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Viral Mutations00:36

Viral Mutations

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A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
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Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.
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Reverse Genetics to Engineer Positive-Sense RNA Virus Variants
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Concepts and methods for predicting viral evolution.

Matthijs Meijers1, Denis Ruchnewitz1, Jan Eberhardt1

  • 1Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany.

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|May 15, 2024
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Summary
This summary is machine-generated.

Predicting seasonal influenza virus evolution is crucial for vaccine development. This study presents a data-driven pipeline integrating diverse data for accurate viral evolution predictions and vaccine strain selection.

Keywords:
antigenic evolutionfitness modelsinfluenza vaccinespopulation immunity

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Area of Science:

  • Virology
  • Epidemiology
  • Computational Biology

Background:

  • Seasonal human influenza viruses evolve rapidly due to adaptive mutations, particularly in haemagglutinin antigenic epitopes.
  • Year-to-year changes in circulating viral strains necessitate continuous monitoring and prediction for effective public health strategies.

Purpose of the Study:

  • To develop and present a consistent, data-driven methodology for predictive analysis of viral evolution.
  • To enable accurate forecasting of viral clade frequencies and assess vaccine strain efficacy.

Main Methods:

  • Integration of global viral isolate sequence data, epidemiological incidence data, antigenic characterization, and intrinsic viral phenotypes.
  • Development of a computational pipeline for analyzing these integrated datasets.
  • Estimation of relative strain fitness and prediction of future clade frequencies.

Main Results:

  • The pipeline provides estimates of relative fitness for currently circulating influenza strains.
  • Predictions of clade frequencies up to one year in advance are generated.
  • Comparative protection estimates for candidate vaccine strains against future viral populations are obtained.

Conclusions:

  • The described methods offer a robust framework for data-driven prediction of viral evolution.
  • This approach supports pre-emptive vaccine strain selection, enhancing influenza vaccine efficacy.
  • Continuously updated predictions for influenza and SARS-CoV-2 are accessible via previr.app.