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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.

ObjectiveTo provide more basic information of comparative medicine for the study of biological changes and pathogenesis of COVID-19 by systematical sorting and analyzing the transcriptome data. Methods Following a retrieval strategy, using COVID-19 and SARS-CoV-2 as key words, transcriptome datasets related to COVID-19 from January 2020 to May 2023 were collected from GEO, ArrayExpress and GEN Transcriptome databases. The composition, distribution, and research application of COVID-19 transcriptome data resources were analyzed. Data distribution was visually displayed and correlation analysis was performed. The research applications and limitations of existing COVID-19 transcriptome data were analyzed from the perspectives of clinical medicine and comparative medicine, focusing on clinical-related molecular mechanisms, biomarkers and related immune responses, treatment intervention strategies, etc. The research value and application prospects were discussed. Results A total of 975 sets of COVID-19 transcriptome data were included. Among three databases, datasets from humans accounted for the highest proportion, namely 71.9%, 77.9%, and 90%, respectively. Species other than humans, such as mice, were the main sources of data, with the respiratory and nervous systems having the highest distribution of data. Twenty-seven datasets were associated with clinical significance. Analysis revealed that respiratory tract injury and other related molecular mechanisms were obtained through transcriptome data mining. Biomarkers such as cfDNA could be used as therapeutic targets. The severity of COVID-19 infection was associated with cell changes and disorders represented by M1 macrophages. Comparative medical analysis showed that mice, hamsters, and other animals were susceptible to SARS-CoV-2. Rhesus monkeys and cynomolgus monkeys exhibited infection characteristics highly similar to human. Apart from respiratory symptoms, hamsters also exhibited digestive system symptoms. SARS-CoV-2 can replicate in the respiratory organs of various susceptible animals, the intestines of ferrets and the ears of minks, resulting in interstitial pneumonia, diffuse lung injury and other pathological changes of varying degrees. Based on the differences in immune responses, hamsters can be used for neutralizing antibody reaction research. Conclusion Currently there is a wealth of COVID-19 transcriptome data, but there is a lack of comparative transcriptome research. Transcriptomics can be combined with comparative medicine to further explore the differences in comparative medicine of COVID-19.

Hundreds of endogenous peptides were released from milk proteins within the human mammary gland and some of them possess a variety of bioactive functions. Thus, it is important to investigate human milk endogenous peptides for infant health. Peptidomics based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been used to investigate human milk endogenous peptides. Extraction of endogenous peptides from human milk is an essential and key procedure for analyzing human milk peptides using LC-MS/MS. This study aimed to compare methods for extracting endogenous peptides from human milk using LC-MS/MS. Ultrafiltration methods including that not involving denaturation (UF 1), that involving heat denaturation (UF 2), and that involving chemical denaturation (UF 3), precipitation methods using trichloroacetic acid (PCPN 1) and alcohol (PCPN 2), and an enrichment method using highly ordered mesoporous carbon (OMC) were used to extract endogenous peptides from human milk. Extracted endogenous peptides were then analyzed using LC-MS/MS. The samples extracted using UF 1 and UF 2 comprised 1161±8 and 1017±91 endogenous peptides, respectively. More than 70% peptide sequences in each sample extracted using UF 1 and UF 2 overlapped. The results revealed that endogenous peptides extracted using UF 1 and UF 2 showed similar characteristics. UF 1 yielded the highest number of peptides, whereas UF 3 extracted the least number of peptides at 366±18. The number of endogenous peptides extracted using PCPN 1 and PCPN 2 were 779±69 and 876±55, respectively. However, their characteristics were quite different, and only about 50% peptide sequences overlapped. The number of peptides extracted using OMC (549±151) was not remarkable compared with that using other methods. However, the isoelectric point (pI) and grand average of hydropathicity (GRAVY) of the peptides extracted using OMC were different from those extracted using other methods. This method presented no selectivity for the endogenous peptides with different pI and GRAVY and may be used to extract unique peptides from human milk. A total of 205 peptides were commonly identified in the samples using each of the six methods. The percent of shared peptides across the six samples ranged from 13% to 23%. The number of unique peptides in the samples extracted using UF 1 and UF 2 (226 and 228, respectively) were the highest among those extracted using the six methods. The results showed that all six methods could be used to extract endogenous peptides from these high-abundance precursor proteins. A total of 21, 38, and 19 peptides were extracted from lactotransferrin using UF 2, UF 3, and OMC, respectively, and the coverage rates of these peptides in lactotransferrin were 14%, 16%, and 19%, respectively. These three methods could extract the endogenous peptides from lactotransferrin in human milk, but PCPN 1 that has been commonly used in previous studies could not. The peptides from β -casein, polymeric immunoglobulin receptor, osteopontia, α S1 -casein, κ -casein, and bile salt-activated lipase were identified in all samples extracted using the six methods. Moreover, these precursor proteins contributed 88% peptides in the samples extracted using the six methods. In conclusion, UF 1 and UF 2 were efficient procedures for extracting endogenous peptides from human milk. In addition, UF 2 could extract peptides from lactotransferrin, which is the optimum choice for extracting endogenous peptides from human milk. Additionally, the OMC enrichment method can be used to enrich and extract specific endogenous peptides from human milk. This study systematically compared the sample preparation methods commonly used in human milk endogenous peptidomics in recent years. The results provide strong support for uniform and standardized sample preparation methods. An ultrafiltration method without denaturation, which is more advantageous than the currently commonly used trichloroacetic acid precipitation method, was also established to prepare human milk endogenous peptide samples. In combination with OMC, this method can help in a more comprehensive and in-depth understanding of the endogenous peptidome of human milk.