NL2030705B1 - Method for establishing comparative transcriptomics database of animal models of coronavirus infections - Google Patents
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Abstract
The present disclosure aims to establish a comparative transcriptomics database of animal models of coronavirus infections, so as to explain similarities and differences between human and animal models infected with the coronavirus infections 5 at a genetic expression level, and provide data support for animal experiments and clinical research. In the present disclosure, microarrays and transcriptome data of the animal models and human infected with the coronavirus (SARS— COV, SARS—CoV—Z, and MERS—CoV) infections are downloaded from a 10 Gene Expression Omnibus (GEO) database and ArrayExpress database. Quality control, standardization, and batch effect removal are conducted on sequencing data. Gene expression changes caused by each virus strain after infecting different species, cells or tissues are analyzed. A database based on a Django web application 15 framework is established. ZX retrieval interface is established. Data analysis and visual display functions are provided.
Description
P1051 /NLpd
TECHNICAL FIELD The present disclosure belongs to the field of bictechnology.
BACKGROUND ART In December 2019, a novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) broke out in China. As of September 2021, SARS-CoV-2 has caused more than 200 million infections and more than 4 million deaths worldwide. The pneumonia caused by the novel coronavirus is extremely harmful. At present, the transmission route, occurrence and development, vaccine research and development, and treatment of the novel coronavirus disease have not been fully elucidated. Animal models are widely used in the study of the pathogenesis of various human diseases, and are essential for virus transmission research, vaccine development and drug screening.
SARS-CoV-2, Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus {SARS-CoV) all belong to a B-coronavirus genus. It has been re- ported that the animal models of the coronavirus infections are roughly divided into three categories, rodents, primates and other mammals, mainly transgenic mice, Syrian hamsters, ferrets, non- human primates, cats, and dogs. They have different susceptibili- ties to the coronaviruses, can respond to human diseases to vary- ing degrees, and play an important role in the rapid application of vaccines and drugs evaluation.
In order to study differences in gene expressions between an- imal models and human patients after being infected with the coro- naviruses, this study establishes a comparative transcriptomics database of the animal models of the coronavirus infections, and acquires and integrates gene sequencing data of the animal models of the coronavirus infections in public databases such as a Gene Expression Omnibus (GEO) database
(https: //www.ncbi.nlm.nih.gov/geo/) and an ArrayExpress database (https://www.ebi.ac.uk/arrayexpress/), so as to conduct mining and analysis of expression data of the animal models of different coronavirus infections. The database can provide gene expression changes of different virus infections in different spe- cies/cells/time. According to differentially expressed genes, sig- nal pathways and biological regulatory networks involved are ana- lyzed.
The comparative transcriptomics database of the animal models of the corcnavirus infections is the world's first gene expression profile database of the animal models of the coronavirus infec- tions. Users can conduct comparative analysis at different levels to explore a molecular mechanism of the animal models of the coro- navirus infections. A molecular mechanism of a host of the corona- virus infections is understood at a RNA level.
SUMMARY In order to solve the problem in the prior art, the present disclosure provides a method for establishing a comparative tran- scriptomics database of animal models of coronavirus infections.
A method for establishing a comparative transcriptomics data- base of animal models of coronavirus infections includes the fol- lowing steps: (1) data acquisition and processing acquiring microarrays and transcriptome data of different hu- man/animal/cells infected with coronaviruses of SARS-CoV, SARS- CoV-2, and MERS-CoV through a GEO database (http: //www.ncbi.nlm.nih.gov/geo/) of National Center for Biotech- nology Information (NCBI), and ArrayExpress database (https://www.ebi.ac.uk/arrayexpress/) of European Molecular Biclo- gy Laboratory's European Bioinformatics Institute (EMBL-ERI) ; ac- quiring background data; and conducting correspondence, integra- tion, quality control, standardization, annotation, and batch ef- fect removal between the background data and gene expression data of different species for visual display and differential gene analysis; (2) database establishment developing the database by using Django (v 2.2.6) software (https: //djangopreject com) and deploying the database on CentOS Linux server (v 7.7), wherein nginx (v 1.10.1) is used for website service; Plotly (v 1.5.10) is used to realize online interactive visual display of expression data; and all pictures generated by online analysis have interactive functions; and (3) access accessing a database website: https://covid.com-med.org.cn/.
Preferably, the background data in step (1) may include: vi- rus strains, infection times, titers, and infected organs or cells.
Preferably, image properties capable of being adjusted online in the database may include: size, style, color matching, and dis- played content.
Preferably, analysis results of the database may be exported as vector graphics (in a scalable vector graphics (SVG) format).
Preferably, generated data may be downloaded by users of the database.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a homepage of a database; FIG. 2 is a single gene expression profile; FIG. 3 is a drawing of a multi-gene expression profile; FIG. 4 shows detection of abnormal samples; FIG. 5 is a heatmap showing the pearson correlation results for all samples; FIG. 6 is a heatmap of differential genes after human-mouse infection with viruses; and FIG. 7 shows enrichment results of a biological process of differentially expressed genes.
1. Method
1.1 Data acquisition and processing Microarrays and transcriptome data of different hu- man/animal/cells infected with coronaviruses of SARS-CoV, SARS- CoV-2, and MERS-CoV are acquired through a GEO database
(http://www.ncbi.nlm.nih.gov/gec/) of NCBI and ArrayExpress data- base (https://www.ebil.ac.uk/arrayexpress/) of EMBL-EBI. Background data is acquired, including virus strains, infection times, ti- ters, and infected organs or cells. Correspondence, integration, quality control, standardization, annotaticn, and batch effect re- moval between the background data and gene expression data of dif- ferent species are conducted for visual display and differential gene analysis.
1.2 Database establishment The database is developed by using Django (v 2.2.6) software (https: //dlangoproject. com) and deployed on CentOS Linux server (v
7.7). nginx (v 1.10.1) is used for website service. Plotly (v
1.5.10) is used to realize online interactive visual display of expression data. All pictures generated by online analysis have interactive functions. Image properties are capable of being ad- justed online, such as size, style, color matching, and displayed content. Analysis results are exported as vector graphics (in a SVG format). Generated data is capable of being downloaded by us- ers of the database.
1.3 Access method A database website is accessed: https: //covid,. com-mad. org. on.
2. Results
2.1 Database structure and content The database consists of five sections: simple search, ad- vanced search, analysis tools, data statistics, and help infor- mation (FIG. 1). The analysis tools include three tools: interspe- cific comparison, intraspecific comparison and multi-gene expres- sion profiling. The database includes 43 data sets, 3 viruses (SARS-CoV, SARS-CoV-2, and MERS-CoV), 22 virus strains, 4 species (human, mouse, monkey, and ferret), 14 tissues/cells, 29 infection times, and 15 virus titers, involving a total of 2373 samples (Ta- ble 1}.
Table 1 Database content Item Description Amount Species Human, mouse, monkey, and ferret 4 Viruses SARS-CoV=2, SARS-CoV, and MERS-CoV 3
Virus strains Urbani, MA15, Tor2, EMC/2012, and USA- 22 WA1/2020...
Tissues/cells Airway epithelial cell, A549, Caco2, 14 Calu-3, Dendritic cell, Fibroblast, and H1299...
Infection 3 h, 12 h, 18 h, 24 h, 30 h, 36 h, 54 29 times h, and 72 h...
Titers 0.2MOI, 0.3MOI, 1MOI, 2MOI, and 5MOI... 15 ~~ 2.2 Search method The database can be searched by two methods, one is a simple search method and the other is an advanced search method. When us- ing the simple search method, users can search using gene names, 5 aliases, annotations, functional descriptions, and pathway infor- mation. The database displays all genes and corresponding descrip- tions related to the search content. The gene name is clicked to enter a detail page. The detail page includes gene information and information on single gene expression profiles linked to the data- base.
For advanced search, users can freely combine a variety of search conditions, including species names, virus names, virus strains, cell/tissue types, and item numbers, to conduct advanced search on samples in the database. Detailed search results are displayed, so as to help users find suitable sample data, and cor- responding statistics are displayed through interactive linkage pie charts. Historical retrieval information can be recorded to facilitate query of historical records.
2.3 Gene expression profile
2.3.1 Single gene expression profile Through simple search, after the names of genes, proteins, and pathways are input, the acquired data sets can be integrated and analyzed at different levels. Different levels of comparison are realized, and the differences in gene expressions between hu- man and animal models infected with the coronavirus infections are intuitively compared. Box plots are used to display comparison re- sults and display a P value. Users can view the differential ex- pression of target genes in different species, virus strains, dif- ferent cells, different infection times, and different titers
(FIG. 2).
2.3.2 Multi-gene expression profile A multi-gene expression (GEM) profile page is entered, and multiple gene names are input and combination parameters are se- lected. For example, different species, different viruses, differ- ent virus strains, different infection times, different titers, infected tissues/organs, and different sequencing data types can be selected. The infection time, group, and GSE number can also be selected in more detail. Finally, a data normalization method (raw or Z-value conversion) is selected for visual display in the form of line chart, column chart, heat map, box chart, and correlation chart (FIG. 3). At the same time, a built-in toolkit is provided to personalize the color, size and style of generated pictures. All pictures can be downloaded and used in the SVG format. Compar- ison and display can be conducted cross species, and display can be conducted by placing different species side by side. Change trends of expressions of multiple genes in multiple species can be detected.
2.4 Comparative analysis of differential genes Differential gene analysis is the most important means to find key marker genes. Users can analyze differences in gene ex- pression of species of interest and experimental conditions. Through the two tools of intraspecific and interspecific differen- tial gene analysis, different indicators are combined online to design experiments, search for susceptible genes, and conduct functional enrichment analysis on the susceptible genes. It is worth noting that the intraspecific differential gene analysis tool is limited to one species. The cross-species differential gene analysis tool allows users to select two species for compara- tive analysis.
After determining a species option, the user can further se- lect a cell type, virus type, time, titer and other information to be studied. After analysis is submitted, the system automatically detects the sample. Users can filter abnormal samples having large differences in sample attributes and being not suitable for subse- quent integrated comparative analysis (FIG. 4). Visual display of sample correlations is conducted in the form of clustered heat maps and PCA cluster maps (FIG. 5). Users can select samples for subseguent analysis based on the heat maps and PCA maps.
The user can freely select a comparison group. Usually, the differential gene can only be compared between a 24-hour infection group (Treat 24 h) and a O-hour infection group (Treat 0 h). The infectivity experiment is quite special. There will be 4 groups of samples (Treat 24 h, mock 24 h, Treat 0 h, and mock 0 h) at two time points. To this end, a complex design comparison mode (Treat 24 h-mock 24 h)-(Treat 0 h-mock 0 h) is introduced, so that the treatment groups are compared with their respective controls be- fore being compared at different time points. This analysis mode is also suitable for comparison of different species. For example, human lung cells and mouse lung cells cannot be directly compared, but (Human lung treat-Human lung control)- (Mouse lung treat-Mouse lung control) are compared with their respective controls before being compared, so as to realize comparison between changes of their respective tissues after treatment, and to analyze which genes are expressed uniformly or specifically in different spe- cies.
The user can set a threshold FDR (less than 0.05 by default) and Log2FC (greater than 1 by default) for differential gene anal- ysis of selected samples, so as to generate a report of differen- tial analysis results. A report catalog can index the results of each part, and the predicted differential genes are displayed in the form of volcano maps and heat maps. FIG. 6 is a heat map of interspecific gene expression clustering after human-mouse infec- tion with SARS virus for cross-species comparative analysis, so as to show clustering of gene expression of different species infect- ed with the SARS virus. Functional enrichment analysis of differ- ential genes can also be conducted, and biological functions, mo- lecular functions, and cellular components of the genes are clus- tered and displayed in the form of bubble charts (FIG. 7).
3 Analysis The comparative transcriptomics database of the animal models of the coronavirus infections is the world's first thematic gene expression profile database after the coronavirus infects differ- ent hosts. Since the beginning of the epidemic, relevant open data in public databases have been acquired and kept updated continu- ously. Users can conduct comparative analysis at different levels, and provide data mining and association analysis at the gene level of the animal models of the coronavirus infections. A pathogenic mechanism of the coronavirus infecting different hosts is studied at a molecular level.
The World Health Organization (WHO) and countries around the world have emphasized the importance of sharing and openness of COVID-19 data. In order to combat the global spread of the COVID- 19 epidemic, open and comprehensive data resources can help re- searchers, policy makers, medical workers and the general public have a deeper understanding of the disease caused by the corona- virus. The development of new digital platforms and open scien- tific practices may make a huge contribution to strengthening global research and innovation cooperation 2%. with the develop- ment of the epidemic, many scientists around the world are con- ducting research projects on COVID-19. These studies rely entirely on data, so the public availability of data is very important. Hundreds of studies have been published through existing open da- tabases.
Databases related to coronavirus research that have been es- tablished and published in public journals include GISAID, CoVDB, ViPR, 2019nCoVR, Drugvirus, CARD, D3Targets-2019-nCoV, and CoV2ID, which mainly store viral gene sequences, epidemiological infor- mation, and drug target information, and conduct analysis mainly based on the viral gene sequences without integrated analysis of gene expression information.
The comparative transcriptomics database of the animal models of the coronavirus infections can display expression profiles of the animal models of the coronavirus infections and screen differ- entially expressed genes. Comparative analysis of the gene expres- sion of different animals, different virus strains, different dos- es, and different time points can be conducted. Since the data set comes from GEO and ArrayExpress, the data format and sample infor- mation are not standardized, the experimental design is complex, involving different virus strains, different cell lines, different virus titers, and different processing times, and the data units are not uniform.
Therefore, it is necessary to conduct preliminary processing, unify and classify the information, and acquire miss- ing information from the corresponding literature.
Through quality control, standardization and batch effect re- moval on sequencing data from different platforms, the database obtains corrected gene expression data, and realizes cross- platform data analysis.
In addition, the cross-species comparative analysis of gene expression profiles is difficult.
For example, humans and animals must be compared with their own controls before being compared.
The database will continue to update the corona- virus expression profile data, and include more data types, such as single-cell sequencing data, to study gene expression at a cel- lular level.
The database provides scientific data basis for dis- covering and predicting key genes or therapeutic targets that may be used in relevant COVID-19 prevention and control research.
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