脊髓性肌萎缩症患者接受 nusinersen 治疗后的蛋白质组学分析和机器学习。

Proteomics profiling and machine learning in nusinersen-treated patients with spinal muscular atrophy.

机构信息

Center of Translational and Experimental Myology, IRCCS Istituto Giannina Gaslini, Via G. Gaslini, 5, I-16147, Genova, Italy.

Department of Biostatistics and Bioinformatics, Institute of Health Sciences, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey.

出版信息

Cell Mol Life Sci. 2024 Sep 10;81(1):393. doi: 10.1007/s00018-024-05426-6.

DOI:10.1007/s00018-024-05426-6

PMID:39254732

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11387582/

Abstract

AIM

The availability of disease-modifying therapies and newborn screening programs for spinal muscular atrophy (SMA) has generated an urgent need for reliable prognostic biomarkers to classify patients according to disease severity. We aim to identify cerebrospinal fluid (CSF) prognostic protein biomarkers in CSF samples of SMA patients collected at baseline (T0), and to describe proteomic profile changes and biological pathways influenced by nusinersen before the sixth nusinersen infusion (T302).

METHODS

In this multicenter retrospective longitudinal study, we employed an untargeted liquid chromatography mass spectrometry (LC-MS)-based proteomic approach on CSF samples collected from 61 SMA patients treated with nusinersen (SMA1 n=19, SMA2 n=19, SMA3 n=23) at T0 at T302. The Random Forest (RF) machine learning algorithm and pathway enrichment analysis were applied for analysis.

RESULTS

The RF algorithm, applied to the protein expression profile of naïve patients, revealed several proteins that could classify the different types of SMA according to their differential abundance at T0. Analysis of changes in proteomic profiles identified a total of 147 differentially expressed proteins after nusinersen treatment in SMA1, 135 in SMA2, and 289 in SMA3. Overall, nusinersen-induced changes on proteomic profile were consistent with i) common effects observed in allSMA types (i.e. regulation of axonogenesis), and ii) disease severity-specific changes, namely regulation of glucose metabolism in SMA1, of coagulation processes in SMA2, and of complement cascade in SMA3.

CONCLUSIONS

This untargeted LC-MS proteomic profiling in the CSF of SMA patients revealed differences in protein expression in naïve patients and showed nusinersen-related modulation in several biological processes after 10 months of treatment. Further confirmatory studies are needed to validate these results in larger number of patients and over abroader timeframe.

摘要

目的

疾病修饰疗法和脊髓性肌萎缩症（SMA）新生儿筛查计划的出现，迫切需要可靠的预后生物标志物来根据疾病严重程度对患者进行分类。我们旨在确定 SMA 患者基线（T0）时采集的脑脊液（CSF）中 CSF 预后蛋白生物标志物，并描述在第 6 次 nusinersen 输注（T302）前受 nusinersen 影响的蛋白质组学特征变化和生物学途径。

方法

在这项多中心回顾性纵向研究中，我们采用基于非靶向液相色谱-质谱（LC-MS）的蛋白质组学方法对 61 名接受 nusinersen 治疗的 SMA 患者（SMA1 n=19、SMA2 n=19、SMA3 n=23）的 CSF 样本进行了分析，分别在 T0 和 T302 时采集。应用随机森林（RF）机器学习算法和通路富集分析进行分析。

结果

RF 算法应用于初始患者的蛋白质表达谱，揭示了一些蛋白质，这些蛋白质可以根据它们在 T0 时的差异丰度对不同类型的 SMA 进行分类。对蛋白质组学图谱变化的分析确定了 SMA1 中 147 个差异表达蛋白，SMA2 中 135 个，SMA3 中 289 个。总体而言，nusinersen 治疗后 SMA1 中观察到的蛋白质组学图谱的变化与 i）所有 SMA 类型中观察到的共同效应（即轴突发生的调节）和 ii）疾病严重程度特异性变化一致，即 SMA1 中葡萄糖代谢的调节，SMA2 中凝血过程的调节，SMA3 中补体级联的调节。