What was reason for thinking coronavirus immunity would not be affected by viral mutations?

Coronaviruses have lower mutation rate than other RNA viruses

Coronaviruses are only RNA viruses with proofreading activity in their polymerase, and so have ~5- to 10-fold lower mutation rate than influenza virus

The average single-nucleotide mutation to SARS-CoV-2 had occurred >10,000 independent times in human-transmitted SARS-CoV-2 by third year of the pandemic.

But evolution of SARS-CoV-2 (and other RNA viruses) is not mutation-limited

Evolution is not just mutation: also involves selection on phenotypic effects of mutations

Evolution of CoV-229E spike

We experimentally generated CoV-229E spikes at ~8 year intervals to study in the lab:

- 1984

- 1992

- 2001

- 2008

- 2016

Eguia et al (2021)

Evolution of CoV-229E spike erodes neutralization by human serum antibodies

Eguia et al (2021)

Ideally vaccines would elicit evolution-resistant antibodies (like those made by person at right) rather than evolution-sensitive antibodies (like those made by person at left)

Evolution erodes neutralization by sera from different people at different rates

Eguia et al (2021)

We now know SARS-CoV-2 evolves to erode neutralizing antibodies, just like CoV-229E

Original vaccine induced high neutralizing antibody titers against early viral strains

Lasrado et al (2023)

We now know SARS-CoV-2 evolves to erode neutralizing antibodies, just like CoV-229E

Lasrado et al (2023)

newer viral variants

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

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)

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, 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

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!

Initially, most people were exposed to early strain, imprinting antibody response (     )

First exposed to early strain in original vaccine

First infected by an early strain (pre-Omicron)

Imprinting to early strain continues to shape antibody response after later exposures

But antibody response of infants is imprinted by more recent strain

Imprinted by recent strain

We measured which spike mutations erode neutralization by antibodies from people imprinted with different strains

Adults vaccinated with original strain followed by various exposures

Dadonaite et al (2025); adult sera from Helen Chu's HAARVI study; infant sera from Mary Staat's IMPRINT cohort

6-12 month infants first infected by XBB*

Adult sera have similar escape (mostly in RBD)

site in spike

Sites of escape from sera of adults imprinted with original vaccine, then exposed to various infections and vaccinations.

adult 1

adult 2

adult 3

adult 4

adult 5

adult 6

2021

Dadonaite et al (2025)

site in spike

Sites of escape from infants with only single infection with XBB*

infant 1

infant 2

infant 3

infant 4

infant 5

infant 6

2023

Infant sera have similar escape (mostly in NTD)

Dadonaite et al (2025)

To examine role of population immune heterogeneity, I will switch focus to human seasonal influenza

Human influenza evolves similarly to SARS-CoV-2, but has circulated in humans for decades, so imprinting and population immunity) are highly heterogeneous.

As described on the next few slides, we developed a new method to characterize immune heterogeneity for influenza by efficiently measuring neutralization of >50 viral strains by ~100 human sera.

Assays measure just one virus against one serum in each row (or column) of a 96-well plate

Loes et al (2024)

Loes et al (2024)

Loes et al (2024)

Loes et al (2024)

Full viral library assayed in each row of plate

Loes et al (2024)

Full viral library assayed in each row of plate

Loes et al (2024)

Full viral library assayed in each row of plate

Loes et al (2024)

Neutralization of all the different viruses is read out simultaneously be sequencing

Loes et al (2024)

Neutralization of all the different viruses is read out simultaneously be sequencing

Loes et al (2024)

Neutralization of all the different viruses is read out simultaneously be sequencing

Loes et al (2024)

Neutralization of all the different viruses is read out simultaneously be sequencing

Loes et al (2024)

Neutralization of all the different viruses is read out simultaneously be sequencing

Loes et al (2024)

Neutralization of all the different viruses is read out simultaneously be sequencing

different recent H3N2 viral strains

Each row of plate measures titers to many viruses. Below are titers for one child serum.

this child has low titers to these viral strains

Kikawa et al (2025)

Person-to-person variation in per-strain titers

Kikawa et al (2025)

Heterogeneity within and between age groups

Kikawa et al (2025)

We measured neutralization for a large set of sera in two ways

sera from 95 individuals of different ages

Measured neutralization by each individual serum

Kikawa et al (2025)

sera from 95 individuals of different ages

Measured neutralization by pool of all sera

We measured neutralization for a large set of sera in two ways

Kikawa et al (2025)

But viral growth rates do not correlate with titers of a pool of all serum

Kikawa et al (2025)

Conclusions

Some viruses evolve rapidly to erode antibody immunity.

We can use deep mutational scanning to measure how mutations affect the function and antibody neutralization of viral proteins.

These measurements help explain success of viral variants in real world.

However, imprinting means there is lots of person-to-person variation in how antibodies target viruses, and hence how viral mutations erode immunity.

Sequencing-based neutralization assays can measure that heterogeneity at scale, and improve our understanding of how immunity drives viral evolution.

Bloom lab

These slides: https://slides.com/jbloom/fda-2025

Thanks

Fred Hutch Cancer Center

Trevor Bedford

University of Washington

Helen Chu and HAARVI cohort

Neil King

David Veesler

Cincinnati Children's Hospital

Mary Staat

David Haslam

Allie Burrell

University of Pennsylvania

Scott Hensley

Seattle Children's Hospital

Janet Englund

Also: Kate Crawford, William Hannon, Caelan Radford

Caroline Kikawa

Andrea Loes