Virus evolution and the spread of infectious disease

Richard Neher
Biozentrum, University of Basel

slides at


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)
  • most abundant organisms on earth $\sim 10^{31}$

Lifecycle of animal viruses

By GrahamColm at English Wikipedia

Development of sequencing technologies

We can now sequence...
  • thousands of bacterial isolates
  • thousands of single cells
  • populations of viruses, bacteria or flies
  • diverse ecosystems

Virus genomes change rapidly through time




... hundreds of thousands of sequences...

Phylogenetic analysis of viral sequences

RNA viruses have a high mutation rate. New mutations arise every few weeks.

Some viruses evolve a million times faster than animals

Animal haemoglobin

HIV protein

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

Immune escape in early HIV infection

Immune escape in early HIV infection

Selective sweeps

  • Viruses carrying a beneficial mutation have more offspring: on average $1+s$ instead of $1$
  • $s$ is called selection coefficient
  • Fraction $x$ of viruses carrying the mutation changes as $$x(t+1) = \frac{(1+s)x(t)}{(1+s)x(t) + (1-x(t))}$$
  • In continuous time → logistic differential equation: $$\frac{dx}{dt} = sx(1-x) \Rightarrow x(t) = \frac{e^{s(t-t_0)}}{1+ e^{s(t-t_0)}}$$

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
Zanini et al, eLife, 2015; antibody data from Richman et al, 2003

Human Influenza A viruses

slide by Trevor Bedford

Weekly numbers of positive influenza tests in the US by subtype

Data by the US CDC

Influenza virus

  • Surface proteins hemagglutinin (HA) and neuraminidase (NA)
  • Influenza A virus
    • Common in birds and mammals
    • Many different subtypes defined by surface proteins
    • H3N2, H1N1, H7N9, H5N1
  • Influenza B virus
    • infects mainly humans
    • two lineages that split 30-40y ago
    • B/Victoria vs B/Yamagata

joint work with Trevor Bedford & his lab

joint work with Trevor Bedford & his lab

Clonal interference and traveling waves

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

Bursts in a tree ↔ high fitness genotypes

Can we read fitness of a tree?

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

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


  • 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

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


Trevor Bedford and his lab -- terrific collaboration since 2014

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

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

Data are shared and curated by GISAID