Real-time analysis and forecasting of influenza virus evolution


Richard Neher
Biozentrum, University of Basel


slides at neherlab.org/201803_IMRP.html



  • Influenza viruses evolve to avoid human immunity
  • Vaccines need frequent updates

Large scale sequencing -- A/H3N2 genomes in GISAID

Joint work with....

  • Boris Shraiman
  • Colin Russell
  • Trevor Bedford

nextflu.org

joint work with Trevor Bedford & his lab

Features

  • Maps mutations to the tree
  • Calculates frequency trajectories of every major mutation
  • Allows subsetting of data to date ranges and geographic region
  • Time-scaled and standard phylogenetic trees
  • Updated frequently, reflects GISAID data
  • Integrates HI data and molecular evolution data

Beyond tracking: can we predict?

Different approaches to predict IAV evolution

  • extrapolation of current frequency trajectories
    • sampling bias can affect this dramatically
  • explicit fitness score based on historical patterns (Lukzsa and Lässig)
    • epitope mutations
    • other mutations -- interfere with virus function
  • fitness inference from branching patterns in the tree (RN, Russell, Shraiman)
    • requires no historical data
    • not influenza specific

Recent review: Morris et al, Trends in Microbiology, 2017

Model of an adapting influenza virus population

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

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

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

Validation on simulated data

RN, Russell, Shraiman, eLife, 2014

Prediction of the dominating H3N2 influenza strain

  • Since 2015: Reports with (conservative) predictions are available on nextflu.org
 RN, Russell, Shraiman, eLife, 2014

Sept 2015: "3c2.a will continue to dominate"

Feb 2016: "...we predict the HA1:171K (now 3c2.a1) variant to dominate..."

Sep 2016: "...we predict that clade 3c2.a1 variant to dominate, but..."

Feb 2017: "...we predict clades 171K/121K (3c2.a1a) and 131K/142K (3c2.a2) to be successful..."

Sep 2017: "...we think clades 3c2.a1a/135K, 3c2.a2, 3c2.a3 are competitive"

A reassortant dominated A/H3N2 circulating this past season

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

Rate of antigenic evolution

  • Cumulative antigenic evolution since the root: $\sum_i d_i$
  • A/H3N2 evolves faster antigenically
  • A/H3N2 has a more rapid population turn-over
  • Proportion of children is high in B vs A/H3N2 infections

How many sites are involved?


Mutationeffect
K158N/N189K 3.64
K158R 2.31
K189N 2.18
S157L 1.29
V186G 1.25
S193F 1.2
K140I 1.1
F159Y 1.08
K144D 1.08
K145N 0.91
S159Y 0.89
I25V 0.88
Q1L 0.85
K145S 0.85
K144N 0.85
N145S 0.85
N8D 0.73
T212S 0.69
N188D 0.65

Exploring HI data relative to individual sera

nextflu and nextstrain.org

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