Transcriptome Data and Comparative Medical Analysis of COVID-19 Virus Infection
10.12300/j.issn.1674-5817.2023.079
- VernacularTitle:新冠病毒感染转录组学数据及比较医学分析
- Author:
Tingting FENG
1
;
Yitong LI
1
;
Yue WU
1
;
Jue WANG
1
;
Qi KONG
1
Author Information
1. Institute of Laboratory Animal Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, National Human Diseases Animal Model Resource Center, NHC Key Laboratory of Human Disease Comparative Medicine, Beijing Key Laboratory for Animal Models of Emerging and Reemerging Infectious Diseases, Beijing Engineering Research Center for Experimental Animal Models of Human Critical Diseases, Beijing 100021, China
- Publication Type:Journal Article
- Keywords:
COVID-19;
Transcriptome;
Big data;
Molecular mechanisms;
Data analysis
- From:
Laboratory Animal and Comparative Medicine
2024;44(1):62-73
- CountryChina
- Language:Chinese
-
Abstract:
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.MethodsFollowing 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.
