In November 2020, messenger RNA catapulted into the public consciousness when the first COVID-19 vaccines were authorized for emergency use. Around the same time, an equally groundbreaking yet relatively unheralded application of mRNA technology was taking place at a London hospital.
Over the past two decades, there's been increasing interest in harnessing mRNA — molecules present in all of our cells that act like digital tape recorders, copying instructions from DNA in the cell nucleus and carrying them to the protein-making structures — to create a whole new class of therapeutics.
Scientists realized that artificial mRNA, designed in the lab, could be used to instruct our cells to produce certain antibodies, turning our bodies into vaccine-making factories, or to recognize and attack tumors. More recently, researchers recognized that mRNA could also be used to make another groundbreaking technology far more accessible to more patients: gene editing. The gene-editing tool CRISPR has generated plenty of hype for its potential to cure inherited diseases. But delivering CRISPR to the body is complicated and costly.
"Most gene editing involves taking cells out of the patient, treating them and then giving them back, which is an extremely expensive process," explains Drew Weissman, professor of medicine at the University of Pennsylvania, who was involved in developing the mRNA technology behind the COVID-19 vaccines.
But last November, a Massachusetts-based biotech company called Intellia Therapeutics showed it was possible to use mRNA to make the CRISPR system inside the body, eliminating the need to extract cells out of the body and edit them in a lab. Just as mRNA can instruct our cells to produce antibodies against a viral infection, it can also teach them to produce one of the two components that make up CRISPR — a cutting protein that snips out a problem gene.
"The pandemic has really shown that not only are mRNA approaches viable, they could in certain circumstances be vastly superior to more traditional technologies."
In Intellia's London-based clinical trial, the company applied this for the first time in a patient with a rare inherited liver disease known as hereditary transthyretin amyloidosis with polyneuropathy. The disease causes a toxic protein to build up in a person's organs and is typically fatal. In a company press release, Intellia's president and CEO John Leonard swiftly declared that its mRNA-based CRISPR therapy could usher in a "new era of potential genome editing cures."
Weissman predicts that turning CRISPR into an affordable therapy will become the next major frontier for mRNA over the coming decade. His lab is currently working on an mRNA-based CRISPR treatment for sickle cell disease. More than 300,000 babies are born with sickle cell every year, mainly in lower income nations.
"There is a FDA-approved cure, but it involves taking the bone marrow out of the person, and then giving it back which is prohibitively expensive," he says. It also requires a patient to have a matched bone marrow done. "We give an intravenous injection of mRNA lipid nanoparticles that target CRISPR to the bone marrow stem cells in the patient, which is easy, and much less expensive."
Meanwhile, the overwhelming success of the COVID-19 vaccines has focused attention on other ways of using mRNA to bolster the immune system against threats ranging from other infectious diseases to cancer.
The practicality of mRNA vaccines – relatively small quantities are required to induce an antibody response – coupled with their adaptable design, mean companies like Moderna are now targeting pathogens like Zika, chikungunya and cytomegalovirus, or CMV, which previously considered commercially unviable for vaccine developers. This is because outbreaks have been relatively sporadic, and these viruses mainly affect people in low-income nations who can't afford to pay premium prices for a vaccine. But mRNA technology means that jabs could be produced on a flexible basis, when required, at relatively low cost.
Other scientists suggest that mRNA could even provide a means of developing a universal influenza vaccine, a goal that's long been the Holy Grail for vaccinologists around the world.
"The mRNA technology allows you to pick out bits of the virus that you want to induce immunity to," says Michael Mulqueen, vice president of business development at eTheRNA, a Belgium-based biotech that's developing mRNA-based vaccines for malaria and HIV, as well as various forms of cancer. "This means you can get the immune system primed to the bits of the virus that don't vary so much between strains. So you could actually have a single vaccine that protects against a whole raft of different variants of the same virus, offering more universal coverage."
Before mRNA became synonymous with vaccines, its biggest potential was for cancer treatments. BioNTech, the German biotech company that collaborated with Pfizer to develop the first authorized COVID-19 vaccine, was initially founded to utilize mRNA for personalized cancer treatments, and the company remains interested in cancers ranging from melanoma to breast cancer.
One of the major hurdles in treating cancer has been the fact that tumors can look very different from one person to the next. It's why conventional approaches, such as chemotherapy or radiation, don't work for every patient. But weaponizing mRNA against cancer primes the immune cells with the tumor's specific genetic sequence, training the patient's body to attack their own unique type of cancer.
"It means you're able to think about personalizing cancer treatments down to specific subgroups of patients," says Mulqueen. "For example, eTheRNA are developing a renal cell carcinoma treatment which will be targeted at around 20% of these patients, who have specific tumor types. We're hoping to take that to human trials next year, but the challenge is trying to identify the right patients for the treatment at an early stage."
Repairing Damaged mRNA
While hopes are high that mRNA could usher in new cancer treatments and make CRISPR more accessible, a growing number of companies are also exploring an alternative to gene editing, known as RNA editing.
In genetic disorders, the mRNA in certain cells is impaired due to a rogue gene defect, and so the body ceases to produce a particular vital protein. Instead of permanently deleting the problem gene with CRISPR, the idea behind RNA editing is to inject small pieces of synthetic mRNA to repair the existing mRNA. Scientists think this approach will allow normal protein production to resume.
Over the past few years, this approach has gathered momentum, as some researchers have recognized that it holds certain key advantages over CRISPR. Companies from Belgium to Japan are now looking at RNA editing to treat all kinds of disorders, from Huntingdon's disease, to amyotrophic lateral sclerosis, or ALS, and certain types of cancer.
"With RNA editing, you don't need to make any changes to the DNA," explains Daniel de Boer, CEO of Dutch biotech ProQR, which is looking to treat rare genetic disorders that cause blindness. "Changes to the DNA are permanent, so if something goes wrong, that may not be desirable. With RNA editing, it's a temporary change, so we dose patients with our drugs once or twice a year."
Last month, ProQR reported a landmark case study, in which a patient with a rare form of blindness called Leber congenital amaurosis, which affects the retina at the back of the eye, recovered vision after three months of treatment.
"We have seen that this RNA therapy restores vision in people that were completely blind for a year or so," says de Boer. "They were able to see again, to read again. We think there are a large number of other genetic diseases we could go after with this technology. There are thousands of different mutations that can lead to blindness, and we think this technology can target approximately 25% of them."
Ultimately, there's likely to be a role for both RNA editing and CRISPR, depending on the disease. "I think CRISPR is ideally suited for illnesses where you would like to permanently correct a genetic defect," says Joshua Rosenthal of the Marine Biology Laboratory in Chicago. "Whereas RNA editing could be used to treat things like pain, where you might want to reset a neural circuit temporarily over a shorter period of time."
Much of this research has been accelerated by the COVID-19 pandemic, which has played a major role in bringing mRNA to the forefront of people's minds as a therapeutic.
"The pandemic has really shown that not only are mRNA approaches viable, they could in certain circumstances be vastly superior to more traditional technologies," says Mulqueen. "In the future, I would not be surprised if many of the top pharma products are mRNA derived."
The Friday Five covers five stories in health research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
One day in recent past, scientists at Columbia University’s Creative Machines Lab set up a robotic arm inside a circle of five streaming video cameras and let the robot watch itself move, turn and twist. For about three hours the robot did exactly that—it looked at itself this way and that, like toddlers exploring themselves in a room full of mirrors. By the time the robot stopped, its internal neural network finished learning the relationship between the robot’s motor actions and the volume it occupied in its environment. In other words, the robot built a spatial self-awareness, just like humans do. “We trained its deep neural network to understand how it moved in space,” says Boyuan Chen, one of the scientists who worked on it.
For decades robots have been doing helpful tasks that are too hard, too dangerous, or physically impossible for humans to carry out themselves. Robots are ultimately superior to humans in complex calculations, following rules to a tee and repeating the same steps perfectly. But even the biggest successes for human-robot collaborations—those in manufacturing and automotive industries—still require separating the two for safety reasons. Hardwired for a limited set of tasks, industrial robots don't have the intelligence to know where their robo-parts are in space, how fast they’re moving and when they can endanger a human.
Over the past decade or so, humans have begun to expect more from robots. Engineers have been building smarter versions that can avoid obstacles, follow voice commands, respond to human speech and make simple decisions. Some of them proved invaluable in many natural and man-made disasters like earthquakes, forest fires, nuclear accidents and chemical spills. These disaster recovery robots helped clean up dangerous chemicals, looked for survivors in crumbled buildings, and ventured into radioactive areas to assess damage.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”