Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
Sci Data. 2024 Apr 2;11(1):328. doi: 10.1038/s41597-024-03124-3.
Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.
人类感染病毒病原体后,会引发一系列复杂的宿主反应,这些反应既能限制疾病的发展、清除感染,又能产生免疫,还可能导致严重疾病或死亡。在这里,我们展示了 45 项独立实验中获得的、针对感染后宿主整体反应的实验方法和多组学数据捕获方法,这些实验涉及正粘病毒科、丝状病毒科、黄病毒科和冠状病毒科的人类病毒。在人类或小鼠宿主模型系统中实施了类似的实验设计,在明确的时间进程中采集纵向样本,并通过微阵列、RNA 测序或质谱分析获取全局多组学数据(转录组学、蛋白质组学、代谢组学和脂质组学)。为了进行比较,我们还包括了用 I 型和 II 型人干扰素处理的细胞的转录组数据集。原始多组学数据和元数据已被存入公共数据库,我们还提供了一个中央位置,可将原始数据与实验元数据以及之前未在任何公共数据库中提供的即用型、质量控制、经过统计学处理的多组学数据集链接起来。本研究中整合的感染诱导宿主反应数据集可重复使用,对于那些努力了解病毒疾病病理生理学和网络生物学的人来说将非常有用。
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