Virus evolution and the spread of infectious disease

Richard Neher
Biozentrum, University of Basel

slides at

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

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

Vaccine selection time line

Slide by Trevor Bedford

Tracking virus spread and evolution by sequencing




... thousands of sequences...

Phylogenetic analysis of viral sequences

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

joint work with Trevor Bedford & his lab

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

Antigenic evolution

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-SIV phylogeny

What makes these viruses so good at evolving?

High mutation rates

Large population sizes

  • Within a chronically infected person, HIV infects $10^8$ cells a day
  • Influenza viruses infects $10^8$ humans a year
  • every infected cell produces 1000s of virions
  • → every possible mutation is produced many times every day


Influenza Virus

HIV-1 evolution within one individual

  • Viruses rapidly diversify
    swarm or quasi-species
  • Different tissues can harbor different viral variants
  • diversity depends on duration of infection
  • number of founder variants
  • immune selection

Drug resistance evolution

Drug targets
  • Reverse transcriptase (NRTI and NNRTI)
  • Protease inhibitors
  • Integrase inhibitors
  • Entry inhibitors
Resistance evolution
  • Every mutations pre-exists → mono-therapy fails
  • Combination therapy requires multiple mutations to become resistant
  • Sufficiently high 'barrier to resistance' results in long-term suppressive therapy
Hedgkog et al, PLOS One, 2010

Sequence evolution vs phenotypic evolution

  • RNA viruses change rapidly under consistent selection pressure
  • Rapid immune escape or drug resistance in HIV or influenza
  • BUT: most mutations are deleterious
  • AND: most observed changes have no clear phenotype

Case in point: "the microcephaly mutation" in Zika

  • Weak evidence that a particular mutation facilitates spread of Zika virus in mouse brains
  • Media trumpets: "microcephaly causing mutation" found
  • Zika virus strains in India lack this mutations
    → authorities claim: Zika not a problem
  • see Open-Ed by Nathan Grubaugh for a good discussion

Development of sequencing technologies

We can now sequence...
  • thousands of bacterial isolates
  • thousands of single cells
  • populations of pathogens, metagenomics
Sequences allow us to reconstruct at great detail how viruses change and spread
But the link between genotype and phenotype is far from being understood.