Decoding distinctive features of plasma extracellular vesicles in amyotrophic lateral sclerosis.
Biomarkers
Extracellular vesicles
HSP90
Machine learning
PPIA
Phosphorylated TDP-43
Plasma
Journal
Molecular neurodegeneration
ISSN: 1750-1326
Titre abrégé: Mol Neurodegener
Pays: England
ID NLM: 101266600
Informations de publication
Date de publication:
10 08 2021
10 08 2021
Historique:
received:
17
09
2020
accepted:
05
07
2021
entrez:
11
8
2021
pubmed:
12
8
2021
medline:
11
1
2022
Statut:
epublish
Résumé
Amyotrophic 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. We analyzed plasma-derived EVs of ALS patients (n = 106) and controls (n = 96), and SOD1 Our 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. Our 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.
Sections du résumé
BACKGROUND
Amyotrophic 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.
METHODS
We analyzed plasma-derived EVs of ALS patients (n = 106) and controls (n = 96), and SOD1
RESULTS
Our 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.
CONCLUSIONS
Our 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.
Identifiants
pubmed: 34376243
doi: 10.1186/s13024-021-00470-3
pii: 10.1186/s13024-021-00470-3
pmc: PMC8353748
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
52Informations de copyright
© 2021. The Author(s).
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