Tracking and predicting evolution and spread of RNA viruses


Richard Neher
Biozentrum, University of Basel


slides at neherlab.org/201709_USB_DoKo.html

Evolution of RNA viruses

HI virus, source wikipedia
  • Constant struggle to adapt to changing environments and host immunity
  • Mutation rates of about 0.00001/site and replication
  • Large viral populations explore every single mutation every day
  • Most mutations are detrimental, but some persist
  • Mutations can act like an approximate clock
Phylogenetic analysis
Phylogenetic analysis

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

HIV-1 sequencing before and after therapy

Zanini et al, eLife, 2015; Brodin et al, eLife, 2016. Collaboration with the group of Jan Albert


Population sequencing to track all mutations above 1%

  • diverge at 0.1-1% per year
  • almost full genomes coverage in 10 patients
  • full data set at hiv.tuebingen.mpg.de
Zanini et al, eLife, 2015

Diversity and rates of change

  • envelope changes fastest, enzymes slowest
  • identical rate of synonymous evolution
  • diversity saturates where evolution is fast
  • synonymous mutations stay at low frequency
Zanini et al, eLife, 2015

Does HIV evolve during therapy?

Brodin et al, eLife, 2016

No evidence of ongoing replication

Brodin et al, eLife, 2016

No evidence of ongoing replication

Brodin et al, eLife, 2016

Sequences record the spread of pathogens

The resolution is limited by the number of mutations!
images by Trevor Bedford

Human seasonal influenza viruses

slide by Trevor Bedford


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

nextflu.org

joint work with Trevor Bedford & his lab

based on sequences and phenotype provided by the WHO CCs and NICs

Beyond tracking: can we predict?

Competition between different viral variants

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

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

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

nextstrain.org

  • integrate data from many different sources
  • analyze those data in near real time
  • disseminate results in an intuitive yet informative way
  • provide actionable insights

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

nextstrain.org

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