Proteomics profiling and machine learning in nusinersen-treated patients with spinal muscular atrophy.

AIM: The availability of disease-modifying therapies and newborn screening programs for spinal muscular atrophy (SMA) has generated an urgent need for reliable prognostic biomarkers to classify patients according to disease severity. We aim to identify cerebrospinal fluid (CSF) prognostic protein biomarkers in CSF samples of SMA patients collected at baseline (T0), and to describe proteomic profile changes and biological pathways influenced by nusinersen before the sixth nusinersen infusion (T302). METHODS: In this multicenter retrospective longitudinal study, we employed an untargeted liquid chromatography mass spectrometry (LC-MS)-based proteomic approach on CSF samples collected from 61 SMA patients treated with nusinersen (SMA1 n=19, SMA2 n=19, SMA3 n=23) at T0 at T302. The Random Forest (RF) machine learning algorithm and pathway enrichment analysis were applied for analysis. RESULTS: The RF algorithm, applied to the protein expression profile of naïve patients, revealed several proteins that could classify the different types of SMA according to their differential abundance at T0. Analysis of changes in proteomic profiles identified a total of 147 differentially expressed proteins after nusinersen treatment in SMA1, 135 in SMA2, and 289 in SMA3. Overall, nusinersen-induced changes on proteomic profile were consistent with i) common effects observed in allSMA types (i.e. regulation of axonogenesis), and ii) disease severity-specific changes, namely regulation of glucose metabolism in SMA1, of coagulation processes in SMA2, and of complement cascade in SMA3. CONCLUSIONS: This untargeted LC-MS proteomic profiling in the CSF of SMA patients revealed differences in protein expression in naïve patients and showed nusinersen-related modulation in several biological processes after 10 months of treatment. Further confirmatory studies are needed to validate these results in larger number of patients and over abroader timeframe.

P2X7 Receptor Antagonist Reduces Fibrosis and Inflammation in a Mouse Model of Alpha-Sarcoglycan Muscular Dystrophy.

Limb-girdle muscular dystrophy R3, a rare genetic disorder affecting the limb proximal muscles, is caused by mutations in the α-sarcoglycan gene (Sgca) and aggravated by an immune-mediated damage, finely modulated by the extracellular (e)ATP/purinoceptors axis. Currently, no specific drugs are available. The aim of this study was to evaluate the therapeutic effectiveness of a selective P2X7 purinoreceptor antagonist, A438079. Sgca knockout mice were treated with A438079 every two days at 3 mg/Kg for 24 weeks. The P2X7 antagonist improved clinical parameters by ameliorating mice motor function and decreasing serum creatine kinase levels. Histological analysis of muscle morphology indicated a significant reduction of the percentage of central nuclei, of fiber size variability and of the extent of local fibrosis and inflammation. A cytometric characterization of the muscle inflammatory infiltrates showed that A438079 significantly decreased innate immune cells and upregulated the immunosuppressive regulatory T cell subpopulation. In α-sarcoglycan null mice, the selective P2X7 antagonist A438079 has been shown to be effective to counteract the progression of the dystrophic phenotype and to reduce the inflammatory response. P2X7 antagonism via selective inhibitors could be included in the immunosuppressant strategies aimed to dampen the basal immune-mediated damage and to favor a better engraftment of gene-cell therapies.