一个综合的多omics数据照亮宿主对致命的人类病毒感染的反应
Amie J Eisfeld1, Lindsey N Anderson2, Shufang Fan3,4
1Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA. amie.eisfeld@wisc.edu.
Scientific data
|April 2, 2024
概括
这项研究详细介绍了一个全面的多omics数据集,捕捉了全球宿主对多个家族病毒感染的反应. 这些数据,包括转录组学和蛋白质组学,是公开可用的,以促进对病毒性疾病的理解.
科学领域:
- 病毒学 病毒学
- 免疫学 免疫学 免疫学
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 病毒感染引发复杂的宿主反应,影响疾病的结果.
- 了解这些反应对于开发有效的治疗和疫苗至关重要.
- 现有的数据是分散的,阻碍了对宿主-病原体相互作用的全面分析.
研究的目的:
- 介绍标准化的实验方法,以捕捉全球宿主对病毒感染的反应.
- 创建一个集中式,公开可访问的多种病毒感染的多omics数据库.
- 促进对病毒性疾病病理生理学和宿主-病原体网络生物学的研究.
主要方法:
- 进行了45项实验,使用人类或老鼠模型感染了来自Orthomyxoviridae,Filoviridae,Flaviviridae和Coronaviridae家族的病毒.
- 在定义的时间段内收集纵向样本.
- 采用各种高通量技术获取全球多omics数据 (转录组学,蛋白组学,代谢组学,脂组学),并将原始数据存储在公共存储库中.
主要成果:
- 创建了感染诱导的主体反应数据的综合汇编.
- 提供质量控制,统计处理的多omics数据集,与实验元数据相关联.
- 包括人类干扰素治疗细胞的比较转录组学数据.
结论:
- 精心策划的多omics数据集为研究病毒感染的研究人员提供了宝贵的资源.
- 这种集中式数据库将加速对病毒性疾病机制和宿主-病原体相互作用的理解.
- 使网络生物学方法能够破译对病毒病原体的复杂宿主反应.
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