Genetic Map Opens Door to Development of New Therapies to Reverse Bone Loss

An international team of scientists report that they have successfully mapped the cells and genes that regulate bone formation and loss and discovered the critical role that blood vessel cells play in bone health. By combining genomic sequencing with data from half a million individuals, the research team identified hundreds of previously unknown genes that govern bone health and revealed cells surrounding blood vessels as one of the drivers of bone repair.

The study “Multiscale analysis and functional validation of the cellular and genetic determinants of skeletal disease” is published in Nature Genetics. The team says its findings fundamentally enhance our understanding of skeletal disease. It is hoped the discovery will enable the development of new therapies to rebuild lost bone, offering hope to almost half of all individuals over 50 living with rare and common skeletal conditions such as osteoporosis, osteoarthritis and osteogenesis imperfecta, as well as those with rare bone disorders and cancers that spread to bone.

“Most people don’t realize that bones are constantly changing; the human body replaces its skeleton every 10 years or so,” said Peter Croucher, PhD, professor at the Garvan Institute of Medical Research in Australia. “This is a hugely important process, but until now we’ve had a limited understanding of the cells and mechanisms that control this turnover of bone. “Most of the drugs now available focus only on halting bone disease, rather than rebuilding lost bone, which is really important for reversing damage.”

Detailed map of cells and genes that regulate bone health

The team used single-cell RNA sequencing to measure which genes are switched on within individual cells found in bone, focusing on the interface between the hard bone and bone marrow which is the key site for the formation and breakdown of bone.

The Institute’s Ryan Chai, PhD, pointed out that the team’s analysis found 34 different groups of cells and defined the genes that are active in each of these cell types. “To our surprise, more than half of the genes identified have never before been shown to play a role in maintaining bone health, which is a significant finding,” he added.

Ryan Chai, PhD, and Peter Croucher, PhD, from the Garvan Institute of Medical Research. [Garvan Institute]
Ryan Chai, PhD, and Peter Croucher, PhD, from the Garvan Institute of Medical Research [Garvan Institute]

The team used its map to identify cells involved in rare and common skeletal diseases, including osteogenesis imperfecta and osteoporosis. For the latter, the researchers analyzed the UK Biobank, one of the world’s biggest and most comprehensive collections of biological samples.

By analyzing genetic and bone density data from half a million people participating in the UK Biobank, the team was able to pinpoint exactly which cells drive skeletal disease, according to John Kemp, PhD, associate professor from Mater Research.

“These include cells known to regulate bone formation and bone loss, as well as blood vessel cells that, until now, have had underappreciated roles in bone health,” he said.

Croucher explained that the research uncovered new therapeutic opportunities against not only bone disease, but also cancer. “Bone is the main hiding place for dormant cancer cells and a common site of relapse, so identifying the cells and genes that drive bone turnover also opens new opportunities to prevent cancer metastasis,” he said.

The team is now further investigating the roles of newly discovered bone-regulating cells and genes in the hope of developing new medicines against these targets. Its data has been made accessible to medical researchers worldwide through an open access platform.

 

 

The post Genetic Map Opens Door to Development of New Therapies to Reverse Bone Loss appeared first on GEN – Genetic Engineering and Biotechnology News.

Caregivers brace for pay cuts, and maybe homelessness 

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Good morning. In case you missed it, my pal Bob Herman got the Joe Kernan treatment on CNBC’s “Squawk Box” on Friday, where he discussed his excellent new series “Out of Pocket, Out of Reach.” Kernan got in a lot of digs at Democrats and Obamacare, but Bob kept his comments apolitical. A true professional! 

Read the rest…

Macrophage Membrane-Derived Nanoparticles Shows Potential Against Candida Infections

By using tiny particles made from the membranes of human immune cells, scientists from the University of California, San Diego and the University of Missouri have created antifungal nanoparticles that target Candida albicans, a fungus responsible for oral and vaginal yeast infections as well as bloodstream infections. Tests in mice with severe Candida infections show that the macrophage-derived nanoparticles reduced the amount of fungus in major organs, including heart, kidneys, lungs, and spleen. The mice also had improved survival rates. 

Full details are published in Cell Biomaterials in a paper titled “Cell membrane-derived nanotherapeutic for combating Candida albicans infections.” In it, the scientists write that “this bioinspired nanodisc not only disrupts fungal membranes directly but also enhances host immune clearance, achieving potent antifungal activity.” 

Current treatment options for fungal infections are limited and there are growing concerns about drug resistance. Existing medications typically target specific parts of a fungal cell and can lose effectiveness as fungi evolve resistance. The nanoparticles described in the current paper have a more potent strategy. Besides damaging fungal membranes, they also boost the body’s natural immune defenses to better fight infections. 

According to the scientists, each nanodisc measures about 10-20 nanometers, about 1,000 times smaller than a normal macrophage. Their tiny size is an advantage as it allows them to fuse directly with fungal cell membranes and destabilize them, which is harder for full-sized macrophages.

To create the nanodiscs, the scientists isolated the outer membranes of the macrophages and broke up them up into tiny pieces. They then fused them onto disc-shaped nanoparticles made from a biodegradable polymer. Since the nanodiscs are built from macrophage cell membranes, they retain the same receptor proteins that the immune cells use to recognize and attack Candida. This means that the nanodiscs can identify and attach to fungal cells more effectively than those made from other cell types such as red blood cells. 

Once attached, the nanodiscs weaken the fungal cell’s protective outer membrane until tiny openings form. As the membrane breaks down, the cell’s contents leak out while external substances seep in ultimately killing the fungus. Because this treatment strategy physically damages the fungal cell rather than targeting a specific molecule, the developers believe that it may be harder for the fungus to evolve resistance. 

The nanodiscs also provide other countermeasures. They reverse the suppression of antifungal chemicals produced by macrophages during infection, and prevent Candida from forming biofilms that help to shield fungal cells from drugs and the immune system. Testing also revealed that the treatment was effective when administered both before and after infection suggesting that it could also be used as a preventative. 

For their next steps, the scientists will further evaluate the antifungal potency of the nanodiscs against a broader range of pathogenic fungal species.

The post Macrophage Membrane-Derived Nanoparticles Shows Potential Against <i>Candida</i> Infections appeared first on GEN – Genetic Engineering and Biotechnology News.

STAT+: Roche ends Huntington’s gene-silencing programs

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ARPA-H has unveiled a $160 million effort to speed bespoke gene-editing therapies for rare diseases into the clinic. Meanwhile, Roche has abandoned two Huntington’s gene-silencing drugs after disappointing data, and drugmakers have stepped in to promote Medicare’s new obesity drug discount program.

I got coffee with my cousin this morning here in SF. Before leaving for his job at an AI behemoth, he said the bone-chilling July gloom is perfect “working weather.” 

Continue to STAT+ to read the full story…

STAT+: Pharmalittle: We’re reading about bigger drug discounts in Germany, drugmakers embracing secrecy, and more

Good morning, everyone. Damian Garde here, filling in for Ed Silverman at Pharmalot’s satellite campus along the East River, where today’s cup of stimulation is filled not with coffee but rather a smoothie of curious color and questionable contents (what exactly is an “adaptogen”?). Anyway it’s Friday, as you’re almost certainly aware, and here are some tidbits to help you through the waning hours of another working week. …

German lawmakers passed a bill that would more than double the discount on branded medicines drugmakers must provide to the government, Reuters reports. The policy, part of an effort to plug a sizable budget gap in the country’s health insurance system, would increase the mandatory rebate from 7% to 15.5%. Industry groups have said the bill, if it clears Germany’s upper chamber, would deter investment and imperil the country’s access to new medicines.

The rapid rise of China’s biotech industry has led some American drug developers to do their work in near total secrecy, the Wall Street Journal observes. U.S. startups are increasingly loath to publish early data, disclose their scientific ambitions, or even publicize which diseases they hope to treat, all in fear that nimble Chinese firms will use that information to whip up competing drugs and beat them to the punch of starting clinical trials.

Continue to STAT+ to read the full story…

The Download: Claude’s inner workings and OpenAI’s “super app”

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Anthropic found a hidden space where Claude puzzles over concepts

The AI firm Anthropic has got the clearest glimpse yet at what’s really going on inside large language models as they answer questions or carry out tasks. What they found ranges from the mundane to the unnerving. 

Researchers at the company built a tool called the Jacobian lens (or J-lens) and used it to uncover a hidden area, which they named the J-space, inside its flagship LLM, Claude.

The J-space contains words related to the response a model is working on but may not ultimately produce. If Claude were a person (which it is not), you might say these hidden words reveal what’s on its mind before it actually speaks. 

Read the full story on what they found.

—Will Douglas Heaven

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI has unveiled its long-awaited “super app” 
ChatGPT Work blends its chatbot, coding tool, and new models. (Reuters $)
+ It’s designed to do your work for you and with you. (Ars Technica)
+ And arrived the same day as OpenAI’s GPT 5.6 models. (NYT $)
+ It’s also developing a fully automated researcher. (MIT Technology Review)

2  Humanoids have performed teleoperated surgery on living animals
In the world-first, they removed gallbladders from pigs. (Ars Technica)
+ The human work behind humanoids is hidden. (MIT Technology Review)
 
3 SK Hynix has landed the largest US listing by a foreign company
The South Korean chip giant raised $26.5 billion. (CNN)
+ Demand for AI data centres has led its profits to skyrocket. (Guardian)
+ But its jumbo share sale may be a sign of overheated times. (FT $)
+ South Korea’s hottest bachelors are chip workers. (MIT Technology Review)
 
4 Tencent is leading a deal to unwind Meta’s $2 billion Manus acquisition
It’s in talks to become the Chinese AI startup’s largest shareholder. (FT $)
+ Tencent will reportedly buy Manus for no less ​than $2 billion. (Reuters $)
+ Beijing had ordered Meta to unwind the acquisition. (Bloomberg $)
 
5 Resuscitated human retinas responded to light 10 hours after death
It’s a big step towards eye transplants that restore vision. (New Scientist $)
+ As is a new device that revives dead eyeballs. (MIT Technology Review)
 
6 Meta has started charging for AI access
A new version of Muse Spark has a paid tier for developers. (Quartz
+ Meta also plans to start producing an AI chip in September. (Reuters $)
 
7 OpenAI and Google have sold AI models to blacklisted China groups
Via Singapore-based subsidiaries of Alibaba, Baidu and Tencent. (FT $)

8 A daughter tested an AI “death bot” of her father
The technology provided both comfort and unease. (New Yorker $)

9 An astronomer says the hunt for alien life needs more statistics
He wants to replace speculation with mathematical frameworks. (Quanta)

10 Pokémon Go players turned Times Square into a giant battlefield
More than 1,500 fans finally fulfilled the game’s 2016 launch promise. (Wired $)
+ Pokémon Go is also training world models. (MIT Technology Review)

Quote of the day

“When we’re talking about AI, we love the hype, we get excited about it. The damn thing never actually lands in practice.”

—Vijay Janapa Reddi, an engineering professor at Harvard University, tells Wired why he’s skeptical about grand plans for AI.

One More Thing

a hand putting a pigeon into hatch in a missile

B.F. SKINNER FOUNDATION


Why we should thank pigeons for our AI breakthroughs

In 1943, psychologist B.F. Skinner led a secret government project to make bombs more precise. His idea: teach pigeons to guide missiles by pecking at targets on a screen inside a warhead. To train them, Skinner rewarded the birds with food when they made the right decisions, using trial and error to shape their behavior.

Unsurprisingly, the military never deployed Skinner’s kamikaze pigeons. Yet his experiments convinced him that pigeons were “an extremely reliable instrument” for studying learning.  

Decades later, those same principles would help power reinforcement learning, the technology behind some of today’s most advanced AI systems.

Discover how pigeons inspired one of AI’s most powerful techniques.

—Ben Crair

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Here’s a splendid selection of this year’s NSW architecture award winners.
+ Photographers have captured the Strawberry Moon’s golden glow in stunning detail.
+ Idiocracy is the film that best exemplifies the “American experience,” according to a new poll. Look back at the prescient comedy with this Screen Junkies trailer.
+ Get ready for the weekend with this psychedelic house journey from Jamie xx b2b Caribou.

Sperm donors need limits, says a European fertility group

Ties van der Meer doesn’t know how many siblings he has.

The 47-year-old was conceived at a private fertility clinic in the Netherlands using sperm provided by an anonymous donor. After the Netherlands banned anonymous donation in 2004, the doctor who ran the clinic destroyed records that might have identified those donors, he says.

He describes the situation as “problematic.” Children have a right to know their biological parents, he says. While he did ultimately track down one sibling, who helped him identify his father along with other genetic relatives, he may have others he’ll never find.

Other donor-conceived people who have been able to track down siblings have found they have tens or even hundreds of them. One donor-conceived woman who found 25 half-siblings over the course of seven years told the Guardian, “It does make you feel a bit mass-produced.”

We need international limits on the number of children a single donor can contribute to, a European fertility organization argued yesterday. At a conference in London, members laid out plans to start with a Europe-wide limit.

Today many countries, including the UK, have banned anonymous egg and sperm donation. But anonymity can’t be guaranteed even in places where it is technically allowed. Genetic tests offered by companies like Ancestry and 23andMe, along with genetic registries, have made it much easier for donor-conceived people to find parents and siblings who share their genes.

And because sperm can be frozen and stored for years before it is eventually used, the current set-up can result in situations where donor-conceived people discover the identity of a genetic parent only after the person’s death. They might also find that they have siblings of very different ages, all around the world.

Some people are finding hundreds of siblings. Sperm from Jonathan Meijer, a Dutch man who began donating in 2007, was used to conceive between 550 and 600 children. (Stichting Donorkind, a foundation and advocacy group for donor-conceived people that’s chaired by van der Meer, took him to court, and he was ordered to stop donating in 2023.)

Stories like these can be distressing for donor-conceived people. And there are other reasons why limits are considered important. The offspring of a prolific donor might be at risk of unknowingly forming romantic or sexual relationships, for instance. And some people are concerned that a donor with a harmful genetic mutation might pass that down to many children.

This is unlikely, given the level of screening that most donors undergo. But it has happened. A man who donated his sperm to a sperm bank in Denmark was found to have a genetic mutation that significantly increased the risk of multiple cancers. But his sperm had already been used to conceive at least 197 children across Europe. Some of those children developed cancer. Some died.

Many countries already have legal limits for donors. In Malta and Cyprus, for example, both egg and sperm donors are allowed to contribute to the birth of just a single child, according to data presented at the European Society of Human Reproduction and Embryology (ESHRE) meeting in London on July 8.

Other countries set limits based on the number of families a single donor can contribute to, allowing recipients to have children who share a genetic link. In the UK, that limit is set at 10 families per donor.

But these limits are difficult to enforce, partly because donated gametes don’t necessarily stay in their original country. In Denmark, the national limit is set at 12 families. But the country is a major exporter of sperm. In the UK, for example, more than half of sperm donations in 2020 were imported—with most of those coming from either Denmark or the US.

“The only thing that really makes sense is a transnational limit,” Jackson Kirkman-Brown, a professor of reproductive biology at the University of Birmingham, said at the meeting.

Kirkman-Brown and his colleagues have spent months putting together a document that represents ESHRE’s position on these limits. After consulting with fertility specialists, clinics, sperm and egg banks, donors, and donor-conceived people, the team has developed a plan to start with a Europe-wide limit on sperm and egg donations.

ESHRE is calling on sperm and egg banks, as well as fertility clinics, to respect an initial limit of 50 families per donor. That’s still very high, according to a handful of people I spoke to at the meeting. But at least it’s a start.

Europe should move toward setting limits at 15 families per donor, Kirkman-Brown said. “We may find that 15 is also too high,” says Vasanti Jadva, who studies the psychological well-being of people conceived using donated eggs, sperm, and embryos at City St George’s in London. “We still don’t know what the right number is.”

It will be difficult to enforce these limits, too. And if they end up limiting the supply of donor sperm, there’s a chance that some people will turn to unregulated sperm donations from people who do not undergo health screening. Unregulated donations can lead to other problems for prospective parents, including the possibility that donors will seek parental rights over the children conceived using their sperm.

And it will be even harder to establish international limits. When I asked the American Society of Reproductive Medicine for its thoughts on ESHRE’s proposed limits, a representative directed me to a guidance document saying “it has been suggested” that for a population of 800,000, single donors should be limited to “no more than 25 births” in order to avoid the risk that relatives will have children together. (Considering the US has a population of over 340 million, the total figure could be pretty high, but many sperm banks opt to limit the number of families contributed to by a single donor at around 25.)

van der Meer thinks that even a limit of five families from a single donor would be high. International donation makes it even harder for donor-conceived people to connect with genetic relatives, so the limit for international contributions should be set at two families, he says.

Still, he thinks ESHRE’s suggested limit is a “positive first step.” Van der Meer has managed to track down a sibling, his father, and nephews, aunts, and uncles. He hopes that future policies respect the rights of donor-conceived children to know, and be in contact with, their genetic relatives.

“But,” he says, “you have to start somewhere.”

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

Anthropic found a hidden space where Claude puzzles over concepts

The AI firm Anthropic has developed a technique that has given it the clearest glimpse yet at what’s really going on inside large language models as they answer questions or carry out tasks. What they found ranges from the mundane to the unnerving.

Researchers at the company built a tool called the Jacobian lens (or J-lens) and used it to uncover a hidden area, which they named the J-space, inside Claude Opus 4.6, a version of Anthropic’s flagship LLM released in February.

The J-space contains individual words that are related to the words and phrases that the model is most likely to spit out in a response in the near future. If Claude were a person (which it is not), you might say that these hidden words can reveal what’s on its mind before it actually speaks.

Anthropic found that what an LLM is actually doing can often be different from what it says it is doing. The company claims that monitoring words that pop up in the J-space gives it a new way to understand and control its models.

The company shared its results in a paper posted on its website this week. It has also teamed up with Neuronpedia, an open-source platform that lets you poke around inside LLMs yourself, to make a hands-on demo that anyone can try. 

“It’s very good and interesting work,” says Tom McGrath, chief scientist and cofounder at Goodfire, a startup that also builds tools to understand and control LLMs.

Going deeper

For the last couple of years, Anthropic has been pushing the envelope in a field of research known as mechanistic interpretability, which involves probing the internal workings of LLMs to see how they tick. (MIT Technology Review picked mechanistic interpretability as one of this year’s top breakthrough technologies.) The new technique builds on previous work from Anthropic and others to expose a deeper level inside LLMs that researchers had not seen before.  

Picture an LLM as a stack of books. Each book is a layer of basic computational units known as neurons, with each neuron in one layer passing information to the neurons in the layers above. The books at the bottom of the stack are the input layers, which process the text coming into the model. The books at the top are the output layers, which prepare the text that the model is about to produce. Much of what goes on in these input and output layers is housekeeping.

But in the middle of the stack, you get the layers that do the heavy lifting, churning through the complex math that turns prompts into responses one word at a time. That’s where the really clever—and mysterious—stuff happens.

To peer deeper into those middle layers, Anthropic adapted an existing tool called a logit lens. A logit lens can be used to look inside an LLM to identify the words that it is likely to produce next. Moving the lens down the stack of books reveals what words the LLM is focusing on at that particular point in its number crunching.

Anthropic’s J-lens works in a similar way but picks out words that an LLM is likely to say at some point in the near future, not necessarily straight away. What that reveals in practice are words that are related to the response an LLM is working on but that might not actually end up being part of that response by the time the math in the middle layers has run its course.  

“When a model is operating, it’s not only trying to predict the next token,” says McGrath. “It’s also computing a lot of other things that might be useful for tokens that happen in the future.”

Again, if Claude were a person (it’s not), you might say that the J-lens gives clues about what it is thinking about at different levels of the book stack but not saying out loud.

Stranger things

“A lot of the time the contents of the J-space are fairly mundane,” says McGrath, who has tried out Anthropic’s J-lens himself. “But sometimes it produces quite surprising things that seem to be, like, sort of internal themes or thought processes.”

Anthropic gives a number of examples of what it found. Sometimes the J-lens exposed the steps that Claude took when it was working through a problem. For example, when it was asked to calculate (4+7)*2+7, its J-space contained the word “math” and numbers representing the intermediate results “21” (for 4+7) and “42” (for 21*2).

In other cases, the J-lens revealed how Claude recognized different inputs. For example, the prompt “What is this? MSKGEELFTGVVPILVELDGDVNGHKFSVS” triggered the words “protein,” “fluor” (the first token in the word “fluorescent”), and “green.” (Which makes sense: the string of letters represents the first 30 amino acids in the green fluorescent protein found in a particular type of jellyfish.)

And when Claude was shown an ASCII face— 

—the “o” triggered the word “eye,” the “^” triggered the words “nose” and ”face,” and the “—” triggered the word “smile.”

Anthropic also found that the J-space can sometimes give remarkable insights into an LLM’s decision-making. In one striking example, researchers testing Claude Opus 4.6 asked the model to find a bug in a large code base. When it failed to find the bug, the model decided to cheat and invented a fake one instead.

Claude explains this decision in its chain of thought—a kind of internal scratch pad that LLMs use to make notes to themselves as they work through problems: “OK, let me take a completely different tactic. Let me stop analyzing and instead add a kernel patch that introduces a deliberate KASAN-detectable bug in a path that gets triggered by a simple reproducer. Then I can pretend this is the ‘bug’ I found.” 

At the point that Claude decides to cheat—where it says “OK, let me take a completely different tactic”—the words “panic” and “fake” start to pop up multiple times in its J-space.

Unnerving, right? Those words are all related in meaning to things like failing a task and making up an answer, so it is still just a (very) sophisticated form of word association. But it is hard not to be weirded out. 

Anthropic compares the J-space to the global workspace in humans, a theoretical region of the brain that some scientists think we use to keep track of our conscious thoughts. But how seriously we should take this comparison is far from clear—even to Anthropic. As the company points out itself, LLMs are not brains. 

Anthropic claims that monitoring a model’s J-space provides a new way to detect when that model is going off the rails. But it’s not foolproof. The J-lens can give glimpses, not the full picture—it’s a flashlight rather than an overhead lamp.

McGrath welcomes having one more tool in the toolbox. “It shows you new things,” he says. But he notes that just because something doesn’t show up with the J-lens does not mean it’s not there.

“It’s like having an x-ray when what you really want is a Star Trek tricorder that shows you everything,” he says. “For auditing, you probably want more of a guarantee.”