A Transcriptomic Meta-Analysis Shows Lipid Metabolism Dysregulation as an Early Pathological Mechanism in the Spinal Cord of SOD1 Mice.
Luis C Fernández-BeltránJuan Miguel Godoy-CorchueloMaria Losa-FontangordoDebbie WilliamsJorge Matias-GuiuSilvia CorrochanoPublished in: International journal of molecular sciences (2021)
Amyotrophic lateral sclerosis (ALS) is a multifactorial and complex fatal degenerative disorder. A number of pathological mechanisms that lead to motor neuron death have been identified, although there are many unknowns in the disease aetiology of ALS. Alterations in lipid metabolism are well documented in the progression of ALS, both at the systemic level and in the spinal cord of mouse models and ALS patients. The origin of these lipid alterations remains unclear. This study aims to identify early lipid metabolic pathways altered before systemic metabolic symptoms in the spinal cord of mouse models of ALS. To do this, we performed a transcriptomic analysis of the spinal cord of SOD1G93A mice at an early disease stage, followed by a robust transcriptomic meta-analysis using publicly available RNA-seq data from the spinal cord of SOD1 mice at early and late symptomatic disease stages. The meta-analyses identified few lipid metabolic pathways dysregulated early that were exacerbated at symptomatic stages; mainly cholesterol biosynthesis, ceramide catabolism, and eicosanoid synthesis pathways. We present an insight into the pathological mechanisms in ALS, confirming that lipid metabolic alterations are transcriptionally dysregulated and are central to ALS aetiology, opening new options for the treatment of these devastating conditions.
Keyphrases
- amyotrophic lateral sclerosis
- spinal cord
- rna seq
- single cell
- spinal cord injury
- neuropathic pain
- systematic review
- meta analyses
- fatty acid
- mouse model
- high fat diet induced
- newly diagnosed
- randomized controlled trial
- ejection fraction
- prognostic factors
- machine learning
- physical activity
- patient reported outcomes
- depressive symptoms
- deep learning
- big data
- data analysis
- drug induced