Computational design of hepatitis C virus immunogens from host-pathogen dynamics over empirical viral fitness landscapes
Authored by Gregory R Hart, Andrew L Ferguson
Date Published: 2019
DOI: 10.1088/1478-3975/aaeec0
Sponsors:
United States National Science Foundation (NSF)
Platforms:
MATLAB
Model Documentation:
Other Narrative
Model Code URLs:
https://github.com/GregoryRHart/Population_Dynamics
Abstract
Hepatitis C virus (HCV) afflicts 170 million people and kills 700 000
annually. Vaccination offers the most realistic and cost effective hope
of controlling this epidemic, but despite 25 years of research, no
vaccine is available. A major obstacle is HCV's extreme genetic
variability and rapid mutational escape from immune pressure. Coupling
maximum entropy inference with population dynamics simulations, we have
employed a computational approach to translate HCV sequence databases
into empirical landscapes of viral fitness and simulate the intrahost
evolution of the viral quasispecies over these landscapes. We explicitly
model the coupled host-pathogen dynamics by combining agent-based models
of viral mutation with stochastically-integrated coupled ordinary
differential equations for the host immune response. We validate our
model in predicting the mutational evolution of the HCV RNA-dependent
RNA polymerase (protein NS5B) within seven individuals for whom
longitudinal sequencing data is available. We then use our approach to
perform exhaustive in silico evaluation of putative immunogen candidates
to rationally design tailored vaccines to simultaneously cripple viral
fitness and block mutational escape within two selected individuals. By
systematically identifying a small number of promising vaccine
candidates, our empirical fitness landscapes and host-pathogen dynamics
simulator can guide and accelerate experimental vaccine design
efforts.Hepatitis C virus (HCV) afflicts 170 million people and kills
700 000 annually. Vaccination offers the most realistic and cost
effective hope of controlling this epidemic, but despite 25 years of
research, no vaccine is available. A major obstacle is HCV's extreme
genetic variability and rapid mutational escape from immune pressure.
Coupling maximum entropy inference with population dynamics simulations,
we have employed a computational approach to translate HCV sequence
databases into empirical landscapes of viral fitness and simulate the
intrahost evolution of the viral quasispecies over these landscapes. We
explicitly model the coupled host-pathogen dynamics by combining
agent-based models of viral mutation with stochastically-integrated
coupled ordinary differential equations for the host immune response. We
validate our model in predicting the mutational evolution of the HCV
RNA-dependent RNA polymerase (protein NS5B) within seven individuals for
whom longitudinal sequencing data is available. We then use our approach
to perform exhaustive in silico evaluation of putative immunogen
candidates to rationally design tailored vaccines to simultaneously
cripple viral fitness and block mutational escape within two selected
individuals. By systematically identifying a small number of promising
vaccine candidates, our empirical fitness landscapes and host-pathogen
dynamics simulator can guide and accelerate experimental vaccine design
efforts.
Tags
Replication
Drug-resistance
In-vivo
T-cells
Neural-networks
Vaccine
Effective population-size
Hcv infection
Recombination
Hepatitis c virus
Vaccine design
Host-pathogen dynamics
Viral fitness
landscape
Hiv evolution