2026-07-22 22:00:00
Researchers could use lab-grown sperm to develop infertility treatments or, more controversially, make babies.
Scientists just transformed a living mouse’s kidney into an incubator for developing human sperm made from blood cells.
It sounds like sci-fi Mad Libs. But a team at the University of Pennsylvania, led by Kotaro Sasaki, pulled it off. For up to nine months, a tiny pouch of human cells nestled beneath a mouse’s kidney gradually developed into immature sperm. The study is the latest in a decade-long quest to grow sperm in the lab.
If successful, lab-grown sperm could open a new window into the earliest stages of sperm development, a process that’s notoriously difficult to study because it begins before birth. The research could also shed light on male infertility—which, in many cases, has no clear cause—and inspire treatments.
More controversially, lab-grown sperm could one day be used to make babies, offering hope to people struggling to conceive and same-sex couples who want to have children genetically related to both parents. That goal is still far off. Though gene activity was similar to their natural counterparts, none of the lab-grown cells were able to develop into functional sperm.
Those results starkly contrast similar attempts in mice. Researchers have already produced functional sperm and egg cells from rodent skin cells, and in two pioneering cases, used them to create healthy pups with two dads. But translating this capability to humans has been difficult, largely because reproductive development differs tons between species.
Still, the new system can help scientists probe the earliest stages of human sperm development. And because any future clinical applications would first need extensive testing in non-human primates, the team also generated immature sperm cells from monkeys, whose reproductive biology more closely mirrors our own.
Recapitulating sperm development in the lab has uses beyond fertility treatment too, such as testing whether drugs interfere with reproduction. The platform “establishes a robust framework for modeling primate germ cell [reproductive cell] development,” the team wrote.
For decades, scientist have been able to rewind adult cells into induced pluripotent stem cells (iPSCs). These cells can go on to become nearly any other cell type. But steering them to become sperm has proven far trickier, largely because human sperm takes years to fully develop.
The journey begins before birth. Early stem cells give rise to spermatogonia, the founder cells that replenish sperm throughout life. These cells are largely dormant until puberty, when some begin meiosis, a special type of cell division that halves their chromosomes. That way, when sperm meets egg, the embryo gains a full genetic set.
But the cells don’t live in a vacuum. Proteins and other molecules instruct immature sperm when to grow, divide, or pause. Physical forces, such as the winding architecture of the testes and the flow of fluid, also play a role. Recreating this intricate environment in a dish has been one of the biggest challenges to the study of sperm development and our ability to grow them in the lab.
Roughly a decade ago, Sasaki and colleagues found a way to transform human iPSCs into early stem cells that could eventually give rise to sperm and egg. On paper, their gene expression profile closely matched that of natural counterparts. But in practice, the cells couldn’t mature further without the right environmental cues.
In an usual workaround, the team next mixed the immature cells with supportive, non-reproductive cells isolated from mice testes. While it was an usual environment, the mice cells provided nutrients and molecular signaling that nudged development forward.
Called xrTestis, the mixture spontaneously organized into tube-like structures resembling those inside testes. “Overall, our culture method accurately recapitulates in vivo human male GC [germ cell] development and allows us to understand the genetic pathways governing this process,” they wrote at the time.
Yet none of the immature sperm advanced beyond developmental stages normally seen in fetuses. And the miniature structure collapsed after 80 days, likely because it lacked a blood supply.
To prolong the mixture’s viability and push sperm development further, the team transplanted it into the kidneys of immunodeficient mice.
The graft organized itself into the hallmark tubular structures found in testes within a month and remained stable for at least half a year. The mice showed no signs of discomfort or immune rejection.
Six months later, some human cells developed into spermatogonia—the self-renewing stem cells that eventually generate sperm. Along the way, they underwent a major event: an epigenetic reset. During this process, chemical tags on DNA that influence whether genes are turned on or off are almost completely wiped clean. If that reset is incomplete, it could compromise any sperm eventually used for reproduction.
Here, the team found a “dramatic” genome-wide epigenetic reset. The cells’ gene activity mirrored their natural counterparts. Even though the graft survived for at least nine months, however, none of the cells were able to develop into mature sperm.
This is likely due to the environment. Human and mice testes don’t share the exact same signaling molecules or respond the same way to hormones and other developmental cues. Replacing the mouse support cells with human versions could help the spermatogonia develop further.
The team also tested the technique in monkeys, with results similar to those found in human cells. “While our human iPSC system provided valuable insight into male gametogenesis [the formation of reproductive cells], future studies of fertility competency must be carried out in non-human primates,” they wrote.
Although the cells also halted at the immature stage, the results are still valuable. Previous studies have shown monkey spermatogonia can generate mature sperm after transplantation into recipient testes, opening the door to eventually testing if lab-grown cells can sire healthy offspring.
That idea is precisely what makes some bioethicists uneasy.
Mass-producing sperm and eggs in the lab could generate far more embryos for selection, making it easier for prospective parents to choose desirable traits such as eye color or height. Pairing the technology with gene editing makes “designer babies” less hypothetical. And if skin scrapings or a single hair can be turned into reproductive cells, someone could theoretically create sperm or eggs from another person without consent.
These scenarios are purely speculation, but regulators are already preparing for that future. In 2025, the United Kingdom’s Human Fertilization and Embryology Authority urged the government to explicitly tackle lab-grown reproductive cells in legislation. The International Society for Stem Cell Research has similarly called for careful oversight and public engagement before clinical use. Most countries, however, are only beginning to grapple with how these technologies should be dealt with.
Meanwhile, companies are pressing forward. Paterna Biosciences in Utah recently announced they had produced functional sperm from immature sperm collected during testicular biopsies. According to the company, early embryos created with the lab-grown sperm seemed comparable to those produced through standard in vitro fertilization (IVF). And California startup Conception recently reported generating early human egg cells from iPSCs. Neither company has released results in a preprint or journal article, making the claims hard to evaluate.
Like germline gene editing, conversations weighing the pros and cons of lab-grown reproductive cells will help decide not only what’s possible, but also what should be permitted. For now, the team stresses that their work is only a research tool—not a fertility treatment—and clinical use is a long way off.
The post Scientists Inch Closer to Creating Human Sperm in the Lab appeared first on SingularityHub.
2026-07-21 06:09:06
Cambridge University researchers say launch costs fell from $87,000 to $3,868 per kilogram between 1960 and 2025—or roughly 96%—and could hit $273 by 2040.
Rapidly falling launch costs are making space more accessible than ever. But new research suggests the economics are improving even faster than most people realize, potentially opening the door to entirely new industries beyond Earth.
For most of the space age, the cost of getting material into space was so vast that only the most well-heeled governments and corporations could participate. In 1960, getting a kilogram of payload into orbit would have cost you more than $87,000 (in 2024 US dollars).
But according to researchers at the University of Cambridge, that figure had collapsed 96 percent to $3,868 by 2025. The team’s modeling suggests this trend will continue apace for at least the next few decades, with prices forecast to hit just $1,569 by 2030 and as little as $273 by 2040.
The rapid decline in prices is thanks to a well-established economic principle known as Wright’s Law, which holds that technologies get predictably cheaper as cumulative production grows. The Cambridge team says the trends seen in launch costs could soon make a host of possibilities previously confined to science fiction commercially viable, including orbital solar power, asteroid mining, and space-based manufacturing.
“Space is no longer a science-fiction fantasy or a purely scientific pursuit, it is becoming a marketplace,” Alessio Terzi, who led the study, said in a press release. “Rapidly falling launch costs could open the way to space colonization and commercial activity far beyond low Earth orbit.”
To conduct their study, published in PNAS Nexus,the researchers assembled a massive dataset of rocket launches covering over 4,400 flights by more than 330 different rocket designs from 1960 to 2025. For each launch, they estimated the “unit flyaway cost,” or the total cost to manufacture, maintain, and launch the vehicles, excluding research and development investments.
They then checked how this data stacked up against Wright’s Law, which predicts that every time production volumes double the cost should fall by a fixed percentage. This is known as a technology’s “learning curve” as the reduction in costs is attributed to an industry getting better at producing the technology with experience.
The researchers found space launches obey the law almost perfectly, with every doubling of payload sent to orbit shaving 21.2 percent off the average cost per kilogram. More importantly, this represents a particularly steep learning curve compared to previous technologies.
Solar panels are often held up as the poster boy for learning curves, with prices falling 99.8 percent between 1975 and 2023. But while solar power’s total price reduction is higher than that achieved by launch vehicles, the technology got there by scaling deployment far more. When accounting for total production, solar’s learning curve lags launch costs at 20.2 percent.
The researchers also compared launch costs to another revolution in transport. Steamships transformed our ability to ship goods like wheat and cotton around the world in the 19th century. They found that steamship costs only fell 15.5 percent with each doubling of cargo.
“The cost of space launch technology is now falling faster than during one of history’s greatest transport revolutions,” said Terzi. “Steamships cut costs through explosive growth in global trade. Space technology, by contrast, has achieved even steeper declines at a far smaller scale. This suggests there is plenty of scope for further cost reductions and the industry may now be on the cusp of a comparable economic boom.”
There are, of course, caveats. The researchers note that the industry’s progress is inextricably tied to the fate of a single company. SpaceX already accounts for roughly 80 percent of payload reaching orbit. If the company successfully scales up its reusable, heavy-lift Starship vehicle it could massively reduce costs.
But a company with a stranglehold on the global launch market may be tempted to take advantage of its monopolistic position. This may also push foreign governments and companies away from relying on SpaceX even if it’s the cheapest option.
There’s also the danger that as costs fall and launching material into space becomes more accessible, low Earth orbit could quickly become clogged with debris that makes it increasingly difficult to reach orbit safely.
If these challenges can be sidestepped, the implications of such rapidly falling costs could be profound. The researchers suggest that everything from zero-gravity research and orbital tourism to factories churning out fiber-optic cables and 3D-bioprinted organs could become financially viable.
The post Spaceflight Nears Its Steamship Era appeared first on SingularityHub.
2026-07-18 22:00:00
China’s Moonshot AI Releases Model to Challenge Top US SystemsTracy Qu and Raffaele Huang | The Wall Street Journal ($)
“Moonshot said Friday that it planned to fully open-source the model, called Kimi K3, by late this month, where people will be generally free to download and adapt. With 2.8 trillion parameters dictating its decision-making, Kimi K3 is the world’s biggest open-source model, according to the Beijing-based startup.”
The Fight Over Humanoid Robots Has Shut Down a Car Factory for the First TimeJiyoung Sohn | The Wall Street Journal ($)
“The union’s response [to Hyundai’s new Atlas robot] was blunt: Atlas would never step onto a production line without workers agreeing first. This week, Hyundai’s auto workers in South Korea have gone on a partial strike. It is the car industry’s first factory stoppage addressing humanoid robots.”
Psiquantum Has a Plan to Make a Massive Quantum Computer Out of LightJames O’Donnell | MIT Technology Review ($)
“PsiQuantum has attracted unusual investment and scrutiny for two reasons: It is one of the few companies aiming directly at building a large and useful machine, and it is already working with a major chip manufacturer to build its systems using existing semiconductor fabs.”
Generative AI Is an Engineering DisasterAlex Reisner | The Atlantic ($)
“I asked a few AI researchers whether they could name any other real-world software that scales so poorly. None of them could think of any. Even outside the world of software, it’s hard to find a comparable example, given that economy of scale is the principle that has made light bulbs, cars, and clothing so affordable. By economic and engineering measures, generative AI might be the worst technology ever deployed.”
How Hard Is It to Build Orbital Data Centers, Actually?Eric Berger | Ars Technica
“You need unprecedented heavy lift: reusable and rapid launch. You need the ability to manufacture the largest satellites humans have ever built and to build 100 times more of them than humans ever have for a single constellation. You have to hope that radiation’s impacts on chips are manageable and that radiational cooling scales. You also need a few trillion bucks.”
Once Again We Are Told AI May Be Conscious—I Study Consciousness, and I Have My DoubtsAnil Seth | The Guardian
“The information processing unfolding inside Claude is no more likely to result in consciousness than a simulation of a weather system is likely to generate a real hurricane. AI systems are getting more powerful every day. But to give ourselves the best chance of navigating this new world, we should remember how different we are from our almost-magical creations. When we sell our minds too cheaply to our machines, we not only overestimate them, we underestimate ourselves.”
AI Teaches a Bitter Biology LessonReed Albergotti | Semafor
“Future discoveries and therapies will come not from a human-like understanding of science, but by simple pattern recognition of new biological information at scale. …[Richard] Sutton’s bitter lesson is applicable to biology because so much of the human body—not just the mind—is still beyond our understanding. And we’ll find the way forward by industrializing trial-and-error experimentation until the breakthroughs materialize.”
Want Experts in 10 Years? Keep AI Away From Your Beginners TodayLaëtitia Vitaud | Fast Company
“A Nordic public school system and a 300-person American law firm aren’t pursuing the same goals. But they arrived at the same conclusion: Beginners must first learn without assistance in order to be, later on, well assisted.”
Astronomers Find an Atmosphere on a Nearby Earthlike PlanetKatrina Miller | The New York Times ($)
“New data collected by the astronomers strongly suggests that LHS 1140b has a helium-rich atmosphere. The detection, published in the journal Science, is the first clear evidence of a potentially habitable planet with an atmosphere, and it reinforces the idea that there exists a population of worlds similar to our own with the properties necessary to sustain life.”
The Big Reason Einstein Would Never Have Used AIEthan Siegel | Big Think
“Irrespective of whether AI counts as actual ‘intelligence’ or not, the fact is that outsourcing the growth and refinement of your critical thinking skills means you not only don’t develop those skills yourself, the ones you already have begin to decay. …This is where humans are needed most. Einstein was uncompromising about humans developing those exact skills.”
Simulating Everything, Sort Of: The Promise and Limits of World ModelsSamuel Axon | Ars Technica
“Instead of or in addition to working with language, world models aim to lay the groundwork for AI systems that are capable of simulating the physical world, or at least a useful approximation of it. To examine what’s different and important about this idea, Ars spoke with three expert practitioners working on world models and related technologies.”
Meet GPT-Red: An LLM Super-Hacker OpenAI Built to Make Its Models SaferWill Douglas Heaven | MIT Technology Review ($)
“GPT-Red automates a type of safety evaluation for software systems known as red-teaming, which is typically done by a team of human testers. The aim is to find as many different ways to break or hijack a system as possible. The weak spots can then be patched before the final version of the software is released.”
The post This Week’s Awesome Tech Stories From Around the Web (Through July 18) appeared first on SingularityHub.
2026-07-18 03:57:06
When you interact with a large language model (LLM)—one of the systems behind chatbots such as ChatGPT and Claude—it can feel as though you are in contact with another conscious mind. But are you, really?
Some prominent scientists, such as Geoff Hinton and Richard Dawkins, claim you are. But most experts remain skeptical, arguing that the impressive cognitive capacities of LLMs occur in the absence of consciousness.
Recently, researchers at Anthropic, the company behind Claude, waded into this debate with an interesting finding. They claim Claude has a normally invisible set of representations of information that guide its internal reasoning and its verbal output.
This is where it gets interesting. The researchers argue this finding can be understood in terms of an influential theory of consciousness called the global workspace theory.
First proposed by the psychologist Bernard Baars in 1998 and further developed by the neuroscientist Stanislas Dehaene and his collaborators, this theory holds that consciousness involves the activity of a “global workspace.” This is a kind of processing hub in the mind or brain that integrates and broadcasts information, allowing it to be used for reasoning, behavior control, and speech.
In a glossy video explaining the work, Anthropic depicts the contents of Claude’s “global workspace” as sailing ships afloat on a vast sea of unconscious mental activity.
How should we react to these developments? Do they provide evidence for artificial consciousness? If so, how strong is that evidence?
We can start by asking whether Claude does indeed have a “global workspace.” This is not straightforward, for the theory gives no formal definition of a global workspace.
The notion is characterized only informally. The (typically implicit) assumption is that any computational workspace “similar enough” to a human’s will qualify as a “global workspace.” But how similar is similar enough?
Claude’s workspace may indeed have much in common with ours, but there do appear to be differences.
For example, the brain’s workspace is sustained by recurrent loops—signals cycling back through the same circuits over time. In contrast, Claude’s workspace evolves over a single pass through the network.
A related difference concerns how representations enter a workspace. Advocates of global workspace theory have long argued that in humans, a process called “ignition” occurs in which a non-linear process amplifies and sustains neural representations, allowing them to enter the workspace. As far as we know, nothing comparable occurs in Claude’s case.
Do these differences matter? The answer is not clear. Global workspace theory is based on data drawn from adult humans. There are questions about how far the notion can be—or should be—extended.
But let’s suppose Claude does have a global workspace. To figure out whether that would be evidence for Claude being conscious we need to consider the status of the global workspace theory of consciousness.
There is no doubt it’s one of the most influential theories of consciousness, but it’s hardly uncontroversial among experts. (In a rather extreme understatement, Anthropic’s paper remarks that “the global workspace model is not universally accepted.”)
Many consciousness experts argue that computational properties alone are enough for consciousness. Even among those who think that consciousness is inherently computational, global workspace theory is only one of many options.
What’s more, there are questions about whether global workspace theory is really a theory of consciousness in the relevant sense at all.
In an influential paper on artificial consciousness, the neuroscientist Dehaene and his collaborators advance the theory as an account of what they call “conscious access”—the availability of information for recall, the voluntary control of behavior, and verbal report. Crucially, they leave open the question of whether global workspace theory should be understood as an account of the subjective or experiential components of consciousness.
But if global workspace theory is just a theory of “conscious access,” then its implications for the artificial consciousness debate lose much of their significance. When we ask whether Claude is conscious we don’t want to know whether it has “conscious access”—instead, we want to know whether there is anything, subjectively speaking, that it’s like to be Claude. Global workspace theory doesn’t speak to that question if we treat it as nothing more than an account of “conscious access.”
Even taking these complications into account, there is no doubt that Anthropic’s findings are noteworthy. Global workspace theory can be understood as a theory of subjective experience, and Claude may indeed turn out to have something akin to a “global workspace.”
None of this is evidence that artificial consciousness has arrived. But it’s not unreasonable to think these findings do move the dial—if only ever so slightly—in the artificial consciousness debate.
But if that’s right, then it’s puzzling why Anthropic is quite so upbeat about these developments. As Anthropic recognizes, the creation of artificial consciousness would be a momentous event with wide-ranging social, ethical, political, and legal ramifications.
If chatbots are conscious then we would need to take their interests seriously. It would no longer be permissible to treat them as mere machines; instead, we would need to consider their welfare.
Anthropic remarks that “it’s time to start thinking about whether we should be building conscious machines.”
I agree we need to have that discussion, but we should also pause work on building machines that might potentially be conscious. If Anthropic were serious, it would surely down tools rather than plough ahead with its attempt to develop conscious AI.
A moratorium on AI research that might be thought to lead to conscious AI would, of course, be far from straightforward. There are questions about the range of research it would affect and who might enforce it. But if we don’t close the stable door now we might find that the horse has already bolted.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
The post Anthropic Says Chatbots Have What May Be a Key Feature of Consciousness. Are They Right? appeared first on SingularityHub.
2026-07-17 07:03:59
Research suggests offloading mental work to AI is like debt: an immediate payoff with long-term consequences. But collaborating with the technology may boost our work without eroding skills.
Thinking is hard. It’s no wonder we lean on technology to lighten the load. We use calculators instead of doing long division by hand, GPS or Google Maps for navigation, and search engines instead of countless trips to the library. Yet just a few decades ago, getting around meant unfolding paper maps, and looking up a word required leafing through a hefty dictionary. Cognitive offloading of mental tasks to tools makes us more efficient. What’s the harm?
Then along came ChatGPT, Claude, and Gemini. Unlike earlier digital tools, AI chatbots can tackle an astonishing range of tasks and are easy to use. At a prompt, AI generates essays, analyzes medical images, writes software, and floods our feeds with AI slop. It’s cognitive offloading to the max.
Now people are asking: Is AI dulling our minds?
Yes and no, according to a new paper written by an international team of psychologists. AI can accelerate learning by giving people immediate guidance and feedback. But take the tool away, and those who rely on it often perform worse than people who learned the material on their own. Similarly, using AI to summarize information, rather than researching and organizing it yourself, often leads to shallower understanding.
But it’s not all bad news. Core cognitive abilities—including attention, reasoning, and working memory—seem to be “stubbornly resistant” to manipulation, the team wrote.
As technology evolves, so does the way we gain knowledge and think for ourselves. AI may reshape not just what we learn, but how we learn to learn. And like any other tool, its impact comes down to how we use it. Completely relying on AI is likely detrimental. But as a collaborator that challenges ideas or fills knowledge gaps, it can boost performance even after the tool is taken away.
“There is clearly a risk that AI can make us ‘stupid’ by compromising our skills (and knowledge) if we completely offload them to AI,” wrote the team. “[But] AI may be less likely to diminish the foundational cognitive capacities that underpin our ability to be smart, rather than ‘stupid’, in the first place.”
It’s easy to rely on large language models (LLMs)—the algorithms behind chatbots—for help. Why read an assigned novel when AI can summarize it in seconds? Gmail has already drafted an email reply; all I need to do is click send. That pesky essay? A few prompts and voila, done.
It seems like an easy hack, but there’s a cost to handing over too much thinking.
Researchers have long studied the consequences of cognitive offloading, or using external tools to reduce mental effort. Writing down a shopping list and keeping appointments in a calendar free up working memory, the brain’s temporary mental workspace, and allow us to focus on more important tasks without having to remember every detail.
AI is different. Beyond memory, it can offload critical thinking itself.
An MIT preprint introduced the idea of “cognitive debt” to describe the tradeoff. Participants wrote essays either with ChatGPT, using only a search engine, or with just their brains. Researchers monitored their brain activity during the task. Those using AI showed the weakest brain connectivity, which suggests they were less engaged. They also struggled to remember their own writing and felt the completed essay didn’t reflect their own ideas. When asked to write again without AI, they produced weaker work according to human judges.
Like financial debt, cognitive debt offers an immediate payoff with long-term consequences. Outsourcing mental effort makes writing faster and easier, but it slashes opportunities to build knowledge, strengthen reasoning, and practice critical thinking.
“While LLMs offer immediate convenience, our findings highlight potential cognitive costs,” wrote the MIT team.
Other studies have found the same pattern. High school students learning a new mathematical concept solved practice questions better with AI help, but they struggled on a later test when left to think on their own. Using AI “impeded the students’ learning by preventing them from engaging in the practice needed to acquire the skill,” wrote the team.
Habitual reliance on AI may even erode already-acquired expertise. In a large study of over 1,400 patients undergoing colonoscopy screening, doctors used an AI system to help detect abnormal growths. Three months later, when the AI was unavailable, their detection rate dropped from 28.4 to 22.4 percent.
“Continuous exposure to AI…[suggests] a negative effect on endoscopist behavior,” wrote the European team.
These effects extend beyond individual skills. AI can also influence how we build knowledge in the first place.
A recent study asked participants to learn about gardening by either Googling and synthesizing the knowledge themselves or by asking ChatGPT for a summary. They were then asked to give advice to someone else without technological help. Answers from those who relied on ChatGPT were rated as generic and less helpful, suggesting a shallower understanding of the topic.
We’re only beginning to understand how AI reshapes the mind. And it’s not all doom and gloom. The crux is how we use it.
In the MIT essay-writing study, for example, people who initially wrote on their own but later gained access to ChatGPT produced work with higher creativity and stronger arguments, while retaining their original perspectives and voice. Likewise, high school students who used AI as a tutor—asking for hints rather than answers—performed well even after the chatbot was taken away.
Used thoughtfully, AI may also enhance collaborative learning and brainstorming or serve as a writing coach, helping people work less and learn more.
Far less is known about if, and how, AI impacts fundamental cognitive capabilities. Attention, reasoning, and working memory have proven remarkably resilient over decades of cognitive research. Becoming better at a task usually reflects learning to use these mental resources more efficiently, not expanding the brain’s processing power. While AI may erode a specific skill, it could spare this core cognitive architecture, wrote the authors.
Whether that remains true over decades of AI use or during early childhood—when the brain is rapidly developing—is an open question.
Plenty other unknowns remain. Will we eventually adapt to AI, just as we’ve embraced calculators, search engines, and smartphones? Can refresher training ward off skill decay, or will some tasks simply become obsolete? How can we encourage people to strategically offload and benefit from AI use? And perhaps more philosophically: As we increasingly share our thinking with machines, will our definition of thinking evolve?
The post Is AI Making Us Dumber? appeared first on SingularityHub.
2026-07-15 05:48:06
A newly discovered brain-gut-bone marrow highway in mice could inspire strategies to protect immunity from chronic stress.
Stress does more than take a toll on mental health. After a particularly taxing week or month, it’s easier to catch a cold and harder to recover. Health issues build up as stress lingers, raising the risk of heart disease, diabetes, cancer, and a weakened immune system.
Chronic stress is often treated as an unavoidable part of modern life. While therapy can help people cope, researchers are increasingly asking a deeper question: How do stress signals in the brain ripple through the rest of the body, and can that damage be stopped?
A new study offers one of clearest answers yet. In mice modeling chronic stress, activity dropped in two brain regions governing emotional resilience. By way of a large nerve to the digestive track, the change wiped out a beneficial bacterial strain key to a healthy microbiome.
Without those microbes, the gut produced less of a crucial molecule that helps cells clear damaged proteins and other molecular junk. These effects impacted the bone marrow, where stem cells generate oxygen-carrying blood cells and components of the immune system. Over time, these stem cells dwindled, leaving signs of premature immune aging in stressed mice.
“One surprising finding of our study was that suppression of only two specific brain regions was sufficient to produce many of the hematopoietic [blood stem cell] defects caused by psychological stress,” study author Linjia Jiang at Sun Yat-sen University said in a press release.
By tracing a direct pathway from brain to gut microbiome and bone marrow, the results could inspire new ways to blunt the biological toll of stress, from targeted probiotics to non-invasive brain stimulation.
De-stressing has become synonymous with self-care. Whether it’s work, family obligations, or a stream of notifications stressing you out, escaping into a good book or a walk in the woods feels like a deep mental exhale.
Stress has its perks. A product of the “fight-or-flight” response, it activates the sympathetic nervous system, a kind of highway connecting brain and body. In extreme cold, the system redirects blood from the skin to vital organs and temporarily slows digestion to prioritize muscles during a marathon. Brief bursts of stress aren’t detrimental. They’re an evolutionary survival hack.
But chronic stress is another story. Decades of research have found that prolonged or repeated mental strain disrupts brain activity and increases the vulnerability to a range of diseases. This is largely related to stress hormones released by the brain. But direct electrical signals traveling to the gut—which is often nicknamed the “second brain”—may also play a major role.
The garden of microbes in our gut roughly matches the number of cells in the body. These bacteria regulate digestion, metabolism, and immunity. They also communicate with the brain. When the ecosystem falls out of balance, it contributes to conditions ranging from diabetes to brain disease.
These beneficial effects can be traced to chemicals gut microbes manufacture. Lactobacillus reuteri, for example, boosts production of spermidine, a molecule that helps cells and tissues clear toxic debris. The process, called autophagy, is essential for the maintenance of healthy tissues but declines with age.
Stress also makes blood stem cells less resilient. Studies have linked prolonged stress to shortened telomeres, the protective caps at the ends of chromosomes, and an accumulation of senescent “zombie” cells. Both are hallmarks of accelerated biological aging.
The brain, gut microbiome, and bone marrow all respond to chronic stress. The new study aimed to find out if they’re connected.
To trace how chronic stress ages the body, the team tested four mouse models. Some experienced mild nerve injury. Others faced subtle disruptions to their daily routines, such as lights switching on earlier than expected or their home cages gently rocking at unpredictable times.
The changes put the mice on edge based on established behavioral tests. Mapping brain activity, the team zeroed in on two regions that consistently quieted. One, the medial prefrontal cortex, orchestrates executive control, or the ability to keep ideas in mind while reaching towards a goal. The other, the periaqueductal grey, coordinates attention to potential threats.
As activity decreased in both regions, blood stem cells struggled to divide and replenish immune cells. Inflammation and other toxic pathways flared up, and the cells developed molecular signatures similar to those seen in much older animals. Silencing either brain region with genetic tools reproduced many of the same symptoms, suggesting neural changes are a cause, not just a correlation.
But how was the brain communicating with the bone marrow? The answer lay in the gut microbiome.
Comparing the levels of chemicals surrounding the bone marrow in stressed and unstressed mice, the team zeroed in on spermidine. The molecule is made by gut bacteria and boosts autophagy, a process that’s linked to healthy aging.
Spermidine levels plummeted in stressed mice due to the loss of Lactobacillus reuteri, a beneficial strain of bacteria in the gut ecosystem that supports spermidine production. Stress-related nerve signals from the brain depleted these microbes, which caused spermidine levels to collapse and leaves blood stem cells unable to maintain themselves.
In another test, transplanting gut microbes from a stressed mouse into a happy-go-lucky mouse triggered early blood stem cell aging in the recipient—even though it didn’t experience stress itself. The results strengthen the case that the gut microbiome is a major link between the brain and bone marrow.
Rather than stress hormones, the pathway seems largely driven by electrical signals traveling from stress-sensitive brain regions to the gut. This means targeted brain stimulation could interrupt the cascade. Supplementing Lactobacillus reuteri as a probiotic or directly providing spermidine in a pill may also restore the missing molecule and slow blood stem cell aging.
This is just speculation though. Stress is deeply personal, and mice can’t capture the entire human experience. The team is now investigating whether the same brain circuits operate in people and if targeting this brain-gut-bone marrow axis can benefit the immune system.
“Our findings raise the possibility that managing psychological stress may not only improve mental well-being but also help preserve immune function and promote healthy aging,” said Jiang.
The post Scientists Find a Surprising New Way Stress Cascades From Brain to Body appeared first on SingularityHub.
