Integrative Analysis of Omics Data in Animal Models of Coronavirus Infection

Abstract

ObjectiveThis 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.

Keywords

• coronavirus infection
• animal models
• omics data
• sars-cov-2
• sars-cov
• mers-cov
• integrative analysis