Article ; Online: 1-L Transcription of SARS-CoV-2 Spike Protein S1 Subunit.
International journal of molecular sciences
2024 Volume 25, Issue 8
Abstract: The COVID-19 pandemic prompted rapid research on SARS-CoV-2 pathogenicity. Consequently, new data can be used to advance the molecular understanding of SARS-CoV-2 infection. The present bioinformatics study discusses the "spikeopathy" at the molecular ... ...
Abstract | The COVID-19 pandemic prompted rapid research on SARS-CoV-2 pathogenicity. Consequently, new data can be used to advance the molecular understanding of SARS-CoV-2 infection. The present bioinformatics study discusses the "spikeopathy" at the molecular level and focuses on the possible post-transcriptional regulation of the SARS-CoV-2 spike protein S1 subunit in the host cell/tissue. A theoretical protein-RNA recognition code was used to check the compatibility of the SARS-CoV-2 spike protein S1 subunit with mRNAs in the human transcriptome (1-L transcription). The principle for this method is elucidated on the defined RNA binding protein GEMIN5 (gem nuclear organelle-associated protein 5) and RNU2-1 (U2 spliceosomal RNA). Using the method described here, it was shown that 45% of the genes/proteins identified by 1-L transcription of the SARS-CoV-2 spike protein S1 subunit are directly linked to COVID-19, 39% are indirectly linked to COVID-19, and 16% cannot currently be associated with COVID-19. The identified genes/proteins are associated with stroke, diabetes, and cardiac injury. |
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MeSH term(s) | Spike Glycoprotein, Coronavirus/genetics ; Spike Glycoprotein, Coronavirus/metabolism ; Humans ; SARS-CoV-2/genetics ; SARS-CoV-2/metabolism ; COVID-19/virology ; COVID-19/metabolism ; COVID-19/genetics ; Transcription, Genetic ; RNA, Messenger/genetics ; RNA, Messenger/metabolism ; Computational Biology/methods ; Transcriptome |
Chemical Substances | Spike Glycoprotein, Coronavirus ; spike protein, SARS-CoV-2 ; RNA, Messenger |
Language | English |
Publishing date | 2024-04-18 |
Publishing country | Switzerland |
Document type | Journal Article |
ZDB-ID | 2019364-6 |
ISSN | 1422-0067 ; 1422-0067 ; 1661-6596 |
ISSN (online) | 1422-0067 |
ISSN | 1422-0067 ; 1661-6596 |
DOI | 10.3390/ijms25084440 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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