@@ -6,7 +6,7 @@ The current COVID-19 pandemic is caused by a novel coronavirus strain, SARS-CoV-
Pathogenic viruses put a selective pressure on the host-viral interacting proteins. Identifying which host genes bear signatures of such evolutionary conflict (e.g. positive selection) can lead to the identification of the proteins that have been the most relevant in the response to a virus family. Here, we have used this evolutionary framework to decipher which interactions between the SARS-CoV-2-like viruses and our cells have been important in vivo. In addition, identifying traces of positive selection in different hosts phylogenetic lineages also sheds lights on ancient epidemics and how virus-host determinants may be species specific. This may help to understand differences in susceptibility and pathogenicity to SARS-CoV-like viruses between hosts.
To achieve this, we characterized the evolutionary history of the SARS-CoV-2 interactome identified in in vitro studies: 332 host proteins identified by mass-spectrometry by Gordon and collaborators [1], as well as two essential SARS-CoV-2 entry factors, the angiotensin converting enzyme 2 (ACE2) and the transmembrane serine protease 2 (TMPRSS2) genes. We characterized their evolution in primates (tracing the human history) and in bats (the natural viral reservoir). To do so, we used DGINN [2], a novel computational pipeline to Detect Genetic INNovations in protein-coding genes, which embeds gold-standard methods to perform phylogenetic and positive selection analyses in a high-throughput manner.
To achieve this, we characterized the evolutionary history of the SARS-CoV-2 interactome identified in in vitro studies: 332 host proteins identified by mass-spectrometry by Gordon and collaborators [1], as well as two essential SARS-CoV-2 entry factors, the angiotensin converting enzyme 2 (ACE2) and the transmembrane serine protease 2 (TMPRSS2) genes. We characterized their evolution in primates (tracing the human history) and in bats (the natural viral reservoir). To do so, we used [DGINN](https://academic.oup.com/nar/article/48/18/e103/5907962?login=true), a novel computational pipeline to Detect Genetic INNovations in protein-coding genes, which embeds gold-standard methods to perform phylogenetic and positive selection analyses in a high-throughput manner.
## Data formating
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@@ -40,7 +40,7 @@ Output tables in **figure/1_xxx**
## Comparaison with MAIC score and pancorona analysis
Script to compare the DGINN screen results to \url[MAIC]{https://www.nature.com/articles/s41598-020-79033-3} score and \url[pancorona data]{https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-020-02480-z}.
Script to compare the DGINN screen results to [MAIC](https://www.nature.com/articles/s41598-020-79033-3) score and [pancorona data](https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-020-02480-z).
