Its additional role, say the researchers, may shift from trash sorter to signal messenger in dorsal root ganglion neurons — cells that convey sensory signals from nerve cells close to the skin to the central nervous system.

Results of their experiments, published April 12 in Cell Reports, show that proteasomes may help those specialized neurons sense the surrounding environment, send signals to each other and potentially differentiate between sensing pain and itch, a finding that could help scientists better understand these sensory processes and new targets for treating pain and other sensory problems.

“Neurons live next to each other for a long time, and they need ways to communicate with each other about what they’re doing and who they are,” says Seth S. Margolis, Ph.D., associate professor of biological chemistry at the Johns Hopkins University School of Medicine. “Proteasomes in the membrane of neurons may help the cells fine tune this messaging process.”

“Proteasomes are more complicated than they appear,” says Margolis. He and his colleagues first found proteasomes in the plasma membranes of central nervous system neurons in mice in 2017, which they dubbed neuronal membrane proteasomes, and have continued studying how these special proteasomes promote messaging, or crosstalk, among neurons.

At the time, Margolis’ focus was on the central nervous system, encompassing the brain and spinal cord. But later, he collaborated with neurobiologist Eric Villalón Landeros, Ph.D., postdoctoral fellow in Margolis’ laboratory at Johns Hopkins, whose work focuses on the peripheral nervous system, the network of neurons running through the rest of the body, closer to the skin, capturing sensory information from the environment.

Margolis and Villalón Landeros wondered whether proteasomes could be found in peripheral neurons, and if so, what they might do.

Using mouse antibodies that glom on to proteasomes, and other methods, the investigators found the proteasomes on the surface of neurons in the spinal cord, dorsal root ganglia, sciatic nerve and peripheral nerves innervating skin.

The researchers were also able to find proteasomes in the same type of peripheral neurons grown in laboratory culture dishes.

To understand the proteasome’s function in peripheral sensory neurons, the researchers gave mice biotin-epoxomicin, a cell membrane-impermeable proteasome inhibitor that blocks the function of neuronal membrane proteasomes. Then, they performed classic sensory tests.

The researchers found that the mice that got injections of the proteasome-blocking drug biotin-epoxomicin on one side of the body were between 25% to 50% slower than the other side to respond to sensory tests.

“This suggests that membrane proteasomes are important for sensation, and they must be facilitating this at the signaling level,” says Margolis.

The researchers used single cell sequencing technology to determine that membrane proteasomes were expressed in a subpopulation of neurons involved in itch sensation and known to be sensitive to histamine, an immune system compound that launches an animal’s (including human’s) response to allergens.

In laboratory culture dishes, the researchers stimulated both itch-related and non-itch related neurons and blocked their membrane proteasomes with biotin-epoxomicin. This resulted in changes to activity in all of the cells. “Blocking proteasomes seems to have an activity-modulatory effect across all the cells, despite being expressed in a subpopulation, suggesting that proteasomes facilitate a kind of cross talk between these cells,” says Margolis.

Proteasome blockers, including one called Velcade, are currently used to treat certain types of cancer.

Villalón Landeros and Margolis plan to continue working together to determine how neuronal membrane proteasomes function in sensory neurons and in sensing pain versus itch. “We want to see if we can manipulate neuronal membrane proteasomes to have a different outcome on pain and itch sensation,” says Villalón Landeros.

Additional scientists who contributed to the research are Samuel Kho, Taylor Church, Anna Brennan, Fulya Türker, Michael Delannoy and Michael Caterina from Johns Hopkins.

Funding for the research was provided by the National Institutes of Health (F32NS119202, R01 NS110754) and a Merkin Peripheral Neuropathy and Nerve Regeneration Center grant.

Researchers are urgently trying to find ways to prevent or slow idiopathic pulmonary fibrosis (IPF) and related lung conditions, which can cause worsening shortness of breath, dry cough, and extreme fatigue. Average survival following diagnosis of IPF is just three to five years, and the disease has no cure.

A recent U-M study from a team led by Sean Fortier, M.D. and Marc Peters-Golden, M.D. of the Division of Pulmonary and Critical Care Medicine at U-M Medical School uncovers a pathway used during normal wound healing that has the potential to reverse IPF.

Using a mouse model, they simulated IPF by administering bleomycin, a chemotherapy agent that causes cell injury and confirmed that the resulting lung scarring resolved itself over the span of about six weeks.

Because of this, “studying fibrosis is kind of tough,” said Fortier. “If we’re going to give experimental drugs to try and resolve fibrosis, we have to do it before it resolves on its own.

Otherwise, we will not be able to tell if the resolution was the action of the drug or natural repair mechanisms of the body.”

However, he said, “there’s actually a lot to learn about how the mouse gets better on its own. If we can learn the molecular mechanisms by which this occurs, we may uncover new targets for IPF.”

The process by which lung injury either leads to healing or fibrosis relies in part on what happens to a cell called a fibroblast, which forms connective tissue.

During injury or illness, fibroblasts are activated, becoming myofibroblasts that form scar tissue by secreting collagen. When the job is done, these fibroblasts must be deactivated, or de-differentiated, to go back to their quiet state or undergo programmed cell death and be cleared.

“This is the major distinction between normal wound healing and fibrosis — the persistence of activated myofibroblasts,” explained Fortier. That deactivation is controlled by molecular brakes. The study examined one of these brakes, called MKP1 — which the team found was expressed at lower levels in fibroblasts from patients with IPF.

By genetically eliminating MKP1 in fibroblasts of mice after establishing lung injury, the team saw that fibrosis continued uncontrolled.

“Instead of at day 63, seeing that nice resolution, you still see fibrosis,” said Fortier.

“We argued by contradiction: when you knock out this brake, fibrosis that would otherwise naturally disappear, persists and therefore MKP1 is necessary for spontaneous resolution of fibrosis.”

They performed several additional studies using CRISPR techniques to demonstrate how MKP1 applies the brakes, mainly by deactivating the enzyme p38α, which is implicated in a cell’s reaction to stress.

Furthermore, they demonstrated that neither of the two current FDA approved drugs for lung fibrosis, pirfenidone and nintedanib, are able to turn off myofibroblasts.

“That’s totally in keeping with the fact that they do slow the progression, but they don’t halt or reverse disease,” said Fortier.

Fortier hopes the discovery that this pathway reverses fibrosis leads to exploration of additional brakes on fibrosis.

“So much work on fibrosis has focused on how we can prevent it, but when a patient presents to my clinic with a dry cough, shortness of breath, and low oxygen as a result of underlying IPF, the scarring is already present. Of course, we’d love a way to prevent the scarring from getting worse, but the Holy Grail is to reverse it.”

Duke University’s Marine Geospatial Ecology Lab led a collaboration of 11 institutions in the United States that pooled 17 years of available visual survey data covering 9.7 million square kilometers of the U.S. Atlantic — roughly the same area as the entire contiguous United States.

This information was coupled with auditory data from almost 500 hydrophone recorders in US Atlantic waters that captured whales’ calls. Lining up visual and acoustic datasets for the first time, researchers built a statistical model to estimate the number of whales per square kilometer at different points in time. Researchers published their findings on March 20, 2024 in Marine Ecology Progress Series.

“The more accurate and detailed the mapping, the better chance we have to save dwindling numbers of right whales from preventable injury and fatality,” said Patrick Halpin, director of Duke’s Marine Geospatial Ecology Lab. The lab studies marine ecology, resource management, and ocean conservation, using data to inform ocean management and governance.

Other current real-time efforts to track and protect the whales from deadly encounters with human activities have been incomplete or ineffective. Electronic tagging can harm whale health, and it is infeasible to continuously monitor more than a small fraction of the population that way.

The statistical model is a revision of a 2016 model that predicts whale density from environmental data, like sea surface temperature. This latest version incorporates new data to reflect whales’ changing migration and feeding patterns, including their presence in new areas that lack protection measures for marine life.

“With nearly three times more aerial survey data than we had before, and confirming evidence from the hydrophones, we were able to show how strongly the population has shifted its distribution,” said Jason Roberts, a Duke research associate and lead author of the study.

Right whales maintain the health and balance of marine environments and the entire food web through their feeding habits. As climate change has reduced the population of their prey, whale migration patterns have become more unpredictable, increasing the chances that human activities, like commercial fishing, may harm whale health and chances of reproduction.

Using maps obtained by satellite ocean monitoring, or from physical ocean models like the recently published one, researchers can more accurately predict whale density across the U.S. east coast.

Muscle strength, especially hand grip strength, can indicate an individual’s physiological resources to protect against age-related diseases and disabilities, as well as their ability to cope with them. Age-related loss of muscle strength is individual and influenced not only by lifestyle but also by genetics.

The study revealed that individuals with a genetic predisposition for higher muscle strength have a slightly lower risk for common noncommunicable diseases and premature mortality. However, it did not predict better survival after acute adverse health events compared to the time before illness onset.

“It seems that a genetic predisposition for higher muscle strength reflects more on an individual’s intrinsic ability to resist and protect oneself against pathological changes that occur during aging than the ability to recover or completely bounce back after severe adversity,” says doctoral researcher Päivi Herranen from the Faculty of Sport and Health Sciences.

The research utilized a unique study population

Muscle strength is a multifactorial trait influenced by lifestyle and environmental factors but also by numerous genetic variants, each with a very small effect on muscle strength. In this study, the genetic predisposition for muscle strength was defined by constructing a polygenic score for muscle strength, which summarizes the effects of hundreds of thousands of genetic variants into a single score. The polygenic score makes it possible to compare participants with an exceptionally high or low genetic predisposition for muscle strength, and to investigate associations with inherited muscle strength and other phenotypes, in this case, common diseases.

“In this study, we were able to utilize both genetic information and health outcomes from over 340,000 Finnish men and women,” Herranen explains.

“To our knowledge, this is the first study to investigate the association between a genetic predisposition for muscle strength and various diseases on this scale.”

Further research on the effects of lifestyles is still needed

Information about the genetic predisposition for muscle strength could be used alongside traditional risk assessment in identifying individuals who are at particularly high risk of common diseases and health adversities. However, further research on the topic is still needed.

“Based on these results, we cannot say how lifestyle factors, such as physical activity, modify an individual’s intrinsic ability to resist diseases and whether their impact on health differs among individuals due to genetics,” Herranen notes.

The study utilized the internationally unique FinnGen dataset, compiled through the collaboration of Finnish biobanks. The dataset consisted of 342,443 Finns who had given their consent and provided a biobank sample. The participants were aged 40 to 108 years, and 53% of them were women. The diagnoses selected for the study were based on the leading causes of death and the most significant noncommunicable diseases in Finland. Selected diagnoses included the most common cardiometabolic and pulmonary diseases, musculoskeletal and connective tissue diseases, falls and fractures, mental health and cognitive disorders, cancers, as well as overall mortality and mortality from cardiovascular diseases.

The study is the second publication of Päivi Herranen’s doctoral thesis, which investigates how genetics and environmental factors affect biological aging, particularly the weakening of muscle strength and functional capacity with age. The research is part of the GenActive project, funded by the Research Council of Finland and the Juho Vainio and Päivikki and Sakari Sohlberg foundations. The project is led by Assistant Professor and Academy Research Fellow Elina Sillanpää. The research was conducted in collaboration with the Gerontology Research Center (GEREC), the Institute for Molecular Medicine Finland (FIMM), and the FinnGen research project.

The design and development of novel materials with superior properties demands a comprehensive analysis of their atomic and electronic structures. Electron energy parameters such as ionization potential (IP), the energy needed to remove an electron from the valence band maximum, and electron affinity (EA), the amount of energy released upon the attachment of an electron to the conduction band minimum, reveal important information about the electronic band structure of surfaces of semiconductors, insulators, and dielectrics. The accurate estimation of IPs and EAs in such nonmetallic materials can indicate their applicability for use as functional surfaces and interfaces in photosensitive equipment and optoelectronic devices.

Additionally, IPs and EAs depend significantly on the surface structures, which adds another dimension to the complex procedure of their quantification. Traditional computation of IPs and EAs involves the use of accurate first-principles calculations, where the bulk and surface systems are separately quantified. This time-consuming process prevents quantifying IPs and EAs for many surfaces, which necessitates the use of computationally efficient approaches.

To address the wide-ranging issues affecting the quantification of IPs and EAs of nonmetallic solids, a team of scientists from Tokyo Institute of Technology (Tokyo Tech), led by Professor Fumiyasu Oba, have turned their focus towards machine learning (ML). Their research findings have been published in theJournal of the American Chemical Society.

Prof. Oba shares the motivation behind the present research, “In recent years, ML has gained a lot of attention in materials science research. The ability to virtually screen materials based on ML technology is a very efficient way to explore novel materials with superior properties. Also, the ability to train large datasets using accurate theoretical calculations allows for the successful prediction of important surface characteristics and their functional implications.”

The researchers employed an artificial neural network to develop a regression model, incorporating the smooth overlap of atom positions (SOAPs) as numerical input data. Their model accurately and efficiently predicted the IPs and EAs of binary oxide surfaces by using the information on bulk crystal structures and surface termination planes.

Moreover, the ML-based prediction model could ‘transfer learning,’ a scenario where a model developed for a particular purpose can be made to incorporate newer datasets and reapplied for additional tasks. The scientists included the effects of multiple cations in their model by developing ‘learnable’ SOAPs and predicted the IPs and EAs of ternary oxides using transfer learning.

Prof. Oba concludes by saying, “Our model is not restricted to the prediction of surface properties of oxides but can be extended to study other compounds and their properties.”