Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, Arizona, 85013, USA.
Cancer & Cell Biology Division, Translational Genomics Research Institute, Phoenix, Arizona, 85004, USA.
Ann Clin Transl Neurol. 2023 Nov;10(11):2025-2042. doi: 10.1002/acn3.51890. Epub 2023 Aug 30.
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.
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.
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.
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.
肌萎缩侧索硬化症（ALS）是一种具有复杂病因的异质性疾病，缺乏预测疾病进展的生物标志物。本研究的目的是使用纵向脑脊液（CSF）样本，确定能够区分快速进展（FP）和缓慢进展（SP）的生物标志物，并评估其时间反应。
我们使用基于质谱（MS）的蛋白质组学方法，使用 SP 和 FP ALS 患者的纵向 CSF 来鉴定候选生物标志物。免疫测定法用于定量和验证顶级生物标志物的水平。使用纵向 MS 数据创建状态转换数学模型，该模型还预测了 FP 与 SP。
我们在所有 ALS 患者的 CSF 中总共鉴定出 1148 种蛋白质。途径分析确定了 FPs 中补体和凝血级联相关途径以及 SPs 中突触发生和葡萄糖代谢的富集。纵向分析揭示了一个由 59 个候选标志物组成的面板，这些标志物可以区分 FP 和 SP ALS。基于多元分析，我们确定了三个标志物（F12、RBP4 和 SERPINA4）作为根据疾病进展速度区分 ALS 的顶级候选标志物。这些蛋白质在发现和另一个验证队列中得到了验证。我们的状态转换模型确定了随时间变化的蛋白质组的整体方差可预测疾病进展速度。
我们确定了区分 ALS 疾病进展速度的途径和蛋白质生物标志物。CSF 蛋白质组的数学模型确定了蛋白质组随时间变化的熵变化可预测 FP 与 SP。
