The Centers for Disease Control and Prevention recently recommended that everyone 6 months and older get an updated vaccination this fall against COVID-19 that is formulated to combat the most prevalent strains of the rapidly evolving virus that upended the world four years ago.
Vaccine upgrades rely on many sources of information about what the SARS-CoV-2 virus that causes the disease is doing in the human population. That data includes changes to its genetic makeup tracked in samples of the virus collected from more than 16 million patients around the world.
Sequencing so many versions of the virus so quickly has helped scientists monitor the emergence of new variants and identify thousands of new genetic mutations that may or may not improve the virus’ survival – vital information that could help us make more effective vaccines and annual boosters.
But finding out which of those mutations matters takes considerably more time using the usual experimental methods and safety protocols. By the time researchers pin down what one mutation does, another dozen may crop up.
Over the last few years, evolutionary biologist Jesse Bloom, PhD, and his team in the Basic Sciences Division at Fred Hutch Cancer Center have figured out a new way to speed that process, allowing them to safely run thousands of experiments simultaneously.
In a study published today in the journal Nature, Bloom and his colleagues describe how they used this method – called pseudovirus-based deep mutational scanning – to identify recent mutations to the infamous “spike” protein that have helped the virus dodge our vaccines and outrun our growing natural immunity.
The analysis partially explains why some variants have had more success in humans than others, providing clues about the next steps the virus will probably take to evade our defenses.
“We don’t think that we will ever be able to predict exactly what will happen because there is some inherent element of randomness, but we can certainly understand the paths that are likely to be taken,” Bloom said.
The method pioneered at the Bloom Lab combines two well-established techniques in biological research.
The first technique involves genetically engineering a pseudovirus that can infect a cell like the real thing, but only once. The pseudovirus cannot reproduce and spread like an actual virus, making it much safer for experimentation.
Work with pseudoviruses can be done in Biosafety Level Two laboratories, or BSL-2, unlike experiments with live viruses, which must be performed under the more restrictive and time-consuming conditions of a BSL-3 lab.
The second technique is called deep mutational scanning, which makes it possible to study the effects of as many as a million protein variants in a single experiment using high-volume gene sequencing.
The Bloom Lab has demonstrated previously how combining these techniques enables the study of effects of mutations on the “spike” of the SARS-CoV-2 virus, a key protein that enabled the virus to gain a foothold in humans and has undergone the most rapid evolution in response to our efforts to defeat it.
— Evolutionary biologist Dr. Jesse Bloom
For this study, Bloom’s team tested the effect of mutations on functions critical to the virus’ evolution, including the spike protein’s ability to grab onto an area on the surface of a human cell called ACE2 and bind to it.
They also tested mutations that could affect its ability to escape antibodies generated by vaccines or previous exposure to the virus, which often try to disrupt ACE2 binding to prevent infection.
To make the pseudovirus, they stripped a live virus commonly used in research and gene therapy down to its backbone, removing its ability to replicate while adding some features to streamline its use in a deep mutational scan.
Then, they made thousands of slightly different versions of the pseudovirus, genetically engineering each version to express a particular variant of the spike protein, allowing the team to measure the effects of 7,000 mutations in a single experiment.
To keep track of the mutations throughout the process, they tagged each variant with a unique genetic barcode. Think of it like the barcodes a library uses to keep track of its books as they are checked out, returned and re-shelved.
Then they grew stocks comprising millions of pseudovirus particles, each one loaded with its own barcode, to create a viral library.
They dripped this mutant brew onto little dishes containing human cells and antibodies sampled from people with varied immunization and infection histories.
Instead of exposing the cells to one mutation at a time, they exposed them to the whole library of 7,000 barcoded mutations in a single experiment.
The next day, they collected the infected cells from the dishes and sequenced their genomes to see which of those pseudovirus particles managed to escape the antibodies and infect the cells.
Then they crunched that huge dataset, using computer analysis to search for those barcodes to see which of the 7,000 mutations made a difference.
The team was able to directly measure which mutations matter for escaping antibodies and made a surprising discovery.
Many antibodies focus on trying to prevent the grabby part of the virus (the receptor-binding domain, or RBD) from grabbing onto the ACE2 area on the surface of a human cell.
As expected, many relevant mutations occur in or near the RBD to get around those antibodies.
But the team also found mutations well outside the RBD that mattered, too. The strongest of those distant mutations appears to move the RBD into an up or down position.
The RBD needs to be in the up position to grab ACE2 and bind to it, but when it’s up, it’s also vulnerable to attack. Moving the RBD down makes it easier for the virus to escape getting neutralized by antibodies, but that means it can’t bind to ACE2 and infect the cell.
“Some mutations that might be very good for receptor-binding might be very bad for neutralization, and vice versa,” said lead author Bernadeta Dadonaite, PhD, a staff scientist in the Bloom Lab. “Evolution figures out what is the exact balance of how much you need to be in the up position and how much you need to be in the down position.”
Somehow that balance of mutations moves the RBD like the periscope on a submarine, raising it for cell attack and lowering it for a stealthy escape.
This escape mechanism happens often enough that it should be monitored, according to the study.
“It’s always a cat and mouse game,” Dadonaite said. “We can try to do many things, but in the end, evolution will still push the virus to figure out how to escape whatever we do.”
Mutations like the RBD periscope that help their pseudovirus escape antibodies also appear in the most successful variants of SARS-CoV-2, partially explaining how they have thrived despite our growing immunity and multiple vaccinations.
The Bloom Lab takes that as an encouraging sign that its scanning method has matured enough to generate useful predictions about what the real virus will do next to stay ahead.
It could provide watch lists of potentially dangerous mutations for other researchers who are tracking the spread of SARS-CoV-2 and other viruses, such as computational biologist Trevor Bedford, PhD in the Vaccine and Infectious Disease Division at Fred Hutch, who is a co-author on the study.
“I don’t want to oversell it because it’s a complicated problem to predict how viruses are going to evolve,” Bloom said. “The idea is to have something that can help you interpret what’s happening as it’s happening.”
This work was supported by grants from the National Institutes of Health, a Pew Biomedical Scholars Award, an Investigators in the Pathogenesis of Infectious Disease Awards from the Burroughs Wellcome Fund, the University of Washington, Arnold and Mabel Beckman Center for Cryo-EM, shared resources of the Fred Hutch/University of Washington/Seattle Children’s Cancer Consortium, SciLifeLab's Pandemic Laboratory Preparedness programme and the Erling Persson Foundation.
John Higgins, a staff writer at Fred Hutch Cancer Center, was an education reporter at The Seattle Times and the Akron Beacon Journal. He was a Knight Science Journalism Fellow at MIT, where he studied the emerging science of teaching. Reach him at jhiggin2@fredhutch.org.
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