Enlarge / Smart insulin has the potential to make injections far less frequent. (credit: A. Martin UW Photography )

People with type I diabetes have to inject themselves multiple times a day with manufactured insulin to maintain healthy levels of the hormone, as their bodies do not naturally produce enough. The injections also have to be timed in response to eating and exercise, as any consumption or use of glucose has to be managed.

Research into glucose-responsive insulin, or “smart” insulin, hopes to improve the quality of life for people with type I diabetes by developing a form of insulin that needs to be injected less frequently, while providing control of blood-glucose levels over a longer period of time.

A team at Zhejiang University, China, has recently released a study documenting an improved smart insulin system in animal models—the current work doesn’t involve any human testing. Their insulin was able to regulate blood-glucose levels for a week in diabetic mice and minipigs after a single subcutaneous injection.

Enlarge / A related species of Fusarium . (credit: Wikimedia Commons )

While it may be the reason behind tires skidding, pipes bursting, and closed roads making traffic a nightmare, ice doesn’t always form as easily as it seems. It often gets an assist from proteins made by fungi.

Never mind the common thinking that ice forms at 0° C (32° F). Though this is water’s freezing point, pure water will only freeze when temperatures plummet as low as minus 46° C (minus 50.8° F). So why does it usually freeze at zero anyway? Organisms such as bacteria, insects, and fungi produce proteins known as ice nucleators (non-protein nucleators can also be of abiotic origin). These proteins can kick-start the formation, or nucleation , of ice at higher temperatures than pure water would freeze at.

While the exact reason fungi make these proteins remains unknown, researchers Valeria Molinero of the University of Utah and Konrad Meister of Boise State University led a study that has revealed more about how fungal ice nucleators can both promote and hold back ice formation more efficiently than those of many other life-forms.

Enlarge (credit: LAGUNA DESIGN )

For most proteins, structure is function. The complex three-dimensional shapes that proteins adopt create folds and pockets that can accomplish the remarkably improbable: driving chemical reactions that would otherwise never happen or binding to a single chemical inside the complex environment of a cell. Protein structure is so important that there's an entire discipline, along with several well-developed approaches, to figuring out what a protein looks like when it's all folded up into its active state.

But that's only most proteins. Scientists have also found a growing catalog of intrinsically disordered proteins. Rather than having a set structure, intrinsically disordered proteins seem to have entire sections that can flap around in the breeze of Brownian motion and, yet, were critical to the protein's structure. People haven't been sure whether these proteins temporarily adopted a specific structure to work or the disorder was critical for function.

Now, a new paper describes a case where two intrinsically disordered proteins induce specific structures in each other when they interact. And Google's new AlphaFold AI software was critical to figuring out that structure.

Enlarge / All steroids past and present share the complex ringed structure but differ in terms of the atoms attached to those rings. (credit: KATERYNA KON/SCIENCE PHOTO LIBRARY )

All of the organisms we can see around us—the plants, animals, and fungi—are eukaryotes composed of complex cells. Their cells have many internal structures enclosed in membranes, which keep things like energy production separated from genetic material, and so on. Even the single-celled organisms on this branch of the tree of life often have membrane-covered structures that they move and rearrange for feeding.

Some of that membrane flexibility comes courtesy of steroids. In multicellular eukaryotes, steroids perform various functions; among other things, they’re used as signaling molecules, like estrogen and testosterone. But all eukaryotes insert various steroids into their membranes, increasing their fluidity and altering their curvature. So the evolution of an elaborate steroid metabolism may have been critical to enabling complex life.

Now, researchers have traced the origin of eukaryotic steroids almost a billion years further back in time. The results suggest that many branches of the eukaryotic family tree once made early versions of steroids. But our branch evolved the ability to produce more elaborate ones—which may have helped us outcompete our relatives.

Enlarge / A Moon jellyfish. (credit: Dan Kitwood / Getty Images )

The sensation of hunger seems pretty simple on the surface, but behind the scenes, it involves complicated networks of sending and signaling, with multiple hormones that influence whether we decide to have another serving or not. The ability to know when to stop eating appears to be widespread among animals, suggesting that it might have deep evolutionary roots.

A new study suggests that at least one part of the system goes back to nearly the origin of animals. Researchers have identified a hormone that jellyfish use to determine when they're full and stop eating. And they found that it's capable of eliciting the same response in fruit flies, suggesting the system may have been operating in the ancestor of these two very distantly related animals. That ancestor would have lived prior to the Cambrian.

Feeding the fish (or jellyfish)

Given they lack any obvious equivalents to a mouth, it might seem like it would be tough to determine whether a jellyfish is even eating, much less hungry. But a team of Japanese researchers showed that the jellyfish species Cladonema pacificum has a bunch of stereotypical behaviors while feeding, including that their tentacles latch onto prey and that they then withdraw the tentacle into the bell so that the prey can be digested. And, if you keep feeding the jellyfish brine shrimp, eventually this process will slow, indicating that the animal is sensing it is well fed. (There's a movie available of the jellyfish feeding.)

Enlarge (credit: CHRISTOPH BURGSTEDT/SCIENCE PHOTO LIBRARY )

The success of ChatGPT and its competitors is based on what's termed emergent behaviors. These systems, called large language models (LLMs), weren't trained to output natural-sounding language (or effective malware ); they were simply tasked with tracking the statistics of word usage. But, given a large enough training set of language samples and a sufficiently complex neural network, their training resulted in an internal representation that "understood" English usage and a large compendium of facts. Their complex behavior emerged from a far simpler training.

A team at Meta has now reasoned that this sort of emergent understanding shouldn't be limited to languages. So it has trained an LLM on the statistics of the appearance of amino acids within proteins and used the system's internal representation of what it learned to extract information about the structure of those proteins. The result is not quite as good as the best competing AI systems for predicting protein structures, but it's considerably faster and still getting better.

LLMs: Not just for language

The first thing you need to know to understand this work is that, while the term "language" in the name "LLM" refers to their original development for language processing tasks, they can potentially be used for a variety of purposes. So, while language processing is a common use case for LLMs, these models have other capabilities as well. In fact, the term "Large" is far more informative, in that all LLMs have a large number of nodes—the "neurons" in a neural network—and an even larger number of values that describe the weights of the connections among those nodes. While they were first developed to process language, they can potentially be used for a variety of tasks.

Enlarge (credit: James Randklev )

The pros and cons of moonlighting—taking up an extra job in addition to full-time employment—are hotly debated. But in biology, moonlighting is not uncommon, as individual proteins often perform multiple functions. For many years, scientists knew that the Unusual Floral Organ (UFO) protein seems to do some moonlighting.

Based on the protein's structure, its role in plants is thought to be targeting proteins for destruction. But it also works with the Leafy (LFY) protein to aid flower formation. A team of scientists from France has now shed light on how this protein performs two roles.

Flowers and a UFO

When it comes to flower formation, the Leafy (LFY) protein is a veritable workhorse. Flowers are built from parts named sepals, petals, stamens, and carpels, which are arranged in whorls. The LFY protein, acting either alone or in combination with other proteins, is responsible for activating genes essential for creating each of these parts. LFY combines with UFO to help form petals and stamens.

Enlarge (credit: VICTOR de SCHWANBERG/SCIENCE PHOTO LIBRARY )

Psychedelic drugs are often used for entertainment purposes. But there have been some recent indications that they can be effective against PTSD and treatment-resistant depression. Figuring out whether these substances work as medicinal drugs can be challenging because (as one researcher helpfully pointed out) it's difficult to do a controlled experiment when it's easy to figure out who's in the treatment group. Still, we've made some progress in understanding what's happening with psychedelics at the molecular level.

Many psychedelics seem to bind to a specific receptor for the neural signaling molecule serotonin, activating it. That would seem to make sense for antidepressive effects, given that many popular antidepressants also alter serotonin signaling (such as in SSRIs, or selective serotonin reuptake inhibitors). But SSRIs don't produce any of the mind-altering effects that drive non-medical interest in psychedelics, so things remain a bit confusing.

New data suggests that psychedelics may activate serotonin signaling in a very different way than serotonin itself can, reaching the receptors in parts of the cell that serotonin can't get to.

Enlarge (credit: Adrienne Bresnahan )

The research community is excited about the potential of DNA to function as long-term archival storage. That's largely because it's extremely dense, chemically stable for tens of thousands of years, and comes in a format we're unlikely to forget how to read. While there has been some interesting progress , efforts have mostly stayed in the research community because of the high costs and extremely slow read and write speeds. These are problems that need to be solved before DNA-based storage can be practical.

So we were surprised to hear that storage giant Seagate had entered into a collaboration with a DNA-based storage company called Catalog. To find out how close the company's technology is to being useful, we talked to Catalog's CEO, Hyunjun Park. Park indicated that Catalog's approach is counterintuitive on two levels: It doesn't store data the way you'd expect, and it isn't focusing on archival storage at all.

A different sort of storage

DNA is a molecule that can be thought of as a linear array of bases, with each base being one of four distinct chemicals: A, T, C, or G. Typically, each base of the DNA molecule is used to hold two bits of information, with the bit values conveyed by the specific base that is present. So A can encode 00, T can encode 01, C can encode 10, and G can encode 11; with this encoding, the molecule AA would store 0000, while AC would store 0010, and so on. We can synthesize DNA molecules hundreds of bases long with high efficiency, and we can add flanking sequences that provide the equivalent of file system information, telling us which part of a chunk of binary data an individual piece of DNA represents.