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Amyotrophic lateral sclerosis (ALS) is characterized by the degeneration of upper and lower motor neurons, yet an increasing number of studies in both mouse models and patients with ALS suggest that altered metabolic homeostasis is a feature of disease. Pre-clinical and clinical studies have shown that modulation of energy balance can be beneficial in ALS. However, our capacity to target specific metabolic pathways or mechanisms requires detailed understanding of metabolic dysregulation in ALS. Here, using the SOD1G93Amouse model of ALS, we demonstrate that an increase in whole-body metabolism occurs at a time when glycolytic muscle exhibits an increased dependence on fatty acid oxidation. Using myotubes derived from muscle of ALS patients, we also show that increased dependence on fatty acid oxidation is associated with increased whole-body energy expenditure. In the present study, increased fatty acid oxidation was associated with slower disease progression. However, we observed considerable heterogeneity in whole-body metabolism and fuel oxidation profiles across our patient cohort. Thus, future studies that decipher specific metabolic changes at an individual patient level are essential for the development of treatments that aim to target metabolic pathways in ALS.

Circulating cell-free DNA (cfDNA) in the bloodstream originates from dying cells and is a promising non-invasive biomarker for cell death. Here, we develop a method to accurately estimate the relative abundances of cell types contributing to cfDNA. We leverage the distinct DNA methylation profile of each cell type throughout the body. Decomposing the cfDNA mixture is difficult, as fragments from relevant cell types may only be present in a small amount. We propose an algorithm, CelFiE, that estimates cell type proportion from both whole genome cfDNA input and reference data. CelFiE accommodates low coverage data, does not rely on CpG site curation, and estimates contributions from multiple unknown cell types that are not available in reference data. In simulations we show that CelFiE can accurately estimate known and unknown cell type of origin of cfDNA mixtures in low coverage and noisy data. Simulations also demonstrate that we can effectively estimate cfDNA originating from rare cell types composing less than 0.01% of the total cfDNA. To validate CelFiE, we use a positive control: cfDNA extracted from pregnant and non-pregnant women. CelFiE estimates a large placenta component specifically in pregnant women (p = 9.1 × 10−5). Finally, we use CelFiE to decompose cfDNA from ALS patients and age matched controls. We find increased cfDNA concentrations in ALS patients (p = 3.0 × 10−3). Specifically, CelFiE estimates increased skeletal muscle component in the cfDNA of ALS patients (p = 2.6 × 10−3), which is consistent with muscle impairment characterizing ALS. Quantification of skeletal muscle death in ALS is novel, and overall suggests that CelFiE may be a useful tool for biomarker discovery and monitoring of disease progression.