Evolution of HIV, immune escape, and latency


Richard Neher
Biozentrum, University of Basel


slides at neherlab.org/201707_KITP.html

HIV acknowledgments

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

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 infection

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

HIV and the immune system

Richman et al, 2003


HIV-1 evolution within one individual



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

Accuracy of minor variant frequencies

Population sequencing to track all mutations above 1%

Zanini et al, eLife, 2015

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

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

Recombination and linkage

Zanini et al, eLife, 2015; Neher and Leitner, 2010

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: $s = \mu/\bar{x}$

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

Blue: all sites; red: without amino acid change

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

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

Hypermutation

Brodin et al, eLife, 2016

Hypermutation

Brodin et al, eLife, 2016