Dettaglio Progetto Finanziato

Lombardy partner:

– Alessio Di Fonzo, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico 

Dystonias encompass a heterogenous group of rare hyperkinetic movement disorders. Leveraging the advance in genetic technology, researchers have been able to elucidate the genetic cause of various monogenic forms of dystonia, revealing different pathogenic mechanisms. However, in most cases the genetic diagnosis fails, hindering the understanding of the pathogenesis, and therefore accurate prognosis and development of targeted therapeutic approaches. Based on our previous data, we hypothesize that undiagnosed forms can be stratified into distinct categories that reflect mechanisms in common with monogenic forms. To test this hypothesis, we will integrate complementary datasets using graph-based statistical and explainable artificial intelligence approaches. Specifically, by applying these analyses to a combination of OMICS and deep phenotyping data, we aim to i) increase the diagnostic yield in dystonias, either by detecting previously unrecognized etiological gene variants or by identifying less penetrant disease-predisposing alleles, to ii) transfer prioritized gene variants into 3-D human-based models to validate the underlying mechanisms, to iii) identify prognostic markers based on the molecularly resolved cases, which can be used to predict the course of undiagnosed patients. To these ends, we will employ clinical cohorts, large collections of samples and patient-derived induced pluripotent stem cell (iPSC) 3-D models that aim to recapitulate dystonia features at the molecular and functional level. The proposed multidisciplinary approach encompasses multiple areas of expertise, gathered at a transnational level by the consortium.