Scientists Attempt to Make Human Cells Resistant to Coronaviruses and Ebola
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.
Under the electronic microscope, the Ebola particles looked like tiny round bubbles floating inside human cells. Except these Ebola particles couldn't get free from their confinement.
They were trapped inside their bubbles, unable to release their RNA into the human cells to start replicating. These cells stopped the Ebola infection. And they did it on their own, without any medications, albeit in a petri dish of immunologist Adam Lacy-Hulbert. He studies how cells fight infections at the Benaroya Research Institute in Seattle, Washington.
These weren't just any ordinary human cells. They had a specific gene turned on—namely CD74, which typically wouldn't be on. Lacy-Hulbert's team was experimenting with turning various genes on and off to see what made cells fight viral infections better. One particular form of the CD74 gene did the trick. Normally, the Ebola particles would use the cells' own proteases—enzymes that are often called "molecular scissors" because they slice proteins—to cut the bubbles open. But CD74 produced a protein that blocked the scissors from cutting the bubbles, leaving Ebola trapped.
"When that gene turns on, it makes the protein that interferes with Ebola replication," Lacy-Hulbert says. "The protein binds to those molecular scissors and stops them from working." Even better, the protein interfered with coronaviruses too, including SARS-CoV-2, as the team published in the journal Science.
This begs the question: If one can turn on cells' viral resistance in a lab, can this be done in a human body so we that we can better fight Ebola, coronaviruses and other viral scourges?
Recent research indeed shows that our ability to fight viral infections is written in our genes. Genetic variability is at least one reason why some coronavirus-infected people don't develop symptoms while others stay on ventilators for weeks—often due to the aberrant response of their immune system, which went on overdrive to kill the pathogen. But if cells activate certain genes early in the infection, they might successfully stop viruses from replicating before the immune system spirals out of control.
"If my father who is 70 years old tests positive, I would recommend he takes interferon as early as possible."
When we talk about fighting infections, we tend to think in terms of highly specialized immune system cells—B-cells that release antibodies and T-cells that stimulate inflammatory responses, says Lacy-Hulbert. But all other cells in the body have the ability to fight infections too via different means. When cells detect the presence of a pathogen, they release interferons—small protein molecules named so because they set off a genetic chain reaction that interferes with viral replication. These molecules work as alarm signals to other cells around them. The neighboring cells transduce these signals inside themselves and turn on genes responsible for cellular defenses.
"There are at least 300 to 400 genes that are stimulated by type I interferons," says professor Jean-Laurent Casanova at Rockefeller University.
Scientists don't yet know exactly what all of these genes do, but they change the molecular behavior of the cells. "The cells go into a dramatic change and start producing hundreds of proteins that interfere with viral replication on the inside," explains Qian Zhang, a researcher at Casanova's lab. "Some block the proteins the virus needs and some physically tether the virus."
Some cells produce only small amount of interferon, enough to alert their neighbors. Others, such microphages and monocytes, whose jobs are to detect foreign invaders, produce a lot, injecting interferons into the blood to sound the alarm throughout the body. "They are professional cells so their jobs [are] to detect a viral or bacterial infection," Zhang explains.
People with impaired interferon responses are more vulnerable to infections, including influenza and coronaviruses. In two recent studies published in the journal Science, Casanova, Zhang and their colleagues found that patients who lacked a certain type of interferon had more severe Covid-19 symptoms and some died from it. The team ran a genetic comparison of blood samples from patients hospitalized with severe coronavirus cases against those with the asymptomatic infections.
They found that people with severe disease had rare variants in the 13 genes responsible for interferon production. More than three percent of them had a genetic mutation resulting in non-functioning genes. And over ten percent had an autoimmune condition, in which misguided antibodies neutralized their interferons, dampening their bodies' defenses—and these patients were predominantly men. These discoveries help explain why some young and seemingly healthy individuals require life support, while others have mild symptoms or none. The findings also offer ways of stimulating cellular resistance.
A New Frontier in the Making
The idea of making human cells genetically resistant to infections—and possibly other stressors like cancer or aging—has been considered before. It is the concept behind the Genome Project-write or GP-write project, which aims to create "ultra-safe" versions of human cells that resist a variety of pathogens by way of "recoding" or rewriting the cells' genes.
To build proteins, cells use combinations of three DNA bases called codons to represent amino acids—the proteins' building blocks. But biologists find that many of the codons are redundant so if they were removed from all genes, the human cells would still make all their proteins. However, the viruses, whose genes would still include these eliminated redundant codons, would no longer successfully be able to replicate inside human cells.
In 2016, the GP-Write team successfully reduced the number of Escherichia coli's codons from 64 to 57. Recoding genes in all human cells would be harder, but some recoded cells may be transplanted into the body, says Harvard Medical School geneticist George Church, the GP-Write core founding member.
"You can recode a subset of the body, such as all of your blood," he says. "You can also grow an organ inside a recoded pig and transplant it."
Church adds that these methods are still in stages that are too early to help us with this pandemic.
LeapsMag exclusively interviewed Church in 2019 about his latest progress with DNA recoding:
The Push for Clinical Trials
In the meantime, interferons may prove an easier medicine. Lacy-Hulbert thinks that interferon gamma might play a role in activating the CD74 gene, which gums up the molecular scissors. There also may be other ways to activate that gene. "So we are now thinking, can we develop a drug that mimics that actual activity?" he says.
Some interferons are already manufactured and used for treating certain diseases, including multiple sclerosis. Theoretically, nothing prevents doctors from prescribing interferons to Covid patients, but it must be done in the early stages of infection—to stimulate genes that trigger cellular defenses before the virus invades too many cells and before the immune systems mobilizes its big guns.
"If my father who is 70 years old tests positive, I would recommend he takes interferon as early as possible," says Zhang. But to make it a mainstream practice, doctors need clear prescription guidelines. "What would really help doctors make these decisions is clinical trials," says Casanova, so that such guidelines can be established. "We are now starting to push for clinical trials," he adds.
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.