Article ; Online: Using big sequencing data to identify chronic SARS-Coronavirus-2 infections.
2024 Volume 15, Issue 1, Page(s) 648
Abstract: The evolution of SARS-Coronavirus-2 (SARS-CoV-2) has been characterized by the periodic emergence of highly divergent variants. One leading hypothesis suggests these variants may have emerged during chronic infections of immunocompromised individuals, ... ...
| Abstract | The evolution of SARS-Coronavirus-2 (SARS-CoV-2) has been characterized by the periodic emergence of highly divergent variants. One leading hypothesis suggests these variants may have emerged during chronic infections of immunocompromised individuals, but limited data from these cases hinders comprehensive analyses. Here, we harnessed millions of SARS-CoV-2 genomes to identify potential chronic infections and used language models (LM) to infer chronic-associated mutations. First, we mined the SARS-CoV-2 phylogeny and identified chronic-like clades with identical metadata (location, age, and sex) spanning over 21 days, suggesting a prolonged infection. We inferred 271 chronic-like clades, which exhibited characteristics similar to confirmed chronic infections. Chronic-associated mutations were often high-fitness immune-evasive mutations located in the spike receptor-binding domain (RBD), yet a minority were unique to chronic infections and absent in global settings. The probability of observing high-fitness RBD mutations was 10-20 times higher in chronic infections than in global transmission chains. The majority of RBD mutations in BA.1/BA.2 chronic-like clades bore predictive value, i.e., went on to display global success. Finally, we used our LM to infer hundreds of additional chronic-like clades in the absence of metadata. Our approach allows mining extensive sequencing data and providing insights into future evolutionary patterns of SARS-CoV-2. |
|---|---|
| MeSH term(s) | Humans ; COVID-19/genetics ; SARS-CoV-2/genetics ; Persistent Infection ; Mutation ; Spike Glycoprotein, Coronavirus/genetics ; Spike Glycoprotein, Coronavirus/chemistry |
| Chemical Substances | Spike Glycoprotein, Coronavirus ; spike protein, SARS-CoV-2 |
| Language | English |
| Publishing date | 2024-01-20 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 2553671-0 |
| ISSN | 2041-1723 ; 2041-1723 |
| ISSN (online) | 2041-1723 |
| ISSN | 2041-1723 |
| DOI | 10.1038/s41467-024-44803-4 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
More links
Kategorien
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.