The evolutionary dynamics of untreated HIV and the maintenance of the latent reservoir


Richard Neher
Biozentrum, University of Basel


slides at neherlab.org/201703_UZurich.html

Evolution of HIV


  • Chimp → human transmission ~1900 gave rise to HIV-1 group M
  • Diversified into subtypes that are ~20% different
  • evolves at a rate of about 0.1% per year
image: Sharp and Hahn, CSH Persp. Med.

HIV infection

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

HIV-1 sequencing before and after therapy

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


Deep sequencing of plasma RNA

Accuracy of minor variant frequencies

Population sequencing to track all mutations above 1%

  • diverge at 0.1-1% per year
  • almost full genomes coverage in 10 patients
  • full data set at hiv.tuebingen.mpg.de
Zanini et al, eLife, 2015

Diversity and mutation rates

  • 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

Mutation rates and diversity and neutral sites

Zanini et al, Virus Evolution, 2017

Frequent version 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

Inference of fitness costs

  • 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: $s = \mu/\bar{x}$

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

Does HIV evolve during therapy?

Brodin et al, eLife, 2016

No evidence of ongoing replication

  • HIV-1 RNA from plasma before treatment started
  • HIV-1 DNA gag-p17 from PBMCs after many years of treatment
Brodin et al, eLife, 2016

No evidence of ongoing replication

Brodin et al, eLife, 2016

Hypermutation

Brodin et al, eLife, 2016

Hypermutation

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

HIV acknowledgments

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

the group