Evolution of HIV

Richard Neher
Biozentrum, University of Basel

slides at neherlab.org/201712_ICTP1.html

Ernst Haeckel, 1879

From qualitative to quantitative

  • What are the relevant parameters?
  • How does adaptation and diversity depend on parameters?
  • How repeatable is evolution?
  • How predictable is evolution?
  • How gradual is evolution?

Development of sequencing technologies

We can now sequence...
  • thousands of bacterial isolates
  • thousands of single cells
  • populations of viruses, bacteria or flies
  • diverse ecosystems

Experimental evolution -- Lenski experiment

Rich Lenski, Ben Good, Michael Desai et al

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.

Evolution of HIV

wikipedia, Klenermann et al

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

Immune escape in early HIV infection

Immune escape in early HIV infection

HIV-1 evolution within one individual

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

Population sequencing to track all mutations above 1%

  • diverge at 0.1-1% per year
  • almost whole genome coverage in 10 patients
  • full data set at hiv.tuebingen.mpg.de
Zanini et al, eLife, 2015; antibody data from Richman et al, 2003

The rate of sequence evolution in HIV

Mutation rate, evolutionary rate, substitution rates

  • Mutation rate != evolutionary rate
  • Neutral divergence = $\mu t$
  • Diversifying selection → faster
  • Purifying selection → slower

Evolution in different parts of the genome

  • 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

Mutation rates and diversity and neutral sites

Zanini et al, Virus Evolution, 2017

HIV recombination

Recombination and linkage

Zanini et al, eLife, 2015

Why recombination?

  • Red and blue: beneficial mutations
  • Black: deleterious mutation

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

Frequent reversion of previously beneficial mutations

  • HIV escapes immune systems
  • most mutations are costly
  • humans selects for different mutations
  • compensation or reversion?

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


  • Deep sequencing allows comprehensive characterization of evolving populations
  • RNA viruses evolve rapidly enough to observe dynamics
  • Fitness landscape can be estimated explicitly
  • Constant struggle between maintaining a functional virus and adaptation to host immunity
  • Latently integrated virus is an accurate snapshot of the virus before therapy
  • Latent virus can serve as barcode for T-cells.


  • Complex dynamics of many mutations: How do they interact?
  • What are appropriate theoretical frameworks?
  • Can we quantify the effect of recombination on HIV adaptation?
  • How important is epistasis?
  • Are there regions in the HIV genome that can't escape the immune system?

HIV acknowledgments

  • Fabio Zanini
  • Jan Albert
  • Johanna Brodin
  • Christa Lanz
  • G├Âran Bratt
  • Lina Thebo
  • Vadim Puller