Real-time tracking of virus evolution


Trevor Bedford (@trvrb)
11 Apr 2016
Ecology and Evolution Seminar
University of Chicago

Slides at bedford.io/talks/

Phylogenies

Phylogenies describe history

Phylogenies describe history

Haeckel 1879

Phylogenies describe history

Phylogenies reveal process

Darwin 1859

Epidemics

Epidemic process

Sample some individuals

Sequence and determine phylogeny

Sequence and determine phylogeny

Localized Middle Eastern MERS-CoV phylogeny

Regional West African Ebola phylogeny

Global influenza phylogeny

Applications of evolutionary analysis for influenza vaccine strain selection and charting spread of Ebola and Zika

Influenza

Influenza virion

Influenza H3N2 vaccine updates

H3N2 phylogeny showing antigenic drift

H3N2 phylogeny showing antigenic drift

Drift variants rapidly take over the virus population


Timely surveillance and rapid analysis essential to understand ongoing influenza evolution

nextflu

Project to provide a real-time view of the evolving influenza population

All in collaboration with Richard Neher

nextflu pipeline

  1. Download all recent HA sequences from GISAID
  2. Filter to remove outliers
  3. Subsample across time and space
  4. Align sequences
  5. Build tree
  6. Estimate frequencies
  7. Export for visualization

Up-to-date analysis publicly available at:

nextflu.org

Antigenic evolution

Influenza hemagglutination inhibition (HI) assay

HI measures cross-reactivity across viruses

Data in the form of table of maximum inhibitory titers

Model HI titers by estimating branch effects

Model is highly predictive of missing titer values

Recent HI data from WHO CC London annual and interim reports

Up-to-date analysis at:

nextflu.org

Forecasting

The future is here, it's just not evenly distributed yet — William Gibson

USA music industry, 2011 dollars per capita

Influenza population turnover

Vaccine strain selection timeline

Seek to explain change in clade frequencies over 1 year

Fitness models can project clade frequencies


Clade frequencies $X$ derive from the fitnesses $f$ and frequencies $x$ of constituent viruses, such that

$$\hat{X}_v(t+\Delta t) = \sum_{i:v} x_i(t) \, \mathrm{exp}(f_i \, \Delta t)$$

This captures clonal interference between competing lineages

Predictive fitness models


A simple predictive model estimates the fitness $f$ of virus $i$ as

$$\hat{f}_i = \beta^\mathrm{ep} \, f_i^\mathrm{ep} + \beta^\mathrm{ne} \, f_i^\mathrm{ne}$$

where $f_i^\mathrm{ep}$ measures cross-immunity via substitutions at epitope sites and $f_i^\mathrm{ep}$ measures mutational load via substitutions at non-epitope sites

We implement a similar model based on two predictors


  1. Clade frequency change
  2. Antigenic advancement

Project frequencies forward,
growing clades have high fitness

Calculate HI drop from ancestor,
drifted clades have high fitness

Fitness model parameterization


Our predictive model estimates the fitness $f$ of virus $i$ as


$$\hat{f}_i = \beta^\mathrm{freq} \, f_i^\mathrm{freq} + \beta^\mathrm{HI} \, f_i^\mathrm{HI}$$


We learn coefficients and validate model based on previous 15 H3N2 seasons

Clade growth rate is well correlated (ρ = 0.66)

Growth vs decline correct in 84% of cases

Trajectories show more detailed congruence

Formalizes intuition about drivers of influenza dynamics


Model Ep coefficient HI coefficient Freq error Growth corr
Epitope only 2.36 -- 0.10 0.57
HI only -- 2.05 0.08 0.63
Epitope + HI -0.11 2.15 0.08 0.67

Further work on predictive modeling


  1. Integrate data predictors and data sources, e.g. geography
  2. Possible to build predictive models for H1N1 and B and to forecast NA evolution

Phylogeny of H3 with geographic history

Geographic location of phylogeny trunk

Real-time analyses are actionable and thus, may inform influenza vaccine strain selection

Outbreak analysis

Ebola

Epidemic nearly contained, but resulted in >28,000 confirmed cases and >11,000 deaths

Outbreaks are independent spillovers from the animal reservoir

Person-to-person spread in the early West African outbreak

Continued spread through Dec 2014

At epidemic height, geographic spread of particular interest

Rambaut 2015

Later on, tracking transmission clusters of primary importance

Evolutionary analyses helped to establish the degree of adaptive evolution occurring

Selective patterns differ across genome

Phylogeographic analyses reveal detailed patterns of spatial movement

Dudas et al 2016

Animation by Gytis Dudas

Dudas et al 2016

Important analyses, let's make them more rapid and more automated

Tracking epidemic spread in real-time:

ebola.nextstrain.org

Zika

Virus endemic to Africa, emergence in Southeast Asia in the last century

Spread eastward through the South Pacific

Isolated epidemics in the South Pacific

Single arrival into the Americas in early 2014

Working on analysis of ongoing evolution:

nextstrain.org/zika/

Moving forward, genetically-informed outbreak response requires:


  • Rapid sharing of sequence data, genetic context critical
  • Technologies to rapidly conduct phylogenetic inference
  • Technologies to explore genetic relationships and inform epidemiological investigation

Future work

Acknowledgements


Influenza: WHO Global Influenza Surveillance Network, Worldwide Influenza Centre at the Francis Crick Institute, Richard Neher, Colin Russell, Andrew Rambaut

Ebola: data producers, Gytis Dudas, Andrew Rambaut, Philipe Lemey, Richard Neher, Nick Loman, Ian Goodfellow, Paul Kellam, Danny Park, Kristian Andersen, Pardis Sabeti

Zika: data producers, Nuno Faria, Andrew Rambaut, Richard Neher, Charlton Callender

 

Contact


  • Website: bedford.io
  • Twitter: @trvrb
  • Slides: bedford.io/talks/real-time-tracking-chicago/