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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, WA 98101, USA.

NAR molecular medicine

|July 12, 2024

概括

这项研究通过分析合成致命 (SL) 相互作用来确定基于宿主的新型抗病毒标. 来自CRISPR淘汰赛 (KO) 屏幕的基因枯竭数据揭示了针对SARS-CoV-2和流感等病毒感染的潜在广谱药物标.

科学领域:

病毒学病毒学
遗传学遗传学是一种遗传学.
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,但可能具有非特异性影响.
针对病毒破坏蛋白质的合成致命 (SL) 合作伙伴,可以选择性地消除受感染的细胞.

研究的目的:

以假设和计算预测基于合成致命 (SL) 相互作用的抗病毒药物标.
调查病毒感染细胞的CRISPR淘汰 (KO) 屏幕中耗尽的基因是否富含感染改变蛋白质的SL伙伴.
识别基于主机的广泛的抗病毒SL目标.

主要方法:

建立了用于预测抗病毒SL药物点的计算管道.
使用omics数据识别了SARS-CoV-2诱导的基因产物改变.
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数据的变化,共同的预测SL目标被确定.
在CRISPR KO枯竭数据集中发现SL目标的显著丰富.
对SARS-CoV-2和流感数据的比较揭示了共享的潜在广泛的抗病毒SL目标.

结论:

由于它们与病毒改变状态的SL关系,CRISPR KO数据包含了常见的抗病毒点.
对OMIC数据和SL预测的分析可以揭示这些广泛的抗病毒标.
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策略.

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