Identification of potential pathways and biomarkers linked to progression in ALS.
Identification of potential pathways and biomarkers linked to progression in ALS.
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DOI：
10.1002/acn3.51697
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发表时间：
2023-02
影响因子：
5.3
通讯作者：
Yeo CJJ
中科院分区：
文献类型：
--
作者：
Huber RG;Pandey S;Chhangani D;Rincon-Limas DE;Staff NP;Yeo CJJ
为肌萎缩侧索硬化症的临床治疗和临床试验寻找潜在的诊断和预后生物标志物。我们分析了ALS患者诱导的多能干细胞衍生运动神经元的蛋白质组学数据，这些数据可以通过AnswerALS联盟获得。在使用临床ALSFRS-R和ALS-CBS量表对患者进行分层后，我们确定了指示ALS疾病严重程度和进展率的差异表达蛋白作为ALS相关和预后的候选生物标志物。用STICCH软件对鉴定的蛋白质进行路径分析。使用连接图工具将蛋白质集与药物的效果相关联，以识别可能影响相似途径的化合物。在果蝇TDP-43 ALS模型中进行RNAi筛选，以验证病理相关性。使用岭回归建立了统计分类机器学习模型，使用蛋白质组学数据来区分ALS患者和对照组。我们分别从ALSFRS-R基线、ALSFRS-R进展斜率、ALS-CBS基线和ALS-CBS进展斜率分层的患者中，分别确定了76、21、71和1个候选ALS相关生物标志物和22、41、27和候选预后生物标志物。19种蛋白质增强或抑制ALS苍蝇模型中的致病眼型。使用连接性地图工具预测营养食品、多巴胺途径调节剂、他汀类药物、抗炎药和抗菌剂是药物再利用的起点。通过机器学习预测了10个诊断生物标志物蛋白，以识别ALS患者，具有较高的准确性和敏感性。这项研究展示了IPSC-运动神经元蛋白质组学结合机器学习和生物学确认在预测ALS新机制以及诊断和预测生物标志物方面的强大方法。
To identify potential diagnostic and prognostic biomarkers for clinical management and clinical trials in amyotrophic lateral sclerosis. We analysed proteomics data of ALS patient‐induced pluripotent stem cell‐derived motor neurons available through the AnswerALS consortium. After stratifying patients using clinical ALSFRS‐R and ALS‐CBS scales, we identified differentially expressed proteins indicative of ALS disease severity and progression rate as candidate ALS‐related and prognostic biomarkers. Pathway analysis for identified proteins was performed using STITCH. Protein sets were correlated with the effects of drugs using the Connectivity Map tool to identify compounds likely to affect similar pathways. RNAi screening was performed in a Drosophila TDP‐43 ALS model to validate pathological relevance. A statistical classification machine learning model was constructed using ridge regression that uses proteomics data to differentiate ALS patients from controls. We identified 76, 21, 71 and 1 candidate ALS‐related biomarkers and 22, 41, 27 and 64 candidate prognostic biomarkers from patients stratified by ALSFRS‐R baseline, ALSFRS‐R progression slope, ALS‐CBS baseline and ALS‐CBS progression slope, respectively. Nineteen proteins enhanced or suppressed pathogenic eye phenotypes in the ALS fly model. Nutraceuticals, dopamine pathway modulators, statins, anti‐inflammatories and antimicrobials were predicted starting points for drug repurposing using the connectivity map tool. Ten diagnostic biomarker proteins were predicted by machine learning to identify ALS patients with high accuracy and sensitivity. This study showcases the powerful approach of iPSC‐motor neuron proteomics combined with machine learning and biological confirmation in the prediction of novel mechanisms and diagnostic and predictive biomarkers in ALS.
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DOI：
10.1007/s00018-021-03872-0
发表时间：
2021-08
期刊：
Cellular and molecular life sciences : CMLS
影响因子：
--
作者：
Kok JR;Palminha NM;Dos Santos Souza C;El-Khamisy SF;Ferraiuolo L
通讯作者：
Ferraiuolo L
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
4.4
作者：
Cedarbaum, JM;Stambler, N;Nakanishi, A
通讯作者：
Nakanishi, A
DOI：
10.1080/21678421.2017.1363780
发表时间：
2017-01-01
影响因子：
2.8
作者：
Abe, Koji;Itoyarna, Yasuto;Yoshino, Hiide
通讯作者：
Yoshino, Hiide
影响因子：
3.1
作者：
Chen, Dong-Hui;Latimer, Caitlin;Bird, Thomas D.
通讯作者：
Bird, Thomas D.