The new platform fueled by AI could help scientists and health officials to catch the next COVVI-19 variant before spreading, offering the world a crucial lead in the fight against future pandemics.
Study: genomic surveillance in silico by coverage predicts and characterizes variants of SRAS-COV-2 interest. Image credit: Peterschreiber.media/Shutterstock.com
Researchers from the Helmholtz Center for infection Research and the German center for research on infections have developed a web platform to identify and characterize the variants of severe acute respiratory syndrome 2 Coronavirus 2 (SARS-COV-2) at the start of their development. The study is published in Nature communications.
Background
SARS-COV-2, the Causal pathogen of the 2019 coronavirus disease (COVVI-19), is a positive meaning RNA virus with a great capacity to acquire mutations during its evolution. These mutations can potentially increase the transmissibility, pathogenicity or immune exhaust capacity of the virus, leading to the emergence of more infectious or more harmful variants, designated as concern variants (VOC) or interest variants (VOI) by the World Health Organization (WHO).
A high immune exhaust capacity allows SARS-COV-2 to escape antiviral immunity developed by previous infection or vaccination. This highlights the need to frequently upgrade COVVI-19 vaccines to maintain their effectiveness against circulating variants.
Large-scale viral genomic surveillance programs have been implemented in several countries around the world to continuously monitor the evolution and adaptation of SAR-COV-2 and the appropriate identification of new VOCs. This has led to the generation of a large amount of viral genome sequencing data in the Gisaid database. Although the Gisaid database has greatly helped researchers and public health officials to characterize viral development, methods are necessary to permanently interpret these sequences and quickly guarantee the continuous efficiency of vaccines.
In this study, researchers have developed an online analysis method, the coverage system, for genomic surveillance of SARS-COV-2.
The cover system
The cover system analyzes the data sequence of Sars-COV-2 genomic sequence of the Gisaw database, which contains more than 16.5 million sequences. The system permanently predicts and characterizes the potential VO emerging by the country of origin for the dynamics of deformations and antigenic changes.
The system includes a series of statistical and bioinformatics methods, including the exact test and Fisher correction for multiple comparisons, which compares the mutations occurring in the advanced protein on the surface of different viral strains during a given month. Viral strains with significantly higher mutations than the average should have higher transmissibility or immune exhaust capacity. They are then displayed on the cover platform in special graphics called “thermal brands” so that users can see when and where important changes in the virus occur.
System validation
The researchers tested the reliability of the coverage system by analyzing the sequence data of the known VOCs genome, including the SARS-COV-2 Omicron variant. They observed that the system can identify these sequences as VOCs on average 79 days before the designation of the WHO.
The system used a method that marks the changes of amino acids based on a viral immune escape capacity to identify the SAR-COV-2 variants with antigenic alterations. These antigenic alteration scores are calculated using a matrix that weighs mutations throughout the advanced protein, not only on known antigenic sites. They are compared against experimental neutralization data for validation.
In thermal cards, these antigenic alteration scores have increased in a clear order, first displaying variants that are not monitored, followed by Voir, and finally, more strongly, VOCs, which are considered particularly harmful.
Meaning study
The study describes the development and validation of a genomic surveillance platform, the coverage, which continuously monitors the data of sequence of the SARS-COV-2 SARS-COV-2 genome to identify and characterize your potentials based on circulating viral strains in time. It also suggests their degree of antigenic modifications and advanced protein alleles with specific amino acid changes which can provide a selective advantage.
The coverage system includes three new methods: a method detects your potentials with higher transmissibility; A second method analyzes the dynamics of amino acid changes through the main surface peak proteins to identify those which can give a selective advantage; And a third method that marks the degree of antigenic alteration of each variant using a unidirectional immune matrix.
The systemic evaluation of the coverage indicates that the system can identify 88% of VOS and VOCs designated by WHO, with a precision of 79% and a recall of 72%, more than two months before their official WHO designation. No VOCs had missed and most of the missed lines were lower public health relevance (variants under surveillance).
The predictions made by the coverage depend on the extent and quality of the viral genomic monitoring programs underway for individual countries. The analysis is carried out in terms of countries and can also be affected by the genetic effects of the population when the number of cases is low. Any reduction in genomic monitoring can thus affect its predictive capacity.
Several other web platforms, including Nextstrain, Covariants, COVIDCG, EESCAPE and SPIKEPRO, monitor the SARS-COV-2 variants and characterize their mutagenic frequencies. However, none of these platforms permanently marks all circulating variants for a potential advantage and an antigenic change in real time. They also do not provide a comparison against the experimental data of antigenicity as does the coverage.
In addition, the coverage system combines Gisaid data with links to alternative web resources. It offers reproducible free access analyzes for additional information on selected variants, providing a full resource for viral genomic monitoring.
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