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Updated: Feb 20, 2026

Author Spotlight: Unveiling the Pathway Linking Obesity to Autoimmune Inflammation in Multiple Sclerosis
Published on: February 23, 2024
Synapsin I deletion reduces neuronal damage and ameliorates clinical progression of experimental autoimmune
Fabrizia C Guarnieri1, Serena Bellani2, Latefa Yekhlef2
1Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Via Olgettina 58, 20132 Milan, Italy.
Abstract:
The classical view of multiple sclerosis (MS) pathogenesis states that inflammation-mediated demyelination is responsible for neuronal damage and loss. However, recent findings show that impairment of neuronal functions and demyelination can be independent events, suggesting the coexistence of other pathogenic mechanisms. Due to the inflammatory milieu, subtle alterations in synaptic function occur, which are probably at the basis of the early cognitive decline that often precedes the neurodegenerative phases in MS patients. In particular, it has been reported that inflammation enhances excitatory synaptic transmission while it decreases GABAergic transmission in vitro and ex vivo. This evidence points to the idea that an excitation/inhibition imbalance occurs in the inflamed MS brain, even though the exact molecular mechanisms leading to this synaptic dysfunction are as yet not completely clear. Along this line, we observed that acute treatment of primary hippocampal neurons in culture with pro-inflammatory cytokines leads to an increased phosphorylation of synapsin I (SynI) by ERK1/2 kinase and to an increase in the frequency of spontaneous synaptic vesicle release events, which is prevented by SynI deletion. In vivo, the ablation of SynI expression is protective in terms of disease progression and neuronal damage in the experimental autoimmune encephalomyelitis mouse model of MS. Our results point to a possible key role in MS pathogenesis of the neuronal protein SynI, a regulator of excitation/inhibition balance in neuronal networks.
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