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从omics数据预测基于宿主,合成致命的抗病毒目标

Jeannette P Staheli¹, Maxwell L Neal¹, Arti Navare¹

¹Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, Washington, 98101, USA.

bioRxiv : the preprint server for biology

|August 30, 2023

概括

研究人员通过分析合成致命 (SL) 相互作用,确定了新的基于宿主的抗病毒点. 这种方法利用CRISPR淘汰屏幕和omics数据来发现病毒感染细胞的漏洞,为广泛的抗病毒药物开发提供了一个有前途的战略.

科学领域:

病毒学病毒学
计算生物学计算生物学
药物发现药物发现药物发现

背景情况:

传统的抗病毒疗法面临着毒性和耐药性的挑战.
基于宿主的抗病毒药物提供了一个替代方案,但可能会导致非特异性影响.
针对病毒破坏蛋白质的合成致命 (SL) 合作伙伴,为选择性细胞消除提供了一个新的策略.

研究的目的:

假设在病毒感染细胞的CRISPR淘汰 (KO) 屏幕中耗尽的基因在感染改变的蛋白质的SL伙伴中得到丰富.
开发一条计算管道,用于预测SL药物针对病毒感染的点.
为了确定潜在的宽频,基于宿主的抗病毒SL目标.

主要方法:

识别了SARS-CoV-2诱导的基因产物改变,使用了omics数据汇编.
确定每个改变基因产品的SL合作伙伴.
对SL合作伙伴进行选的CRISPR KO数据对于感染细胞活力至关重要.

主要成果:

尽管omics数据存在差异,但预测的SL目标在不同的数据集中共享.
在CRISPR KO枯竭数据集中观察到SL目标的显著丰富.
在SARS-CoV-2和流感感染之间的比较显示出常见的预测SL目标.

结论:

CRISPR KO数据包含许多常见的抗病毒点,因为它们与病毒改变的细胞状态的SL关系.
对OMIC数据集的分析与SL预测相结合,可以发现这些广泛的抗病毒目标.
这种方法为开发基于宿主的新型抗病毒疗法提供了一个有希望的途径.

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