Exploration of omics mechanism and drug prediction of coronavirus-induced heart failure based on clinical bioinformatics.
10.3760/cma.j.cn112148-20200308-00172
- Author:
Xi Meng CHEN
1
;
Feng CAO
2
;
Hao Min ZHANG
1
;
Hao Ran CHEN
3
;
Jun Dong ZHANG
4
;
Peng ZHI
3
;
Zhuo Yang LI
3
;
Yi Xing WANG
5
;
Xue Chun LU
1
Author Information
1. National Medical Center for Geriatric Diseases, Department of Hematology, Second Medical Center, General Hospital of Chinese People's Liberation Army, Beijing 100853, China.
2. National Medical Center for Geriatric Diseases, Second Medical Center, General Hospital of Chinese People's Liberation Army, Beijing 100853, China.
3. School of Management, Shanxi Medical University, Taiyuan 030001, China.
4. Graduate School of Medical College of Chinese People's Liberation Army, Beijing 100853, China.
5. Department of Traditional Chinese Medicine, Dongfang Hospital Affiliated to Tongji University, Shanghai 200120, China.
- Publication Type:Journal Article
- Keywords:
Bioinformatics;
Coronavirus infections;
Drug prediction;
Heart failure
- MeSH:
Betacoronavirus;
COVID-19;
Computational Biology;
Coronavirus Infections/complications*;
Gene Expression Profiling;
Gene Ontology;
Heart Failure/virology*;
Humans;
Pandemics;
Pneumonia, Viral/complications*;
SARS-CoV-2
- From:
Chinese Journal of Cardiology
2020;48(7):587-592
- CountryChina
- Language:Chinese
-
Abstract:
Objective: Present study investigated the mechanism of heart failure associated with coronavirus infection and predicted potential effective therapeutic drugs against heart failure associated with coronavirus infection. Methods: Coronavirus and heart failure were searched in the Gene Expression Omnibus (GEO) and omics data were selected to meet experimental requirements. Differentially expressed genes were analyzed using the Limma package in R language to screen for differentially expressed genes. The two sets of differential genes were introduced into the R language cluster Profiler package for gene ontology (GO) and Kyoto gene and genome encyclopedia (KEGG) pathway enrichment analysis. Two sets of intersections were taken. A protein interaction network was constructed for all differentially expressed genes using STRING database and core genes were screened. Finally, the apparently accurate treatment prediction platform (EpiMed) independently developed by the team was used to predict the therapeutic drug. Results: The GSE59185 coronavirus data set was searched and screened in the GEO database, and divided into wt group, ΔE group, Δ3 group, Δ5 group according to different subtypes, and compared with control group. After the difference analysis, 191 up-regulated genes and 18 down-regulated genes were defined. The GEO126062 heart failure data set was retrieved and screened from the GEO database. A total of 495 differentially expressed genes were screened, of which 165 were up-regulated and 330 were down-regulated. Correlation analysis of differentially expressed genes between coronavirus and heart failure was performed. After cross processing, there were 20 GO entries, which were mainly enriched in virus response, virus defense response, type Ⅰ interferon response, γ interferon regulation, innate immune response regulation, negative regulation of virus life cycle, replication regulation of viral genome, etc. There were 5 KEGG pathways, mainly interacting with tumor necrosis factor (TNF) signaling pathway, interleukin (IL)-17 signaling pathway, cytokine and receptor interaction, Toll-like receptor signaling pathway, human giant cells viral infection related. All differentially expressed genes were introduced into the STRING online analysis website for protein interaction network analysis, and core genes such as signal transducer and activator of transcription 3, IL-10, IL17, TNF, interferon regulatory factor 9, 2'-5'-oligoadenylate synthetase 1, mitogen-activated protein kinase 3, radical s-adenosyl methionine domain containing 2, c-x-c motif chemokine ligand 10, caspase 3 and other genes were screened. The drugs predicted by EpiMed's apparent precision treatment prediction platform for disease-drug association analysis were mainly TNF-α inhibitors, resveratrol, ritonavir, paeony, retinoic acid, forsythia, and houttuynia cordata. Conclusions: The abnormal activation of multiple inflammatory pathways may be the cause of heart failure in patients after coronavirus infection. Resveratrol, ritonavir, retinoic acid, amaranth, forsythia, houttuynia may have therapeutic effects. Future basic and clinical research is warranted to validate present results and hypothesis.
