Quantifying the evolutionary constraints and potential of hepatitis c virus ns5a protein

Lei Dai, Yushen Du, Hangfei Qi, Christian D. Huber, Dongdong Chen, Tian Hao Zhang, Nicholas C. Wu, Ergang Wang, James O. Lloyd-Smith, Ren Sun

Research output: Contribution to journalArticlepeer-review

Abstract

RNA viruses, such as hepatitis C virus (HCV), influenza virus, and SARSCoV- 2, are notorious for their ability to evolve rapidly under selection in novel environments. It is known that the high mutation rate of RNA viruses can generate huge genetic diversity to facilitate viral adaptation. However, less attention has been paid to the underlying fitness landscape that represents the selection forces on viral genomes, especially under different selection conditions. Here, we systematically quantified the distribution of fitness effects of about 1,600 single amino acid substitutions in the drug-targeted region of NS5A protein of HCV. We found that the majority of nonsynonymous substitutions incur large fitness costs, suggesting that NS5A protein is highly optimized. The replication fitness of viruses is correlated with the pattern of sequence conservation in nature, and viral evolution is constrained by the need to maintain protein stability. We characterized the adaptive potential of HCV by subjecting the mutant viruses to selection by the antiviral drug daclatasvir at multiple concentrations. Both the relative fitness values and the number of beneficial mutations were found to increase with the increasing concentrations of daclatasvir. The changes in the spectrum of beneficial mutations in NS5A protein can be explained by a pharmacodynamics model describing viral fitness as a function of drug concentration. Overall, our results show that the distribution of fitness effects of mutations is modulated by both the constraints on the biophysical properties of proteins (i.e., selection pressure for protein stability) and the level of environmental stress (i.e., selection pressure for drug resistance).

Original languageEnglish (US)
Article numbere01111-20
JournalmSystems
Volume6
Issue number2
DOIs
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • Microbiology
  • Ecology, Evolution, Behavior and Systematics
  • Biochemistry
  • Physiology
  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computer Science Applications

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