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

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?

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

Summary

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.