Dysregulated N<sup>6</sup>-methyladenosine modification in peripheral immune cells contributes to the pathogenesis of amyotrophic lateral sclerosis

doi:10.1007/s11684-023-1035-5

2024, Vol. 18

Issue (2) : 285-302 https://doi.org/10.1007/s11684-023-1035-5

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

Di He¹, Xunzhe Yang¹, Liyang Liu^2,³, Dongchao Shen¹, Qing Liu¹, Mingsheng Liu¹, Xue Zhang^3,⁴(

), Liying Cui¹(

)

¹. Department of Neurology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
². Medical Doctor Program, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
³. McKusick-Zhang Center for Genetic Medicine, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100730, China
⁴. Neuroscience Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China

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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 N⁶-methyladenosine epi-transcriptome proteome single cell RNA sequencing analysis CX3CR1

Corresponding Author(s): Xue Zhang,Liying Cui

Just Accepted Date: 30 January 2024 Online First Date: 15 March 2024 Issue Date: 27 May 2024

Cite this article:

Di He,Xunzhe Yang,Liyang Liu, et al. Dysregulated N⁶-methyladenosine modification in peripheral immune cells contributes to the pathogenesis of amyotrophic lateral sclerosis[J]. Front. Med., 2024, 18(2): 285-302.

URL:

https://academic.hep.com.cn/fmd/EN/10.1007/s11684-023-1035-5
https://academic.hep.com.cn/fmd/EN/Y2024/V18/I2/285

Fig.1 Schematic illustrating the research design of this study.

Fig.2 Differential RNA methylation and expression in ALS peripheral immune cells. (A) Distribution of m⁶A-containing peaks across the transcriptome in samples collected from ALS (n = 16) and control (n = 6) subjects. (B) Percentage of mRNAs with varying degree of m⁶A abundance. (C) m⁶A peak distribution throughout mRNA and the top consensus motif identified by HOMER of m⁶A peaks in ALS (top) and control (bottom). (D) Volcano plots of differentially methylated peaks in ALS_b (n = 7, top) and ALS_s (n = 9, bottom). (E) Heatmaps and hierarchical clustering of the top 50 differentially expressed genes in ALS_b (top) and ALS_s (bottom). (F) Volcano plots depicting differential gene expression (x-axis) and differential methylation peaks (y-axis) in ALS_b (top) and ALS_s (bottom).

Fig.3 Potential m⁶A target genes implicated in ALS pathogenesis screened by differential methylation and expression. (A) GO terms of biological process (top) and KEGG enrichment (bottom) analysis of genes with differential m⁶A peaks in ALS_b (n = 7). (B) GO terms of biological process (top) and KEGG enrichment (bottom) analysis of genes with differential m⁶A peaks in ALS_s (n = 9). (C) Heatmap of the overlapping 15 candidate genes differentially methylated and expressed identified in the two ALS subgroups. (D) Validation of the differential expression of the selected candidate genes by qPCR (n = 5). (E) Visualization of CX3CR1 m⁶A read density in ALS (n = 16) and control (n = 6) samples by Integrative Genomics Viewer. y-axis represents the normalized m⁶A signal along the transcript. The identified differential peaks are indicated at the bottom. The arrow indicates the peak region targeted by MeRIP-qPCR primers for validation. (F) Validation of the differential methylation of CX3CR1 by MeRIP-qPCR (n = 5). HC, healthy controls. (G) Relative serum levels of FKN in patients with ALS and controls (n = 6).

Fig.4 CX3CR1 signaling primarily affecting macrophage migration in vitro. (A) Bubble plots of the top enriched biological processes from GO analysis in LPS-primed cells with/without treatment of FKN (left) and AZD8797 (right). The results were derived from three biological replicates. (B) Gene set enrichment analysis demonstrating enhanced chemotaxis-associated pathways in FKN-treated cells. FDR Q value < 0.25 was considered significant. (C) Gene set enrichment analysis demonstrating reduced chemotaxis-associated pathways in AZD-treated cells. (D) Expression levels of selected immunity-related genes verified by RT–qPCR (n = 3). (E) Reduced macrophage migration in the absence of CX3CR1 signaling. Cell monolayers were scratched and treated with AZD8797, with images taken at 0 and 12 h (n = 3).

Fig.5 Changes in serum proteome and implicated biological pathways in ALS. (A) Heatmaps of significantly altered protein expression in ALS_b (n = 7, top) and ALS_s (bottom). (B) Volcanic plots of differentially expressed proteins in ALS_b (n = 9, top) and ALS_s (bottom). Proteins with differential m⁶A methylation were labeled. (C) Protein–protein interaction (PPI) network and the associated biological processes in ALS_b (left) and ALS_s (right) by STRING web tool. The network nodes were MCL-clustered with disconnected nodes in the network hided. The line thickness between each node indicates the strength of data support. The differentially methylated proteins at the RNA level were highlighted in red (hypermethylated) or green (hypomethylated). (D) Serum levels of ENG, CDH13, TNXB, CLEC3B, and DEFA1 measured by parallel reaction monitoring in the validation cohort (n = 8). HC, healthy controls.

Fig.6 Expression of differentially methylated genes in prefrontal motor cortex. (A) Uniform manifold approximation and projection (UMAP) plot of the identified cell populations, including glial cells, excitatory/inhibitory neurons, and vascular cells. (B) UMAP plot of DM_score calculated by AddModuleScore function in Seurat package. The actual scores have no physical meaning, whereas a positive score suggests that the genes are expressed in a particular cell more highly than expected by chance. (C) UMAP plot of identified glial cell subtypes after sub-clustering (top) and corresponding DM_score (bottom). (D) Violin plot comparing the DM_score between ALS and pathologically normal (PN) subjects across different cellular subtypes of glial cells. (E) Random Forest regression model displaying the top candidate genes ranked by the increase in mean square error (MSE) and node purity, which correlate to their relative importance. (F) Selection of tuning parameter (λ) in the LASSO model via 10-fold cross-testing based on minimum criteria. The curve was plotted against log (λ), and the dotted vertical lines were drawn at the optimal values by using the minimum criteria and the 1-standard error of the minimum criteria (top); the distribution of regression coefficients for different λ values (bottom). (G) Differentially methylated hub genes identified by intersecting the candidate genes from the two regression algorithms, as demonstrated by the Venn plot.

Fig.7 CellChat analyses of cell–cell communication network between neuronal and non-neuronal cells in primary motor cortex. (A) Circle plots of differential interaction quantity (top) and interaction strength (bottom) between neuronal and non-neuronal cells, with blue edges indicating decreased communication and red edges indicating increased communication in ALS compared with pathologically normal (PN) subjects. (B) Significant signaling pathways ranked based on their differences of overall information flow within the inferred networks between ALS and PN. The top signaling pathways colored in red were more enriched in ALS, whereas the bottom signaling pathways colored in green were more enriched in PN. (C) Bar plot of the ranking of signaling axes between ALS and PN samples by pairwise Euclidean distance. (D) Heatmaps of the overall signaling flows of each cell population mediated by individual signaling axes in ALS (left) and PN (right). The relative strength is indicated by the color bar. (E) Hierarchical plot illustrating the inferred intercellular communication network of CX3C signaling between neuronal and non-neuronal cells. Left and right columns show the autocrine and paracrine signaling to non-neuronal cells, respectively, and edge width represents the probability of communication. (F) Expression distribution of ligand CX3CL1 and its receptor CX3CR1 in ALS (red) and PN (green) cells. (G) Hierarchical plot illustrating the inferred intercellular communication network of GAS signaling between neuronal and non-neuronal cells. (H) Expression distribution of ligand GAS6 and its receptors in ALS (red) and PN (green) cells. (I) Inferred incoming communication patterns of target cells visualized by Sankey plot, which demonstrated the correspondence between the inferred cell groups, latent patterns, and signaling pathways, with the flow thickness representing the contribution of the cell group or signaling pathway to each latent pattern. (J) Inferred outgoing communication patterns of secreting cells.

Fig.8 Schematic illustrating the potential pathogenic roles of dysregulated m⁶A modification in ALS.

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