Muscular Biomarkers in Amyotrophic Lateral Sclerosis

Overview

The first objective is to find some biomarkers, or a profile of biomarkers of ALS to help to diagnosis. The second objective is to better understand the pathogenesis of this disease by the exploration of muscle, blood and satellite cells metabolomes and transcriptomes.

Amyotrophic Lateral Sclerosis (ALS), the most common MND, is a fatal adult-onset neuromuscular disease. Due to clinical heterogeneity and absence of biological tools to diagnose ALS, the delay between the first symptoms and diagnosis averages 9-13 months. A group of pathophysiological processes, including oxidative stress and glutamate-mediated excitotoxicity contribute to cell death, but the triggering factor, the timing and the interaction of different cellular events await elucidation \[2\]. Unknown pathogenesis for most patients means few available treatments. The search for biomarkers that can aid diagnosis, characterize phenotype, define pathophysiology, identify endpoints in trials and measure disease progression is of utmost importance for the field. Some studies have advocated that muscle per se may be impaired by pathogenesis of the diseases. Muscle has been poorly studied and its central role in energetic metabolism suggests that this tissue, quite easily available, should be more analyzed to find biomarkers and to compare muscular metabolism with those of brain and overall body. Specific aims of our subjects are: Specific aims are focused on: 1. the acquisition of metabolites profiles of the muscle, blood and satellite cells using an analytical platform enable a deep exploration. For that, the use of three analytical modalities (NMR, mass spectrometry coupled to GC or UPLC) ensures the best coverage of the metabolite population with a high range of concentration variability and molecular diversity. 2. the building of metabolites profiles models that discriminate pathological and control situations. 3. the identification of metabolites implicated in the discriminant model. 4. the generation of metabolism pathways hypothesis related to the discriminant model. 5. the acquisition of transcriptomics data to confirm and add complementary results to metabolomics data

Conditions

Interventions

Eligibility

Locations (2)

Outcomes