Scientists have long been aware that some people live with what's known as "congenital insensitivity to pain"—the inability to register the tingles, jolts, and aches that alert most people to injury or illness.
"If you break the chain of transmission somewhere along there, it doesn't matter what the message is—the recipient will not get it."
On the ospposite end of the spectrum, others suffer from hyperalgesia, or extreme pain; for those with erythromelalgia, also known as "Man on Fire Syndrome," warm temperatures can feel like searing heat—even wearing socks and shoes can make walking unbearable.
Strangely enough, the two conditions can be traced to mutations in the same gene, SCN9A. It produces a protein that exists in spinal cells—specifically, in the dorsal root ganglion—which transmits the sensation of pain from the nerves at the peripheral site of an injury into the central nervous system and to the brain. This fact may become the key to pain relief for the roughly 20 percent of Americans who suffer from chronic pain, and countless other patients around the world.
"If you break the chain of transmission somewhere along there, it doesn't matter what the message is—the recipient will not get it," said Dr. Fyodor Urnov, director of the Innovative Genomics Institute and a professor of molecular and cell biology at the University of California, Berkeley. "For scientists and clinicians who study this, [there's] this consistent tracking of: You break this gene, you stop feeling pain; make this gene hyperactive, you feel lots of pain—that really cuts through the correlation versus causation question."
Researchers tried for years, without much success, to find a chemical that would block that protein from working and therefore mute the pain sensation. The CRISPR-Cas9 gene editing tool could completely sidestep that approach and "turn off" pain directly.
Yet as CRISPR makes such targeted therapies increasingly possible, the ethical questions surrounding gene editing have taken on a new and more urgent cast—particularly in light of the work of the disgraced Chinese scientist He Jiankui, who announced in late 2018 that he had created the world's first genetically edited babies. He used CRISPR to edit two embryos, with the goal of disabling a gene that makes people susceptible to HIV infection; but then took the unprecedented step of implanting the edited embryos for pregnancy and birth.
Edits to germline cells, like the ones He undertook, involve alterations to gametes or embryos and carry much higher risk than somatic cell edits, since changes will be passed on to any future generations. There are also concerns that imprecise edits could result in mutations and end up causing more disorders. Recent developments, particularly the "search-and replace" prime-editing technique published last fall, will help minimize those accidental edits, but the fact remains that we have little understanding of the long-term effects of these germline edits—for the future of the patients themselves, or for the broader gene pool.
"We need to have appropriate venues where we deliberate and consider the ethical, legal and social implications of gene editing as a society."
It is much harder to predict the effects, harmful or otherwise, on the larger human population as a result of interactions with the environment or other genetic variations; with somatic cell edits, on the other hand— like the ones that would be made in an individual to turn off pain—only the person receiving the treatment is affected.
Beyond the somatic/germline distinction, there is also a larger ethical question over how much genetic interference society is willing to tolerate, which may be couched as the difference between therapeutic editing—interventions in response to a demonstrated medical need—and "enhancement" editing. The Chinese scientist He was roundly criticized in the scientific community for the fact that there are already much safer and more proven methods of preventing the parent-to-child transmission of HIV through the IVF process, making his genetic edits medically unnecessary. (The edits may also have increased the girls' risk of susceptibility to other viruses, like influenza and the West Nile virus.)
Yet there are even more extreme goals that CRISPR could be used to reach, ones further removed from any sort of medical treatment. The 1997 science fiction movie Gattaca imagined a dystopian future where genetic selection for strength and intelligence is common, creating a society that explicitly and unapologetically endorses eugenics. In the real world, Russian President Vladimir Putin has commented that genetic editing could be used to create "a genius mathematician, a brilliant musician or a soldier, a man who can fight without fear, compassion, regret or pain."
"[Such uses] would be considered using gene editing for 'enhancement,'" said Dr. Zubin Master, an associate professor of biomedical ethics at the Mayo Clinic, who noted that a series of studies have strongly suggested that members of the public, in the U.S. and around the world, are much less amenable to the prospect of gene editing for these purposes than for the treatment of illness and disease.
Putin's comments were made in 2017, before news of He's experiment broke; since then no country has moved to continue experiments on germline editing (although one Russian IVF specialist, Denis Rebrikov, appears ready to do so, if given approval). Master noted that the World Health Organization has an 18-person committee currently dedicated to considering these questions. The Expert Advisory Committee on Developing Global Standards for Governance and Oversight of Human Genome Editing first convened in March 2019; that July, it issued a recommendation to regulatory and ethics authorities in all countries to refrain from approving clinical application requests for work on human germline genome editing—the kind of alterations to genetic cells used by He. The committee's report and a fleshed-out set of guidelines is expected after its final meeting, in Geneva this September (unless the COVID-19 pandemic disrupts the timeline).
Regardless of the WHO's report, in the U.S., all regulations of new medical procedures are overseen at the federal level, subjected to extensive regulatory review by the FDA; the chance of any doctor or company going rogue is minimal to none. Likewise, the challenges we face are more on the regulatory end of the spectrum than the Gattaca end. Dr. Stephanie Malia Fullerton, a bioethics professor at the University of Washington, pointed out that eugenics not only typically involves state-sponsored control of reproduction, but requires a much more clearly delineated genetic basis of common complex traits—indeed, SCN9A is one way to get to pain, but is not the only source—and suggested that current concerns about over-prescribing opioids are a more pressing question for society to address.
In fact, Navega Therapeutics, based in San Diego, hopes to find out whether the intersection of this research into SCN9A and CRISPR would be an effective way to address the U.S. opioid crisis. Currently in a preclinical funding stage, Navega's approach focuses on editing epigenetic molecules attached to the basic DNA strand—the idea is that the gene's expression can be activated or suppressed rather than removed entirely, reducing the risk of unwanted side effects from permanently altering the genetic code.
As these studies focused on the sensation of pain go forward, what we are likely to see simultaneously is the use of CRISPR to target diseases that are the root causes of that pain. Last summer, Victoria Gray, a Mississippi woman with sickle cell disease was the second-ever person to be treated with CRISPR therapy in the U.S. The disease is caused by a genetic mutation that creates malformed blood cells, which can't carry oxygen as normal and get stuck inside blood vessels, causing debilitating pain. For the study, conducted in concert with CRISPR Therapeutics, of Cambridge, Mass., cells were removed from Gray's bone marrow, modified using CRISPR, and infused back into her body, a technique called ex vivo editing.
In early February this year, researchers at the University of Pennsylvania published a study on a first-in-human phase 1 clinical trial, in which three patients with advanced cancer received an infusion of ex vivo engineered T cells in an effort to improve antitumor immunity. The modified cells persisted for up to nine months, and the patients experienced no serious adverse side effects, suggesting that this sort of therapeutic gene editing can be performed safely and could potentially allow patients to avoid the excruciating process of chemotherapy.
Then, just this spring, researchers made another advance: The first attempt at in vivo CRISPR editing—where the edits happen inside the patient's body—is currently underway, as doctors attempt to treat a patient blinded by Leber congenital amaurosis, a rare genetic disorder. In an Oregon study sponsored by Editas Medicine and Allergan, the patient, a volunteer, was injected with a harmless virus carrying CRISPR gene-editing machinery; the hope is that the tool will be able to edit out the genetic defect and restore production of a crucial protein. Based on preliminary safety reports, the study has been cleared to continue, and data on higher doses may be available by the end of 2020. Editas Medicine and CRISPR Therapeutics are joined in this sphere by Intellia Therapeutics, which is seeking approval for a trial later this year on amyloidosis, a rare liver condition.
For any such treatment targeting SCN9A to make its way to human subjects, it would first need to undergo years' worth of testing—on mice, on primates, and then on volunteer patients after an extended informed-consent process. If everything went perfectly, Urnov estimates it could take at least three to four years end to end and cost between $5 and 10 million—but that "if" is huge.
"The idea of a regular human being, genetically pure of pain?"
And as that happens, "we need to have appropriate venues where we deliberate and consider the ethical, legal and social implications of gene editing as a society," Master said. CRISPR itself is open-source, but its application is subject to the approval of governments, institutions, and societies, which will need to figure out where to draw the line between miracle treatments and playing God. Something as unpleasant and ubiquitous as pain may in fact be the most appropriate place to start.
"The pain circuit is very old," Urnov said. "We have evolved with the senses that we have, and have become the species that we are, as a result of who we are, physiologically. Yes, I take Advil—but when I get a headache! The idea of a regular human being, genetically pure of pain?... The permanent disabling or turning down of the pain sensation, for anything other than a medical reason? … That seems to be challenging Mother Nature in the wrong ways."
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.”