Tracking and predicting the evolution RNA viruses

Richard Neher
Biozentrum, University of Basel

slides at

RNA viruses are primary pandemic threats

  • influenza virus (spanish flu 1918, "swine flu" 2009, H5N1, ...)
  • SARS (Severe Acute Respiratory Syndrome, coronavirus)
  • MERS (Middle East respiratory syndrome, coronavirus)
  • Ebola (filovirus)
  • Zika virus (flavivirus)
  • ...

Human seasonal influenza viruses

slide by Trevor Bedford

Surveillance of human seasonal influenza viruses

  • WHO CCs and NICs sequence and phenotype 100s of viruses per month
  • Sequences allow us to track how the virus is spreading and changing
images by Trevor Bedford

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

joint work with Trevor Bedford & his lab

joint work with Trevor Bedford & his lab

NextStrain architecture

Using treetime to rapidly compute timetrees

Modeling and predicting influenza evolution

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

RN, Russell, Shraiman, eLife, 2014

Prediction of the dominating H3N2 influenza strain

RN, Russell, Shraiman, eLife, 2014

Virus evolution takes place within the host

Deep longitudinal sampling is necessary to monitor evolution in detail

Evolution of HIV

  • Chimp → human transmission ~1900 gave rise to HIV-1 group M
  • Diversified into subtypes that are ~20% different
  • evolves at a rate of about 0.1% per year
image: Sharp and Hahn, CSH Persp. Med.

HIV infection

chronic infection:
  • $10^8$ cells are infected every day
  • the virus repeatedly escapes immune recognition
  • integrates into T-cell as latent provirus
image: wikipedia

HIV-1 sequencing before and after therapy

Zanini et al, eLife, 2015; Brodin et al, eLife, 2016

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

Diversity and mutation rates

  • 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

Frequent version of previously beneficial mutations

  • HIV escapes immune systems
  • most mutations are costly
  • humans selects for different mutations
  • compensation or reversion?
Zanini et al, eLife, 2015

Fitness landscape of HIV-1

Zanini et al, Virus Evolution, 2017

Does HIV evolve during therapy?

Brodin et al, eLife, 2016

No evidence of ongoing replication

  • HIV-1 RNA from plasma before treatment started
  • HIV-1 DNA gag-p17 from PBMCs after many years of treatment
Brodin et al, eLife, 2016

No evidence of ongoing replication

Brodin et al, eLife, 2016

T-cell turnover is fast in untreated infection

  • latent HIV → barcode of a T-cell lineage
  • all latent integrated virus derives from late infection
  • untreated: T-cell lineages are short lived
  • on therapy: T-cell clones live decades
Brodin et al, eLife, 2016

Sharing of viral NGS data is challenging!

  • Short reads need to be filtered, mapped, assembled, ...
  • Metadata is critical (template input, CD4 counts, etc)
  • Processed data much more useful than raw reads

Horizontal transfer, pangenomes, and bacterial diversity

  • bacterial phylogenetics is more complicated!
  • much bigger data sets
  • horizontal transfer
  • messy assemblies

panX at

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 team

  • Colin Megill
  • Trevor Bedford
  • James Hadfield
  • Sidney Bell

the group

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