Decoding distinctive features of plasma extracellular vesicles in amyotrophic lateral sclerosis

Molecular Neurodegeneration

Cristina Potrich¹  Marcella Chiari²  Marina Cretich²  Maria Pennuto³  Vito Giuseppe D’Agostino⁴  Deborah Ferrara⁴  Alessandro Quattrone⁴  Francesco Rinaldi⁵  Stefano Callegaro⁵  Massimo Corbo⁶  Gabriele Mora⁷  Gianni Sorarù⁸  Alessandro Corbelli⁹  Valentina Bonetto⁹  Laura Pasetto⁹  Laura Brunelli⁹  Roberta Pastorelli⁹  Fabio Fiordaliso⁹  Giovanna Sestito⁹  Elisa Bianchi⁹  Manuela Basso^1,10  Christian Lunetta^1,11  Adriano Chiò^1,12  Andrea Calvo^1,12  Cristina Moglia^1,12 

[1] Centre for Materials and Microsystems, Fondazione Bruno Kessler, Trento, Italy;Istituto di Biofisica, Consiglio Nazionale delle Ricerche, Trento, Italy;Consiglio Nazionale delle Ricerche, Istituto di Scienze e Tecnologie Chimiche “Giulio Natta” (SCITEC-CNR), Milan, Italy;Department of Biomedical Sciences (DBS), University of Padova, 35131, Padova, Italy;Veneto Institute of Molecular Medicine (VIMM), 35129, Padova, Italy;Department of Cellular, Computational and Integrative Biology – CIBIO, University of Trento, Trento, Italy;Department of Mathematics “Tullio Levi-Civita”, University of Padova, Padova, Italy;Department of Neurorehabilitation Sciences, Casa Cura Policlinico (CCP), Milan, Italy;Department of Neurorehabilitation, ICS Maugeri IRCCS, Milan, Italy;Department of Neuroscience, University of Padova, 35122, Padova, Italy;Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy;Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy;Department of Cellular, Computational and Integrative Biology – CIBIO, University of Trento, Trento, Italy;NEuroMuscular Omnicentre (NEMO), Serena Onlus Foundation, Milan, Italy;‘Rita Levi Montalcini’ Department of Neuroscience, Università degli Studi di Torino, Torino, Italy;

关键词: Extracellular vesicles;    HSP90;    PPIA;    Phosphorylated TDP-43;    Biomarkers;    Machine learning;    Plasma;   

DOI  :  10.1186/s13024-021-00470-3

来源: Springer

【 摘 要 】

BackgroundAmyotrophic lateral sclerosis (ALS) is a multifactorial, multisystem motor neuron disease for which currently there is no effective treatment. There is an urgent need to identify biomarkers to tackle the disease’s complexity and help in early diagnosis, prognosis, and therapy. Extracellular vesicles (EVs) are nanostructures released by any cell type into body fluids. Their biophysical and biochemical characteristics vary with the parent cell’s physiological and pathological state and make them an attractive source of multidimensional data for patient classification and stratification.MethodsWe analyzed plasma-derived EVs of ALS patients (n = 106) and controls (n = 96), and SOD1G93A and TDP-43Q331K mouse models of ALS. We purified plasma EVs by nickel-based isolation, characterized their EV size distribution and morphology respectively by nanotracking analysis and transmission electron microscopy, and analyzed EV markers and protein cargos by Western blot and proteomics. We used machine learning techniques to predict diagnosis and prognosis.ResultsOur procedure resulted in high-yield isolation of intact and polydisperse plasma EVs, with minimal lipoprotein contamination. EVs in the plasma of ALS patients and the two mouse models of ALS had a distinctive size distribution and lower HSP90 levels compared to the controls. In terms of disease progression, the levels of cyclophilin A with the EV size distribution distinguished fast and slow disease progressors, a possibly new means for patient stratification. Immuno-electron microscopy also suggested that phosphorylated TDP-43 is not an intravesicular cargo of plasma-derived EVs.ConclusionsOur analysis unmasked features in plasma EVs of ALS patients with potential straightforward clinical application. We conceived an innovative mathematical model based on machine learning which, by integrating EV size distribution data with protein cargoes, gave very high prediction rates for disease diagnosis and prognosis.

【 授权许可】

CC BY