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.”
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.