Estimating the in-vivo fitness landscape of HIV-1


Fabio Zanini, Vadim Puller, Jan Albert, and Richard Neher
Biozentrum, University of Basel

HIV-1 sequencing before and after therapy

Zanini et al, eLife, 2015; Brodin et al, eLife, 2016. Collaboration with the group of Jan Albert

Population sequencing to track all mutations above 1%

Zanini et al, eLife, 2015

Dynamics of single nucleotide variants

  • the average frequency of neutral variants grows as $\mu t$
  • individual trajectories of SNVs are very noisy
  • many sites $\Rightarrow$ average mutation rates
  • ASSUMPTION: synonymous and globally variable $\Rightarrow$ approximately neutral

Mutation rates and diversity and neutral sites

Zanini et al, Virus Evolution, 2017

Inferred vs measured mutations rates (Abram et al)

Zanini et al, Virus Evolution, 2017

Inference of fitness costs

  • mutation away from preferred state at rate $\mu$
  • selection against non-preferred state with strength $s$
  • variant frequency dynamics: $\frac{d x}{dt} = \mu -s x $
  • equilibrium frequency: $\bar{x} = \mu/s $
  • fitness cost: $s = \mu/\bar{x}$
Zanini et al, Virus Evolution, 2017

Inference of fitness costs

  • Frequencies of costly mutations decorrelate fast $\frac{d x}{dt} = \mu -s x $
  • $\Rightarrow$ average many samples to obtain accurate estimates
  • Assumption: The global consensus is the preferred state
  • Only use sites that initially agree with consensus
  • Only use sites that don't chance majority nucleotide

Fitness landscape of HIV-1

Zanini et al, Virus Evolution, 2017

Selection on RNA structures and regulatory sites

Zanini et al, Virus Evolution, 2017

The distribution of fitness costs

Zanini et al, Virus Evolution, 2017

Fitness - diversity correlation

Zanini et al, Virus Evolution, 2017

Costly HLA associated positions have high diversity

Zanini et al, Virus Evolution, 2017

HIV acknowledgments

  • Fabio Zanini
  • Jan Albert
  • Johanna Brodin
  • Vadim Puller

Frequent reversion of previously beneficial mutations

  • HIV escapes immune systems
  • most mutations are costly
  • humans selects for different mutations
  • compensation or reversion?
Zanini et al, eLife, 2015

Deep sequencing of plasma RNA

Accuracy of minor variant frequencies