The evolution of untreated HIV and maintenance of the latent reservoir


Richard Neher
Biozentrum, University of Basel


slides at neherlab.org/201809_PEI.html

Evolution of HIV


  • Chimp → human transmission around 1900 gave rise to HIV-1 group M
  • ~100 million infected people since
  • subtypes differ at 10-20% of their genome
  • HIV-1 evolves ~0.1% per year
image: Sharp and Hahn, CSH Persp. Med.

HIV infection

  • $10^8$ cells are infected every day
  • the virus repeatedly escapes immune recognition
  • integrates into T-cells as latent provirus
image: wikipedia

HIV-1 evolution within one individual



silouhette: clipartfest.com, Zanini at al, 2015. Collaboration with Jan Albert and his group

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

Approximately neutral divergence -- silent mutations

Zanini et al, Virus Evolution, 2017

In vivo mutation rate estimates

Zanini et al, Virus Evolution, 2017

Divergence at increasingly conserved positions

  • Six categories from high to low conservation
  • mutation away from preferred state with 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: $s = \mu/\bar{x}$
  • Fit model of minor variation to categories of conservation
  • $\Rightarrow$ harmonic average fitness cost in category

Accurate frequency estimates by averaging many samples

  • Frequencies of costly mutations decorrelate fast $\frac{d x}{dt} = \mu -s x $
  • $\Rightarrow$ average many samples to obtain accurate estimates

Fitness landscape of HIV-1

Zanini et al, Virus Evolution, 2017

Selection on RNA structures and regulatory sites

  • Blue: all mutations
  • Red: only mutations that don't change amino acids
Zanini et al, Virus Evolution, 2017

Diversity and hitchhiking

  • envelope changes fastest, enzymes lowest
  • identical rate of synonymous evolution
  • diversity saturates where evolution is fast
  • synonymous mutations stay at low frequency
Zanini et al, eLife, 2015

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

Does HIV evolve during therapy?

Brodin et al, eLife, 2016

No evidence of ongoing replication

Brodin et al, eLife, 2016

No evidence of ongoing replication

Brodin et al, eLife, 2016

T-cell turnover is fast in untreated infection

  • latent HIV → barcode of a T-cell lineage
  • all latent integrated virus derives from late infection
  • untreated: T-cell lineages are short lived
  • on therapy: T-cell clones live decades
Brodin et al, eLife, 2016

nextstrain.org

joint work with Trevor Bedford & his lab

code at github.com/nextstrain

Summary

  • Intra-host HIV evolution is governed by a universal fitness landscape, modulated by host-specific immune response
  • Landscape of fitness costs can be estimated from intra-host diversity
  • The latent HIV DNA reservoir turns over fast
  • No evidence for evolution under therapy
  • all data are available at hiv.biozentrum.unibas.ch

Thank you for your attention!

Acknowledgments

  • Fabio Zanini
  • Jan Albert
  • Johanna Brodin
  • Christa Lanz
  • Göran Bratt
  • Lina Thebo
  • Vadim Puller

nextstrain.org

  • Trevor Bedford
  • Colin Megill
  • Pavel Sagulenko
  • Sidney Bell
  • James Hadfield
  • Wei Ding
  • Tom Sibley
  • Emma Hodcroft

Amplification bias and template input

Accuracy of minor variant frequencies

Frequency concordance in samples 4 weeks apart

The distribution of fitness costs

Zanini et al, Virus Evolution, 2017

Fitness costs vs consensus amino acid

Zanini et al, Virus Evolution, 2017