Yiğit Esra Nur, Sönmez Ekin, Yüksel İsa, Aksan Kurnaz Işıl, Çakır Tunahan
Institute of Biotechnology, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey.
Research Institute for Health Sciences and Technologies (SABITA), İstanbul Medipol University, İstanbul, Turkey.
Mol Omics. 2023 Mar 27;19(3):218-228. doi: 10.1039/d2mo00267a.
The most common treatment strategies for Parkinson's disease (PD) aim to slow down the neurodegeneration process or control the symptoms. In this study, using an PD model we carried out a transcriptome-based drug target prediction strategy. We identified novel drug target candidates by mapping genes upregulated in 6-OHDA-treated cells on a human protein-protein interaction network. Among the predicted targets, we show that and are promising in validating our bioinformatics approach since their known ligands, rutin and quercetin, respectively, act as neuroprotective drugs that effectively decrease cell death, and restore the expression profiles of key genes upregulated in 6-OHDA-treated cells. We also show that these two genes upregulated in our PD model are downregulated to basal levels upon drug administration. As a further validation of our methodology, we further confirm that the potential target genes identified with our bioinformatics approach are also upregulated in post-mortem transcriptome samples of PD patients from the literature. Therefore, we propose that this methodology predicts novel drug targets and , which are relevant to future clinical applications as potential drug repurposing targets for PD. Our systems-based computational approach to predict candidate drug targets can be employed in identifying novel drug targets in other diseases without assumption.
帕金森病（PD）最常见的治疗策略旨在减缓神经退行性变过程或控制症状。在本研究中，我们使用PD模型开展了基于转录组的药物靶点预测策略。我们通过将6-OHDA处理细胞中上调的基因映射到人类蛋白质-蛋白质相互作用网络上，确定了新的药物靶点候选物。在预测的靶点中，我们发现[此处原文缺失两个靶点名称]很有希望验证我们的生物信息学方法，因为它们已知的配体芦丁和槲皮素分别作为神经保护药物，可有效减少细胞死亡，并恢复6-OHDA处理细胞中上调的关键基因的表达谱。我们还表明，在我们的PD模型中上调的这两个基因在给药后下调至基础水平。作为对我们方法的进一步验证，我们进一步证实，通过我们的生物信息学方法确定的潜在靶基因在文献中PD患者的死后转录组样本中也上调。因此，我们提出这种方法预测了新的药物靶点[此处原文缺失两个靶点名称]，它们作为PD潜在的药物再利用靶点与未来的临床应用相关。我们基于系统的计算方法来预测候选药物靶点，可用于在无[此处原文缺失相关假设内容]假设的情况下识别其他疾病中的新药物靶点。
