Pseudovirus deep mutational to forecast SARS-CoV-2 evolution

 

Jesse Bloom

Fred Hutch Cancer Center / HHMI

 

These slides at https://slides.com/jbloom/sars2dms

 

Most viral vaccines induce neutralizing antibodies to the viral entry protein

All viruses have one or more entry proteins that bind receptor and then fuse with the cell membrane:

  • SARS-CoV-2 spike
  • influenza hemagglutinin
  • HIV envelope protein
  • Lassa virus glycoprotein
  • Nipah virus G and F proteins
  • RSV G and F proteins

Mutations to viral entry proteins affect antigenicity and transmissibility

For SARS-CoV-2 spike, we know following molecular phenotypes are important:

  • Cell entry: if spike cannot mediate cell entry, virus will not be fit
  • ACE2 binding: if spike does not bind ACE2 strongly enough, virus will not be fit
  • Antibody escape: if spike is resistant to antibodies, virus will be more fit

 

Viruses constantly acquiring mutations that potentially affect these phenotypes

How can we rapidly and safely measure how mutations affect these phenotypes?

  • General: can work for many viral entry proteins
  • Comprehensive: measure mutations throughout spike, not just RBD
  • High-throughput: can be applied new variants and antibodies
  • Safe: we want to characterize mutations with making novel pathogenic viruses
  • Applicable directly to sera, to capture heterogeneity due to imprinting

Lentiviral pseudotyping

  • Many viruses have entry proteins amenable to lentiviral pseudotyping.
  • However, traditional pseudotyping does not create genotype-phenotype link.

Two-step method to create genotype-phenotype linked spike-pseudotypes

Two-step method to create genotype-phenotype linked spike-pseudotypes

Sequencing measures relative amounts, so include neutralization standard

Example: RBD antibody LY-CoV1404

Validates very well

Example: S2 antibody CC67.105

days before present

fraction of USA sequences

Estimates of clade growth from multinomial logistic regression

Experimentally measured spike phenotypes correlate with SARS-CoV-2 clade growth 

OLS regression combining spike phenotypes predicts clade growth quite well

We can measure how mutations affect key phenotypes that actually shape SARS-CoV-2 evolution

What could we hope to do next?

Develop a sustainable approach to keep generating these phenotypic data as the virus and population immunity evolve.

 

Share the data so they can be easily visualized and used by other scientists and variant trackers.

 

Partner with groups developing models to leverage these data for more sophisticated predictions and countermeasure development.

 

Overall, the last decade has seen tremendous advances in viral surveillance. What we need now is advances in interpreting the results of this surveillance.

Bloom lab

Bernadeta Dadonaite

Kate Crawford

Caelan Radford

 

University of Washington

David Veesler

 

Karolinska Institute

Ben Murrell

 

Penn

Raiees Andrabi

 

Scripps

Dennis Burton

 

Thanks

sars2dms

By Jesse Bloom

sars2dms

Pseudovirus deep mutational scanning to forecast SARS-CoV-2 evolution

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