In Vivo Dopamine Neuron Imaging-Based Small Molecule Screen Identifies Novel Neuroprotective Compounds and Targets.
In Vivo Dopamine Neuron Imaging-Based Small Molecule Screen Identifies Novel Neuroprotective Compounds and Targets.
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基于体内多巴胺神经元成像的小分子筛选可识别新型的神经保护化合物和靶标。
DOI：
10.3389/fphar.2022.837756
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发表时间：
2022
影响因子：
5.6
通讯作者：
Guo S
中科院分区：
文献类型：
--
作者：
Kim GJ;Mo H;Liu H;Okorie M;Chen S;Zheng J;Li H;Arkin M;Huang B;Guo S
帕金森病 (PD) 是第二常见的神经退行性疾病，具有显着的多巴胺 (DA) 神经元变性。 PD 影响着全世界数百万人，但目前可用的治疗方法仅限于暂时缓解症状。为了发现缓解疾病的疗法，我们在转基因斑马鱼幼虫中使用体内整体有机体筛选试验，对 1,403 种生物活性小分子化合物进行了筛选。转基因模型表达由酪氨酸羟化酶 (th) 启动子驱动的细菌酶硝基还原酶 (NTR)。 NTR 将常用的抗生素前药甲硝唑 (MTZ) 转化为有毒的亚硝基形式，从而诱导 DA 神经元损失。经鉴定，57 种化合物的大脑健康评分 (BHS) 与 FDR 调整后单独使用 MTZ 治疗相比显着改善 (padj<0.05)。我们通过药物分类、已知作用机制、适应症、IC50 和靶标对每种化合物进行注释，独立地整理了高通量筛选 (HTS) 数据。使用 Reactome 数据库，我们进行了通路分析，除了验证先前已知的与 PD 相关的通路外，还发现了先前未知的通路。对筛选数据的非拓扑通路分析进一步确定细胞凋亡、雌激素、二肽基肽酶 4 和阿片受体 Mu1 是参与神经保护的潜在重要通路和靶点。通过对化合物处理前后的 DA 神经元进行成像的二次测定，总共检查了 12 种化合物。该二次测定的 z' 因子确定为 0.58，表明它是一种出色的筛选测定。依托度酸、奈帕芬胺、阿洛佩林、丙硫异烟胺和奥美沙坦显示出显着的神经保护作用，并且还通过盲法手动 DA 神经元计数进行了验证。为了确定这些化合物是否与神经保护作用广泛相关，我们在Conduritol-b-环氧化物（CBE）诱导的戈谢病（GD）模型上对其进行了测试，在该模型中，葡萄糖脑苷脂酶（GBA）（一种众所周知的帕金森病遗传风险因素）的活性受到抑制。在此测定中，阿哌林、奥美沙坦和奈帕芬胺显示出对 DA 神经元的显着保护作用。这项工作结合了基于体内成像的高内涵全生物体筛选和筛选数据集的生物信息学通路分析，描绘了一种以前未知的方法，用于识别命中先导候选物，并暗示以前未知的参与 DA 神经元保护的通路和靶点。
Parkinson’s disease (PD) is the second most common neurodegenerative disorder with prominent dopamine (DA) neuron degeneration. PD affects millions of people worldwide, but currently available therapies are limited to temporary relief of symptoms. As an effort to discover disease-modifying therapeutics, we have conducted a screen of 1,403 bioactive small molecule compounds using an in vivo whole organism screening assay in transgenic larval zebrafish. The transgenic model expresses the bacterial enzyme nitroreductase (NTR) driven by the tyrosine hydroxylase (th) promotor. NTR converts the commonly used antibiotic pro-drug metronidazole (MTZ) to the toxic nitroso radical form to induce DA neuronal loss. 57 compounds were identified with a brain health score (BHS) that was significantly improved compared to the MTZ treatment alone after FDR adjustment (padj<0.05). Independently, we curated the high throughput screening (HTS) data by annotating each compound with pharmaceutical classification, known mechanism of action, indication, IC50, and target. Using the Reactome database, we performed pathway analysis, which uncovered previously unknown pathways in addition to validating previously known pathways associated with PD. Non-topology-based pathway analysis of the screening data further identified apoptosis, estrogen hormone, dipeptidyl-peptidase 4, and opioid receptor Mu1 to be potentially significant pathways and targets involved in neuroprotection. A total of 12 compounds were examined with a secondary assay that imaged DA neurons before and after compound treatment. The z’-factor of this secondary assay was determined to be 0.58, suggesting it is an excellent assay for screening. Etodolac, nepafenac, aloperine, protionamide, and olmesartan showed significant neuroprotection and was also validated by blinded manual DA neuronal counting. To determine whether these compounds are broadly relevant for neuroprotection, we tested them on a conduritol-b-epoxide (CBE)-induced Gaucher disease (GD) model, in which the activity of glucocerebrosidase (GBA), a commonly known genetic risk factor for PD, was inhibited. Aloperine, olmesartan, and nepafenac showed significant protection of DA neurons in this assay. Together, this work, which combines high content whole organism in vivo imaging-based screen and bioinformatic pathway analysis of the screening dataset, delineates a previously uncharted approach for identifying hit-to-lead candidates and for implicating previously unknown pathways and targets involved in DA neuron protection.
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影响因子：
9.8
作者：
McQuin C;Goodman A;Chernyshev V;Kamentsky L;Cimini BA;Karhohs KW;Doan M;Ding L;Rafelski SM;Thirstrup D;Wiegraebe W;Singh S;Becker T;Caicedo JC;Carpenter AE
通讯作者：
Carpenter AE
影响因子：
5.1
作者：
Cai, Y.;Liu, S.;Chan, P.
通讯作者：
Chan, P.
影响因子：
11
作者：
Jankovic, Joseph;Tan, Eng King
通讯作者：
Tan, Eng King
影响因子：
16.2
作者：
Hu, Gang;Jousilahti, Pekka;Tuomilehto, Jaakko
通讯作者：
Tuomilehto, Jaakko
影响因子：
7.7
作者：
Ehlen, J. Christopher;Brager, Allison J.;Paul, Ketema N.
通讯作者：
Paul, Ketema N.