Virus evolution and the predictability of next year's flu


Richard Neher
Biozentrum, University of Basel


slides at neherlab.org/201705_Bethe.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)
  • many infectious diseases are caused by viruses
  • very important function in microbial eco-systems
  • 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


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

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!

Clonal interference and traveling waves

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

Neutral/Kingman coalescent

strong selection

Bolthausen-Sznitman Coalescent

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

Traveling waves and the Bolthausen-Snitman coalescent

  • Branching process approximation: $P(n_i, t|x_i)$
  • Does a sample (blue dots) have a common ancestor $\tau$ generations ago?
    $\quad Q_b = \langle \sum_i \left(\frac{n_i}{\sum_j n_j}\right)^b\rangle \approx \frac{\tau-T_c}{T_c(b-1)} $
  • All other merger rates are also consistent with the Bolthausen-Sznitman coalescent:
    $\quad\lambda_{b,k} = \frac{(k-2)!(b-k)!}{T_c (b-1)!}$
RN, Hallatschek, PNAS, 2013; see also Brunet and Derrida, PRE, 2007

U-shaped polarized site frequency spectra



RN, Hallatschek, PNAS, 2013
Zanini et al, eLife, 2015

Bursts in a tree ↔ high fitness genotypes

Can we read fitness of a tree?

Human seasonal influenza viruses

slide by Trevor Bedford


  • Influenza virus evolves to avoid human immunity
  • Vaccines need frequent updates

Predicting evolution

Given the branching pattern:

  • can we predict fitness?
  • pick the closest relative of the future?
RN, Russell, Shraiman, eLife, 2014

Fitness inference from trees

$$P(\mathbf{x}|T) = \frac{1}{Z(T)} p_0(x_0) \prod_{i=0}^{n_{int}} g(x_{i_1}, t_{i_1}| x_i, t_i)g(x_{i_2}, t_{i_2}| x_i, t_i)$$
RN, Russell, Shraiman, eLife, 2014

Validate on simulation data

  • simulate evolution
  • sample sequences
  • reconstruct trees
  • infer fitness
  • predict ancestor of future
  • compare to truth
RN, Russell, Shraiman, eLife, 2014

Validation on simulated data

RN, Russell, Shraiman, eLife, 2014

Prediction of the dominating H3N2 influenza strain

  • no influenza specific input
  • how can the model be improved? (see model by Luksza & Laessig)
  • what other context might this apply?
 RN, Russell, Shraiman, eLife, 2014

nextstrain.org

Summary

  • RNA virus evolution can be observed directly
  • Extensive reversion to preferred amino acid sequence
  • Rapidly adapting population require new population genetic models
  • Those model can be used to infer fit clades
  • Future influenza population can be anticipated
  • Automated real-time analysis can help fight the spread of disease

HIV acknowledgments

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

Influenza and Theory acknowledgments

  • Boris Shraiman
  • Colin Russell
  • Trevor Bedford
  • Oskar Hallatschek

nextstrain.org

  • Trevor Bedford
  • Colin Megill
  • Pavel Sagulenko
  • Wei Ding
  • Sidney Bell
  • James Hadfield