Interpreting viral evolution

Jesse Bloom

Fred Hutch Cancer Center / HHMI

These slides at https://slides.com/jbloom/vaxco-2025

Can we predict evolutionary success of new 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

Deep mutational scanning to measure effects of mutations 

Deep mutational scanning to measure effects of mutations 

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

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!

But human population in which SARS-CoV-2 evolves is changing...

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)

How does it affect evolution if viral mutations only affect immunity of some individuals in the population?

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

Loes et al (2024)

Overall we generate triplicate neutralization curves like one below for 50-100 different viral strains in each row of the 96-well plate

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

Person-to-person variation in per-strain titers

Heterogeneity within and between age groups

How do these heterogeneous neutralization titers for different humans shape viral evolution?

Unpublished data available here

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

Unpublished data available here

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

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

Population immune heterogeneity is important for viral evolution.

What matters is fraction of population susceptible to each viral variant.

Experimental approaches I have described are applicable to many viral entry proteins

• influenza H5 hemagglutinin (Dadonaite et al, 2024)
• HIV envelope protein (Radford et al, 2023)
• Lassa virus glycoprotein (Carr et al, 2024)
• Nipah virus RBP and F proteins (Larsen et al, 2024)
• RSV G and F proteins
• Rabies G protein (Aditham et al, 2024)
• Chikungunya virus E proteins

Pseudovirus of clade 2.3.4.4b H5 HA

Dadonaite et al, 2024

HA molecular phenotypes relevant to pandemic risk

Dadonaite et al, 2024

HA molecular phenotypes relevant to pandemic risk

Dadonaite et al, 2024

HA molecular phenotypes relevant to pandemic risk

Dadonaite et al, 2024

HA molecular phenotypes relevant to pandemic risk

Dadonaite et al, 2024

HA molecular phenotypes relevant to pandemic risk

Dadonaite et al, 2024

These data can inform surveillance of H5 influenza viruses spreading in animals

We prospectively identified A160T as strongly reducing neutralization by sera elicited by candidate vaccine strains

(L122Q, P162Q, T199I)

Dadonaite et al, 2024

A160T in recent human case in Missouri

Bloom lab

These slides: https://slides.com/jbloom/vaxco-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