Interpreting evolution of SARS-CoV-2 and other viruses

Jesse Bloom

Fred Hutch Cancer Center / HHMI

These slides at https://slides.com/jbloom/hhmi-meeting-2024

New variants (mutants) are always arising during viral evolution

Can we predict evolutionary success of viral variants from the biochemical effects of their constituent mutations?

SARS-CoV-2's spike binds receptor and mediates viral entry

viral membrane

cell membrane

spike

spike conformational change

Image adapted from here

ACE2

antibody

Image adapted from here

SARS-CoV-2's spike binds receptor and mediates viral entry

Deep mutational scanning to measure effects of mutations 

RBD

fluorescent ACE2

yeast

fluorescent tag on RBD

Yeast display of spike receptor-binding domain (RBD) to measure ACE2 binding

Starr et al (2020)

cell sorting

Deep mutational scanning: selections on libraries of mutants, read out by sequencing

Experiments identified N501Y as a mutation that increases ACE2 binding

Starr et al (2020)

RBD

fluorescently labeled antibody

yeast

fluorescent tag on RBD

Yeast display to measure how all RBD mutations escape antibody binding

Greaney et al (2021)

These anecdotes suggest experiments can prospectively identify mutations subsequently selected by evolution

Yeast display only measures binding, which is just part of spike's biological function

cell sorting

Viral infection is more biologically relevant measure of mutation effects

However, we are cognizant of biosafety concerns of mutating actual virus

actual SARS-CoV-2 virion: pathogen capable of spread in humans

pseudotyped lentiviral particle: not a pathogen, cannot spread in humans

actual SARS-CoV-2 virion: pathogen capable of spread in humans

pseudotyped lentiviral particle: not a pathogen, cannot spread in humans

Therefore we use pseudoviruses rather than actual replicative SARS-CoV-2

Pseudoviruses are modified viruses that can only undergo single round of infection

Crawford et al (2020)

Crawford et al (2020)

We need to link phenotype (spike mutant) and genotype (sequence encoded by virus)

To link genotype to phenotype link, we encode spike in viral genome with barcode

Dadonaite et al (2023)

We then create genotype-phenotype linked libraries by two-step process

Dadonaite et al (2023)

Result is library of spike mutant pseudoviruses with identifying barcodes

• Captures full cell-entry function of spike
• Each mutant identified by short barcode
• Pseudoviruses safe at biosafety-level 2
• Broadly applicable to viral entry proteins

Dadonaite et al (2023)

Dadonaite et al (2024)

Measured how spike mutations affect three molecular phenotypes

Dadonaite et al (2024)

Measured how spike mutations affect three molecular phenotypes

How do these experimental measurements relate to actual evolution?

Estimating variant fitness (multinomial logistic regression)

With Trevor Bedford & Ben Murrell

Deep mutational scanning measurements correlate with SARS-CoV-2 variant fitness

Dadonaite et al (2024)

Combining phenotypes offers best predictions (because mutations can involve tradeoffs)

Dadonaite et al (2024)

We can explain ~55% of the variance in growth of different clades, with largest fraction of variance uniquely explained by sera escape.

Dadonaite et al (2024)

Can partially predict outcome of real-world evolutionary competition from experiments!

Bloom lab

Bernadeta Dadonaite

Kate Crawford

Caelan Radford

Tyler Starr

Allie Greaney

Rachel Eguia

William Hannon

Jenny Ahn

Fred Hutch Cancer Center

Trevor Bedford

John Huddleston

University of Washington

Helen Chu and HAARVI cohort

Neil King

David Veesler

Slides: https://slides.com/jbloom/hhmi-meeting-2024

Thanks

Pirbright Institute

Thomas Peacock

University of Pennsylvania

Scott Hensley

Louise Moncla

Jordan Ort

St Jude Children's Hospital

Richard Webby