Multimodal analysis of cell-free DNA identifies epigenetic biomarkers for amyotrophic lateral sclerosis diagnosis and progression
Sebastian Michels, Chaorong Chen, Wolfgang P. Ruf, M. Madhy Garcia Garcia, Frederick J. Arnold, Zhuoxing Wu, Craig L. Bennett, Daniel Shams, Leslie M. Thompson, Alyssa C. Walker, Dennis W. Dickson, Leonard Petrucelli, Johannes Dorst, Mercedes Prudencio, Wei Li, Albert R. La Spada
Sebastian Michels, Chaorong Chen, Wolfgang P. Ruf, M. Madhy Garcia Garcia, Frederick J. Arnold, Zhuoxing Wu, Craig L. Bennett, Daniel Shams, Leslie M. Thompson, Alyssa C. Walker, Dennis W. Dickson, Leonard Petrucelli, Johannes Dorst, Mercedes Prudencio, Wei Li, Albert R. La Spada
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Research Article Genetics Neuroscience

Multimodal analysis of cell-free DNA identifies epigenetic biomarkers for amyotrophic lateral sclerosis diagnosis and progression

Abstract

The role of the epigenome in age-related neurodegenerative disorders remains understudied. Here, we analyzed circulating cell-free DNA (cfDNA) from blood to detect methylation changes as a liquid biopsy for Amyotrophic Lateral Sclerosis (ALS). Our study included 20 patients with sporadic ALS, 10 patients with C9orf72-associated ALS, 10 asymptomatic carriers of the C9orf72 repeat expansion mutation, and 21 nondisease control individuals. Following targeted enzymatic methyl-sequencing (EM-seq) of approximately 4 million CpG sites, we detected numerous differentially methylated genes, including several implicated in ALS disease risk and pathogenesis. By integrating multiple epigenetic features, we delineated a distinct epigenetic signature, which achieved an average area under the curve (AUC) of 0.91 ± 0.10 upon receiver operator characteristic (ROC) analysis, which enabled detection of approximately 70% of patients with ALS with close to 100% specificity. Furthermore, we also identified a set of genes whose methylation status significantly correlated with clinical disease progression and cerebrospinal fluid (CSF) neurofilament levels. Our results reveal the potential of cfDNA-based biomarkers to accurately diagnose ALS and potentially predict disease progression.

Authors

Sebastian Michels, Chaorong Chen, Wolfgang P. Ruf, M. Madhy Garcia Garcia, Frederick J. Arnold, Zhuoxing Wu, Craig L. Bennett, Daniel Shams, Leslie M. Thompson, Alyssa C. Walker, Dennis W. Dickson, Leonard Petrucelli, Johannes Dorst, Mercedes Prudencio, Wei Li, Albert R. La Spada

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