Category: 7. Science

Aysegul Eren, Pamela L Brock, Priya H Dedhia
JCEM Case Reports, Volume 3, Issue 7, July 2025, luaf119
https://doi.org/10.1210/jcemcr/luaf119

Abstract

Germline pathogenic variants in MYC-associated factor X (MAX) are a rare cause of hereditary pheochromocytoma and paraganglioma (PPGL) syndrome, typically presenting with pheochromocytomas (PCC). Although MAX-related PPGLs are generally characterized by an adrenergic phenotype and bilateral tumors in 67% of cases, the tumor spectrum associated with MAX pathogenic variants remains poorly understood. We present a case of a 28-year-old man with a germline MAX pathogenic variant (c.64-2A>G) who developed bilateral PCC and later, a liver sarcoma with a TP53 variant and PLEKHO2::BRAF gene fusion. The diagnosis of sarcoma in this young patient underscores a potential association between MAX pathogenic variants and an increased predisposition to sarcoma development. Our findings suggest that MAX-related PPGLs may be associated with other malignancies, including sarcoma, and support expanding surveillance guidelines to include whole-body imaging for early detection of extra-adrenal tumors. Given the rarity of MAX pathogenic variants, further studies are needed to elucidate the full spectrum of presentation and establish comprehensive evidence-based surveillance strategies.

 

A new strategy for strengthening polymer materials could lead to more durable plastics and cut down on plastic waste, according to researchers at MIT and Duke University.

Using machine learning, the researchers identified crosslinker molecules that can be added to polymer materials, allowing them to withstand more force before tearing. These crosslinkers belong to a class of molecules known as mechanophores, which change their shape or other properties in response to mechanical force.

“These molecules can be useful for making polymers that would be stronger in response to force. You apply some stress to them, and rather than cracking or breaking, you instead see something that has higher resilience,” says Heather Kulik, the Lammot du Pont Professor of Chemical Engineering at MIT, who is also a professor of chemistry and the senior author of the study.

The crosslinkers that the researchers identified in this study are iron-containing compounds known as ferrocenes, which until now had not been broadly explored for their potential as mechanophores. Experimentally evaluating a single mechanophore can take weeks, but the researchers showed that they could use a machine-learning model to dramatically speed up this process.

MIT postdoc Ilia Kevlishvili is the lead author of the open-access paper , which appeared Friday in ACS Central Science. Other authors include Jafer Vakil, a Duke graduate student; David Kastner and Xiao Huang, both MIT graduate students; and Stephen Craig, a professor of chemistry at Duke.

The weakest link

Mechanophores are molecules that respond to force in unique ways, typically by changing their color, structure, or other properties. In the new study, the MIT and Duke team wanted to investigate whether they could be used to help make polymers more resilient to damage.

The new work builds on a 2023 study from Craig and Jeremiah Johnson, the A. Thomas Guertin Professor of Chemistry at MIT, and their colleagues. In that work, the researchers found that, surprisingly, incorporating weak crosslinkers into a polymer network can make the overall material stronger. When materials with these weak crosslinkers are stretched to the breaking point, any cracks propagating through the material try to avoid the stronger bonds and go through the weaker bonds instead. This means the crack has to break more bonds than it would if all of the bonds were the same strength.

To find new ways to exploit that phenomenon, Craig and Kulik joined forces to try to identify mechanophores that could be used as weak crosslinkers.

“We had this new mechanistic insight and opportunity, but it came with a big challenge: Of all possible compositions of matter, how do we zero in on the ones with the greatest potential?” Craig says. “Full credit to Heather and Ilia for both identifying this challenge and devising an approach to meet it.”

Discovering and characterizing mechanophores is a difficult task that requires either time-consuming experiments or computationally intense simulations of molecular interactions. Most of the known mechanophores are organic compounds, such as cyclobutane, which was used as a crosslinker in the 2023 study.

In the new study, the researchers wanted to focus on molecules known as ferrocenes, which are believed to hold potential as mechanophores. Ferrocenes are organometallic compounds that have an iron atom sandwiched between two carbon-containing rings. Those rings can have different chemical groups added to them, which alter their chemical and mechanical properties.

Many ferrocenes are used as pharmaceuticals or catalysts, and a handful are known to be good mechanophores, but most have not been evaluated for that use. Experimental tests on a single potential mechanophore can take several weeks, and computational simulations, while faster, still take a couple of days. Evaluating thousands of candidates using these strategies is a daunting task.

Realizing that a machine-learning approach could dramatically speed up the characterization of these molecules, the MIT and Duke team decided to use a neural network to identify ferrocenes that could be promising mechanophores.

They began with information from a database known as the Cambridge Structural Database, which contains the structures of 5,000 different ferrocenes that have already been synthesized.

“We knew that we didn’t have to worry about the question of synthesizability, at least from the perspective of the mechanophore itself. This allowed us to pick a really large space to explore with a lot of chemical diversity, that also would be synthetically realizable,” Kevlishvili says.

First, the researchers performed computational simulations for about 400 of these compounds, allowing them to calculate how much force is necessary to pull atoms apart within each molecule. For this application, they were looking for molecules that would break apart quickly, as these weak links could make polymer materials more resistant to tearing.

Then they used this data, along with information on the structure of each compound, to train a machine-learning model. This model was able to predict the force needed to activate the mechanophore, which in turn influences resistance to tearing, for the remaining 4,500 compounds in the database, plus an additional 7,000 compounds that are similar to those in the database but have some atoms rearranged.

The researchers discovered two main features that seemed likely to increase tear resistance. One was interactions between the chemical groups that are attached to the ferrocene rings. Additionally, the presence of large, bulky molecules attached to both rings of the ferrocene made the molecule more likely to break apart in response to applied forces.

While the first of these features was not surprising, the second trait was not something a chemist would have predicted beforehand, and could not have been detected without AI, the researchers say. “This was something truly surprising,” Kulik says.

Tougher plastics

Once the researchers identified about 100 promising candidates, Craig’s lab at Duke synthesized a polymer material incorporating one of them, known as m-TMS-Fc. Within the material, m-TMS-Fc acts as a crosslinker, connecting the polymer strands that make up polyacrylate, a type of plastic.

By applying force to each polymer until it tore, the researchers found that the weak m-TMS-Fc linker produced a strong, tear-resistant polymer. This polymer turned out to be about four times tougher than polymers made with standard ferrocene as the crosslinker.

“That really has big implications because if we think of all the plastics that we use and all the plastic waste accumulation, if you make materials tougher, that means their lifetime will be longer. They will be usable for a longer period of time, which could reduce plastic production in the long term,” Kevlishvili says.

The researchers now hope to use their machine-learning approach to identify mechanophores with other desirable properties, such as the ability to change color or become catalytically active in response to force. Such materials could be used as stress sensors or switchable catalysts, and they could also be useful for biomedical applications such as drug delivery.

In those studies, the researchers plan to focus on ferrocenes and other metal-containing mechanophores that have already been synthesized but whose properties are not fully understood.

“Transition metal mechanophores are relatively underexplored, and they’re probably a little bit more challenging to make,” Kulik says. “This computational workflow can be broadly used to enlarge the space of mechanophores that people have studied.”

The research was funded by the National Science Foundation Center for the Chemistry of Molecularly Optimized Networks (MONET).

Get the key facts from this story in our 1-minute read

Co‑orbital rocks: Earth has several tiny co‑orbital bodies – objects orbiting the Sun but influenced by Earth’s gravity – some in horseshoe, tadpole, or quasi‑satellite orbits

Lunar Origin? One of these, the 40 m wide asteroid Kamo‘oalewa, has a spectral signature closely matching lunar rocks, suggesting it may be Moon ejecta

Simulated trajectories: Simulations of 54,000 particles launched from the lunar surface show that about 6.7 % become Earth co‑orbitals, with over a quarter evolving into quasi‑satellite paths

Impact site: Kamo‘oalewa likely originated from the Giordano Bruno crater on the Moon’s far side, a relatively young 22 km crater formed about 4 million years ago

Capture conditions: Material most likely becomes co‑orbital if ejected from the Moon’s trailing (western) equatorial region, matching simulation patterns

Mission opportunity: China’s Tianwen‑2 mission launched in May 2025 aims to return a sample of Kamo‘oalewa by 2028, potentially confirming its lunar origin

Broader implications: If confirmed, lunar‑derived asteroids offer a new category of near‑Earth objects, helping refine models of impacts and lunar ejecta dynamics

Scientific value: Studying these Moon‑sourced bodies may deepen understanding of both lunar geology and the origin of small near‑Earth objects

A new strategy for strengthening polymer materials could lead to more durable plastics and cut down on plastic waste, according to researchers at MIT and Duke University.

Using machine learning, the researchers identified crosslinker molecules that can be added to polymer materials, allowing them to withstand more force before tearing. These crosslinkers belong to a class of molecules known as mechanophores, which change their shape or other properties in response to mechanical force.

“These molecules can be useful for making polymers that would be stronger in response to force. You apply some stress to them, and rather than cracking or breaking, you instead see something that has higher resilience,” says Heather Kulik, the Lammot du Pont Professor of Chemical Engineering at MIT, who is also a professor of chemistry and the senior author of the study.

The crosslinkers that the researchers identified in this study are iron-containing compounds known as ferrocenes, which until now had not been broadly explored for their potential as mechanophores. Experimentally evaluating a single mechanophore can take weeks, but the researchers showed that they could use a machine-learning model to dramatically speed up this process.

MIT postdoc Ilia Kevlishvili is the lead author of the open-access paper, which appeared Friday in ACS Central Science. Other authors include Jafer Vakil, a Duke graduate student; David Kastner and Xiao Huang, both MIT graduate students; and Stephen Craig, a professor of chemistry at Duke.

The weakest link

Mechanophores are molecules that respond to force in unique ways, typically by changing their color, structure, or other properties. In the new study, the MIT and Duke team wanted to investigate whether they could be used to help make polymers more resilient to damage.

The new work builds on a 2023 study from Craig and Jeremiah Johnson, the A. Thomas Guertin Professor of Chemistry at MIT, and their colleagues. In that work, the researchers found that, surprisingly, incorporating weak crosslinkers into a polymer network can make the overall material stronger. When materials with these weak crosslinkers are stretched to the breaking point, any cracks propagating through the material try to avoid the stronger bonds and go through the weaker bonds instead. This means the crack has to break more bonds than it would if all of the bonds were the same strength.

To find new ways to exploit that phenomenon, Craig and Kulik joined forces to try to identify mechanophores that could be used as weak crosslinkers.

“We had this new mechanistic insight and opportunity, but it came with a big challenge: Of all possible compositions of matter, how do we zero in on the ones with the greatest potential?” Craig says. “Full credit to Heather and Ilia for both identifying this challenge and devising an approach to meet it.”

Discovering and characterizing mechanophores is a difficult task that requires either time-consuming experiments or computationally intense simulations of molecular interactions. Most of the known mechanophores are organic compounds, such as cyclobutane, which was used as a crosslinker in the 2023 study.

In the new study, the researchers wanted to focus on molecules known as ferrocenes, which are believed to hold potential as mechanophores. Ferrocenes are organometallic compounds that have an iron atom sandwiched between two carbon-containing rings. Those rings can have different chemical groups added to them, which alter their chemical and mechanical properties.

Many ferrocenes are used as pharmaceuticals or catalysts, and a handful are known to be good mechanophores, but most have not been evaluated for that use. Experimental tests on a single potential mechanophore can take several weeks, and computational simulations, while faster, still take a couple of days. Evaluating thousands of candidates using these strategies is a daunting task.

Realizing that a machine-learning approach could dramatically speed up the characterization of these molecules, the MIT and Duke team decided to use a neural network to identify ferrocenes that could be promising mechanophores.

They began with information from a database known as the Cambridge Structural Database, which contains the structures of 5,000 different ferrocenes that have already been synthesized.

“We knew that we didn’t have to worry about the question of synthesizability, at least from the perspective of the mechanophore itself. This allowed us to pick a really large space to explore with a lot of chemical diversity, that also would be synthetically realizable,” Kevlishvili says.

First, the researchers performed computational simulations for about 400 of these compounds, allowing them to calculate how much force is necessary to pull atoms apart within each molecule. For this application, they were looking for molecules that would break apart quickly, as these weak links could make polymer materials more resistant to tearing.

Then they used this data, along with information on the structure of each compound, to train a machine-learning model. This model was able to predict the force needed to activate the mechanophore, which in turn influences resistance to tearing, for the remaining 4,500 compounds in the database, plus an additional 7,000 compounds that are similar to those in the database but have some atoms rearranged.

The researchers discovered two main features that seemed likely to increase tear resistance. One was interactions between the chemical groups that are attached to the ferrocene rings. Additionally, the presence of large, bulky molecules attached to both rings of the ferrocene made the molecule more likely to break apart in response to applied forces.

While the first of these features was not surprising, the second trait was not something a chemist would have predicted beforehand, and could not have been detected without AI, the researchers say. “This was something truly surprising,” Kulik says.

Tougher plastics

Once the researchers identified about 100 promising candidates, Craig’s lab at Duke synthesized a polymer material incorporating one of them, known as m-TMS-Fc. Within the material, m-TMS-Fc acts as a crosslinker, connecting the polymer strands that make up polyacrylate, a type of plastic.

By applying force to each polymer until it tore, the researchers found that the weak m-TMS-Fc linker produced a strong, tear-resistant polymer. This polymer turned out to be about four times tougher than polymers made with standard ferrocene as the crosslinker.

“That really has big implications because if we think of all the plastics that we use and all the plastic waste accumulation, if you make materials tougher, that means their lifetime will be longer. They will be usable for a longer period of time, which could reduce plastic production in the long term,” Kevlishvili says.

The researchers now hope to use their machine-learning approach to identify mechanophores with other desirable properties, such as the ability to change color or become catalytically active in response to force. Such materials could be used as stress sensors or switchable catalysts, and they could also be useful for biomedical applications such as drug delivery.

In those studies, the researchers plan to focus on ferrocenes and other metal-containing mechanophores that have already been synthesized but whose properties are not fully understood.

“Transition metal mechanophores are relatively underexplored, and they’re probably a little bit more challenging to make,” Kulik says. “This computational workflow can be broadly used to enlarge the space of mechanophores that people have studied.”

The research was funded by the National Science Foundation Center for the Chemistry of Molecularly Optimized Networks (MONET).

The moon looks almost full tonight, but it’s not quite there. We’ve even got a few more days before we hit that part of the lunar cycle.

The lunar cycle is a series of eight unique phases of the moon’s visibility. The whole cycle takes about 29.5 days, according to NASA, and these different phases happen as the Sun lights up different parts of the moon whilst it orbits Earth. 

So, what’s happening with the moon tonight, Aug. 5?

What is today’s moon phase?

As of Tuesday, Aug. 5, the moon phase is Waxing Gibbous. According to NASA’s Daily Moon Observation, the moon will be 85% lit up tonight, the 12th day of the lunar cycle.

With each night we progress through the lunar cycle, there is more visibility for us on Earth. With your unaided eye, you’ll be able to spot many things tonight, but most notably the Mare Vaporum, the Mare Tranquillitatis, and the Tycho Crater.

Pull out the binoculars to add the Mare Humorum, the Apennine Mountains, and the Archimedes Crater, which, according to NASA, is about 3/4 the size of Washington, DC. And with a telescope, enjoy glimpses of the Schiller Crater, the Descartes Highlands, and the Gruithuisen Domes, something NASA calls a “geologic mystery.” One mile tall, these volcanic mountains are steeper than normal lunar volcanoes, despite missing water and plate tectonics that help volcanoes form on Earth.

When is the next full moon?

The next full moon will be on August 9. The last full moon was on July 10.

Mashable Light Speed

What are moon phases?

According to NASA, moon phases are caused by the 29.5-day cycle of the moon’s orbit, which changes the angles between the Sun, Moon, and Earth. Moon phases are how the moon looks from Earth as it goes around us. We always see the same side of the moon, but how much of it is lit up by the Sun changes depending on where it is in its orbit. This is how we get full moons, half moons, and moons that appear completely invisible. There are eight main moon phases, and they follow a repeating cycle:

New Moon – The moon is between Earth and the sun, so the side we see is dark (in other words, it’s invisible to the eye).

Waxing Crescent – A small sliver of light appears on the right side (Northern Hemisphere).

First Quarter – Half of the moon is lit on the right side. It looks like a half-moon.

Waxing Gibbous – More than half is lit up, but it’s not quite full yet.

Full Moon – The whole face of the moon is illuminated and fully visible.

Waning Gibbous – The moon starts losing light on the right side.

Last Quarter (or Third Quarter) – Another half-moon, but now the left side is lit.

Waning Crescent – A thin sliver of light remains on the left side before going dark again.

Gram-negative bacteria pose a significant threat to global health due to their high resistance to antibiotics compared to that of Gram-positive bacteria. Their formidable defensive capabilities stem from their outer membrane (OM), which acts as a selective barrier against harmful compounds. The OM is not merely a static shield but a dynamic structure crucial for the bacteria’s survival and virulence. Thus, understanding how the OM is built and maintained is critical in our battle against drug-resistant infections.

To construct such an effective protective layer, bacteria rely on specialized molecular machinery. The lipopolysaccharide transport (Lpt) system is a key player in this process, as it integrates functional lipopolysaccharide complexes into the OM. Although some components of this transport pathway, such as the LptDE complex, are known to be essential for bacterial survival, the precise mechanisms governing their assembly and maturation remain unclear. 

In a recent study, a research team led by Assistant Professor Ryoji Miyazaki from the Nara Institute of Science and Technology (NAIST), Japan, made an important discovery toward a better understanding of these processes. Their study, made available online on July 16, 2025, and scheduled for publication on August 26, 2025, in Volume 44, Issue 8 of the journal Cell Reports, reveals the critical role of a small protein called LptM in maturing and stabilizing LptD, which, together with LptE, forms the LptDE complex. The study was co-authored by Mai Kimoto, Dr. Hidetaka Kohga, and Professor Tomoya Tsukazaki from NAIST.

The team employed a combination of advanced techniques to shed light on the function of LptM. They investigated the precise timing of various events during LptD maturation, demonstrating that LptM acts at a later stage, influencing already-folded LptD intermediates. Through comprehensive mutational analyses, they identified a short region of LptM, comprising fewer than ten amino acid residues, as essential for its purpose. The researchers then used cryo-electron microscopy to acquire a high-resolution structure of the Escherichia coli LptDEM complex. This analysis, combined with biochemical experiments, provided an unprecedented molecular view of how LptM directly interacts with and stabilizes the LptDE complex.

Their results revealed that LptM positions itself at a critical interface within LptD, suggesting its role in fine-tuning the structure of this protein for Lpt. This enhanced understanding of the LptDE assembly process has significant implications for future therapeutic advances. “Our study highlights the essential role of LptM, providing fundamental insights that may support antibiotic design, as the LptDE complex has been identified as a potential target for novel antibiotics,” states Dr. Miyazaki. “Thus, our findings contribute to the advancement of research that could guide future drug discovery.”

Beyond potential drug targets, this research also sheds important light on a broader principle in biology. “Our findings suggest that small proteins, many of which have been previously overlooked, may play critical roles in the assembly and regulation of larger membrane protein complexes. This opens up a new perspective in basic biology, underscoring the functional relevance of small proteins,” remarks Dr. Miyazaki.

Indeed, such results could open doors to new avenues for exploring the previously unrecognized functions of these “microproteins” in various cellular processes.

Original publication

Yudhajeet Basak, Christian Lorent, Jae-Hun Jeoung, Ingo Zebger, Holger Dobbek; “Metalloradical-driven enzymatic CO2 reduction by a dynamic Ni–Fe cluster”; Nature Catalysis, 2025-8-1

Jakob Ruickoldt, Julian Kreibich, Thomas Bick, Jae-Hun Jeoung, Benjamin R. Duffus, Silke Leimkühler, Holger Dobbek, Petra Wendler; “Ligand binding to a Ni–Fe cluster orchestrates conformational changes of the CO-dehydrogenase–acetyl-CoA synthase complex”; Nature Catalysis, Volume 8, 2025-7-11