دورية أكاديمية
Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progression
| العنوان: | Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progression |
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| المؤلفون: | Lucas Vu, Krystine Garcia‐Mansfield, Antonio Pompeiano, Jiyan An, Victoria David‐Dirgo, Ritin Sharma, Vinisha Venugopal, Harkeerat Halait, Guido Marcucci, Ya‐Huei Kuo, Lisa Uechi, Russell C. Rockne, Patrick Pirrotte, Robert Bowser |
| المصدر: | Annals of Clinical and Translational Neurology, Vol 10, Iss 11, Pp 2025-2042 (2023) |
| بيانات النشر: | Wiley, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Neurosciences. Biological psychiatry. Neuropsychiatry LCC:Neurology. Diseases of the nervous system |
| مصطلحات موضوعية: | Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2328-9503 |
| العلاقة: | https://doaj.org/toc/2328-9503Test |
| DOI: | 10.1002/acn3.51890 |
| الوصول الحر: | https://doaj.org/article/0d4c2e099206491a9ddd1e16461a4d54Test |
| رقم الانضمام: | edsdoj.0d4c2e099206491a9ddd1e16461a4d54 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 23289503 |
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| DOI: | 10.1002/acn3.51890 |