Epigenetic profiles of tissue informative CpGs inform ALS disease status and progression

Abstract

Background: Cell-free DNA (cfDNA), derived from dying cells, has demonstrated utility across multiple clinical applications. However, its potential in neurodegenerative diseases remains underexplored, with most existing cfDNA technologies tailored to specific disease contexts like cancer or non-invasive prenatal screening.

Methods: To address this gap, we developed a novel approach to characterize epigenetic cfDNA profiles by identifying key regions of DNA methylation that reveal the tissues origins undergoing apoptosis or necrosis. We evaluated this method in the largest cfDNA study of amyotrophic lateral sclerosis (ALS) and other neurological diseases (OND) to date, encompassing two independent cohorts (n = 192) from Australia (UQ N_cases = 48, N_controls = 32, N_OND = 15) and the USA, (UCSF N_cases = 50, N_controls = 45)).

Results: Our approach accurately distinguished ALS patients from controls (UQ AUC = 0.82, UCSF AUC = 0.99) and from individuals with other neurological diseases (AUC = 0.91). It also identified an asymptomatic carrier of a pathogenic C9orf72 variant, and strongly correlated with ALS disease progression measures (Pearson's R = 0.66, p = 3.71 × 10⁻⁹).

Conclusions: We identified DNA methylation signals from multiple tissue types in ALS cfDNA, highlighting diverse tissue involvement in ALS pathology. These findings promote epigenetic cfDNA analysis as a powerful tool for advancing our understanding of neurodegenerative disease.

Keywords: Cell-free DNA; Epigenetics; Neurodegeneration.

Fig. 1

Overview of epigenetic cfDNA biomarker development approach. a Firstly, tissue informative markers (TIMs) were selected using WGBS data to capture CpG sites that were hypermethylated or hypomethylated in a tissue of interest using publicly available WGBS reference data. b Next, cfDNA was extracted from the blood plasma of ALS cases and controls. c The cfDNA was bisulfite-treated, hybridized to capture probes, designed as complementary to TIMs, and then sequenced. Some off-target reads were also captured. d Using computational approaches, we analyzed the tissue of origin of the cfDNA samples and performed machine learning to identify features of ALS

Fig. 2

Cohort demographic and clinical characteristics. For the UQ (n = 43) and UCSF (n = 42) ALS patients. a The distribution of the age of onset of ALS disease symptoms, where the dotted line indicates the median age of onset, b patient ALSFRS-R scores, c FVC, and d the number of days between cfDNA collection and date ALS symptoms were observed. In the density plots, the shaded area indicates the continuous probability curve using kernel density estimation. In the box plots, the centerline of the box indicates the mean, the outer edges of the box indicate the upper and lower quartiles, and the whiskers indicate the maxima and minima of the distribution. Each dot indicates an individual

Fig. 3

Capture panel design. a The panel was designed to capture both hypomethylated TIMs, which were CpG sites that were less methylated in a tissue of interest relative to other tissues, and hypermethylated TIMs, which were designed to capture sites more methylated in a tissue of interest than other tissues. b The methylation proportion of reference tissues at either the site the TIM was selected for or all other tissues. c The distance hyper- or hypo-methylated TIMs are from the transcription start site of a gene. d The number of hyper- and hypo-methylated TIMs in different genomic regions. e For samples where the true genome-wide methylation proportion was between 0.0 and 1.0 (red dots), the observed methylation proportion after capture and sequencing. For all box plots, the centerline of the box indicates the mean, the outer edges of the box indicate the upper and lower quartiles, and the whiskers indicate the maxima and minima of the distribution. Each dot indicates an individual

Fig. 4

Capture panel performance on cfDNA data. a The starting cfDNA concentration of ALS patients and controls for each cohort, where each point represents one individual. b Coverage of the on-target and off-target CpG sites of each cohort, where each dot represents one sample. c Correlation between the UQ and UCSF methylation proportions at on-target sites. A single point represents a TIM. d The proportion of cfDNA from the controls and cases in each cohort that was estimated to originate from skeletal muscle. The gray-shaded circle indicates outlier control individuals. For all box plots, the centerline of the box indicates the mean, the outer edges of the box indicate the upper and lower quartiles, and the whiskers indicate the maxima and minima of the distribution. Each dot indicates an individual

Fig. 5

ALS disease classification with cfDNA epigenetic features. The false-positive rate versus true-positive rate for models trained and tested using CpG coverage, CpG methylation, and covariates as input features for a tenfold cross-validation within UQ samples, b tenfold cross-validation within UCSF samples, c trained on UCSF data and tested on UQ data, and d trained on UQ data and tested on UCSF data

Fig. 6

Features selected by the elastic net algorithm. a For each tissue, the TIMs were selected for, and for the type of TIM, the total absolute β value. A larger absolute β sum indicated that the feature type contributed more to model predictions. The β values for the b methylation proportion and c the read coverage of individual TIMs selected to be hypermethylated and the β values for the d methylation proportion and e read coverage of individual TIMs selected to be hypomethylated. f The methylation proportion of cases and controls for each cohort for a hypermethylated TIM in the SHISA5 gene. g The read coverage of cases and controls for each cohort for a hypermethylated TIM located in the XRCC6 gene. For all box plots, the centerline of the box indicates the mean, the outer edges of the box indicate the upper and lower quartiles, and the whiskers indicate the maxima and minima of the distribution. Each individual dot indicates a cfDNA sample

Fig. 7

Predictive performance of cfDNA epigenetic features for ALS phenotypes. For a tenfold cross-validated model trained using cfDNA methylation proportion and coverage features of the predicted versus true a ALSFRS-R, b FVC, and c ALSFRS-R slope. Each point represents one ALS case