Virus evolution and the predictability of next year's flu


Richard Neher
Biozentrum, University of Basel


slides at neherlab.org/201711_NIBR.html

Human seasonal influenza viruses

slide by Trevor Bedford


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

nextflu.org

joint work with Trevor Bedford & his lab

Beyond tracking: can we predict?

Clonal interference and traveling waves

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

Theoretical framework for virus evolution -- population genetics

evolutionary processes ↔ trees ↔ genetic diversity

Typical tree

Bolthausen-Sznitman Coalescent

RN, Hallatschek, PNAS, 2013; see also Brunet and Derrida, PRE, 2007; Desai, Walczak, Fisher, Genetics, 2013

Bursts in a tree ↔ high fitness genotypes

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

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

Hemagglutination Inhibition assays

Slide by Trevor Bedford

HI data sets

  • Long list of distances between sera and viruses
  • Tables are sparse, only close by pairs
  • Structure of space is not immediately clear
  • MDS in 2 or 3 dimensions
Smith et al, Science 2002
Slide by Trevor Bedford

Integrating antigenic and molecular evolution

  • $H_{a\beta} = v_a + p_\beta + \sum_{i\in (a,b)} d_i$
  • each branch contributes $d_i$ to antigenic distance
  • sparse solution for $d_i$ through $l_1$ regularization
  • related model where $d_i$ are associated with substitutions
RN et al, PNAS, 2016

Integrating antigenic and molecular evolution

  • MDS: $(d+1)$ parameters per virus
  • Tree model: $2$ parameters per virus
  • Sparse solution
    → identify branches or substitutions that cause titer drop
RN et al, PNAS, 2016

HI distances on the phylogenetic tree

nextstrain.org

joint work with Trevor Bedford & his lab

NextStrain architecture

Using treetime to rapidly compute timetrees

HIV-1 evolution within one individual



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

HIV-1 sequencing before and after therapy

Zanini et al, eLife, 2015; Brodin et al, eLife, 2016. Collaboration with the group of Jan Albert


Population sequencing to track all mutations above 1%

  • diverge at 0.1-1% per year
  • almost full genomes coverage in 10 patients
  • full data set at hiv.tuebingen.mpg.de
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: $s = \mu/\bar{x}$

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

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

  • RNA virus evolution can be observed directly
  • 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

Influenza and Theory acknowledgments

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

nextstrain.org

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

Acknowledgments

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