Whole-genome bisulfite sequencing of cell-free DNA unveils age-dependent and ALS-associated methylation alterations

Abstract

Background: Cell-free DNA (cfDNA) in plasma carries epigenetic signatures specific to tissue or cell of origin. Aberrant methylation patterns in circulating cfDNA have emerged as valuable tools for noninvasive cancer detection, prenatal diagnostics, and organ transplant assessment. Such epigenetic changes also hold significant promise for the diagnosis of neurodegenerative diseases, which often progresses slowly and has a lengthy asymptomatic period. However, genome-wide cfDNA methylation changes in neurodegenerative diseases remain poorly understood.

Results: We used whole-genome bisulfite sequencing (WGBS) to profile age-dependent and ALS-associated methylation signatures in cfDNA from 30 individuals, including young and middle-aged controls, as well as ALS patients with matched controls. We identified 5,223 age-related differentially methylated loci (DMLs) (FDR < 0.05), with 51.6% showing hypomethylation in older individuals. Our results significantly overlapped with age-associated CpGs identified in a large blood-based epigenome-wide association study (EWAS). Comparing ALS patients to controls, we detected 1,045 differentially methylated regions (DMRs) in gene bodies, promoters, and intergenic regions. Notably, these DMRs were linked to key ALS-associated pathways, including endocytosis and cell adhesion. Integration with spinal cord transcriptomics revealed that 31% of DMR-associated genes exhibited differential expression in ALS patients compared to controls, with over 20 genes significantly correlating with disease duration. Furthermore, comparison with published single-nucleus RNA sequencing (snRNA-Seq) data of ALS demonstrated that cfDNA methylation changes reflects cell-type-specific gene dysregulation in the brain of ALS patients, particularly in excitatory neurons and astrocytes. Deconvolution of cfDNA methylation profiles suggested altered proportions of immune and liver-derived cfDNA in ALS patients.

Conclusions: cfDNA methylation is a powerful tool for assessing age-related changes and ALS-specific molecular dysregulation by revealing perturbed locus, genes, and the proportional contributions of different tissues/cells to the plasma. This technique holds promise for clinical application in biomarker discovery across a broad spectrum of neurodegenerative disorders.

Fig. 1

Overview of study workflow utilizing genome-wide methylation analysis of plasma-derived cfDNA to identify age-related and ALS-associated methylation signatures. Plasma samples were collected from 30 individuals, including healthy participants from various age groups, ALS patients, and age- and gender-matched control subjects. Methylome profiling of cfDNA was conducted using WGBS on bisulfite-treated cfDNA. Age-related and ALS-associated methylation signatures were identified through comprehensive bioinformatic analysis. Additionally, the correlation between ALS-related methylation profiles and gene dysregulation in CNS tissues derived from ALS patients was investigated at both the bulk and single-cell levels. A reference-based deconvolution analysis was performed to determine the tissue and cell-type origins of the cfDNA

Fig. 2

Identification of age-related methylation features in cfDNA. (A) Bar graph showing average genome-wide mCG levels in young and middle-aged groups. No significant difference of methylation levels was observed between groups. (B) Identification of DML between the two groups. (C) Heatmap visualization of the methylation profiles of DMLs identified between the two groups. (D) Genomic annotation of the identified DMLs to their percentage of each genomic feature. (E) KEGG analysis for DML-associated genes to examine their biological significance

Fig. 3

Characterization of cfDNA-derived DNA methylation landscapes in ALS. (A) Bar graph showing average genome-wide mCG levels in ALS and control groups. No significant difference of methylation levels was observed between groups. (B) Metagene plots showing mCG (top) levels across transcriptional start sites (TSS), transcriptional end sites (TES) and RefSeq gene bodies. Ten kb upstream and downstream of given genomic features were plotted. (C) Identification of DMRs between ALS and control groups (top), with a heatmap (bottom) visualizing the methylation profiles of the identified DMRs. (D) Genomic annotation of the identified DMRs on their percentage of each genomic feature. (E) Proportion of ALS-related DMRs within CpG islands (CGIs), CpG shelves, CpG shores, and other genomic regions. (F) Top: Proportion of hypermethylated DMRs and hypomethylated DMRs on their percentage of each genomic feature. Bottom: Fold enrichment of DMRs over genomic background. (G) GO and KEGG pathway analysis of DMR-associated genes to explore their functional relevance to ALS

Fig. 4

Integration of cfDNA methylation with spinal cord gene expression in ALS [49]. (A) Venn diagram illustrating the overlap between cfDNA-derived DMR-associated genes and DEGs identified in three spinal cord segments of ALS patients. (B) Violin plots displaying the log2 fold-change (LFC) of DEGs with differential methylation in cfDNA from the spinal cords of ALS patients compared to controls. C-D. KEGG pathway analysis of overlapping genes in the cervical and lumbar spinal cord segments. E. Venn diagram displaying the overlap between DMR-associated genes and genes linked to disease duration in ALS. F. Detailed information on DMRs annotated to ARRB2 and CYBA, along with their gene expression statistics in the spinal cord

Fig. 5

Integration of cfDNA methylation with snRNA-Seq for frontal cortex and motor cortex of ALS [32]. (A) Venn diagram showing the overlap between cfDNA-derived DMR-associated genes and DEGs identified in ALS patients’ frontal cortex and motor cortex compared to controls. (B) Number of DEGs overlapping with DMR-associated genes in six major cell types of the frontal cortex (left) and motor cortex (right), respectively. (C) Bar graphs showing the percentage of DEGs in each cell type associated with DMRs in cfDNA. (D) Genomic annotation of the DMRs from overlapping genes, showing their percentage of each genomic feature. (E) Box plots displaying the LogFC of DEGs with differential methylation in cfDNA across each major cell type in the frontal cortex (left) and motor cortex (right), respectively. F-G. KEGG pathway analysis of overlapping genes to assess their functional significance in ALS’s frontal cortex (left) and motor cortex (right)

Fig. 6

Box plot for estimated 14 tissue proportions from WGBS data of ALS patients and control subjects using Type I and Type II methylation markers. ALS patients exhibited a significantly lower proportion of cfDNA from T-cells and B-cells compared to controls, while showed an increased proportion of cfDNA originating from the liver