Unveiling HuB genes and drug design against Helicobacter pylori infection by network biology and biophysics techniques

Saba Javed , Sajjad Ahmad , Anam Naz , Asad Ullah , Salma Mohammed Aljahdali , Yasir Waheed , Alhanouf I. Al-Harbi , Syed Ainul Abideen , Adnan Rehman , Muhammad Khurram
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Abstract

Helicobacter pylori (H. pylori) is mainly considered for causing chronic gastritis, which can lead to several secondary complications like peptic ulcer and pre-malignant lesions for example atrophic gastritis, intestinal dysplasia and metaplasia, with the etiological factor of developing gastric cancer. Recent research demonstrates that H.pylori colonizes the stomach mucosa of more than fifty populations around the globe. This research focuses on unveiling hub genes, and diagnostic and drug targets against said organism by utilizing various types of networking biology and biophysical approaches. In data retrieval, the GSE19826 dataset was obtained from the gene expression omnibus database and microarray data set from array express. Geo2r analysis predicted a total number of 7 DEGs and 10 hub genes, next functional protein association network analysis (STRING) unveiled that among 10 Hub genes only 3 genes were found more interactive with other genes and involved in pathogenesis, The shortlisted three genes were further analyzed for survival analysis using Gene Expression Profiling Interactive Analysis (GEPIA) and predicted the survival rate of targeted genes. Moreover, functional enchainment analysis was done using the ToppFun server, the server predicted that COL11A1 and COL10A1 were more involved in the pathogenesis of the H. pylori infection. Furthermore, the COL10A1 gene was subjected to protein structure prediction. In molecular docking analysis, the asinex antibacterial library was screened for potential inhibitors, and one compound was predicted as a strong inhibitor with the best binding at −10.23 kcal/mol. The docking results were further validated through molecular dynamic simulation analysis and the MD simulation analysis evaluated the dynamic movement of the docked complex in various nanoseconds, the MD simulation results predicted that the docked complexes are stable throughout the simulation and can be used as a potential inhibitor against the said pathogen, however experimental study is required to further validate the predicted results and design drug against targeted pathogen.

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利用网络生物学和生物物理学技术揭示 HuB 基因并设计抗幽门螺旋杆菌感染的药物
幽门螺杆菌(Helicobacter pylori,H.pylori)主要被认为是导致慢性胃炎的元凶,可引发多种继发性并发症,如消化性溃疡和恶性前期病变,如萎缩性胃炎、肠道发育不良和变性,以及胃癌的致病因素。最新研究表明,幽门螺杆菌在全球五十多个人群的胃黏膜中定植。这项研究的重点是利用各种类型的网络生物学和生物物理学方法,揭示枢纽基因以及针对该生物的诊断和药物靶标。在数据检索方面,GSE19826 数据集来自基因表达总括数据库,微阵列数据集来自 array express。Geo2r分析预测出了7个DEGs和10个枢纽基因,接下来的功能蛋白关联网络分析(STRING)揭示了在10个枢纽基因中,只有3个基因与其他基因有更多的交互作用,并参与了发病机制。此外,还使用 ToppFun 服务器进行了功能连锁分析,该服务器预测 COL11A1 和 COL10A1 在幽门螺杆菌感染的发病机制中参与度更高。此外,还对 COL10A1 基因进行了蛋白质结构预测。在分子对接分析中,asinex 抗菌库筛选出了潜在的抑制剂,其中一个化合物被预测为强抑制剂,其最佳结合力为 -10.23 kcal/mol。通过分子动态模拟分析进一步验证了对接结果,分子动态模拟分析评估了对接复合物在不同纳秒内的动态运动,分子动态模拟结果预测对接复合物在整个模拟过程中都是稳定的,可用作对上述病原体的潜在抑制剂,但还需要进行实验研究来进一步验证预测结果和设计针对目标病原体的药物。
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来源期刊
Informatics in Medicine Unlocked
Informatics in Medicine Unlocked Medicine-Health Informatics
CiteScore
9.50
自引率
0.00%
发文量
282
审稿时长
39 days
期刊介绍: Informatics in Medicine Unlocked (IMU) is an international gold open access journal covering a broad spectrum of topics within medical informatics, including (but not limited to) papers focusing on imaging, pathology, teledermatology, public health, ophthalmological, nursing and translational medicine informatics. The full papers that are published in the journal are accessible to all who visit the website.
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