi A, Pasterkamp RJ, Veldink JH, van den Berg LH. Amyotrophic lateral sclerosis. Lancet 2017; 390(10107): 2084–2098

[3] Brown RH, Al-Chalabi A. Amyotrophic lateral sclerosis. N Engl J Med 2017; 377(2): 162–172

[4] Feldman EL, Goutman SA, Petri S, Mazzini L, Savelieff MG, Shaw PJ, Sobue G. Amyotrophic lateral sclerosis. Lancet 2022; 400(10360): 1363–1380

[5] Keller MF, Ferrucci L, Singleton AB, Tienari PJ, Laaksovirta H, Restagno G, Chiò A, Traynor BJ, Nalls MA. Genome-wide analysis of the heritability of amyotrophic lateral sclerosis. JAMA Neurol 2014; 71(9): 1123–1134

[6] Chia R, Chiò A, Traynor BJ. Novel genes associated with amyotrophic lateral sclerosis: diagnostic and clinical implications. Lancet Neurol 2018; 17(1): 94–102

[7] Cook C, Petrucelli L. Genetic convergence brings clarity to the enigmatic red line in ALS. Neuron 2019; 101(6): 1057–1069

[8] McCauley ME, O’Rourke JG, Yáñez A, Markman JL, Ho R, Wang X, Chen S, Lall D, Jin M, Muhammad AKMG, Bell S, Landeros J, Valencia V, Harms M, Arditi M, Jefferies C, Baloh RH. C9orf72 in myeloid cells suppresses STING-induced inflammation. Nature 2020; 585(7823): 96–101

[9] Yu CH, Davidson S, Harapas CR, Hilton JB, Mlodzianoski MJ, Laohamonthonkul P, Louis C, Low RRJ, Moecking J, De Nardo D, Balka KR, Calleja DJ, Moghaddas F, Ni E, McLean CA, Samson AL, Tyebji S, Tonkin CJ, Bye CR, Turner BJ, Pepin G, Gantier MP, Rogers KL, McArthur K, Crouch PJ, Masters SL. TDP-43 triggers mitochondrial DNA release via mPTP to activate cGAS/STING in ALS. Cell 2020; 183(3): 636–649.e18

[10] Coque E, Salsac C, Espinosa-Carrasco G, Varga B, Degauque N, Cadoux M, Crabé R, Virenque A, Soulard C, Fierle JK, Brodovitch A, Libralato M, Végh AG, Venteo S, Scamps F, Boucraut J, Laplaud D, Hernandez J, Gergely C, Vincent T, Raoul C. Cytotoxic CD8⁺ T lymphocytes expressing ALS-causing SOD1 mutant selectively trigger death of spinal motoneurons. Proc Natl Acad Sci USA 2019; 116(6): 2312–2317

[11] Beers DR, Appel SH. Immune dysregulation in amyotrophic lateral sclerosis: mechanisms and emerging therapies. Lancet Neurol 2019; 18(2): 211–220

[12] McCauley ME, Baloh RH. Inflammation in ALS/FTD pathogenesis. Acta Neuropathol 2019; 137(5): 715–730

[13] Fournier CN, Schoenfeld D, Berry JD, Cudkowicz ME, Chan J, Quinn C, Brown RH, Salameh JS, Tansey MG, Beers DR, Appel SH, Glass JD. An open label study of a novel immunosuppression intervention for the treatment of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Frontotemporal Degener 2018; 19(3–4): 242–249

[14] Huang H, Weng H, Chen J. The biogenesis and precise control of RNA m⁶A methylation. Trends Genet 2020; 36(1): 44–52

[15] Roundtree IA, Evans ME, Pan T, He C. Dynamic RNA modifications in gene expression regulation. Cell 2017; 169(7): 1187–1200

[16] Zhao BS, Roundtree IA, He C. Post-transcriptional gene regulation by mRNA modifications. Nat Rev Mol Cell Biol 2017; 18(1): 31–42

[17] Yang Y, Hsu PJ, Chen YS, Yang YG. Dynamic transcriptomic m⁶A decoration: writers, erasers, readers and functions in RNA metabolism. Cell Res 2018; 28(6): 616–624

[18] Zaccara S, Ries RJ, Jaffrey SR. Reading, writing and erasing mRNA methylation. Nat Rev Mol Cell Biol 2019; 20(10): 608–624

[19] Xiong X, Hou L, Park YP, Molinie B; GTEx Consortium; Gregory RI, Kellis M. Genetic drivers of m⁶A methylation in human brain, lung, heart and muscle. Nat Genet 2021; 53(8): 1156–1165

[20] McMillan M, Gomez N, Hsieh C, Bekier M, Li X, Miguez R, Tank EMH, Barmada SJ. RNA methylation influences TDP43 binding and disease pathogenesis in models of amyotrophic lateral sclerosis and frontotemporal dementia. Mol Cell 2023; 83(2): 219–236.e7

[21] He F, Krans A, Freibaum BD, Taylor JP, Todd PK. TDP-43 suppresses CGG repeat-induced neurotoxicity through interactions with HnRNP A2/B1. Hum Mol Genet 2014; 23(19): 5036–5051

[22] Wang L, Wen M, Cao X. Nuclear hnRNPA2B1 initiates and amplifies the innate immune response to DNA viruses. Science 2019; 365(6454): eaav0758

[23] Winkler R, Gillis E, Lasman L, Safra M, Geula S, Soyris C, Nachshon A, Tai-Schmiedel J, Friedman N, Le-Trilling VTK, Trilling M, Mandelboim M, Hanna JH, Schwartz S, Stern-Ginossar N. m⁶A modification controls the innate immune response to infection by targeting type I interferons. Nat Immunol 2019; 20(2): 173–182

[24] Vahsen BF, Gray E, Thompson AG, Ansorge O, Anthony DC, Cowley SA, Talbot K, Turner MR. Non-neuronal cells in amyotrophic lateral sclerosis—from pathogenesis to biomarkers. Nat Rev Neurol 2021; 17(6): 333–348

[25] Spiller KJ, Restrepo CR, Khan T, Dominique MA, Fang TC, Canter RG, Roberts CJ, Miller KR, Ransohoff RM, Trojanowski JQ, Lee VM. Microglia-mediated recovery from ALS-relevant motor neuron degeneration in a mouse model of TDP-43 proteinopathy. Nat Neurosci 2018; 21(3): 329–340

[26] Greenhalgh AD, David S, Bennett FC. Immune cell regulation of glia during CNS injury and disease. Nat Rev Neurosci 2020; 21(3): 139–152

[27] Garbuzova-Davis S, Sanberg PR. Blood-CNS barrier impairment in ALS patients versus an animal model. Front Cell Neurosci 2014; 8: 21

[28] Turner MR, Goldacre R, Ramagopalan S, Talbot K, Goldacre MJ. Autoimmune disease preceding amyotrophic lateral sclerosis: an epidemiologic study. Neurology 2013; 81(14): 1222–1225

[29] Zhao W, Beers DR, Hooten KG, Sieglaff DH, Zhang A, Kalyana-Sundaram S, Traini CM, Halsey WS, Hughes AM, Sathe GM, Livi GP, Fan GH, Appel SH. Characterization of gene expression phenotype in amyotrophic lateral sclerosis monocytes. JAMA Neurol 2017; 74(6): 677–685

[30] Chiot A, Zaïdi S, Iltis C, Ribon M, Berriat F, Schiaffino L, Jolly A, de la Grange P, Mallat M, Bohl D, Millecamps S, Seilhean D, Lobsiger CS, Boillée S. Modifying macrophages at the periphery has the capacity to change microglial reactivity and to extend ALS survival. Nat Neurosci 2020; 23(11): 1339–1351

[31] Sheean RK, McKay FC, Cretney E, Bye CR, Perera ND, Tomas D, Weston RA, Scheller KJ, Djouma E, Menon P, Schibeci SD, Marmash N, Yerbury JJ, Nutt SL, Booth DR, Stewart GJ, Kiernan MC, Vucic S, Turner BJ. Association of regulatory T-cell expansion with progression of amyotrophic lateral sclerosis: a study of humans and a transgenic mouse model. JAMA Neurol 2018; 75(6): 681–689

[32] Zhang Z, Luo K, Zou Z, Qiu M, Tian J, Sieh L, Shi H, Zou Y, Wang G, Morrison J, Zhu AC, Qiao M, Li Z, Stephens M, He X, He C. Genetic analyses support the contribution of mRNA N⁶-methyladenosine (m⁶A) modification to human disease heritability. Nat Genet 2020; 52(9): 939–949

[33] Ridderstad Wollberg A, Ericsson-Dahlstrand A, Juréus A, Ekerot P, Simon S, Nilsson M, Wiklund SJ, Berg AL, Ferm M, Sunnemark D, Johansson R. Pharmacological inhibition of the chemokine receptor CX3CR1 attenuates disease in a chronic-relapsing rat model for multiple sclerosis. Proc Natl Acad Sci USA 2014; 111(14): 5409–5414

[34] Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez JY, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A. Fiji: an open-source platform for biological-image analysis. Nat Methods 2012; 9(7): 676–682

[35] Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005; 102(43): 15545–15550

[36] Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M, Hoffman P, Stoeckius M, Papalexi E, Mimitou EP, Jain J, Srivastava A, Stuart T, Fleming LM, Yeung B, Rogers AJ, McElrath JM, Blish CA, Gottardo R, Smibert P, Satija R. Integrated analysis of multimodal single-cell data. Cell 2021; 184(13): 3573–3587.e29

[37] PinedaSSLee HFitzwalterBEMohammadiSPregentLJ GardashliMEMantero JEngelberg-CookEDeJesus-HernandezMvan Blitterswijk MPottierCRademakersROskarsson BShahJSPetersenRCGraff-Radford NRBoeveBFKnopmanDSJosephsKA DeTureMMurray MEDicksonDWHeimanMBelzilVV KellisM. Single-cell profiling of the human primary motor cortex in ALS and FTLD. bioRvix 2021; doi:10.1101/2021.07.07.451374

[38] Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH, Myung P, Plikus MV, Nie Q. Inference and analysis of cell-cell communication using CellChat. Nat Commun 2021; 12(1): 1088

[39] Zhou Y, Zeng P, Li YH, Zhang Z, Cui Q. SRAMP: prediction of mammalian N⁶-methyladenosine (m⁶A) sites based on sequence-derived features. Nucleic Acids Res 2016; 44(10): e91

[40] Deng S, Zhang H, Zhu K, Li X, Ye Y, Li R, Liu X, Lin D, Zuo Z, Zheng J. M6A2Target: a comprehensive database for targets of m⁶A writers, erasers and readers. Brief Bioinform 2021; 22(3): bbaa055

[41] Cui L, Ma R, Cai J, Guo C, Chen Z, Yao L, Wang Y, Fan R, Wang X, Shi Y. RNA modifications: importance in immune cell biology and related diseases. Signal Transduct Target Ther 2022; 7(1): 334

[42] Yerbury JJ, Farrawell NE, McAlary L. Proteome homeostasis dysfunction: a unifying principle in ALS pathogenesis. Trends Neurosci 2020; 43(5): 274–284

[43] Katzeff JS, Bright F, Lo K, Kril JJ, Connolly A, Crossett B, Ittner LM, Kassiou M, Loy CT, Hodges JR, Piguet O, Kiernan MC, Halliday GM, Kim WS. Altered serum protein levels in frontotemporal dementia and amyotrophic lateral sclerosis indicate calcium and immunity dysregulation. Sci Rep 2020; 10(1): 13741

[44] Xu Z, Lee A, Nouwens A, Henderson RD, McCombe PA. Mass spectrometry analysis of plasma from amyotrophic lateral sclerosis and control subjects. Amyotroph Lateral Scler Frontotemporal Degener 2018; 19(5–6): 362–376

[45] King ZT, Butler MT, Hockenberry MA, Subramanian BC, Siesser PF, Graham DM, Legant WR, Bear JE. Coro1B and Coro1C regulate lamellipodia dynamics and cell motility by tuning branched actin turnover. J Cell Biol 2022; 221(8): e202111126

[46] Zaritsky A, Tseng YY, Rabadán MA, Krishna S, Overholtzer M, Danuser G, Hall A. Diverse roles of guanine nucleotide exchange factors in regulating collective cell migration. J Cell Biol 2017; 216(6): 1543–1556

[47] Jia Z, Wan X. ISYNA1: an immunomodulatory-related prognostic biomarker in colon adenocarcinoma and pan-cancer. Front Cell Dev Biol 2022; 10: 792564

[48] Goutman SA, Hardiman O, Al-Chalabi A, Chió A, Savelieff MG, Kiernan MC, Feldman EL. Emerging insights into the complex genetics and pathophysiology of amyotrophic lateral sclerosis. Lancet Neurol 2022; 21(5): 465–479

[49] He D, Xu Y, Liu M, Cui L. The inflammatory puzzle: piecing together the links between neuroinflammation and amyotrophic lateral sclerosis. Aging Dis 2023; 15(1): 96–114

[50] Shulman Z, Stern-Ginossar N. The RNA modification N⁶-methyladenosine as a novel regulator of the immune system. Nat Immunol 2020; 21(5): 501–512

[51] Li Y, Dou X, Liu J, Xiao Y, Zhang Z, Hayes L, Wu R, Fu X, Ye Y, Yang B, Ostrow LW, He C, Sun S. Globally reduced N⁶-methyladenosine (m⁶A) in C9ORF72-ALS/FTD dysregulates RNA metabolism and contributes to neurodegeneration. Nat Neurosci 2023; 26(8): 1328–1338

[52] Wang X, Lu Z, Gomez A, Hon GC, Yue Y, Han D, Fu Y, Parisien M, Dai Q, Jia G, Ren B, Pan T, He C. N⁶-methyladenosine-dependent regulation of messenger RNA stability. Nature 2014; 505(7481): 117–120

[53] Huang H, Weng H, Sun W, Qin X, Shi H, Wu H, Zhao BS, Mesquita A, Liu C, Yuan CL, Hu YC, Hüttelmaier S, Skibbe JR, Su R, Deng X, Dong L, Sun M, Li C, Nachtergaele S, Wang Y, Hu C, Ferchen K, Greis KD, Jiang X, Wei M, Qu L, Guan JL, He C, Yang J, Chen J. Recognition of RNA N⁶-methyladenosine by IGF2BP proteins enhances mRNA stability and translation. Nat Cell Biol 2018; 20(3): 285–295

[54] Song RH, Du P, Gao CQ, Liu XR, Zhang JA. METTL3 is involved in the development of Graves’ disease by inducing SOCS mRNA m⁶A modification. Front Endocrinol (Lausanne) 2021; 12: 666393

[55] Li HB, Tong J, Zhu S, Batista PJ, Duffy EE, Zhao J, Bailis W, Cao G, Kroehling L, Chen Y, Wang G, Broughton JP, Chen YG, Kluger Y, Simon MD, Chang HY, Yin Z, Flavell RA. m⁶A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways. Nature 2017; 548(7667): 338–342

[56] Ip WKE, Hoshi N, Shouval DS, Snapper S, Medzhitov R. Anti-inflammatory effect of IL-10 mediated by metabolic reprogramming of macrophages. Science 2017; 5; 356(6337): 513–519

[57] Qin Y, Li B, Arumugam S, Lu Q, Mankash SM, Li J, Sun B, Li J, Flavell RA, Li HB, Ouyang X. m⁶A mRNA methylation-directed myeloid cell activation controls progression of NAFLD and obesity. Cell Rep 2021; 37(6): 109968

[58] Goodman WA, Bedoyan SM, Havran HL, Richardson B, Cameron MJ, Pizarro TT. Impaired estrogen signaling underlies regulatory T cell loss-of-function in the chronically inflamed intestine. Proc Natl Acad Sci USA 2020; 117(29): 17166–17176

[59] Smeets E, Huang S, Lee XY, Van Nieuwenhove E, Helsen C, Handle F, Moris L, El Kharraz S, Eerlings R, Devlies W, Willemsen M, Bücken L, Prezzemolo T, Humblet-Baron S, Voet A, Rochtus A, Van Schepdael A, de Zegher F, Claessens F. A disease-associated missense mutation in CYP4F3 affects the metabolism of leukotriene B4 via disruption of electron transfer. J Cachexia Sarcopenia Muscle 2022; 13(4): 2242–2253

[60] Lee HK, Chaboub LS, Zhu W, Zollinger D, Rasband MN, Fancy SP, Deneen B. Daam2-PIP5K is a regulatory pathway for Wnt signaling and therapeutic target for remyelination in the CNS. Neuron 2015; 85(6): 1227–1243

[61] Ding X, Jo J, Wang CY, Cristobal CD, Zuo Z, Ye Q, Wirianto M, Lindeke-Myers A, Choi JM, Mohila CA, Kawabe H, Jung SY, Bellen HJ, Yoo SH, Lee HK. The Daam2-VHL-Nedd4 axis governs developmental and regenerative oligodendrocyte differentiation. Genes Dev 2020; 34(17–18): 1177–1189

[62] Jo J, Woo J, Cristobal CD, Choi JM, Wang CY, Ye Q, Smith JA, Ung K, Liu G, Cortes D, Jung SY, Arenkiel BR, Lee HK. Regional heterogeneity of astrocyte morphogenesis dictated by the formin protein, Daam2, modifies circuit function. EMBO Rep 2021; 22(12): e53200

[63] Zhao D, Zhou Y, Huo Y, Meng J, Xiao X, Han L, Zhang X, Luo H, Can D, Sun H, Huang TY, Wang X, Zhang J, Liu FR, Xu H, Zhang YW. RPS23RG1 modulates tau phosphorylation and axon outgrowth through regulating p35 proteasomal degradation. Cell Death Differ 2021; 28(1): 337–348

[64] Calvo A, Moglia C, Canosa A, Cammarosano S, Ilardi A, Bertuzzo D, Traynor BJ, Brunetti M, Barberis M, Mora G, Casale F, Chiò A. Common polymorphisms of chemokine (C-X3-C motif) receptor 1 gene modify amyotrophic lateral sclerosis outcome: a population-based study. Muscle Nerve 2018; 57(2): 212–216

[65] Lopez-Lopez A, Gamez J, Syriani E, Morales M, Salvado M, Rodríguez MJ, Mahy N, Vidal-Taboada JM. CX3CR1 is a modifying gene of survival and progression in amyotrophic lateral sclerosis. PLoS One 2014; 9(5): e96528

[66] Subbarayan MS, Joly-Amado A, Bickford PC, Nash KR. CX3CL1/CX3CR1 signaling targets for the treatment of neurodegenerative diseases. Pharmacol Ther 2022; 231: 107989

[67] Hickman S, Izzy S, Sen P, Morsett L, El Khoury J. Microglia in neurodegeneration. Nat Neurosci 2018; 21(10): 1359–1369

[68] Cardona AE, Pioro EP, Sasse ME, Kostenko V, Cardona SM, Dijkstra IM, Huang D, Kidd G, Dombrowski S, Dutta R, Lee JC, Cook DN, Jung S, Lira SA, Littman DR, Ransohoff RM. Control of microglial neurotoxicity by the fractalkine receptor. Nat Neurosci 2006; 9(7): 917–924

[69] Cardona SM, Kim SV, Church KA, Torres VO, Cleary IA, Mendiola AS, Saville SP, Watowich SS, Parker-Thornburg J, Soto-Ospina A, Araque P, Ransohoff RM, Cardona AE. Role of the fractalkine receptor in CNS autoimmune inflammation: new approach utilizing a mouse model expressing the human CX3CR1I249/M280 variant. Front Cell Neurosci 2018; 12(October): 365

[70] Inoue K, Morimoto H, Ohgidani M, Ueki T. Modulation of inflammatory responses by fractalkine signaling in microglia. PLoS One 2021; 16(5): e0252118

[71] Fernández de Cossío L, Lacabanne C, Bordeleau M, Castino G, Kyriakakis P, Tremblay MÈ. Lipopolysaccharide-induced maternal immune activation modulates microglial CX3CR1 protein expression and morphological phenotype in the hippocampus and dentate gyrus, resulting in cognitive inflexibility during late adolescence. Brain Behav Immun 2021; 97(April): 440–454

[72] Wang J, Yan S, Lu H, Wang S, Xu D. METTL3 attenuates LPS-induced inflammatory response in macrophages via NF-κB signaling pathway. Mediators Inflamm 2019; 2019: 3120391

[73] Wang H, Hu X, Huang M, Liu J, Gu Y, Ma L, Zhou Q, Cao X. Mettl3-mediated mRNA m⁶A methylation promotes dendritic cell activation. Nat Commun 2019; 10(1): 1898

[74] Feng Z, Li Q, Meng R, Yi B, Xu Q. METTL3 regulates alternative splicing of MyD88 upon the lipopolysaccharide-induced inflammatory response in human dental pulp cells. J Cell Mol Med 2018; 22(5): 2558–2568

[75] Jara JH, Gautam M, Kocak N, Xie EF, Mao Q, Bigio EH, Özdinler PH. MCP1-CCR2 and neuroinflammation in the ALS motor cortex with TDP-43 pathology. J Neuroinflammation 2019; 16(1): 196

[76] Garofalo S, Cocozza G, Porzia A, Inghilleri M, Raspa M, Scavizzi F, Aronica E, Bernardini G, Peng L, Ransohoff RM, Santoni A, Limatola C. Natural killer cells modulate motor neuron-immune cell cross talk in models of Amyotrophic Lateral Sclerosis. Nat Commun 2020; 11(1): 1773

[77] Fourgeaud L, Través PG, Tufail Y, Leal-Bailey H, Lew ED, Burrola PG, Callaway P, Zagórska A, Rothlin CV, Nimmerjahn A, Lemke G. TAM receptors regulate multiple features of microglial physiology. Nature 2016; 532(7598): 240–244

[78] Huang Y, Happonen K, Burrola P, O’Connor C, Hah N, Huang L, Nimmerjahn A, Lemke G. Microglia use TAM receptors to detect and engulf amyloid beta plaques. Nat. Immunol 2021; 22(5): 586–594

[79] Zhou X, Sun L, Bracko O, Choi JW, Jia Y, Nana AL, Brady OA, Hernandez JCC, Nishimura N, Seeley WW, Hu F. Impaired prosaposin lysosomal trafficking in frontotemporal lobar degeneration due to progranulin mutations. Nat Commun 2017; 8(1): 15277

[80] Zhao W, Beers DR, Bell S, Wang J, Wen S, Baloh RH, Appel SH. TDP-43 activates microglia through NF-κB and NLRP3 inflammasome. Exp Neurol 2015; 273: 24–35

RIGHTS & PERMISSIONS

Higher Education Press