A gene signature seen in antibody-producing cells in the blood of vaccinated study participants could expedite vaccine development.
October 31, 2022 - By Emily Moskal
Scientists developing the COVID-19 vaccines accelerated clinical trials, but a major holdup was waiting to see whether the vaccine protected the study participants. What if there was a way to predict a person’s vaccine-induced immunity?
By studying the immune responses of 820 adults to 13 vaccines, Stanford Medicine researchers found that the strongest predictor of immunity for many vaccines was a gene signature recognizable in blood cells that produce antibodies — plasmablasts — seven days post-vaccination.
With the finding of such a universal predictor of immunity from vaccination, scientists at Stanford Medicine are hoping to accelerate vaccine development by measuring the gene signature of plasmablasts in a person in the days following vaccination, rather than waiting weeks to see if that person’s immune system will respond appropriately.
“Our integrated analysis has revealed a common gene signature that predicts the strength of the antibody response to most vaccines,” said Bali Pulendran, PhD, a professor of microbiology and immunology. “This paves the way for the development of a ‘vaccine chip’ that can be used as an early screening strategy of future vaccine candidates, accelerating the timeline for research and development efforts.”
The paper was published online Oct. 31 in Nature Immunology. Pulendran is the senior author, and Thomas Hagan, PhD, a former postdoctoral researcher in Pulendran’s lab and now an assistant professor at the University of Cincinnati College of Medicine, is the lead author. Steve Kleinstein, PhD, a professor of pathology and immunology at Yale School of Medicine, is a senior co-author with Pulendran.
Clues on the search for a universal predictor
Pulendran has been mulling the idea of a universal immunity predictor since he wrote a 2008 paper examining the yellow fever vaccine, a vaccine with 97% efficacy, which is seen as a gold standard in understanding how a successful vaccine works, according to Pulendran, the Violetta L. Horton Professor II.
He and his colleagues studied cells, genes and proteins to see how they responded to the yellow fever vaccine. Using machine learning, the researchers found biomarkers, molecular signs that the vaccines were working, in the first week after vaccination that could predict immune antibody response at 30, 60 and 90 days.
The paper jump-started the field of systems vaccinology, a global look at the molecular landscape of vaccine response. Following the 2008 paper, researchers explored other vaccines, such as the flu, malaria, and meningococcal and pneumococcal vaccines, producing a 2021 study also led by Pulendran detailing the Pfizer-BioNTech mRNA COVID-19 vaccine. They identified the molecular machinery each vaccine initiated, but the research left open the question of whether there was a universal predictor.
To find the common marker in the current study, researchers created an “atlas of immunity to vaccination,” a compilation of the previously published genomic readouts of the molecular material, including plasmablast genes, produced once someone’s immune system is activated by a particular vaccine. Pulendran said it’s the most comprehensive dataset of vaccine responses gathered.
“This molecular atlas of immunity to vaccination serves as a kind of immune fingerprint registry to cross-reference for any future vaccines that scientists will develop to anticipate the immunity of patients who receive the vaccine,” said Pulendran.
When people are vaccinated, their immune response follows a classic roll-out: The foreign object (antigen) activates the white blood cells that make antibodies and attack invaders (B cells) as well as cells that destroy a body’s traitors (T cells), which then can transform into memory cells, before fading away once the immune system disarms the invader.
Finding a commonality among study participants’ vaccines is possible because our immune system responses tend to vary, if only slightly, from one vaccine to another. Our innate immune reaction is ancient and straightforward: Barriers, such as skin, that prevent foreign objects from entering our bodies are common all the way from “sea slugs to stockbrokers,” Pulendran said. But it takes about seven days for our adaptive immune response — the one we’re more familiar with, which includes the antibodies and memories of pathogens gone bad — to kick in.
In their study, the scientists found that key elements of the participants’ vaccine-induced immune cascade were similar in all vaccines except for one: yellow fever. The yellow fever immune response followed the same pathway as other vaccine responses but had a delay in the time that plasmablasts were deployed. Pulendran said that is likely because it is a live vaccine that builds a slow progression of immune response, the way a symphony progresses by adding an instrument at a time following the conductor’s baton raise.
Once the scientists adjusted for time, the algorithm revealed the predictor of a person’s response to virtually any vaccine: the plasmablast signature, specifically M156.1, a module or specific set of genes expressed in plasmablasts.
Vaccine chip on the horizon
In their study, Pulendran and colleagues proposed a “vaccine chip” that would use a PCR test — what lab technicians use to find COVID-19 virus genetic material — to measure a set of genes that predict the outcome of a vaccine response by studying which genes are activated and therefore which immune products are present in the body. The study suggests that if your plasmablast M156.1 gene readout reaches a threshold level, you’re more likely to be protected from the targeted pathogen.
Scientists could incorporate the chip into clinical trials to speed up their evaluation of candidate vaccines. Clinicians may also be able to use the chip to predict an immunocompromised person’s response to a vaccine to see if they need a booster sooner than most.
Although the study found a threshold of immunity, at the peak of the antibody response, among participants, it doesn’t suggest how long the immunity will last. Pulendran and his colleagues are now focusing on finding a universal predictor of durability to understand how long we can go until we need booster shots of new vaccines.
“We’re at an exciting new time in this field of systems vaccinology, with the prospect of customizing vaccines to the recipient based on these molecular signatures,” Pulendran said. “The COVID-19 pandemic has underscored the global imperative to be ready to fast track the deployment of multiple vaccine candidates when the next pandemic emerges. Here’s a big step in the right direction.”
Pulendran is a member of Stanford Bio-X.
Researchers from the Icahn School of Medicine at Mount Sinai, Emory University School of Medicine, the National Institutes of Health’s NIAID Human Immunology Project Consortium, Cambridge University, UC San Francisco, Fred Hutchinson Cancer Research Center, Boston Children’s Hospital, Harvard Medical School, NG Health Solutions, Broad Institute of MIT and Harvard, University of Lausanne, Lausanne University Hospital, and the Swiss Institute of Bioinformatics also contributed to the work.
The study was funded in part by the National Institutes of Health (grants U19AI118608, U19AI128949, U19AI090023, U19AI118626, U19AI089992, U19AI128914, U19AI128910, U19AI118610, U19AI128913, R37 DK057665, R01 AI048638, U19 AI057266 and U19 AI167903); the Department of Pediatrics at Boston Children’s Hospital; the Bill and Melinda Gates Foundation; Open Philanthropy; and the Violetta L. Horton, and Soffer and Open Philanthropy Endowments.
About Stanford Medicine
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