Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progression
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F23%3A00079728" target="_blank" >RIV/00159816:_____/23:00079728 - isvavai.cz</a>
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1002/acn3.51890" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/acn3.51890</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/acn3.51890" target="_blank" >10.1002/acn3.51890</a>
Alternative languages
Result language
angličtina
Original language name
Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progression
Original language description
Objective: Amyotrophic lateral sclerosis (ALS) is a heterogeneous disease with a complex etiology that lacks biomarkers predicting disease progression. The objective of this study was to use longitudinal cerebrospinal fluid (CSF) samples to identify biomarkers that distinguish fast progression (FP) from slow progression (SP) and assess their temporal response.Methods: We utilized mass spectrometry (MS)-based proteomics to identify candidate biomarkers using longitudinal CSF from a discovery cohort of SP and FP ALS patients. Immunoassays were used to quantify and validate levels of the top biomarkers. A state-transition mathematical model was created using the longitudinal MS data that also predicted FP versus SP.Results: We identified a total of 1148 proteins in the CSF of all ALS patients. Pathway analysis determined enrichment of pathways related to complement and coagulation cascades in FPs and synaptogenesis and glucose metabolism in SPs. Longitudinal analysis revealed a panel of 59 candidate markers that could segregate FP and SP ALS. Based on multivariate analysis, we identified three biomarkers (F12, RBP4, and SERPINA4) as top candidates that segregate ALS based on rate of disease progression. These proteins were validated in the discovery and a separate validation cohort. Our state-transition model determined that the overall variance of the proteome over time was predictive of the disease progression rate.Interpretation: We identified pathways and protein biomarkers that distinguish rate of ALS disease progression. A mathematical model of the CSF proteome determined that the change in entropy of the proteome over time was predictive of FP versus SP.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30210 - Clinical neurology
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Annals of Clinical and Translational Neurology
ISSN
2328-9503
e-ISSN
2328-9503
Volume of the periodical
10
Issue of the periodical within the volume
11
Country of publishing house
US - UNITED STATES
Number of pages
18
Pages from-to
2025-2042
UT code for WoS article
001069656900001
EID of the result in the Scopus database
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