Amity Institute of Neuropsychology & Neurosciences, Amity University, Noida, UP 201303, India.
Dept. of Plant Molecular Biology, University of Delhi, South Campus, New Delhi 110021, India.
Int J Biol Macromol. 2022 Sep 30;217:853-863. doi: 10.1016/j.ijbiomac.2022.07.200. Epub 2022 Jul 28.
The global coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 virus has had unprecedented social and economic ramifications. Identifying targets for drug repurposing could be an effective means to present new and fast treatments. Furthermore, the risk of morbidity and mortality from COVID-19 goes up when there are coexisting medical conditions, however, the underlying mechanisms remain unclear. In the current study, we have adopted a network-based systems biology approach to investigate the RNA binding proteins (RBPs)-based molecular interplay between COVID-19, various human cancers, and neurological disorders. The network based on RBPs commonly involved in the three disease conditions consisted of nine RBPs connecting 10 different cancer types, 22 brain disorders, and COVID-19 infection, ultimately hinting at the comorbidities and complexity of COVID-19. Further, we underscored five miRNAs with reported antiviral properties that target all of the nine shared RBPs and are thus therapeutically valuable. As a strategy to improve the clinical conditions in comorbidities associated with COVID-19, we propose perturbing the shared RBPs by drug repurposing. The network-based analysis presented hereby contributes to a better knowledge of the molecular underpinnings of the comorbidities associated with COVID-19.
由 SARS-CoV-2 病毒引起的全球 2019 年冠状病毒病(COVID-19)大流行对社会和经济产生了前所未有的影响。确定药物再利用的靶点可能是提供新的快速治疗方法的有效手段。此外,当存在共存的医疗条件时,COVID-19 的发病率和死亡率会增加,但潜在机制尚不清楚。在本研究中,我们采用基于网络的系统生物学方法来研究 COVID-19、各种人类癌症和神经紊乱之间基于 RNA 结合蛋白(RBPs)的分子相互作用。基于 RBPs 的网络常见于三种疾病条件,由九个 RBPs 连接 10 种不同的癌症类型、22 种脑疾病和 COVID-19 感染,最终暗示 COVID-19 的合并症和复杂性。此外,我们强调了五种具有报道的抗病毒特性的 miRNA,它们靶向所有九个共享的 RBPs,因此具有治疗价值。作为改善与 COVID-19 相关合并症的临床状况的一种策略,我们建议通过药物再利用来干扰共享的 RBPs。本文提出的基于网络的分析有助于更好地了解与 COVID-19 相关合并症的分子基础。