ABSTRACT
A series of stringent non-pharmacological and pharmacological interventions were implemented to contain the pandemic but the pandemic continues. Moreover, vaccination breakthrough infection and reinfection in convalescent coronavirus disease 2019 (COVID-19) cases have been reported. Further, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants emerged with mutations in spike (S) gene, the target of most current vaccines. Importantly, the mutations exhibit a trend of immune escape from the vaccination. Herein the scientific question that if the vaccination drives genetic or antigenic drifts of SARS-CoV-2 remains elusive. We performed correlation analyses to uncover the impacts of wide vaccination on epidemiological characteristics of COVID-19. In addition, we investigated the evolutionary dynamics and genetic diversity of SARS-CoV-2 under immune pressure by utilizing the Bayesian phylodynamic inferences and the lineage entropy calculation respectively. We found that vaccination coverage was negatively related to the infections, severe cases, and deaths of COVID-19 respectively. With the increasing vaccination coverage, the lineage diversity of SARS-CoV-2 dampened, but the rapid mutation rates of the S gene were identified, and the vaccination could be one of the explanations for driving mutations in S gene. Moreover, new epidemics resurged in several countries with high vaccination coverage, questioning their current pandemic control strategies. Hence, integrated vaccination and non-pharmacological interventions are critical to control the pandemic. Furthermore, novel vaccine preparation should enhance its capabilities to curb both disease severity and infection possibility.
ABSTRACT
Impact Statement We present a method of mapping data from publicly available genomics and publication resources to the Resource Description Framework (RDF) and implement a server to publish linked open data (LOD). As one of the largest and most comprehensive semantic databases about coronaviruses, the resulted gcCov database demonstrates the capability of using data in the LOD framework to promote correlations between genotypes and phenotypes. These correlations will be helpful for future research on fundamental viral mechanisms and drug and vaccine designs. These LOD with 62,168,127 semantic triplets and their visualizations are freely accessible through gcCov at https://nmdc.cn/gccov/.
ABSTRACT
The genomic variations of SARS-CoV-2 continue to emerge and spread worldwide. Some mutant strains show increased transmissibility and virulence, which may cause reduced protection provided by vaccines. Thus, it is necessary to continuously monitor and analyze the genomic variations of SARS-COV-2 genomes. We established an evaluation and prewarning system, SARS-CoV-2 variations evaluation and prewarning system (VarEPS), including known and virtual mutations of SARS-CoV-2 genomes to achieve rapid evaluation of the risks posed by mutant strains. From the perspective of genomics and structural biology, the database comprehensively analyzes the effects of known variations and virtual variations on physicochemical properties, translation efficiency, secondary structure, and binding capacity of ACE2 and neutralizing antibodies. An AI-based algorithm was used to verify the effectiveness of these genomics and structural biology characteristic quantities for risk prediction. This classifier could be further used to group viral strains by their transmissibility and affinity to neutralizing antibodies. This unique resource makes it possible to quickly evaluate the variation risks of key sites, and guide the research and development of vaccines and drugs. The database is freely accessible at www.nmdc.cn/ncovn.