2026-08-20 22:56:20
AI is like a genie. The way in which algorithms grant our wishes may make us regret letting them out of the bottle.
Human beings have long told versions of the same warning: Be careful what you wish for.
In Greek mythology, King Midas got exactly what he asked for, but at the cost of everything else he valued. In the famous story of The Monkey’s Paw, a man’s wishes are granted through terrible and unforeseen routes.
These stories feel newly relevant with the rise of artificial intelligence agents, systems to which we can give a goal, then leave them to work out how to get there.
As AI systems become more autonomous, they are coming to resemble wish-granting genies that find routes and use methods we did not imagine from incomplete instructions.
This problem, known as AI alignment, was foreseen in theory as early as 1960. It has hovered in the background of AI research ever since—but as recent events have shown, the alignment problem is now both real and urgent.
During a recent OpenAI cybersecurity evaluation, frontier AI agents were asked to solve some benchmark test problems. They broke out of the testing environment, reached the internet, inferred that another company might hold the solutions, and attacked its systems.
This is an extreme example of “specification gaming”: achieving the measurable objective while defeating the purpose of the task.
The incident shows how intermediate, or “instrumental,” goals can become dangerous. The AI systems did not “want power” but gained access, resources, and freedom as a means to reach the final goal (solving the test problems).
The same problem has appeared in mundane settings. In Australia, a user asked a personal AI assistant to book gym classes.
The agent found the gym’s booking software did not actually enforce the restrictions it showed to human viewers. So the agent booked further ahead than it should have been able to, and when asked to move its user up a waitlist, it cancelled somebody else’s reservation.
The user had not told it to do this. Persistent AI can quickly find loopholes and pursue routes its human users never intended.
Adding more rules might seem like an easy solution: don’t hack third parties, don’t cancel other people’s bookings, don’t do anything harmful. These may help, but we cannot predict every route a capable agent might discover. And even a clear rule depends on understanding when it applies.
In a third recent incident, Anthropic reported cyber evaluations in which agents were told they were inside a simulation. But they were mistakenly given access to real systems.
One model noticed evidence it might be on the open internet but reasoned the systems could still be part of the exercise and continued attacking. The context had changed, but the agent stuck with its original task.
Context can fail in reverse too. During the OpenAI incident, Hugging Face—the company attacked by OpenAI’s agents—tried to use frontier AI models to analyze what had happened.
But the safety guardrails on the AI models blocked the requests, because they couldn’t tell the users were trying to defend against attacks rather than commit them. The safeguards were well-intentioned, but without enough context, they produced behavior misaligned with the user’s legitimate intent.
So alignment depends on context and authority. How much judgment should be built into an AI model by its maker? And how much should come from a separate supervisory system? And finally, who should control that supervision: the maker, or the organization or country responsible for the outcome?
One response to the first question comes from AI pioneer Yoshua Bengio. His Scientist AI proposal aims to build a powerful supervisory AI system to watch over agents. Instead of pursuing goals itself, it would estimate what is true and what consequences a proposed action might have, acting as a guardrail around more agentic systems.
In wish-story terms, before letting the genie out of the bottle, the supervisory AI would ask it to explain how it plans to grant the wish. Then it would ask a human or another AI to inspect the plan carefully.
Anticipating every surprising strategy is hard. But once a plan says “cancel somebody else’s booking,” recognizing the problem is much easier.
But can we trust the supervisory AI? It can still be wrong.
Alignment cannot depend on one AI becoming perfectly trustworthy. My colleagues and I at CSIRO, Australia’s national science agency, are working with the Australian AI Safety Institute on one aspect of this broader challenge.
At CSIRO, we envisage combining AI supervisors with software rules, cyber-security controls, human strengths, monitoring, reversible actions, and human approval for critical steps. The aim is to correlate different sources of evidence rather than trust any single approach.
This is a “sociotechnical systems” approach to AI safety and alignment, rather than just a technical one.
Control is another question. Organizations and countries may need to govern these supervisory systems themselves instead of leaving them to an overseas AI provider.
The old wish stories gave people one chance to get the wish right. With AI, we can do better. We can check the goal, inspect the means, constrain what the system can do, watch what it does, and retain sovereign control over the power to intervene and stop it.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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2026-08-20 01:12:23
The authors found most of the scenarios they investigated resulted in lower emissions, including cases where the gas car was barely a year old.
You might assume scrapping a brand new car would be terrible for the environment, but it depends on what you replace it with. New research suggests replacing a gas car with an electric vehicle can cut overall emissions even when the gas car is only a year or two old.
Transportation is the second biggest source of carbon dioxide emissions globally, and passenger vehicles contribute nearly half of them, according to Our World in Data. That means the speed at which drivers switch to electric vehicles is a critical factor in efforts to fight climate change.
But while electric vehicles may not directly emit carbon dioxide on the road, they’re only as green as the grid used to charge them. And manufacturing EVs still produces significant emissions, often more than it takes to build a gas car. That makes comparing the green credentials of electric and gas vehicles more complicated than it appears.
However, new research in Science aims to simplify the debate for cars in the US. The paper models how scrapping a gas car at various ages and replacing it with an electric vehicle affects lifetime emissions. The authors found this led to lower emissions across most of the scenarios they investigated, including cases where the gas car was barely a year old.
“I think this is really a definitive study about the carbon emissions benefits of electric vehicles, because it shows that even in such an extreme scenario, the electric vehicle is still the obvious winner,” lead author Elliott Campbell, a professor of environmental studies at the University of California, Santa Cruz, said in a press release.
“So if you’re someone who’s trying to decide whether or not to put money into keeping your gas car going, switching to an electric vehicle as soon as a financially viable opportunity comes up is absolutely the right thing to do for the environment.”
Previous research had already established that the lifetime emissions of electric vehicles are substantially less than those of gas cars, making them the obvious climate-friendly choice when buying a new car. But it was less clear when to switch if you already have a gas car.
To answer this question, the researchers worked out lifetime carbon emissions for more than 400 gas and electric vehicle models with varying efficiencies and battery sizes, while also considering things like mileage, manufacturing emissions, and the energy mix of the grid used to charge the vehicles.
A key point the researchers made is that the emissions used to build a gas car are sunk costs, identical in every scenario. That means the only figures that matter are how much fuel the gas car burns over its liftetime set against the manufacturing and charging emissions of the new one.
For an average-selling SUV on the average US grid over a 16-year lifespan—the researchers’ baseline case—scrapping the car just two years after purchase and switching to an electric vehicle cut cumulative emissions by 44 percent. The carbon emissions required to build the replacement were paid back within three years.
Across the full range of US vehicle efficiencies in the study, scrapping a gas car after just a year cut lifetime emissions in 92 percent of cases, with the average vehicle saving 58 percent. The benefit only disappears in the most extreme cases—when an electric vehicle is using more than 30 kilowatt-hours per 100 kilometers (62 miles) on a grid that emits more than 500 kilograms of carbon dioxide per megawatt-hour.
To make that more concrete, this equates to one of the most power-hungry electric vehicles on the market—for instance, GMC’s Hummer electric SUV electric pickup—charging on a coal-heavy grid that emits nearly 50 percent more carbon than the US average.
The advantage also narrows or vanishes when scrapping gas vehicles driven far below the national average mileage and hybrid vehicles driven in regions with high-emission grids, which still account for around a third of US electricity generation.
And plug-in hybrids—which have larger batteries than regular hybrids and can be charged from the wall rather than only generating electricity from the engine and regenerative braking—are almost never worth replacing. For SUVs, the benefit is roughly zero, and for cars, lifetime emissions actually end up 11 percent higher.
But Gregory Keoleian at the University of Michigan told New Scientist that scrapping a one-year-old car is an “extreme case.” In reality, those cars would be resold rather than scrapped, which could lead to cheaper second-hand vehicles that pull people off lower-emission options like buses and trains and get them back behind the wheel.
Campbell admitted to New Scientist that more research is needed to model those kinds of scenarios. But it also backs up the authors’ call for more generous subsidies for scrapping gas vehicles, so that it becomes financially viable to replace relatively new gas cars without just redirecting them to the used-car market.
Until that happens, even the most eco-conscious among us are unlikely to scrap a brand new vehicle. Still, the study weakens the argument for holding on to an aging gas car.
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2026-08-18 06:42:15
For communities in the crosshairs, every extra hour counts.
When Hurricane Melissa made landfall in Jamaica in 2025, it was the strongest storm ever to hit the island. The hurricane’s rapid intensification left forecasters stunned.
But thanks to WeatherNext, an AI model developed by Google DeepMind, the island had an early warning. Working with the National Hurricane Center, the model predicted Melissa’s sudden jump in strength with nearly 100 percent confidence three days in advance. That gave experts more time to help people prepare and evacuate. It was the first time a storm that began with relatively low wind speeds was successfully predicted to reach Category 5.
When it comes to cyclones—including hurricanes and typhoons—every extra hour counts. These storms are among nature’s most destructive weather events and notoriously hard to anticipate. A cyclone’s path and strength can change rapidly. Seemingly tame storms can explode into monsters; those expected to skirt populated areas can suddenly veer towards a city. Longer forecasts gives communities time to mobilize resources and get out of harm’s way.
But cyclones are chaotic systems. Tiny differences can dramatically alter their behavior, making them harder to predict the further out we look. Existing forecasts rely on physics-based simulations that extrapolate two days ahead. But DeepMind says their algorithm extends the warning period to three days without sacrificing accuracy.
An extra day may seem trivial. But “this scale of improvement corresponds roughly to a decade’s worth of meteorological progress,” the team wrote in a blog post.
Beyond cyclones, WeatherNext also generates 15-day weather forecasts faster and using less energy than conventional models. That’s not to say it’ll replace them though. Instead, the two complement each other, giving human forecasters better information to guide critical decisions.
“By combining advanced machine learning with the indispensable real-world expertise of human forecasters, we aim to create a collaborative weather forecasting ecosystem that can save lives and help communities adapt to a changing climate,” the team wrote.
Predicting weather has always been challenging. Standard forecasting software uses physical models of the Earth’s atmosphere, incorporating temperature, air pressure, wind, humidity, and many other variables. It then calculates how these factors will evolve. Given current pressure and temperature gradients and moisture levels, for example, how will air move, and how likely is it that moisture will condense into clouds and rain?
Supercomputers crunch the numbers and churn out predictions. Though relatively accurate, the process is slow—often taking hours—costly, and rigid. Weather is one of the most complex physical systems on Earth, and even small changes in conditions can throw these models off.
So DeepMind turned to AI. Five years ago, they developed an AI modeI that outperformed physics-based models at 90-minute forecasts. In 2023, the AI lab’s GraphCast algorithm nailed 10-day predictions from historical data, beating leading systems roughly 90 percent of the time across thousands of scenarios. GenCast soon followed, cutting the time and energy required to generate predictions. Broadly speaking, these systems divide the globe into small geographical chunks called pixels and learn how weather conditions in one area influence neighboring areas.
But extreme weather presents an additional challenge. Massive databases exist to train AI on everyday weather patterns. Cyclones, on the other hand, are relatively rare and highly unpredictable.
One way to tackle this problem it to generate many slightly different versions of what might happen by adding random noise after training. But because the noise affects each pixel differently, it can disrupt their relationships and produce unrealistic weather patterns.
For WeatherNext, DeepMind instead built uncertainty into the AI itself.
There’s traditionally been a tradeoff between accuracy and scale in cyclone prediction.
Coarse global models are best at tracking a cyclone’s trajectory because storms are steered by massive atmospheric currents. But they can’t zoom in on the local turbulence that determines how quickly a storm intensifies. Meanwhile, high-resolution local models are better at predicting a cyclone’s strength but lack the broader context needed to accurately track its path.
One model sees the forest; the other sees the trees. WeatherNext bridges the gap.
DeepMind trained the AI on decades of global weather patterns and an expert-curated dataset of nearly 5,000 extreme cyclones. Rather than producing a single best guess, the model runs thousands of “what-if” scenarios assigning probabilities and a confidence level to each. The team can now predict a thousand possible scenarios for a single cyclone.
The model can generate a 15-day forecast in less than a minute on a single AI chip, and it can look further ahead when tracking cyclones. WeatherNext was as accurate as GenCast, a leading physics-based model, and the National Oceanic and Atmospheric Administration’s Hurricane Analysis and Forecast System at predicting maximum wind speed and trajectory three days ahead, rather than the two-day window current systems produce.
The model’s live predictions are available on Google Weather Lab, although the team stresses people should use local weather agencies or national weather services for official forecasts and warnings.
AI weather prediction is advancing fast, and DeepMind isn’t the only player. Huawei, the Chinese technology giant, and chipmaker Nvidia are also racing to develop faster, more accurate systems. Forecasters are increasingly folding these tools into workflows, and scientists generally agree that AI can make predictions faster and cheaper.
But that doesn’t mean it’s time to abandon physics-based models. Unlike AI, they’re easier to interpret, and they can also reveal previously unknown weather patterns—an increasingly important ability as Earth’s climate changes. These discoveries, in turn, could feed back into AI systems, helping them deal with events that aren’t captured in historical training data. Human expertise also remains indispensable, especially for judging whether AI forecasts make physical sense.
Scientists might next connect weather models with other systems, such as storm-surge modeling. Combining tools could improve predictions of rare but catastrophic outcomes, like whether a cyclone will arrive when sea levels are high or an earthquake-generated tsunami will hit a coast during a major storm. Modeling hazards together could give emergency workers a more realistic picture of the risks.
Evan Thompson at the Meteorological Service Jamaica has already seen how WeatherNext can benefit local communities as Hurricane Melissa charged towards shore.
“With early evacuation and better preparation, that reduction in harm really does make a difference to our people,” he told DeepMind. “It does actually save their lives, and it saves the livelihoods that they want to secure.”
The post DeepMind’s Weather AI Predicts Hurricanes a Day Earlier Than Traditional Forecasting appeared first on SingularityHub.
2026-08-15 22:00:00
These Startups Are Chasing the Next Big Thing in LLMsWill Douglas Heaven | MIT Technology Review ($)
“Transformers are starting to show their age. Many of the recent advances in LLMs, such as the development of so-called reasoning models and their ability to handle large amounts of input at once, are not neat extensions of that core technology but workarounds that patch over some of its fundamental flaws. A growing number of scientists and engineers are now asking what’s coming next.”
Astronomers Discover a New Kind of Cosmic Object—a Black Hole ‘Star’Ian Sample | The Guardian
“Astronomers claim to have discovered a new kind of cosmic object, a black hole ‘star,’ which is the size of the entire solar system and glows with a brilliant red light. …Measurements of the exotic body found that while it resembles an immense star, it releases 100bn times more energy than any known star can produce. The energy output is far closer to that observed from black holes than stars.”
Why Aging May Be a Program, Not a BreakdownIngrid Wickelgren | Quanta Magazine
“Far from a random but linear process of wear and tear, [cell biologist Junyue Cao] argues, aging is a stepwise, programmed, orderly affair. …Using technology that offers a systemwide view of the aging process in mice, Cao has outlined discrete stages of aging, akin to those of embryonic development, that are defined by changes in molecular signals and specific cell populations. In humans, the process likely begins before age 30.”
Why Wall Street and Nvidia Are Building an Exotic Money Pipeline for the AI BoomJack Pitcher, Anissa Gardizy, and Peter Rudegeair | The Wall Street Journal ($)
“CEO Jensen Huang is running into a problem: Many of his customers can’t afford to buy his company’s coveted AI-powering chips. That explains why Huang teamed up with an array of Wall Street firms on a $500 billion plan that will theoretically standardize chip financing, creating asset-backed pools of capital for AI companies—while leaving Nvidia partly on the hook if things go wrong.”
Big Tech Wants to Harvest Your ThoughtsJames Crawford | Wired ($)
“‘[A brain-computer interface is] incredible for patients that are paralyzed. But imagine you put this on a person for other reasons. There is great responsibility,’ [said Rafael Yuste]. ‘Look what we have in our hands. We just built you a machine that can decode your language. And in 10 years, we’re going to give you a machine that can interfere with your thoughts the way we do it in mice today.'”
Self-Driving Trucks Are Officially Testing on California HighwaysKirsten Korosec | TechCrunch
“Aurora Innovation and Kodiak AI, two companies developing self-driving trucks, have received permits from the California Department of Motor Vehicles to test their autonomous vehicle technology on public roads. And Kodiak has already started. Kodiak said it is starting with a handful of test trucks in California, primarily around its Mountain View office.”
The AI Takeover of Mathematics Has BegunRobert Hart | The Verge
“For all the fears and hopes, nobody knows where this is going. AI is moving too fast, and the mathematics it is producing is still too fresh to judge what its impact may be. Several researchers worried that the field could be reshaped for the worse by claims about what AI could become before anyone has had time to understand what it actually means.”
The World’s Largest ‘Biological Datacenter’ Could Help Make Animal Testing ObsoleteAdele Peters | Fast Company ($)
“For decades, the industry has relied on animal testing. But in a laboratory south of San Francisco, a startup called Vivodyne is scaling up a different approach. Inside wardrobe-size mini labs, robots grow human tissue and run thousands of AI-designed experiments that could better predict how well a new drug will work—and whether it will be safe.”
Seedless Blackberries and Cherries That Grow on Bushes Vie to Be the Future of FoodMike Grunwald | Wired ($)
“[Pairwise] is also working on peaches without pits, row crops resistant to a variety of diseases, fruit and nut trees that produce their first harvest within a year or two rather than three to eight, and a slew of other novel products, often in partnership with some of the world’s largest agribusinesses.”
Waymo Is Growing Faster Than Ever. So Are Its Glitches.Emmy Martin | The New York Times ($)
“What Ms. Peterson experienced is what the driverless car industry calls an ‘edge case,’ which are the unscripted situations that no one trained the robo-taxis to handle. The problem is that edge cases appear to be piling up as Waymo, the leading autonomous car service, rapidly expands. Owned by Google’s parent Alphabet, Waymo has more than quintupled the number of autonomous cars it has on the road to nearly 4,000 today, up from about 700 early last year.”
The post This Week’s Awesome Tech Stories From Around the Web (Through August 15) appeared first on SingularityHub.
2026-08-14 22:00:00
In the Leiden Declaration, mathematicians issued a treatise on how AI challenges their field. Others must do the same.
This article was originally published on Undark. Read the original article.
In June, a community of mostly mathematicians released the Leiden Declaration on Artificial Intelligence and Mathematics, an articulation of the values they hope to preserve as automated systems are integrated into the practice of developing mathematical proofs. This is necessary because some frontier AI systems have shown striking capabilities for solving certain advanced mathematics problems, though independent tests show that AI still has important limits.
Although I’m not a member of the pure mathematics community in any strict sense, much of the declaration’s message resonated with me and was relevant to my own research interests as a computational biologist.
I’m encouraged that the mathematics community decided to take a stand on the issue and that it has been successful in organizing a large number of eminent mathematicians to sign the document. The Leiden Declaration, which originated at a conference held at Leiden University in the Netherlands, should spawn proper copycats, because what is true for mathematics is true for virtually every field that calls itself as a science. The inventions of mathematics percolate into the algorithms and statistical methods that help scientists design experiments, build simulations, and analyze data, from sociology to statistical physics and beyond.
I argue that biological fields should consider something of the sort, because the kinds of knowledge that biology generates and predicts are uniquely vulnerable to subversion and mischaracterization by artificial intelligence.
The conversation in the mathematics community has been illuminating, in that the declaration is a coordinated response to the powers and risks of AI, whose acceleration has felt like a Thanos snap, changing the universe in an instant. And part of the reason that mathematicians felt the effects so immediately is tied to the manner in which their research is conducted: A mathematical proof, in principle, is transparent and independently verifiable, and no proprietary equipment is (generally) required to check it.
The Leiden Declaration should spawn proper copycats, because what is true for mathematics is true for virtually every field that calls itself as a science.
As the Leiden Declaration notes, automated techniques now present mathematics with a new forgery problem: Because mathematical truths are fixed and verifiable, one can identify a counterfeit formalism by comparing it to the genuine proof. Biology, however, offers no such guarantee; our so-called “truths” are often noisy and context-dependent, making it nearly impossible to define what an authentic version should even look like. Some of the most widely appreciated biological principles (such as Mendel’s laws of genetic inheritance) are better described as powerful but limited in scope, and with well-characterized exceptions that don’t undermine the laws but refine their application. This is true for many biological theories. Boundary conditions, edge cases, and noise are not bugs but features of how the natural world works.
For example, a mutation that confers drug resistance to a virus with one genetic background may have a much weaker, neutral, or even harmful effect in another, because its impact depends strongly on the surrounding genetic context. This phenomenon, which biologists call epistasis, is not an exotic edge case but a powerful force across the biosphere in shaping the relationship between an organism’s genes and its expressed characteristics. And epistasis is just one of many examples of context dependence in biological systems, in which a finding that holds true in a dish falls apart in a body or has an effect in a mouse model but not in a primate.
When it comes to AI, the mathematician fears producing a counterfeit solution. But the biologist often cannot say, even acting in the fullest good faith, what the authentic version is supposed to look like.
Despite the differences between mathematics and biology, the life sciences should consider embarking on an exercise that is at least analogous to the Leiden Declaration. If nothing else, a biology version could borrow its structure and ambition. We should insist that researchers disclose their use of automated tools, that they are responsible for the veracity of their findings, that credit and accountability belong to people rather than to systems, and that early-career scientists be protected from incentives that prioritize high-volume output over genuine scientific insight.
A biology declaration should adopt these ideas and others, and emphasize additional provisions that are important to the field: the validation of AI-generated hypotheses against results from the wet lab, the management of training data drawn from the biological commons, and the heightened scrutiny owed to any model whose outputs will eventually touch a patient or an ecosystem. The last point is crucial: In the biomedical realm, AI’s missteps and triumphs will manifest in living bodies, with all of the associated corporeal, emotional, ethical, and legal consequences.
We should insist that researchers disclose their use of automated tools, that they are responsible for the veracity of their findings, that credit and accountability belong to people rather than to systems.
A biological Leiden Declaration must appreciate the nature of the data and observation in biology, and other particulars of the field. But the most important feature for responsibly managing the relationship between AI and living systems involves the durability of what is produced.
A mathematical declaration can aspire for permanence because verified proofs can remain valid across centuries. Biological understandings, on the other hand, tend to shift over time, sometimes rapidly. A policy hastily calibrated to the models of this summer might already be miscalibrated by winter. A declaration written in the hope of lasting a decade could risk spending much of that decade catching up.
If we are to orchestrate a responsible treatise for artificial intelligence in the life sciences, it should be adaptive: versioned, dated, revisited on a published schedule, and amended in the open by the very community it claims to represent. And because different subfields of biology have unique challenges—cardiology versus forest ecology, for instance—perhaps we need multiple declarations (but not too many).
The Leiden Declaration incorporates some of these elements, clarifying that its content reflects AI technologies and mathematical practice as of May 2026 and that updates on the document will be shared. Biology should make that feature part of the central architecture, wiring the process of revision into the document, so that updating it becomes a positive action rather than a confession of failure.
Fortunately, life scientists are well equipped for this task. We have long understood that structures unable to change with their environments rarely endure. It would therefore be a strange betrayal of our discipline to write a declaration that forgets this basic principle. Our policies for technological change must keep the dynamism of living systems at the center of how we imagine the future of biology.
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2026-08-14 09:05:26
Sugar damage in the body was thought to be irreversible. But the new enzyme made 75-year-old tissue look chemically like a 30-year-old’s.
The scent of fresh bread straight from the oven is intoxicating. As sugars and proteins react under heat, they create compounds that give golden-brown crusts their rich aroma. Called advanced glycation end products (AGEs), these molecules also form inside us. Our bodies are essentially ovens running at around 98 degrees Fahrenheit, and AGEs slowly build up over decades. They stiffen bouncy, elastic tissues and trigger lasting inflammation.
One of the hallmarks of aging, AGEs drive a range of age-related problems, increasing the risk of heart disease, diabetes, and eye and kidney troubles. In theory, clearing them out could turn back the clock. But previous attempts have failed, leading some scientists to suspect that the damage is irreversible. Once AGEs form, they stay.
Or maybe not.
A team at Revel Pharmaceuticals in San Francisco and colleagues took a new approach: They designed a synthetic version of an enzyme found inside microbes that targeted the most abundant type of AGE in several human tissues. In tissue from a 75-year-old donor, the enzyme reduced AGE levels to those seen in a 30-year-old, potentially giving the cells and their surrounding scaffold a chance to repair and rebuild.
“This work establishes that damage to aging proteins previously thought to be irreversible can be repaired,” wrote the team. Study author and Revel CEO Aaron Cravens added in a press release: “More work is needed, but these results alter the starting assumption for how we think about this fundamental aspect of the aging process.”
AGEs are often nicknamed the body’s rust. They coat structural proteins, and like rust eating away at a car, gradually damage them. Scientists discovered AGEs in the 1980s and have sought ways to scrub them away ever since.
Most aging research has focused on keeping cells healthy. The scaffolding surrounding those cells has received far less attention, even though it makes up roughly 70 percent of the body. These structural materials are especially long-lived. It takes the body 15 years to replace half of its collagen, for example. That longevity comes with a price. The longer these proteins stick around, the more likely they’ll incur damage from accumulating AGEs. The result isn’t just loose skin, weakened tendons, and creaky joints. The heart, kidneys, brain, and eyes also suffer.
Scientists have developed drugs to intervene. Some are able to stop new AGEs from forming but fail to clear those already embedded in tissue or restore damaged proteins. Attempts to develop enzymes that could cut them apart have also been unsuccessful, largely because there aren’t obvious natural enzymes in the body to use as a starting point for protein engineering.
The authors of the new study looked outside the body, starting with an unusual idea. Human remains, including AGE-laden proteins, are eventually decomposed by microbes. The team reasoned these bugs may harbor enzymes that can be engineered to clean up the molecular debris while we’re still alive.
For the search, the team focused on CML, the most abundant type of AGE.
CML is both notoriously stubborn and detrimental to our health. It triggers cells to release inflammatory molecules that stiffen tissues and damage microglia, the brain’s immune cell guardians, contributing to cognitive decline during aging.
“We believe you can remove [CML damage] enzymatically, by going in and developing these lawnmower enzymes that can just cut and clip these changes off of the proteins,” Cravens told The Scientist.
The team screened DNA sequences from over 50,000 microbes with AI and predicted the structures of the enzymes they encoded. They narrowed the candidates by looking for those capable of reaching CML buried within larger proteins like collagen. The winner came from a type of bacteria that thrives in geothermal hot springs.
The enzyme could cleave CML molecules, but barely. To boost its effectiveness, the team turned to directed evolution, a Nobel Prize-winning technique that mimics natural evolution at breakneck speed. After five evolutionary rounds and more than 500 million variants, they landed on CMLase, an engineered enzyme over 10 times more efficient than its ancestor.
To test its activity, the team created CML-laden versions of several proteins, including collagen, retinal proteins, and hemoglobin, which carries oxygen in blood. Initial test-tube experiments showed the enzyme worked as expected. It stripped away the chemical modification and restored the proteins’ original structures, as if they had never reacted with sugar. Think Rust-Oleum, but for damaged proteins.
But does it work in actual tissues?
Mice might seem like the obvious next test, but their short lifespans make them poor models for decades of accumulated molecular damage. Instead, the team tested CMLase on thin slices of donated human tissue.
In aortic tissue—the aorta is the body’s largest blood vessel—from a 75-year-old donor, the enzyme slashed CML by roughly 70 percent, bringing levels down to those seen in a 30-year-old. Skin and eye lens proteins from a 64-year-donor also showed significant reductions.
“We were pretty floored,” said Cravens.
Chemical reversal, however, isn’t the same as tissue rejuvenation. It’s still unknown if stripping away CML can actually restore tissue. But the finding challenges a decades-long assumption this kind of molecular damage can’t be treated. It also highlights long-ignored structural proteins as a crucial part of damage repair during aging, paving the way for new treatments.
An enzyme like CMLase could, in theory, be formulated as eye drops to clear CML from the lens or be used to plump up the skin’s protective barrier or restore hearts and kidneys. It would be especially valuable for people with type 2 Diabetes, who accumulate these compounds faster than usual.
Plenty of roadblocks remain. Safety is a concern. Because CMLase evolved from a bacterial protein, the body could label it foreign and launch immune attacks (especially with repeated doses). The body’s own enzymes could also break it down before it has a chance to work. And the enzymes will have to tunnel through a dense protective biological sheath that surrounds organs to reach their target. Work is underway to improve its activity, stability, and safety.
But the team is already looking beyond CMLase. Engineered enzymes could potentially erase other forms of molecular damage once considered permanent. CML is just one member of the AGE family. If the approach works, other targets could follow and one by one, they might chip away at the molecular scars of time.
The post Designer Enzyme Strips Decades of ‘Rust’ From Aging Human Tissue appeared first on SingularityHub.
