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Updated: Jan 27, 2026

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency
Published on: June 16, 2011
Mutational pathway maps and founder effects define the within-host spectrum of hepatitis C virus mutants resistant to
Rubesh Raja1, Aditya Pareek1, Kapil Newar1
1Department of Chemical Engineering, Indian Institute of Science, Bangalore, India.
Abstract:
Knowledge of the within-host frequencies of resistance-associated amino acid variants (RAVs) is important to the identification of optimal drug combinations for the treatment of hepatitis C virus (HCV) infection. Multiple RAVs may exist in infected individuals, often below detection limits, at any resistance locus, defining the diversity of accessible resistance pathways. We developed a multiscale mathematical model to estimate the pre-treatment frequencies of the entire spectrum of mutants at chosen loci. Using a codon-level description of amino acids, we performed stochastic simulations of intracellular dynamics with every possible nucleotide variant as the infecting strain and estimated the relative infectivity of each variant and the resulting distribution of variants produced. We employed these quantities in a deterministic multi-strain model of extracellular dynamics and estimated mutant frequencies. Our predictions captured database frequencies of the RAV R155K, resistant to NS3/4A protease inhibitors, presenting a successful test of our formalism. We found that mutational pathway maps, interconnecting all viable mutants, and strong founder effects determined the mutant spectrum. The spectra were vastly different for HCV genotypes 1a and 1b, underlying their differential responses to drugs. Using a fitness landscape determined recently, we estimated that 13 amino acid variants, encoded by 44 codons, exist at the residue 93 of the NS5A protein, illustrating the massive diversity of accessible resistance pathways at specific loci. Accounting for this diversity, which our model enables, would help optimize drug combinations. Our model may be applied to describe the within-host evolution of other flaviviruses and inform vaccine design strategies.
Insights
Understanding hepatitis C virus (HCV) resistance mutations within the body is key for effective drug combinations. Our model predicts these mutation frequencies, revealing vast diversity and guiding better treatment strategies.
Area of Science:
- Virology
- Mathematical Biology
- Drug Resistance Studies
Background:
- Within-host frequencies of resistance-associated amino acid variants (RAVs) are crucial for optimizing hepatitis C virus (HCV) drug combinations.
- Multiple RAVs can exist below detection limits, creating diverse resistance pathways.
Purpose of the Study:
- To develop a multiscale mathematical model for estimating pre-treatment frequencies of the entire spectrum of HCV mutants at specific resistance loci.
- To analyze the diversity of accessible resistance pathways and inform drug combination strategies.
Main Methods:
- Developed a multiscale mathematical model combining stochastic intracellular and deterministic extracellular dynamics.
- Used codon-level amino acid descriptions and simulated nucleotide variants to estimate relative infectivity and variant distributions.
- Validated the model by accurately predicting database frequencies of the R155K RAV.
Main Results:
- The model successfully predicted known RAV frequencies, validating its formalism.
- Mutational pathway maps and founder effects were identified as key determinants of the mutant spectrum.
- Significant differences in mutant spectra were observed between HCV genotypes 1a and 1b, explaining differential drug responses.
- Massive diversity of accessible resistance pathways was illustrated at specific loci, such as residue 93 of the NS5A protein.
Conclusions:
- The developed model accurately estimates within-host HCV mutant frequencies, accounting for extensive diversity.
- Understanding this diversity is essential for optimizing antiviral drug combinations.
- The model has potential applications in studying other flaviviruses and informing vaccine design.
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