Within-host evolution of HIV


Richard Neher
Biozentrum, University of Basel


slides at neherlab.org/201910_Amsterdam.html

The people who made this happen...

  • Fabio Zanini
  • Jan Albert
  • Johanna Brodin
  • Christa Lanz
  • Göran Bratt
  • Lina Thebo
  • Vadim Puller
  • The participants of this study!

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 individuals



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

Untreated early to chronic infection:

Virus-immune system coevolution

Population sequencing to track all mutations above 1%

Zanini et al, eLife, 2015

Evolution and diversity across the genome

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

Approximately neutral divergence at silent mutations

Zanini et al, Virus Evolution, 2017

In vivo mutation rate estimates

Zanini et al, Virus Evolution, 2017

Frequent reversion towards the center of HIV phylogeny

  • Almost one third of all mutations are reversions (~5% expected by chance)
  • Reversions accumulate slowly throughout chronic infection
  • Reversion can explain much of the intra/inter-host evolutionary rate mismatch
Zanini et al, eLife, 2015

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

Fitness landscape of HIV-1

Zanini et al, Virus Evolution, 2017

Suppressive therapy and the latent reservoir:

Where is HIV hiding?

The latent proviral DNA reservoir

  • Viruses integrate but don't express → pro-viral DNA reservoir
  • Therapy fully suppresses virus replication, but interruption results in rebound
  • When is the reservoir established? How is it maintained?

p17 sequencing from PBMCs on fully suppressive therapy

Brodin et al, eLife, 2016

Proviral DNA mostly identical to pre-therapy RNA

Brodin et al, eLife, 2016

No evidence of ongoing evolution

Brodin et al, eLife, 2016, see also Abrahams et al, bioRxiv, 2019

Most proviral DNA originates from shortly after start of therapy

  • latent HIV → barcode of a T-cell lineage
  • all latent integrated virus derives from late infection
Brodin et al, eLife, 2016

Summary

  • Continuous reversion to ancestral states throughout chronic infection
  • Minor diversity allows fitness cost estimates at every position in the genome
  • The latent reservoir is a snapshot of the pre-treatment population
  • No evidence of ongoing replication
  • No clear trend of diversity loss on treatment
  • Proviral DNA can serve as a T-cell lineage marker

Acknowledgments

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

No evidence of ongoing evolution -- hypermutated provirus

Brodin et al, eLife, 2016

Hypermutated and intact proviral p17 sequences

  • Between 0 and 43% hypermutants, median 13%
  • Between 3 and 43 unique sequences >1% read frequency, median 25
  • Majority of hypermutant sequences have stop codons
Brodin et al, eLife, 2016