Dataset Information

On the origin and evolution of SARS-CoV-2.

ABSTRACT: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the ongoing global outbreak of a coronavirus disease (herein referred to as COVID-19). Other viruses in the same phylogenetic group have been responsible for previous regional outbreaks, including SARS and MERS. SARS-CoV-2 has a zoonotic origin, similar to the causative viruses of these previous outbreaks. The repetitive introduction of animal viruses into human populations resulting in disease outbreaks suggests that similar future epidemics are inevitable. Therefore, understanding the molecular origin and ongoing evolution of SARS-CoV-2 will provide critical insights for preparing for and preventing future outbreaks. A key feature of SARS-CoV-2 is its propensity for genetic recombination across host species boundaries. Consequently, the genome of SARS-CoV-2 harbors signatures of multiple recombination events, likely encompassing multiple species and broad geographic regions. Other regions of the SARS-CoV-2 genome show the impact of purifying selection. The spike (S) protein of SARS-CoV-2, which enables the virus to enter host cells, exhibits signatures of both purifying selection and ancestral recombination events, leading to an effective S protein capable of infecting human and many other mammalian cells. The global spread and explosive growth of the SARS-CoV-2 population (within human hosts) has contributed additional mutational variability into this genome, increasing opportunities for future recombination.

SUBMITTER: Singh D

PROVIDER: S-EPMC8050477 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Various lineages of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have contributed to prolongation of the Coronavirus Disease 2019 (COVID-19) pandemic. Several non-synonymous mutations in SARS-CoV-2 proteins have generated multiple SARS-CoV-2 variants. In our previous report, we have shown that an evenly uneven distribution of unique protein variants of SARS-CoV-2 is geo-location or demography-specific. However, the correlation between the demographic transmutability of the SARS-CoV-2 infection and mutations in various proteins remains unknown due to hidden symmetry/asymmetry in the occurrence of mutations. This study tracked how these mutations are emerging in SARS-CoV-2 proteins in six model countries and globally. In a geo-location, considering the mutations having a frequency of detection of at least 500 in each SARS-CoV-2 protein, we studied the country-wise percentage of invariant residues. Our data revealed that since October 2020, highly frequent mutations in SARS-CoV-2 have been observed mostly in the Open Reading Frame (ORF) 7b and ORF8, worldwide. No such highly frequent mutations in any of the SARS-CoV-2 proteins were found in the UK, India, and Brazil, which does not correlate with the degree of transmissibility of the virus in India and Brazil. However, we have found a signature that SARS-CoV-2 proteins were evolving at a higher rate, and considering global data, mutations are detected in the majority of the available amino acid locations. Fractal analysis of each protein's normalized factor time series showed a periodically aperiodic emergence of dominant variants for SARS-CoV-2 protein mutations across different countries. It was noticed that certain high-frequency variants have emerged in the last couple of months, and thus the emerging SARS-CoV-2 strains are expected to contain prevalent mutations in the ORF3a, membrane, and ORF8 proteins. In contrast to other beta-coronaviruses, SARS-CoV-2 variants have rapidly emerged based on demographically dependent mutations. Characterization of the periodically aperiodic nature of the demographic spread of SARS-CoV-2 variants in various countries can contribute to the identification of the origin of SARS-CoV-2.

Dataset Information

On the origin and evolution of SARS-CoV-2.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets