Integrative Analysis of Omics Data in Animal Models of Coronavirus Infection

doi:10.12300/j.issn.1674-5817.2024.008

Abstract

Abstract:

Objective This study analyzes the omics data resources in human-infecting coronavirus animal models collected from various public databases, focusing on data distribution, dataset quantity, data types, species, strains, and research content. It aims to enhance our understanding of biological characteristics and pathogenic mechanisms of coronaviruses, thereby providing a solid foundation for devising effective therapeutic strategies and preventive measures. Methods Query strategies, including specific virus names, time ranges, and inclusion and exclusion criteria, were defined to retrieve data from major public omics databases such as GEO and ArrayExpress. Secondary filtering was performed based on different field types to obtain a more accurate data list. An omics data text database was established for bibliometric analysis. Co-occurrence networks were constructed for the analysis of the correlation strengths between different research themes, technical methods, and involved species. The cell types, organs, and biological pathways involved in studies were examined to further elucidate the pathogenic interplay between pathogens and hosts. Results About twenty public databases containing coronavirus-related omics data were identified, with a primary focus on novel coronavirus infection. Commonly used species include humans, mice, hamsters, and monkeys, while the commonly used virus strains are Wuhan-Hu-1 and USA-WA1/2020. Lung tissues are primarily used in animal models such as mice, macaques, and ferrets, while airway epithelial cells and Calu-3 cells are predominantly employed in human-related studies. Expression profiling data indicate that gene pathways involved in inflammation, cytokine response, complement pathway, cell damage, proliferation, and differentiation are significantly upregulated after infection. Proteomics studies reveal significant changes in phosphoproteome, ubiquitinome, and total proteome of patient samples at different infection stages. Specific protein categories, including viral receptors and proteases, transcription factors, cytokines, proteins associated with coagulation system, angiogenesis-related proteins, and fibrosis markers, show alterations after coronavirus infection. In addition, metabolomics data suggest that phosphocholine, phosphoethanolamine, arachidonic acid, and oleic acid could serve as potential metabolic markers. Epigenomics research indicates m6A methylation plays a role in SARS-CoV-2 replication, infection, and transmission, affecting host cell-virus interactions. Among these, N, S, and non-structural proteins 2 and 3 exhibit the most significant ubiquitination. Trends in microbiomics research suggest that microbial communities in the gut and wastewater are emerging as new research focuses. Conclusion The data types of coronavirus omics are diverse, with a wide variety of models and cell types used. The selection of species and technical methods for modelling varies based on the characteristics of different viruses. Multi-omics data from animal models of coronavirus infection can reveal key interactions between hosts and pathogens, identifying biomarkers and potential therapeutic targets, and provide valuable information for a deeper understanding of biological characteristics and infection mechanisms of coronaviruses.

Key words: Coronavirus infection, Animal models, Omics data, SARS-CoV-2, SARS-CoV, MERS-CoV, Integrative analysis

CLC Number:

Q95-33

WU Yue,LI Lu,ZHANG Yang,et al. Integrative Analysis of Omics Data in Animal Models of Coronavirus Infection[J]. Laboratory Animal and Comparative Medicine, 2024, 44(4): 357-373. DOI: 10.12300/j.issn.1674-5817.2024.008.

Figures/Tables 8

References 69

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数据库名称 Database name	网址 URL	简介 Brief description
Biostudies	https://www.ebi.ac.uk/biostudies/	包含生物学研究数据集，以及与EMBL-EBI数据库或其他公共数据库的数据链接
ENA (European Nucleotide Archive)	https://www.ebi.ac.uk/ena	核酸测序原始数据、序列拼装和功能注释信息
PRIDE (Proteomics Identifications Database)	https://www.ebi.ac.uk/pride	蛋白质组学质谱数据存储平台包含蛋白质鉴定、翻译后修饰和光谱数据
GEO (Gene Expression Omnibus)	https://www.ncbi.nlm.nih.gov/geo/	存储各种高通量实验数据的公共数据库，以表达谱、芯片数据为主
EGA (European Genome-Phenome Archive)	https://ega-archive.org	存储生物医学研究中产生的遗传和表型数据
ArrayExpress	https://www.ebi.ac.uk/arrayexpress/	主要包括微阵列芯片和高通量测序数据，与GEO数据库类似
iProX (Integrated Proteome Resources)	https://www.iprox.org	国家蛋白质科学中心网站，收录蛋白质组学相关数据集
MassIVE (Mass Spectrometry Interactive Virtual Environment)	https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp	储存蛋白、多肽、质谱数据
FAIRDOMHub	https://fairdomhub.org/	共享多种类型的科学研究数据集、模型或研究过程和成果数据
BioModels	https://www.ebi.ac.uk/biomodels/	储存生物和医学相关的数学模型、生理学和药学相关机制模型
MetaboLights	https://www.ebi.ac.uk/metabolights/	储存代谢组学和相关衍生信息的数据库，包含代谢物的结构、浓度、功能数据
dbGap (Database of Genotypes and Phenotypes)	https://dbgap.ncbi.nlm.nih.gov/	储存基因型和表型互作数据
PanoramaPublic	https://panoramaweb.org/	储存蛋白质组学和小分子数据
GNPS (Global Natural Products Social Molecular Networking)	https://gnps.ucsd.edu/	储存原始、处理或注释过的质谱数据
Metabolomics Workbench	https://www.metabolomicsworkbench.org/	代谢组学元数据和实验数据的公共数据库，包含不同物种的质谱（MS）和核磁共振（NMR）光谱数据
ExpressionAtlas	https://www.ebi.ac.uk/gxa/home	提供不同条件下基因表达信息，包含芯片与转录组数据
jPOST (Japan Proteome Standard Repository Database)	https://jpostdb.org/	包含蛋白质组数据及分析结果数据
EVA (European Variation Archive)	https://www.ebi.ac.uk/eva/	包含不同物种遗传变异数据
NODE (National Omics Data Encyclopedia)	https://www.biosino.org/node/	储存多组学数据资源，包括测序数据、蛋白质组学数据、代谢组学数据及荧光成像数据
PeptideAtlas	https://peptideatlas.org/	收录质谱、蛋白质组学数据。主要为人、小鼠、酵母数据

数据库名称 Database name	网址 URL	简介 Brief description
Biostudies	https://www.ebi.ac.uk/biostudies/	包含生物学研究数据集，以及与EMBL-EBI数据库或其他公共数据库的数据链接
ENA (European Nucleotide Archive)	https://www.ebi.ac.uk/ena	核酸测序原始数据、序列拼装和功能注释信息
PRIDE (Proteomics Identifications Database)	https://www.ebi.ac.uk/pride	蛋白质组学质谱数据存储平台包含蛋白质鉴定、翻译后修饰和光谱数据
GEO (Gene Expression Omnibus)	https://www.ncbi.nlm.nih.gov/geo/	存储各种高通量实验数据的公共数据库，以表达谱、芯片数据为主
EGA (European Genome-Phenome Archive)	https://ega-archive.org	存储生物医学研究中产生的遗传和表型数据
ArrayExpress	https://www.ebi.ac.uk/arrayexpress/	主要包括微阵列芯片和高通量测序数据，与GEO数据库类似
iProX (Integrated Proteome Resources)	https://www.iprox.org	国家蛋白质科学中心网站，收录蛋白质组学相关数据集
MassIVE (Mass Spectrometry Interactive Virtual Environment)	https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp	储存蛋白、多肽、质谱数据
FAIRDOMHub	https://fairdomhub.org/	共享多种类型的科学研究数据集、模型或研究过程和成果数据
BioModels	https://www.ebi.ac.uk/biomodels/	储存生物和医学相关的数学模型、生理学和药学相关机制模型
MetaboLights	https://www.ebi.ac.uk/metabolights/	储存代谢组学和相关衍生信息的数据库，包含代谢物的结构、浓度、功能数据
dbGap (Database of Genotypes and Phenotypes)	https://dbgap.ncbi.nlm.nih.gov/	储存基因型和表型互作数据
PanoramaPublic	https://panoramaweb.org/	储存蛋白质组学和小分子数据
GNPS (Global Natural Products Social Molecular Networking)	https://gnps.ucsd.edu/	储存原始、处理或注释过的质谱数据
Metabolomics Workbench	https://www.metabolomicsworkbench.org/	代谢组学元数据和实验数据的公共数据库，包含不同物种的质谱（MS）和核磁共振（NMR）光谱数据
ExpressionAtlas	https://www.ebi.ac.uk/gxa/home	提供不同条件下基因表达信息，包含芯片与转录组数据
jPOST (Japan Proteome Standard Repository Database)	https://jpostdb.org/	包含蛋白质组数据及分析结果数据
EVA (European Variation Archive)	https://www.ebi.ac.uk/eva/	包含不同物种遗传变异数据
NODE (National Omics Data Encyclopedia)	https://www.biosino.org/node/	储存多组学数据资源，包括测序数据、蛋白质组学数据、代谢组学数据及荧光成像数据
PeptideAtlas	https://peptideatlas.org/	收录质谱、蛋白质组学数据。主要为人、小鼠、酵母数据

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[2]	. Guidelines for the Selection of Animal Models and Preclinical Drug Trials for Spontaneous Intracerebral Hemorrhage (2024 Edition) [J]. Laboratory Animal and Comparative Medicine, 2024, 44(1): 3-30.
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