Population genetics of rapid adaptation and influenza virus evolution

Richard Neher
Biozentrum & SIB, University of Basel

slides at neherlab.org/202101_.html

Human seasonal influenza viruses

slide by Trevor Bedford

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

Influenza B viruses have split into two lineages

Le Yan, RN, Shraiman, bioRxiv, 2018

Fitness variation in rapidly adapting populations

  • Speed of adaptation is logarithmic in population size
  • Environment (fitness landscape), not mutation supply, determines adaptation
  • Different models have universal emerging properties
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; Desai, Walczak, Fisher, Genetics, 2013

Burst in the tree ↔ high fitness

Simple heuristic: Local branching index

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

Limits of predictability

Barrat-Charlaix et al, 2020

Fixation probability

A/H3N2 influenza
Simulations with increasing interference
Barrat-Charlaix et al, 2020

Diversity in immune responses

Lee et al, 2019

A/H3N2 influenza doesn't quite fit

  • Strong signal of positive selection
  • Rapid clade displacement, interference
  • Any given escape only allows to escape a fraction of the population
    → expiration of selection long before a mutation is common
  • The more diverse the immune landscape, the more neutral it looks
  • Still, evolutionary rate at antigenic sites very high

Influenza and Theory acknowledgments

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

  • All the NICs and WHO CCs that provide influenza sequence data
  • The WHO CCs in London and Atlanta for providing titer data


Trevor Bedford and his lab -- terrific collaboration since 2014

especially James Hadfield, Emma Hodcroft, Ivan Aksamentov, Moira Zuber, and Tom Sibley

Data we analyze are contributed by scientists from all over the world

Data are shared and curated by GISAID

Why do predictions work

Barrat-Charlaix et al, 2020