Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progression

Lucas Vu, Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, Arizona, 85013, USA.
Krystine Garcia-Mansfield, Cancer & Cell Biology Division, Translational Genomics Research Institute, Phoenix, Arizona, 85004, USA.
Antonio Pompeiano, International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic.
Jiyan An, Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, Arizona, 85013, USA.
Victoria David-Dirgo, Integrated Mass Spectrometry, City of Hope Comprehensive Cancer Center, Duarte, California, 19050, USA.
Ritin Sharma, Cancer & Cell Biology Division, Translational Genomics Research Institute, Phoenix, Arizona, 85004, USA.
Vinisha Venugopal, Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, Arizona, 85013, USA.
Harkeerat Halait, Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, Arizona, 85013, USA.
Guido Marcucci, Department of Hematologic Malignances Translational Science, Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California, 91010, USA.
Ya-Huei Kuo, Department of Hematologic Malignances Translational Science, Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California, 91010, USA.
Lisa Uechi, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope Medical Center, Duarte, California, 91010, USA.
Russell C. Rockne, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope Medical Center, Duarte, California, 91010, USA.
Patrick Pirrotte, Cancer & Cell Biology Division, Translational Genomics Research Institute, Phoenix, Arizona, 85004, USA.
Robert Bowser, Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, Arizona, 85013, USA.

Document Type

Article

Abstract

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.

Medical Subject Headings

Humans; Amyotrophic Lateral Sclerosis; Proteome (metabolism); Proteomics (methods); Biomarkers (cerebrospinal fluid); Disease Progression; Retinol-Binding Proteins, Plasma

Publication Date

11-1-2023

Publication Title

Annals of clinical and translational neurology

E-ISSN

2328-9503

Volume

10

Issue

11

First Page

2025

Last Page

2042

PubMed ID

37646115

Digital Object Identifier (DOI)

10.1002/acn3.51890

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