Laboratory of Pharmaceutical Biotechnology and Bioinformatics, Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jessore, Bangladesh.
Department of Biosciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan.
J Cell Mol Med. 2024 Aug;28(16):e18588. doi: 10.1111/jcmm.18588.
Huntington's disease (HD) is a gradually severe neurodegenerative ailment characterised by an increase of a specific trinucleotide repeat sequence (cytosine-adenine-guanine, CAG). It is passed down as a dominant characteristic that worsens over time, creating a significant risk. Despite being monogenetic, the underlying mechanisms as well as biomarkers remain poorly understood. Furthermore, early detection of HD is challenging, and the available diagnostic procedures have low precision and accuracy. The research was conducted to provide knowledge of the biomarkers, pathways and therapeutic targets involved in the molecular processes of HD using informatic based analysis and applying network-based systems biology approaches. The gene expression profile datasets GSE97100 and GSE74201 relevant to HD were studied. As a consequence, 46 differentially expressed genes (DEGs) were identified. 10 hub genes (TPM1, EIF2S3, CCN2, ACTN1, ACTG2, CCN1, CSRP1, EIF1AX, BEX2 and TCEAL5) were further differentiated in the protein-protein interaction (PPI) network. These hub genes were typically down-regulated. Additionally, DEGs-transcription factors (TFs) connections (e.g. GATA2, YY1 and FOXC1), DEG-microRNA (miRNA) interactions (e.g. hsa-miR-124-3p and has-miR-26b-5p) were also comprehensively forecast. Additionally, related gene ontology concepts (e.g. sequence-specific DNA binding and TF activity) connected to DEGs in HD were identified using gene set enrichment analysis (GSEA). Finally, in silico drug design was employed to find candidate drugs for the treatment HD, and while the possible modest therapeutic compounds (e.g. cortistatin A, 13,16-Epoxy-25-hydroxy-17-cheilanthen-19,25-olide, Hecogenin) against HD were expected. Consequently, the results from this study may give researchers useful resources for the experimental validation of Huntington's diagnosis and therapeutic approaches.
亨廷顿病 (HD) 是一种逐渐严重的神经退行性疾病，其特征是特定的三核苷酸重复序列 (胞嘧啶-腺嘌呤-鸟嘌呤，CAG) 增加。它作为一种随时间恶化的显性特征遗传，造成重大风险。尽管是单基因遗传，但潜在机制和生物标志物仍知之甚少。此外，HD 的早期检测具有挑战性，并且现有的诊断程序精度和准确性较低。本研究旨在利用基于信息学的分析和应用基于网络的系统生物学方法，提供涉及 HD 分子过程的生物标志物、途径和治疗靶点的知识。研究了与 HD 相关的基因表达谱数据集 GSE97100 和 GSE74201。结果确定了 46 个差异表达基因 (DEGs)。在蛋白质-蛋白质相互作用 (PPI) 网络中进一步区分了 10 个关键基因 (TPM1、EIF2S3、CCN2、ACTN1、ACTG2、CCN1、CSRP1、EIF1AX、BEX2 和 TCEAL5)。这些关键基因通常下调。此外，还全面预测了 DEGs-转录因子 (TF) 连接 (例如 GATA2、YY1 和 FOXC1)、DEG- microRNA (miRNA) 相互作用 (例如 hsa-miR-124-3p 和 has-miR-26b-5p)。此外，还使用基因集富集分析 (GSEA) 确定了与 HD 中 DEGs 相关的基因本体论概念 (例如序列特异性 DNA 结合和 TF 活性)。最后，进行了计算机药物设计以寻找治疗 HD 的候选药物，预计可能有一些温和的治疗化合物 (例如 cortistatin A、13,16-环氧-25-羟基-17- cheilanthen-19,25-内酯、hecogenin)。因此，本研究的结果可为亨廷顿病的实验验证和治疗方法的研究人员提供有用的资源。
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