Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progression
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, Research Support, N.I.H., Extramural, práce podpořená grantem
Grantová podpora
R56 NS061867
NINDS NIH HHS - United States
R01 NS061867
NINDS NIH HHS - United States
P30 CA033572
NCI NIH HHS - United States
PubMed
37646115
PubMed Central
PMC10647001
DOI
10.1002/acn3.51890
Knihovny.cz E-zdroje
- MeSH
- amyotrofická laterální skleróza * MeSH
- biologické markery mozkomíšní mok MeSH
- lidé MeSH
- plazmatické proteiny vázající retinol MeSH
- progrese nemoci MeSH
- proteom metabolismus MeSH
- proteomika metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- Názvy látek
- biologické markery MeSH
- plazmatické proteiny vázající retinol MeSH
- proteom MeSH
- RBP4 protein, human MeSH Prohlížeč
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.
Cancer and Cell Biology Division Translational Genomics Research Institute Phoenix Arizona 85004 USA
Department of Translational Neuroscience Barrow Neurological Institute Phoenix Arizona 85013 USA
Integrated Mass Spectrometry City of Hope Comprehensive Cancer Center Duarte California 19050 USA
International Clinical Research Center St Anne's University Hospital Brno Czech Republic
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