Intravenous immunoglobulin (IVIG) therapy involves infusing patients with naturally occurring IgG antibodies to treat autoimmune conditions. With origins dating back to the 1950s, IVIG is currently FDA-approved for four diseases but widely prescribed off-label to treat more than 80 additional ones because it’s often the only medicine that has any impact on these conditions.

But IVIG has serious shortcomings. Treatment can require multi-hour, high-volume infusions several times per month, the cost is exorbitant, and because the antibodies are sourced from donated human plasma, there are frequent supply shortages.

Now scientists from Rockefeller University’s Leonard Wagner Laboratory of Molecular Genetics and Immunology have used their discovery of previously unknown mechanisms in an anti-inflammatory pathway to develop a powerful upgrade: an engineered antibody that delivers the effectiveness of IVIG at a fraction of the dose in mice, and can be synthesized without the need for human plasma.

The study is published in the journal Science.

“We discovered that by enhancing the binding of a certain pair of receptors we can significantly lower the dose yet have an equal effect,” says first author Andrew Jones, a research associate in the lab, which is led by Jeffrey Ravetch.

These advances build upon earlier research from the lab that has already led them to develop a molecule 10 times more potent than IVIG, which is currently in phase 2 clinical trials through the biotech company Nuvig, which Ravetch co-founded. The current findings dramatically improve upon that molecule.

40 years of research

The findings build on 40 years of research in Ravetch’s lab on Fc receptors, a family of proteins found on the surface of nearly all immune cells to which antibodies bind to coordinate the immune system’s effector responses. The most common serum antibody is immunoglobulin G (IgG), which represents 75% of the infection-fighting force found in blood—and is the key component of IVIG.

The work on the anti-inflammatory properties of IVIG began about 25 years ago when Ravetch discovered that a small fraction of serum IgGs present in IVIG possessed a naturally occurring modification: a sugar modification called sialylation, which conferred its anti-inflammatory properties.

Subsequent studies in his lab identified two additional components that were required to trigger an anti-inflammatory response by IVIG: an inhibitory Fc receptor called FcγRIIB and a lectin (a carbohydrate-binding protein) called DC-SIGN. These insights led them to be able to develop the drug currently in phase 2 clinical trials, now known as NVG-2089, which is 10 times more potent than IVIG in suppressing autoimmune inflammation.

“Those were the pieces that we had figured out,” Ravetch says. “The question was, how do these three components come together to mediate the anti-inflammatory activity? That’s the work we undertook for the current study.”

That earlier work had also been done studying IVIG activity in mice with their own native Fc receptors—not human ones. Since then, Ravetch has developed mice that express human Fc receptors.

“We thought we could potentially develop a next-generation therapeutic IVIG if we had a better understanding of how IVIG works specifically on cells expressing human Fc receptors,” Jones says.

Powering up

To understand how these components come together to mediate IVIG activity, the researchers conducted many in vitro experiments testing a variety of scenarios of activation and interaction.

“We discovered that the type 1 FcγRIIB receptor and the type 2 DC-SIGN co-receptor are actually binding to each other on the cell surface, and that seems to be important for the anti-inflammatory effect of IgG,” Jones says. “This was a novel configuration we hadn’t seen before. We think that when they bind, they enhance the ability of the sialylated IgG antibody to trigger the anti-inflammatory signaling cascade.”

They next engineered a recombinantly expressed IgG to have enhanced binding to these receptors and infused them into mice with human Fc receptors into which they had induced arthritis (meaning they’d been injected with serum isolated from a mouse with naturally occurring arthritis). A similar group of arthritic mice was treated with the conventional IVIG infusion.

Both groups benefited from the infusion, seeing reduced joint swelling. But the doses were dramatically different: 100 times as much IVIG was required to achieve the same effect as one dose of the new molecule.

“This is a really substantial difference, and there are several factors why it’s important. For one, this new molecule is a recombinant protein that we can produce in vitro, so it does not need to come from human plasma. That’s an enormous advantage,” Ravetch says. “And then there are the many autoimmune diseases that currently aren’t treated with IVIG because we haven’t achieved the correct dosing. With a very high-potency product, it’s possible to achieve the correct dosage and expand use to more autoimmune diseases.”

A second test run used a mouse model stand-in for multiple sclerosis, an autoimmune disease that causes both cognitive and mobility deterioration. The molecule protected mice from neuro-inflammation by preventing cell destruction, and did so at the same small dose.

Going forward, the lab will look into the structure and molecular dynamics of the type 1 and type 2 receptors. They’ve identified many over the years, but how they pair up and what their functions are remain to be discovered.

“What we have discovered opens the door to exploring how they might function in different biological pathways,” Jones says.

They’ll also pursue clinical potential.

“As of now, we’ve optioned the molecule to Nuvig, and they’ll test further to determine if they want to pursue it as a clinical product,” Ravetch says. “I hope they do. We want to see it get into patients.”

Researchers in Shanghai have reported in a study, recently published in Science Advances, that they’ve successfully decoded Mandarin Chinese language in real time with the help of a brain-computer interface (BCI) framework, a first for BCIs working with tonal languages. The participant involved in the study was also capable of controlling a robotic arm and digital avatar and interacting with a large language model using this new system.

What are mind-reading BCIs used for?

While most people may not want a computer reading their mind, those who are unable to speak due to neurological conditions, like strokes or amyotrophic lateral sclerosis (ALS), need to find alternative ways to communicate. Speech decoding BCIs, capable of decoding neural signals, offer a promising way to restore communication in such individuals. In addition to communication, BCIs also offer ways to control devices directly through thought. This is particularly helpful for neurological conditions in which disabilities extend beyond speech loss.

These types of devices are not exactly a novel technology, but most BCI speech decoding research has focused on English, a non-tonal language.

“A leading approach focuses on the ventral sensorimotor cortex, which encodes articulatory kinematic trajectories. Neural signals from this region can be transformed into discrete linguistic units or articulatory gesture parameters and subsequently synthesized into words, sentences, or sounds. This strategy is especially suitable for individuals with intact speech motor areas, aiming to re-enable functional communication.

“Recent advances in English language decoding have enabled real-time translation of brain activity into text or speech for patients with severe dysarthria caused by conditions such as amyotrophic lateral sclerosis (ALS) or brainstem stroke,” the study authors write.

Overcoming the difficulties of decoding Mandarin

Advances in BCIs capable of decoding tonal languages, like Mandarin, have been more limited. Because Mandarin is a tonal, monosyllabic language with high homophone density, speech decoding is more challenging. Some previous studies have decoded small sets of Mandarin syllables or tones, but not the full range needed for practical use and not in real time.

However, a clinical study on an epilepsy patient has enabled the researchers involved in the new study to take a different approach. The study, conducted on a 43-year-old woman, used an implanted 256-channel high-density electrocorticography (ECoG) array to monitor and record neural signals through a series of single-character and sentence reading tasks over 11 days. A 3-gram Mandarin language model was integrated to improve sentence decoding.

The team says analysis of the ECoG signals revealed distinct neural correlates for syllable and tone processing. The system achieved a median syllable identification accuracy of 71.2% in single-character tasks. Real-time sentence decoding reached 73.1% character accuracy with a language model, and a communication rate of 49.7 characters per minute.

“Our study demonstrates that combining high-density, ultraconformal ECoG grids with a syllablecentric decoding framework can yield substantial improvements. The ECoG arrays provided broad and stable cortical coverage, particularly over speech-related regions, and enabled us to decode a large set of 394 Mandarin tonal syllables with high accuracy—based primarily on neural features before any linguistic postprocessing,” the study authors write.

Refining future BCIs for speech loss

While the study demonstrates marked improvement for BCIs decoding Mandarin, the authors do note some limitations and areas that could use improvement. The study only included one participant, limiting generalizability. And because the ECoG array was meant for clinical epilepsy monitoring, the electrode coverage did not include all tone-relevant brain regions. However, future studies can build upon this one, further increasing accuracy and generalizability.

The study authors hope to extend BCI applicability to a range of patients. They say, “Beyond improvements in decoding accuracy and hardware performance, expanding the neural targets of speech BCIs represents an exciting frontier.

“Although current approaches primarily leverage signals from motor and premotor cortices responsible for articulation, future systems may benefit from incorporating activity in higher-order language, such as the middle temporal gyrus, inferior frontal gyrus, and supramarginal gyrus. Integrating the semantic and syntactic information processed within these regions may help build more stable and accurate speech decoders.”

Inside every cell, a finely tuned metabolic network determines when to build, recycle, or stop producing essential molecules. A central part of this network is folate metabolism, a process that provides vital chemical units for the synthesis of DNA, RNA, and amino acids.

When this system is disturbed—for example through genetic mutations or a lack of dietary folates—the consequences can range from developmental disorders to cancer.

Now, researchers from CeMM, the Research Center for Molecular Medicine of the Austrian Academy of Sciences, together with collaborators from the University of Oxford, have identified an unexpected player in this metabolic balance: the enzyme NUDT5.

Their study, published in Science, shows that NUDT5 helps to switch off purine production—the chemical pathway that generates the building blocks of DNA—but does so without using its enzymatic activity. Instead, the protein acts as a kind of molecular scaffold that physically restrains a key biosynthetic step when purine levels are already high.

A new role for an old enzyme

Purines are essential molecules that cells use to build DNA and RNA and to store energy. They can be recycled from existing material, or produced from scratch through the so-called de novo pathway—an energy-intensive process that must be tightly controlled.

In their study, the researchers explored this control mechanism by studying cells with mutations in the gene MTHFD1, a crucial enzyme in the folate cycle. Folate metabolism provides the one-carbon units required for purine synthesis, and defects in this pathway cause rare genetic diseases and influence cancer risk.

Using a combination of genetic screening, metabolomics, and chemical biology, the team discovered that the protein NUDT5 interacts with another enzyme, PPAT, which catalyzes the first step of purine synthesis. When purine levels rise, NUDT5 binds to PPAT and likely locks it into an inactive form—effectively telling the cell to stop producing more purines.

Surprisingly, this function of NUDT5 does not rely on its known enzymatic activity, which breaks down nucleotide derivatives. Even when its catalytic site was chemically blocked or genetically disabled, the protein continued to regulate purine synthesis. Only when NUDT5 was completely removed—either through genetic knockout or a newly developed molecule that selectively degrades it—did cells lose this control mechanism.

Metabolic control with medical implications

The discovery sheds new light on how cells sense and respond to changes in their metabolic environment.

“NUDT5 has long been classified as an enzyme that hydrolyzes metabolites,” says Stefan Kubicek, Principal Investigator at CeMM and senior author of the study.

“But our work reveals a completely different role—it acts as a structural regulator that determines whether the cell keeps producing purines or not.”

This mechanism may also explain why some cells become resistant to certain cancer drugs. “Many chemotherapies, such as 6-thioguanine, work by mimicking purine molecules and blocking DNA synthesis,” explains Tuan-Anh Nguyen, co-first author of the study.

“But we found that cells without functional NUDT5–PPAT interaction were less sensitive to these treatments, suggesting that mutations in NUDT5 could contribute to drug resistance in tumors.”

The key role of NUDT5 in controlling cancer drug sensitivity is also supported by similar findings from Ralph DeBerardinis’ laboratory that are also published in the same issue of Science.

In addition, the research connects the dots between folate metabolism, purine synthesis, and diseases caused by MTHFD1 deficiency, a rare genetic disorder that affects immune and neurological development.

“Because the folate and purine pathways are tightly linked, understanding this regulatory network could eventually inform new therapeutic approaches,” Jung-Ming George Lin, co-first author of the study, adds.

The collaborators in Kilian Huber’s lab in Oxford also developed a chemical degrader called dNUDT5, which can selectively eliminate NUDT5 from cells. This tool will allow scientists to study the pathway in more detail and may offer future possibilities for protecting healthy cells from chemotherapy side effects.

“Our findings highlight that enzymes not only can act via the chemical reactions they catalyze, but also through their structure,” concludes Kubicek. “Sometimes, it’s the physical presence of a protein that makes the crucial difference.”

DNA isn’t just a long string of genetic code, but an intricate 3D structure folded inside each cell. That means the tools used to study DNA need to be just as sophisticated—able to read not only the code itself, but how it’s arranged in space.

Researchers at Case Western Reserve University compared different computer tools used to analyze how DNA folds and interacts inside individual cells. Their work, published in Nature Communications, could help scientists better understand how to read the body’s genetic “instruction manual” in different circumstances—like understanding what goes wrong when diseases develop or how cells change their jobs as we grow.

“The 3D structure of DNA affects how genes interact with each other, just like the layout of a house affects how people move through it,” said Fulai Jin, professor in the Department of Genetics and Genome Sciences at the Case Western Reserve School of Medicine. “Understanding this structure is crucial for figuring out how diseases develop and how we might treat them.”

The team addressed a key challenge: existing tools for analyzing DNA structure often produced inconsistent results. It’s like having multiple translators who can’t agree on what a foreign language text says, he said.

Jin was joined in the research by Jing Li, the Arthur L. Parker Professor in the Department of Computer and Data Sciences at the Case School of Engineering, and Yan Li, associate professor and vice chair of research in the genetics and genome sciences department.

The researchers tested 13 software tools on 10 datasets from mice and humans and found that different computer tools work better for different types of data. They also discovered that changing how data is prepared before analysis can dramatically improve results. Artificial intelligence computer programs work especially well with lower-quality and complex datasets.

“We’re essentially helping scientists find or build better microscopes to see how DNA works inside individual cells,” Jin said. “This could lead to a better understanding of genetic diseases and potentially new treatment strategies.”

Jin said the improved tools could help scientists see which genes switch on or off in diseased cells, explain why treatments work for some patients but not others and track how cells change during early development.

The research team also created a software package other scientists can use to find the best method to analyze their specific research—like how a GPS app finds the best route for your destination.

“Instead of researchers having to guess which tool might work best, our software can test multiple approaches and recommend the optimal one,” Jin said.

The methods are freely available to scientists worldwide through GitHub, an open-source platform that allows developers to create, store, manage and share their code. Jin said the widespread accessibility has the potential to accelerate discoveries across multiple fields of biomedical research.

“This is a significant step toward making sense of the massive genetic data from modern sequencing—and toward understanding how our genetic blueprint truly works,” Jin said.

The FDA awards a second round of Commissioner’s National Priority vouchers to six larger biopharma companies. And this time, with the exception of Eli Lilly’s orforglipron, the vouchers are for drugs that are already on the market.

The FDA announced the second batch of recipients of its new Commissioner’s National Priority vouchers Thursday, and the beneficiaries strike a different tone than those awarded in the first round.

These vouchers, first introduced in June, are intended for programs that are “aligned with U.S. national priorities,” under a number of different categories, including addressing a public health crisis in the country, addressing unmet needs, onshoring drug development or increasing affordability. With a CNPV voucher, the drug review process could be shortened from 10–12 months to 1–2 months.

The products receiving vouchers in this round are:

• Novo Nordisk’s Wegovy for obesity and related health conditions. Wegovy, or semaglutide, was first approved in 2021.
• Vertex and CRISPR Therapeutics’ Casgevy for sickle cell disease. Casgevy was approved for sickle cell disease in December 2023.
• Eli Lilly’s orforglipron for treating obesity and related health conditions. Orforglipron is the only drug on this list not already approved.

• Boehringer Ingelheim’s zongertinib, for HER2 lung cancer. Zongertinib was approved as Hernexeos for non-small cell lung cancer in August.
• Johnson and Johnson’s bedaquiline for drug-resistant tuberculosis in young children. J&J subsidiary Janssen sells bedaquiline as Sirturo, approved under an accelerated pathway in 2012 before winning full FDA approval in 2024 for treating tuberculosis.
• GSK’s dostarlimab for rectal cancer. Dostarlimab is sold as Jemperli and was approved in 2023 for endometrial cancer.

The first round of CNPV recipients was announced on October 17. The nine recipients from that round represented a range of large and small pharmas and a variety of stages along the drug development pipeline. This new group, on the other hand, focuses on larger companies and drugs that are largely already approved for at least one indication.

“National priority vouchers are granted to a select group of products,” FDA Commissioner Marty Makary said in a statement Thursday afternoon, “where the company has agreed to increase affordability, domesticate manufacturing as a national security issue, or address an unmet public health need.” Priority voucher holders will receive decisions “within months” of submitting a complete application, according to the announcement, though the FDA could extend the timeline for review as needed.

Why vouchers were awarded to drugs that are already approved for the indications listed—like Wegovy for obesity, Sirturo for tuberculosis or Casgevy for sickle cell disease—was not made clear in the FDA’s announcement. BioSpace has reached out to the agency for clarification and will update this article accordingly.

“The first nine vouchers embodied a new definition of ‘unmet medical need,’ one not tied to the traditional standard of serious and life-threatening diseases,” Steven Grossman, policy and regulatory consultant, and author of the FDA Matters blog, told BioSpace in an email. Grossman pointed to vouchers for investigational drugs addressing deafness, blindness, infertility and onshoring to reduce ongoing drug shortages.

“So far, we have less information about the new set and their intended use. Hopefully, they will meet the criteria I applied to the first batch: they address unmet medical needs that are legitimately on FDA’s priority list and can be justified without reference to separate dialogues about pricing and other national (but not necessarily FDA) priorities.”  

Executives from Novo Nordisk and Eli Lilly were at the White House shortly before the vouchers were announced, discussing an agreement made by both companies with the Trump administration to sell their GLP-1 medicines—semaglutide (including Wegovy) and tirzepatide, respectively—at around $350 per month through the administration’s direct-to-consumer platform TrumpRx.

The agreement will also secure a $150 price for future weight loss pills from Novo Nordisk and Eli Lilly—at least initially.

Eli Lilly and Novo Nordisk will offer their GLP-1 drugs through President Donald Trump’s direct-to-consumer marketplace for about $350 per month. Future pills manufactured by the companies—such as Novo’s oral Wegovy or any other GLP-1 pill later approved—will be offered at $150 for the initial doses.

The deal was officially announced from the White House Thursday afternoon, with Novo CEO Maziar Mike Doustdar and Eli Lilly’s Davis Ricks in attendance. Stunningly, the live press conference was cut off as Ricks spoke when an attendee standing next to Doustdar collapsed. The feed had not returned as of publication but a White House official reportedly told reporters present that the man was with one of the companies and was “okay.”

Prior to the feed cutting off, Trump’s cabinet officials had touted the deal as a way to help millions of Americans lose weight and return to productivity.

“This is the biggest drug in our country. That’s why this is the most important of all the MFN [Most Favored Nation] announcements we’ve made,” said Health Secretary Robert F. Kennedy Jr. “This is going to have the biggest impact on the American people.”

The agreement with the two GLP-1 leaders marks the third MFN drug pricing deal for the White House after Pfizer and AstraZeneca. The president in an executive order earlier this year set a framework for companies to bring their drug prices in line with what other equivalent nations pay.

In a fact sheet about the latest deal, the White House said that the price of Novo’s Ozempic and Wegovy will fall from $1,000 and $1,350 per month, respectively, to $350 for either drug via the TrumpRx platform, which is set to launch in the new year.

Lilly will offer Zepbound, which is priced at $1,086, and its forthcoming weight loss pill orforglipron at an average of $346 on the platform.

The agreement will also secure a $150 price for future weight loss pills—at least initially. The fact sheet states that Novo’s forthcoming Wegovy oral and “certain similar ‘GLP-1’ drugs in each company’s pipeline” will be priced at $150 for the initial doses.

As for Mounjaro, the diabetes version of Lilly’s tirzepatide franchise, the agreement will see the drug offered under Medicare for $245, the same cost of Zepbound, Wegovy and Ozempic in that program.

The deal also secures an expansion of coverage for the weight loss drugs under Medicare for the first time, the White House said.

“I want to thank these extraordinary CEOs for sticking with us on this, or allowing us to make this happen,” Kennedy said. “Mike Doustdar said to me, we’ve known we’ve had to do this for many years. We just haven’t been able to get over the edge. And that President Trump’s order, his executive order, was a catalyst to do something that we always knew needed to be done.”

In October, Trump promised to bring the price of GLP-1 drugs down to $150 during a press conference with Merck on a price reduction for certain fertility drugs. Centers for Medicare & Medicaid Services Administrator Mehmet Oz rushed to the microphone to say that those deals had not been sealed yet.

Both Novo and Lilly have taken steps through their own direct-to-consumer platforms to lower the price of their mega-blockbuster GLP-1 drugs. Wegovy is available for $499 with manufacturer rebates or as little as $224 per month through commercial payers. GoodRx offers the drug for $499 per month. Single-dose vials of Zepbound can now be purchased at Walmart through LillyDirect for $349 per month.

Reporting earnings last week, Ricks detailed the pricing conundrum his company is facing with orforglipron, which has emerged as one of the most anticipated obesity drugs in the pipeline at the moment.

Caricature artists exaggerate distinctive features of an individual, deepening a cleft chin or multiplying freckles. Yale researchers have now applied a similar approach to maps of neural connections, emphasizing individual differences to see if they yield useful information.

Turns out they do, according to the researchers’ findings published in Nature Neuroscience.

Researchers have been constructing and studying these maps, known as connectomes, to see if they might be predictive of, for instance, behaviors or mental health conditions.

This research has so far found that connectome activity that is similar across individuals is important and can be predictive of behavior all by itself. So the remaining activity has largely been cast aside.

“But what’s going on in that activity? It has been left behind, so we really don’t know whether there’s value in it,” says lead author Raimundo Rodriguez, a Ph.D. student in the Interdepartmental Neuroscience Program at Yale School of Medicine (YSM).

When Rodriguez caricatured connectome data, minimizing shared activity and thereby emphasizing individual differences, he found that connectomes were better predictors of several features, including age, IQ, and emotion processing.

“What we’re finding is that information carried in caricatured data is distinct from non-caricatured data,” he says.

Caricaturing brain activity

For the study, the researchers used Human Connectome Project, UCLA Consortium for Neuropsychiatric Phenomics, and Yale-developed functional magnetic resonance imaging (fMRI) datasets.

First, Rodriguez identified key patterns in the datasets where, across individuals, different brain regions activated or deactivated together while individuals were completing some sort of task. And then he removed those shared patterns from the fMRI data that were collected when the individuals were at rest.

Doing so, Rodriguez confirmed, made individuals look less like each other in the data. It also made separate scans from the same individual easier to identify from the rest of the scans. “That told us that this method really was caricaturing the data,” says Rodriguez.

The question that remained was how this might affect efforts to predict behavior and characteristics.

“We looked at a range of features and showed that often times, this caricaturing method actually improves predictive capabilities,” says Rodriguez, who works in the lab of Dustin Scheinost, Ph.D., associate professor of radiology and biomedical imaging at YSM. “But what’s interesting is that it didn’t do so for everything.”

Caricatured connectomes better predicted individuals’ ages, IQs, sex, and BMI, as well as performance on tasks assessing emotional processing and the ability to identify similarities across objects. But caricaturing was less predictive of borderline personality disorder.

“That there’s this nuance in where prediction improves and where it doesn’t tells us that this method isn’t simply ‘cleaning’ the data. It’s not just removing noise,” says Scheinost, senior author of the study and associate director of biomedical imaging technology at the Yale Biomedical Imaging Institute.

“Instead, these findings suggest that differing activity patterns may hold important information for predicting some characteristics and behaviors while shared patterns hold importance for others, meaning the two are offering different types of information.”

The researchers found support for that idea as well. When they combined caricatured data with non-caricatured data, they got even better predictions than with either one separately.

Going forward, the researchers want to identify what behaviors caricatured data does and doesn’t work for, and whether there are patterns underlying why.

“Ultimately, we’ve shown that there is this new source of information that we’ve so far been ignoring,” says Rodriguez. “But we can use it to improve prediction.”

The pursuit of a longer life may currently be trending for tech bros, but the notion of a fountain of youth, or even immortality, has intrigued people for millennia. Yet, some of the more evidence-based methods to increase longevity, such as dieting, are decidedly unpleasant to maintain over time.

Research from the lab of Scott Leiser, Ph.D., of Molecular and Integrative Physiology Department at University of Michigan Medical School, uncovers interesting connections between a longevity gene, behavior and the environment. The findings bring scientists closer to understanding the underlying biological mechanisms that might be exploited to extend life without the downsides.

The first study, appearing in PNAS, uses a worm (the popular research model species C. elegans) to further explain the effect of environmental cues and food access on longevity.

“Believe it or not, most of the central ideas and types of metabolism we study are conserved from worms to people,” said Leiser. “When we perceive the environment, we release hormones like adrenaline or dopamine. Worms do the exact same thing; their neurons respond to the environment and change their physiology accordingly.”

Previous research has shown that stress like food scarcity can promote survival. Intriguingly, foundational work on flies from Leiser’s U-M colleague Scott Pletcher, Ph.D., showed that the mere smell of food can reverse this effect.

Leiser, along with project leader Elizabeth Kitto, Ph.D., and with support from Safa Beydoun, Ph.D., wondered whether other sensory inputs, like touch, would also mitigate the life-extending effects of dietary restriction, and if so, how?

To test this, they placed worms on a bed of beads with a texture similar to the E. coli buffet they would normally encounter during feeding.

The touch of the beads was enough to blunt the expression of a gene in the intestine related to longevity (fmo-2) and in doing so, reduced the life extension effect of dietary restriction. Leiser discovered that fmo-2 is a gene that is necessary and sufficient to extend lifespan downstream from dietary restriction in 2015.

“The fmo-2 enzyme remodels metabolism, and as a result increases lifespan,” he explained. “Without the enzyme, dietary restriction does not lead to a longer lifespan.”

Specifically, their experiment showed that touch activates a circuit that modulates signals from cells that release dopamine and tyramine, which decreases intestinal fmo-2 induction and thus the longevity effect of a restricted diet.

Most importantly for human health, the work demonstrates that these circuits can be manipulated, said Leiser, adding, “If we could induce fmo-2 without taking away food, we could activate the stress response and trick your brain into making you long-lived.”

Before this can happen, however, it’s important to understand how else fmo-2 affects organisms.

In another study, published in Science Advances, the team demonstrated that the enzyme affects behavior in noticeable ways.

Worms engineered to overexpress fmo-2 were apathetic to positive and negative changes in their environment: They did not flee from potentially harmful bacteria, and when presented with food, didn’t slow to eat after a brief fast the way normal worms did.

Worms engineered to completely lack fmo-2 also explored their environments less often than normal worms did. Both behavioral states, the team found, were caused by a change in tryptophan metabolism.

“There are going to be side effects to any intervention to extend life–and we think one of the side effects will be behavioral,” said Leiser. “By understanding this pathway, we could potentially provide supplements to offset some of these negative behavioral effects.”

Leiser plans to continue to study the connection between the brain, metabolism, behavior and health with the hopes of contributing to the development of drugs to target these innate pathways.

“Investigating all of the individual signals that our brain is responding to from the gut is a hot but not well understood area,” he concluded.

Australian researchers have created tiny compartments to help supercharge photosynthesis, potentially boosting wheat and rice yields while slashing water and nitrogen use.

Researchers from Associate Professor Yu Heng Lau’s group at the University of Sydney and Professor Spencer Whitney’s group at Australian National University have spent five years tackling a fundamental problem: How can we make plants fix carbon more efficiently?

The team engineered nanoscale “offices” that can house an enzyme called Rubisco in a confined space, enabling scientists to fine-tune compatibility for future use in crops, which should allow them to produce food with fewer resources. Their research is published in Nature Communications.

Rubisco is a common enzyme in plants that is essential for “fixing” carbon dioxide for photosynthesis, the chemical process that uses sunlight to make food and energy for plants.

“Despite being one of the most important enzymes on Earth, Rubisco is surprisingly inefficient,” said lead researcher Dr. Taylor Szyszka from the ARC Center of Excellence in Synthetic Biology and School of Chemistry at the University of Sydney.

“Rubisco is very slow and can mistakenly react with oxygen instead of CO2 which triggers a whole other process that wastes energy and resources. This mistake is so common that important food crops such as wheat, rice, canola and potatoes have evolved a brute-force solution: mass-produce Rubisco,” she said.

In some leaves, up to 50% of the soluble protein is just copies of this one enzyme, representing a huge energy and nitrogen expense for the plant. “It’s a major bottleneck in how efficiently plants can grow,” said Davin Wijaya, a Ph.D. candidate at the Australian National University, who co-led the study.

Some organisms solved this problem millions of years ago. Algae and cyanobacteria house Rubisco in specialized compartments and supply them with concentrated CO2. They’re like tiny home offices that allow the enzyme to work faster and more efficiently, with everything it needs close at hand.

Scientists have been trying for years to install these natural CO2-concentrating systems into crops. But even the simplest of these Rubisco-containing compartments from cyanobacteria, called carboxysomes, are structurally complicated. They need multiple genes working in precise balance and can only house their native Rubisco.

The Lau and Whitney team took a different approach, using encapsulins. These are simple bacterial protein cages that require just one gene to build. Think of it like Lego blocks that automatically snap into place, rather than assembling complicated flat-pack furniture.

To load Rubisco inside, the researchers added a short “address tag” of 14 amino acids to the enzyme that, like a zip code, directs the enzyme to its destination inside the assembling compartment.

The team tested three Rubisco varieties: one from a plant and two from bacteria. They found that timing matters. For more complex forms of the enzyme, they needed to build Rubisco first, then build the protein shell around it.

“Rubisco didn’t assemble properly when trying to do both at once,” Wijaya said.

Dr. Szyszka said, “Another cool advantage of our system is that it’s modular. Carboxysomes can only package their own Rubisco, whereas our encapsulin system can package any type.

“Most excitingly we found the pores in the encapsulin shell allow for the entry and exit of Rubisco’s substrate and products,” she said.

The researchers emphasize this is just a proof of concept. They need to add the additional components that will give Rubisco the high-performance environment it needs. Early-stage plant experiments are already under way at ANU. “We know we can produce encapsulins in bacteria or yeast; making them in plants is the next sensible step. Our preliminary results look promising,” Wijaya said.

If successful, crops with this elevated CO2-fixing technology could produce higher yields while using less water and nitrogen fertilizer. These are critical advantages as climate change and population growth put pressure on global food systems.

With a newly developed method that compares AI-generated protein sequences with naturally occurring ones, function- and structure-regulating amino acids can be determined much more precisely than before.

Proteins are among the most important building blocks of nature and play a central role in biological processes in all organisms. Accordingly, scientists are keen to understand them as precisely as possible. As polymers of different amino acids, proteins can have different 3-dimensional structures and various functions. However, it is often difficult to determine which amino acids influence protein function and which influence structural stability.

A team led by Andreas Winkler and Oliver Eder from the Institute of Biochemistry at Graz University of Technology (TU Graz) have developed the Function-Structure-Adaptability (FSA) approach, which compares machine-learning-generated, idealized protein sequences with natural sequences that have developed over millions of years of evolution. This allows the amino acids that are crucial for function and stability to be identified with unprecedented accuracy.

This knowledge provides an important basis for the production and modification of proteins and thus for the development of new drugs, for the targeted improvement of proteins in industrial applications and for a better understanding of protein changes, for example in connection with antibiotic resistance. The research is published in the journal Structure.

Understanding the building blocks of life better

“As biochemists, we want to understand how proteins have evolved in nature and thus find out which amino acids are relevant for specific functions,” says Winkler. “To do this, we combined what nature has conserved during evolution with what an AI model considers relevant for the stability and structure of a protein. This combination of millions of years of evolutionary history and the latest technology greatly simplifies the analysis and understanding of proteins.”

The team used the deep learning model ProteinMPNN, which generates new protein sequences with the aim of ensuring that they adopt a predetermined stable, three-dimensional structure. The researchers compared these sequences with those in natural proteins. As a test system, the bacteriophytochrome protein family was utilized, which in nature serves as a photoreceptor for some bacteria and plays a central role in the perception of environmental influences such as light.

The new analysis method revealed that if an amino acid is repeatedly represented in the natural sequences, but does not appear to be significant for ProteinMPNN, this indicates a functional role. However, if it is strongly present in both sequence collections, this is an indication of structural significance.

Validation in the laboratory

For their approach, the researchers had to group the amino acids based on chemical properties in order to then statistically compare natural and AI-generated proteins. This made it possible to classify amino acids into three categories: “functional” (important for the specific role of the protein), “structural” (relevant for stability and folding) and “adaptable” (a third category that still requires further research). The team validated the results by means of extensive laboratory experiments in which they were able to influence the functional properties of proteins by making specific changes to correspondingly classified amino acids.

This made it possible, for example, to significantly influence the light perception of the photoreceptor test system. The comparison with functional residues already known from the literature also confirmed the high hit rate of the new analysis method.

“In the past, it often took several months or even years of preparatory work and laboratory work to carry out an analysis like this,” says Eder. “The preliminary work to identify potentially interesting natural protein sequences is now possible for a new protein within a week. And because our method allows us to pre-filter the functional amino acids much more specifically, we don’t have to spend so much time in the laboratory on testing and characterization.

“As the method can in principle be applied to all protein classes, we can now appreciate the intricate details of how proteins work in a more targeted way.”