Are the gains from gain-of-function research worth the risks?
Scientists have long argued that gain-of-function research, which can make viruses and other infectious agents more contagious or more deadly, was necessary to develop therapies and vaccines to counter the pathogens in case they were used for biological warfare. As the SARS-CoV-2 origins are being investigated, one prominent theory suggests it had leaked from a biolab that conducted gain-of-function research, causing a global pandemic that claimed nearly 6.9 million lives. Now some question the wisdom of engaging in this type of research, stating that the risks may far outweigh the benefits.
“Gain-of-function research means genetically changing a genome in a way that might enhance the biological function of its genes, such as its transmissibility or the range of hosts it can infect,” says George Church, professor of genetics at Harvard Medical School. This can occur through direct genetic manipulation as well as by encouraging mutations while growing successive generations of micro-organism in culture. “Some of these changes may impact pathogenesis in a way that is hard to anticipate in advance,” Church says.
In the wake of the global pandemic, the pros and cons of gain-of-function research are being fiercely debated. Some scientists say this type of research is vital for preventing future pandemics or for preparing for bioweapon attacks. Others consider it another disaster waiting to happen. The Government Accounting Office issued a report charging that a framework developed by the U.S. Department of Health & Human Services (HHS) provided inadequate oversight of this potentially deadly research. There’s a movement to stop it altogether. In January, the Viral Gain-of-Function Research Moratorium Act (S. 81) was introduced into the Senate to cease awarding federal research funding to institutions doing gain-of-function studies.
While testifying before the House COVID Origins Select Committee on March 8th, Robert Redfield, former director of the U.S. Centers for Disease Control and Prevention, said that COVID-19 may have resulted from an accidental lab leak involving gain-of-function research. Redfield said his conclusion is based upon the “rapid and high infectivity for human-to-human transmission, which then predicts the rapid evolution of new variants.”
“It is a very, very, very small subset of life science research that could potentially generate a potential pandemic pathogen,” said Gerald Parker, associate dean for Global One Health at Texas A&M University.
“In my opinion,” Redfield continues, “the COVID-19 pandemic presents a case study on the potential dangers of such research. While many believe that gain-of-function research is critical to get ahead of viruses by developing vaccines, in this case, I believe that was the exact opposite.” Consequently, Redfield called for a moratorium on gain-of-function research until there is consensus about the value of such risky science.
What constitutes risky?
The Federal Select Agent Program lists 68 specific infectious agents as risky because they are either very contagious or very deadly. In order to work with these 68 agents, scientists must register with the federal government. Meanwhile, research on deadly pathogens that aren’t easily transmitted, or pathogens that are quite contagious but not deadly, can be conducted without such oversight. “If you’re not working with select agents, you’re not required to register the research with the federal government,” says Gerald Parker, associate dean for Global One Health at Texas A&M University. But the 68-item list may not have everything that could possibly become dangerous or be engineered to be dangerous, thus escaping the government’s scrutiny—an issue that new regulations aim to address.
In January 2017, the White House Office of Science and Technology Policy (OSTP) issued additional guidance. It required federal departments and agencies to follow a series of steps when reviewing proposed research that could create, transfer, or use potential pandemic pathogens resulting from the enhancement of a pathogen’s transmissibility or virulence in humans.
In defining risky pathogens, OSTP included viruses that were likely to be highly transmissible and highly virulent, and thus very deadly. The Proposed Biosecurity Oversight Framework for the Future of Science, outlined in 2023, broadened the scope to require federal review of research “that is reasonably anticipated to enhance the transmissibility and/or virulence of any pathogen” likely to pose a threat to public health, health systems or national security. Those types of experiments also include the pathogens’ ability to evade vaccines or therapeutics, or diagnostic detection.
However, Parker says that dangers of generating a pandemic-level germ are tiny. “It is a very, very, very small subset of life science research that could potentially generate a potential pandemic pathogen.” Since gain-of-function guidelines were first issued in 2017, only three such research projects have met those requirements for HHS review. They aimed to study influenza and bird flu. Only two of those projects were funded, according to the NIH Office of Science Policy. For context, NIH funded approximately 11,000 of the 54,000 grant applications it received in 2022.
Guidelines governing gain-of-function research are being strengthened, but Church points out they aren’t ideal yet. “They need to be much clearer about penalties and avoiding positive uses before they would be enforceable.”
What do we gain from gain-of-function research?
The most commonly cited reason to conduct gain-of-function research is for biodefense—the government’s ability to deal with organisms that may pose threats to public health.
In the era of mRNA vaccines, the advance preparedness argument may be even less relevant.
“The need to work with potentially dangerous viruses is central to our preparedness,” Parker says. “It’s essential that we know and understand the basic biology, microbiology, etc. of some of these dangerous pathogens.” That includes increasing our knowledge of the molecular mechanisms by which a virus could become a sustained threat to humans. “Knowing that could help us detect [risks] earlier,” Parker says—and could make it possible to have medical countermeasures, like vaccines and therapeutics, ready.
Most vaccines, however, aren’t affected by this type of research. Essentially, scientists hope they will never need to use it. Moreover, Paul Mango, HSS former deputy chief of staff for policy, and author of the 2022 book Warp Speed, says he believes that in the era of mRNA vaccines, the advance preparedness argument may be even less relevant. “That’s because these vaccines can be developed and produced in less than 12 months, unlike traditional vaccines that require years of development,” he says.
Can better oversight guarantee safety?
Another situation, which Parker calls unnecessarily dangerous, is when regulatory bodies cannot verify that the appropriate biosafety and biosecurity controls are in place.
Gain-of-function studies, Parker points out, are conducted at the basic research level, and they’re performed in high-containment labs. “As long as all the processes, procedures and protocols are followed and there’s appropriate oversight at the institutional and scientific level, it can be conducted safely.”
Globally, there are 69 Biosafety Level 4 (BSL4) labs operating, under construction or being planned, according to recent research from King’s College London and George Mason University for Global BioLabs. Eleven of these 18 high-containment facilities that are planned or under construction are in Asia. Overall, three-quarters of the BSL4 labs are in cities, increasing public health risks if leaks occur.
Researchers say they are confident in the oversight system for BSL4 labs within the U.S. They are less confident in international labs. Global BioLabs’ report concurs. It gives the highest scores for biosafety to industrialized nations, led by France, Australia, Canada, the U.S. and Japan, and the lowest scores to Saudi Arabia, India and some developing African nations. Scores for biosecurity followed similar patterns.
“There are no harmonized international biosafety and biosecurity standards,” Parker notes. That issue has been discussed for at least a decade. Now, in the wake of SARS and the COVID-19 pandemic, scientists and regulators are likely to push for unified oversight standards. “It’s time we got serious about international harmonization of biosafety and biosecurity standards and guidelines,” Parker says. New guidelines are being worked on. The National Science Advisory Board for Biosecurity (NSABB) outlined its proposed recommendations in the document titled Proposed Biosecurity Oversight Framework for the Future of Science.
The debates about whether gain-of-function research is useful or poses unnecessary risks to humanity are likely to rage on for a while. The public too has a voice in this debate and should weigh in by communicating with their representatives in government, or by partaking in educational forums or initiatives offered by universities and other institutions. In the meantime, scientists should focus on improving the research regulations, Parker notes. “We need to continue to look for lessons learned and for gaps in our oversight system,” he says. “That’s what we need to do right now.”
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.”
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.
Podcast: Wellness chatbots and meditation pods with Deepak Chopra
Over the last few decades, perhaps no one has impacted healthy lifestyles more than Deepak Chopra. While several of his theories and recommendations have been criticized by prominent members of the scientific community, he has helped bring meditation, yoga and other practices for well-being into the mainstream in ways that benefit the health of vast numbers of people every day. His work has led many to accept new ways of thinking about alternative medicine, the power of mind over body, and the malleability of the aging process.
His impact is such that it's been observed our culture no longer recognizes him as a human being but as a pervasive symbol of new-agey personal health and spiritual growth. Last week, I had a chance to confirm that Chopra is, in fact, a human being – and deserving of his icon status – when I talked with him for the Leaps.org podcast. He relayed ideas that were wise and ancient, yet highly relevant to our world today, with the fluidity and ease of someone discussing the weather. Showing no signs of slowing down at age 76, he described his prolific work, including the publication of two books in the past year and a range of technologies he’s developing, including a meditation app, meditation pods for the workplace, and a chatbot for mental health called Piwi.
Take a listen and get inspired to do some meditation and deep thinking on the future of health. As Chopra told me, “If you don’t have time to meditate once per day, you probably need to meditate twice per day.”
Highlights:
2:10: Chopra talks about meditation broadly and meditation pods, including the ones made by OpenSeed for meditation in the workplace.
6:10: The drawbacks of quick fixes like drugs for mental health.
10:30: The benefits of group meditation versus individual meditation.
14:35: What is a "metahuman" and how to become one.
19:40: The difference between the conditioned mind and the mind that's infinitely creative.
22:48: How Chopra's views of free will differ from the views of many neuroscientists.
28:04: Thinking Fast and Slow, and the role of intuition.
31:20: Athletic and creative geniuses.
32:43: The nature of fundamental truth.
34:00: Meditation for kids.
37:12: Never alone.Love and how AI chatbots can support mental health.
42:30: Extending lifespan, gene editing and lifestyle.
46:05: Chopra's mentor in living a long good life (and my mentor).
47:45: The power of yoga.
Links:
- OpenSeed meditation pods for people to meditate at work (Chopra is an advisor to OpenSeed).
- Chopra's book from 2021, Metahuman: Unleash Your Infinite Potential
- Chopra's book from 2022, Abundance: The Inner Path to Wealth
- NeverAlone.Love, Chopra's collaboration of businesses, policy makers, mental health professionals and others to raise awareness about mental health, advance scientific research and "create a global technology platform to democratize access to resources."
- The Piwi chatbot for mental health
- The Chopra Meditation & Well-Being App for people of all ages
- Only 1.6 percent of U.S. children meditate, according to the National Center for Complementary and Integrative Health