tetano
Editor, Senior Moderator
Front Mol Biosci
. 2022 Feb 8;9:831876.
doi: 10.3389/fmolb.2022.831876. eCollection 2022.
In Silico Infection Analysis (iSFA) Identified Coronavirus Infection and Potential Transmission Risk in Mammals
Yanyan Zou[SUP] 1 2 [/SUP], Xiaojian Cao[SUP] 1 3 [/SUP], Bing Yang[SUP] 1 3 [/SUP], Lulu Deng[SUP] 1 3 [/SUP], Yangyang Xu[SUP] 1 3 [/SUP], Shuang Dong[SUP] 4 [/SUP], Wentao Li[SUP] 3 [/SUP], Chengchao Wu[SUP] 1 3 [/SUP], Gang Cao[SUP] 1 3 5 [/SUP]
Affiliations
Abstract
Coronaviruses are a great source of threat to public health which could infect various species and cause diverse diseases. However, the epidemic's spreading among different species remains elusive. This study proposed an in silico infection analysis (iSFA) system that includes pathogen genome or transcript mining in transcriptome data of the potential host and performed a comprehensive analysis about the infection of 38 coronaviruses in wild animals, based on 2,257 transcriptome datasets from 89 mammals' lung and intestine, and revealed multiple potential coronavirus infections including porcine epidemic diarrhea virus (PEDV) infection in Equus burchellii. Then, through our transmission network analysis, potential intermediate hosts of five coronaviruses were identified. Notably, iSFA results suggested that the expression of coronavirus receptor genes tended to be downregulated after infection by another virus. Finally, binding affinity and interactive interface analysis of S1 protein and ACE2 from different species demonstrated the potential inter-species transmission barrier and cross-species transmission of SARS-CoV-2. Meanwhile, the iSFA system developed in this study could be further applied to conduct the source tracing and host prediction of other pathogen-induced diseases, thus contributing to the epidemic prevention and control.
Keywords: COVID-19; coronaviruses; data mining; in silico docking; in silico infection analysis.
. 2022 Feb 8;9:831876.
doi: 10.3389/fmolb.2022.831876. eCollection 2022.
In Silico Infection Analysis (iSFA) Identified Coronavirus Infection and Potential Transmission Risk in Mammals
Yanyan Zou[SUP] 1 2 [/SUP], Xiaojian Cao[SUP] 1 3 [/SUP], Bing Yang[SUP] 1 3 [/SUP], Lulu Deng[SUP] 1 3 [/SUP], Yangyang Xu[SUP] 1 3 [/SUP], Shuang Dong[SUP] 4 [/SUP], Wentao Li[SUP] 3 [/SUP], Chengchao Wu[SUP] 1 3 [/SUP], Gang Cao[SUP] 1 3 5 [/SUP]
Affiliations
- PMID: 35211513
- PMCID: PMC8861533
- DOI: 10.3389/fmolb.2022.831876
Abstract
Coronaviruses are a great source of threat to public health which could infect various species and cause diverse diseases. However, the epidemic's spreading among different species remains elusive. This study proposed an in silico infection analysis (iSFA) system that includes pathogen genome or transcript mining in transcriptome data of the potential host and performed a comprehensive analysis about the infection of 38 coronaviruses in wild animals, based on 2,257 transcriptome datasets from 89 mammals' lung and intestine, and revealed multiple potential coronavirus infections including porcine epidemic diarrhea virus (PEDV) infection in Equus burchellii. Then, through our transmission network analysis, potential intermediate hosts of five coronaviruses were identified. Notably, iSFA results suggested that the expression of coronavirus receptor genes tended to be downregulated after infection by another virus. Finally, binding affinity and interactive interface analysis of S1 protein and ACE2 from different species demonstrated the potential inter-species transmission barrier and cross-species transmission of SARS-CoV-2. Meanwhile, the iSFA system developed in this study could be further applied to conduct the source tracing and host prediction of other pathogen-induced diseases, thus contributing to the epidemic prevention and control.
Keywords: COVID-19; coronaviruses; data mining; in silico docking; in silico infection analysis.