One of the 12 labors of Hercules, according to ancient tradition, was to destroy a nine-headed monster called the Hydra. The challenge was that when Hercules used his sword to cut off one of the monster’s heads, two would grow back in its place. Therefore, he needed an additional weapon, a torch, to defeat his enemy.
There are parallels between this legend and our three-year and counting battle against SARS-Cov-2, the virus that causes Covid-19. Every time scientists thought they had subdued one strain of the virus, be it alpha, beta, delta, or omicron, another variant or subvariant emerged a short time later.
For this reason, researchers at MIT and other institutions are preparing a new strategy against the virus: a novel vaccine that, unlike those used today, could counteract all variants of the disease, with a property called ” panvariance” that could avoid the need for a different booster injection each time a new strain enters circulation. in a paper published today in the magazine Frontiers in Immunologythe team reports on experiments with mice demonstrating the efficacy of the vaccine in preventing death from Covid-19 infection.
Viral vaccines generally work by exposing the immune system to a small part of the virus. That can create learned responses that protect people later when they are exposed to the real virus. The premise of standard covid-19 vaccines, such as those produced by Moderna and Pfizer, is to activate the part of the immune system that releases neutralizing antibodies. They do this by giving cells instructions (in the form of mRNA molecules) to make the spike protein, a protein found on the surface of the Covid-19 virus whose presence can trigger an immune reaction. “The problem with that approach is that the target keeps changing” (the spike protein itself can vary between different viral strains) “and that can render the vaccine ineffective,” says David Gifford, a professor of electrical and computer engineering and MIT biology. engineering, as well as co-author of the borders paper.
He and his colleagues have therefore taken a different approach, selecting a different target for their vaccine: activating the part of the immune system that releases “killer” T cells, which attack cells infected with the virus. Such a vaccine will not prevent people from getting covid-19, but it could prevent them from becoming seriously ill or dying.
A key innovation made by this group, which included researchers from MIT, the University of Texas, Boston University, Tufts University, Massachusetts General Hospital, and Acuitas Therapeutics, was to incorporate machine learning techniques into the design process of vaccines. A critical aspect of that process involves determining which parts of SARS-Cov-2, which peptides (chains of amino acids that are the building blocks of proteins), should be included in the vaccine. That means looking at thousands of peptides in the virus and selecting only about 30 that should be incorporated.
But that decision must take into account so-called HLA molecules, protein fragments on the surface of cells that serve as “billboards” that tell immune cells (which lack X-ray vision) what’s going on inside. other cells. The visualization of specific protein fragments can indicate, for example, that a certain cell is infected with SARS-Cov-2 and must be eliminated.
Machine learning algorithms were used to solve a complicated set of “optimization problems,” says Brandon Carter, a doctoral student in MIT’s Department of Electrical Engineering and Computer Science, affiliated with the Computer Science and Intelligence Laboratory. Artificial Intelligence at MIT (CSAIL), and a lead author of the new paper. The main goal is to select peptides that are present or “conserved” in all variants of the virus. But those peptides must also associate with HLA molecules that have a high probability of showing up so they can alert the immune system. “You want this to happen in as many people as possible to get maximum population coverage from your vaccine,” says Carter. In addition, he wants each individual to be covered multiple times by the vaccine, he adds. “This means that more than one peptide in the vaccine is predicted to show up in some HLAs in each person.” Achieving these various goals is a task that can be significantly sped up with machine learning tools.
While that touches on the theoretical end of this project, the latest results come from experiments by collaborators at the University of Texas Medical Branch at Galveston, who showed a strong immune response in mice that received the vaccine. The mice in this experiment did not die, but were “humanized,” meaning they had an HLA molecule found in human cells. “This study,” Carter says, “provides proof in a living system, a real mouse, that the vaccines we designed using machine learning can provide protection against the Covid virus.” Gifford characterizes his work as “the first experimental evidence that a vaccine formulated in this way would be effective.”
Paul Offit, a professor of pediatrics in the Division of Infectious Diseases at the Children’s Hospital of Philadelphia, calls the results encouraging. “Many people wonder what approaches will be used to make vaccines against covid-19 in the future,” says Offit. cellular responses will be an important step forward in the next generation of vaccines.”
More animal studies, and eventual human studies, would have to be done before this work could usher in the “next generation of vaccines.” The fact that 24 percent of the lung cells in the vaccinated mice were T cells, Gifford says, “showed that their immune systems were primed to fight the viral infection.” But care must be taken to avoid too strong an immune response, he cautions, so as not to cause lung damage.
Other questions abound. Should T-cell vaccines be used instead of, or in combination with, standard spike protein vaccines? While it is possible to improve existing vaccines by including a T-cell component, Gifford says, “putting two things together may not be strictly additive, as one part of the vaccine could mask the other.”
However, he and his colleagues believe their T-cell vaccine has the potential to help immunosuppressed people who cannot produce neutralizing antibodies and therefore may not benefit from traditional Covid vaccines. His vaccine may also ease the suffering of “long covid” in people who continue to harbor reservoirs of the virus long after their initial infection.
The mechanism behind current flu vaccines, like current covid-19 vaccines, is to induce neutralizing antibodies, but those vaccines don’t always work for different strains of flu. Carter sees potential for flu vaccines based on a T-cell response, “which may be more effective and provide broader coverage, due to their high variation.”
The methods they are developing are not limited to covid-19 or the flu either, he maintains, as they could one day be applied to cancer. Gifford agrees, saying that a T-cell vaccine, designed to maximize immune protection both within an individual and among the largest number of individuals, could become a key asset in the fight against cancer. “That’s not within the scope of our current study,” she says, “but could be the subject of future work.”
Other MIT contributors to the work included Ge Liu and Alexander Dimitrakakis. The work was supported, in part, by Schmidt Futures and a grant from the C3.ai Digital Transformation Institute to David Gifford.
