Virus evolution and the predictability of next year's flu

Richard Neher
Biozentrum, University of Basel

slides at neherlab.org/201709_GCB.html

Viruses

tobacco mosaic virus (Thomas Splettstoesser, wikipedia)

bacteria phage (adenosine, wikipedia)

influenza virus wikipedia

human immunodeficiency virus wikipedia

rely on host to replicate
little more than genome + capsid
genomes typically 5-200k bases (+exceptions)
most abundant organisms on earth $\sim 10^{31}$

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 one individual

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

HIV acknowledgments

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

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

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

Mutation rates and diversity at neutral sites

Zanini et al, Virus Evolution, 2017

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

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

Theoretical framework for virus evolution -- population genetics

evolutionary processes ↔ trees ↔ genetic diversity

Neutral models and beyond

Neutral models

all individuals are identical → same offspring distribution
Kingman coalesence and diffusion theory are dual descriptions
everything is easy to calculate
perturbations like background selection can be included

But: neutral models not suitable for RNA viruses!

Influenza and Theory acknowledgments

Boris Shraiman
Colin Russell
Trevor Bedford
Oskar Hallatschek

Clonal interference and traveling waves

RN, Annual Reviews, 2013; Desai & Fisher; Brunet & Derrida; Kessler & Levine

Neutral/Kingman coalescent

strong selection

Bolthausen-Sznitman Coalescent

RN, Hallatschek, PNAS, 2013; see also Brunet and Derrida, PRE, 2007

Bursts in a tree ↔ high fitness genotypes

Can we read fitness of a tree?

Influenza virus evolves to avoid human immunity
Vaccines need frequent updates