Viruses
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 $\sim 10^{31}$
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
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 hiv.tuebingen.mpg.de
Zanini et al, eLife, 2015; antibody data from Richman et al, 2003
Evolution in different parts of the genome
- envelope changes fastest, enzymes lowest
- identical rate of synonymous evolution
- diversity saturates where evolution is fast
- synonymous mutations stay at low frequency
Zanini et al, eLife, 2015
Theoretical framework for virus evolution -- population genetics
evolutionary processes ↔ trees ↔ genetic diversity
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?
Human seasonal influenza viruses
slide by Trevor Bedford
- Influenza virus evolves to avoid human immunity
- Vaccines need frequent updates
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
Summary
- 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
nextstrain.org
- Trevor Bedford
- Colin Megill
- Pavel Sagulenko
- Sidney Bell
- James Hadfield
- Wei Ding